Study of Subdivision Requirements as a Regulatory Barrier

by HUDUSER

Study of Subdivision Requirements as a
Regulatory Barrier
Prepared for:
U.S. Department of Housing and Urban Development

Office of Policy Development and Research

Washington, DC

Prepared by:

NAHB Research Center

Upper Marlboro, MD

April 2007

Acknowledgement
The NAHB Research Center would like to recognize the contributions of ECONorthwest of
Eugene Oregon in cooperation with the Community Planning Workshop at the University of
Oregon who prepared Section 3 (Existing Regulations), Section 5 (Regulatory Cost Barriers),
and Appendix A – Survey of Regulations for this report. The literature review and comments
on the draft final report were provided by Professor Gerrit Knapp of the University of
Maryland.
The research team also appreciates the assistance provided by the National Association of
Counties (NACo) Center for Sustainable Communities in securing participation by
municipal/county land planners in its expert survey for land development benchmarks. Mr.
Martin Harris of NACo obtained cooperation from several county land planning commissions
that greatly facilitated the focus group evaluation of regulatory barriers. The following
individuals participated in the expert panel for land development benchmarks: Bill Kreager,
architect, Mithun Partners, Inc.; Edward Tombari, planner, NAHB; Douglas Nataluk, developer,
Neumann Homes; Gerald Eid, developer, Eid Companies; Charles L. Baker, general manager,
Department of Land Use New Castle County Delaware; Eric Young, land planner, Washoe
County, Nevada; Bill Florea, land planner, Boone County, Missouri; David Petersen, land
planner, Spring Hills, Kansas; Martin Rivarola, community development director, City of
Mission, Kansas; James E. Harpool, principal/president, Dial Realty; Phil L. Stuepfert, director of
planning, Illinois SEC Planning Consultants; Richard Dooley, environmental land planner,
Arlington County, Virginia.
Special thanks also to Ms. Debra Bassert, vice president for land development policy at NAHB,
for thoughtful review and comments on the benchmarks and unit cost methods. Finally, the
Research Center would like to acknowledge the support, project guidance and helpful
comments on the Executive Summary provided by Mr. Edwin Stromberg of HUD’s Office of
Policy Development and Research.
Disclaimer
Neither the NAHB Research Center, Inc., nor any person acting on its behalf, makes any
warranty, express or implied, with respect to the use of any information, apparatus, method, or
process disclosed in this publication or that such use may not infringe privately owned rights, or
assumes any liabilities with respect to the use of, or for damages resulting from the use of, any
information, apparatus, method or process disclosed in this publication, or is responsible for
statements made or opinions expressed by individual authors.

Execut
t
i
i
ve Summary
Introduction
Local subdivision regulations represent a major tool by which local governments manage and
shape the housing development process. In addition to laying land plats or site plans, these
regulations establish infrastructure or site requirements to support new residential
development, i.e., they establish specifications for streets, sidewalks, water and sewer, drainage,
curbs and gutters, street signs, landscaping. In many cases, subdivision regulations also provide
for trees, utility easements, and dedications of land or fees for recreational and/or school
facilities. Subdivision regulations are intended to ensure that proposed housing developments
are cost-effective (i.e., reduce extensive long-term maintenance by the locality), meet health and
safety requirements, are properly designed, and have a favorable impact on the community.
The cost of these requirements represents a significant share of the cost of producing new
housing. Such requirements can reasonably be considered “regulatory barriers” to affordable
housing if the locally determined requirements are greater (and hence, more costly) than those
necessary to achieve health and safety requirements in the community. This has been a
concern for many years, and has been identified in the report, Not in My Backyard (prepared by
the President’s Commission on Regulatory Barriers to Affordable Housing) as a potential major
contributor in raising the cost of housing and limiting the supply of affordable housing in
communities.
To determine whether subdivision requirements exceed what is necessary to meet health and
safety requirements, and hence likely to become a regulatory barrier, the Department of
Housing and Urban Development commissioned a nationwide study. The key objective of the
study was to develop a national estimate of the cost of excessive land and site development
standards on single-family detached housing built in subdivisions. This is the type of housing
most closely associated with the idea of homeownership in America. In order to measure
which local standards are in fact excessive, the study commissioned nationally recognized land
development experts to devise benchmark subdivision standards. Benchmark standards are
standards necessary to achieve minimum acceptable health and safety benefit for the
community. Such standards, and their corresponding monetary values, were devised for the
most important development standards: lot size, floor space requirements, lot width, roadway
width, sidewalk requirements, and curb & gutter drainage. The expert group in land and
housing development consisted of residential land developers, architects, civil engineers, and
land planners and was asked to develop benchmark standards for this study. Based on the

responses from this group, consensus benchmark standards were developed and used in this
study as the basis for determining whether existing local standards were excessive.
METHODOLOGY: The study used a four-step methodology for developing the national cost
estimates for those subdivision rules that constitute regulatory barriers: (1) collect regulatory
standards from a nationally representative sample of jurisdictions for the selected subdivision
and related zoning rules; (2) establish benchmark values and unit costs for each requirement;
(3) produce a cost estimate for excessive regulation based on the application of the benchmark
values and costs to the regulatory standards from the national sample of jurisdictions; and (4)
create a national estimate of the costs of excessive regulations. For practical reasons, this study
focused on only one type of zoning district- those that allowed the densest single-family
residential development. If the study had examined all zoning districts in all jurisdictions, which
would have included lower density districts, greater levels of regulatory cost barriers most
likely would have been found.
SUMMARY OF FINDINGS
The overwhelming majority of communities in the study—94%-- mandate one or more land
development standards for residential subdivisions. The most common regulatory standards
were for lot size, front setbacks, off-street parking, and lot width. The least common
requirements were for floor area and open space.
Ninety-one percent (91%) of all the communities had one or more regulatory standards that
exceeded the benchmarks. Jurisdictions exceeded the benchmarks most frequently for off-
street parking, front setbacks, lot width, and lot size. Jurisdictions exceeded the benchmarks
least frequently for floor area, sidewalk requirements, open space, and sidewalk width
requirements.
Lot size, lot width, and floor area accounted for the largest percentage of total costs for a
variety of reasons:
Excessive lot size regulations accounted for the largest percentage of cost (65%). The cost of
land is a major component of the cost of housing, so it should not be surprising that regulations
mandating excessive lot sizes results in such a finding. Coincidentally, sixty-five percent of
jurisdictions in the sample exceeded the lot size benchmark. On average, jurisdictions with
excessive lot size requirements exceeded the benchmarks by 6,573 square feet (or more than
one-seventh of an acre). The frequency and magnitude of lot size requirements greater than
the benchmarks, combined with the cost of land, resulted in the regulatory cost barrier for lot
size accounting for the majority of total costs of the regulatory cost barriers for all the land and
site development variables considered in this study.
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Excessive lot width requirements account for a moderate share of costs (9%), with 63 percent
of jurisdictions containing lot width regulations exceeding the benchmarks. Excessive lot widths
also have clear secondary cost impacts, i.e., they increase costing for land, sanitary sewer main,
water main, street paving, curb and gutter, sidewalk construction, landscaping, and storm
sewer.
Floor area had a disproportionately large impact on total regulatory barrier costs in proportion
to the number of jurisdictions with excessive floor area requirements. While only 8 percent of
jurisdictions had excessive floor area requirements, the regulatory cost barriers for floor area
for those jurisdictions accounted for 17 percent of the total regulatory cost barriers for all land
development variables for all the jurisdictions in the study. This resulted from the relatively high
cost per square foot of required floor area and the fact that the mean differential between
required floor area and benchmark floor areas was 354 square feet (or more than $26,000 per
dwelling unit).
Key Findings
The average cost of excessive regulation resulting from subdivision standards for one dwelling
unit was about 5 percent of the average cost of a new home. For the land development
standards studied, the average regulatory barrier cost for one dwelling unit was $11,910. In
comparison with the average cost for a new single-family dwelling in the United States in 2004
($244,000), the average per-unit regulatory cost barrier is 4.8 percent of that average selling
price.
The regulatory barriers cost varied. The regulatory barriers cost varied considerably across
communities, as well as across regions and Metropolitan Statistical Area (MSA) status. Given
that the sample is representative of all jurisdictions in the nation, the national regulatory cost
barriers will vary by region and whether the jurisdiction is part of an MSA. The actual
regulatory barrier cost for any given jurisdiction of course, depends on actual local regulations
and costs of development.
Of the two types of “models” used to make the national estimates—aggregated and
disaggregated--the disaggregated model provides the best national estimate of the land
development regulatory barrier costs. The disaggregated model accounts for variations in
regulatory standards and costs among the Census regions and MSA status better than the
aggregated model. The total mean regulatory cost barriers for land and site development
standards in the disaggregated model was about $14.6 billion for the nation in 2004.
Conclusion
This study is the first effort to quantify, on a nationwide basis, the costs of excessive site
development regulation on affordable housing. The common rationale for such requirements is
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that they enhance the soundness, livability, and sustainability of a community. The purpose of
this study, however, has not been to deny that such standards have important benefits for the
community, but to broaden the perspective of what other values and goals can and should be
embodied in the housing development regulatory process, and to call attention to the impacts
that excessive requirements have upon affordable housing. The additional cost pressures such
standards impose on new housing may also help increase the prices of existing housing.
The findings themselves are not surprising; but they confirm and quantify, through an empirical
study, what has been well-known, but unverified, throughout the workforce housing
community—that large lot zoning and various site development requirements—limit or prevent
the development of affordable housing. Such requirements, then, can harm a community’s
ability to provide what is, or ought to be, a high priority community goal.
The $14.6 billion national estimate for the land development regulatory barriers costs is a very
conservative estimate. For practical reasons, this study focused on only on one type of zoning
district, i.e., one that allowed the densest single-family residential development. If the study had
examined all zoning districts in all jurisdictions, which most likely would have included lower
density districts, far greater levels of regulatory barriers cost would have been found. For
example, in medium-density residential zoned districts typically found next to the dense zoning
districts, required minimum lot areas are generally larger. One would expect to find greater
regulatory barrier costs in such neighborhoods vis a vis. high-density areas. Hence, the
regulatory barriers cost resulting from required lot sizes greater than the benchmark lot areas
would have been larger if these medium-density residential zoned districts had been included in
the regulatory barrier cost analysis. Also, the estimate of housing construction costs on a
square foot basis associated with excessive floor area standards was relatively conservative.
Further, in lower density areas experiencing high housing construction, regulatory cost barriers
based on average land costs and total housing construction starts for the Census region may
underestimate the actual regulatory cost barrier from lot size requirements within that region.
The results presented here can serve as an opportunity and an invitation for communities as
part of their commitment to affordable housing, to review and assess their own land and site
development requirements. In order for local and state policies to have the greatest impact on
the reduction of regulatory cost barriers associated with land and site development standards,
efforts should focus on the most significant land and site development regulatory barriers
identified in this study: excessive zoning regulations and excessive house size requirements.
Local advocates for reduction in regulatory cost barriers may wish to use the regulatory barrier
costing tool, described in section 4.2.1.1 (and presented in Appendix G) to determine the
regulatory barriers costs that apply in specific locations. This computer-based tool incorporates
the benchmark standards used in this study as the baseline for judging a community’s regulatory
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standards. A community’s calculation would be based on local land costs and on the cost of
land development standards adopted by the local land planning jurisdiction or jurisdictions.
Communities committing to this self-assessment exercise, will not only be contributing to our
understanding of a significant dimension of regulatory barriers costs, but will also be advancing
their own efforts to expand affordable housing opportunities.
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Study of Subdivision Requirements as a Regulatory Barrier
Table of Contents
1. Introduction ............................................................................................................................................1-1

2. Background..............................................................................................................................................2-1

2.1 Subdivision Regulations and Zoning Ordinances..................................................................2-2

2.1.1 Literature Review...........................................................................................................2-3

2.1.2. Regulatory Cost Barriers .............................................................................................2-3

2.1.3 Quantifying Cost Barriers for Specific Land-Use Regulations..............................2-5

3. Existing Regulations...............................................................................................................................3-1

3.1 Sampling of Jurisdictions.............................................................................................................3-2

3.2 Selection of Land Development Standards............................................................................3-4

3.3 Definitions .....................................................................................................................................3-5

3.4 Analytical Framework.................................................................................................................3-5

4. Standards, Benchmarks, and Unit Costs...........................................................................................4-1

4.1 Benchmarks...................................................................................................................................4-1

4.1.1 Lot size .............................................................................................................................4-4

4.1.2 Lot Width ........................................................................................................................4-7

4.1.3 Setbacks............................................................................................................................4-8

4.1.4 Floor Area Minimums ...................................................................................................4-9

4.1.5 Road Widths .................................................................................................................4-10

4.1.6 Sidewalks........................................................................................................................4-11

4.1.7 Open Space ...................................................................................................................4-11

4.2 Unit Costs and Regulatory Cost Barriers............................................................................4-11

4.2.1 Lot Size...........................................................................................................................4-12

4.2.2 Minimum Square Footage Dwelling Requirements ..............................................4-15

4.2.3 Lot Width ......................................................................................................................4-15

4.2.4 Road Width...................................................................................................................4-16

4.2.5 Front yard set-back requirements............................................................................4-17

4.2.6 Sidewalk Requirements...............................................................................................4-17

4.2.7 Open Space Requirements ........................................................................................4-18

5. Regulatory Cost Barriers .....................................................................................................................5-1

5.1 Cost Analysis of the Sample Jurisdictions ..............................................................................5-1

5.1.1 Methods ...........................................................................................................................5-1

5.1.2 Results ..............................................................................................................................5-4

5.2 National Estimates of the Regulatory Cost Barriers for Housing in Subdivisions ........5-8

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5.3
Study of Subdivision Requirements as a Regulatory Barrier
5.2.1 Methods ...........................................................................................................................5-8

5.2.2 Results ............................................................................................................................5-13

5.2.3 Sensitivity Analysis .......................................................................................................5-15

Key Findings and Conclusions.................................................................................................5-16

6. Conclusions.............................................................................................................................................6-1

7. References...............................................................................................................................................7-1

8. Endnotes ..................................................................................................................................................8-1

Appendix A: Survey of Regulatory Standards……………………………………………………A-1
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List of Tables
Table 2.1: Average Per-Unit-of-Measure Costs of Selected Land Improvements in

Waukesha County, Wisconsin ..................................................................................................................2-7

Table 2.2: Lot Improvement Costs for Various Lot Widths in Waukesha

County, Wisconsin.......................................................................................................................................2-7

Table 3.2: Summary of Means Testing for Variance within Five Subcomponents

of the Sample ...............................................................................................................................................3-10

Table 4.1: Expert Responses for Land Development Benchmarks for “More Dense”

Development, Statistical Summary of Responses Used for Comparison in MSAs (N=12) .........4-2

Table 4.2: Expert Responses for Land Development Benchmarks for “Less Dense”

Development, Statistical Summary of Responses Used for Comparison in MSAs (N=8) ...........4-3

Table 4.3: Estimated Raw Land Costs by U.S. Bureau of Census Region......................................4-13

Table 5.1: Summary of Jurisdictions with Regulatory Standards and Jurisdictions

Exceeding the Benchmarks by Variable ...................................................................................................5-5

Table 5.2: Summary of Regulatory Cost Barriers for All Jurisdictions and Costs Per

Dwelling Unit by Variable ...........................................................................................................................5-6

Table 5.3: Results of Sensitivity Testing on Total and Mean Regulatory Cost

Barriers in the Sample Jurisdictions..........................................................................................................5-8

Table 5.4: Number of Building Permits Issued for Single-Family Detached Dwellings in the

United States and Each Census Region and MSA Status, 2004........................................................5-12

Table 5.5: Aggregated Model Estimates of the Regulatory Cost Barriers for All

Building Permits Issued for Detached Single-Family Dwelling Units in the Nation, 2004..........5-13

Table 5.6: Disaggregated Model Estimates of Regulatory Cost Barriers for All

Building Permits Issued for Single-Family Dwelling Units in the Nation, 2004.............................5-14

Table 5.7: Results of Sensitivity Tests of the Aggregated and Disaggregated Models of the

National Regulatory Cost Barrier Estimates, 2004.............................................................................5-15

List of Figures
Figure 3.1: Sample Database Report Output Format Showing Data Collected for Each

Jurisdiction......................................................................................................................................................3-2

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Table 3.1: Summary of Descriptive Statistics of the Variables Reviewed ........................................3-7

Figure 4.1: Lot Area Comparison of Benchmarks to Reference Values ..........................................4-5

Figure 4.2: Benchmarks for Lot Width Compared with Reference Values.....................................4-7

Figure 4.3: Jurisdictions with Standards for Lot Width in Width Ranges as a

Percentage of Total Jurisdictions with Standards (342).......................................................................4-8

Figure 4.4: Interior Floor Space Benchmarks and Reference Criteria..............................................4-9

Figure 5.1: Process for Determining the Costs of Excessive Land Use Regulation for

Jurisdictions in the Sample..........................................................................................................................5-2

Figure 5.2: Process for Modeling the Nationwide Regulatory Cost Barriers ...............................5-10

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1. I
I
nt
t
roduct
t
i
i
on
This study addresses the characterization on a national basis of the regulatory cost barriers
associated with land subdivision, specifically barriers to the subdivision of land that can be
developed with single-family detached (SFD) dwellings. Previously, this issue has been addressed
only on a very small geographic scale. Previous approaches have not been used to examine
regulatory cost barriers at the national level.
As discussed in the Executive Summary, there are two distinguishable types of regulatory
barriers for the subdivision of land to construct single family detached dwellings: —
a) barriers which lengthen the time for approval of a subdivision and b) land development and
site development standards which are more costly than a set of minimum “benchmark”
standards selected to provide for public health and safety. Benchmark standards are intended to
be appropriate for affordable single-family detached dwellings without adding costs that would
not bring commensurate public health and safety benefits. There are added costs when land is
developed according to standards larger than the benchmark standards and there may also be
benefits associated with those larger standards. The question of whether the added benefits are
commensurate with the added costs is dependent upon many considerations. The evaluation of
whether some of the benefits may be commensurate with the costs is an area that merits
considerable investigation but is outside the scope of this study.
Land development standards investigated by the research team included standards associated
with zoning requirements such as lot size as well as standards associated with subdivision rules
such as lot width, interior floor space, building setbacks from the street, rear and side property
lines, street widths, requirements for sidewalks and requirements for open space. This specific
set of land development standards was selected for study by the research team based on our
literature review. These specific standards have been identified as having the potential to
significantly increase the costs of residential building lots. Based on existing research
approaches, measuring the costs which are attributable to the differences between the existing
land development standards and the benchmark standards require:
1. Knowledge and analysis of existing local requirements or standards
2. A set of alternative standards (the benchmark standards) which provide for public health
and safety and could apply to a broad range of jurisdictions across the county
3. A methodology for costing the existing and the benchmark standards
4. A methodology for projecting the implications of the cost differences, if any, to the price
of housing
5. A methodology to estimate the number of families affected by the cost differential
between the existing standards and the “benchmark“ standards
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Measuring administrative and process costs in a quantitative approach was outside of the scope
of this study. A quantitative approach to these costs would require extensive knowledge of the
local administrative review process, the out-of-pocket costs associated with the local review
process, assumptions about time and opportunity costs, a methodology for measuring such
costs, and a benchmark review process, either actual or model, against which to compare actual
administrative and process costs.
The present study examined the standards that are currently enforced in subdivision controls
and zoning ordinances. Toward that end, the research team developed and executed a plan to
systematically collect and analyze, via a Microsoft Access database, a nationally-representative
sample of the land development and site development standards contained in subdivision rules
and zoning ordinances.
A set of benchmark standards was developed using a survey of land development professionals
in both the private and public sectors. The benchmark standards represent “best professional
judgment” of the respondents to the survey. All such surveys are open to criticism with respect
to the selection of the group being surveyed and the possible bias present in the individuals
responding to the survey as compared with those individuals who did not respond to the
survey. The research team reviewed the benchmark standards as compared with published
model standards by several organizations. Where applicable, the benchmark standards were
also reviewed with respect to median or the average values for the standards contained in our
nationally representative database of existing land development regulations and with the median
or average values of dwelling and house lot characteristics from the 2005 U.S. Census of
Residential Construction.
The currently enforceable land and site development standards were then compared with the
benchmark standards that would promote public health and safety. The research team also used
engineering construction cost estimating methods to establish the incremental cost differences
between the land development standards contained in the nationally representative database
and the benchmark standards. This approach allowed the team to estimate the regulatory cost
barriers due to land or site development standards greater than the benchmark standards for
each of the planning jurisdictions recorded in the database.
Where the standards of a given jurisdiction were found to be the same or less expensive that
the benchmark standards, there were no cost barriers related to land or site development
standards in that planning jurisdiction. Conversely, if the standards of the jurisdiction were
greater and more costly than the benchmark standards, the team assigned quantifiable
regulatory cost barriers due to the standards enforceable in that jurisdiction.
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A data weighting method was developed using the cost barrier estimates from the study’s
nationally representative sampling of planning jurisdictions to estimate the cost barriers existing
in the general population of all planning jurisdictions in the United States.
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2. Background
Since the early 20
th
century, there has been concern about the impact of regulations on
housing costs, and whether certain regulations are too complex or have excessive standards.
Many studies have been conducted to address these issues. One of the missions of the U.S.
Department of Housing and Urban Development (HUD) is to promote policies which lead to
a sufficient amount of affordable housing nationwide. In support of that mission, HUD
maintains the “Regulatory Barriers Clearinghouse” for issues and information relevant to the
promotion of affordable housing.
i.
Ratios of median house prices to median household
incomes are currently at a 25-year high in more than half the evaluated metropolitan areas in
the United States (Harvard, 2005).
Most previous research on the regulatory cost barriers to affordable housing considered the
effects of whole categories of regulations, of non-subdivision regulations (such as building
codes), or of general development patterns. Few studies have focused on individual,
subdivision rules or zoning ordinances, or have analyzed them on a national scale. One reason
for this, as noted by the U.S. President’s Commission on Urban Housing (1969), has been
“the lack of a comprehensive, up-to-date survey on the provision of ordinances throughout
the nation…” For the purpose of the present study, a regulatory barrier is a public regulatory
requirement or process that increases the cost of single-family detached dwellings with
respect to benchmark standards that are intended to protect public health and safety.
One seminal study in Waukesha County, Wisconsin, used a cost barrier evaluation method
based on estimated construction costs for various infrastructure standards. This study
compared the costs for the range of land development standards present within that county.
According to government definitions of affordable housing, families should devote no more
than 30 percent of their income to rent or mortgage payments and utilities.
ii
“Affordable
housing“ often simply means housing whose residents do not pay too large a share of their
income on rent or mortgage. In the context of this study, “affordable means affordable to
families earning less than four-fifths (80%) of the area's median income.“
iii
These families are
officially ”lower income" households. The median family income in the United States is
approximately $60,000 per year.
iv
Based on the guideline of the 30 percent of income for
housing and 80 percent of median income standards, affordable housing on a national average
would cost no more than $1,200 per month for rent or mortgage plus real estate taxes.
While often motivated by the intention to preserve existing property values within the
community, subdivision rules and zoning ordinances may, as a side effect, increase the cost to
build new single-family dwellings. The goal of this research project was to quantify the
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Study of Subdivision Requirements as a Regulatory Barrier
regulatory cost barriers crated by planning jurisdiction requirements that are greater than
benchmark standards for house square foot floor area, lot size, road width, etc.
This study also qualitatively characterized the reasons for delays that occur during the
interactive process between a land developer and a planning jurisdiction during the approval
of a proposed residential land subdivision. According to a developer in Phoenix, Arizona, who
participated in a regulatory cost barrier focus group managed by the research team, the delay
of one year in obtaining a subdivision approval for a typical subdivision of 40 acres and 160
homes would add about $2,250 to the cost of each home for interest charges alone.
v
2.1 Subdivision Regulations and Zoning Ordinances
Local subdivision regulations represent a major tool by which local governments shape and
control the housing development process. Subdivision regulations establish infrastructure
(land development) and site (building lot development) requirements to support new
residential development—i.e., they establish specifications for streets, sidewalks, water and
sewer, drainage, curbs and gutters, street signs, landscaping, trees, utility easements, and
dedications of land or fees for recreational and/or school facilities. Subdivision regulations are
intended to ensure that proposed housing developments are cost-effective (i.e., reduce long-
term maintenance), meet health and safety requirements, are properly designed, and have a
favorable impact on the community.
Land use controls which exceed appropriate requirements, particularly subdivision regulation
and zoning, which determine the type and design of development that may be built in a
community, is identified in Not in My Backyard (HUD, 1991) as a major contributor in raising
the cost of housing and limiting the supply of affordable housing in communities. This study
stated that regulations (as a group, and not just their excessive elements) add 20-35 percent
to the costs of new homes. According to U.S. Department of Housing and Urban Development
(HUD)- sponsored Joint Venture for Affordable Housing demonstration, reducing the cost of the
vi
“developed lot” was the greatest single factor in achieving housing affordability.
These studies have identified two significant categories of costs associated with subdivision
regulation: (1) administrative or process costs, which refer to the costs associated with
developing and processing subdivision requests through the local government review process,
including the out-of-pocket costs, application costs, and review costs, and costs of delay (time
costs, opportunity costs); and (2) costs resulting from land and site development standards
( specified design and materials requirements for infrastructure and site features, such as
requirements for rights of way, curbs/gutters, water and drainage systems, and land
dedication requirements) which are greater than the standards needed solely to protect
public health and safety.
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Since the purpose of this study is to probe the link between subdivision regulations/zoning
ordinances and housing prices, the research team’s review of the literature covers two
topics—model regulations and the cost of regulations. The first topic concerns what are
considered minimum or typical standards. It can be argued that some regulations that exceed
minimum standards add unnecessary costs to the subdivision process, and these costs can
show up in the price of housing.
2.1.1 Literature Review
Introduction
The literature review for the most part revealed that very little published work has been
done that has direct relevance to the quantification of regulatory cost barriers on a national
basis. Few studies have been done examining the costs associated with subdivision regulations
in general, much less those analyzing individual regulations or done on a national scale. One
relevant study that quantified regulatory cost barriers for a single county in Wisconsin is
reviewed in section 2.1.3.
A review of literature related to subdivision regulations could cover numerous topics, such as
legal framework, historical rationale, and the actual subdivision process. As the purpose of
this study is to probe the link between subdivision regulations and housing prices, this review
covers existing land use regulations, model land use regulations, benchmark standards and
costs of regulations where the required standard is greater than an applicable benchmark.
The literature of model land use regulations discusses what are considered minimum or
typical standards. These standards are generally approached only from a performance
perspective, without regard to the cost effectiveness of the proposed or recommended
standard. The literature on the second topic, costs of land use regulations, is even more
incomplete, because few studies have been done examining the costs associated with
subdivision regulations in general, much less those analyzing individual regulations or done on
a national scale.
Many codes also address one or more other subdivision issues, such as administrative
processes, growth management tools such as fees and exactions, and environmental
protection. These are not discussed here, for one or more reasons (such as, their presence
varies widely across codes, or it is difficult to assign costs to them).
2.1.2. Regulatory Cost Barriers
Several references on model land use regulations were reviewed as part of the literature
vii
review ; however these references were not of significant value in developing our estimates
of the regulatory cost barriers associated with land and site development standards.
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Nelson et al (2002), The Link Between Growth Management and Housing Affordability: The
Academic Evidence, is a comparison of the economic impacts on housing prices of two land use
regulation categories: traditional zoning and growth management. This paper makes an
important point—housing prices are influenced by many factors, not just the costs of
development and construction. Luger and Temkin (2000), Red Tape and Housing Costs: How
Regulations Affects New Residential Development, documents procedural and administrative cost
barriers to land development. From interviews of people involved in development in several
communities, the authors of this document developed an idea of reasonable versus excessive
regulations, and then calculated the costs of those excessive regulations to be $10,000 to
$20,000 per unit. In the current context, a significant limitation of this study is the fact that it
calculated costs for regulations as a group, not for just subdivision regulations and not for
individual regulations.
Kennedy (2002), in The Impact of Municipal Governments on Residential Housing: A Case Study of
Single-Family Detached Housing, in Cary North Carolina, identifies five development activities that
affect housing costs—impact fees, restrictive zoning interpretation, administrative fees,
excessive building codes, and required capital improvements (on- and off-site). The
significance of this study’s conclusions related to the overall costs of the development
activities is limited by the fact that it is a case study of only one town, and the determination
of “excessive” was made based on interviews of two employees of a developer.
Reducing Housing Costs Through Regulatory Reform: A Handbook for Colorado Communities (1999)
reviews the literature on two issues—the financial impacts of various regulations, and the
relative costs of regulations in general. On the first issue, the only study it mentions related to
subdivision regulations is by Weitz (1982), Affordable Housing: How Local Regulatory
Improvements Can Help. That study found the cost of excessive right-of-way widths was $700
per lot. Excessive was defined as the magnitude or quality of the standard exceeding what a
developer would provide absent the regulation.
A study by German (1993), Under Siege: What Regulations Cost Builders and Buyers, compared
the costs of building houses in two jurisdictions with different levels of regulation. The study
found that regulations added $20,420 more to housing costs in the more regulated
jurisdiction. However, this study only considered fees and building codes.
In 1987, the NAHB Research Center published a paper called “Affordable Residential Land
Development: A Guide for Local Government and Developers.” This paper reported results
from the Joint Venture for Affordable Housing, an effort involving multiple organizations and
more than 100 demonstration projects across the country to reform regulations and
administration and determine the housing cost savings. Reforms fell into four categories—
zoning regulations (primarily related to density), subdivision regulations, building construction
innovations, and administration. On average, reforms saved $8,573 per unit, with three-
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fourths of this coming from land use regulations, which means at most one-fourth, or $2,143,
came from subdivision regulations.
2.1.3 Quantifying Cost Barriers for Specific Land-Use Regulations
As a first step in identifying the land development requirements that were appropriate to
examine from the cost barrier perspective, the research team identified those infrastructure
elements with standards that are most commonly referenced in subdivision rules and zoning
ordinances (see Table B-1, Appendix B) :
• Streets (right-of-way width, grade)
• Street access (block length)
• Sidewalks (width)
• Parking (number of parking spaces required)
• Sanitary sewerage
• Utilities (easement widths)
• Open space (amount required)
However, there is considerable discrepancy across codes on the numerical standards
proposed for these infrastructure elements. For some, such as sidewalk surface thickness and
minimum number of parking spaces, there seems to be general agreement on standards:
• Sidewalk minimum surface thickness = 4 inches
• Minimum number of parking spaces = 2 per unit
But for most regulations that are commonly covered by subdivision codes, the proposed
standards vary significantly. For example the following ranges of land use standards were
present in the regulations reviewed:
• Minimum right-of-way width = 47-60 feet
• Vehicle lane width = 9-15 feet
• Maximum grade = 8-15 percent
• Minimum curb radius at intersection = 5-25 feet
• Minimum block length = 125-400 feet
• Sidewalk requirement criteria = from “where appropriate” to “always”
• Minimum amount of open space = 5-10 percent of gross area
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One of the few studies to examine the costs of individual regulations is Schuetz and White
(1992), Identifying and Mitigating Local Regulatory Barriers to Affordable Housing in Waukesha
County, Wisconsin. The authors established three ways in which subdivision and zoning
regulations can affect housing costs—through land costs, lot improvement costs, and housing
construction costs. The effect of subdivision application processing delays and the impact of
the delays on interest charges paid by the developer were not explicitly addressed. Schuetz
and White collected the subdivision and zoning regulations for several jurisdictions in
Waukesha County, and examined the minimum standards for each regulation. Minimum
standards were defined by reputable agencies or by using the smallest values found in the
County’s jurisdictions. They then calculated the costs of County and minimum regulations,
and compared these costs to determine which regulations unnecessarily raised housing costs
and by how much.
The subdivision regulations that were found to have a significant impact on costs in the
Waukesha study were:
• Sidewalk requirements
• Curb and gutter requirements
• Storm sewer requirements
• Impact fees
• Excessive right-of-way widths
Additionally, they found that certain excessive zoning regulations unnecessarily increase the
costs of certain subdivision elements, by increasing the amount of materials needed.
• Excessive lot widths increase the costs of sewer mains, water mains, streets,

sidewalks, storm sewers, and curbs and gutter.

• Excessive front yard setbacks increase the costs of sewer and water laterals.
The costs associated with these elements of subdivision construction are reported in Table
2.1. Note that these costs are per some unit associated with the material (for example, the
street pavement costs are per square foot of pavement), not per housing unit.
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Table 2.1: Average Per-Unit-of-Measure Costs of Selected

Land Improvements in Waukesha County, Wisconsin

Selected improvements Average Cost
Sanitary sewer/front foot (FF) $25.01
Sanitary sewer laterals/lineal foot (LF) $23.62
Water main total/FF $18.80
Water lateral/LF $16.00
Storm sewer/FF $18.52
Street without curb and gutter/FF $18.72
Mountable curb/LF $5.48
Boulevard curb/LF $6.23
Concrete sidewalk 4' W x 4" D/FF $6.00
Source: Village of Menomonee Falls; Mike Mucha, City Engineer, Mequon, WI.;

the University of Wisconsin-Milwaukee Urban Research Center, 1992

The Waukesha study estimated the unit costs for infrastructure elements related to the width
of the lot based on a tabulation of contractor bids for land development. These unit costs
were utilized to estimate the aggregate infrastructure costs for the roads, sidewalks, and
utilities associated with the range of lot widths specified in each municipality within the
county—from 60 feet, the smallest allowable lot width, to 200 feet, the most restrictive
requirement for lot widths within the county (See Table 2.2).
Table 2.2: Lot Improvement Costs for Various Lot Widths in Waukesha County, Wisconsin
Lot Front Street Curb and Sanitary Storm Total Cost of
Width Setback Width Gutter Sewer Water Water Street Sidewalk Improvements
60 50 27 $ 328.80 $2,569 $1,788 $ 957 $ 907 $ 360 $ 6,910
66 50 27 $ 361.68 $2,699 $1,884 $1,053 $ 998 $ 396 $ 7,391
80 50 27 $ 438.40 $3,000 $2,110 $1,276 $1,210 $ 480 $ 8,515
100 50 27 $ 548.00 $3,431 $2,433 $1,595 $1,512 $ 600 $10,120
120 50 27 $ 657.60 $3,862 $2,756 $1,914 $1,814 $ 720 $11,724
130 50 27 $ 712.40 $4,078 $2,917 $2,074 $1,966 $ 780 $12,527
180 50 27 $ 986.40 $5,155 $3,724 $2,871 $2,722 $1,080 $16,539
200 50 27 $1,096.00 $5,586 $4,047 $3,190 $3,024 $1,200 $18,144
Source: Minimum zoning regulations adopted by Waukesha County municipalities, average local improvement
costs. University of Wisconsin-Milwaukee Urban Research Center, 1992.
Other factors that need to be accounted for in assessing infrastructure costs for wider lots
are costs for the extensions in the length of the sidewalks and the planting strips between the
edge of the roadway and sidewalks. In addition to the costs to improve the raw land for the
roads, sidewalks, and planting strips, there is an additional cost for the purchase of the raw
land for these elements of infrastructure. The cost of infrastructure improvements associated
with the street length (water supply, sanitary sewer, paving of road, sidewalk construction,
and storm drainage) increased from approximately $7,000 to $18,000 per lot as the frontage
per lot increased from 60 to 200 feet—equivalent to approximately $80 per linear foot of lot
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frontage requirement. To put these on a per dwelling unit basis, you would divide by two
assuming that you need to divide the additional cost s between the two facing houses across
the street from each other. Also keep in mind these costs are in 1992 dollars.
The minimum and the median front setback requirements for Waukesha County, Wisconsin
were 25 feet and 50 feet, respectively. In the study, the cost for the utility lateral lines plus
the cost for driveway paving at the 25-foot setback was $2,000, and the cost at the 50-foot
setback was $3,200. The difference in costs for these values of front setbacks was
approximately $50 per foot of setback (1992 dollars).
Within Waukesha County, communities which are more urbanized or in high-growth areas
allow development of smaller single-family lots than do more rural towns and villages. The
smallest lot size approved for SFD homes in Waukesha County, Wisconsin in 1992 was 4,800
square feet. The median value for the minimum lot size for the highest density residential
district permitting SFD homes for the 32 communities in Waukesha County was 20,000
square feet. The report did not attribute a specific cost differential to larger lot size
requirements, probably due to the high spatial variability of land cost.
Minimum interior floor area requirements were noted as a possible significant contributor to
regulatory cost barriers for affordable housing in the Waukesha study. The adopted
minimums for the communities ranged from 900 to 1,500 square feet. The median required
minimum floor area adopted in Waukesha County for a SFD residence in 1992 was 1,100
square feet; although the most frequently adopted minimum floor area was 1,200 square feet.
These values for minimum floor area requirements are near the average values found by the
research team for the nationally representative sample of jurisdictions. The study noted that
although most of these minimum floor area requirements are considerably smaller than the
homes built during the previous decade, the minimums can still be adjusted to allow more
affordable housing to be constructed.
Open space requirements in subdivision rules may be stated in one of the principal formulae:
• Percentage of total subdivision land
• Number of square feet per dwelling unit
• Number of square feet per person
Using the applicable minimum lot area for a subdivision and the median household population
in the United States—2.69 people per household based on the 2000 U.S. Census—the three
measures of open space can be expressed in the same units of number of square feet per
dwelling unit.
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Open space requirements in Waukesha County at the time of the study ranged from 1,500 to
7,500 square feet per dwelling unit when sewer was available, and from 9,400 to 15,000
square feet in areas without municipal sanitary sewer service. Depending on the costs for
additional raw land to provide for subdivision open space requirements, these standards may
be a significant contributor to regulatory cost barriers.
Based on a comparison with the minimum development standards adopted in the county
which were considered to be reasonable guideline minimums, the Waukesha County study
listed the following five items as the most obvious steps to reduce the cost of finished
dwellings:
• Decreasing the minimum lot size in the highest density single-family zone
• Reducing required lot width in these zones
• Reducing the front yard setback requirements in these zones
• Reducing the minimum floor area required in these zones
• Reducing the street pavement width in these zones
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3. Exi
i
st
t
i
i
ng Regul
l
at
t
i
i
ons
For the study detailed in this report, the project team assessed the cost impacts of regulatory
barriers to the construction of SFD dwellings within subdivisions. In the quantitative portion of
this study, the research team estimated the cost impacts of subdivision rules and zoning
ordinances which require land or site development standards in excess of what is needed to
protect public health and safety.
For the quantitative assessment of the cost barriers, the team sampled the land and site
development standards in the subdivision rules and zoning ordinances of 469 separate municipal
or county level planning jurisdictions. The sample was selected using statistical considerations in
order to be representative of planning jurisdictions throughout the United States. Specifically,
the team looked at the standards that applied to the subdivision of land for the construction of
SFD dwellings. In most jurisdictions there is more than a single zoning district that allows these
dwellings. In these cases, the study considered the zoning district that allowed the smallest lot
sizes for SFD dwellings.
Land development and site development standards data were entered into a Microsoft Access
database where they could be analyzed for a range of statistical measures and to quantify the
effect of subcomponents of the sample. The team recorded key characteristics of each planning
jurisdiction in the sample to ascertain the role of these characteristics with respect to the
requirements for land and site development standards. These subcomponents of the sample
included:
• Planning jurisdiction government type
• U.S. Bureau of Census region (Northeast, South, Mid-West, and West)
• Membership in a Metropolitan Statistical Area (MSA)
• Central city or outside central city
• Population separated into quartiles
The research team developed a basic descriptive reporting format for the collected data, which
presents the recorded standards for each jurisdiction. An example of this format is presented in
Figure 3.1.
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Figure 3.1: Sample Database Report Output Format Showing Data Collected for Each Jurisdiction
Source: EcoNorthwest, 2006
3.1 Sampling of Jurisdictions
The foundation of this study is a statistically representative sample of local governments
(municipal and county level) in the United States that have the authority to adopt land use
regulations. The sampling challenge was to develop a methodology that resulted in a random
sample that is representative of the population. The objective of the project was to develop a
sample that was: (1) geographically representative; (2) reflected the national distribution of
population (including jurisdiction size); (3) reflected both fast and slow growing jurisdictions;
and (4) represented a range of government types.
The project team selected jurisdictions based on weighing the sample by population in states
and the amount of growth in each local government between 1996 and 2000. This methodology
placed emphasis on the amount of population in each state, and ensured that both fast and slow
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growing governments were represented. The rationale for the sampling methodology is
described in sub-appendix A-2, contained in Appendix A – Survey of Regulatory Standards.
The sampling methodology originally intended to examine subdivision rules from 1,100
jurisdictions. When the project team began evaluating which standards to measure, it found
that many of the relevant standards were in zoning ordinances, rather than subdivision rules.
After consultation with HUD, the team decided to review both the zoning ordinances and
subdivision rules and to reduce the sample size to 500 jurisdictions, reflecting the increased
labor required for the collection and analysis of the zoning ordinances. In cases where the
ordinances could not be obtained from the jurisdiction, the team employed a substitution
method to choose a different jurisdiction. Ultimately 469 separate jurisdictions were included in
the sample utilized for the national regulatory barrier cost estimates.
After selecting the sample, the project team collected zoning ordinances for review. The
structure of a typical zoning ordinance presents some inherent analytical challenges. A typical
zoning ordinance has three or more residential districts. The research team concluded that
reviewing every residential district for each jurisdiction in the sample would be infeasible. Thus,
the team developed a protocol for gathering ordinances that significantly reduced the data
collection effort. The team focused on the “border” zone between low-density single-family
development and high-density multifamily development because the land requirements are
smaller (e.g., minimum lot sizes are typically smaller), which should result in lower housing
costs. The “border” zone was defined based on the following characteristics: it permitted
detached single-family houses outright; it had the smallest minimum lot size and setbacks; and,
where applicable, it allowed a mixture of detached single-family houses and duplexes or
multifamily housing. In cases where it was unclear which zone to choose after evaluating these
characteristics, the research team opted for the zone with the smallest minimum lot size where
SFD homes are permitted outright.
After collecting ordinances, identifying the appropriate zone for analysis, and inputting data, the
research team conducted a statistical analysis of the sample. The analytical approach focused on
two types of statistical analysis: (1) basic descriptive analysis; and (2) inferential statistics in the
form of means testing. The basic descriptive analysis consisted of the following statistics: mean,
median, mode (most frequently reported value), frequencies, range, and standard deviation. The
means testing used chi-square and ANOVA with post-hoc testing to determine if the variables
varied systematically by class membership within subcomponents of the sample including
government type, Census region, membership in a Metropolitan Statistical Area (MSA), central
city, and population quartiles.
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3.2 Selection of Land Development Standards
The project began with a list of about 75 land or site development standards (referred to as
“variables”) that were considered for inclusion in this study. The team narrowed the list of
variables by reviewing ordinances from 10 jurisdictions to assess whether subdivision
ordinances commonly contained the standards on the list. Many site-specific variables on the
original list were not in the initial 10 ordinances.
At that point, the team began considering expanding the scope of the project to include some
variables from zoning ordinances because the preliminary research indicated that many
standards, especially those related to lot size, which have substantial impact on housing costs,
are not typically included in subdivision ordinances. The team reduced the number of variables
to 15 and conducted a second review of 10 jurisdictions' subdivision and zoning ordinances to
determine how frequently these variables occurred in the ordinances. This review showed that
the variables on the reduced list were frequently found.
The list of variables was finalized based on the following criteria:
• Expected impact of the variable on housing cost. This was a critical factor. Some
of the variables initially considered were estimated to have minimal impact on the cost of
housing. For example, many of the jurisdictions in the preliminary review contained
standards for the angle of street intersections, but, based on the team’s expertise in
residential construction, it was understood that this factor has little impact on the cost of
housing in a subdivision.
• Likelihood and ease of finding the variables within a zoning or subdivision
ordinance. A number of the variables that the team was originally interested in measuring
were not generally found in either subdivision or zoning ordinances. For example, the
minimum diameter of a sewer lateral or street pavement surface thickness was not often
found in either the zoning or subdivision ordinances.
• Ease of measuring the variables. Some of the variables that were considered were
difficult to measure. For example, landscaping standards vary substantially among ordinances.
The team was unable to find a way to quantify such diverse standards. Instead, the
researchers chose to identify whether or not each jurisdiction had landscaping requirements
in their zoning or subdivision ordinances.
The following variables were selected for inclusion in the review of subdivision rules and zoning
ordinances for this study:
• Lot width minimums • Yard set-back minimums (front yard,
• Lot size minimums side yards, rear yard)
• Floor area minimums
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• Off-street parking requirements • Sidewalk requirements
• Curb and gutter requirements • Open space requirements
• Minimum street right-of-way width • Landscaping requirements
• Minimum pavement width
3.3 Definitions
This section includes definitions of terms that are used for the statistical analysis.
Means testing. This includes tests that describe the variation with in the sample. Types of
means tests include: chi-squared and ANOVA.
Statistically significant. Results are referred to as statistically significant or significant if a
statistical test shows a difference that is unlikely to occur by chance.
Chi-square. A statistical procedure used to test for differences between groups of categorical
data. This technique makes use of data classified into a contingency table, and the results are
based on a comparison of expected frequencies with observed frequencies. (For further details
see http://www.statistics.com/content/glossary/c/chisqtest.php.)
ANOVA (Analysis of Variance). Techniques used to determine if differences between two
or more groups are significant. This type of test is based on an assessment of the variation
between groups relative to variation not associated with differences in group membership. (For
further details see http://www.animatedsoftware.com/statglos/sg_anova.htm.)
3.4 Analytical Framework
For this project, the analytical approach focused on two types of analysis—basic descriptive
analysis, and means testing. The basic descriptive analysis consisted of the following statistics:
mean, median, mode, frequencies, range, and standard deviation. The means testing consisted of
using chi-square and ANOVA tests.
The means tests showed statistically significant
1
differences among the jurisdictions. Jurisdictions
were separated by certain characteristics, such as population size or whether the jurisdiction
belonged to an MSA. These groupings, which represent subcomponents of the sample, allowed
the research team to perform the means testing to compare the regulatory standards between
regions within the sample, rather than the entire sample. Using these groupings allowed for
comparisons that showed regional variation among the subcomponents of the sample. The
subcomponents included:
1
For the remainder of the report, “statistically significant” results will be denoted as “significant.”
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Government type. Jurisdictions were separated by six government types—county, city,
town, township, village, and other government type. Counties and parishes were combined
because there are few parishes, and they serve a similar function as counties. All the other
government types were combined as well because they made up less than 3 percent of the
governments in the study.
Census region. States were grouped into the four regions used by the U.S. Census:
viii
Northeast, Midwest, South, and West.
Part of an MSA. Jurisdictions were grouped by whether they are a part of a Metropolitan
Statistical Area (MSA), as defined by the U.S. Census. Jurisdictions belonging to an MSA are
more likely to be located in an area where the population is densely distributed.
Central city. Jurisdictions were grouped based on whether they are a central city, as defined
by the U.S. Census. A central city is the largest city of a Metropolitan Area (MA) and is a basis
for establishment of an MA. Jurisdictions that are a central city are typically more densely
populated than jurisdictions that are not a central city.
Population. The sample jurisdictions were grouped into quartiles based on their populations
from the 2000 U.S. Census. The groups were as follows: fewer than 5,491 people; 5,492 to
ix
25,176 people; 25,177 to 97,268 people; and more than 97,268 people.
The means tests indicated whether sample subcomponents, such as government type or Census
region, make a difference in the standards that jurisdictions establish. For example, the means
testing tells whether a variable such as lot size is likely to be different if the government is a city
or county or if it is located in the east or west, etc. The research team used two forms of
means testing—chi-square and ANOVA.
The team performed chi-square tests on each variable using the five subcomponents of the
sample. The chi-square indicated which variables had significant differences for the
subcomponents of the sample. It is likely that significant differences were caused by differences
in the variables for the subcomponents. In other words, if the chi-square for lot size by
government type is significant, then it is likely that lot size varied in a significant pattern by
government type.
The research team then performed an ANOVA test to identify which subcomponents had
significant differences. Where the chi-square test can indicate a significant difference among all
of the subcomponents, the ANOVA can show the significant differences between each of the
subcomponents. For example, this test might show that lot sizes are statistically different in
cities than in counties. Researchers performed this test for the government type, Census
region, and population subcomponents.
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In cases where the team found standards for the variables in fewer than 100 jurisdictions, it did
not perform any means testing because there were too few data to produce meaningful results.
Appendix A – Survey of Regulatory Standards includes additional details about statistical
procedures.
3.5 Summary of Descriptive Analysis of Regulatory Standards
Table 3.1 below and the following general observations on the sample provide summary of
descriptive statistics for the variables reviewed in the study.
Table 3.1: Summary of Descriptive Statistics of the Variables Reviewed
Standard
N Mean Median Mode Deviation Minimum Maximum
Lot size 419 9,924 6,000 5,000 16,946 750 217,800
Lot width 342 62 60 50 25 20 250
Front yard 413 25 25 25 13 0 100
Side yard 417 8 8 5 5 0 30
Rear yard 404 21 20 25 9 0 65
Minimum floor area 86 1,060 1,000 1,000 359 500 2,500
Off-street parking 367 2 2 2 1 0 4
Open space requirements
Percent of total land in subdivision 47 13 10 10 9 3 50
Number of square feet per dwelling unit 18 1,562 795 871 3,447 310 15,246
Number of square feet per person 34 229 218 218 112 87 436
Sidewalk width 153 4 4 4 1 3 10
Planting strip width 37 5 5 5 1 2 8
Street pavement width
Street right-of-way width
192
262
28
52
28
50
30
50
6
8
16
20
45
80
Source: Study of Subdivision Requirements as a Regulatory Barrier to Affordable Housing, Descriptive Analysis, CPW 2006
Units are linear feet except minimum floor are in square feet
The statistical analysis led the research team to the following broad conclusions:
The sample is roughly representative of each state by population and geography.
The sampling methodology was designed to draw a sample of jurisdictions based on the states'
populations proportionate to the U.S. population. It was also designed to ensure geographic
diversity by including a minimum of two jurisdictions per state, regardless of population.
Jurisdictions in the sample represent 26 percent of the entire U.S. population.
Most of the 469 jurisdictions reviewed had zoning and subdivision ordinances.
Ninety percent of the jurisdictions had a zoning ordinance and 86 percent had a subdivision
ordinance. Six percent of the jurisdictions had neither a subdivision nor a zoning ordinance.
Lot size requirements were highly variable among jurisdictions. The smallest minimum
lot size in the study was 750 square feet and the largest was 217,800 square feet (5 acres).
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Forty-one percent of the jurisdictions in the sample had minimum lot sizes between 5,000 and
6,999 square feet. Statistical testing showed significant differences in lot size requirements for
each subcomponent of the sample. For example, there were significant differences in minimum
lot sizes for each of the four Census regions (one of the subcomponents of the sample), with
larger minimum lot sizes in the Northeast than in the other three regions.
Lot widths varied across jurisdictions. The smallest lot width requirement was 20 feet, the
largest was 250 feet, and the median was 60 feet. Fifty-five percent of jurisdictions required
minimum lot widths of 50 to 69 feet. As with lot sizes, statistical testing showed significant
differences in lot width requirements for each subcomponent of the sample.
The mean front set-back requirement was 25 feet. Fifty-six percent of jurisdictions had
front setbacks between 20 and 29 feet. Like lot size and width, front setbacks differ in a
significant way for each subcomponent of the sample.
The mean side set-back requirement was 8 feet per side. Fifty-eight percent of
jurisdictions required minimum side yard setbacks of between 5 to 9 feet. Side setbacks differed
in significant ways for each subcomponent of the sample.
The mean rear set-back was 21 feet. The smallest requirement for a rear set-back was
zero feet and the largest was 65 feet. Fifty-five percent of jurisdictions required rear set-back of
between 20 and 29 feet. Rear yard setbacks differed in significant ways for each of the five
subcomponents of the sample, except for population quartiles.
Fewer than 20 percent of jurisdictions had minimum floor area requirements. For
those jurisdictions that have such requirements, the mean floor area was 1,060 square feet and
the median 1,000 square feet. The smallest floor area requirement was 500 square feet per
dwelling unit and the largest 2,500 square feet.
More than three-quarters of the jurisdictions required two off-street parking
spaces. The mean number of off-street parking spaces required per dwelling unit was 1.88 and
the median was 2 parking spaces. Further statistical testing showed that the number of off-
street parking spaces required differed in a statistically significant way based on population
quartile, as well as between central cities and non-central cities.
Fewer than half of the jurisdictions had landscaping standards. Forty-two percent of
the jurisdictions had landscaping standards specifically for subdivisions or the zoning district that
were examined for the study. Differences in landscaping requirements are significant for the
following subcomponents of the sample: Census region, if the jurisdiction is part of an MSA, and
population quartile.
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Twenty percent of jurisdictions had quantitative open space requirements. The
research team collected three types of requirements for open space: (1) percent of total land in
the subdivision; (2) number of square feet per dwelling unit; and (3) number of square feet per
person. About 20 percent of the jurisdictions in the sample used at least one of these methods
for determining open space requirements.
Fifty-one percent of all jurisdictions n the sample explicitly required sidewalks. Of
those jurisdictions, 51 percent (e.g., about one-quarter of all jurisdictions in the sample)
required sidewalks on both sides of the street. The average sidewalk width was 4 feet.
Requirements for sidewalks varied systematically by the following subcomponents of the
sample: government type; membership in an MSA; and the jurisdiction's population. In contrast,
sidewalk width requirements did not vary systematically based on any of the subcomponents of
the sample.
Relatively few (8%) jurisdictions had requirements for planting strips. The planting
strip is a landscaped area between the sidewalk and curb. The mean and median planting strip
width was 5 feet.
Curbs and gutters were required by 50 percent of the jurisdictions in the study.
The mean and median pavement width for streets was 28 feet. Most jurisdictions'
standards for pavement width were either from 20 to 24 feet wide, from 25 to 29 feet wide, or
from 30 to 34 feet wide. Pavement width differed in a statistically significant way for each
subcomponent of the sample.
The mean street right-of-way was 52 feet. Fifty-six percent of jurisdictions with right-of-
way standards require right-of-ways no smaller than 50 to 54 feet; and 24 percent of
jurisdictions require right-of-ways at least 60 to 64 feet wide. Street right-of-way requirements
vary systematically by Census region.
3.6 Summary of Means Testing Of Regulatory Standards
The results of means testing, including chi-square and ANOVA tests, are summarized by
variable below. Table 3.2 shows a summary of the results of the chi-square tests. Statistically
significant results are denoted with a star (*). A significance level of less than 0.05 is considered
significant.
A statistically significant result means that it is highly probable that there is a difference within
the subcomponent for the variable. For instance, off-street parking requirements are only
statistically significant for central city and population. That means that off-street parking
requirements are different for jurisdictions that are a central city and jurisdictions that are not a
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Study of Subdivision Requirements as a Regulatory Barrier
central city. Likewise, off-street parking standards are different for jurisdictions with different
sized populations.
Table 3.2: Summary of Means Testing for Variance within Five Subcomponents of the Sample
Government Census Part of
type region MSA Central city Population
Lot size 0.000* 0.000* 0.000* 0.000* 0.000*
Lot width 0.000* 0.000* 0.000* 0.000* 0.000*
Front yard 0.000* 0.000* 0.000* 0.000* 0.000*
Side yard 0.000* 0.000* 0.000* 0.000* 0.000*
Rear yard 0.007* 0.000* 0.001* 0.029* 0.055
Off-street parking 0.804 0.556 0.622 0.000* 0.011*
Open space requirement 0.216 0.003* 0.058 0.460 0.200
Landscaping requirement 0.054 0.002* 0.018* 0.783 0.000*
Sidewalk required 0.007* 0.096 0.000* 0.143 0.001*
Sidewalk width 0.841 0.060 0.565 0.156 0.251
Street pavement width
Street right-of-way width
0.001*
0.903
0.000*
0.000*
0.050*
0.233
0.046*
0.323
0.021*
0.387
Source: Study of Subdivision Requirements as a Regulatory Barrier to Affordable Housing, Descriptive Analysis, CPW
2006. Calculations by ECONorthwest.
Note: Statistically significant results are noted with an star (*) for p < .05.
The means tests led the research team to the following broad conclusions:
Lot size requirements were statistically significant for all subcomponents of the
sample. Table 3.2 shows that lot size requirements were significantly different for each
subcomponent of the sample. For example, lot size requirements were different based on the
jurisdiction’s type of government or whether the jurisdictions was within an MSA.
Further statistical testing showed the differences in lot sizes among subcomponents of the
sample.
2
Lot size varied within each subcomponent in the following ways:
Government type: City lot sizes were significantly smaller than county, town, and
township lot sizes.
Census region: Lot sizes in the Northeast were statistically larger than lot sizes in the
other three regions.
Population: Lot sizes in the first quartile, jurisdictions with the fewest people, were
statistically larger than lot sizes in other quartiles.
2
This form of statistical testing, an ANOVA, required a minimum of three groups within the subcomponent. The project team
conducted this test for the government type, Census region, and population subcomponents.
NAHB Research Center 3-10 April 2007

Study of Subdivision Requirements as a Regulatory Barrier
The differences observed within both types of means testing reinforce the conclusion that
statistical differences resulted from differences in lot size within the subcomponents.
Lot width requirements were statistically significant for all subcomponents of the
sample. Table 3.2 shows that lot width requirements were significantly different for each
subcomponent of the sample. For example, jurisdictions located in different Census regions had
significantly different lot width requirements. Further statistical testing showed that lot width
varied in the following ways:
Government type: Lot widths were statistically different between the following
groups—cities had smaller average lot widths than villages, towns, or townships; and
counties had larger average lot widths than towns or townships.
Census region: Lot widths were statistically different between the Northeast and all
other regions. The Northeast had larger average lot widths than any other region. Lot
widths in the Midwest were statistically larger than those in the West.
Population: Lot widths were statistically different between the first population
quartile, jurisdictions with the fewest people, and the other quartiles. The first quartile
had larger average lot widths than the other quartiles. In addition, the second quartile
had significantly larger lot widths than the fourth quartile.
The differences observed within both types of means testing reinforce the conclusion that
statistical differences resulted from differences in lot width within the subcomponents.
Front yard requirements were statistically significant for all subcomponents of the
sample. Table 3.2 shows that front yard requirements were significantly different for each
subcomponent of the sample. Further statistical testing showed that front yard requirements
varied in the following ways:
Government type: Front setbacks were statistically different for the following
groups—counties had smaller average front setbacks than townships; and cities had
smaller average front setbacks than towns, townships, and villages.
Census region: The West had statistically smaller average front setbacks than any
other region. The South had statistically smaller average front setbacks than the
Midwest and Northeast.
The differences observed within both types of means testing reinforce the conclusion that the
statistical differences result from fundamental differences in front setbacks within the
subcomponents.
NAHB Research Center 3-11 April 2007

Study of Subdivision Requirements as a Regulatory Barrier
Side yard requirements were significant for all subcomponents of the sample. Table
3.2 shows that side yard requirements were significantly different for each subcomponent of the
sample. Further statistical testing showed that side yard requirements varied in the following
ways:
Government type: Side setbacks are statistically different for the following groups—
counties have smaller average front setbacks than towns and townships; and cities have
smaller average front setbacks than towns, townships, and villages.
Census region: The Northeast has larger average side setbacks, a significant difference
between the Northeast and the other regions. The West has smaller average side
setbacks than any other region, which is also significant.
Population: The first population quartile—jurisdictions with the fewest people—have
larger average side setbacks, which is significantly different from each other quartile.
Likewise, the second population quartile has larger average side setbacks than the third
and fourth quartiles, which is also significant.
The differences observed within both types of means testing reinforce the conclusion that the
statistical differences resulted from fundamental differences in side yard requirements within
the subcomponents.
Rear yard requirements were significantly different for most subcomponents of the
sample. Table 3.2 shows that rear yard requirements were statistically significant for each
subcomponent except population. Further statistical testing showed that side yard
requirements varied in the following ways:
Government type: Rear setbacks were on average smaller in counties than townships,
a significant difference. Cities had smaller average rear setbacks than towns and
townships.
Census region: The West had significantly different re0ar setbacks, which were
generally smaller than the other regions. The South’s rear setbacks were also
statistically different from other regions. The South had larger rear setbacks than the
West and smaller rear setbacks than the Northeast or Midwest.
Population: Jurisdictions in the fourth quartile of population (having the largest
populations) had smaller average setbacks than any other quartile. This difference was
significant between the fourth quartile and the first and second quartiles.
NAHB Research Center 3-12 April 2007

Study of Subdivision Requirements as a Regulatory Barrier
The differences observed within both types of means testing reinforce the conclusion that the
statistical differences resulted from fundamental differences in rear yard requirements within
the subcomponents.
Off-street parking requirements varied significantly for two subcomponents of the
sample. Table 3.2 shows that off-street parking requirements were significantly different within
the central city and population subcomponents. In other words, the number of off-street
parking spaces required varied, depending on whether the jurisdiction was a central city and the
amount of population the jurisdiction had.
Additional statistical testing showed that off-street parking requirements varied for the
population subcomponent. Jurisdictions in the fourth quartile of population (having the largest
populations) require less off-street parking than jurisdictions in the second population quartile.
This result reinforces the conclusion that the statistical differences resulted from fundamental
differences in off-street parking requirements within population quartiles.
Open space requirements varied significantly among Census regions. Table 3.2 shows
that open space requirements only varied significantly among Census regions. For example,
whether a jurisdiction required open space or not was not significantly different for jurisdictions
located in a central city and those not within a central city. The research team did not perform
further tests for open space because open space had only two possible values (yes or no) and
the ANOVA test requires three or more possible values (i.e., yes, no, maybe).
Landscaping requirements varied significantly for some subcomponents. Landscaping
requirements varied significantly for the following subcomponents—Census region,
membership in an MSA, and population. For example, landscaping requirements varied
significantly based on whether the jurisdiction was part of an MSA or not. The research team
did not perform further tests for landscaping because landscaping had only two possible values
(yes or no) and the ANOVA test requires three or more possible values (i.e., yes, no, maybe).
Sidewalk requirements varied significantly for some subcomponents but sidewalk
width requirements did not vary significantly for any subcomponent. Whether a
jurisdiction required a sidewalk varied significantly among the following subcomponents&mdash;
government type, membership in an MSA, and population. The research team did not perform
further tests for sidewalk requirements because it had only two possible values (yes or no) and
the ANOVA test requires three or more possible values (i.e., yes, no, maybe).
Sidewalk width requirements did not vary significantly by any subcomponent of the sample,
which is consistent with the findings shown in Table 3.2 that sidewalk widths had little variation.
NAHB Research Center 3-13 April 2007

Study of Subdivision Requirements as a Regulatory Barrier
Street pavement width varied significantly for each subcomponent of the sample.
Table 3.2 shows that street pavement widths varied significantly for all subcomponents. For
instance, street pavement requirements were significantly different for the Census regions, with
jurisdictions in some regions requiring wider pavement widths than in other regions.
Additional statistical testing showed differences in pavement widths within subcomponents of
the sample. The tests showed differences within government type and Census region, indicating
that pavement widths varied significantly by government type and Census region. Although the
chi-square statistical test indicated that pavement widths were statistically different among
population quartiles, additional statistical testing indicated that there are no significant
differences in pavement widths for population quartiles. Pavement widths varied within each
subcomponent in the following ways:
Government type: Cities had the largest average pavement widths of any government
type. This difference in pavement widths was significant between cities and towns.
Census region: Pavement widths were statistically different between the West and the
Northeast and South. The West had larger average pavement widths.
Population: There were no significant differences in pavement widths when analyzed
by population quartiles.
The differences observed within both types of means testing reinforce the conclusion that the
statistical differences resulted from fundamental differences in pavement width requirements
within the subcomponents.
Street right-of-way width varied significantly among Census regions. Table 3.2 shows
that right-of-way widths only varied significantly among Census regions. Additional statistical
testing shows that street right-of-way widths were statistically different between the Midwest
and all other regions. The Midwest had larger average right-of-way widths than the other
regions. This result reinforces the conclusion that the statistical differences resulted from
fundamental differences in right-of-way widths within Census regions
NAHB Research Center 3-14 April 2007

Study of Subdivision Requirements as a Regulatory Barrier
4. St
t
andards,
,
Benchmarks,
,
and Uni
i
t
t
Cost
t
s
The research team assumed that an SFD dwelling developed in conformance with appropriate
land and site development standards for the protection of public health and safety would be
characterized by a minimum benchmark set of land development standards for lot size, floor
space requirements, lot width, etc. The benchmark standards are based on 12 individuals
responding to a survey of 25 land development professionals including residential land
developers, civil engineers, architects, land planners in private practice, and land planners
employed by planning jurisdictions. The individuals solicited in the survey were recommended
by the National Association of Home Builders (NAHB) and the National Association of
Counties (NACo).
4.1 Benchmarks
The benchmark standards in Tables 4.1 and 4.2 were set at the mean value of the benchmark
values suggested by the 12 respondents to the survey described above. The respondents were
asked to submit benchmark standards appropriate to geographic areas with “more dense”
development. The more dense development scenario was defined as a median lot size of 7,000
square feet or 0.16 acre.
x
These benchmarks were used for comparison with jurisdiction requirements for Metropolitan
Statistical Areas (MSA). Separate standards were solicited for communities with “less dense”
development. The “less dense” development scenario was described as a community with a
xi
median lot area of 22,000 square feet or 0.50 acre. These benchmarks were used for
comparison with jurisdiction requirements outside Metropolitan Statistical Areas (non-MSA).
NAHB Research Center 4-1 April 2007

Study of Subdivision Requirements as a Regulatory Barrier
Table 4.1: Expert Responses for Land Development Benchmarks for “More Dense” Development,

Statistical Summary of Responses Used for Comparison in MSAs (N=12)

Land Development
Standard
Lot size ( feet )
2
Mean
4,250
Minimum
2,750
Maximum
7,000
Lot widths (feet) 39 30 60
Front, side, and rear setbacks
FRONT (feet) 13 0 30
SIDE (feet) 5 3.5 6
REAR (feet) 16 10 30
Floor area minimums (feet)
2
981 400 1,750
Paved roadway width (feet)
On-street parking
allowed one side only
24 21 28
On-street parking
allowed on both sides
27 22.5 32
Width of planting strip
required (feet)
5.1 3.5 10
Sidewalk width (feet) 3.94 3 5
Number of off-street parking
places required
Open space requirements
(% of total land in subdivision)
1.56
12.9
1
0
2
40
NAHB Research Center 4-2 April 2007

Study of Subdivision Requirements as a Regulatory Barrier
Table 4.2: Expert Responses for Land Development Benchmarks for “Less Dense” Development,

Statistical Summary of Responses Used for Comparison in MSAs (N=8)

Land Mean Minimum Maximum
Development
Standard
Lot size (feet )
2
9,411 3,200
20,000
Lot widths (feet)
54 20.0
90
Front, side, and rear setbacks
FRONT
19 0.0
35
(feet)

SIDE

8 0.0
20
(feet)

REAR

21 10
50
(feet)
Floor area (feet)
2
1,481 800
2,500
Paved roadway width (feet)
On-street
parking allowed
23 20 28
one side only
On-street
parking allowed
28 22.5 36
both sides
Width of planting
strip required
6.1 3.5 10
(feet)
Sidewalk width
(feet)
3.93 3 5
7 out of 8 respondents
indicate swales/ drainage
Curb and Gutter drainage
ditches should be considered
for alternate drainage
Number of off-
street parking
1.6 1 2
places required
Should required
parking be under
consensus of 7 out of 8
cover?
respondents : No
Open space
requirements
11.5 4.0 25.0
(% of total land in
subdivision)
NAHB Research Center 4-3 April 2007

Study of Subdivision Requirements as a Regulatory Barrier
As expected, the expert survey result for the benchmark lot sizes, lot widths, minimum front,
rear, and side setbacks, and for minimum square foot floor areas are all significantly smaller for
the “more dense” population scenario as compared with the “less dense” population scenario.
This reflects that a more compact development pattern is generally followed in more urbanized
locations.
Costs to a homebuyer for developed lots which conform to the benchmark standards will be
lower in general as compared to developed lots with larger lot areas, frontage widths, and
interior floor space. The cost differential between a lot developed according to the benchmark
standards and the more common larger lot sizes and dwelling characteristics seen in existing
residential subdivisions are classified as a regulatory cost barrier in this report. Notwithstanding
this designation, larger, wider lots with larger interior floor space are considered more
desirable single-family homes to many potential homebuyers who would be willing to pay more
for these dwellings as compared with a benchmark lot and dwelling. The requirement for large,
wide lots and large interior floor space is an actual cost barrier to the home buyer who has the
resources to afford a home developed according to the benchmark standards but is less able to
afford a home developed according to the larger and more costly standards adopted by many
planning jurisdictions.
4.1.1 Lot Size
The average benchmark value for lot size developed by the expert panel was 4,250 square feet
for “more dense” communities, and 9,411 square feet for “less dense” communities (see figure
4.1). As can be seen, the “required lot sizes”, i.e., the median zoned lot sizes for the “more
dense” and the “less dense” communities—7000 square feet and 22,000 square feet,
respectively—were both significantly in excess of the benchmarks used for these communities.
In the more dense communities, required lot sizes were sixty-five percent (65%) greater than
the benchmark standard; in the less dense communities,required lot sizes were over 200%
greater than the benchmark standard.
NAHB Research Center 4-4 April 2007

Study of Subdivision Requirements as a Regulatory Barrier
Figure 4.1: Lot Area Comparison of Benchmarks to Reference Values
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
20000
Benchmark for
“more dense”
communities
Benchmark for
“less dense”
communities
2005 SFD new
sales Median
2005 SFD new 419 419 HUD Average HUD Average Design Center
construction construction
sales Average
jurisdicitons w/
standards: standards: (gross) (estimated Sustainability
Median
jurisdicitons w/
Average
for 7 SFDs for 7 SFDs
Net)
for
benchmark
Identification of benchmark or comparison value
ar
ea
i
n
sq
u
a
r
e
f
e
e
t
4250
9411
9110
17567
6000
9924
5150
4230
3230
The U.S. Census Survey of Construction classifies new SFD homes in the following categories
of lot size—under 7,000 square feet; 7,000 to 9,000 square feet; 9,000 to 11,000 square feet;
11,000 to 22,000 square feet; and over 22,000 square feet. On a national basis, including 2005
sales in all price ranges, 29 percent of sales fell in the category which included the benchmark
value for “more dense” communities—less than 7,000 square feet. The applicable percentages
for the other lot size categories for 2005 sales were 19 percent for 7,000 to 9,000 square feet,
17 percent for 9,000 to 11,000 square feet, 22 percent for 11,000 to 22,000 square feet, and 13
percent for over 22,000 square feet.
xii
The median lot size for 2005 sales of SFD homes in the
United States was 9,110 square feet and the average lot size was 17,567 square feet.
These values for the benchmark lot sizes show the same trend to larger minimum lot sizes in
“less dense” communities as is noted in the statistical characteristics for lot size in the survey of
existing regulations—planning jurisdictions in non-MSAs had significantly larger minimum lot
size requirements than jurisdictions located in MSAs.
The benchmark minimum lot size for “more dense” communities is 28 percent smaller (1,750
square feet)than the median lot size standard of 6,000 square feet for the 419 jurisdictions with
standards listed in Table 3.1 The benchmark minimum lot size standard of 4,250 square feet in
“more dense” communities is larger than existing minimum lot size standards in 37 of the 419
NAHB Research Center 4-5 April 2007

Study of Subdivision Requirements as a Regulatory Barrier
jurisdictions with lot size standards—only 37 jurisdictions had lot size standards of less than
4,000 square feet. The 4,250 square foot benchmark is smaller than lot size standards in 348 of
the 419 jurisdictions with standards. Those 348 jurisdictions had standards which were 5,000
square feet or greater. Thirty jurisdictions (seven percent of the 419 with standards) are in the
range that includes the benchmark standard (4,000-5,000 square feet). Because the
“benchmark” lot size for “more dense” communities is considerably smaller than the median
and the mean lot size requirements on a national basis, land planning jurisdictions require on
average 2000-6000 square feet more than the benchmark and at a national average raw land
cost of about $2 per square foot, this is a regulatory cost barrier of $4000 -12000 for lot size
alone.
The benchmark lot size of 9,411 square feet for “less dense” communities is very close to the
mean minimum lot size of 9,924 square feet for the 419 jurisdictions in the sample which had
standards for lot area. The median value for the lot sizes for new SFD home sales nationally in
the U.S. Census of Construction for 2005 was about 3 percent less than the 9,411 square foot
benchmark.
Of the seven SFD developments listed in the HUD web-based resource “Affordable Housing
xiii
Design Advisor,” the number of dwelling units per acre ranged from 5 to 16 with an average
of 8.4 (gross area of 5,150 square feet per unit). The actual average lot size for those seven SFD
developments will be smaller than 5,150 square feet since the number of units per acre density
includes the roadway areas serving 8.4 lots. Assuming 40-foot lot frontages, a 50-foot right of
way, and a 26-foot wide road, the road area would occupy about 18 percent of the total area,
leaving each lot with an area of approximately 4,230 square feet. This is very close to the
“benchmark” standard for “more dense” communities developed by the research team, which
lends independent support for the benchmark used in this study.
An even smaller lot size standard that the average lot size based on the seven SFD
developments referenced above has been suggested as a means of reducing the costs of
developed lots by the Design Center for Sustainability at the University of British Columbia
(Canada). They recommended decreasing in the minimum residential single family detached
building lot size from a current standard of 7,247 square feet to 3,230 square feet, a standard
xiv
which was adopted in a neighboring community.
NAHB Research Center 4-6 April 2007

Study of Subdivision Requirements as a Regulatory Barrier
4.1.2 Lot Width
The benchmark lot width of 39 feet in “more dense” communities is 65 percent of the median
lot width of 60 feet listed in Table 3.1 for the 342 jurisdictions which had minimum lot width
standards (see Figure 4.2).
Figure 4.2: Benchmarks for Lot Width Compared with Reference Values
39
54
60
62
0
10
20
30
40
50
60
70
Benchmark for
“More Dense”
Communities
Benchmark for
“Less Dense”
Communities
Median for
Jurisdictions
with Lot Width
Standards
Average for
Jurisdictions
with Lot Width
Standards
Lot Width Benchmark or Reference Comparison
Lot Wid
t
h (fe
e
t)
The benchmark lot width of 54 feet in “less dense” communities is 90 percent of the median lot
width in the 342 jurisdictions with lot width standards. Of those jurisdictions, 6.4 percent (22)
had standards equal to or lower than the 39-foot benchmark for “more dense” communities
and 93.6 percent (320) had lot-width standards greater than this benchmark (Figure 4.3).
NAHB Research Center 4-7 April 2007

Study of Subdivision Requirements as a Regulatory Barrier
Figure 4.3: Jurisdictions with Standards for Lot Width in Width Ranges as a Percentage of Total

Jurisdictions with Standards (342)

# jurisdictions with
standards less than or
equal to 39 feet
6%
10%
# jurisdictions with
standards 40-49 feet
# jurisdictions with
52%
standards 50-59 feet
# jurisdictions with
standards 60 feet or
greater
32%
In comparison with the 54-foot lot width benchmark for “less dense” communities, 110 of 342
jurisdictions (32%) had standards within the range of 50 to 60 feet, more than any other 10-
foot width increment range. Fifty-five jurisdictions (16%) had lot width standards smaller than
50 feet; 177 jurisdictions (52%) had standards that were 60 feet or greater.
Lot widths of 40 feet or smaller have been proposed by a number of jurisdictions in
conjunction with “zero lot line” zoning. This is a placement of house with the edge of the
building coincident with one of the lot lines. The single side yard extends from each dwelling to
the lot line on the opposite side. This configuration of house and side yard preserves a sense of
xv
private open space for narrow lots.
4.1.3 Setbacks
The average value for the benchmark front setback is 13 feet in “more dense” communities and
19 feet in less dense communities. Of the 419 jurisdictions in the sample with front setback
standards, 9 percent were 14 feet or less, 80 percent were 20 feet or more, and 11 percent
were in the range of 14 to 20 feet. The range of the benchmarks for “more dense”
communities was 0 to 30 feet, and 0 to 35 feet for “less dense” communities. In the range of 13
to 19 feet, approximately one car length can be accommodated between the street and the
front of the dwelling. If the driveway were 16 feet wide, then two vehicles could comfortably be
accommodated side by side. At the high end of the benchmark range, two vehicles may be
accommodated end to end between the dwelling and the street.
NAHB Research Center 4-8 April 2007

Interior Floor Space Benchmarks and Reference
Criteria
Study of Subdivision Requirements as a Regulatory Barrier
A number of planning jurisdictions have adopted reduced setbacks to reduce the cost of lot
development. In its affordable housing overlay district, the front setback for the San Louis
Obispo, California planning jurisdiction is 18 feet.
4.1.4 Floor Area Minimums
The benchmark floor area minimums of 981 square feet in MSAs and 1,481 square feet in non-MSAs
are both significantly smaller than the median national interior floor areas for construction of
single-family detached dwellings in 2005—2,275 square feet for MSAs and 1,841 square feet for
xvi
non-MSAs (Figure 4.4). Interestingly, the 981-square foot average benchmark value of
minimum floor area for “more dense” communities is very close to the 1,000-square foot
median value for the floor area required in the 86 out of 469 jurisdictions with standards for
interior floor area.
Figure 4.4: Interior Floor Space Benchmarks and Reference Criteria
981
1481
1000
2275
1841
0
500
1000
1500
2000
2500
“more
dense”
community
benchmark
“less dense”
community
benchmark
median for
jurisdictions
with floor
area
standards
median for
SFD sales
in 2005 in
MSAs
median for
SFD sales
in 2005 in
non-MSAs
Benchmark or Reference Criteria
Square Feet
Of reported sales of 2005 new SFD homes in the United States, 2 percent of interior floor
areas fell in the range of the benchmark standards for MSA areas—less than 1,200 square feet
floor area. Thirteen percent of those homes fell in the range that includes the non-MSA
benchmark interior floor area—1,200 to 1,600 square feet. Most sales fell in larger size ranges
for floor areas—19 percent in the range 1,600 to 2,000 square feet; 19 percent in the range or
2,000 to 2,400 square feet; 22 percent in the range of 2,400 to 3,000 square feet; and 25
xvii
percent greater than 3,000 square feet.
NAHB Research Center 4-9 April 2007

Study of Subdivision Requirements as a Regulatory Barrier
4.1.5 Road Widths
The average value of the benchmark standard for paved street width for streets with parking
restricted to one side only in “more dense” communities is 24 feet. For streets with parking on
both sides of street, the benchmark standard is 27 feet. For “less dense” communities, the
average benchmark is 23 feet for parking on one side only and 28 feet for parking allowed on
both sides. The difference between 23 feet “more dense“ communities and 24 feet “less dense”
communities for one side only parking is not statistically significant given the small sample size.
Also not significant for the same reason is the difference between 27 feet in “more dense”
communities” and 28 feet in “less dense" communities for both sides of street parking.
The average value of the benchmark standard for paved street width for streets with parking
restricted to one side only in “more dense” communities is 24 feet. For streets with parking on
both sides of street, the benchmark standard is 27 feet. For “less dense” communities, the
average benchmark is 23 feet for parking on one side only and 28 feet for parking allowed on
both sides. In the database of existing regulations, the average required street width is 28 feet
and the most frequent street width requirement, the mode, is 30 feet. The required minimum
road widths based on the survey of existing regulations were in the range of 20 to 24 feet for
55 out of the 192 (29%) jurisdictions with road width standards. Road widths of 25 to 29 feet
were required by 53 jurisdictions (28%) with road width standards.
The NAHB Research Center (1993) provides guidance for required road widths. For access
roads with estimated vehicular traffic of 0 to 250 average trips per day, and a maximum posted
speed of 20 miles per hour, with both on-street and off-street parking, the listed range of
minimum acceptable street widths is 18 to 22 feet. For the larger sub-collector streets with
both on- and off-street parking, the listed minimum paved width is 26 to 28 feet.
A standard 32-foot wide paved street with parking on both sides of the street will typically have
a 7-foot wide lane on each side for parking and two 9-foot wide travel lanes. Under traditional
design standards, where parking is restricted to one side of the street, the parking lane may
have a width of 8 feet with two 10-foot wide travel lanes, for a total width of 28 feet.
Street widths can be reduced by using parking lanes as “queuing” lanes. For example, a 26-foot
wide street with parking allowed on both sides of the street may have two queuing lanes and a
single travel lane. The travel lane would typically be 12 feet wide with each queuing lane at 7
feet wide. Cars need to move from the travel lane into the opposite queuing lanes as two
vehicles pass each other. For parking restricted to one side only, the use of a 7-foot wide
queuing lane and a 13-foot wide travel lane for a total width of 20 feet is recommend to
minimize street widths (NAHB Research Center, 2003). Suggested street width standards in
these subdivision design guides generally reference the need to consider the wheelbase and
turning requirements of fire and emergency equipment that will serve the development.
NAHB Research Center 4-10 April 2007

Study of Subdivision Requirements as a Regulatory Barrier
Current trends for the reduction of development impacts to the water or hydrologic cycle,
known as “low impact development,” emphasize the benefits of reducing paved road surfaces as
a means of reducing stormwater runoff amounts. The Seattle Department of Public Works
(2005) pioneered a low impact development residential access road design with a paved width
of 14 feet. All parking is off-street in this area. The design includes 2-foot flat concrete curbs on
each side for a total width of 18 feet from the outside edge of curbing to the outside edge of
opposite curbing. Besides cost savings on actual paving, the quantity of stormwater runoff is
reduced due to less paved surface and having all drainage flow off the curbing into grass
drainage swales, eliminating the need for more costly curb and gutter drainage.
4.1.6 Sidewalks
The benchmark survey for this study indicated a consensus that alternative pedestrian paths
could be utilized and that sidewalks should not be required in “less dense” communities. This is
the benchmark used to compare requirements in non-MSA jurisdictions.
4.1.7 Open Space
The average benchmarks for required minimum amounts of open space are 12.9 percent of the
total subdivision area for “more dense” communities and 11.5 percent of the subdivision area
for “less dense” communities. For the 47 out of 133 jurisdictions with open space requirements
based on a percentage of land area in the subdivision, the mean value was 13 percent. The
majority (60%) of the jurisdictions with standards for open space had requirements in the range
of 10 to 19 percent. About one-fifth of the jurisdictions with standards for open space had
higher requirements for open space and approximately the same percentage had lower
requirements for open space.
Much higher open space requirements are generally applied to “open space conservation
subdivisions," also known as “cluster” subdivisions, as compared with standard lot layout
subdivisions. In the cluster design, the allowable number of dwellings on the parcel being
subdivided is often set at the number of dwellings that could be constructed at the underlying
zoning density. The dwellings are placed on the parcel in a manner to maximize the percent of
undeveloped land, usually in the range of 40 to 60 percent, while the lot size for each dwelling
is reduced proportionately.
4.2 Unit Costs and Regulatory Cost Barriers
The research team for this study compared the individual jurisdiction land development
standards (lot size, lot width, etc.) with benchmark values for those standards. Where the
requirements of the individual land development standard contained in a jurisdiction’s
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Study of Subdivision Requirements as a Regulatory Barrier
subdivision rules or zoning ordinances exceed the benchmarks, a regulatory cost barrier was
estimated using raw land costs together with data taken from the 2005 R.S. Means Site Work
and Landscape Cost Data manual and the 2005 RS Means Residential Cost Data manual.
The team quantified the incremental unit costs for both raw land and for additional
infrastructure such as road paving, lengths of storm sewer pipe, sanitary sewer pipe, and water
supply piping within a subdivision. These individual cost estimation methods are presented
xviii
below for each land development or site development standard considered.
4.2.1 Lot Size
Due to the high cost of land in MSAs, especially in the Northeast and West U.S. Census
regions, the cost differential for building lot sizes larger than the benchmark lot standards can
be significant. It is not completely straightforward to attribute a fixed cost of the lot per square
foot because the per-square-foot cost of land will generally be lower for larger lots. Within a
jurisdiction, the per-square-foot cost of a buildable lot will be a function of the required area
for the minimum lot size. If the requirements are changed, there could be a direct effect on the
cost of lots on a square-foot basis.
One particular concern for the research team was obtaining a credible source of data to
estimate the unit cost of the raw land in order to quantify the lot size cost barrier. Land costs
vary on a smaller spatial scale than that of the easily available data on averages for finished lot
costs and lot sizes from U.S. Bureau of Census, while costs for finished building lots vary
significantly between neighboring communities. ,The building lot cost averages used by the
research team were from very large geographic regions of the United States, as summarized by
the U.S. Census of Residential Construction for the following census regions: Northeast, South,
Midwest, and West. The costs are broken down as to lots located in MSAs and lots located in
non-MSAs.
The team used these average costs and average lot sizes from U.S. Census Bureau data with the
understanding and anticipation that if more accurate and representative local data were 

required for a specific jurisdiction, those local costs, if available from local sources of land sale
records, could be substituted for the average land costs used in this chapter.
The data are based on responses to the 2005 U.S. Census of Residential Construction survey.
The data from this survey serve as the basis of average costs for residential building lots and
average lot sizes for 2004 annual recorded sales in each of the four U.S. Census regions (see
Appendix B – Methods for Unit Infrastructure Costing).
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The U.S. Census of Residential Construction survey asks builders what they paid for the lot
that was permitted for construction in 2004. If this lot was bought several years prior to
construction, land prices would generally have been lower at that time.
For the purpose of this research, the lot costs were modified by a factor of 50 percent to
represent a national average for the ratio of the raw land cost to the finished lot cost (Carliner,
2006). Carliner used builder-supplied data from all four major U.S. Census regions to estimate
the breakdown of raw land versus development costs for building lots.
For each of the 469 jurisdictions in the database, the average raw land cost to the builder per
square foot (2005 costs) was estimated separately for each of the four major U.S. Census
regions and either MSAs or non-MSAs based on the following quantities:
A. Average cost of SFD building lots
B. Percentage of the cost of the lot attributed to raw land (50%)
C. Average size of the single-family dwelling building lots
The average raw land cost per square foot is set at the product of the lot price A, multiplied by
50 percent, then divided by the lot area C.
These average raw land costs calculated using the method referenced above are presented in
Table 4.3.
Table 4.3: Estimated Raw Land Costs by U.S. Bureau of Census Region
2004 Average Raw Land Cost (per square foot)
(Inflated to 2005 $)
Northeast Midwest South West National
Metro $1.61 $1.55 $1.74 $4.36 $2.22
Non-Metro $0.87 $0.80 $0.85 $1.80 $1.05
For each jurisdiction with a lot area requirement greater than the benchmark, the research
team estimated the regulatory cost barrier for required lot sizes greater 4,250 square feet for
MSA areas and 9,963 square feet for non-MSAs listed in Tables 4.1 and 4.2. For example, if the
jurisdiction is located in an MSA in the Northeast Census region, and the jurisdiction minimum
lot size is 1,000 square feet greater than the benchmark, the regulatory cost barrier due to the
lot size regulation is estimated at $1.61 per square foot multiplied by 1,000 square feet, which
equals $1,610.
Infrastructure and land development costs vary somewhat from region to region due to
differing costs for labor but the material costs do not vary significantly. The jurisdictions in the
West U.S. Census region MSAs have the highest finished lot costs, and the estimation method
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indicates they have the highest raw land costs, as well. Conversely, in the Midwest U.S. Census
region non-MSAs, the land costs are much lower than the national average.
Once the team determined the aggregate cost barrier for excess raw land requirements for the
entire United States, it weighted the average raw land regulatory cost barrier for each of the
four Census regions and MSA or non-MSA by the annual numbers of housing starts within that
area, and then summed the results from each region and area. It should be noted that the spatial
scale of available land cost data is at a gross level and is not conclusive as to whether or not the
housing starts are evenly distributed across areas within a Census region/MSA area that have
lower than average, average, or greater than average land costs. If the actual number of housing
starts that occur within higher-than-average cost neighborhoods is greater that the number of
housing starts in neighborhoods with below-average land costs, then the estimated aggregate
Census region/MSA area regulatory cost barrier for excess lot size requirements will be
skewed on the low side.
4.2.1.1 Use of Localized Land Costs
For more locally accurate estimates of the regulatory cost barrier due to lot size requirements
greater than the benchmarks, it would be necessary to use locally appropriate building lot costs.
To address that possibility the research team developed a template for a regulatory barrier
costing (RBC) tool that could be made available to users over the Internet. As of this date the
tool has not been verified. The use of the tool can not be recommended until this verification
xix
has taken place.
The RCB tool would allow the user to input locally relevant cost factors such as land costs. The
user is asked to supply the local jurisdiction’s requirements for the various standards utilized by
the research team in developing the regulatory barrier cost analysis—lot size, lot width, street
width (including whether or not one side or both side parking is permitted), front setback
distance, interior floor space requirements, sidewalk requirements, off-street parking
requirements, and open space requirements.
The RBC tool also provides user input options for unit infrastructure costs where the user
wishes to modify the assumptions used by the research team. The tool would then perform the
set of calculations used by the research team in Section 5.0 of this report to estimate the
regulatory cost barriers for each of the land planning jurisdictions in the database used for the
current study. The RBC tool would calculate the regulatory cost barrier for each land
development requirement and would sum all costs to show the aggregate or total regulatory
cost barrier. The RCB tool would provide an option for sharing the results with the
administrator of a database containing all shared responses. An Excel file prototype for this tool
is included in Appendix G.
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4.2.2 Minimum Square Footage Dwelling Requirements
Where the planning jurisdiction required a minimum square foot floor area that is larger than
the benchmark standards of 981(MSA) to 1,481 (non-MSA) square feet listed in Tables 4.1 and
4.2, a regulatory cost barrier was calculated. The unit cost factor for square foot floor area
requirements greater than the benchmark standards was estimated to be $74.60 per square
foot. This cost is based on an average of the costs for various sizes and qualities of slab-on-
grade structures, and is based on combinations of siding and framing types for economy grade
xx
and average grade slab foundation houses.
According to the 2005 U.S. Census of Construction, 14 percent of all new single-family
dwellings had reported costs for construction per square foot in the category that includes the
xxi
value selected for this project—$70-80 per square foot. For all new detached house sales in
xxii
the United States in 2005, the median cost of construction per square foot is $82.59.
The regulatory cost barrier for excess floor area requirements is equal to the difference
between the jurisdiction-required interior floor area square foot space and the benchmark
square foot area, multiplied by the residential unit construction cost factor. As an example, if the
jurisdiction requires a minimum square foot floor area that is 100 square feet greater than the
benchmark, the regulatory cost barrier for this requirement is estimated at $74.60 per square
feet multiplied by 100 square feet, which equals $7,460.
4.2.3 Lot Width
If a planning jurisdiction requires a minimum lot width, or street frontage, larger than the
benchmark widths of 39 feet (MSA) or 54 feet (non-MSA), the research team calculated a
regulatory cost barrier. The greater costs are due to the requirement for construction of the
additional length of street, the additional land for the longer street, and the associated
infrastructure within the street as compared with the benchmark lot width.
Lot width is a significant determinant of total land improvement costs because the most
expensive site improvements (i.e., roads, sewer, and water) cost proportionately more as the
required length of street frontage for each lot increases. The requirement by a planning
jurisdiction for greater than the benchmark lot widths listed in Tables 4.1 and 4.2 will cost the
subdivision developer more for both the land and the paving required for longer streets. If the
required lot widths are 50 percent greater than the benchmark widths, the street length
required for a given number of fronting lots will be 50 percent longer than for lots which utilize
the benchmark widths. The length of the sanitary sewer, storm sewer, water main, and other
utilities which run in easements within the right-of-way associated with the street will also be
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increased in proportion to the road length. Sewer and water supply connection fees are
generally based on the length of the street frontage for each property being connected.
The aggregate unit costs for road construction for a given length of roadway depends on the
required width of the roadway. For a typical subdivision access roadway width of 28 feet, which
is the median value for the jurisdictions sampled, using national average cost estimates for
sanitary sewer, storm sewer, water main, and 4- foot concrete sidewalks, and using national
average MSA land costs, the unit cost for excess lot width is approximately $125 per linear
xxiii
foot. This is one-half of the total cost of the greater frontage length since it accounts for
the distribution of the costs between two dwellings on opposite sides of the street.
The regulatory cost barrier for lot width requirements is equal to the difference between the
jurisdiction-required lot width and the benchmark lot width, multiplied by the unit length cost
for the roadway (which is dependent upon the required roadway paved width) and associated
utilities. For example, if the required lot width exceeds the benchmark lot width by 20 feet, the
regulatory cost barrier is estimated at $125/linear foot multiplied by 20 feet, which equals
$2,500.
4.2.4 Road Width
If the planning jurisdiction required a minimum paved road width larger than benchmark widths
of 23 to 24 feet listed in Tables 4.1 and 4.2, researchers estimated a regulatory cost barrier
based on the additional land required and additional paving of the road.
The unit cost factor for bituminous asphalt paved road construction, based on estimates
xxiv
developed using construction cost data in RS Means Site Work Construction Costs, 2005, is
projected to be $1.15 per square foot. The estimate of $2.30 per square foot was reduced by
50 percent to account for the costs per dwelling unit being split between two units on opposite
sides of the street.
The regulatory cost barrier for road widths greater than the benchmark value, is estimated to
be the difference between the jurisdiction-required and the benchmark value for street widths, in
feet, multiplied by the jurisdiction-required minimum lot frontage in feet, multiplied by cost per
square foot for roadway construction. For example, if the required road width is 5 feet greater
than the benchmark width and if the required lot width is 60 feet, the cost for the additional 5
feet of road width is $1.15 multiplied by 5 feet, multiplied by 60 feet, which equals $345 per
dwelling unit. Furthermore, researchers assumed that additional land costs of approximately the
same unit costs as the paving would be incurred as well as additional costs for lengths of sewer
laterals and water supply laterals.
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Study of Subdivision Requirements as a Regulatory Barrier
4.2.5 Front Yard Setback Requirements
If the planning jurisdiction required a minimum dwelling setback distance from the edge of the
street larger than the 13-foot “more dense” benchmark or 19-foot “less dense” benchmarks
listed in Tables 4.1 and 4.2, the research team calculated a regulatory cost barrier due to the
additional required infrastructure as compared with the benchmark setbacks.
Dwelling setback requirements from the paved edge of the street affect the cost of housing
because they impact the distance between the house utility connections to the water and sewer
laterals, and the right-of-way area utilized for the water supply and the sewer main pipes. In
some cases, these water and sewer pipes may be beneath the roadway itself or it may be
beneath the grassed boulevard strip between the edge of the sidewalk pavement and the edge
of the roadway. The front setback distance will also affect the required driveway length and will
thus affect driveway construction costs as well. Front, rear, and side setbacks greater than the
benchmark values can also drive the total lot size requirement if the setbacks, when added to a
proposed building footprint, comprise a total area which exceeds the minimum lot size specified
in the zoning.
The cost factor per linear foot for the longer driveway will depend on the material of
construction (asphalt or concrete) and on the width of the driveway. For this study, the team
assumed a conservative width of eight feet, which is a required design width for a single-vehicle
parking space. The estimated construction costs were taken as the average of the unit costs for
concrete driveways and asphalt paved driveways. Adding the costs for installation of water
supply lateral piping and sewer drain piping to the cost for driveway construction, the additional
cost for each foot of front setback is approximately $60.
If the required setback between the building and the edge of the roadway exceeds the
benchmark setbacks listed in Table 4.1 and 4.2, the regulatory cost barrier is the difference
between the jurisdiction-required front setback and the benchmark front setback, multiplied by
the combined unit length costs for longer driveways, sanitary sewer lateral, and water supply
xxv
lateral installation. If the required front setback is 10 feet greater than the benchmark front
setback, then the cost barrier for this requirement is approximately $600 per dwelling unit.
4.2.6 Sidewalk Requirements
Due to a lack of consensus regarding the standards for sidewalks in denser MSAs, the research
team did not attribute any cost barrier to sidewalk requirements in MSAs. The unit cost for
concrete sidewalks, as indicated in Appendix B – Methods for Unit Infrastructure Costing, is
$4.18 per square foot. The cost barrier for sidewalk requirements in non-MSAs was equal to
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Study of Subdivision Requirements as a Regulatory Barrier
the unit cost factor for sidewalks, multiplied by the required width of the sidewalk (typically 4
feet), multiplied by the required lot width.
For a street frontage of 60 feet, the cost barrier for one sidewalk in a non-MSA area is 60 feet,
multiplied by 4 feet, multiplied by $4.18 per square foot, which equals $1,003. This cost is then
divided by two to obtain a per-dwelling unit cost of $501.50, which accounts for costs being
spread over two dwellings on opposite sides of the street.
4.2.7 Open Space Requirements
If a planning jurisdiction required a minimum percentage of land reserved for open space larger
than benchmarks indicated in Tables 4.1 and 4.2—11.5 percent of the total land in the
subdivision in “less dense” communities or 12.9 percent in “more dense” communities—the
research team calculated a regulatory cost barrier due to the additional land required with
respect to the benchmark standards for open space.
In some cases the open spaces will be designated as active recreational areas and will be
landscaped and possibly include additional plantings. If the rules of the subdivisions or zoning
ordinances require that the open space be developed as “parkland,” there will be additional
costs not addressed in this study for footpaths or sidewalks, street lights, landscaping, park
benches, and possibly other park amenities. The costs for any required landscaping or park-
related amenities were not accounted for in this study because the need for these amenities of
the required open space is generally not specified in either subdivision rules or zoning
ordinances.
The unit cost for open space was taken to be the raw land cost presented in Table 4.3. The
cost depends on whether or not the jurisdiction is in an MSA or non-MSA, and on the U.S.
Census region in which the jurisdiction is located. In areas where non-buildable wetland or
stormwater retention areas can satisfy the jurisdiction requirements for open space, the actual
unit cost would likely be lower than the national average raw land costs in Table 4.3.
If the jurisdiction-required subdivision open space exceeds the benchmark open space listed in
Tables 4.1 and 4.2, the regulatory cost barrier is the difference between the jurisdiction-
required open space and the benchmark open space, multiplied by the cost per unit area for raw
land. To convert the values to a per-dwelling-unit basis, the percentage difference between
benchmark and the required open space is multiplied by the required minimum lot area. For
example, if the required minimum lot area for an MSA jurisdiction is 6,000 square feet (based
on national average MSA land costs), and the difference between the required open space and
the benchmark open space is 5 percent, the regulatory cost barrier for this requirement is
6,000 square feet, multiplied by 0.05, multiplied by $2.22 per square foot, which equals $666.
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Study of Subdivision Requirements as a Regulatory Barrier
5. Regul
l
at
t
ory Cost
t
Barri
i
ers
A key objective of this study was to develop a national estimate of the cost of excessive land
and site development standards on SFD housing built in subdivisions. The study uses a four-step
methodology for developing the national cost estimates: (1) collect regulatory standards from a
sample of jurisdictions for selected variables; (2) establish benchmark values and unit costs for
each variable; (3) produce a cost estimate for regulatory cost barriers based on the application
of the benchmark values and costs to the regulatory standards from the sample jurisdictions;
and (4) create a national estimate of the regulatory cost barriers.
The previous chapter presented the results of the survey of regulatory standards, the
benchmarks used to estimate regulatory cost barriers, and the unit costs attributed to each
variable. This chapter presents the results of applying the benchmarks to the regulatory
standards for the sample jurisdictions and the resulting regulatory cost barriers. It presents two
models for generalizing the regulatory cost barriers from the sample jurisdictions to the nation,
and includes four cost estimates of regulatory cost barriers for the nation.
5.1 Cost Analysis of the Sample Jurisdictions
This section presents the methodology and results of the cost analysis for the sample
jurisdictions. It includes a description of the methodology for the estimated cost of excessive
regulation for the sample jurisdictions. It concludes with a sensitivity analysis of the costs of
excessive regulation.
5.1.1 Methods
The first step in developing the national cost estimates was to produce an estimate of the
regulatory cost barriers for the 469 sample jurisdictions. The estimate of the total regulatory
cost barriers of the sample jurisdictions was built from estimates of the regulatory cost barriers
for each of the nine variables. This sample estimate also allowed average per-dwelling costs to
be calculated for the variables.
The data presented in Sections 3.0 Existing Regulations, and 4.0 Standards Benchmarks and Unit
Costs provide the foundation for the national costs estimates: (1) a summary of the survey of
regulatory standards; (2) a summary of the benchmark survey; and (3) the unit cost of
exceeding the benchmark values for each of the variables.
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Figure 5.1 shows the steps in developing the estimates of the regulatory cost barriers for the
469 sample jurisdictions. The process for estimating the regulatory cost barriers in the sample
jurisdictions is summarized below Figure 5.1 and presented in detail in Appendix E – Cost
Estimation Methods for Sample Jurisdictions and National Costs.
Figure 5.1: Process for Determining the Costs of Excessive Land Use Regulation for Jurisdictions
in the Sample
Source: ECONorthwest, 2006
The process for estimating the regulatory cost barriers in the sample jurisdictions included the
following steps:
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1. Select variables. The first step was to select the land and site development variables to
examine from subdivision and zoning ordinances.
2. Review ordinances from sample jurisdictions. The next step was ordinance review, which
involved collecting regulatory standards for each of the variables from the zoning and
subdivision ordinances from the sample jurisdictions.
3. Establish benchmarks. The benchmark survey established benchmark values for each
variable.
4. Develop unit cost assumptions. The cost analysis required unit cost assumptions for each
variable. For example, cost per square foot of land or per lineal foot of sewer line.
5. Identify variance from benchmarks. The benchmarks (from Step 3) were applied to the
data collected for each variable in each jurisdiction (from Step 2) to determine the
degree to which a category of land development standard exceeded the benchmark
value for that category of standard.
6. For each variable, estimate cost of regulations requiring a variance from the benchmarks. The
unit cost of each variable (from Step 4) was applied to the amounts of variance from the
benchmark (from Step 5) to estimate the costs of the regulation that required the
variance from the benchmark. These costs are all on a per-dwelling-unit basis.
7. Aggregate costs to reflect total costs for sample. The costs of regulations requiring a
variance from the benchmark (from Step 6) were summed for all jurisdictions in the
sample to produce a total regulatory cost barrier for the required variances from each
of the benchmarks.
8. Estimate mean and median costs of regulatory barriers for the sampled jurisdictions. From the
total regulatory cost barrier for each variable , summed over the sample of 469
jurisdictions (from Step 7), the mean and median regulatory cost barriers for each
variable were determined. These means and medians apply to the subset of the total
sample of jurisdictions in which there was a calculated non-zero regulatory cost barrier
for that variable.
The following provides an example of how these steps were applied to regulatory
requirements for one of the nine variables, minimum floor areas.
1. The project team included minimum floor area requirements as a variable in the study.
2. Ordinance review showed that 86 of the sample jurisdictions had minimum floor area
requirements.
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3. The benchmark survey established maximum values for floor area requirements of 981
square feet for jurisdictions within an MSA and 1,481 square feet for jurisdictions
outside an MSA.
4. The project team established a unit cost factor of $75 per square foot for excessive
floor area requirements.
5. The benchmark value (from Step 3) was subtracted from the floor area requirement in
each jurisdiction (from Step 2), resulting in the variance between the required and the
benchmark floor area requirement for each jurisdiction.
6. The variance in floor area requirements (from Step 5) was multiplied by $75 per square
foot (from Step 4), resulting in the total regulatory barrier cost of floor area regulation
xxvi
for each jurisdiction.
7. The total regulatory cost barrier for floor area regulation for each jurisdiction was
summed, producing an aggregate regulatory cost barrier of excessive floor area
requirements for the sample jurisdictions. As indicated in Table 5.2, this value is
$880,000.
8. The mean and median costs of excessive floor area regulation for the sample of
jurisdictions were calculated, resulting in two estimates of the regulatory cost barrier of
floor area regulation for one SFD dwelling unit. As indicated in Table 5.2, the mean was
$26,581 and the median was $16,425.
5.1.2 Results
Table 5.1 shows a summary of the number of jurisdictions with requirements from each
variable. Of the 469 jurisdictions in the study, 439 had regulatory standards for one or more of
the variables listed in the table. The most common regulatory standards were for lot size, front
setbacks, off-street parking, and lot width. The least common requirements were floor area and
open space.
Table 5.1 also shows the number of jurisdictions that exceeded the benchmarks. Of the 439
jurisdictions with one or more regulatory standards, 425 jurisdictions had one or more
regulatory standards that exceeded the benchmarks. Jurisdictions exceeded the benchmarks
most frequently for off-street parking, front setbacks, lot width, and lot size. Jurisdictions
exceeded the benchmarks least frequently for floor area, sidewalk requirements, open space,
and sidewalk width requirements.
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Table 5.1: Summary of Jurisdictions with Regulatory Standards and Jurisdictions Exceeding the
Benchmarks by Variable
Jurisdictions with
regulatory standards
Percent of
Variable
Number total
Lot size 419 89%
Lot width 342 73%
Front setbacks 413 88%
Floor area 86 18%
Pavement 192 41%
Sidewalk 241 51%
Sidewalk width 153 33%
Off-street parking 367 78%
Open space 99 21%
Total 439 94%
Jurisdictions that exceed benchmarks
Percent of
jurisdictions
with regulatory Percent of all
Number standards jurisdictions
305 73% 65%
295 86% 63%
370 90% 79%
36 42% 8%
134 70% 29%
40 17% 9%
60 39% 13%
15 4% 3%
27 27% 6%
425 97% 91%
Source: Survey of Regulatory Standards, University of Oregon Community Planning Workshop, with calculations by ECONorthwest
Table 5.2 shows the costs of excessive regulation by variable for all jurisdictions.
The total regulatory cost barriers for the jurisdictions in the sample was about $5.6 million and
the average cost per dwelling unit was more than $13,000.
The variables with the largest share of total costs were lot size, floor area, and lot width. The
variables with the smallest share of the costs were sidewalk width, off-street parking, and open
space requirements.
The detailed analysis of the results for each land development variable is provided in Appendix
F – Excel Workbook for Regulatory Cost Barrier Calculations. Lot size, lot width, and floor
area accounted for the largest percentage of total costs for a variety of reasons, described
below.
• Lot size regulations accounted for the largest percentage of cost (65 percent). Land that is
permitted for residential development is costly and is the major component of the costs of
excessive lot size requirements.
• Sixty-five percent of jurisdictions in the sample exceeded the lot size benchmark. On
average, jurisdictions with excessive lot size requirements exceeded the benchmarks by
6,573 square feet. The frequency and magnitude of lot size requirements greater than the
benchmarks, combined with the cost of land resulted in the regulatory cost barrier for lot
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Study of Subdivision Requirements as a Regulatory Barrier
size accounting for the majority of total costs of the regulatory cost barriers for all the land
and site development variables considered in this study.
• Excessive lot width requirements account for a moderate share of costs (9%) because 63
percent of jurisdictions with lot width regulations exceeded the benchmarks. The costs
associated with lot widths included land, sanitary sewer main, water main, street paving,
curb and gutter, sidewalk construction, landscaping, and storm sewer.
• Floor area had a disproportionately large impact on total regulatory barrier costs in
proportion to the number of jurisdictions with excessive floor area requirements. Only 8
percent of jurisdictions had excessive floor area requirements, but the regulatory cost
barriers for floor area for those jurisdictions accounted for 17 percent of the total
regulatory cost barriers for all land development variables for all the jurisdictions in the
study. This resulted from the relatively high cost per square foot of required floor area and
the fact that the mean differential between required floor area and benchmark floor areas
was 354 square feet (or more than $26,000 per dwelling unit).
Table 5.2 shows that the mean cost per dwelling unit for regulatory cost barriers associated
with all the land development variables was $13,142 and the median cost was $4,950. Floor
area had the largest mean and median cost per dwelling unit, followed by lot size. Sidewalk
width and street pavement width had the smallest mean and median cost per dwelling unit.
Table 5.2: Regulatory Cost Barriers for All Jurisdictions and Costs Per Dwelling Unit Variable
Costs for all
jurisdictions
Percent
Variable Total of total
Lot size $ 3,648,726 65%
Lot width $ 526,529 9%
Front setbacks $ 231,952 4%
Floor area $ 956,925 17%
Pavement $ 64,993 1%
Sidewalk $ 63,001 1%
Sidewalk width $ 14,631 0%
Off-street parking $ 22,183 0%
Open space $ 56,620 1%
Total $ 5,585,560 100%
Costs of excessive regulation per dwelling unit
Mean Median Minimum Maximum
$ 11,963 $ 5,596 $ 216 $ 405,099
$ 1,785 $ 1,120 $ 36 $ 16,142
$ 627 $ 570 $ 52 $ 4,195
$ 26,581 $ 16,425 $ 1,425 $ 113,925
$ 485 $ 354 $ 12 $ 2,134
$ 1,575 $ 1,672 $ 468 $ 3,344
$ 244 $ 211 $ 0 $ 1,782
$ 1,479 $ 1,079 $ 270 $ 2,159
$ 2,097 $ 616 $ 7 $ 24,846
$ 13,142 $ 4,950 $ 2 $ 407,429
Source: Survey of Regulatory Standards, University of Oregon Community Planning Workshop, with calculations by ECONorthwest
5.1.3 Sensitivity Analysis
The benchmark values were determined by a survey of land-use experts, as described in
Chapter 4. The purpose of the survey was to establish a consensus on reasonable minimum
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regulatory requirements for each variable. The benchmark standards are the average value for
each surveyed standard from all survey respondents.
Recognizing that reasonable people might have different conclusions with respect to where the
benchmarks should be set, the research team performed sensitivity testing on the benchmarks
to determine how changes would impact the cost estimates. The team used the minimum and
maximum benchmark values for each variable as input values for the sensitivity testing.
Table 5.3 shows the results of sensitivity testing on the total and mean costs for the sample
jurisdictions. The estimate of the cost of excessive regulation for the study sample using the
mean values for the benchmarks was $5.6 million for all jurisdictions in the sample. The mean
estimate of cost of excessive regulation per dwelling unit was $13,142. The total cost for the
sample jurisdictions using the smallest benchmark values was $10.7 million and the mean cost
per dwelling was $24,475 (smaller benchmark values resulted in more jurisdictions exceeding
the benchmarks). When the research team used the largest benchmarks (which decrease the
amount of excessive regulation), the total cost for all jurisdictions in the sample was $2.6
million and the mean cost per dwelling unit was $10,013.
The data in Table 5.2 show that the variables with the greatest share of the costs were lot size
and floor area. When using the smallest and largest benchmark values these variables continue
to make-up the majority of costs of excessive regulation.
Using the mean value benchmarks, lot size accounted for 65 percent of the total costs and floor
area accounted for 17 percent of total costs. The total cost of excessive regulation of lot size
was $3.6 million and floor area cost about $957,000.
• With the smallest benchmarks, lot size accounted for just over 50 percent of the total
regulatory cost barrier. The total cost of excessive regulation of lot size was about $5.5
million.
• With the smallest benchmarks, floor area accounted for one-third of the total costs of
excessive regulation because using the smallest floor area benchmark resulted in
considerably larger variances between the required floor areas and the benchmark floor
areas. This change in benchmark values resulted in a much larger total regulatory cost
barrier for floor size which was estimated at $3.4 million for all sampled jurisdictions.
• With the largest benchmarks, lot size accounted for about 82 percent of total regulatory
cost barriers. The total regulatory cost barrier was about $2.1 million. The maximum lot
size benchmark value was much larger than the mean lot size benchmark, resulting in a
smaller number of jurisdictions exceeding benchmarks. However, the aggregate regulatory
cost barrier for lot size for all jurisdictions did not show a proportional decrease to the
decrease in the numbers of jurisdictions which exceeded benchmarks. This resulted in a
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Study of Subdivision Requirements as a Regulatory Barrier
larger average regulatory cost barrier for lot size using the largest benchmarks ($21,279 for
largest benchmarks versus $11,963 for the mean benchmarks).
• With the largest benchmark, floor area accounted for 4 percent of the total costs of
excessive regulation because the maximum floor area benchmark was much bigger than the
mean floor area benchmark. This change in benchmark values resulted in smaller regulatory
cost barriers for floor area requirements, decreasing the percentage of total costs
accounted for by floor area regulation. The total regulatory cost barrier using the largest
benchmarks was only $101,250. Only 7 out of the 86 jurisdictions with standards for floor
area had regulatory cost barriers using the largest floor area benchmarks.
Table 5.3: Results of Sensitivity Testing on Total and Mean Regulatory Cost Barriers in the Sample
Jurisdictions
Study benchmark assumptions Smallest benchmark values Largest benchmark values
% of % of % of
total Mean total Mean total Mean
Total Costs costs Costs Total Costs costs Costs Total Costs costs Costs
Lot size $ 3,648,726 65% $ 11,963 $ 5,492,460 51% $ 13,697 $ 2,106,601 82% $ 21,279
Lot width $ 526,529 9% $ 1,785 $ 899,847 8% $ 2,719 $ 142,864 6% $ 1,742
Front setbacks $ 231,952 4% $ 627 $ 537,979 5% $ 1,306 $ 43,814 2% $ 996
Floor area $ 956,925 17% $ 26,581 $ 3,438,000 32% $ 43,519 $ 101,250 4% $ 14,464
Pavement $ 64,993 1% $ 485 $ 59,082 1% $ 372 $ 54,154 2% $ 685
Sidewalk $ 63,001 1% $ 1,575 $ 63,001 1% $ 1,575 $ 63,001 2% $ 1,575
Sidewalk width $ 14,631 0% $ 244 $ 14,631 0% $ 244 $ 14,631 1% $ 244
Off-street parking $ 22,183 0% $ 1,479 $ 22,183 0% $ 1,479 $ 22,183 1% $ 1,479
Open space $ 56,620 1% $ 2,097 $ 192,970 2% $ 2,053 $ 24,812 1% $ 6,203
Total $ 5,585,560 100% $ 13,142 $ 10,720,154 100% $ 24,475 $ 2,573,310 100% $ 10,013
Source: Survey of Regulatory Standards, University of Oregon Community Planning Workshop, with calculations by ECONorthwest
5.2 National Estimates of the Regulatory Cost Barriers for Housing in
Subdivisions
This section presents the methodology and results of the national cost estimate. It includes a
brief description of two ways to model the national costs of excessive regulation. The section
presents the cost of excessive regulation for the nation and sensitivity testing of the national
cost of excessive regulation.
5.2.1 Methods
A key objective of this study was to develop a national estimate of the cost of excessive
regulation on housing developed in subdivisions. The methodology used (a national sample of
jurisdictions with the authority to adopt regulations) was intended to result in sample data that
could be inferred to the national level. To accomplish this, the research team developed and
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Study of Subdivision Requirements as a Regulatory Barrier
tested several models to weight the sample data to represent the regulatory cost barriers for
land development requirements for the entire United States.
The models for estimating the national regulatory cost barriers build from the results of the
cost analysis, and use the mean estimates of regulatory cost barriers per dwelling unit for the
sample jurisdictions. Because the sample data resulted in cost estimates on a per-dwelling-unit
basis, the team weighted the regulatory cost barriers from the sample to the national level by
using the U.S. Census Bureau’s Census of Construction estimates of single-family building
xxvii
permits issued for the nation in 2004.
The research team developed two models of the national regulatory cost barriers—an
aggregated model and a disaggregated model. The models use different assumptions about the
geographic distribution of regulatory standards for the land development variables listed in
Table 5.3.
The aggregated model assumed that the geographic distribution of regulatory
standards for the land development variables would not vary based on Census
region or MSA status within the nation. The aggregated model is based on the assumption
that development of new SFD housing in each of the four U.S. Census regions and whether or
not the development is located within an MSA would average about the same regulatory cost
barriers for each land development variable as the 469 sample jurisdictions. In other words, the
regulatory cost barriers would be about the same for all parts of the nation.
The advantage of the aggregated model is that the size of the study sample is big enough that
we are relatively certain that the average regulatory cost barrier that we found for each
variable in the study is close to the true average of the regulatory cost barriers for that variable,
on a national basis.
The disadvantage of the aggregated model is that it doesn’t allow for separate consideration of
regional variations in the distributions of standards, which results in higher estimates of national
aggregate regulatory cost barrier as compared with the national disaggregated estimate of the
regulatory cost barrier.
The disaggregated model assumed that the regulatory cost barriers would vary
across regions and urban and rural areas within the nation. The disaggregated model is
based on the assumption that the regulatory cost barriers vary between different regions in the
nation and between urban and rural areas. The disaggregated model applies different regulatory
cost barriers to new SFD development based on location (one of the four Census regions) and
density of development (whether the development takes place in an MSA or not).
NAHB Research Center 5-9 April 2007

Study of Subdivision Requirements as a Regulatory Barrier
The research team developed two models of the national regulatory cost barriers —an
aggregated model and a disaggregated model. Figure 5-2 shows the steps in developing the
national estimate of the cost of excessive regulation on housing in subdivisions. Below is a
summary of the process used in each model. A detailed description of the methodologies for
the models is presented in Appendix E – Cost Estimation Methods for Sample Jurisdictions and
National Costs.
Figure 5.2: Process for Modeling the Nationwide Regulatory Cost Barriers
Source: ECONorthwest, 2006
The process for estimating the regulatory cost barriers at the national level included the
following steps:
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Study of Subdivision Requirements as a Regulatory Barrier
1. Develop sample cost estimates. The cost analysis provided the following information: the
number of jurisdictions in the sample with requirements for each variable; estimates of
xxviii
total regulatory cost barriers for each variable; the mean cost of each variable for
the sample jurisdictions; and the percent of jurisdictions in the sample with regulatory
cost barriers for each variable.
2. Collect building permit data. The next step was to gather the number of building permits
issued in 2004 for the nation and the four Census regions. Table 5.4 presents this
information.
3. Apply building permit data to sample cost estimates. The information from the cost analysis
(Step 1) was combined with the number of building permits issued in 2004 (Step 2) to
produce two estimates of the national regulatory cost barriers for each variable. These
estimates were based on the following models:
• The aggregated model used the cost estimates for the sample jurisdictions from
Tables 5.1 and 5.2 and the number of building permits issued nationwide in 2004
(Table 5.4).
• The disaggregated model used cost estimates for the sample jurisdictions where
the jurisdictions were grouped by the Census regions and whether they belonged
to an MSA. These cost estimates are presented in Appendix E – Cost Estimation
Methods for Sample Jurisdictions and National Costs in Tables E.3 through E.9.
The disaggregated model used the number of building permits issued for each
Census region and MSA status in 2004 (Table 5.4).
4. Weight costs to national level. The final step was to sum the estimates of the regulatory
cost barriers for each variable to produce two estimates of the national costs of
excessive regulation, one estimate for the aggregated model and another estimate for
the disaggregated model.
Table 5.4 shows the number of building permits issued for SFD dwellings in 2004. The
aggregated model used the estimate of approximately 1.6 million building permits issued for
SFD dwellings in the United States. The disaggregated model used the estimates of the number
of building permits issued for SFD dwellings in each of the Census regions and MSA status.
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Study of Subdivision Requirements as a Regulatory Barrier
Table 5.4: Number of Building Permits Issued for Single-Family Detached Dwellings
in the United States and Each Census Region and MSA Status, 2004
2004
Building
Region and MSA status
permits
U.S. 1,613,445
Northeast MSA 102,571
Northeast non-MSA 29,187
Midwest MSA 240,241
Midwest non-MSA 55,281
South MSA 665,526
South non-MSA 90,613
West MSA 384,627
West non-MSA
45,399
Source: U.S. Census of Construction, 2006
The following example shows how these steps were applied to determine national costs of
regulatory cost barriers for lot size using the aggregated model.
1. The cost analysis provided the following information about lot sizes from the sample
jurisdictions—65 percent of jurisdictions had regulatory barriers for lot size (Table 5.1),
and the mean regulatory cost barrier for lot size regulation was $11,963 per dwelling
unit (Table 5.2)
2. Table 3.4 shows that approximately 1.6 million building permits were issued for SFD
dwelling units in the United States in 2004.
3. The regulatory cost barrier for lot size was calculated in the following way—the percent
of jurisdictions with excessive lot size regulation (65%) was multiplied by the number of
building permits issued for the nation in 2004 (1,613,445 dwelling units). The result
(1,048,739 dwelling units with regulatory cost barriers for lot size) was multiplied by the
mean regulatory cost barrier for lot size regulation from the sample ($11,963 per
dwelling unit). The result was that the regulatory cost barrier for regulation of lot sizes
was approximately $12.5 billion nationwide in 2004.
4. This process was repeated for the other eight variables to produce national estimates of
the regulatory cost barrier for each variable. These costs were added to produce a
national estimate of the regulatory cost barriers of the land use standards considered in
this study of approximately $19.2 billion.
The process for estimating the costs of excessive regulation for the disaggregated model was
very similar to the process presented above. The difference is that the disaggregated model
used the information presented in Tables E-3 through E-9 in Appendix E to create cost
estimates for each Census region and MSA status.
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Study of Subdivision Requirements as a Regulatory Barrier
5.2.2 Results
Table 5.5 shows the results of the aggregated model of the regulatory cost barriers for the
nation. The national estimate of regulatory cost barriers for land and site development
regulations was $19.2 billion for 2004.
The variables with the largest proportion of excessive costs were lot size, floor area, and lot
width. The average regulatory cost barriers for one dwelling unit was $11,910. This is equal to
the mean cost per dwelling unit for regulatory cost barriers ($13,142), multiplied by the percent
of jurisdictions in the sample that exceeded any benchmark standards (91%). In comparison
with the average cost for a new single-family dwelling in the United States in 2004, which was
$244,000, the average per-unit regulatory cost barrier is approximately 5 percent of that
average selling price.
Table 5.5: Aggregated Model Estimates of the Regulatory Cost Barriers for All Building Permits
Issued for Detached Single-Family Dwelling Units in the Nation, 2004
Aggregated model
Cost estimates
National cost of excessive regulation $ 19,215,338,860
Lot size $ 12,552,280,392
Lot width $ 1,811,355,507
Front setbacks $ 797,956,481
Floor area $ 3,291,995,430
Pavement $ 223,586,056
Sidewalk $ 216,734,720
Sidewalk width $ 50,334,294
Off-street parking $ 76,312,500
Open space $ 194,783,479
Per single family dwelling unit $ 11,910
Source: ECONorthwest, 2006
Table 5.6 shows the estimate of regulatory cost barriers for the disaggregated model. The total
cost of regulatory cost barriers for the nation according to the disaggregated estimate was
about $14.6 billion for 2004.
In this model, the variables with the largest proportion of excessive costs were lot size, lot
width, and front setbacks. The average regulatory cost barriers for one dwelling unit was
$9,051.
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Study of Subdivision Requirements as a Regulatory Barrier
Table 5.6: Disaggregated Model Estimates of Regulatory Cost Barriers for All Building Permits

Issued for Single-Family Dwelling Units in the Nation, 2004

Disaggregated model
Cost estimates
National cost of excessive regulation $ 14,603,018,827
Lot size $ 11,137,670,615
Lot width $ 1,551,371,592
Front setbacks $ 743,335,966
Floor area $ 872,800,196
Pavement $ 96,777,852
Sidewalk $ 36,107,707
Sidewalk width $ 21,564,900
Off-street parking $ 73,915,367
Open space $ 69,474,632
Per single family dwelling unit $ 9,051
Source: ECONorthwest, 2006
The cost estimates for the aggregated model are higher than for the disaggregated model 
because the aggregated model costs did not take into account variations in the regulatory cost
barriers among the regions and MSA status. The aggregated model used the same mean cost
estimate for each variable for all Census regions and for both MSAs and non-MSAs. The
disaggregated model took these variations into account, which resulted in lower estimates of
the cost of excessive regulation.
Table 5.2 shows the mean costs of excessive regulation per dwelling unit for each variable,
which were used in the aggregated model. The cost estimates used in the disaggregated model
are shown in Appendix E Table E-8 and are separated by region and MSA status. The mean and
median regulatory cost barriers for each variable differed between the aggregated and
disaggregated models. Some instances of these variations are illustrated below.
• Differences in assumptions about excessive floor area requirements accounted for more
than half of the difference in total costs between the aggregated and disaggregated models.
The aggregated model estimated that the mean regulatory cost barrier for regulation of
floor area was $26,581 and that 8 percent of jurisdictions exceeded the benchmark for
floor area requirements (Tables 6.1 and 6.2). In Appendix E, Table E-11 shows that these
assumptions resulted in a total regulatory cost barrier for floor area regulation of about
$3.3 billion for the nation in 2004. In contrast, in the disaggregated model nearly all the
jurisdictions with excessive floor area requirements were located within an MSA. The
frequency of excessive floor area requirements within an MSA ranged between a high of 16
percent of jurisdictions in the South, to a low of 6 percent of jurisdictions in the West
(Appendix E, Table E-6). The disaggregated model used mean regulatory cost barriers for
floor area requirements of between $35,175 per dwelling unit in the Northeast to $16,425
per dwelling unit in the Midwest (Appendix E, Table E-8). Table E-13 shows that the total
regulatory cost barrier for floor area from these assumptions was about $872 million for
the nation in 2004.
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Study of Subdivision Requirements as a Regulatory Barrier
• Differences in lot size assumptions accounted for nearly one-third of the cost differences
between the aggregated and disaggregated models. Table 5.2 shows that the mean
regulatory cost barrier for lot size requirements for all jurisdictions in the sample was
$11,963. Table E-8 in Appendix E shows that the mean cost of excessive regulation of lot
size varied across regions and whether the jurisdiction belonged to an MSA. For instance,
jurisdictions in the sample located outside an MSA in the South had a mean regulatory cost
barrier for lot size of $4,488 per dwelling unit. Jurisdictions located in the Midwest within
an MSA had mean regulatory cost barrier for lot size of $16,425. These differences were
caused by differences in the cost of land between the regions and differences in the degree
of variance from the benchmarks between regions. The aggregated model does not allow
for these types of variations in costs and requirements, resulting in greater estimates of the
regulatory cost barriers for lot size requirements. The regulatory barrier estimate for lot
size requirements for the disaggregated model was $11.1 billion as compared with $12.5
billion for the aggregated model.
5.2.3 Sensitivity Analysis
The research team performed the same sensitivity analysis on the national models of the
regulatory cost barriers as it performed on the cost analysis for the sample jurisdictions. The
team used the smallest and largest benchmark values to get an idea of what the range of
national regulatory cost barriers might be.
Table 5.7 shows the results of the sensitivity analysis. For the aggregate model, the total
national regulatory cost barrier using the study benchmark values was $19.2 billion for 2004.
With the smallest benchmarks, this rose to $36.9 billion in 2004. Using the largest benchmark
values, it dropped to $8 billion for 2004.
For the disaggregated model, the total national regulatory cost barrier using the study
benchmark values was $14.6 billion for 2004. With the smallest benchmarks, it rose to $24.6
billion in 2004. Using the largest benchmarks, the total national regulatory cost barrier estimate
dropped to $6.6 billion for 2004.
Table 5.7: Results of Sensitivity Tests of the Aggregated and Disaggregated Models
of the National Regulatory Cost Barrier Estimates, 2004
Study Smallest Largest
benchmarks benchmarks benchmarks
Aggregated model $ 19,215,338,860 $ 36,879,271,157 $ 8,852,653,304
Disaggregated model $ 14,603,018,827 24,665,695,261 $ 6,622,266,833
NAHB Research Center 5-15 April 2007

Study of Subdivision Requirements as a Regulatory Barrier
5.3 Key Findings and Conclusions
• The average cost of excessive regulation for one dwelling unit was about 5
percent of the average cost of a new home. For the land development standards
studied by the research team, the average regulatory cost barrier for one dwelling unit was
$11,910. This is equal to the mean cost per dwelling unit for excess regulatory costs
($13,142), multiplied by the percent of jurisdictions in the sample that exceeded any
benchmark standards (91%). In comparison with the average cost for a new single-family
dwelling in the United States in 2004 ($244,000), the average per-unit regulatory cost
barrier is 4.8 percent of that average selling price.
• The regulatory cost barriers varied. The regulatory cost barriers varied considerably
in the sample jurisdictions, as well as across regions and MSA status. Assuming that the
sample is representative of all jurisdictions in the nation, the national regulatory cost
barriers should vary by region and whether the jurisdiction is part of an MSA.
It is probable that the regulatory cost barriers also vary substantially within the U.S. Census
regions. For instance, the cost of land often varies within a single metropolitan area, which
would produce variation in the cost of excessive regulation within the metropolitan area.
The actual regulatory cost barrier for any given jurisdiction depends on local regulations and
costs of development.
• The estimates of the regulatory cost barriers varied with changes in the
benchmarks but they remained within an order of magnitude of the estimates.
The sensitivity analysis showed that the costs for the sample jurisdiction and national
models of the regulatory cost barriers varied substantially depending on the benchmark
values. However, the changes in either direction were neither twice nor one-half of the cost
estimates. In other words, the actual regulatory cost barriers may be somewhat more or
less than the estimates but the actual costs are relatively close to the estimates.
• The disaggregated model provided the best national estimate of the regulatory
cost barriers. The disaggregated model accounted for variations in regulatory standards
and costs among the Census regions and MSA status better than the aggregated model. The
total mean regulatory cost barriers for land and site development standards in the
disaggregated model was about $14.6 billion for the nation in 2004.
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Study of Subdivision Requirements as a Regulatory Barrier
• The $14.6 billion estimate of the national regulatory cost barriers is a
conservative estimate. Given the results of the study, the research team believes this
estimate is conservative for several reasons. In the survey of regulatory standards, where
there were multiple zoning districts in a jurisdiction, the team examined the residential
zoning district that allowed the densest development. Where multiple zoning districts were
present in a jurisdiction, the team collected data for the “border” zoning districts which
were the densest zoned districts that allowed detached single-family dwellings. These
border zones are generally located between high-density (apartments, townhouses) and
medium-density single-family detached residential development. If the researchers had
examined all zoning districts for every jurisdiction in the sample (which was impractical),
greater levels of regulatory cost barriers may have been found. In the medium-density
residential zoned districts adjacent to the border zones, required minimum lot areas are
generally larger than in the border zones; hence the regulatory cost barriers due to
required lot sizes greater than the benchmark lot areas would have been larger if these
medium-density residential zoned districts had been included in the regulatory cost barrier
analysis.
The estimates of costs associated with the variables were relatively conservative. The cost
assumption for floor area was $75 per square foot. This is highly dependent on the quality
of the construction and the architectural and interior design features of the house. This cost
was an average estimate for “economy” and “average” grade of construction for homes
built with slab-on-grade foundations. If a jurisdiction requires expensive architectural details
within its building code standards, the additional unit cost of floor area construction could
result in considerably higher costs for excessive regulation of floor area. Of single-family
detached homes sold nationwide in 2005, 31 percent had costs (exclusive of the lot cost) of
less than $70 per square foot; 14 percent had costs in the range $70 to $80 per square
xxix
foot; and 56 percent had costs of greater than $80 per square foot.
In reality, land costs may vary substantially on a neighborhood-to-neighborhood level in
some areas. Compared with the “average” communities within a U.S. Census region, the
communities within that Census region that have higher than average land costs may also be
experiencing higher than average rates of new construction starts. In this case, regulatory
cost barriers based on average land costs and total housing construction starts for the
Census region may underestimate the actual regulatory cost barriers due to lot size
requirements within that region.
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Study of Subdivision Requirements as a Regulatory Barrier
6. Concl
l
usi
i
ons
Residential land developers and home builders recognize that excessive requirements for lot
xxx xxxi
size and lot width add significantly to the cost of the finished dwelling.
The costs to the developer, to the builder, and ultimately to the homebuyer, for lots and
dwellings using smaller lot widths and smaller lot areas will be significantly lower than for
dwellings developed on larger lots.
When a land planning jurisdiction has adopted land and site development standards which are
larger than the set of minimum benchmark land development standards intended for protection
of safety and public health, a significant regulatory cost barrier may be imposed on new
residential single-family detached dwellings. There are added costs for the land development
standards greater than the benchmark standards and there may also be benefits associated with
those higher standards. The question of whether the added benefits are commensurate with
the added costs is dependent upon many considerations. The evaluation of whether some of
the benefits may be commensurate with the costs is an area that merits considerable
investigation but is outside the scope of the present study.
For those jurisdictions with one or more regulatory cost barriers for land development
standards, the mean per-housing-unit regulatory cost barrier is approx $12,000. Of all the
planning jurisdictions in the nation, approximately 91 percent have one or more regulatory cost
barriers for land development standards. The mean regulatory cost barrier for jurisdictions
with a barrier, multiplied by the percentage of all jurisdictions that have one or more barriers,
equals the average regulatory cost barrier for all planning jurisdictions. On a national average
basis, the estimate for this regulatory cost barrier for the 1.6 million new SFD housing units
constructed in the United States in 2004 is approximately $11,000.
The research team’s estimate of the regulatory cost barrier due to land and site development
standards imposed by planning jurisdictions is based on the “disaggregated” national regulatory
cost barrier model, which accounts for differences between U.S. Census regions and between
MSAs and non-MSAs. To the extent that the increased cost of new dwellings also raises the
costs for existing dwellings or for existing vacant buildable lots
,xxxii
there are additional cost
barriers, not quantified in this study, due to the influence of new housing prices on existing lot
prices in the same general area.
In order for HUD’s policies to have greatest impact on the reduction of regulatory cost
barriers associated with land and site development standards, effort should focus on the most
significant land and site development standards listed below in order of cost on a national basis:
NAHB Research Center 6-1 April 2007

Study of Subdivision Requirements as a Regulatory Barrier
• Lot size
• Interior square foot floor area
• Lot width
The other site and land development standards evaluated in the study had relatively small cost
barriers in comparison with these three.
Local advocates for reduction in regulatory cost barriers may wish to use the regulatory barrier
costing tool described in section 4.2.1.1 and presented in Appendix G to determine the
regulatory cost barriers that apply in specific locations. This calculation would be based on local
land costs and on the land development standards adopted by the local land planning
jurisdiction or jurisdictions.
Based on the research team’s focus group evaluation of procedural barriers to the land
subdivision process, presented as Appendices 3 and 4, the three main procedural barriers
resulting in increased costs to residential land developers which are passed on to builders and
ultimately to homebuyers are:
• Local anti-growth political climate
• Inefficiency in the application process by large numbers of government review agencies
• Critical infrastructure deficiency in rural areas
Recently, the U.S. Department of Housing and Urban Development awarded 14 communities
with the Robert L Woodson, Jr. Award for reducing burdensome regulations that unnecessarily
inflate the cost of homes (NAHB, 2006, d). Specific noted reasons for the award that are
consistent with the results of this study generally fall into the following categories:
• Reduction in land cost to the developer
• Reduction in the site and land development standards, leading to lower infrastructure costs
for development of lots and for the construction of dwellings
• Reduction in time and costs to the developer for obtaining the required permits from a
range of municipal land development authorities
The actions by these communities should be considered in other communities seeking to
reduce regulatory cost barriers within the subdivision rules and zoning ordinances.
NAHB Research Center 6-2 April 2007

Study of Subdivision Requirements as a Regulatory Barrier
7. Ref
f
er
r
ences
Brilz, 2006, Personal Communication, Mike Brilz, VP of Planning and Development, Pulte
Homes, West Valley Division, Scottsdale AZ, March 14 2006.
Carliner, 2006, Michael Carliner, NAHB Economics Division, personal communication via email.
Eddy, 2006, “Wastewater Planning is an Integral Part of Smart Growth”, Natalie Eddy, Small
Flows Quarterly, Winter 2006, published by the National Small Flows Clearinghouse,
downloaded from http://www.nesc.wvu.edu/nsfc/Articles/SFQ/SFQ_w06/smartgrowth.pdf.
Colorado Division of Housing, Department of Local Affairs, 1999. Reducing Housing Costs
Through Regulatory Reform: A Handbook for Colorado Communities. Available online:
http://www.dola.state.co.us/Doh/Documents/ReducingCosts.htm
Duany, A., 2004. Smartcode: A comprehensive smart growth municipal ordinance. New Urban
Publications Inc. available on line; http://www.placemakers.net/info/SCdownloads.html
Harvard, 2005, “The State of the Nation’s Housing 2005”, Joint Center for Housing Studies of
Harvard University, p. 2, 2005, downloaded from
http://www.jchs.harvard.edu/publications/markets/son2005/son2005.pdf.
HUD Advisory Commission on Regulatory Barriers to Affordable Housing, 1991. Not in My
Backyard: Removing Barriers to Affordable Housing. U.S. Department of Housing and Urban
Development, report 5806. Available online:
http://www.huduser.org/publications/pdfnotinmybackyard.pdf
Freilich, R. and Schultz, M., 1995. Model Subdivision Regulations: Planning and Law. APA
Planners Press, 2nd ed.
German, B., 1993. Under Siege: What Regulations Cost Builders and Buyers. Builder, August
1993, p 46.
Kennedy, J., 2002. The Impact of Municipal Governments on Residential Housing: A Case Study
of Single-Family Detached Housing, in Cary North Carolina. ASC Proceedings of the 38th
Annual Conference. Virginia Polytechnic Institute and State University, pp 381-395. Blacksburg,
Virginia. Available online: http://asceditor.unl.edu/archives/2002/Kennedy02.htm
Luger, M. and Temkin, K., 2000. Red Tape and Housing Costs: How Regulations Affects New
Residential Development. New Brunswick, New Jersey : Center for Urban Policy Research.
Maine State Planning Office. Model Subdivision Ordinance. Maine State Planning Office.
Available online: http://www.smrpc.org/Subdivision/landusesubdivision.htm
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Study of Subdivision Requirements as a Regulatory Barrier
Maryland Department of Planning, 2001. Smart Neighborhoods: Managing Maryland’s Growth.
Baltimore, MD: Maryland Department of Planning, Sep 2001, pub 2001-04. Available online:
http://www.mdp.state.md.us/pdf/smartneighborhoods.pdf
Metropolitan Planning Commission of Knoxville and Knox County, 2004. Knoxville-Knox
County minimum subdivision regulations. Knoxville, TN: The Commission. Available online:
http://www.knoxmpc.org/zoning/subreg.pdf
NAHB, 2006, a, New Ideas for Detached Density, David A. Clinger, Land Development,
National Association of Home Builders, Winter 2006 Vol 19, No. 1, Winter 2006, p. 36-41.
NAHB, 2006, b, “A Misunderstood Concept”, Interview with Denver Land Planner David
Clinger, Land Development, National Association of Home Builders, Vol 19, No. 1 Winter
2006.
NAHB, 2006, c, “Supreme Court to hear Wetlands Cases”, Land Development, Vol. 19, No. 1,
Winter 2006, published by the National Association of Home Builders, p. 4-5.
NAHB, 2006, d, “Honoring 14 Communities for reducing Regulatory Barriers to Affordable
Housing”, Land Development, Vol.19, No.1 Winter 2006, National Association of Home
Builders, p. 10-16.
National Association of Home Builders Research Center, 1987. “Affordable Residential Land
Development: A Guide for Local Government and Developers.” Volume I of Affordable Housing:
Challenge and Response. Upper Marlboro, Maryland: NAHB Research Center.
National Association of Home Builders Research Center, 1993. Proposed Model Land
Development Standards and Accompanying Model State Enabling Legislation, 1993 Edition.
Washington, DC: U. S. Department of Housing and Urban Development, Office of Policy
Development and Research
Nelson, A., Pendall, R., Dawkins, C. and Knaap, G., 2002. The Link Between Growth
Management and Housing Affordability: The Academic Evidence. Working paper for Brookings
Institution Center on Urban and Metropolitan Policy. Available online:
http://www.brook.edu/dybdocroot/es/urban/publications/growthmang.pdf
Schuetz and White (1992) Identifying and Mitigating Local Regulatory Barriers to Affordable
Housing in Waukesha County, Wisconsin, Mary Kay Schuetz, Professor Sammis B. White,
Urban Research Center, University of Wisconsin-Milwaukee, May 29, 1992.
Seattle Washington Department of Public Works (2005) Seattle Street Edge Alternatives
website
http://www.ci.seattle.wa.us/util/About_SPU/Drainage_&_Sewer_System/Natural_Drainage_Syst
ems/Street_Edge_Alternatives/index.asp.
NAHB Research Center 7-2 April 2007

Study of Subdivision Requirements as a Regulatory Barrier
Planning Advisory Service, 2004. Codifying New Urbanism. Chicago, IL: American Planning
Association, report no 526.
Planning Advisory Service, 1998. Principles of Smart Development. Chicago, IL: American
Planning Association, report no 479.
Schuetz, M. and White, S., 1992. Identifying and Mitigating Local Regulatory Barriers to
Affordable Housing in Waukesha County, Wisconsin. Working paper for Urban Research
Center, University of Wisconsin-Milwaukee. Available online:
http://www.huduser.org/rbc/docs/milwaukee071102.pdf
Transportation and Growth Management Program, 1999. Model development code and user’s
guide for small cities. Salem, OR: Oregon Department of Transportation. Available online:
http://www.oregon.gov/lcd/tgm/docs/model_dev.zip
U.S. President’s Committee on Urban Housing, 1968. A decent home: the report of the
President’s Committee on Urban Housing. Washington, DC: Govt. Printing Office.
Weitz, S., 1982. Affordable Housing: How Local Regulatory Improvements Can Help. Product
of the Joint Venture for Affordable Housing. Washington, DC: U.S. Department of Housing and
Urban Development, Office of Policy Development and Research, September 1982.
NAHB Research Center 7-3 April 2007

Study of Subdivision Requirements as a Regulatory Barrier
8. Endnot
t
es
i
Regulatory Barriers Clearinghouse P.O. Box 23268 Washington, DC 20026-3268 available
online: http://www.huduser.org/rbc/
ii
Myths and Facts About Affordable and High Density Housing, California Planning Roundtable,
downloaded from http://www.abag.ca.gov/services/finance/fan/housingmyths2.htm
iii
Ibid
iv
U.S. Department of Housing and Urban Development NOTICE PDR-2006-01
downloaded from http://www.huduser.org/datasets/il/il06/MedianNotice_2006.pdf
v
“It's a relatively simple formula -- developer builders are ’for profit‘ entities and each dollar
added to construction goes right back into the cost of the home. The ability to manage/reduce
construction costs by increasing efficiency will result in lower home pricing. Reducing
processing times ultimately reduces the carrying costs associated with projects. Locally,
processing times for some municipalities have almost doubled in the past five years. Quick math
would indicate that a year longer in processing theoretically adds ~$2,250 per home in carrying
costs (purchase 40 acres @ $150k = $6m; at 6 percent assume $360,000 in annual interest
charges incurred; assuming 4 units per acre yields 160 homes; $360,000 interest/160 homes =
$2,250).” Personal Communication, Mike Brilz, VP of Planning and Development, Pulte Homes,
West Valley Division, Scottsdale AZ, March 14 2006
vi
US Department of Housing and Urban Development Proposed Model land Development
Standards and Accompanying Model State Enabling Legislation, 1993 edition, p. 1.
vii
Freilich and Schultz (1995), Model Subdivision Regulations: Planning and Law; Knoxville-Knox
County Minimum Subdivision Regulations (2004); Proposed Model Land Development Standards and
Accompanying Model State Enabling Legislation (1993), written by the NAHB Research Center for
the U.S. Department of Housing and Urban Development;; Smart Neighborhoods: Managing
Maryland’s Growth (2001), and Oregon’s model code, Model Development Code and User’s Guide
for Small Cities (1999); American Planning Association’s (APA) Principles of Smart Development
(1998); APA’s Codifying New Urbanism (2004); Nelson et al (2002), The Link Between Growth
Management and Housing Affordability: The Academic Evidence;;
viii
The U.S. Census groups states by region in two ways. One grouping method is by four
regions, which include: the Northeast, Midwest, South, and West. The other grouping method
divides the four regions into nine divisions, which include: New England, Middle Atlantic, South
Atlantic, East South Atlantic, West South Central, East North Central, West North Central,
Mountain, and Pacific. This study uses the four regions to group states.
NAHB Research Center 8-1 April 2007

Study of Subdivision Requirements as a Regulatory Barrier
ix
The research team created four quartiles for population of jurisdictions within the sample.
Each quartile is comprised of 25 percent of the jurisdictions in the sample
x
The U.S. Census of Construction for 2004 lists the median lot size in the West census sub
region as 7000 sq feet.
xi
The U.S. census of Construction for 2004 lists the median lot size in the New England sub
region as 22,000 sq. feet.
xii
U.S. Census of Construction for 2005. Downloaded from
http://www.census.gov/const/C25Ann/lotsizesolddetpr.pdf
xiii
HUD’s Affordable Housing Design Advisor Web site http://www.designadvisor.org/
xiv
Design Centre for Sustainability at UBC downloaded from
http://www.sgog.bc.ca/uplo/Sq1Housing.pdf
xv
Ibid
xvi
U.S. Census of Construction Survey for 2005, Median and Average Square Feet of Floor
Area in New One-Family Houses Completed by Location: Built For Sale. Downloaded from
http://www.census.gov/const/C25Ann/sfforsalemedavgsqft.pdf
xvii
U.S. Census of Construction Survey for 2005: Interior floor areas. Downloaded from
http://www.census.gov/const/C25Ann/soldsqftdetach.pdf
xviii
The details on the land cost estimation is given in the Appendix B. The details on the
assumptions used with the RS Means costing approach is given in Appendix B. The details on
the unit costs for infrastructure elements are given in the Appendix B and in Excel spreadsheet
“Current cost analysis.” Appendix F. Summaries for all land and site development standards are
given in the “cost” tab. Details for each land or site development standard given in the
worksheet labeled as that standard.
xix
Discussions with potential local user groups such as Home Builder Associations are needed
to verify that there is interest in using the tool to obtain estimates of regulatory cost barriers
for a relatively well defined geographic area.
The research team is evaluating possible funding mechanisms for the verification and
deployment of this tool. Possible steps that would need to be taken for verification include:
a) one or more local user groups supplies the local land cost information and possibly local
infrastructure cost factors; b) the tool computes the regulatory cost barriers associated with
NAHB Research Center 8-2 April 2007

Study of Subdivision Requirements as a Regulatory Barrier
land and infrastructure and provides the results in a simple report format; c) the local user
evaluates the utility of the report format and provides feedback for modification if necessary
xx
1 story economy grade - 1200 square foot structure $70.25; 1800 square foot structure
$61.50; 2 story average quality grade 1800 square foot structure $85.03; 2,000 square foot
average grade structure $81.63 from 2005 RS Means Residential Construction costs. see
Appendix B Unit Cost Estimates
xxi
U.S. Census of Construction data for price of housing per square foot. Downloaded from
http://www.census.gov/const/C25Ann/soldpricesqftdetach.pdf
xxii
U.S. Census of Construction data for median cost of construction per square foot.
Downloaded from http://www.census.gov/const/C25Ann/soldmedavgppsfdetach.pdf
xxiii
See Appendix B; detailed formula given in “Lot Width” worksheet in Subdivision
Requirements as a Regulatory Barrier to Affordable Housing ; Cost Analysis Workbook,
Appendix F
xxiv
See Appendix 3.3; detailed formula given in “Pavement Width” worksheet in Subdivision
Requirements as a Regulatory Barrier to Affordable Housing current cost analysis 0329.xls
;
xxv
See Appendix 3.3; detailed formula given in “Front Setback” worksheet in Subdivision
Requirements as a Regulatory Barrier to Affordable Housing current cost analysis 0329.xls;
xxvi
If the benchmark was larger than a jurisdiction’s minimum requirement for a variable, the
result of Step 6 would be a negative cost. In other words, the jurisdiction’s requirements
allowed for less expensive development than the benchmark. We excluded negative costs from
our calculations because the focus on the study was the costs of excessive regulation.
xxvii
This figure includes all permits issued for single-family dwellings in 2004. This includes
dwellings on lots in subdivisions and other dwellings. The Census Bureau’s Current
Construction Reports, the data source used for building permits, does not distinguish between
single-family permits issued in subdivisions and other permits.
xxviii
The cost analysis included calculation of the mean and median cost of regulatory cost
barriers for each variable. In creating the national estimate of the cost of excessive regulation,
we used the mean cost estimates. The mean cost estimates provide a better estimate of
regulatory cost barriers because mean cost estimates use the average costs, rather than the
mid-point costs. In other words, the mean cost estimates reflect the total aggregate regulatory
cost barriers for all jurisdictions divided by the number of jurisdictions with one or more
regulatory cost barrier.
xxix
U.S. Census of Construction 2006, New Detached One-Family Housing Units Sold by Price
per Square Foot, downloaded from
http://www.census.gov/const/C25Ann/soldpricesqftdetach.pdf
NAHB Research Center 8-3 April 2007

Study of Subdivision Requirements as a Regulatory Barrier
xxx
“On the development side, some municipalities locally are starting to require minimum lot
sizes (i.e., no lots less than 60 feet wide). This type of ordinance will likely eliminate entry level
housing, as land costs are ~25 percent of a house cost -- the ability to efficiently use the land
over a few more lots reduces the overall cost per home.” (Brilz, 2006)
xxxi
According to Denver land planner David Clinger, as quoted a recent issue of Land
Development Magazine (NAHB 2006, a) “Some cities realize that four to the acre zoning prices
their own employees out of homeownership.” Using non rectangular lot geometries and
alternating wider and narrower lot street frontages known as “keyhole” or “zipper” type lot
design, as well as judicious use of side yard use easements and creative landscaping and privacy
fences, a wide range of award-wining designs have been developed for SFD homes at 7 to 10
units per acre (NAHB, 2006, b)
xxxii
According to a King County Washington news release in Dec 2000, “The average cost of
vacant urban land as a proportion of the average price of a new home has remained nearly the
same over the last 20 years.“ downloaded from
http://www.metrokc.gov/exec/news/2000/121300.htm
NAHB Research Center 8-4 April 2007

Appendix A
Survey of Regulatory
Standards
A-1

Summary
This report presents a statistical analysis of regulatory data gathered from subdivision and
zoning ordinances for the national Study of Subdivision Requirements as a Regulatory Barrier to
Affordable Housing. It serves as a technical appendix to the main report that discusses the
implications of regulation on housing cost in the U.S.
The broader purpose of this study is to explore the connection between subdivision and
zoning regulations and housing prices. The approach of the study is to investigate regulatory
standards and requirements that raise the cost of residential development. This is done by
examining regulatory standards that lend themselves to direct measurement and analysis of
their cost implications.
The project has several distinct parts: (1) conducting review of literature available about the
topic; (2) choosing regulatory variables and collecting data on them for a nationwide
representative sample of jurisdictions; (3) determining excessive values for the variables and
determining the associated costs; and (4) assessing the administrative and processing costs of
the regulations.
This report documents the second part of the study: data collection for specific variables
for a national sample of jurisdictions. The core of the report is a descriptive analysis of the 14
variables included as part of the sample. The report presents the sampling methods and
procedures for collecting the data.
METHODS
SAMPLING METHODS
This report is based on a sample of 469 local governments in the U.S. that have the
authority to adopt land use regulations. The sampling challenge was to develop a methodology
that resulted in a random sample that is representative of the population. The objective was to
develop a sample that was (1) geographically representative of jurisdictions across the nation,
(2) reflected the national distribution of population (including jurisdiction size), (3) reflected
both fast and slow growing jurisdictions, and (4) represented a range of government types.
The methodology we used to draw the sample weighted the sample by population in states
(e.g., the number of samples for each state is proportional to its population) and then by
amount of population growth in each local government between 1996 and 2000. This
methodology placed emphasis on the amount of population in each state, and ensured that both
fast and slow growing governments were represented.
The sampling methodology originally intended to examine subdivision ordinances from
1,100 jurisdictions. When the Project Team began evaluating which standards to measure, we
found that many of the relevant standards were in zoning ordinances, rather than subdivision
ordinances. After consultation with HUD, the Project Team decided to review zoning
ordinances and subdivision ordinances and to reduce the sample size to 500 jurisdictions to
reflect the increased work of gathering data from multiple ordinances. We chose the smaller
sample of 500 jurisdictions from the previous sample of 1,100 jurisdictions. In cases where the
Appendix A: Statistical Analysis of Regulations A-2

ordinances could not be obtained from the jurisdiction, we used a substitution method to
choose a different jurisdiction.
Analysis of the sample jurisdictions performed after data collection was completed showed
that the sample is roughly representative of each state by population and geography.
Jurisdictions in the sample represent 26% of the entire U.S. population.
ORDINANCE REVIEW PROCEDURES
Ordinance review consisted of five steps.
1. Choose the residential district to review. The first step in ordinance review was choosing
the correct residential district to review. This step was the most critical step in
ordinance review. The methods for choosing the zoning district are presented below.
2. Gather data from the zoning ordinance. The following information was generally found in
the section of the ordinance that described the chosen zoning district: lot width, lot
size, floor area, and setbacks. The zoning ordinance often contained landscaping
requirements and off-street requirements.
3. Gather data from the subdivision ordinance. The following standards were generally found
in the design standards section of the subdivision ordinance: sidewalk requirements,
street width standards, open space requirements, and curb and gutter requirements.
Some jurisdictions included landscaping or off-street parking requirements in the
subdivision ordinance.
4. Search both ordinances for missing standards. When researchers did not find standards in
the usual ordinance, they searched the other ordinance for the standards. For example,
if landscaping standards were not found in the subdivision ordinance, the researcher
searched the zoning ordinance for landscaping standards for the chosen zoning district.
5. Gather administrative data. The final step was to gather administrative data about the
ordinances, such as the last date the ordinance was updated and the type of update.
METHODS FOR CHOOSING ZONING DISTRICTS
Zoning ordinances presented some inherent challenges to analysis of the variables. A typical
zoning ordinance has three or more residential districts. The Project Team quickly concluded
that reviewing every residential district for each jurisdiction in the sample would be infeasible.
Thus, we developed a protocol for gathering standards that significantly reduced the data
collection effort.
We focused our attention on the “border” zone between low-density single-family
development and high-density multifamily development. These “border” zones typically had
smaller minimum regulations for lot size and other lot dimension variables than other single-
family zones. We focused on this zone because it presents a greater opportunity for
development of affordable housing than zones that require larger lot sizes.
We chose the “border” zone based on the following characteristics: it permitted detached
single-family houses outright; it had the smallest minimum lot size and setbacks; and (where
Appendix A: Statistical Analysis of Regulations A-3

Appendix A: Statistical Analysis of Regulations A-4
applicable) it allowed a mixture of detached single-family houses and duplexes or multifamily
housing. In cases where it was unclear which zone to choose after evaluating these
characteristics, we always chose the zone with the smallest minimum lot size where detached
single-family homes are permitted outright.
ANALYTICAL APPROACH
The analytical approach focused on two types of statistical analysis: (1) basic descriptive
analysis; and (2) means testing. The basic descriptive analysis consisted of the following
statistics: mean, median, mode, frequencies, range, and standard deviation. The means testing
used chi-square and ANOVA with post-hoc testing to determine if the variables varied
significantly by class membership within subcomponents of the sample including government
type, census region, membership in a Metropolitan Statistical Area (MSA), central city, and
population quartiles. These tests showed whether and how the variables differed by these
subcomponents and which differences were significant.
SUMMARY OF FINDINGS
Table 1 provides a summary of descriptive statistics for the variables reviewed in the study.
The findings for each variable are discussed below.
Table 1. Summary of descriptive statistics of the variables reviewed
NMeanMedianMode
Standard
Deviation Minimum Maximum
Minimum lot size (square feet) 419 9924 6,000 5000 16,946 750 217,800
Minimum lot width (linear feet) 342 61.92 60 50 25 20 250
Minimum front setbacks (linear feet) 413 25.15 25 25 13 0 100
Minimum side setbacks (linear feet) 417 8.257 8 5 5 0 30
Minimum rear setbacks (linear feet) 404 20.55 20 25 9 0 65
Minimum floor area (square feet) 86 1060 1,000 1000 359 500 2,500
Minimum off-street parking spaces 367 1.878 2 2 1 0 4
Minimum open space requirements
Percent of total land in subdivision 47 13.1 10 10 9 3 50
Number of square feet per dwelling unit 18 1562 795 871 3,447 310 15,246
Number of square feet per person 34 229.4 218 218 112 87 436
Minimum sidewalk width (linear feet) 153 4.487 4 4 1 3 10
Minimum planting strip width (linear feet) 37 4.608 5 5 1 2 8
Minimum street pavement width (linear feet) 192 27.81 28 30 6 16 45
Minimum street right-of-way width (linear feet) 262 52.2450 50 8 20 80

Source: Survey of Regulatory Standards, CPW 2006
Most of the 469 jurisdictions reviewed had zoning and subdivision ordinances.
Eighty-three percent of the jurisdictions had both zoning and subdivision ordinances. Six
percent of jurisdictions only had zoning ordinances, 4% of jurisdictions only had subdivision
ordinances, and 6% of jurisdictions had neither ordinance. The jurisdictions with neither
ordinance were included in the study to represent jurisdictions with less regulation.
Lot size requirements were highly variable among jurisdictions. The smallest
minimum lot size in the study was 750 square feet and the largest was 217,800 square feet (5
acres). Forty-one percent of the jurisdictions in the sample had minimum lot sizes between
5,000 and 6,999 square feet. Statistical testing showed significant differences in lot size

requirements for each subcomponent of the sample. This indicated that there were
fundamental differences in minimum lot sizes for each of the subcomponents of the sample. For
example, there were significant differences in minimum lot sizes for each of the four Census
regions (one of the subcomponents of the sample), with larger minimum lot sizes in the
Northeast than in the other three regions.
Lot widths varied across jurisdictions. The smallest lot width requirement was 20 feet,
the largest 250 feet, and the median 60 feet. Fifty-five percent of jurisdictions required minimum
lot widths of 50 to 69 feet. As with lot sizes, statistical testing showed significant differences in
lot width requirements for each subcomponent of the sample.
The mean front setback requirement was 25 feet. Fifty-six percent of jurisdictions
had front setbacks between 20 to 29 feet. Like lot size and width, front setbacks differ in a
significant way for each subcomponent of the sample.
The mean side setback requirement was eight feet per side.. Fifty-eight percent of
jurisdictions required minimum side yard setbacks of between five to nine feet. Side setbacks
differed in significant ways for each subcomponent of the sample.
The mean rear setback was 21 feet. The smallest requirement for a rear setback was
zero feet and the largest was 65 feet. Fifty-five percent of jurisdictions required rear setback of
between 20 to 29 feet. Rear yard setbacks differed in significant ways for each of the five
subcomponents of the sample, except for population quartiles.
Fewer than 20% of jurisdictions had minimum floor area requirements. For these,
the mean floor area was 1,060 square feet and the median 1,000 square feet. The smallest floor
area requirement was 500 square feet per dwelling unit and the largest 2,500 square feet.
More than three-quarters of the jurisdictions required two off-street parking
spaces. For these, the mean number of off-street parking spaces required per dwelling unit was
1.88 and the median was 2 parking spaces. Further statistical testing showed that the number of
off-street parking spaces required differed in a significant way based on population quartile, as
well as between central cities and non-central cities.
Fewer than half of the jurisdictions had landscaping standards. Forty-two percent
of the jurisdictions had landscaping standards specifically for subdivisions or the zoning district
that we examined for the study. Differences in landscaping requirements were significant for
the following subcomponents of the sample: Census region, whether the jurisdiction is part of
an MSA, and population quartile.
Open space standards show substantial variation. We collected three types of
requirements for open space: (1) percent of total land in the subdivision, (2) number of square
feet per dwelling unit, and (3) number of square feet per person. About 20% of the jurisdictions
in the sample used one of these methods for determining open space requirements.
Fifty-one percent of jurisdictions explicitly required sidewalks. Twenty-six percent
of all jurisdictions required sidewalks on both sides of the street. The average sidewalk width
Appendix A: Statistical Analysis of Regulations A-5

Appendix A: Statistical Analysis of Regulations A-6
was four feet. Requirements for sidewalks varied in a significant way by the following
subcomponents of the sample: government type, whether the jurisdiction is part of an MSA, and
the jurisdiction's population. In contrast, sidewalk width requirements did not vary significantly
based on any of the subcomponents of the sample.
Relatively few (8%) jurisdictions had requirements for planting strips. The planting
strip is a landscaped area between the sidewalk and curb. The mean and median planting strip
width was five feet.
Curbs and gutters were required by 50% of the jurisdictions in the study.
The mean and median pavement width for streets was 28 feet. Most jurisdictions'
standards for pavement width were either 20 to 24 feet wide, 25 to 29 feet wide, or 30 to 34
feet wide. Pavement width differed in a significant way for each subcomponent of the sample.
The mean street right-of-way was 52 feet. Fifty-six percent of jurisdictions with right-
of-way standards require right-of-ways no smaller than 50 to 54 feet and 24% of jurisdictions
require right-of-ways at least 60 to 64 feet wide. Street right-of-way requirements varied
significantly by Census region.
GENERAL OBSERVATIONS ABOUT DATA COLLECTION AND ANALYSIS
The following section presents observations about the data collection and analysis phases of the
survey of regulatory standards.
Most jurisdictions regulated one or more of the variables. Ninety-four percent of
the jurisdictions in the sample had standards for one or more of the study variables. More than
three-quarters of the jurisdictions had standards for lot size, front setbacks, and off-street
parking spaces. About one-fifth of the jurisdictions had standards for open space and floor area.
The population varied among jurisdictions. The size of jurisdictions within the sample
varied substantially. They ranged in population from 9.5 million people to 132 people, from
some of the largest cities and most densely developed counties in the U.S. to small rural towns.
These differences presented challenges in analyzing the regulatory standards. We addressed
these challenges by separating the sample into population quartiles and comparing regulatory
standards among the quartiles.
Some of the variables had a broad range of values. This indicates that jurisdictions
are applying a wide range of regulatory standards. The basic statistical analysis showed that
several of the variables had substantial variation. The following variables had a large range of
values and large standard deviation compared to their mean: lot size, lot width, front setback,
side setback, and open space. For example, a rural jurisdiction is likely to have larger minimum
requirements for lot size and lot width than an urban city. We addressed these differences by
separating the sample subcomponents and comparing regulatory standards within the
subcomponent groupings. The subcomponents included government type, census region,
membership in a Metropolitan Statistical Area (MSA), and central city.

Introduction
This report presents a statistical analysis of regulatory data gathered from subdivision and
zoning ordinances for the national Study of Subdivision Requirements as a Regulatory Barrier to
Affordable Housing. It serves as a technical appendix to the main report that discusses the
implications of regulation on housing cost in the U.S. This report begins with an overview of the
study—the context and the research approach. It then describes characteristics of the sample
of zoning and subdivision regulations from 469 local governments. The core of the report is a
descriptive analysis of the 14 variables included as part of the sample. It discusses the
implications of the sample in the context of the broader study. This report also includes a
number of appendices that describe the sampling methods and procedures.
BACKGROUND
This study is designed to assess, on a nationwide scale, the occurrence and magnitude of
land use controls as regulatory barriers to building affordable housing. To date there is only
local and regional research available on the topic, much of which is outdated. Prior studies have
focused on the effect of regulatory barriers to affordable housing from a broad perspective. For
instance, Not In My Backyard: Removing Barriers to Affordable Housing, published by the President’s
Advisory Commission in 1991, found that exclusionary, discriminatory, and unnecessary
regulations were significant barriers to affordable housing. According to that study, these
barriers deter the development of housing within the means of lower-income, and increasingly
middle-income “work force” families.
The regulations that this study focused on are generally found in subdivision and zoning
ordinances. Subdivision and zoning regulations can increase the cost of housing through setting
excessive standards for development, such as lot size or street width. Until now, there had
been no nationwide study that explores the impact of these specific regulations on the
affordability of housing.
Subdivision and zoning ordinances are used by jurisdictions to regulate development. The
ordinances serve different purposes in the development process.
• Subdivision ordinances regulate the division of land for development, most
frequently for residential purposes. Subdivision ordinances generally include two types of
requirements: (1) administrative procedures and requirements for dividing land into smaller lots
and (2) standards for developing shared infrastructure, such as streets and sidewalks, within the
area of development.
• Zoning ordinances regulate development by dividing the jurisdiction into multiple
zones, based on compatible uses. For example, a jurisdiction may have several different zones
for residential development, each geared towards different densities of residential development.
Zoning ordinances include many types of regulation and requirements. This study focuses on
the requirements found in the densest residential zone that permits development of detached
single-family housing outright. This zone represents the “border” between multi-family housing
and low-density single-family housing. The requirements for less land in this zone increase the
likelihood of affordable housing being developed in this zone because land accounts for a
substantial share of the costs of residential development.
Appendix A: Statistical Analysis of Regulations A-7

• The United States Department of Housing and Urban Development (HUD) contracted
with the National Association of Homebuilders (NAHB) Research Center to conduct this
study. The NAHB assembled a Project Team to investigate this issue, which included the
National Center for Smart Growth Research and Education at the University of Maryland and
ECONorthwest. ECONorthwest contracted with the Community Planning Workshop (CPW)
to conduct the data collection portion of the study. This technical appendix was written by
CPW and ECONorthwest.
PURPOSE
The broader purpose of this study is to explore the connection between subdivision and
zoning regulations and housing prices. The approach of the study is to investigate regulatory
standards and requirements that raise the cost of residential development. This is done by
examining regulatory standards that lend themselves to direct measurement and analysis of
their cost implications.
The project has several distinct parts: (1) conducting review of literature available about the
topic, (2) choosing regulatory variables and collecting data on them for a nationwide
representative sample of jurisdictions, (3) determining excessive values for the variables and
determining the associated costs, and (4) assessing the administrative and processing costs of
the regulations.
This report documents the second part of the study, data collection for specific variables
for a national sample of jurisdictions. The report presents the methodology for collecting the
data, as well as a descriptive analysis of the variables. Specifically this report presents:
• Protocol and methods used to obtain the data
• Characteristics of the sample as compared to all jurisdictions in the U.S.
• Descriptive analysis of each variable included in the study
• Discussion of deviation among jurisdictions for each variable
• Comparisons of geographic regions, jurisdiction types, and population for selected
variables
MEASURING REGULATORY STANDARDS
The Project Team worked together to design and execute the data collection portion of the
study. This part of the study involved the following components:
• Choosing the variables. This involved creating a list of land use regulatory variables
that contribute to construction costs and evaluating whether these variables are found in
subdivision ordinances. During the design phase we found that some of the variables that
contribute the most to residential development costs are found in zoning ordinances, rather
than subdivision ordinances. As a result, we chose to expand the study to include these zoning
standards. This process is summarized in Appendix 1.
Appendix A: Statistical Analysis of Regulations A-8

• Sampling methodology. One goal of the study was to produce conclusions that can
be applied to the entire U.S. An essential part of this task was to creating a representative
nationwide sample of jurisdictions to include in the data collection portion of the study.
• Collecting ordinances. Ordinance collection was an essential task for the study.
• Ordinance review. Ordinance review involved locating the variables in either the
subdivision or zoning ordinance and choosing the minimum values for each variable. We
focused on regulations impacting development of new single-family detached dwellings,
especially development in subdivisions.
• The steps above resulted in a sample of 469 local governments with the authority to
adopt land use regulations.
ORGANIZATION OF THE REPORT
The remainder of this report is organized as follows:
Chapter 2: Characteristics of the sample presents information about the sample,
including a summary of the sampling methods used to choose jurisdictions, variations among
ordinances, and comparisons of the sample with all local governments in the U.S.
Chapter 3: Descriptive analysis of variables from sample jurisdictions presents
analysis of the variables taken from the ordinances. The analysis includes lot standards,
landscaping standards, open space standards, and street standards.
Chapter 4: Observations presents our general observations about the variables and
study.
This report also contains an Appendix 1: Choice of review variables summarizes the
variables used in the project and the reasons for including each variable.

Appendix A: Statistical Analysis of Regulations A-9

CHARACTERISTICS OF THE SAMPLE
The Project Team developed a sampling methodology to choose a nationwide
representative sample of jurisdictions. This chapter contains a description of the sample and the
methods used to draw the sample. The chapter is separated into the following sections: a
summary of methods, a comparison of the sample against all jurisdictions in the U.S., and a
description of the ordinances in the sample.
SUMMARY OF METHODS
This section provides short summaries of our sampling methods and our the method for
choosing zoning districts for the study.
SAMPLING METHODS
This report is based on a sample of 469 local governments in the U.S. that have the
authority to adopt land use regulations. The sampling challenge was to develop a methodology
that resulted in a random sample that is representative of the population. The objective of this
exercise was to develop a sample that was (1) geographically representative of jurisdictions
across the nation, (2) reflected the national distribution of population (including jurisdiction
size), (3) reflected both fast and slow growing jurisdictions, and (4) represented a range of
government types.
According to the U.S. Census of Local Governments, 38,966 governmental entities existed
in 2002 that had the authority to develop and adopt subdivision regulations. These local
governments represent the population of interest in this study. Further evaluation of the data
indicates that local governments are far from being evenly distributed through the U.S.
Moreover, the geographic distribution of local governments is significantly different than the
geographic distribution of population. Thus, weighting the sample by the number of
governmental entities would result in a sample that had a much higher proportion of small
jurisdictions.
The methodology we used to draw the sample weighted the sample by population in states
(e.g., the number of samples for each state is proportional to its population) and them by
amount of population growth in each local government between 1996 and 2000. This
methodology placed emphasis on the amount of population in each state, and ensured that both
fast and slow growing governments were represented.
The sampling methodology originally intended to examine subdivision ordinances from
1,100 jurisdictions. When the Project Team began evaluating which standards to measure, we
found that many of the relevant standards were in zoning ordinances, rather than subdivision
ordinances. After consultation with HUD, the Project Team decided to review zoning
ordinances and subdivision ordinances and to reduce the sample size to 500 jurisdictions to
reflect the increased work of gathering data from multiple ordinances. We chose the smaller
sample of 500 jurisdictions from the previous sample of 1,100 jurisdictions. In cases where the
ordinances could not be obtained from the jurisdiction, we used a substitution method to
choose a different jurisdiction.
By the time we made the decision to reduce the number of jurisdictions in the study, we
had already pulled a sample of 1,100 jurisdictions. We selected the 500 jurisdictions from the
Appendix A: Statistical Analysis of Regulations A-10

1,100 jurisdictions. We separated the process of choosing jurisdictions into two parts: (1) we
chose the two largest jurisdictions in each state to ensure that each state was represented in
the study and (2) we chose the remaining 400 jurisdictions randomly from the remaining 1,000
jurisdictions.
In cases where we were unable to obtain the ordinances from the jurisdiction, we used a
substitution method to choose a different jurisdiction. In short, we returned to the remainder
of the sample of 1,100 jurisdictions and chose a new jurisdiction from the same state, with a
similar size and government type if possible.
COMPARISON OF THE SAMPLE WITH ALL JURISDICTIONS IN THE U.S.
A comparison of the jurisdictions included in the sample provides an indication of whether
the sample is representative of all governments in the U.S. If it is not representative, this
comparison should show the ways that the sample is not representative. This section includes
comparisons of the sample against the entire U.S. for the following: jurisdictions by type of
government, jurisdictions by region, jurisdictions by state based on population, and jurisdictions’
population by region.
It is worth reviewing the sampling priorities since they determined the characteristics of the
sample. The sample was weighted by the following factors for each jurisdiction:
1. Population
2. Growth rate
3. Geographic representation (each state got at least 2 samples regardless of population)
Note that the key weighting criteria did not include the number of local governments. If we
had used the number of local governments as a weighting criteria, then states such as North
Dakota that have a lot of local governments would have had many more samples that states like
California that have far fewer local governments. We focused on jurisdictions’ population and
growth rate rather than the type of local government because the focus of the study is the cost
of excessive regulation on new development, which occurs more frequently in areas with more
population and higher growth rates.
Table 2-1 shows the total number of each government type in the U.S. and the study
sample, as well as the percentage differences. The table shows that the study sample over-
represents cities and under-represents townships. To a lesser degree, villages and towns are
also underrepresented. The main reason that townships, towns, and villages are
underrepresented in the sample is that our sampling methodology favored geographic diversity
and population distribution over government type. This finding is consistent with the sampling
methodology described above.
Appendix A: Statistical Analysis of Regulations A-11

Table 2-1. Comparison between the number and proportion of the types of
governments in the United States and the study sample
Government type Number Percent Number Percent
Borough 1,233 3.2% 7 1.5% -1.7%
Charter Township 126 0.3% 4 0.9% 0.5%
City 10,048 25.8% 258 55.0% 29.2%
City and Borough 3 0.0% 0 0.0% 0.0%
City and County 5 0.0% 3 0.6% 0.6%
City/Parish 2 0.0% 0 0.0% 0.0%
Civil Township 5 0.0% 0 0.0% 0.0%
Consolidated Government 3 0.0% 0 0.0% 0.0%
Corporation 2 0.0% 0 0.0% 0.0%
County 2,963 7.6% 47 10.0% 2.4%
Metropolitan Government 3 0.0% 1 0.2% 0.2%
Municipality 4 0.0% 1 0.2% 0.2%
Parish 60 0.2% 1 0.2% 0.1%
Plantation 35 0.1% 0 0.0% -0.1%
Town 7,973 20.5% 77 16.4% -4.0%
Township 12,759 32.7% 44 9.4% -23.4%
Unified Government 2 0.0% 1 0.2% 0.2%
Urban County Government 1 0.0% 1 0.2% 0.2%
Village 3,740 9.6% 24 5.1% -4.5%
Total
38,967 100.0% 469 100.0% 0.0%
U.S. Study Sample Percent
difference

Source: U.S. Census and Source: Survey of Regulatory Standards, CPW 2006
Note: The selection of the jurisdictions is not proportionate between different government types for the U.S. and the sample because
our sampling methods placed a stronger emphasis on geography and jurisdiction size than government type.
Table 2-2 shows the number of jurisdictions in each Census region for the entire U.S. and
the study sample. The South and West make up a higher proportion of jurisdictions in the
study sample than in the entire U.S. The South has the greatest representation in the sample,
with 164 jurisdictions. The Northeast has the least representation in the sample, with 84
jurisdictions. This is proportionate to the region's share of the total number of jurisdictions in
the U.S. The Midwest has the greatest number of jurisdictions in the U.S. (56%), but its
representation in the sample is proportionately smaller (23%). The selection of the jurisdictions
is not proportionate between different regions within the U.S. and the sample because our
sampling methods placed a stronger emphasis on jurisdiction size than geographic distribution.
Appendix A: Statistical Analysis of Regulations A-12

Appendix A: Statistical Analysis of Regulations A-13
Table 2-2. Number and percentage of jurisdictions by region
for the U.S. and the study sample
Number Percent Number Percent
Northeast 6,440 17% 84 18%
South 7,898 20% 164 35%
Midwest 21,918 56% 109 23%
West 2,710 7% 112 24%
Total 38,966 100% 469 100%
U.S. Sample

Source: U.S. Census and Study of Subdivision Requirements as a
Regulatory Barrier to Affordable Housing Descriptive Analysis, CPW 2006
Note: The selection of the jurisdictions is not proportionate between different regions within the U.S. and the sample because our
sampling methods placed a stronger emphasis on jurisdiction size than geographic distribution.
The sampling methodology was designed to draw a sample of jurisdictions based on the
states' populations, proportionate to the U.S. population. The exception was that, to ensure
geographic diversity, we chose two jurisdictions from each state, regardless of population.
Proportionate to each state’s population, five states were under-represented by at least three
jurisdictions in the sample: Florida, New York, Ohio, Pennsylvania, and Texas. One reason for
this under-representation is the fact that some jurisdictions were not responsive to our
attempts to obtain their ordinances.
1
Another explanation for the under-representation is that
every state had a minimum of two jurisdictions in the sample, which redistributed jurisdictions
from states with larger populations to states with small populations.
Table 2-3 shows the 2000 Census population and percentage by region for the U.S. and the
study sample. The study sample represents 26% of the entire U.S. population. The region with
greatest representation by population in the sample is the West, with 39% of the population in
the sample. Population in the West is over represented in the sample and population in the
South and Midwest are underrepresented in the sample. One explanation for this discrepancy is
that six of the thirteen jurisdictions in the sample with greater than 1 million residents are
located in the West, including Los Angeles County, which has approximately 9.5 million
residents.

1
We chose substitute jurisdictions to replace the unresponsive jurisdictions but were unable to contact some of the substitute
jurisdictions. We stopped substituting jurisdictions near the end of data collection because there was not enough time to obtain
and review ordinances for additional substitute jurisdictions.

Table 2-3. Number and percent of population per
region for the U.S. and the study sample, 2000
Persons Percent Persons Percent
Northeast 53,594,378 19% 12,843,013 17%
South 100,236,820 36% 19,937,361 27%
Midwest 64,392,776 23% 12,108,851 16%
West 63,197,932 22% 29,163,713 39%
Total 281,421,906 100% 74,052,938 100%
U.S. Sample

Source: U.S. Census and Study of Subdivision Requirements as a Regulatory
Barrier to Affordable Housing Descriptive Analysis, CPW 2006
Note: The figures for the sample population in Table 2-3 are approximations because the sample had some instances where a
county and a jurisdiction within the county were included in the sample. In those cases, we subtracted the population for the
jurisdiction but counted the rest of the county’s population.
CHARACTERISTICS OF THE ORDINANCES
The ordinances that we reviewed varied in a number of ways, including: whether the
jurisdiction had both subdivision and zoning ordinances, the last date the ordinances were
modified, the type of update, and the media the ordinance was available in. This section
describes these differences among the ordinances.
Our sample included 500 jurisdictions, and CPW was able to perform ordinance reviews for
469 of them. We were unable to obtain ordinances for the remaining 31 jurisdictions, many of
which were substitute jurisdictions. We chose to stop attempting to collect ordinances towards
the end of the ordinance review process because it took several weeks to receive the
requested ordinances. The main problem in obtaining ordinances was that some jurisdictions
did not have their ordinances available on the Internet and were unresponsive to our attempts
at contact.
In some cases, staff at the jurisdictions that we contacted indicated that they did not have a
subdivision and/or zoning ordinance. We included these jurisdictions in the review because the
lack of regulation for particular standards could affect housing affordability.
Table 2-4 shows 83% of the jurisdictions had both ordinances. Six percent of the
jurisdictions only had a zoning ordinance and 4% of the jurisdictions only had a subdivision
ordinance. Twenty-seven (6%) had neither a subdivision nor a zoning ordinance.

Table 2-4. Jurisdictions with ordinances
Number Percent
Both ordinances 391 83%
Zoning only 30 6%
Subdivision only 21 4%
Neither ordinance 27 6%
Total jurisdictions 469 100%

Source: Survey of Regulatory Standards, CPW 2006

One indication that the ordinances are frequently used or that there has been development
activity is the frequency of ordinance updates. Table 2-5 shows the date that the ordinances
were last updated or adopted. The oldest zoning ordinance in our sample was last updated in
Appendix A: Statistical Analysis of Regulations A-14

June 1966. The most recently updated zoning ordinance was updated in November 2005. Most
zoning ordinances in our sample have been updated since April 2003.
The subdivision ordinances were generally not updated as recently as the zoning
ordinances. The oldest subdivision ordinance in our sample was updated in January 1950, and
the most recent update was December 2005. Most subdivision ordinances in our sample have
been updated since February 2001.
Table 2-5. Date that the ordinances
were last updated or adopted
Date
Zoning ordinances
Date last updated
Oldest June 1966
Most recent November 2005
Mean date December 1999
Median date April 2003
Subdivision ordinances
Date last updated
Oldest January 1950
Most recent December 2005
Mean date April 1997
Median date February 2001

Source: Survey of Regulatory Standards, CPW 2006

We also collected information about the type of ordinance update. If the ordinance has not
been updated since adoption, we classified the ordinance as “adopted.” If the ordinance had
been updated since adoption, we classified the ordinance as “amended.” In some cases, we were
unable to determine the type of update and left this data blank. We experienced this problem
most frequently in cases where the subdivision or zoning ordinances were a part of a larger
ordinance, such as a unified development code. In these cases, we frequently only had the
zoning and/or subdivision sections of the larger ordinance and could not determine the data of
last update and/or the type of update.
Table 2-6 shows the most recent type of update for the ordinances. Forty-five percent of
the zoning ordinances in the sample were amended, while 22% were not updated since
adoption. We were unable to determine the type of update for the remaining 33% of zoning
ordinances. Thirty-eight percent of the subdivision ordinances were amended and 24%
remained as adopted. We were unable to determine the type of update for the remaining 37%
of subdivision ordinances.
Appendix A: Statistical Analysis of Regulations A-15

Appendix A: Statistical Analysis of Regulations A-16
Table 2-6. Ordinance last update
Number Percent
% of Total
Sample
Zoning ordinances
Adopted 101 32% 22%
Amended 211 68% 45%
Total 312 67%
Subdivision ordinances
Adopted 114 39% 24%
Amended 180 61% 38%
Total 294 63%

Source: Survey of Regulatory Standards, CPW 2006

We collected electronic copies of the ordinances where possible. Table 2-7 shows the type
of media for each ordinance, either electronic or paper. Of the jurisdictions with a zoning
ordinance, 77% of the ordinances were available in electronic form and 23% were only available
in paper form. Of the jurisdictions with a subdivision ordinance, 73% of the ordinances were
available in electronic form and 27% were only available in paper form.

Table 2-7. Ordinance media
Number Percent
% of Total
Sample
Zoning ordinances
Electronic 326 77% 70%
Paper 95 23% 20%
Total 421 90%
Subdivision ordinances
Electronic 294 73% 63%
Paper 109 27% 23%
Total 403 86%

Source: Survey of Regulatory Standards, CPW 2006
Note: The total for “% of Total Sample” does not equal 100% because not all jurisdictions in our sample had both ordinances, as
noted previously.
In summary, the study sample was geographically diverse and represented at least two
jurisdictions from each state. Cities were over-represented in the sample and villages, towns,
and townships under-represented.
The study sample consisted of 469 jurisdictions, 83% of which had both a zoning and
subdivision ordinance. More than half of the ordinances that gave the data of last update were
last modified within the last five years. And about three-quarters of ordinances were in
electronic form.
One final note: the researchers from the Community Planning Workshop at the University
of Oregon found it surprisingly difficult and time consuming to obtain and compile ordinances
for review. The initial hypothesis was that the majority of ordinances would be easy to find on-
line. This was far from the case. Researchers considering such samples in the future are
encouraged to provide plenty of time and budget for the seemingly simple process of finding
and obtaining ordinances. In addition, not all ordinances available in electronic format are easy

Appendix A: Statistical Analysis of Regulations A-17
to work with. CPW found a surprising number of ordinances that were in html or pdf format
and were published section-by-section, or page by page in separate files.

Appendix A: Statistical Analysis of Regulations A-18
Analysis of Variables
This chapter presents an analysis of the variables collected from zoning and
subdivision ordinances. It represents the core of this report and the basis for the cost
estimates that are the overarching objective of this study. The categories of variables in
this study include: lot standards, landscaping standards, open space standards, sidewalk
standards, and street standards.
ANALYTICAL FRAMEWORK
Our analytical approach focused on two types of analysis: (1) basic descriptive
analysis, and (2) inferential statistics in the form of means testing. The basic descriptive
analysis consisted of the following statistics: mean, median, mode, frequencies, range,
and standard deviation. The means testing consisted of using chi-square and ANOVA
tests.
The means tests showed statistically significant
2
differences among the jurisdictions.
We separated the jurisdictions by certain characteristics, such as population size or
whether the jurisdiction belonged to an MSA. These groupings, which represent
subcomponents of the sample, allowed us to perform the means testing between the
subcomponents of the sample, rather than the entire sample. This was helpful because
the types of statistical tests we used work best when there is little variation within the
sample and the descriptive analysis showed that the data had substantial variation. In
other words, grouping the jurisdictions by subcomponents created more homogenous
groups and allowed for comparisons of the standards within the subcomponents. The
subcomponents included:
• Government type: We separated government types into six categories: county,
city, town, township, village, and other government types. We combined counties and
parishes because there are few parishes, and they serve a similar function as counties.
We combined all the other government types presented in Table 2-1 because they
made up less than 3% of the governments in the study.
• Census region: We grouped states into the four regions used by the U.S.
Census: Northeast, Midwest, South, and West.
3

• Part of an MSA: We grouped jurisdictions by whether they are a part of a
Metropolitan Statistical Area (MSA), as defined by the U.S. Census. Jurisdictions
belonging to a MSA are more likely to be located in an area where the population is
densely distributed.

2
For the remainder of the chapter, we refer to “statistically significant” results as “significant.”
3
The U.S. Census groups states by region in two ways. One grouping method is by four regions, which include: the
Northeast, Midwest, South, and West. The other grouping method divides the four regions into nine divisions, which
include: New England, Middle Atlantic, South Atlantic, East South Atlantic, West South Central, East North Central,
West North Central, Mountain, and Pacific. This study uses the four regions to group states.

A-18

Appendix A: Statistical Analysis of Regulations A-19
• Central city: We grouped jurisdictions based on whether they are a central city,
as defined by the U.S. Census. A central city is the largest city of a Metropolitan Area
(MA) and is a basis for establishment of an MA. Jurisdictions that are a central city are
typically more densely populated than jurisdictions that are not a central city.
• U.S. population: We grouped the sample jurisdictions into quartiles based on
their populations from the 2000 U.S. Census. The groups were as follows: fewer than
5,491 people; 5,492 to 25,176 people; 25,177 to 97,268 people, and more than 97,268
people.
4

The means tests allowed us to determine if the variables varied in a significant
pattern by class membership within certain subcomponents of our sample. In other
words, the means testing indicated whether sample subcomponents, such as
government type or Census region, make a difference in the standards that jurisdictions
establish. For example, the means testing tells whether a variable such as lot size is likely
to be different if the government is a city or county or if it is located in the east or west,
etc. We used two forms of means testing: chi-square and ANOVA.
We performed chi-square tests on each variable using the five subcomponents of
the sample. The chi-square indicated which variables had significant differences for the
subcomponents of the sample. It is likely that significant
5
differences were caused by
differences in the variables for the subcomponents. In other words, if the chi-square for
lot size by government type is significant, then it is likely that lot size varied in a
significant pattern by government type.
We then performed an ANOVA with a Bonferroni post-hoc test to identify which
subcomponents had significant differences. This type of test required subcomponents
with three or more categories. In our data, qualifying subcomponents included:
government type, census region, and population. It also required continuous data in the
variables. Where the chi-square test can indicate a significant difference among all of the
subcomponents, the ANOVA with a Bonferroni post-hoc test can show the significant
differences between each of the subcomponents. For example, this test might show that
lot sizes are statistically different in cities than in counties.
In cases where we found standards for the variables in fewer than 100 jurisdictions,
we did not perform any means testing because we had too little data to produce
meaningful results.
METHODS FOR CHOOSING ZONING DISTRICTS
Zoning ordinances presented some inherent challenges to the standards analysis. A
typical zoning ordinance has three or more residential districts. The Project Team
quickly concluded that reviewing every residential district for each jurisdiction in the

4
We created four quartiles for population of jurisdictions within the sample. Each quartile is comprised of 25% of the
jurisdictions in the sample. The first quartile had jurisdictions with 5,491 or fewer residents. The second quartile had
populations of 5,492 to 25,176. The third quartile had jurisdictions with populations of 25,177 to 97,268. And the
fourth quartile had jurisdictions with 97,269 or more residents.
5
When we use the term “significant” in the context of a statistical analysis, we mean “significant.”

sample would be infeasible. As a result, we developed a protocol for gathering
ordinances that significantly reduced the data collection effort.
The review of each jurisdiction's zoning requirements was based on the standards
from one zone in the zoning ordinance. The reason that we focused on one zoning
ordinance is that most jurisdictions have three or more zoning districts that allow for
single-family housing development, each with different requirements for the study
variables. Collecting information about each of these zones was not practical because of
the amount of time and resources involved in doing so. Instead, we focused our
attention on the “border” zone between low-density single-family development and
high-density multifamily development. The “border” zone generally allowed a mixture of
high-density single-family dwellings with duplexes and multifamily dwellings.
We chose the “border” zone based on the following characteristics: it outright
permitted detached single-family houses; it had the smallest minimum lot size and
setbacks; and (where applicable) it allowed a mixture of detached single-family and
duplexes or multifamily housing. In cases where it was unclear which zone to choose,
we always selected the zone with the smallest minimum lot size that outright permitted
detached single-family housing. We focused on the “border” zone because the land
requirements are smaller, which should result in lower housing costs.
LOT STANDARDS
The Project Team collected seven lot-related variables, including minimums for: lot
size, lot width, front yard setbacks, side yard setbacks, rear yard setbacks, floor area,
and off-street parking. In all cases, we took standards from the densest zone that
outright permitted detached single-family dwellings. Our assumption was that housing in
this zone would be more affordable than zones with larger minimum lot size
requirements because land cost is a large contributor to housing costs. We used
standards for interior lots, rather than corner or other lot configurations.
We found the lot variables in the zoning ordinance more than 90% of the time, with
10% of the lot variables coming from the subdivision ordinance. In most cases, the lot
standards were single-point values, which we recorded in the database. In a few cases,
one or more of the lot standards was given as a range or formula, which we could not
record in the database. For example, several jurisdictions gave formula for calculating
minimum side yard setbacks based on the lot width and other factors. In those cases, we
did not record the side setbacks in the database but tracked them in a separate text
document. These cases are not included in this analysis.
Table 3-1 presents summary statistics for each of the lot variables. The statistics are
discussed in conjunction with each standard.
Appendix A: Statistical Analysis of Regulations A-20

Table 3-1. Minimum lot variable summary statistics
N Mean Median Mode
Standard
Deviation Minimum Maximum
Lot size 419 9,924 6,000 5,000 16,946 750 217,800
Lot width 342 62 60 50 25 20 250
Front yard 413 25 25 25 13 0 100
Side yard 417 8 8 5 5 0 30
Rear yard 404 21 20 25 9 0 65
Minimum floor area 86 1,060 1,000 1,000 359 500 2,500
Off-street parking
367
1.88 2.00 2.00 0.51 0.00 4.00

Source: Survey of Regulatory Standards, CPW 2006
LOT SIZE
Lot size is one of the key variables in this study because the minimum lot size
determines the amount of land required for a single-family detached dwelling. Most of
the other lot variables are related to size and placement of the dwelling on the lot.
Lot size is the most commonly encountered standard in the study, and it has the
greatest variation. We found lot size standards in 95% of the jurisdictions with zoning or
subdivision ordinances. Table 3-1 shows that minimum lot size has a mean value of 9,924
square feet and a median of 6,000 square feet, with a standard deviation of 16,946
square feet. This shows that minimum lot sizes vary substantially. The smallest minimum
lot size in the study was 750 square feet and the largest was 217,800 square feet (5
acres).
Figure 3-1 shows a breakdown of the minimum lot sizes, grouped in 2,000 square
foot increments. About two-thirds of jurisdictions set their minimum lot sizes between
4,000 and 7,999 square feet. Only seven jurisdictions allow lots smaller than 2,000
square feet. Eighteen jurisdictions require lot sizes of at least 30,000 square feet or
more.
Appendix A: Statistical Analysis of Regulations A-21

Figure 3-1. Minimum lot size requirements, percentage, and frequency,
grouped by 2,000 square foot increments
0% 5% 10% 15% 20% 25% 30% 35%
Less than 1,999
2,000 to 3,999
4,000 to 5,999
6,000 to 7,999
8,000 to 9,999
10,000 to 11,999
12,000 to 13,999
14,000 to 15,999
16,000 to 17,999
18,000 to 19,999
20,000 to 21,999
22,000 to 23,999
30,000 or more
S
quar
e f
eet
Percent
7
30
127
133
31
29
14
15
1
12
1
1
18

Source: Survey of Regulatory Standards, CPW 2006
Figure 3-2 shows lot sizes grouped in 1,000 square foot increments. This closer
examination of lot size requirements show that 23% of jurisdictions have minimum lot
sizes between 5,000 and 5,999 square feet and an additional 18% of jurisdictions allow
lots between 6,000 and 6,999 square feet.
Appendix A: Statistical Analysis of Regulations A-22

Figure 3-2. Minimum lot size percentage and frequency, grouped by 1,000
square foot increments
0% 5% 10% 15% 20% 25%
Less than 1,999
2,000-2,999
3,000-3,999
4,000-4,999
5,000-5,999
6,000-6,999
7,000-7,999
8,000-8,999
9,000-9,999
10,000-10,999
more than 11,000
Squa
r
e f
ee
t
Percent
7
10
20
31
96
77
56
21
10
28
63

Source: Survey of Regulatory Standards, CPW 2006
Table 3-2 shows the results of statistical analysis of lot size compared to the five
subcomponents. The significance level column describes the statistical relationship
between the variable and each subcomponent of the sample. A significance level of less
than 0.05 is considered significant. If the significance level is less than 0.05, we have
entered “yes” in the “statistically significant difference” column, and if it is greater than
0.05, we have entered “no.” The analysis shows that lot size is statistically different for
each subcomponent of the sample.
Table 3-2. Statistical comparisons of lot sizes for
jurisdictions grouped by subcomponents of the sample
Statistically
significant
difference
Significance
level
Government type Yes 0.000
Census region Yes 0.000
Part of MSA / Not part of MSA Yes 0.000
Central city / Not central city Yes 0.000
U.S. Population grouped in quartiles Yes 0.000

Source: Survey of Regulatory Standards, CPW 2006

Appendix A: Statistical Analysis of Regulations A-23

Appendix A: Statistical Analysis of Regulations A-24
Further, statistical testing shows the differences in lot sizes among subcomponents
of the sample.
6
The differences observed within both types of means testing reinforce
the conclusion that statistical differences result from differences in lot size within the
subcomponents. Lot size varies within each subcomponent in the following ways:
• Government type City lot sizes are significantly smaller than county, town,
and township lot sizes.
• Census region Lot sizes in the Northeast are statistically larger than lot sizes in
the other three regions.
• U.S. population Lot sizes in the first quartile, jurisdictions with the fewest
people, are statistically larger than lot sizes in other quartiles.
LOT WIDTH
Lot widths varied across jurisdictions but not as greatly as lot size. Table 3-1
shows that 342 or about 77% of jurisdictions with ordinances had minimum lot width
standards. The mean lot width requirement was 62 feet and the median was 60 feet,
with a standard deviation of 25 feet. The smallest lot width requirement was 20 feet and
the largest was 250 feet.
Figure 3-3 shows lot widths in 10-foot increments. Most jurisdictions with minimum lot
widths require lot widths of at least 50 to 69 feet. Thirty two percent of jurisdictions
require minimum lot widths of 50 to 59 feet and 23% of jurisdictions require lot width
minimums between 60 to 69 feet. Three percent of jurisdictions have minimum lot
widths of less than 30 feet and 10% require lot widths of more than 100 feet.

6
This form of statistical testing, an ANOVA with a Bonferroni post-hoc test, required a minimum of three groups
within the subcomponent. We conducted this test for the following subcomponents: government type, census region,
and U.S. population.

Figure 3-3. Minimum lot widths percentage and frequency, grouped in 10-
foot increments
0% 5% 10% 15% 20% 25% 30% 35%
Less than 29
30 to 39
40 to 49
50 to 59
60 to 69
70 to 79
80 to 89
90 to 99
More than 100
Lo
t

w
i
dt
h i
n f
ee
t
Percent of total
12
33
22
39
79
110
34
3
10

Source: Survey of Regulatory Standards, CPW 2006
Table 3-3 shows statistical comparisons between lot widths and five subcomponents
of the sample. A significance level of less than 0.05 is considered significant. If the
significance level is less than 0.05, we have entered “yes” in the “statistically significant
difference” column, and if it is greater than 0.05, we have entered “no.” Like lot size,
comparison of lot widths indicated significant differences within each of the five
subcomponents of the sample.
Table 3-3. Statistical comparisons of lot widths for
jurisdictions grouped by subcomponents of the sample
Statistically
significant
difference
Significance
level
Government type Yes 0.000
Census region Yes 0.000
Part of MSA / Not part of MSA Yes 0.000
Central city / Not central city Yes 0.000
U.S. Population grouped in quartiles Yes 0.000

Source: Survey of Regulatory Standards, CPW 2006

Additional statistical testing using ANOVA shows the differences in minimum lot
widths within subcomponents of the sample. These differences reinforce the conclusion
that the statistical differences result from fundamental differences in lot widths within
the subcomponents. Lot widths vary within each subcomponent in the following ways:
• Government type Lot widths are statistically different between the following
groups: Cities have smaller average lot widths than villages, towns, or townships; and
Appendix A: Statistical Analysis of Regulations A-25

Appendix A: Statistical Analysis of Regulations A-26
counties have larger average lot widths than towns or townships. The county
regulations did not generally apply to the incorporated jurisdictions located within the
county.
• Census region. Lot widths are statistically different between the Northeast and
all other regions. The Northeast has larger average lot widths than any other region.
Lot widths in the Midwest are statistically larger than those in the West.
• U.S. population. Lot widths are statistically different between the first
population quartile, jurisdictions with the fewest people, and the other quartiles. The
first quartile has larger average lot widths than the other quartiles. In addition, the
second quartile has significantly larger lot widths than the fourth quartile.
SETBACKS
We examined three types of setbacks in this study: front, side, and rear setbacks. In
each case, we examined setbacks for interior lots.
The front yard setback is the distance from the front of the dwelling unit to the
property line or street. Table 3-1 shows that 93% or 413 of jurisdictions with
ordinances had minimum standards for front yard setbacks. The mean and median front
setback requirement was 25 feet, with a standard deviation of 13 feet. Minimum front
setbacks ranged from zero to 100 feet.
Table 3-4 shows front yard setback requirements in 5-foot increments. The majority
of jurisdictions had minimum setbacks between 20 and 29 feet. Twenty-five percent of
jurisdictions had minimum setbacks between 20 and 24 feet and minimum 31% of
jurisdictions had setbacks of 25 to 29 feet. One percent of jurisdictions had minimum
setbacks less than 9 feet and 3% of jurisdictions had minimum setbacks of 55 feet or
greater.

Table 3-4. Minimum front yard
setbacks in 5-foot increments
Frequency
Percent
Less than 9 4 1%
10 to 14 31 8%
15 to 19 45 11%
20 to 24 103 25%
25 to 29 126 31%
30 to 34 49 12%
35 to 39 16 4%
40 to 44 10 2%
45 to 49 41%
50 to 54
13 3%
More than 55 12 3%
Total
413 100%

Source: Survey of Regulatory Standards, CPW 2006

Table 3-5 shows statistical comparisons between front yard setbacks and five
subcomponents of the sample. A significance level of less than 0.05 is considered
significant. If the significance level is less than 0.05, we have entered “yes” in the
“statistically significant difference” column, and if it is greater than 0.05, we have entered
“no.” Differences in front yard setbacks were significant within each of the five
subcomponents of the sample.
Table 3-5. Statistical comparisons of front yard setbacks
with subcomponents of the sample
Statistically
significant
difference
Significance
level
Government type Yes 0.000
Census region Yes 0.000
Part of MSA / Not part of MSA Yes 0.000
Central city / Not central city Yes 0.000
U.S. Population grouped in quartiles Yes 0.000

Source: Survey of Regulatory Standards, CPW 2006

Additional ANOVA statistical testing shows the differences in front setbacks within
subcomponents of the sample. The differences observed within both types of means
testing reinforce the conclusion that the statistical differences result from fundamental
differences front setbacks within the subcomponents. Front setbacks vary within each
subcomponent in the following ways:
• Government type Front setbacks are statistically different for the following
groups: Counties have smaller average front setbacks than townships; and cities have
smaller average front setbacks than towns, townships, and villages.
• Census region. The West has statistically smaller average front setbacks than
any other region. The South has statistically smaller average front setbacks than the
Midwest and Northeast.
• U.S. population. Front setbacks are statistically different between the first
population quartile (the fewest people per jurisdiction) and the other quartiles. The first
quartile has larger average front setbacks than the other quartiles. In addition, the
second quartile has statistically larger front setbacks than the fourth quartile.
We also examined side yard setbacks for interior lots. Table 3-1 shows that 417 or
94% of jurisdictions with ordinances had minimum side yard setback requirements. The
mean and median side yard setback was eight feet per side, with a standard deviation of
five feet. The smallest side yard setback requirement was zero feet and the largest was
thirty feet per side.
Table 3-6 shows the side yard setbacks in five-foot increments. Fifty-eight percent of
jurisdictions required minimum side yard setbacks of between five to nine feet. One-
quarter of jurisdictions required side setbacks between ten to fourteen feet. Six percent
required minimum side setbacks of less than five feet and 3% required setbacks of
greater than twenty-five feet.
Appendix A: Statistical Analysis of Regulations A-27

Appendix A: Statistical Analysis of Regulations A-28
Table 3-6. Minimum side yard setbacks
in 5-foot increments
Frequency
Percent
Less than 5 27 6%
5 to 9 240 58%
10 to 14 104 25%
15 to 19 24 6%
20 to 24 10 2%
More than 25 12 3%
Total
417 100%

Source: Survey of Regulatory Standards, CPW 2006

Side yard setbacks consist of a setback for each side of the yard. Most ordinances
present the setbacks for one side of the yard but some ordinances present a total size
for both side setbacks. Seventeen percent of the jurisdictions with side yard setback
requirements presented their minimum setbacks as a total number to be divided
between the two sides. For example, a jurisdiction might require ten feet of side yard
setbacks between the two sides but the minimum setback on either side might be four
feet. The other setback would have to be at least six feet. In these cases, we divided the
combined side setback in half and recorded that number. In our example, we would
record five feet as the minimum side setback.
Table 3-7 shows statistical comparisons between side yard setbacks and five
subcomponents of the sample. A significance level of less than 0.05 is considered
significant. If the significance level is less than 0.05, we have entered “yes” in the
“statistically significant difference” column, and if it is greater than 0.05, we have entered
“no.” Differences in side yard setbacks were significant for each of the five
subcomponents of the sample.
Table 3-7. Statistical comparisons of side yard setbacks for jurisdictions
grouped by subcomponents of the sample
Statistically
significant
difference
Significance
level
Government type Yes 0.000
Census region Yes 0.000
Part of MSA / Not part of MSA Yes 0.000
Central city / Not central city Yes 0.000
U.S. Population grouped in quartiles Yes 0.000

Source: Survey of Regulatory Standards, CPW 2006

Further statistical testing using ANOVA shows the differences in minimum side
setbacks among subcomponents of the sample. The differences observed within both
types of means testing reinforce the conclusion that the statistical differences result
from fundamental differences side setbacks within the subcomponents. Side setbacks
vary within each subcomponent in the following ways:

• Government type Side setbacks are statistically different for the following
groups: Counties have smaller average front setbacks than towns and townships; and
cities have smaller average front setbacks than towns, townships, and villages.
• Census region The Northeast has larger average side setbacks, a significant
difference between the Northeast and the other regions. The West has smaller average
side setbacks than any other region, which is also significant.
• U.S. Population The first population quartile, jurisdictions with the fewest
people, have larger average side setbacks, which is significantly different from each other
quartile. Likewise, the second population quartile has larger average side setbacks than
the third and fourth quartiles, which is also significant.
The final type of setback was rear yard setbacks. Table 3-1 shows that 404 or 91% of
jurisdictions with ordinances had minimum rear yard setback requirements. The mean
rear setback was 21 feet and the median was 20 feet, with a standard deviation of 9 feet.
The smallest requirement for a rear setback was zero feet and the largest was 65 feet.
Figure 3-4 shows the rear yard setback requirements in five-foot increments. The
majority of rear yard setback minimums range from 15 feet to 29 feet. The most
common minimum rear yard setbacks are between 20 to 24 feet (22% of jurisdictions)
and 25 to 29 feet (23% of jurisdictions). One percent of jurisdictions have minimum rear
setbacks of less than five feet and four percent of jurisdictions have minimum rear
setbacks of greater than 40 feet.
Appendix A: Statistical Analysis of Regulations A-29

Figure 3-4. Minimum rear yard setbacks percentage and frequency,
grouped in 5-foot increments
0% 5% 10% 15% 20% 25%
Less than 5
5 to 9
10 to 14
15 to 19
20 to 24
25 to 29
30 to 34
35 to 39
More than 40
Rear
yar
d set
bac
k i
n
f
eet
Percent of total
5
23
15
14
45
94
89
66
53

Source: Survey of Regulatory Standards, CPW 2006
Table 3-8 shows statistical comparisons between rear setbacks and five
subcomponents of the sample. A significance level of less than 0.05 is considered
significant. If the significance level is less than 0.05, we have entered “yes” in the
“statistically significant difference” column, and if it is greater than 0.05, we have entered
“no.”
The differences were significant for each of the five subcomponents of the sample,
except for the jurisdictions grouped by U.S. population. This means that rear setbacks
are not statistically different based on the population quartile.

Table 3-8. Statistical comparisons of rear yard setbacks for jurisdictions
grouped by subcomponents of the sample
Statistically
significant
difference
Significance
level
Government type Yes 0.007
Census region Yes 0.000
Part of MSA / Not part of MSA Yes 0.001
Central city / Not central city Yes 0.029
U.S. Population grouped in quartiles No 0.055

Source: Survey of Regulatory Standards, CPW 2006

Additional statistical testing using ANOVA shows the differences in minimum rear
setbacks within subcomponents of the sample. The differences observed reinforce the
conclusion that the statistical differences result from fundamental differences rear
Appendix A: Statistical Analysis of Regulations A-30

Appendix A: Statistical Analysis of Regulations A-31
setbacks within the subcomponents. Rear setbacks vary within each subcomponent in
the following ways:
• Government type Rear setbacks are on average smaller in counties than
townships, a significant difference. Cities are statistically different than towns and
townships, with smaller average rear setbacks.
• Census region The West has significantly different rear setbacks, which are
generally smaller than the other regions. The South’s rear setbacks are also statistically
different from other regions. The South has larger rear setbacks than the West and
smaller rear setbacks than the Northeast or Midwest.
• U.S. Population Jurisdictions in the fourth quartile of population (having the
largest populations) have smaller average setbacks than any other quartile. This
difference is significant between the fourth quartile and the first and second quartiles.
FLOOR AREA
The lot variables so far are related to the size of the lot and placement of the
dwelling on the lot. The next lot variable, floor area, is related to the amount of living
space within the dwelling unit, expressed as the minimum number of square feet of floor
space within the dwelling.
Table 3-1 shows that 86 jurisdictions or 18% of jurisdictions with ordinances had
requirements for floor area. The mean floor area requirement was 1,060 square feet
and the median requirement was 1,000 square feet, with a standard deviation of 359
square feet. The smallest floor area requirement was 500 square feet per dwelling unit
and the largest 2,500 square feet.
Table 3-9 shows the distribution of the floor area minimum requirements. Twenty-
seven percent of jurisdictions with floor area requirements required no less than 800 to
999 square feet and 24% required no less than 1,000 to 1,199 square feet. Twenty
percent of jurisdictions with floor area standards had minimums less than 800 square
feet and 10% required more than 1,600 square feet.
Table 3-9. Minimum floor area in square feet
Frequency
Percent
Less than 800 17 20%
800 to 999 23 27%
1,000 to 1,199 21 24%
1,200 to 1,399 12 14%
1,400 to 1,599 4 5%
More than 1,600 9 10%
Total
86 100%

Source: Survey of Regulatory Standards, CPW 2006

We did not do statistical analysis of floor area requirements based on the five
subcomponents of the sample because the number of jurisdictions in the study with
floor area requirements was so small that the statistical analysis would have little
meaning.

Appendix A: Statistical Analysis of Regulations A-32
OFF-STREET PARKING REQUIREMENTS
The final lot variable was requirement of off-street parking spaces. Table 3-1 shows
that 367 or 83% of the jurisdictions had off-street parking requirements. The mean
number of off-street parking spaces required was 1.88 and the median was two spaces.
The standard deviation was 0.51 spaces. The fewest spaces required was zero and the
most spaces required was four. Off-street parking requirements were found most
frequently in the zoning ordinance.
Table 3-10 shows that more than three-quarters of jurisdictions with off-street
parking standards require two spaces. Sixteen percent of jurisdictions require one off-
street parking space. Four percent of jurisdictions require three or four off-street
parking spaces.

Table 3-10. Minimum number of
parking spaces per dwelling unit
Frequency Percent
1 per du 57 16%
1.5 per du 9 2%
2 per du 284 78%
2.25 per du 1 0%
2.3 per du 1 0%
3 per du 6 2%
4 per du 7 2%
Total 365

Source: Survey of Regulatory Standards, CPW 2006

About 4% of the jurisdictions based their requirement on the number of bedrooms.
In general, the more bedrooms, the greater the requirements for off-street parking. In
half of these cases, the minimum number of off-street parking spaces was two. In the
remaining cases, the minimum requirement was one or 1.5 spaces.
Table 3-11 shows statistical comparisons between off-street parking requirements
within five subcomponents of the sample. A significance level of less than 0.05 is
considered significant. If the significance level is less than 0.05, we have entered “yes” in
the “statistically significant difference” column, and if it is greater than 0.05, we have
entered “no.”
Differences were significant for the following subcomponents: central city and
population quartiles. In other words, the number of off-street parking spaces required
varies, depending on whether the jurisdiction is a central city and what the population
quartile is. Additional ANOVA testing shows that jurisdictions in the fourth quartile of
population (having the largest populations) require less off-street parking than
jurisdictions in the second population quartile.

Table 3-11. Statistical comparisons of the number of off-street parking
spaces for jurisdictions grouped by subcomponents of the sample
Statistically
significant
difference
Significance
level
Government type No 0.804
Census region No 0.556
Part of MSA / Not part of MSA No 0.622
Central city / Not central city Yes 0.000
U.S. Population grouped in quartiles Yes 0.011

Source: Survey of Regulatory Standards, CPW 2006
LANDSCAPING STANDARDS
Many jurisdictions include landscaping standards in their ordinances. Landscaping
standards vary by jurisdiction and the type of development. We were concerned with
landscaping standards related to residential subdivisions and residential zones, especially
the residential zone we examined.
Landscaping requirements vary in several ways. First, landscaping requirements vary
in the amount of landscaping required. Some jurisdictions require extensive landscaping
and others require less landscaping. Secondly, ordinances vary in the level of detail about
landscaping requirements. Some jurisdictions' ordinances have general language about
landscaping requirements and its locations. Other ordinances have very specific
requirements about the types, locations, and size of the plants in the landscaping. Finally,
different types of landscaping may be required in different circumstances. For example,
street trees may be required along streets and other types of landscaping may be
required at the entrance to a subdivision.
Quantifying these standards would pose significant difficulties. As a result, we chose
to track whether ordinances contain landscaping standards for development of
residential subdivisions or development in the zoning district that we reviewed. Table 3-
12 shows that 42% of the jurisdictions in the study had landscaping requirements.
Appendix A: Statistical Analysis of Regulations A-33

Appendix A: Statistical Analysis of Regulations A-34
Table 3-12. Requirements of landscaping
Frequency
Percent
Required standards 195 42%
No standards required* 274 58%

Source: Survey of Regulatory Standards, CPW 2006,
*Note: We found no landscaping standards for these jurisdictions in the ordinances we reviewed but they may have
required landscaping standards in other ordinances.
Table 3-13 shows that we found landscaping standards in both subdivision and
zoning ordinances. The total number of ordinances that we found landscaping standards
in is larger than the total number of jurisdictions that required landscaping because
some ordinances said that landscaping may be required under some circumstances, such
as trees along certain streets or in parking lots, in swales or other drainage areas, or at
the entrance to the subdivision.
Table 3-13. Ordinances that
specified landscaping standards
Frequency
Percent
Subdivision 122 47%
Zoning 83 32%
Both 53 21%
Total 258

Source: Survey of Regulatory Standards, CPW 2006

Table 3-14 shows statistical comparisons on whether jurisdictions require
landscaping within the five subcomponents of the sample. A significance level of less than
0.05 is considered significant. If the significance level is less than 0.05, we have entered
“yes” in the “statistically significant difference” column, and if it is greater than 0.05, we
have entered “no.”
Differences in landscaping requirements were significant for Census regions,
whether the jurisdiction is part of an MSA, and the jurisdiction's population. We did not
perform an ANOVA test for landscaping because landscaping had two possible values
(yes or no) and the ANOVA requires three or more possible values (i.e. yes, no, or
maybe).

Table 3-14. Statistical comparisons of landscaping requirements for
jurisdictions grouped by subcomponents of the sample
Statistically
significant
difference
Significance
level
Government type No 0.054
Census region Yes 0.002
Part of MSA / Not part of MSA Yes 0.018
Central city / Not central city No 0.783
U.S. Population grouped in quartiles Yes 0.000

Source: Survey of Regulatory Standards, CPW 2006

Appendix A: Statistical Analysis of Regulations A-35
The following are some examples of landscaping requirements, quoted from several
different ordinances. The examples are fragments of the landscaping standard, related to
one type of development.
1. All lots shall be planted with grass or other suitable ground cover approved
by the Planning Board, from the roadside edge of the unpaved right-of-way
back to a distance of 25 feet behind the principal residence on the lot.
7

2. A minimum of two (2) trees shall be required per single or two-family
residential lot. The trees shall be placed in the front yard area at least ten
(10) feet from the curb line. On corner lots and cul-de-sac lots, one of the
trees may be placed in the side yard area. All remaining lot area not used for
structures, parking area, or driveway shall be landscaped with turf grass,
native grasses, ground cover, or other perennial flowering plants, vines,
shrubs, or trees.
8

3. All single-family developments will have one tree per 40 lineal feet, or
fraction thereof. These trees will be located in the swale (green) area in
between the roads edge and the sidewalks. Trees that are located in swales
that are six to eight foot in width will require the use of a root barrier to
protect the sidewalk from root damage. The root barrier will be installed per
the manufacturer’s recommendation. In right-of-ways with less than a six-
foot swale area the street trees will be located in the front yard five feet
from the sidewalk and a root barrier will be used along the sidewalk adjacent
to the tree.
9

4. Single-family residential landscaping requirements.
a. These standards shall apply to all detached and attached single-family
residential districts. These standards may be met by saving existing trees
on the site or planting new trees from the approved list. Lot size
designations shall apply to the zoning classification(s) of the subdivision
rather than to each individual lot.
b. One shade tree (2.5“ caliper minimum) shall be provided for all single-
family residential lots less than 6,000 square feet.
c. Two shade trees (2.5” caliper minimum) shall be provided for all single-
family residential lots of 6,000 square feet to less than 9,000 square feet.
d. Three shade trees (2.5" caliper minimum) shall be provided for all single-
family residential lots of 9,000 square feet or more.

7
Millbrook Village, NY subdivision ordinance, p 21.
8
Faribault, MN subdivision ordinance, chapter 4.
9
Pembroke Pines, FL code of ordinances Title XV, Section 153, Section 153.19.

Appendix A: Statistical Analysis of Regulations A-36
e. All required trees must be planted prior to request for final building
inspection of dwelling units.
10

OPEN SPACE STANDARDS
Open space standards refer to land that is undeveloped and devoted to public uses,
such as parkland. About 28% or 133 of the jurisdictions in the sample had regulations
requiring dedication of land for open space uses. Of these jurisdictions, 59% or 78
jurisdictions allowed payments (fee-in-lieu) of land dedications. These requirements
were most often found in the subdivision ordinance.
While open space requirements do not vary as much as landscaping standards, the
types of open space requirements do vary substantially. We found three common
standards for dedicating land to open space in new development: a percentage of the
total land in a subdivision, a number of square feet per dwelling unit, and a number of
square feet per person. Ninety-nine of the 133 jurisdictions that with open space
requirements used one of these three methods for specifying the amount of open space
required. Table 3-15 provides a statistical summary of these standards, which we will
discuss below.

Table 3-15. Statistical summary of different standards for requiring open
space
Open space standard N Mean Median Mode
Standard
deviation Minimum Maximum
Percent of total land in subdivision 47 13 10 10 9.0 3 50
Number of square feet per dwelling unit 18 1,562 795 871 3,446.9 310 15,246
Number of square feet per person
34 229 218 218 112.0 87 436

Source: Survey of Regulatory Standards, CPW 2006
The most common method for establishing the amount of open space for new
residential development is requiring dedication of a percentage of land within the
subdivision for open space. Table 3-15 shows that 47 or 47% of jurisdictions with open
space standards use this method of calculating open space requirements. The mean
percentage of total land required for open space in a new subdivision is 13% and the
median is 10%, with a standard deviation of 9%. The smallest requirement is 3% and the
greatest requirement is for 50% of the land in a subdivision.
Table 3-16 shows a breakdown of the amount of land required for open space. Sixty
percent of the jurisdictions using this method require between 10% and 19% of the land
in the subdivision for open space. Twenty-one percent of jurisdictions require
dedications of 20% or more of the land in the subdivision for open space. Nineteen
percent of jurisdictions require that a minimum of 9% or less of the land in the
subdivision is dedicated to open space.

10
Lewisville, TX General Development Ordinance Chapter 6, Article VI, Section 6-122.

Table 3-16. Percent of land in a subdivision required
for open space
Frequency
% of
jurisdictions
Percent of all
jurisdictions
9% of land or less 9 19% 2%
10% to 19% of land 28 60% 6%
20% or more of land 10 21% 2%
Total 47 100% 10%

Source: Survey of Regulatory Standards, CPW 2006

Another method for calculating the amount of land to dedicate to open space is
based on the number of dwelling units in the subdivision, with requirements for a
specific amount of land for open space per dwelling unit. Table 3-15 shows that 18
jurisdictions use this method of calculation. The mean number of square feet of land per
dwelling unit is 1,562, and the median amount is 795 square feet of land per dwelling
unit, with a standard deviation of 3,447 square feet of land per dwelling unit. The
smallest amount of land is 310 square feet and the largest is 15,246 square feet per
dwelling unit.
Table 3-17 presents a breakdown by percent of jurisdictions of the number of
square feet of land per dwelling unit required for open space. It shows the variability in
the amount of land required per dwelling unit. Forty-four percent of jurisdictions
require a minimum of 500 or fewer square feet per dwelling unit. An equal number of
jurisdictions require 500 to 999 square feet per dwelling unit or more than 1,000 square
feet per dwelling unit.
Table 3-17. Number of square feet per dwelling unit required for open space
Frequency
% of
jurisdictions
Percent of all
jurisdictions
Fewer than 500 square feet per du 8 44% 2%
500 to 999 square feet per du 5 28% 1%
More than 1,000 square feet per du 5 28% 1%
Total 18 100% 4%

Source: Survey of Regulatory Standards, CPW 2006
The third method for calculating open space requirements is based on the number
residents in the subdivision, with dedications of a certain number of square feet per
resident. Table 3-15 shows that 34 jurisdictions, 34% of the jurisdictions who require
open space, use this method. The mean amount of land required in this method is 229
square feet per person, and the median is 218 square feet per person, with a standard
deviation of 112 square feet per person. The smallest amount of land required is 87
square feet per person, and the largest is 436 square feet per person.
Table 3-18 shows a breakdown of the amount of land required for open space per
resident. Seventy-six percent of jurisdictions require either fewer than 200 square feet
of land per person or 200 to 299 square feet of land per person.
Appendix A: Statistical Analysis of Regulations A-37

Appendix A: Statistical Analysis of Regulations A-38
Table 3-18. Number of square feet per person required for open space
Frequency
% of
jurisdictions
Percent of all
jurisdictions
Fewer than 200 square feet per person 13 38% 3%
200 to 299 square feet per person 13 38% 3%
300 to 399 square feet per person 3 9% 1%
More than 400 square feet per person 5 15% 1%
Total 34 100% 7%

Source: Survey of Regulatory Standards, CPW 2006
One-hundred thirty-three of the jurisdictions in the sample had open space
requirements. Table 3-19 shows statistical comparisons on whether jurisdictions require
open space within the five subcomponents of the sample. A significance level of less than
0.05 is considered significant. If the significance level is less than 0.05, we have entered
“yes” in the “statistically significant difference” column, and if it is greater than 0.05, we
have entered “no.”
Differences in landscaping requirements were significant for Census regions. We did
not perform an ANOVA test for requiring open space because requiring open space had
two possible values (yes or no) and the ANOVA requires three or more possible values
(i.e. yes, no, or maybe).

Table 3-19. Statistical comparisons of landscaping requirements for
jurisdictions grouped by subcomponents of the sample
Statistically
significant
difference
Significance
level
Government type No 0.216
Census region Yes 0.003
Part of MSA / Not part of MSA No 0.058
Central city / Not central city No 0.460
U.S. Population grouped in quartiles No 0.200

Source: Survey of Regulatory Standards, CPW 2006
SIDEWALK STANDARDS
One of the most commonly required improvements in new residential development
is sidewalks. This section includes information collected in the study about sidewalks,
planting strips, and curbs.
In our review, we kept track of the jurisdictions that explicitly require sidewalks in
either their subdivision or zoning ordinances. A number of other jurisdictions implied
that sidewalks might be required or stated that they would be required under certain
circumstances. We did not count these jurisdictions as requiring sidewalks, even though
it is possible that they do.
Fifty-one percent of the jurisdictions in the study explicitly require sidewalks. These
requirements are most frequently found in the subdivision ordinances. Table 3-20 shows
that sidewalks are required on both sides of the street in 51% of the jurisdictions with

Appendix A: Statistical Analysis of Regulations A-39
sidewalk standards. Fourteen percent of jurisdictions with sidewalk standards require
them on one side of the street and the remaining jurisdictions do not specify where
sidewalks are required.

Table 3-20. Where sidewalks are required
Frequency
% of
jurisdictions
Percent of all
jurisdictions
One side 33 14% 7%
Both sides 123 51% 26%
Unspecified 85 35% 18%
Not required 228 N/A 49%

Source: Survey of Regulatory Standards, CPW 2006

Table 3-21 provides a summary of required sidewalk width and planting strip width.
Thirty-three percent of jurisdictions in the study had a minimum sidewalk width. The
mean and median sidewalk width is four feet, with a standard deviation of one foot. The
smallest requirement is three feet and the largest requirement is 10 feet.

Table 3-21. Summary of statistical standards for sidewalks, in feet
Street standard N Mean Median Mode
Standard
deviation Minimum Maximum
Sidewalk width 153 4 4 4 1 3 10
Planting strip width
37 5 5 5 1 2 8

Source: Survey of Regulatory Standards, CPW 2006
Table 3-22 shows a distribution of sidewalk widths. It shows that 59% of jurisdictions
with sidewalk width standards require sidewalks of at least four feet wide. Thirty-one
percent require sidewalks at least five feet wide.

Table 3-22. Minimum required sidewalk width
Frequency
% of
jurisdictions
Percent of all
jurisdictions
3 ft. 2 1% 0%
4 ft. 91 59% 19%
4.5 ft. 4 3% 1%
5 ft. 48 31% 10%
6 ft. 4 3% 1%
More than 6 ft. 4 3% 1%
Total
153 100% 33%

Source: Survey of Regulatory Standards, CPW 2006

Table 3-23 shows statistical comparisons between whether sidewalks are required
and five subcomponents of the sample. A significance level of less than 0.05 is
considered significant. If the significance level is less than 0.05, we have entered “yes” in
the “statistically significant difference” column, and if it is greater than 0.05, we have
entered “no.”

Appendix A: Statistical Analysis of Regulations A-40
Differences were significant for government type, whether the jurisdiction is part of
an MSA, and the jurisdiction's population. Sidewalk requirements were not significantly
different for jurisdictions grouped by region or whether the jurisdiction is a central city.

Table 3-23. Statistical comparisons of whether sidewalks
are required for jurisdictions grouped by subcomponents
of the sample
Statistically
significant
difference
Significance
level
Government type Yes 0.007
Census region No 0.096
Part of MSA / Not part of MSA Yes 0.000
Central city / Not central city No 0.143
U.S. Population grouped in quartiles Yes 0.001

Source: Survey of Regulatory Standards, CPW 2006

Table 3-24 shows statistical comparisons between sidewalk widths and five
subcomponents of the sample. A significance level of less than 0.05 is considered
significant. If the significance level is less than 0.05, we have entered “yes” in the
“statistically significant difference” column, and if it is greater than 0.05, we have entered
“no.”
Differences in sidewalk widths are not significant for any of the five groupings of
jurisdictions. This means that sidewalk width does not vary significantly based on these
subcomponents of the sample.
Table 3-24. Statistical comparisons of sidewalk widths for jurisdictions
grouped by subcomponents of the sample
Statistically
significant
difference
Significance
level
Government type No 0.841
Census region No 0.060
Part of MSA / Not part of MSA No 0.565
Central city / Not central city No 0.156
U.S. Population grouped in quartiles No 0.251

Source: Survey of Regulatory Standards, CPW 2006
Table 3-25 shows statistical comparisons between the number of sides of the street
that sidewalks are required on and five subcomponents of the sample. A significance
level of less than 0.05 is considered significant. If the significance level is less than 0.05,
we have entered “yes” in the “statistically significant difference” column, and if it is
greater than 0.05, we have entered “no.”
Statistical comparison sidewalk widths are not significant for groupings of
jurisdictions, except for region. The ANOVA test shows that there is no significant
difference in sidewalk widths within the subcomponents of the sample.

Appendix A: Statistical Analysis of Regulations A-41
Table 3-25. Statistical comparisons of the number of sides of the street that
sidewalk are required on for jurisdictions grouped by subcomponents of
the sample
Statistically
significant
difference
Significance
level
Government type No 0.856
Census region Yes 0.040
Part of MSA / Not part of MSA No 0.638
Central city / Not central city No 0.400
U.S. Population grouped in quartiles No 0.831

Source: Survey of Regulatory Standards, CPW 2006
Table 3-21 shows that 37 (8%) jurisdictions in the study have requirements for
planting strips, a grassy buffer between the sidewalk and the curb The mean and median
width of the strip was five feet, with a one-foot standard deviation. The smallest width
requirement was two feet and the largest was eight feet.
Table 3-26 shows the distribution of planting strip minimum widths. Of the
jurisdictions with planting strip width standards, 30% required five foot wide planting
strips and 19% of jurisdictions required either four foot or six foot planting strips.

Table 3-26. Minimum width for
planting strip
Frequency
% of
jurisdictions
Percent of all
jurisdictions
2 ft. 4 11% 1%
3 ft. 4 11% 1%
3.5 ft. 1 3% 0%
4 ft. 7 19% 1%
5 ft. 11 30% 2%
6 ft. 7 19% 1%
5% 0%7 ft. 2
8 ft. 1 3% 0%
Total
37 100% 8%

Source: Survey of Regulatory Standards, CPW 2006

Another commonly required improvement in new residential development is curbs
and gutters. Like sidewalks, we recorded the jurisdictions that explicitly require curbs
and gutters in either their subdivision or zoning ordinances. A number of jurisdictions
implied that curbs and gutters were required improvements or may be required. We
did not count these jurisdictions as requiring curbs and gutters, even though it is
possible that they do.
Curbs and gutters were required by 234 or 50% of the jurisdictions in the study. We
found most of the requirements in the subdivision ordinance.

Appendix A: Statistical Analysis of Regulations  A-42
STREET VARIABLES
Streets are an essential improvement in residential development. In this study we
collected information about minimum pavement width and minimum street right-of-way
width. When we found these standards they were in the subdivision ordinances 92% of
the time.
Table 3-27 provides a summary of pavement and right-of-way widths. Forty-one
percent of the jurisdictions in the study had minimum pavement widths in their
subdivision or zoning ordinances. The mean and median minimum pavement width was
28 feet, with a standard deviation of six feet. The smallest minimum pavement width was
16 feet and the largest minimum pavement width was 45 feet.

Table 3-27. Statistical summary of minimum street pavement and right-of-
way widths
Street standard N Mean Median Mode
Standard
deviation Minimum Maximum
Street pavement width
192 28 28 30 6 16 45
Street right-of-way width
262 52 50 50 8 20 80

Source: Survey of Regulatory Standards, CPW 2006
Figure 3-5 shows minimum pavement width, grouped in increments of five feet. The
majority of jurisdictions' standards were divided between 20 to 24 feet wide, 25 to 29
feet wide, and 30 to 34 feet wide. Three percent of jurisdictions with this standard
allowed streets in new residential development that are less than 20 feet wide and 6%
required pavement widths of 40 feet or greater.

Figure 3-5. Minimum pavement width percentage and frequency, grouped
in 5-foot increments
0% 5% 10% 15% 20% 25% 30% 35%
Less than 20 ft.
20 to 24 ft.
25 to 29 ft.
30 to 34 ft.
35 to 39 ft.
40 ft. or more
P
avem
en
t
w
i
d
t
h
Percent
6
10
13
55
53
55

Source: Survey of Regulatory Standards, CPW 2006
Table 3-28 shows statistical comparisons between pavement width and five
subcomponents of the sample. A significance level of less than 0.05 is considered
significant. If the significance level is less than 0.05, we have entered “yes” in the
“statistically significant difference” column, and if it is greater than 0.05, we have entered
“no.” Differences in pavement width were significant for each of the five subcomponents
of the sample.

Table 3-28. Statistical comparisons of pavement widths for jurisdictions
grouped by subcomponents of the sample
Statistically
significant
difference
Significance
level
Government type Yes 0.001
Census region Yes 0.000
Part of MSA / Not part of MSA Yes 0.050
Central city / Not central city Yes 0.046
U.S. Population grouped in quartiles Yes 0.021

Source: Survey of Regulatory Standards, CPW 2006

Additional statistical testing showed the differences in pavement widths within
subcomponents of the sample. The tests showed differences within government type
and census region, indicating that pavement widths vary significantly by government type
and Census region. Although the chi-square statistical test indicated that pavement
width are statistically different by population quartiles, additional post-hoc statistical
Appendix A: Statistical Analysis of Regulations A-43

testing indicates that there are no significant differences in pavement widths for
population quartiles. Pavement widths vary within each subcomponent in the following
ways:
• Government type. Cities have the largest average pavement widths of any
government type. This difference in pavement widths is significant between cities and
towns.
• Census region. Pavement widths are statistically different between the West
and the Northeast and South. The West has larger average pavement widths.
• U.S. Population. There are no significant differences in pavement widths when
analyzed by population quartiles.
Table 3-27 shows that 262 or 56% of jurisdictions had minimum right-of-way widths
in their ordinances. The mean right-of-way was 52 feet and the median was 50 feet, with
a standard deviation of 8 feet. The smallest right-of-way width was 20 feet and the
largest was 80 feet.
Figure 3-6 shows the distribution of street right-of-way widths in five-foot increments.
Fifty-six percent of jurisdictions with this standard require right-of-ways no smaller than
50 to 54 feet, and 24% of jurisdictions require right-of-ways at least 60 to 64 feet wide.
Four percent of jurisdictions' minimum right-of-way width is less than 40 feet, and 6%
have a minimum of 65 feet or more.
Appendix A: Statistical Analysis of Regulations A-44

Figure 3-6. Minimum right-of-way width percentage and frequency, grouped
in 5-foot increments
0% 10% 20% 30% 40% 50% 60%
Less than 40 ft.
40 to 44 ft.
45 to 49 ft.
50 to 54 ft.
55 to 59 ft.
60 to 64 ft.
65 ft. or more
Ri
gh
t
-
of
-
w
a
y
w
i
dt
h
Percent
11
15
63
4
148
10
11

Source: Survey of Regulatory Standards, CPW 2006
Table 3-29 shows statistical comparisons between street right-of-way widths and
five subcomponents of the sample. A significance level of less than 0.05 is considered
significant. If the significance level is less than 0.05, we have entered “yes” in the
“statistically significant difference” column, and if it is greater than 0.05, we have entered
“no.”
Statistical comparisons of right-of-way widths were significant for jurisdictions
grouped by region. Street right-of-way widths did not vary significantly based on
government type, whether the jurisdiction is part of an MSA, whether the jurisdiction is
a central city, or the jurisdiction's population.

Appendix A: Statistical Analysis of Regulations A-45

Table 3-29. Statistical comparisons of street right-of-way
width for jurisdictions grouped by subcomponents
of the sample
Statistically
significant
difference
Significance
level
Government type No 0.903
Census region Yes 0.000
Part of MSA / Not part of MSA No 0.233
Central city / Not central city No 0.323
U.S. Population grouped in quartiles No 0.387

Source: Survey of Regulatory Standards, CPW 2006

Additional statistical testing shows that street right-of-way widths are statistically
different between the Midwest and all other regions. The Midwest has larger average
right-of-way widths than the other regions. This reinforces the conclusion that right-of-
way width varied significantly by region.
Appendix A: Statistical Analysis of Regulations A-46

OBSERVATIONS
This chapter presents a few observations about the data collection phase of the
study. The focus of this phase of the study was gathering data and presenting a
descriptive analysis of the data. This majority of the conclusions from the study will
come from the cost analysis portion of the study. Our observations about the data and
data collection are as follows.
Most jurisdictions regulated one or more of the variables. Ninety-four
percent of the jurisdictions in the sample had standards for one or more of the study
variables. More than three-quarters of the jurisdictions had standards for lot size, front
setbacks, and off-street parking spaces. About one-fifth of the jurisdictions had standards
for open space and floor area.
The population varied among jurisdictions. The size of jurisdictions within the
sample varied substantially. They ranged in population from 9.5 million people to 132
people, from some of the largest cities and most densely developed counties in the U.S.
to small rural towns. These differences presented challenges in analyzing the regulatory
standards. We addressed these challenges by separating the sample into population
quartiles and comparing regulatory standards among the quartiles.
Some of the variables had a broad range of values. The basic statistical
analysis showed that several of the variables had substantial variation. The following
variables had a large range of values and large standard deviation compared to their
mean: lot size, lot width, front setback, side setback, and open space. For example, a
rural jurisdiction is likely to have larger minimum requirements for lot size and lot width
than an urban city. We addressed these differences by separating the sample
subcomponents and comparing regulatory standards within the subcomponent
groupings. The subcomponents included government type, census region, membership
in a Metropolitan Statistical Area (MSA), and central city.
Difficulty in obtaining ordinances. Obtaining ordinances was difficult, especially
with smaller jurisdictions. Some jurisdictions made their ordinances available via the
Internet, making it easy to get their ordinances. Where the ordinance was only available
directly from the jurisdiction, we were more likely to have problems getting the
ordinances. Some jurisdictions were unresponsive to our phone calls and requests for
ordinances, resulting in their exclusion from the sample.
Appendix A: Statistical Analysis of Regulations A-47

Appendix A: Statistical Analysis of Regulations A-48
Choice of Review Variables
This appendix summarizes the process for choosing variables for the database. It
includes our reasoning for choosing particular variables, as well as the list of variables
for the study.
PROCESS FOR CHOOSING VARIABLES
The entire team participated in choosing the variables for reviewing the ordinances,
including representatives from the NAHB, ECONorthwest, and CPW. This process
took several weeks and was documented in several memorandums, which are
summarized in this appendix.
We began the project with a list of about 75 variables that we considered including
in this study. We narrowed the list of variables by reviewing ordinances from ten
jurisdictions to assess whether subdivision ordinances commonly contained the
standards in our list. We found that many of site-specific variables in our original list
were not in the initial ten ordinances.
At that point, we began considering expanding the scope of the project to include
some variables from zoning ordinances because our research indicated that these
variables, especially those related to lot size, have substantial impact on housing costs.
As a result of the decision to include variables from zoning ordinances, we reduced our
sample size from 1,100 jurisdictions to 400 jurisdictions
11
.
We reduced the number of variables to about fifteen and conducted a second
review of ten jurisdictions' subdivision and zoning ordinances to determine how
frequently these variables occurred in the ordinances. This review showed that the
variables in our list were frequently found.
We finalized our list of variables, based on the following criteria:
• Likelihood and ease of finding the variables within a zoning or subdivision
ordinance. A number of the variables that we were originally interested in
measuring were not generally found in either of these ordinances. For example, the
minimum diameter of a sewer lateral or street pavement surface thickness were not
often found in either the zoning or subdivision ordinances.
• Ease of measuring the variables. Some of the variables that we considered were
difficult to measure. For example, landscaping standards vary substantially among
ordinances. We were unable to find a way to quantify such diverse standards.
Instead, we chose to collect whether or not each jurisdiction had landscaping
requirements in their zoning or subdivision ordinances.
• Expected impact of the variable on housing cost. Some of the variables that
we had originally considered including in the study probably had minimal impact on
the cost of housing. For example, many of the jurisdictions in our preliminary review

11
Ordinance review and collection took less resources than we initially thought, and we were able to increase the
sample size. We ultimately reviewed 469 ordinances.

Appendix A: Statistical Analysis of Regulations A-49
contained standards for the angle of street intersections but the angle of an
intersection probably has little impact on the cost of housing in a subdivision.
The variables that we selected for review included:
• Lot-width minimums
• Lot-size minimums
• Yard setback minimums (front yard, side yards, rear yard)
• Floor area minimums
• Off-street parking requirements
• Curb and gutter requirements
• Minimum street right-of-way width
• Minimum pavement width
• Sidewalk requirements
• Open space requirements
• Landscaping requirements
As we began ordinance review, we added several variables to address administrative
concerns. These variables gave us information about the ordinances themselves and
allowed for certain types of comparison between ordinances. They include:
• The type(s) of ordinance: zoning and/or subdivision
• Last update for each ordinance
• Type of update (adopted or amended)
• Ordinance media (electronic or paper)
• Name of the zoning district used for review
• Type of quality assurance review (short check or long check)
The next step was quantifying each variable an