HomeMy WebLinkAbout1999-11-09 Info Packet (1)CITY COUNCIL INFORMATION PACKET
October 22, 1999
MISCELLANEOUS ITEMS
IP1 Meeting Schedule and Tentative Work Session Agendas
IP2
Letter from City Manager to Johnson County Board of Supervisors Chair: Land Use
Planning
IP3 Memorandum from City Manager: Friday after Thanksgiving Parking Enforcement
IP4 Letter from City Manager to Nancy Purington, Arts Iowa City: Funding
IP5 Memorandum from City Attorney: City of Iowa City v. Winebrenner Ford, Inc.
IP6
Memorandum from Assistant City Attorney Matthews: Partial Litigation Update:
Rudman v. City of Iowa City, City of Coralville, Johnson County
IP7 Memorandum from City Clerk: Special Work Session of November 18
IP8 Letter from Steve Siglin to Traffic Engineering Planner Ripley: Friendship Street
IP9 Article: Clear Vision Essential for Municipal Airport Expansions [Kubby]
IP10
Minutes: October 21 -Johnson CountyBoard of Supervisors
Information from the 10/21 joint meeting regaring the Rural
Initiative meeting.
Development
10-22-99 I
IP1
City Council Meeting Schedule and ootober 22, ~999
Tentative Work Session Agendas
i November 8
6:30p
SPECIAL COUNCIL WORK SESSION
Monday
Council Chambers
I November 9
7:00p
SPECIAL FORMAL COUNCIL MEETING
Tuesday
Council Chambers
I November 10
6:30p
I November 11
CITY HALL DAY
6:30p Reception
7:00p Program Begins
VETERANS' DAY HOLIDAY - CITY OFFICES CLOSED
Wednesday
Council Chambers
Thursday I
November 15
7:00p
SPECIAL COUNCIL WORK SESSION
Joint Meeting with Library Board
Monday
IC Public Library, Room A
I November 18
7:OOp - 8:30p
SPECIAL COUNCIL WORK SESSION
Thursday
Council Chambers
I November 22
6:30p
SPECIAL COUNCIL WORK SESSION
Monday
Council Chambers
I November 23
7:00p
SPECIAL FORMAL COUNCIL MEETING
Tuesday
Council Chambers
Meeting dates/times subject to change
FUTURE WORK SESSION ITEMS
Hickory Hill West Council Goals
Commercial Use of Sidewalks Newspaper Vending Machines
Y2K Update Transit Interchange
Communication Towers Liquor Licenses
Kirkwood Signalization Residing in Vehicles
October 15, 1999
CITY OF I0 WA CITY
Jonathan Jordahl, Chair
Johnson County Board of Supervisors
913 S. Dubuque Street
Iowa City, IA 52240
Dear Jonathan:
As a member of the Iowa League of Cities (ILC) Board of Directors one of our
responsibilities is to approve our annual legislative program. The ILC represents the 949
incorporated cities in Iowa. One of our legislative positions (enclosed) involves land use
planning. The Iowa League of Cities Board has chosen to make comprehensive land use
planning a priority in our State legislative contacts. In that County Board of Supervisors
has expressed interest in the land use planning and fringe agreements are currently under
discussion, I thought you might find this legislative priority of interest.
Please feel free to share this with the members of the Board and your county planning
staff. As this legislation is pursued I will do my best to keep you advised of the ILC
position.
Sincerely,
Stephen J. Atkins
City Manager
Enclosure
cc: City Council
Karin Franklin
tp4-2sa,doc
410 EAST WASHINGTON STREET · IOWA CITY,
IOWA 52240-1826 · (319) 356-5000 · FAX (319) 356-5009
Priority:
Pursue legislation to establish pilot projects for comprehensive land use
planning to develop models that can be emulated statewide and also
direct the state to conduct a land use inventory.
The topic of land use has received much attention over the past few years. Land use means
different things to different people. To some, land use equals annexation practices. To
others, it is the difficulty of meshing urban uses with rural. For others, it is preserving Iowa'.s
farmland. Actually, all of these components and many more are part of the land use equation.
Rather than attempting to enact policy that seeks to remedy perceived problems related to
narrow issues, a more holistic view should be taken.
The first step in developing meaningful land use policy is to evaluate current conditions in
the state. Gathering empirical evidence encourages an informed discussion rooted in actual
land use practices, rather than reacting to flash point situations with policies that do not
address the broader issues. The first step in gathering information is to build upon the work
of Iowa State University Extension's pilot land use inventory project. Selecting seven
counties in the state, ISU Extension researched several areas to provide an accurate overview
of land use. The inventory compiled data on conversions of agricultural property to non-
agricultural classification. This included every conversion regardless of whether it was for
commercial, residential or conservation purposes. The inventory also looked at the amount
of prime farmland (high corn suitability rating) that was converted compared to less optimum
land. The inventory provides a snapshot of Iowa's landscape and a sound basis from which
to determine appropriate land use goals. It should be completed in all 99 counties.
The second step is to take preventive measures that will reduce potential conflict.
Comprehensive joint planning between cities and counties is the mechanism to plan for
growth and achieve the best use of the land. Comprehensive planning is more than land use
planning; it includes planning for transportation, residential and recreational needs. Some
cities and counties are already attempting to control residential development by jointly
approving subdivision development outside the city limits. This represents recognition of the
need to control development, promote orde~y growth and maximize land use.
The League proposes establishing a pilot planning initiative to provide cities and counties
with planning models that can eventually be applied statewide. To accomplish this task, the
state should invest through the Department of Economic Development's Community
Development program. This will send a message that the state wants to partner with local
government in being proactive in balancing the citizens' needs with preservation of our
natural resources.
City of Iowa City
MEMORANDUM
Date: October 18, 1999
To: City Council
From: City Manager
Re: Friday after Thanksgiving Parking Enforcement
We have received a request from the Downtown Association for free parking on Friday,
November 26.
The day after Thanksgiving is a contract holiday for City employees. Those scheduled to work
receive their regular pay for the day plus time and a half as comp time or pay. The additional
cost to the Parking Division to operate this day is $3,357. Ramp revenue from last year was
approximately $2,000 for the day after Thanksgiving. Revenue from parking meters and the
Linn Street Lot was approximately $1,000. Operation of the system is therefore done strictly for
traffic control.
The DTA has agreed to provide traffic control within the parking garages from 11:00 AM until
5:00 PM, the peak shopping time.
Because the revenue generated would be equal to the cost of operation and the DTA has
agreed to assume the responsibility for traffic control, we plan to waive parking fees on
November 26.
cc: Joe Fowler
Chief of Police
indexbc\mernos\5-1 SA.doc
IP4
CITY OF I0 WA CITY
October 21, 1999
Nancy Purington
Arts Iowa City
207 E. Washington St.
Iowa City, IA 52240
Dear Nancy:
At their work session of October 18 the Iowa City City Council authorized $15,000 in City funds
to assist Arts Iowa City in their operating costs, and general program responsibilities. At that
meeting it was the Council's desire to provide you with short-term financial support as Arts Iowa
City initiates plans to address its space needs and related issues.
I would like to receive a copy of any lease agreements or other documents you believe relevant
to the City's financial support, in particular any new agreements reached with the property
owner.
As Arts Iowa City begins discussion with the property owner I will assure you that with
reasonable notice, preferably two weeks, a $15,000 payment to Arts Iowa City will be available.
I believe it was the Council's intent that within three months, on or around February 1, you
should have a more formal plan for the future of Arts Iowa City in the downtown area. At that
time I believe the Council would like to review your formal plan, and additional financial support
as they feel appropriate would be considered.
Please call with any questions.
Sincerely,
City Manager
cc: City Council
Kevin O'Malley
jw/~tr/sa-arts .doc
410 EAST WASHINGTON STREET · IOWA CITY, IOWA 52240-1826 · (319) 356-5000 , FAX (319) 356-5009
City of Iowa City
MEMORANDUM
Date: October 20, 1999
To: City Council ~
From: Eleanor M. Dilkes, City Attorney
Re: City of Iowa City v. Winebrenner Ford, Inc.
The Court has now rescheduled the trial in the above-referenced case for April 17, 2000
at 9:00 a.m.
CC:
Steve Atkins
Dale Helling
Marian Karr
Chuck Schmadeke
eleanorNmem\ed 10~18.doc
City of Iowa City
MEMORANDUM
IP6
Date:
To:
From:
Re:
October 21, 1999
City Council
Andrew Matthews, Assistant City Attomey
Partial Litigation Update: Rudman v. City of Iowa City, City of Coralville,
Johnson County
This memo is provided as an update in the above-referenced litigation. You will recall that the
Plaintiffs claims against the City of Iowa City alleged that the City was negligent in failing to
arrest an individual, who it was determined in the course of discovery was Plaintiffs former
boyfriend, for violating a claimed no-contact order, and that as a result, this person allegedly
went to Plaintiffs' residence, attacked, abducted, assaulted, and sexually abused her. In the
course of discovery, Plaintiff claimed damages well in excess of $1,000,000. This lawsuit
attracted considerable press attention when it was filed.
Following discovery, we filed a motion for summary judgment, as did Coralville. We just
received the Court's ruling on our motions. The Court granted our motions for summary
judgment, effectively ending our involvement in this lawsuit.
The Court ruled that the Iowa City police complied with state code provisions in their
investigation of Plaintiffs claims of a violation of a domestic abuse no-contact order, that
Plaintiff failed to prove any special relationship with the police that would except her claim from
the immunity provided by the "public duty" doctrine, which generally establishes that police owe
a general duty to the public at large to investigate and prevent crime, rather than a duty to protect
specific individuals. The underlying basis for the public duty doctrine is to insure that police are
flee to vigorously pursue criminal investigations without constantly risking being exposed to
liability claims for their investigations.
If you have any questions about this ruling or this litigation, feel flee to call me.
CC:
Steve Atkins, City Manager
Dale Helling, Assistant City manager
Marian Karr, City Clerk
R.J. Winkelhake, Police Chief
Kevin O'Malley, Finance Director
City of Iowa City
MEMORANDUM
DATE:
TO:
FROM:
RE:
October 21, 1999
Mayor and City Council
Marian K. Karr, City Clerk ~
Special Work Session of November 18
Please reserve Thursday evening, November 18, from 7:00-8:30 P.M. for a joint meeting
of the 1999 and 2000 City Council Members. The meeting will be in Council Chambers.
More information will be provided at a later time.
Just a reminder that the employee luncheon is planned for 11:00-1:00 that same day.
.., OCT 2t 1999
Doug Ripley ~,
JCCOG Traffic Engineering Planner CITY MANAGER'$ OFjFIC[
City of Iowa City
4 ] 0 East Washington Street
Iowa City, IA 52240-1826
Mr. Pdpley:
I'd like to commend the city for attempting to address the need for traffic calming on
Friendship Street, but I am disappointed by your October 12 letter indicating that the
proposal for a raised crosswalk has been killed. While you interpret the survey results to
mean an absence of wide support for the idea, I interpret them to show that the majority
of respondents favor the idea. In a democracy, (theoretically) we are supposed to honor
the majority wishes following a vote.
Having said that, my concem now is with the next step. My fear is that the issue of
motorists driving 40, 50, and 60 miles per hour (that's fight) on Friendship Street will be
dropped because of an interpretation that residents don't really care. Are there other plans
to attempt to control a problem that may cause a pedestrian death at some point? In the
eight years I have lived near the proposed crosswalk site, I have only seen the police
using radar to control speeding one time - the two weeks before the traffic calming
survey was sent out. Is increased police presence an option? Are there other plans to
address the problem?
As the parent of a small child, I realize my responsibility to keep her safely away from
the street, but I also think the city has a responsibility to address unsafe situations. The
proposed crosswalk area is adjacent to a city park entrance, and is a place that children
pass through on their way to school. I appreciated the city's attempt to be proactive with
the traffic calming survey; don't stop now.
Steve Siglin -- ·
ee: City Council
Clear vision essential for municipal airport expansions
committee, consisting of members
from the city, county, Poweshiek
Area Development, Grinnell
Chamber of Commerce, a major
employer and a long-time
stakeholder in the regional airport
are soliciting funds to complete a
$434,000 project. So far, they've
raised enough to give the go-ahead
for design work. Subcommittees
continue to work on marketing,
financing and implementation.
Contruction has begun in Pella,
where the main runway will be
expanded from 4,000 to 5,400 feet.
A new airport entrance must be
constructed, as the extension will
cut off the old entrance. Existing
power lines will become an
obstruction, so MidAmerican
Energy will place these
underground in a new location.
As Pella planned its airport
enhancement, the business
community and airplane pilots
were brought on board to
determine community needs and
the best way to meet those needs.
"We gathered a committee of
larger employers who use the
airport to bring in dealers and
customers, and airplane pilots, to
determine what we needed," said
Jim Twombly, Peila city adminis-
trator. "Their people provided the
support to move the project ahead
and we received financial support
from two of the businesses."
In fact, Pella Corporation and
Vetracer Manufacturing are providing 50
percent of the funding. The remaining funds
will come from the sale of general obligation
bonds (tax-free investments).
While it is difficult to forecast the future
use of the Pella airport, its past track record
shows that ten years ago there were only
seven or eight aircraft based there. Today 30
aircraft are in hangers. Future plans include
a parallel taxiway, which the city hopes will
qualify for federal assistance.
Planning is a prerequisite
Whether a city plans an airport enhancement
Existing power lines will obstruct Pella's
extended runway. MidAm erican Energy will
place these underground.
unicipal airports are increasingly
important to economic
development efforts to attract
industry to Iowa's cities of all
sizes.
"Most cities want longer runways so
they can accommodate business jet
activity," say Bill Grabe of Clapsaddle-
Garber Associates. "The local
development corporation is trying to
attract industries to a city and those
industries need to be able to fly in
customers and vendors to grow their
operations."
Funding for longer runways (many
municipal airports were built prior to
the proliferation of small business jets)
becomes a challenge as runway exten-
sions don't qualify for Federal Avia-
tion Administration (FAA) and the
Department of Transportation (DOT)
funding until an established need
exists. "It becomes a chicken and egg
situation," explains Grabe. "An indus-
try won't locate in a city that doesn't
have a long enough runway to handle
business jet actvity, but the city can't
get funding to improve their facility
until traffic justifies it."
So what are cities to do'?
Some are raising the funds
themselves. Pella and Grinnell are two
cities doing just that. They've acquired
some funding from county and city
budgets, ,but most comes from private
donations. Existing industries are
kicking in to help local airports
In Grinnell, a chamber-airport
14 CITYSCAPE October 1999
· /AIUNICIPAL AIRPORTS
that qualifies for FAA or DOT
assistance or does not, planning
studies are a prerequisite for
serious grant applications.
A planning study looks at
airport needs 20 years out. Steps
in the process arc:
1. Take a current inventory of
the facility and gather demo-
graphic information.
2. Forecast airport activity for
the next 20 years.
3. Identify facilities needed to
accommodate the plan.
4. Put together an activity layout
plan.
5. Develop a capital improvement
plan and how that capital will be
derived.
No matter what the size of the
airport, a plan kept current will be an
asset when enhancements must be
made. The selection of an engineer
that specializes in aviation is
extremely important to the success of
the project. Usually these engineers
are pilots themselves and understand
airport operations and regulations.
They can help pinpoint funding for
particular projects and advise
municipalities about the type of
project more likely to get funding.
Cad Byers, manager of aviation services,
Howard R. Green, also contributed to this
article.
t, L/
Corporate business travelers are a target of cities' business development efforts.
kJTII. ITV
I
~ t~ella
,,~Corporation
Pella is a major funder of the runway e~l~ansion
and has a hanger of its own at the Pella Airpoll.
t
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CIIYSCAPtl October 1999 15
B/2B/99 BB:4B:OB
->' +319-q56~i11119 IDYll CITY CLBItX
Johnson County
IOWA m~
1
Jonathan Jordahl, Chair
Charles D. Duffy
Michael E. Lehman
Sally Stutsman
Carol Thompson
-'BOARD OF SUPERVISORS
Agenda
B0ardroom - 2nd Floor
Jghnson County Administ~t,ign Building
913 South Dubuque Street
Iowa City, Iowa 52240
October 21, 1999
Call to order 9:00 a.m.
2. Action re: claims
Action re: formal minutes for canvass of votes for Iowa
Election of October 7m~, axxd .:the.. formal minutes of October 14th
Action re: payroll authorizations'
. "~: ...'.
Business from the County Auditor
a) Action re: permits
b) Action re: repo~ ¢ ..;i~,~ ;i :: '., (.
c)
1. CoUnty Recorder' s monthly rei~ort' of fees collected
Other.
, : :.,
City Primary
6. Business from. ~ :Planning and Zoning Administrator
a) Final con,sid~ation of ,application Zpp2,9of. Bernard and Phyllis Marak.
b) Final consideration of application Z9~36 of John Conner.
c) Final conSide. ration of application Z9937 of Keith and Karen Millard.
d) Final eonSide:r~tio,n,.p.[~app!iC~!on Z9938of Claudette Stratton.
e) Other
· .....: :.,: .:... ..':,,,:
913 SOUTH DUBUQUE STREET, SUITE 201 "' . ,IOWA CITY, IOWA 52240-4207
.,:' .' ..:. . ':.~ ..... :~:i :.5,'.;!..',! '
TEL: (319) 356-6000 FAX: (319) 354-4213
18/28/99 08:40:38 319-]54-4213 -> +31915GSB09 IOg~ CITY ELERR Page 082
Agenda 10-21-99
Page 2
Continuation of Public Hearing on Zoning application:
a) First and SecOnd consideration of the following Zoning application:
Application Z9935 of Terry Duwa, Lone Tree, Iowa, requesting
rezoning of 3.00 acres from A1 Rural to RS-3 Suburban
Residential of certain property described as being Lot 1 of
Schomberg Farmstead located in the NE 1/4 of Section 21;
Township 77 North; Range 5, W, est, of the 5th P.M. in Johnson
County, Iowa (This property is located on the west side of Wapsi
Avenue SE, approximately 560 feet south of its intersection with
640th Street SE in Fremont Twp.).
Business from the County Attorney
a) Disdussion/action re: authorizing Chairperson to sign subcontracts for
the FY 2000 J.uvenile,.. Crime prevemi0n C. ommunity Grant program.
b) Discussion/action re: Administrative Unit Collective Bargaining
Agreeme.nt Grievance regarding Hours of Work posting grievance.
c) Other
Business ffom'th~ Board of Supervisors,
a) Discussi6n/~icti0n r~:' proposal .for videoi'aping the Board of Supervisors'
weekly 're. formal and .f0rm. a~ ~eetings .for .the 1999-2000 year.
b) Discussion/aCti0n. re:..var!?U.s' .options ,regarding videotaping of Board of
Supervisors' informal and formal meetings.
e) Discussion/action re: options for increasing viewership of Board of
Supervisors' meetings.
d) Discussion/action re: Capital projects-funding of fire code compliance
project at ambulance service.
e) Discussion/actlori' re':' "ReSoluiion i~6r Axiicles of Agreement for the
Region 10 Chief Elected Official Consortium Workforce Investment
Act of 1998.' ........ ; ' '
Discussion/action re: Regional Workforce Investment Board and
r)
g)
Elected Officials BoardAgreement.
Other '
.;;,~. . ,~. :.- i('... .! .... ,
Chief
18/20/99 88:41:15 919-354-4213 -> +319~565809 IOW~ CITY CLgRR Page 889
Agenda 10-21-99
10. Adjoum to informal meeting
Page 3
11.
a) Reports and inquiries from the County Attomey
b) Inquiries and reports from the public
c) Reports and inquiries from the members of the Board of Supervisors
d) Other '
e) Announcements
f) Executive-Session re: Administrative Unit Collective
Agreement Grievance regarding Hours of Work posting
discussion/action
Bargaining
griovanc¢.
1:30 p.m. - Evaluation and goals of the Administrative Assistant to the
Board of Supervisors: (possible executive session to evaluate the
professional competency of individuals whose appointment~ hinng~
performance, or. dis_ch~g~.~s being considered... ) discussion
12. Adjournment
ZT4 C~ c~
IOWA STATE UNIVERSITY
OF SCIENCE AND TECHNOLOGY
Cooperative Extension
October 26, 1999
City of Iowa City
City of Coralvitle
Iowa City Chamber of Commerce
Board of Supervisors
Johnson County
4-H Fairgrounds
4265 Oak Crest Hill Road SE
Iowa City, Iowa 52246-5881
319-337-2145 Phone
319-337-7864 Fax
I want to take this opportunity to thank you for attending the Rural Development
Initiative meeting held on Thursday, October 21, at the Coralville City Hall. As promised
I am providing you with copies of items that were presented that evening. They are as
follows:
· Iowa Retail Trade Market Share
· Retail Trade Analysis for Iowa City and Coralville
I have requested a new analysis of the past year' s data to include Coralridge Mall
and will have that for you after the first of the year.
· 1998 Pilot Land Use inventory
· Iowa City RDI Report
· Coralville RDI Report
Please share these items with those interested. Again, thank you for using ISU Extension.
Sincerely,
Gene Mohling
County Extension Education Director
Iowa State University and U.S. Department of Agriculture cooperating
Extension programs are available to all without regard to race, color, national origin, religion, sex, age, and disability.
IOWA STATE UNIVERSITY
OF SCIENCE AND TECHNOLOGY
Department of Economics
Heady Hall
Ames, Iowa 5ool ~-lo7o
515 294-67-to
FAX 515 294-o221
October 25, 1999
Gene Mohling
Johnson County Extension Education Director
4265 Oak Crest Hill Road, SE
Iowa City, IA 52246
Dear Gene:
Enclosed please find an updated Retail Trade Analysis for Iowa City and Coralville. The data for
th~ii/TCbi'~iidge i~tl is n01r inClu~(~'d in this report because the Department of Revenue and
Finance reports their data on fiscal years running from April 1 through March 31. For example,
the fiscal year 1998 ran from April 1, 1997 to March 31, 1998. Since the mall opened in July of
1998, it won~ be apparent in the data until fiscal year 1999. We hope to have the data for FY99
soon, but have yet to receive anything from IDRF.
I hope this is helpful. Please feel free to contact me if I may be of any further assistance.
Sincerely,
Georgeanne M. Artz
Extension Program Specialist
(515) 294-6271
em all: gartz ~ i astate. edu
Retail Trade.AnalySis .f998..
Iowa City &
Johnson County, Iowa
Prepared by:
Dr. Kenneth E. Stone
&
Georgeanne M. Artz
460 Heady Hall, ISU
Ames, IA 50011
Ph. (515) 294-7318
FAX (515) 294-1700
Email kstone@iastate.edu
or gartz~iastate.edu
Developed by Scott J. Baumler
Iowa State University
University Extension
Ames, Iowa
...and justice for all
The Iowa Cooperative Extension Service's programs and policies are consistent with pedinent federal and state laws and regulations on
nondiscrimination regarding race, color, national origin, religion, sex, age and disability.
Issued in fudheranoe of Cooperative Extension work, Acts of May 8 and June 30, 1914, in cooperation with the U.S. Department of
Agriculture. Stanley R. Johnson, direotor, Cooperative Extension Service, Iowa State University of Science and Technology, Ames, Iowa.
L Introduction
Iowa State University Extension Service has been
conducting retail trade analyses for more than 20
years. The main purpose of these studies is to
inform business people and other dtizens of the
history and current status of their retail sectors. By
knowing the relative strengths and weaknesses of a
town's business distdct compared to those of
competing towns, it is hoped that merchants will
build upon strengths and capitalize on the areas of
opportunity. The following paragraphs will attempt to
answer some of the questions most frequently asked
over the last several years.
Sources of Data: Most of the data in the analysis is based on the Iowa Retail Sales and
Use Tax Report. This report is compiled by the Iowa Department of Revenue and Finance
from state sales tax returns. The report is published quarterly, with an annual summary.
The repods publish data from all towns in the state that have at least 10 businesses with
sales tax permits. For towns above 2,500 population, the report also lists the sales for
broad merchandise categories such as building materials, food, apparel, etc. The sales for
counties are also listed for merchandise categories and in total. Although there are some
minor quirks in this data base, it is more complete and more reliable than those from most
other states.
The income data come pdmadly from Survey of Buying Power, published by Sales and
Marketing Management Magazine. This organization updates Census baseline data
annually and has a good overall accuracy record. Many professional marketing research
firms use this data. Population statistics are based on Census Bureau estimates.
Fiscal Year Period: All the sales data shown in this report are reported by fiscal year.
These fiscal years correspond to the Department of Revenue and Finance's fiscal year,
which runs from Apdl I through March 31. For example, fiscal year 1998 began on Apdl 1,
1997 and ended on March 31, 1998. It should be noted that these fiscal years are different
from the State's budgeting fiscal years which begin on July 1 and end on June 30.
Taxable Goods and Services: The sales reported in this report are based on goods and
services subject to the retail sales tax. An approximation of food and drug sales which were
exempted in 1974 has been added in each year since then to maintain continuity. This
addition has vaded between 15 and 18 percent. Other notable exemptions are feed, seed,
and fertilizer; new and used automobiles (these are subject to a use tax that is credited to
the county of the purchaser); professional services such as medical and legal; and farm
machinery and equipment. It can therefore be seen that the sales reported herein
understate total retail sales. However, all towns and counties are compared on the same
basis.
Prepared by ISU Extension 10/22/99 Page 1
Number of Retail 'Firms: Business counts are based on the number of quarterly sales tax
returns filed and are converted to "full-time equivalents."
· Current Dollar Sales: Current dollar sales are sales as reported by the state. In other
words, no correction has been made for price inflation. In general this measure of sales is
not very satisfactory for comparisons over time since it does not take into consideration
changes in population, inflation, or the state's economy. Current dollars are also known as
"nominal dollars."
Constant Dollar Salesi Constant dollar sales reflect changes in pdce inflation. The
method used in this report uses the Consumer Pdce Index (CPI-W calculated to match
fiscal years) to adjust current dollar sales. Constant dollar sales indicate the real sales
level with respect to'some base year. This is a more realistic method of evaluating sales
over time than current dollar comparisons, but still does not take into consideration
changes in population or changes in the state's economy. Constant dollars are sometimes
referred to as "real dollars" as well.
Per Capita Sales: Per capita (or "per person") sales are calculated by dividing current
dollar sales by the population estimate. In areas where population is subject to substantial
change, this is a more satisfactory measure of sales activity than sales alone. However, it
still does not reflect changes in the state economy.
Pull Factor: The pull factor was developed by Iowa State University Extension Service to
provide a precise measure of sales activity in a locality. It is derived by dividing the per
capita current dollar sales of a town or county by the per capita sales for the state. For
example, if a town's per capita sales were $20,000 per year and the state per capita sales
were $10,000 per year, the pull factor is 2.0 ($20,000 + $10,000). The interpretation is that
the town is selling to 200 percent of the town population in full-time customer equivalents.
Pull factors are good measures of sales activity because they reflect changes in population,
inflation, and the state economy. Pull factors are available through the Extension Service
for total taxable sales for all towns with reported sales over the last 25 years. For towns
with populations greater than 2,500, pull factors are available by merchandise category for
the past 20 years. The pull factors listed in this report are not adjusted for purchasing
power; they are simply the ratio of local per person sales to the state average. Income
levels are accounted for in the expected sales and potential sales formulas, described
below.
Effective BuVin¢~ Income (EBI): A statistic developed by Sales & Marketing Management
(Bill Communications, Inc.). EBI is defined as income (wages, salary, dividends, interest,
pensions, etc.) less taxes and certain other nontax items. Commonly it is referred to as
"income after taxes" or "disposable personal income."
Index of Income: This index is calculated by dividing local per capita income by state per
capita income. It is a relative measure of income, with the base being 100 (sometimes it is
Prepared by ISU Extension 10/22/99 Page 2
expressed in a dedmal format, such as 1.00). For example, an index number of 120 would
indicate that per capita income in that area is 20 percent above th'~9 state average.
Expected Sales: Expected sales is an estimate of the sales level a town should achieve if
it were performing on a par with towns of a similar size in Iowa. In essence, it is a retail
performance benchmark. In addition to population and income variables, expected sales
incorporates the typical strength of comparable communities via the typical pull factor
component in the equation below:
Population x Capita Sales x Similarly- Sized Towns x = Expected Sales
For example, if a town had a population of 5,000, the state per capita sales were $9,000,
the typical pull factor was 1.30, and the index of income was 1.03, expected sales would be
approxjmately $60 million per year (5,000 x $9,000 x 1.30 x 1.03). This provides a means
of comparing what is expected for a town of a certain size to what is actually happening.
Variance Between Actual Sales & Expected Sales: The vadance between actual and
expected sales is how much retail sales differ from the "norm" (i .e., the amount above or
below the standard established by the expected sales formula). The set of similarly-
sized towns in Iowa is the "peer group" to which the comparison is being made.
Discrepandes between expected and actual sales occur for a vadety of reasons. Proximity
to larger population centers, management, marketing, and transportation patterns are just a
few factors that can cause the retail sales of a particular town to deviate substantially from
expected sales. It is important that derision-makers consider these influences when
constructing policies, plans, or projects. The vadance between actual and expected sales
is expressed in dollars, percentages, and customer equivalents. The use of the analysis
will dictate which measure best conveys the information, though all are equivalent.
Potential Sales: Potential sales is a term used with reference to counties. The formula is:
County State Per
Potential sales is an estimate of the amount of money that could be spent on retail
goods and services by residents of the county. The potential sales concept for counties
is similar to the expected sales calculations for towns, though it differs in that potential sales
does not utilize a measure of typical pulling power (like the typical pull factor that is used in
the expected sales equation). Since a county is a large, well-defined region within which
retail business takes place, counties are compared to other counties without
adjustments for trade area size (the pull factor is set at 1.0).
Prepared by ISU Extension 10/22/99 Page 3
Surplus or Leakage: If the estimate of available money does not match what is
actually spent, then business is apparently going elsewhere. This is the "surplus" or
the "leakage", which is calculated by finding the difference between potential county
sales and actual county sales. This statistic attempts to quantify the actual net inflow
or net outflow of money.
The "trade area population gain or loss" statistic transforms surplus and leakage dollar
figures into full-time customer equivalents. Note that the equivalent gain or loss of
customers and the dollar gain or loss to county are gross measures, while surplus or
leakage as a percent of potential sales is a relative measure.
The Utility Effect: Because utilities are subject to the sales tax in Iowa, cedain areas
may seem to have exaggerated retail sales. Occasionally, utility companies relocate or
change billing operations. This can result in a large, abrupt change in reported retail
sales. These facts should be considered when analyzing total sales.
The College Effect: Counties such as Story and Johnson often appear as leakage
counties. People assume that with all the sporting, entertainment, and cultural events
surrounding the universities, a retail sales surplus will automatically ensue. However,
the student enrollment at these universities is counted as part of that county's
population. Past studies indicate that college students spend only about half as much
per capita in the college county as do the full-time residents. Consequently, the
counties seem to have retail leakages.
Leakage From Metropolitan Counties: Most people assume that metropolitan
counties will have a surplus in retail trade. However, occasionally a county such as
Pottawattamie will indicate a retail sales leakage. These situations are difficult to
explain, but usually counties of this type are near competing large trade centers which
capture retail sales from the surrounding areas.
Town & County Performance: Sometimes a county exhibits an overall trade surplus
while the statistics for a large community within the county indicate under-performance,
or vice versa. Apparent discrepancies between town and county performance can
occur due to the manner in which potential sales are calculated for counties and
expected sales are calculated for towns.
The statistics have different meanings and uses. As described above, surplus and
leakage statistics are derived by comparing actual sales to potential sales. The
resultant surpluses or leakages indicate trade inflows or outflows. For towns, expected
sales is a "par value" performance criterion. As such, variances from expected sales
indicate retail strength relative to comparable communities across Iowa.
Revisions: Population data are frequently updated and methods for calculating typical
pull factors are periodically revised. As such, some statistics in this trade analysis may
not match previous versions.
Prepared by ISU Extension 1 0122/99 Page 4
II. Overview
Retail sales in Iowa City totaled $806.31 million in 1998. Compared to 1997, sales
grew 3.45%. Adjusting for inflation, that was a "real" change of +1.64%.
· For the state as a whole, total retail sales were $29.13 billion. Sales increased
3.28% between 1997 and 1998; 1.47% in inflation-adjusted dollars.
Sales per business in Iowa City rose 2.53% in that same time frame, which
translates into growth of 0.74% once inflation is taken into account. Statewide,
sales per business' rose 4.43% in nominal terms and rose 2.61% in real terms.
In 1998, Iowa City had 175% as many retail businesses as it had in 1971. In real
terms, average. revenue per business was 98% of what it was in 1971. The
comparable state statistics are 124% and 98%, respectively.
The rate of growth in total retail sales between 1971 and 1998 averaged 7.38%
annually for Iowa City. In inflation-adjusted terms, the average annual rate of
change was +2.01%.
For the state, the nominal average annual rate of growth between 1971 and 1998
was 6.06%. The real rate was +0.75%
Retail sales in Iowa City amounted to $13,193 per capita in 1998. This was 129%
of the state average, $10,206.
$9O0.0
Iowa City: Total Retail Sales
$800.0 ........................................................................................................................
$700.0 .........................................................................................................................
$500.0
$400.0 .....................................................................................................................
$300.0 .........................................................................................................................
$0.0 ~1 I I I I I I I I I I I I I I I I I I I
71 74 77 80 83 86 89 92 95 98
Fiscal Year
Currents Total Sales --m--Constants Total Sales
Prepared by ISU Extension 10/22/99 Page 5
Retail sales in Coralville totaled $196.84 million in 1998. Compared to 1997, sales
declined 1.17%. Adjusting for inflation, that was a "real" change of -2.9%.
For the state as a whole, total retail sales were $29.13 billion. Sales increased
3.28% between 1997 and 1998; 1.47% in inflation-adjusted dollars.
Sales per business in Coralville fell 2.52% in that same time frame, which translates
into a decline of 4.22% once inflation is taken into account. Statewide, sales per
business rose 4.43% in nominal terms and rose 2.61% in real terms.
In 1998, Coralville had 318% as many retail businesses as it had in 1971. In real
terms, average. revenue per business was 113% of what it was in 1971. The
comparable state statistics are 124% and 98%, respectively.
The rate of decline in total retail sales between 1971 and 1998 averaged 10.36%
annually for Coralville. In inflation-adjusted terms, the average annual rate of
change was +4.83%.
For the state, the nominal average annual rate of growth between 1971 and 1998
was 6.06%. The real rate was +0.75%
Retail sales in Coralville amounted to $16,645 per capita in 1998. This was 163%
of the state average, $10,206.
$250.0
Coralville: Total Retail Sales
$200.0 .........................................................................................................................
$150.0 .........................................................................................................................
$100.0 ............
$50.0 .....................................................................................................--~ ....;
,=.==:.~:=: ~
, : ,.'. ,
$O.Olllllllttlllllllllllll II I
71 74 77 80 83 86 89 92 95 98
FiscalYear
~CuffentSTotalSales --m-ConstantSTotalSales
Prepared by ISU Extension 10/22/99 Page 5a
Currants Total
Fiscal Retail Sales
Year (in millions)
1971 $117.79
1972 $128.22
1973 $135.80
1974 $151.08
1975 $169.34
1976 $187.83
1977 $213.65
1978 $237.69
1979 $255.26
1980 $277.43
1981 $289.74
1982 $314.56
1983 $343.75
1984 $375.21
1985 $398.05
1986 $408.16
1987 $450.48
1988 $463.11
1989 $503.06
1990 $534.61
1991 $553.11
1992 $580.98
1993 $617.45
1994 $665.60
1995 $707.82
1996 $756.33
1997 $779.39
1998 $806.31
% Change
'71 to '98 +584.54%
% Change
'97 to '98 +3.45%
Iowa City: Total Retail Sales
Constants Total Currents
Retail Sales Number of Sales Per
(in millions, base=FY71)Retail FirmsFirm
$117.79 821 $143,513
$123.28 847 $151,376
$126.91 843 $161,090
$130.24 835 $181,038
$131.27 838 $202,079
$135.13 778 $241,576
$145.34 795 $268,825
$151.40 898 $264,841
$150.15 971 $262,886
$145.25 1,004 $276,397
$134.76 1,049 $276,270
$133.86 1,056 $297,953
$139.17 1,097 $313,428
$147.14 1,105 $339,637
$150.78 1,131 $351,947
$150.06 1,168 $349,375
$163.22 1,199 $375,873
$161.36 1,210 $382,658
$168.25 1,232 $408,414
$170.26 1,272 $420,459
$167.10 1,285 $430,435
$169.88 1,333 $436,006
$175.41 1,358 $454,763
$183.87 1,369 $486,372
$190.79 1,406 $503,607
$197.99 1,406 $538,120
$198.32 1,421 $548,481
$201.58 1,434 $562,376
Constants
sales Per
Firm
$143,513
$145,554
$150,551
$156,067
$156,650
$173,796
$182,874
$168,689
$154,639
$144,711
$128,498
$126,789
$126,894
$133,191
$133,313
$128,447
$136,186
$133,330
$136,593
$133,904
$130,041
$127,487
$129,194
$134,357
$135,743
$140,869
$139,563
$140,594
Per Capita
Sales
$2,514
$2,716
$2,855
$3,153
$3,507
$3,861
$4,359
$4,813
$5,130
$5,534
$5,736
$6,124
$6,581
$7,064
$7,369
$7,431
$8,O65
$8,153
$8,709
$9,101
$9,259
$9,698
$10,247
$10,995
$11,553
$12,346
$12,793
$13,193
Pull
Factor
1.19
1.21
1.17
1.13
1.11
1.10
1.11
1.13
1.07
1.05
1.09
1.13
1.17
1.21
1.23
1.24
1.26
1.21
1.23
1.22
1.20
1.21
1.23
1.26
1.27
1.30
1.29
1.29
+71.14% +74.69% +291.86% -2.03% +424.77% +8.38%
+1.64% +0.90% +2.53% +0.74% +3.13% -0.05%
Prepared by ISU Extension 10/22/99 Page 6
currents Total
Fiscal Retail Sales
Year (in millions)
1971 $13.75
1972 $15.03
1973 $17.20
1974 $22.36
1975 $25.53.
1976 $30.47
1977 $37.87
1978 $39.30
1979 $43.80
1980 $49.51
1981 $56.55
1982 $57.98
1983 $69.81
1984 $77.94
1985 $79.43
1986 $83.98
1987 $89.71
1988 $96.55
1989 $112.31
1990 $123.56
1991 $133.65
1992 $141.53
1993 $159.02
1994 $168.50
1995 $183.22
1996 $178.64
1997 $199.18
1998 $196.84
% Change
'71 to '98 +1,331.12%
% Change
'97 to '98 -1.17%
Coralville: Total Retail Sales
Con~ant$ Total Currents
Retail Sales Number of Sales Per
(in millions, base=FY71)Retail FirmsFirm
$13.75 128 $107,877
$14.45 133 $113,190
$16.07 142 $121,332
$19.28 152 $147,343
$19.79 161 $158,310
$21.92 149 $204,511
$25.76 161 $234,838
$25.03 172 $228,483
$25.77 188 $233,311
$25.92 203 $243,915
$26.30 213 $265,497
$24.67 216 $268,446
$28.26 227 $307,174
$30.57 245 $318,786
$30.09 248 $320,283
$30.87 265 $317,194
$32.50 275 $326,800
$33.64 265 $363,988
$37.56 270 $415,981
$39.35 279 $442,882
$40.38 281 $476,460
$41.38 299 $472,956
$45.18 340 $468,406
$46.55 349 $482,796
$49.39 361 $508,243
$46.76 382 $467,950
$50.68 400 $498,261
$49.21 405 $485,726
constants
sales Per
Firm
$107,877
$108,836
$113,395
$127,020
$122,721
$147,130
$159,754
$145,531
$137,242
$127,704
$123,487
$114,232
$124,362
$125,014
$121,319
$116,615
$118,406
$126,825
$139,124
$141,045
$143,946
$138,291
$133,070
$133,369
$136,993
$122,500
$126,784
$121,432
Per Capita
Sales
$2,244
$2,396
$2,681
$3,408
$3,804
$4,439
$5,393
$5,472
$5,962
$6,589
$7,357
$7,322
$8,557
$9,275
$9,175
$9,417
$9,765
$10,202
$11,51 9
$12,302
$12,917
$13,501
$14,609
$15,067
$16,026
$15,412
$16,895
$16,645
Pull
Factor
1.06
1.07
1.10
1.22
1.20
1.27
1.38
1.28
1.24
1.25
1.40
1.35
1.53
1.59
1.54
1.57
1.52
1.52
1.63
1.65
1.67
1.68
1.75
1.73
1.76
1.62
1.71
1.63
+257.78% +217.84% +350.26% +12.56% +641.82% +53.21%
-2.90% +1.38% -2.52% -4.22% -1.48% -4.52%
Prepared by ISU Extension 10t22/99 Page 6a
III. Historical Performance
Iowa City
Individual Firms
200%
180% .................................................................................................................................
160%
140%
120%
loo%
80%
60%
40%
20%
0%
77 80 83 86 89 92 95 98
Fiscal Year
[] Number of Firms [] Constants Sales per Firm
$120.0
Constants Seasonal Retail Sales
$100.0 ................................................................................................................
$80.0
$60.0 ~ !i '
$40.O
$20.0
$0.0
80 82 84 86 88 90 92 94 96
Fiscal Year
DQ1 OQ2 IQ3
Note: Q'I ends June 30, Q2 ends Sept. 30, Q3 ends Dec. 3`1, & Q4 ends March 3'1.
iilQ4
98
Prepared by ISU Extension 10/22/99 Page 7
2.5O
Iowa City
Index of "Pulling Power" by Merchandise Category
2.00 .....................................................................................................................................
I~ 1.50 --
~,~
~ 1.00 '
, .................., ................ ............. .....
0.50 ~>----¢------%----:----: ...........................................
0.00 I I I I I I I I I I I { I { I I I I I I
76 78 80 82 84 86 88 90 92 94 96 98
Fiscal Year
,~ General --m- Food
Merchandise Stores
Utility ~> Building x Motor ¢ Apparel
Services Materials Vehicles Stores
Fiscal Utility Building General Food Motor Apparel
Year Services Materials Merchandise Stores Vehicles Stores
76 1.28 0.55 1.71 1.43 0.75 1.38
77 1.25 0.61 1.72 1.39 0.78 1.43
78 1.27 0.66 1.77 1.36 0.83 1.45
79 1.27 0.56 1.72 1.41 0.80 1.39
80 1.24 0.51 1.64 1.43 0.75 1.37
81 1.23 0.54 1.57 1.44 0.74 1.36
82 1.19 0.53 1.52 1.53 0.77 1.67
83 1.18 0.61 1.45 1.49 0.83 1.80
84 1.12 0.72 1.57 1.55 0.83 1.72
85 1.15 0.65 1.56 1,45 0.90 1.86
86 1.14 0.61 1.51 1.41 0,91 1,95
87 1.14 0.56 1.44 1.50 0.88 1.85
88 1.13 0,59 1.37 1.55 0.87 1,80
89 1.16 0.61 1.28 1.54 0.85 1.68
90 1.12 0,65 1.32 1.52 0.85 1,67
91 1.10 0.61 1.30 1.51 0.80 1.59
92 1.11 0.62 1.38 1.46 0.82 1.49
93 1.12 0.61 1.51 1.36 0.80 1.39
94 1.20 0.66 1.53 1.45 0.84 1.48
95 1.19 1.03 1.50 1.39 0.87 1.47
96 1.19 1.24 1.45 1.51 0.89 1.50
97 1.15 1.25 1.44 1.60 0.89 1.64
98 1.10 1.37 1.50 1.74 0.98 1.68
% Chg.
'76 to '98 -14.12% +150.57% -11.92% +22.01% +30.32% +21.87%
% Chg.
'97 to '98 -4.80% +9.71% +4.19% +8.83% +9.58% +1.93%
Prepared by ISU Extension 10/Z2/99 Page 8
Coralville
Index of "Pulling Power" by Merchandise Category
4.50
4.00 ...........................- ...........................................................................................................
3.50
.... m,~
3.00
2.50 ...................................................................................................
2.00 ......................................................................................................................................
0.00 I I I I I I I I I I I I I I I I I I I 1
76 78 80 82 84 86 88 90 92 94 96 98
Fiscal Year
* General --m-- Food
Merchandise Stores
Utility o Building · Motor +Apparel
Services Materials Vehicles Stores
Fiscal Utility Building General Food Motor Apparel
Year Services Materials Merchandise Stores Vehicles Stores
76 0.15 #N/A #N/A #N/A 1.06 #N/A
77 #N/A #N/A #N/A 2.92 1.11 #N/A
78 #N/A #N/A #N/A 2.75 1.15 #N/A
79 #N/A 2.13 #N/A 2.78 1.08 #N/A
80 #N/A 2.21 #N/A #N/A 1.18 #N/A
81 #N/A 2.54 #N/A #N/A 1.72 #N/A
82 #N/A 2.99 #N/A #N/A 1.39 #N/A
83 #N/A 3.35 #N/A #N/A 1.37 #N/A
84 #N/A 3.49 #N/A 2.54 1.24 #N/A
85 #N/A 3.55 1.82 2.54 1.21 #N/A
86 #N/A 3.65 1.72 2.59 1.42 #N/A
87 #N/A #N/A 1.29 #N/A 1.41 #N/A
88 #N/A #N/A 0.98 #N/A 1.17 #N/A
89 #N/A 3.22 0.91 #N/A 1.15 #N/A
90 #N/A 3.26 0.87 #N/A 1.13 #N/A
91 #N/A #N/A 0.80 #N/A 1.16 #N/A
92 #N/A #N/A 0.77 #N/A · 1.19 #N/A
93 0.22 3.47 0.69 2.23 1.21 #N/A
94 0.24 3.83 0.70 2.52 1.07 #N/A
95 0.22 3.30 0.66 2.53 1.13 #N/A
96 0.31 2.56 0.20 2.84 1.19 #N/A
97 0.27 2.23 0.22 2.45 1.24 0.36
98 0.27 2.07 0.31 2.78 1.23 #NIA
% Chg.
'76 to '98 +78.95% #N/A #N/A #N/A +15.79% #N/A
% Chg.
'97 to '98 +0.48% -7.27% +39.56% +13.40% -1,30% #N/A
#N/A means the information was not available.
Prepared by ISU Extension 10/22/99 Page 8a
~OWa City
Index of "Pulling Power" by Merchandise Category
4.00
3.so .......................................: ..........................................................................................................
3.00 ..............................................................................................................: .................................
2.so ................................................................................................................................................
2.00 ~ ....;: ....-~ ....~- ....~ .....':"' ~ "'~""'~ ....'~' ....~: .........................................i ....'~:' .~~
4.50 -;-~ ....-~ ....~- ....j- ......~ ....; ......~ ......: ........- .....-=- ....~ .....~ ....-=- ....~ .....~ ....
~.oo .........................................................................................................................
~ ~- ........................................ ~ ....~ · · ~ · X
0.00 I I I I I I I
76 78 80 82
84 86 88 90 92 94 96 98
Fiscal Year
Home e Eating & ,~ Specialty + Services ·Wholesale
Furnishings Drinking Stores
Fiscal Home Eating & Specialty
Year Furnishings Drinking Stores Services Wholesale
76 1.00 1.26 1.89 1.25 0.46
77 1.05 1.38 1.81 1.22 0.48
78 0.97 1.36 1.81 1.22 0.44
79 0.97 1.35 1.87 1.22 0.40
80 0.99 1.40 1.91 1.17 0.38
81 1.11 1.42 1.92 1.20 0.44
82 1.15 1.53 2.00 1.22 0.41
83 1.23 1.70 1.95 1.23 0.46
84 1.30 1.69 1.93 1.22 0.50
65 1.34 1.72 1.91 1.34 0.52
86 1.21 1.72 1.84 1.34 0.60
87 1.32 1.95 1.83 1.31 0.78
88 1.71 1.77 1.67 1.17 0.66
89 1.67 1.76 1.64 1.27 0.85
90 1.69 1.74 1.61 1.30 0.72
91 1.72 1.74 1.62 1.26 0.58
92 1.75 1.78 1.67 1.25 0.59
93 1.87 1.86 1.69 1.27 0.51
94 1.92 1.89 1.74 1.27 0.51
95 2.03 1.89 1.74 1.25 0.52
96 2.72 1.87 1.73 1.24 0.50
97 2.98 1.86 1.72 1.20 0.52
98 3.41 1.71 1.52 1.19 0.51
% Chg.
'76 to '98 +239.03% +35.38% -19.70% -4.42% +10.71%
% Chg.
'97 to '98 +14.09% -8.20% -11.67% -0.24% -1.66%
Prepared by ISU Extension 10/22/99 Page 9
Coralville
Index of "Pulling Power" by Merchandise Category
6.00
5,00 .........................i~ .......................................................................................................................
4.00 ~- ....o --Z ............................................................................................/
3,00 ..........................................................................~ ._.~...e....'~__, ...... o
2.00 -'* ........-\ ..........................................: ........................' ....: ....."- .....! ......
1,00 ~ ........ -~ ....~- ....
. ~
0,00 I I I I I I I I I I ] I I I I I I I I I I
76 78 80 82 84 86 88 90 92 94 96 98
Fiscal Year
Home o Eating& *Specialty -m-Services ·
Furnishings Drinking Stores
- Wholesale
Fiscal Home Eating & Specialty
Year Furnishings Drinking Stores Services Wholesale
76 2,27 3,88 1.43 2,50 0,92
77 2,60 3,96 1,25 2,20 1,23
78 2,62 4,09 1,22 1,83 0,96
79 1,89 4,82 1,09 1,84 0,71
80 1,87 4,82 1,04 1,73 0,72
81 1,89 4,74 1,04 1,78 0,70
82 2,13 4,50 1,06 1,69 0,66
83 2,48 4,26 1,12 1,80 0,80
84 3,26 4,04 1,12 1,95 1,19
85 2,66 4,17 0,95 1,81 0,95
86 2.25 4,57 0,98 1,77 0,87
87 2,35 4,41 0,90 1,71 1,07
88 2,35 3,72 2,73 1,77 1,12
89 2,59 3,36 3,74 1,80 0,91
90 2.95 3,49 3.72 1,90 0,89
91 3,44 3,51 3,27 2,08 0,95
92 2,69 3,46 3,48 2,11 1,01
93 3,82 3,42 3,43 2,28 1,08
94 3,76 3,03 2,80 2,26 1,18
95 3,71 3,34 3,43 2,18 1,19
96 3,57 3,27 3,69 1,96 1,18
97 3,92 3,35 3,36 2,03 2,35
98 4,26 3,40 3,08 2,10 1,31
% Chg,
'76 to '98 +87,71% -12,30% +115,42% -15,99% +41,58%
% Chg,
'97 to '98 +8,66% +1,46% -8,25% +3,75% -44,46%
Prepared by ISU Extension 10/22/99 Page 9a
IV. Historical Comparisons
Currents Total Retail Sales
(in millions)
$2,500.0
$2,000.0 .....................................................................
$1,500.0 ..............................................................
$1,000.0 ..............................................................
$500.0 ............................................................
$o.o + *"'+ ,*,******" ' '
71 74 77 80 83 86 89 92 95 98
Fiscal Year
Constants Total Retail Sales
(in millions)
$700.0
$600.0 .......................................................................
$500.0
$400.0 ............................................
$300.0 ~
$2oo.o ..........................................
$0.0 "++~*******"" **A*****" *'
71 74 77 80 83 86 89 92 95 98
Fiscal Year
$25,000
Per Capita Retail Sales
2.50
Index of "Pulling Power"
$20,000
$15,000
$10,000
$5,000
71 74 77 80 83 86 89 92 95 98
Fiscal Year
2.00 ....................................................................
1.50
000~~
1.00 ............................................................................
0.50 ............................................................................
0.00
71
74 77 80 83 86 89 92 95
Fiscal Year
98
--E}-- Cedar Rapids
x Davenport
Coralville
Iowa City
Prepared by ISU Extension 10/22/99 Page 10
V. Comparative Analysis
Iowa City
Trade Area Analysis of Retail Sales, 1997
Variance Between Actual & Expected
Expected Actual In Full-Time
Sales Sales In Dollars As % of Customer Number
Merchandise Group (in millions) (in millions)(millions) Expected Equivalents of Firms
Building Materials $57.47 $41.94 -$15.52 -27.0% -24,766 31
General Merchandise $156.10 $106.86 -$49.25 -31.5% -35,581 27
Food $137.10 $131.93 -$5.17 -3.8% -3,365 29
Apparel $27.35 $21.78 -$5.57 -20.4% -22,527 56
Home Furnishings $32.97 $62.69 +$29.73 +90.2% +75,771 69
Eating &Drinking $83.90 $83.95 +$0.05 +0.1% +61 132
Specialty Stores $68.81 $66.80 -$2.01 -2.9% -2,764 297
Services $109.31 $83.97 -$25.34 -23.2% -19,344 473
Wholesale $84.33 $27.35 -$56.98 -67.6% -57,950 77
Total Sales* $1,009.39 $779.39 -$229.99 -22.8% -20,432 1,421
Percent of
Total Sales
5.4%
13.7%
16.9%
2.8%
8.0%
10.8%
8.6%
10.8%
3.5%
100.0%
Iowa City
Trade Area Analysis of Retail Sales, 1998
Variance Between Actual & Expected
Expected Actual In Full-Time
Sales Sales In Dollars As % of Customer Number
Merchandise Group (in millions) (in millions)(millions) Expected Equivalents of Firms
Building Materials $58.91 $47.00 -$11.92 -20.2% -18,740 32
General Merchandise $162.48 $110.84 -$51.65 -31.8% -37,731 29
Food $134.02 $144.98 +$10.95 +8.2% +7,106 31
Apparel $28.13 $23.30 -$4.83 -17.2% -18,741 57
Home Furnishings $35.59 $74.20 +$38.62 +108.5% +95,516 66
Eating &Drinking $92.10 $83.30 -$8.80 -9.6% -9,717 132
Specialty Stores $80.43 $66.31 -$14.13 -17.6% -17,422 307
Services $115.62 $87.46 -$28.16 -24.4% -20,723 472
Wholesale $88.22 $27.26 -$60.97 -69.1% -61,596 74
Total Sales* $1,052.47 $806.31 -$246.16 -23.4% -21,268 1,434
Percent of
Total Sales
5.8%
13.7%
18.0%
2.9%
9.2%
10.3%
8.2%
10.8%
3.4%
100.0%
NA means the data were not available.
* All categories are included in the total sales category, including the Utilities, Miscellaneous, and Motor Vehicles merchandise groups.
Prepared by ISU Extension 10/22/99 Page 11
Coralville
Trade Area Analysis of Retail Sales, 1997
Variance Between Actual & Expected
Expected Actual In Full-Time
Sales Sales In Dollars As % of Customer Number
Merchandise Group (in millions) (in millions)(millions) Expected Equivalents of Firms
Building Materials $6.23 $14.48 +$8.25 +132.4% +13,160 6
General Merchandise $22.64 $3.14 -$19.50 -86.1% -14,090 5
Food $34.02 $39.06 +$5.04 +14.8% +3,281 6
Apparel $2.78 $0.92 -$1.86 -66.8% -7,512 5
Home Furnishings $4.25 $15.93 +$11.68 +274.7% +29,768 22
Eating &Drinking $13.43 $29.28 +$15.86 +118.1% +18,795 44
Specialty Stores $8.71 $25.30 +$16.59 +190.5% +22,839 64
Services $16.32 $27.52 +$11.20 +68.7% +8,552 149
Wholesale $10.52 $23.96 +$13.45 +127.8% +13,675 28
Total Sales* $155.43 $199.18 +$43.75 +28.1% +3,887 400
Percent of
Total Sales
7.3%
1.6%
19.6%
0.5%
8.0%
14.7%
12.7%
13.8%
12.0%
100.0%
Coralville
Trade Area Analysis of Retail Sales, 1998
variance Between Actual & Expected
Expected Actual In Full-Time
Sales Sales In Dollars As % of Customer Number
Merchandise Group (in millions) (in millions)(millions) Expected Equivalents of Firms
Building Materials $5.68 $13.71 +$8.03 +141.2% +12,621 5
General Merchandise $23.01 $4.37 -$18.65 -81.0% -13,623 6
Food $31.15 $44.72 +$13.57 +43.6% +8,803 7
Apparel $2.76 NA NA NA NA NA
Home Furnishings $4.58 $17.96 +$13.37 +291.7% +33,076 25
Eating &Drinking $13.76 $32.11 +$18.35 +133.3% +20,258 41
Specialty Stores $10.28 $26.08 +$15.80 +153.6% +19,483 68
Services $17.48 $29.81 +$12.33 +70.5% +9,075 148
Wholesale $8.74 $13.49 +$4.75 +54.3% +4,794 32
Total Sales* $167.91 $196.84 +$28.93 +17.2% +2,500 405
NA means the data were not available.
* All categories are included in the total sales category, including the Utilities, Miscellaneous, and Motor Vehicles merchandise groups.
Percent of
Total Sales
7.0%
2.2%
22.7%
NA
9.1%
16.3%
13.2%
15.1%
6.9%
100.0%
Prepared by ISU Extension 10/22/99 Page 11 a
Iowa CitylCoralville Combined
Trade Area Analysis of Retail Sales, 1997
variance Between Actual & Expected
Expected Actual In Full-Time
Sales Sales In Dollars As % of Customer Number
Merchandise Group (in millions) (in millions)(millions) Expected Equivalents of Firms
Building Materials $68.59 $56.42 -$12.17 -17.7% -19,409 36
General Merchandise $i86.31 $110.00 -$76.31 -41.0% -55,136 32
Food $163.63 $170,99 +$7.36 +4.5% +4,787 34
Apparel $32.64 $22.70 -$9.94 -30.5% -40,187 61
Home Furnishings $39.35 $78.62 +$39.28 +99.8% +100,116 91
Eating &Drinking $100.14 $113.24 +$13.10 +13.1% +15,527 176
Specialty Stores $82.12 $92.10 +$9.97 +12.1% +13,731 360
Services $130.46 $111.49 -$18.97 -14.5% -14,482 623
Wholesale $100.65 $51.32 -$49.33 -49.0% -50,175 105
Total Sales* $1,204.71 $978.57 -$226.14 -18.8% -20,090 1,821
Percent of
Total Sales
5.8%
11.2%
17.5%
2.3%
8.0%
11.6%
9.4%
11.4%
5.2%
100.0%
Iowa City/Coralville Combined
Trade Area Analysis of Retail Sales, 1998
variance Between Actual & Expected
Expected Actual In Full-Time
Sales Sales In Dollars As % of Customer Number
Merchandise Group (in millions) (in millions) (millions) Expected Equivalents of Firms
Building Materials $70.32 $60.71 -$9.61 -13.7% -15,106 37
General Merchandise $193.92 $115.20 -$78.72 -40.6% -57,512 35
Food $159.95 $189.70 +$29.74 + 18.6% + 19,294 38
Apparel $33,57 $23.30 -$10.28 -30.6% -39,843 57
Home Furnishings $42.47 $92.16 +$49.69 +117.0% +122,899 90
Eating &Drinking $109.93 $115.42 +$5.49 +5.0% +6,060 173
Specialty Stores $96.00 $92.39 -$3.61 -3.8% -4,452 374
Services $137.99 $117.27 -$20.72 -15.0% -15,248 620
Wholesale $105,30 $40.75 -$64.55 -61.3% -65,217 105
Total Sales* $1,256.13 $1,003.15 -$252.98 -20.1% -21,858 1,839
NA means the data were not available.
* All categories are included in the total sales category, including the Utilities, Miscellaneous, and Motor Vehicles merchandise groups.
Prepared by ISU Extension 10/25/99
Percent of
Total Sales
6.1%
11.5%
18.9%
2.3%
9.2%
11.5%
9.2%
11.7%
4.1%
100.0%
Page 11 b
Building Materials
General Merchandise
Food
Apparel
Home Furnishings
Iowa City
Percentage Above or Below Expected Sales, 1998
-20.2% ~
-31.8% ~
+8.2%
-17.2% ~
+108.5%
Eating &Drinking -9.6% I~
Specialty Stores ·-~7.6% ~
Services -24.4% ~
........................ ...............................................................
Total Sales* -23.4% ~
I
-150% -100% -50% 0% +50% +100%
NA means the data were not available.
*All categories are included in the total sales category, including the Utilities, Misc., & Motor Vehicles merchandise groups.
+150%
"Expected sales" is a standard to which actual performance is compared. In calculating
expected sales, population, income, and typical "pulling power" characteristics are taken into
account. The formula is provided in the introductory material.
As a whole, retail sales in Iowa City were below what might be expected given the income
attributes of the community and typical performance for towns this size in 1998.
Expected sales can be used as a guideline or "par value" in analyzing retail strength. The
tables on the preceeding page provide information by merchandise category as well.
Deviations from these norms can be analyzed to first judge whether they should be considered
"material." If the differences appear to be significant (whether in dollar amounts or relatively
with percentages), additional consideration is merited.
Categories with undesirable performance may be further examined for potential corrective
action. It is also impodant to determine whether or not the situation is relatively uncontrollable
due to external or extenuating circumstances.
In cases of favorable differences from expectations, the positive aspects should be identified
and built upon.
Prepared by I SU Extension 10/22/99 Page 12
Building Materials
General Merchandise
Food
Apparel
Home Furnishings
Coralville
Percentage Above or Below Expected Sales, 1998
-81.0%
,~+43.6%
NA
Eating & Drinking ~ ,133.3%
Specialty Stores' ~ +153.6%
Services ~ +70.5%
Wholesale ~ +54.3%
Total Sales* ~] +17.2%
-300 -250 -200 -150 -100 -50% 0% +50 +100 +150 +200 +250 +300
NA means the data were not av:(l~ble.
*All categories are included in the total sales category, including the Utilities, Misc., & Motor Vehicles merchandise groups.
"Expected sales" is a standard to which actual performance is compared. In calculating
expected sales, population, income, and typical "pulling power" characteristics are taken into
account. The formula is provided in the introductory material.
As a whole, retail sales in Coralville were above what might be expected given the income
attributes of the community and typical performance for towns this size in 1998.
Expected sales can be used as a guideline or "par value" in analyzing retail strength. The
tables on the preceeding page provide information by merchandise category as well.
Deviations from these norms can be analyzed to first judge whether they should be considered
"material." If the differences appear to be significant (whether in dollar amounts or relatively
with percentages), additional consideration is merited.
Categories with undesirable performance may be further examined for potential corrective
action. It is also important to determine whether or not the situation is relatively uncontrollable
due to external or extenuating circumstances.
In cases of favorable differences from expectations, the positive aspects should be identified
and built upon.
Prepared by ISU Extension 10/22J99
Page 12a
Building Materials
General Merchandise
Food
Apparel
Home Furnishings
Iowa CitylCoralville Combined
Percentage Above or Below Expected Sales, 1998
-13.7% ~
.~+18.6%
+117.0%
Eating & Drinking ~ +s.o%
Specialty Stores --3.s% ~
Se~jCes-15.0% ~
Wholesale-6~ .3% ~
............................................. ...............................................................
I I I
-150% -100% -50% 0% +50% +100%
NA means the data were not available.
*All categories are included in the total sales category, including the Utilities, Misc., & Motor Vehicles merchandise groups.
+150%
"Expected sales" is a standard to which actual performance is compared. In calculating
expected sales, population, income, and typical "pulling power" characteristics are taken into
account. The formula is provided in the introductory material.
As a whole, retail sales in Iowa City/Coralville Combined were below what might be expected
given the income attributes of the community and typical performance for towns this size in
Expected sales can be used as a guideline or "par value" in analyzing retail strength. The
tables on the preceeding page provide information by merchandise category as well.
Deviations from these norms can be analyzed to first judge whether they should be considered
"material." If the differences appear to be significant (whether in dollar amounts or relatively
with percentages), additional consideration is merited.
Categories with undesirable performance may be further examined for potential corrective
action. It is also important to determine whether or not the situation is relatively uncontrollable
due to external or extenuating circumstances.
In cases of favorable differences from expectations, the positive aspects should be identified
and built upon.
Prepared by ISU Extension 10/25/99 Page 12b
Category
Building Materials Dealers
General Merchandise Stores
Food Stores
Apparel Stores
Home Furnishings Dealers
Eating & Drinking Establishments
Specialty Stores
Service Firms
Wholesale Dealers
Total Sales*
Iowa City
Components of Change
1997 to 1998
Actual Sales
1997
$41,943,360
$106,856,380
$131,929,024
$21,775,320
$62,692,460
$83,954,100
$66,801,540
$83,970,520
$27,352,980
$779,391,191
Actual Sales
1998
$46,997,340
$110,835,500
$144,975,232
$23,296,000
$74,203,720
$83,302,080
$66,308,900
$87,462,700
$27,258,700
$806,305,919
Dollar
Change
+$5,053,980
+$3,979,120
+$13,046,208
+$1,520,680
+$11,511,260
-$652,020
-$492,640
+$3,492,180
-$94,280
+$26,914,729
* All categories are included in the Total Sales category. including the Utilities, Miscellaneous, and Motor Vehicles merchandise groups.
Figures not adjusted for inflation.
Percent
Change
+12.05%
+3,72%
+9.89%
+6.98%
+18.36%
-0.78%
-0.74%
+4.16%
-0.34%
+3.45%
+$13,046,208
Dollar Changes by Category
FY97 to FY98
+$11,511,260
+$5,053,980
+$3,979, 120
+$1,520,680
Bldg. Mat. Gen. Mdse. Food Apparel
+$3,492,180
Home Furn. 'Eat & Drink'f"Sl~i~lty' Services
-$652,020 -$492,6~
Wsle.
-$94,280
Prepared by ISU Extension 10/22/99 Page 13
Category
Building Materials Dealers
General Merchandise Stores
Food Stores
Apparel Stores
Home Furnishings Dealers
Eating & Ddnking Establishments
Specialty Stores
Service Firms
Wholesale Dealers
Total Sales*
Coralville
Components of Change
1997 to 1998
ActuaiSales ActuaiSales Dollar
1997 1998 Change
$14,477,900 $13,711,440 -$766,460
$3,141,760 $4,365,240 +$1,223,480
$39,058,944 $44,722,816 +$5,663,872
$922,940 NA NA
$15,930,140 $17,957,600 +$2,027,460
$29,283,360 $32,113,300 +$2,829,940
$25,296,980 $26,080,980 +$784,000
$27,521,100 $29,811,780 +$2,290,680
$23,963,240 $13,488,380 -$10,474,860
$199,179,771 $196,840,592 -$2,339,179
* All categories are included in the Total Sales category, including the Utilities, Miscellaneous, and Motor Vehicles merchandise groups.
Figures not adjusted for inflation.
Percent
Change
-5.29%
+38.94%
+14.50%
NA
+12.73%
+9.66%
+3.10%
+8.32%
-43.71%
-1.17%
Dollar Changes by Category
FY97 to FY98
+$5,663,872
+$1,223,480
Mat. Gen. Mdse. Food
-$766,460
NA
+$2,829,940
+$2,027,460 +$2,290,680
+$784,000
Apparel Home Furn. Eat & Drink Specialty Services
-$I 0,474,860
Prepared by ISU Extension 10/22/99 Page 13a
Category
Building Materials Dealers
General Merchandise Stores
Food Stores
Apparel Stores
Home Furnishings Dealers
Eating & Drinking Establishments
Specialty Stores
Service Firms
Wholesale Dealers
Total Sales*
Iowa City/Coralville Combined
Components of Change
1997 to 1998
ActuaiSales
1997
$56,421,260
$109,998,140
$170,987,968
$22,698,260
$78~622,600
$113,237,460
$92,098,520
$111,491,620
$51,316,220
$978,570,962
Actual Sales
1998
$60,708,780
$115,200,740
$189,698,048
$23,296,000
$92,161,320
$115,415,380
$92,389,880
$117,274,480
$40,747,080
$1,003,146,511
Dollar
Change
+$4,287,520
+$5,202,600
+$18,710,080
+$597,740
+$13,538,720
+$2,177,920
+$291,360
+$5,782,860
-$10,569,140
+$24,575,549
* All categories are included in the Total Sales category, including the Utilities, Miscellaneous, and Motor Vehicles merchandise groups.
Figures not adjusted for inflation.
Percent
Change
+7.60%
+4.73%
+10.94%
+2.63%
+17.22%
+1.92%
+0.32%
+5.19%
-20.60%
+2.51%
Dollar Changes by Category
FY97 to FY98
+$18,710,080
+$4,287,520
+$5,202,600
+$597,740
+$13,538,720
+$2,177,920
Bldg. Mat. Gen. Mdse. Food Apparel Home Furn. Eat & Drink
+$291,360
Specialty
+$5,782,860
Services
-$10,569,140
Prepared by ISU Extension 10/25/99 Page 13a
Town
Ames
Cedar Falls
Cedar Rapids
Clinton
Coralville
Davenport
Iowa City
Marion
Waterloo
Trade Area Analysis of Retail Sales for Selected Iowa Towns, 1998
Expected Actual $ Above or Below % Above or
Population Sales Sales Expected Sales Below
Estimate (in millions) (in millions)(in millions) Expected
47,734 $711.86 $595.23 -$116.63 -16.4%
34,540 $399.76 $375.78 -$23.98 -6.0%
114,670 $1,976.52 $2,377.48 +$400.96 +20.3%
28,101 $302.44 $308.98 +$6.54 +2.2%
11,826 $167.91 $196.84 +$28.93 +17.2%
97,311 $1,517.71 $1,632.59 +$114.88 +7.6%
61,114 $1,052.47 $806.31 -$246.16 -23.4%
23,136 $292.87 $256.11 -$36.76 -12.6%
64,380 $963.04 $1,028.39 +$65.36 +6.8%
Customer
Equivalents
-11,636
-2,386
+34,612
+664
+2,500
+10,960
-21,268
-3,174
+6,501
Expected & Actual Sales for Selected Iowa Towns, 1998
Ames
Cedar Rapids
Clinton
Coralville
Davenpo~
Iowa City
Waterloo
$0 $500 $1,000
D Expected Sales
$1,500
in millions
~';-',' Actual Sales
$2,000
$2,500
Prepared by ISU Extension 10/22/99 Page 14
Pull Factors
1998 Index of "Pulling Power"
Towns with Populations between 40,000 & 200,000
Building General Home Eat & Total
Town Population Materials Merch, Food Apparel Fum. Drink Specialty Services Wsle. Sales
Ames 47,734 1.05 1.94 1.44 1.60 0.98 1.69 1.68 1.36 0.56 1.22
Council Bluffs 55,801 1.44 1.89 1.44 1.38 0.47 1.82 1.20 1.19 0,45 1.28
Dubuque 57,276 2.32 1.95 1,32 1.47 1.44 1.41 1.56 1.40 1.24 1,41
Iowa City 61,114 1.37 1.50 1,74 1.68 3.41 1.71 1.52 1,1 9 0,51 1.29
Waterloo 64,380 - 2.15 2.02 1.09 1.29 1,44 1.54 1.44 1.38 1.93 1.57
Sioux City 83,288 1.71 1.81 1.33 2.28 1.81 1.36 1.52 1.1 5 1.48 1.36
Davenport 97,311 1.37 1.99 1.01 1.90 2.30 1.82 1.90 1.55 1,62 1,64
Cedar Rapids 114,670 1.59 2.15 1.45 2.01 1.32 1.70 2.41 2.23 2.42 2.03
Des Moines 194,,564 1.92 1.79 1 ,,56 1.95 3,06 1.6,:3 2.07 3.00 3,23 2.28
Unadjusted Average: * 1.66 1.89 1.38 1.73 1.80 1.63 1.70 1.61 1.49
Raw averages; not adjusted for special circumstances. Outliers were considered for calculating typical pull factors used in the expected sales formula.
1.67
Rankings
Building General Home Eat & Total
Town Population Materials Merch, Food Apparel Fum. Drink Specialty Services Wsle. Sales
Ames #9 #9 #5 #5 #6 #8 #5 #4 #6 #7 #9
Council Bluffs # 8 # 6 # 6 # 4 # 8 # 9 # 1 # 9 # 8 # 9 # 8
Dubuque #7 # 1 #4 #7 #7 #6 #8 #5 #4 #6 #5
Iowa City #6 #7 #9 #1 #5 #1 #3 #7 #7 #8 #7
Waterloo #5 #2 #2 #8 #9 #5 #7 #8 #5 #3 #4
Sioux City #4 #4 #7 #6 #1 #4 #9 #6 #9 #5 #6
Davenport #3 #8 #3 #9 #4 #3 #2 #3 #3 #4 #3
Cedar Rapids # 2 # 5 # 1 # 3 # 2 # 7 # 4 # 1 # 2 # 2 # 2
Des Moines # 1 # 3 # 8 # 2 # 3 # 2 # 6 # 2 # 1 # 1 # 1
Above are all communities in the poputation range listed in the title with data available by merchandise category. Adjustments for special circumstances may be necessary
for accurate comparisons.
Prepared by ISU Extension 10122/99 Page 15
Pull Factors
1998 Index of "Pulling Power'
Towns with Populations between 8,800 & 14,800
(Range: Population of Coralville +/- ~ 25%.) '
BuiTding General Home Eat & Total
Town Population Materials Merch. Food Apparel Fum. Ddnk Specialty Services Wsle. Sales
Le Mars 8,993 0.92 1.56 t .31 0.24 t .49 0.96 1.18 2.11 1.30
Pella 9,600 1.78 1.10 1.15 1.52 1.32 1.18 1.76 1.17 0.68 1.23
Fairfield 1 O, 126 0.61 1.20 2.40 0.55 0.52 0.96 1.61 1.01 0.63 1,15
Carroll 1 O, 153 1.69 2.83 2.04 2.71 1.73 1.28 1.28 1.50 2.35 1.73
Clive 10,477 ; 0.40 2.32 3.65 3.95 1.07 2.06 4.12 1.95
Oskaloosa 10,605 0.73 2.37 2.33 1.33 2.02 1.60 0.90 1.03 1.03 1.47
Spencer 11,202 1.16 2.08 2.22 2.60 2.62 1.57 1.50 1.47 1.06 1.61
Fort Madison 11,553 0.76 0.99 1.36 0.80 0.96 1.11 0.59 0.87 0.36 0.94
Coralville 11,826 2.07 0.31 2.78 4.26 3.40 3.08 2.10 1.31 1.63
Keokuk 12,251 0.62 1.99 1.36 1.28 0.64 1.53 3.12 0.98 1.15 1.33
Indianola 12,696 0.57 1.56 1.70 0.33 0.63 0.91 0.40 0.73 0.48 0.84
Boone 12,896 0.76 1.41 1.26 0.56 2.70 1.37 0.66 0,64 0.47 1.04
Unadjusted Average: * 1.01 1.58 1.86 1.39 1.77 1.70 1.41 1.23 1.31
· Raw averages; not adjusted for special circumstances, Outliers were considered for calculating typical pull factors used in the expected sales formula.
1.35
Rankings
Building General Home
Town Populab~n Materials Merch. Food Apparel Fum.
LeMars #12 #5 #6 #6 #12
Pella #11 #2 #9 #10 #4 #7
Fairfield #10 #10 #8 #2 #10 #11
Carroll # 9 # 3 # 1 # 5 # 1 # 6
Clive # 8 # 12 # 3 # 2
Oskaloosa # 7 # 8 # 2 # 3 # 5 # 5
Spencer # 6 # 4 # 3 # 4 # 2 # 4
Fort Madison # 5 # 6 # 10 # 8 # 8 # 8
Coralville # 4 # 1 # 11 # 1 # 1
Keokuk #3 #9 #4 #7 #7 #9
Indianola # 2 # 11 # 5 # 6 # 11 # 10
Boone # 1 #7 #7 #9 #9 #3
Eat & Total
Ddnk Specialty Services Wsle. Sales
#6 #8 #5 #3 #7
#9 #3 #6 #8 #8
#11 #4 #8 #9 #9
#8 #6 #3 #2 #2
#1 #7 #2 #1 #1
#3 #9 #7 #7 #5
#4 #5 #4 #6 #4
#10 #11 #10 #12 #11
#2 #2 #1 #4 #3
#5 #1 #9 #5 #6
#12 #12 #11 #10 #12
#7 #10 #12 #11 #10
Above are all communities in the population range listed in the title with data available by merchandise category. Adjustments for special circumstances may be neces3a~/
for accurate comparisons.
Prepared by ISU Extension 10/22/99 Page 15a
Pull Factors
1998 Index of "Pulling Power"
Towns with Populations between 40,000 & 200,000
Building General Home Eat & Total
Town Population Materials Merch. Food Apparel Fum. Drink Specialty Services Wsle. Sales
Ames 47,734 1.05 1.94 1.44 1.60 0.98 1.69 1,68 1.36 0,56 1.22
Council Bluffs 55,801 1.44 1.89 1.44 1.38 0.47 1.82 1.20 1.19 0.45 1.28
Dubuque 57,276 2.32 1.95 1.32 1.47 1.44 1.41 1.56 1.40 1.24 1.41
Waterloo 64,380 2.15 2.02 1.09 1.29 1.44 1.54 1.44 1.38 1.93 1.57
Iowa City/Coralville 72,940 1.48 1.31 1.91 1.40 3.54 1.98 1.06 2.25 0.64 1.35
Sioux City 83,288 1.71 1.81 1.33 2.28 1.81 1.36 1.52 1.15 1.48 1.36
Davenport 97,311 1,37 1.99 1.01 1.90 2.30 1.82 1.90 1.55 1.62 1.64
Cedar Rapids 114,670 1.59 2.15 1.45 2.01 1.32 1.70 2.41 2.23 2.42 2.03
Des Moines 194,504 1.92 1.79 1 .,56 1.95 3.06 1.63 2.07 3.00 3.23 2.28
Unadjusted Average: * 1.67 1.87 1.40 1.70 1.82 1.66 1.65 1.72 1.61
· Raw averages; not adjusted for special circumstances. Outliers were considered for calculating htpical pull factors used in the expected sales formula.
.67
Rankings
Building General Home Eat & Total
Town Population Materials Merch. Food Apparel Furn. Drink Specialty Services Wsle. Sales
Ames #9 #9 #5 #5 #5 #8 #5 #4 #7 #8 #9
Council Bluffs # 8 # 7 # 6 # 4 # 8 # 9 # 2 # 8 # 8 # 9 # 8
Dubuque #7 #1 #4 #7 #6 #6 #8 #5 #5 #6 #5
Waterloo # 6 # 2 # 2 # 8 # 9 # 5 # 7 # 7 # 6 # 3 # 4
iowa City/Coralville # 5 # 6 # 9 # 1 # 7 # 1 # 1 # 9 # 2 # 7 # 7
Sioux City #4 #4 #7 #6 #1 #4 #9 #6 #9 #5 #6
Davenport #3 #8 #3 #9 #4 #3 #3 #3 #4 #4 #3
Cedar Rapids # 2 # 5 # 1 # 3 # 2 # 7 # 4 # 1 # 3 # 2 # 2
Des Moines # '1 # 3 # 8 # 2 # 3 # 2 # 6 # 2 # 1 # 1 # 1
Above are all communities in the population range listed in the title with data available by merchandise category. Adjustments for special circumstances may be necessary
f~ accurate comparisons.
Prepared by ISU Extension 10/25/99 Page 15b
VI. County Statistics'
Johnson County: Total Retail Sales
currents Total Constants Total Currents
Fiscal Retail Sales Retail Sales Number of Sales Per
Year (in millions) (in millions, ba~=FY71)Retail FirmsFirm
1971 $145.05 $145.05 1,359 $106,771
1972 $157.33 $151.28 1,421 $110,738
1973 $168.09 $157.09 1,416 $118,748
1974 $192.42 $165.88 1,458 $131,997
1975 $218.29 $169.22 1,499 $145,622
1976 $240.77 $173.22 1,388 $173,436
1977 $282.36 $192.08 1,441 $195,980
1978 $315.06 $200.68 1,574 $200,166
1979 $340.07 $200.04 1,650 $206,100
1980 $366.75 $192.02 1,714 $213,973
1981 $387.71 $180.33 1,787 $216,992
1982 $415.82 $176.95 1,787 $232,758
1983 $459.34 $185.97 1,856 $247,555
1984 $497.89 $195.25 1,890 $263,433
1985 $518.16 $196.27 1,914 $270,790
1986 $528.08 $194.15 1,964 $268,950
1987 $578.28 $209.52 2,003 $288,780
1988 $597.30 $208.12 1,994 $299,623
1989 $656.47 $219.55 2,046 $320,893
1990 $705.15 $224.57 2,108 $334,510
1991 $738.39 $223.08 2,136 $345,768
1992 $774.42 $226.44 2,185 $354,466
1993 $825.56 $234.53 2,225 $371,037
1994 $888.23 $245.37 2,248 $395,074
1995 $947.02 $255.26 2,300 $411,838
1996 $990.93 $259.41 2,334 $424,653
1997 $1,042.79 $265.34 2,374 $439,346
1998 $1,071.53 $267.88 2,417 $443,330
%Change
'71 to '98 +638.74% +84.68% +77.92% +315.22%
% Change
'97 to '98 +2.76% +0.96% +1.83% +0.91%
constants
Sales Per
Firm
$106,771
$106,479
$110,979
$113,790
$112,885
$124,774
$133,320
$127,494
$121,235
$112,028
$100,927
$99,046
$1 O0,225
$103,307
$102,572
$98,879
$104,630
$104,398
$107,322
$106,532
$104,462
$103,645
$105,408
$109,137
$111,007
$111,166
$111,793
$110,832
+3.80%
-0.86%
Per Capita
Sales
$2,011
$2,138
$2,299
$2,569
$2,868
$3,147
$3,570
$3,978
$4,278
$4,506
$4,745
$4,969
$5,427
$5,789
$5,934
$5,964
$6,468
$6,600
$7,092
$7,472
$7,682
$8,006
$8,419
$8,965
$9,412
$9,773
$10,263
$10,513
+422.75%
+2.43%
Pull
Factor
0.95
0.95
0.94
0.92
0.91
0.90
0.91
0.93
0.89
0.85
0.90
0.92
0.97
0.99
0.99
0.99
1.01
0.98
1.00
1.00
0.99
1.00
1.01
1.03
1.03
1.03
1.04
1.03
+7.96%
-0.73%
Prepared by ISU Extension 10/22/99 Page 16
Johnson County
Trade Area Analysis of Retail Sales,
1997
Potential Actual Surplus or Surplus or Trade Area
Sales Sales Leakage Leakage as % Population
Merchandise Group (in millions)(in millions)(in millions)Of Potential Gain or Loss
Building Materials $63.69 $65.46 +$1.76 +2.8% +2,815
General Merchandise $140.63 $112.85 -$27.78 -19.8% -20,070
Food $156.17 $179,44 +$23.27 +14.9% +15,137
Apparel* $25.14 $22.70 -$2.44 -9.7% -9,866
Home Furnishings $39.86 $79.60 +$39.73 +99.7% +101,278
Eating &Drinking $85.72 $118.89 +$33.17 +38.7% +39,320
Specialty Stores $73.81 $95.22 +$21.41 +29.0% +29,479
Services $133.10 $121.25 -$11,85 -8.9% -9,048
Wholesale $99.90 $57.18 -$42.72 -42.8% -43,453
Total Sales** $1,143.76 $1,042.79 -$100.97 -8.8% -8,970
Number Percent of
of Firms Total Sales
51 6.3%
43 10.8%
43 17.2%
61 2.2%
105 7.6%
212 11.4%
473 9,1%
789 11.6%
142 5.5%
2,374 100.0%
Johnson County
Trade Area Analysis of Retail Sales,
1998
Potential Actual Surplus or Surplus or Trade Area
Sales Sales Leakage Leakage as % Population
Merchandise Group (in millions)(in millions)(in millions)Of Potential Gain or Loss
Building Materials $64.82 $70.99 +$6.17 +9.5% +9,707
General Merchandise $139,52 $118.52 -$20.99 -15.0% -15,337
Food $157.13 $197.85 +$40.72 +25.9% +26,414
Apparel $26.29 $23.38 -$2.91 -11.1 % ~11,297
Home Furnishings $41.21 $93.17 +$51.96 +126.1% +128,528
Eating &Drinking $92.33 $122.27 +$29.95 +32.4% +33,060
Specialty Stores $82.65 $95.27 +$12.62 +15.3% +15,566
Services $138.48 $126.27 -$12.21 -8.8% -8,990
Wholesale $100.88 $45.90 -$54.99 -54.5% -55,556
Total Sales** $1,179.73 $1,071.53 -$108.20 -9.2% -9,349
Number Percent of
of Firms Total Sales
52 6.6%
46 11.1%
47 18.5%
62 2.2%
104 8.7%
214 11.4%
490 8.9%
793 11.8%
141 4.3%
2,417 100.0%
** All categories are included in Total Sales, including the Utilities, Miscellaneous, and Motor Vehicles merchandise groups.
Prepared by ISU Extension 10/22/99
Page 17
Johnson County Retail Trade Surplus or Leakage
County Surplus or Leakage as a Percent of Potential
+2.0% -'
+0.0% [
-2.0% --
-4.0% .......................
-6.0% .......................
-8.0% ............. : .........
-10.0% .......................
-12.0% .......................
-14.0% .......................
-16.0%
80 82
84 86 88 90 92 94 96 98
Fiscal Year
Fiscal
Year
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
Population
Estimate
81 400
81.717
83.689
84.633
86.012
87.324
88 538
89.404
90.496
92 566
94.369
96,119
96,729
98,063
99,081
100,619
101,398
101,609
101,928
Potential Actual
Index of Sales Sales
Income (in millions) (in millions)
87.0 $373.57 $366.75
89.9 $387.20 $387.71
90.5 $409.71 $415.82
112.3 $533.15 $459.34
113.3 $567.39 $497.89
108.1 $563.29 $518.16
105.7 $562.48 $528.08
105.0 $602.76 $578.28
101.4 $617.35 $597.30
99.8 $654.73 $656.47
98.9 $695.49 $705.15
111.5 $830.18 $738.39
117.0 $909.31 $774.42
117.2 $960.61 $825.56
118.1 $1,021.59 $888.23
115.0 $1,055.26 $947.02
114.7 $1,107.13 $990.93
113.8 $1,143.76 $1,042.79
113.4 $1,179.73 $1,071.53
Surplus or
Leakage
(in millions)
-$6.82
$o.51
$6.12
-$73.81
-$69.50
-$45.13
-$34.39
-$24.48
-$2o.o5
$1.74
$9.66
-$91.79
-$134.89
-$135.05
-$133.36
-$108.24
-$116.2o
-$100.97
-$108.20
Surplus or
Leakage as %
of Potential
-1.8%
+0.1%
+1.5%
-13.8%
-12.2%
-8.0%
-6.1%
-4.1%
-3.2%
+0.3%
+1.4%
-11.1%
-14.8%
-14.1%
-13.1%
-10.3%
-10.5%
-8.8%
-9.2%
Trade Area
Population
Gain or Loss
-1,487
+108
+1,249
-11,717
-10,535
-6,997
-5,414
-3,631
-2,939
+246
+1,310
-10,627
-14,349
-13,787
-12,934
-10,321
-10,642
-8,970
-9,349
Prepared by ISU Extension 10/22/99 Page 18
1998 Trade Data for Selected Iowa Counties
Income Characteristics ~
Percent of Households by EBI Group
Total EBI Median Less Than $20,000 to $35.000 to $50,000
County ($000) Household EBI $20,000 $34,999 $49,999 Over
Cedar $280,805 $36,457 24.3% 23.5% 22.1% 30.1%
Iowa $261,466 $36,010 23.6% 24.8% 21,9% 29.7%
Johnson $1,791:656 $35,532 27.6% 21.8% 16,9% 33.7%
Linn $3,187,607 $38.233 23.0% 22.4% 21.1% 33.5%
Muscatine $661,971 $37,173 24.7% 22.3% 21.7% 31.3%
Washington $295,143 $31,870 26.5% 29.6% 21.0% 22.9%
State $44,235,017 $32,694 28.4% 25.2% 19.7% 26.7%
Index of
Income
100.7
108.9
113.4
113.5
104.3
91.1
100.0
Population Characteristics
Less Than 18 18 to 24
County Population Years Years
Cedar 17,992 26.8% 5.7%
Iowa 15,487 25.4% 5.4%
Johnson 101,928 20.6% 22.2%
Linn 181,289 25.3% 9.2%
M uscati ne 40,939 27.8% 7.4%
Washington 20,899 26.8% 6.1%
State 2,854,330 25.8% 8.7°/0
I
Percent of Population by Age Group
Avg. No.
25 to 34 35 to 49 50 & Number of People per
Years Years Over Households Household
12.5% 22.8% 32.2% 6,800 2.6
13.1% 20.9% 35.2% 6,200 2~5
18.5% 21.7% 17.0% 38,400 2.7
14.9% 24.1% 26.5% 71,300 2.5
14.1% 23.0% 27.7% 15.500 2.6
12.8% 21.3% 33.0% 8,000 2.6
13.4% 22.1% 30.0% 1.107,200 2.6
Trade Migration Dollar Gain
1998 Total 1998 Sales or Loss to
Retail Sales Potential County
County (in millions) (in millions) (in millions)
Cedar $84.95 $184.92 -$99.97
Iowa $221.69 $172.14 +$49.55
Johnson $1,071.53 $1,179.73 -$108.20
Linn $2,786.34 $2,100.12 +$686.22
Muscatine $361.89 $435.81 -$73.92
Washington $128.00 $194.32 -$66.32
Percent Trade Area
Gain or Population
Loss of Gain or
Customers Loss
-54.1% -9,727
+28.8% +4,458
In full-time
customer -9.2% -9,349
equivalents. +32.70/0 +59,237
-17.0% -6,944
-34.1% -7,133
Prepared by ISU Extension 10/22/99 Page 19
VII. State Statistics -
Retail Market Share for Various Size Towns
Iowa, 1976 & 1998 *
Popula~on Group
Over 50,000
25,000 to 50,000
10,000 to 25,000
5,000 to 10,000
2,500 to 5,000
1,000 to 2,500
500 to 1,000
Less Than 500
Rural & Others
0%
~6.8%
~ 5.3%
~ 2.6%
~ 3.0%
1.7%
~ 4.7%
1.8%
· 1976
[] 1998
10% 20% 30% 40% 50%
Share of Market
Percentages may not add to 100% due to rounding.
Share of Total Businesses & Total Sales
by Amount of Gross Revenue
Iowa, 1998
Gross Revenue
Under $25,000
$25,000 to $49,999
$50,000 to $99,999
$100,000 to $249,999
$250,000 to $499,999
$500,000 to $999,999
~i!~:~i!~ 9.2%
2.0%
$1,000,000 & Over
7.6%
7.3%
3.6%
7.7%
2.0%
8.4%
2.6%
0% 10%
': =' '."' ' .................. ' ............' ............}67.2%
[] % of Total Businesses
· % of Total Sales
20% 30% 40% 50% 60%
Share of Total
69.3%
70%
Percentages may not add to 100% due to rounding.
80%
Prepared by ISU Extension 10/22/99 Page 20
State of Iowa Per Capita Taxable Retail Sales &
Threshold Levels for Selected Goods and Services
FY98: April 1, 1997 to March 31, 1998
Threshold/eve/refers to the number of people per business, which can be used as a general guide
for determining the "critical mass" necessary to support a business. These are broad averages for
the state as a whole and do not reflect differences in income, tourism, agglomeration, establishment,
etc. Further. the business counts are based on the number of sales tax returns filed and are
converted to "full-time equivalents." Multiplying people per business by sales per capita yields
average sales per firm.
Business Activity / Store Type
People Per Sales Per
Business Capita
Utilities & Transportation Group
Communication Utilities
Electric & Gas Utilities
Water & Sanitation Utilities
Transportation Companies
Utilities Group Total
Building Materials Group
Building Material Dealers
Paint & Glass Stores
Hardware Stores
Garden Supply Store
Mobile Home Dealers
Building Materials Group Total
General Merchandise Group
Department Stores
Variety Stores
Misc. General Merchandise Stores
General Merchandise Group Total
4,O47 $336.86
5,429 $766.61
3,012 $108.93
3,352 $30.28
$1,242.68
3,566 $423.53
17,041 $22.81
5,658 $91.34
6,558 $21.73
57,956 $1.39
$560.80
13,432 $776.74
16,790 $162.43
1,817 $267.86
$1,207.03
Food Dealers Group
Grocery Stores
Meat & Fish Markets
Fruit & Vegetable Markets
Confectionary Stores
Dairy Product Stores
Bakeries
Miscellaneous Food Stores
Subtotal
X Adjustment for Untaxed Items
Food Group Total
Motor Vehicles Group
Automobile Dealers
Automobile Parts
Gas Stations
Recreational Vehicles
Motor Vehicles Group Total
3,009 $405.73
71,807 $0.24
104,746 $0.15
30,528 $3.36
42,444 $4.12
14,009 $2.55
16,451 $8.68
$424.83
3.2
$1,359.45
3,356 $134.19
2,098 $152.58
2,131 $117.67
7,026 $37.59
$442.03
Prepared by ISU Extension 10/22/99 Page 21
Business Activity / Store Type
Apparel Group
Men's & Boys' Apparel Stores
Women's Apparel Stores
Family & Children's Apparel Stores
Shoe Stores
Other Apparel Stores
Apparel Group Total
Home Furnishings & Appliances Group Furniture Stores
Home Furnishings Stores
Appliance, Entertainment Equipment Stores
Home Furnishings Group Total
Eating & Drinking Places Group
Eating & Drinking Group Total
Specialty Retail Stores Group
Drug Stores
Liquor Stores
Used Merchandise Stores
Sporting Goods Store
Books & Stationery Stores
Jewelry Stores
Hobby & Toy Stores
Gift & Novelty Stores
Mail Order Stores
Vending Machines
Direct Selling
Fuel & Ice Dealers
Florists
Other Specialty Shops
Specialty Group Total
Services Group
Finance, Insurance and Real Estate
Hotels & Other Lodging Places
Laundry & Cleaning
Photographic Studios
Beauty 8hops
Barber Shops
Shoe Repair Shops
Funeral Homes
Other Personal Services
Building Maintenance
Employment Agencies
Other Business Services
Automobile Rental & Storage
Automobile Repair & Services
Electrical Repair
Watch, Jewelry Repair
Furniture Repair
People Per
Business
27,645
5,537
6,047
10,286
7,319
4,689
5,016
1,999
410
5,225
28,687
1,157
1,678
4,894
5,732
708
2,829
18,326
6,890
1,936
12,330
4,805
758
3,173
2,977
2,750
3,886
554
3,595
34,390
6,487
2,107
2,556
25,657
1,014
10,314
599
3,530
53,352
4,704
Sales Per
Capita
$16.02
$73.17
$79.46
$41.59
$17.22
$227.47
$117.53
$53.42
$185.57
$356.53
$798.76
$84.68
$11.62
$30.56
$73.02
$50.56
$54.59
$59.25
$99.73
$7.42
$22.ol
$23.81
$2o.87
$24.78
$152.14
$715.o2
$40.91
$154.05
$43.57
$19.71
$67.28
$8.67
$1.28
$28.98
$25.05
$33.27
$14.44
$174.46
$26.92
$202.56
$29.95
$1.04
$4.19
Prepared by ISU Extension 10/22/99 Page 22
Business Activity / Store Type
Miscellaneous Repairs
Motion Picture Theaters
Amusement Parks & Services
Education Institutions
Other Services
Services Group Total
Wholesale Goods Group Motor Vehicle
Furniture & Home Furnishings
Construction Matedal
Farm & Construction Machinery
Miscellaneous Durable Goods
Apparel, Piece Goods
Groceries & Farm Products
Miscellaneous Nondurable Goods
Wholesale Group Total
Miscellaneous Group
Agriculture Production & Services
Mining
General Contractors
Plumbing & Heating Contractors
Painting Contractors
Electrical Contractors
Carpentry Contractors
Other Special Trade Contractors
Food Manufacturers
Apparel & Textile Manufacturers
Furniture, Wood, & Paper Manufacturers
Publishers of Books & Newspapers
Commercial Printers
Nonmetallic Product Manufacturers
Industrial Equipment Manufacturers
Miscellaneous Manufacturers
Temporary Retailers
Miscellaneous Group Total
Other
Total
X Adjustment for Untaxed Items
Grand Total
People Per
Business
926
18,356
1,446
17,729
1,936
11,238
113,043
2,091
1,004
12,397
131,234
13,199
1,323
1,237
14,694
1,762
1,899
4,238
3,052
4,474
2,407
14,218
67,161
7,074
12,602
4,763
7,591
3,344
9,851
2,928
Sales Per
Capita
$85.09
$19.67
$108.91
$11.91
$96.17
$1,198.10
$28.32
$2.30
$269.94
$382.90
$17.29
$0.70
$22.46
$148.89
$872.81
$60.69
$25.32
$74.98
$87.78
$10.21
$53.88
$20.04
$86.38
$13.o2
$3.29
$36.42
$7.72
$50.72
$61.75
$89.12
$18.62
$12.78
$712.74
$116.40
$8,875.20
1.15
$10,206.48
Prepared by ISU Extension 10122/99 Page 23
RESIDENTS OF IOWA CITY SPEAK OUT
During June 1997, you were one of 4,750 households asked to participate in a statewide survey of
Iowa urban residents. The purpose of this survey was to identify the problems and opportunities
facing the state' s urban communities. The households that were asked to take part in the study were
randomly selected from telephone directories of 15 cities in Iowa. Selection of cities was also
random where 15 cities were selected from Iowa's 30 cities having at least 10,000 residents. Of the
4,750 questionnaires mailed out, 2,901 (or 61%) were completed and returned. Below are highlights
of the results as reported by Iowa City's 282 survey participants.
MA.IIH{ I{EASON.',; F()R I,IVING IN IOVv'A CITY
IIp [1~ ~ ICil~,(lll~. ~.Clc t'llCtl hV cjt.'h rc~l~t,ldcnl~
Close' to relativeS/in-laws'
Percent Reporting
51%
.... '..::.7:,...y~,;.~.~........
=
:.:..
. ;: ::ii'.'.; ...
..... "" . 2991,
RATING SERVICES AND FACILITIES AVAILABLE IN IOWA CITY
Nine local services and facili-
ties were listed on the ques-
tionnaire, along with the in-
structions to rate each as
"very good," "good," "fair,"
or "poor." Of the 282 respon-
dents from Iowa City, over
three-fourths gave public
schools, senior programs,
recreation and entertainment,
and medical services a rating
of either "good" or "very
good." Following behind,
child care services, jobs,
housing, youth programs, and
shopping were also rated ei-
ther "good" or "very good" by
over half of the Iowa City
respondents.
Jobs
Medical Service8
Public Schools
Shopping
Housing
RecJEntertalnment
Child Care Services
Senior Programs
Youth Programs
I 65
46
54
139
61
~~;,~. 24 76
~ ~4~
86
20 40 60 80
Percent*
· Good/Very Good r~ Fair/Poor
100
*"Don't know" and "undecided" responses not included.
This report was prepared through the Rural Development Initiative Project, funded by the I
College of Agriculture, Iowa State University, Ames, IA.
LOCAL PURCHASING PATTERNS
Primary Health Care
Spec. Medical Serv.
,
0 T
- ,
Shop (big ticket) ~~ '~.. ,~.~ ..~,..~i~ ~8
Church 61
0 20 40 60 80 100
Percent
ilD o Not Use BMoatly Local [:]Mostly Other ~
For a variety of reasons,
many Iowa residents rely
on neighboring cities for
services. Based on re-
turned questionnaires,
Iowa City respondents do
not follow this pattern. A
large majority reported re-
maining in Iowa City for
primary health care~ spe-
cialized medical services,
recreation and entertain-
ment, and shopping for big
ticket and daily needs
items. In fact, church was
the only service for which
about one-third of the peo-
ple reported leaving Iowa
City.
Seven services normally
provided through local gov-
ernments were included
with the instructions to rate
each as "very good,"
"good," "fair," or "poor."
Emergency response ser-
vices (EMS) was rated high-
est with 94% of the respon-
dents giving it a "very good"
or "good" rating. Library
services and fire protection
followed closely behind.
Street conditions and water
service ranked the lowest
with a majority of the re-
spondents giving both a rat-
ing of "fair" or "poor."
RATING GOVERNMENT SERVICES
Police Protection
Street Conditions
Park Conditions
Water
Fire Protection
56
65
Library
EMS
20 40 60 80
Percent*
Go__odNe_ry_ G~oo__d___F_aly/_P_oo__r
*"Don't know" and "undecided" responses not included.
91
100
FRIENDLINESS AND EVIDENCE OF COMMUNITY SUPPORT
In addition to describing cities by their physical traits, important social features are also significant
when evaluating local conditions. Accordingly, questions were included in the survey asking
residents to assess various social characteristics of their communities.
Of the eight attributes evaluated on a
7-point scale, Iowa City respondants
assigned the highest rating to the
friendliness of its residents, fol-
lowed by the safety and well-kept
appearance of Iowa City. The lowest
rating was given to the amount of
excitement offered to the residents.
6
5---
5.2
4.8
5.0 5.0
i
Contribute/Gov.
Affairs
Quick
Responsa/Gov.
Orgs,/Intere~t In
All
Emergency/All
Assist
Problem/All
Respond
89
65
58
0 20 40 60 60 100
Percent*
*"Don't know" and "undecided" responses not included.
According to survey respondants,
Iowa City's responsiveness to per-
sonal and community problems is
generally quite favorable. A majority
of the respondants agreed that all were
allowed to contribute to local govern-
mental affairs, that everyone would
help in case of an emergency, that a
city office would give a quick re-
sponse in regard to a complaint, and
that organizations are interested in
what is best for all residents. Over
half of the respondents felt that when
something' needs to be done, not ev-
eryone gets behind it.
INTEREST AND PARTICIPATION IN IOWA CITY ACTIVITIES
Over three-fourths of Iowa City respondents are interested in being informed of community
activities. Yet, only 50% reported having participated over the past year in any community
improvement project. When asked to describe their level of involvement in local community
improvement activities and events, 36% indicated being "very active" or "somewhat active."
Ties between local residents often are related to commitment to the community. In Iowa City' s case,
51% of the respondents indicated knowing the name of more than 100 adult residents. In addition,
64% indicated that half or more of their close personal friends live in Iowa City. As for the
respondents' adult relatives and in-laws, 16% indicated that half or more of them also live in Iowa
City.
The future of Iowa's ur-
ban communities will
probably depend on
whether or not important
trends will continue over
the course of the next few
years. In the case of Iowa
City, at least three-fourths
of the respondents see in-
creased crime, resident in-
difference, and loss of
small businesses as condi-
tions that pose threats
("some" or "severe") to
the future of the commu-
nity. Lack of leadership
and residents not working
together followed closely
behind. Over half of re-
spondants expressed that
there was no threat con-
cerning quality of schools,
people moving into the
community, and people
moving out of the com-
munity.
PERCEIVED COMMUNITY THREATS
Loss Family Farms
Loss Small Bus.
Indifference
Lack Leadership
Lack of Jobs ~6
Quality of Schools
Increased Crime ~ ~ ~ ~'~ ;~ ~ ~ ~ ~ 61
More Single Parents 44
45
31
Not Work Together
28
Loss Comm. Spirit
31
Both Parents Work
Out Migration
In Migration
' 0 20 40 60 80 100
Percent*
!1 No Threat [] Some Threat [] Severe Threat i
*"Don't know" and "undecided" responses not included.
OVERALL COMMUNITY ATTACHMENT
How important is it for Iowa City residents to feel a part of their city? When asked this question,
86% of the survey respondents ~reported that it was important for them to feel a part of the city.
When asked whether they feel "at home" in Iowa City, 92% said that they did. Furthermore, 78%
indicated they would be sorry if forced to move away from Iowa City. In spite of the community
concerns as indicated in the previous charts, the majority of respondents see Iowa City as their home
and are reluctant to move away from the area.
Prepared by Veto Ryan, Lori Merntt, Nicole Ca'ewe, Jeremy Judldns, Department of Sociology. Iowa State University. For further
information about this report, contact Jeff Zacharakis-Jutz, Linn County Extension Office, 3279 7th Ave, Marion, IA 52302;
Telephone (319) 377-9839; Fax (319) 377-0475; xlzach@exnet. iastate.edu. For information on other reports in the RDI series,
contact Vern Ryan. 317C East Hall, Iowa State University, Ames, IA 50011; Telephone (515) 294-5011; Fax (515) 294-2303;
vryan @iastate.edu.
RESIDENTS OF HILLS SPEAK OUT
During July 1994, you were one of 15,000 households asked to participate in a statewide survey.
The purpose of this survey was to identify the problems and opportunities facing Iowa's rural
communities. The households asked to take part in the study were randomly selected from
telephone directories of 100 Iowa communities (150 households per directory). Selection of
communities was also random where one community with 500-10,000 residents was selected
from each of Iowa's 99 counties. (Because of its geographic size, two communities were
included for Pottawattamie County, one from West and another from East Pottawattamie.) Of
the 15,000 questionnaires mailed out, 10,798 (or 72%) were completed and returned.
Below are highlights of the results as reported by Hills' 100 survey participants.
MAJOR:REASONS FOR:: LIVING: IN BILLS"..':i::!'='~.=2.':~
Cup:to 3 reasonS=,we~e cited:by each respondent.) · :-. :?i! ';~i:
· · :. Majo,, t e.so.s ::::.::.""::.:::::':'='
1. Affordable hoUsiq'2L" .........':"
2. Close to:job ....".:il;".. ........~i'i..:~'i: :'::4S% '..:
3. Safe arei.~2..2i'~'~:~.:..~ .........~:..........".~"iLZ~ii*~':. 35%, :i.:rr ~,:~.. :.:~.;5 ::(2; :...~.....-.'.~,.:... :::'.:~(~
4. Friendliness:of people ......:.:.'....' ......:.~:~:~;':.2~. 30~(<.
RATING SERVICES AND FACILITIES AVAILABLE IN HILLS
Nine local services and
facilities were listed on
the questionnaire along
with the instructions to
rate each as "very
good," "good," "fair,"
"poor," or "not
available." Of the 100
respondents from Hills,
half or more gave
public schools, housing
and senior citizen
programs a rating of
either good or very
good. But jobs,
recreation/
entertainment, child
care and youth
Jobs
Medical services
Public schools
Shopping
Housing
Reclentertain
Child care serv
Senior programs
Youth programs
r~-i: '7
:t 60
65
· ::::,:,: .....[lq'- r -~ 7o
34
55
51
0 20 40 60
Percent
iGoodlvery good mFairlpoor
80 100
r-nNot available
programs were rated no better than fair by at least half of the people. For medical services at
least half of the respondents indicated the service was not available in Hills.
This report was prepared through the Rural Development Initiative Project,
funded by the College of Agriculture, Iowa State University, Ames, IA.
LOCAL PURCHASING PATTERNS
For a variety of
reasons, many
residents of Iowa's
smaller towns rely on
neighboring cities for
services. Based on
retumed
questionnaires, Hills
residents follow this
pattem. At least
three-fourths of the
people reported
leaving Hills for
primary and
specialized health
care, shopping for
daily needs and "big
ticket" items, and
recreation/
Primary health care
Special medical serv
Shop (daily needs)
Shop (big ticket)
Reclentertaln
Church
0 20
40 60 80 100
Percent
I~ Mostly other E3 Do not use
iMostly local
entertainment. In fact, there were the no services where at least half of the people reported
remaining in Hills.
RATING GOVERNMENT SERVICES
Seven services
normally provided
through local
governments were
included with the
instructions to rate
each as "very good,"
"good, .... fair," or
"poor." Fire protection
was rated the highest
with 92 percent giving
it a positive (very
good or good) rating.
Over half of the
individuals also rated
park conditions,
Police protection
39
56
Street conditions
Park condition
Water
Fire protection
Garbage collection
EMS
0 20
iGoodlvery good
49
40 60
Percent
S Fair/poor
9O
92
83
91
80 100
E:3Not receive
garbage collection, and emergency response service (EMS) positively, while police protection
and street conditions ~received lower ratings (fair or poor) by a majority of the respondents.
FRIENDLINESS AND EVIDENCE OF COMMUNITY SUPPORT
In spite of the frequently publicized economic problems recently experienced by many of Iowa's
smaller communities, reference often is made of their favorable social climates. However, no
information to date has been available to determine the extent to which Iowa's rural communities
do in fact possess favorable social environments. Accordingly, questions were included in the
survey asking residents to
evaluate various social
attributes of their
communities.
Unfriendly
Dangerous
Indifferent
Bodng
Prejudiced
Rejecting of
new Ideas
Not trusting
Run down
Of the eight attributes
evaluated on a 7-point scale,
Hills residents assigned the
highest rating to the safety of
Hills, followed closely by
how well-kept the community
is. The lowest rating was
given to the amount of
excitement offered.
2O
91
80
Contdbute/gov aft
Orgslinterest in all
12
Ernel
4
63
88
Prohlent/all respond;
0 20 40 60
Percent
IIYes imNo
96
80 100
town gets behind it where 37 percent of the respondents
.6 Friendly
..... 6 Safe
..... 9 - Supportive
.................... Exciting
......... 4,2 .......... Tolerant
Open to
..... 4.5 .... new Ideas
- - * 5,3 · Trusting
· Well-kept
2 3 4 S 6 7
Average Score
1=lowest 7=highest
According to survey respondents, Hills'
responsiveness to personal and
community problems is generally quite
favorable. Most everyone agreed that in
the case of an emergency all residents
would help. At least three-fourths also
felt that everyone is allowed to
contribute to local governmental affairs,
that a city office would give a quick
response in regards to a complaint and
that organizations are interested in what
is best for all residents. If any concern
was noted about how Hills responds to
problems, it pertained to when
something needs to get done the whole
reported dissatisfaction in this area.
INTEREST AND PARTICIPATION IN HILLS ACTIVITIES
Over three-fourths of Hills residents are interested in being informed of community activities.
Yet only about 42 percent reported having participated over the past year in any community
improvement project. When asked to describe their level of involvement in local community
improvement activities and events, 35 percent indicated being very or somewhat active.
Ties with o~her local residents often are an indication of their commitment to the community. In
Hills' case, 39 percent of the respondents indicated knowing the name of half or more of the
adult residents. Also, 25 percent indicated that half or more of their close personal friends live in
Hills. As for their adult relatives and in-laws, 11 percent indicated that half or more of them live
in Hills.
PERCEIVED COMMUNITY THREATS
The future of Iowa's
rural communities
will probably depend
on whether or not
important trends will
continue over the
course of the next few
years. Not a single
condition was
reported as being a
threat to the future of
Hills by one-third of
the residents.
Minimal concern was
expressed with lack of
jobs, quality of
schools, increase in
crime, increase in
single parent family,
increase in homes
where both parents
Indifference
Lack leadership
Not work together
Loss comm spirit
More 2/parents work
Lack of jobs
Quality of schools
Increased crime
More single parents
Loss family farm
Loss small bus.
Out migration ~ ~.
In migration ~.,:~.,.~, ,~
0 20
E]No threat
40 60
Percent
maSome threat
work, people moving out of the community, and people moving into the community.
I 76
I 70
I 83
80 100
ISevere threat
OVERALL COMMUNITY ATTACHMENT
How important is it for Hills residents to feel a part of this community? When asked this
question, 89 percent responded that it was important for them to feel a part of the community.
When asked whether they feel "at home" in Hills, 94 percent said that they did. Furthermore 74
percent indicated they would be sorry to move away from Hills. In spite of the community
concerns as indicated in the previous charts, the majority of residents see Hills as their home and
are reluctant to move away from the community.
Prepared by Veto Ryan, Terry Besser, Jan Flora, and Paul Lasley, Deparm~ent of Sociology, Iowa State University.
For further information about this report, contact JeffZacharakis-Jutz, Linn County Extension Office, 655 12th Street,
Marion, 1A 52302: Tele (3 ! 9) 377-9839; Fax (319) 377-0475; x I zach@exnet.iastate.edu.
For infom~ation on other reports in the RDI series, contact Vern Ryan, 317 East Hall, Iowa State University, Ames, IA
5001 I; Tele (515) 294-501 I; Fax (515) 294-2303; x I vryan@exnet. iastate.edu.
RESIDENTS OF CORALVILLE SPEAK OUT
During June 1997, you were one of 4,750 households asked to participate in a statewide survey of
Iowa urban residents. The purpose of this survey was to identify the problems and opportunities
facing the state' s urban communities. The households that were asked to take part in the study were
randomly selected from telephone directories of 15 cities in Iowa. Selection of cities was also
random where 15 cities were selected from Iowa's 30 cities having at least 10,000 residents. Of the
4,750 questionnaires mailed out, 2,901 (or 61%) were completed and returned. Below are highlights
of the results as reported by Coralville's 146 surve~y participants.
Percent Reporting
68%
45% ..
33 '
22%
RATING SERVICES AND FACILITIES AVAILABLE IN CORALVILLE
Nine local services and facili-
ties were listed on the ques-
tionnaire, along with the in-
structions to rate each as
"very good," "good," "fair,"
or "poor." Of the 146 respon-
dents from Coralville, ap-
proximately three-fourths or
more gave public schools,
housing, and medical services
a rating of either "good" or
"very good." On the contrary,
shopping and senior pro-
grams were rated either "fair"
or "poor" by. over half of the
Coralville respondents.
Jobs
Medical Services
Public Schools
Shopping
Housing
RecdEntertainment
Child Care Services
Senior Programs
Youth Programs
38
62
74~
J 83
20 40 60 80
Percent*
· Good/Very Good [] Fair/Poor T
100
*"Don't know" and "undecided" responses not included.
This report was prepared through the Rural Development Initiative Project, funded by the ]
College of Agriculture, Iowa State University, Ames, IA.
LOCAL PURCHASING PATTERNS
Primary Hsalth Care
Spec. Medical Serv.
Shop (daily needs)
:.
Shop (big ticket)
FlecJEntertain.
Church
'- T
182
79
143
40
181
0 20
4O
Percent
ilDo Not Use BMostly Local DMostly Other
100
For a variety of reasons,
many Iowa residents rely
on neighboring cities for
services. Based on re-
turned questionnaires,
Coralville respondents of-
ten follow this pattern.
Half or more o~ the people
reported leaving Coralville
for primary health care,
specialized medical ser-
vices, recreation and enter-
tainment, and shopping for
big ticket items. Shopping
for daily needs items was
the only service for which
about three-fourths of the
people reported remaining
in Coralville.
RATING GOVERNMENT SERVICES
Seven services normally
provided through local gov-
ernments were included
with the instructions to rate
each as "very good,"
"good," "fair," or "poor."
Emergency response ser-
vices and park conditions
were rated highest with 90%
of the respondents giving
both a "very good" or
"good" rating. Police pro-
tection, library services, and
fire protection followed
closely behind. Water
service ranked the lowest
while still maintaining a ma-
jority rating of "very good"
or "good."
Police Protection
Street Conditions
Park Conditions
Water
Fire Protection
Library
EMS
73
20 40 60 80
Percent*
· Good/Very Good [] Fair/Poor
*"Don't know" and "undecided" responses not included.
89
100
FRIENDLINESS AND EVIDENCE OF COMMUNITY SUPPORT
In addition to describing cities by their physical waits, important social features are also significant
when evaluating local conditions. Accordingly, questions were included in the survey asking
residents to assess various social characteristics of their communities.
Of the eight attributes evaluated on a
7-point scale, Coralville respondents
assigned the highest ratings to the
safety of Coralville and the friendli-
ness of its residents. The lowest
rating was given to the amount of
excitement offered to the residents.
6--~5,6~----5.6 ............................................
! 4.8 4.9 4.9
5
3.9
4
2---
1
Contribute/Gov.
Affairs
Quick
Response/Gov.
OrgsJInterest In
All
Emergency/All
Assist
Problem/All
Respond
85
8O
83
4 96
8O
0 20 40 60 80 100
Percent*
*"Don't know" and "undecided" responses not included.
According to survey respondents,
Coralville's'responsiveness to per~
sonal and community problems is
generally quite favorable. Most ev-
eryone agreed that all were allowed to
contribute to 'loCal governmental af-
fairs and that everyone would help in
case of an emergency. At' least three-
fourths also felt that a city office
would give a quick response in regard
to a complaint, that organizations are
interested:in what is best for all resi-
dents, and that when something needs
to be done, everyone gets behind it.
INTEREST AND PARTICIPATION IN CORALVILLE ACTIVITIES
Over three-fourths of Coralville respondents are interested in being informed of community
activities. Yet, only 31% reported having participated over the past year in any community
improvement project. When asked to describe their level of involvement in local community
improvement activities and events, 21% indicated being "very active" or "somewhat active."
Ties between local residents often are related to commitment to the community. In Coralville's case,
15% of the respondents indicated knowing the name of more than 100 adult residents. In addition,
28% indicated that half or more of their close personal friends live in Coralville. As for the
respondents' adult relatives and in-laws, 6% indicated that half or more of them also live in
Coralville.
The future of Iowa's ur-
ban communities will
probably depend on
whether or not important
trends will continue over
the course of the next
few years. In the case of
Coralville, at least half of
the respondents see in-
creased crime, resident
indifference, residents
not working together,
and loss of small busi-
nesses as conditions that
pose threats ("some" or
"severe") to the future of
the community. People
moving out of the com-
munity was considered a
threat by about one-third
of Coralville respon-
dents. Less concern was
expressed with quality of
schools and people mov-
ing into the community.
PERCEIVED COMMUNITY THREATS
Lack of Jobs
Quality of Schools
Increased Crime
More Single Parents
Loss Family Farms
Loss Small Bus.
Indifference
Lack Leadership
Not Work Together
Loss Coma. Spirit
Both Parents Work
Out Migration
In Migration
24
51
38
B1
52
59
41
52
48
52
~8
71'
85
0 20 40 60 100
Percent*
:I No Threat rsSome Threat [3Severe Threat
*"Don't ~now" and "undecided" responses not ~ncludcd.
OVERALL COMMUNITY ATTACHMENT
How important is it for Coralville residents to feel a part of their city? When asked this question,
80% of the survey respondents reported that it was important for them to feel a part of the city.
When asked whether they feel "at home" in Coralville, 91% said that they did. Furthermore, 73%
indicated they would be sorry if forced to move away from Coralville. In spite of the community
concerns as indicated in the previous charts, the majority of respondents see Coralville as their home
and are reluctant to move away from the area.
Prepared by Vern Ryan, Lori Merritt, Nicole Grewe, Jeremy Judkins, Department of Sociology, Iowa State University. For further
information about this report, contact Jeff Zacharakis-Jutz, Linn County Extension Office, 3279 7th Ave, Marion, IA 52302;
Telephone (319) 377-9839; Fax (319) 377-0475; xlzach@exnet.iastate.edu. For information on other reports in the RDI series,
contact Veto Ryan, 317C East Hall, Iowa State University, Ames, IA 50011; Telephone (515) 294-5011; Fax (515) 294-2303;
vryan @iastate.edu.
Table of Contents
Paine
Study Design .........................................................................................................2
Table 1. Participating small cities (10,000-50,000)
Table 2. Participating metropolitan cities (minimum 50,000)
Who Lives in Sigma? ............................................................................................4
Figure 1. Length of residence
Figure 2. Employment status
Figure 3. Educational achievements
Reasons for Living in Sigma ................................................................................7
Figure 4. Major reasons for living here
Community Services ............................................................................................8
Figure 5. Ratings of selected services and facilities
Figure 6. Where selected services are acquired
Figure 7. Ratings of government services
Community Sentiments and Involvement ............................................................11
Figure 8. Ties with other adults in community
Figure 9. Interest and participation in community
Figure 10. Residents rate social features of community
Figure 11. Residents rate how community responds
Figure 12. Measure of community attachment
Community Description .......................................................................................16
Figure 13. How residents describe their community
Threats to the Community ....................................................................................17
Figure 14. Severe threats to the community
Involvement in Organizations ..............................................................................18
Figure 15. Number of organizational memberships
Figure 16. Organization and group memberships
Summary ...............................................................................................................20
Acknowledgements ..............................................................................................21
Comparing Coralville with Sigma City
(Iowa's Typical Small City)
Questions are being raised concerning the future
of Iowa's urban places. For instance, will they
replace the traditional rural lifestyle of earlier
generations? Are they capable of serving the
demands of larger and more diverse populations?
What about their role as trade centers for
residents of outlying areas that lack their own
services and employment opportunities? Who is
responsible for initiating programs and policies
on behalf of this ever-expanding constituency?
Recognizing that Iowa' s urban areas continue to
face challenging circumstances and
opportunities, the 1994 Rural Development
Initiative (RDI) expanded in 1997 to consider
living conditions in cities of 10,000 or more
residents. Similar to the 1994 study of rural
communities of 500-10,000 residents, the 1997
study involved a survey of urban residents to
obtain a variety of information including the
availability and quality of local services and
facilities. Altogether, 4,750 residents across 15
Iowa cities were asked to participate in this study.
This report is designed to help citizens and city
officials assess local living conditions as viewed
by the residents themselves. Throughout the
report, figures appearing on the top of the page
are averages of the results from all cities similar
in size to the city under evaluation. The
hypothetical name "Sigma City" refers to the
average responses for the 11 cities included in
this study with populations between 10,000 and
50,000. Figures at the bottom of the page provide
specific findings for Coralville. The
accompanying text is limited to a discussion of
statewide trends as illustrated by Sigma City's
figures, leaving interpretation of the results for
Coralville to be completed by its citizens.
Study Design
The 1997 study of Iowa's urban places was
designed to obtain information useful for both
local and statewide planning. Fifteen cities were
randomly selected so that various regions of the
state would be represented in the study.
Information obtained from residents was then
summarized for each city, making it possible to
compare results across all cities participating in
the study.
The 15 cities were further divided into two
categories. The first category is comprised of the
11 smaller cities (10,000-50,000) that were used
to create "Sigma City." Coralville's population
falls into this size range and is therefore
compared to Sigma City.
The second category consists of four
metropolitan cities with minimum populations of
50,000 residents. The hypothetical name "Metro
Sigma" represents the aggregation of four
metropolitan cities. (See RDI 118-121 reports for
metropolitan city results.) (See Tables 1 and 2 for
all community listings, respective response rates,
and population categorizations.)
Using local telephone directories, households
were randomly selected from each community to
take part in the study: 250 households were
selected from each of the 11 smaller cities, while
500 households were chosen from directories of
the four metropolitan cities. Within each
household, adult participants were selected by
randomizing the request between male and
female heads/co-heads. In the event the
requested gender was not present, instructions
asked that another adult member complete and
return the questionnaire.
Community
Bettendoff
Burlington
Cedar Falls
Clinton
Coralville
Fort Dodge
Mason City
Muscatine
Newton
Spencer
West Des Moines
Table 1. Participating Small Cities
"Sigma City"
Small City = 10,000-50,000 residents
Response Rate
68%
63%
67%
60%
58%
69c~
719~
59Q
58Ci
Small City average rcsl.,n.~c rtztc = 64%
Table 2. Participating Metropolitan Cities
"Metro Sigma"
Metropolitan = minimum of 50, O00 residents
Community Response Rate
Cedar Rapids 61%
Des Moines 53%
Iowa City 56%
Sioux City 57%
Metropolitan City average response rate = 57%
Questionnaires were mailed to 4,750 households
during spring 1997. Two weeks following the
initial mailing, postcards were sent to all
households thanking those who had returned their
questionnaires and reminding nonrespondents to
do so. Two weeks later, replacement
questionnaires were mailed to nonrespondents.
Altogether, 2,901 households returned completed
questionnaires yielding an average response rate
of 61%. Response rates by city ranged from 53%
to 71% (see Tables 1 and 2).
When using this report, two definitions should be
noted. First, "resident" is used broadly by
including individuals living both in and around
selected cities. Second, individuals participating
in the study were limited to household heads and
co-heads. Accordingly, certain groups of adults
(e.g., young adults living with parents or elde~y
parents living with adult sons or daughters) were
excluded from this study.
Purpose of the Report
This report is one of 15 reports comparing the
profiles of the communities studied with a
hypothetical city that typifies other similar cities
included in the study. It is prepared to allow
citizens the opportunity to analyze responses
given by their community residents and compare
them with results obtained in comparable cities.
Sigma City will serve as a benchmark from
which citizens and city officials can compare and
evaluate their own community.
Prepared by Vern Ryan and Nicole Grewe, Department of Sociology, Iowa State University. For further information about this
report, contact Jeff Zacharakis-Jutz, Lima County Extension Office, 3279 7th Ave, Marion, IA 52302; Telephone (319) 377-9839;
Fax (319) 377-0475; xlzach@exnet. iastate.edu. For information on other reports in the RDI series, contact Veto Ryan, 317C East
Hall, Iowa State University, Ames IA 5001 I; Telephone (515) 294-5011; Fax (515) 294-2303; vryan@iastate.edu
3
Who Lives in Sigma City?
Sigma City, a composite of the 11
smaller cities, has a population of
approximately 25,000 residents.
Slightly more females live in Sigma
City than males (56% versus 44%).
The large majority of Sigma City
residents are working age citizens
with almost three-quarters being 64
years of age and under. The largest
group, 25 to 44 years of age, consisted
of 36% of adult residents. The
average age of Sigma City adults is
approximately 51 years.
Years of residence in Sigma City is
closely associated with the age of
adult residents; almost half (46%)
have lived there 19 years or less (Fig.
l a). On the other hand, one-fourth
(25%) have lived in the community
for at least 40 years.
Sigma City
Fig. I a. Length of Residence
Less than 10
years
10-19 years
2O
49
20-29 years
15
13
30-39 years
14
40 years and
over
0 25 50 75 100
Percent
I" In neighborhood [] In community I
Coralville
Fig. I b. Length of Residence
Less than 10
years
10-19 years
20-29 years
30-39 years
7
8
20
16
72
40 years and ~ 2
over I
25 50 75
Percent
I• In neighborhood [] In community
100
Length of residency is also
noted by the number of
years Sigma City residents
have lived in their current
neighborhood. Almost half
(49%) have been residents
of the neighborhood less
than ten years. This
suggests the existence of
considerable residence
mobility within Sigma City.
In fact, while the average
length of residence in Sigma
City is 25 years, residents on
average have only lived in
their current neighborhood
for about 15 years.
Employment status also
denotes a working age
citizenry. When asked to
indicate their employment
status, 65% of Sigma City
males and 44% of females
reported full-time
employment (Fig. 2a).
While males outnumber
the females employed on a
full-time basis, more
females than males are
employed part-time (14%
versus 4%). Only slightly
more than one-quarter of
males (28%) and females
(26%) are retired.
Unemployment is very
rare; only 1% of men and
2% of women reported
being unemployed.
Sigma City
Fig. 2a. Employment Status
Employed (FT)
Employed (PT)
Retired
Homemaker
Student
Unemployed
65
44
14
50
Percent
75 100
Fig. 2b.
Employed (FT)
Employed (PT)
Retired
0
Homemaker 1
Student
Unemployed ~ 2
1
Coralville
10
13
Employment Status
15
6O
25 50 75
Percent
81
100
Of all adults employed,
49% work either in
professional or managerial/
administrative occupations,
while 21% are employed in
clerical or sales positions.
Only one-in-ten of Sigma
City residents report service
as their primary occupation.
When asked to rate their
overall satisfaction with
their present employment
situation, most Sigma City
residents indicated they
were satisfied (89%); only
11% expressed
dissatisfaction.
5
Sigma City residents
inherently value
education as they
reflect national trends
of pursuing higher
educational goals.
Figure 3a clearly
illustrates Sigma City
residents' educational
achievements. Fifteen
percent of males and
14% of females have
completed a graduate
or professional degree.
Another 21% of males
and 18% of females
have attained a
bachelor' s degree.
Sigma City
Fig. 3a. Educational Achievements
Associate's degree
High
.6Some high school5
Less than 9th grade2
50 75 100
25
Percent
II Female [] Male I
Coralville
Fig. 3b. Educational Achievements
GradJprof. degree
Bachelor's degree
Associate's degree
Some college, no degree
High school graduate
Some high school
Less than 9th grade 0
1
0
50 75
Percent
100
Furthermore, almost one-
in-ten of Sigma City
residents completed an
associate's degree. In
contrast, only 8% of the
men and 7% of the
women reported not
completing a high school
education.
.- 6
Reasons for Living in Sigma City
Why do residents live in Sigma City rather than
another community? Up to three reasons were
cited by each resident. The most frequently
stated reasons are: a) it is close to my job (55%);
b) it is close to my relatives and/or in-laws
(42%); c) I grew up here (31%); and d) it is a safe
area (31%) (Fig. 4a).
Reasons seldom mentioned included low
property taxes (4%), caring for aging relatives
(4%), and presence of strong local leadership
(2%). The latter may suggest a void in the quality
of leadership, or perhaps it implies that Sigma
City residents do not consider leadership as
important as other factors.
Coralville
Fig. 4b. Major Reasons for Living
in Coralville
Grew up here
Close to relJin-lew
Friendliness
Close to job
Affordable housing
Scenic area
Safe area
Strong schools
Medical service
Good leadership
Low property taxes
Can't afford to leave
Care for aging relatives
0 25
* "Other" responses not included.
,10
~ 20
~15
~ 68
'14
~33
~ 22
2
7
Percent*
100
Fig. 4a. Major Reasons for Living
in Sigma City
Grew up here ~ 31
Close to relJ1n-law
Friendliness
Close to job
Affordable housing
Scenic area
Safe area
Strong schools
Medical service
Good leadership
Low property taxes
Can't afford to leave
Care for aging relatives 4
0 25
* "Other" responses not included.
m17
~19
18
I/31
~ 26
~13
2
14
,8
50 75 100
Percent*
Reasons for living in a community may vary
depending upon the size of the community.
Within the population range used to define small
cities (10,000-50,000), the frequency of reasons
cited varies between different population sizes.
Medical services, strong local leadership, low
property taxes, cannot afford to leave, and caring
for aging relatives are of equal importance to
residents of smaller and larger communities.
However, a significant variation by community
size exists for other reasons. Specifically,
residents of smaller communities chose close to
job and affordable housing more commonly than
residents of larger communities. On the other
hand, residents of larger communities selected
close proximity to relatives and strong school
system more frequently than residents of smaller
communities.
7
Community Services
Considering all of the local
services and facilities included
in the questionnaire, public
schools received the most
positive evaluation (Fig. 5a). In
addition, over half rated local
jobs, medical services,
shopping, housing, child care,
and senior citizen programs as
"good" or "very good."
Conversely, less than one-half of
Sigma City residents viewed
youth programs and recreation
and entertainment favorably.
Fig. 5a.
Jobs
Medical
Public schools
Shopping
Housing
RecJentertain.
Child care
Senior programs
Youth programs
0 25
* "Don't know" responses not included.
Sig!l'la City
Ratings of Selected Services and Facilities
~ 57
~ 74
~ 56
~ 59
~ 42
50 75 100
Fig. 5b.
Jobs
Medical
Public schools
Shopping
Housing
RecJentertain.
Child care
Senior programs
Youth programs
Coralville
Ratings of Selected Services and Facilities
~ 62
0 25 50
* "Don't know" responses =or included. ~ · Good/Very good
100
In towns larger than Sigma City,
residents are more likely to think
that shopping and senior
programs are "good" or "very
good." People in smaller
communities evaluated these
same services less positively.
Jobs, medical services, housing,
recreation and entertainment, and
child care are evaluated about the
same regardless of the
community size.
When residents were asked to
give an overall rating of local
services, three-fourths (76%) of
Sigma City residents responded
with a "good" or "very good."
8
Figure 6a illustrates the proportion
of Sigma City citizens who obtain
services locally versus those going
outside Sigma City. At least three-
fourths of the residents receive
primary health care, shop for daily
needs, and attend church within
Sigma City. In contrast,
specialized health care is the
service that the least amount of
people obtain within Sigma City.
Only 35% of residents reported
remaining within Sigma City for
specialized health care.
In addition, approximately half
reported leaving Sigma City to
recreate and shop for big tickets
items.
Siglrla City
Fig. 6a. Where Selected Services Are Acquired
Primary health
care
Specialize health
Care
Shopping
(daily needs)
Shopping
(big ticket items)
RecJentertainment
Church
0
* "Do not use or purchase"
responses not included.
35
75
51
25 50 75
Percent*
· Mostly outside community
· Mostly Inside community
81
100
Coralville
Fig. 6b. Where Selected Services Are Acquired
Primary health
Care
Specialized health
care
Shopping
(dally needs)
Shopping
(big ticket items)
RecJentertainment
Church
illill[5
79
39
61
72
28
25 50 75 100
Percent*
* "Do HOt use or purchase"
responses not included.
· Mostly outside community
· Mostly inside community
It comes as no surprise that
residents of communities larger
than Sigma City are more likely to
shop for big ticket items and attend
church within their city.
Furthermore, population size made
no significant difference in the
amount of residents who reported
remaining within their respective
communities to shop for daily
needs items.
9
Residents of Sigma City
expressed positive ratings
for their local government
services (Fig. 7a). Virtually
everyone rated fire
protection and emergency
response services
positively. Police
protection, parks, libraries,
and water services received
slightly lower ratings,
although still over three-
fourths reported ratings of
"good" or "very good."
Street conditions, on the
other hand, received the
lowest rating with only 41%
of Sigma City residents
giving a rating of "good" or
"very good."
Siglfla City
Fig. 7a. Ratings of Government Services
Police protection
Street conditions
Park conditions
Water
Fire protection
Library
Emergency response
~78
~ 41
~ 82
0 25
* "Don't know" responses not included.
50 75
Percent*
.oo Very .oo. I
92
100
Coralville
Fig. 7b. Ratings of Government Services
Police protection
Street conditions
Park conditions
89
90
Water
Fire protection
Library
Emergency response
0 25 50 75
Percent*
* "Don't know" responses not included.
89
9O
100
When asked to rate their
overall opinion of the quality
of government services, 62%
of Sigma City residents
believed that the local
government is doing a "good"
or "very good" job.
Population differences of
similar cities do not appear to
create significant differences
in residents' opinions of local
gov. emment services.
10
Community Sentiments
and Attachments
Sigma City
Continued improvements in
transportation and
communication systems
have increased the
opportunities for
individuals to look beyond
their communities for social
interaction. However, as
the number of these
opportunities increases
there is also the risk of
declining commitments to
one's own community.
Fig. 8a. Ties With Other Adults in Community
Relatives living in community
Relatives living in neighborhood
Friends living in community
Friends living in neighborhood
Adults known by name in
neighborhood
In Sigma City, we find
evidence suggesting
considerable dependence on
outside contacts (Fig. 8a).
In Sigma City, for example, few citizens reported
having a majority of their relatives (one-half or
more) living in the same neighborhood (6%) or
27
Coralville
Fig. 8b. Ties With Other Adults in Community
73
17
Relatives living in community
Relatives living in neighborhood
Friends living in community
Friends living in neighborhood
Adults known by name in
neighborhood
94
60
40
12
88
57
0 25 50 75 1 O0
Percent
II Less than half [] Half or more I
even in the same community (27%). Also, few
reported a majority of their friends living in the
same neighborhood (12%), although most
residents (60%) continue to
depend on other Sigma City
residents when it comes to
developing friendships.
Finally, slightly less than
one-half (43%) indicated
knowing the majority of
95 neighborhood residents by
name.
72
25 50 75
Percent
I [] Less than half [] Half or more I
100
At the extreme, we note that
approximately one-third
(34%) have no relatives
living in Sigma City and
almost one-in-ten have no
local friends. In addition,
one in every five Sigma City
residents indicated knowing
none or very few of their
neighbors.
Despite indications of social
isolation, almost everyone
(93%) in Sigma City is
interested in what goes on in
the community (Fig. 9a). At
the same time, less than half
(44%) reported serving as a
community volunteer during
the past year. When asked to
describe their level of
involvement in community
improvement projects, only
a third (32%) indicated being
"somewhat" or "very"
active.
Sigma City
Fig. 9a. Interest and Participation in Community
Interested in
community
Volunteered last
year
93
Considers
oneself active
44
The pattern of citizen
participation in Sigma City
is typical of communities
throughout the country.
While most residents
express interest in their local communities, few
(or at least less than half) get involved in
community improvement efforts. The challenge
32
0 25 50 75 1 O0
Percent
for local leaders is to actively recruit a broader
base of citizen support.
Coralville
Fig. 9b. Interest and Participation in Community
Interested in
community
Volunteered last
year
31
Considers
oneself active
21
90
0 25 50 75 100
Volunteerism in Sigma City
can be considered a glass
that is half full or half
empty. Community leaders
in other parts of the nation,
urban and rural alike, would
be overjoyed if half of their
citizenry engaged in
community betterment.
However, the results also
suggest that there is
considerable room for
improvement. How does
your community compare
with Sigma City? Why are
its citizens more or less
active than Iowa's typical
small city?
Percent
The ratings of Sigma City as a
place to live are for the most part
positive. Most agreed, for instance,
that they can usually find someone
to talk to when they want to chat
(Fig. 10a). Interestingly, however,
more agreed that it was easier to
find someone to talk to in the
community (87%) than in their own
neighborhood (76%). At the same
time, only one-fourth agreed that
residents of either Sigma City
(26%) or their own neighborhood
(24%) look out for each other. Yet,
two-thirds rated residents of both
the community (66%) and their
neighborhood (73%) as accepting
of racial and ethnic diversity.
Sigma City
Fig. 10a. Residents Rate Social Features of
Community and Neighborhood
someone to talk to 76
Residents look out
for each other
Residents accept
racial/ethnic
diversity
Information not included in Figure
10a further reveals that 81%
believed that their neighbors are more trusting of
each other than in other Sigma City
neighborhoods. In contrast, only 52% of Sigma
City residents felt that living in their
26
!4
66
73
0 25 50 75
Percent*
I
IAgree within neighborhood []Agree within community I
* "Undecided" responses not included.
neighborhood is like living with a group friends.
Overall, 78% felt Sigma City has more things
going for it than other communities of similar
size.
Coralville
Fig. 10b. Residents Rate Social Features of
Community and Neighborhood
Can usually find
someone to talk to
71
65
Residents look out
for each other
Residents accept
racial/ethnic
diversity
15
20
0 25 50 75
Percent*
IIAgree within neighborhood mAgree within community I
92
H)
100
The negativity of responses to
questions related to concern for
fellow community and
neighborhood residents are
worthy of further exploration.
The belief by a large majority
that "every person for
themselves" accurately
describes fellow resident
attitudes within the community
and neighborhoods may affect
the ability to act in other realms.
For instance, how might lack of
concern for fellow residents
affect Sigma City's success in
community betterment?
* "Undecided" responses not included.
- 13
Additional ratings of Sigma
City were obtained on a
number of quality of life
dimensions (Fig. 1 la). For
instance, most (91%) felt
that in an emergency
situation, complete strangers
from within the community
would be willing to help.
Furthermore, most (84%)
felt that anyone who wanted
to, could contribute to Sigma
City's governmental affairs.
Three-fourths (74%) felt that
organizations in Sigma City
are interested in what is best
for everyone and almost two-
thirds (65%) felt that a city
office in Sigma City would
quickly respond to a call.
Sigma City
Fig. 11 a. Residents Rate How Community Responds
In an emergency, residents I
don't know would help
Everyone can contribute to
governmental affairs
Organizations interested in
what is best for ell
If I cell a city office, I would
get a quick response
When a community problem
exists, all residents help out
When a neighborhood problem
exists, all residents help out
* "Undecided" responses not included.
0 25 50 75
Percent*
100
Coralville
Fig. 11 b. Residents Rate How Community Responds
In an emergency, residents I
don't know would help
Everyone can contribute to
governmental affairs
Organizations interested In
what is best for 811
If I call a city office, I would
get a quick response
When a community problem
exists, all residents help out
When a neighborhood problem
exists, all residents help out
* "Undecided" responses not included.
~96
83
~80
~80
~57
0 25 50
Percent*
I,Ag__ I
75 100
Slightly more than one-
half believed that all
residents would step
forward to address either a
community (61%) or
neighborhood (53%)
problem.
The greater confidence of
a response to a community
problem as opposed to a
neighborhood problem
might suggest an eroding
confidence in the ability of
neighborhoods to address
local problems. It may
also signify a declining
commitment among
neighbors to deal with
issues of mutual interest.
In spite of the few concerns
previously discussed, most
residents remain attached to
Sigma City (Fig. 12a). This
attachment is demonstrated by the
fact that virtually everyone (95%)
said they feel "at home" in Sigma
City. In addition, most (87%)
considered it important to feel a
part of their community and three-
fourths (75 %) would regret having
to move out of Sigma City.
What determines this strong
attachment to Sigma City? Many
reasons may exist, some perhaps
more important than others
depending on each person' s
situation. For some, local
acquaintances, friendships, and a
feeling of belonging might take
precedence.
Sigma City
Fig. 12a. Measures of Community Attachment
Feel "at home" 1
in this
community
Important to
feel part of
community
Sorry if had to
move out of
community
25
5O
Percent
75
95
87
75 100
Coralville
Fig. 12b. Measures of Community Attachment
Feel "at home"
in this
community
Important to
feel part of
community
Sorry if had to
move out of
community
50
Percent
79
73
91
For others, it may be a matter of
physical traits such as a clean
environment, less congestion, and
easy access to their localities. Still
others might value the opportunities
available for employment, shopping,
and health care. Additionally, in the
same way that residents express
different reasons for being attached
to their community, a warm and
caring community to some may be
intolerant and prejudiced to others.
100
-- 15
Community Description
Several traits typically
used to evaluate Dangerous
communities were
included in the survey. Unfriendly
For each positive trait
listed, a polar negative Run Down
was also included.
Instructions directed sot Truering
respondents to circle a
number along a Indifferent
continuum (1-7) between
Rejecting of
the extremes that best New Ideas
reflects their rating of
Sigma City. Prejudiced
Boring
Sigma City
Fig. 13a. How Residents Describe Their Community
.0
................. 5.3 ---
....... 5:0 - -
Safe
Fdendly
Well-kept
Trusting
As reported in Fig. 13a,
highest ratings were given
to Sigma City's
friendliness (5.3), safety
(5.0), and well-kept appearance (5.0). The level
of excitement (3.7) in Sigma City received the
lowest rating. Sigma City's level of tolerance
Supportive
Open to New
Ideas
I 2 3 4 5 6 7
Average Score
1=lowest 7=highest
Tolerant
Exciting
(4.1) and openness to new ideas (4.2) also
received relatively low ratings.
Fig. 13b.
Dangerous
Coralville
How Residents Describe Their Community
· Safe
5.6
Unfriendly .......... *5:6 ' ' Friendly
Run down
Not trusting
Indifferent
Rejecting of
new ideas
Prejudiced
Boring
...... 5.5 _ _ Well-kept
............ 4.9 ........ Trusting
· ' 4;7 _ _ Supportive
Open to
.......... 4.9 ...... new ideas
......... . .... Tolerant
Exciting
I 2 3 4 5 6 7
Average Score
Interestingly, size of
community does not
significantly affect
residents' description
of Sigma City's
qualities. Although, it
is probably safe to say
that people in larger
cities may evaluate
their community as
more exciting and
tolerant than those who
live in smaller cities.
However, residents of
smaller cities probably
feel safer in comparison
to people living in
larger communities.
1=lowest 7=highest
-- 16
Threats to the Community
What about the future of Sigma City
as viewed by its residents? When
asked to indicate the degree of threat
posed by a series of possibilities,
several items stood out (Fig. 14a).
Most notable was the fear of losing
small businesses with one-third
(34%) rating this as a severe threat
to Sigma City' s future. Other severe
threats pertained to lack of local
leadership (28%), indifference about
the community (27%), failure of
people to work together (27%), and
loss of community spirit (25%).
These items obviously demonstrate
concern expressed by residents
related to "people" issues, or how
Sigma City will function together to
address future challenges.
Sigma City
Fig. 14a. Severe Threats to Community
Lack of jobs
Quality of schools
Increased crime
More single parents
Loss of family farms
Loss of small businesses ~ 34
Indifference ~ 27
Lack of leadership ~ 28
Not work together ~ 27
Loss of community spirit ~ 25
More than 2 parents work ~ 16
Out migration ~ 22
In migration · 5
0 25
I16
* "Undecided" responses not included.
50 75 100
Percent*
Coralville
Fig. 14b. Severe Threats to Community
14
Lack of jobs
Quality of schools
Increased crime
More single parents
Loss of family farms
Loss of small businesses
Indifference
Lack of leadership
Not work together
Loss of community spirit
More than 2 parents work
Out migration
In migration
0
* "Undecided" responses not included.
13
Is
17
15
14
25
50 75
Percent*
In addition, the summation of these
specific factors may cause or
contribute to other community
problems. For example, is resident
indifference and loss of community
spirit related to the lack of concern
for fellow residents (see Fig. 10a)?
On the positive side, quality of
schools (8%) and in migration (5%)
were seldom viewed as severe
threats to Sigma City's future. In
fact, out migration (22%)
apparently was viewed as a far
greater concern than was in
migration.
Involvement in
Organizations
Figure 15a illustrates the
proportion of Sigma City
residents reporting
memberships in various
neighborhood and community
organizations. Organizational
membership and participation
are useful measures for
understanding the extent to
which residents are involved
in civic, political, and social
activities. In Sigma City,
33% do not belong to any
community organizations,
63% do not belong to any
organizations outside the
community, and 76% do not
belong to any groups within
their neighborhood.
Sigma City
Fig. 15a. Number of Organizational Memberships
NODe
76
18
One 18
14
~19
~.. Izl~_,,,,~,~
3
Four or more
~!~ 16
1
0 25 50 75
Percent
100
Coralville
Fig. 15b. Number of Organizational Memberships
...................... ' ........ ' ............. '5:':i'l'i 63
None
77
!17
Orle 16
14
Two 16
Three
Four or more
7
0 25 50 75 100
Percent
IIIrmide neighborhood []Outside community Binside community I
Eighteen percent of Sigma City
residents belong to one local
community organization and 19%
report belonging to two local
community organizations. This is
quite similar to the proportions
reporting memberships in one
(18%) or two (10%) organizations
outside Sigma City. However,
memberships in three or more
organizations are more common
within the community. Thirty
percent of Sigma City residents
reported belonging to three or
more community organizations
versus only 9% belonging to three
or more outside organizations.
Neighborhood group
memberships were quite low.
Only 24% expressed membership
in any neighborhood
organizations.
Within categories of local
clubs, associations, and
organizations, there is
considerable variability in
membership (Fig. 16a).
Church-related groups received
the highest percentage of Sigma
City residents claiming
membership. One-in-two
Sigma City residents belongs to
a church-related group such as
church committees and bible
study groups. In addition,
approximately one-third of
residents are members of
recreational groups or job-
related organizations. Sports
teams, bowling leagues, and
card clubs are all examples of
recreational groups. Job-
related organizations include,
but are not limited to, labor
unions and professional associations.
Sigma City
Fig. 16a. Organizations and Group Memberships
Church-related groups
Recreational groups
Job-related groups
Political and civic groups
Service and fraternal groups
~50
~32
18
0 25 50
Percent
75 100
Coraiviile
Fig. 16b. Organizations and Group Memberships
Church-related groups
Recreational groups
Job-related groups
Political and civic groups
Service and fraternal groups
20
20
8
0 25 50 75 100
Less support was shown for
political and civic groups.
Only one-in-four Sigma City
residents is a member of
political and civic groups,
including historical groups,
local development
organizations, and the PTA.
Service and fraternal
organizations receive the
least membership support.
Only 18% of residents
indicated memberships to
service and fraternal
organizations such as the
Lions, Kiwanis, and Eastern
Star.
Percent
19
Summary
Sigma City is a hypothetical small city in
Iowa with a population of approximately
25,000 people. It is a stable community
where the average length of residence is 25
years within the city and 15 years within
respective current neighborhoods. The
primary reasons people choose to live in
Sigma City are because it is close to their
place of work, they grew up there, it is close
to relatives, and it is a safe area.
Unemployment is very rare with almost
everyone being either employed, retired,
homemakers, or students. Overall, the
majority of Sigma City residents do not feel
that the future of the community is severely
threatened by any one significant factor.
Although, approximately one-third believe
that loss of small businesses may be a
source of potential danger.
The citizens of Sigma City are linked to
each other through ties of family, friendship,
acquaintanceship, and shared involvement
in community activities and organizations.
A very large majority feel at home in Sigma
City and would be sorry if they had to move
away. In addition, almost everyone
expresses that it is important to feel they are
part of Sigma City and is interested in what
is going on in the city. On the other hand,
unfortunately less than half of residents
volunteered last year or even consider
themselves active in community
improvement activities. Furthermore, only
one-quarter of Sigma City citizens believe
that fellow neighborhood and community
residents look out for each other, although a
large majority think that residents are
accepting of racial and ethnic diversity.
The fact that the majority of Sigma City
residents stay inside the community for
primary health care, shopping for daily needs,
church, and to recreate is reflected in their
overall positive evaluation of the quality of
local community services. Shopping for big
ticket items and specialized health care are
the only services that over half of residents
leave Sigma City to obtain. Furthermore,
recreation/entertainment and youth programs
are the only community services viewed as
significantly lacking in quality by a majority
of residents.
On the other hand, Sigma City residents are
extremely positive about the quality of local
government services. Fire protection and
emergency response services are rated
favorably by almost everyone. Street
conditions was the only government service
that is perceived as unsatisfactory by a
majority of Sigma City residents. In addition,
over one-third believe that a city office would
not respond quickly to a complaint. Lastly, a
large majority think that everyone is allowed
to contribute to governmental affairs if they
so desire.
No matter what the data reveals about Sigma
City, remember that Sigma City does not
exist. It is an imaginary community created
from the averages of results from all 11 small
cities included in the study. It is created so
that citizens would have a point of
eornparigon for their gpeeifie community
findings. Therefore, the real value of this
report can only be realized when
communities use the information for self-
examination and as a tool for community
improvement.
How does your community compare?
2O
Acknowledgements
This report is one of 15 reports available as
part of Iowa State University's Rural
Development Initiative Project. Funding for
this project was provided through the Iowa
Agriculture and Home Economics
Experiment Station, College of Agriculture,
Iowa State University.
We wish to thank each of the students and
staff in the Department of Sociology who
contributed to the completion of these
reports. At every stage of the research,
beginning with the random selection of more
than 4,750 households from 15 local
telephone directories, and ending with the
construction of more than 510 graphs that
appear in these reports, the following
individuals played a major role:
Terry Besser
Kyong Hee Chee
Chris Colvin
Jan Flora
Jeremy Judldns
Amy Lonsdale
Loft Merrit
Tom Rice
We also thank each of the communities that
took part in this research. Specifically, we
appreciate the support received from
representatives of local media who publicized
the purpose of the study and notified citizens
when the research was to occur. Hopefully,
the content included in these reports will be
used by media when disseminating
information useful for local development
projects.
Last but not least, we acknowledge the
cooperation of the 2,901 Iowa citizens who
contributed to this research project by
completing and returning their
questionnaires. There is no better indication
of the importance of community to Iowa
citizens than the fact that so many individuals
voluntarily participated in a study of this type.
We hope it was worth the effort.
Table of Contents
Pa~e
Study Design .........................................................................................................2
Table 1. Participating small cities (10,000-50,000)
Table 2. Participating metropolitan cities (minimum 50,000)
Who Lives in Sigma? ............................................................................................4
Figure 1. Length of residence
Figure 2. Employment status
Figure 3. Educational achievements
Reasons for Living in Sigma ................................................................................7
Figure 4. Major reasons for living here
Community Services ............................................................................................8
Figure 5. Ratings of selected services and facilities
Figure 6. Where selected services are acquired
Figure 7. Ratings of government services
Community Sentiments and Involvement ............................................................11
Figure 8. Ties with other adults in community
Figure 9. Interest and participation in community
Figure 10. Residents rate social features of community
Figure 11. Residents rate how community responds
Figure 12. Measure of community attachment
Community Description .......................................................................................16
Figure 13. How residents describe their community
Threats to the Community ....................................................................................17
Figure 14. Severe threats to the community
Involvement in Organizations ..............................................................................18
Figure 15. Number of organizational memberships
Figure 16. Organization and group memberships
Summary ...............................................................................................................20
Acknowledgements ..............................................................................................21
Comparing Iowa City with Metro Sigma
(lowa's Typical Metropolitan City)
Questions are being raised concerning the future
of Iowa's urban places. For instance, will they
replace the traditional rural lifestyle of earlier
generations? Are they capable of serving the
demands of larger and more diverse populations?
What about their role as trade centers for
residents of outlying areas that lack their own
services and employment opportunities? Who is
responsible for initiating programs and policies
on behalf of this ever-expanding constituency?
Recognizing that Iowa's urban areas continue to
face challenging circumstances and
opportunities, the 1994 Rural Development
Initiative (RDI) expanded in 1997 to consider
living conditions in cities of 10,000 or more
residents. Similar to the 1994 study of rural
communities of 500-10,000 residents, the 1997
study involved a survey of urban residents to
obtain a variety of information including the
availability and quality of local services and
facilities. Altogether, 4,750 residents across 15
Iowa cities were asked to participate in this study.
This report is designed to help citizens and city
officials assess local living conditions as viewed
by the residents themselves. Throughout the
report, figures appearing on the top of the page
are averages of the results from all cities similar
in size to the city under evaluation. The
hypothetical name "Metro Sigma" refers to the
average responses for the four metropolitan cities
included in this study with minimum populations
of 50,000 residents. Figures at the bottom of the
page provide specific findings for Iowa City. The
accompanying text is limited to a discussion of
statewide trends as illustrated by Metro Sigma's
figures, leaving interpretation of the results for
Iowa City to be completed by its citizens.
Study Design
The 1997 study of Iowa's urban places was
designed to obtain information useful for both local
and statewide planning. Fifteen cities were
randomly selected so that various regions of the
state would be represented in the study.
Information obtained from residents was then
summarized for each city, making it possible to
compare results across all cities participating in the
study.
The 15 cities were further divided into two
categories. The first category is comprised of the
11 smaller cities (10,000-50,000) that were used to
create "Sigma City." (See RDI 107-117 reports for
small city results.)
The second category consists of four metropolitan
cities with minimum populations of 50,000
residents. The hypothetical name "Metro Sigma"
represents the aggregation of the four metropolitan
cities. Iowa City's population falls into this size
range and is therefore compared to Metro Sigma.
(See Tables 1 and 2 for all community listings,
respective response rates, and population
categorizations.)
Using local telephone directories, households were
randomly selected from each community to take
part in the study: 250 households were selected
from each of the 11 smaller cities, while 500
households were chosen from directories of the
four metropolitan cities. Within each household,
adult participants were selected by randomizing the
request between male and female heads/co-heads.
In the event the requested gender was not present,
instructions asked that another adult member
complete and return the questionnaire.
Community
Bettendorf
Burlington
Cedar Falls
Clinton
Coralville
Fort Dodge
Mason City
Muscatine
Newton
Spencer
West Des Moines
Table 1. Participating Small Cities
"Sigma City"
Small City = 10,000-50,000 residents
Response Rate
68%
63%
67%
60%
58%
69%
71%
59%
66%
66%
58%
Small City average response rate = 64%
Table 2. Participating Metropolitan Cities
"Metro Sigma"
Metropolitan = minimum of 50, O00 residents
Community Response Rate
Cedar Rapids 61%
Des Moines 53%
Iowa City 56%
Sioux City 57%
Metropolitan City average response rate = 57%
Questionnaires were mailed to 4,750 households
during spring 1997. Two weeks following the
initial mailing, postcards were sent to all
households thanking those who had returned their
questionnaires and reminding nonrespondents to
do so. Two weeks later, replacement
questionnaires were mailed to nonrespondents.
Altogether, 2,901 households returned completed
questionnaires yielding an average response rate
of 61%. Response rates by city ranged from 53%
to 71% (see Tables 1 and 2).
When using this report, two definitions should be
noted. First, "resident" is used broadly by
including individuals living both in and around
selected cities. Second, individuals participating
in the study were limited to household heads and
co-heads. Accordingly, certain groups of adults
(e.g., young adults living with parents or elderly
parents living with adult sons or daughters) were
excluded from this study.
Purpose of the Report
This report is one of 15 reports comparing the
profiles of the communities studied with a
hypothetical city that typifies all other similar
cities included in the study. It is prepared to
allow citizens the opportunity to analyze
responses given by their community residents and
compare them with results obtained in
comparable cities. Metro Sigma will serve as a
benchmark from which citizens and city officials
can compare and evaluate their own community.
Prepared by Vern Ryan and Nicole Grewe, Department of Sociology, Iowa State University. For further information about this
report, contact Jeff Zacharakis-Jutz, Linn County Extension Office, 3279 7th Ave, Marion, IA 52302; Telephone (319) 377-9839;
Fax (319) 377-0475; xlzach@exnet.iastate.edu. For information on other reports in the RDI series, contact Vern Ryan, 317C East
Hall, Iowa State University, Ames IA 50011; Telephone (515) 294-5011; Fax (515) 294-2303; vryan@iastate.edu
Who Lives in Metro Sigma?
Metro Sigma, a composite of the four
metropolitan cities, has a population
of approximately 110,000 residents.
Slightly more females live in Metro
Sigma than males (57% versus 43%).
The large majority of Metro Sigma
residents are working age citizens
with over three-quarters (80%) being
64 years of age and under. The largest
group, 25 to 44 years of age, consisted
of 42% of adult residents. The
average age of Metro Sigma adults is
approximately 47 years.
Years of residence in Metro Sigma is
closely associated with the age of
adult residents; almost half (47%)
have lived there 19 years or less (Fig.
l a). On the other hand, one-fourth
(24%) have lived in the community
for at least 40 years.
Metro Sigma
Fig. I a. Length of Residence
Less than 10
years
10-19 years
13
14
62
20-29 years
15
12
30-39 years
14
40 years and
over
0 25 50 75
Percent
IIIn neighborhood QIn communityI
100
Iowa City
Fig. I b. Length of Residence
Less than 10
years
10-19 years
13
15
46
65
20-29 years
30-39 years
40 years and
over
16
12
5
14
0
25 50 75
Percent
100
Length of residency is also noted by
the number of years Metro Sigma
residents have lived in their current
neighborhood. Over half (62%)
have been residents of the
neighborhood less than ten years.
This suggests the existence of
considerable residence mobility
within Metro Sigma. In fact, while
the average length of residence in
Metro Sigma is 24 years, residents
on average have only lived in their
current neighborhood for about 12
years.
IIIn neighborhood rain community I
4
Employment status also
denotes a working age
citizenry. When asked to
indicate their employment
status, 69% of Metro Sigma
males and 48% of females
reported full-time employment
(Fig. 2a). While males
outnumber the females
employed on a full-time basis,
more females than males are
employed part-time ( 13 %
versus 6%). Only slightly less
than one-quarter of males
(19%) and females (23%) are
retired. Unemployment is very
rare; only 3% of men and 2%
of women reported being
unemployed.
Fig. 2a.
Employed (FT)
Employed (PT)
Retired
Homemaker
Student
Unemployed
Metro Sigma
Employment Status
48
&13
23
0
50
Percent
75 100
Employed (FT)
Employed (PT)
Retired
Homemaker
Student
Unemployed
lowa City
Fig. 2b. Employment Status
17
118
17 ·
10
7
12
13
0 25
50
Percent
100
Of all adults employed, 52%
work either in professional
or managerial/administrative
occupations, while 23% are
employed in clerical or sales
positions. Only one-in-ten
of Metro Sigma residents
report service as their
primary occupation.
When asked to rate their
overall satisfaction with
their present employment
situation, most Metro Sigma
residents indicated they were
satisfied (85%); only 15%
expressed dissatisfaction.
Metro Sigma residents
inherently value
education as they reflect
national trends of
pursuing higher
educational goals. Figure
3a clearly illustrates
Metro Sigma residents'
educational
achievements. Twenty-
one percent of males and
15% of females have
completed a graduate or
professional degree.
Another 26% of males
and 22% of females have
attained a bachelor' s
degree.
Metro Sigma
Fig. 3a. Educational Achievements
GradJprof. degree
Bachelor's degree
Associate's degree
Some college, no degree
High school graduate
5
Some high school i 5
Less than 9th grade ~ 2
1
0
26
25 50 75
Percent
100
Iowa City
Fig. 3b. Educational Achievements
GradJprof.
25
36
Bachelor's de~
Associate's degree
Some college, no degree
High school graduate
Some high school
Less than 9th grade
~8
24
0 25
31
50
Percent
Female [] Male
100
Furthermore, almost
one-in-ten of Metro
Sigma residents
completed an
associate's degree. In
contrast, only about 7%
of men and 6% of
women reported not
completing a high
school education.
6
Reasons for Living in Metro Sigma
Why do residents live in Metro Sigma rather than
another community? Up to three masons were
cited by each resident. The most frequently
stated reasons are: a) it is close to my job (54%);
b) it is close to my relatives and/or in-laws
(47%); and c) I grew up here (34%) (Fig. 4a).
Reasons seldom mentioned included scenic area
(4%), low property taxes (2%), caring for aging
relatives (5%), and strong local leadership (2%).
The latter may suggest a void in the quality of
leadership, or perhaps it implies that Metro
Sigma residents do not consider leadership as
important as other factors.
Iowa City
Fig. 4b. Major Reasons for Living
in Iowa City
Grew up here
Close to relJin-law
Friendliness
Close to .Job
Affordable housing
Scenic area
Safe area
Strong schools
Medical service
Good leadership 1
Low property taxes 0
Can't afford to leave ~14r
Care for aging relatives
0
~18
~ 29
~18
~ 51
15
16
~ 30
~ 29
25 50 75
* "Other" responses not included.
100
Metro Sigma
Fig. 4a. Major Reasons for Living
in Metro Sigma
Grew up here
Close to relJin-law ~ 47
Friendliness ~ 18
Close to job
Affordable housing ~ 14
Scenic area 14
Safe area / 23
Strong schools ~ 19
Medical service ~ 13
Good leadership 2
Low property taxes 2
Can't afford to leave ~55 11
Care for aging relatives
0 25
* "Other" responses not included.
50 75
Percent*
100
Reasons for living in a community may vary
depending upon the size of the community.
Using the population range used to define
metropolitan cities (minimum of 50,000
residents), the frequency of reasons cited varies
between different population sizes. Friendliness
of community, scenic area, medical services,
strong local leadership, low property taxes, and
caring for aging relatives are of equal importance
to residents of smaller and larger communities.
However, a significant variation by community
size exists for other reasons. Specifically,
residents of larger communities chose grew up
here, close to relatives, and affordable housing
more commonly than residents of smaller
communities. On the other hand, residents of
smaller communities selected strong school
system more frequently than residents of larger
communities.
Community Services
Considering all of the local services
and facilities included in the
questionnaire, medical services
received the most positive
evaluation (Fig. 5a). In addition,
approximately three-quarters rated
local jobs, public schools,
shopping, and senior citizen
programs as "good" or "very
good." Conversely, about one-half
or less of Metro Sigma residents
viewed youth programs and
recreation and entertainment
favorably.
Metro Sigrlla
Fig. 5a. Ratings of Selected Services and Facilities
Jobs
Medical
Public schools
Shopping
Housing
RecJentertain.
Child care
Senior programs
Youth programs
~ 79
~ 70
~ 53
~ 58
~ 46
0 25 50
Percent*
* "Don't know" responses not included. i · Good/Very good I
65
75 1 O0
lowa City
Fig. 5b. Ratings of Selected Services and Facilities
Jobs
Medical
Public schools
Shopping
Housing
RecJentertain.
Child care
Senior programs
Youth programs
~ 99
91
54
~ 61
~76
~ 70
0 25 50 75 100
Percent*
* "Don't know" responses not included. I IGood/Very good I
In towns smaller than Metro
Sigma, residents are more likely to
think that public schools,
recreation and entertainment, child
care, senior programs, and youth
programs are "good" or "very
good." People in larger
communities evaluated these same
services less positively. Jobs,
medical services, and housing are
evaluated about the same
regardless of the community size.
When residents were asked to give
an overall rating of local services,
three-fourths (78%) of Metro
Sigma residents responded with a
"good" or "very good."
Figure 6a illustrates where Metro
Sigma citizens acquire important
services. It compares resource
accessibility within neighborhoods
and communities. Overall, an
overwhelming majority acquire all
of the services within the
community, although
neighborhood differences exists. At
least three-fourths of Metro Sigma
residents receive primary health
care, specialized health care,
recreate, and shop for big items
outside their neighborhoods, but
within Metro Sigma. Shopping for
daily needs items and church are
the services that are most likely to
be obtained within the
neighborhood, although only 47%
and 38% (respectively) acquire
these services within their
neighborhoods.
Mett o Sigma
Fig. 6a. Where Selected Services Are Acquired
Primary health care ~!~!~i:;~!:;i~!;!!~i!!;!~i,~i~i;!:i~i;~;ii:=!~:i::~:i~i:! 20
Specialized health 4 . 7' ~~.:._~ 82
C8r8
Shopping (daily
needs)
Shopping (big
ticket items)
Recdentertainment
Church
0 25 50 75
100
Percent'
* "Do not use or purchase"
responses not included.
· Mostly outside neighborhood, but in community
· Mostly inside neighborhood
· Mostly outside community
Iowa City
Primary health care
Specialized health
care
Shopping (daily
needs)
Shopping (big
ticket items)
Fig. 6b. Where Selected Services Are Acquired
............................... ""'::""5::";:;. ~'~:.,.~~~ 70
~~::,,::;:;~:T ............. ~ .... 59
3
.......... ~ 74
................................ ~::-~ ~
R~denteaainment ~ 14
......... ~:~~= ~
Church ~!i~ii~i!..................~ii:i~E:i~E:i~ 24
s r
0 25 50 75
Percent*
* "Do not use or purchase"
responses not included.
· Mostly outside neighborhood, but in community
[]Mostly inside neighborhood
· Mostly outside community
100
It comes as no surprise that
residents of communities larger
than Metro Sigma are more likely
to shop for daily needs items within
their neighborhoods. Residents of
smaller communities are more
likely to shop for daily needs items
and attend church outside their
neighborhood, but still within
Metro Sigma. Furthermore,
population size made no significant
difference in the amount of
residents who reported remaining
within their respective
communities but outside of their
neighborhoods to shop for big
ticket items and to recreate.
9
Residents of Metro Sigma
expressed positive ratings
for their local government
services (Fig. 7a). Virtually
everyone rated fire
protection and emergency
response services
positively. Police
protection, parks, and
libraries received slightly
lower ratings, although still
approximately three-fourths
of the residents reported
ratings of "good" or "very
good." Street conditions,
on the other hand, received
the lowest rating with only
33% of Metro Sigma
residents giving a rating of
"good" or "very good."
Metro Sigma
Fig. 7a. Ratings of Government Services
Police protection
Street conditions
Park conditions
Water
Fire protection
Library
Emergency response
~ 74
~ 87
|
0 25
* "Don't know" responses not included.
50 75
100
Iowa City
Fig. 7b. Ratings of Government Services
Police protection
Street conditions
Park conditions
Water
Fire protection
Library
Emergency response
* "Don't know" responses not included.
50 75
When asked to rate their
overall opinion of the quality
of government services, 58%
of Metro Sigma residents
believed that local
government is doing a "good"
or "very good" job.
Population differences of
similar cities do not appear to
create significant differences
in residents' opinions of local
government services.
100
10
Community Sentiments
and Attachments
Metro Sigma
Continued improvements
in transportation and
communication systems
have increased the
opportunities for
individuals to look beyond
their communities for
social interaction.
However, as the number of
these opportunities
increases there is also the
risk of declining
commitments to one' s own
community.
Fig. 8a. Ties With Other Adults in Community
Relatives living in community
Relatives living in neighborhood
Friends living in community
Friends living in neighborhood
33
Adults known by name in
neighborhood
In Metro Sigma, we find
evidence suggesting
considerable dependence
on outside contacts (Fig. 8a). In Metro Sigma,
for example, few citizens reported having a
majority of their relatives (one-half or more)
living in the same neighborhood (7%) or even in
Iowa City
Fig. 8b. Ties With Other Adults in Community
12
67
93
Relatives living in community
Relatives livin9 in neighborhood
Friends living in community
68
32
11
Friends living in neighborhood
89
32
68
25 50 75 100
Percent
~ · Less than half [] Half or more ~
Adults known by name in
neighborhood
the same community (33%). Furthermore, few
reported a majority of their friends living in the
same neighborhood (11%), although most
residents (68%) continue to depend on other
Metro Sigma residents when
it comes to developing
friendships. Finally,
slightly less than one-third
(32%) indicated knowing
the majority of
neighborhood residents by
halTleo
64
36
88
32
68
25 50 75
Percent
Less than half [] Half or more
97 At the extreme, we note that
approximately one-quarter
(27%) have no relatives
living in Metro Sigma and
only 5%-have no local
friends. In addition, three in
every ten Metro Sigma
residents indicated knowing
none or very few of their
100 neighbors.
11
Despite indications of social
isolation, almost everyone
(93%) in Metro Sigma is
interested in what goes on in
the community (Fig. 9a). At
the same time, less than half
(48%) reported serving as a
community volunteer during
the past year. When asked to
describe their level of
involvement in community
improvement projects, only
a third (31%) indicated being
"somewhat" or "very"
active.
Metro Sigma
Fig. 9a. Interest and Participation in Community
Interested in
community
Volunteered last
year
93
Considers
oneself active
The pattern of citizen
participation in Metro Sigma 0
is typical of communities
throughout the country.
While most residents
express interest in their local communities, few
(or at least less than half) get involved in
community improvement efforts. The challenge
31
25 50 75 1 O0
Percent
for local leaders is to actively recruit a broader
base of citizen support.
Iowa City
Fig. 9b. Interest and Participation in Community
Interested in
community
Volunteered last
year
5O
Considers
oneself active
0 25
36
94
100
Volunteerism in Metro
Sigma can be considered a
glass that is half full or half
empty. Community leaders
in other parts of the nation,
urban and rural alike, would
be overjoyed if half of their
citizenry engaged in
community betterment.
However, the results also
suggest that there is
considerable room for
improvement. How does
your community compare
with Metro Sigma? Why
are its citizens more or less
active than Iowa's typical
metropolitan city?
Percent
,- 12
The ratings of Metro Sigma as
a place to live are for the most
part positive. Most agreed, for
instance, that they can usually
find someone to talk to when
they want to chat (Fig. 10a).
Interestingly, however, more
agreed that it was easier to find
someone to talk to in the
community (91%) than in their
own neighborhood (68%). At
the same time, only one-fourth
agreed that residents of either
Metro Sigma (23%) or their
own neighborhood (28%) look
out for each other. Yet, three-
fourths rated residents of both
the community (70%) and
their neighborhood (79%) as
accepting of racial and ethnic
diversity.
Metro Sigma
Fig. 10a. Residents Rate Social Features of
Community and Neighborhood
someone to talk to 68
Residents look out
for each other
Residents accept
racial/ethnic
diversity
23
28
70
79
0 25 50 75
Percent*
I
lAgree within neighborhood BAgme within community I
* "Undecided" responses not included.
Information not included in Figure 10a further
reveals that 76% believed that their neighbors are
more trusting of each other than in other Metro
100
Sigma neighborhoods. In contrast, only 43% of
Metro Sigma residents felt that living in their
neighborhood is like living with a group friends.
Overall, 80% felt Metro Sigma has more things
going for it than other communities of similar size.
,Iowa City
Fig. 10b. Residents Rate Social Features of
Community and Neighborhood
someone to talk to 68
90
Residents look out
for each other
Residents accept '
racial/ethnic
diversity
18
27
25 50 75
Percent*
IAgree within neighborhood []Agree within community
92
The negativity of responses
to questions related to
concern for fellow
community and
neighborhood residents are
worthy of further
exploration. The belief by a
large majority that "every
person for themselves"
accurately describes fellow
resident attitudes within the
community and
neighborhoods may affect
the ability to act in other
realms. For instance, how
might lack of concem for
fellow residents affect Metro
Sigma's success in
community betterment?
13
Additional ratings of Metro
Sigma were obtained on a
number of quality of life
dimensions (Fig. lla). For
instance, most (92%) felt
that in an emergency
situation, complete strangers
from within the community
would be willing to help.
Furthermore, most (81%)
felt that anyone who wanted
to, could contribute to Metro
Sigma' s governmental
affairs. Slightly less that
two-thirds (62%) felt that
organizations in Metro
Sigma are interested in what
is best for everyone and
about half (52%) felt that a
city office in Metro Sigma
would quickly respond to a
call.
]t/Ietro Sig!Ha
Fig. 11a. Residents Rate How Community Responds
In an emergency, residents I
don't know would help
Everyone can contribute to
governmental affairs
Organizations interested in
what is best for all
If I call a city africa, I would
get a quick response
When a community problem
exists, all residents help out
When a neighborhood problem
exists, ell residents help out
* "Undecided" responses not included.
62
~52
~45
0 25 50
Percent*
I"'"' I
75 100
Iowa City
Fig. 11 b. Residents Rate How Community Responds
In an emergency, residents I
don't know would help
Everyone can contribute to
governmental affairs
Organizations interested in
what is best for all
If I call a city ice, I would
get a quick response
When a community problem
exists, all residents help out
When a neighborhood problem
exists, all residents help out
* "Undecided" responses not included.
96
~89
~65
~63
~42
25 50 75 100
Percent*
Slightly less than one-half
believed that all residents
would step forward to
address either a community
(41%) or neighborhood
(45%) problem.
The greater confidence of a
response to a neighborhood
problem as opposed to a
community problem might
suggest an eroding
confidence in the ability of
communities to address
local problems. It may also
signify a declining
commitment among
communities to deal with
issues of mutual interest.
14
In spite of the few concerns
previously discussed, most
residents remain attached to Metro
Sigma (Fig. 12a). This attachment
is demonstrated by the fact that
virtually everyone (92%) said they
feel "at home" in Metro Sigma. In
addition, most (86%) considered it
important to feel a part of their
community and three-fourths
(73%) would regret having to
move out of Metro Sigma.
What determines this strong
attachment to Metro Sigma?
Many reasons may exist, some
perhaps more important than
others depending on each person's
situation.
Metro Sigma
Fig. 12a. Measures of Community
Attachment
Feel "at home"
in this
community
Important to feel
part of
community
Sorry if had to
move out of
community
25
5O
Percent
92
86
73
75 100
Iowa City
Fig. 12b. Measures of Community
Attachment
Feel "at home"
in this
community
Important to
feel part of
community
Sorry if had to
move out of
community
25
5O
Percent
75
92
86
78
100
For some, local acquaintances,
friendships, and a feeling of
belonging might take precedence.
For others, it may be a matter of
physical traits such as a clean
environment, less congestion, and
easy access to their localities. Still
others might value the opportunities
available for employment,
shopping, and health care.
Additionally, in the same way that
residents express different reasons
for being attached to their
community, a warm and caring
community to some may be
intolerant and prejudiced to others.
15
Community Description
Several traits typically used to
evaluate communities were
included in the survey. For
each positive trait listed, a polar
negative was also included.
Instructions directed respondents
to circle a number along a
continuum (1-7) between the
extremes that best reflects their
rating of Metro Sigma. As
reported in Fig. 13a, highest
ratings were given to Metro
Sigma' s friendliness (5.3),
safety (4.7), and well-kept
appearance (4.8). The level of
excitement (3.9) in Metro
Sigma received the lowest
rating. Metro Sigma's level of
tolerance (4.3) and openness to
new ideas (4.3) also received
relatively low ratings.
Metro Sigma
Fig. 13a. How Residents Describe Their Community
Dangerous Safe
7
Unfriendly .... 5.3 - - Friendly
Run down - ' ' 4.8 ' Well-kept
Not trusting ......... ~ .... Trusting
Indifferent ......... * 4~5 .... Supportive
Rejecting of
Open to
new ideas "' ' '4:3 .... new ideas
Prejudiced ...... ;4;3 ' ' - Tolerant
Boring e/~.9 Exciting
I 2 3 4 5 6 7
Average Score
1=lowest 7=highest
Iowa City
Fig. 13b. How Residents Describe Their Community
Dangerous .2 Safe
Unfriendly ...... 5.5 - - - Friendly
Run down ........ 5;2 - - - Well-kept
Not trusting ....... 4.7 ' - - Trusting
Indifferent ...... . - - - Supportive
Rejecting of
new ideas ..... "5:0 ..... Open to
new ideas
Prejudiced ..... ,~.~5:0 ' Tolerant
Boring · Exciting
I 2 3 4 5 6 7
Average Score
1=lowest 7=highest
Interestingly, size of
community does not
significantly affect
residents' description of
Metro Sigma's qualities.
Although, it is probably
safe to say that people in
larger cities may evaluate
their corctrnunity as more
exciting and tolerant than
those who live in smaller
cities. However, residents
of smaller cities probably
feel safer in comparison to
people living in larger
communities.
16
Threats to the Community
What about the future of Metro
Sigma as viewed by its residents?
When asked to indicate the degree
of threat posed by a series of
possibilities, several items stood
out (Fig. 14a). Most notable was
the fear of losing small businesses
with almost half (41%) rating this
as a severe threat to Metro
Sigma's future. Other severe
threats pertained to increased
crime (36%), loss of family farms
(38%), resident indifference about
the community (36%), lack of
local leadership (33%), failure of
people to work together (32%),
and loss of community spirit
(31%).
Metro Sigma
Fig. 14a. Severe Threats to Community
Lack of jobs l
Quality of schools
Increased crime
More single parents
Loss of family farms
Loss of smell businesses
Indifference
Lack of leadership
Not work together
Loss of community spirit
More than 2 parents work
Out migration
In migration ~ 8
0
* "Undecided" responses not included.
/13
~ 36
38
41
~ 36
~ 33
~ 32
~ 31
25 50 75 1 O0
Percent*
Iowa City
Fig. 14b. Severe Threats to Community
Lack of jobs ' ~ 11
Quality of schools' · 6
Increased crime ~ 17
More single parents ~ 11
Loss of family farms
Loss of small businesses ~ 35
Indifference
Lack of leadership
Not work together ~ 24
Loss of community spirit ~ 19
More than 2 parents work ~ 12
Out migration' ~ 9
In migration' · 6
0 25
* "Undecided" responses not included.
50 75
Percent*
100
These items obviously demonstrate
concern expressed by residents
related to "people" issues, or how
Metro Sigma will function together
to address future challenges.
In addition, the summation of these
specific factors may cause or
contribute to other community
problems. For example, is resident
indifference and loss of community
spirit related to the lack of concern
for fellow residents (see Fig. 10a)?
On the positive side, quality of
schools (13%) and in migration
(8%) were seldom viewed as severe
threats to Metro Sigma's future. In
fact, out migration (22%) apparently
was viewed as a far greater concern
than was in migration.
17
Involvement in Organizations
Figure 15a illustrates the
proportion of Metro Sigma
residents reporting memberships
in various neighborhood and
community organizations.
Organizational membership and
participation are useful
measures for understanding the
extent to which residents are
involved in civic, political, and
social activities. In Metro
Sigma, 28% do not belong to
any community organizations,
71% do not belong to any
organizations outside the
community, and 75% do not
belong to any groups within
their neighborhood.
None
Metro Sigma
Fig. 15a. Number of Organizational
Memberships
16
One 16
17
~~ 19
Three ~ 14 2
Four or more ~ 3
1
0 25
50 75 1 O0
Percent
IIInside neighborhood BOutside community Binside community
Iowa City
Fig. 15b. Number of Organizational
Memberships
................ ~""~""=~=~ 2-~
None
One
16
18
24
Two 11 6
__15
Three 2
2
: ::.._:..=:~:~-:i;~ 23
Four or more ~ 5
2
0 25
72
50 75
Percent
100
IIInside neighborhood BOutside community Binside communiW I
Sixteen percent of Metro Sigma
residents belong to only one local
community organization and 19%
report belonging to two local
community organizations. This is
similar to the proportions reporting
memberships in one (16%) or two
(7%) organizations outside Metro
Sigma. However, memberships in
three or more organizations are
more common within the
community. Thirty-seven percent
of Metro Sigma residents reported
belonging to three or more
community organizations versus
only 6% belonging to three or more
outside organizations.
Neighborhood group memberships
were quite low. Only 25%
expressed membership in any
neighborhood organizations.
18
Within categories of local
clubs, associations, and
organizations, there is
considerable variability in
membership (Fig. 16a).
Church-related groups
received the highest
percentage of Metro Sigma
residents claiming
membership. One-in-two
Metro Sigma residents
belong to a church-related
group such as church
committees or bible study
groups. Following closely
behind, 40% of residents are
members of j ob-related
groups. Job-related groups
can include, but are not
limited to, labor unions and
professional organizations.
Metro Sigma
Fig. 16a. Organizations and Group Memberships
Church-Related Groups
Recreational Groups
Job-Related Groups
Political and Civic Groups
Service and Fraternal Groups
~37
~ 40
~30
0 25
50 75 100
Percent
lowa City
Fig. 16b. Organizations and Group Memberships
Church-Related Groups
Recreational Groups
Job-Related Groups
Political and Civic Groups
Service and Fraternal Groups
~40
~33
~ 30
0 25 50 75
Percent
100
In addition, approximately
one-third of residents are
members of political and
civic organizations or
recreational groups. Sports
teams, bowling leagues, and
card clubs are all examples
of recreational groups.
Political and civic
organizations include
historical groups, local
development organizations,
and the PTA. Service and
fraternal organizations
received the least
membership support. Only
17% of residents indicated
memberships to service and
fraternal organizations such
as the Lions, Kiwanis, and
Eastern Star.
19
Summary
Metro Sigma is a hypothetical metropolitan
city in Iowa with a population of
approximately 110,000 people. It is a stable
community where the average length of
residence is 24 years within the city and 12
years within respective current
neighborhoods. The primary reasons people
choose to live in Metro Sigma are because it
is close to their place of work, they grew up
there, and it is close to relatives.
Unemployment is very rare with almost
everyone being either employed, retired,
homemakers, or students. Overall, the
majority of Metro Sigma residents do not feel
that the future of the community is severely
threatened by any one significant factor.
Although, approximately one-third believe
that loss of small businesses, loss of family
farms, increased crime, resident indifference,
lack of leadership, residents not working
together, and loss of community spirit may be
sources of potential danger.
The citizens of Metro Sigma are linked to
each other through ties of family, friendship,
acquaintanceship, and shared involvement in
community activities and organizations. A
very large majority feel at home in Metro
Sigma and would be sorry if they had to move
away. In addition, almost everyone expresses
that it is important to feel they are part of
Metro Sigma and is interested in what is
going on in the city. On the other hand,
unfortunately less than half of residents
volunteered last year and even less consider
themselves active in community
improvement activities. Furthermore, only
about one-quarter of Metro Sigma citizens
believe that fellow neighborhood and
community residents look out for each other,
although a large majority think that residents
are accepting of racial and ethnic diversity.
The fact that the majority of Metro Sigma
residents stay inside the community for
medical care, shopping needs, church, and to
recreate is reflected in their overall positive
evaluation of the quality of local community
services. Residents are especially satisfied
with medical services, public schools, and
shopping opportunities. On the other hand,
recreation/entertainment and youth programs
are the only community services viewed as
significantly lacking in quality by about half
of residents.
Metro Sigma residents are also positive about
the quality of local government services. Fire
protection, libraries, and emergency response
services are rated favorably by almost
everyone. Street conditions is the only
government service that is perceived as
unsatisfactory by a majority of Metro Sigma
residents. In addition, almost one-half believe
that a city office would not respond quickly to
a complaint. Lastly, a large majority think
that everyone is allowed to contribute to
governmental affairs if they so desire.
No matter what the data reveals about Metro
Sigma, remember that Metro Sigma does not
exist. It is an imaginary community created
from the averages of results from all four
metropolitan cities included in the study. It is
created so that citizens would have a point of
comparison for their specific community
findings. Therefore, the real value of this
report can only be realized when
communities use the information for self-
examination and as a tool for community
improvement.
How does your community compare?
2O
Acknowledgements
This report is one of 15 reports available as
part of Iowa State University's Rural
Development Initiative Project. Funding for
this project was provided through the Iowa
Agriculture and Home Economics
Experiment Station, College of Agriculture,
Iowa State University.
We wish to thank each of the students and
staff in the Department of Sociology who
contributed to the completion of these
reports. At every stage of the research,
beginning with the random selection of
more than 4,750 households from 15 local
telephone directories, and ending with the
construction of more than 510 graphs that
appear in these reports, the following
individuals played a major role:
Terry Besser
Kyong Hee Chee
Chris Colvin
Jan Flora
Jeremy Judldns
Amy Lonsdale
Lori Merrit
Tom Rice
We also thank each of the communities that
took part in this research. Specifically, we
appreciate the support received from
representatives of local media who
publicized the purpose of the study and
notified citizens when the research was to
occur. Hopefully, the content included in
these reports will be used by media when
disseminating information useful for local
development projects.
Last but not least, we acknowledge the
cooperation of the 2,901 Iowa citizens who
contributed to this research project by
completing and returning their
questionnaires. There is no better indication
of the importance of community to Iowa
citizens than the fact that so many
individuals voluntarily participated in a
study of this type.
We hope it was worth the effort.
21
TABLE OF CONTENTS
Purpose of the Report ......................................................................................
Study Design ....................................................................................................
Table 1. Iowa Communities (500-10,000)
Who Lives in Sigma.'? .......................................................................................3
Figure 1. Length of residence
Figure 2. Employment status
Figure 3. Place of employment
Reasons for Living in Sigma ............................................................................6
Figure 4. Major reasons for living here
Community Services ........................................................................................7
Figure 5. Ratings of selected services and facilities
Figure 6. Where selected services are acquired
Figure 7. Ratings of government services
Community Sentiments and Involvement .........................................................10
Figure 8. Ties with other adults in community
Figure 9. Interest and participation in community
Figure 10. Residents rate social features of community
Figure 11. Residents rate how community responds
Figure 12. Measures of community attachment
Community Description ...................................................................................15
Figure 13. How residents describe their community
Threats to Community .....................................................................................16
Figure 14. Severe threats to community
Involvement in Organizations ..........................................................................17
Figure 15. Number of organizational memberships
Figure 16. Location of greatest amount of organizational
involvement
Summary ..........................................................................................................19
Acknowledgments ...........................................................................................19
Appendix A: Participating Communities .........................................................20
Pa~e
2
2
Comparing Hills with
Sigma (lowa's Typical Community)
Numerous questions have been raised concerning the future of Iowa's rural communities:
Can they survive in spite of their well-documented steady out-migration of people and
jobs? What can be done to reverse the familiar patterns of declining populations and
corresponding losses of local businesses? Who is responsible for initiating programs and
policies on behalf of communities plagued by these circumstances?
Recognizing that communities face differing circumstances and opportunities for
development, a major research effort was initiated in 1994 to assess the present and future
conditions facing Iowa's smaller communities. In each of Iowa's 99 counties, one
community with a population of between 500 and 10,000 was randomly selected to be
included in the study.
Within each selected community, a sample of residents was asked to rate the living
conditions in their community. Their responses were aggregated to produce a profile for
each of the 99 communities. Comparisons of these profiles can be used to assess the
social and economic status of Iowa communities in different regions of the state.
Prepared by Vem Ryan, Teny Besser, Jan Flora, and Paul Lasley, Department of Sociology, Iowa State
University. For further information about this report, contact JeffZacharakis-Jutz, Linn County Extension
Office, 655 12th Street, Marion, IA 52302; Tele (319) 377-9839; Fax (319) 377-0475;
x 17ach@exnet. iastate. edu.
For information on other reports in the RDI series, contact Vern Ryan, 317 East Hall, Iowa State University,
Ames, IA 50011; Tele (515) 294-5011; Fax (515) 294-2303; x I vryan@exnet. iastate.edu.
Purpose of the Report
This report is one of 99 reports comparing the profile of each of the communities with a
hypothetical community that typifies all of the communities included in the study. It is
prepared to permit citizens from participating communities the opportunity to analyze
responses given by their residents and compare them with results obtained in other
communities. Sigma, a pseudonym of the hypothetical community, is used in the report as
a basis for comparison, recognizing that Sigma itself does not exist. Sigma will serve as a
benchmark from which citizens can compare and evaluate their own community.
This' report is designed to help citizens conduct a Self-study of their own community.
Throughout the report, figures in the left hand column show findings for Sigma. Figures
in the right hand column give findings for the specific community identified in the title of
the report. The text is limited to a discussion of the statewide trends as illustrated in
Sigma's figures, leaving the interpretation of results for each community to be completed
by its own citizens.
Study Design
The study was designed to obtain information useful for both local and statewide rural
community development efforts. Selected communities were identified so that all regions
of the state would be represented in the study. Information obtained from residents was
then summarized for each community, making it possible to compare results across all of
the communities participating in the study.
Selection of communities was done by randomly choosing one community per county that
fell within a population range of 500 to 10,000 residents. Communities smaller than 500
were excluded since many of these communities have few if any local services beyond
those provided by local governments. Because of this, plans are to focus on their unusual
circumstances in a later study. Excluding towns with more than 10,000 residents was
done to allow for the more rural-oriented regional centers to be included in the study,
while excluding larger cities that are less dependent on Iowa's rural population.
Accordingly, "rural" as used in this report does not include the smallest of communities
(less than 500 inhabitants)yet goes beyond the conventional population size of 2500.
Table 1 shows that the communities chosen represent all of Iowa's communities that fall
between 500 and 10,000 population.
2
Table 1. Iowa Communities (500-10,000)
Size Sample Total
500-999 40 (40%) 167 (44%)
1000-2499 41 (42%) 135 (36%)
2500-9999 .18 (18%) 75 (20%)
Total 99 (100%) 377 (100%)
From directories of local telephone districts, 150 households were randomly selected from
each community to receive mail questionnaires. Half of the letters accompanying the
questionnaires asked the female head or co-head to complete the questionnaire; the other
half instructed the male head or co-head to complete the questionnaire and return it in the
enclosed postage-paid envelope. In situations where the gender requested to participate
was not present in the household, the letter asked a person of the opposite gender to
participate. Questionnaires coming back as postal returns were replaced with another
randomly selected household.
Two weeks following the initial mailing, postcards were sent to everyone thanking those
who had returned their questionnaires, and asking nonrespondents for their cooperation.
Two weeks later, replacement questionnaires were sent to households that had not yet
returned their surveys.
Altogether 10,798 households completed and returned questionnaires for a total response
rate of 72 percent. For individual communities, response rates ranged from 62 to 83
percent. (See Appendix A for a listing of the communities included in the study and their
respective response rates.)
When using this report, two definitions are particularly relevant. First, "resident" is used
broadly to include individuals living in and around selected communities. This was done
to extend the meaning of community beyond political jurisdictions so that rural farm and
non-farm residents would be included. Second, individuals participating in the study were
limited to household heads and co-heads. Accordingly, certain groups of adults, e.g.,
young adults living with parents or elderly parents living with adult sons or daughters,
were excluded from the study.
Who lives in Sigma?
Sigma, the composite of all 99 communities included in the study, has a population of
1800 residents. Slightly more females live in Sigma than males (55% versus 45%). Adult
Sigmans are older on average than was true in earlier times; in fact, one-third are 65 years
of age and over, and their average age is approaching 55 years.
Less than 10 yrs
10-19 yrs
Sigma
Fig. 1 a. Length of residence
I In community I~! In neighborhood
18 33
15 ~ 22
20-29 yrs
16
17
30-39 yrs
14
11
Less than 10 yrs
10-19 yrs
20-29 yrs
30-39 yrs
40 yrs and over
Fig. 1 b. Length of residence
I In community I~ In neighborhood
36
52
23
21
12 12
11 5
18 10
I I I
100 80 60 40 20 0 20 40 60 80 100
Percent
40 yrs and over
100
37 17
I I I I I I
80 60 40 20 0 20 40 60
Percent
100
Years of residence in Sigma is closely associated with the age of its residents. Half (51%)
of Sigma's adults have lived there for at least 30 years (Fig. la); about one-fifth (18%)
have lived in the community less than 10 years.
Long-term residency is also noted by the length of time that Sigma's residents have lived
in their current neighborhood. Yet, one-third have been a resident of the neighborhood
less than 10 years, suggesting that there is some mobility of residence within Sigma. In
fact, while the average length of residence in Sigma is 31 years, residents have lived in
their current neighborhood for about 20 years.
Employment status also denotes an older population. When asked to indicate their
employment status, almost a third (31%) respond that they are retired (Fig. 2a). Only half
of the Sigma adults are employed or self-employed on a full-time basis, and another 10
percent work part-time. While males outnumber the females employed on a full-time basis
(64% versus 37%), more females than males are employed in part-time positions (14% versus
6%).
Sigma
Fi . 2a. Employment status
IMale I~Female
Employed (FT) 64 ~ 37
Employed (PT) 6 g 14
Retired 29
33
Homemaker 0 g
Student 0:1
13
Unemployed I I
I I I I
100 80 60 40 20 0
2
Percent
Employed (FT)
Employed (PT)
Retired
Homemaker
Student
Unemployed
Hills
Fig. 2b. Employment status
IMale EBFemale
71 ' 55
I
7
2
25
2
27
9
2
100 80 60 40 20 0 20 40 60 80 100
Percent
100
Of the adults employed, almost 30 percent work in either professional or
managerial/administrative occupations; 20 percent are employed in clerical or sales
positions. Only one-in-ten of Sigma's employed residents report farming as their primary
occupation.
4
Sigma's employed work force is very reliant on employment opportunities in neighboring
communities; only half (53%) are working in Sigma, while others either commute to a
neighboring community or their employment requires traveling to multiple locations (Fig.
3a). This is tree for both males and females. On average, Sigma commuters travel about
11 miles one-way. When asked about their overall satisfaction with their present
employment, most everyone (90%) indicated they were satisfied; only 10 percent
expressed dissatisfaction with their present employment situation.
Household and family characteristics are also important when describing Sigma. Over
two-thirds (70%) of Sigma's household heads and co-heads are presently married and
living together. Sigma also has a rather large number of adults who are widowed (16%),
S/g/t/a
Fig. 3a. Place of employment
Home
community
IMale I~lFemale
I
53
Home
community
Other
community
Hills
Fig. 3b. Place of employment
IMale I~Female
1 O0 80 60 40 20 O0 20 40 60 80 1 O0
Percent
Other
community
100
46
I I I
80 60 40
47
20 0 20 40 60 80 1 O0
Percent
5
and fewer who are divorced/separated (8%) or never married (6%). As previously noted,
the low proportion of individuals never married is due to limiting the study to heads and
co-heads of households, thus excluding many of Sigma's younger adult residents.
Home ownership is high in Sigma (84%). The number of individuals living in the
household, however, is small; one-fourth (24%) of all households include one person and
over one-third (38%) have two members. In contrast, only one-fourth of Sigma's
households include four or more persons. Two-thirds of Sigma's households have no one
under 18 years of age.
Reasons for Living in Sigma
Residents were asked to select up to three major reasons for living in Sigma. The most
frequently mentioned reasons are: a) I grew up here; b) it is close to my relatives and/or
in-laws; and c) it is close to my job (Fig. 4a). Each of these reasons are mentioned by two
out of every five Sigma residents.
Reasons seldom mentioned indicate some surprises. For example, few (10%) mention
low property taxes as an important reason for living in Sigma. Also, virtually no one
(1%) mentioned strong local leadership as a reason. Perhaps this suggests a void in the
quality of leadership in Sigma, or perhaps it implies that Sigma residents do not consider
leadership as particularly important.
Sigma
Fig. 4a. Major reasons for living here
Grew up here
Close to rel/in-laws
Close to job
Safe area
Affordable housing
Friendliness
Strong schools
Low property taxes
Can't afford to leave
Medical service
Scenic area
Care of aging rels
Good leadership
42
40
~40
~32
~26
~23
10
8
7
3
20 40 60 80 100
Percent
Hills
Fig. 4b. Major reasons for living here
Grew up here
Close to rellin-laws
Close to job
Safe area
Affordable housing
Friendliness
Strong schools
Low property taxes
Can't afford to leave
Medical service
Scenic area
Care of aging rels
Good leadership
23
~30
~13
5
9
3
5
I I I
59
0 20 40 60 80 100
Percent
Reasons for living in a community may vary depending on the size of the community. In
the size range included in this study, the frequency of reasons cited are remarkably similar
whether the community has 500 or 10,000 residents. As examples, growing up in the
community and being close to relatives and/or in-laws is as important to residents of larger
or smaller communities as it is to Sigma residents. Being close to one's job, however, is
mentioned more frequently by residents of larger communities. Residents of larger
communities also list closeness to medical services more often than do residents of Sigma.
In communities smaller than Sigma, friendliness, affordable housing and lower taxes are
mentioned more often. As for other reasons listed in Figure 4a (e.g., safety, strong
schools, can't afford to leave), residents of both larger and smaller communities respond
no differently than Sigma residents.
Community Services
Among all of the local services and facilities included in the questionnaire, public schools
received the most positive evaluation (Fig. 5a). Eighty-three percent of Sigma citizens
responded that local schools are good or very good. (Residents who indicated local
schools and other services as unavailable were excluded before calculating the percentages
reported in Fig. 5a/b.) More than half (55%) think senior citizens programs are good or
very good. Other services, including medical, child care and housing are rated positively
by more than a third but less than half the residents. Youth programs, recreation,
entertainment, jobs and shopping are viewed favorably by less than a third of Sigma
residents.
Sigma
Fig. 5a. Ratings of selected services and facilities
Public schools
Senior programs
55
83
Medical 46
I
Child care 46
Housing
Youth programs
32
42
Rec/entertain 23
Jobs 19
IGoodlvery good
Shopping
16
0 20
40 60 80 100
Percent
Fig. 5b. Ratings of selected services and facilities
Public schools
Senior programs
Medical
Child care
Housing
Youth programs
Rec/entertain
Jobs
Shopping
0
3
26
11
18
20
39
72
55
50
66
IGood/very good
40 60 80 100
Percent
In towns larger than Sigma, residents are more likely to think that senior programs,
medical services, child care services, youth programs, recreation and jobs are good or very
good. People in smaller communities evaluate these same services less positively. Public
schools, shopping, and housing were evaluated about the same regardless of community
size.
When residents were asked to give an overall rating of local services, 59 percent of Sigma
residents believe that on the whole, community services are good or very good. Opinions
about the overall quality of community services in Sigma are similar to residents living in
both smaller and larger communities.
In Figure 6a, the proportion of Sigma citizens who obtain services locally versus those
going outside Sigma are shown; only those who actually use each service are included in
the graph. For example, only six percent of the Sigmans who use specialized medical
services obtain these services locally.
As the figure below shows, people are most likely to go outside Sigma for specialized
medical services, big ticket items, recreation/entertainment, and primary medical care.
Eighty-one percent of Sigma citizens attend a local church and 50 percent shop for daily
needs in Sigma.
Fig. 6a.
Sigma
Where selected services are acquired
81
Church ~ 19
50
Shopping (dally needs) r/y///"jTfy~7//~/~ 50
37
Primary health care 'V/Zyff,,Z~/7~~/~,~/~f/,ff~ 63
~7
Reclente~ainment 73
Shopping (big ticket items) 89
Specialized health care
0 20 40 60 80
Percent
I Mostly local $ Mostly outside
94
100
Hills
Fig. 6b. Where selected services are acquired
Church
Shopping (daily needs)
Primary health care
Reclentertainment
Shopping (big ticket items)
Specialized health care
59
12
98
13
97
91
99
12
0 24 48 72 96
Percent
Mostly local
Mostly outside
It comes as no surprise that residents in communities larger than Sigma are more likely to
shop, recreate, and seek medical services within the community. It may be less obvious
that church attendance is similarly affected by community size. People from communities
smaller than Sigma are more likely to attend a church in another town than are residents of
larger towns.
8
Residents of Sigma have very positive feelings toward their local government services
(Fig. 7a). Only residents who reported receiving each service are included in the graph.
They are especially pleased with fire protection, emergency response, garbage collection,
parks and water services. Police protection and street maintenance are viewed less
favorably, although even they receive good or very good evaluations from about half of
the respondents.
Fig. 7b. Ratings of government services
Fire protection
Emergency response
services
93
93
Fig. 7a.
Ratings of government services
Fire protection 92
Garbage collection
Park conditions
Water
71
84
91
Emergency response
91
Police protection
41
Garbage collection
Park conditions
Water
Police protection
Street conditions
0 20
88
81
70
57
50 IGood/very good
I I I
40 60 80 100
Percent
Street conditions
49 IGoodlvery good
0 20 40 60 80 100
Percent
Only the evaluation of police is affected by community size. People in communities larger
than Sigma generally view police protection more favorably, while police protection in
smaller towns receives lower ratings from their citizens. In regard to other government
services, the size of the community makes no difference in people's evaluations.
When asked to rate their overall opinion of the quality of government services, 60 percent
of Sigmans believe that local government is doing a very good or good job. This rating is
no different in communities larger or smaller than Sigma.
Community Sentiments
and Involvement
Hills
Fig. 8b. Ties with other adults in community
The degree to which residents know each other is quite high in Sigma (Fig. 8a). Five of nine
Sigmans know at least half of the community's adult residents by name. In towns larger than
Sigma, it may be more difficult to know the majority of people by name, while in towns which
are smaller than Sigma, it is easier to know half or more of the residents by name.
Fig. 8a. Ties with other adults in community
Proportion of adults
you know by name
Proportion of friends
living in community
45
46
55
54
IHalf or more
I~Less than half
Proportion of relatives
living in community
76
0 20 40 60 80
Percent
100
Proportion of adults
you know by name
Proportion of friends
living in community
39
61
IHalf or more
I~!Less than half
75
Proportion of relatives
living in community
89
0 20 40 60 80
Percent
100
In Sigma, there appears to be a correspondence between knowing people and developing
friendships. Five of nine adult residents have half or more of their close personal adult friends
in Sigma. For nearly one-fourth of Sigmans, half or more of their adult relatives and in-laws
live in Sigma.
At the other extreme, about one-third of Sigma's respondents have no relatives living in the
community, and about one-in-ten know none or few of their fellow residents and/or have no
close friends in the community. Taking friends, relatives, and acquaintances together, we can
say that well over half are very rooted through ties to other indMduals in their community.
Only a small minority is not.
10
Most Sigma residents are interested in what is going on in their community (Fig 9a); only
one-in-twelve is not. That does not necessarily translate into active involvement in community
affairs, since only half had volunteered in a community improvement project or done volunteer
fund raising for such a project in the past year. Four-of-nine Sigrnans consider themselves
somewhat or very active in community improvement activities.
Sigma
Fig. 9a. Interest and participation in community
Interested
in community
92
Volunteered
during past year
Hills
Fig. 9b. Interest and participation in community
/
Interested
in community 92
Volunteered
during past year
42
Considers
oneself active
35
0 20 40 60 80 100
Percent
Considers
oneself active
0 20 60 80
43
40
Percent
100
Volunteerism in Sigma can be considered a glass which is half full or half empty. Community
leaders in other parts of the country--urban and rural alike--would be overjoyed if half of their
citizenry engaged in community betterment. The results also suggest, however, that there is
considerable room for improvement. How does your community compare with Sigma? Why
are its citizens more or less active than Iowa's typical rural community?
11
Most residents find Sigma to be a friendly and caring place (Fig. 10a). For most, it is a
community accepting of newcomers to serve in leadership positions and of people with racial
and ethnic differences. Still, viewed from the glass-is-half-empty perspective, about one-third
of the residents consider Sigma as non-receptive to newcomers taking leadership positions and
not fully accepting of racial and ethnic minorities. It is worth noting that fully one-third of
Sigma's respondents are undecided on these two issues. Therefore, fewer than half of all
respondents judge their fellow residents to be receptive to newcomers taking leadership
positions and to people from different racial and ethnic groups?
Sigma
Fig. 10a. Residents rate social features of community
Can usually find
someone to talk to
Living in community is
like living with friends
I
24
76
92
Residents look out
for each other
26
Fig. 10b.
Residents receptive to
new residents as leaders
Residents accept
racial/ethnic diversity
32
36
Hills
Residents rate social features of community
2O
4O
6O
Can usually find
someone to talk to
Living in community iS
like living with friends
Percent
Residents look out
for each other
Residents receptive to
new residents as leaders
Residents accept
racial/ethnic diversity
15
24
39
36
39
61
64
61
85
76
IAgree
li] Disagree
0 20 40 60 80 100
74 Percent
68
IAgree
E~Disagree
64
8O
100
The divergence between questions related to inclusion of those who are new or different and
the general patterns of concem in the community would be worthy of further exploration. The
perceived hesitancy of the community by a slight majority of its residents to involve or
welcome new or non-majority people may affect its ability to act in other reahns. How, for
instance, might their reservations affect Sigma's success in community betterment?
~ To make the graph more readable, undecided responses were excluded from the
percentages in the table. For other questions in Fig. 10a, inclusion of undecided
respondents would have had little impact.
12
Generally, citizens perceive that Sigma responds well to the needs of its members (Fig. 11 a).
Individuals and community clubs/organizations attend to collective needs and indMdual
emergencies, and generally act in the best interest of fail residents. Sigmans think that most
everyone is allowed to contribute to local government affairs if they want to.
Fig. 11 a.
Sigma
Residents rate how community responds
In an emergency, residents
I don't know would help
Everyone can contribute
to governmental affairs
Organizations interested
in what is best for all
If I call a city office,
would get quick response
When community problem,
all residents help out
4
10
0 20
40 60
Percent
IAgree ISBDisagree
74
73
80
90
96
Hills
Fig. ~ J b. Residents rate how community responds
In an emergency, residents
I don't know would help
Everyone can contribute
to governmental affairs
Organizations interested
in what is best for all
4
~9
12
91
88
96
If I call a city office,
would get quick response
20
80
When community problem,
all residents help out
37
63
0 20 40 60 80 100
87 Percent
100
IAgree E]Disagree
Only if respondents were to call a city office with a complaint would s/he be uncertain about
getting a quick response. When the undecided respondents (three-in-ten) are included with
respondents disagreeing with this item, barely half of the total believe they would get a quick
response. Based on this, Sigmans appear divided as to the responsiveness of their local
government.
13
We can look at Sigma in terms of how its residents fed about their community by measuring
their degree of community attachment. Nine of ten Sigmans believe it is very or somewhat
important to feel that they are part of the community they live in (Fig. 12a). An even higher
proportion feel "at home" in Sigma. And perhaps the real test is whether one would be sorry if
forced to move away from Sigma. Four-of-five would be very or somewhat sorry if they had
to move from Sigma. One-of-five would be indifferent or actually pleased to leave.
Fig. 12a.
Measures of community attachment
Hills
Fig. 12b. Measures of community attachment
Feel "at home"
in this community
Important to feel
part of community
94
1
89
Feel "at home"
in this community
Important to feel
part of community
94
90
Sorry if had to move
out of community
74
0 20 40 60 80 100
Percent
Sorry if had to move
out of community
81
0 20 40 60 80 100
Percent
What determines community attachment? There are many contributors and they may be
different for different people. For instance, residents may feel at home in Sigma or be sorry to
leave because of their friends, relatives and acquaintances, or because they view the community
as a caring place where people look out for each other. Others may like Sigma because it is
accepting of diverse kinds of people and leadership, e.g., newcomers, women, ethnic and racial
minorities. Still others may gain community attachment through being active in community
affairs. Not all these reasons are compatible with one another, however, since a community
which is warm and caring to some may be intolerant to others.
14
Community Description
Respondents were asked to describe Sigma on eight sets of adjectives (e.g., dangerous
versus safe) using a seven-point scale (higher scores indicating more positive ratings).
Figure 13a summarizes how the people of Sigma responded. Sigma, as viewed by its
residents, is a place where people feel safe. It is supportive, trusting and friendly. Citizens
also rate Sigma as being open to new ideas and tolerant, although there is much room for
improvement in both areas. The only area where Sigma is viewed unfavorably is in regard
to the level of excitement. Respondents indicate that Sigma is more boring than exciting.
Sigma
Fig. 13a. How residents describe their community
Dangerous 5.7 Safe
Unfriendly ............................ 5.6 ........... Friendly
Run down .......................... 5.-3 ............ Well-kept
Not trusting ........................ 5;2 ............. Trusting
Indifferent ....................... 4.9 .............. Supportive
Rejecting of Open to
new ideas ................... 4:4 ................... new ideas
Fig. 13b. How residents describe their community
Dangerous
Unfriendly
........................... 5~7
Run down
Not trusting
Indifferent ....................... 4.9 .............
Rejecting of .................. 4.5 ...............
new ideas
Prejudiced .................... 4.2 ...................
3.3
Boring
I 2 3 4 5 6
Average Score
Safe
Friendly
Well-kept
Trusting
Supportive
Open to
new ideas
Tolerant
7 Exciting
Prejudiced ............... 4.3/, ................. Tolerant
Exciting
I 2 3 4 5 6 7
Average Score
Interestingly, size of community has no effect on residents' description of its qualities,
except in four areas. People in towns larger than Sigma evaluate their community as
friendlier, more trusting and more supportive than those who live in smaller towns.
However, residents of smaller towns feel safer than people living in larger communities.
15
Threats to Community
Figure 14a shows what Sigma residents perceive as severe threats to the community's
future. Economic factors are considered to pose the greatest threat to Sigma. Residents
feel that the loss of small businesses, loss of family farms, and lack of jobs are the greatest
threats to their community. The middle category of threats (indifference, lack of
leadership, people unwilling to work together and loss of community spirit) are similar in
that they are all related to community cohesiveness. About one-fourth of Sigma residents
believe that the town is threatened by these factors. Still fewer people see single parenting
and the increasing incidence of both parents in the labor force as community threats.
Finally, a small percentage view quality of schools, crime and in-migration as
Fig. 14a.
Loss of small bus.
Loss of family farms
Lack of jobs
Out migration
Indifference
Lack leadership
Not work together
Loss comm spirit
More 2/parents work
More single parents
Quality of school
Increased crime
In migration
Sigma
Severe threats to community
~ 51
~ 39
~29
26
~ 22
~22
~ 22
~17
~ 14
/11
111
~5
35
20 40 60 80 100
Percent
Hills
Fig. 14b. Severe threats to community
Loss of small bus.
Loss of family farms
Lack of jobs
Out migration
Indifference
Lack leadership
Not work together
Loss comm spirit
More 2/parents work
More single parents
Quality of school
Increased crime
In migration
/11
/8
~6
~17
~17
~17
~21
/6
~6
13
~5
0
30
I
t ~
0 20 40 60 80 100
Percent
serious threats to Sigma. This corresponds with the positive evaluation reported earlier of
Sigma's public schools (see Fig. 5a) and the high level of safety felt by Sigma residents
(see Fig. 13a).
16
Regardless of the size of community, residents share similar views about what things
severely threaten their community. However, there is one exception: Citizens of
communities smaller than Sigma are more likely than those living in larger towns to
perceive quality of schools as a severe future threat. A possible explanation of this
difference may be that maintaining quality schools is becoming increasingly more difficult
for small towns. One solution to this problem has been to consolidate with neighboring
school systems. In the process, some communities are left without school facilities within
their boundaries. Thus, residents may see the loss of their school as a threat to the
community.
Involvement in Organizations
Figure 15a shows the proportion of Sigma residents reporting memberships in organizations.
Organizational membership and participation are useful measures for understanding the extent to
which residents are involved in civic, political, and social activities. In Sigma, one-third (32%) of
the residents do not belong to any local organizations, and over half (55%) report no
memberships in groups or organizations outside of Sigma.
S/gtt/a
Fig. 15a. Number of organizational memberships
Inside community E$ Outside community
None 32
Orle
18 20
55
Two 18
12
Hills
Fig. 15b. Number of organizational memberships
Inside community
None 53
One
Outside community
54
25
21
Two
Three
Four or more
13
15
8 5
100 80 60 40 20 0 20 40 60 80 100
Percent
Three
Four or more
100
13
19 8
80 60 40 20 0 20 40 60 80 100
Percent
About one-fifth (18%) of Sigma residents belong to one local organization and the same
proportion report belonging to two local organizations. This is quite similar to the
proportions reporting memberships in one (20%) or two (12%) organizations outside of
Sigma. However, memberships in three or more organizations are more common locally.
One-third (32%) of Sigma residents report at least three local memberships, while only
one-in-eight (13%) are members of three or more organizations outside of Sigma. Less
than 10 percent report belonging to three or more organizations beyond the local
community.
17
Within categories of local clubs, associations and organizations, them is considerable variability
in membership (Fig. 16a). In Sigma, about one-in-five belong to a service club, such as Lions,
Kiwanis or Eastern Star. One-third report belonging to a recreational group, such as a ball
team, bowling or card club. One-fourth of Sigma residents belong to a political or civic group,
such as PTA, PEO, historical society or a local development group. One-fifth belong to job
related organizations, such as a labor union or professional association. Membership in church-
related groups, such as a church committee or a Bible study group, is reported by 60 percent of
Sigma residents.
Sigma
Fig. 16a. Location of greatest amount of
organizational involvement
Mostly Local
46%
/
~Z.2L:.Z.......: ....~: ~
.22222222222222222227.v.v.v.'.'.'.':.'.'.'.'.'.'.'.'.'.'.':.'.v.','.v.v~ ....~.~_
':Z i:' Z:' i:':i:':i .5'.'~t..$. ;.'.: . !~' &:" ~.' ~
-.:.:.:.:.:.: I, ii.r.'.~.;~..
'-2-:.2-2. m:.:::,..".~. ',z'..~i~ .'
No involveme ~$~,!~$h;;~
26%
About the same
12%
Mostly Outside
16%
Hills
Fig. ~6b. Location of greatest amount of
organizational involvement
Mostly Local
26%
/
30%
:-:.:.~
.-?~.?
[vII!, ~ -~-); ?:~v- -7 ':-;~ ~ :% ?' '~- ~
About the same ~'~-~ ~' Mostly Outside
16% 28%
Respondents were asked whether they consider themselves more involved in local
organizations or those outside of Sigma (Fig. 16a). Almost half (46%) report being more
involvement locally. In contrast, only one-in-six (16%) are more involved outside of
Sigma, and 12 percent consider their involvement equally divided between local and
outside organizations. One-fourth (26%) of Sigma residents are not involved in either
local or nonlocal organizations.
18
Summary
Sigma is a hypothetical small Iowa town. It's a stable community where the average
length of residence is 31 years. The primary reasons people choose to live in Sigma are
because they grew up there, it is close to relatives, and it is close to their place of work. It
is not surprising then that most Sigmans are likely to recognize, know by name, and be
close friends with those they see daily at local stores, sporting events, etc. A majority
believe that living in Sigma is like living with a group of close friends. Sigmans are
interested in what happens in town and would be sorry if they had to leave.
The citizens of Sigma are linked to each other through ties of acquaintanceship, friendship,
and shared involvement in community activities and organizations. Over half of the
residents have volunteered in at least one community activity in the last year, and slightly
more than two out of three belong to at least one local organization. This corresponds to
feelings among the majority of residents that not only do people in Sigma feel that the
community is important, but they act on those feelings. As a result, most people say they
can count on others to help out if they had a problem, that citizens look out for each
other, and that if the community had a problem, all residents would help solve it. At the
same time, fewer than half judge their fellow residents to be receptive to newcomers in
leadership positions and to people from different racial and ethnic groups.
The fact that most people in Sigma go outside the community for medical care, recreation,
and shopping for big ticket items is reflected in their low evaluation of the quality of local
community services. Only public schools and senior citizen programs receive positive
endorsements from a majority of residents. The provision of jobs, shopping, recreation
and youth programs are viewed as particularly lacking. On the other hand, Sigmans are
positive about the quality of local government services, with fire protection and
emergency response services rated favorably by almost everyone.
No matter what the data reveals about Sigma, remember that Sigma does not exist. It is
an imaginary community created from the averages of results from all 99 communities
included in the study. It was created so that citizens would have a point of comparison for
their community findings. Therefore, the real value of this report can only be realized
when communities use the information for self-examination and as a tool for community
improvement.
How does your community compare?
19
ACKNOWLEDGMENTS
This report is one of 99 community reports available as part of Iowa State University's
Rural Development Initiative Project. Funding for this project was provided through the
Iowa Agriculture and Home Economics Experiment Station, College of Agriculture, Iowa
State University. Support for printing the reports was provided by the Iowa Agriculture
and Home Economics Experiment Station and Iowa State University Extension.
We wish to thank each of the students and staff working in the Department of Sociology
who contributed to the completion of these reports. At every stage of the research,
beginning with the random selection of more than 15,000 households from 99 local
telephone directories and ending with the construction of more than 1600 graphs that
appear in these reports, the following individuals played a major role:
R.D. Blount
Veronique Canttell
Rita Conner
JeffDoran
Bonnie Green
Duane Halbur
Amy Lammar
Qiaoming Liu
Julia McClendon
Lori Merritt
Renea Miller
Chukwudi Okafor
Lisa Peters
Jennifer Petersen
Sandy Pollard
Joan Steffen-Baker
Yumei Sun
Andy Terry
Danyal Woebke
We also thank each of the communities that took part in this research. Specifically, we
appreciate the support received from representatives of local media who publicized the
purpose of the study and notified citizens when the research was to occur. Hopefully, the
content included in these reports will be used by media when disseminating information
useful for local development projects.
Last but not least, we acknowledge the cooperation of the 10,798 Iowa citizens who
contributed to this research by completing and returning their questionnaires. There is no
better indication of the importance of community to Iowans than the fact that so many
individuals voluntarily participated in a study of this type.
We hope it was worth the effort.
; . : I
Appendix A:
Participating Communities
Community
Afton
Agency
Ainsworth
Albert City
Albia
Albion
Allerton
Altoona
Atkins
Audubon
Bancroft
Batavia
Battle Creek
Bayard
Bedford
Bloomfield
Buffalo Center
Calmar
Center Point
Chaff ton
Cherokee
Clarence
Ciarinda
Colo
Columbus Junction
Coming
Correctionville
Denison
Donnelison
Dumont
Eagle Grove
Elgin
Elk Horn
Response
Ra,__,g
77%
82%
72%
735/0
66%
69%
76%
80%
79%
62%
73%
67%
71%
81%
71%
67%
70%
79%
78%
69%
69%
69%
74%
78%
81%
Community
Elma
Epworth
Estherville
Everly
Farmington
Fontanelle
Fruitland
Gamavillo
George
Gilbertville
Glidden
Gowrie
Graettinger
Grand Mound
Hamburg
Hartford
HartIcy
Hills
Hopkinton
Hospers
Humboldt
Jefferson
Kanawha
Lainore
Lc Claire
Lake Park
LcMars
Madrid
Mapleton
Mediapolls
Missouri Valley
Monroe
Montczuma
Moulton
Response
Rate
83%
80%
80%
75%
67%
74%
69%
79%
67%
67%
75%
81%
79%
77%
74%
71%
83%
70%
72%
79%
77%
74%
78%
73%
78%
75%
70%
66%
78%
74%
75%
73%
69%
67%
Community
Mount Ayr
Murray
Nashua
Neola
Nora Springs
Northwood
Oakland
Olin
Pacific Junction
Pleasantvillc
Pocahontas
Pomeroy
Quasqueton
Radcliffe
Sabula
Sac City
Saint Ansgar
Saint Charles
Sheffield
Sibley
Traer
University Park
Ventura
Villisca
Waukon
Wavefly
Webster City
Wellsburg
What Cheer
Williamsburg
Winfield
Woodward
Total
Response
Bate
69%
69%
79%
70%
77%
67%
78%
78%
71%
65%
78%
75%
76%
81%
76%
76%
77%
75%
81%
73%
80%
63%
77%
75%
74%
77%
69%
77%
70%
75%
73%
66%
72%
2O
Final Report
on the
1998 Pilot Land Use Inventory
The Commission on Urban Planning,
Growth Management of Cities,
and Protection of Farmland
by
Iowa State University Extension
Ames, Iowa
November 30, 1998
Final Report
on the
1998 Pilot Land Use Inventory
Submitted to
The Commission on Urban Planning,
Growth Management of Cities,
and Protection of Farmland
by
Iowa State University Extension
Ames, Iowa
November 30, 1998
Table of Contents
Acknowledgments
Executive summary
Introduction
A. Background
B. Purpose and objectives
C. Project components
II.
Pilot counties
B.
C.
D.
E.
Request for participation
Letters of interest
Selection criteria
Profile of characteristics
Final selection of counties
III.
Statewide issues and trends
A. Meetings with state and county officials
B. Survey of county officials
C. Changes in assessment classification
D. Land use in incorporated areas
IV.
Land use inventory
A. Data sources
B. Procedures
C. Results
Agricultural quality of land
A. Measures of agricultural quality
B. Data sources
C. Procedures
D. Results
VI.
Conclusions and recommendations
A. Conclusions
B. Recommendations
Appendices
A.
B1.
B2.
C.
Summary of the 1983 Land Use Inventory report
Iowa Communications Network Meeting 1
Iowa Communications Network Meeting 2
Request for county participation in the pilot
land use inventory
Letter to county officials regarding the telephone survey
111
ix
1
3
4
7
7
7
8
10
12
12
23
38
41
42
43
50
57
62
80
106
111
115
121
127
131
137
Appendices (continued)
El.
E2.
G.
H1.
H2.
H3.
I--I4.
H5.
H6.
H7.
H8.
H9.
H10.
Questionnaire for telephone survey of county assessors
Questionnaire for telephone survey of county
zoning administrators
Definitions of farmland protection programs
Definitions of real estate classifications
Bremer County data
Cen'o Gordo County data
Dallas County data
Monroe County data
Pottawattamie County data
Scott County data
Story County data
City of Ames data
City of Davenport data
City of Mason City data
References
139
145
151
157
165
167
169
171
173
175
177
179
181
183
185
ii '
Acknowledgments
This project involved gathering and analyzing a great deal of data in a very short time.
This would not have been possible without the dedication and expertise of many
individuals.
Foremost is Paul F. Anderson, who serves on the faculties of both the Department of
Landscape Architecture and the Department of Agronomy. From the beginning,
Professor Anderson took charge of this project. He recruited and directed a cadre of
outstanding student researchers, analyzed all of the data from the seven pilot counties,
and provided leadership in all areas of the project. He also was the primary author of this
report.
Troy A. Siefert, predoctoral associate in the Department of Landscape Architecture,
prepared a schedule and time line to keep the project moving forward, arranged the
meetings on the Iowa Communications Network, helped with the initial research, and
prepared sections of the final report.
Nora M. Ladjahasan, research associate with the Institute for Design Research and
Outreach (IDRO) in the College of Design, supervised and conducted much of the data
gathering for the project. She also designed and supervised the telephone survey that
resulted in data being gathered and analyzed from all Iowa counties, and prepared
substantial sections of this report.
Heather N. Sauer, communications specialist for the College of Design, brought her
considerable editing skills to bear on the draft and final reports, causing this work of
many hands to flow easily from section to section.
Karen L. F. Ormsbee, graduate research assistant in the Department of Landscape
Architecture, helped to develop techniques for data gathering which made this daunting
task more manageable.
Landscape architecture graduate students Jane J. Chen, Sandra L. Peterson, and Benjamin
J. Swanson worked closely with Professor Anderson in digit,zing and analyzing county
data.
Student researchers Carmen Chan, Megan McClellan, Radhika Sakhamuri, and
Christopher Wall worked with Nora Ladjahasan in gathering data from the seven pilot
counties and completing the statewide telephone survey.
Without exception, team members worked tirelessly to complete this effort on time and in
good order. Their extra hours and devotion to this undertaking are gratefully
acknowledged.
111
Pilot county representatives
Supervisors, assessors, and other officials in the pilot counties also rendered invaluable
assistance to the project. They volunteered to cooperate in a difficult study on short
notice, interrupted other tasks to assist members of the ISU research team in gathering
and understanding data, and provided financial and in-kind support to the land use
inventory. The contributions of all those from the seven pilot counties who assisted in
this project are gratefully recognized:
Bremer County
Board of Supervisors
Gaylord Hinderaker, Chair
James Block
Steven Reuter
Assessor
Jean Keller
Extension Education Director
Jack Dillon
Zoning A dministra tor
Doug Bird
Cerro Gordo County
Board of Supervisors
Robert K. Ermer, Chair
Jay Urdahl
Roger Broers
Assessor
Johll Boedeker
Extension Education Director
Darwin Miller
Zoning Administrator
Tom Drzycimski
Dallas County
BoardofSupervisors
Marvin Shirley, Chair
Julius Linle
Joe Reece
iv
Dallas County (continued)
Assessor
Ronald G. Potter
Extension Education Director
Linda Nelson
Zoning Administrator
Murray McConnell
Monroe County
Board of Supervisors
Paul Koffman, Chair
Michael R. Beary
Dennis Ryan
Assessor
Don Cook
Extension Education Director
Sue Delaney
Zoning Administrator
Juanita Murphy
Pottawattamie County
Board of Supervisors
Arlyn Danker, Chair
Stan Grote
Robert Williams
Connie Lehan
Delmar Goos
Assessor
James O'Neill
Extension Education Director
Perry Beedie
Thomas Jensen
Zoning Administrator
Kay Mocha
V
Scott County
Board of Supervisors
Ed Winborn, Chair
Otto Ewoldt
Tom Otting
Forrest Kilmer
Jim Hancock
Assessor
Dale Denklau
Extension Education Director
Becky Bray
Zoning Administrator
Tim Huey
Story County
Board of Supervisors
Fred Mathison, Chair
Jack Whitmer
Jane Halliburton
Asses$or
Gary Bilyeu
Extension Education Director
Carolyn Manning
Zoning Administrator
Leslie Beck
Many other staff members in the county assessors' and zoning administrators' offices
also provided assistance in compiling and interpreting land use data, and their efforts are
most appreciated. -
Other organizations
The research team wishes to thank the Commission on Urban Planning, Growth
Management of Cities, and Protection of Farmland for its sponsorship of this research.
Co-chairpersons Senator Mary Lundby and Representative Russell Teig and members of
the commission provided support and encouragement throughout the study period. Tom
Bredeweg, executive director of the Iowa League of Cities, and executive director
William Peterson and public policy analyst Robert Mulqueen of the Iowa State
vi '
Association of Counties were helpful in providing contacts and in commenting on work
in progress. The contract was administered by the Legislative Service Bureau. Doug
Adkisson, legal counsel in the bureau, was most helpful. The team gratefully
acknowledges the assistance provided by Dick Davidson of the Iowa Department of
Revenue and Finance, who guided the team in understanding the reconciIiation reports
filed annually by Iowa county assessors. Gerald Miller, associate dean of the Iowa State
University College of Agriculture, reviewed portions of the final report; his contributions
are appreciated. Finally, the team wishes to thank members of the Subcommission on
Farmland Inventories and Preservation: Jay Howe, chair, Tim Keller, and Jill Knapp.
These are the people who suggested the need for this study and followed up to make sure
that it became a reality.
Smart H. Huntington
Principal Investi!Tator
vii
Executive Summary
Introduction
The primary purpose of this pilot inventory was to determine the extent to which land in
this state has been convened from agricultural use to residential, commercial, industrial, or
public uses (including recreational areas, natural areas, and public facilities and
infrastructure), and to report on the quality of agricultural land converted to these uses.
The Iowa State University research team identified foUr principal objectives to be met by
the end of the project:
· Involve the public and public officials
· Assess statewide needs and trends
· Inventory land-use changes since 1983
· Quantify agricultural quality of land convened from agricultural use
Project components included the following:
· Conduct a telephone survey of officials in all 99 Iowa counties
· Analyze statewide changes in assessment classification
· Schedule meetings of county and state officials
· Inventory agricultural land converted in the seven pilot counties
· Quantify agricultural quality of land convened in the seven pilot counties
The first and second components, which involved all 99 Iowa counties, represent work
not required by the contract, but the project team considered them important in providing
context for interpreting the results of the other components.
The counties that expressed interest in participating in this study represented a good
cross-section of Iowa counties and thus were selected for inclusion in the land-use
inventory. The counties represent an adequate mix of urban and rural areas and levels of
growth pressure as well as geographic location. The seven pilot study counties were
Bremer, Cerro Gordo, Dallas, Monroe, Pottawattamie, Scott, and Story.
Project components
1. Statewide telephone survey. During the period from September 21 to
October 23, 1998, the ISU project team conducted a survey of county zoning
administrators and assessors with the goal of providing decision-makers with up-
to-date information on land use changes across the state. The ]and use survey was
designed to gain the following information relating to land use policy across the
state:
· Identify methods and procedures for valuation of agricultural land
· Farmland protection strategies that are in place in counties
· Procedures for recording and monitoring land use changes at the county
level
· Local issues and concerns in regard to land use changes taking place in
Iowa's 99 counties
The results of the telephone survey are based on the responses elicited from 98
county assessors and 60 county, zoning administrators and on observations made
by Iowa State University researchers during county visits for data gathering. One
hundred fifty-eight out of 164 potential subjects participated in the survey, with
an overall response rate of 96 percent. Ninety-eight of 99 assessors participated
with a response rate of 99 percent (one refusal). Sixty out of 65 zoning
administrators participated with a response rate of 92 percent. These response
rates are considered very high for this type of survey.
Resui~
Agricultural land valuation. The most common index used for agricultural
land valuation throughout the state' s 99 counties is the corn suitability
rating (CSR) system, Some counties use a combination of CSR, crop yield
and Land Capability Class to determine agricultural land valuation.
Monitoring farmland change. Most Iowa counties use the state-mandated
reconciliation report to monitor changes in farmland. Some counties use
other monitoring systems, including visual inspections, real estate
transactions, property assessment cards, active zoning, aerial photos,
geographic information systems (GIS), plat books, and so forth.
Farmland protection programs and strategies. Forty-four counties have
farmland protection programs or strategies in place. The most common
state-level farmland protection strategies in effect among the sample
counties are agricultural zoning district and conservation easement. Among
the local-level programs, comprehensive planning and agricultural
protection zoning are the most common.
Issues of concern related to farmland protection. Overall, both groups
of respondents (65 percent of zoning administrators and 40 percent of
assessors) were concerned about the rate of urban growth in their counties.
They indicated that efforts should be made to preserve prime agricultural
land from being transferred to other uses. However, there was a surprising
difference between the two groups' perceptions about the issue of
farmland protection, indicating how complex an issue it is.
2. Statewide changes in assessment classification. Statewide land use changes
were reflected in data collected by the Iowa Department of Revenue and Finance
(IDRF) and analyzed by ISU researchers as part of this study. Assessment
classification is based on primary use of land parcels. Common assessment
classes include the following:
· Agricultural
· Residential
· Commercial
· Industrial
· Exempt
· Other
In addition to these six common classes, some county assessors include other
classes, such as forest reserve, rural residential, and annexed. Because incidental
uses are permitted in each class, assessment classification indicates only primary
use. Mixed use can occur in each class and is particularly common in the exempt
class. Because mixed use is permitted, assessment class is therefore considered an
indicator of land use, rather than a direct measure of land use.
Results
Change from agricultural to nonagricultural classes. Statewide data from
reconciliation reports were available from the IDRF for the period 1986 to
1997. Parcels that changed fi'om the agricultural class to a nonagricultural
class (that is. residential, commercial, industrial, exempt, or other) totaled
480,567 acres and had a total assessed value of $314,781,679.
Each year since 1986 (except 1993), more land area changed from the
agricultural class to the unincorporated exempt class than to any other
class increasing from 13,615 acres in 1986 to 22,601 acres in 1997. This
change reflects a rate of increase of approximately 750 acres per year.
Each year since 1986, the assessed value of land that changed from the
agricultural class to the unincorporated residential class was higher than
any other class. The area that changed from the agricultural class to the
unincorporated residential class increased in total assessed value from
$7,934,167 in 1986 to $14,952,743 in 1997. The rate of increase was
approximately $585,000 per year.
The unincorporated residential class ranked second in each year except
1993, when its total exceeded the unincorporated exempt class. This class
increased from 6,237 acres in 1986 to 16,566 acres in 1997, a rate of
increase of approximately 860 acres per year. The unincorporated exempt
class ranked second in each year. The area that changed from the
agricultural class to the unincorporated exempt class increased in total
assessed value from $5,130,491 in 1986 to $9,969.625 in 1997. The rate
of increase was approximately $403,000 per year.
This same IDRF report revealed that. from 1988 to 1997, the seven pilot
counties selected for this study contained parcels that changed from the
agricultural class to a nonagricultural class totaling 53,801 acres with a total
assessed value of $34,072,770.
Change from nonagricultural to agricultural class. Parcels that changed
from a nonagricultural class to the agricultural class statewide between
1986 and 1997 totaled 165,848 acres and had a total assessed value of
$212,661,997.
The area that changed to the agricultural class from the
unincorporated exempt class decreased from 10,516 acres in
1986 to 653 acres in 1997.
The area that changed to the agricultural class from the
unincorporated exempt class increased from 3,301 acres in 1986
to 4,221 acres in 1997.
The area that changed to the agricultural class from the
unincorporated residential class increased from 2,601 acres in
1986 to 3,460 acres in 1997.
Each year since 1986, the assessed value of land that changed to
the agricultural class from the unincorporated residential class
was higher than to any other class. The area that changed to
the agricultural class from the unincorporated residential class
increased in total assessed value from $5,397,369 in 1986 to
$11,726,155 in 1997.
Net change from agricultural to nonagricultural classes. The net change
in parcels that changed from the agricultural class to a nonagricultural class
between 1986 and 1997 totaled 314,719 acres and had a total assessed
value of$102,119,682. In the seven pilot counties from 1988 to 1997,
parcels that changed from the agricultural class to a nonagricultural class
had a net total of 35,979 acres and had a net total assessed value of
$14,128,871
Land use in incorporated areas. According to land use data collected
from 19?5 to 1984, incorporated areas in Iowa contained nearly equal
amounts of agricultural land use and nonagricultural land use.
3. Meetings with state and county officials. Two Iowa Communications
Network (ICN) sessions were scheduled to allow state and county officials and
interest groups to comment on the study and inform the research. The first of
these was held September 23, 1998. The second was held November 18, 1998.
Each meeting gave the Iowa State University team an opportunity to provide
updates on the progress of the land use inventory and to solicit information
regarding participants' concerns and interests. At the second session, draft
recommendations and conclusions were discussed and evaluated. The results of
these discussions are found in Appendices B 1 and B2.
4. Seven-county land conversion study. A preliminary visit to the seven pilot
counties selected was made to determine the kind of data available and the staffing
required to gather the data for use in this inventory. In addition to county
assessors, three of the pilot counties had separate city assessors: Ames (Story
County), Davenport (Scott County,), and Mason City (Cerro Gordo County).
The land use change data for these cities were gathered separately from their
respective counties.
Results
Data gathering. In four of the seven counties (Bremer, Cerro Gordo,
Dallas and Scott), the ISU research team entered the data. Data entry in
Cerro Gordo and Dallas counties was done in the assessor' s office, while
data entry for Bremer and Scott counties was done at Iowa State
University.. For each county, it took an average of two to three full days
for three persons to enter data into an MS Excel spreadsheet.
Data format. Data for three counties were provided to the research team
in digital form (Monroe, Pottawattamie, and Story). Some of the data
were in spreadsheet format and some in database format. Pottawattamie
County data, which were in FileMaker Pro format, were convened into
Microsoft Excel format for debugging.
Debugging techniques. Once the data for each city and county had been
entered, a series of debugging techniques were used to test their accuracy.
The first level of debugging done was to determine whether any data were
missing. The county or city. assessor was contacted to supply/verify the
missing information. The second level involved finding duplicate dam
(parcel number and legal descriptions) using the Filemaker Pro program.
The third level of debugging involved determining the reliability of the data.
When very high or very low values were generated, assessors were asked
to verify the accuracy of the data. The final data set was submitted for
analysis and digitizing.
Data variations. The seven counties were found to record different kinds
and amounts of data, maintain records in different formats for different
lengths of time and store them in different locations. For example, some
records are kept' in full-sheet and half-sheet assessment cards, some in
digital format, and some as computer printouts. Sometimes it was difficult
for researchers to determine whether an actual land use change had taken
place; in these cases, researchers relied on other methods to confirm any
actual change in land use.
Four of the pilot study counties (Cerro Gordo, Dallas, Pottawattamie and
Scott) have records of farmland change from 1982 to 1998. Bremer
County had 10 years of data related to fanrdand change (1988 to 1998)
available to researchers. Story County has data from 1983 to 1998, and
Monroe County's data ranges from 1987 to 1998.
Data limitations. Because the data varied widely from county to county,
it was difficult to compare one county with the others in a meaningful
way. There was no standard record-keeping system among the pilot
counties; some maintain records from as far back as 1982 and earlier, while
others keep only more recent records. The amount of detail included on
the assessment cards and printouts also varies from county to county. In
some cases, this led researchers to spend a great deal of time verifying
what type of transaction had taken place for each parcel.
The data gathered from the seven counties cannot be considered a complete
list. Some of the complexities involved splits. Also, county assessors
have different ways of classifying property. For example, Bremer County
places forest reserve designations in the exempt category, while Dallas and
Monroe counties consider forest reserve to be a separate classification.
Pottawattamie County has a separate entry or classification for land that
has been annexed, but for other counties, annexed land falls under a
residential class change.
Acres converted. Of the four counties for which 17 years of data (from
1982 to 1998) on land use change were available, Dallas had the greatest
number of total acres (11,851 acres) convened from agricultural use to
other uses, followed by Pottawattamie (6,825.6 acres), Scott (3,454.5
acres), and Cerro Gordo (5,958.9 acres).
A comparison between the statewide data and data for the seven pilot
counties reveals that the pilot counties see a higher-than-average amount of
agricultural land (382.77 acres per county) convened per year. The
statewide average is 264.9 acres per county per year. Statewide data are
based on Iowa Department of Revenue and Finance data collected from
reconciliation reports between 1986 to 1997.
To present a consistent picture of what is taking place in the seven
counties, counties were compared with each other based upon acres
convened from 1988 to 1998. A total of 34,577.41 farm acres were
convened into different uses from 1988 to 1998 for the seven counties. Of
the total acres, 52 percent were converted into residential use, 24 percent
to exempt, 9 percent to forest reserve, 8 percent to commercial use, 4
percent annexed and nearly 1 percent to rural residential or industrial use.
Dallas Counv?, had the largest area convened from agricultural to
residential use (5,699.2 acres), followed by Pottawattamie (4, 129.4 acres),
Story (2,903.5 acres) and Bremer (2,201.5 acres). Scott, Monroe and
Cerro Gordo counties had 1.822. 1,157.64 and 186.8 acres convened into
residential use. respectively. Monroe County had the highest number of
acres of farm area convened to industrial use, while Portawattamie had the
greatest number convened to commercial use and Cerro Gordo County had
the largest number of exempt parcels. Only Potmwattamie and Story
counties indicated they have some agricultural areas that were annexed.
Trends and changes over time. Most of the agricultural conversion
within the seven counties took place between 1989 and 1997. Dallas
County consistently had the most agricultural land convened to other uses
from 1989 to 1994, with its peak amount of conversion occurring in 1996,
1994 and 1993. The rate of conversion tapered off in 1995, but increased
from 1996 to 1997. Cerro Gordo County saw most of its conversions in
1995, 1997 and 1996. Bremer County had its peak of conversion in 1997,
Monroe in 1994, and Pottawattamie in 1995. For an individual county, no
pattern was seen in the amount of agricultural land conversion. It
fluctuated from year to year.
From 1993 to 1994, all seven counties experienced an increase in
agricultural conversion ranging from 0.01 percent (Bremer) to 13.27
percent (Monroe). However, from 1996 to 1997, agricultural conversion
decreased for four counties (-10.89 for Story -5.49 for Dallas, 3.19 for
Ponawattamie, and -0.98 for Scott). It is also in these years where Bremer
had the highest increase in agricultural conversion (+26.87 percent).
Bremer had the highest increase in agriculture conversion from 1996 to
1997 (+26.87 percent), Cen-o Gordo from 1994 to 1995 (+13.3 percent),
Dallas from 1995 to 1996 (+5.48 percent), Monroe from 1992 to 1993
(+12.4 percent), Pottawattamie from 1994 to 1995 (+4.45 percent), Scott
from 1993 to 1994 (+6.28 percent), and Story from 1997 to 1998 (+18.47
percent).
The peak rate of conversion of agricultural land to residential use was
experienced by the seven counties from 1994 to 1997. In Story County,
most conversion occurred in 1998, while the same was true for Bremer
County in 1997, Dallas in 1996, Pottawattamie and Cerro Gordo in 1995,
Scott and Monroe in 1994. The data indicate that conversion to
commercial use in Story County occurred mostly in 1985, while the same
was true for Pottawattamie County in 1995 and 1996, Scott in 1995,and
Cerro Gordo in 1983.
5. Seven-county agricultural quality study. In this study, data on soil
characteristics were used as the primary measure of relative potential for
agricultural use. In addition, survey data on farmland value provided context and a
basis for comparing measures from soil characteristics. The agricultural quality of
land converted from agricultural use to nonagricultural use was measured using
four Soil survey interpretations: Corn Suitability Rating (CSR), Estimated Corn
Yield (ECY), Land Capability Class (LCC) and USDA Prime Farmlands
classification.
Resul~
Parcel area. Of the 4,005 parcels (totaling 48,564 acres) included in the
study database for seven pilot counties, 2,567 parcels (totaling 36,93 1
acres) had a land use change and 1,438 parcels (totaling 11,633 acres) had
no land use change even though the assessment class changed.
Of the 2,567 parcels (totaling 36,931 acres) that had a land use change,
1,463 parcels (totaling 32,417 acres) were digitized and 1,104 parcels
(totaling 4,464 acres) were not digitized due to incomplete data.
Approximately 57 percent of the parcels in which land use changed were
digitized. These digitized parcels included approximately 88 percent of
the area in which land use changed.
In the seven pilot study counties, the number of acres convened from
agricultural to nonagricultural classes averaged 336 acres per county per
year. The values ranged from 129 acres per year in Bremer County to 592
acres per year in Scott County. By comparison, the average size farm
ranges from 241 acres in Bremer County to 396 acres in Pottawattamie
County. The seven-county average farm size is 335 acres and the Iowa
average is 339 acres.
The majority (64 percent) of digitized parcels were converted from the
agricultural class to the residential class. However, only 62 percent of the
area was convened from the agricultural class to the residential class. Of
the total area convened from agriculture, approximately 22 percent was
convened to the exempt class. Approximately 5 percent of the digitized
parcels and 5 percent of the parcel area was convened to other assessment
classes (forest reserve, annexed, or other). These other assessment classes
or designations were found in only a few of the seven pilot study counties
and generally do not represent a land use change.
The average number of parcels per year increased sliodhtly during the 1982
to 1998 study period. The average area of parcels per year decreased in
the middle of the study period (74 acres per county in 1987), then
increased again to an average of 406 acres in 1998.
Parcel location. For the 32,417 acres digitized in this study,
approximately 67 percent were in incorporated areas or within 2 miles.
Approximately 32 percent were located more than 2 miles from
incorporated areas. These results indicate that, for the parcels digitized,
non-farm development was not necessarily close to incorporated areas.
This was particularly true given that the acreage in the 0-1 mile zone
(10,791 acres) was almost the same as the acreage in the 2+ mile zone
(10,382 acres).
For parcels changed to the industrial class, 70.2 percent of the area was in
incorporated areas or within 2 miles. In contrast, only 52.0 percent of the
exempt class was in incorporated areas or within 2 miles. This was a
logical result given that industrial uses rely on urban services more than the
variety of uses in the exempt class.
Corn suitability rating (CSR). For the parcels digitized in this study, the
area-weighted average CSR was 57.6. For the entire area of all seven pilot
counties, the average was 67.4, slightly above the state average. Average
CSR in digitized parcels ranged from 39.2 in Monroe County to 68.1 in
Story County. In each county, the CSR of digitized parcels was below the
average CSR for the entire county. This indicates that the agricultural
quality of parcels convened from agricultural to nonagricultural classes
was below average in each county.
In the seven pilot counties, parcels converted to the commercial class had
an average CSR of 57.1. The average CSR for the exempt class and
residential class were similar to the CSR for the commercial class. In
contrast, parcels convened to the industrial class had an average CSR of
72.6, much higher than the averages for the other classes. This indicated
that industrial uses may compete with agriculture for high quality land.
Soils that are high quality for agriculture are typically highly suited for
induslrial sites because of little slope, adequate drainage, and other
mutually desirable soil characteristics.
Estimated corn yield (ECY). For the parcels in this study, the area-
weighted average ECY was 106.8 bushels per acre. Average ECY in
digitized parcels ranged from 76.9 bushels per acre in Monroe County to
121.3 bushels per acre in Story County. In each county, the ECY of
parcels was below the average ECY for the entire county and also below
the average ECY for Iowa. Among the assessment classes, parcels
convened to the industrial class showed the highest ECY, 129.5 bushels
per acre.
Land Capability Class (LCC). For the parcels in this study, approximately
47 percent of the acreage was classified by the USDA as Land Capability
Class I or Class II. These two classes have few or no limitations for
intensive agriculture.
The proportion of the area convened to the commercial, exempt, and
residential classes classified as Land Capability Class I or Class II was
approximately 49 percent. For the area convened to the industrial class,
the proportion was 84.7 percent. This pattem was similar to those
described earlier for CSR and ECY.
As with CSR and ECY, the long-term trend from 1982 to 1998 showed
little change in the percentage of Class I and Class II land. The long-term
average was between 45 and 50 percent Class I and Class II land.
USDA Prime Farmland. According to the USDA Prime Farmland
classification, approximately 48 percent of the parcel area convened from
agricultural to nonagricultural class was considered prime agricultural land.
Approximately 26 percent was considered of state importance, 22 percent
was of local importance, and 3 percent was not rated.
From 47.2 to 56.0 percent of the area convened to commercial, exempt,
and residential classes was considered prime agricultural land by the
USDA. In contrast, over 84 percent of the area convened to the industrial
class was considered prime agricultural land by the USDA.
The average percentage of land classified as prime by the USDA showed
no clear trend before 1991. However, beginning in 1991 there was an
overall increase in the annual average and three-year moving average above
50 percent prime land.
Story County. Additional data analysis in Story County in three
incorporation zones (incorporated, zero- to one-mile extraterritorial zone,
and one- to two-mile extraterritorial zone) showed that the area weighted
average CSR increased with distance from the incorporated zone, from
72.8 to 79.9. The area weighted average ECY also increased with distance
from the incorporated zone, from 127.6 to 142.3. In contrast, the average
CSR and average ECY decreased with distance from the incorporated zone
for the parcels in each zone. except for parcels in the 2+-mile zone.
Therefore, in general, as distance from incorporated areas increased in
Story County, the agricultural quality of all land increased, but the
agricultural qualit>., of the parcels decreased.
Analyses of flood zones. hazard zones, and conservation zones also were
completed for Story County. These analyses suggest additional
applications of parcel data and GIS technology to land management.
Emergency management, disaster preparedness, conservation planning, and
other land management applications can benefit from data on land
characteristics and land use changes. These data can be used to identify
limitations and hazards that endanger public health, safety, and welfare.
Information on limitations and hazards can be effectively used to protect
both people and the environment, minimize expenditure of public funds,
and increase the quality of life for Iowa' s citizens.
Assessment classification as an indicator of land use change. Data on
assessment class from county assessors were used in this study as an
indicator of land use change. Data on assessment class provides an indirect
measure of land use change for three reasons. First, a change in assessment
class doesn't necessarily result in a change in land use. Second, assessment
class is based on the principal land use in each parcel; by law, incidental
land uses and mixed uses also are permitted. Third, the assessment class
"exempt" is a better indicator of land ov,~aership rather than land use.
Other data sources, such as field surveys and aerial surveys, provide a
more direct measure of land use change than assessment class. However,
these direct measures were not used in this study due to time and budget
limitations.
How effective was assessment class in indicating land use change? One
measure in this study was the number of parcels with a land use change
compared to the number of parcels without a land use change. Of the total
4,005 parcels analyzed in this study, 2,567 (64 percent) had a land use
change. Of the total 48,564 acres analyzed in this study, 36,931 (76
percent) had a land use change. Therefore, in this study assessment class
change was from 64 to 76 percent effective as an indicator of land use
change.
Recommendations
Digitize and analyze additional parcels in each pilot study county. Most,
but not all, parcels in which land use changed were included in this study.
Because of data and time limitations, 57 percent of the parcels and 88 percent
of the area that changed land use were digitized for this study. Though this
sample of convenience provides sufficient data for conclusions about the vast
majority of area that changed land use, it was biased toward larger parcels and
parcels with complete dam. Digitizing the remaining parcels in each pilot
study county would provide a more representative sample on which to base
conclusions.
Confirm land use changes in each pilot study county. In some parcels that
changed from agricultural to nonagricultural assessment classes (especially to
residential and exempt classes), land use changed on only a portion of the
parcel. An example is a municipal well field in Story County (now classified
as exempt but used primarily for agriculture). Field surveys and aerial surveys
could provide more detailed data that could be used to refine the results.
Other parcels that change from agricultural to nonagricultural classes directly
support agriculture. An example is a soybean plant in Pottawattamie County.
Monitor future land use changes in all counties. An analysis of land
converted from agricultural to nonagricultural use should be conducted ever3'
year or two. For example, the procedure used in this study could be
institutionalized annually using data in each county assessor' s reconciliation
report to the Iowa Department of Revenue and Finance. In addition to
reporting total acres and total assessed value (as is done currently), the number
and location of individual parcels could be included in each county, report.
Such data, combined with aerial imagery, would provide even higher quality
data and more consistent results.
Assist all counties in modernizing land records. As shown in this study,
digital parcel records become an efficient and powerful database for monitoring
land use changes. Pilot counties with parcel records in digital form quickly
provided data needed for this study. Another key to efficient data analysis is
a digital parcel map. Several counties in Iowa, including Story County, already
have a digital parcel map. Others are in the process of creating. one. Helping
all counties create a digital parcel map and modernize their land records in a
consistent way would make future monitoring much more efficient.
Inventory land use and resources statewide. This study provides data and
conclusions based on a sample of seven counties with a diversity. of
characteristics. Statewide inventories of land use and resources would provide
more current and complete data on land use, agricultural quality of land, urban
growth patterns, and population changes. For example, the agricultural quality
of land in and near all incorporated areas could be mapped to provide guidance
to public officials in making decisions about location of future development.
Interpret the results of future inventories and assessments in both a state
and national context. This would help determine the significance of rates of
change as well as size and number of converted parcels/acres. An impartial
steering committee could be appointed to effectively evaluate the findings of a
statewide inventory. This would allow policy makers to gain a better
understanding of the implications of land use change in the state.
Assist county personnel to ensure consistency in implementation of future
land use inventories. A common theme in the ]983 land use inventor/
reports was the variation in implementation of the inventory due to
differences in opinion regarding proper methods to be used and the definitions
of various land use types.
Apply data to other land management issues and needs. Data on land use
and resources are useful not only for agricultural applications but also for a
variety of other land management applications. As shown earlier in this
report, such data can be used for emergency management, disaster
preparedness, and conservation planning. Other applications include urban
growth management, watershed planning, and water quality monitoring.
Though some government agencies and nongovernmental organizations are
already using geographic information system (GIS) databases and technology,
many more would find these tools useful as they work to protect public
health, safety, and welfare. Information produced from data on land use and
resources can be effectively used to protect both people and the environment,
minimize expenditure of public funds, and increase the quality of life for
Iowa's citizens.
Introduction
Background
Low-density, single-family suburbanization, the expansion of highway-oriented
commercial shopping areas, minimal voluntary infill development, disinvestment
in many central cities, significantly increasing infrastructure costs, and the loss of
agricultural and forest lands are all factors that have raised concerns among public
officials nationwide. In the past several years, Maryland, New Jersey, Arizona,
and nine other states have passed legislation aimed specifically at curbing urban
sprawl. The purposes of Maryland's Smart Growth Areas Act of 1997 are to
revitalize older development areas, to preserve valuable resources and open space,
and to discourage continued sprawl. Other states have taken similar actions to
achieve similar goals.
While Iowa is in some respects more rural than the states just mentioned, it has
not been immune to the factors causing land use change. Some highly visible
changes in recent years have led concerned individuals to hypothesize that the rate
of land use change in Iowa is accelerating and that prime agricultural land is being
lost to urban development. In order to adequately address this problem, or even
to determine whether a problem exists, state decision makers require accurate and
timely information.
In 1997, the Legislative Council of the Iowa General Assembly established the
Commission on Urban Planning, Growth Management of Cities, and Protection of
Farmland to study these issues and formulate recommendations for future land use
policies. Chaired by Senator Mar>.' Lundby and Representative Russell Teig, the
commission consists of 21 voting members with diverse expertise in planning,
development, design, zoning, annexation. agriculture, historic preservation,
transportation, and conservation.
The cornmission is charged to
· review county land-use inventories;
evaluate the effectiveness of current state, regional, and local planning
and zoning laws and assess their impact on farmland, natural areas, and
cities of the state;
review model legislation and studies on farmland protection and urban
planning, and collect information on states that have undertaken reform
efforts and have effective programs;
propose innovative and cooperative planning and land-use approaches
that will protect farmland, accommodate and guide growth and
development, and ensure the planning and construction of adequate
supporting services and infrastructure, provide for or eliminate barriers
to affordable housing, protect the environment, and minimize exposure
to natural hazards;
survey the status of Iowa farmland and natural areas over the past
20 years to determine how much of these areas has been converted to
residential, commercial, industrial. or public use, and report on the
agricultural quality of the farmland convened to these uses;
· survey the problems facing the state's cities;
· survey property developers and local government agencies to seek
their advice on solutions to local planning problems; and
· hold public hearings around the state.
To better cover this wide range of issues, in December 1997 the commission
formed six subcommissions, each comprising three members, to address the
following topics:
· Annexation
· Private property fights
· Land-use planning and policies/urban revitalization
· Public parks and recreation areas/natural and historic areas
· Farmland inventories/farmland preservation
· Infrastructure costs and subsidies/tax implications of development
The subcommissions met monthly to gather information, interview knowledgeable
individuals and prepare reports to the commission. In addition, a series of 10
public forums was held throughout the state during July, August, and September
1998. Citizens and representatives of interest groups who attended these forums
provided the commission with testimony on land use issues and other related
topics.
Commission members and others observe land going out of agricultural production
as subdivisions, highways, commercial areas and other facilities are constructed
throughout the state. While such changes are highly visible and often have a
strong impact on the viewer, it is necessary to determine the actual extent of the
land use changes taking place in order to understand their true implications.
To address this need for empirical evidence, in February 1998 the Legislative
Service Bureau circulated a Request for Proposals by potential vendors to conduct
four tasks related to the commission's charge. In late June 1998, Iowa State
University Extension entered into a contract with the bureau to conduct Task
One, a land use inventory.
B. Purpose and objectives
The primary purposes of this inventory were to determine the extent to which
land in Iowa has been converted from agricultural use to other uses, including
residential, commercial, industrial, or public uses (such as public facilities and
infrastructure or recreation and natural areas), and to report on the quality of
agricultural land converted to these uses.
To achieve this end, the Iowa State University research team was asked to
identify at least five counties with different population characteristics, proximity
to major urban areas, geographic location, and growth pressure to serve as pilots
for the land use inventor>,. Of the counties that expressed interest in this project,
seven were selected as pilots to be included in the research. A complete
explanation of the selection process may be found in Section II of this report.
Four principal objectives also were identified by the research team:
Involve the public and public officials
The research team wanted to provide opportunities for review and
comment by commission members, especially those who served on the
Subcommission on Farmland Inventories and Preservation. The
involvement of other ~oups. including the Iowa League of Cities, the
Iowa Association of Counties. and the Legislative Service Bureau, also
was sought. Officials from the seven pilot counties also were heavily
involved in this study. In addition to numerous trips by research staff
to the pilot counties, two meetings using the Iowa Communications
Network (ICN) were held to update and allow comment by all those
with an interest in the research. Important points raised during these
ICN sessions are summarized in Section Ill. A: Meetings with state
and county officials.
Assess statewide needs and trends
Although it was not specifically called for in the contract, the Iowa
State University team designed and conducted a survey of all 99 Iowa
counties to gauge the extent of concern about land use issues and to
gather data on measures being taken to limit the loss of farmland
throughout the state. Results of this telephone survey may be found
in Section IILB: Survey of county officials.
Inventory land use changes since 1983
For the seven pilot counties, land use changes were inventorled using a
sampling technique. Assessors" records were examined to identify
parcels whose tax classification had been changed, which was assumed
to be a potential indicator of land use change. The process is explained
further in Section IV: Land use inventory.
Quantify agricultural quality of land converted from agricultural use
In addition to the amount of land being converted from agricultural use
to other uses, the research team worked to determine the quality of
land going out of agricultural production. This issue is presented in
Section V.' Agricultural quality of land.
Finally, this report also provides a link to previous efforts to inventory land use
change across Iowa. In 1982, the Iowa Legislature passed Senate File 2218,
requiting all counties to inventory the loss of agricultural land from 1960 through
1980. Work done at that time in the seven pilot counties was reviewed and
considered in preparation of this report. See Appendix A for a summary of the
1983 Land Use Inventory.
C. Project components
Initial information gathering included contacts with the Iowa Department of
Revenue and Finance (IDRF) to determine the content and utility of reconciliation
reports that county assessors file annually with the department. The IDRF uses
these reports to ensure equalization of assessment for property taxation. The
reports also contain some information regarding land use changes, and were used
during this land use inventory as an indirect indicator of such changes around the
state. Dick Davidson of the IDRF's Property Tax Section provided a great deal of
information to the Iowa State University team during this stage of the project.
Primary project components included the following.
Schedule meetings with state and county officials
In addition to the ISU research team, this component involved
representatives of the seven pilot counties and of several statewide
organizations, including the Iowa State Association of Counties and the
Iowa League of Cities. In addition to numerous meetings between
research staff and officials in each pilot county, two sessions for
everyone involved in the project were scheduled using the Iowa
Communications Network (ICN). The first, held September 23, 1998,
introduced state and county officials to the purposes and components
of the project and allowed comment on issues of concern about land
use across the state. Draft copies of this land use inventory report
were distributed prior to the second meeting, which was held
November 18 to update all those with an interest in the project on the
results of the telephone survey and pilot land use inventory and to
invite feedback on the draft report. Summaries of each ICN session
may be found in Appendices B 1 and B2.
Conduct a telephone survey of officials in all 99 Iowa counties
A telephone survey was designed and conducted to assess statewide
issues and trends regarding land use change and the loss of prime
agricultural land. All county assessors and county zoning
administrators were contacted during the course of the survey, which
looked at local concerns and issues and gathered data on farmland
protection strategies throughout the state.
Analyze statewide changes in assessment classification
This component provided a statewide overview of changes in land
assessment classification. Data were obtained from annual
reconciliation reports submitted by county and city assessors to the
Iowa Department of Revenue and Finance. Changes in assessment
classification provided indirect measures of land use changes, both in
acres and assessed value. A summary and analyses of these data are
presented in Section Ill. C: Changes in assessment classification.
Inventory agricultural land converted in the seven pilot counties
To inventory land that changed from agricultural to other uses in the
seven counties, researchers used a sampling technique that involved
examining large parcels f~rst and then successively smaller parcels
(Section IV: Land use inventory). This was done because land use
changes were verified more easily on large parcels and because large
parcels have a potentially greater impact. During the process, such
parcels also were found to be more likely than smaller parcels to have
experienced an actual land use change.
Quantify agricultural quality of land convened in the seven
pilot counties
This component provided four measures of the agricultural quality of
land that changed assessment classification since 1982. Measures of
corn suitability rating (CSR), estimated corn yield (ECY), Land
Capability Class (LCC), and USDA Prime Farmland were obtained
from county soil surveys. Analyses of these data are presented in
Section V: Agricultural quality of land.
The second and third components, which involved all 99 Iowa counties, represent
work not required by the contract, but which the project team considered
important in providing context for interpreting the results of the first, fourth, and
fifth components.
Pilot Counties
IL Request for participation
Early in July 1998, letters were sent from ISU Extension to all county assessors,
board of supervisors chairpersons, and county Extension directors. County
officials were asked to respond if they had an interest in being one of the pilot
counties for this land use study. A copy of the correspondence sent to counties
may be found in Appendix C. Nine counties telephoned initially to inquire about
being part of the pilot land use inventory.
B. Letters of interest
Of the nine counties that telephoned to express initial interest in the study, seven
followed up with letters indicating their willingness to cooperate with and provide
support for this effort (Figure 1 ). Three counties -- Bremer, Pottawaztamie, and
Scott -- agreed to provide $5,000 to partially support the work done in their
county. Monroe County agreed to provide $5,000 in in-kind services, which
consisted of county employees' time spent helping the ISU team to obtain the
necessary data. The other three counties -- Cerro Gordo, Dallas, and Story --
agreed to provide a mixture of financial and in-kind support.
C. Selection criteria
In its contract with Iowa State University Extension, the Legislative Service
Bureau prescribed the following criteria to be used in selecting pilot counties for
the land use inventory:
Level of interest
the level of interest in the project expressed by persons in the county,
including the count3' board of supervisors, public and private
community decisions makers, and residents of the county
Availability of ISU staff
the availability of lSU field staff in a county necessary to conduct the
land use inventory
· Growth pressure
the degree to which the county is affected by urban growth
7
· Geographic location
the location of the county in the state
Urban areas
the number and size of urban areas located within or adjacent to the
county
Availability of data
the availability of data regarding land use changes in the county based
on existing research, including the quantity and quality of the data
All nine counties that expressed interest in the study were evaluated to determine
how well they met the selection criteria. To assist in the selection process,
background data were collected and analyzed for each county. These background
data were compiled into profiles of county characteristics, which are presented in
the following section.
O. Profile of characteristics
Data on the following 15 characteristics were analyzed for each county that
expressed interest in being pan of the pilot land use inventory (Tables 1 through
4). Data were obtained from the US Bureau of the Census, US Department of
Agriculture, Iowa Department of Revenue and Finance, and ISU Extension.
· Land assessment class changes (1986-1997)
· Percent of county in farmland (1993-1997)
· Number of farms (1992)
· Average farm size (1992)
· Average value of farmland (1997)
· Total agricultural land valuation (1997)
· Average corn suitability rating (CSR)
· Landform region
· Commuter region
· Rural/urban category (rural, rural adjacent to metro, urban non-metro,
metro)
· Rural/urban population (1994)
· Total population (1990 population and 1997 estimate)
· Population change (1980 to 1990)
· Population change ( 1990 to 1997)
· Households and household income (1990)
For numeric characteristics (such as corn suitability rating), the range of county
values were compared to statewide averages and ranges to ensure that diversity
was represented by the counties. For non-numeric characteristics (such as
rural/urban category), the number of categories present in the counties was
compared to the total number of categories to ensure that diversity was
represented by the counties.
In addition, the status of available land use data and soils data was analyzed for
each county. These data sources needed for the study included USDA county soil
surveys, Natural Resources Conservation Service digital soils data, ISU Iowa Soil
Properties and Interpretations Database (ISPAID), Iowa Department of Natural
Resources GIS data (NRGIS), US Geological Survey topographic quadrangles
(DRGs), and US Geological SUrvey Orthophotos (DOQs).
Table 1. Agricultural characteristics
1997 Average Percent Average size
County land value CSR in farms farm (acres)
Bremer 1,997 73.4 91.2 24 l
Cass 1,558 61.8 96.5 384
Cerro Gordo 2, 185 71.4 89.9 383
Dallas 1,977 73.6 88.9 348
Jasper 1,856 64.1 91.6 322
Monroe 1,004 40.6 94.1 380
Pottawattarnie 1,653 60.8 89.9 396
Scott 2,913 74.2 77.7 261
Stor-,, 2.525 77.6 90.1 333
Nine-county average 1.963 66.4 90.0 339
State average 1,837 62.8 85.9 339
State minimum 757 35.1 62.9 208
State maximum 2,913 84.7 97.5 516
Table 2.
Regional characteristics
Rural/urban Commuter Landform
County cate~Jory reqion re~Jion
Bremer Rural adjacent 9 northeast Erosion surface
Cass Rural adjacent 4 west central Drift plain
Cerro Gordo Urban nonmetro 12 north central Des Moines lobe
Dallas Metro 5 central Des Moines lobe
Jasper Rural adjacent 5 central Drift plain
Monroe Rural 2 south central Drift plain
Pottawattamie Metro 4 west central Fioodplain/Loess Hills
Scott Metro 7 east central Drift plain
Story Urban nonmetro 5 central Des Moines lobe
9
Table 3, Rural/urbanpopulation characteristics
1990 population 1990 population 1994 population 1994 population
County mini urban unincorpomted incorporated
Bremer 62.6% 37.4% 29.0% 71.0%
Cass 50.9% 49.1% 28.2% 71.8%
Cerro Gordo 20.5% 79.5% 12.8% 87.2%
Dallas 58.1% 41.9% 32.3% 67.7%
Jasper 57.5% 42.5% 30.6% 69.4%
Monroe 52.3% 47.7% 42.5% 57.5%
Pottawattamie 27.5% 72.5% 20.3% 79.7%
Scott 12.4% 87.6% 9.1% 90.9%
Story 24.4% 75.6% 12.0% 88.0%
Nine-county average 40.7% 59.3% 24.1% 75.9%
Table 4. Population growth characteristics
1980-1990 1990-1997 1990 1997
County change change population estimate
Bremer -8.1% 2.2% 22,813 23,304
Cass - 10.7% -2.5% 15, 128 14,743
Cerro Gordo -3.6% -0.8% 46,733 46,371
Dallas 1.0% 20.2% 29,755 35,765
Jasper --4.5% 2.6% 34,795 35,700
Monroe - 11.9% -0.9% 8, 114 8,045
Pottawattamie -4.5% 3.4% 82,628 85,405
Scott -5.7% 4.3% 150,979 157,433
Story 2.7% 0.4% 74.252 74.582
Nine-county average -5.0% 3.2% 51,689 53,483
State average -4.7% 2.7% 28,049 28,812
State minimum - 17.1% -9.2% 4,866 4,420
State maximum 17.6% 20.2% 327,140 354,232
E, Final selection of counties
Based on the selection criteria set forth by the contract with the Legislative
Service Bureau and on the profiles of characteristics developed to aid in the
selection process, the seven counties that submitted letters of interest were judged
to represent a fairly good cross section of Iowa counties and thus were chosen for
inclusion in the land use inventory (Figure 1 ). The counties selected -- Bremer,
Cerro Gordo, Dallas, Monroe, Pottawattamie, Scott, and Story -- represent an
adequate mix of urban and rural areas and levels of growth pressure as well as
geographic location. No northwest Iowa county responded to the request for
proposals, however, so that area could not be represented in the inventory.
10 '
Figure 1. The seven pilot study counties
]1
III.
Statewide issues and trends
A, Meetings with state and county officials
During the project period, two meetings were held with participating local
officials, representatives of the Iowa League of Cities and the Iowa State
Association of Counties, and others interested in the inventory. To save travel
time and expense, the Iowa Communications Network was used for these
sessions. Summaries of both meetings may be found in Appendices B 1 and B2.
B. Survey of county officials
Although it was not required by its contract with the Legislative Bureau, the Iowa
State University research team believed a survey of officials in all 99 Iowa
counties would help determine the extent and implications of land use change
around the state. During the period from September 21 to October 23, 1998, the
team conducted a survey of county assessors and zoning administrators with the
goal of providing decision-makers with current information on land use changes
and concerns across Iowa.
Purposes of the survey
The land use survey was designed to gather several types of information. In
general, the objectives were to collect information about methods and procedures
for valuation of agricultural land, identify farmland protection strategies that are in
place in counties, identify procedures for recording and monitoring land use
changes at the county level, and identify local issues and concems in regard to land
use changes taking place in Iowa' s 99 counties. Specifically, the survey was
designed to identify:
productivity indices used for agricultural land valuation, including data
sources and formats, local modifications, and index categories, ranges,
thregholdg or groupg;
dates that valuation procedures were implemented or last updated;
procedures and definitions used to identify land that qualifies for
agricultural land valuation;
· procedures used to record land use changes and opinions about the
accuracy, reliability and appropriateness of those procedures;
12
· the availability of summary reports documenting farm land change;
· counties where zoning or land-use plans are in place;
farmland protection strategies that are in place in counties, how those
strategies have been implemented and how effective they have been;
and
· local issues and concems related to changes in land use at the county
level.
The objectives just described reflect the preliminary recommendations made by
the Subcommission on Farmland Inventories and Preservation of the Commission
on Urban Planning, Growth Management of Cities, and Protection of Farmland.
These recommendations resulted from a meeting held on March 19, 1998, artended
by Jim Gulliford of the Iowa Department of Agriculture and Land Stewardship,
Les Beck of the Story County Planning and Zoning Department, and Gerald
Miller and Paul Anderson of Iowa State University.
Methods
The survey involved questioning two groups of county officials: assessors and
zoning administrators. These officials were selected for the study because they
are involved in recording and evaluating changes in land use and thus are in a
position to provide reliable information about those changes. Each county in the
state has an assessor, resulting in a population of 99 individuals for the assessors'
portion of the survey. A total of 65 counties have zoning administrators, resulting
in a population of 164 for both groups. Because of the relatively small number of
people who would need to be contacted, researchers made a complete enumeration
of the total population.
Project time constraints required that researchers contact officials and gather
information quickly. Because of the short time frame, a telephone survey was
selected for this study. Although phone surveys are more labor intensive than a
mail survey, they provide information more immediately and allow for a greater
degree of control over the survey process, as researchers are able to schedule
remm calls when potential subjects are less busy and keep track of the number of
times that attempts are made to contact each individual.
To inform possible respondents about this telephone survey, a letter was sent to
each potential subject indicating the purpose of the survey, the organization(s)
sponsoring the research, and when the individual would be contacted. A copy of
this correspondence may be found in Appendix D. When phone contacts were
made with potential subjects, interviewers briefly explained the purpose of the
13
study again, informing respondents that they did not need to answer any
questions that made them feel uncomfortable and that all of their responses would
be treated confidentially. Individuals then were asked if they were willing to
participate; participation in the survey was voluntary. Only two of the potential
subjects refused to participate in the study.
When an individual agreed to participate, the interviewer read through the survey
and recorded the subject's responses directly onto the survey form. Two slightly
different surveys, based on the different areas of expertise of the assessors and
zoning administrators, were developed for the two groups of subjects (see
Appendices E1 and E2 for a copy of each of the survey instruments). In order to
be able to compare and contrast results, however, the majority of the sun, ey
questions were the same for the two groups.
Contacts with potential subjects were tracked on call sheets that listed the
assessor or zoning administrator for each countv along with their telephone
numbers. In many instances, initial contacts were made at a time that was not
convenient for the respondent. In these cases, arrangements were made to call
back at a better time. A potential subject was contacted up to four times before
being dropped from the list without completing the survey. During the course of
the survey, 13 individuals requested that a copy of the survey instrument be faxed
to their office for completion at their leisure.
After the surveys were completed, data were entered into a computer for analysis.
Responses to closed-ended questions were pre-coded in the survey in order to
simplify data entry. Responses to open-ended questions were coded during data
entry. The data were analyzed using Microsoft Excel and SPSS 8.0 (Statistical
Package for Social Sciences).
Limitations
In this survey, as in most studies of this type, certain limitations should be
considered when interpreting and discussing the results that follow. This study
represents a snapshot in time, and caution should be utilized when projecting the
data contained in this report into the future. A number of the subjects in this
survey reported that their counties are in the process of updating various policies,
procedures and plans, implying that some of the data from the survey may be
outdated in the near future.
The populations that were chosen for the study present an additional limitation.
Assessors and zoning administrators were selected because of their expertise in
the areas of interest for the survey. This does not mean, however, that theirs are
the only opinions that matter in their counties. Most of the survey questions
involved objective, technical information, but some questions elicited more
subjective responses from subjects. It is important to realize that there may be
other significant viewpoints held in these counties that are somewhat (or very)
dissimilar to those held by the subjects of this survey.
Results
Sample. One-hundred fifty-seven out of 164 potential subjects participated in
the survey, with an overall response rate of 96 percent, which is a very high
participation rate for this type of study. Ninety-seven of 99 assessors
participated with a response rate of 98 percent (one refusal). Sixty out of 65
zoning administrators participated with a response rate of 92 percent. Three of
the zoning administrators were unavailable after four or more contacts, so they
were dropped from the list. Eleven of the 13 questionnaires that were faxed were
completed and returned. These extremely high response rates are one indication
that the respondents believe agricultural land change is an important issue that
should not be ignored. As they were informed that this land use inventory was
requested by the Iowa Legislature, it is possible that they were more inclined to
have their concems included in the analysis and so with state policy decision-
making.
In general, potential subjects were accessible and willing to participate in the
study. In the week prior to October 1, which is the deadline for property' tax
notices, it was more difficult to reach assessors, but this did not pose serious
problems for the study. Most respondents seemed interested in the study, and all
but a few (98 percent of the assessors and 93 percent of the zoning
administrators) were interested in receiving a summary of the report when the
study is complete.
index for agricultural land valuation. Several different indices are used for
agricultural land valuation. The most common is corn suitability rating (CSR),
used by 66 counties or 68 percent of the sample. This is followed by a combined
index of crop yield, CSR and land capability. class, which is used by nine counties.
Seven counties utilize a land soil survey and the Iowa Department of Revenue and
Finance (IDRF) five-year average. and two counties use the per-acre value index
(Table 5).
15
Table 5. Index for agricultural land valuation (n=97)
Index
Corn suitability rating (CSR)
Combined CSR, crop yield,
and Land Capability Class
Land soil survey
IDRF five-year average index
Per-acre value index
Missing
Total
Number of
Counties
66
Percent
68.1
9 9.3
7 7.2
5 5.2
2 2.0
8 8.2
97 100.00
Eighty-five percent of the respondents indicated that for a parcel to qualify. for
agricultural land valuation, its primary use should be for agriculture (either crops
or livestock). In addition to the indices listed in Table 5, aerial photos and zoning
also are used for agricultural land valuation. Fewer than one-fourth of the counties
last updated their agricultural land valuation procedure prior to 1970, while nearly
25 percent updated the valuation procedure between 1990 and 1998 (Table 6).
Table 6. Year that county land valuation procedure was last updated (n--97)
Number of
Year counties Percent
1930-1970 20 20.7
1971-1974 3 3.0
1975-1979 15 15.5
1980-1983 12 12.4
1984-1989 10 10.3
1990-1994 9 9.3
1995-1998 13 13.4
Don't Know 15 15.4
Total 97 100.00
Monitoring of farmland change. According to survey responses by county
assessors, most of Iowa's counties do not monitor changes in farmland in any
manner other than the state-mandated reconciliation report. Fewer than one-
fourth of the assessors surveyed mentioned using visual inspections, real estate
transactions, property value cards, active zoning, aerial photos, geographic
information systems (GIS), mapping systems, plat books or other reports in the
office for recording and monitoring agricultural changes (Table 7).
Eighty-three percent said they believe their recording or monitoring system is
appropriate, reliable and accurate. These same respondents said their monitoring
system seems to work well or adequately for their purposes and they are satisfied
with it. Some respondents indicated, however, that little change is taking place in
their counties and that there is no need to be monitoring things that weren't
happening.
Several subjects mentioned GIS as a better way to monitor land use changes, but
indicated that the counties need technical help to implement such a system. Also,
a number of counties were in the process of updating their procedures at the time
of the survey, and several respondents mentioned that land use changes at the
county level had reached the point where they probably need to keep more
complete and consistent records.
Table 7. Procedure for recording fannland change (n=97)
Number of
Recordingl system counties Percent
Reconciliation report 97 100.0
Visual inspection 23 24.2
Real estate transaction 18 18.9
Property value card 15 15.8
Active zoning 4 4.2
Aerial photograph 4 4.2
Geographic information system 3 3.2
Mapping system 2 2.1
Plat book 2 2.1
Report in office 2 2.1
Subdivision or annexation 2 2.1
No System At All 1 1.0
Others 6 6.3
Oata on tarrnlantl chan!le. One of the goals of this survey was to determine the
kind and amount of available data on farmland changes. Iowa State University
researchers sought information on the number of years counties have kept records
and in what forms those records are maintained (i.e., digital or paper).
One-half (50 percent) of the counties surveyed kept records of farmland changes
in any form prior to 1982 (1948-1981); 18 percent had records from 1988, 10
percent from 1993, and one to two counties had data available beginning in each
year 1982, 1983, 1986. 1989, 1990, 1992, 1994 and 1995. Only one county did
not have any records at all, and 13 assessors surveyed did not know what data
their counties keep. Eighty-one or 98 percent of the counties that kept data on
farmland change had records up to 1998, while one county had records up to 1997.
When asked in what form their records are kept, a majority of the respondents
indicated that their records are in paper form (green cards or printouts). In
addition, 13 counties had started keeping the records in digital form, with available
data ranging from 1983 to 1998 (Table 8).
17
Table 8. Year from which counties have kept records of farmland changes
Year
1948-1981'
1982
1983
1984
1985
1986 3 3.6
1987
1988 15 18.1
1989 2 2.4
1990 3 3.6
1991
1992 2 2.4
1993 8 9.6
1994 1 1.2
1995 1 1.2
1996
1997
1998 1 1.2
Total 83 100.0
Data available In paper format
No. of No. of
counties Percent counties Percent
42 50.7 42 51.8
2 2.4 2 2.5
3 3.6 4 4.9
3 3.7
13 16.0
2 2.5
2 2.5
In digJital format
No. of
counties Percent
1 7.7
1 7.7
I 7.7
1 7.7
2 15.4
2 2.5
8 9.9 3 23.0
2 2,7 1 7.7
1 1.0 2 15.4
1 7.7
81 100.0 13 100.0
Nothing I 4 68
Don't know 13 12 16
*A total of 42 counties had data on farmland changes available prior to 1982, with three
having records from as far back as 1948. As this study focused on data from the period
1982 to 1998, the table does not provide a year-by-year breakdown from 1948 to 1981.
Zoning ordinances, land-use plans, and farmland protection strategies. The
study shows that zoning administrators generally are familiar with their county' s
farmland protection policies and issues, while county assessors are more familiar
with record-keeping procedures. Because of this, data related to farmland
protection strategies were based on the responses of the 60 zoning administrators
in the sample.
Respondents from 59 counties mentioned that they have zoning ordinances, land-
use plans or policies. Of these, 44 counties (74 percent) have farmland protection
strategies. Ninety-three percent or 41 counties have farmland protection
strategies included in their zoning ordinance or land-use plan. Most indicated that
these strategies are in effect in their county (91 percent or 40 counties); only one
county's farmland protection strategies are not being implemented currently.
Such strategies have been in place in many counties for a long time; the first was
implemented in 1949, with nearly three-fourths of the counties implementing such
a strategy prior to 1980 (73 percent or 27 counties). Only two counties
implemented farmland protection strategies after 1985 (Table 9).
Table 9. First time county implemented farmland protection strategies (n=44)
Number of
Year counties Percent
1949-1970 9 24.3
1971-1974 7 19.0
1975-1980 11 29°7
1981o1984 8 21.6
1985-1990 1 2.7
1991-1994 0 0.0
1995-1996 I 2.7
Missing 7
Total 44 100.0
Two types of farmland protection strategies are found in the counties: those
implemented at the state level and those that are implemented locally (see
Appendix F for detailed descriptions of these programs). Several strategies are
used to protect farmland at the state level. These are agricultural zoning district,
conservation easement, differential assessment tax relief, purchase of agricultural
easement programs (PACE), and Circuit breaker tax relief credits (Table 10). The
most common technique is agricultural zoning district (33 counties or 75 percent
of those with farmland protection strategies), followed by conservation easement
(11 counties or 25 percent).
Agricultural district laws allow farmers to form special areas where commercial
agriculture is encouraged and protected. Agricultural district programs are a
unique farmland protection technique because they use a combination of
incentives to achieve the same goals as regulator3.' strategies. Instead of controlling
land use, agricultural district laws offer farmers benefits for keeping their land in
agriculture. Agricultural districts are implemented mainly through zoning
ordinances and in accordance with Iowa Code. A majority (88 percent) of the
survey resp0ndents said they believe the agricultural zoning district technique is
an effective way of protecting farmland in their counties.
On the local level, the most common strategies used to protect farmland are
comprehensive planning, agricultural protection zoning, and cluster zoning. A
comprehensive plan can form the foundation of a local farmland protection
strategy by identifying areas to be protected for agricultural use and areas where
growth will be encouraged. It may include policies designed to conserve natural
resources and provide affordable housing and adequate public services. It is also
called the master plan for an area. Any changes in the plan have to go through a
series of meetings and hearings with the public, county board of supervisors, and
all the offices involved. Eighty-six percent of the counties that have adopted this
technique said it is an effective tool.
19
Agricultural zoning is being adopted by 63 percent of the respondents' counties.
Agricultural protection zoning (APZ) ordinances designate areas where fanning is
the primary land use and discourage other land uses in those areas. APZ limits the
activities that are permitted in agricultural zones. The most restrictive regulations
prohibit any uses that might be incompatible with commercial farming. More than
three-fourths of the implementors believe this is an effective tool. The other
techniques implemented at the local level are cluster zoning, local right-to-farm
ordinances, mitigation ordinances and policies, and transfer of development rights
(TDR). All of these techniques seem to work well for most of the counties except
for TDR (zero percent effective) (Table 11 ).
These different farmland protection strategies are intended to protect all types of
land, including farmland, open space, and protected areas. However, they are
targeted primarily to protect prime agricultural land. Fifty percent of the
respondents said their counties protect agricultural land that has moderate to high
CSR (from 55 to 75 and higher). Thirty-three percent said their counties protect
land with "high agricultural value" but did not specifically mention CSR as an
indicator of value. Five percent said they protect farms larger than 35 acres, while
four respondents specifically mentioned that farmland protection strategies are
geared toward protecting small farms and grain farms.
Table 10.
State-level farmland protection strategies and ~eir perceived effectiveness
Strate{jy
Counties usinq strateqy
Counties that consider
strategy effective
Number Percent Number Percent
Agricultural zoning district 33 75.0 29 87.89
Conservation easement 11 25.0 6 54.6
Differential assessment tax relief 2 4.6 1 50.0
PACE 1 2.3 1 100.0
Circuit breaker tax relief I 2.3 1 1 O0.0
Others 4 9.1 2 50.0
Table 11. Local-level farmland protection programs and their perceived effectiveness
Strategy
Counties usin; strate~Jy
Counties that consider
stratec~y effective
Number Percent Number Percent
Comprehensive planning 38 86.4 32 84.2
Agricultural protection zoning 28 63.6 22 ?8.6
Cluster zoning or open-space zoning 9 20.4 8 88.9
Pjght-to-farm laws 8 18.2 4 50.0
Local right-to-farm ordinances 4 9.1 2 50.0
Mitigation ordinances and policies 3 6.8 3 100.0
Transfer of development rights 3 6.8 0 0.0
Others 9 20.4 7 77.8
20 '
Issues of concern related to farmland protection. All respondents (county
assessors and zoning administrators) were asked whether farmland protection is
an issue of concem in their own county, and the majority of both groups indicated
that it is (Table 12). However, a greater number of zoning administrators (77
percent) than county assessors (53 percent) believe it is something that should not
be taken for granted in their counties.
Table lZ Perception of farmland protection as an issue of concern
Zoning administrator Assessor
Issue of Concern (n=60) (n=97)
Number Percent Number Percent
Yes 46 76.7 51 52.6
No 11 18.3 35 36.1
Missingdon't know 3 5.0 11 11.3
An attempt was made to determine whether both the assessor and zoning
administrator from a single county have the same perception about the issue of
farmland protection. Of the 57 counties that answered this question and have
both an assessor and a zoning administrator, 29 counties (50 percent) said yes (it
is an issue), five counties (9 percent) said no, and 23 counties (40 percent) had
officials with differing opinions (one said yes, the other said no) (Table 13).
Of the counties in which both the assessor and zoning administrator believe that
farmland protection is an issue of concern, 13 (45 percent) are classified as rural,
nine as metropolitan, and seven as urban-nonmetropolitan counties (based upon
the rural/urban classification provided by Iowa Profiles Public Resource Online).
In only five counties (9 percent) do both the zoning administrator and county
assessor believe that farmland protection is not an issue of concern at all. All five
are rural counties (two are rural adjacent to metropolitan counties) not currently
experiencing much farmland change; it is also possible that leaders in these
counties desire increased economic development and are willing to give up some
agricultural land to commercial. industrial. and residential uses.
However, in more than one-third of the counties (23 out of 57), either the assessor
or the zoning administrator believes that farmland protection is not an issue in
their county. Of this group, seven counties are classified as rural, 11 as rural
adjacent to metropolitan, and five as urban-nonmetropolitan. These data indicate
that county assessors and zoning administrators have different perceptions about
farmland protection in their counties, and the rural/urban county classification
seems to have little correlation with the type of response.
Generally, the rural/urban analysis of the farmland protection issue indicates that
respondents within each group have different perceptions about the issue;
21
therefore, no real trend is apparent. This seems to indicate that concern about
farmland protection varies widely and is more of a local than a statewide issue.
Table 13.
Consistency in perception of county assessors and zoning administrators on the
issue of farmland protection by county classification (n--57)
County classification
Rural
Rural adjacent
to metropolitan county
Urban-nonmetro county
Metropolitan county
Total
Issue of concern
Yes No Either yes or no
Number Percent Number Percent Number Percent
13 44.8 3 60.0 7 30.4
0 - 2 40.0 11 47.8
9 24.1 0 - 5 21.8
9 31.1 0 - 0 -
29 100.0 5 100.0 23 100.0
As stated previously in this section, farmland protection is an issue of more
concern for county zoning administrators (77 percent) than for county assessors
(55 percent of the sample). Other issues mentioned by survey respondents also
differ between the two groups (Table 14). A main point of interest for both
groups was the fear of losing prime agricultural land (34 percent of the total
respondents). Neither group wants to lose prime agricultural land (with specific
mention of high C SR as a measure of prime farmland); however, nearly half of the
assessors had this concern, compared with only 26 percent of the zoning
administrators. A number of county assessors were concerned that any
agricultural land taken out of production, even for exemption or preservation,
leads to a loss of tax dollars.
The second issue of concern was the prevalence of urban sprawl. This is the
primary concern of the zoning administrators (30 percent). These two issues --
loss of prime agricultural land and urban sprawl -- are closely related for both
groups of survey respondents. They are worried about uncontrolled growth of
residential areas, especially of subdivisions, which are perceived as taking prime
agricultural land out of production for residential or commercial purposes. Both
groups want to keep growth out of prime agricultural land or leave the prime land
for production. Likewise, they are concerned about the effect of urban growth on
current residents of their counties' communities. Some respondents mentioned an
increase in people moving from cities and building homes in rural subdivisions,
which can lead to higher service costs.
One concern mentioned many times by the county assessors during the survey
was hog or livestock confinements (37 percent). These confinements do not
represent a land change from agricultural use to other uses, but instead represent a
change in types of agricultural enterprises being conducted on the land. Many
assessors view confinements as a problem (with potentially wide-ranging effects)
that they are unable to deal with because of provisions contained in the Iowa State
Code. Only nine percent of the zoning administrators expressed the need to
address this issue,
Only four respondents specifically mentioned saving family farms as being an
issue of concem. Other land use concerns mentioned by the respondents were
pollution, odor, and protection-from-nuisance lawsuits (presumably in regard
again to livestock confinement operations, although this was not always
specifically stated).
Many respondents (29 percent of the total respondents) indicated that farmland
protection was not an issue of concern at all. Six respondents said it was an issue
in their county, but not a significant or alarming one. These respondents come
mainly from rural communities where little land use change is taking place.
Table 14. Issues of concern indicated by county assessors and zoning administrators
Issue
Zoning administrators
(n=46)
Number Percent
12 26.1
Loss of prime agricultural land
(high CSR, productive soil)
Prevention of urban sprawl 14 3 0.4 10 19.6
Hog or livestock confinements 4 8.7 19 3 7.3
Odor, pollution, protection from nuisance 3 6.5 2 3.9
Development is undermining agriculture 3 6.5 0 -
Impact on family farms 4 8.7 0 -
Artacting tourism 0 - 3 5.9
Zoning 0 - 3 5.9
Not a big concern 2 4.3 4 7.8
Others 6 13.0 13 25.5
Assessors Total
(n=51) (n=97)
Number Percent Number Percent
21 41.2 33 34.0
24 24.7
23 23.7
5 5.2
3 3.1
2 2.1
3 3.1
3 3.1
6 6.2
19 19.6
C. Changes in assessment classification
Statewide land use changes were reflected in data collected by the Iowa
Department of Revenue and Finance (IDRF) and analyzed by Iowa State
University researchers as part of this land use inventory. County assessors
submit annual reconciliation reports that summarize the total area and value of
land parcels that changed assessment classification. Assessors' reconciliation
reports are part of the Abstract of Assessment required by the Iowa
Administrative Code. Data from reconciliation reports are used in the property
tax equalization procedure administered by the IDRF.
23
Assessment classification is based on primary use of land parcels. Assessment
classes in the reconciliation reports include the following:
· Agriculture
· Residential
· Commercial
· Industrial
· Exempt
· Other
A complete description of each class is included in Appendix G. The class
"Other" has a special use in reconciliation reports. It is used to make
administrative adjustments and corrections, such as resurveys, updated plats,
changes involving utilities and railroads, and transfers of agricultural dwellings on
agricultural land. Not all of these changes and adjustments represent land use
changes. In the following tables, "Other" is included to present the complete data
set. However, for comparison purposes, summary tables at the end of this
section show both totals with "Other" and totals without "Other."
Because incidental uses are permitted in each class, assessment classification
indicates only primary use. Mixed use can occur in each class and is particularly
common in the exempt class. For example, a municipal government that buys land
to drill a water supply well may rent part of the land as cropland. In this case, the
assessment class is exempt, but the uses are water extraction and agriculture.
Because mixed use is permitted, assessment class is therefore considered an
indicator of land use, rather than a direct measure of land use.
Change from agricultural to nonagricultural classes. Statewide data from
reconciliation reports was available from the IDRF for the period 1986 to 1997.
Parcels that changed from the agricultural class to a nonagricultural class (that is,
residential, commercial, industrial, exempt, or other) totaled 480,567 acres and had
a total assessed value of $314,781,679 (Table 15).
24 '
Table
Area and assessed value of parcels changed from agricultural to nonagricultural
classes in Iowa (1986 to 1997)
To class
Residential
Commercial
Industrial
Exempt
Other
99-county total acres
Percent of total acres
Unincorporated Incorporated Total acres Percent of total
138,021 30,281 168,303 35.0%
21,445 17,233 38,678 8.0%
6,477 4,544 11,020 2.3%
204, 183 17,436 221,619 46.1%
36.753 4.194 40,947 8.5%
406,879 73,688 480,567 100.0%
84.7% 15.3% 100.0%
To class Unincorporated Incorporated Total value Percent of total
Residential $130,013,228 $23,522,038 $153,535,266 48.8%
Commercial $20,772,583 $15,694,852 $36,467,435 11.6%
Industrial $3,844,768 $3,896,655 $7,741,423 2.5%
Exempt $83,755,544 $10,721,396 $94,476,940 30.0%
Other $20,477.734 $2.082.881 $22.560,615 7.2%
99-county total value $258,863,857 $55,917,822 $314,781,679 100.0%
Percent of total value 82.2% 17.8% 100.0%
Each year since 1986 (except 1993), more land area changed from the agricultural
class to the unincorporated exempt class than to any other class (Figure 2). The
area that changed from the agricultural class to the unincorporated exempt class
increased from 13,615 acres in 1986 to 22,601 acres in 1997. The rate of increase
was approximately 750 acres per year. The unincorporated residential class
ranked second in each year except 1993, when its total exceeded the
unincorporated exempt class. The area that changed from the agricultural class to
the unincorporated residential class increased from 6,237 acres in 1986 to 16,566
acres in 1997. The rate of increase was approximately 860 acres per year.
Each year since 1986, the assessed value of land that changed from the agricultural
class to the unincorporated residential class was higher than any other class
(Figure 3). The area that changed from the agricultural class to the unincorporated
residential class increased in total assessed value from $7,934,167 in 1986 to
$14,952,743 in 1997. The rate of increase was approximately $585,000 per year.
The unincorporated exempt class ranked second in each year. The area that
changed from the agricultural class to the unincorporated exempt class increased in
total assessed value from $5,130,491 in 1986 to $9,969,625 in 1997. The rate of
increase was approximately $403.000 per year.
In the seven pilot counties from 1988 to 1997, parcels that changed from the
agricultural class to a nonagricultural class totaled 53,801 acres and had a total
assessed value of $34,072,770 (Table 16).
25
Figure 2.
Total acreage of parcels changed from agricultural to nonagricultural classes
(1986 to 1997)
25,000 ,
20,000 '-::'!:' i':: ': ·: ::' _.~,/i -.-e- Unincorp res
-. :.: . /tt~ /~"+ Unincorp com
! "',,/
/.,x.,.,~: ~ / / ,.,/~ ~ / .......... ..-. Unincorp ind
15,000 ~
-.--~----Unincorp exe
10,000 i +lncorpres
~ _,,,e..-- - -e-Incorp com
I
· .s ~ Incorp ind
5,000 ~~
', :... ': _ Incorp exe
~ ~ T"" ~ ~ ~ I=~- I''='- ~ ~ ~ ~
Figure 3.
Total assessed value of parcels changed from agricultural to nonagricultorel
classes (1986 to 1987)
0,000,000
8,000,000
6,000,000
z,./''~ i ........::ii .........Uni ncorp ind
e ,"x""x.-'''K'' ./<,,.., ,. ; ----~---: Unincorp exe
x//X,.,,,,y,..~,/ "-x-"' , : Incorp res
,- + Incorp corn
...~ Incorp ind
~ Incorp exe
26 '
Table 16.
Area and assessed value of parcels changed from agricultural to nonagricultural
classes in the seven pilot study counties
To class Unincorporated Incorporated Total acres Percent of total
Residential 17,803 3,504 21,307 39.6%
Commercial 2,661 1,955 4,616 8.6%
Industrial 329 175 504 0.9%
Exempt 16,285 1,536 17,821 33.1%
Other 9.354 197 9.551 17.8%
Seven-county total acres 46,432 ? ,369 53,801 100.0%
Percent of total acres 86.3% 13.7% 100.0%
To class Unincorporated Incorporated Total value Percent of total
Residential $16,569,057 $2,986,155 $19,555,212 57.4%
Commercial $2,567,698 $1,824,082 $4,391,780 12.9%
Industrial $211,126 $153,602 $364,728 1.1%
Exempt $8,125,576 $875,834 $9,001,410 26.4%
Other $660.680 $98.960 $759.640 2.2%
Seven-county total value $28,134,137 $5,938,633 $34,072,770 100.0%
Percent of total value 82.6% 17.4% 100.0%
County totals for annual acreage and assessed value were available from IDRF for
the period 1988 to 1997. The total amount of unincorporated area that changed
from the agricultural class to nonagricultural classes was much higher than average
in Pottawattamie, Polk, Johnson, Jasper, and Dallas counties (Table 17). The
total value of unincorporated area that changed from the agricultural class to
nonagricultural classes was much higher than average in Polk, Jasper, Dallas,
Pottawattamie, Johnson, Story., Bremer, Marshall, and Iowa counties (Figure 4).
Table 17.
Unincorporated area and assessed value changed from agricultural
to nonagricultural classes in top-ranking Iowa counties
County Unincorporated acres
Portawartamie 12.614
Polk 11.710
Johnson 10.024
Jasper 8.985
Dallas 8.677
99-count' mean 3.577
99-count, median 2.609
County Unincorporated value
Polk $6,837,570
Jasper $5,980,630
Dallas $5,586,868
Pottawattamie $5,467,070
Johnson $5,154,802
Story. $5,143,893
Bremer $4,986,009
Marshall $4,958,365
Iowa $4.936.683
99-county mean $2,252,507
99-county median $1,837,105
The seven-county mean and seven-county median were above those for all 99
counties, both for acres and for assessed value in unincorporated areas (Table 18).
27
Figure 4.
Acreage and assessed value of parcels changed from agricultural
to nonagricultural classes in unincorporated areas (1988 to 1997)
Acres u
~ 177-4.323
' 4,324 - 6,468
8,4e;- 12.614
Value,
' 225,781 - 2,429.710
2.429.711 - 4,L~L_~,640
4,533,641 - 6,837,570
Figure 5.
Acreage and assessed value of parcels changed from agricultural
to nonagricultural classes in incorporated areas (1988 to 1997)
~ 7 - 4,301
' 4,3D2 - 8,596
B,597 - 12,.,,891
Value $
~ 1,399 - 2,739,692
' 2,739,693 - 5,478.386
5,4763,67 - B .216,880
Rgura6.
AcIII
Total acreage and assessed value of parcels changed from agricultural
to nonagricultural classes (1988 to 1997)
' 206 - 8,.337
6,338 - 16,489
t 6.470 - 24,601
V~lue S
242,652 - 5.179,918
Table 18.
Unincorporated area and assessed value of parcels changed from agricultural
to nonagricultural classes in the seven pilot study counties
County Unincorporated acres County Unincorporated value
Bremer 6,437 Bremer $4,986,009
Cerro Gordo 7,662 Cerro Gordo $3,333,834
Dallas 8,677 Dallas $5,586,868
Monroe 2,419 Monroe $731,019
Pottawanam ie 12,614 Pottawartamie $5,467,070
Scott 3,267 Scott $2,885,444
Stor~' 5.355 Story $5.143.893
Seven-county mean 6,633 Seven-county mean' $4,019, 162
Seven-county median 6,437 Seven-county median $4,986,009
The total amount of incorporated area that changed from the agricultural class to
nonagricultural classes was much higher than average in Polk and Lirm counties
(Table 19). The total value of unincorporated area that changed from the
agricultural class to nonagricultural classes was much higher than average in Polk,
Black Hawk, Linn, and Scott counties (Figure 5).
Table 19.
Incorporated area and assessed value changed from agricultural to nonagricultural
classes in top-ranking Iowa counties
County Incorporated acres County Incorporated value
Polk 12,891 Polk $8,216,880
Linn 6.236 Black Hawk $3,652,210
99-county mean 678 Linn $3,574,256
99-county median 205 Scott $3.490.245
99-county mean $505,039
99-county median $197,270
The seven-county mean and seven-county median were above those for all 99
counties, both for acres and for assessed value in incorporated areas (Table 20).
Table 21).
Incorporated area and assessed value changed from agricultural
to nonagricultural classes in the seven pilot study counties
County Incorporated acres County Incorporated value
Bremer 643 Bremer $921,126
Cerro Gordo 1,450 Cerro Gordo $800,688
Dallas 2.422 Dallas $1,636,962
Monroe 172 Monroe $56,383
Ponawartamie 1.552 Pottawattamie $1,111,63 1
Scott 3,900 Scott $3,490,245
Story 2,056 Story $I .740.208
Seven-county mean 1,742 Seven-county mean $1,393,892
Seven-county median 1,552 Seven-county median $1,111,63 1
The total amount of area (both unincorporated and incorporated) that changed
from the agricultural class to nonagricultural classes was much higher than average
in Polk, Pottawanarnie, Johnson, Linn, Dallas, Jasper, and Cerro Gordo counties
(Table 21 ). The total value of area (both unincorporated and incorporated) that
29
changed from the agricultural class to nonagricultural classes was much higher than
average in Polk, Johnson, Dallas, Story, Black Hawk, Ponawattarnie, Linn, Jasper,
Scott, Bremer, and Marshall counties (Figure 6).
Table 21. Total area and assessed value changed from agricultural to nonagricultural
classes'in top-ranking Iowa counties
County Total acres County Total value
Polk 26,601 Polk $l 5,054,450
Pottawattamie 14, 166 Johnson $7,232,356
Johnson 13,426 Dallas $7,233,830
Linn 13.073 Story $6,884,101
Dallas 11,099 Black Hawk $6,796,060
Jasper 9,694 Pottawattamie $6,578,701
Cerro Gordo 9. l 12 Linn $6,546,715
99-county mean 4.255 Jasper $6,482,304
99-county-median 3,125 Scott $6,375,689
Bremer $5,907, 135
Marshall $5.388.463
99-county mean $2,757,546
99-county median $2,106,647
The seven-county mean and seven-count)' median were above those for all 99
counties, both for acres and for assessed value in unincorporated areas and
incorporated areas (Table 22).
Table 22,
Total area and assessed value changed from agricultural to nonagricultural
classes in the seven pilot study counties
County Total acres County Total value
Bremer 7,097 Bremer $5,907, 135
Cerro Gordo 9, 112 Cerro Gordo $4,134,522
Dallas 11,099 Dallas $7,233,830
Monroe 2,591 Monroe $787,402
Portawattamie 14, 166 Pottawattamie $6,578,701
Scott 7, 167 Scott $6,375,689
Storv 7,411 Story $6.884.101
Seven-county mean 8,375 Seven-county mean $5,413,054
Seven-county median 7,411 Seven-county median $6,375,689
Change from nonagricultural to agricultural class. Parcels that changed from a
nonagricultural class to the agricultural class between 1986 and 1997 totaled
165,848 acres and had a total assessed value of $212,661,997 (Table 23).
30 '
Table 23.
Area and assessed value of parcels changed from nonagricultural to agricultural
class in iowa
From class Unincorporated Incorporated Total acres Percent of total
Residential 3 l, 133 8,766 39,899 24.1%
Commercial 10,353 2,802 13,154 7.9%
Industrial 1,019 748 1,767 I. 1%
Exempt 50,793 4, 126 54,920 31.1%
Other 52,847 3.262 56.109 33.8%
99-county total acres 146, 145 19,704 165,848 100.0%
Percent of total acres 88.1% 11.9% 100.0%
From class Unincorporated Incorporated Total value Percent of total
Residential $82,237,231 $19,107,463 $101,344,694 47.7%
Commercial $30,841,773 $24,450,337 $55,292,110 26.0%
Industrial $4,878,346 $2,267,278 $7,145,624 3.4%
Exempt $23,683,548 $2,590,632 $26,274,180 12.4%
Other $19,819,839 $2.785.550 $22.605.389 10.6%
99-county total value $161,460,737 $51,201,260 $212,661,997 100.0%
Percent of total value 75.9% 24.1% 100.0%
In 1986, 1990, 1993, and 1996, more land area changed to the agricultural class
from the unincorporated other class than from any other class (Figure 7). The area
that changed to the agricultural class from the unincorporated exempt class
decreased from 10,516 acres in 1986 to 653 acres in 1997. The rate of decrease
was approximately 820 acres per year. In 1987, 1988, 1989, 1994, 1995, and
1996, more land area changed to the agricultural class from the unincorporated
exempt class than from any other class. The area that changed to the agricultural
class from the unincorporated exempt class increased from 3,301 acres in 1986 to
4,221 acres in 1997. The rate of increase was approximately 75 acres per year.
The unincorporated residential class ranked first in 1991, when its total exceeded
the unincorporated exempt class and unincorporated other class. The area that
changed to the agricultural class from the unincorporated residential class increased
from 2,601 acres in 1986 to 3,460 acres in 1997. The rate of increase was
approximately 70 acres per year.
Each year since 1986, the assessed value of land that changed to the agricultural
class from the unincorporated residential class was higher than to any other class
(Figure 8). The area that changed to the agricultural class from the unincorporated
residential class increased in total assessed value from $5,397,369 in 1986 to
$11,726, 155 in 1997. The rate of increase was approximately $527,000 per year.
In the seven pilot counties from 1988 to 1997, parcels that changed to the
agricultural class from a nonagricultural class totaled 17,822 acres and had a total
assessed value of $19,943,899 (Table 24).
31
Figure 7.
Total acreage of parcels changed from nonagricultural to agricultural class
(1986 to 1997)
6,000
5,000
t~ 4,000
,,~ 3,000
2,000
/ ~
/ ,,x
"'%% ' / %'~"Z ' '
--e- Unincorp res
----- Unincorp com
· -..::ii,:.-.. Unincorp ind
-y-- Unincorp exe
z Incorp res
+ Incorp com
: Incorp ind
· Incorp exe
Rgure 8.
Total assessed value of parcels changed from nonagricultural to agricultural class
{1986 to 1997)
2,000,000 ,
0,000,000 ~ Unincorp res
+ Unincorp com
.... -x----. Unincorp exe
6,000,000
x Incorp res
4,000,000 !:::'::; --e-Incorp corn
~ ~ : Incorp ind
Table 24.
Area and assessed value changed from nonagricultural to agricultural class in the
seven pilot study counties
From class Unincorporated incorporated Total acres Percent of total
Residential 3,192 498 3,690 20.7%
Commercial 418 397 815 4.6%
Industrial 251 31 282 1.6%
Exempt 5,428 315 5,743 32.2%
Other 7.152 138 7.290 40.9%
Seven-county total acres 16,442 1,380 17,822 100.0%
Percent of total acres 92.3% 7.7% 100.0%
From class Unincorporated Incorporated Total value Percent of total
Residential $ l 1,643,993 $ 1,499,254 $13, ] 43,247 65.9%
Commercial $1,415,439 $1,172,258 $2,587,697 13.0%
Industrial $235,518 $136,540 $372,058 1.9%
Exempt $2,716,326 $123,293 $2,839,619 14.2%
Other $976.388 $24.890 $1.001.278 5.0%
Seven-county total value $16,987,664 $2,956,235 $19,943,899 100.0%
Percent of total value 85.2% 14.8% 100.0%
County totals for annual acreage and assessed value were available from IDRF for
the period 1988 to 1997. The total amount of unincorporated area that changed to
the agricultural class from nonagricultural classes was much higher than average in
Ponawattamie, Lucas, Sioux, Guthrie, Ringgold, and Washington (Table 25). The
total value of unincorporated area that changed to the agricultural class from
nonagricultural classes was much higher than average in Polk, Sioux, Story,
Guthrie, Linn, Bremer, Boone, Dallas, Marshall, Tama, Mills, Wright,
Washington, Benton, Portawattamie. Black Hawk, and Mahaska counties
(Figure 9).
Table Z5.
Unincorporated area and assessed value changed from nonagricultural to
agricultural class in top-ranking Iowa counties
C o u nty Un incorporated value
Polk $6,582,790.
Sioux $4,985,156
Story $4,885,798
Guthrie $4,241,409
Lirm $4,066,8 ! 9
Bremer $3,428,206
Boone $3,341,894
Dallas $3,183,240
Marshall $3,150,868
Tama $3,018,327
Mills $2,980,319
Wright $2,955,670
Washingon $2,897,249
Benton $2,808,184
Pottawattamie $2,650,532
Black Hawk $2,313, 130
Mahaska $2.288.744
99-county mean $1,372,493
99-county median $999,164
County Unincorporated acres
Pottawanamie 7,953
Lucas 5,493
Sioux 5.156
Guthrie 4.468
Ringgold 3.924
Washino_ton 2.849
99-count' mean 1.153
99-county media 857
33
The seven-county mean and seven-county median were above those for all 99
counties, both for acres and for assessed value in unincorporated areas (Table 26).
Table26.
Unincorporated area and assessed value changed from nonagricultural to
agricultural class in the seven pilot study counties
County Unincorporated acres County Unincorporated value
Bremer 2, 161 Bremer $3,428,206
Cerro Gordo 1~ 152 Cerro Gordo $986,232
Dallas 1,820 Dallas $3,183,240
Monroe 343 Monroe $495,053
Pottawattam ie 7,953 Pottaw attam ie $ 2.650,532
Scott 644 Scott S 1,358.603
Story 2.370 Storv S4.885~798
Seven-county mean 2,349 Seven-county mean $2,426.809
Seven-county median 1.820 Seven-county median $2,650,532
The total amount of incorporated area that changed from the agricultural class to
nonagricultural classes was much higher than average in Polk and Linn Counties
(Table 27). The total value of unincorporated area that changed from the
agricultural class to nonagricultural classes was much higher than average in Polk,
Black Hawk, Linn, and Scott Counties (Figure 10).
Table27.
Incorporated area and assessed value changed from nonagricultural to agricultural
class in top-ranking Iowa counties
County Incorporated acres County Incorporated value
Woodbury 1,706 Polk $6,948,470
Scott 1,087 Linn $5,421,935
Black Hawk 1,052 Black Hawk $3,825,920
Polk 952 Flovd $2.872,700
Linn 940 99-county mean $438,782
Sioux 880 99-county median $159,345
99-county mean 157
99-county median 79
The seven-county mean and seven-county median were above those for all 99
counties, both for acres and for assessed value in incorporated areas (Table 28).
Table28.
Incorporated area and assessed value changed from nonagricultural
to agricultural class in the seven pilot study counties
County Incorporated acres County
Bremer 54 Bremer
Cerro Gordo 327 Cerro Gordo
Dallas 400 Dallas
Monroe 13 Monroe
Pottawattamie 316 Pottawattamie
Scott 1,087 Scott
Storv 297 Story
Seven-county mean 356 Seven-county mean
Seven-county median 316 Seven-county median
Incorporated value
$77,494
$218,364
$788,450
$21,690
$917,939
$1,782,029
$561.968
$623,991
$561,968
Figure 9.
Acreage and assessed value of parcels changed from nonagricultural to
agricultural class in unincorporated areas (1988 to 1997)
i"l.'.
2S86.397 - 5319.793
5319.793 - 7953.19
Value $
--
2231 t 66 - 440~677
4406978 - 6582790
Figure 10.
Acreage and assessed value of parcels changed from nonagricultural
to agricultural class in incorporated areas (1988 to 1997)
0-56E,
569 - 1,137
1,138 - 1,706
Value S
' 0 - 2,316,156
2,316,157 - 4,632,313
4,632,314- 6,948,470
Figure 11.
Total acreage and assessed value of parcels changed from nonagricultural
to agricultural cJass (1988 to 1997)
61.38 - 2797.283
27973.33 - -_K-_;L_3.187
5533.187 - 6269.09
Value $
' 69935 - 4570376
45713377 - 9050818
9050819 - 13531260
J
i
35
The total mount of area (both unincorporated and incorporated) that changed
from the agricultural class to nonagricultural classes was much higher than average
in Pottawattamie, Sioux, Lucas, Guthrie, Ringgold, Polk, Washington, and
Woodbury Cerro Gordo counties (Table 29). The total value of area (both
unincorporated and incorporated) that changed from the agricultural class to
nonagricultural classes was much higher than average in Polk, Linn, Black Hawk,
Sioux, and Story counties (Figure 11 ).
Table29.
Total area and assessed value changed from nonagricultural to agricultural class in
top-ranking Iowa counties
County Total acres County Total value
Pottawattamie 8,269 Polk $13,531,260
Sioux 6,036 Lirm $9,488,754
Lucas 5,509 Black Hawk $6,139,050
Guthrie 4,553 Sioux $5,958,223
Ringgold 4,014 Storv $5,447.766
Polk 3,223 99-county mean $1,811,274
Washington 2,901 99-county median $1,272, 199
Woodbury 2.825
99-county mean 1,311
99-cotmty-rnedian 919
The seven-county mean and seven-county median were above those for all 99
counties, both for acres and for assessed value in unincorporated areas and
incorporated areas (Table 30).
Table30.
Total area and assessed value changed from nonagricultural to agricultural class in
the seven pilot study counties
County Total acres County Total value
Bremer 2,215 Bremer $3,505,700
Cerro Gordo 1,480 Cerro Gordo $1,204,596
Dallas 2,220 Dallas $3,9? 1,690
Monroe 356 Monroe $5 ] 6,?43
Pottawattamie 8,269 Pottawattamie $3,568,471
Scott 1,731 Scott $3,140,632
Story 2.667 Story $ 5.447.766
Seven-county mean 2,705 Seven-county mean $3,050,800
Seven-county median 2,215 Seven-county median $3,505,700
Net change from agricultural to nonagricultural class. The net change in parcels
that were ~'ansferred from the agricultural class to a nonagricultural class between
1986 and 1997 totaled 314,719 acres (Table 31). That is, the total number of
acres that changed from the agricultural class exceeded the number of acres that
changed to the agricultural class in each assessment class. The same was mac both
in unincorporated areas and incorporated areas.
36
Table 31. Net area changed from agricultural to nonagricultural class in Iowa
Net to class Unincorporated Incorporated Total acres Percent of total
Residential 106,889 21,525 128,404 40.8%
Commercial 11,093 14,431 25,524 8.1%
Industrial 5,458 3,796 9,254 2.9%
Exempt 153,389 13,310 166,700 53.0%
Other - ! 6.094 932 - 15.162 -4.8%
99-county total acres 260,734 53,984 314,719 100.0%
Percent of total acres 82.8% 17.2% 100.0%
99-county average 2,634 545 3,179
99-county average per year 219 45 265
In the seven pilot counties from 1988 to 1997, parcels that changed from the
agricultural class to a nonagricultural class had a net total of 35,979 acres (Table
32). Of this total, 29,991 acres (83.4 percent) were in unincorporated areas. This
is nearly the same percentage as for all 99 counties (82.8 percent). The percentage
of acres in incorporated areas is likewise similar in the seven pilot counties as in all
99 counties.
Table 32.
Net area changed from agricultural to nonagricultural class in the seven
pilot study counties
Net to class Unincorporated Incorporated Total acres Percent of total
Residential 14,611 3,006 17,617 49.0%
Commercial 2,243 1,558 3,801 10.6%
Industrial 78 144 222 0.6%
Exempt 10,857 1,221 12,078 33.6%
Other 2.202 59 2.261 6.3%
Seven-county total acres 29,991 5,988 35,979 100.0%
Percent of total acres 83.4% 16.6% 100.0%
Seven-county average 4.284 855 5, 140
Seven-county' average per year 428 86 514
The number of acres per county averages 5, 140. Over the 10-year reporting
period, the number of acres per county per year averages 514. This average is
nearly double (94 percent higher than) the 99-county average of 265 acres per
county per year over the 12-year reporting period. This indicates that the seven
pilot study counties (as a group) are not typical of the statewide pattern.
According to the IDRF, the class "other" includes some land that has not changed
land use. If the class "other" is excluded from the totals, the averages are different.
In this case, the average number of acres per county per year increases from 265
to 278 for all 99 counties (Table 33).
37
Table 33.
Net area changed from agricultural to nonagricultural class in Iowa
(excludes "other')
Net to class Unincorporated Incorporated Total acres
Residential 106,889 21,525 128,404
Commercial 11,093 14.431 25,524
Induslxial 5,458 3,796 9,254
Exempt 153,389 13.310 166,700
99-cottory total acres 276.829 53.062 329,882
Percent of total acres 83.9% 16.1% 100.0%
99-county average 2,796 536 3,332
99-county average per year 233 45 278
Percent of total
38.9%
7.7%
2.8%
50.5%
100.0%
Excluding the class "other" in the seven pilot counties decreases the average
number of acres per county per year from 514 to 482 (Table 34). In this case; the
seven-county yearly average is 73 percent higher than the 99-county yearly
average. Again, this indicates that the seven pilot study counties (as a group) are
not typical of the statewide pattern.
Table 34.
Net area changed from agricultural to nonagricultural class in the seven
pilot study counties (excludes "other")
Net to class Unincorporated Incorporated Total acres
Residential 14,611 3,006 17,617
Commercial 2,243 1,558 3,801
Industrial 78 144 222
Exempt 10.857 1.221 12.078
Seven-county total acres 27,789 5,929 33,718
Percent of total acres 82.4% 17.6% 100.0%
Seven-county average 3,970 847 4, 817
Seven-county average per year 397 85 482
Percent of total
52.2%
11.3%
0.7%
35.8%
100.0%
The seven pilot counties also differ from the statewide pattern in the percentage
of land transferred to each assessment class. Statewide, the majority of acres
transferred (50.5 percent) went to the exempt class. In the seven pilot study
counties, the majority of acres transferred (52.2 percent) went to the residential
class. Also, in the seven pilot study counties, the percentage of acres transferred
to the commercial class (11.3 percent) is higher that the statewide percentage (7.7
percent). However, the percentage of acres transferred to the industrial class (0.7
percent) is lower that the statewide percentage (2.8 percent).
O. Land use in incorporated areas
According to land use data collected from 1975 to 1984, incorporated areas in
Iowa contained nearly equal amounts of agricultural land use and nonagricultural
land use (Table 35).
Table :3,E Land use in incorporated areas
Land use Acres Percent
Agricultural 611,060 52. l
Nonagricultural 561.130 47.9
Total 1,172, 190 100.0
Land use data were prepared by the US Geological Survey from high-altitude
NASA aerial photographs, ranging in date from 1975 to 1984. Maps of land use
were prepared from the aerial photographs at a scale of 1:125,000 in 1982-1984.
GIRAS data were later digitized by USGS at a scale of 1:250,000. The GIRAS
land use codes utilize the Anderson and others (1976) level II land use/land cover
classification system. Nonagricultural land uses include urban or built-up land,
forest, water, wetland, and barren land (such as extraction and mining areas).
Data on the location and boundaries of incorporated areas were obtained from the
Iowa Department of Transportation. The data source was last updated in 1992
(Figure 12).
39
Figure 12. Agricultural land use in incorporated areas
Incorporated agricultural land
Incorporated non-agricultural land
IV.
Land use inventory
IL Data sources
A preliminary visit to each of the seven pilot counties selected was made to
determine the kind of data available and the staffing necessary to gather the data
for use in this pilot land use inventory. Prior to this first visit, county assessors
were given a Microsoft Excel spreadsheet describing the kind of data needed by
the researchers to conduct the inventory. The spreadsheet included legal
description, parcel identification number, number of acres convened, the new
classification, value of the land and farm structures or improvements convened,
and corn suitability rating (CSR) if available. When the ISU project staff then
visited the counties, they looked at what type of data each county has and how
far back they keep records on fannland change to determine how much time and
how many staff would be required for data gathering. Based on the availability of
the ISU research team and assessor staff, a schedule for data gathering was created.
For Bremer, Cerro Gordo, Dallas, and Scott counties, the ISU research team
entered all the data gathered. Each county assessor's office assisted the research
team by pulling the assessment cards from the files and helping to interpret the
data. Data entry in Cerro Gordo and Dallas was done at the assessor's office. For
each count),, it took an average of two to three full days for three persons to enter
data in the computer. Data were entered directly into the MS Excel spreadsheet.
Data entry for Bremer and Scott counties was done at Iowa State University.
Research staff brought actual assessment cards from Bremer County back to ISU,
while for Scott County, it was necessary to photocopy computer printouts and
bring the photocopies to ISU to complete the data-entry process.
In addition to county assessors, three of the pilot counties had separate city
assessors: Cerro Gordo County. (Mason City), Scott County (Davenport), and
Story County (Ames). The data on land use change for these cities were gathered
separately from their respective counties. Data from Ames and Mason City were
entered in the assessors' offices by the ISU research team. Davenport data were
photocopied and entered at ISU.
Data from Monroe, Pottawattamie, and Story counties were provided to the
research team in digital form (Table 36). Some of the data were in spreadsheet
form and some in database form. Pottawattamie County data were converted to
MS Excel format from FileMaker Pro. All data then were "debugged" to locate
and correct discrepancies.
41
Table36.
Data sources and processing in the pilot counties and cities
Cnunty Data source Data entry
Bremer Assessment card ISU research team
Cerro Gordo Assessment card ISU research team
Dallas Assessment card ISU research team
Monroe Assessment card County staff
Pottawanamie Digital &assessment card County staff
Scott Computer printout ISU research team
Story Digital &assessment card County staff
City
Ames Assessment card ISU research team
Mason City Assessment card ISU research team
Davenport Computer printout ISU research team
B. Procedures
Once the data for each city and county had been entered, a series of debugging
techniques were used to test their accuracy. The first level of debugging done was
to discover whether any data were missing. This was determined by sorting the
data in Microsoft Excel and locating blank cells in any of the columns. The
county assessor was contacted to supply/verify missing information. Once the
data were completed, they were converted into another database format, Filemaker
Pro. This formatting allowed the addition of ISU reference numbers to all entries
for cross-checking. It also allowed the creation of additional fields in the database
program.
Additional debugging involved searching all entries for duplications in the parcel
number and legal description fields. Duplications then were sorted to compare
reference numbers and were cross-referenced, with all like entries being noted in
the identification fields. After all records were searched and the duplicate entries
were noted, the data set was exported back into MS Excel, where the duplicates
were eliminated.
The third level of debugging involved determining the reliability of the data. Value
of land per acre was computed to determine if the data were within the range of
values stated in the IowaPROfiles (http://www.profiles.iastate.edu/) agricultural
land value data for each county. When very high or very low values were
generated, thus signaling a potential error in the original data source or in the data-
entry process, county assessors were asked to verify the accuracy of the data.
The final data set was submitted for analysis and digitizing.
C. Results
Data variations. Because the seven pilot counties participating in this inventory
have different record-keeping policies and systems, farmland change data were
analyzed on a county-by-county basis. The seven counties were found to record
different kinds and amounts of data, maintain records in different formats for
different lengths of time and store records in different locations. For example,
some records are kept in full-sheet and half-sheet cards, some in digital format, and
some as computer printouts. Cards typically are colored coded: green indicates
agricultural class, light yellow is residential, orange is industrial, white is
commercial and blue is exempt (although some variation is found among counties
for designations of white, blue and orange cards).
In terms of recording actual land use change, some counties have made a practice
of writing down the year when the parcel was convened, the number of acres
convened, and the value of land and structures converted, while other counties
omit some or all of this information. Corn suitability rating (CSR) values often are
not included on the cards. In several cases, researchers in this study needed to ask
for assistance in locating missing information that was relevant to the inventors'.
It was sometimes difficult to determine whether an actual land use change had
taken place; in these cases, the researchers relied on comments written on the
cards for indication of a change, then confirmed their conclusions with staff in the
assessor' s office. The assessor' s staff used other sources- computer records,
deeds, or other cards related to the specific parcel, and even staff members'
knowledge about the parcel, owner or court history -- to determine whether an
actual change in land use had occurred. In some counties, cards that recorded
farmland change were not filed separately from other cards. Some were arranged
by township, making it more difficult to pull individual records. In addition, some
of the records had been discarded or were kept in a location outside the assessor's
office due to space limitations.
Four of the pilot study counties (Cerro Gordo, Dallas, Pottawattamie, and Scott)
have records of farmland change from 1982 to 1998. Bremer County had 10 years
of data related to farmland change ( 1988-1998) available to researchers. Some
1982-1987 data are included in the Bremer County summary; however, not all of
the older records were available at the time of this inventory. Story County has
data from 1983 to 1998, and Monroe County's data ranges from 1987 to 1998.
(Table 37).
43
Table 37, Available data by county and city
Available data
County (years)
Bremer 1988-1998
Cerro Gordo 1982-1998
Dallas 1982-1998
Monroe 1987-1998
Ponawanam ie 1982-1998
Scott 1982-1998
Story 1983-1998
City
Ames 1988-1998
Mason City 1982-1998
Davenport 1987-1998
Data limitations. Again, because the data varied widely from count3.' to county, it
was difficult to compare one county with the others in a meaningful way. There
is no standard record-keeping system among the counties; some maintain records
from as far back as 1982 and earlier, while others keep only more recent records.
The amount of detail included on the assessment cards and printouts also varies
from county to county. In some cases, this led researchers to spend a great deal of
time verifying what type of transaction had taken place for each parcel.
The data gathered from the seven counties cannot be considered a complete list;
for example, some Dallas County data are missing information on the years in
which land conversions took place. This will affect the time-series analysis later
in this report. In addition, after the data were digitized for parcel analysis, it was
discovered that not all of the subdivisions were included in the data set (Dallas and
Scott counties). Some of the complexities involved splits. Also, county assessors
have different ways of classifying property. For instance, Bremer County places
forest reserve designations in the exempt category, while Dallas and Monroe
counties consider forest reserve to be a separate classification. Pottawattamie has
a separate entry or classification for land that has been annexed, but for other
counties, annexed land falls under a residential class change.
Acres converted. Of the four counties for which 17 years of data (from 1982-
1998) on land use change were available, Dallas had the greatest number of total
acres (11,851 acres) converted from agricultural use to other uses (residential,
commercial, industrial, exempt, and others) with a total of 851 parcels. This was
followed by Pottawanamie with 960 parcels and a total area of 6,825.6 acres,
Scott with 561 parcels and 3,454.5 acres, and Cerro Gordo with 383 parcels and
5,958.9 acres.
Of the seven pilot study counties, Cerro Gordo had the highest average
agricultural acres per parcel that were convened (15.6 acres per parcel), followed
by Dallas (13.9 acre per parcel), Story (12.4 acre per parcel), Ponawanamie (8.4
acres per parcel), Monroe (7.4 acres per parcel), Bremer (6.9 acres per parcel), and
Scott (6.2 acres per parcel). Cerro Gordo and Dallas had the lowest (minimum)
amount of agricultural land converted (0.01 acre per parcel) while Pottawartamie
had the largest (maximum) area of 173.3 acres per parcel convened (Table 38).
Although the table seems to indicate that Dallas County has the highest maximum
acres per parcel convened, this value (*345 acres) actually refers to the total
number of acres convened in one subdivision, not in a specific parcel.
The average number of acres per parcel convened does not differ much among the
three cities; Ames had an average of 16.2 acres per parcel, while Mason City,' had
15.9 acres per parcel and Davenport had 10.5 acres per parcel (Table 38).
However, the city average in acres per parcel converted was generally higher than
county totals.
Table 38. Total parcels and acres convened by county and city
County No. of Total Average Minimum Maximum
parcels acres acres/parcel acres/parcel acres/parcel
Bremer 443 3,046.6 6.900 0.057 39.00
Cerro Gordo 383 5,958.9 15.600 0.010 83.28
Dallas 851 11,851.0 13.920 0.010 *345.00
Monroe 277 2,040.8 7.400 0.100 67.80
Ponawattamie 960 6,825.6 8.400 0.030 173.34
Scott 561 3,454.5 6.200 0.005 96.99
Story,' 535 6,664.6 12.468 0.040 134.01
City
Ames 95 1,537.2 16.200 0.140 65.38
Mason City, 104 1,652.5 15.900 0.100 93.56
Davenport 145 1,521.3 10.500 0.070 79.94
A comparison between the state,Mde data and data for the seven pilot counties
reveals that the pilot counties see a higher-than-average amount of agricultural land
convened per year. The statewide average was 264.9 acres per county per year,
while the average for the seven pilot counties was 382.77 acres per county per
year. The statewide data used in this analysis is based on Iowa Department of
Revenue and Finance (IDRF) data collected from reconciliation reports between
1986 to 1997.
Agricultural land converted in each of the seven pilot counties has gone to a
number of different uses, including rural residential, residential, industrial,
commercial, exempt, forest reserve, and annexed (see Appendix G for definitions
of these real estate classifications). To present a consistent picture of what is
taking place in the seven counties, the following analysis is based only on those
45
acres that were convened from 1988 to 1998, as data for this period were available
from all seven counties. A total of 34,577.41 farm acres were convened into
different uses from 1988 to 1998 for the seven counties. Of the total acres, 52
percent were convened into residential use, 24 percent to exempt, 9 percent to
forest reserve, 8 percent to commercial use, 4 percent annexed and nearly 1
percent to rural residential or industrial use. As stated in the previous section on
data limitations, the "forest reserve" classification was treated differently by the
various counties -- Bremer included forest reserve in its exempt category., while
Dallas and Monroe counties treated it as a separate class. Likewise, the rural
residential and amiexed categories were often treated as part of the residential
classification; however, rural residental is a separate class in Bremer County, while
in Pottawattamie County, annexed land is considered a separate class.
Of the seven pilot counties, Dallas had the largest area convened from agricultural
to residential use (5,699.2 acres), followed by Pottawattamie (4, 129.4 acres),
Story (2,903.5 acres) and Bremer (2,201.5 acres). Scott, Monroe and Cerro Gordo
counties had 1,822, 1,157.64 and 186.8 acres convened to residential use,
respectively. Monroe County had the highest number of acres of farm area
convened to industrial use, while Pottawattarnie had the greatest number
convened to commercial use and Cerro Gordo County had the largest number of
exempt parcels. Only Pottawattamie and Story counties indicated they have some
agricultural areas that were annexed (Table 39, Figures 13 and 14).
Table 39.
Total acres convened by county by class, 1987 to 1998
(rounded to the nearest whole acre)
Rural Forest
County residential Residential Industrial Commercial Exempt reserve Annexed
Bremer 318 2,202 44 274 210 0 0
Cerro Gordo 0 187 17 292 4,659 0
Dallas 0 5,699 1 149 327 3, 189 0
Mortroe 0 1,152 303 101 437 42 0
Pottawattamie 0 4,129 3 1,070 1,478 0 228
Scott 0 1,822 21 245 390 0 0
Story. 0 2,904 0 514 873 0 1,300
Seven-county 318 18,094 389 2,644 8,373 3,231 1,528
total
Percent 0.92 52.33 1.13 7.65 24.21 9.35 4.42
Total
3,047
5,154
9,366
2,035
6,908
2,477
5,590
34,577
100.00
Figure 13.
Total acres converted by class for the seven pilot counties (1988 to 1998)
60
50
40
30
20
10¸
0
/
/
/
/
/
/
,/
/
/
/
R.R. R.
Y
Y
Y
Y
y
y
y
I.
C. E. F.R. A.
.E Rural residentel
'; ResClentid
m Induslrial
~, Comrmrcial
;~ Exempt
~,' Foest reserve
III; Annexed
Rgure 14. Total acres converted by county by class (1988 to 1998)
6000
5000
4000
3000
2000
1000
0
~ Bremer
= Cerro Gordo
~ Dallas
.. x Monroe
x Pottawattarne
--e-- Sco~ : Stoq/
47
Year
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
Unknown
Trends and changes over time. Most of the agricultural conversion within the
seven counties took place bevween 1989 and 1997. Dallas County consistently
had the most agricultural land convened to other uses from 1989 to 1994, with its
peak mount of conversion occurring in 1996, 1994 and 1993. The rate of
conversion tapered off in 1995, but increased again from 1996 to 1997. Cerro
Gordo County saw most of its conversions in 1995, 1997 and 1996. Bremer
Count~ had its peak rate of conversion in 1997, Monroe in 1994, and
Portawattamie in 1995. For an individual county, no partern was seen in the
amount of agricultural land conversion. It fluctuated from year to year (Table 40,
Figure 15).
Table 40.
Bremer
74.8
71.8
133.8
210.6
213.2
131.3
131.5
195.5
385.8
1,204.4
294.2
Total acres convened by
county by year for the seven pilot counties
Cerro Gordo Dallas Monroe Pottawattamie Scott Story
1.1 216.2 106.8 262.0
623.1 807.0 62.9 134.8 190.5
19.8 247.3 108.6 160.1 72.3
91.1 104.0 312.9 129.5 502.4
34.0 462.0 265.9 181.9 141.9
36.7 517.8 6.2 302.0 108.9 167.5
82.1 291.0 2.8 503.5 155.7 232.7
163.9 1,117.5 271.2 639.1 247.7 759.4
332.6 505.9 70.9 427.3 110.1 77.6
476.2 778.3 8Q.6 247.2 103.7 103.5
258.0 723.0 21.4 563.4 243.8 204.0
276.5 '1,242.3 275.3 575.9 149.8 280.8
414.5 1,254.7 546.2 588.5 366.7 373.9
1,207.3 738.7 105.2 947.1 355.9 651.6
692.1 1,388.5 136.4 923.9 342.5 1,042.5
1,191.4 737.8 294.3 666.6 308.5 316.6
59.6 587.9 230.6 825.7 93.0 1547.4
131.0
Figure 15.
Total acres converted by county by year for the seven pilot counties
1800
1600
1400
1200
1888
600
400
200
· Bre~qer
Ceil'0 Gord0
Dalas
Monroe
Pottavmttamie
_- Scott
Stow
48
From 1993 to 1994, all seven pilot counties experienced an increase in agricultural
conversion ranging from 0.01 percent (Bremer) to 13.27 percent (Monroe).
However, from 1996 to 1997, agricultural conversion decreased for four counties
(-10.89 for Story, -5.49 for Dallas, -3.19 for Pottawattamie, and -0.98 for Scott).
Bremer had the highest increase in conversion of agricultural land from 1996 to
1997 (+26.87 percent); Cerro Gordo from 199410 1995 (+13.3 percent), Dallas
from 1995 to 1996 (+5.48 percent); Monroe from 1992 to 1993 (+12.4 percent);
Pottawattamie from 1994 to 1995 (+4.45 percent); Scott from 1993 to 1994
(+6.28 percent); and Story. from 1997 to 1998 (+18.47 percent) (Table 41).
Table 41. Rate of increase/decrease in agricultural conversion by county by year
Year Bremer Cerro Gordo Dallas Monroe Pottawattamie Scott Story
1982
1983 10.44 4.99 -0.54 -3.68
1984 - I 0.12 -4.72 0.57 0.73 -1.77
1985 -0.33 -1.21 2.53 -0.89 6.45
1986 -0.96 3.02 -0.58 1.52 -5.41
1987 0.05 0.47 0.45 -2.11 0.38
1988 0.76 -1.91 -0. i7 2.50 1.35 0.98
1989 -0.113 1.37 6.97 13.15 1.68 2.66 7.90
1990 2.03 2.83 -5.16 -9.81 -2.63 -3.98 -10.23
1991 2.52 2.41 2.30 0.48 -2.23 -0.19 0.39
1992 0.09 -3.66 -0.47 -2.90 3.92 4.06 1.51
1993 -2.69 0.31 4.38 12.44 0.15 -2.72 1.15
1994 0.01 2.32 0.10 13.27 0.16 6.28 1.40
1995 2.10 13.30 -4.35 -21.61 4.45 -0.31 4.17
1996 6.25 -8.64 5.48 1.53 -0.29 -0.39 5.87
1997 26.87 8.38 -5.49 7.74 -3.19 -0.98 -10.89
1998 -29.87 -18.99 -1.26 -3.12 1.97 -6.24 18.47
The peak rate of conversion of agricultural land to residential use was experienced by the
seven counties from 1994 to 1997. In Story County', most conversion occurred in 1998,
while the same was true for Bremer County, in 1997, Dallas in 1996, Pottawattamie and
Cerro Gordo in 1995, Scott and Monroe in 1994. The data indicate that conversion to
commercial use in Story Count' occurred mostly in 1985, while the same was tree for
Pottawartamie County. in 1995 and 1996, Scott in 1995,and Cerro Gordo in 1983
(Appendices H1 to H7).
For the counties that have separate city assessors' offices, most of the agricultural land
that was converted to residential use changed classes in 1994 for Ames (Story County), in
1996 for Mason City (Cerro Gordo County), and in 1989 for Davenport (Scott County).
Ames had a total of 253.97 acres converted to residential use in 1994; Mason City had
112.27 acres and Davenport had 408.5 acres. Most of the conversion to commercial uses
in Ames and Davenport took place in the late 1990s. On the other hand, Mason City had
sporadic commercial-use change from 1984 to 1998 (Appendices H8 to H 10).
49
Agricultural quality of land
IL Measures of agricultural quality
The quality of land for agricultural use can be measured in several ways. Surveys
of farm owners and operators can measure perceptions of agricultural quality.
Data on farm income can identify economically productive areas. Data on
farmland sales can measure the market value of agricultural land. Data on soil
characteristics can measure relative potential for agricultural use.
In this study, data on soil characteristics were used as the primary. measure of
relative potential for agricultural use. In addition, survey data on farmland value
provided context and a basis for comparing measures from soil characteristics.
Surveys of agricultural land value. The Iowa land value survey, conducted
annually by Iowa State University since 1941, is co-sponsored by the Iowa
Agriculture and Home Economics Experiment Station and ISU Extension. Each
year, surveys are mailed to more than 1,000 licensed real estate brokers and
selected individuals with a knowledge of agricultural land values. Survey response
rates typically are in the range of 50 to 60 percent.
According to 1995 survey respondents (Duffy 1995), agricultural land value was
influenced by crop prices (47 percent), interest rates (34 percent), livestock prices
(23 percent), crop yields (20 percent), weather (20 percent), government farm
programs (20 percent), and number of listings (15 percent). Additional influences
listed by survey respondents in recent years included investment or development
demand (24 percent), farm expansion (17 percent), shortage of farmland on the
market (16 percent), rising production costs (9 percent).
The average value of an acre offarmland in Iowa climbed to $1,837 in 1997, the
fourth highest average recorded since 1941. The only years in the past half
century with higher average values were 1979, 1980 and 1981, when the average
value peaked at $2,147. Following the 1981 peak, Iowa land lost nearly two-
thirds of its value, bottoming out at $787 an acre in 1986 (Figure 16). The 1997
survey findings indicated that the average value of an acre of land has increased
133 percent in the past 11 years, and it has regained 86 percent of the peak 1981
value.
The average price statewide of an acre of land increased 9.2 percent in 1997. That
was a gain of $155 from 1996 when the average was $1,682 an acre. Generally,
counties with the lowest average values saw the greatest percentage of increase.
Many of these counties are located in southern Iowa where values increased 13.5
50 '
percent in the southwest crop reporting district and 12.5 percent in the south
central crop reporting district. Highest overall average value was $2,295 per acre
in the central district, up 9.8 percent from last year.
The 1997 survey asked questions about who was buying farmland in Iowa. The
majority of sales, 73 percent, were to existing farmers. Investors represented 22
percent of the sales. New farmers represented 3 percent of the sales. Other
purchases represented 2 percent of sales (Duffy 1997).
The state average and district averages were based directly on the survey data.
County estimates were derived by using a procedure that combines survey results
with data from the U.S. Census of Agriculture. Land values derived from the ISU
Extension survey are typically somewhat higher than other surveys of Iowa land
values because of differences in the time period covered. The ISU survey is
conducted annually; other surveys are not. The latest data available for the seven
pilot counties and their crop reporting districts are from the 1997 survey (Table
42).
Table 42. 1997 average value per acre for the seven pilot study counties
Crop reporting District County County
County district averoqe avera~Je rank
Bremer Northeast $1,721 $1,997 45
Cerro Gordo North Central $2,194 $2,185 28
Dallas Central $2,295 $1,977 47
Monroe South Central $957 $1,004 92
Pottawattamie Southwest $1,369 $1,656 63
Scott East Central $2,110 $2,913 1
Storv Central $2.295 $2,525 5
Seven-county average $1.849 $2,037 40
State average $1,837 $1,837
The 1997 average value of farmland in Scott County, $2,913, was the highest of
any count' in the state. Other pilot study counties with above average values in
1997 included Story, Cerro Gordo, Bremer, and Dallas counties. The 1997
average values in Pottawattamie County and Monroe County were below the
state average. The seven-count, average, $2,037, was 11 percent above the state
average of $1,837.
51
Figure 16. Average land value statewide and in the seven pilot counties (1983 to 1998)
estate ave.
--.!-- Bremer
~ Cerro Gordo
.... :"~:i:.--. Dallas
--.t--- Pottawatlamie
t, Scott
~ Story
Table 43. Increase in average value per acre for the seven pilot study counties
1983 1983-1997 1996 1996-1997
C o u nty value increase value increase
Bremer $ 1,934 3.3% $ 1,814 ! 0. 1%
Cerro Gordo $2,203 -0.8% $2,001 9.2%
Dallas $1,658 19.2% $1,816 8.9%
Monroe $879 14.2% $920 9.1%
Portawattamie $1,433 15.6% $1,490 11.1%
Scott $2,856 2.0% $2,735 6.5%
Storv $2.239 12.8% $2.329 8.4%
Seven-county average $1,886 9.5% $1,872 9.0%
State average $1,691 8.6% $1,682 9.2%
Of the seven pilot counties, Dallas had the largest increase in land value from 1983
to 1997 (19.2 percent). Other pilot study counties with above-average increases
since 1983 included Pottawattamie, Monroe, and Story (Table 43). The 1983-
1997 increases in Bremer, Scott, and Cerro Gordo counties were below the state
average. The seven-county average, 9.5 percent, was 10 percent above the state
average of 8.6 percent.
52
Of the seven pilot counties, Pottawattamie had the largest percentage increase in
land value from 1996 to 1997 (11.1 percent). Bremer County also had an above-
average pementage increase since 1996. Cerro Gordo and Monroe counties had a
1996-1997 percentage increase similar to the state average of 9.2 percent. The
1996-1997 percentage increases in Dallas, Story and Scott counties were below
the state average. The seven-county average percentage increase, 9.0 percent, was
2 percent below the state average of 9.2 percent.
Coun~ soil surveys. Data on soil characteristics can also be used to measure the
relative potential of land for agricultural use. Soil surveys have been completed at
least twice for all 99 counties since 1910. Newer surveys are more detailed than
older surveys, both spatially (map detail) and categorically (map legend). As of
June 1, 1998, modem soil survey reports had been published for all counties
except Allamakee, Humboldt, Jefferson, Lucas, Monona, Polk, and Van Buren.
Recent surveys were being correlated for Allarnakee, Jefferson, Lucas, Monona,
Polk, and Van Buren. Counties being resurveyed and updated include Black
Hawk, Clay, Crawford, Humboldt, and Woodbury. Digital soil maps were
available for all counties except Humboldt, Monona, Polk, and Van Buren.
Interpretations of soil maps aid in determining opportunities and limitations in the
use and management of land. Physical, chemical, biological and hydrologic
interpretations of soil maps are available in published county soil survey reports
and in a digital database. the Iowa Soil Properties and Interpretations Database
(ISPAID). ISPAID interpretations are customized for each count>, soil sun, ey.
(ISPAID data are being updated for Humboldt, Monona, and Van Buren counties.)
Several ISPAID interpretations are designed specifically to indicate relative
potential of land for agricultural use. These include land capability class (LCC),
USDA Prime Farmland. LEAG farmland units, and corn suitability. rating (CSR).
In addition, estimated crop yields are included for corn, soybeans, oats, wheat,
alfalfa-bromegrass, tall introduced grasses, and Kentucky, bluegrass. The following
information about each interpretation is from the ISPAID 6.0 manual (Miller and
Fenton 1997).
Land capability classification shows. in a general way, the suitability of soils for
most kinds of field crops. Crops that require special management are excluded.
The soils are grouped according to their limitations for field crops, the risk of
damage if they are used for crops, and the way they respond to management.
Criteria used in grouping the soils do not include major and generally expensive
landforming that would change slope, depth, or other characteristics of the soils,
nor do they include possible but unlikely major reclamation projects. Capability
classification is not a substitute for interpretations designed to show suitability
and limitations of groups of soils for woodland and for engineering purposes. The
53
numbers 1 through 7 indicate progressively greater limitations and narrower
choices for practical use. The capital letters (E, W, and S) indicate the soils' main
limitation within one class. There are no subclasses in class 1 because the soils Of
this class have few limitations.
Class 1
Class 2
Class 3
Class 4
Class 5
Class 6
Class 7
Soils have few limitations that restrict their use.
Soils have moderate limitations that reduce the choice of
plants or that require moderate conservation practices.
Soils have severe limitations that reduce the choice of plants or
that require very careful management or both.
Soils have very severe limitations that reduce the choice of
plants or that require very careful management or both.
Soils are not likely to erode but have other limitations,
impractical to remove, that limit their use.
Soils have severe limitations that make them generally
unsuitable for cultivation.
Soils have very severe limitations that make them unavailable
for cultivation.
Subclass E
Subclass W
Subclass S
Risk of erosion unless close-growing plant cover is maintained.
Water in or on the soil interferes with plant growth or
cultivation (in some soils wetness can be partly corrected by
artificial drainage).
Shallow, droughty, or stony.
Prirnefarmland, as defined by the USDA, is the land that is best suited to food,
feed, forage, fiber, and oilseed crops. It may be cropland, pasture, woodland, or
other land, but is not urban or built-up land or water areas. It either is used for
food or fiber or is available for these uses. The soil qualities, growing season, and
moisture supply are those needed for a well-managed soil to produce economically
a sustained high yield of crops. Prime farmland produces the highest yields with
minimal inputs of energy and economic resources, and fanning it results in the
least damage to the environment as compared to other uses. Prime farmland
usually has an adequate and dependable supply of moisture from precipitation or
irrigation. The temperature and growing season are favorable. The level of acidity
or alkalinity is acceptable. Prime farmland has few or no rocks and is permeable to
wafer and air. It is not excessively erodible or saturated with water for long
periods and is not frequently flooded during the growing season. The slopes range
mainly from 0 to 6 percent. Some soils have a seasonal high water table and soils
that are frequently flooded qualify for prime farmland only in areas where these
limitations have been overcome by a drainage system or flood control. The need
for these measures is indicated by a number following the letter designation for
54
prime farmland (see list). On-site evaluation is needed to determine whether or
not these limitations have been overcome by corrective measures.
P Prime
P2 Prime, where drained
P3 Prime, if protected from flooding or does not flood more than once in
two years during a growing season
P5 Prime, where drained and protected from flooding
S Statewide importance. These are soils that generally also can be
highly productive for cropland, but occur on slopes greater than 6
percent or have limitations in drainage or flood control that are more
difficult to overcome. These soils are in capability class 3 or 4. At
this time, the soils identified as statewide importance are a potential
listing as it has not yet been approved by the state of Iowa.
L Local importance. These are soils that generally are poorly suited or
unsuited to cropland because of the steepness of slope or flooding and
wetness limitations. They may be important in the county, however,
for other uses such as pasture, wildlife, or recreation. The soils
identified as local importance are a potential listing of soils that may
be considered by county officials for this designation.
LEAGfarmland units are a refinemere of the USDA Prime Farmland units. The
LEAG definition of prime farmland is based on land capability classes and native
productivity. The LEAG farmland units are:
P 1 Most SMUs listed in capability classes 1 and 2 but does not include
those soils that have profile features that limit rooting depth and
water-holding capacid'. All are on slopes of 0-5 percent.
P2 Those SMUs with profile features that limit rooting depth or water-
holding capacity, and have slopes of 0-5 percent.
P3 Highly productive soils on slopes of 5-9 percent that can be major
sediment producers if they are intensively used for row-crop
production without conservation practices. Includes prairie-derived
soils that are in erosion classes slight and moderate and transitional
and forest-derived soils that are in erosion class slight.
P4 Those SMUs protected from flooding or that do not flood more than
once in two years during the growing season.
S1 SMUs that generally are sloping (5-9 percent), that are severely
eroded prairie soils, or are moderately or severely eroded transition
and forested units. Includes some less productive soils on slopes less
than 5-9 percent.
55
S2 SMUs with desirable profile characteristics but occur on slopes 9-14
percent. Erosion classes 1 and 2 are included. Includes some less
productive soils on slopes less than 9-14 percent.
S3 All other units that have more desirable properties than land of local
importance.
O SMUs of local importance.
U Organic soils and some sandy soils that are suited for vegetable crops
under high-level management resulting in high yields.
Corn suitability ratings provide a relative ranking of all soils mapped in the state
of Iowa based on their potential to be utilized for intensive row-crop production.
The CSR is an index that can be used to rate one soil's potential yield against
another over a period of time (Table 44). The CSR considers average weather
conditions as well as frequency of use of the soil for row crop production.
Ratings range from 100 for soils that have no physical limitations, occur on
minimal slopes, and can be continuously row cropped to as low' as 5 for soils ~vith
severe limitations for row crops. The ratings listed assume (a) adequate
management, (b) natural weather conditions (no irrigation), (c) artificial drainage
where required, (d) that soils lower on the landscape are not affected by frequent
floods, and (e) no land leveling or terracing. The weighed CSR for a given field can
be modified by the occurrence of sandy spots, local deposits, rock and gravel
outcroppings, field boundaries, noncrossable drainageways, and so forth. Even
though predicted average yields will change with time, the CSRs are expected to
remain relatively constant in relation to one another over time.
Table 44. Average corn suitability rating (CSR) and rank for the seven pilot study counties
County Averatje CSR Rank
Bremer 73 17
Cerro Gordo 71 28
Dallas 74 16
Monroe 41 94
Pottawattarnie 61 62
ScoUt 74 13
Story 7g 3
Seven-county average 67 ~ ~
State average 63
Corn yield in bushels per acre. The benchmark yield is listed and may be adjusted
for weather conditions in a specific county. The yield estimate for each SMU is
based on kind of parent material, slope class, erosion class, natural drainage class,
and nature of the subsoil in terms of rooting environment to include limiting
layers, soil depth, and plant available water capacity. In addition, potential for
periodic flooding and weather conditions are included. Com yields are estimated
for high-level management and are normalized for a 5-year average (Table 45).
56
High-level management includes the adoption of the best available technology for
crop production to include agronomic, engineering, and economic practices.
Table 45. 1993-1997 average yield (bushels per acre) for the seven pilot study counties
County Corn Soybeans Oats
Bremer 129.1 45. l 66.2
Cerro Gordo 125.7 40.6 66.6
Dallas 132.3 44.3 61.5
Monroe 106.5 38.1 46.1
Pottawattam ie 121.7 43.4 64.3
Scott 136.0 50.6 70.7
Stor,,' 132.2 46.1 67.6
Seven-county average 126.2 44.0 63.3
State average 123.0 42.8 60.6
Though the ISPAID database includes estimated yield for seven crops, corn yield
was selected for this pilot study as an indicator of agricultural quality because
corn is the principal agricultural crop in Iowa (approximately 12.4 million acres).
In 1997, Iowa ranked first in the nation in corn production (USDA 1998). USDA
Prime Farmland was selected for this study instead of LEAG farmland units
because USDA Prime Farmland has categories appropriate for this study of
agricultural quality of soils.
B. Data sources
County soil surveys. Count), soil surveys were the major data source used in
computing the agricultural qualit).' of land convened from agricultural use to
nonagricultural use (Table 46). The ISPAID 6.0 Manual described previously
(Miller and Fenton 1997) provided the interpretations used for each soil mapping
unit.
Table tli. Soil survey dates and digital data status
County Publication Correlation Digital data
Bremer 1967 1965 Preliminary,
Cerro Gordo 1981 1978 Final
Dallas 1983 1980 Preliminary
Monroe 1984 1982 Preliminary
Ponawattamie 19g9 1986 Preliminary
Scott 1996 1989 Preliminary
Store 1984 1981 Preliminary
Soil survey atlas sheets were converted to geographic information system (GIS)
digital form by Iowa State University, Iowa Department of Agriculture and Land
Stewardship, and USDA Natural Resources Conservation Service. Data were
available in the form of coverages in the Arc/Info format or Arcanfo export format
57
(E00 files) (Table 47). Final checking had been completed for Cerro Gordo
County soil survey data. Soil survey data for all other counties were in
preliminary form and had not been through the final checking procedure. Digital
soil survey data for 11 sections in one township in Dallas County were not
available (T78 R.26). Data were digitized for the needed areas from Dallas County
soil survey atlas sheets. Axcanfo Export (E00) files were imported using the
Import utility supplied with ArcView 3.0. In Scott County (and township T78
R27 in Dallas County), sectional E00 files were imported, then tiled into
township shape files.
Table 47. Digital soil survey data
Township Township Sectional
County coverage EO0 EO0
Bremer 12
Cerro Gordo 16
Dallas 15 36
Monroe 12
Pottawattam ie 29
Scott 493
Sto~, 16
Total counties 1 5 2
Total township files 16 84
Total sectional files 529
Digital soils data for Cerro Gordo and Potmwattamie counties were obtained from
the GIS Research and Support Facility of Iowa State University. Digital soils
data for the other five counties were obtained from the USDA Natural Resources
Conservation Service data server.
Supplementan/data sources. Several GIS data sources were used to help
visually locate and digitize parcel boundaries in conjunction with legal descriptions
provided by county assessors. These dam sources included satellite images
drainage features, section lines, and Digital Orthophoto Quadrangles (DOQs).
DOQ files were obtained for Pottawattamie County from the US Geological
Survey CUSGS). All other files were obtained from the I0wa Department of
Natural Resources GIS server, Natural Resources Geographic Information System
(NRGIS).
Satellite images. Two satellite images were available for each county: one imaged
during late spring or early summer, the other imaged during late summer or early
fall (Table 48). In general, the late spring or early summer images were more
useful in identifying land cover patterns for this study. This Was because the lack
of crop cover created a high contrast between fields and trees/lawn areas. The
satellite images used in this study were imaged between June 19, 1989, and
September 4, 1991.
58
Table 48. GIS satellite imagery and digital data
Satellite Satellite Drainage Section
Co u nty image image features lines
Bremer 040691 081291 Rivers09 PL S S 09
Cerro Gordo 040691 081291 Riversl7 PLSS17
Dallas 061691 090491 Rivers25 P L S S 25
Monroe 061989 092690 Rivers68 PLSS68
Ponawattam ie 062090 092490 Rivers78 PL S S 78
Scon 050790 091290 Rivers82 PLS S82
Stor~' 061989 092690 Rivers85 PLSS85
The satellite images have a resolution of 25 meters. Each pixel represents
approximately 0. 15 acres. These false-color images were derived from Landsat 5
Themarie Mapper. Three spectral bands were used to create this representation:
red was from TM band 4 (0.76-0.90 tun), green from TM band 5 (1.55-1.7Sum)
and blue from TM band 3 (0.63-0.69um). The three band TM data was
compressed into an 8-bit, 256-color image using a fast parallelpiped classification
program in the ELAS image processing package. The image file was geometrically
corrected to a UTM grid reference system. Spring images can be visually
interpreted in a similar way to a color-inffared aerial photograph taken in mid-
spring. In general, red hues indicate healthy vegetation such as grass, trees and
early crops such as oats and alfalfa. Green hues represent bare soil. Black is
indicative of water. Blues and white showed areas of human activity such as
urban areas, roads, and quarries. These images were made to be used as a primary
data layer in a geographic information system as a source document for on-screen
digitizing or a background image for comparison with other point, line or polygon
data.
Drainage features. Drainage features were developed from the USEPA's REACH
FILE 3 data system. Graphic elements were from 1: 100,000 scale digital line
graphs (DLG) files from the USGS. Attribute data for each arc were added by
USEPA contractors. Rivers were originally subdivided by river basin, but then
merged and clipped by county. The RF3 data files were an interim version with
many errors and incomplete attributes and documentation. A new version of this
file from the improved RF3 data eventually will be produced.
Section lines. Section lines were from the Public Land Survey System (PLSS).
These lines form polygons which were labeled for PLSS township, range, and
section number. Coordinates were digitized from US Geological Survey 7.5
minute topographic maps (paper copies) using a digitizing program developed in-
house by the Geological Survey Bureau, Iowa Department 0fNatural Resources.
The digitizing tablet accuracy was 1/50 inch. Section lines from individual quads
were combined and edited using PC Arc/Info.
59
Digital Orthophoto Quadrangles. Digital Orthophoto Quadrangles (DOQs) were
available only for Pottawattamie County (Table 49). DOQs are specially
prepared aerial photographs in which geometric distortions have been minimized
and GIS coordinates have been added. A digital orthophoto is a digital image that
has the properties of an orthographic projection. It is derived from a digitized
perspective aerial photograph by differential rectification so that image
displacements caused by camera tilt and relief of terrain are removed. A digital
orthophoto may be made up of several images which are mosaicked together to
form the final image. Each separate piece of the mosaic that contributes to the
final image is called a "chip." Orthophotos combine the image characteristics of a
photograph with the geometric qualities of a map. They serve a variety. of
purposes, from interim maps to field references for earth science investigations
and analysis. Digital orthophotos are useful as a layer of a geographic information
system and as a tool for revision of digital line graphs and topographic maps.
USGS DOQs are available as 1-meter resolution quarter-quadrangles from
1:40,000-scale National Aerial Photography Program (NAPP).
Table 49. Aerial photography quarter quadrangles for Pottawattamie County
Quarter quadrancJle
Atlantic SW NE
Atlantic SW NW
Atlantic SW SE
Atlantic SW SW
Avoca NE
Avoca NW
Avoca SE
Avoca SW
Avoca NW NE
Avoca NW NW
Avoca NW SE
Avoca NW SW
Avoca SE NE
Avoca SE NW
Avoca SE SE
Avoca SE SW
l~eebeetown gE
Beebeetown SW
Carson NE
Carson NW
Carson SE
Carson SW
Carson NE NE
Carson NE NW
Carson NE SE
Carson NE SW
Corley SE
Corley SW
Council Bluffs North NE
Council Bluffs North NW
Council Bluffs North SE
File name Photo date
04109547. NEC / NEH 3/3/90
04109547. NWC / NWH 3/3/90
04109547. SEC / SEH 3/3/90
04109547. SWC / SWH 3/3/90
04109538. NEC / NEH 3/3/90
04109538. NWC / NWH 3/3/90
04109538. SEC / SEH 3/3/90
04109538. SWC / SWH 3/3/90
04109537. NEC / NEH 3/3/90
04109537. NWC / NWH 3/3/90
04109537. SEC / SEH 3/3/90
04109537. SWC / SWH 3/3/90
04109546. NEC /NEH 3/3/90
04109546. NWC / NWH 3/3/90
04109546. SEC / SEH 3/3/90
04109546. SWC / SWH 3/3/90
04109526. gEC / SEI-I 3/3/90
04109526. SWC / gWIq 3/3/90
04109553. NEC / NEH 3/3/90
04109553. NWC / NWH 3/3/90
04109553. SEC / SEH 3/3/90
04109553. SWC / SWH 3/3/90
04109554. NEC / NEH 3/3/90
04109554. NWC / NWH 3/3/90
04109554. SEC / SEH 3/3/90
04109554. SWC / SWH 3/3/90
04109530. SEC / SEH 3/3/90
04109530. SWC / SWH 3/3/90
04109542. NEC / NEH 3/3/90
04109542. NWC / NWH 3/3/90
04109542. SEC / SEH 3/3/90
60
Quarter quadrantlie
Council Bluffs North SW
Council Bluffs South NE
Council Bluffs South NW
Council Bluffs South SE
Council Bluffs South SW
Fort Calhoun NE
Griswold NE
Griswold NW
Griswold SE
Griswold SW
Hard Scratch SE
Hard Scratch SW
Honey Creek NE
Honey Creek NW
Honey Creek SE
Honey Creek SW
Loveland NE
Loveland NW
Loveland SE
Loveland SW
McClelland NE
McClelland NW
McClelland SE
McClelland SW
Mineola NE
Mineola NW
Mineola SE
Mineola SW
Missouri Valley SE
Missouri Valley SW
Modale SE
Neola NE
Neola NW
Neola SE
Neola SW
Oakland NE
Oakland NW
Oakland SE
Oakland SW
Omaha North NE
Omaha North NW
Omaha North SE
Omaha South NE
Omaha South SE
Persia SE
Persia SW
Prairie Rose Lake SE
Prairie Rose Lake SW
Shelby SE
Shelby SW
Iaylor NE
Taylor NW
Taylor SE
Taylor SW
Treynor NE
Treynor NW
Treynor SE
File name
04109542. SWC / SWH
04109550. NEC / NEH
04109550. NWC / NWH
04109550. SEC / SEH
04109550. SWC /
04109640. NEC / NEH
04109555. NEC /NEH
04109555. NWC / NWH
04109555. SEC / SEH
04109555. SWC / SWH
04109527. SEC / SEH
04109527. SWC / SWH
04109534. NEC / NEH
04109534. NWC / NWH
04109534. SEC / SEH
04109534. SWC / SWH
04109533. NEC /NEH
04109533. NWC / NWH
04109533. SEC / SEH
04109533. SWC / SWH
04109543. NEC / NEH
04109543. NWC / NWH
04109543. SEC / SEH
04109543. SWC / SWH
04109551. NEC / NEH
04109551. NWC /NWH
04109551. SEC / SEH
04109551. SWC / SWH
04109525. SEC / SEH
04109525. SWC / SWH
04109632. SEC / SEH
04109536. NEC / NEH
04109536. NWC / NVv'H
04109536. SEC / SEH
04109536. SWC /
04109545. NEC / NEH
04109545. NWC / NWH
04109545. SEC / SEH
04109545. SWC / SWH
04109541. NEC / NEH
04109541. NWC / NWH
04109541. SEC / SEH
04109549. NEC / NEH
04109549. SEC / SEH
04109528. SEC / SEH
04109528. SWC / SWH
04109531. SEC / SEH
04109531. SWC / SWH
04109529. SEC / SEH
04109529. SWC / SWH
04 109544. NEC / NEH
04109544. NWC / NWH
04109544. SEC / SEH
04109544. SWC / SWH
04109552. NEC / NEH
04109552. NWC / NWH
04109552. SEC / SEH
Photo date
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
6/27/88
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
6/27/88
6/27/88
6/27/88
6/27/88
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
6/27/88
6/27/88
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
3/3/90
61
Quarter quadranqle
Treynor SW
Underwood NE
Underwood NW
Underwood SE
Underwood SW
Walnut NE
Walnut NW
Walnut SE
Walnut SW
File name Photo date
O4109552. SWC / SWH 3/3/90
04109535. NEC /NEH 3/3/90
04109535. NWC / NWH 3/3/90
04109535. SEC / SEH 3/3/90
O4109535. SWC / SWH 3/3/90
04109539. NEC / NEH 3/3/90
04109539. NWC / NWH 3/3/90
04109539. SEC / SEH 3/3/90
04109539. SWC / SWH 3/3/90
Paper maps. In addition to digital GIS data, paper maps were used as reference
materials to help locate and digitize parcel boundaries (Table 50). These included
county plat books, subdivision maps, and zoning maps. County plat books
contain township plat maps that show the owner, size, and general boundaries for
larger parcels, typically those larger than 10 acres. County plat books are
published by private comp,anies from county records and are typically used as
directories to locate rural residents. Zoning maps show the location and
classification of zoning districts. These were useful in locating rural residential
subdivisions. Subdivision maps show the location, size, and identification of
individual lots or parcels in residential subdivisions. These were useful in locating
parcels within rural residential subdivisions.
Table 50. Paper maps used in locating parcels
Plat Subdivision
County book(s) plats
Bremer 1987, 1998 1956-1998
Cerro Gordo 1997
Dallas 1998
Monroe 1997
Pottawattamie 1991 - 1997
Scott 77, 80, 90, 93, 97
Story
Subdivision Zoning Highway
maps maps maps
1992
1998
1990 1997
Plat books and other maps were not needed for Story County because parcels had
been digitized previously by the Story County Planning and Zoning Department.
C. Procedures
The procedure for measuring the agricultural quality of land convened from
agricultural use included three major tasks: securing needed equipment, digitizing
parcels, and analyzing dam.
Equipment. GIS data for parcels were digitized using ArcView 3.0 on Pentium-
class microcomputers. Data analysis was completed using ArcView 3.0 with the
Spatial Analyst extension and MS Excel 97.
62 '
Parcel digitizing. The parcel digitizing task included six major steps:
1. Evaluate parcel priority
2. Prepare spreadsheet
3. Locate each parcel
4. Digitize parcel boundaries
5. Evaluate digitizing confidence
6. Check dam
]. Evaluate parcelpriority. Each parcel in the spreadsheet database was
evaluated for its digitizing priority. Digitizing priority was based on several
factors: relevance to the study, completeness of the spreadsheet data, parcel
location, and parcel size.
The spreadsheet database for each county contained records for parcels that had
changed assessment class. However, not all parcels that had changed assessment
class had changed land use. This was particularly true of parcels that changed
from the a~m'icultural class to the residential class and also had an existing dwelling.
Typically, these small parcels were farmsteads that were split from a larger
agricultural tract. These parcels were assigned a low priority for parcel digitizing
because they were not relevant in a study of land use change.
Some records in the database were incomplete, making it difficult or impossible to
digitize parcel boundaries. The most important data field for digitizing parcel
boundaries was legal description. Records with legal descriptions that were
missing or incomplete were assigned a low priority for parcel digitizing. Many
small parcels were difficult to digitize for several reasons: lack of essential
location information in the legal description, irregular shape, lack of sufficient
detail in satellite images and other supplementary, data (such as plat books).
Only some of the parcels located within incorporated areas were digitized. The
reason was that soil surveys generally do not map soils in urban and built-up
areas. This is because consu-uction activities (land filling and excavation)
significantly change the soils and make classification impossible. In these areas,
measures of agricultural quality, of soils are not included in soil surveys.
63
Criteria on relevance, record completeness, parcel location, and parcel size were
combined into a priority rating system. The rating system used a 5-point scale:
5. Large or medium-size parcels relevant to this study with complete data
records
4. Large or medium-size parcels relevant to this study with nearly
complete data records
3. Small parcels relevant to this study with complete data records;
large/medium-size parcels with incomplete data records
2. Small parcels relevant to this study with incomplete data records
1. Parcels not relevant to this study (that is, no land use change or no
soils data)
In general, for six of the seven pilot counties, all parcels with a priority rating of 5
were digitized. Most parcels with a priority rating of 4 were digitized. Some
parcels with a priority rating of 3 were digitized. No parcels with a priority rating
of 2 or 1 were digitized. One exception was Story County, 'where all parcels were
digitized (Table 51 ).
Table 51. Number and area of parcels in the seven pilot study counties
Number of Total Parcels with Parcels Percent of
parcels parcels land use change diQitized chanqed parcels
Bremer 443 335 170 51%
C erro Gordo 383 294 254 86%
Dallas 819 535 222 41%
Monroe 277 165 107 65%
Ponawattamie 962 368 167 45%
Scott 586 583 256 44%
Story 535 287 287 100%
Total 4,005 2,567 1,463 57%
Area of Total Acres with Acres Percent of
parcels acres land use chanqe dicjitized chancJed acres
Bremer 3,022 2, 195 2,080 95%
Cerro Gordo 5,959 5,687 5,322 94%
Dallas 12,690 8,388 8, 13 1 97%
Monroe 2,041 1,330 1328 99%
Pottawattamie 8,098 4,401 2,667 61%
Scott 10,090 10,071 8,030 80%
Story 6.664 4,859 4.859 100%
Total 48,564 36,93 1 32,417 88%
A 100 percent sample of parcels was achieved in Story County. In the other six
counties, the sample ranged from 41 to 86 percent of the number of parcels that
had a land use change and from 61 to 99 percent of the acreag~ involved in land use
changes.
Overall, in the seven pilot counties, 57 percent of the parcels that had a land use
change were digitized and 88 percent of the acres with a land use change were
digitized.
This type of sample was not a random sample, but rather a sample of
convenience. Descriptive statistics (such as frequency, range, mean, and median)
are appropriate measures of agricultural quality in a sample of convenience. This
sample of convenience was biased toward larger parcels and those with complete,
unambiguous legal descriptions. This approach may introduce bias in measuring
the quality of agricultural land, but the bias (if it was present) was unintentional.
In the future, a random-sampling procedure in a pilot county could provide
another means of measuring agricultural quality and also a measure of
representativeness of the sample of convenience.
The mean parcel sized digitized ranged from 12.2 acres in Bremer County to 36.6
acres in Dallas County (Table 52). For the 1,463 parcels digitized, the mean size
was 20.9 acres, the median was 11.4 acres, the minimum averaged 0.8 acres, and
the maximum averaged 173.7 acres. In general, parcels larger than 2.5 acres were
digitized.
Table 52. Size of digitized parcels
Parcel size lacres) Mean Median Minimum . Maximum
Bremer 12.2 10.0 0.4 49.4
Cerro Gordo 21.0 15.5 0.6 85.1
Dallas 36.6 20.0 2.5 345.0
Monroe 12.4 7.1 0.4 72.6
Pottawartamie 16.0 9.1 0.6 174.4
Scott 31.4 8.4 1. l 355.7
Story ] 6.9 9.6 0.1 134.0
Average 20.9 11.4 0.8 173.7
2. Prepare spreadsheet. After the spreadsheet data for each county were
inspected for accuracy and completeness, the data were then used as a guide for
digitizing parcels. The first step in preparing the spreadsheet as a guide for
digitizing parcels was to remove the records for parcels with a priority rating of 2
or 1. Then, the dam fields most useful for digitizing were identified. Typically,
these included the following fields:
· ISU reference number
· Parcel identification code
· Township, section
· Legal description
· Year converted
· New assessment class
65
· Acres
· Corn suitability rating
· Dwelling value
The records were then sorted in order by township and section, then by size.
hard copy. of the spreadsheet was primed to aid in planning the sequence and
priority of parcel digitizing.
A
3. Locate each parceL In ArcView 3.0, a new project was started for each
county. A new polygon theme was started for the parcel boundary data. Themes
were added for each satellite image, drainage features, and section lines (PLSS
data). PLSS data were used in conjunction with township and section information
from the spreadsheets to help locate the section for each parcel. Patterns in the
drainage features and on the satellite images helped verify that the correct section
was located for each parcel.
4. Digitize parcel boundaries. After the correct section had been located for each
parcel, the legal description for each record provided the primary guidance in
locating the parcel within the section. Also, plat books were inspected for more
detailed information about parcel shape and location. Unfortunately, individual
parcels smaller than eight to 10 acres were not shown in the county plat books.
However, the word "tracts" or the abbreviation "TR" was typically found on the
plat maps to indicate a rural subdivision or cluster of smaller parcels. Patterns in
the drainage features and on the satellite images also helped locate each parcel
within a section. If CSR was available in the spreadsheet data from county
assessors, it was also used as corroborating evidence that the location within the
section was logical (that is, higher CSR on flatter ground, lower CSR on steeper
ground).
In ArcView, the Polygon digitizing tool was used to trace the boundary of each
parcel. The status bar at the bottom of the program window displayed the length
of each line segment and the area of each polygon as it was being digitized.
Because of limitations in the spreadsheet data and GIS data (such as lack of
information about utility and road rights-of-way), minor differences occurred in
the size of the parcel digitized and listed in the spreadsheet. Differences of 10
percent or less were considered acceptable for this study. After comparing
digitized area with spreadsheet area on a parcel-by-parcel basis, most differences
were found to be compensating rather than cumulative, so that the total area
digitized was well within 10 percent of the total area shown on the spreadsheets.
The location of each parcel and its boundaries, therefore, was based on
convergence of evidence from legal descriptions, plat maps, subdivision maps,
zoning maps, CSR, satellite images, drainage features, transportation panems,
new assessment class, year converted, and parcel size.
5. Evaluate digitizing confidence. After each parcel was digitized, it was
evaluated for the degree of certainty or confidence that the parcel was located and
digitized correctly. If the evidence was complete, there was visual confirmation
on the satellite images, and there were no discrepancies among the data sources,
then the certainty or confidence was considered high. Conversely, if the evidence
was incomplete and there were discrepancies, then the certainty or confidence was
considered low.
Confidence was indicated in the ArcView polygon attribute tables in two ways.
Annotations were included in a comments field. Also, a confidence rating system
was used that was similar to the parcel priority. rating system described earlier.
Ratings of 5 and 4 indicated high confidence. A rating of 3 indicated moderate
confidence. Ratings of 2 and 1 indicated low confidence.
6. Check data. After digitizing each parcel, the spreadsheet data were reviewed to
make sure that each parcel was in a logical location, size, and shape. If
discrepancies were discovered concerning a parcel, the entire digitizing procedure
was reviewed to identify errors in digitizing. If any errors were discovered, the
parcel was edited to make needed corrections.
Count), differences in digitizing parcels. Most steps of the digitizing procedure
were the same for each of the seven pilot counties. However, there were some
differences due to minor differences in assessors' records, GIS data available,
supplementary data sources. and sequence in the digitizing process.
Bremer Countv. Seven complete rural subdivisions were digitized each as a single
polygon rather than a series of individual polygons, one for each parcel
(subdivision lot). This saved time because fewer polygons needed to be digitized.
However, in several cases, unsold lots were included in a subdivision polygon.
Subdivisions digitized as complete polygons included Barrick Road Estates,
Centennial Estates, Kelly, Lane. Rolling Meadows, Strong Haven, and Willow
Lawn. Other rural subdivisions were not digitized as a single polygon because few
lots were included in the parcel database. In the parcel database, the year listed
for each subdivision polygon was the year in which most development activity
occurred. In general, the miramum size parcel digitized was 2.5 acres (Figure 17).
Cerro Gordo Countv. Parcel digitizing for Cerro Gordo County was completed
using the standard procedures described above. No special procedures were
needed for parcel digitizing. In general, the minimum size parcel digitized was 2.5
acres (Figure 18).
67
Figure 17. Digitized parcels in Bremer County (with incorporation zones)
..I
ml
, i T~li'i
· · 'Ill=
Incorporated area
D-1 mile zone
1-2 mile zone
· /,,
0 2 4 6 Miles
Figure 18.
Digitized parcels in Cerro Gordo County (with incorporation zones)
Incorporated area
0-1 mile zone
1-2 mile zone
I
Miles
Dallas County. Each rural subdivision was digitized as single parcel, rather than a
series of individual lots. This resulted in the number of digitized acres exceeding
the number of acres in the spreadsheet database. This digitizing strategy was
justified by the fact that not all residential parcels were included in the
spreadsheet database because of limitations in collecting parcel dam. Therefore,
though the digitized parcel data overestimated the acreage of land use changes
based on the spreadsheet data, the spreadsheet data underestimated the acreage.
The combined effect was compensating. The error in one estimate compensated
for the error in the other estimate, resulting in an overall estimate that better
reflected the mount of residential development.
Rural subdivisions were digitized from a Dallas County Subdivision Map dated
May 5, 1998. However, only rural subdivisions started after 1981 were included
in the parcel database. Starting dates for subdivisions were obtained from a
spreadsheet database provided by the Dallas County Department of Planning and
Zoning.
Parcels for all other assessment classes (commercial, exempt, and industrial) were
digitized individually. In general for all assessment classes, the minimum size
parcel digitized was approximately 2.5 acres (Figure 19).
Monroe County. Two complete rural subdivisions were digitized each as a single
polygon rather than a series of individual polygons, one for each parcel
(subdivision lot). This saved time because fewer polygons needed to be digitized.
However, several unsold lots may have been included in the subdivision polygons.
The subdivisions digitized each as a complete polygon were the Falvey's Addition
and Krotz Addition.
The 1997 plat book available for Monroe County included the location of small
parcels which increased the number of digitized parcels and their confidence
ratings. In general, the minimum size parcel digitized was 2.5 acres (Figure 20).
Pottawattarnie County. Parcel digitizing for Pottawattamie County was the first
to be completed. The priority rating system and confidence rating system
described previously were developed while digitizing parcels for Pottawattamie
County. Only a few parcels in the Annexed class were digitized. Digital
Orthophoto Quadrangles (DOQs) were helpful in locating many parcels in which
the land use changed before 1990 (the year the aerial photos were taken). Because
of higher resolution and detail, the DOQs were used rather than satellite imagery.
Because of this greater detail, in general, the minimum size parcel digitized was 2.0
acres (rather than 2.5 acres as in other counties) (Figure 21).
7O
Figure 19. Digitized parcels in Dallas County (with incorporation zones)
m In~rporald area
0-1 mile ~ne ~ 0 2 4 6 Miles
1-2 mile ~ne
71
Figure 20. Digitized parcels in Monroe County (with incorporation zones)
.,.,..,,,.
i'
~ Incorp~ramd area
................... 0-1 mile zone
~1-2 mile zone
0
6 Miles
Rgure 21. Digitized parcels in pottawattamie County (with incorporation zones)
Incorporated area
0-1 mile zone
1-2 mite zone
..........·
.,.........·,,..
.......
.,......,.·,...
...,...-...:
0 2 4 6 Miles
73
Scott County. Each rural subdivision was digitized as a single parcel, rather than a
series of individual lots. This resulted in the number of digitized acres exceeding
the ntunber of acres in the spreadsheet database. This digitizing strategy was
justified by the fact that not all residential parcels were included in the
spreadsheet database because of the difficulties in interpreting the many joins and
splits involved in creating typical residential parcels in Scott County. Therefore,
though the digitized parcel data overestimated the acreage of land use changes
based on the spreadsheet data, the spreadsheet data underestimated the acreage.
The combined effect was compensating. The error in one estimate compensated
for tlie error in the other estimate, resulting in an overall estimate that better
reflected the amount of residential development.
Rural subdivisions were digitized from township plat maps in the 1997 published
county plat book. However, only rural subdivisions started after 1980 were
included in the parcel database. In general for all assessment classes, the minimum
size parcel digitized was approximately 2.5 acres (Figure 22).
Storv Countv. Parcels were not digitized as part of this study because they were
previously digitized by the Story County Planning and Zoning Department. In
the earlier procedure for developing the spreadsheet data from assessor' s records,
535 parcels were identified that had changed assessment class during the 1983-
1998 period. These parcels were extracted from the digital parcel map of the
entire county. The county assessor suggested that some oft he 535 parcels that
had changed assessment class did not change land use. These were parcels of the
residential class that had an existing dwelling. In the spreadsheet data, these
parcels showed a dwelling value greater than zero. There were 248 parcels that fit
this description, leaving 287 parcels that had a land use change along with the
assessment class change.
This complete parcel database (100 percent sample) allowed additional analysis
not possible in other counties, where only parcels that had both a class change and
a land use change were digitized. Therefore, analysis of agricultural quality for
Story County parcels was available for two groups of parcels: (a) 535 parcels
that had an assessment class change and (b) 287 parcels that had both a class
change and a land use change. Data for only the second group (287 parcels that
had both a class change and a land use change) were included in the county
comparison tables in this report (Figure 23).
Rgure 22. Digitized parcels in Scott County (with incorporation zones)
BIL D 2 4 6 Miles
lncorporated area
0-1 mile zone
4-2 m.. zone
Figure 23. Digitized parcels in Story County (with incorporation zones)
Incorporated :,tea
D-1 mile zone
1-2 mile zone
Miles
76
Data analysis. The data analysis task included seven major steps:
1. Join spreadsheet to parcel attribute table
2. Recompute parcel acreage
3. Summarize parcel frequency and acreage by class and year
4. Clip and merge soils data
5. Summarize CSR and ECY averages by class and year
6. Summarize LCC and prime farmland acreages by class and year
7. Summarize acreage by incorporation zones
1. Join spreadsheet to parcel attribute table. For each county, the spreadsheet
data were joined with the attribute table that was created by ArcView when the
parcels were digitized. This provided more complete data about each parcel
digitized. The ArcView attribute tables included fields for only parcel number,
digitizing comments, and acreage. By joining the attribute table with the
spreadsheet, data on class, year, dwelling value, and other parcel characteristics
could be analyzed spatially using ArcView software.
First, each spreadsheet was loaded into MS Excel. Field names were edited for
clarity, and consistency. Only one row of the spreadsheet was used for field
names. Second. the portion of the spreadsheet containing data was selected,
including the first row with field names. Third, the name "database" was inserted
using the menu item Insert > Name > Define. Fourth, the spreadsheet was saved
as a dBase 3.0 (DBF) file. This format was compatible with ArcVie,,,,'.
In ArcView. the spreadsheet table was added to the project table of contents. A
field was selected that was also included in the parcel attribute table. Typically,
this field was either the assessor's parcel identification number or the ISU
identification number. After selecting the corresponding fields in both tables, the
menu item Table > Join was used to join the two tables. The joined table was
inspected to make sure that each parcel was correctly matched with a record in the
spreadsheet. For parcels that were not correctly matched, the identification
numbers were checked and edited until a successful match was made.
2. Recompute parcel acreage. Because the acreage of some digitized parcels
varied slightly (less than 10 percent) from the acreage in the assessor' s data, it was
necessary, to recompute the acreage of each parcel. The XTools function Update
Area, Perimeter, Hectares. and Length was used to recompute the area for each
parcel. A new field was then added to each attribute table and the area in square
meters was convened to acres using the Field Calculator function.
3. Summarize parcel frequency and acreage by class and year. ArcView was
used to summarize the number and acreage of parcels for each assessment class
77
(residential, commercial, and so on) and for each year (for example 1983 to 1997).
The menu item Field > Summarize was used with the Sum option to create
summary tables with frequency (count) and area (acres). One table was created
for assessment classes and another was created for year of change.
The resultant tables (for example, sumi.dbfand sum2. dbj) were then opened in
MS Excel. Table values were compiled on a summary spreadsheet for each
county. Spreadsheet functions were used to compute total frequency and acreage,
percent, and average parcel size. MS Excel was also used to produce summary,
graphs and charts.
4. Clip and merge soils data. This step made the analysis process more efficient
by reducing the amount of soils data required. The total file size for digital soils
data ranged from 50 to 100 MB per count>.'. Though ArcView 3.0 will operate
with themes this large, it is quite slow and inefficient, even with a 400 MHz
processor and 128 MB of RAM.
To make the procedure more efficient, the soils data for each township were
clipped to parcel boundaries, then merged together into one shapefile with soils
for all digitized parcels in a count'. The XTools function Clip with Polygon(s)
was used along with the XTools function Merge Themes. The shape files that
resulted from the procedure ranged in size from 0.25 MB to 2.0 MB (total for
SHP, SHX, and DBF).
5. Summarize CSR and ECY averages by class and year. The Spatial Analyst
extension was used in ArcView to summarize Com Suitability Rating (CSR) and
Estimated Com Yield (ECY) for the digitized parcels by class and year. For all
parcels, the menu item Analysis > Summarize Zones computed (among other
measures) the mean value (of CSR or ECY) as an area-weighted average.
However, to use this Spatial Analyst function, the soils polygon theme first was
converted to a grid theme using the menu item Theme > Convert to Grid. In each
case, the Output Grid Extent was set to match the soils polygon theme and the
Output Grid Cell Size was set to 20 meters. The grid themes that resulted from
the conversion ranged in size from 50K bytes to 200K bytes.
The resultant tables (for example, zstat]. dbfand zstat2. dbf) were then opened in
MS Excel. Table values were compiled on a summary spreadsheet for each
county. MS Excel was also used to produce summary graphs and charts.
6. Summarize LCC and Prime Farmland acreages by class and year. The Spatial
Analyst extension was used in ArcView to summarize the Land Capability Class
(LCC) and USDA Prime Farmland classification of the soils in each digitized
parcel by class and year. For all parcels, the menu item Analysis > Tabulate Areas
was used twice to compute the total area of each Land capability class and USDA
Prime Farmland category.
The resultant tables (for example, tareal.dbfand tarea2. dbj) were then opened in
MS Excel. Table values were compiled on a summary spreadsheet for each
count% Spreadsheet functions were used to convert from square meters to acres
and to compute total acreage and percentage for each class or category. MS Excel
was also used to produce summary graphs and charts.
7. Summarize acreage by incorporation zones. Summaries were also prepared to
determine the frequency and acreage of parcels within the incorporated limits of
municipalities and within the 2 mile extraterritorial zone. The 2 mile
extraterritorial zone was divided into the 0-1 mile zone and the 1-2 mile zone. GIS
data on the location and boundaries of incorporated areas were obtained from the
Iowa Department of Transportation through the Iowa Department of Natural
Resources GIS server, Natural Resources Geographic Information System
(NRGIS). The data source (INCORP Arc/Info coverage) was last updated in
1992.
Completion of each incorporation zone summary. required a series of steps. First,
the INCORP coverage was added as a theme, converted to a shapefile, then edited
to include incorporated areas onlv within the count3, and adjacent counties.
Second. the XTools function Buffer Selected Features was used to create two new
shapefiles: one with a 1-mile distance and another with a 2-mile distance. Third,
the two new buffer shapefiles were combined using the XTools function Union
Polygon Themes. In a similar way. this new shapefile was combined with the
edited INCORP theme also using the XTools function Union Polygon Themes.
Fourth. the resultant shapefile (with the incorporated area plus two distance
zones) was clipped to the count' boundaries using the XTools function Clip I~'ith
Polygonfs). Fifth, the clipped incorporation zones theme was combined with the
parcel theme using the XTools function ldentiO,. The parcels theme was the
"input coverage" and the incorporation zones theme was the "identity coverage."
Sixth, the acreage values were updated with the XTools function and field
calculator. Seventh. Spatial Analyst function Analysis > Tabulate Areas was used
to compute the total area of the parcels in each incorporation zone.
The resultant table (for example, tarea3. dbJ) was then opened in MS Excel. Table
values were compiled on a summary. spreadsheet for each county. Spreadsheet
functions were used to convert from square meters to acres and to compute total
acreage and percentage for each distance zone. MS Excel was also used to produce
summar2,.' graphs and charts.
79
County differences in data analysis. Most steps of the data analysis procedure
were the same for each of the seven pilot counties. However, for demonstration
purposes, additional analysis was done in Story County because of a more
complete database.
Additional data analysis for Story County included summaries of CSR, ECY,
LCC, and USDA Prime Farmland in the three incorporation zones (incorporated,
0-1 mile extraterritorial zone, and 1-2 mile extraterritorial zone). This analysis
included the entire area within each zone. not just the area within the digitized
parcels. Procedures used were similar to those described for steps 4, 5, and 6.
Additional data analysis was completed for the digitized parcels to measure the
area of parcels in FEMA flood zones and in chemical hazard zones. Three FEMA
flood zones included the 100-year floodplain, 500-year floodplain, and neither.
Three chemical hazard zones were created using procedures similar to those
described above for step 7:0-1 mile zone. 1-2 mile zone. and 2+ mile zone.
Chemical hazard sites in the database included abandoned underground storage
tanks, anhydrous ammonia storage facilities. and outdoor public swimming pools.
Additional data analysis was also completed for the digitized parcels to measure
the area of parcels in conservation zones. Four conservation zones were created
using procedures similar to those described above for step 7: conservation area, 0-
1 mile zone, 1-2 mile zone, and 2+ mile zone. Conservation areas include public
land used for parks, recreation areas, wildlife habitat, prairie, wetland, forestry,,
and environmental education.
D. Results
The agricultural quality of land convened from agricultural use to nonagricultural
use was measured using four soil survey interpretations: corn suitability, rating
(CSR), estimated corn yield (ECY), land capability class (LCC) and USDA Prime
Farmland classification. Results are shown for each county by total acres or
average, by assessment class (commercial, exempt, industrial, and residential), and
by year.
To provide context for these four measures of agricultural quality of soils, results
also include comparative summaries of parcel area, parcel size, and parcel location
(relative to incorporated areas). Results are shown for each county by total or
average, by assessment class, and by year.
Parcel area. Of the 4,005 parcels (totaling 48,564 acres) included in the study
database for the seven pilot counties, 2,567 parcels (totaling 36,931 acres) had a
8O
land use change and 1,438 parcels (totaling 11,633 acres) had no land use change
even though the assessment class changed (Table 53).
Table 53. Number and area of parcels with land use change
Number of All Land use Land use
parcels parcels chanqed not chan~)ed
Bremer 443 335 108
Cerro Gordo 383 294 89
Dallas 819 535 284
Monroe 277 165 112
Pottawanamie 962 368 594
Scon 586 583 3
Story 535 287 248
Total 4.005 2,567 1,438
Percent 100% 64% 36%
Average 572 367 205
Parcel AJ] Land use Land use
area (acres) parcels chanoed not changed
Bremer 3,022 2, 195 827
Cerro Gorclo 5,959 5,687 272
Dallas 12,690 8,388 4,302
Monroe 2.041 1,330 711
Potlawattamie 8,098 4,401 3,697
Scott 10,090 10,071 19
Stor,, 6.664 4.859 1.805
Total 48.564 36.931 11,633
Percent 100% 76% 24%
Average 6.938 5.276 1,662
Of the 2.567 parcels (totaling 36.931 acres) that had a land use change, 1,463
parcels (totaling 32.417 acres) were digitized and 1,104 parcels (totaling 4.464
acres) were not digitized due to incomplete data (Table 54). Approximately 57
percent of the parcels in which land use changed were digitized. These digitized
parcels included approximately 88 percent of the area in which land use changed.
81
Table 54. Number and area of parcels digitized
Number of Land use Parcels Parcels
parcels chanqed diQitized not dicjitized
Bremer 335 170 165
Cerro Gordo 294 254 40
Dallas 535 222 3 13
Monroe 165 107 58
Pottawattamie 368 167 201
Scott 583 256 327
Story 287 287 0
Total 2,567 1.463 1.104
Percent 100% 57% 43%
Average 367 209 158
Parcel Land use Parcels Parcels
area (acres) chanqed diqitized not di~Jitized
Bremer 2, 195 2.080 l 15
Cerro Gordo 5,687 5.322 365
Dallas 8,388 8.131 207
Monroe 1,330 1,328 2
Pottawattamie 4,401 2.667 1,734
Scott 10,071 8,030 2,041
Story 4,859 4,859 0
Total 36,931 32.417 4,464
Percent 100% 88% 12%
Average 5,276 4,631 638
In the seven pilot study counties, the number of acres converted from agricultural
to nonagricultural classes averaged 336 acres per county per year (Table 55). The
values ranged from 129 acres per year in Bremer County to 592 acres per year in
Scott County. By comparison, the average size farm ranges from 241 acres in
Bremer County to 396 acres in Pottawattamie County (US Department of
Agriculture 1998). The seven-county average farm size is 335 acres and the Iowa
average is 339 acres.
Table 55. Average area of parcels per year and farm size
Parcel Land use Years Acres Average size
'area {acres) chanqed of data per year farm (1992)
Bremer 2,195 l 7 129 241
Cerro Gordo 5,687 16 355 383
Dallas 8,388 17 493 348
Monroe 1,330 I 0 133 380
Pottawartamie 4,401 17 259 396
Scott 10,071 17 592 261
Stor~' 4,859 16 304 333
Total 36,931 110
Average 336. 335
Iowa 339
The majority (64 percent) of digitized parcels were convened from the agricultural
class to the residential class (Table 56). However, only 62 percent of the area was
convened from the agricultural class to the residential class. Of the total area
convened from agricultural use, approximately 22 percent was convened to the
exempt class. Approximately 5 percent of the digitized parcels and 5 percent of
the parcel area was convened to other assessment classes (forest reserve, annexed,
or other). These other assessment classes or designations were found in only a
few of the seven pilot study counties and generally do not represent a land use
change. Therefore, these were not included when measuring quality of agricultural
land convened from agricultural class.
Table 56. Number and area of parcels by class
Number of
parcels Commercial Exempt Industrial Residential Other classes
Bremer 9 9 4 148 0
Cerro Gordo 27 153 1 73 0
Dallas 5 28 0 189 0
Monroe 6 14 12 71 4
Pottawattam ie 29 28 0 102 8
Scott 12 29 0 215 0
Story 39 43 1 142 62
Total 127 304 18 940 74
Percent 9% 21% 1% 64% 5%
Average 18 43 3 134 11
Parcel
area (acres) Commercial Exempt Industrial Residential Other classes
Bremer 178 171 42 1,689 0
Cerro Gordo 826 4.245 3 247 0
Dallas 85 183 0 7,862 0
Monroe 99 435 298 459 37
Pottawartamie 857 736 0 935 112
Scott 179 384 0 7,467 0
Story 883 1,046 38 1.287 1.606
Total 3,107 7.200 381 19.946 1.755
Percent 10% 22% 1% 62% 5%
Average 444 1.029 54 2.849 251
The average number of parcels per year increased slightly during the 1982 to 1998
study period (Table 57). The average area of parcels per year decreased in the
middle of the study period (74 acres per count' in 1987), then increased again to
an average of 406 acres in 1998 (Figure 24).
83
Table 57. Number and area of parcels by year
Number of parcels 82 83 84 85 86
Bremer i 0 I 0 1
Cerro Gordo 20 8 I0 6
Dallas 5 4 5 2 5
Monroe
Pottawartamie 7 0 6 8 7
Scott 45 12 20 23 6
Storx~ 8 5 14 13
Average 15 7 8 10 6
87 88 89 90 91 9Z 91 91 ~ 96 97 tl
2 2 3 9 7 6 8 8 12 17 74 19
6 8 5 12 16 10 14 19 41 27 50 2
3 6 6 6 I0 14 26 32 27 35 20 16
8 5 3 7 10 28 10 9 13 14
7 7 4 I1 10 14 14 10 19 18 14 ll
5 16 13 9 11 21 4 32 23 6 5 5
12 17 95 10 13 14 21 25 54 80 45 109
6 9 19 9 10 12 14 22 27 27 32 25
Parcel area (acres} 82 83 84 85 86 87 88
Bremer 20 0 I I 0 3 13 69
CerroGordo 579 20 56 55 24 80
Dallas 257 168 153 40 441 133 286
Monroe
Potxawanamie 44 0 35 119 81 42 128
Scott 16202289731193 95 63428
Stor~, 190 72 502 142 168 233
Average 485 194 211 318 136 74 204
31 60120 75 63 61 103320924207
158 325 473 245 253 3791068638 916 53
340 400 301 353 1015 1048 674 1294 660 565
261 57 49 149 47 201 82 72 206 203
69 100 145 335 356 96 599 232 109 176
907 184317 178 94430452646 130 93
'~59 78 104 204 281 374 6521042 3171547
361 172 216 220 301 370 519 606 466 406
Parcel size. For the total 4,005 parcels included in this study, the average size
was 11.9 acres (Table 58). Average sizes ranged from 6.8 acres in Bremer Count'
to 17.2 acres in Scott County. For the 2.567 parcels in which land use changed.
the average size was 13.7 acres. Average sizes ranged from 6.6 acres in Bremer
County to 19.3 acres in Cerro Gordo County. For the 1,438 parcels in which land
use did not change, the average size was 7.4 acres. Average sizes ranged from 3.1
acres in Cerro Gordo County to 15.1 acres in Bremer County.
Table 511. Average size of parcels with land use change
Average parcel A! Land use Land use
size (acres} parcels chan~led not changed
Bremer 6.8 6.6 7.7
Cerro Gordo 15.6 19.3 3. I
Dallas 15.5 15.7 15. I
Monroe 7.4 8.1 6.3
Ponawanamie 8.4 12.0 6.2
Scott 17.2 17.3 6.3
Story 12.5 16.9 7.3
Average 11.9 13.7 7.4
For the 1,463 parcels digitized in this study, the average size was 13.7 acres
(Table 59). Average sizes ranged from 6.6 acres in Bremer County to 19.3 acres in
Cerro Gordo County. For the 1,104 parcels not digitized in this study, the
average size was 5.7 acres. Average sizes ranged from 3.5 acres in Bremer County
to 7.3 acres in Dallas and Story counties.
84
Figure 24.
Summary of parcel area in the seven pilot counties
9000
8000
6000
5000
4OOO
3000
2000
10oo
0
[] Bremer
ICerro Gordo
BDaJlas
13 Monroe
· Pottawatlamie
BScott
:~ :''" ~ ' ' ! [] Story
· Average
Commercial Exempt Industrial Residential Other
1800 ...........................................
1400 T
100o
400
..iii..
0 _, -~ , , '~, ........
~ Bremer
+ Cerro Gordo
...... ~i:.- ......Dallas
...... · )~ ......Monroe
:)l Pottawatlamie
Scott
Story
Year ave.
· ""-' "CumulaWe ave.
~ ~m Moving ave.
85
Table 59. Average size of parcels digitized
Average parcel Land use Parcels Parcels
size (acres) chan~)ed diqitized not diqitized
Bremer 6.6 12.2 3.5
Cerro Gordo 19.3 21.0 4.9
Dallas 15.7 36.6 7.3
Monroe 8.1 12.4 4.2
Pottawartamie 12.0 16.0 6.8
Scott 17.2 31.4 6.2
Story 16.9 ] 6.9 7.3
Average 13.7 15.7 5.7
The average size parcel digitized (15.7 acres) was 15 percent larger than the
average size parcel in which the land use changed (13.7 acres). This reflects the
fact that not all parcels in which the land use changed were digitized. In general.
parcels smaller than 2.5 acres were not digiti-zed due to limited data and limited
time.
For digitized parcels, average parcel size varied by assessment class (Table 60).
On average, parcels in the commercial class were largest (averaging 21.6 acres),
followed by the exempt class (averaging 21.3 acres). Parcels in the residential
class were the smallest, averaging 16.4 acres.
Table 60. Average size of digitized parcels by class
Average parcel
size (acres) Commercial Exempt Industrial Residential
Bremer 19.8 19.0 10.5 11.5
Cerro Gordo 30.6 27.7 3.2 3.4
Dallas 17.0 6.5 41.6
Monroe 16.6 31.1 24.8 6.5
Pottawattamie 29.5 27.3 9.2
Scott 14.9 13.2 34.7
Story 22.6 24.3 38.0 7.9
Average 21.6 21.3 19.1 16.4
For digitized parcels, average parcel size also varied by year (Table 61 ). Parcels
converted in 1987 and 1994 were relatively small. Parcels converted in 1989 and
1996 were relatively large. The long-term trend showed variation in average parcel
size between 15 and 32 acres (Figur.e 25).
Rgure 25.
Summary of average parcel size in the seven pilot counties
4O
35 -.-
30
0 ...........
CommerciaJ
Exempt
Indus~'ial ResidentiaJ
[] Bremer
i Cerm Gordo
E3 Dallas
gMonroe
· Pottawattamie
lScott
IStory
· Averag e
120 - -
?::i. : ::': ¢ Bremer
80 + Cerro Gordo
!~?""""'/' ,':~::%i,! ! =
;~::""~:::'~i~:.:. t 'StO~
eCumula!We ave.
,
.,:,.,
'
0 .......
87
Table 61. Average size of digitized parcels by year
Averagesize(acres) 82 93 84 85 1t6 87 Ill 89 90 91 92 93 9~ 95 95 97 93
Bremer 20 ll 3 ? 35 l-0 7 l? 13 8 8 9 19 13 II
CerroGordo 29 3 6 9 4 l0 32 27 30 25 18 20 26 24 18 27
Dallas 52 42 31 20 88 44 48 57 67 30 25 39 33 25 37 33 35
Monroe 33 l l 17 21 5 7 8 8 16 15
Ponawanarnie 6 6 15 12 6 18 17 9 15 24 26 10 32 13 8 16
Scott 36 19 49 52 16 13 27 70 20 29 9 23 13 20 108 26 19
Stor~' 24 15 36 11 14 14 8 8 8 15 13 15 12 13 7 14
Average 29 29 19 26 23 15 25 32 21 21 19 19 15 19 32 17 20
Parcel location. For the 32,417 acres digitized in this study, approximately 67
percent were in incorporated areas or within 2 miles. Approximately 32 percent
were located more than 2 miles from incorporated areas. These results indicate
that, for the parcels digitized, non-farm development was not necessarily close to
incorporated areas. This was particularly true given that the acreage in the 0-1
mile zone (10,791 acres) was almost the same as the acreage in the 2+ mile zone
( 10,3 82 acres) (Table 62).
Table 62. Total area of digitized parcels by location
Parcel location Incor- 0-1 mile 1-2 mile 2+ mile
(acres} porated zone zone zone
Bremer 358 387 592 744
Cerro Gordo 108 606 1,764 2.843
Dallas 21 2,496 2,791 2,823
Mortroe 0 263 361 705
Pottawattamie 534 961 315 856
Scott 996 4,320 1,643 1,071
Story 686 1,758 1,040 1,340
Total acres 2,703 10,791 8,506 10,382
Percent 8% 33% 26% 32%
Average acres 386 1,542 1,215 1,483
For parcels changed to the industrial class, 70.2 percent of the area was in
incorporated areas or within 2 miles (Table 63). In contrast, only 52.0 percent of
the exempt class was in incorporated areas or within 2 miles. This was a logical
result given that industrial uses rely on urban services more than the variety of
uses in the exempt class.
Table 63. Percent of area of digitized parcels in incorporated areas or within 2 miles by class
Incorporated and 0-2 mile zones
(percent of area) Commercial Exempt Industrial Residential
Bremer 56.2 41. l 100.0 66.6
Cerro Gordo 63.3 42.6 0.0 58.9
Dallas 98.4 82.7 64.6
Monroe 34.3 40.0 80.7 33.0
Ponawattamie 73.7 82.2 47.6
Scott 60.7 47.1 89.3
Story 75.8 28.0 100.0 72.5
Average 66.1 52.0 70.2 61.8
The percentage of parcel area in incorporated areas or within 2 miles varied each
year, with relatively high percentages in 1985 and 1986 (Table 64). Relatively
low percentages were in 1983, 1987, 1990, and 1993 to 1996. For the parcels
digitized, the long-term trend was decreasing amounts in and near incorporated
areas over time, with many averages over 70 percent before 1989 and man>,
averages under 70 percent after 1988 (Figure 26). This indicated that recent non-
farm development was relatively far from incorporated areas. However, recent
annexations not reflected in the database may reduce the average distance of recent
non-farm development in some areas. (Data on incorporated areas used
throughout this study were developed by the Iowa Department of Transportation
in 1992.)
Table 64. Percent of area of digitized parcels in incorporated areas or within 2 miles by year
Incorporated and 0-2 mile zones
(percent of area) 82 83 84 I~i 86 87 88 89 90 91 92 93 94 96 96 97 98
Bremer 100 100 100 100 46 89 89 94 29 60 51 53 52 68 60
CerroGordo 49 100 82 78 62 93 100 4 32 66 47 9 15 56 86 100
Dallas 100 47 39 100 100 30 99 97 37 58 54 76 43 76 68 50 58
Monroe 1 34 7 98 63 24 71 44 88 52
Pottawattamie 38 0 78 63 14 82 4 57 91 85 81 100 48 67 69 91
Scott 89 58 99 83 100 100 82 94 96 93 96 17 45 80 100 92 100
Storx' 95 100 100 99 78 77 85 100100 79 77 74 82 74 58 40
Average 82 62 73 89 90 64 80 67 60 68 72 60 49 61 66 73 72
Corn suitability rating (CSR). For the parcels digitized in this study, the area-
weighted average CSR was 57.6 (Table 65). For the entire area of all seven pilot
counties, the average was 67.4. slightly above the state average. Average CSR in
digidzed parcels ranged from 39.2 in Monroe County to 68.1 in Story County. In
each count3,, the CSR of digitized parcels was below the average CSR for the
entire county. This indicates that the agricultural quality of parcels convened
from agriculture to nonagricultural classes was below average in each county.
89
figure26. Summary of parcel location in the seven pilot counties
70
60;
I
~ 50
,;
40
I
20 -'
10.-
Commercial Exempt
Irldust]'jaJ
ResidenliaJ
[] Bremer
· Cerro Gordo
aDallas
[] Monroe
IPottawattamie
BScot~
lAveage
100
90
80
70
50
40
20
10
0
:5
[
O)
O'~ {:3) O) (:3) (3) (:3) O)
,~ Bremer
--!---- Cerro Gordo
...... -.-.'.:~:: ......Dallas
...... ]K. ......Monroe
]1: PoRawattamie
~' Scott
~ Ston/
Yea~ ave,
====~ ~Cumula~jve ave,
~ {=' Moving ave,
Table
Area-weighted average corn suitability rating for entire county
and digitized parcels
Average Entire Digitized
CSR county parcels
Bremer 73.4 64.3
Cerro Gordo 71.4 62.2
Dallas 73.6 57.7
Monroe 40.6 39.2
Pottawattamie 60.8 53.0
Scott 74.2 58.8
Story 77.6 68.1
Seven-county average 67.4 57.6
State average 62.8
In the seven pilot counties, parcels converted to the commercial class had an
average CSR of 57.1 (Table 66). The average CSR for the exempt class and
residential class were similar to the CSR for the commercial class. In contrast,
parcels convened to the industrial class had an average CSR of 72.6, much higher
than the averages for the other classes. This indicated that industrial uses may
compete with agriculture for high quality land. Soils that are high quality for
agriculture are typically highly suited for industrial sites because of little slope,
adequate drainage, and other mutually desirable soil characteristics.
Table IlL Area-weighted average corn suitability rating by class
Avera~je CSR Commercial Exempt Industrial Residential
Bremer 64.9 52.9 75.2 65.0
Cerro Gordo 46.5 64.4 72.3 76.4
Dallas 61.1 66.5 57.4
Monroe 53.2 33.8 53.4 32.5
Pottawattam ie 51.2 57.5 50.3
Scott 64.4 69.3 58.1
Story 58.1 75.7 89.4 66.1
Average 57.1 60.0 72.6 58.0
The long-term trend from 1982 to 1998 showed little change in the average CSR of
approximately 58, especially since 1986 (Table 67, Figure 27).
Table I;7. Area-weighted average corn suitability rating by year
Avera~JeCSR 82 83 84 85 86 87 88 89 90 91 9~ 93 94 t 96 97 98
Bremer 68 61 36 55 78 65 47 59 65 79 62 66 56 67 64
CerroGordo 44 80 47 72 79 61 58 71 61 63 64 57 62 68 71 53
Dallas 51 49 51 42 80 43 56 60 52 49 55 64 48 64 63 54 55
Monroe 31 63 36 49 33 33 35 42 37 48
Pottawattamie 43 47 45 57 50 59 52 40 53 54 59 56 55 31 63 64
Scott 56 59 67 54 59 70 54 60 63 50 67 75 6164 55 66 79
Storv 63 61 55 62 60 66 69 72 73 62 72 66 70 70 71 75
Average 55 54 61 49 61 60 62 56 58 54 59 64 55 59 55 61 63
91
Summary of parcel average CSR in the seven pilot counties
Figure 27.
loo
70
60
40~-
30- .
20 ~ '
0
Commercial Exempt Indus~'ial ResidentaJ
~ Bremer
ICerm Gordo
E3DaJlas
[] Monroe
· PottawaUamie
gScott
ISDry
lAyerage
I=
10
O)
O)
O)
; Bremer
+ Cerro Gordo
....... ~-',- ......Dallas
...... · :~:. ......Monroe
~ Pottawattamie
Scott
StDry
Year ave.
e 'Cumulative ave.
m ~m IVkNing ave.
92
Estimated corn yield (ECY). For the parcels in this study, the area-weighted
average ECY was 106.8 bushels per acre (Table 68). Average ECY in digitized
parcels ranged from 76.9 bushels per acre in Monroe County to 121.3 bushels per
acre in Story, County. In each county, the ECY of parcels was below the average
ECY for the entire county and also below the average ECY for Iowa.
Table 68. Area-weighted average estimated corn yield for entire county and digitized parcels
Average Entire Digitized
ECY (bu/ac) county parcels
Bremer 129. l 117.5
Cerro Gordo 125.7 118.1
Dallas 132.3 108.9
Monroe 106.5 76.9
Pottawattamie 121.7 100.7
Scoff 136.0 104.0
Story 132.2 121.3
7-county average 126.2 106.8
State average 123.0
Among the assessment classes, parcels converted to the industrial class showed
the highest ECY, 129.5 bushels per acre (Table 69).
Table 69. Area-weighted average estimated corn yield by class
Average ECY
(bushels per acre) Commercial Exempt Industrial Residential
Bremer 129.3 81.6 139.6 l 19.2
Cerro Gordo 86.3 122.9 124.7 140.7
Dallas 105.6 112.3 108.9
Monroe 106.2 66.7 99.9 65,7
Portawattamie 96. l 103.4 101.4
Scott 118.7 126.6 102.4
Stor~' 102.0 132.2 153.9 I 18,4
Average 106.3 106.5 129.5 108, 1
The trend from 1982 to 1998 showed little change in ECY, with a long-term
average between 105 and 110 bushels per acre (Table 70, Figure 28).
Table 70. Area-weighted average estimated corn yield by year
Average ECY
(bushels her acre) 82 83 84
Bremer 128 129
Cerro Gordo 82 145
Dallas 92 93 111
Monroe
Pottawartamie 79 104
Scott 104 102 124
Storv 108 101
Average 101 96 119
· 1tl5 87
63 124
85 135 145
73 142 78
78 104 113
93 108 122
94 98 102
85 108 114
147 118 84 122 126 142 112 125 95 122 115
120 104 130 117 117 122 120 119 127 131 103
109 117 107 100 103 122 87 125 118 99 99
63 119 71 91 68 65 73 85 72 92
111 Ill 75 102 97 109 107 108 58 128 121
81 104 103 79 113 146 108 112 97 122 147
117 124 122 134 115 129 125 127 126 125 134
114 106 106 104 109 120 103 113 101 114 116
93
Figure 28. Summary of parcel average ECY in the seven pilot counties
160
140
20-
CommerciaJ Exempt Indus~'ial Residenljal
El Bremer
ICerm Gordo
[] DaJlas
rl Mort me
· Pottawatlamie
gScott
IStory
· Averag e
Pottawattamie
Scott
s~ory
Year ave.
m,,,mm ,CumulalWe ave.
m ~m Moving ave.
94
Land Capability Class (LCC). For the parcels in this study, approximately 47
percent of the acreage was classified by the USDA as Land Capability Class I or
Class II (Table 7 1 ). These two classes have few or no limitations for intensive
agriculture.
Table 71. Area of each Land Capability Class
LCC (acres) Class I Class II Class III Class IV Class V Class VI Class VII None
Bremer 116 1,203 375 182 128 44 14 18
Cerro Gordo 198 3,213 1,313 138 6 169 42 239
Dallas 367 3,126 2,424 539 153 555 843 99
Monroe 10 291 405 183 0 98 293 53
Ponawartamie 416 744 737 417 0 122 158 74
Scott 443 1,972 2,454 823 288 780 822 411
Story 455 2,695 831 190 180 201 98 200
Total acres 2,005 13,244 8,539 2,472 755 1,969 2,270 1,094
Percent 6% 41% 26% 8% 2% 6% 7% 3%
Average acres 286 1892 1220 353 108 281 324 156
The proportion of the area convened to the commercial, exempt, and residential
classes classified as Land Capability Class I or Class II was approximately 49
percent (Table 72). For the area convened to the industrial class, the proportion
was 84.7 percent. This pattern was similar to those described earlier for CSR and
ECY.
Table 7Z.
Percent of parcel area in each assessment class classified as
Land Capability Class I or II
LCC I & II
(percent of area) Commercial Exempt Industrial Residential
Bremer 55.2 47.2 94.5 65.1
Cerro Gordo 57.7 63.8 100.0 90.8
Dallas 47.9 71.7 42.3
Monroe 29.5 17.8 46.3 12.1
Pottawattarnie 49.1 58.3 26.3
Scott 50.5 33.6 29.5
Story 52.8 81.7 97.8 49.5
Average 49.0 53.4 84.7 45.1
As with CSR and ECY, the long-term trend from 1982 to 1998 showed little
change in the percentage of Class I and Class II land (Table 73). The long-term
average was between 45 and 50 percent Class I and Class II land (Figure 29).
95
Figure Zg, Summary of parcel LCC in the seven pilot counties
IO0
8O
7O
E 50
!.
30- ;.
20 -:
10-~
0
Commercial
Exempt Indusb'iaJ Residential
BI Bremer
:~ : ':! :i ICerro Gordo
: : i [] Dallas
[] Monroe
· Pottawattamie
· eaScott
: IStory
ilAyerage
70 e. :,~: !:~: :i~ : :i! ,~ Bremer
:.: !: /:i: ~ ,~,: i: : ' ~ --' Cerro Gordo
60 ~ ~.. ....Dallas
i 50 : : ~ Pottawattamie
-- Scott
ao ~ :' ~::::: i:,., .i i::.:'i ~:i.!:~
· :: : ;~: i!~ ::.:
10 '
· ...
:: ·
0 ,
('%1L'~ ',q'li~ (D I",,-(:Oa) Cl {M ~ ",q'Ul ~ r,,.. OD
· --~ ,v- s-- ,,,.-~ ,,- v=. v=,
96
Table 73. Percent of parcel area in each year classified as Land Capability Class I or II
LCC I & II
{percent of area) 82 83 84 85 86 87 88 89 90 91 92 93 94 95 95 97 98
Bremer 73 19 36 2 79 50 13 46 67 74 68 76 62 67 67
CerroGordo 48 93 56 83 87 45 ?1 82 44 82 70 58 53 77 76 75
Dallas 28 37 17 44 90 29 42 38 27 29 44 51 31 42 51 41 42
Monroe 16 62 17 40 7 16 4 8 23 32
Pottawattamie 27 12 51 43 12 66 19 17 37 49 51 46 45 18 39 72
Scott 24 32 36 26 38 43 31 29 27 22 41 29 38 42 31 40 27
Storv 53 52 53 66 53 58 59 85 66 66 72 42 64 65 61 82
Average 38 43 38 46 59 38 54 40 45 37 56 51 43 47 45 50 57
USDA Prime Farmland. According to the USDA Prime Farmland classification,
approximately 48 percent of the parcel area convened from agriculture to
nonagricultural class was considered prime agricultural land (Table 74).
Approximately 26 percent was considered of state importance, 22 percent was of
local importance, and 3 percent was not rated.
Table 74. Area of each USDA Prime Farmland category
USDA Prime Prime Prime if Prime if Prime if State Local Not
Farmland (acres) drained not flooded drain/not fl importance importance rated
Bremer 1,037 307 39 50 308 321 18
Cerro Gordo 1,792 1,601 11 147 1,251 274 239
Dallas 2,281 582 261 418 2,435 2,030 99
Monroe 125 4.8 143 62 339 562 53
Potmwattamie 373 I 521 209 800 691 74
Scott 2,092 365 0 0 2,485 2,639 411
Story 1,771 1.000 0 476 793 610 200
Total acres 9,471 3,904 975 1,362 8,411 7,127 1,094
Percent 29% 12% 3% 4% 26% 22% 3%
Average acres 1,353 558 139 195 1,202 1,018 156
From 47.2 to 56.0 percent of the area convened to commercial, exempt, and
residential classes was considered prime agricultural land by the USDA (Table
75). In contrast, over 84 percent of the area convened to the industrial class was
considered prime agricultural land by the USDA.
Table 75.
Percent of parcel area in each assessment class classified as
USDA Prime Farmland
USDA Prime Farmland
(percent of area) Commercial Exempt Industrial Residential
Bremer 57.2 49.5 94.5 71.4
Cerro Gordo 65.2 65.6 100.0 92.0
Dallas 52.6 71.7 42.9
Monroe 37.5 28.0 46.3 15.3
Pottawattamie 43.6 57.0 26.3
Scott 53.5 38.0 29.8
Story 57.9 82.2 97.8 52.5
Average 52.5 56.0 84.7 47.2
97
The average percentage of land classified as prime by the USDA showed no clear
trend before 1991. However, beginning in 1991 there was an overall increase in
the annual average and 3-year moving average above 50 percent prime land (Table
76, Figure 30).
Table 76. Percent of parcel area in each year classified as USDA Prime Farmland
USDA Prime Farmland
(percent of area) 82 83 84 85 85 87 88 89 90 91 91Z 93 94 95 95 97 98
Bremer 97 19 36 2 79 82 13 46 67 74 82 79 64 73 76
CerroGordo 59 98 56 83 87 45 81 82 52 82 70 58 54 78 78 75
Dallas 29 37 17 44 90 29 42 38 27 29 44 54 31 43 51 42 43
Monroe 27 62 17 40 8 20 17 31 24 36
Pottawattarnie 27 12 36 43 12 61 19 17 37 49 51 46 44 18 39 59
Scott 24 32 36 26 38 43 31 29 27 22 41 28 40 43 31 54 27
Story 56 52 54 66 53 62 59 85 66 66 79 52 65 68 62 82
Average 44 46 39 43 59 38 53 48 45 38 56 52 47 49 49 53 57
Story Count>'. Additional data analysis included summaries of CSR, ECY, LCC,
and USDA Prime Farmland in three incorporation zones (incorporated zone, 0-1
mile extraterritorial zone, and 1-2 mile extraterritorial zone). This analysis
included the entire area within each zone, not just the area within the digitized
parcels (Figure 23). The area weighted average CSR increased with distance from
the incorporated zone, from 72.8 to 79.9 (Table 77). The area weighted average
ECY also increased with distance from the incorporated zone, from 127.6 to
142.3.
In contrast, the average CSR and average ECY decreased with distance from the
incorporated zone for the parcels in each zone, except for parcels in the 2+ mile
zone. Therefore, in general, as distance from incorporated areas increased, the
agricultural quality of all land increased, but the agricultural quality of the parcels
decreased. Therefore, in Story, County, land that was changed from the
agricultural class to a nonagricultural class in or near urban areas was relatively
high quality and land in rural areas was relatively low quality.
Table r/.
Area-weighted average corn suitability rating and estimated corn yield
in incorporated areas or within 2 miles in Stow County
Story County Ave. CSR Ave. CSR Ave. ECY Ave. ECY
Entire zone Parcels Entire zone Parcels
Incorporated 72.8 73.2 127.6 130.0
0-1 mile zone 77.6 67.6 137.6 120.7
1-2 mile zone 79.9 64.7 142.3 115.4
2+ mile zone 71.7 127.4
County average 77.6 68.1 132.2 121.3
figure 30.
Summary of parcel USDA Prime Farmland in the seven pilot counties
(percent prime)
n I
40 - "~'
20-
10-
0
Commercial Exempt IndustriaJ ResidentiaJ
ra Bremer
· Cerm Gordo
n Dallas
[] Monroe
[] Scott
: ~:ii'. · Ave'age
j
100
i ; Bremer
70 ~ ~ii .... Cerro Gordo
....... ~i~... .......Dallas
'° ...... ,:. ......
;! ~ Pottawattamie
-~-+----Story
:/':' .s: ~ ~ '~ t~ ' ""' 'Cumulative ave.
~o~:; . .i~ ' . ::i~::'""-~-:~:~ :i::i~ '~ :~
m {mMoving ave.
lo ~ ' ·
· .. .. .
..
0 , e ' ..'
, ,
99
Similarly, the percentage of area classified as Land Capability Class I or Class II
increased with distance from the incorporated zone, from 76.1 percent to 83.2
percent (Table 78). The percentage of area classified as prime farmland also
increased with distance from the incorporated zone, from 76.9 percent to 83.6
percent. In contrast, the percentage of area classified as Class I or Class II and the
percentage of area classified as prime farmland decreased with distance from the
incorporated zone for the parcels in each zone, except for parcels in the two-plus-
mile zone. Therefore, in general, as distance from incorporated areas increased, the
agricultural quality of all land increased, but the agricultural quality of the parcels
decreased. This was similar to the pattern for CSR and ECY.
Table 78.
Percent of area classified as Land Capability Class I or II in incorporated areas
or within two miles in Story County
Story County PcL LCC 1/11 Pct LCC 1/11 Pct. Prime PcL Prime
Percent Entire zone Parcels Entire zone Parcels
Incorporated 76. l 73.7 76.9 73.7
0-1 mile zone 80.3 64.8 80.9 65.4
1-2 mile zone 83.2 56.1 83.6 59.9
2- mile zone 71.3 73.0
County average 64.9 67.0
Additional data analysis was also completed for parcels in FEMA flood zones and
in chemical hazard zones. Three FEMA flood zones included the 100-year
floodplain, 500-year floodplain, and neither flood zone (Figure 31 ). Over 14
percent of the parcel acreage was included in the 100-year or 500-year flood zones
(Table 79).
Table 79. Area parcels in flood zones in Story County
Story County parcels
Flood zones Acres Percent
100-year floodplain 696 14.3
500-year floodplain 15 0.3
Not a flood zone 4, 153 85.4
Total 4,864 100.0
A much higher percentage of the parcel acreage converted to the exempt class
(28.6 percent) was in the 100-year or 500-year flood zones (Table 80). No
industrial parcels were located in the 100-year or 500-year flood zones.
100 '
figure 31. FEMA flood zones and parcels with land use change in Stow County
!
~ 100-~;ear floodplain
I~ 500-year floodplain
D 2 4 ~ Miss
101
Table 80. Percent of parcel area in flood zones in Stow County by assessment class
Stow County parcels Annexed Commer- Exempt Forest Indus- Residen-
Flood zones (percent) cial Reserve trial tial
100-y ear ~oodp lain 9.5 13.0 28.6 7.7 0.0 9.8
500-year fioodp Iain 0.1 0.8 0.0 0.0 0.0 0.5
Not a flood zone 90.4 86.2 71.4 92.3 100.0 89.7
Total 100.0 100.0 100.0 100.0 100.0 100.0
Three chemical hazard zones were 04 mile zone. 1-2 mile zone, and 24. mile zone
(Figure 32). Twenty-seven chemical hazard sites in the database included
abandoned underground storage tanks, anhydrous ammonia storage facilities, and
outdoor public swimming pools (Table 81 ). Over 19 percent of the parcel area
was located within one mile of a hazard site.
Table WI. Area parcels in hazard zones in Stow County
Stow County parcels
Hazard zones Acres Percent
0-1 mile 935 19.2
1-2 miles 1,300 26.7
2+ miles 2.628 54.0
Total 4,864 100.0
A much higher percentage of the parcel acreage converted to the exempt class
(39.9 percent) was within one mile of a hazard site (Table 82). No industrial or
forest reserve parcels were located within one mile of a hazard site.
Table 82. Percent of parcel area in hazard zones in Stow County by assessment class
Stow County parcels Annexed Commer- Exempt Forest Indus- Residen-
Hazard zones (percent) cial Reserve trial tial
0-I mile 21.2 12.1 39.9 0.0 0.0 7.4
1-2 miles 16.5 19.3 39.8 28.3 100.0 30.8
2~- miles 62.3 68.5 20.3 71.7 0.0 61.8
Total 100.0 100.0 100.0 100.0 100.0 100.0
Several chemical hazard sites are located in flood zones. Of the 27 chemical
hazard sites included in the Story County database, two sites are in the 100-year
flood zone and 23 additional sites are within one mile of the 100-year flood zone.
These chemical hazard sites may need special management because of their
proximity to flood zones.
102 '
Rgure 32. Chemical hazard zones and parcels with land use change in Story County
k
..,.,..,..-...,..
0-1 mile zone A 2 4 6 Miles
1-2 mile zone N '
103
Additional data analysis was also completed for the digitized parcels to measure
the area of parcels in conservation zones. Four conservation zones were created:
conservation area, 0-1 mile zone, 1-2 mile zone, and 2+ mile zone (Figure 33).
Conservation areas include public land used for parks, recreation areas, wildlife
habitat, prairie, wetland, forestry, and environmental education. Only 2.0 percent
of the parcel area was in a conservation area (Table 83). However, 45.0 percent of
the parcel area was within one mile of a conservation area.
Table ID. Area parcels in conservation zones in Story County
Story County parcels
Conservation zones Acres Percent
Conservation area 99 2.0
0-1 mile 2,181 45.0
1-2 miles 1,480 30.5
2+ miles 1,089 22.5
Total 4,864 100.0
Parcels that converted to the exempt class and the residential class had nearly
double the average percentage within conservation areas (Table 84). However,
parcels that convened to the commercial class and forest reserve class had higher
than average area within one mile of conservation areas. Rural development,
especially residential development, was often attracted to conservation areas
because of the scenic quality and other amenities nearby. Such development,
however, can be incompatible with conservation areas if nearby development
inhibits wildlife movement, fragments habitat, reduces biodiversity, lowers water
quality, or causes other negative environmental impacts.
Table IB. Percent of parcel area in conservation zones in Story County by assessment class
Story County parcels Annexed Commer- Exempt Forest Indus- Residen-
Conservation zones (pct) cial Reserve trial tial
Conservation area 0.0 0.9 3.8 0.0 0.0 3.7
0-1 mile 38.2 66.6 34.9 59.6 0.0 46.6
1-2 miles 39.0 20.5 44.5 0.0 15.7 19.6
2'~ miles 22.8 12.1 16.9 40.4 84.3 30.1
Total 100.0 100.0 100.0 100.0 100.0 100.0
These analyses of flood zones, hazard zones, and conservation zones suggest
additional applications of parcel data and GIS technology to land management.
Emergency management, disaster preparedness, conservation planning, and other
land management applications can benefit from data on land characteristics and
land use changes. These data can be used to identify limitations and hazards that
endanger public health, safety, and welfare. Information on limitations and
hazards can be effectively used to protect both people and the environment,
minimize expenditure of public funds, and increase the quality of life for Iowa' s
citizens.
104
Figure :33. Conservation zones and parcels with land use change in Stow County
Corservadon area A
0-1 mile zone o 2 4 6 Mass
I-2 mile zone , ' '
105
V!. Conclusions and recommendations
The following conclusions and recommendations are based on an analysis of land use and
resource data in the seven pilot study counties. They are also based on an analysis of
statewide issues and trends identified in a telephone survey, meetings and interviews with
public officials, and land assessment data from the Iowa Department of Revenue and
Finance (IDRF). Recommendations are not listed in priority order.
Conclusions
Agricultural land valuation. The most comrnon index used for agricultural land
valuation throughout the state's 99 counties is the com suitability rating (CSR)
system. Some counties use a combination of CSR, crop yield and land capability
class to determine agricultural land valuation. The last time that land valuation
procedures were updated in Iowa counties ranged from 1930 to 1998. Half were
updated prior to 1983. However, 13 count>' assessors surveyed weren't sure
when they last updated their land valuation procedure.
Monitoring farmland change. Most Iowa counties use the state-mandated
reconciliation report to monitor changes in farmland. This report is a summary of
all the land use changes (from agricultural to Other uses, and vice versa) that have
been made during a single year. In most cases, the files or list of individual parcels
used to obtain the figures in the reconciliation report are discarded after the report
is completed.
Some counties use other monitoring systems, including visual inspections, real
estate transactions, property assessment cards, active zoning, aerial photographs,
geographic information systems (GIS), plat books, and so forth. The majority (86
percent or 83 counties) keep records of farmland changes in their counties up to
1998. Forty-two counties have records of farmland changes beginning anywhere
from 1948 to 1981. The remaining counties have records beginning from 1982 to
1998. Thirteen counties have data in digital form, while the other counties keep
most records in paper form. Thus, dam on farmland changes are available in most
of the counties, but they are in different formats and in different locations (e.g.,
some in filing cabinets in various offices, some in storage, etc.)
Farmland protection programs and strategies. Forty-four Iowa counties have
farmland protection strategies in place. In many cases, these strategies are quite
old; 27 counties in the survey sample had implemented theirs prior to 1980. The
oldest farmland protection program has been in existence since 1949. Only two of
the counties began implementing such a program after 1985.
106
The most common state-level farmland protection strategies in effect among the
sample counties are agricultural zoning dislrict and conservation easement.
Among the local-level programs, comprehensive planning and agricultural
protection zoning are the most common. All of the farmland protection strategies
mentioned by survey respondents were rated as effective except for transfer of
development rights (TDR), which was considered ineffective by those who have
used it (three counties).
Issues of concern related to farmland protection. Overall, both groups of
survey respondents (77 percent of zoning administrators and 53 percent of
assessors) were concemed about the rate of urban growth in their counties. They
indicated that efforts should be made to preserve prime agricultural land from
being transferred to other uses. Urban sprawl, specifically the uncontrolled
growth of subdivisions into highly productive agricultural land, was a major
concern for both groups, especially for the zoning administrators. Other concerns
that were mentioned during the survey were hog or livestock confinements, saving
family farms, and pollution, odor, and protection from nuisance (presumably in
regard again to livestock confinement operations, although this was not always
specifically stated).
A striking degree of difference was found in assessors' and zoning administrators'
perceptions about farmland protection as an issue of concern in their counties. Of
the 57 counties that have both an assessor and a zoning administrator, 29 (50.
percent) said it is an issue, five counties (9 percent) said it is not an issue, and 23
counties (40 percent) had officials holding opposite opinions. County. assessors'
and zoning administrators' perceptions about the significance of the farmland
protection issue were different in nearly 29 percent of the sample counties. This
finding is significant in that it shows how complex an issue farmland protection
can be, and that decisions about farmland protection across the state probably
should take into account the views and expertise of a greater number of
individuals.
No relationship is found be~,een the respondents' level of concem about
farmland protection and their coun~"s classification (rural versus urban). This
could be true for a varietx, of reasons: some counties may not be experiencing
much growth; some count' officials may welcome development regardless of the
amount of agricultural land being converted for that purpose; some officials in
counties experiencing a greater rate of growth may view it as a problem while
others see it as a benefit; and so forth.
Amount of land converted from agricultural to nonagricultural classes. Based
on statewide data from reconciliation reports prepared by county assessors for
the Iowa Department of Revenue and Finance during the period 1986 to 1997,
107
parcels that changed from the agricultural class to a nonagricultural class (that is,
residential, commercial, industrial, exempt, or other) totaled 480,567 acres and had
a total assessed value of $314,781,679. The rate of conversion from the
agricultural class was approximately 40,050 acres per year.
Parcels that changed from a nonagricultural class to the agricultural class for the
same period totaled 165,848 acres and had a total assessed value of $212,661,997.
The rate of conversion to the agricultural class was approximately 13,820 acres
per year. According to IDRF data for the period 1986 to 1997, the net change
from the agricultural class statewide was approximately 26,230 acres per year.
This is equivalent to approximately 0.07 percent of the state's total land area per
year. This area is also equivalent to approximately 265 acres per county per year.
Based on IDRF reconciliation reports from the seven pilot study counties. the net
area of parcels in which assessment class changed from agricultural to
nonagricultural (excluding "other") averaged approximately 482 acres per county
per year, far above the state average of 278 acres per county per year. Based on
data provided by assessors in the seven pilot study counties, the area of parcels in
which land use changed from agricultural to nonagricultural averaged
approximately 336 acres per county per year. This area is almost the same as the
335 acre average farm size in the seven pilot study counties. This area also is
almost the same as the 339 acre average size farm in Iowa.
According to the 1983 land use reports from the seven pilot study counties, a
total of approximately 40,900 acres was convened from agricultural to
nonagricultural use during the period 1960 to 1983. This is equivalent to
approximately 255 acres per county per year. This is approximately 25 percent
less than the average of 336 acres per county per year for the 1982 to 1998
reporting period of this study.
In their 1997 report, Farming on the Edge, the American Farmland Trust reported
that 50,000 acres of Iowa farmland had been convened to urban uses during the
period 1982 to 1992 (Sorensen and others 1997). On average, this area was
equivalent to approximately 50 acres per county per year. All of the convened
land was classified as prime or unique farmland. The data were developed from
the USDA National Resources Inventory conducted in 1982 and 1992 from a
sample of 800,000 sites throughout the US including 21,000 sites in Iowa.
Assessment classification of land converted from agricultural to
nonagricultural classes. According to the Iowa Department of Revenue and
Finance, 46 percent of the area transferred from agricultural to nonagricultural
classes statewide was transferred to the exempt class. Approximately 35 percent
was transferred to residential, 8 percent to commercial, 2 percent to industrial, and
108
9 percent to other. These data are from assessors' annual reconciliation reports
collected by IDRF during the period 1986 to 1997.
Of the total area converted from the agricultural class in the seven pilot study
counties, approximately 22 percent (21 percent of the digitized parcels) was
converted to the exempt class. Approximately 62 percent of the total area (64
percent of digitized parcels) was converted to the residential class, 10 percent (9
percent of parcels) to commercial, 5 percent (5 percent of parcels) to other
classes, and 1 percent (1 percent of parcels) to industrial.
Location of land converted from agricultural to nonagricultural classes. For the
32,417 acres digitized in this study, approximately 67 percent were in
incorporated areas or within two miles. Approximately 32 percent were located
more than two miles from incorporated areas. These results indicate that, for the
parcels digitized, non-farm development was not necessarily close to incorporated
areas. This was particularly true given that the acreage in the zero- to one-mile
zone (10,791 acres) was almost the same as the acreage in the two-plus-mile zone
(10,382 acres).
The percentage of parcel area in incorporated areas or within two miles varied each
year, with relatively high percentages in 1985 and 1986. Relatively low
percentages were in 1983, 1987, 1990, and 1993 to 1996. For the parcels
digitized, the long-term trend was decreasing amounts in and near incorporated
areas over time, with man>, averages over 70 percent before 1989 and many
averages under 70 percent after 1988. This indicated that recent non-farm
development was relatively far from incorporated areas. However, recent
annexations not reflected in the database may reduce the average distance of recent
non-farm development in some areas. (Data on incorporated areas used
throughout this study were developed by the Iowa Department of Transportation
in 1992.)
Although the trend since 1982 was decreasing amounts in and near incorporated
areas, several data sources indicated a relatively large proportion of agricultural
land in incorporated areas. According to land use data collected from 1975 to
1984, incorporated areas in Iowa contained nearly equal amounts of agricultural
land use (611,060 acres; 52.1 percent) and nonagricultural land use (561,130
acres; 47.9 percent). These data were from high-altitude NASA aerial
photographs and boundaries of incorporated areas obtained from the Iowa
Department of Transportation last updated in 1992. By comparison, the 1983
land use reports from the seven pilot study counties reported that approximately
45 percent of the land in incorporated areas was taxed as agricultural land.
109
Agricultural quality of land converted from agricultural to nonagricultural
classes. According to the USDA Prime Farmland classification, approximately
48 percent of the parcel area convened from agricultural to nonagricultural classes
was considered prime agricultural land (Table 65). Approximately 26 percent was
considered of state importance, 22 percent was of local importance, and 3 percent
was not rated. From 47.2 to 56.0 percent of the area converted to commercial,
exempt, and residential classes was considered prime agricultural land by the
USDA. In contrast, over 84 percent of the area converted to the industrial class
was considered prime agricultural land by the USDA.
Table 8~ Agricultural quality of parcels in the seven pilot study counties
Quality measure Parcel avera~Je State avera~Je
Corn suitability rating (CSR) 58 63
Estimated corn yield (ECY) 107 123
Land Capability Class (LCC I or II) 47% 56%
USDA Prime Farmland 48% 52%
In their 1997 report, Farming on the Edge, the American Farmland Trust reported
that approximately 52 percent of Iowa' s land was considered prime or unique
farmland (Sorensen and others 1997). This percentage is near the 48 percent of
land converted in the seven pilot counties that was classified as prime farmland.
This suggests that prime farmland was converted at a rate approximately
proportional to the amount of prime farmland in the state. In other words, the
amount of prime farmland being convened was not disproportionately high or low'
compared with the entire state.
As with CSR, ECY, and LCC, the long-term trend from 1982 to 1998 showed
little change in the percentage of prime agricultural land classified by the USDA.
The long-term averages were between 45 and 50 percent prime land, between 45
and 50 percent Class I and Class II land, between 105 and 110 bushels per acre
ECY, and between 55 and 60. The average CSR and average ECY of the parcels
digitized were below the state averages. The percentages of parcel area classified
as USDA Prime Farmland and Land Capability Class I or II also were below the
state average.
Additional data analysis in Story County in three incorporation z0ncs
(incorporated, zero- to one-mile extraterritorial zone, and one- to two-mile
extraterritorial zone) showed that the area weighted average CSR increased with
distance from the incorporated zone, from 72.8 to 79.9. The area weighted
average ECY also increased with distance from the incorporated zone, from 127.6
to 142.3. In contrast, the average CSR and average ECY decreased with distance
from the incorporated zone for the parcels in each zone, except for parcels in the
two-plus-mile zone. Therefore, in general, as distance from incorporated areas
110
increased in Story County, the agricultural quality of all land increased, but the
agricultural quality of the parcels decreased.
Assessment classification as an indicator of land use change. Data on
assessment class from county assessors were used in this study as an indicator of
land use change. Data on assessment class provides an indirect measure of land
use change for three reasons. First, a change in assessment class doesn't
necessarily result in a change in land use. Second, assessment class is based on the
principal land use in each parcel; by law, incidental land uses and mixed uses are
also permitted. Third, the assessment class "exempt" is a better indicator of land
ownership rather than land use.
Other data sources, such as field surveys and aerial surveys, provide a more direct
measure of land use change than assessment class. However, these direct
measures were not used in this study due to time and budget limitations.
How effective was assessment class in indicating land use change? One measure in
this study was the number of parcels with a land use change compared to the
number of parcels without a land use change. Of the total 4,005 parcels analyzed
in this study, 2,567 (64 percent) had a land use change. Of the total 48,564 acres
analyzed in this study, 36,931 (76 percent) had a land use change. Therefore, in
this study assessment class change was from 64 to 76 percent effective as an
indicator of land use change.
Recommendations
Digitize and analyze additional parcels in each pilot study county.
Most, but not all, parcels in which land use changed were included in
this studv. Because of data and time limitations, 57 percent of the
parcels and 88 percent of the area that changed land use were digitized
for this study. Though this sample of convenience provides sufficient
data for conclusions about the vast majority of area that changed land
use, it was biased toward larger parcels and parcels with complete data.
Digitizing the remaining parcels in each pilot study county would
provide a more representative sample on which to base conclusions.
Confirm land use changes in each pilot study county. In some
parcels that changed from agricultural to nonagricultural assessment
classes (especially to residential and exempt classes), land use changed
on only a portion of the parcel. An example is a municipal well field in
Story County (now classified as exempt but used primarily for
agriculture). Field surveys and aerial surveys could provide more
111
detailed data that could be used to refine the results. Other parcels that
change from agricultural to nonagricultural classes directly support
agriculture. An example is a soybean plant in Pottawattamie County.
Monitor future land use changes in all counties. An analysis of land
converted from agricultural to nonagricultural use should be conducted
every year or two. For example, the procedure used in this study
could be institutionalized annually using data in each county assessor's
reconciliation report to the Iowa Department of Revenue and Finance.
In addition to reporting total acres and total assessed value (as is done
currently), the number and location of individual parcels could be
included in each county report. Such data, combined with aerial
imagery, would provide even higher quality data and more consistent
results.
Assist all counties in modernizing land records. As shown in this
study, digital parcel records become an efficient and powerful database
for monitoring land use changes. Pilot counties with parcel records in
digital form quickly provided data needed for this study. Another key
to efficient data analysis is a digital parcel map. Several counties in
Iowa, including Story County, already have a digital parcel map.
Others are in the process of creating one. Helping all counties create a
digital parcel map and modemize their land records in a consistent way
would make future monitoring much more efficient.
Inventor,/land use and resources statewide. This study provides
data and conclusions based on a sample of seven counties with a
diversity of characteristics. Statewide inventories of land use and
resources would provide more current and complete data on land use,
agricultural quality of land, urban growth panems, and population
changes. For example, the agricultural quality of land in and near all
incorporated areas could be mapped to provide guidance to public
officials in makin~ decisions about location of future development.
Interpret the results of future inventories and assessments in both a
state and national context. This would help determine the
significance of rates of change as well as size and number of converted
parcels/acres. An impartial steering committee could be appointed to
effectively evaluate the findings of a statewide inventory. This would
allow policy makers to gain a better understanding of the implications
of land use change in the state.
112
Assist county personnel to ensure consistency in implementation of
future land use inventories. A common theme in the 1983 land use
inventory reports was the variation in implementation of the inventory
due to differences in opinion regarding proper methods to be used and
the definitions of various land use types.
Apply data to other land management issues and needs. Data on
land use and resources are useful not only for agricultural applications
but also for a variety of other land management applications. As
shown earlier in this report, such data can be used for emergency
management, disaster preparedness, and conservation planning. Other
applications include urban growth management, watershed planning,
and water quality monitoring. Though some govemment agencies and
nongovernmental organizations are already using geographic
information system (GIS) databases and technology, many more would
find these tools useful as they work to protect public health, safety,
and welfare. Information produced from data on land use and
resources can be effectively used to protect both people and the
environment, minimize expenditure of public funds, and increase the
quality of life for Iowa' s citizens.
113
Appendix A.
Summary of the 1983 Land Use Inventory
report
In 1983, the 69th General Assembly of Iowa passed Senate File 2218, the Land Use Bill,
which was signed into law by then Govemor Robert D. Ray. An outcome of this law was
to create County Land Preservation and Use Commissions. One of the responsibilities of
these commissions was to complete a county land use inventory by January 1, 1984.
To aid in the completion of this task, the County Land Use Inventory Guidebook was
developed by the Inter-Agency Resource Council. This guidebook outlined a process for
County Land Preservation and Use Commissions to use to complete the county land use
inventory using information from 1960 to 1983. This process involved documenting
current land use through the use of aerial photographs, many of which were obtained
from county Agricultural Stabilization and Conservation Service (ASCS) offices. In
many cases, the land use was verified by a windshield survey after the initial
identification was done.
Land uses were identified within township sections as one of nine types:
· incorporated areas
· high~quality agriculture
· low-quality agriculture
· public facilities
· private open spaces
· commercial
· industrial
· residential
· transportation
The figures were then totaled by township and by county. The inventory resulted in three
major types of information:
· existing land use by county
· land conversion from agricultural use to one of six alternative uses
· agricultural land ~4thin incorporated areas
The first of these results, existing land use by county, is represented for this seven-county
pilot study in Table 86. As the table indicates, total acres in the counties ranged from
approximately 271,560 in Bremer County to 612,447 in Pottawattamie County.
The predominant land use in all seven counties for the period inventoried was
agricultural, ranging from 189,505 acres in Scott County to 537,670 acres in
Pottawattamie County. These figures include acres categorized as both high and low
115
quality. Com suitability rating and Land Capability Class were at least two ways of
detennining land quality, but these rating systems were not used consistently among
counties. Because of variation in the definition of high and low quality agricultural land,
a detailed analysis of both types is difficult.
Public facilities, defined as "land used for public and private facilities for education,
health, religious activities, government facilities, recreation and conservation," ranged
from 619 acres in Dallas County to 6,892 acres in Cerro Gordo County. These figures,
perhaps more than any other, reflect the character of existing natural resources such as
lakes and open space areas which greatly influence the totals.
Commercial and industrial land uses were similar in appearance on the aerial photo base
maps and resulted in some counties having difficulty in differentiating them. The
definitions help to clarify the differences. Commercial land is defined as "land used for
retail sales or trade of goods and/or services including enclosed arenas, lodging and
motels and any type of office facility...and their associated land uses." Industrial land
use, on the other hand, is defined as "land used for extraction or mining of raw materials,
manufacture of goods, warehousing and wholesale trade, bulk storage and their
associated land uses .... "Figures for commercial land use ranged from 62 acres in
Monroe County to 803 acres in Cerro Gordo County while figures for industrial land use
ranged from 110 acres in Bremer county to 2,558 acres in Monroe Count3'.
Private open space is defined as "land owned privately and may include woodlands not
used for agriculture, wetlands, water bodies, native prairie and wildlife habitat .... "This
land use type was also difficult for some counties to verify because of similarities with
other uses, such as pastured woodlands, etc., which would be classified as agricultural or
rural residential development in some cases. Figures for this land use type ranged from
135 acres in Cerro Gordo to 44,312 acres in Monroe. The extreme range would indicate
some difficulty in identifying this land use type in a consistent manner.
Residential land use is defined as "land used for non-farm residential uses that are
permanent or seasonal and all their associated areas including accessory buildings on
lots .... "Figures range from 641 acres in Monroe County to 4,828 acres in Scott County.
Again, some difficulty in identifying this land use from the aerial photo base maps may
have contributed to the wide range in values.
Transportation land use is defined as "land uses relating to transportation, commtmication
facilities, and utilities .... "Examples include highways, airports and rail facilities.
Figures for this land use ranged from 5,731 acres in Scott county to 22,665 acres in
Pottawattamie county. Again, these figures reflect the unique nature of the representative
counties, with Pottawattamie County heavily impacted by the interstate highway system
in this land use area.
Incorporated area figures ranged from 2,948 acres in Monroe County to 71,126 acres in
Scott county between the years documented. Although the acreage in many incorporated
areas is high, much of this land is actually in agricultural use. Even though the 1983
inventory excluded inventory of lands within incorporated areas, the examination of
116
county assessor records determined those acres that are taxed as agricultural lands within
incorporated areas (Table 88).
Another significant component of the 1983 Land Use Inventory was the documentation
of land transferred from agricultural use to one of six alternative uses (Table 87). These
uses include public facilities, public open space, commercial, industrial, residential and
transportation. This examination was very helpful in clarifying trends in the change of
land use over the period studied.
The amount of total acres of all categories transferred by county ranged from 404 acres in
Bremer county to 11,830 acres transferred in Scott County. Of these alternative uses,
residential use was the category with the largest amount of change with an average of
1873 acres for all seven counties. While most counties used information dating back to
1960, there is some variability in the years from which data was accumulated. These
figures would suggest that the transfer to residential land use is the most significant threat
to agricultural lands.
The final category examined in the 1983 Land Use Inventory is agricultural land in
incorporated areas (Table 88). This analysis gives some indication of the amount of land
present in these areas based upon county assessor records. As indicated, the total land
present in incorporated areas ranges from 2,948 acres in Monroe County to 71,126 acres
in Scott county. The total acres of land present in incorporated areas for all seven
counties is 177,041 acres. Of this amount, approximately 80,144 acres (roughly 45
percent) are taxed as agricultural. This suggests that large amounts of agricultural land
exist within the incorporated boundaries of many cities in Iowa.
Summary. information in the seven reports examined for this study was limited, with four
including some narrative and three without any narrative summary. Of those that did
include summar>.' information, one included a formal recommendations section and
another eluded to some recommendations for future inventories. These include (1)
conducting a more complete and comprehensive survey in the future, such as including
cities in any future studies and looking at private open space conversion; and (2)
ensuring more compatibility and consistencv in inventory methods among counties.
The 1983 Land Use Inventor>,' illustrated many of the issues affecting land use change at
that time. The report also pointed out many issues affecting the process of performing an
accurate and meaningful land use inventory. Many things were learned that can inform
future statewide inventories. Some of these include training of implementers to help
ensure consistency in analysis and reporting, the importance of valid data, the limitations
of the methods used, the need for consistent methods of record keeping and the
usefulness of involving all concerned and interested parties in the process. This
information will serve to inform this pilot land use study as well as future studies relating
to land use in the state.
117
Table 86.
Summary of 1983 land use totals for the seven pilot study counties
Land Use Type County
Bremer Cerro Cordo nailas Monroe PoUawallamle
Acres % Acres % Acres % Acres % Acres %
Incorporated 10,785.40 400 25,55267 700 10,505.00 ')80 *)948 22 1.06 31,909(N) 5.21
High Qualily Agricullure 186,501.00 · 68.70 315,61622 8600 237,287 00 6230 65,632.55 23.64 3~4,899 O0 628%
Low Qualily Agricuhute 29,093.40 10.70 0.00 0 00 84,30200 2230 152,024.23 5476 152,77100 24 94
PublicFacililies 3,715.20 1.40 6,892.48 2.00 619.00 020 3,17063 1.14 4,58000 0.75
Privnle Open Space 31,72210 11.70 135.76 0.04 25,595.00 670 44,312.43 15.96 12,33000 201
Commercial 119.90 0.04 803.97 0 20 172.00 0 04 62 15 002 7500 0 01
Induslrial 110.30 004 1,58438 040 56200 O. 10 2,55844 0.92 R63.00 O. 14
Residenlial 1,445.20 0.50 2,618.95 0.70 2,659.00 070 641 66 0.23 2,355 0) 0.39
Ttanspotlalion 8,067.50 300 13,399.91 4.00 14,863.130 3.90 6,256.24 2.25 22.66500 3.70
Tolal County Acres 271,560.30 I00.00 365,960.32 100.00 379,401.00 I00.00 277,606.55 I00.00 612,447.00 100.00
Totals are taken from the 1983 Land Use Inventory reports for the seven participaling counties. Any discrepancies in IotaIs rollcot
Scott
SI,ry
Acres % Acres
7 I, 126.00 25 (~) 211,9'14.1X}
133,210(X) 4600 295,665(X)
56,295 O0 19(X) 25,TR2(X)
5.681.00 200 2.64 I00
12, 158 O0 4 O0 6,669 (X)
.12000 0 Io 158.00
344 O0 0 I(} 672.00
4.~2~00 2.00 2.~i21.00
5 331.00 2 (1(1 I 1,481.00
289,69.1 O0 I00.00 366.583.(X)
Ibose found in the
%
5.70
X0,70
700
0 70
I.~O
0
0.20
0 70
3 10
II)0,(X)
original reports.
Table 87. Summary of land convened from agricultural to nonagricultural use
Alternative land-use type County
Bremer Cerro Gordo Dallas Monroe PottawaUamie Scott Story
Acres Acres Acres Acres Acres Acres Acres
Public facilities 77.70 2099.92 15.00 I ?01.89 299.00 2899.00 2306.00
Private open space 98.30 41.62 27].00 0.00 134.00 2354.00 ?6.00
Commercial 39.40 28 !.75 82.00 52.56 99.00 341.00 353.00
Industrial 23.00 595.19 22.00 ] ?:53.29 792.00 904.00 949.00
Residential 155.30 1821.63 1939.00 362.81 1846,00 4417.00 25?2.00
Transportation ] 0.40 1411.49 38.00 259.69 4563.00 915.00 1926.00
Total county acres transferred 404.10 6251.60 2367.00 4 130.24 7733.00 ! i 830.00 8182.00
Totals are taken from the 1983 Land Use Inventory reports for the seven pilot study counties. Variation exists in the time period for the study
due to the variability of records available for the 1983 inventory. Please see 1983 reports for details.
Table 88. Summary of agriculturally taxed land in incorporated areas
Agriculturally taxed
County Acres in incorporated areas acres in incorpoTated areas
Bremer 10,785.40 5664.00
Cerro Gordo 23,392.67 12,596.73
Dallas 15,886.00 6507.00
Monroe 2948.22 I i 13.00
Pottawattam ie 31,909.00 10,376.00
Scott 7 I, 126.00 37,597.93
Story 20,994.00 6,289.00
Total acres in incorporated areas* 177,041.00 80, 144.00
Totals are taken l?om tile 1983 Lancl Use Inventory reports fbr the seven pilot study counties.
*Acres in incorporated areas data for Dallas County are taken from 1992 Iowa Department of Transportation statewide data layer.
Appendix B1.
Iowa Communications Network Meeting 1
September 23, 1998
Participants
Iowa State University
Smart Huntington
Paul Anderson
Carmen Chan
Nora Ladjahasan
Karen Ormsbee
Sandy Peterson
Troy Siefert
Ben Swanson
Heather Sauer
Brerner County
Jean Keller
Monroe County
Diane Durian
Paul Koffman
Juanira Murphy
Dermy Ryan
Peggy Vandenberg
Pottawattamie County
Tom Bredewag
Stanley Grote
Kay Mocha
Laura Romano
Scott County
Keith Blake
Dale Denklau
Otto Ewoldt
Timothy Huey
Story County
Les Beck
Gary Bilyeu
Principal Investigator
Co-Principal Investigator
Research Assistant
Research Assistant
Research Assistant
Research Assistant
Research Assistant
Research Assistant
Editor
County Assessor
Director, Veterans Affairs
Chair, County Board of Supervisors
Zoning Administrator,
Office Manager- County Secondary Roads
Member, County Board of Supervisors
Deputy Treasurer
Executive Director, Iowa League of Cities
Member, County Board of Supervisors
Director, County Planning and Development
Clerical Coordinator, Assessor's Office
President, Scott County Fann Bureau
County Assessor
Member, County Board of Supervisors
Director, Planning and Development
Director, Planning and Zoning Department
County Assessor
121
Kyle Croner Intern
Other Interested Parties
Bill Peterson
Executive Director, Iowa State Association of Cities
**Note:
Due to technical problems. some participants' names were not received
and therefore are not listed here.
Session Summary
Stuart
Huntington
Expressed appreciation and thanks for the help from counties participating in this
study. Gave an introduction and described the impetus for the project. He
mentioned that this is the first of two ICN sessions to be held. He reviewed the
purpose of the project and posed sample questions to county representatives and
asked for their input.
Gave a PowerPoint presentation with an overview of the project:
Purposes of the land use inventory
Schedule of activities
Why the 99-county survey?
Paul Anderson
Described his analysis work to date regarding land use and statewide trends. This
discussion included statewide dam, incorporated areas and pilot counties. The
presentation included numerous tables, figures, charts and graphs. (This material
is included in the body of the report.)
Stuart Huntington
Solicited input from counties and posed sample questions for discussion:
The Preservation of Agricultural Land
Is this a concern in your area?
Is it seen as a priority?
Is ag land preservation thought to be a legitimate area for:
Public policy debate
Local government regulation
State-level interest, regulation
What are your other concerns?
122
What is Being Tried?
Does your county have zoning?
Does the zoning protect ag land?
Does your county have a right-to-farm ordinance?
Axe there other measures in place which seek to protect ag land?
Assistance Needed to Protect Farm Land Axe legislative changes needed?
Do counties need technical assistance to identify and protect
prime ag land?
Is there a need for additional funding?
What other needs exist?
Pottawattamie County
Discussed the issue of "worst first." The point was made that land that is
considered "worst" for farming is also very difficult to build on. An example
given was that developing steep slopes in the Loess Hills area is inappropriate due
to erosion. It was noted that very good land often is not utilized as the
topography isn't as desirable for residential development. A moderate-value land
with rolling topography is probably most desirable for various land use
conversions.
Another example of changing land use was that of a soybean processing plant.
This use displaced land being used for one type of agricultural practice for
another. Much of this high-quality agricultural land was used for a transportation
network to service the facility. The question was asked, what alternative land use
is desirable?
As another example, the point was made that the county has acquired two 80-acre
sites for preservation. The comment was made that land transferred from
agricultural use to preservation shouldn't count against the county.
The suggestion was made to look at the agricultural exemption clause. gometimes
this is viewed to help farmers and sometimes not. For example, a recent attempt
to direct land use met with resistance in Pottawattamie County.
Other interested parties
It was noted that people are "all over the board" on regulations: some feel there is
areal concern and others feel local control is better. The comment was made that
it will be triclcy to do something statewide that will fit the-whole state but some
general things can be done.
123
Bremer County
Noted that one problem of this type is the Avenue of the Saints area. There is a
dispute between city and county officials on jurisdiction such as regulating areas
and determining what should happen here. So far there has been "limited
cohesion."
Keith Blake
Said there is a concem over preserving high-quality agricultural land. Some
protection is needed, whether working with local or state officials. Farmers who
are unwilling sellers need protection. He suggested directing some agricultural land
uses to the city rather than to rural areas. He also noted that he doesn't feel the
govemment should be able to condemn and/or annex land for other peoples
purposes.
Dale Denklau
Said the issue of development along highway corridors is a tough question. What
is the best use of land? Can rural areas and urban areas be separated in this study?
Development in rural areas is limited at this time.
Story CounW
Is agricultural land protection a concern? Yes. There is a balancing act between
protection and growth and development. This is an area for public policy debate.
This is an area for an overarching state framework. An issue is, should there be a
policy that is "one size fits all" or is there a need for policy that addresses
growing urban versus stable rural areas?
Funding is an issue as well. Perhaps Iowa should fund an agricultural land
protection fund.
Story County's development is scattered all over. Refining the data is needed to
address change in land use versus change in assessment. An assessment change is
not equal to a land use change.
Growth management issues should be explored. Compact development as a
growth objective: sacrifice good agricultural land for compact development
patterns versus scattered development panems with additional demands and the
cost of providing additional services; lose lower-quality agricultural lands. Is this
a preferred way of planning?
Is land preservation a concem? It is something to be talked about. Regulation
doesn't seem to work. Does zoning work for agricultural land preservation?
Zoning for agricultural pressure is designed to control development but it doesn't
function well.
124
Things are in place for preservation but they don't work. Residential land use
conversion is going up. As far as moving to a rural area is concerned, people want
to be in the country.
The agricultural land preservation act was overruled today in Kossuth County.
This will have some significant impacts in the future.
Monroe County
Preservation of agricultural land is very important. We are looking at development
and our comprehensive plan. The intent is to have public debate. Slowly but
surely residents are accepting land use policy. Technical assistance and funding
assistance is needed.
Stuart Huntington
Is there resistance to regulations locally or is there acceptance?
Monroe County
Slowly but surely there is acceptance.
Stuart
Huntington
Noted these have been excellent comments. Following a request for any additional
comments, he indicated that "This gives us a grasp of what is going on as a state.
Our inventory is a large job but only a small piece of the pn771e. Some difficult
choices are required to balance issues of development and preservation in Iowa.
This will give us a better grasp of what's going on. I believe a balanced approach
is fight on: balancing agricultural use and development."
Story County
The question is the importance of this issue and how it is iMayed out in the State
Commission for Urban Sprawl.
Bill Peterson
Representatives on the state commission are working with count), offices to get an
idea of what their feelings are on this issue and how it's playing out. The policy
setting process statevAde is about half done at this point.
Steering committees have spent large amounts of time on this. Groups meet again
in October to formalize a position to bring before members. A lot of interest and
discussion has taken place but there is not a position at this time.
Tom Bredewag
Adopt a set of objectives. Land use is very important to this organization,
perhaps first or second in importance in what they do in 1999. They believe
125
development must be balanced with agriculture. They are concerned with state
regulations. They believe local problems can be solved by local officials.
Management from Des Moines won't work. An overall state wide structure is
needed and then have local and county flexibility. Growth in an orderly fashion is
good.
Paul Anderson
Requested suggestions for results and feedback. Is this (ICN session) a good
format?
Scott County
Suggested sending out information on inventoD' results before the next ICN
session.
Paul Anderson
Requested any final suggestions, then thanked all participants for their input.
Stuart Huntington
Indicated that a draft report will be sent out before the next ICN session.
Thanked everyone for their input and ended the meeting.
126 "
Appendix B2. Iowa Communications Network Meeting 2
November 18, 1998
Participants
Iowa State University
Stuart Huntington
'Paul Anderson
Nora Ladjahasan
Karen Ormsbee
Troy Siefert
Ben Swanson
Heather Sauer
Tim Keller
Tim Borich
Bremer County
Jack Dillon
Scoff County
Dale Denklau
Principal Investigator
Co-Principal Investigator
Research Assistant
Research Assistant
Research Assistant
Research Assistant
Editor
Chair, Department of Landscape Architecture
Assistant Dean for Research and Outreach
Bremer County Extension Office
County Assessor
Session Summary
Stuart Huntington
Discussed the material prepared to date in the form of the draft report sent out to
all counties. Noted that the final report is due November 30. Indicated that this
session is being held to give project participants an oppommity to review the
findings and ask any questions they would like. Introduced Nora Ladjahasan to
discuss the telephone survey.
Nora Ladjahasan
Discussed the telephone survey, including the participants, methods, findings, and
recommendations ,all of which are included in the report. This overview included
several tables and lists to outline the data presented.
Paul Anderson
Discussed the agricultural quality and inventory of land use change components
of the report. This discussion included statewide data, incorporated areas, and
pilot counties. The presentation included numerous tables, figures, charts, and
graphs. This material also is included in the report.
127
Jack Dillon
Inquired about what is done with Forest Reserve lands in the report. Paul
Anderson explained that Forest Reserve is included in the Agricultural class and is
not considered a land use change.
Tim Keller
Inquired whether there is a disparity in parcels that changed to the Industrial class
compared to other class changes, because they tend to be a higher-quality
agricultural land.
Paul Anderson
Noted that he felt there would not be a large disparity because the number of
parcels that changed from the Agricultural class were few, perhaps about 2
percent.
Tim Keller
Noted that several speakers brought before the commission have been concemed
with agricultural land that was "condemned" through annexation into adjacent
communities.
Paul Anderson
Said the sample of counties chosen for the study has not illustrated that fact, but
noted that the sample is one of convenience and not random, and therefore may
not adequately represent the realities of this issue. The publication "Farming on
the Edge" was mentioned as a reference for comparing this study's results with
one done by another entity. There appears to be a large difference in the rates and
quantities of land use change between the two studies.
Jack Dillon
Inquired whether the numbers in the report were actually counted and would
therefore represent a minimum number of acres converted.
Paul Anderson
Noted that because a class change does not necessarily constitute a land use
change, this measure could not be used as a direct measure of change but rather
should be considered as an indicator.
Stuart
Huntington
Noted that it is possible that some counties are using adverse condemnation or
imminent domain to create industrial parks in communities, as this has been
known to happen.
128
Tim Borieh
Recommended that local land use control be implemented within a two-mile radius
from towns in order to assist them in holding the line against conversion.
Stuart Huntington
Noted that counties do have a hard time holding the line and that changes probably
do need to be made.
Tim Borieh
Asked whether local ordinances have any affect on land use patterns.
Jack Dillon
Recalled an instance in which county supervisors denied a request for a land use
change, which resulted in the landowner going to the city to get annexed and
having the land use change approved anyway.
Requested that the commission make this quantitative information public so that
it may be disseminated among interested parties statewide.
Stuart Huntington
Thanked everyone for their comments and adjourned the meeting.
129
Appendix C. Request for county participation
in the pilot land use inventory
Cover letter to Extension staff
Date: July 8, 1998
To:
From:
Re:
County Extension Education Directors
Extension Area Directors
Extension to Communities Staff
Stanley R. Johnson, Vice Provost for Extensiq~
Land Resource Inventory
Attached please find a copy of a letter which I am sending to all county
boards of supervisors and assessors. Like the public that we serve, we need
to be concerned not only with the quality of Iowa farmland, but with the
quantity also. In many areas of the state, land is being converted from
agriculture to other land uses at an increasing rate. We are being asked by
the legislature to assist in a ~eater understanding of this change in land use
patterns.
I hope you will make a serious effort to support my request to county
officials by urging them to provide data to the researchers undertaking this
work. I also hope that you will encourage counties to become more
involved in understanding changing Iowa land use patterns by applying to
serve as model counties.
131
Letter to county officials
Date: July 8, 1998
From:
County Board of Supervisor Chairpersons
Assessors
Land Use Inventory
Iowa State University Extension has recently entered into a contract with the
Legislative Service Bureau to conduct a land use inventory for the Commission
on Urban Planning, Growth Management of Cities, and Protection of
Farmland, created by the Iowa Legislature. The Commission has asked ISU
Extension to determine the extent to which land in this state has been convened
from agricultural use to residential, commercial, or industrial, or public uses.
To this end, researchers College of Design/ISU Extension to Communities will
soon contact you to ask about local concerns and issues regarding the
conversion of agricultural land to other land uses in your county. Is this seen as a
problem in your area? Is there public concern about this issue?
In addition, five to seven "model counties" will be selected for more in depth
analysis of changing land use patterns, and the forces contributing to these
changes. Instructions for applying to become a model county are attached.
With your assistance, we can all achieve a greater understanding of changes in
Iowa land use patterns. Please note that the deadline for the expression of
interest by potential model counties is July 27, 1998.
132
Five to Seven Model Counties to be Selected
Benefits to model counties:
Selected counties will receive a detailed report including maps indicating the location and
extent of land use conversions. For affected land parcels, the Com Suitability Ratings
(CSR) or other similar measures will be analyzed to determine the quality of land being
taken out of agricultural production. This report will provide useful background
information on county concerns such as land use, public safety, emergency management,
cultural and natural resources, tourism, and place competitiveness. In addition, the ISU
team can provide assistance in applying new technology to efficiently manage county data.
Criteria for selecting the model counties:
1. The Commission has asked that ISU Extension select a variety of types of
counties for the "model counties" analysis.
2. Selected counties will need to provide researchers access to the records
maintained by the county assessor. These records should be in good order and,
preferably, in digital format. Other county officials will also be consulted.
3. The Commission has provided ISU with $5,000 per county. The contract calls
for this amount to be matched by $5,000 from the County Board of Supervisors. If
this creates a hardship for the county, a portion of this local match requirement
could be met through the provision of in-kind contributions such as providing
clerical assistance to comile data.
The ISU team, in consultation with the Iowa State Association of Counties, will select the
counties which most closely meet the above criteria.
Deadline and how to apply:
Because of the fight time flame of this contract, those interested in having their counties
considered for model counties are asked, by July 27. to send a letter of interest to:
Smart Huntington
Extension Planning and Development Specialist
Dept. of Community, and Regional Planning- ISU
Ames, IA 50011
Tel: 515-294-2973
Fax: 515-294-5156
E-mail: x 1 huntin@exnet. iastate .edu
The letter of interest should include name and telephone number of a contact person, how
the county proposes to meet the matching requirement, and how the county will
providenecessary data (see attachment on Data Needs).
133
Data Needs for Model Counties
Here' s an example of the kind of data that would help the Iowa Legislature' s Commission
on Urban Planning, Growth Management of Cities, and Protection of Farmland evaluate
the issues involved in conversion of farmland.
Working from assessors' annual reconciliation reports, the ISU team can summarize,
compare, and map the number of acres and assessed value of land and structures converted
from the agricultural classification to four other classifications (residential, commercial,
industrial, and exempt). These summary data are already available from the Iowa
Department of Revenue and Finance for each county during the period 1986 through 1997.
However, more detailed data are needed for the five to seven counties selected for the pilot
study. The Commission would like to know the agricultural quality of the land convened
from ag to non-ag classifications. To do this, we need to know the location of individual
parcels or tracts. Here's what the ISU team would like to know about each parcel or tract
convened from ag to non-ag classifications:
A. Reference number (including legal description so we can map it)
B. Year convened (1982 through 1997)
C. New classification (residential, commercial, industrial, or exempt)
D. Net acres
E. Dollar value of land and structures
F. CSR per tract (area weighted average)
Data for items B, C, D, and E (Year convened, New classification, Net acres, and Dollar
value of land and smacmres) allow comparisons with the county and state totals contained
in the reconciliation reports filed with the Iowa Department of Revenue and Finance.
Typically, all six items could be compiled directly from property record cards (green cards)
or from digital computer files. The Commission would like us to obtain data back as far as
1982, when the last statewide inventory was made of agricultural use and quality.
134 '
Contact people
Contact People
who are familiar with this project:
Paul Anderson
Associate Professor
Deptartment of Landscape Architecture - ISU
Ames, IA 50011
Tel: 515-294-8943
Gary, Bilyeu
Story County Assessor
900 Sixth St.
Nevada, IA 50201
Tel: 515-382-7320
Tim Borich
Associate Professor
Department of Community and Regional Planning - ISU
Ames, IA 50011
Tel: 515-294-0220
Robert Mulqueen
Public Policy Analyst
Iowa State Association of Counties
701 E. Court Ave., Suite A
Des Moines, IA 50309-4901
James O'Neill
Pottawattamie County Assessor
227 S. Sixth St.
Council Bluffs. IA 51501
Tel: 712-328-5617
135
Appendix O. Letter to county officials regarding
the telephone survey
September 4, 199g
Dear Zoning Administrator (County Assessor):
During the past several years, the state of Iowa has seen changes in land use with a
substantial amount of agricultural land converted to other uses. To address these
changes, the Iowa Legislature created the Commission on Urban Planning, Growth
Management of Cities, and Protection of Farmland, and directed it to examine growth in
the state. The commission was also instructed to investigate changes in both the quality
and quantity of agricultural land in the state.
As part of this effort, the Commission has contracted with Iowa State University to
examine the changes in the state's agricultural land. Researchers from the university
soon will be contacting you to ask about issues and concerns in your county.
Specifically, the survey will ask about:
a) whether planning and zoning have been implemented in your county,
b) any changes in the quality, and quantity of fanrdand in your county,
c) productivity indices (com suitabilit3' rating, etc.) that are used for valuations,
along with local modifications.
d) processes for monitoring land use changes and ensuring reliability and
consistency of land-use designations, and
e) the availability, of data (reports, state forms, tracking systems, etc.) related to
agricultural land use changes taking place in your county.
Agricultural change is taking place continually throughout the state, and the information
described above will be essential for a complete and accurate description of these events.
Telephone surveys will be conducted during the period from September 14 to 30, 1998.
The telephone interview will take approximately 30 minutes of yottr time. If the
interview falls at an inconvenient time, feel free to resehedule the call. Your assistance in
providing information to us will be greatly appreciated.
Sincerely,
Smart Huntington
Principal Investigator
137
Appendix El. Questionnaire for telephone survey
of county assessors
Phone:
Hello, this is
May I speak with
CO!lilly aSSeSSOr:
8tart time:
Land Resource Inventory
(YOUR NAME)
AM / PM
calling from Iowa State University.
(county assessor)?
[READ INTRODUCTION WHEN SUBJECT COMES TO THE TELEPHONE]
Iowa State University Extension has entered into a contract with the
Legislative Service Bureau to conduct a land use inventory for the
Commission on Urban Planning, Growth Managemere of Cities, and
Protection of Farmland.
Did you receive the letter that we sent dated September 4, 1998, regarding
the land-use inventory?
1= YES
2= NO
We are interested in determining the extent to which agricultural land in
the state of Iowa has been convened to either residential, commercial,
industrial, or public uses. We also want to know your local concerns and
issues regarding this topic in your county.
May we ask some questions in regard to agricultural land conversion in
your count,?
1= YES
2= NO (STOP THE INTERVIEW AND THANK THE RESPONDENT)
139
Do you have time now to answer some questions? This interview should take only 20
minutes, and you do not have to answer any questions that make you feel uncomfortable.
All of your responses will be treated confidentially.
1= YES (GO to Q1)
2= NO (ASK for alternate date/time)
index do you use for agricultural land valuation?
Crop yield
Land Capability Class
Corn suitability rating
USDA Prime Farmland
80 percent of its actual value for agricultural or horticultural purpose
Others (please specify: )
2. What procedures and definitions do you use to identify land that qualifies for
agricultural land valuation?
3. When was the last time your county's land valuation procedure was updated?
19
Now I would like to move on to some questions about how your county monitors
changes in land use and about the data sources that might be available to researchers.
4. Please describe the procedure (green cards, GIS, reports) your county uses to monitor
and record farmland changes.
5a. Do you believe that your county's procedure for monitoring and recording farmland
change is appropriate, reliable and accurate.'?
1 =YES (G0 to Q5b)
2 = NO (G0 to Q5c)
3 = Don't Know (G0 to Q6a)
140
b. If YES, why do you think your monitoring system appropriate, reliable and accurate.'?
Please be specific.
c. If NO, what would be a preferable method for monitoring and recording farmland
change in your county? Please be specific.
6a.
7a.
For what years does your county possess data on farmland changes in your county?
19 to 19
b. What years are in digital form? 19 to 19
c. What years are in paper form? 19 to 19
Do you prepare summar>, reports (other than the reconciliation report required by
the state) that document farmland changes in your county?
I=YES (G0 to Q7b)
2= NO (G0 to Q8)
3= Don't Know (GO to Q8)
b. If yes, what type of information is contained in the summary reports?
c. How frequently do you prepare the summary reports?
__ quarterly
__ every 6 months
__ yearly
__ Other (please specify:
Now rd like to move on to some questions that deal with farmland protection strategies
that may be in place in your county.
141
8. Do you have a farmland protection program in you county?
I=YES (G0 to Q9)
2=NO (G0 to Q11a)
3=Don't Know (G0 to Q11a)
For each of the following strategies, I will ask whether the strategy is in use in your
county, the process by which it is implemented in your county, and whether or not
you feel that it has been effective in protecting farmland in your county.
State-Level Programs
In place? How implemented? Effective?
Y / N Agricultural zoning district Y / N
Y / N Conservation easement Y / N
Y/N
Y/N
PACE (Purchase of agricultural
conservation easement program)
Circuit breaker tax relief
Y/N ,
Y/N
Y / N Differential assessment
tax relief
Y/N
Y / N Ag Enterprise Zones Y / N
Y / N Others (please specify) Y / N
142
Local-Level Programs
In place?
Y/N
Y/N
Y/N
Y/N
YIN
Y/N
Y/N
Y/N
How implemented?
Agricultural protection zoning
Cluster or open space zoning
Comprehensive planning
Mitigation ordinances/policies
Local right-to-farm ordinances_
Right-to-farm laws
Transfer of development rights
Others (please specify)
143
Effective?
Y/N
Y/N
Y/N
Y/N
Y/N
Y/N
Y/N
YIN
lOa.
Do any of the farmland protection strategies just described contain a requirement
for a minimum parcel size in order to be eligible?
I=YES (G0 to Q10b)
2=NO (G0 to Qll)
3=Don't Know (G0 to Qll)
If YES, please indicate the specific strategies and the minimum size requirement for
that strategy.
Strategy Parcel size
Finally, I have a couple of general questions regarding farmland protection in your
county.
11 a. Is farmland protection an issue of concern in your county?
I=YES (G0 to Q11b)
2= NO (G0 to Q12)
3= Don't Know (G0 to 012)
b. If YES, what are the concems?
12. Is there anything else you would like to tell us about farmland change in your county?
Those are all the questions we have for you. We appreciate your help very much. If you
think of anything else you want to tell us, or if you have any questions about the research
project, please contact Nora Ladjahasan at (515) 294-07~14. Thank you for your time.
13. Are you interested in having a summary of our findings? 1 =YES
2 = NO
End time: AM / PM
144
Appendix E2. Questionnaire for telephone survey
of county zoning administrators
County zoning administrator:
Phone: Start time: AM / PM
Land Resource Inventory
Hello, this is (YOUR NAME) calling from Iowa State University.
May I speak with
(county zoning administrator)?
[READ INTRODUCTION WHEN SUBJECT COMES TO THE TELEPHONE]
Iowa State University Extension has entered into a contract with the
Legislative Service Bureau to conduct a land-use inventory for the
Commission on Urban Planning, Growth Management of Cities, and
Protection of Farmland.
Did you receive the letter that we sent dated September 4, 1998, regarding
the land-use inventory?
1= YES
2= NO
We are interested in determining the extent to which agricultural land in
the State of Iowa has been convened to either residential, commercial,
industrial, or public uses. We also want to know your local concerns and
issues regarding this topic in your county.
May we ask some questions in regard to agricultural land conversion in
your county?
1= YES
2= NO (STOP THE INTERVIEW AND THANK THE RESPONDENT)
145
Do you have time now to answer some questions? This interview should take only 20
minutes, and you do not have to answer any questions that make you feel uncomfortable.
All of your responses will be treated confidentially.
1= YES (GO to Q1)
2= NO (ASK for alternate date/time)
What index do you use for agricultural land valuation? Crop yield
Land Capability Class
Com suitability rating
USDA Prime Farmland
80 percent 'of its actual value for agricultural or horticultural purpose
Others (please specify: )
What procedures and definitions do you use to identify land that qualifies for
agricultural land valuation?
3. When was the last time your county's land valuation procedure was updated?
19
Now I would like to move on to some questions about how your county monitors
changes in land use and about the data sources that might be available to researchers.
4. Please describe the procedure (~een cazds, GIS, reports) }'our co~ty uses to monitor
and record farmland changes
5a. Do you believe that your county's procedure for monitoring and recording farmland
change is appropriate, reliable and accurate?
I=YES (G0 to Q5b)
2= NO (G0 to Q5c)
3= Don't Know (G0 to Q6a)
146
b. If YES, why do you think your monitoring system appropriate, reliable and accurate.'?
Please be specific.
c. If NO, what would be a preferable method for monitoring and recording farmland
change in your county.'? Please be specific.
6a.
For what years does your county possess data on farmland changes in your county?
19 to 19
b. What years are in digital form? 19 to 19
c. What years are in paper form? 19__ to 19
7a.
Do you prepare summary reports (other than the reconciliation report required by
the state) that document farmland changes in your county?
I=YES (G0 to Q7b)
2= NO (G0 to Q8)
3= Don't Know (G0 to Q8)
b. If yes, what type of information is contained in the summary reports?
c. How frequently do you prepare the summary reports?
__ quarterly
__ every 6 months
__ yearly
__ Other (please specify:
Now rd like to move on to some questions that deal with farmland protection strategies
that may be in place in your county.
147
8. Do you have a farmland protection program in you county?
I=YES (GO to Q9)
2=NO (GO to Q11a)
3=Don't Know (GO to Q11a)
For each of the following strategies, I will ask whether the strategy is in use in your
county, the process by which it is implemented in your county, and whether or not
you feel that it has been effective in protecting farmland in your county.
How implemented?
Agricultural zoning district
State-Level Programs
In place?
Y/N
Effective?
Y/N
Y / N Conservation easement Y / N
Y/N
PACE
(Purchase of agricultural
conservation easement program)
Y/N
Y/N
Circuit breaker tax relief
Y/N
Y/N
Differential assessment tax relief
Y/N
Y/N
Ag Enterprise Zones
Y/N
Y/N
Others (please specify)
Y/N
148
Local-Level Programs
In place?
Y/N
Y/N
Y/N
Y/N
Y/N
Y/N
Y/N
Y/N
How implemented?
Agricultural protection zoning
Cluster or open space zoning
Comprehensive planning
Mitigation ordinances/policies
Local right-to-farm ordinances
Right to farm laws
Transfer of development rights
Others (please specify)
149
Effective?
Y/N
Y/N
Y/N
Y/N
Y/N
Y/N
Y/N
Y/N
lOa.
Do any of the farmland protection strategies just described comain a requirement
for a minimum parcel size in order to be eligible?
I=YES (G0 to Q10b)
2=NO (G0 to Qll)
3=Don't Know (G0 to Qll)
If YES, please indicate the specific strategies and the minimum size requirement for
that strategy.
Strategy Parcel size
Finally, I have a couple of general questions in regard to farmland protection in your
county.
1 la. Is farmland protection an issue of concern in your county?
1=YES (G0 to Q11b)
2= NO (GO to Q12)
3= Don't Know (GO to Q12)
b. If YES, what are the concerns?
12. Is there anything else you would like to tell us about farmland change in your county?
Those are all the questions we have for you. We appreciate your help very much. If you
think of anythin9 else you want to tell us, or if you have any questions about the resean;h
project, please contact Nora Ladjahasan at (515) 294-0734. Thank you for your time.
13. Axe you interested in having a summary of our findings? 1 =YES
2 = NO
End time: AM / PM
150 -
Appendix F. Definitions of farmland protection programs
Source:
American Farmland Trust, Saving American Farmland: What Works
(Northampton, Mass., 1997)
Programs that generally are enacted at the state level
Agricultural district laws
Agricultural district laws allow farmers to form special areas where commercial
agriculture is encouraged and protected. Programs are authorized by state legislatures and
implemented at the local level. Common benefits of enrollment in a district include
automatic eligibility for differential assessment, protection eminent domain and
municipal annexation, enhanced right-to-farm protection, exemption from special local
tax assessments and eligibility for state PACE programs. Some agricultural district laws
require farmers to sign agreements that prohibit development for the term of enrollment.
Agricultural district programs are a unique farmland protection technique because
they use a combination of incentives to achieve the same goals as regulatory strategies.
Instead of controlling land use, agricultural district laws offer farmers benefits for
keeping their land in agriculture.
Conservation easements
Agricultural conservation easements are designed specifically to protect farmland.
Grantors retain the right to use their land for fanning, ranching and other purposes that do
not interfere with or reduce agricultural viability. They hold title to their properties, and
may restrict public access, sell, give or transfer their property, as they desire. Producers
also remain eligible for any state or federal farm program for which they qualified before
entering into the conservation agreement.
Conservation easements limit land to specific uses and thus protect it from
development. These voluntary legal agreements are created between private landowners
(grantors) and qualified land trusts, conservation organizations or government agencies
(grantees). Grantors can receive federal tax benefits as a result of donating easements.
Grantees are responsible for monitoring the land and enforcing the terms of the
easements.
Easements may apply to entire parcels of land or to specific parts of a property.
Most easements are permanent; term easements impose restrictions for a limited number
of years. All conservation easements legally bind future landowners. Land protected by
conservation easements remains on the tax rolls and is privately owned and managed.
While conservation easements limit development, they do not affect other private
property rights.
Agricultural conservation easements are a flexible farmland protection tool.
Private land trusts and other conservation organizations educate farmers about the tax
benefits of donating easements, and state and local governments have developed
programs to purchase agricultural conservation easements from landowners. In addition,
151
agricultural conservation easements can be designed to protect other natural resources,
such as wetlands and wildlife habitat.
Executive orders
State executive orders have the potential to build public and institutional support
for other farmland protection programs. By restricting the use of state funds for projects
that would result in the loss of agricultural land, executive orders also can influence the
actions of local governments. To the extent that they call attention to the problem of
farmland conversion and facilitate discussion about solutions, executive orders can serve
as a building block of a comprehensive farmland protection program.
State growth management laws
Growth management laws are designed to control the timing and phasing of urban
growth and to determine the types of land use that will be permitted at the local and
regional levels. Growth management laws take a comprehensive approach to regulating
the pattern and rate of development and set policies to ensure that most new construction
is concentrated within designated urban growth areas or boundaries CUGBs). They direct
local govenments to identify lands with high resource value and protect them from
development. Some growth management laws require that public services such as water
and sewer lines, roads and schools be in place before new development is approved.
Others direct local governments to make decisions in accordance with a comprehensive
plan that is consistent with plans for adjoining areas.
Purchase of agricultural conservation easement (PACE) programs
Purchase of agricultural conservation easement programs pay farmers to protect
their land from development. PACE is known by a variety of other terms, the most
common being purchase of development rights. Landowners sell agricultural
conservation easements to a government agency or private conservation organization.
The agency or organization usually pays them the difference between the value of the
land for agriculture and the value of the land for its "highest and best use," which is
generally residential or commercial development. Easement value is most often
determined by professional appraisals, but may also be established through the use of a
numerical scoring system which evaluates the suitability for agriculture of a piece of
property.
PACE programs allow farmers to cash in a fair percentage of the equity in their
land, thus creating a financially competitive alternative to selling land for nonagricultural
uses. Permanent easements prevent development that would effectively foreclose the
possibility of fanning. Removing the development potential from farmland generally
reduces its future market value. This may help facilitate farm transfer to the children of
farmers and make the land more affordable to beginning farmers and others who want to
buy it for agricultural purposes. P ACE provides landowners with liquid capital that can
enhance the economic viability of individual farming operations and help perpetuate
family tenure on the land. Finally, PACE gives communities a way to share the costs of
protecting agricultural land with farmers. '
152 '
Right-w-farm laws
State right-to-farm laws are intended to protect farmers and ranchers from
nuisance lawsuits. Every state in the nation has at least one right-to-farm law. Some
statutes protect farms and ranches from lawsuits filed by neighbors who moved in after
the agricultural operation was established. Others protect farmers who use generally
accepted agricultural and management practices and comply with federal and state laws.
Twenty-three right-to-farm laws also prohibit local governments from enacting
ordinances that would impose unreasonable restrictions on agriculture.
Right-to-farm laws are a state policy assertion that commercial agriculture is an
important activity. The statutes also help support the economic viability of farming by
discouraging neighbors from filling lawsuits against agricultural operations. Beyond
these protections, it is unclear whether right-to-farm laws help maintain the land base.
Tax relief
Circuit breaker tax relief credits
Circuit breaker tax programs offer tax credits to offset farmers' property
tax bills. In Iowa, farmers receive school tax credits from their local
governments when school taxes exceed a statutory limit. The counties and
municipalities are then reimbursed from a state fund.
Like differential assessment laws, circuit breaker tax relief credits reduce
the amount farmers are required to pay in taxes. The key differences between the
programs are that most circuit breaker programs are based on farmer income and
are funded by state governments.
Differential assessment laws
Differential assessment laws direct local governments to assess
agricultural land at its value for agriculture, instead of its full fair market value,
which is generally higher. Differential assessment laws are enacted by states and
implemented at the local level. With a few exceptions, the cost of the programs is
borne at the local level.
Differential assessment programs help ensure the economic viability of
agriculture. Since high taxes reduce profits, and lack of profitability is a major
motivation for farmers to sell land for development, differential assessment laws
also protect the land base. Finally, these laws help correct inequities in the
property tax system. Owners of farmland demand fewer local public getvices
than residential landowners. but they pay a disproportionately high share of local
property taxes. Differential assessment helps bring farmers' property taxes in line
with what it actually costs local governments to provide services to the land.
Differential assessment is also known as current use assessment, current
use valuation, farm use valuation. use assessment and use value assessment.
153
Programs that are enacted at the local level
· Agricultural protection zoning
Agricultural protection zoning ordinances designate areas where farming is the
primary land use and discourage other land uses in those areas. APZ limits the activities
that are permitted in agricultural zones. The most restrictive regulations prohibit any uses
that might be incompatible with commercial farming.
APZ ordinances also restrict the density. of residential development in agricultural
zones. Maximum densities range from one house per 20 acres in the eastern United
States to one house per 640 acres in the west. Some local ordinances also contain right-
to-farm provisions and authorize commercial agricultural activities, such as farmstands,
that enhance farm profitability. Occasionally, farmers in an agricultural zone are required
to prepare farm management plans.
In most states, APZ is implemented at the counry level, although towns and
townships may also have APZ ordinances. Zoning can be modified through the local
political process. Generally, the enactment of an APZ ordinance results in a reduction of
permitted residential densities in the new zone. This reduction in density, also called
downzoning, is generally controversial because it can reduce the market value of land. A
change in zoning that increases permitted residential densities is known as upzoning. A
change in the zoning designation of an area-from agricultural to commercial, for
example-is known as rezoning. Successful petitions for upzoning and rezoning in
agricultural protection zones often result in farmland conversion.
APZ stabilizes the agricultural land base by keeping large tracts of land relatively
free of non-farm development. This can reduce the likelihood of conflicts between
farmers and their non-farming neighbors. Communities can use APZ to conserve
a"cfitical mass" of agricultural land, enough to keep individual farms from becoming
isolated islands in a sea of residential neighborhoods. Maintaining a critical mass of
agricultural land can ensure that there will be enough farms to support local agricultural
service businesses. By restricting the development potential of large properties, APZ
limits land speculation and helps keep land affordable to farmers and ranchers. Finally,
APZ helps promote orderly growth by preventing sprawl into rural areas, and benefits
farmers and non-farmers alike by protecting scenic landscapes and maintaining open
space.
Cluster zoning
Cluster zoning ordinances allow or require houses to be grouped close together on
small lots to protect open land. The portion of the parcel that is not developed may be
restricted by a conservation easement. Cluster developments are also known as cluster
subdivisions, open space or open land subdivisions.
Cluster subdivisions can keep land available for agricultural use, but generally
they are not designed to support commercial agriculture. The protected land is typically
owned by developers or homeowners' associations. Homeowners may object to renting
their property to farmers and ranchers because of the noise, dust and odors associated
with commercial agricultural production. Even if the owners are willing to let the land be
used for agriculture, undeveloped portions of cluster subdivisions may not be large
enough for farmers to operate efficienfiy, and access can also be a problem. For these
154
reasons, cluster zoning has been used more successfully to preserve open space or to
create transitional areas between farms and residential areas than to protect farmland.
Comprehensive planning
Comprehensive planning allows counties, cities, towns and townships to create a
vision for their joint future. Comprehensive plans, which are also known as master or
general plans, outline local government policies, objectives and decision guidelines, and
serve as blueprints for development. They typically identify areas targeted for a variety
of different land uses, including agriculture, forestry, residential, commercial, industrial
and recreational activities. Comprehensive plans provide a rationale for zoning and
promote the orderly development of public services.
A comprehensive plan can form the foundation of a local farmland protection
strategy by identifying areas to be protected for agricultural use and areas where growth
will be encouraged. It may include policies designed to conserve natural resources and
provide affordable housing and adequate public services. Some counties have used the
comprehensive planning process to encourage their cities and towns to develop UGBs
and adopt agricultural protection zoning. Others have incorporated the use of PACE and
transfer of development fights into their master plans.
Mitigation ordinances and policies
Generally, developers place an agricultural conservation easement on farmland in
another part of the city, although mitigation may also be satisfied by paying a fee. While
most of the regulatory farmland protection techniques restrict the property rights of
farmers, the mitigation ordinance makes developers pay for farmland protection.
King County, Wash., has a "no net loss of farmland" policy in its comprehensive
plan. The policy prohibits the conversion of land subject to APZ unless an equal amount
of agricultural land of the same or better quali~' is added to the county's agricultural
production zones.
Local right-to-farm ordinances
Local governments around the nation are enacting their own fight-to-farm laws to
strengthen and clarify weak language in state laws. Local right-to-farm laws are most
widespread in California, where the state farm bureau developed and distributed a model
right-to-farm ordinance during the 1980s.
A local fight-to-farm ordinance can serve as a formal policy statement that
agriculture is a valuable pan of the cotmr3., or tovm economy and culture. Some require
that a notice be placed on the deed to all properties in agricultural areas, cautioning
potential buyers that the3' may experience noise, dust, odors and other inconveniences
due to fanning and ranching operations. Local ordinances help educate residents about
the'needs of commercial agriculture and reassure farmers that their communities support
them.
155
Transfer of development rights (TDR)
Transfer of development rights (TDR) programs allow landowners to transfer the
right to develop one parcel of land to a different parcel of land. Generally established
through local zoning ordinances, TDR programs can protect farmland by shifting
development from agricultural areas to areas planned for growth. When the development
fights are transferred from a piece of property, the land is restricted with a permanent
agricultural conservation easement. Buying development fights generally allows
landowners to build at a higher density than ordinarily permitted by the base zoning
Most TDR transactions are between private landowners and developers. Local
governments approve transactions and monitor easements. A few jurisdictions have
created "TDR banks" that buy development fights with public funds and sell them to
developers and other private landowners.
TDR programs are designed to accomplish the same purposes as publicly funded
PACE programs. They prevent nonagricultural development of farmland, reduce the
market value of protected farms and provide farmland owners with liquid capital that can
be used to enhance farm viability.
TDR programs also offer a potential solution to the political and legal problems
that many communities face when they try to restrict development of farmland.
Landowners often oppose agricultural protection zoning and other land use regulations
because they can reduce equity. APZ can benefit farmers by preventing urbanization, but
it may also reduce the fair market value of their land. When downzoning is combined
with a TDR program, however, landowners can retain their equity by selling development
rights.
Other strategies to protect farmland and support agriculture
Competition for land is only one of the problems facing farmers and ranchers.
Financial problems and the burden of complying with regulations are also significant
challenges for commercial agricultural operations. Most farmers say the best way to
protect farmland is to keep farming profitable. State and local governments have created
a variety of marketing programs to support and enhance the economics of agriculture.
Several states and a few local governments have developed programs that compensate
farmers for protecting natural resources.
156
Appendix G. Definitions of real estate classifications
Sources:
Some of the sources used for this appendix include the Code of Iowa
1997, Iowa Administrative Code 1997 (IAC), the Real Property. Appraisal
Manual, "Duties and Responsibilities of Iowa Assessors" and An
Introduction to Iowa Property Tax, The Iowa Depmunent of Revenue and
Finance prepares the latter three documents.
Real estate is classified according to IAC 701-71.1 12/4/96, 11/5/97 (1-6), and
determined by city and county assessors as provided in this rule. The assessor determines
classification by following the guidelines of this rule and the status of real estate as of
January 1 of the assessing year. Subrule 71.1(8) details exceptions that property be
classified according to its use.
In the state of Iowa, property subject to taxation is classified as real estate or personal
property. It is essential to determine the proper classification to insure the correct
property tax liabilities. Real estate includes land, buildings, and structures. In addition,
property not ordinarily removed when the owner moves to a new location is also assessed
as real estate. Examples of personal property include inventories, farm machinery, office
furnishings, boats, etc. There are certain types of property that are not assessable. In lieu
of property taxes on motor vehicles and mobile homes, registration fees are paid instead.
The controlling factor in determining the classification of realty is the primary use or
intended primary use of the property. Other incidental use may be made of a portion of
the property as long as a single primary use of the property can be identified. As an
example, a dwelling would be classified as residential property although a portion may be
used as an office for a commercial venture. The commercial use of this property is
incidental to its primary use, and retains the original assessment of residential property.
Also, while zoning laws may affect property classification, they are not necessarily the
controlling factor. A house located in a zoned industrial area is classified as residential
property if it is used as a residence. If the house were razed, the vacant lot would revert
to industrial realty.
Classification of Real Estate
Iowa Administrative Code, Chapter 71, Assessment Practices and Equalization
701-71.1 (3) Agricultural real estate. Agricultural real estate shall include all
tracts of land and the improvements and structures located on them which are in good
faith used primarily for agricultural purposes except buildings which are primarily used
or intended for human habitation as defined in subrule 71.1 (4). Land and the
nonresidential improvements and structures located on it shall be considered to be used
primarily for agricultural purposes if its principal use is devoted to the raising and
harvesting of crops or forest or fruit trees, the rearing, feeding, and management of
livestock, or horticulture, all for intended profit.
157
Agricultural real estate shall also include woodland, wasteland, and pastureland,
but only if that land is held or operated in conjunction with agricultural real estate
as defined in this subrule.
71.1(4) Residential real estate. Residential real estate shall include all lands and
buildings which are primarily used or intended for human habitation, including those
buildings located on agricultural land. Buildings used primarily or intended for human
habitation shall include the dwelling as well as structures and improvements used
primarily as part of, or in conjunction with, the dwelling. This includes but is not limited
to garages, whether attached or detached, tennis courts, swimming pools, guest cottages,
and storage sheds for household goods. Residential real estate located on agricultural
land shall include only buildings as defined in this subrule. Buildings for human
habitation that are used as commercial ventures, including but not limited to hotels,
motels, rest homes, and structures containing three or more separate living quarters shall
not be considered residential real estate. However, regardless of the number of separate
living quarters, condominiums, multiple housing cooperatives organized under Iowa
Code chapter 499A, and land and buildings owned and operated by organizations that
have received tax-exempt status under Section 501 (c)(3) of the Internal Revenue Code, if
the rental income from the property is not taxed as unrelated business income under Iowa
Code section 422.33 (1A), shall be considered residential real estate.
71.1(5) Commercial real estate. Commercial real estate shall include all lands
and improvements and structures located thereon which are primarily used or intended as
a place of business where goods, wares, services or merchandise are stored or offered for
sale at wholesale or retail. Commercial realty shall also include hotels, motels, rest
homes, structures consisting of three or mbre separate living quarters and any other
buildings for human habitation that are used as a commercial venture. Commercial real
estate shall also include data processing equipment as defined in Iowa Code section
427A. 1 (1)"j ," except data processing equipment used in the manufacturing process.
However, regardless of the number of separate living quarters or any commercial use of
the property, single- and two-family dwellings, condominiums, multiple housing
cooperatives organized under Iowa Code chapter 499A, and land and buildings used
primarily for human habitation and owned and operated by organizations that have
received m-exempt status under Section 501 (c)(3) of the Internal Revenue Code, if the
rental income from the property is not taxed as unrelated business income under Iowa
Code section 422.33(1A), shall be classified as residential real estate.
71.1 (6) Industrial real estate.
a. Land and buildings.
(1) Industrial real estate includes land, buildings, structures, and improvements
used primarily as a manufacturing establishment. A manufacturing establishment is a
business entity in which the primary activity consists of adding to the value of personal
property by any process of manufacturing, refining, purifying, the packing of meats, or
the combination of different materials with the intent of selling the product for gain or
profit. Industrial real estate includes land .and buildings used for the storage of raw
materials or finished products and which are an integral part of the m_anufacturing
establishment, and also includes office space used as part of a manufacturing
establishment.
(2) Whether property is used primarily as a manufacturing establishment and,
therefore, assessed as industrial real estate depends upon the extent to which the property
158
is used for the activities enumerated in subparagraph 71.1 (6)"a"( 1 ). Property in which
the performance of these activities is only incidental to the property's primary use for
another purpose is not a manufacturing establishment. For example, a grocery store in
which bakery goods are prepared would be assessed as commercial real estate since the
primary use of the grocery store premises is for the sale of goods not manufactured by the
grocery and the industrial activity, i.e., baking, is only incidental to the store premises'
primary use. However, property which is used primarily as a bakery would be assessed
as industrial real estate even if baked goods are sold at retail on the premises since the
bakery premieses' primary use would be for an industrial activity to which the retail sale
of baked goods is merely incidental.
(3) Property used primarily for the extraction of rock or mineral substances from
the earth is not a manufacturing establishment if the only processing performed on the
substance is to change its size by crushing or pulverizing.
Responsibility of Local Assessors
Iowa Administrative Code, Chapter 78, Property Tax Exemptions
701-78.1 (427,441 ) Responsibility of local assessors.
701-78.1 ( 1 ) The assessor shall determine the taxable status of all property. If an
application for exemption is required to be filed under Iowa Code subsection 427.1 (23),
the assessor shall consider the information contained in the application in determining the
taxable status of the property. The assessor may also request fi'om any property owner or
claimant any additional information necessary to the determination of the taxable status
of the property. However, the assessor shall not base the determination of the taxable
status of property solely on the statement of objects or purposes of the organization,
institution, or society seeking an exemption. The use of the property rather than the
objects or purposes of the organization, institution, or society shall be the controlling
factor in determining the taxable status of property.
Tax Exempt Status
Code of Iowa 1997 Chapter 427, Property Exempt and Taxable
427.]
1.
.~.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
Exemptions. The following classes of property shall not be taxed:
Federal and state property.
Municipal and military property.
Publle grounds and cemeteries.
Fire company buildings and grounds.
Property of association of war veterans.
Property of cemetery associations.
Libraries and art galleries.
Property of religious, literary, and charitable societies.
Property of educational institutions.
Homes for soldiers.
Agricultural produce.
Government lands.
Public airports.
159
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
Statement of objects and uses filed. A society or organization claiming
an exemption under subsection 5 or subsection 8 of this section shall
file with the assessor not later than July 1 a statement upon forms to be
prescribed by the director of revenue and finance, describing the
nature of the property upon which the exemption is claimed and
setting out in detail any uses and income from the property derived
from the rentals, leases, or other uses of the property not solely for the
appropriate objects of the society or organization.
Mandatory denial. No exemption shall be granted upon any property
which is the location of federally licensed devices not lawfully
permitted to operate under the laws of the state.
Revoking exemption. Any taxpayer or any taxing district may make
application to the director of revenue and finance for revocation for
any exemption, based upon alleged violations of this chapter. The
director of revenue and finance may also on the director' s own motion
set aside any exemption which has been granted upon property. for
which exemption is claimed under this chapter.
Rural water sales.
Assessed value of exempt property. Each county and city assessor
shall determine the assessment value that would be assigned to the
property if it were taxable and value all tax-exempt property, within the
assessor' s jurisdiction. A summary report of tax exempt property shall
be filed with the director of revenue and finance and the local board of
review on or before April 16 of each year on forms prescribed by the
director of revenue and finance.
Pollution control and recycling.
Impoundmerit structures.
Low-rent housing.
Natural conservation or wildlife areas.
Native prairie and wetland.
Land certified as a wildlife habitat.
Right-of-way.
Public television station.
Speculative shell buildings of certain organizations.
Joint water utilities.
Methane gas conversion.
Valuation of Real Estate
"Property taxes are not determined by a single individual who assesses your
property and sends you a bill. The final tax rate is the result of budgets established to
provide services, an assessor's assessment, a county auditor's calculations, and laws
administered by the Iowa Department of Revenue and Finance." (An Introduction to Iowa
Property Tax intro page)
160
Iowa Administrative Code, Chapter 71, Assessment Practices and Equalization
701-71.2(421,428,441 ) Assessment and valuation of real estate.
71.2(1 ) Responsibility of assessor. The valuation of real estate as established by
city and county assessors shall be the actual value of the real estate as of January 1 of the
year in which the assessment is made. New parcels of real estate created by the division
of existing parcels of real estate shall be assessed separately as of January 1 of the year
following the division of the existing parcel of real estate.
71.2(2) Responsiblity of other assessing o~cials. Whenever local boards of
review, county auditors, and county treasurers exercise assessment functions allowed or
required by law, they shall follow the provisions of subrule 71.2( 1 ) and rules
71.3 (421,428,441 ) to 71.7 (421,427A,428,441 ).
This rule is intended to implement Iowa Code sections 421.17, 428.4 and 441.21.
701-71.3 (421,428,4-41 ) Valuation of agricultural real estate. Agricultural real
estate shall be assessed at its actual value as defined in Iowa Code section 44 1.21 by
giving exclusive consideration to its productivity and net earning Capacity. In
determining the actual value of agricultural real estate, city and county assessors shall use
the "Iowa Real Property Appraisal Manual" and any other guidelines issued by the
department of revenue and finance pursuant to Iowa Code section 421.17(18).
In determining the productivity and net earning capacity of agricultural real estate
the assessor shall also use available data from Iowa State University, the Iowa crop and
livestock reporting service, the department of revenue and finance, or other reliable
sources. The assessor shall also consider the results of a modern soil survey, if
completed.
The assessor shall determine the actual valuation of agricultural real estate within
the assessing jurisdiction and spread such valuation throughout the jurisdiction so that
each parcel of real estate is assessed at its actual value as defmed in Iowa Code section
441.21.
This rule is intended to implement Iowa Code sections 421.17, 428.4 and 441.21.
701-71.4(421,428,441 ) Valuation of residential real estate. Residential real
estate shall be assessed at its actual value as defined in Iowa Code section 44 1.21.
In determining the actual value of residential real estate, city and county assessors
shall use the appraisal manual issued by the department of revenue and finance pursuant
to Iowa Code section 421.17(18) as well as a locally conducted assessment/sales ratio
study, an analvsis of sales o mpa~able properties, and any other relevant data
, fco
available.
This rule is intended to implement Iowa Code sections 421.17, 428.4 and 441.21.
701-71.5(421,428,44 1 ) Valuation of commercial real estate. Commercial real
estate shall be assessed at its actual value as defined in Iowa Code section 441.21. The
director of revenue and finance shall assess the property of long distance telephone
companies as defined in Iowa Code section 476.1 D(10) which property is first assessed
for taxation on or after January 1, 1996, in the same manner as commercial real estate.
In determining the actual value of commercial real estate, city and county
assessors shall use the appraisal manual issued by the department of revenue and finance
pursuant to Iowa Code section 421.17(18) as well as locally conducted assessment/sales
ratio study, an analysis of sales of comparable properties, and any other relevant data
available.
161
Reconciliation Report
Iowa Administrative Code, Chapter 71, Assessment Practices and Equalization
701-71.9(428,441 ) Reconciliation report. The assessor's report of any
revaluation required by Iowa Code section 428.4 shall be made on the reconciliation
report prescribed and furnished by the department of revenue and finance. The assessor
shall enter on the report all information required by the department. The reconciliation
report shall be a part of the abstract of assessment required by Iowa Code section 441.45
and shall be reviewed and considered by the director in equalizing valuations of classes
of property,.
This rule is intended to implement Iowa Code section 428.4 and 441.45.
Assessment/Sales Ratio Study
Iowa Administrative Code, Chapter 71, Assessment Practices and Equalization
701-71.10(421 ) Assessment/sales ratio study.
71.10(1) Basic data. Basic data shall be that submitted to the deparunent of
revenue and finance by county recorders and city and county assessors on forms
prescribed and provided by the department, information furnished by parties to real estate
transactions, and information obtained by field investigations made by the department of
revenue and finance.
71.10(2) Responsibility of recorders and assessors. County recorders and city
and county assessors shall complete the prescribed forms as required by Iowa Code
subsection 421.17(6) and rule 701-79.3(428A) in accordance with instructions issued by
the department. Assessed values entered on the prescribed form shall be those
established as of January 1 of the year in which the sale takes place.
71.10(3) Normal sales. All real estate transfers shall be considered by the
department of revenue and finance to be normal sales unless there exists definite
information which would indicate the transfer was not an arms-length transaction or is of
an excludable nature as provided in Iowa Code section 441.21.
This rule is intended to implement Iowa Code section 421.17.
163
Appendix H1. Bremer County data
Table 89,
Total acres convened by class and year (to the nearest whole acre)
Rural
Residential Residential industrial Commercial Exempt
1988 0 43 0 31 0
1989 0 49 3 17 4
1990 40 94 0 0 0
1991 19 41 18 76 57
1992 38 115 0 55 5
1993 40 56 0 3 32
1994 33 98 0 0 0
1995 49 93 9 38 6
1996 13 229 0 47 97
1997 71 1,116 ll 6 1
1998 15 268 3 1 8
Figure 34. Total acres converted by class and year
1200
800
600
400
200
! · RurEt R~icl~rti~l
[--- · ....Residential
: ....· ....Indu~Ifi~l
' x Cornmecial
L · Exeml::t
165
Appendix H2. Cerro Gordo County data
Table 90.
Total acres converted by class and year (to the nearest whole acre)
Residential Industrial Commercial Exempt
1982 ] 0 0 0
1983 ]8 0 603 3
1984 20 0 0 0
1985 28 0 52 11
1986 20 0 3 12
1987 37 0 0 0
1988 27 0 0 55
1989 3 0 43 118
1990 19 0 7 306
1991 8 0 0 468
1992 I0 0 167 80
1993 27 0 0 250
1994 25 3 21 365
1995 49 14 26 1,119
1996 19 0 9 663
1997 0 0 11 1,180
1998 0 0 7 53
Figure 35. Total acres convened by class and year
1400
1200
1000
800
600
400
200
X
r * Residential
-' Industrial
i .i, Commercial
! ....x--- Exempt
167
Appendix H3. Dallas County data
Table 91.
Total acres converted by class and year (to the nearest whole acre)
Residential Industrial Commercial
]982 ]39 0 0
]983 ]56 0 0
1984 154 0 0
1985 23 0 0
1986 454 0 0
1987 284 0 0
1988 114 0 0
1989 317 0 0
1990 237 0 21
1991 131 1 0
1992 216 0 9
1993 910 0 1
1994 967 0 65
1995 583 0 0
1996 1,132 0 39
1997 553 0 0
1998 539 0 13
No year 131 0 0
indicted
Exempt
Forest Reserve
77
4 647
1 93
1 80
3 5
25 209
37 141
58 743
30 218
4 643
11 486
38 294
7 215
38 117
51 167
45 140
8 27
0 0
Figure 36. Total acres converted by class and year
1200
1000 ,
800
,e / "\ e~ / ,, ' \
600 ..
\\
200
O
Z
j- - +- - Residential
= Industrial
Commercial
o Exempt
· Forest Reserve
169
Appendix H4. Monroe County data
Table 92.
Total acres convened by class and year
Residential Industrial Commercial Exempt Forest Reserve
1987 6.0 0.0 0.0 0.2 0.0
1988 2.8 0.0 0.0 0.0 0.0
1989 12.2 0.0 0.0 257.0 2.0
1990 25.6 18.3 27.0 0.0 0.0
1991 40.6 0.0 40.0 0.0 0.0
1992 20.4 0.0 1.0 0.0 0.0
1993 133.0 138.7 0.3 0.0 3.3
1994 524.7 1.5 0.0 0.0 20.0
1995 89.3 0.0 0.0 1.9 14.0
1996 101.9 0.0 6.8 24.7 3.0
1997 125.8 8.5 7.0 153.0 0.0
1998 75.6 136.0 19.0 0.0 0.0
Figure 37. Total acres convened by class and year
500
400
300
200
100
~ ; Resi~rtial
· Indus~'iel
~, Comme'dal
-- - x- -- Exempt
C Forest Reserve
171
Appendix H5. Pottawattamie County data
Table 93,
Total acres convened by class and year (to the nearest whole acre)
Residential Indu~dal Commercial Exempt Annexed
1982 99 0 8 0 0
1983 63 0 0 0 0
1984 109 0 0 0 0
1985 240 0 73 0 0
1986 218 0 5 43 0
1987 302 0 0 0 0
1988 352 0 95 56 0
1989 595 0 45 0 0
1990 381 0 41 5 0
1991 217 0 1 29 0
1992 247 0 74 243 0
1993 3356 0 78 143 0
1994 235 0 0 354 0
1995 446 0 297 166 38
1996 449 0 310 160 5
1997 518 0 0 16 132
1998 334 3 130 305 53
Figure 38.
Total acres convened by class and year
7o0
600
500
400
300
200
100 :a
O!l,.l,
I\
I \
I \
I ,,
· Ill \
i- -e- - Residential
= Industrial
Commercjal
Exempt
Annexed
173
Appendix H6. Scott County data
Table 94.
Total acres converted by class and year
Residential Industrial Commercial Exempt
] 982 ] 85.6 0.0 66.9? 9.46
1983 45.8 82.6 0.00 6.40
1984 153.8 0.0 2.30 4.00
1985 121.8 0.0 2.60 5.10
1986 143.7 0.0 1.70 36.50
1987 87.5 0.0 9.50 11.90
1988 133.4 0.0 1.80 20.50
1989 247.5 0.0 0.00 0.20
1990 109.7 0.0 0.00 0.40
1991 98.6 0.0 3.30 1.80
1992 155.4 0.0 16.50 71.90
1993 66.8 0.0 4.10 78.90
1994 197.1 16.4 36.10 117.10
1995 183.1 0.0 129.50 43.30
1996 309.8 4.7 27.30 0.70
1997 252.4 0..0 23.00 33.10
1998 68.2 0.0 2.90 21.90
Figure 39. Total acres converted by class and year
350
250 ~ 1
' "' / l , Residential
200 ~> / "/ ,,,~' '/ .. = Industrial
150 " ~ /" ' {r ,," i, - ~- -- Commercial
1 O0 // ~ \~...._ .~/' "\", "' ..'~'. '. t,, = Exempt
50~ ~
0 ~"~ '~ ,
175
Appendix H7. Story County data
Table 95. Total acres convened by class and year
Residential Industrial Commercial Exempt
Annex Forest Reserve
1983 6.54 38.0 130.42 0.00 0.00 15.52
1984 1.02 0.0 43.75 0.00 0.00 27.50
1985 54.45 0.0 194.67 35.54 217.70 0.00
1986 96.38 0.0 0.00 0.00 45.55 0.00
1987 29.98 0.0 0.00 137.57 0.00 0.00
1988 68.94 0.0 42.67 0.04 121.04 0.00
1989 533.29 0.0 42.89 44.74 138.51 0.00
1990 37.92 0.0 0.00 0.00 39.65 0.00
1991 79.18 0.0 3.06 3.39 17.89 0.00
1992 103.62 0.0 0.11 31.16 69.17 0.00
1993 116.61 0.0 105.91 0.00 58.26 0.00
1994 140.63 0.0 58.29 4.00 170.99 0.00
1995 391.59 0.0 81.55 57.93 120.50 0.00
1996 468.25 0.0 103.67 75.25 395.32 0.00
1997 288.75 0.0 25.71 2.11 0.00 0.00
1998 674.75 0.0 50.40 653.93 168.34 0.00
Figure 40. Total acres convened by class and year
800
700
600
500
400
·
300
200 .' . ' " =
1 O00 ~.~~.~._, '
I!
"'t Residential
Industrial
, [ ,a. Commercial
.' i ~ Exempt
, , ~ {---~--Annex
Forest Resem9
177
Appendix H8. City of Ames data
Table 96. Total acres converted by class and year (to the nearest whole acre)
Residential Indu~rial Commercial Exempt
1988 0 0 0 14
]989 0 0 0 0
1990 lll 0 28 0
1991 48 0 55 0
1992 70 0 61 0
1993 55 24 70 0
1994 254 0 104 0
1995 53 0 10 0
1996 85 0 57 0
1997 31 0 25 0
1998 138 185 60 0
Figure 41.
Total acres converted by class and year
300
250
200
150
100
50
1988 1989 1990 1991 1992 1993 199,4 1995 1996 1997 1998
, Resdential
= Industnal
.... &-.- Commeraal
x Exempt
179
Appendix H9. City of Davenport data
Table 97,
Total acres convened by class and year
Residential Industrial Commercial Exempt
]98? 0.0 0.0 0,0 4.5
1988 23.8 0.0 0.0 0.0
1989 408.5 63.5 15.3 0.0
1990 22.9 0.0 40.1 2.3
1991 20.8 3 ] .4 7.8 7.4
1992 42.4 0,0 11.3 0.0
1993 117.4 0,0 12.7 0.1
1994 69.0 6.4 49.5 0.0
1995 82.9 0.0 7?. 1 0.0
1996 114.6 0.0 62.2 18.6
1997 33.4 0.0 98.4 0.0
1998 26.7 0.0 0,0 50.4
Figure 42. Total acres convened by class and year
450
400
350
300
250
200
150 '
50i !,
0
19~7 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998
,, Fi~sidential
i+ In~u~ hal
~ ,k Comm~cial
~ E~mpt
181
Appendix HIO.
City of Mason City data
Table
Total acres convened by class and year (to the nearest whole acre)
Residential Industrial Commercial Exempt
1982 223 0 0 0
1983 0 0 0 0
1984 14 0 11 97
1985 18 2 29 5
1986 0 0 19 0
1987 0 0 163 12
1988 8 50 0 6
1989 0 0 0 104
1990 6 52 5 0
1991 0 2 16 37
1992 26 60 15 0
1993 55 0 0 0
1994 0 0 3 0
1995 3 0 7 0
1996 112 40 20 0
1997 64 310 0 0
1998 12 0 46 0
Figure 43.
Total acres convened by class and year
350
300
250
200 '. '
5~ ,,_~,__~_ I ." , [] . ,e '.
· Residential
.... me-- - Industria]
· Comme'ciai
x Exeml~
183
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(ISPAID 6.0). Iowa Agriculture and Home Economics Experiment Station and University Extension
Service, Iowa State Universih.'. Ames. p. 28.
Monroe County Land Use Commission. June 4, 1984. Monroe County, Iowa 1983 Land Use Inventory.
Pottawattamie County Land Preservation and Use Commission. January, 1984. Pottctwattamie County
Land Preservation and Use Commission County Land Use Inventory.
Scott County Land Preservation and Use Commission. July 27, 1983. Scott County, Iowa 1983 Land Use
Inventory.
185
Sorer~en, A. Ann, Richard P. Greene, arid Karen Russ. 1997. Farming on the Edge. American Farmland
Trust, Center for Agriculture in the Environment, Northern Illinois University, DeKalb. p. 29.
Story. County Land Preservation and Use Commission. January 24, 1984. Story County, Iowa 1983 Land
Use Inventory.
Tweeten, Luther. 1998. Competing for Scarce Land: Food Security and Farmland Preservation.
Department of Agricultural, Environmental, and Development Economics, Ohio State University, p.
28.
US Department of Agriculture. 1998. lowa Agricultural Statistics. National Agricultural Statistics
Service.
Vogelmann. J.E., T. Sohl, and S.M. Howard. 1998. Regional Characterization of Land Cover Using
Multiple Sources of Data. Photogrammetric Engineering and Remote Sensing 64( 1 ): 45-57.
186
CITY COUNCIL INFORMATION PACKET
October 29, 1999
MISCELLANEOUS ITEMS
IP1 Meeting Schedule and Tentative Work Session Agendas
IP2
Letter from City Manager to Johnson County Board of Supervisors Chair: Transit
Service to Chatham Oaks [staff memorandum also included]
IP3 Memorandum from City Manager: Iowa League of Cities Board Meeting
IP4 Memorandum from City Manager: Video Tapes
IP5 Memorandum from Parking and Transit Director to City Manager: Capitol Street Ramp
IP6
Letter from Andrew Boeddeker (HUD) to Mayor: Public Housing Management
Assessment Program (PHMAP) Score and Status for Fiscal Year Ended June 30,
1999
IP7 Letter from Johnson County Board of Supervisors Chair to Mayor: Joint Effort
IP8 Memorandum from City Clerk: September 27 Council Work Session
IP9 Memorandum from City Clerk: October 11 Council Work Session
IPlO Memorandum from City Clerk: Employee Computer Equipment Policy
IPll
Letter from David Lyons (Iowa Department of Economic Development) to Parks and
Recreation Director: Funding
IP12 ICAD Accomplishments/Activity Report: 1990-1999
IP13 Minutes: October 25, 26, and 28 - Johnson County Board of Supervisors
IP14
Minutes: September 30 East Central Iowa Employment and Training Consortium
[Vanderhoef]
IP15 Minutes: September 30 East Central Iowa Council of Governments [Vanderhoef]
,elfeI ~1
I November 8
6:30p
I November 9
7:00p
I November 10
6:30p
I November 11
November 15
7:00p
I November 18
7:00p - 8:30p
I November 22
6:30p
City Council Meeting Schedule and
Tentative Work Session Agendas
Octob~
SPECIAL COUNCIL WORK SESSION
SPECIAL FORMAL COUNCIL MEETING
CITY HALL DAY
6:30p Reception
7:00p Program Begins
VETERANS' DAY HOLIDAY - CITY OFFICES CLOSED
Monday
Council Chambers
Tuesday
Council Chambers
Wednesday
Council Chambers
Thursday
SPECIAL COUNCIL WORK SESSION
Joint Meeting with Library Board
Monday
ic Public Library, Room A
SPECIAL COUNCIL WORK SESSION
Council and Council Elect
Thursday
Council Chambers
SPECIAL COUNCIL WORK SESSION
Monday
Council Chambers
Meeting dates/times subject to change
FUTURE WORK SESSION ITEMS
Hickory Hill West Council Goals
Commercial Use of Sidewalks Newspaper Vending Machines
Y2K Update Liquor Licenses
Communication Towers Residing in Vehicles
Kirkwood Signalization
10-29-99
IP2
CITY OF I0 WA CITY
October 28, 1999
Jonathan Jordahl, Chair
Johnson County Board of Supervisors
County Administration Building
P.O. Box 1350
Iowa City, IA 52244-1350
Dear Jonathan:
At the most recent joint Council/Board of Supervisor meeting a request was made to
evaluate the possibility of transit service to Chatham Oaks. Attached is a copy of a
memorandum that was prepared at my request and has been directed to the City Council's
attention. As you will see in your review of this memorandum the cost of such service is
significant and I could not recommend proceeding. Our budget for public transit service
simply does not have the flexibility to absorb a major increase of this consequence.
Sincerely,
~kins
City Manager
" cc: City Council
tp2-1 sa.doc
410 EAST WASHINGTON STREET · IOWA CITY. IOWA 52240-1826 · (319) 356-5000 · FAX (319) 356-5009
City of Iowa City
MEMORANDUM
Date: October 26, 1999
To: Steve Atkins, City Manager
From: Joe Fowler, Director Parking & Transit
Re: Chatham Oaks Bus Service
I understand the County has requested transit service to Chatham Oaks. It was stated that
approximately 20 people per day would likely use this service.
Chatham Oaks is 1.4 miles from the nearest current transit route. There are two ways to provide
the additional service, extend an existing route or create a new route.
The routes currently serving the far west side of Iowa City am 45 minutes long. Extending
service to Chatham Oaks would require at least a one-hour route. A one-hour route is, in our
judgment, too long to provide customers with efficient service. A one-hour route also does not fit
into the current mute structure. It would require changing the current routes and times to
incorporate a new mute into the system. We recently revamped our route structure and new
informational material was prepared.
Chatham Oaks is 3.8 miles from the transit interchange. If we establish a new route to serve this
area it could be a 30-minute route. If this service were added there is no route to pair with it. The
bus would either sit downtown for 30 minutes or have 30-minute service to Chatham Oaks. The
additional cost to provide this service 12 hours per day would be $420 per day, or $107,000 per
year for Monday through Friday service.
An additional concern that would need to be addressed is if service were extended to Chatham
Oaks there is no place to turn a 40-foot bus around at the facility. The current parking lot is not
substantial enough to withstand the constant weight of a bus.
After reviewing the options for extending service to Chatham Oaks and the limited passenger
count I cannot recommend additional service at this time. Please contact me if you would like to
discuss this further.
indexbc\memos~l -3JF.doc
City of Iowa City
MEMORANDUM
Date: October 28, 1999
To: City Council
From: City Manager
Re: Iowa League of Cities Board Meeting
I will be out of the office Thursday, November 4, attending the Iowa League of Cities
Board of Directors meeting.
cc: Dept. Directors
tp3-1 sa.doc
City of Iowa City
MEMORANDUM
Date: October 28, 1999
To: City Council
From: City Manager
Re: Video Tapes
I have a copy of the new promotional video tape from ICAD and the video of our recent
Y2K meeting at West High. If anyone is interested in borrowing, let me know.
cc: Dept. Directors
tp3-2sa.doc
City of Iowa City
MEMORANDUM
Date: October 29, 1999
To: Steve Atkins, City Manager
From: Joe Fowler, Director Parking & Transit(~r
Re: Capitol Street Ramp
Two months ago the Parking Division began a series of improvements in the Capitol Street
Ramp. These improvements were undertaken to upgrade the facility from its 1970's style. This
included creating an open feeling on the lower level, accommodating the new pedestrian flow,
and brightening the interior.
The project started with a good house cleaning. While the lobbies and parking areas were
clean the interior ledges and signs had accumulated a layer of dust. Every sign in the ramp was
cleaned and bird nests and other residue were removed. The north wall on levels one and two
and the cashier booths were painted white to brighten the interior.
The most notable change occurred on the Clinton Street side of the structure. With the
assistance of Parks and Recreation all the plantings on this side of the building were removed
and the trees were trimmed to increase the natural light entering the ramp. The east wall was
cut down to the sidewalk grade. These two changes allowed natural light to enter the facility
and created a new exit to accommodate pedestrian movement. This change also allows visitors
to the central business district to look into the facility, identify it as a parking area, and see that
there is activity inside.
An additional benefit to these changes is that there has been an increase in the usage of the
bicycle racks located inside the ramp.
To complete the project we will be increasing the size of the pedestrian exit located south of the
entrance drive. This will require pouring an additional section of concrete between the sidewalk
and the ramp. The remaining area between the sidewalk and the ramp will be mulched. The
planters that have been removed during the streetscape will be placed in this area.
Shive-Hattery is currently designing a lighting package for the ramp. It is anticipated that this
work will be bid in early 2000 with work scheduled to begin in the spring. When the project is
complete light levels should be similar to those in Chauncey Swan.
indexbc\memos\4-1 J F.doc
Honorable Ernest Lehman
Mayor
410 E. Washington Street
Iowa City, IA 52240-1826
U.S. DEPARTMENT OF HOUSING AND URBAN DEVELOPMENT
KANSAS/MISSOURI STATE OFFICE
Gateway Tower II, Room 200
400 State Avenue
Kansas City, KS 66101-2406
HUD Home Page: http:llwww. hud. gov ,~
October 22, 1999 ~, . ..
Dear Mayor Lehman:
SUBJECT: Public Housing Management Assessment Program (PHMAP) score and
status for fiscal year ended June 30, 1999
This letter is to inform you of the total weighted PHMAP score and status for
the Housing Authority for the City of Iowa City, Iowa (Housing Authority). The
enclosed scoring report shows the indicator name and grade for each indicator and
components.
The Housing Authority's total weighted score for the assessment period is
93.44 percent. This percentage is derived by dividing the Housing Authority's actual
number of points achieved by the potential number of possible points with the ratio
multiplied by 100.
A Housing Authority which achieves a total weighted PHMAP score of 90
percent or more with no individual indicator grade less than "C" shall be
designated as a high performer. The Housing Authority is hereby designated as a
high performer.
A Housing Authority which achieves a total weighted PHMAP modernization
score of 90 percent or more and whose overall weighted PHMAP score is 90 percent
shall be designated as a mod-high performer. The Housing Authority's total weighted
PHMAP modemization score (Indicator Number 2) is 100 percent and is hereby
designated as rood-high performer.
Incentives afforded to High and Standard performing Housing Authorities are in
accordance with 24 CFR 901.130.
The Housing Authority did not submit any modification or exclusion requests.
Therefore, no changes were made to the calculation of the score or grade for any of the
indicators and components included in this assessment.
Since the Housing Authority has less than 250 units, it was not graded on Indicator
Number 7, Resident Services and Community Building, and Indicator Number 8,
Security. Although the Housing Authority was not evaluated for PHMAP purposes, the
functions covered by these two indicators are very important to the successful operation
of your Housing Authority and may, in the future be evaluated by this office for
compliance with statutes, regulations and programmatic guidance.
A Housing Authority may appeal. its PHMAP rating. An appeal can be made only
on the basis of data errors, denial of modification or exclusion requests (when their denial
affects the Housing Authority's total score), denial of an adjustment based on the physical
condition and neighborhood environment of the Housing Authority's developments, or
determination of intentional false certification. An appeal must be received by this office
by close of business on November 8, 1999, or it will not be considered.
This letter will serve as the INITIAL NOTIFICATION and, if no appeal is
received from the Housing Authority by the required timeframe, this letter will also serve
as the FINAL NOTIFICATION.
You should be aware that the Housing Authority's overall score and its score for an
indicator or component may be changed subsequently by the Field Office pursuant to data
included in an independent auditor's report or data acquired in the course of an on-site
confirmatory review.
If you have any questions concerning this assessment or need assistance in
correcting any deficiencies, please contact Ms. Dean Downs at (913) 551-5809.
Sincerely,
Andrew L. oed~
Director
Office of Public Housing
Enclosure
U.S. Department of Housing and Urban Development
PHMAP Housing Authority Scoring Report
All Iterations
As Of I 0/l 8~99
IA022 IOWA CITY
6~30~99 Assessment - In Progress
No. Indicator/Component Name Iter. 1
1 Vacancy Rate and Unit Turnaround Time B
1A Vacancy Rate B
1B Unit Turnaround Time X
2 Modernization A
2A Unexpended Funds A
2B Funds Obligation A
2C Adequacy of Contract Administration A
2D Quality of Physical Work A
2E Budget Controls A
3 Rents Uncollected B
4 Work Orders A
4A Emergency Work Orders A
413 Non-Emergency Work Orders A
5 Inspection of Units and Systems A
5A Inspection of Units A
5B Inspection of Systems A
6 Financial Management A
6A Cash Reserves A
6B Energy Consumption/Utility Expenses X
7 Resident Services and Community Buildin9 X
7A Economic Uplift and Self-Improvement X
7B Resident Organization X
7C Resident Involvement X
7D Resident Programs Management X
8 Security X
8A Tracking and Reporting Crime X
8B Screening of Applicants X
8C Lease Enforcement X
8D Grant Program Goals X
93.44 %
100.00 %
TOTAL SCORE:
MOD SCORE:
Note: X denotes an excluded indicator or component.
All 1996 PHMAP assessments appear as complete in this report, although they may still be in progress. Final scores and designations may
change as result of the monthly updates that will take place until February 1, 1998.
Page 1 of I
Johnson County
% IOWA ~
X_
Jonathan Jordahl, Chair
Charles D. Duffy
Mike Lehman
Sally Stutsman
Carol Thompson
Mayor Ernie Lehman
City of Iowa City
410 East Washington Street
iowa City, iowa 52240
Dear Ma~61~an:
10-29-99
IP7
BOARD OF SUPERVISORS
October 14, 1999
The Johnson County Board of Supervisors would like to accept Iowa City' s offer to take the lead and
invite information services and other relevant staff from each of our governments to discuss and plan
for ways of saving money, time and staff by sharing resources. Possibilities that should be
investigated include:
1. Joint purchasing of software.
2. Coordinating types software purchased so that information can be shared more easily.
Establishing regular communication so that this coordination can be continued as our systems
develop.
3. Sharing staff positions, either for the sake of coordinating efforts or because we do not need to
each hire a full-time person to perform a particular specialized function.
4. Dividing labor between our entities so that we do not unnecessarily duplicate effort. (One unit
could provide a service for all of the others.)
5. New perspectives on shared arrangements which would get around the problem of control by one
entity.
6. Any other good ideas.
It has been suggested that cooperation may be possible in the areas of payroll, human resources,
financial management and Geographical Information Systems. Jean Schultz, our Information
Services Director will be our contact person. Her telephone number is 356-6080. Her address is the
same as ours, 913 South Dubuque Street, Iowa City, Iowa 52240. She will contact staff from other
Johnson County departments to participate as necessary. We look forward to hearing from you.
cerel~'4~d/k_
nathan Jordahl
CC:
Cities of Coralville, North Liberty, Tiffin and University Heights
Iowa City Community School District Board of Directors
913 SOUTH DUBUQUE ST. P.O. BOX 1350 IOWA CITY, IOWA 52244-1350 TEL: (319) 356-6000 FAX: (319) 354-4213
City of iowa City
MEMORANDUM
IP8
Date:
To:
From:
Re:
October 26, 1999
Mayor and City Council
Marian K. Karr City Clerk
Council Work Session, September 27, 1999, 6:30 P.M.
Council: Lehman, Champion, Kubby, Norton, O'Donnell, Thornberry, Vanderhoef
Staff: Atkins, Helling, Karr, Dilkes, Franklin, Davidson, Yapp, Fosse
Tapes: Reel 99-92, Both Sides; 99-93, Side 1.
A complete transcription is available in the City Clerk's Office.
REVIEW ZONING ITEMS
Planning and Community Development Director Franklin presented the following Planning and
Zoning items for discussion:
A. PUBLIC HEARING ON AN ORDINANCE AMENDING TITLE 14. CHAPTER 6. ZONING, TO
ALLOW BANNER SIGNS IN SHOPPING CENTERS.
P&Z Commissioner Member Bovbjerg presented information. Council agreed to continue the
public hearing to allow the Planning and Zoning Commission to review the issue of number
of banners that may be permitted and address celebratory language.
B. PUBLIC HEARING ON AN ORDINANCE AMENDING TITLE 14. CHAPTER 6. ZONING. TO
AMEND THE PROVISIONS RELATING TO HOME OCCUPATIONS.
C. PUBLIC HEARING ON AN ORDINANCE AMENDING TITLE 14, CHAPTER 6. ZONING. TO
ALLOW PUBLIC UTILITIES IN COMMERCIAL AND INDUSTRIAL ZONES.
ORDINANCE CONDITIONALLY CHANGING THE ZONING DESIGNATION FROM
MEDIUM DENSITY SINGLE-FAMILY RESIDENTIAL (RS-8) TO PLANNED
DEVELOPMENT HOUSING OVERLAY (OPDH-8). AND APPROVING A PRELIMINARY
PLANNED DEVELOPMENT HOUSING OVERLAY PLAN FOR 24 TOWNHOUSE-STYLE
DWELLING UNITS FOR APPROXIMATELY 7.72 ACRES LOCATED AT THE NORTHEAST
CORNER OF BARRINGTON ROAD AND HUNTINGTON DRIVE. (Windsor Ridge Part
13/REZ99-0007) (SECOND CONSIDERATION)
ORDINANCE CONDITIONALLY CHANGING THE ZONING DESIGNATION OF
APPROXIMATELY 7.46 ACRES FROM MEDIUM DENSITY SINGLE-FAMILY
RESIDENTIAL (RS-8) TO PLANNED DEVELOPMENT HOUSING OVERLAY (OPDH-8)
AND THE APPROVAL OF A PRELIMINARY OPDH PLAN FOR 72 RESIDENTIAL
Council Work Session
September 27, 1999
Page 2
DWELLING UNITS WITHIN THE WINDSOR RIDGE SUBDIVISION LOCATED AT THE
EAST TERMINUS OF COURT STREET. (Windsor Ridge Part 12/REZ99-0006) (PASS AND
ADOPT)
ORDINANCE VACATING AN APPROXIMATE 7.720 SQUARE FOOT UNIMPROVED
PORTION OF VIRGINIA DRIVE LOCATED BETWEEN LOTS 2 AND 14 OF NORTH HILLS
SUBDIVISION IMMEDIATELY NORTHEAST OF THE INTERSECTION OF VIRGINIA
DRIVE AND RIDGEWOOD LANE. (VAC87-0001) (PASS AND ADOPT)
REVIEW AGENDA ITEMS
(Agenda #3e(1 ) - RESOLUTION AUTHORIZING HISTORIC PRESERVATION
COMMISSION TO APPLY...PRESERVATION SERVICES FUND (PSF)
GRANT .... REHABILITATION OF THE MONTGOMERY-BUTLER HOUSE) Majority of
Council clarified that support of this request should not be interpreted that the Council was
inclined to spend large amounts of money to restore this house.
(Agenda #3e(4) - RESOLUTION AUTHORIZING ...AGREEMENT .... FOR PEDESTRIAN
USE EASEMENT...WITHIN PLAZA CENTRE ONE...) In response to Kubby, PCD Director
Franklin explained that a new hallway is being planned through the former Eby's that would
allow access other than through the present court yard.
(Agenda #11 - RESOLUTION AUTHORIZING...AGREEMENT...LEPIC-KROEGER
REALTORS) Asst. PCD Director Davidson explained special strategies would be utilized in
marketing the project (Tower Place and Parking). Council requested that a representative
from Lepic-Kroeger be present at the formal meeting to address the additional one-half
percent.
(Agenda #13 - ORDINANCE AMENDING TITLE 8 .... EMPLOYEE EDUCATION -
CIGARETTE SALES ...... ELIMINATING THE REQUIREMENT THAT EMPLOYEES SIGN
AN AFFIDAVIT...) In response to Kubby, City Clerk Karr stated she would forward a copy of
the proposed ordinance to the Johnson County Coalition for Tobacco Free Youth.
(Agenda #20 - RESOLUTION AWARDING CONTRACT .... CHAUNCEY SWAN FOUNTAIN
PROJECT) City Clerk Karr noted staff was recommending rejecting bids and the resolution
should be defeated.
(Agenda #26 - RESOLUTION AUTHORIZING EXECUTION OF AN
EASEMENT .... TEMPORARY USE OF RIGHT-OF-WAY...GENE, L.L.C) In response to
Kubby, PCD Director Franklin explained maximizing the intensity of the development on the
site with design issues. City Atty. Dilkes stated the all utility poles in the alley right-of-way
are going to be removed, a widening of the alley as you enter Court Street, and two parking
spaces taken be replaced across the street.
(Agenda #24 - RESOLUTION AUTHORIZING...AGREEMENT...DROLLINGER RIDES
FOR PURCHASE OF CERTAIN AMUSEMENT RIDES) City Arty. Dilkes stated the
agreement had changed to allow remaining equipment to be removed by April 1, rather than
October 15.
Council Work Session
September 27, 1999
Page 3
28E WITH CORALVILLE - SUBDIVISION REVIEW
(Agenda Item #12)
JCCOG Executive Director Davidson present for discussion. Council agreed to proceed as
presented.
LONGFFLLOW-TWAIN PEDESTRIAN TUNNEL
(Agenda Item #7)
Project Coordinator Yapp presented information. After discussion, majority of Council agreed to
proceed with the project utilizing the option 1 location.
COUNCIL TIME
1. Champion stated she had responded to her first e-mail message.
2. O'Donnell requested consideration of forming a solid waste advisory committee. Majority of
Council were interested in the idea as a means of promoting dialog with various
communities in the county, and organizing a recycling program within the schools. The City
Manager will follow up with JCCOG.
3. O'Donnell noted the tremendous response to the fountain dedication.
4. Vanderhoef reminded Council Members that Iowa City would be hosting the Iowa League of
Cities City Hall Day for our area on Wednesday, November 10. Council agreed to a
reception from 6:30-7:00 p.m., forum from 7:00-9:00 p.m., and no cable casting.
5. In response to Norton, City Atty. Dilkes stated she would have information concerning the
matter of sleeping/camping in your car on public streets by the second meeting in October.
6. In response to Norton, City Manager Arkins noted conversations on possible construction of
a building on Taft Speedway for the Chamber of Commerce and/or University that would
require rising Dubuque Street, and that no City money was currently budgeted for the
Dubuque Street feature.
7. (IPll of 9/17 info packet - OFFICER INITIATED TRAFFIC STOPS) The City Manager
stated that the information will be statically validated by someone outside of the community
and a report furnished.
8. (IP5 of 9/24 info packet - Urban Renewal Parcel 64-1a/Library Project) The City Manager
stated his opinion that further discussion of the parcel should wait until a report is forwarded
from the Library on their expansion plans. The Mayor reported that a special work session
and joint meeting with the Library Board will be scheduled. Council agreed to meet at 7:00
p.m. on November 15 with the Library Board.
9. O'Donnell raised concerns regarding long term parking at Terrill Mill park. The City Manager
stated P&R Director Trueblood was aware of the problem.
Council Work Session
September 27, 1999
Page 4
10. (Agenda #3f(4) - JEFF GILLITZER RE KIRKWOOD TRAFFIC STOPS) There was a
majority of Council interested in looking at signalization on Kirkwood Avenue, and requested
it be scheduled for a November work session.
11, Vanderhoef acknowledged the new stencils on the sidewalks in the Ped Mall prohibiting
skateboarders and bicyclists.
12. (Agenda #3f(2) - ELLEN SWEET RE WEED ORDINANCE) Vanderhoef requested more
information the City's position. The City Manager will follow up.
Adjourned: 8:15 p.m.
clerk~nin\9-27ws.doc
City of Iowa City
MEMORANDUM
Date:
To:
From:
Re:
October 27, 1999
Mayor and City Council
Marian K. Karr City Clerk
Council Work Session, October 11, 1999 - 6:30 p.m.
Council: Lehman, Champion, Kubby, Norton, O'Donnell, Thornberry, Vanderhoef
Staff: Atkins, Karr, Dilkes, Franklin, Schoon, Fosse
Tapes: Reel 99-93, Side 2; 99-98, Side 1.
A complete transcription is available in the City Clerk's Office.
REVIEW ZONING ITEMS
Planning and Community Development Director Franklin presented the following Planning and
Zoning items for discussion:
A. ORDINANCE AMENDING TITLE 14. CHAPTER 6. ZONING, TO AMEND THE
PROVISIONS RELATING TO HOME OCCUPATIONS. (FIRST CONSIDERATION)
B. ORDINANCE AMENDING TITLE 14. CHAPTER 6. ZONING. TO ALLOW PUBLIC
UTILITIES IN COMMERCIAL AND INDUSTRIAL ZONES. (FIRST CONSIDERATION)
ORDINANCE CONDITIONALLY CHANGING THE ZONING DESIGNATION FROM
MEDIUM DENSITY SINGLE-FAMILY RESIDENTIAL (RS-8) TO PLANNED
DEVELOPMENT HOUSING OVERLAY (OPDH-8). AND APPROVING A PRELIMINARY
PLANNED DEVELOPMENT HOUSING OVERLAY PLAN FOR 24 TOWNHOUSE-STYLE
DWELLING UNITS FOR APPROXIMATELY 7.72 ACRES LOCATED AT THE NORTHEAST
CORNER OF BARRINGTON ROAD AND HUNTINGTON DRIVE. (Windsor Ridge Part
13/REZ99-0007) (PASS AND ADOPT)
REVIEW AGENDA ITEMS
(Agenda #14 - RESOLUTION AUTHORIZING...EMPLOYMENT AGREEMENT...CITY
ATTORNEY .... CITY CLERK) In response to the Mayor, Council agreed to proceed as
presented.
(Agenda #13 - RESOLUTION PROVIDING FOR COOPERATION
GOVERNMENTAL UNITS...REGION 10...INTERGOVERNMENTAL
AGREEMENT...) Vanderhoef explained the program.
WITH OTHER
COOPERATIVE
(Agenda fl4d(1 ) - RESOLUTION SETTING PUBLIC HEARING ... FOR THE
CONSTRUCTION OF .THE IOWA CITY WATER FACILITY IMPROVEMENTS ·..WELL
HOUSES...) Norton and Kubby requested details of the project prior to the public hearing.
The City Manager stated there would be a staff presentation on October 18.
Council Work Session
October 11, 1999
Page 2
Council Member Vanderhoef left room.
IOWA AVFNUE STREETSCAPE
(Agenda #7)
City Engr. Fosse, PCD Director Franklin, and Brian Clark, Brian Clark and Associates outlined
the project. After discussion Council directed staff to prepare a five minute presentation showing
each of the three blocks and a width comparison to Linn Street. Majority' of Council directed staff
to explore armrests in the middle of all benches.
Council Member Vanderhoef returned to the meeting.
APPOINTMENT
Riverfront & Natural Areas Commission - Kathleen Janz
COUNCIL TIME
1. O'Donnell asked the Mayor to encourage people to vote in the November election during the
formal meetings between now and the election.
In response to Vanderhoef, PCD Director stated there was a strategic plan for Sturgis Ferry
Park but indicated that prairie grasses and poplar trees could be considered as a more up-
to-date solution.
Vanderhoef reported that the EPA was changing their rules on grants available for cleaning
up areas, and suggested the city re-apply for the area around the public works/transit
building.
4. In response to Norton, City Arty. Dilkes stated staff would have a report on the
sleeping/camping in cars on public streets issue for the next work session.
5. In response to Norton, the City Manager stated staff would be presenting information on the
proposed transportation center on October 18.
In response to Norton, PCD Director Franklin stated communication towers and co-location
of towers is scheduled for Planning and Zoning in November and December and should be
to Council sometime after the budget in March.
7. Norton requested Parks & Recreation Commission or the Riverfront Commission consider
the issue of outdoor storage as it relates to unsightliness of the river corridor trail.
8. Norton requested an update on the letter sent to Ellen Sweet regarding natural areas in
September. The City Manager will report back.
9. The Mayor asked if there was Council interest in researching limiting of liquor licenses in the
downtown area. Majority of Council directed the City Atty. to research the possibility.
10. The Mayor reminded Council of the Y2K Community Conversation planned October 14.
Council Work Session
October 11, 1999
Page 3
11. O'Donnell and Lehman reported on their meeting with the county on the 28E agreement and
stated there was agreement to regulate commercial development to be as compatible as we
can with the interest of the City; establishment of a committee of council and supervisors to
settle disagreement on interpretation of the printed agreement; a five year automatic
renewable clause; and stressing the importance of both parties abiding with the agreement.
12. The City Manager distributed a memo "Raw Water Resources" and stated that the testing for
the water wells far exceeded expectation both in quality and quantity:
Adjoumed: 7:45 p.m.
derkJrnin/10-11-99ws.doc
City of Iowa City
MEMORANDUM
DATE:
TO:
FROM:
RE:
October 28, 1999
Mayor and City Council
Marian K. Karr, City Clerk ,0~~
Employee Computer Equipment Policy
Attached is a new policy regarding the purchase of computer equipment for departing
employees. This policy is effective immediately and will be utilized with outgoing
Council Members. Please call Kevin O'Malley (356-5052) or myself (356-5041) with
any questions.
Cc: Kevin O'Malley, Finance Director
CITY OF I0 I/VA CITY
COMPUTER EQUIPMENT POLICY
Sale of Equipment to Departing Employees
It shall be the policy of the City of Iowa City that, upon termination of employment (retirement,
resignation, etc.) the departing employee may request to purchase the computer and other
direct supporting equipment such as modem, printer, etc. that has been assigned to the
employee and used exclusively by the employee. It shall be the responsibility of the Director of
Finance to determine the value of the equipment at the time of the departure of the employee,
and communicate the value to the departing employee. The City shall have the sole right to
accept or reject the employee's request to purchase equipment in whole or in part. If the
equipment is determined to be of value to the organization, the equipment shall not be made
available for sale to the departing employee. Notwithstanding the above, equipment with a value
of more than that allowed by Section 362.5(10) of the Iowa Code (currently $1,500) shall not be
sold to the departing employee. Any purchase of equipment by the departing employee shall be
"as is" and the City makes no warranties, expressed or implied, with respect to said equipment.
Payment in full for the purchase of such equipment must be made prior to the employee's last
day of work. Equipment shall not be removed from the work site until payment has been made.
Employees who transfer within the City organization are not eligible to request a computer
purchase under this policy
10/99
mgr\pcsale.doc
410 EAST WASHINGTON STREET · IOWA CITY, IOWA 52240-1826 · (319) 356-5000 · FAX (319) 356-5009
October 15, 1999
Terry Treeblood
Director, Parks and Recreation
City of Iowa City
220 South Gilbert Street
Iowa City, IA 52240
Dear Terry:
Thank you for your application to the Community Attraction and Tourism Development
program for the Riverside Festival Stage project. The committee has met to review your
application, and I regret to inform you that the application did not score enough points to
be considered for funding.
Areas of the application that did not score well included economic impact and leveraged
activity.
Please do not interpret this decision as non-supportive. I applaud your efforts to construct
an Elizabethan stage for the Riverside Theatre Shakespeare Festival and other events.
David J. Lyons
Director
THOMAS J. VILSACK. GOVERNOR DAVID J. LYONS, DIRECTOR
200 East Grand Avenue · Suite 150 * Des Moines, Iowa 50309-1834 · 515/242-4700 · Fax: 515/242-4809
info@ided.state.ia.us ° TTY: 1/800-735-2942 · www. state.ia.us/ided
ICAD Accomplishments/Activity Report: 1990 - 1999
Oral B Laboratories - Expansion
NCS - 3 Expansions
Moore - Expansion
General ,Mills - Recruitment
UTA (Lear) - Expansion
Noel Levitz - Expansion
ACT - Expansion
Blooming .~.irie - 2 E.x'pam,'io~
CoralviHe
Integrated DNA Technologies - Expansion
Oakdale Systems - Expansion
Uro-Surge - Recruitment
UPS - Expansion
Applied Systems - Recruitment
111 Transportation - Expansion
GEIC O - Recruitment
Amana Warehouse - Recruitment
Veridical - Recruitment
Neural Applications - Recruitment
Can Shed Recycling - Recruitment
North Libert-~
Employment Impact
973 Employees
150 Employees
! 00 Employees
35 Employees
200 Employees
50 Employees
50 Employees
8 Employees
18 Employees
15 Employees
105 Employees
35 Employees
350 Employees
25 Employees
4 Employees
58 Employees
ICAD fields on average
2 75-300 inquiries per
year, of which
approximately 40% are
retail in nature. These
figures equate to nearly
2600 inquiries during the
period recorded on page
1, with 28projects or 10%
coming to fruition.
North Liberty Plastics - Recruitment
Crystal Clear - Recruitment
West Branch
Sauer Sunstrand - Recruitment
Wausau Buildings - Recruitment
entro - Expansion
West Liberty
Minn Rubber Co. - Recruitment
W. L. Turkey Processing - Reorganization
Kalona
172 Employees
15 Employees
35 Employees
15 Employees
150 Employees
135 Employees
ICAD 's assistance in
these recruitment and
expansion projects
resulted in the creation
appro;c 3188 new jobs
with a payroll average
@$9. 50per hour or _ $6.2'9m~9~880
Kalona Hastics - Expansion
CIVCO - Expansion
Riverside
Wilkinson Precast - Recruitment
Riverside Pallet Co. Expansion
50 Employees
85 Employees
5 Employees
35 Employees
ICAD 's assistance in the
recruitment and
expansion projects
resulted in estimated new
Capital Investments of
$35 - $40 Million.
1B/22/99 88:49:22
319-354-4213
-> IOYfi CITY CLERX
10-29-99
IP13
Jo~ Cmm~ I
iiOW~' ~ "-
_ .?
Jonathan Jordahl, Chair
Charles D. Duffy
Michael E. Lehman
Sally Stutsman
Carol Thompson
BOARD OF SUPERVISORS
Agenda
Boardroom - 2nd Floor
Johnson County Administration Building
913 South Dubuque Street
Iowa City, Iowa 52240
October 25, 1999
INFORMAL MEETING
Work Session
1. Call to order 1:30 p.m.
Discussion re: evaluation and goals of General Relief Director: (possible
executive session to evaluate the professional competency of individuals
whose appointment, hiring, performance, or discharge is being considered... )
discussion
2:30 p.m. - Evaluation and goals of the General Relief Director: (possible
executive session to evaluate the professional competency of individuals
whose appointment, hiring, performance, or discharge is being considered... )
discussion
4. Discussion from the public
5. Adjournment
913 SOUTHDUBUQUE STREET, SUITE 201 4, IOWA CITY, IOWA 522404207 + PHONE: (319) 356.-6000 , FAX: (319) 3544213
18/25/99 BB:41:BB 319-354-4213 -> +319~Sfi5089 IOg~ EITY ELERX Page
JohnsonCounty
]I'OWA~)
Jonathan Jordahl, Chair
Charles D. Duffy
Michael E. Lehman
Sally Slutsman
Carol Thompson
BOARD OF SUPERVISORS
Agenda
Boardroom- 2nd'Fio0r:
Johnson County Administration Building
913 South Dubuque Street
Iowa City, Iowa 52240
:- October26, 1-999.-
INFORMAL MEETING
1. Call to order 9:00 a.m.
2. Review of the formal minutes of October 2 1 st
3. Business from Lisa Dewey, S.E.A.T.S. Director re: approve budget request
for $18,000 for Mobile., Data Terminals (MDTs) and Automatic Vehicle
Locators (AVLs) Matching Grant. ~ discussion/action needed
4. Business from the Roadside Vegetation Manager/Weed Commissioner re:
1999 Weed Commissioner' s Report. discussion/action needed
5. Business from the County Auditor
a) Discussion/action needed re:
Reader Machines.
b) Other
purchasing up to ten used Optech Ballot
913 SOUTH DUBU(~UE STREET, SUITE 201 : ' IOWA CITY, IOWA 52240-4207 TEL: (319) 356-6000 FAX: (319) 354-4213
18/25/99 08:41:28 319-354-4213 ~> +3193565809 IOW~ CITY CLERR Page 882
Agenda 10-26-99
Page 2
6. Business from the Board of Supervisors
,
a)
b)
c)
d)
e)
g)
h)
Discussion/action needed re: various options regarding videotaping of
Board of Supervisors' informal and formal meetings.
Discussion/action needed re: options for increasing viewership of
Board of Supervisors' meetings.
Discussion/action needed re: Community Center Development.
Discussion/~ction needed re: extension.of Lease Agreement between
Chatham Oaks, Inc. and Johnson Cotmty.
Discussion/action needed re: Confidentiality Agreement for New
Cons~truction Program, United States Census 2000 and appointing New
Construction Program liaison.
Minutes, received
1. Johnson County Paratransit Advisory Board Committee for August
2. Mid-Eastern Council 'on Chemical Abuse Board of Directors for
September 23, 1999
3. Iowa~,Ci.ty'. Area Chamber of Co..mmeree Board of Directors for
September 23'; 1999
4. Sixth Judicial Diitiict Depar[ment of Correctional Services Board
of D~ectors for September 29, 1999
5.Communicatidn COmmittee for October 13, 1999
Reports
Discussion from the public
Recess
10/Z?/99 88:41:22 319-354-4213 -> +319~SfiSOBg IDW~ CITY CLERX Page BB1
Johnston Count'
]/IOWA w~
Jonathan Jordahl, Chair
Charles D. Duffy
Michael E. Lehman
Sally Stutsman
Carol Thompson
BOARD OF SUPERVISORS
Agenda
Boardroom - 2na Floor
Johnson County Administration Building
913 South Dubuque Street
Iowa City, Iowa 52240
Thursday, October 28, 1999
FORMAL MEETING
1. Call to order 9:00 a.m.
2. Action re:
3. Action re:
claims
formal minutes of October 21 st
a) Action re:
b) Action re:
c) Other
Action re: payroll authorizations
Business from the County Auditor
permits
reports
Business from the Planning and Zoning Administrator
a) Final consideration of application Z9935 of Terry Duwa.
b) Other
913 SOUTH DUBUQUE STREET, SUITE 201 IOWA CITY, IOWA 52240-4207 TEL: (319) 356-6000 FAX: (319) 354-4213
18/27/99 B8:41:46 319-354-4213 -> +31935r-,SBB9 IOWa CITY CLERK Page 882
Agenda 10-28-99
Page 2
7. Business from the County Attorney
a) Other
8. Business from the Board of Supervisors
a)
Motion to approve the budget request for $18,000 for N~_~,te IZbta
Terminals (1VIDTs) and Automatic Vehicle Locators (AVL~K/Xatc~g
Grant.
b) Motion authorizing Chair to sign 1999 Weed Commissioners Report.
c) Motion authorizing the County Auditor to purchase up to 10 used
Optech R Ballot Reader Machines with memory packs for no more than
$500 per machine including delivery and a 90 day money-back
guarantee, using available funds in the Technology Fund allocated for
the future replacement of ballot reader machines, recognizing the
potential need to amend the FY00 Technology budget by this amount
later during this fiscal year.
d) Discussion/action re: extension of Lease Agreement between Chatham
Oaks, Inc. and Johnson County.
e) Discussion/action re: Confidentiality Agreement for New Construction
Program, United States Census 2000 and appointing New Construction
Program liaison.
f) Motion authorizing Chair to send Anne Annknecht a letter of
appreciation and certificate for serving on the Johnson County Mental
Health/Developmental Disabilities Planning Council.
g) Other
9. Adjourn to informal meeting
a) Reports and inquiries from the County Attorney
b) Inquiries and reports from the public
c) Reports and inquiries from the members of the Board of Supervisors
d) Other
e) Announcements
f) Executive Session re: collective bargaining strategy for collective
bargaining agreements. discussion
10. Adjournment
10-29-99
IP14
EAST CENTRAL IOWA
EMPLOYMENT and TRAINING CONSORTIUM
September 30, 1999
MINUTES
LOCAL ELECTED OFFICIALS PRESENT:
Lu Barton
Lee Clancoy
Lumir Dostal
Henry Herwig
Jim Houser
Ole Munson
Bob Stout
Sally Stutsman
Dee Vanderhoef
Linn County Board of Supervisors
Mayor, City of Cedar Rapids
Linn County Board of Supervisors
Council Member, City of Coralville
Linn County Board of Supervisors
Council Member, City of Cedar Rapids
Washington County Board of Supervisors
Johnson County Board of Supervisors
Council Member, City of Iowa City
VISITORS PRESENT:
Robert L. Ballantyne
Mark Moore
Steve Rackis
Bonnie A. Pisarik
Title II A Administrative Entity Staff
Iowa Workforce Development
Iowa Workforce Development/Kirkwood Community College
Title IIA Administrative Entity Staff
Lumir Dostal, chair of the East Central Iowa Employment and Training Consortium,
called the meeting to order at 11:46 a.m.
APPROVE AGENDA
Lu Barron made a motion to approve of the agenda as presented. Jim Houser
seconded the motion and it passed.
APPROVE MINUTES
Sally Stutsman made a motion to approve of the minutes. Lumir Dostal seconded the
motion and it was passed.
RECOGNIZE VISITORS
Mark Moore, Consultant, for the State of Iowa Workforce Development Department
and Steve Rackis, Iowa Workforce Development/Kirkwood Community College were
recognized.
Bob Ballantyne indicated that the following agenda items needed action by the Local
1
Elected Officials: all under New Business--B. Audit Bids; C. Workers Compensation
Insurance Bids; D. CSP Contribution of $500.00; and E. Fund Transfer.
NEW BUSINESS
B. Audit Bids
Bob Ballantyne reminded the Local Elected Officials (LEOs) that a Request for
Quotation for unified audit services had been issued. Four responses had been
received. Two respondents declined bidding, citing other time constraints.
Those two respondents were: Redmond & Broghammer PC from Cedar Rapids
and Bergan Paulsen & Company PJ from Cedar Rapids. The two respondents
who submitted bids were:
Latta, Harris, Hanon & Penningroth L.L.P. from Iowa City--Year Ending 6-30-99
bid was $13,750 and Year Ending 6-30-2000 bid was $14,250
State of Iowa Auditor's Office from Des Moines--Year Ending 6-30-99 bid was
$13,750 and Year Ending 6-30-2000 was $14,100
Oie Munson made a motion to accept the two year bids submitted by the State
of Iowa Auditor's Office and to authorize the Chair of the Consortlure to sign the
audit agreement. Sally Stutsman seconded the motion and it passed.
C. Workers Compensation Insurance Bids
Bob Ba!lantyne explained the renewal date of the Consortium's workers
compensation policy is not the same as the renewal date of the Consortium's
comprehensive policy. Insurance companies did not want to bid on solely a
workers compensation policy without also bidding an agency's comprehensive
package. Bob reported that staff were working with our current insurance
broker to obtain an extension on the Consortium's workers compensation
policy. Such an extension would make the renewal dates of both the workers
compensation package and the Consortium's comprehensive package the
same date. A Request for Quotation would then be issued for both policies.
The Local Elected Officials will need to review the bids.
Henry Herwig made a motion to issue a Request for Quotation (RFQ) containing
coinciding dates for both the Consortium's comprehensive and workers
compensation packages. Jim Houser seconded the motion and it passed.
A suggestion of using the ending date, because of Workforce Investment Act
(WIA) activities, of June 30, 2000 in the RFQ was made.
D. CSP contribution of $500.00
Bob Ballantyne explained that he was seeking approval to contribute $500.00
of grant funds towards SDR Ten's Coordinating Service Provider budget. Each
2
participating member of SDR Ten's Coordinating Service Provider was asked to
contribute $500.00 for workforce development activities under the auspices of
the Coordinating Service Provider. Bob informed members that the Consortium
had contributed $500.00 towards CSP's workforce efforts and budget last year.
Jim Houser made a motion to approve of contributing $500.00 towards a
combined Coordinating Service Provider (CSP) budget for the year beginning
July 1, 1999 through June 30, 2000. This budget is to be used for workforce
development activities within Service Delivery Region Ten. Lu Barron
seconded the motion and it passed.
E. Fund Transfer
Bob Ballantyne commented that there will likely be no summer program in 2000
which resembles the summer programs of previous years. He said that success
in summer programs under the Job Training Partnership Act was measured by
the number of youth returning to school and/or kept in school. Success was not
measured in the JTPA summer programs by the number of youth obtaining
employment. However, youth enrolled in summer programming under the
Workforce Investment Act must meet its new placement standards. Bob
commented that the school-to-work focus of the Workforce Investment Act (WIA)
appears to be leaving youth with disabilities and youth with extraordinary
barriers behind.
The Program Year 1999 Transfer Request moved $15,000 of Title lIB Summer
funding to the Title IIC Year Round Youth Program. Bob reported that the
Title IIC Year Round Youth Program is close to being obligated. This transfer of
funds will allow more youth to be enrolled in the Consortium's Title IIC Year
Round Youth Program. Approximately $10,000 will remain available to
contribute to the start-up of a summer program next spring, should the Youth
Advisory Council decide to obligate using youth funds for a summer program.
Lee Clancey made a motion to approve of transferring $15,000 of Title lIB
Summer funding to the Title IIC Year Round Youth Program. This transfer
would leave approximately $10,000 to contribute to the start-up of a summer
program next spring, should the Youth Advisory Council decide to obligate
youth funds for a summer program. Sally Stutsman seconded the motion and it
was passed.
F. Life Skills Purchase
The Life Skills class is a part of the Promise Jobs Program. It provides welfare
recipients with information regarding basic life skills (i.e. budgeting and
nutrition) as well as information regarding problem solving skills, coping skills
and parenting skills.
3
Handout #3 detailed the request to spend $1,235.00 of Life Skills grant funding
for new curriculum materials. Bob Ballantyne explained that since the total
amount of this purchase is over $250.00 then it requires approval from the
Local Elected Officials. Jim Houser made a motion to approve the purchase of
Life Skills curriculum materials totaling $1,235.00. Sally Stutsman seconded
the motion and it was passed.
Mark Moore, Consultant from the Iowa Workforce Development Department, provided
the following information regarding state activities related to the Workforce Investment
Act. Mark reported that Cedar County had signed the Chief Elected Officials 28-E
Agreement. Discussion took place regarding the Chief Elected Officials 28-E
Agreement which had been mailed to all of the Local Elected Officials. Some Local
Elected Officials reported that their legal staff had recommended suggestions and/or
changes to this document. Sentiment of the group was that this document had been at
least "agreed to" in principal by all parties. Mark Moore reminded the Local Elected
Officials that they could not officially conduct Workforce Investment Act activities until
their Chief Elected Officials 28-E Agreement was signed by all governmental units.
Dee Vanderhoef handed out a revised Chief Elected Official/Regional Workforce
'Investment Board (CEO/RWIB) Agreement and reviewed the changes which had been
made to this agreement since the last LEO meeting. Henry Herwig made a motion to
have the Local Elected Officials "agree in principal" with the CEO/RWIB Agreement
even though it may undergo more discussion prior to being signed. Ole Munson
seconded the motion and it was passed.
Mark Moore informed the Local Elected Officials that the Regional Workforce
Investment Board had selected members to serve on two committees. Mark indicated
that he was looking for volunteers from the Local Elected Officials to also serve on
these committees. The committees are short-term in nature and focused on specific
Workforce Investment Act tasks. Lu Barron volunteered to serve on the committee
recommending appointments to the Youth Advisory Council. Lumir Dostal and Henry
Herwig both volunteered to serve on the committee which will decide which process
will be used in selecting the one-stop operator and coordinating service provider
under WIA. Mark informed the Local Elected Officials that three types of selection
processes are possible: competitive bid; "grandparent" the current Coordinating
Service Provider structure; or appoint a consortium of three agencies.
A. Needs Analysis Retreat
Bob Ballantyne encouraged Local Elected Officials to participate in a one day
regional workforce needs assessment meeting which had been scheduled from
10:00 a.m. until 3:00 p.m. on Friday, October 15. The needs assessment
meeting will take place at Camp IO-DIS-E-CA, 3271 Sandy Beach Road N.E.,
Solon. Bob explained that the Workforce Investment Act will begin July 1, 2000.
Last year the RAB (formerly called the Regional Advisory Board but now called
4
the Regional Workforce Investment Board) conducted a regional workforce
development needs assessment and it became the backbone for SDR Ten's
Customer Service Plan. The Regional Workforce Investment Board (RWIB) will
need to conduct another needs assessment regarding seven major areas by
November I of this year. These seven areas are:
1. Workforce Development Needs of Employers
2. Workforce Development Needs of Job Seekers
3. Workforce Development Needs of Workers
4. Workforce Development Needs of Youth
5. Support Service Needs of Customers
6. Accessibility of Services in Each County
7. Other Workforce Investment Issues
Regional Workforce Investment Board members and the Pdvate Industry
Council will also be involved in this process.
The next LEO meeting is scheduled for October 28.
Sally Stutsman made a motion to adjourn. The meeting adjourned at 12:45 p.m.
l:Tonn .A. Pdsc , -LK
5
Because of a ti~ne restraint. Vanderhoef stated titat action items would be addressed first so
a quorum was present.
MINUTES
East Central Iowa Council of Governments
Board Meeting
September 30, 1999 - ECICOG Office
108 Third Street SE, Suite 300, Cedar Rapids, Iowa
MEMBERS PRESENT
Ann Heam-Linn County Citizen
Gary Edwards-lowa County Citizen
Rod Straub-lowa County Supervisor
Dee Vanderhoef-lowa City City Council
Henry Herwig-Coralvilte City Council
Sally Stutsman-Johnson County Supervisor
Bob Stout-Washington County Supervisor
Carol Casey-Johnson County Citizen
Ole Munson-Cedar Rapids Commissioner
Jim Houser-Linn County Supervisor
Lu Barron-Linn County Supervisor
MEtMBERS ABSENT
Dell Hanson-Benton County Supervisor
Tom Tjelmeland-Mayor of Ely
Leo Cook-Jones County Supervisor
David Cavey-Mayor of Olin
Dennis Hansen-Jones County Citizen
Edward Brown-Mayor of Washington
Charles Montross-lowa County Supervisor
Don Magdefrau-Benton County Citizen
Washington County Citizen (not appointed)
Benton County elected official (not appointed)
ALTERNATES PRESENT
Lumir Dostal-Linn County
OTHER'S PRESENT - None
STAFF PRESENT
Doug EIliott-Executive Director
Gina Peters-Administrative Assistant
Angela Williams-Housing Planner
Mary Rump-Transportation Planner
Marie DeVries-Solid Waste Planner
Jim Hardcastle-ECICOG intern
1.0 CALL TO ORDER
The meeting was called to order by Vice-Chairperson, Dee Vanderhoef.
.1 Recognition of Alternates
Lumir Dostal for Tom Tjelmeland
.2 Public Discussion - None
.3 Approval of Agenda
M/S/C (Barron/Munson) to approve the changes to the agenda. All ayes
2.0
.1
M/S/C
ROUTINE MATTERS
Approval of Minutes (August 26, 1999)
(Stutsman/Hearn) to approve the minutes as written. All ayes.
.2 Preceding Month's Budget Reports/Balance Sheets
Elliott gave an overview of the August financial statements.
M/S/C (Munsort/Stutsman) to receive and file the August financial statements for audit.
ayes.
All
3.5 Housing Report
Williams told the board that information was included in the board packet. Elliott told the board
that the staff members that were not present were in Des Moines at IARC.
Elliott handed out information on a property in Vinton to be considered for purchase for the GRO
program. (attached)
(Straub joined the meeting at this time.)
Discussion followed on the information presented and the fact that a loss was already anticipated
before the project was started. Elliott stated that as with the "traditional" rehab program, a loss is
most generally the outcome. The purpose of the GRO program is to rehab homes using
environmentally friendly materials. These materials typically last longer but are sometimes more
expensive adding to the cost of the project.
M/S/C (Stutsman/Munson) to authorize Vanderhoef to sign a contingency release for the
property at 107 West Ninth Street in Vinton. All ayes.
Dostal left the meeting at this time.
More discussion followed on the GRO program.
Staff is to prepare a maximum loss recommendation for the board for future GRO projects.
4.1 Personnel Committee
Edwards told the board that the personnel committee met about two weeks ago. The committee
decided to change the timing of the executive director's performance review to occur at the end
of the calendar year rather that at the end of the fiscal year. With this change, board members
who were evaluating would have sat on the board for at least a year.
The committee also decided to have a RFP sent out to update the agency personnel policy.
Also, the committee reviewed the current salary levels for planners and is forwarding a
recommendation to the budget committee for increases in FY2001 salaries.
Elliott referred to page 24 of the board packet and gave an overview of the proposed GIS
workplan. The personnel committee is recommending to the board that they approve a new
staffing proposal as presented on page 25 of the board packet. Community development and
transportation in the past have had a shared departmental planner and now it is proposed that
each department would have their own departmental planner as well as a lead planner.
Discussion followed on the need for GIS. ECICOG would be able to provide GIS assistance to
the region. Rump handed out a training proposal associated with GIS to the board. Discussion
followed on GIS functions and how GIS could be used throughout the region.
The personnel committee recommends to the board the GIS workplan be implemented as
proposed, including a revision of the job description of the lead transportation planner to include
GIS responsibilities and the creation of a new departmental planner position.
Discussion followed about the salary for the GIS position and whether or not it is competitive.
The salary for this position will need to be reviewed by the personnel committee.
M/S/C (Munson/Casey) to implement the GIS workplan, including a revision of the job
description of the lead transportation planner to include GIS responsibilities and the creation of a
new departmental planner position. All ayes.
4.6 Ad Hoc Committee Reports
Rump handed out a contract restructuring recommendation from the Ad Hoc Committee.
(attached) Discussion followed on the recommendations. Munson asked about item b, the
transit training sessions. He recommended third party training instead of training by ECICOG
staff. Discussion followed on the October 1, 1999 implementation date. Rump was asked to
forward these possible recommendations to Mini Bus after the Ad Hoc Committee meeting and
did so. Elliott went through each item on the list and noted that none of these items were either
not in the current contract or something that Mini Bus had not been informed of after the Ad Hoc
Committee meeting.
M/S/C (Casey/Straub) to accept the contract restructuring recommendations from the Ad Hoc
Committee effective October 1. All ayes.
Rump told the board that the Transit Operator's Group met on September 23 to review the
distribution formula for operating assistance. They decided to table a formal recommendation to
the ECICOG board until the November meeting.
5.0 IOWA INTERGOVERNMENTAL REVIEW SYSTEM
~M/S/C (Houser/Munson) to approve all Intergovernmental Reviews with favorable review. All
ayes.
6.1 Approval of Expenditures
M/S/C (Herwig/Stout) to approve payment of expenditures. All ayes.
3.1 Chairperson's Report - None
3.2 Board Members' Reports - None
3.3 Director's Report
Barron left the meeting at this time.
Elliott told the board that the grant that was approved by IDED for the joint purchasing position
was for $36,000. This was not the amount that was requested or the amount that was to be
awarded after the additional local match was secured. Elliott will call 12DED to find out the
details. The interview process will begin after these details are worked out.
3.4 Community Development Report
Elliott referred the board to pages 11-12 of the board packet, a letter from Iowa Emergency
Management regarding hazard mitigation planning. Elliott said that each city and county on page
12 can submit an application to IEMD and that they can contact ECICOG for that service.
3.6 Transportation Report - None
3.7 Solid Waste Report
DeVries told the board that the solid waste comprehensive plan will be updated this fall and that
meetings are scheduled in each county. A calendar was handed out listing the meeting
information. (attached)
DeVries showed the board some samples of "green-building" materials similar to the ones that
could be used for the GRO Program.
.2 Markets Identification
Vanderhoef gave an overview of the discussion in August for this committee.
.3 Brand Identity Committee
Edwards gave an overview of the discussion in August for this committee.
.4 Development/Training Committee
Casey gave an overview of the discussion in August for this committee.
.5 Position for Future Committee
Herwig gave an overview of the discussion in August for this committee.
Casey presented everyone with a "road map" of the pro~ess to date for the strategic planning
process.
7.0 NEW BUShNESS - None
8.0 NEXT MEETING: October 28, 1999
Carol Casey, Secretary/Treasurer
September 30, 1999
Date
4