Cityscape March 08

by HUD USER

•
U.S. Department of Housing
and Urban Development
A Journal of Policy
Development and Research
Policy Issues in Public and Assisted Housing
Volume 10, Number 1 2008
U.S. Department of Housing and Urban Development
Office of Policy Development and Research

Managing Editor: Mark D. Shroder
Advisory Board
Dr. Stephen Malpezzi
University of Wisconsin
Dr. Denise DiPasquale
City Research, Boston, MA
Dr. Lance Freeman
Columbia University
Dr. William J. Siembieda
California Polytechnic State University
Dr. Donald R. Haurin
The Ohio State University
Dr. Anthony Yetzer
George Washington University
Dr. Rosemary Goss
Virginia Polytechnic Institute and State University

A Journal of Policy
Development and Research
U.S. Department of Housing and Urban Development
Office of Policy Development and Research
Cityscape
Policy issues in Public and assisted Housing
Volume 10, number 1 • 2008

The goal of Cityscape is to bring high-quality original research on housing and community
development issues to scholars, government officials, and practitioners. Cityscape is open to
all relevant disciplines, including architecture, consumer research, demography, economics,
engineering, ethnography, finance, geography, law, planning, political science, public policy,
regional science, sociology, statistics, and urban studies.
Cityscape is published three times a year by the Office of Policy Development and Research
(PD&R) of the U.S. Department of Housing and Urban Development. Subscriptions are available
at no charge and single copies at a nominal fee. The journal is also available on line at http://www.
huduser.org/periodicals/cityscape.html.
PD&R welcomes submissions to the Refereed Papers section of the journal. Our referee process
is double blind and timely, and our referees are highly qualified. The managing editor will also
respond to authors who submit outlines of proposed papers regarding the suitability of those
proposals for inclusion in Cityscape. Send manuscripts or outlines to Cityscape@hud.gov.
Opinions expressed in the articles are those of the authors and do not necessarily reflect
the views and policies of HUD or the U.S. government.

iiiCityscape
Contents
Symposium: Policy Issues in Public and Assisted Housing
Guest Editor: Barbara A. Haley
Guest Editor’s Introduction .........................................................................................................1
Portability Moves in the Housing Choice Voucher Program, 1998–2005..................................5
Carissa G. Climaco, Christopher N. Rodger, Judith D. Feins, and Ken Lam
The Role of Social Networks in Making Housing Choices: The Experience of the
Gautreaux Two Residential Mobility Program ..........................................................................41
Melody L. Boyd
Measuring the Deconcentration of Housing Choice Voucher Program Recipients in
Eight U.S. Metropolitan Areas Using Hot Spot Analysis ..........................................................65
Xinhao Wang, David Varady, and Yimei Wang
Use of Flat Rents in the Public Housing Program .....................................................................91
Meryl Finkel and Ken Lam
Household Life Cycle and Length of Stay in Housing Assistance Programs .........................117
Alvaro Cortes, Ken Lam, and David Fein
Are Census Variables Highly Correlated With Housing Choice Voucher Holders’
Perception of the Quality of Their Neighborhoods? ...............................................................157
Larry Buron and Satyendra Patrabansh
Refereed Papers
Housing in the Nation’s Micropolitan Areas: A First Look.....................................................185
Robert E. Lang and Karen A. Danielsen
Departments
Policy Briefs
Using American Community Survey Data in HUD’s Income Limits and Fair Market Rents ...199
Kurt Usowski, Lynn Rodgers, Marie Lihn, and Peter Kahn
Data Shop
HUD-Assisted Housing 101: Using “A Picture of Subsidized Households: 2000” ................211
Lydia B. Taghavi
Correction
Ten Years of Smart Growth ......................................................................................................221
Regina C. Gray

?Cityscape: A Journal of Policy Development and Research • Volume 10, Number 1 • 2008
U.S. Department of Housing and Urban Development • Office of Policy Development and Research
Cityscape
Guest Editor’s Introduction
Barbara A. Haley
U.S. Department of Housing and Urban Development
Office of Policy Development and Research
Program Monitoring and Research Division
The symposium section in this issue of Cityscape represents the second set of articles devoted to
research on the 4.3 million households that receive housing assistance from the U.S. Department
of Housing and Urban Development (HUD).
1
Assisted housing is found in every metropolitan area
and in every state. About 15 percent is in nonmetropolitan areas.
2

The largest housing assistance program is the Housing Choice Voucher Program (HCVP), formerly
known as tenant-based Section 8, in which households are expected to find individual housing
units owned by private landlords. Approximately 1.9 million households participate in the HCVP.
An additional 1.4 million households live in HUD-subsidized, privately owned multifamily proj-
ects, supported by the project-based Section 8 program and other multifamily assisted programs.
Slightly under 1 million households live in public housing units that are managed by some 3,200
public housing agencies (PHAs).
3
Housing assistance programs serve large numbers of vulnerable people. As of 2007, 37 percent of
households receiving housing assistance were headed by an elderly person.
4
Another 26 percent
were headed by a person who was disabled, but not elderly, and 54 percent were families with
children. A small percentage, about 13 percent, fit in none of the categories listed above, such as
formerly homeless individuals and people with AIDS.
5
Policymakers and the public want to know more about how these housing assistance programs
perform. Much can be learned from HUD’s administrative records, the Housing Choice Voucher
Program Customer Satisfaction Survey, and qualitative interviews with participants in the Gau-
treaux Two Housing Mobility Study.
1
The first issue is available at http://www.huduser.org/periodicals/cityscpe/vol8num2/.
2
The author thanks Mark Perdue for his assistance in producing these estimates. The author also thanks David Chase and
Mark Shroder for helpful comments.
3
Further information about these programs is available at http://www.hud.gov/offices/pih/programs/ph/index.cfm; http://
www.hud.gov/offices/pih/programs/hcv/project.cfm; and http://www.hud.gov/offices/pih/programs/hcv/tenant.cfm.
4
Elderly is defined here as 62 years and older.
5
Further information about HUD’s homeless assistance programs, created by the McKinney-Vento Homeless Assistance Act,
and Housing Opportunities for Persons with AIDS is available at http://www.hud.gov/offices/cpd/homeless/ and http://www.
hud.gov/offices/cpd/aidshousing/.

?
Haley
Policy Issues in Public and Assisted Housing
In all but one case, the authors of the symposium articles are members of HUD’s Research Cadre,
which is supported by the Office of Policy Development and Research in order to investigate issues
of policy in the administration of housing assistance. (The exception is author Melody Boyd.)
Cityscape policy allows guest editors wide latitude in choosing symposium articles, as long as all
articles meet standards of scholarship, relevance to the mission of HUD, and thematic commonal-
ity. As the manager of the contract that funds the Research Cadre and as guest editor for this issue
of Cityscape, I am favorably impressed by the quality of the articles presented in this issue. I deeply
appreciate the cooperation and effort of the authors.
Articles in the Symposium Section of This Issue
The authors bring a variety of theoretical and methodological tools to the research questions posed.
One set of articles in this issue focuses on the issue of the extent that various aspects of housing
assistance contribute to the goal of deconcentrating poverty. One article addresses the extent that
the age mix of children in households that use vouchers affects the longevity of households in this
program. Another article explores the extent that census tract indicators of neighborhood quality
predict perceptions of neighborhood quality reported by those who use housing vouchers.
Program Dynamics
Carissa G. Climaco, Christopher N. Rodger, Judith D. Feins, and Ken Lam investigated
“portability” of vouchers in the HCVP. This policy allows a family to use a voucher issued in one
jurisdiction to move to another jurisdiction where the program is administered by a different local
public housing agency. The authors examined portability moves in the HCVP between 1998 and
2005 and analyzed household and neighborhood characteristics associated with portability moves.
They found that, of the 3.4 million households that received housing assistance in the voucher
program from 1998 to 2005, 8.9 percent made a portability move. The rate of portability movers
was highest among African-American households (10.3 percent), compared with White house-
holds (8.1 percent) and Hispanic households (8.6 percent). The authors also found that, compared
with households in the HCVP overall, portability movers are more likely to be households with
young children and are more likely to have a younger head of household than households that
are otherwise similar to them. The authors examined the association between length of stay
and portability moves and the timing of portability moves. Most are likely to occur between the
fourth and fifth years of HCVP participation. The article examines public housing jurisdictions by
program costs. Three-fifths of portability moves were made to lower cost jurisdictions compared
with the originating jurisdiction. The data also show reductions in census tract poverty rates for
households that completed portability moves.
Melody L. Boyd presents the results of indepth interviews with voucher holders who participated
in the Gautreaux Two Housing Mobility Program study. Her respondents were women who used
vouchers to move out of segregated, highly concentrated poverty neighborhoods into more affluent
areas. This qualitative analysis compares residents who made secondary moves with residents who
stayed at their Gautreaux placement addresses, focusing on the role of social networks in making
housing decisions. Ms. Boyd reports that secondary movers were motivated by a number of social
network factors, including feelings of social isolation in the placement neighborhood, distance

?Cityscape
Guest Editor’s Introduction
from kin, and transportation difficulties. Conversely, she found that strong social networks were
also crucial reasons why some families remained in their Gautreaux neighborhoods or moved on to
other similarly advantaged neighborhoods.
Xinhao Wang, David Varady, and Yimei Wang used hot spot analysis to measure changes in
spatial clustering of HCVP recipients. The authors conducted hot spot analyses of HCVP recipientsof HCVP recipients.. The authors conducted hot spot analyses of HCVP recipients
in eight metropolitan areas (New York, Baltimore, Chicago, Cincinnati, Miami, Houston, Los
Angeles, and Phoenix), using a tenant-based data system from HUD’s Office of Public and Indian
Housing. The 2000 and 2005 hot spots were overlaid with 2000 Census block group data. The hot. The 2000 and 2005 hot spots e overlaid with 2000 Census block group data. The hot
spot results show that the tendency of HCVP households to cluster varies by metropolitan area.
Moreover, no evidence indicates that HCVP clustering is declining. Although HCVP participants
are becoming less concentrated in hot spots in Chicago and Phoenix, the opposite is true in otherother
metropolitan areas, especially New York, Cincinnati, and Baltimore. The authors conclude thatNew York,k, Cincinnati, and Baltimore. The authors conclude that
this type of HCVP concentration is likely to continue as long as affordable rental housing is largely
confined to central cities and older inner suburbs.
Meryl Finkel and Ken Lam analyzed one outcome of the 1998 Quality Housing Work Respon-
sibility Act (QHWRA), a requirement that PHAs offer the option of a flat rent (as opposed to an
income-based rent) to residents of public housing. Flat rents are based on market rents and, there-
fore, the tenant rent does not vary with income. As of the end of 2005, about 105,000 families
were identified on HUD’s data system as paying flat rents. The authors found that, although nearly
all PHAs have at least some flat-rent units, the proportion of flat-rent units in each PHA is generally
small. Households paying flat rent have much higher incomes compared with other public housing
residents. Similarly, a much higher percentage of households paying flat rent reported that the
majority of their income was from wages, compared with other public housing households. Thus,
flat rents appear to be succeeding in allowing residents in these units to increase income through
employment and to remain in their units even as their income increases. Rents in units where resi-
dents are paying flat rents are substantially higher than in other public housing units. At the same
time, households paying flat rents are virtually always paying less than 30 percent of their income
for rent. Properties with flat-rent units have a higher degree of income mixing than other proper-
ties, which is to be expected, because households in units with flat rents have higher incomes than
most other public housing residents have.
Duration of Receipt of Housing Assistance
Alvaro Cortes, Ken Lam, and David Fein used HUD administrative data to explore household
characteristics that are associated with a household’s length of stay in the HCVP. The first is the
degree to which the presence of children of varying ages is related to a household’s length of stay
(longevity) in the program. The second is the degree to which older children, as a potential source
of childcare, may mitigate a longer duration of assistance for households containing infants and
toddlers. The third is the degree to which disability status of the household head or children
affect program longevity. In 1998, PHAs were given considerable discretion to select tenants
on the basis of local PHAs’ preferences rather than on old federal preferences for households
experiencing housing-related hardships. Many PHAs adopted other categorical preferences. As
a result, the demographic profile and household composition of tenants have changed. These

?
Haley
Policy Issues in Public and Assisted Housing
changes have important implications for the HCVP, because past research has found that household
characteristics, as well as location factors, significantly affect a household’s length of stay in the
program. The authors found that the median length of stay among nonelderly households with a
child or children is about 2.8 years, which is nearly two-thirds of the median (4.4 years) associated
with nonelderly households with at least one disabled child. The presence of an infant or toddler
increases a household’s length of stay in the voucher program, after data are controlled for an array
of household and location characteristics, but the presence of other children in the same household
attenuates this effect. Conversely, they found that the presence of teenagers, especially boys, magni-
fies the lengthening of spells associated with infants and toddlers.
Housing Assistance and Neighborhood Quality
Larry Buron and Satyendra Patrabansh present the results of a study examining voucher holders’
ratings of their neighborhoods in HUD’s HCVP Customer Satisfaction Survey. The authors found
that voucher holders’ neighborhood ratings were consistent with their answers to more specific
questions about the attributes of their neighborhoods (that is, they were internally consistent), but
that the ratings were only weakly correlated with census-based measures of neighborhood quality.
Internal consistency was demonstrated by the strong correlation between neighborhood ratings
and voucher holders’ perception of crime problems and physical disorder in their neighborhoods.
The comparison with census-based measures of the neighborhoods showed that, although a very
systematic correlation exists in the expected direction between the neighborhood rating and census
measures of the neighborhoods, the correlation was not very strong for any of the census variables
tested. The variable with the highest correlation was the percentage of female-headed households
with children, but the variable explained less than 5 percent of the variation in tenants’ neighbor-
hood ratings. Furthermore, combining multiple census variables into a single neighborhood quality
indicator increased the variables’ explanatory power by only a small amount.
Conclusion
Changes in the legislation regulating the federal housing assistance programs occur regularly and
not always in an atmosphere of clarity and understanding. The Office of Policy Development and
Research is pleased to present these articles to the public, in the belief that their information can
contribute to informed debate about programs that serve 4.3 million households.

?Cityscape: A Journal of Policy Development and Research • Volume 10, Number 1 • 2008
U.S. Department of Housing and Urban Development • Office of Policy Development and Research
Cityscape
Portability Moves in the
Housing Choice Voucher
Program, 1998–2005
Carissa G. Climaco
Christopher N. Rodger
Judith D. Feins
Ken Lam
Abt Associates Inc.
Abstract
Portability in the Housing Choice Voucher Program (HCVP) enables a household to
use a voucher issued in one jurisdiction when moving to another jurisdiction where
the program is administered by a different local public housing agency. This article
reports the results from a study examining portability moves in the HCVP from 1998
to 2005. Using a specially constructed longitudinal data set developed from U.S.
Housing and Urban Development administrative records, the study identifies records
that represent portability moves and then analyzes household and neighborhood
characteristics associated with portability moves. Of the 3.4 million households that
received housing assistance in the voucher program from 1998 to 2005, 8.9 percent
made a portability move. The rate of portability movers was highest among African-
American households (10.3 percent) compared with White households (8.1 percent)
and Hispanic households (8.6 percent). Compared with households in the HCVP
overall, portability movers are more likely to comprise households with young children
and more likely to have a younger head of household. Length of stay in the HCVP
is correlated with portability moves, and portability moves are most likely to occur
between the fourth and fifth years of HCVP participation. When examining public
housing jurisdictions by program costs, three-fifths of portability moves were made
to lower cost jurisdictions compared with the originating jurisdiction. The data also
show reductions in census tract poverty rates and other neighborhood indicators for
households that completed portability moves.

?
Climaco, Rodger, Feins, and Lam
Policy Issues in Public and Assisted Housing
Introduction
The U.S. Department of Housing and Urban Development’s (HUD’s) Housing Choice Voucher
Program (HCVP) provides program participants with considerable flexibility in choosing their own
housing. Participants may decide to use their voucher to continue renting in their current housing
unit (lease in place), move to another housing unit in the immediate vicinity, or move to a jurisdiction
where the program is administered by a different local public housing agency (PHA). Moves to areas
outside the jurisdiction that issued the voucher are permitted under the HCVP’s portability provisions.
Research conducted in 2003 on mobility in the HCVP (Feins and Patterson, 2005) determined
the extent to which families with children use the voucher to move to another unit and described
selected characteristics of the neighborhoods those households chose. This article focuses on the
portability of HCVP vouchers and whether portability is an important contributor to mobility
in the HCVP. This research extends the methods developed in the mobility study (Feins and
Patterson, 2005). Using longitudinal data for the years 1998 to 2005, this study on portability
determines the extent to which families use their vouchers to make portability moves.
In this article, we describe the analysis steps undertaken to determine the number of portability
moves that occurred from 1998 to 2005. Through a series of tables, we assess the extent of
portability moves in the HCVP. The descriptive analysis tables summarize the characteristics of
the households that have undertaken portability and the characteristics of the neighborhoods and
jurisdictions where portability households lived before and after their moves. The origination or
preportability location is where the household lived before making a portability move, and the
destination location is where the household lived as a result of completing a portability move. Also,
using a logit model, we examine the characteristics of households that undertook a portability
move and explore whether households with certain demographic characteristics are statistically
more likely to exercise portability in the HCVP.
Data and Sources
The primary data source for this research was longitudinal HCVP data, collected through HUD
Form 50058, from HUD’s Multifamily Tenant Characteristics System/Public and Indian Housing
Information Center (MTCS/PIC) database.
1
Our approach to the data was based on our experience
conducting the HCVP geographical mobility study described earlier, and we have used the same
approaches to adjust for temporal discontinuities in the data. We have paid particular attention to
portability moves that originate in one of the 25 most racially segregated metropolitan statistical
areas (MSAs). We have relied on the racial housing pattern analysis that the U.S. Census Bureau
produced to identify the 25 most racially segregated MSAs nationally.
1
The earliest HUD Form 50058 data records were collected through the Multifamily Tenant Characteristics System, and
the later HUD Form 50058 data records were collected through the Public and Indian Housing Information Center, Form
HUD-50058 submodule. The later data are referred to as “PIC data.”

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Portability Moves in the Housing Choice Voucher Program, 1998–2005
Cityscape
For this research, we have used the following four data sources.
1. Multifamily Tenant Characteristics System/Public and Indian Housing Information Center database.
The MTCS/PIC database from 1997 to 2005
2
contains nearly 14 million records—3.3 million
certificate records and 10.5 million voucher records. The total number of records by year
increases steadily, from about 1.1 million in 1997 to 1.9 million in 2005. Each file, consisting of
an 18-month snapshot of data taken in December each year, contains a record for each house-
hold reported to HUD as receiving tenant-based housing assistance during the 1997-to-2005
period. Households that received assistance continually during this period have one record
for each year of program participation, assuming that the relevant housing agency reported
the household’s data each year. This data set includes selected household characteristics and
location data, based on the geocoding of address data. Actual address data, however, were
not included in the MTCS/PIC data files. In addition to the MTCS/PIC data files it originally
provided for this research, HUD also provided geocoding updates and data based on HUD Form
50058 that specifically deal with portability.
3
2. Census 2000 Summary File 3. Census data were used in the analyses to describe the neighborhood
characteristics of HCVP households at origination and destination. Neighborhood descriptors
include the percentage of persons who are (1) in poverty, (2) minorities, and (3) African
Americans and the percentage of households that are (1) female-headed with children and (2)
renters. We compiled these data using the Census 2000 Summary File 3 state files on DVD and
merging the census tract data into the MTCS/PIC records using the 2000 Census census tract
identifier. In this analysis, we also used a census tract-level data file indicating metropolitan
status (central city, suburb, or nonmetropolitan).
4

We used census data to create a dissimilarity index to identify the metropolitan areas most
segregated by income. This index examined levels of segregation between the estimated num-
ber of families with an annual income below 30 percent of metropolitan area median income
and families with an annual income of more than $200,000. Based on metropolitan statistical
area/primary metropolitan statistical area (MSA/PMSA) definitions of June 30, 1999, we chose
the 25 metropolitan areas with the highest income dissimilarity index score and a population
of at least 250,000 to compose a list of the metropolitan areas with the highest levels of income
segregation. The list of these metropolitan areas appears in the appendix.
2
The Multifamily Tenant Characteristics System/Public and Indian Housing Information Center database contains records
of data snapshots taken from 1997 to 2005. Analysis focused on portability moves made from 1998 to 2005. We used the
data from 1997 to help determine if a 1998 action was a portability move.
3
The questions for the supplemental data from HUD Form 50058 read as follows:
Question 12d: “Did family move into your PHA [public housing agency] jurisdiction under portability? (Y or N)”
Question 12e: “Cost billed per month (put 0 if absorbed)”
Question 12f: “PHA code billed”
4
We have used a file created by HUD for the HUD National Low Income Housing Tax Credit database, which uses census
tract populations to classify each census tract as being located in a metropolitan area central city, a metropolitan area but not
in the central city (suburb), or a nonmetropolitan area. Data for Puerto Rico were not available in this data file. Metropolitan
areas are defined according to the metropolitan statistical area/primary metropolitan statistical area definitions published
June 30, 1999.

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Climaco, Rodger, Feins, and Lam
Policy Issues in Public and Assisted Housing
3. Housing Patterns Data, Decennial Census. To complete the analysis that examines areas with high
levels of segregation, we relied on the racial housing pattern analyses produced by the U.S. Census
Bureau (Iceland, Weinberg, and Steinmetz, 2002; U.S. Census Bureau, 2005). These analyses
produced segregation indices for metropolitan areas using census tracts or block groups as the
unit of analysis. For the voucher portability analysis, we chose the most commonly used index,
the dissimilarity index. As with the income segregation index, we chose the 25 metropolitan
areas with the highest dissimilarity index score and a population of at least 250,000 to compose
a list of metropolitan areas with the highest levels of racial segregation. The list of these
metropolitan areas appears in the appendix.
4. Data on PHA Costs Per Unit. To support analysis of whether portability moves are made to
lower or higher cost jurisdictions, HUD provided data on PHA costs per unit. These data, from
information in HUD’s Voucher Management System, are monthly averages based on data from
April 2005 to March 2006. Monthly average costs were derived by totaling subsidy costs and
administrative fees, then dividing them by the total number of occupied units (as measured by
the unit months of voucher utilization).
5
Analysis Approach
Analysis included a series of data processing steps to create household-based analytic records,
determine if and when a portability move may have occurred, and assess which of the possible
portability moves could be confirmed to represent an HCVP portability move.
Accounting for Time Discontinuities
To analyze household data from year to year for this research, we needed continuity in household
records. Continuity was important because we wanted to determine portability moves during ongoing
participation in the HCVP, as well as portability at entry. We also wanted to more accurately determine
the size of the HCVP, as defined by the number of households,
6
during each of the analysis years.
To create a set of analysis records, we selected households and household records with sufficient
year-to-year data. We believe that multiyear gaps in program data do not provide sufficiently reli-
able information; therefore, when a household was missing in two or more consecutive MTCS/PIC
reference year files, the household was dropped from the analysis. For households with data gaps
of only one reference year file, when the previous and succeeding locations appeared to be the
same based on geocoding data, we assumed the household was at the same address during the
missing year. When the previous and succeeding census tract identifiers were different, we inferred
that a move occurred between the effective dates of the previous and succeeding records, and a
5
Public housing agencies (PHAs) missing from the PHA Costs Per Unit data file included the 25 PHAs in the Moving to
Work Demonstration and other, mostly smaller, housing agencies that have left the Housing Choice Voucher Program or are
nonreporting.
6
We considered using only active households as the basis for determining the size of the Housing Choice Voucher Program
(HCVP). Households appear only once in each reference year of data. Although a household’s transaction may represent an
end of participation, that household was still active in the program at some point in the year. For that reason, we included
households coded as leaving the HCVP as an annual participant in the HCVP.

?
Portability Moves in the Housing Choice Voucher Program, 1998–2005
Cityscape
move date was set by interpolation. These moves did not necessarily represent portability moves,
although they did indicate a change in household residence location.
Identifying Possible Portability Moves
A key step in these analyses was identifying the possible portability moves. The MTCS/PIC data
contain no single item that consistently labeled a record as a portability move, so we needed to
evaluate related items and develop a decision rule. The process included a series of tests for a pos-
sible portability move. Each test created a flag for the record, tracking the basis for identifying the
record as a possible portability move. Following is a description of the series of data steps we took
to create flags for possible portability moves.
1. Signify when the type of action code is a portability move-in. If the type of action code
on HUD Form 50058 indicated a portability move-in, the record was flagged as a possible
portability move. The date of the portability move was based on the record’s effective date.
2. Signify when the type of action code is a portability move-out. If the type of action code
indicated a portability move-out, the household’s succeeding record was flagged as a possible
portability move. The date of the portability move was based on the effective date of the move-
out record.
3. Identify changes in the housing agency code. If a household record’s housing agency code
changed from the same household’s previous record, the latter record was flagged as a possible
portability move. The date of the portability move was based on the effective date of the latter
record.
4. Identify changes in the household’s metropolitan area location. Because of varying sizes
of housing agency jurisdictions, changes in census place alone cannot be considered a reliable
indicator for identifying a possible portability move;
7
however, changes in metropolitan area or
state should be reliable indicators of a portability move. If a household record’s metropolitan
area changed from the same household’s previous record, the latter record was flagged as a
possible portability move. The date of the portability move was based on the latter record’s
effective date. This flag could be created only for successively geocoded records.
5. Identify changes in the household’s state location. Similarly, if a household record’s state
changed from the same household’s previous record, the latter record was flagged as a possible
portability move. The date of the portability move was based on the latter record’s effective date.
This flag could be created only for successively geocoded records.
Of special consideration were the instances in which a household exercised portability on
admission to the HCVP. According to the HUD Form 50058 instruction booklet, any household
that was a new admission should be coded as a new admission, even though the household
possibly could be moving into the PHA program through portability. The data steps to identify
and flag possible portability moves described earlier may also identify households whose initial
unit in the HCVP was a portability move. Other methods used in the study to identify possible
portability moves specifically at admission to the HCVP are described next.
7
For example, a change of place does not imply portability in countywide housing agencies.

?0
Climaco, Rodger, Feins, and Lam
Policy Issues in Public and Assisted Housing
6. Check the new admission records for changes in location based on previous location
ZIP Code. In addition to including the geocoding data representing the household’s current
location, each record included a ZIP Code that we believe indicated the ZIP Code of the
preprogram location. This field was available mostly for the new admission records. Using the
Census 2000 files of 5-digit ZIP Code tabulation areas, we determined the state location of the
five-digit ZIP Codes. For new admission records that indicated a difference in state based on the
preprogram ZIP Code field and the current record’s state, the record was flagged as a possible
portability move on admission to the HCVP. The date of portability was based on the effective
date. This flag could be created only for geocoded records with a state location identified.
7. Check the new admission record using HUD Form 50058 question 12d. Another way to
determine portability moves upon admission involved using other data from the HUD Form
50058. According to the HUD Form 50058 instruction booklet, any household that was a new
admission should be coded as a new admission, even though the household possibly could be
moving into the PHA program through portability. Question 12d asks whether the family is
or was a portability move-in. For new admission records that indicated “yes” to question 12d,
the record was flagged as a possible portability move upon admission to the HCVP. The date of
portability was based on the effective date.
Assessing and Confirming Possible Portability Moves
We used those seven tests and criteria to identify possible portability moves. After we created those
data flags to indicate the possible moves, we reviewed the data to assess which flag or combination
of flags would most reliably indicate a portability move. Our goal was to be able to identify porta-
bility moves and count them with considerable certainty.
We supposed that if a portability move record had more than one portability move flag, sufficient
reason existed to believe the record was a portability move, but portability move records determined
by only one flag merited further review. We had theorized that, for all the portability moves by
households continuing in the HCVP, portability move records would be flagged because of a
change in housing agency code. In fact, comparing all the portability flags, by far, the largest share
of records flagged as possible portability moves included a change in housing agency code. We had
also theorized that with a portability move, we would expect to see a change in location and—in
most cases—a change in census tract location. After closer review of the intersection of possible
portability moves determined by one portability move criterion and whether or not there was a
change in census tract location, we noticed that if a possible portability move was flagged only be-
cause of the change in housing agency code, no change appeared in census tract location in about
50 percent of the cases.
8
8
We have gained some understanding of these results with anecdotal support. We have heard from colleagues familiar with
the Housing Choice Voucher Program that, in some statewide programs, agreements have been made with local housing
agencies such that the administration of certain household vouchers is transferred to the local agency. A household would
not move, but the administering housing agency for it would change—a relatively rare occurrence. For such cases, the year-
to-year data would show that the household had not moved; yet, the reporting housing agency for the household would
have changed. In this analysis, in that scenario, we would have flagged the housing agency change record as a possible
portability move. It is not clear how often these cases can explain our results, but the cases in our analysis do seem to be
isolated to certain states, and the change in housing agency codes is for housing agencies in the same state.

??
Portability Moves in the Housing Choice Voucher Program, 1998–2005
Cityscape
The set of records in which the housing agency code changed but the residence location did not
change was the only group with possible portability moves that we did not count as portabil-
ity moves. If three conditions were met—(1) a record was flagged as a possible portability move,
(2) the only basis was a change in housing agency code, and (3) there was no change in census
tract location—it was likely not a real portability move.
Following is a summary of the decision rules we applied to the possible portability moves before
we accepted them as valid portability moves for analysis:
• If the possible portability move was identified by more than one criterion, it was accepted as a
portability move.
• If the possible portability move was identified by only one criterion, then—
• If the single criterion was not the change in housing agency code, it was accepted as a
portability move.
• If the single criterion was the change in housing agency code, then—
• If the census tract location changed from the previous location, it was accepted as a porta-
bility move.
• If the census tract location did not change from the previous location, it was excluded
from the portability move analysis.
Results of Determining HCVP Portability Moves
In exhibit 1, we show the year-by-year results of the decision rules to determine portability moves
in the HCVP from 1998 to 2005. Of all 3.4 million households ever in the federal housing voucher
program from 1998 to 2005, we estimate that 8.9 percent used their voucher to exercise a por-
tability move to another jurisdiction, counting both initial moves (at admission) and later moves
while continuing to receive assistance in the program (after admission). By year, the percentage
of households with a portability move appears to be decreasing, from 5.1 percent in 1998 to 1.6
percent in 2005.
9
New admission portability moves occurred for about only 1 percent of all HCVP
households during the study period.
Of the households with a portability move from 1998 to 2005, 71.2 percent showed a change
in housing agency code. Nearly 40 percent showed a change in metropolitan area code. Records
showing a change in state location accounted for 34 percent of those portability move house-
holds, and 27.4 percent of portability move households were coded as a portability move-in by
their MTCS/PIC record transaction code. Of all portability move households, we estimate that
9
We have seen in reviewing the Multifamily Tenant Characteristics System/Public and Indian Housing Information Center
data that slightly more than 1 percent of the households coded by transaction type exercised portability, both from 1997
to 2005 and in 2005. Because we use not only the transaction codes but also other household record data—including
geocodes and location information—to determine a portability move, we expected to find a larger portion of households
with portability moves. In fact, the rate has been two to three times greater in most years of the study period.

??
Climaco, Rodger, Feins, and Lam
Policy Issues in Public and Assisted Housing
Exhibit 1
1998 1999 2000 2001 2002 2003 2004 2005
1998–
2005
Percent of HCVP Households With Portability Moves During the Year, 1998–2005
(thousands)
Number of HCVP households 1,078 1,310 1,443 1,344 1,627 1,848 1,831 1,718 3,390
All portability moves (percent)
Percent with portability moves 5.1 3.4 2.9 2.8 3.4 2.6 2.4 1.6 8.9
Among all portability moves
Percent portability move-in code 11.7 16.5 22.6 25.8 29.8 36.4 27.5 31.0 27.4
Percent portability move-out code 6.4 6.0 5.9 9.5 12.7 13.0 11.0 NA 9.9
Percent change in housing
agency code
44.6 61.9 68.9 75.0 77.4 78.3 83.9 83.3 71.2
Percent change in metropolitan
area code
20.9 31.0 36.9 37.7 39.0 44.1 50.5 52.1 38.9
Percent change in state code 50.8 40.7 30.8 24.7 23.0 25.4 29.5 30.0 34.0
Percent current state location
differs from preprogram ZIP
Code’s state location
0.0 0.1 0.1 0.1 0.0 0.0 0.1 0.1 0.1
Percent new admission record
with Form 50058 portability flag
9.4 4.8 7.5 8.8 11.3 10.1 9.5 12.5 10.6
Portability moves at admission
Percent with portability move at
admission
0.5 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.9
Percent of portability moves that
were at admission
9.4 4.9 7.6 8.9 11.3 10.1 9.5 12.5 10.6
Portability moves after admission
Percent with portability moves
after admission
4.6 3.3 2.6 2.5 3.0 2.3 2.2 1.4 8.1
Percent of portability moves that
were after admission
90.6 95.1 92.4 91.1 88.8 90.0 90.5 87.5 91.4
HCVP = Housing Choice Voucher Program. NA = not available.
Notes: The sum of portability moves at admission and portability moves after admission in the HCVP may not equal the
number of total portability moves because households may have completed more than one portability move in a calendar
year. Data for the All portability moves column count households only once by year. Data for the 1998–2005 column are
based on a nonduplicated count of analysis households during the entire time period.
Sources: Multifamily Tenant Characteristics System/Public and Indian Housing Information Center data, 1997–2005; Census
2000 Summary File 3
10.6 percent moved as they entered the HCVP. No trend appeared in the proportion of portability
moves occurring at admission from year to year. The percentage of portability moves that were new
admissions ranged from 4.9 percent in 1999 to 12.5 percent in 2005.

??
Portability Moves in the Housing Choice Voucher Program, 1998–2005
Cityscape
Most portability moves appeared to occur not at admission to the HCVP, but after admission, while
a household was already leased up.
10
Portability move households that exercised portability after
admission to the voucher program accounted for 91.4 percent of portability move households from
1998 to 2005. The percentage of households exercising portability after admission varied from year
to year, but it was lowest in 2005 when 87.5 percent of portability move households were house-
holds continuing participation in the HCVP.
In recent years, HUD has used certain HUD Form 50058 data to assess the degree to which house-
holds exercise portability in the HCVP (HUD, 2006).
11
According to unpublished numbers provided
by HUD from a standard HUD monthly report
12
from February 2006, approximately 111,000
households—representing 6 percent of families that were currently receiving rental assistance—
reported ever using portability. These statistics were based on PIC data, counting households who
used portability based on data from HUD Form 50058 indicating whether a household had moved
into a housing agency jurisdiction under portability. The processes we have used to determine
portability moves also used MTCS/PIC data, but they included transaction data and geocoding
information to examine portability moves through an 8-year period. As a result of examining and
using more data fields, we find somewhat higher rates of portability movement than the standard
HUD monthly report suggests.
These two statistics on portability—the first based on this study’s estimates of instances of portabil-
ity and the second based on the HUD Form 50058 indicator of portability showing households
in the HCVP that ever ported—are difficult to compare. They are based on different time periods
(1998 to 2005 compared with 2005 to 2006) and cover a different set of households (all HCVP
households compared with initial and continuing participation households). Still, these statistics
are similar in scale. We are confident in our estimates of portability moves, and we have used these
records of accepted portability moves for the remainder of the analysis.
HCVP Portability Behavior and Household and Neighborhood
Characteristics
In this section, we summarize characteristics of households that have exercised portability in the HCVP
from 1998 to 2005. We also discuss neighborhood characteristics associated with portability moves.
10
A combination of factors could account for the far larger portion of portability moves by continuing households. Of
particular interest is whether the 1-year residency rule affects a household’s decision to not exercise portability at admission
but to use it after initial lease-up in the Housing Choice Voucher Program. With the data available for analysis, we have
been unable to determine which households have delayed a portability move because of a 1-year residency rule. Housing
agencies typically require that a household that is issued a housing voucher must reside in the housing agency’s jurisdiction
for at least the first year before porting to another jurisdiction. Households already residing in a housing agency’s
jurisdiction when a voucher is issued generally are allowed to exercise portability immediately.
11
According to this guidebook, HUD’s statistics on the number of households that have completed portability are based on
HUD Form 50058 question 12d.
12
This Public and Indian Housing Information Center (PIC) Mobility and Portability report, generated within HUD’s PIC
System, covered a 16-month period, which was the previous 12 months and the succeeding 4 months. In this case, the
February 28, 2006, report covers March 1, 2005, to June 30, 2006. Only records with transaction types of new admission,
annual reexamination, interim reexamination, portability move-in, and other change of unit are included in this PIC System
report.


??
Climaco, Rodger, Feins, and Lam
Policy Issues in Public and Assisted Housing
Exhibit 2 compares rates of portability moves by the race and by the ethnicity of the householder.
Each year and for all years, we calculated the percent of White householders, African-American
householders, and Hispanic householders in the HCVP that completed a portability move in the
study period. White householders and African-American householders could be Hispanic, and
Hispanic householders could be of any race. Overall, from 1998 to 2005, the proportion of house-
holds with a portability move was highest for African Americans. About 10 percent of households
headed by an African-American householder completed a portability move, compared with 8.1
percent of White households and 8.6 percent of Hispanic households. Year by year, the compara-
tive proportions of households by race or ethnicity were similar. The percent of portability moves
among African-American households was highest, compared with White households and Hispanic
households.
Exhibit 3 shows further data on portability moves by minority voucher holders. Minority voucher
holders are defined as households in which the head of the household is African American, American
Indian, Alaska Native, Asian, Native Hawaiian, Other Pacific Islander, or Hispanic. In the overall
HCVP, minority households comprised 56.8 percent of all voucher households from 1998 to 2005.
Higher proportions of minority households comprised portability move households during the
same period. Overall, 63.7 percent of portability households were minority households.
To assess whether minority voucher holders moved to neighborhoods with lower rates of minorities,
we examined the extent to which the census tract minority rate changed for minority households
with a portability move. Overall, with portability moves, the average minority rate decreased from
58.4 percent in the preportability location to 55.8 percent in the portability move location. In fact,
in every year from 1998 to 2005, with the exception of 1999, for minority voucher holders the av-
erage minority percent decreased from the preportability location to the portability move location.
Exhibit 4 shows our analysis of portability moves by extremely low-income voucher holders,
defined as households with an adjusted annual income of less than 30 percent of area median
income. The data in this exhibit, which examine the use of portability by extremely low-income
1998 1999 2000 2001 2002 2003 2004 2005
1998–
2005
Exhibit 2
Rates of Portability Moves by Race and Ethnicity, 1998–2005
Percent of White householders
with portability moves
4.9 3.3 2.7 2.6 3.0 2.3 2.2 1.5 8.1
Percent of African-American
householders with portability
moves
5.0 3.6 3.0 3.1 3.9 3.0 2.7 1.8 10.3
Percent of Hispanic householders
with portability moves (may be of
any race)
4.8 3.3 3.0 2.8 3.1 2.4 2.3 1.5 8.6
Notes: We excluded records with missing data on the race and ethnicity of the head of the household. Data in the
1998–2005 column are based on a nonduplicated count of analysis households during the entire time period. The number
of nonduplicated household analysis records included approximately 1.9 million White householders, 1.3 million African-
American householders, and 493,000 Hispanic householders.
Source: Multifamily Tenant Characteristics System/Public and Indian Housing Information Center data, 1997–2005

??
Portability Moves in the Housing Choice Voucher Program, 1998–2005
Cityscape
1998 1999 2000 2001 2002 2003 2004 2005
1998–
2005
Exhibit 3
Portability Moves by Minority Voucher Holders, 1998–2005, and Changes in
Neighborhood Minority Rate
Percent of portability moves by
minority voucher holders
60.0 61.7 63.0 63.4 64.7 66.4 66.3 65.6 63.7
Minorities as a percent of all HCVP
households
55.2 57.4 58.3 57.8 59.4 60.6 61.3 61.3 56.8
Average census tract minority rate
Percent preportability location 60.3 58.6 60.3 59.7 58.8 58.3 56.1 57.1 58.4
Percent portability move location 59.8 59.5 59.4 57.0 54.3 55.0 53.2 51.8 55.8
HCVP = Housing Choice Voucher Program.
Notes: This exhibit includes only the portability moves by households that were not at admission to the HCVP. We excluded
portability moves at admission to the HCVP because of a lack of information on the preportability location. We completed the
analysis by using records geocoded with 2000 Census census tracts; preportability location and portability move location
needed to be different census tracts. We excluded records with missing data on the race and ethnicity of the head of
household. Data for the 1998–2005 column are based on a nonduplicated count of analysis households during the entire time
period. The number of nonduplicated household analysis records was approximately 182,000. For the category, Minorities as
a percent of all HCVP households, the number of nonduplicated household analysis records was approximately 3.3 million.
Sources: Multifamily Tenant Characteristics System/Public and Indian Housing Information Center data, 1997–2005; Census
2000 Summary File 3
1998 1999 2000 2001 2002 2003 2004 2005
1998–
2005
Exhibit 4
Portability Moves by Extremely Low-Income Voucher Holders, 1998–2005, and
Changes in Neighborhood Poverty Rate
Percent of portability moves by
extremely low-income voucher
holders
89.0 88.5 88.3 87.5 87.9 91.2 90.3 90.4 88.8
Average census tract poverty rate
Percent preportability location 18.4 18.1 18.7 19.0 18.7 18.7 18.1 18.0 18.5
Percent portability move location 17.7 17.4 17.6 17.0 15.9 15.8 15.6 15.3 16.3
HCVP = Housing Choice Voucher Program.
Notes: This exhibit includes only the portability moves by households that were not at admission to the HCVP. We excluded
portability moves at admission to the HCVP because of a lack of information on the preportability location. We completed the
analysis by using records geocoded with 2000 Census census tracts; preportability locations and portability move locations
needed to be different census tracts. We excluded records with missing data on annual household income. Data for the
1998–2005 column are based on a nonduplicated count of analysis households during the entire time period. The number of
nonduplicated household analysis records was approximately 182,000.
Sources: Multifamily Tenant Characteristics System/Public and Indian Housing Information Center data, 1997–2005; Census
2000 Summary File 3
voucher holders, indicate, on average, a reduction in the percentage of low-income households be-
tween the new tract and the old one. Extremely low-income households made a very large portion of
the portability moves. Overall, 88.8 percent of portability households were extremely low-income
households. With portability moves, the average poverty rate decreased from 18.5 percent in the

??
Climaco, Rodger, Feins, and Lam
Policy Issues in Public and Assisted Housing
preportability location to 16.3 percent in the portability move location. In every year from 1998 to
2005, the average poverty rate decreased from the preportability location to the portability move
location.
Exhibits 5 through 8 present data on the characteristics of households that completed portability
moves compared with household characteristics for the HCVP overall. (HCVP household charac-
teristics include portability mover household characteristics.) Exhibit 5 shows the characteristics
of the heads of household, including age, race, and ethnicity. We included elderly households in
this analysis. On average, portability moves were completed by households with a younger head
of household than in the HCVP overall. From 1998 to 2005, on average, the head of household
among portability movers was 39.5 years old compared with the head of household in the overall
HCVP, which was 43 years old. Portability move householders were also more likely to be African
American and less likely to be White. Overall, 45.6 percent of portability households had an African-
American head of household, compared with 39.8 percent of HCVP households overall. During
the 8-year study period, 51.4 percent of portability move householders were White, compared
with 56.6 percent of HCVP householders overall. Portability householders were also more likely to
be minority (61.5 percent compared with 56.8 percent in the overall HCVP) and less likely to be
Hispanic (14.1 percent compared with 14.8 percent in the overall HCVP).
Exhibit 6 compares the characteristics of portability move households and HCVP households by
household type and the presence of children. Most HCVP households have children present. Each
year, and overall from 1998 to 2005, portability move households were more likely to be house-
holds with children, compared with all HCVP households. Of the portability moves during the
analysis period, 58.7 percent were by households with children, but not with an elderly head of
household or disabled members. In the overall HCVP, 51 percent of households had children but
not an elderly head of household or disabled members.
Exhibit 7 shows the length of time HCVP households have been in the program, defined as the
difference between the effective date of the household record in the MTCS/PIC file and the date the
household was admitted to the program. Households newer to the program appear somewhat more
likely to make portability moves because their median HCVP tenure is about 5.4 months shorter
when compared with all HCVP households. Based on our identification of portability movers, the median
tenure for mover households in the HCVP was 2.63 years at the time of completing a portability
move. From 1998 to 2005, the median tenure of all households in the HCVP was 3.08 years.
Exhibit 8 displays data on sources and levels of household annual income for portability movers
and all HCVP households. Portability households are less likely to have wage income and slightly
more likely to have welfare income. During the analysis period, 40.5 percent of HCVP households
had wage income compared with 33.3 percent of portability movers. Although 24.2 percent of
HCVP households had welfare income, 25.8 percent of portability mover households had welfare
income. An examination of average income by specified sources, other than welfare, showed that
average income was less for the portability households compared with all HCVP households.

??
Portability Moves in the Housing Choice Voucher Program, 1998–2005
Cityscape
Exhibit 5 Portability Households and All HCVP Households, 1998–2005, by Characteristics of Head of Household
Portability Households
All HCVP Households
1998
1999
2000
2001
2002
2003
2004
2005
1998–
2005
1998
1999
2000
2001
2002
2003
2004
2005
1998–
2005
Head of household

(age)
(age)
A
verage age of head
of household
40.2
39.9
39.1
39.0
38.5
38.4
38.8
39.8
39.5
43.4
43.6
43.5
43.1
43.4
43.3
43.8
44.4
43.0
Head of household
by race

(per
cent)
(per
cent)
White
56.6
53.6
52.6
50.8
48.1
47.6
47.9
49.4
51.4
57.4
55.7
55.0
55.1
54.0
53.2
52.8
53.3
56.6
African American
39.8
43.2
44.6
46.9
49.2
49.7
49.3
48.0
45.6
39.2
41.1
41.9
41.9
42.8
43.2
43.6
43.1
39.8
American Indian/
Alaska Native
1.1
1.0
1.1
0.9
1.0
0.8
1.0
0.8
1.0
0.9
0.8
0.9
0.9
0.9
0.9
0.9
0.9
1.1
Asian/Native
Hawaiian/Other
Pacific Islander
2.5
2.2
1.7
1.3
1.7
1.8
1.9
1.9
1.9
2.5
2.3
2.2
2.1
2.2
2.6
2.7
2.8
2.5
Hispanic householder
(per
cent)
(per
cent)
Per
cent Hispanic
13.5
14.1
15.4
14.4
13.6
14.1
14.6
14.7
14.1
13.9
14.6
14.7
14.2
14.8
15.2
15.5
16.0
14.8
Minority householder
(per
cent)
(per
cent)
Per
cent minority
55.6
59.3
61.5
62.4
64.3
65.2
65.5
64.2
61.5
55.2
57.4
58.3
57.8
59.4
60.6
61.3
61.3
56.8
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??
Climaco, Rodger, Feins, and Lam
Policy Issues in Public and Assisted Housing
Exhibit 6 Portability Households and All HCVP Households, 1998–2005, by Household T
ypes and Pr
esence of Childr
en
Portability Households
All HCVP Households
1998
1999
2000
2001
2002
2003
2004
2005
1998–
2005
1998
1999
2000
2001
2002
2003
2004
2005
1998–
2005
T
ype of household
(per
cent)
(per
cent)
Elderly
, no childr
en
10.3
9.4
8.4
7.8
7.1
7.2
7.7
8.7
8.8
15.5
15.6
15.4
14.7
14.9
14.7
15.1
15.9
14.7
Disabled, no childr
en
13.1
13.3
12.6
14.5
13.6
14.0
13.8
15.3
13.8
14.2
14.3
13.4
16.1
16.6
16.7
17.0
17.5
15.2
Other
, no childr
en
18.1
13.9
11.8
7.9
6.0
6.3
6.3
6.5
10.2
12.1
10.3
10.9
9.7
8.3
8.6
8.9
9.1
11.7
Elderly with childr
en
0.6
0.7
0.7
0.9
0.9
0.8
0.8
0.9
0.8
0.9
1.0
1.0
0.9
1.0
1.0
1.0
1.0
0.9
Disabled with childr
en
5.7
6.3
2.9
8.8
9.5
9.5
9.6
10.1
7.7
6.1
5.9
2.3
7.3
7.7
7.7
7.8
7.8
6.5
Other with childr
en
52.1
56.4
63.6
60.1
62.8
62.1
61.7
58.5
58.7
51.1
53.0
57.0
51.4
51.6
51.3
50.1
48.6
51.0
Childr
en pr
esent, by age
(per
cent)
(per
cent)
0–3 years
20.2
21.8
25.4
26.2
27.7
28.1
27.4
25.1
24.5
18.1
19.2
20.4
20.8
21.0
21.2
19.9
18.6
20.4
4–5 years
15.5
16.5
17.7
18.2
19.5
20.4
20.4
19.6
18.0
13.9
14.0
13.8
13.8
14.2
14.6
14.6
14.2
14.6
6–12 years
38.7
42.5
43.9
45.5
47.4
46.3
46.3
45.1
43.6
37.1
37.9
37.7
36.6
36.7
36.1
35.7
34.8
34.7
13–17 years
21.6
23.4
23.5
25.7
27.4
26.7
27.7
27.9
25.3
23.5
24.1
23.8
23.2
24.1
24.1
24.7
24.8
22.9
Any child under 18 years
58.5
63.4
67.2
69.8
73.2
72.5
72.2
69.5
67.2
58.1
59.8
60.3
59.6
60.3
60.0
59.0
57.4
58.4
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9
9
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??
Portability Moves in the Housing Choice Voucher Program, 1998–2005
Cityscape
Exhibit 7 Portability Households and All HCVP Households by Length of Time in the HCVP
, 1998–2005
Portability Households
All HCVP Households
1998
1999
2000
2001
2002
2003
2004
2005
1998–
2005
1998
1999
2000
2001
2002
2003
2004
2005
1998–
2005
T
ime in program
(years)
(years)
A
verage
2.48
2.62
3.15
3.79
3.83
3.92
4.48
5.04
3.70
4.13
4.44
4.58
4.51
4.66
4.73
5.14
5.53
4.71
Median
1.50
1.82
2.17
2.76
2.81
2.83
3.33
3.95
2.63
2.89
3.00
3.00
3.00
3.04
3.00
3.49
4.00
3.08
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9
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5

?0
Climaco, Rodger, Feins, and Lam
Policy Issues in Public and Assisted Housing
Source of income

(per
cent)
(per
cent)
W
ages
32.2
30.8
32.8
31.5
33.5
32.8
34.2
33.2
33.3
37.6
38.1
38.6
36.9
36.9
37.9
38.1
38.6
40.5
W
elfar
e
26.9
23.9
23.9
23.0
25.2
28.1
30.4
32.0
25.8
25.4
22.1
20.5
18.4
19.3
21.7
26.1
29.3
24.2
SSI
22.1
23.4
25.3
26.7
27.9
29.7
29.2
31.2
26.6
26.4
27.4
28.2
27.4
29.3
30.7
31.4
31.9
27.5
SS/pension
20.5
20.4
21.5
22.1
22.3
23.5
24.8
27.4
22.9
27.7
27.7
28.0
27.1
28.1
29.0
29.8
30.9
27.7
Other sour
ce of
income
18.7
17.6
19.1
21.4
26.1
29.5
31.5
31.9
24.4
20.8
18.9
19.4
20.1
22.7
25.2
27.1
28.6
25.7
Average annual income by source
(US$)
(US$)
W
ages
12,624
12,618
13,039
13,311
13,948
13,860
14,148
14,483
13,601
12,199
12,374
12,757
13,046
13,332
13,605
14,025
14,473
14,620
W
elfar
e
4,938
4,726
4,848
4,851
4,898
4,960
5,063
5,020
4,906
4,422
4,324
4,340
4,350
4,414
4,443
4,457
4,351
4,399
SSI
5,711
5,656
5,812
6,019
6,302
6,627
6,587
6,613
6,199
5,390
5,434
5,543
5,719
5,964
6,190
6,363
6,496
6,244
SS/pension
7,191
6,920
7,008
7,217
7,435
7,485
7,759
7,914
7,443
7,220
7,167
7,295
7,527
7,749
7,920
8,137
8,383
8,185
Other sour
ce of
income
3,536
3,889
3,985
4,253
4,640
4,743
4,606
4,599
4,375
3,316
3,711
3,847
4,027
4,353
4,458
4,386
4,409
4,325
Exhibit 8 Portability Households and All HCVP Households, 1998–2005, by Sour
ces and Levels of Household Income
Portability Households
All HCVP Households
1998
1999
2000
2001
2002
2003
2004
2005
1998–
2005
1998
1999
2000
2001
2002
2003
2004
2005
1998–
2005
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1
9
9
7
–
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5

??
Portability Moves in the Housing Choice Voucher Program, 1998–2005
Cityscape
Exhibit 9 shows data on how far participants typically move when exercising portability. Distance
calculations were completed using the latitude and longitude data provided in the MTCS/PIC data.
Because we found portability moves that appeared not to represent a change in residence location,
we decided to limit the distance calculations to moves of at least one-quarter mile. This same rule
was used in the HCVP mobility study (Feins and Patterson, 2005) to determine household moves.
We also restricted this analysis to portability moves within the 48 contiguous United States; thus,
we excluded portability moves to or from Alaska, Hawaii, or Puerto Rico in this analysis.
With these restrictions, we have estimated the average portability move to be more than 200 miles,
with a median distance of 25 miles. About one-third of these portability moves were of at least 100
miles; however, 19.4 percent of households overall made portability moves of less than 5 miles,
13.7 percent made moves of between 5 and 10 miles, and 16.9 percent made moves of between 10
and 25 miles.
Exhibit 10 compares portability moves by geographic jurisdiction, including the percentages that
were moves between metropolitan areas and between states. The HCVP primarily operates in
metropolitan areas. When examining the portability moves within metropolitan areas, we observed
that households that exercised portability were more likely to stay in the same metropolitan area.
More than 40 percent of portability moves were within the same metropolitan area, although a
marked decline was apparent in the proportion of moves, 46.0 to 36.6 percent, within the same
metropolitan area from 1998 to 2005. During the period covered by the study, 37.3 percent of
1998 1999 2000 2001 2002 2003 2004 2005
1998–
2005
Exhibit 9
Portability Moves by Distance, 1998–2005
Distance moved (miles)
Average 196 205 221 223 218 230 229 243 219
Median 19 21 24 28 27 28 28 31 25
Range (percent)
0.25 to 4.99 miles 25.8 23.7 20.8 18.7 17.7 16.7 17.7 15.8 19.4
5 to 9.99 miles 13.5 13.8 13.5 12.8 13.8 13.6 13.5 13.5 13.7
10 to 24.99 miles 15.2 15.6 16.1 17.0 17.5 17.9 17.0 17.5 16.9
25 to 49.99 miles 8.5 8.7 8.7 9.9 9.7 9.3 9.2 9.6 9.3
50 to 99.99 miles 8.2 7.8 8.4 8.7 8.2 8.5 8.6 8.5 8.4
100 miles or more 28.8 30.4 32.6 32.8 33.0 33.9 34.0 35.0 32.3
Notes: This exhibit includes only the portability moves by households that were not at admission to the Housing Choice
Voucher Program (HCVP). We excluded portability moves at admission to the HCVP because of a lack of information on the
preportability location. We completed the analysis using records geocoded with 2000 Census census tracts; preportability
location and portability move location needed to be different census tracts. Distance moved was based on calculations
using the latitude and longitude data for geocoded records. Only moves within the 48 contiguous United States and of at
least one-quarter mile are included in these calculations. Data for the 1998–2005 column are based on a nonduplicated
count of analysis households during the entire time period. The number of nonduplicated household analysis records was
approximately 144,000.
Sources: Multifamily Tenant Characteristics System/Public and Indian Housing Information Center data, 1997–2005; Census
2000 Summary File 3

??
Climaco, Rodger, Feins, and Lam
Policy Issues in Public and Assisted Housing
households making portability moves changed from one metropolitan area to another; the proportion
of households moving from one metropolitan area to another in that period increased.
Portability households were more likely to move from a nonmetropolitan area to a metropolitan area
than from a metropolitan area to a nonmetropolitan area. Each year and overall, about 10 percent of
portability movers moved from a nonmetropolitan area to a metropolitan area and about 6 percent
moved in the other direction. Portability moves within and among nonmetropolitan areas equaled
5.5 percent of portability households from 1998 to 2005.
Portability moves across state boundaries can involve a portability move of any kind. (That is, they
can originate in a metropolitan area and then move within the same metropolitan area, move to a
different metropolitan area, or move to a nonmetropolitan area; or, they can originate in a nonmet-
ropolitan area and then move to a metropolitan area or another nonmetropolitan area.) When ex-
amining the last row of exhibit 10, moves between states also appears to have increased from 1998
to 2005. In 1998, 25.6 percent of households completing a portability move went from one state
to another; by 2005, 32.2 percent of households completing a portability move went from one
state to another. For the full study period, slightly more than 29 percent of households completing
a portability move crossed state boundaries with their portability moves.
Exhibit 11 focuses on the portability moves within the same metropolitan area, across different
metropolitan areas, and involving nonmetropolitan areas by examining city locations compared
with suburb locations. (The summed proportions of portability moves by categories shown
in exhibit 11 may differ slightly from exhibit 10 because data on central city, suburb, and
1998 1999 2000 2001 2002 2003 2004 2005
1998–
2005
(percent)
Exhibit 10
Portability Moves by Geographic Jurisdiction, 1998–2005
Within same metropolitan area 46.0 44.8 41.8 39.8 40.7 40.3 39.0 36.6 41.3
Metropolitan area to different
metropolitan area
32.6 33.6 36.6 37.7 37.9 39.6 40.2 43.0 37.3
Metropolitan area to
nonmetropolitan area
6.3 6.1 6.4 6.1 5.5 5.2 5.6 6.8 5.8
Nonmetropolitan area to
metropolitan area
9.0 9.7 9.3 10.2 10.5 10.4 10.4 9.2 10.2
Nonmetropolitan area to
nonmetropolitan area
6.0 5.8 5.8 6.2 5.5 4.5 4.8 4.5 5.5
Change in state 25.6 27.1 28.8 30.2 30.0 30.8 30.9 32.2 29.2
Notes: This exhibit includes only the portability moves by households that were not at admission to the Housing Choice
Voucher Program (HCVP). We excluded portability moves at admission to the HCVP because of a lack of information on the
preportability location. We completed the analysis using records geocoded with 2000 Census census tracts; preportability
location and portability move location needed to be different census tracts. Data for the 1998–2005 column are based on a
nonduplicated count of analysis households during the entire time period. The number of nonduplicated household analysis
records was approximately 146,000.
Sources: Multifamily Tenant Characteristics System/Public and Indian Housing Information Center data, 1997–2005; Census
2000 Summary File 3

??
Portability Moves in the Housing Choice Voucher Program, 1998–2005
Cityscape
nonmetropolitan areas in Puerto Rico were not compiled and available for this analysis.) In this
analysis, a suburb location is any area in a metropolitan area that is not a central city. Across all
types of portability moves, voucher households were most likely to move from a suburb location to
a suburb location within the same metropolitan area. Nearly 13 percent of portability moves were
between suburb locations in the same metropolitan area. The next most likely type of move was
across different metropolitan areas, from one central city to another. Slightly more than 12 percent
of portability moves were between central cities in different metropolitan areas.
Some trends are evident in exhibit 11 across the categories of portability moves by geographic
jurisdiction and by central city and suburb locations. As noted earlier, from 1998 to 2005 the
proportion of portability moves occurring within the same metropolitan area decreased. Much
of the decrease appears in moves from a central city location to a central city location within the
1998 1999 2000 2001 2002 2003 2004 2005
1998–
2005
Exhibit 11
Portability Moves by Geographic Jurisdiction and Central City and Suburban Areas,
1998–2005
Within the same metropolitan area (percent)
Central city to central city 14.5 12.1 10.1 8.9 7.7 7.2 7.0 5.6 8.9
Central city to suburb 8.0 8.8 8.3 7.7 6.9 8.4 8.2 7.6 8.1
Suburb to central city 9.6 10.5 11.0 10.5 12.5 11.7 11.8 11.2 11.3
Suburb to suburb 13.6 13.4 12.7 12.6 13.6 13.1 12.1 12.1 12.9
Across different metropolitan areas (percent)
Central city to central city 11.5 11.9 12.7 12.7 12.7 13.1 12.2 12.9 12.2
Central city to suburb 6.2 6.8 6.8 6.7 6.2 6.8 7.6 8.2 6.8
Suburb to central city 8.7 7.9 9.6 10.2 10.9 11.2 11.1 11.9 10.2
Suburb to suburb 6.4 6.9 7.5 8.2 8.0 8.6 9.3 10.0 8.2
Involving nonmetropolitan locations (percent)
Central city to nonmetropolitan area 5.2 5.8 5.5 5.8 5.9 5.6 5.5 4.8 5.7
Suburb to nonmetropolitan area 3.9 4.0 4.0 4.4 4.6 4.8 4.9 4.4 4.5
Nonmetropolitan area to central city 3.5 3.3 3.2 3.3 2.9 2.6 2.8 3.1 2.9
Nonmetropolitan area to suburb 2.9 2.8 2.8 2.8 2.6 2.6 2.9 3.7 2.8
Nonmetropolitan area to
nonmetropolitan area
6.1 5.9 5.9 6.2 5.5 4.5 4.8 4.5 5.5
Notes: This exhibit includes only the portability moves by households that were not at admission to the Housing Choice
Voucher Program (HCVP). We excluded portability moves at admission to the HCVP because of a lack of information on the
preportability location. We completed the analysis using records geocoded with 2000 Census census tracts; preportability
location and portability move location needed to be different census tracts. Data on central city, suburb, and nonmetropolitan
areas in Puerto Rico were unavailable for this analysis. Within the same metropolitan area, a portability move from central city
to central city may be a portability move within the same city. A suburb is a location within a metropolitan area that is not part
of a central city. Data for the 1998–2005 column are based on a nonduplicated count of analysis households during the entire
time period. The number of nonduplicated household analysis records was approximately 146,000.
Sources: Multifamily Tenant Characteristics System/Public and Indian Housing Information Center data, 1997–2005; Census
2000 Summary File 3

??
Climaco, Rodger, Feins, and Lam
Policy Issues in Public and Assisted Housing
same metropolitan area. These moves decreased from 14.5 percent of all portability moves in 1998
to 5.6 percent of all portability moves in 2005. The only type of portability moves that increased
within the same metropolitan area involved suburb-to-central city moves, which increased from
9.6 percent in 1998 to 11.2 percent in 2005.
Portability moves across metropolitan areas generally increased from 1998 to 2005, including
increases for all types of central city and suburb portability moves. Portability moves that crossed
metropolitan area boundaries increased from 6.4 to 10.0 percent across suburb locations, from 8.7
to 11.9 percent from suburb areas to central cities, from 6.2 to 8.2 percent from central cities to
suburb areas, and from 11.5 to 12.9 percent from one central city to another central city.
Using Census 2000 data at the census tract level, we show in exhibit 12 the neighborhood char-
acteristics of the preportability location and portability move location. We reported similar data in
previous analysis tables for certain groups of portability movers, including minority households
and households with extremely low incomes. Those analyses, when compared with the preport-
ability location, showed that the average census tract poverty rate and average minority rate were
lower in the portability move location. Similar results appear in the tabulation for all portability
movers. Exhibit 12 compares the census tract poverty rate and minority rate of the preportability
location and portability move location. On average, from 1998 to 2005, the census tract poverty
rate decreased from 18.3 to 16.3 percent with a portability move. Average poverty rates in the
portability move locations decreased from 17.6 percent in 1998 to 15.3 percent in 2005. The average
minority rate decreased from 45.9 to 43.8 percent. No consistent increase or decrease in the average
census tract minority rate in the preportability locations is apparent across the analysis period, al-
though, for the most part, the minority rate decreased from year to year with the portability locations.
When we compared the preportability locations to the portability move locations during the study
period, we found the neighborhood average percentage of families headed by a single female de-
creased from 26.9 to 25.4 percent. When examining only the portability move locations from 1998
to 2005, we found that the census tract average percentage of families headed by a single female
decreased from 26.2 to 24.4 percent. The average percentage of renter-occupied units decreased
from 47.5 percent in the preportability location to 44.6 percent in the portability move location.
When we analyzed the portability move locations from 1998 to 2005, we found that the census
tract average percentage of renter-occupied units decreased from 46.4 to 43.0 percent.
Overall, it appears that HCVP households that have exercised a portability move have been able to
move to somewhat better neighborhoods with lower rates of poverty and lower concentrations of
minorities and families headed by single females. The new destination neighborhoods for HCVP
households also have had lower concentrations of renter-occupied units. In fact, portability house-
holds were moving to continuously better neighborhoods from 1998 to 2005, although, overall,
the magnitude of these changes was small.

??
Portability Moves in the Housing Choice Voucher Program, 1998–2005
Cityscape
Poverty rate A
verage
18.5
18.0
18.6
18.8
18.5
18.5
18.0
17.6
18.3
17.6
17.3
17.5
17.0
15.9
15.8
15.5
15.3
16.3
Minority rate A
verage
46.9
45.7
47.2
46.6
46.6
46.8
45.0
44.4
45.9
45.1
45.7
46.1
44.7
43.2
44.2
42.7
41.3
43.8
Percent of families headed by a single female A
verage
26.6
26.5
27.2
27.1
27.5
27.6
26.7
26.4
26.9
26.2
26.5
26.5
25.9
25.2
25.2
24.8
24.4
25.4
Percent of r
enter
-occupied units
A
verage
48.2
47.3
48.3
47.8
47.7
48.1
47.0
47.1
47.5
46.4
46.3
46.3
45.6
44.4
44.2
43.5
43.0
44.6
Exhibit 12 Neighborhood Characteristics: Pr
eportability Locations and Portability Move Locations, 1998–2005
Pr
eportability Location
Portability Move Location
1998
1999
2000
2001
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??
Climaco, Rodger, Feins, and Lam
Policy Issues in Public and Assisted Housing
HCVP Portability Moves and Area Characteristics
In this section, we discuss some additional analyses of portability moves made from 1998 to 2005.
We examine the portability moves originating in the 25 metropolitan areas most highly segregated
by race and the portability moves originating in the 25 metropolitan areas most segregated by
income. We also examine portability moves in relation to local area HCVP subsidy costs.
Portability Moves in Racially Segregated MSAs
Exhibit 13 shows information about portability moves by voucher holders who were initially
located in one of the 25 U.S. metropolitan areas most segregated by race. As described earlier,
to complete this analysis we relied on the racial housing pattern analyses produced by the U.S.
Census Bureau (Iceland, Weinberg, and Steinmetz, 2002). Those analyses produced segregation
indices for metropolitan areas, using census tracts or block groups as the unit of analysis. We chose
the most commonly used index, the dissimilarity index. Exhibit A-1 lists the 25 metropolitan areas
with the highest dissimilarity index scores based on racial segregation of African Americans and a
metropolitan area population of at least 250,000.
Portability moves by households originally living in these 25 metropolitan areas accounted for 14.6
percent of all portability moves from 1998 to 2005. To assess whether these households move to
more or less segregated areas, we examined the minority rate and percentage of African Americans
in the preportability locations compared with the portability move locations. For both measures,
1998 1999 2000 2001 2002 2003 2004 2005
1998–
2005
Exhibit 13
Portability Moves by Voucher Holders Initially Located in One of the 25 Metropolitan
Areas Most Segregated by Race, 1998–2005, and Changes in Neighborhood
Minority Rate and Percentage of African Americans
Percent of portability moves
by voucher holders in highly
segregated metropolitan areas
14.6 11.9 14.1 13.7 15.4 15.1 14.9 16.9 14.6
Average census tract minority rate
Percent preportability location 61.0 56.4 62.9 65.9 67.1 65.5 62.3 63.2 63.3
Percent portability move location 52.3 52.6 54.9 51.9 51.4 51.8 49.0 48.5 51.0
Average census tract percent
African American
Percent preportability location 38.6 37.0 40.8 39.7 42.0 43.0 40.0 38.8 40.0
Percent portability move location 31.9 33.3 34.8 32.3 31.6 32.4 29.3 28.4 31.3
Notes: This exhibit includes only the portability moves by households that were not at admission to the Housing Choice
Voucher Program (HCVP). We excluded portability moves at admission to the HCVP because of a lack of information on
the preportability move location. We completed the analysis using records geocoded with 2000 Census census tracts;
preportability location and portability move location needed to be different census tracts. Exhibit A-1 lists the 25 most
segregated metropolitan areas by race used in this analysis. Data for the 1998–2005 column are based on a nonduplicated
count of analysis households during the entire time period. The number of nonduplicated household analysis records was
approximately 182,000.
Sources: Multifamily Tenant Characteristics System/Public and Indian Housing Information Center data, 1997–2005; Census
2000 Summary File 3; Housing Patterns Data, 2000 Decennial Census

??
Portability Moves in the Housing Choice Voucher Program, 1998–2005
Cityscape
rates consistently decreased each year and overall. The overall average minority rate decreased from
63.3 to 51.0 percent. The overall average percentage of African Americans decreased from 40.0
to 31.3 percent. In general, it appears that the households porting from highly segregated areas
moved to less segregated neighborhoods, shown by the decrease in census tract minority rate.
Considering that this analysis focuses on areas that are highly segregated by race, it is not surprising
that the overall average minority rate for the preportability location and portability move location
in highly segregated areas is higher than the same measures for portability movers in all areas. The
average minority rate for all portability households decreased from 45.9 percent in the preportability
location to 43.8 percent in the portability move location (exhibit 12). The average census tract mi-
nority rate for portability move locations of households originating in one of the highly segregated
areas was still much higher than even the preportability locations of porting households overall.
In exhibit 14, we examine more specifically the minority households in these highly segregated
metropolitan areas. About 80 percent of portability move households in areas that are highly
segregated by race were headed by minority group members.
Distinct changes in the minority rate are apparent when examining preportability locations and
portability move locations. Exhibit 14 includes data on portability households that originated in
one of the 25 metropolitan areas highly segregated by race, but the exhibit does not control for the
portability move location. In other words, although a household in this analysis may have started
in a highly segregated area, the household could have ported to an area that was not highly segre-
gated. For minority households originating in one of the 25 metropolitan areas segregated by race,
the average minority rate in the preportability location was 71.7 percent. As shown in exhibit 3, for
all minority households, the average minority rate in the preportability location was 58.4 percent.
The average minority rate of portability move locations for minority households originating in one
1998 1999 2000 2001 2002 2003 2004 2005
1998–
2005
Exhibit 14
Portability Moves by Minority Voucher Holders Initially Located in One of the
25 Metropolitan Areas Most Segregated by Race, 1998–2005, and Changes in
Neighborhood Minority Rate
Percent of portability moves by
minority voucher holders in highly
segregated metropolitan areas
77.2 77.1 80.4 79.7 81.7 82.1 81.1 81.0 79.8
Average census tract minority rate
Percent preportability location 71.0 66.6 71.2 74.1 74.6 72.9 69.8 71.4 71.7
Percent portability move location 61.2 62.4 62.4 58.2 57.4 57.5 54.9 54.5 57.9
Notes: This exhibit includes only the portability moves by households that were not at admission to the Housing Choice
Voucher Program (HCVP). We excluded portability moves at admission to the HCVP because of a lack of information on the
preportability location. We completed the analysis using records geocoded with 2000 Census census tracts; preportability
location and portability move location needed to be different census tracts. Exhibit A-1 lists the 25 most segregated metro-
politan areas by race used in this analysis. We excluded records missing data on the head of household race and ethnicity.
Data for the 1998–2005 column are based on a nonduplicated count of analysis households during the entire time period.
The number of nonduplicated household analysis records was approximately 21,000.
Sources: Multifamily Tenant Characteristics System/Public and Indian Housing Information Center data, 1997–2005; Census
2000 Summary File 3

??
Climaco, Rodger, Feins, and Lam
Policy Issues in Public and Assisted Housing
of the highly segregated areas was 57.9 percent, and, for minority households overall, the average
minority rate in the portability move locations was 55.8 percent. Compared with the large differ-
ence in the minority rates in the preportability locations, the minority rates in the portability move
locations were similar.
Minorities who ported from all areas and minorities who ported from areas highly segregated by
race could, with portability, move to neighborhoods with similar average minority rates (although
the households from the highly segregated areas appear to have moved to areas with slightly higher
rates of minorities).
Portability Moves in MSAs Segregated by Income
In this section, we use a different measure of segregation, namely segregation by income, to discuss
our analysis. We calculated this metropolitan area segregation index using Census 2000 census tract
data on families. Using a dissimilarity index, we measured levels of segregation for low-income
and high-income families. We defined low-income families as having an annual income of less than
30 percent of the metropolitan area median income. We defined high-income families as having an
annual income of more than $200,000. Exhibit A-2 lists the 25 metropolitan areas with the highest
levels of segregation by income.
Exhibit 15 shows the change in the neighborhood poverty rate for porting households that originated
in one of the 25 metropolitan areas with the highest levels of segregation by income. From 1998 to
2005, 21.9 percent of households completing portability moves originated in one of those metro-
politan areas. The average neighborhood poverty rate in the originating location was 20.6 percent,
and the average neighborhood poverty rate in the portability move location was 17.2 percent.
These average poverty rates are higher than the neighborhood poverty rates for portability movers
1998 1999 2000 2001 2002 2003 2004 2005
1998–
2005
Exhibit 15
Portability Moves by Voucher Holders Initially Located in One of the 25 Metropolitan
Areas Most Segregated by Income, 1998–2005, and Changes in Neighborhood
Poverty Rate
Percent of portability moves
by voucher holders in highly
segregated metropolitan areas
24.7 20.8 22.2 22.0 21.4 21.9 21.5 22.5 21.9
Average census tract poverty rate
Percent preportability location 21.5 20.1 20.9 21.0 21.0 20.5 19.7 20.4 20.6
Percent portability move location 19.8 19.4 19.1 18.0 16.6 16.4 15.7 15.7 17.2
Notes: This exhibit includes only the portability moves by households that were not at admission to the Housing Choice
Voucher Program (HCVP). We excluded portability moves at admission to the HCVP because of a lack of information on the
preportability location. We completed the analysis using records geocoded with 2000 Census census tracts; preportability
location and portability move location needed to be different census tracts. Exhibit A-2 lists the 25 most segregated
metropolitan areas by income used in this analysis. We excluded records missing data on annual household income. Data for
the 1998–2005 column are based on a nonduplicated count of analysis households during the entire time period. The number
of nonduplicated household analysis records was approximately 146,000.
Sources: Multifamily Tenant Characteristics System/Public and Indian Housing Information Center data, 1997–2005; Census
2000 Summary File 3

??
Portability Moves in the Housing Choice Voucher Program, 1998–2005
Cityscape
overall. As shown in exhibit 12, the originating location average poverty rate was 18.3 percent and
the portability move location poverty rate was 16.3 percent.
Changes in the Level of Segregation With Portability Moves
In exhibit 16, we show the degree to which a portability move resulted in a move to a more or less
segregated metropolitan area. Analysis was not limited to only the 25 metropolitan areas with the
highest levels of either racial or income segregation. We included porting households originating
in a large metropolitan area (a population of at least 250,000). To compare how each metropolitan
area ranked with other metropolitan areas, we used specific measures of segregation for 182 large
metropolitan areas. Possible options for portability moves originating in a large metropolitan area
included moving to a more segregated MSA, staying in the originating MSA (no change in the level
of segregation), moving to a less segregated MSA, moving to a small MSA, or moving to a non-
metropolitan area.
1998 1999 2000 2001 2002 2003 2004 2005
1998–
2005
Exhibit 16
Portability Moves by Households Initially Located in a Large Metropolitan Area,
1998–2005, and Measures of Metropolitan Area Racial Segregation and Income
Segregation
Portability moves by measures of
racial segregation
(percent)
Moved to more segregated MSA 14.4 15.7 15.9 16.0 16.0 18.5 18.5 18.4 16.6
Moved in the MSA (same level of
segregation)
57.4 56.1 52.7 50.4 51.9 50.8 49.5 45.2 52.2
Moved to less segregated MSA 18.5 18.6 21.7 23.4 22.7 22.3 22.9 25.5 21.8
Moved to small MSA 3.9 3.9 4.2 4.7 4.5 3.7 4.0 4.7 4.1
Moved to nonmetropolitan area 5.8 5.8 5.6 5.6 4.9 4.8 5.2 6.2 5.3
Portability moves by measures of
income segregation
(percent)
Moved to more segregated MSA 14.9 16.6 17.3 17.8 17.3 19.2 19.7 20.2 17.8
Moved in the MSA (same level of
segregation)
57.4 56.1 52.7 50.4 51.9 50.8 49.5 45.2 52.2
Moved to less segregated MSA 18.0 17.7 20.3 21.6 21.4 21.6 21.6 23.7 20.6
Moved to small MSA 3.9 3.9 4.2 4.7 4.5 3.7 4.0 4.7 4.1
Moved to nonmetropolitan area 5.8 5.8 5.6 5.6 4.9 4.8 5.2 6.2 5.3
MSA = metropolitan statistical area.
Notes: This exhibit includes only the portability moves by households that were not at admission to the Housing Choice
Voucher Program (HCVP). We excluded portability moves at admission to the HCVP because of a lack of information on the
preportability location. We completed the analysis using records geocoded with 2000 Census census tracts; preportability
location and portability move location needed to be different census tracts. The exhibits in the appendix list information on
the measures of segregation used in the analysis. Large metropolitan areas have populations of at least 250,000. Portability
moves to areas with the same level of segregation are considered as moves in the same metropolitan area. Data for the
1998–2005 column are based on a nonduplicated count of analysis households during the entire time period. The number of
nonduplicated household analysis records was approximately 107,000.
Sources: Multifamily Tenant Characteristics System/Public and Indian Housing Information Center data, 1997–2005; Census
2000 Summary File 3; Housing Patterns data, 2000 Decennial Census

?0
Climaco, Rodger, Feins, and Lam
Policy Issues in Public and Assisted Housing
Slightly more than half of the porting households from large metropolitan areas stayed within
their MSAs (52.2 percent). When examining either measures of racial segregation or measures of
segregation by income, the rates of movement to a more segregated or a less segregated area were
very similar. When examining measures of racial segregation (exhibit 16, upper panel), during
the study period 16.6 percent of porting households moved to a more segregated metropolitan
area and 21.8 percent moved to a less segregated metropolitan area. When examining measures
of income segregation (exhibit 16, lower panel), during the study period 17.8 percent of porting
households moved to a more segregated metropolitan area and 20.6 percent moved to a less
segregated metropolitan area.
Portability Moves in High-Cost and Low-Cost Areas
Exhibit 17 shows information on the portability moves made from higher cost and lower cost ju-
risdictions. Jurisdiction was based on the reporting housing agency. Jurisdiction cost data analyzed
were PHA average monthly per-unit costs, including subsidies and administrative fees, for April
2005 to March 2006 from HUD’s Voucher Management System.
The preponderance of portability moves was made from higher cost to lower cost jurisdictions.
From 1998 to 2005, an average of 60.7 percent of portability moves were made from higher cost
to lower cost jurisdictions. With these portability moves, households were moving to areas where
the average subsidy cost was lower, on average, by 11.1 percent. The average monthly subsidy cost
was $580 in the preportability location and $505 in the portability move location.
For the 39.3 percent of portability moves from lower cost to higher cost jurisdictions, households
were moving to areas where the average subsidy cost was higher, on average, by 35 percent. The
average monthly subsidy cost was $478 in the preportability location and $621 in the portability
move location.
Considering that most portability moves were from higher cost to lower cost jurisdictions, it ap-
pears that portability has resulted in savings of subsidy costs to HUD; however, the percentage of
moves from lower cost to higher cost areas increased over time, from 21 percent in 1998 to 44.1
percent in 2005, with a high of 47.8 percent in 2003. Although moves from lower cost to higher
cost areas imply higher subsidy costs to HUD, the moves also show that, with portability, house-
holds with HCVP assistance are increasingly able to move to higher rent markets.
Determinants of HCVP Portability Moves
In addition to conducting the analyses described earlier, we undertook a multivariate analysis fo-
cused on identifying the factors associated with portability moves. We were particularly interested
in exploring whether households with certain demographic characteristics—such as race, ethnicity,
household composition, sources of income, and length of HCVP stay—statistically are more likely
to exercise the portability move option. Multivariate analysis is helpful because it enables us to
examine the effect of each characteristic on the likelihood of a portability move, while holding all
other factors constant.

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Portability Moves in the Housing Choice Voucher Program, 1998–2005
Cityscape
The outcome or dependent variable in this analysis is a 0/1 dummy variable indicating whether a
household has used the option of a portability move in year T. A household’s decision to make a
portability move was modeled statistically using a logistic regression model with repeated observa-
tions for the same set of household units (Wooldridge, 2001). Formally, the model specification is:
Notes: This exhibit includes only the portability moves by households that were not at admission to the Housing Choice
Voucher Program (HCVP). We excluded portability moves at admission to the HCVP because of a lack of information on the
preportability location. Jurisdiction was based on the recording housing agency. PHA average monthly per-unit costs include
subsidies and administrative fees. The Lower to higher cost jurisdictions column includes records in which the percentage of
change in the originating and receiving housing agencies’ average HCVP subsidy costs was greater than or equal to zero. For
those records, data in the Largest increase row present the maximum percentage of change (increase) in the average subsidy
cost associated with a portability move from a lower to a higher cost jurisdiction. The Higher to lower cost jurisdictions
column includes records in which the percentage of change in the originating and receiving housing agencies’ average HCVP
subsidy costs was less than zero. For those records, the Largest decrease row presents the maximum percentage of change
(decrease) in average subsidy cost associated with a portability move from a higher to a lower cost jurisdiction. Data for the
1998–2005 column are based on a nonduplicated count of analysis households during the entire time period. The number of
nonduplicated household analysis records was approximately 189,000.
Sources: Multifamily Tenant Characteristics System/Public and Indian Housing Information Center data, 1997–2005; public
housing agency cost per unit data (Voucher Management System, April 2005–March 2006)
1998 1999 2000 2001 2002 2003 2004 2005
1998–
2005
Exhibit 17
Portability Moves and Lower and Higher Cost Jurisdictions, 1998–2005
(Based on Average Monthly per-Unit HCVP Subsidy Costs)
Higher to lower cost jurisdictions
Percent of all portability moves 79.0 67.5 61.5 59.2 53.6 52.2 52.8 55.9 60.7
Average subsidy cost (US$)
Preportability location 536 568 581 590 600 612 614 620 580
Portability move location 496 509 503 495 503 520 519 517 505
Percent change in average
subsidy cost with move
Average – 5.4 – 8.6 – 11.6 – 14.1 – 14.6 – 13.6 – 14.0 – 15.0 – 11.1
Largest decrease – 77.1 – 77.3 – 73.9 – 77.0 – 78.1 – 79.5 – 78.7 – 79.6 – 79.6
Lower to higher cost jurisdictions
Percent of all portability moves 21.0 32.5 38.5 40.8 46.4 47.8 47.2 44.1 39.3
Average subsidy cost
(US$)
Preportability location 470 465 471 469 479 487 486 493 478
Portability move location 611 625 611 604 623 633 631 622 621
Percent change in average
subsidy cost with move
Average + 35.0 + 39.9 + 34.9 + 33.5 + 34.7 + 35.2 + 34.8 + 30.8 + 35.0
Largest increase + 300 + 341 + 421 + 457 + 362 + 425 + 519 + 478 + 519

??
Climaco, Rodger, Feins, and Lam
Policy Issues in Public and Assisted Housing
44332211
1
log bbbbα
iiii
it
it
XXXX
P
P
++++=








−

(1)
where
it
P

represents the probability that household i has used the portability move option at year t;
α is the constant term;
1i
X
is a set of flags for the entry year (cohort);
2i
X
is a vector of household demographic variables;
3i
X
is a set of geographic covariates;
4i
X
is a continuous variable measuring the number of years household i remains in the program
at year t; and
1
b ,
2
b ,
3
b , and
4
b are vectors of regression coefficients.
The following set of household characteristics and program covariates are included in the model:
• Disability status of household head.
• Elderly status of household head.
• Race/ethnicity of household head.
• Presence of prime-age adults (ages 18–49) in the household.
• Presence of children by age group in the household.
• Total number of household members.
• Whether welfare income accounts for more than half of total household income.
• Whether the household was previously homeless.
• Length of stay in the HCVP (in years) and its squared term.
• Program entry year (cohort).
The set of geographic covariates included dummy variables indicating the census division of a
household’s location; whether the household was located in the central city, suburb, or nonmetro-
politan areas; and the census tract poverty rate (in categories). These variables were intended to
measure a household’s baseline location (before the portability move, if any).
Unlike ordinary regression models, which include one observation per household (or person), this
type of logit model allows for multiple observations for the same household; therefore, the data
should be organized in a household-period format. For example, the first observation for household
A in the data would describe the characteristics of household A in the first year of program partici-
pation; the second observation would show the characteristics for household A in the second year;
and so on. Thus, time-varying characteristics, such as household income, household size, and the
presence of children, can be incorporated into the model.

??
Portability Moves in the Housing Choice Voucher Program, 1998–2005
Cityscape
Considering the size of the MTCS/PIC database and the type of analysis involved, we conducted
the modeling on a 10-percent random sample of the households included in the 1997–2005 data
extract. We constructed an analysis file consisting of households that entered the HCVP in 1997
or later. Some of these households may have had spells of housing assistance before 1997 but, at
some point, they exited the program and then reentered during the 1997-to-2005 period. To avoid
bias in the sample (which would result from truncated household records), we excluded households
with ongoing housing assistance that entered before 1997 and never left. This sampling scheme
also enabled us to focus on the population of households that entered the program since 1997.
13
Because coefficient estimates associated with a logit model are difficult to interpret, researchers
often convert them into an “odds ratio” format. Statistically, an odds ratio is defined as the prob-
ability of the event occurring divided by one minus the probability of the event. In other words,
an odds ratio measures the relative likelihood that the effect of a factor will influence the outcome
event (portability move, in this case). Variables with an odds ratio estimate of greater than one are
interpreted as having a positive effect on the decision a household makes to use the portability
move option, while variables with an odds ratio estimate of less than one suggest that the presence
of these variables decreases the likelihood of portability moves.
Exhibit 18 lists the logit model’s odds ratio estimates of a portability move. Most of the estimates
are statistically significant and have the expected sign/direction. The following summary of obser-
vations is based on the model:
• No evidence indicates that a head of household with disabilities will use the portability move
differently than other households would.
• Compared with other households, households headed by elderly people are less likely (odds of
0.55 times which is statistically significant) to exercise the portability move option, everything
else being equal.
• Compared with other households, households headed by non-Hispanic African Americans are
more likely (odds of 1.3 times) to use the portability move option.
• The presence of prime-age adults (ages 18 to 49) in a household is associated with a slightly
higher likelihood of a portability move (odds of 1.086 times), holding all other factors constant.
• Households with preschool-aged children (ages 0 to 5) are more likely (odds of 1.4 times) to
exercise portability than households with similar demographic and location characteristics. The
presence of children in older age groups has no effect on the probability of portability moves.
• A household that has welfare income accounting for more than half of its income has a higher
likelihood (1 percent, which is statistically significant) of making a portability move than other
household types do.
13
Portability moves were determined and analyzed for the period of 1998 to 2005. Household data from 1997 were
included to help determine if a 1998 action was a portability move.

??
Climaco, Rodger, Feins, and Lam
Policy Issues in Public and Assisted Housing
Exhibit 18
Odds Ratio
Coefficient Estimates From the Logistic Model of a Portability Move
Household characteristics
Household head is disabled 1.006
(0.037)
Household head is elderly 0.550 ***
(0.037)
Household head is non-Hispanic White Reference
Household head is non-Hispanic African American 1.349 ***
(0.044)
Household head is Hispanic 1.050
(0.047)
Household head is other races/ethnicities 0.861 *
(0.076)
Presence of prime-age adults (ages 18–49) 1.086 *
(0.052)
Presence of preschool-aged children (ages 0–5) 1.440 ***
(0.052)
Presence of young children (ages 6–12) 1.000
(0.035)
Presence of teenagers (ages 13–17) 0.916 **
(0.036)
Number of persons in household 0.993
(0.014)
Welfare income accounted for more than half of total household income 1.366 ***
(0.047)
Previously homeless 0.927
(0.088)
Program characteristics
Length of HCVP stay (in years) 3.131 ***
(0.118)
Length of HCVP stay squared 0.860 ***
(0.005)
1997 entering cohort Reference
1998 entering cohort 0.552
(0.243)
1999 entering cohort 0.442 *
(0.195)
2000 entering cohort 0.330 **
(0.145)
2001 entering cohort 0.299 ***
(0.131)
2002 entering cohort 0.262 ***
(0.115)
2003 entering cohort 0.242 ***
(0.107)

??
Portability Moves in the Housing Choice Voucher Program, 1998–2005
Cityscape
Notes: Dependent variable = 1 if household i exercised portability move at year j.
= 0 otherwise.
Standard errors are in parentheses.
* Significant at 10 percent; ** significant at 5 percent; *** significant at 1 percent.
Sources: Multifamily Tenant Characteristics System/Public and Indian Housing Information Center data 1997–2005; Census
2000 Summary File 3
Exhibit 18
Odds Ratio
Coefficient Estimates From the Logistic Model of a Portability Move (continued)
2004 entering cohort 0.144 ***
(0.064)
2005 entering cohort 0.283 **
(0.164)
Household location (before move)
Central city Reference
Suburb 1.406 ***
(0.044)
Nonmetropolitan 1.271 ***
(0.049)
Neighborhood poverty rate less than 10 percent 1.061 *
(0.036)
Neighborhood poverty rate 10–20 percent Reference
Neighborhood poverty rate 21–30 percent 0.939 *
(0.034)
Neighborhood poverty rate greater than 30 percent 0.881 ***
(0.036)
New England census division 1.575
(0.452)
Middle Atlantic census division 0.994
(0.284)
East North Central census division 0.870
(0.249)
West North Central census division 1.314
(0.377)
South Atlantic census division 1.091
(0.312)
East South Central census division 0.794
(0.230)
West South Central census division 0.997
(0.285)
Mountain census division 1.482
(0.426)
Pacific census division 1.336
(0.381)
Puerto Rico and other outlying territories Reference
Log likelihood = – 29,700
Number of households = 96,560
Number of household-year observations = 321,163

??
Climaco, Rodger, Feins, and Lam
Policy Issues in Public and Assisted Housing
• The length of stay in the HCVP correlates with a household’s portability move decision.
Exhibit 19 shows the relationship between the duration of program stay (in years) and the
probability of portability move, using coefficient estimates from the logit model.
14
For an
average HCVP participant, the likelihood of making a portability move increases monotonically
from program entry and peaks between the fourth and fifth years. Starting in the fifth year, the
probability of a portability move begins to decrease.
• Households that entered the HCVP in recent years tend to be less likely to exercise the
portability move option than households from the earlier cohorts.
• Compared with households in central cities, households in suburbs and nonmetropolitan areas
have a higher likelihood of making a portability move, all else being equal.
• Households in low-poverty areas (defined as census tract poverty rate below 10 percent) have
slightly higher odds of portability; households in high-poverty neighborhoods (census tract
poverty rate greater than 20 percent) are less likely to exercise portability. The reference category
in this analysis is households in neighborhoods with poverty rates between 10 and 20 percent.
The multivariate analysis thus bears out the relationships observed in many of the earlier tables.
Exhibit 19
Relationship Between the Length of HCVP Stay and Probability of a Portability Move
HCVP = Housing Choice Voucher Program.
14
To compute the predicted probability, we varied the value of the Housing Choice Voucher Program stay variables (from 0
to 12 by an increment of 0.5) and set the other variables in the mode to the sample mean values.

??
Portability Moves in the Housing Choice Voucher Program, 1998–2005
Cityscape
Conclusion
Through portability, participants in the Housing Choice Voucher Program have the option of us-
ing a voucher to move to a unit outside the issuing housing agency’s jurisdiction. In this article,
we discussed the findings of an analysis of portability moves made from 1998 to 2005. Using a
specially constructed longitudinal data set developed from U.S. Housing and Urban Development
administrative records, we first identified records that represented portability moves, and then we
analyzed household and neighborhood characteristics associated with portability moves. Of the
3.4 million households that received housing assistance in the voucher program during these years,
8.9 percent made a portability move.
Portability movers were less likely to have a White head of household (51.4 percent) compared
with households in the overall HCVP (56.6 percent), and they were more likely to have an African-
American head of household (45.6 percent) than households in the overall HCVP (39.8 percent).
Portability mover households on average were younger than households in the overall HCVP
(39.5 years old compared with 43 years old, respectively). Portability movers were more likely to
be households with children, and households with preschool-aged children were 1.4 times more
likely to complete a portability move compared with other households in the HCVP with simi-
lar demographic and location characteristics. When examining annual income sources, porting
households were less likely to have wage income and slightly more likely to have welfare income.
Households with welfare income accounting for at least half of household income had a higher
likelihood of completing a portability move compared with other households. Length of stay in the
HCVP also correlated with portability moves—portability moves were most likely to occur between
the fourth and fifth years of HCVP participation.
Portability movers typically moved to census tracts with lower poverty rates and lower minority
rates. In the preportability location, the average census tract poverty rate was 18.3 percent, and
in the portability move location, the average census tract poverty rate was 16.3 percent. Overall,
average census tract minority rates decreased from 45.9 percent in the preportability location to
43.8 percent in the portability move location. Statistics for portability households originating in a
metropolitan area highly segregated by race show the average census tract minority rate was 63.3
percent in the preportability location and 51.0 percent in the portability move location.
The HCVP operates primarily in metropolitan areas, and portability movers were most likely to
move to a metropolitan area. The highest portion of portability moves was within the same met-
ropolitan area (41.3 percent), the next highest portion of portability moves was from one metro-
politan area to another metropolitan area (37.3 percent), and another 10.2 percent moved from a
nonmetropolitan area to a metropolitan area. Nearly one-third of portability moves were moves of
more than 100 miles, and the median portability move was 25 miles. The analysis of public hous-
ing jurisdictions by program costs indicated that three-fifths of portability moves were made to
lower cost jurisdictions compared with the originating jurisdiction.

??
Climaco, Rodger, Feins, and Lam
Policy Issues in Public and Assisted Housing
Appendix
Highly Segregated Metropolitan Areas, 2000
Exhibit A–1
MSA/PMSA Name
MSA/PMSA
Code
Total
Population
Dissimilarity
Index
The 25 Most Racially Segregated Metropolitan Areas, With a Population of 250,000
or More, in 2000
Detroit, MI PMSA 2160 4,441,551 0.846
Gary, IN PMSA 2960 631,362 0.839
Milwaukee-Waukesha, WI PMSA 5080 1,500,741 0.818
New York, NY PMSA 5600 9,314,235 0.810
Newark, NJ PMSA 5640 2,032,989 0.801
Chicago, IL PMSA 1600 8,272,768 0.797
Cleveland-Lorain-Elyria, OH PMSA 1680 2,250,871 0.768
Buffalo-Niagara Falls, NY MSA 1280 1,170,111 0.766
Flint, MI PMSA 2640 436,141 0.765
Cincinnati, OH-KY-IN PMSA 1640 1,646,395 0.739
Bridgeport, CT PMSA 1160 459,479 0.737
Saginaw-Bay City-Midland, MI MSA 6960 403,070 0.732
St. Louis, MO-IL MSA 7040 2,603,607 0.731
Nassau-Suffolk, NY PMSA 5380 2,753,913 0.730
Bergen-Passaic, NJ PMSA 0875 1,373,167 0.723
Philadelphia, PA-NJ PMSA 6160 5,100,931 0.720
Youngstown-Warren, OH MSA 9320 594,746 0.719
Fort Wayne, IN MSA 2760 502,141 0.706
Indianapolis, IN MSA 3480 1,607,486 0.704
Birmingham, AL MSA 1000 921,106 0.701
Harrisburg-Lebanon-Carlisle, PA MSA 3240 629,401 0.699
Peoria-Pekin, IL MSA 6120 347,387 0.699
Dayton-Springfield, OH MSA 2000 950,558 0.698
Beaumont-Port Arthur, TX MSA 0840 385,090 0.694
Miami, FL PMSA 5000 2,253,362 0.694
MSA = metropolitan statistical area. PMSA = primary metropolitan statistical area.
a
This exhibit lists the 25 metropolitan areas, each with a population of 250,000 or more, with the highest levels of racial
segregation based on the following data components:
Year: 2000 Census.
Racial minority: African Americans.
Measure of segregation: dissimilarity index.
Unit of analysis for computing the dissimilarity index: census tract.
Note: The metropolitan areas are MSAs/PMSAs based on the Office of Management and Budget definitions for metropolitan
areas as of June 30, 1999.
Source: U.S. Census Bureau (http://www.census.gov/hhes/www/housing/housing_patterns/excel_msa.html)

??
Portability Moves in the Housing Choice Voucher Program, 1998–2005
Cityscape
Exhibit A–2
MSA/PMSA Name
MSA/PMSA
Code
Total
Population
Dissimilarity
Index
The 25 Metropolitan Areas Most Segregated by Income, With a Population of
250,000 or More, in 2000
New York, NY PMSA 5600 9,314,235 0.787
Milwaukee-Waukesha, WI PMSA 5080 1,500,741 0.778
Newark, NJ PMSA 5640 2,032,989 0.773
Philadelphia, PA-NJ PMSA 6160 5,100,931 0.769
Birmingham, AL MSA 1000 921,106 0.758
Toledo, OH MSA 8400 618,203 0.758
Louisville, KY-IN MSA 4520 1,025,598 0.750
Cleveland-Lorain-Elyria, OH PMSA 1680 2,250,871 0.749
Bridgeport, CT PMSA 1160 459,479 0.742
Memphis, TN-AR-MS MSA 4920 1,135,614 0.742
Los Angeles-Long Beach, CA PMSA 4480 9,519,338 0.739
Dallas, TX PMSA 1920 3,519,176 0.737
Detroit, MI PMSA 2160 4,441,551 0.736
Trenton, NJ PMSA 8480 350,761 0.735
Columbus, OH MSA 1840 1,540,157 0.731
Chicago, IL PMSA 1600 8,272,768 0.728
Tucson, AZ MSA 8520 843,746 0.726
Denver, CO PMSA 2080 2,109,282 0.725
Omaha, NE-IA MSA 5920 716,998 0.725
Houston, TX PMSA 3360 4,177,646 0.724
Akron, OH PMSA 0080 694,960 0.722
Phoenix-Mesa, AZ MSA 6200 3,251,876 0.722
Baltimore, MD PMSA 0720 2,552,994 0.719
St. Louis, MO-IL MSA 7040 2,603,607 0.717
San Antonio, TX MSA 7240 1,592,383 0.717
MSA = metropolitan statistical area. PMSA = primary metropolitan statistical area.
a
This exhibit lists the 25 metropolitan areas, each with a population of 250,000 or more, with the highest levels of income
segregation based on the following data components:
Year: 2000 Census.
Low income: estimated number of families with an annual income of less than 30 percent of MSA/PMSA median.
High income: number of families with an annual income of more than $200,000.
Measure of segregation: dissimilarity index.
Unit of analysis for computing the dissimilarity index: census tract.
Note: The metropolitan areas are MSA/PMSAs based on the Office of Management and Budget definitions for metropolitan
areas as of June 30, 1999.
Sources: Census 2000 Summary File 3, Tables P76 and P77

?0
Climaco, Rodger, Feins, and Lam
Policy Issues in Public and Assisted Housing
Authors
Carissa G. Climaco is an associate at Abt Associates Inc.
Christopher N. Rodger is a senior analyst at Abt Associates Inc.
Judith D. Feins is a principal associate at Abt Associates Inc.
Ken Lam is an associate at Abt Associates Inc.
Acknowledgments
The authors gratefully acknowledge support from the U.S. Department of Housing and Urban
Development (HUD), Office of Policy Development and Research, for this work. In particular,
Barbara A. Haley and Robert W. Gray provided guidance on the analysis and on working with the
HUD data files. At Abt Associates, we thank Meryl Finkel and Victoria Main (now with Edgemere
Consulting Corporation) for their input in this study.
References
Feins, Judith D., and Rhiannon Patterson. 2005. “Geographic Mobility in the Housing Choice
Voucher Program: A Study of Families Entering the Program, 1995–2002,” Cityscape 8 (2): 21–47.
Iceland, John, Daniel H. Weinberg, and Erika Steinmetz. August 2002. Racial and Ethnic Residential
Segregation in the United States: 1980-2000. U.S. Census Bureau, Series CENSR-3. Washington, DC.
U.S. Government Printing Office.
U.S. Census Bureau. 2005.“Housing Patterns. MSA/PMSA MS Excel Files.” http://www.census.
gov/hhes/www/housing/housing_patterns/excel_msa.html (accessed September 13, 2006).
U.S. Department of Housing and Urban Development (HUD). 2006. Mobility and Portability Report
Guide. Public and Indian Housing Information Center (PIC) Form 50058 Module.
Wooldridge, Jeffrey M. 2001. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA:
The MIT Press.

??Cityscape: A Journal of Policy Development and Research • Volume 10, Number 1 • 2008
U.S. Department of Housing and Urban Development • Office of Policy Development and Research
Cityscape
The Role of Social Networks in
Making Housing Choices:
The Experience of the
Gautreaux Two Residential
Mobility Program
Melody L. Boyd
Temple University
Abstract
This article explores the experiences of participants in the Gautreaux Two housing
mobility program, which was implemented in 2002. The program gave low-income
residents of Chicago public housing a special voucher providing them the opportunity to
move to more advantaged neighborhoods, designated as neighborhoods in which at least
76.5 percent of households were nonpoor and 70 percent were non-African American.
Four waves of indepth, qualitative interviews were conducted by Northwestern Uni-
versity’s Institute for Policy Research (IPR) between 2002 and 2005 with a randomly
chosen sample of 91 families. Within the 3-year study window, this qualitative analysis
of the IPR data compares residents who made secondary moves with those who stayed
at their Gautreaux placement addresses. In this article, I apply insight from feminist
urbanism and a focus on social networks to a comparison of the reasons some residents
moved while others stayed. Secondary movers were motivated by several social network
factors, including feelings of social isolation in the placement neighborhood, distance
from kin, and transportation difficulties. Conversely, strong social networks were crucial
reasons why some families remained in their Gautreaux neighborhoods or moved on to
other similarly advantaged neighborhoods. This analysis explores policy implications for
the success of mobility programs, including the need for continued program assistance to
build and maintain strong social networks beyond the initial placement.

?? Policy Issues in Public and Assisted Housing
Boyd
Introduction
During the past three decades, housing mobility programs, such as the Chicago Housing
Authority’s (CHA’s) Gautreaux program and the federal Moving to Opportunity program, provided
low-income families with a unique opportunity to relocate from some of the poorest, most segre-
gated, and crime-ridden neighborhoods in the nation—large inner-city housing projects—to safer
and more prosperous neighborhoods.
1
Previous research has shown that over time not all families
who move through these programs remain in these more advantaged neighborhoods. The benefit
of such a move presumably depends on the length of exposure to more advantaged neighborhoods
(Clark, 1991).
2
The results of the original Gautreaux program, which was implemented in 1976, have been largely
favorable. Indications are that the program had a long-term effect on the residential locations of
participants and has improved employment and health outcomes for participants and their families
(Keels, Forthcoming a, b; Keels et al., 2005; Mendenhall, DeLuca, and Duncan, 2006; Rosenbaum
and DeLuca, 2000; Rosenbaum, DeLuca, and Tuck, 2005; Rubinowitz and Rosenbaum, 2001).
The transition from public housing to more prosperous city neighborhoods or the suburbs is not
a smooth and straightforward one; it is rather complicated and nuanced. This article provides an
analysis exploring the nuances of this transition in the second Gautreaux program.
As with its predecessor, the Gautreaux Two program gave low-income residents of Chicago public
housing, located in areas with very high rates of economic and racial segregation, the opportunity
to move to racially diverse and more affluent neighborhoods.
In October 2001, CHA sent letters inviting all tenants to participate in the program. A total of 549
families attended orientation sessions, and those who completed the required followup activities
received a voucher from the federal Housing Choice Voucher Program (HCVP) for low-income
families. Unlike other vouchers, however, these vouchers had a set of special requirements: they
could be used only for units in census tracts with no more than 23.49 percent of residents living in
poverty and no more than 30 percent of residents being African American.
3
Such neighborhoods
were designated “opportunity areas.” After residing in these opportunity areas for 1 year, the
families could either remain in their units or use their vouchers to move—without the poverty and
race restrictions—to any neighborhood they chose.
1
Although most lived in large housing projects, a few families came from scattered-site public housing, which was typically
located in neighborhoods with lower poverty and less segregation.
2
Researchers find various benefits of moving from segregated, high-poverty areas to more diverse and wealthier
neighborhoods. Research on the original Gautreaux program found that these benefits included educational, employment,
and health benefits (see Rosenbaum and DeLuca, 2000; Rosenbaum, DeLuca, and Tuck, 2005; Rubinowitz and Rosenbaum,
2001).
3
Gautreaux and Gautreaux Two both were court-ordered remedies for a racial discrimination suit brought by public
housing residents (including Dorothy Gautreaux) against the Chicago Housing Authority. Thus, unlike the Moving to
Opportunity program, the Gautreaux programs have racial as well as economic restrictions on the neighborhoods where
residents can use their vouchers.

??Cityscape
The Role of Social Networks in Making Housing Choices:
The Experience of the Gautreaux Two Residential Mobility Program
The experiences of participants in the Gautreaux Two program relate to the HCVP more generally.
The HCVP is the U.S. Department of Housing and Urban Development’s (HUD’s) largest housing
subsidy program; it is implemented in every metropolitan area throughout the country. As Devine
et al. (2003: vii) explain in their report on HCVP location patterns, the program allows participants
to secure housing in the private rental market and “encourages participants to avoid high-poverty
neighborhoods.” Administrative data on the HCVP show that a low percentage of voucher recipients
continue to move to more advantaged neighborhoods (see Devine et al., 2003; Feins and Patterson,
2005). Understanding the experiences of the Gautreaux Two program participants provides insight
into these location patterns by exploring how voucher holders make housing choices.
A previous analysis of the Gautreaux Two program by Pashup et al. (2005) examined the process
by which families participating in the program moved through the program and the difficulties they
faced in trying to relocate to a new neighborhood. The study established that some participants
found it difficult to move through the Gautreaux program because of both external and internal
obstacles. External obstacles included a tight rental market, landlord discrimination against
housing vouchers, and bureaucratic delays. Internal obstacles included a poor understanding of
program requirements, large household size, and mental or physical health problems.
Another analysis of Gautreaux Two by Reed, Pashup, and Snell (2005) assessed the ways the pro-
gram affected participation of female movers in the labor force. The study found several primary
obstacles to working, including childcare responsibilities, illness and health issues, transportation
difficulties, and layoffs from temporary jobs. This analysis also found that moving had little effect
on the employment situation of most study participants.
A third analysis of the Gautreaux Two program (see Boyd et al., 2007) provides an overview of the
various influences on respondents’ decisions to move. Hassles with landlords and poor-quality
units were primary factors, along with social network-related factors.
This article uses indepth qualitative interviews with program participants to assess the ways that
social networks influence families’ decisions about remaining in their Gautreaux neighborhoods
or moving on to other neighborhoods. The analysis focuses on family experiences in the first
months and years of adjusting to a new neighborhood, because those experiences may prove vital
to understanding the factors underlying the frequency of secondary moves and the variation in
subsequent neighborhood quality, focusing primarily on factors related to social networks. This
analysis addresses the following questions: What social network factors prompt families to make
secondary moves within 3 years after placement? What social network factors determine the kind
of neighborhoods secondary movers choose?
Literature Review
To understand the context of the experiences of the Gautreaux Two participants, it is important to
consider the ways in which residential segregation influences neighborhoods and to understand
how social networks intersect with neighborhood life.

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Boyd
Racial Residential Segregation and Neighborhood Effects
The original Gautreaux program was implemented in 1976 after a lawsuit argued that HUD and
the CHA were discriminating on the basis of race by engaging in “systematic and illegal segrega-
tion” (Keels et al., 2005: 53). Racial segregation is the primary residential pattern in cities in the
United States, and this pattern is not simply the result of historical processes. Racial segregation
continues because of ongoing individual and institutional discrimination (Bobo and Zubrinsky,
1996; Massey and Denton, 1993). African Americans are less likely than Whites to be able to move
out of low-income areas, more likely than Whites to move into low-income areas, and less capable
of relocating to suburbs, even when socioeconomic status is taken into account (Crowder, 2001;
Logan, Alba, and Leung, 1996; South and Crowder, 1997). Thus, African Americans’ decisions
about residential location are determined largely by external forces rather than simply by personal
preferences (Crowder, 2001; Massey, Condran, and Denton, 1987).
Researchers point to the negative effect that living in areas of concentrated poverty and high crime
rates may have on individual outcomes, including health, education, employment opportunities,
safety, and mortality (Allard and Danzinger, 2003; Brooks-Gunn et al., 1997; Crane, 1991; Mayer
and Jencks, 1989; Peterson and Krivo, 1993). Neighborhoods are so-called “opportunity structures”
consisting of systems, networks, and institutions (Galster and Killen, 1995: 15) that vary in their
ability to provide opportunity for upward mobility. Segregated ghettos have attenuated opportunity
structures, particularly for employment (Wilson, 1996, 1987).
Positive effects are presumed to follow movement to more affluent neighborhoods with greater
racial diversity. Although previous research does not consistently find benefits of living in affluent
neighborhoods, some recent evidence indicates that neighborhoods can confer both advantages
and disadvantages to residents, particularly children (Newman and Schnare, 1997). Children’s
neighborhoods are related to their cognitive development, and children living in affluent areas are
surrounded by greater resources and more enrichment opportunities (Brooks-Gunn et al., 1997).
The outcomes for parents and children may be related to the quality and availability of services
in their neighborhoods, or of jobs, because living closer to job opportunities is associated with a
higher probability of working (Allard and Danzinger, 2003; Ellen and Turner, 1997).
This recognition of the advantages and disadvantages associated with neighborhoods relates to the
goals of the HCVP, which assists 1.9 million households throughout the United States. As Devine
et al. (2003: vii) state, “Because the [HCVP] program encourages participants to avoid high-poverty
neighborhoods, and encourages the recruitment of landlords with rental properties in lower-poverty
neighborhoods, it has the potential to affect both the welfare of participants and the welfare of the
neighborhoods where they live.” The potential to improve the welfare of participants, however,
depends largely on continued residence in more advantaged neighborhoods. Using longitudinal
data from HUD administrative records, Feins and Patterson (2005: 21) find that after entering the
program, “a small but consistent tendency exists for families making later moves to choose slightly
better neighborhoods.” Thus, it is important to understand why only a small percentage of families
make moves to more advantaged neighborhoods through the HCVP. Although the vouchers that
the Gautreaux Two participants received had special requirements, housing decisions the partici-
pants made provide insight into factors affecting how families use the HCVP in metropolitan areas
more generally.

??Cityscape
The Role of Social Networks in Making Housing Choices:
The Experience of the Gautreaux Two Residential Mobility Program
Social Networks
Housing mobility programs and related policies assume that individuals and families who live in
segregated urban areas are more disadvantaged than households living in more resource-rich areas
and that moving to safer and wealthier neighborhoods will result in a better quality of life and
increased life chances. Among the mechanisms that transmit neighborhood-level characteristics
to individual outcomes, social networks are primary because they offer access to social capital and
opportunities (Briggs, 1997; Ellen and Turner, 1997; Mendenhall, 2005). Portes (1998: 6) defines
social capital as the “ability of actors to secure benefits by virtue of membership in social networks
or other social structures.” The focus on development and maintenance of social networks is
significant for housing mobility program research (Briggs, 1997; Clampet-Lundquist, 2004;
Mendenhall, 2005).
Briggs (1997) critiques the housing program assumption that moving to more advantaged
neighborhoods results in various personal benefits and reminds researchers and policymakers
that moving low-income families into affluent neighborhoods does not automatically result in
positive effects for these families, because there are challenges with creating connections in the
new neighborhoods and with benefiting from the resources of those neighborhoods. Clampet-
Lundquist (2004) shows that, although policymakers assume that children and adults who move
through mobility programs will create the kind of social ties in their new neighborhoods that
will enable them to become more economically independent, this assumption does not always
materialize because forming ties is not easy and these ties take time to develop.
Because people in racially segregated areas with high levels of poverty tend to use local social ties
and make ties with others who are very similar to themselves (Briggs, 1997; Clampet-Lundquist,
2004; Gilbert, 1998), one goal of mobility programs is to relocate such families to areas where they
can form more diverse social ties with people who differ from them in resources and networks.
Presumably, these ties should lead to more diverse information sources and access to new oppor-
tunities (Granovetter, 1973). Clampet-Lundquist (2004) suggests that families from high-poverty,
racially segregated areas may lack the resources to use the newly available ties in a way that
improves their situation (see also Kleit, 2001).
Barriers of race, class, and gender can inhibit the creation of diverse networks (Kissane and
Clampet-Lundquist, 2005; Mendenhall, 2005). For example, in her analysis of the original
Gautreaux program, Mendenhall (2005) found that, for the women who participated in the
program, the development of networks and social capital was negatively influenced by the social
distance created by race, class, and gender differences between the women and their neighbors.
These barriers can serve as types of negative (or exclusionary) social capital because the social
relations that contribute to a sense of support and cooperation among community members can
also lead to the exclusion of outsiders (Portes, 1998; Waldinger, 1995). Allport’s contact theory
argues that prejudice “may be reduced by equal status contact between majority and minority
groups in the pursuit of common goals” (1954: 281). This concept of equal status contact is
important, because families who moved through Gautreaux Two had to move to neighborhoods
with different racial and class compositions than their original neighborhoods and because they
did not have equal status in race and class with many of their new neighbors. Thus, low-income,
African-American families face both race and class barriers when moving to predominantly

?? Policy Issues in Public and Assisted Housing
Boyd
wealthy, White neighborhoods (Kissane and Clampet-Lundquist, 2005). These barriers are
exacerbated by gender, because low-income, African-American single mothers often face social
stigma and gender discrimination (Mendenhall, 2005).
Feminist Urbanism
Although little previous literature on housing mobility programs includes feminist urbanism
theory, it is a useful perspective that provides ways of conceptualizing the importance of social
networks for the women who participated in the Gautreaux program in ways that much of the
literature on neighborhood effects overlooks. The literature on racial residential segregation and
neighborhood effects does not always consider gender in its picture of neighborhoods and the
differential effects of neighborhoods on family and individual outcomes. On the other hand, a
feminist urbanism perspective provides ways to consider the nuances of neighborhoods and social
networks by drawing attention to the unique experiences of women in urban spaces (Deutsch,
2000; Domosh, 1998; England, 1996; Hayden, 1981; Jacobs, 1961; McDowell, 1993; Wyly, 1999).
Feminist urbanism is particularly relevant in analyzing housing mobility programs, because most
families who are affected by these policies and programs are households headed by females. Single
mothers and their children are the poorest demographic group in the United States (Edin and
Lein, 1997), and it is crucial to recognize that different inequalities intersect to shape the lives of
women (Collins, 1990; Gilbert, 1997). This concept is especially true when seeking to understand
the stories of the participants in the Gautreaux program, because all but one are female, all are low
income, and nearly all are African American. Specifically, family responsibilities, transportation,
and social networks are central issues to consider from a feminist urbanism perspective, because
these issues largely shaped the experiences of the women in their placement neighborhoods.
A primary family responsibility is childcare, and childcare is often a salient issue for women,
particularly women who are working. Childcare access is place based, and employed mothers of
young children often have trouble finding affordable, good-quality childcare in their neighborhoods.
This circumstance leads many mothers to develop informal solutions to childcare, pointing out an
important way that social networks serve to ameliorate some of the difficulties that single, working
mothers face (Dyck, 1996). Another family responsibility is elder care. Women are typically the
primary caregivers for elderly parents or ill family members, which results in more time constraints,
especially for employed women (Spain, 2002). Complicating these issues is the fact that women
often are limited by poor public transportation, adding difficulties in various areas of their lives
(England, 1996; Shlay and DiGregorio, 1985).
Women’s social networks are typically kin and neighbor oriented, and women with families often
want to live near their friends, family, and relatives (Gilbert, 1998; Shlay and DiGregorio, 1985;
Stack, 1974). Race intersects with class and gender to shape women’s “spatial rootedness”—their
social networks and survival strategies (Gilbert, 1998: 595). The personal networks of African-
American and White women are different, and African-American women living in low-income,
inner-city areas have the most intensive local ties, and they are not spatially diverse (Gilbert, 1998).
Church and family networks are especially central in the lives of many African Americans because
both of these institutions historically have helped relieve the pressures of living in a racist society
(Gilbert, 1998).

??Cityscape
The Role of Social Networks in Making Housing Choices:
The Experience of the Gautreaux Two Residential Mobility Program
Social networks, specifically kin networks, are crucial in low-income, African-American communities,
particularly for women and especially for childcare (Edin and Lein, 1997; Stack, 1974). Among
women who have low levels of education, those who live in high-poverty areas use informal contacts,
such as family, friends, and neighbors, more than women living in low-poverty areas do (Elliott,
1999). In her analysis of the participants in the first Gautreaux program, Mendenhall (2005: 85)
found that social networks were one of the primary “culturally influenced adaptive responses”
that the women made to the challenges they faced. When the participants in the Gautreaux Two
program moved away from their baseline communities, this process of creating new social ties and
changing networks became an important part of their story.
Data and Methods
This analysis is based on data collected by a research team headed by Kathryn Edin, Greg Duncan,
and James Rosenbaum at Northwestern University’s Institute for Policy Research. These researchers
observed all of the Gautreaux Two program orientation sessions and conducted qualitative, indepth,
semistructured interviews between 2002 and 2005 with 91 of the 549 clients who attended the
Gautreaux Two orientation sessions. Researchers initially randomly sampled 20 percent of all
clients, 71 of whom agreed to participate in the study; however, because of the unexpectedly low
number of participants who moved through the Gautreaux program,
4
a second sample of families
who seemed to be likely movers was drawn, adding 20 more families to the study.
Researchers conducted four waves of indepth, focused interviews with respondents in their
homes over a 3-year period. The interviews were semistructured, open-ended interviews that
lasted between 2 and 4 hours. The initial interviews occurred within 3 months of the participants’
orientation session, and the subsequent interviews took place approximately 6 to 9 months after
the previous interview. Thus, most respondents were interviewed four times between 2002 and
2005. Some respondents were interviewed less than four times if they were not found at some
point during the 3-year period. The indepth interviews covered various topics related to program
participation, and topics were adjusted after each wave to reflect the new subjects that emerged
throughout the previous interviews.
Almost all the respondents were African American. The remaining few were Caribbean or Puerto
Rican, and all but one of the respondents were female heads of households. The average age of
adult respondents at baseline was 32 years, and the average household size was four members.
Respondents lived in their current housing development for an average of 8 1/2 years at the time of
the study.
I analyzed the transcripts of the interviews with all of the respondents in the sample who moved
through the Gautreaux program. To gain a broad understanding of the reasons why respondents
stayed in or moved from their placement neighborhoods, I read several waves of interviews for
each of the respondents and coded the interviews for patterns that addressed the research questions.
4
See Pashup et al. (2005) for a detailed analysis of the factors that made making an initial move through the Gautreaux Two
program difficult.

?? Policy Issues in Public and Assisted Housing
Boyd
I then constructed a profile of the experiences of each respondent in the new neighborhood and
the reasons for either staying or leaving in relation to social networks. After creating these profiles,
I counted the cases for each category that emerged from the coding and created a narrative analysis
of reasons for moving and staying. I also used the extensive field notes that the interviewers took
after each interview to get a better picture of each respondent, his or her unit, and his or her
neighborhood.
I specifically examined those respondents who moved through the Gautreaux program and divide
them into two categories—those who stayed in their placement neighborhoods (“stayers”) and
those who made a subsequent move from their placement neighborhoods (“secondary movers”).
I also focused on the types of neighborhoods to which the secondary movers moved and classified
them as either opportunity areas or nonopportunity areas based on the original Gautreaux program
requirements for what constitutes an opportunity area.
Results
Of the 58 respondents in our sample who moved through the Gautreaux Two program by 2005,
27 (47 percent) were stayers and 31 (53 percent) made secondary moves. Only 19 percent of
secondary movers moved on to opportunity areas, while the remaining 81 percent moved to
nonopportunity areas, as defined by the Gautreaux Two program’s race and poverty requirements.
I divide the analysis into two sections: the first assesses the social network factors that influenced
why secondary movers made subsequent moves, and the second examines the social network
factors that contributed to stayer respondents’ decisions to remain in their Gautreaux units.
Secondary Movers
Although most secondary movers moved to nonopportunity areas, the families who moved to
another opportunity area provide insight into the importance of social networks as well. Of the six
families that made a secondary move to another opportunity area, three of those families moved to
other opportunity areas where they had family living, which demonstrates the importance of family
networks in respondents’ decisions about where to move. The other three respondents who made
secondary moves to opportunity areas all moved because they had problems with their Gautreaux
units and landlords but otherwise would have stayed in their placement neighborhood. Half of the
families who moved to another opportunity area made decisions primarily based on proximity to
kin networks, as did many of the movers to nonopportunity areas. The difference for the secondary
movers to opportunity areas is that they had kin networks based in more affluent and diverse
neighborhoods, while many of the movers who returned to nonopportunity areas did not. It is
important to analyze what aspects of social networks affect families’ decisions to make a secondary
move to more disadvantaged neighborhoods.
Social Isolation
Many respondents who moved through the program initially relocated to areas where they did
not have any family or friends and, for many, transportation issues made it difficult to visit these
kin networks. This lack of kin and transportation resulted not only in a sense of social isolation

??Cityscape
The Role of Social Networks in Making Housing Choices:
The Experience of the Gautreaux Two Residential Mobility Program
for respondents, but it also removed them from their primary support networks, particularly for
childcare. Some respondents also had ill family members for whom they cared. Living far away
made it difficult for them to fulfill their care responsibilities. For example, Latisha’s
5
placement
neighborhood was in the suburbs, and the distance from her family made it difficult for her to
fulfill her familial responsibilities. She was the primary caregiver for her diabetic mother, and her
main reason for moving back to Chicago was to care for her. When asked why she moved back,
Latisha said:
“Well for one reason, my mom. She had got sick, and this was one of the reasons why I moved
back to Chicago, so I could kinda help my mom out, you know. And I just started lookin,’ cause
she had got real sick real bad. She’s a diabetic and she’s partially blind and she had like four
mini strokes. And then she had, her hemoglobin was low. You know? It was, it was like she was
goin’—you know, she was gettin’ down. She, you know, just everything was outta control. So by
me runnin’ back and forth on the train, back and forth, you know—[it was hard].”
Latisha was socially isolated in her placement neighborhood; her family did not visit her often
because of the distance, and, after making her secondary move, Latisha was better able to care for
her mother. The distance made her fulfillment of familial responsibilities untenable, leading her to
become a secondary mover back to a nonopportunity area.
To some extent, all the respondents who discussed distance from kin as a problem were socially
isolated; however, many respondents were not only far from their family and friend networks, they
also did not immediately connect with their neighbors and were not involved in neighborhood
activities. Some said they just lived in their unit and went elsewhere to socialize; therefore, they did
not fully engage with the neighborhood or develop neighborhood networks. Other respondents
did not feel completely accepted in their new neighborhoods, which made it difficult for them to
form friendships. Some respondents saw their Gautreaux neighborhoods as transitional and were
just biding their time until they could move again. For these respondents, the ultimate goal was
getting a voucher that would enable them to move wherever they wanted. The demand for Hous-
ing Choice Vouchers has always outstripped their supply, so some families were willing to move for
a year to an opportunity area so they could subsequently move on to where they wanted to live.
Talia, for example, considered her placement neighborhood to be too far from her family and saw
her Gautreaux move as temporary:
“I’m so used to the South Side. I’ve been there all my life. This is something new to me. I don’t
want to get to know this place. Nope, because I’m ready to move. So I ain’t trying to get to know
this place.”
When asked where she wanted to move, Talia responded:
“South Side, like Southwest. Like over there where my mama lives.”
5
All respondents’ names are pseudonyms.

?0 Policy Issues in Public and Assisted Housing
Boyd
Talia made a secondary move back to a neighborhood near the area where, before their demolition,
the Robert Taylor Homes stood. Robert Taylor Homes, a public housing project located in one of
the city’s poorest and most racially segregated neighborhoods, was where she grew up.
Tara continued to work and take classes on the South Side of Chicago after her Gautreaux move
to the North Side, just as she had before the move. Thus, she spent a lot of time commuting and,
not surprisingly, found the location of her Gautreaux apartment to be very inconvenient. No
neighborhoods near her school or job had qualified as opportunity areas. The familiarity and ease
of her routine of working, going to school, and shopping that she had developed on the South Side
before her move is why she maintained ties with her baseline neighborhood on the South Side and
did not even try to become familiar with her North Side Gautreaux neighborhood:
“When I go to the show, even though there’s a show right here, I’ll go to the show on the South
Side. When I go out to eat, I go out to eat on the South Side. So, there’s no type of activity or
anything going…. I’m not even FAMILIAR with this North Side. All I can do is get to my house,
and back out to where I need to go. I’m not familiar; I couldn’t tell you how to get to a store
around here.”
Not surprisingly, Tara ended up making a secondary move to the South Side of Chicago to a
nonopportunity area to be closer to her family and job.
One primary thing respondents missed about their baseline neighborhoods was the regular social
interactions they had with their neighbors in the housing projects. Francine said:
“Actually, like I said, since I been here, I don’t feel I been happy, because this place depresses
me, because there’s nothin’ to do, nothin’, you know? [I] can’t get out much because the buses
stop runnin’ early. I don’t have a car. I don’t know how to drive. So that’s another bad thing, you
know? So I don’t get out.”
When asked what she missed about public housing, Francine said:
“Just bein’ able to go outside, sit down and talk. Because in the projects you can sit down and
talk to everybody. Someone’s always walkin’ around. Here you sit on your porch and that’s it,
you know? It’s always somethin’ goin’ on in the project, there’s never nothin’ goin’ on here. So,
yeah I do miss that, but the projects itself, no, I don’t miss [the projects]. Since I been here [my
health] seems to me it’s been worser, yeah, because I guess, I don’t know. It was like when I was
livin’, you know, down there in the projects, I got out more and did more, you know? So it helped
my strength, whatever. Now here it’s just like blah, you know? I don’t do nothin’, it seem like I
depressin’ myself, makin’ myself sick.”
Tina also explained how difficult it is to be removed from a neighborhood where neighbors know
you and are willing to provide support. When asked what she missed about her baseline neighbor-
hood, Tina said:
“Well, the people, because I know a lot of people. Like all over the neighborhood. I know a lot
people. Where, it’s not like here. OK, if I get stuck I can’t hit no one [up for help]. You know what
I’m saying? Over there everybody knows me. [If] I need [something] or I don’t have any money
and I need a ride to the emergency [room]; there’s always somebody because they all know me.”

??Cityscape
The Role of Social Networks in Making Housing Choices:
The Experience of the Gautreaux Two Residential Mobility Program
Felicia echoed these sentiments of missing her support network in her old neighborhood:
“[I miss] being able to just walk out and talk to my friends, because here it’s just quiet. That’s the
only thing I miss, is being able like to walk out. And I know people but I really don’t know nobody
here. So it’s like you have to budget better than you did before, because there you didn’t have to
worry about it, because you could just walk out the door and say, ‘Hey, I need.’”
Many of the Gautreaux Two respondents who made secondary moves felt disconnected from their
primary support networks in their new neighborhoods and were unable to replace these with
social ties in their placement neighborhoods. When asked what she thought about the Gautreaux
program, Nikki said:
“I mean, I understand what they were trying to do. I do understand what they were trying to do
and they were hoping to give people better opportunities, but to force people away, to force people
away from their family, their support system. You know, just common things that people need to
have. It’s not beneficial. It’s not beneficial and it causes more harm than good.”
For many of the secondary movers, wanting to live closer to their families and to others in their
networks—often very much like them—who would offer ready support was a primary reason for
their decision to make a secondary move. The desire to live closer to their families and others in
their network also influenced the location of the second move. Sophie’s Gautreaux unit was in
Rogers Park, a neighborhood in Chicago’s far North Side, and both she and her children missed the
proximity to South Side family and friends. Sophie said:
“[I’m planning to] move. I want to go back down south where I come from. I don’t too much
care for the North Side. You know, now…I find my way around, but still, there ain’t nothing like
home. Well I don’t have family over here. I got to go all the way out to visit them.”
Thus, Sophie still considered the South Side to be her home because of her familial ties and
familiarity with the area.
Childcare
One primary way in which removal from social networks was difficult for participants was lack
of access to previous forms of childcare. Many participants used informal childcare networks no
longer accessible from their new neighborhoods or that required long commutes to drop their
children off with family or friends, which created pressure in their daily schedules. Nikki, whose
Gautreaux unit was in the suburbs, has three children. She had issues with childcare and had to
travel quite a distance to drop the children off at their grandparents’ house when she was working
and they were not in school. Nikki explained this difficulty:
“I don’t have anybody to get these kids. I don’t have the money to take ‘em to the daycare center
over here, you know? The transportation. This place won’t take ‘em until this time. By the time I
get them there, and I get back on my route, I’m late for work.”
Moving closer to family to receive childcare assistance was common among those respondents
who made secondary moves. When asked about her primary reason for making a secondary move,
Francine said:

?? Policy Issues in Public and Assisted Housing
Boyd
“Actually, to be closer to my family, actually, and the benefit of somethin’, you know? When I
get sick, you know, I have someone [to] watch my daughter. So, that’s really the number one
priority.”
Transportation
Gautreaux Two respondents who moved farther away from the city proper had a difficult time
with transportation, because public transportation becomes less available and more sporadic in
these areas. Owning an automobile was a prohibitive expense for many of these families; therefore,
reliance on public transportation was necessary for many. Some complained about more minor
issues of parking, while others found the lack of good transportation and distance from relatives to
be incredibly difficult, making transportation a primary reason for moving. Yolanda explained how
lack of parking was a deterrent to her family’s visiting her:
“[My family] don’t come too much. Because when they come, [there’s] nowhere to park. Yeah,
[there’s] nowhere to park so they like stoppin’ me from havin’ visitors. And then when they do
come, you know, because from here to Chicago, it don’t seem too far to some people, but some of
‘em say it’s too far to be, too far to drive. And then they come, [and they] have to turn around an’
go back home [because there is no parking]. The parking is crazy out here.”
Some respondents had difficulty getting to their jobs, to childcare, and to their children’s schools.
When she moved to her Gautreaux unit in the suburbs, Lashonda kept the same job at a retail
store that she had when she lived in her baseline neighborhood. A few months after making the
Gautreaux move, she lost her job because she had a lot of transportation problems and was often
late for work. Living closer to jobs was a primary reason for Lashonda’s secondary move back to a
nonopportunity area in the city.
For those respondents who had cars, the long commute time was very cumbersome. Many
respondents also had a hard time with shopping and visiting healthcare providers; thus, navigating
the opportunity areas was difficult. Some respondents lacked the support network of people who
previously gave them rides, or they previously lived closer to these services. Many respondents still
used their baseline area services, including healthcare services, and the distance to these services
presented challenges. Tara still used the hospital in her baseline area and never became familiar
with her Gautreaux neighborhood resources:
“I don’t even know where the nearest hospital is. No. I would have to drive all the way to the
South Side if I had [an] emergency. I would have to go to South Side.”
Public transportation was a primary reason why Joan left her Gautreaux area in the suburbs to
move back to Chicago. She explained:
“What made me want to come back to the city? Well, the transportation. Transportation-wise, I
don’t have a car. It was like, if I’m in the city, [there are] buses here, buses there. I like that.”
Joan’s story demonstrates that transportation is a primary issue; the suburbs do not provide
adequate public transportation. For respondents who have no vehicle and who are removed from
their social networks of people who previously gave them rides, the need for accessible public
transportation was a motivating factor in their decision to move back to Chicago from the suburbs.

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The Role of Social Networks in Making Housing Choices:
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Children
The experiences of the respondents’ children in the new neighborhoods also demonstrate the
importance of social networks and the potential barriers to creating new networks. Some children
experienced racially motivated incidents in their neighborhoods and schools, which contributed
to the inability of some of the children to adapt to their new neighborhoods and schools. Akilah’s
10-year-old son was called “nigger” repeatedly by several of the young children in their Gautreaux
neighborhood in the North Side of Chicago, and such incidents point to the salience of race in
creating possible barriers to the formation of social ties in the respondents’ new neighborhoods.
Some respondents explained that their children found the Gautreaux neighborhoods boring and
missed their baseline friends and visited them frequently, which illustrates the effect that distance
from social networks had, even on the children. Nikki explained that her children thought the
Gautreaux neighborhood in the suburbs was boring:
“[My children] play with some of the kids on the block, but not too much, not too much. So it’s,
I mean, it’s really boring out here. It’s really boring. It’s nothing. Hey, you know, when I’m here
and I’m out, I’ll turn jump rope for one and you know, come out and play. But, it’s like, they don’t
know what to do. I stick them outside, and they don’t know what to do.”
Nikki made a secondary move to a nonopportunity area in the city because she wanted to be closer
to her family network and her children wanted to be closer to their friends. Her oldest son was
experiencing many problems in the school in the suburbs, and she sent him to live in the city with
his grandparents, even before the rest of the family moved back to the city, so that he could attend
a public school in the city. The experiences of her children and the difficulty they had making the
transition to life in the suburbs were crucial factors in Nikki’s decision to move back to Chicago
from the suburbs.
Some respondents kept their children in their baseline schools even after they moved to their
Gautreaux units. Maria made a Gautreaux move to the North Side of Chicago, but she kept her
three children in the same school they attended when they lived in Robert Taylor Homes. She
explained:
“I just want them to stay in one school because it changes them, you know? How schools be
different, teaches them different. It would probably knock them off, so since they used to that,
I let them go, just stay right there. They been going there, and I was raised like that, getting
transferred, transferred, transferred, transferred, and I didn’t want that [for my kids].”
For Maria, keeping her children in the same school provided them with a sense of stability. Other
respondents recognized that their children were doing better in the higher quality Gautreaux
area schools, but this was not a strong enough factor to keep them from moving. Olivia, whose
grandson lives with her, said one of the main reasons she moved to the suburbs was to provide her
grandson with a better education. She recognized that he was doing much better in the school in
her Gautreaux neighborhood, but she ended up moving to a nonopportunity area anyway because
of her health issues and her desire to be in an area with better public transportation. Olivia said:
“That’s the only part that makes me really hate to move. ‘Cause he doing really, really good in
school. He doing better in school out here than he ever did in his whole entire life.”

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Karen also discussed the importance of education and the quality of schools in opportunity areas,
but she also made a secondary move from the suburbs to a nonopportunity area:
“Like I said, I’m leaving the door open for an opportunity area. I know if I find an opportunity
area, and move there, I know the schools are much better, and they have more programs, you
know, available for your children, ‘cause education is very important, you know?”
Even though respondents recognized the quality of the schools in the opportunity areas, other
costs, such as transportation and distance from family networks, outweighed this benefit.
Most of the respondents who made secondary moves were geographically distant from their social
networks in their baseline neighborhoods, which resulted in issues of childcare, distance to em-
ployment, and transportation, particularly for those respondents whose placement neighborhoods
were in the suburbs. As Shlay and DiGregorio (1985) discuss, women, particularly low-income
single mothers, rely on public transportation provided by cities, and they want to live near their
social and kin networks.
Public Housing
Respondents who moved through the Gautreaux program did not automatically adjust to their new
neighborhoods and faced other issues, including poor-quality units and problematic landlords, all
of which contributed to their desires to make secondary moves, often to nonopportunity areas. Yet,
these respondents did not move back to public housing, and, although they missed things about
their baseline areas, most were incredibly glad to be away from public housing. Whitney explained
how happy she was to move out of Altgeld Gardens, one of Chicago’s most isolated South Side
public housing developments:
“The Gardens…it was depressing. I had to get [my kids] out of there. There’s nothing I miss from
out there and nothing I wanna go back to. Nothing. The Gardens just pushed me into the real
world. That’s all it did. And it’s real world, real situations. I’m not gonna say I wanna go back.
No.”
The Gautreaux program was still incredibly valuable for the respondents who made secondary
moves, and it is important to compare the experiences of those who moved to nonopportunity
areas with those who made secondary moves to other opportunity areas.
Stayer Comparison
Of the Gautreaux movers in the sample, 47 percent were still in their Gautreaux units by 2005.
Comparing the stories of the secondary movers with the stories of the stayers once again highlights
the importance of social networks, childcare, and transportation in shaping the experiences of
respondents in their Gautreaux neighborhoods. The secondary movers appreciated many of the
same things about their Gautreaux neighborhoods as the stayers did. Likewise, the respondents
who stayed in their placement neighborhoods faced many of the same challenges that the movers
did, but they were better able to finds ways to adapt. The stayer respondents either had family or
friends in their placement neighborhoods or were still able to see their family and friends despite
the distance, and they still received network support from them (for example, childcare). Some

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The Role of Social Networks in Making Housing Choices:
The Experience of the Gautreaux Two Residential Mobility Program
respondents specifically chose their placement neighborhoods because they already had family or
friends living in those areas.
Melissa moved to the suburbs from LeClaire Courts on Chicago’s Southwest Side, and some of
her friends from this area also moved through Gautreaux Two to the suburbs. These friends really
helped Melissa with her transition to the suburbs:
“Yeah, [I have] friends that came from my old neighborhood. [They live] about five minutes
[away], and I go visit them, go to the store and stuff like that. It helps me adjust more, you know,
because I know somebody from my old neighborhood here.”
Beatrice chose her Gautreaux unit in the suburbs specifically for its proximity to her family:
“The reason why, we just looked [in] this area, [was] because I live[d] in this area. And it wasn’t
too far from my aunt, or you know, her sisters and stuff like that. So we wanted to stay in the
same area with our family right down the street.”
For these stayers, having family and friends in the area to which they moved was a significant fac-
tor in their ability to adjust to their new areas and to continue to receive crucial network support.
Neighborhood Networks
The stayer respondents were more likely to create social ties in their new neighborhoods by getting
involved in neighborhood activities and making friends with their neighbors than the secondary
movers were. Vanessa moved to the far North Side of Chicago and made friends with some of her
neighbors. She even provided daycare for one neighbor’s children. In contrast to the stories of
several respondents who made secondary moves, Vanessa said she got outside more after moving
to her Gautreaux neighborhood:
“I mean, I do more things than I used to. The only thing I used to do, was either go to the show or
go to my mom’s house or something like that. But here, I take more walks. I’m an inside person,
but I find myself now going outside more. I’ll walk down the bike path. Or I may decide to walk
further up Sheridan into Evanston. I find myself outside doing a lot more walking than [before].
Everywhere I went there, I would take the bus. It’s just, I learn the neighborhood by walking
around and learning the different little things, the activities and stuff they have in the neighbor-
hood. So I find myself getting outside more here, around here, than I did [before].”
Evelyn moved to an opportunity area on the Southwest Side of Chicago, and she quickly devel-
oped a strong neighborhood network. On the day that she moved into her unit, her neighbors
introduced themselves and showed her around the neighborhood:
“I had a couple of people in the neighborhood to show me around, different little places, little
social groups, where to vote, play bingo, stuff like that.”
This outreach helped Evelyn feel connected to both her neighbors and the resources in the area and
made the transition to her new neighborhood smoother. She took advantage of the opportunities
around her, and having her neighbors connect her to the area resources was crucial to her adjustment.

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Transportation
The respondents who stayed in their placement neighborhoods also found ways to make trans-
portation work—they either had cars or lived in areas with good public transportation. Other
respondents received rides from family or friends who lived nearby. Many respondents appreciated
the convenience of the areas where they lived. Another reason that Evelyn loved her Gautreaux
neighborhood so much is the convenience of it:
“I love it, ‘cause everything’s right here. The store’s on the corner, restaurant’s right up the street.
Either way you go, restaurants around, little places. Bus stop right outside. Don’t have a car,
drop you off, so, nope…love everything. Love everything.”
Some respondents found opportunity areas close to their jobs, and this helped make the transition
easier. Lauren felt like she got her job at a department store because her Gautreaux unit is close to
the store:
“If I would have never moved out here, I wouldn’t have never had this job. You know, ‘cause I
would have never looked down here.”
Adele also found a Gautreaux unit closer to her job in the suburbs:
“So, that’s the advantage of me moving, because when they gave it to me and I could move, I
said, ‘Well, let me see if I can find something closer to work.’”
Respondents who did not need to travel long distances for work or childcare and who either had
cars or the network support of people who gave them rides were much more capable of adjusting
to their new neighborhoods than those who had difficulties with transportation. Adequate access
to transportation was a crucial factor that kept respondents in their Gautreaux neighborhoods.
Children
Many of the respondents wanted their children to be in areas with diversity, as Vanessa said when
she discussed how her Gautreaux neighborhood on the North Side of Chicago has been for her
two children:
“It’s been really good for them. Deanna has a lot of different friends as far as races, and that’s
something that I wanted them to experience—the different nationalities of people and how it can
be an advantage or disadvantage to you.”
Many of the stayer children became involved in their neighborhoods in different activities and
made friends in their schools and the neighborhood in general. Evelyn’s two children were in-
volved in an after-school program at the Boys and Girls Club, which is an income-based program,
making it affordable for her to send them there:
“I wanted a change, new environment. Like I was explaining at first over there, Altgeld Gardens,
which is a projects, it was bad: drug dealers, shootings and the kids really couldn’t come out and
play. At a certain [time you would be told], hey, you can’t have your kids out, ‘cause such and
such is going to shoot, or whatever. A whole bunch of stuff. But now, they go out. [There are]
parks around. Basketball court, lunch, social activities and stuff, after school events for them. A
whole bunch of little stuff, so it’s nice.”

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The Role of Social Networks in Making Housing Choices:
The Experience of the Gautreaux Two Residential Mobility Program
Having an income-based program was an important resource that the community provided that
enabled Evelyn’s children to take advantage of neighborhood activities.
A lot of children did better in their Gautreaux area schools than in their baseline schools, and
respondents recognized the high quality of the opportunity area schools. Veronica discussed
wanting to stay in her Gautreaux area in the suburbs for the sake of her niece who is doing well in
her new school:
“Yeah, because I want to keep Tiffany in [her new school]. You know, that’s my whole thing. You
know, she’s doing so well. Like I said, she’s in honors at school, and I don’t want to pull her away
from that. Wherever I move, she might get discouraged and go down, you know? I want to keep
her head up.”
Stayer respondents also reported that their children are doing better in their opportunity areas in
general and that they like their neighborhoods. The children of the respondents who stayed in
their placement neighborhoods were more likely to become involved in different activities in their
neighborhoods and make friends in their schools than the children of the movers.
Vanessa reported that her daughter is doing better in the Gautreaux area than she did in her
baseline neighborhood:
“There, you have to let people know, I’m not scared of you, and you know, you have to be always
on the defensive where you have to have your guard up at all times. But here, she’s starting to
let her guard down. She’s starting to be more relaxed. Her temper has changed. She’s now less
aggressive than she used to be. She won’t let nobody pick on her or walk on her, but she’s not as
aggressive as she was before. So I think her surroundings [are part of that]; her teachers that she
have, the input that they give, her friends, and me. You know, [I’m] still saying the same things
that I said before, [but now] she starting to hear it. It’s kind of sticking now.”
Mia explained that her son did not like the Gautreaux area at first, but he adjusted and now does
not want to move:
“It was a culture shock to him. The first year, he didn’t like it here. He really hated it. He was the
only black kid in his classes. Now he don’t want to move from here. He also doesn’t like going to
the projects anymore.”
The story of Mia’s son demonstrates that it takes time for children to adjust to new neighborhoods
and develop connections with other youth as well as neighborhood and school resources.
Conclusion
By 2005, 53 percent of the Gautreaux Two participants in the qualitative sample had made a
secondary move, and 81 percent of these moves were to nonopportunity areas. This high percent-
age of secondary moves to less advantaged neighborhoods calls for assessment of the factors
influencing families’ experiences in their new neighborhoods and decisions about whether to stay
in their placement neighborhoods. The results of this analysis show that social networks are a key
factor in decisions about moving, because distance from kin and support networks and difficulty

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Boyd
in creating new social ties in placement neighborhoods result in social isolation and transportation
difficulties and motivate secondary moves. Family responsibilities such as childcare and caring for
other family members exacerbated the effect of moving away from baseline neighborhoods with
kin networks. On the other hand, social network factors were also primary reasons why some
respondents remained in their placement neighborhood. Stayer respondents were more likely
than secondary movers to have moved to a neighborhood where they already knew people, and
they were able to maintain ties with kin, develop relationships with new neighbors, and become
involved in their placement neighborhoods. Access to good public transportation or the use of a
car facilitated the maintenance of social ties.
Briggs (1998) offers an analysis of social capital that provides insight into the different facets of
the Gautreaux participants’ social networks. He classifies two dimensions of social capital: social
support and social leverage. Social support is the type of social capital that helps one to get by and
cope with one’s circumstances. This type of social capital is particularly important for the poor and
involves having locally based, homogenous social ties. Social leverage, on the other hand, is having
access to more diverse ties that enhance one’s opportunities and help one get ahead (Briggs, 1998).
Although the baseline neighborhoods of the Gautreaux participants may not have provided much
social leverage, they did provide crucial social support. When the women moved to new neighbor-
hoods, these social support networks were often disrupted because the social ties needed to create
these networks take time to develop, and race, class, gender, and spatial barriers can make the
creation of these social ties challenging.
It is important to recognize the barriers to creating social ties in new neighborhoods and to
consider what other support services need to be implemented to assist movers in their transi-
tions to new neighborhoods. As Reed, Pashup, and Snell (2005) discuss in their analysis of the
Gautreaux Two program’s influence on labor force participation, many participants who moved
did not choose their neighborhoods based on specific occupational or educational opportunities
and, therefore, do not have these specific ties to their placement neighborhoods. As this analysis
shows, participants who moved to a neighborhood where they already had a social network were
more likely to feel connected and remain in the placement neighborhood. Thus, having preexisting
ties to a neighborhood assists in the transition process and the lack of those ties makes the process
much more difficult.
As Boyd et al. (2007) suggest, several policy recommendations include encouraging families to
move to neighborhoods where they have family or friends, or to facilitate the ability for people to
move in family groups rather than individually. Another possibility is to require the initial voucher
to be used for 2 years in the placement neighborhood rather than just 1, which would provide a
longer timeframe for participants to develop new social ties before making decisions about moving
elsewhere. These policy recommendations involve possible changes in the way housing mobility
programs are designed and emphasize the need for further preplacement location counseling
to give voucher recipients a more realistic picture of the challenges they may face and ways to
mitigate these challenges. An additional policy implication of this analysis is the importance of
continued program assistance for participants beyond the initial placement, because families who
move to new neighborhoods need more support connecting with neighborhood networks and
services to overcome potential barriers of race, class, and gender differences. Local institutions

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The Role of Social Networks in Making Housing Choices:
The Experience of the Gautreaux Two Residential Mobility Program
such as churches, community groups, and schools can help families make the transition into new
neighborhoods and support both adults and youth in connecting to peers and resources in the
community to gain necessary social capital that could result in participants’ ability to get ahead.
The results of the Gautreaux Two program have implications for the HCVP because families who
receive a voucher likely face many of the same obstacles that the Gautreaux Two participants did.
The insight from the Gautreaux Two participants’ experiences can shed light on the processes and
outcomes of the HCVP, because it is important to know the reasons behind families’ residential
moves. The policy recommendations for the Gautreaux Two program could prove helpful when
considering what further support systems would benefit HCVP recipients. Future qualitative
research should be done with HCVP recipients in other metropolitan areas to further our under-
standing of how recipients make housing choices and what additional resources recipients need.
Acknowledgments
I am grateful to Kathryn Edin, Greg Duncan, and James Rosenbaum for providing access to the
data and to the researchers who collected and processed the data. I thank Kathryn Edin, Susan
Clampet-Lundquist, Kimberly Goyette, Anne Shlay, David Elesh, Sherri Grasmuck, Dustin Kidd,
and Barbara Haley for helpful feedback.
Author
Melody L. Boyd is a Ph.D. candidate in sociology at Temple University.
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Community, and Metropolitan Influences,” American Journal of Sociology 102 (4): 1040–1084.
Spain, Daphne. 2002. “What Happened to Gender Relations on the Way from Chicago to Los
Angeles?” City and Community 1 (2): 155–168.
Stack, Carol B. 1974. All Our Kin: Strategies for Survival in a Black Community. New York: Harper &
Row.
Waldinger, Roger. 1995. “The ‘Other Side’ of Embeddedness: A Case Study of the Interplay
between Economy and Ethnicity,” Ethnic and Racial Studies 18: 555–580.
Wilson, William Julius. 1996. When Work Disappears: The World of the New Urban Poor. New York:
Vintage Books.

??Cityscape
The Role of Social Networks in Making Housing Choices:
The Experience of the Gautreaux Two Residential Mobility Program
———. 1987. The Truly Disadvantaged: The Inner City, The Underclass and Public Policy. Chicago:
The University of Chicago Press.
Wyly, Elvin K. 1999. “Continuity and Change in the Restless Urban Landscape,” Economic
Geography 75 (4): 309–338.

?? Policy Issues in Public and Assisted Housing

??Cityscape: A Journal of Policy Development and Research • Volume 10, Number 1 • 2008
U.S. Department of Housing and Urban Development • Office of Policy Development and Research
Cityscape
Measuring the
Deconcentration of
Housing Choice Voucher
Program Recipients in
Eight U.S. Metropolitan Areas
Using Hot Spot Analysis
Xinhao Wang
David Varady
Yimei Wang
University of Cincinnati
Abstract
This article describes the use of hot spot analysis to measure changes in the clustering of
U.S. Department of Housing and Urban Development (HUD) Housing Choice Voucher
Program (HCVP) recipients. Hot spot analysis for HCVP recipients in eight metropolitan
areas (New York, Baltimore, Chicago, Cincinnati, Miami, Houston, Los Angeles, and
Phoenix) was performed using the tenant-based data system from HUD’s Office of Public
and Indian Housing. The 2000 and 2005 hot spots were overlaid with 2000 Census
block group data. The hot spot results show that the tendency of HCVP households to
cluster varies by metropolitan area; however, no evidence indicates that HCVP clustering
is declining. Although HCVPs are becoming less concentrated in hot spots in Chicago
and Phoenix, the opposite is true in other metropolitan areas, especially in New York,
Cincinnati, and Baltimore. This type of HCVP concentration is likely to continue as long
as affordable rental housing is confined largely to central cities and older inner suburbs.

?? Policy Issues in Public and Assisted Housing
X. Wang, Varady, and Y. Wang
Introduction
Since 1980, the thrust of U.S. low-income housing policy has shifted from supply-side to demand-
side subsidies. The main focus of the U.S. Department of Housing and Urban Development’s
(HUD’s) Housing Choice Voucher Program (HCVP)—a demand-side subsidy—is to enable low-
income households to afford safe and decent housing. HUD is also using the HCVP to improve
access to low-poverty neighborhoods, and, in turn, to deconcentrate poverty.
1

This study builds on previous HUD national research on the spatial distribution of HCVP recipi-
ents (HUD, 2003, 1998) as well as our own 2005 Housing Studies article (Wang and Varady, 2005)
that provides a snapshot view of the spatial distribution of HCVP recipients in Hamilton County,
Ohio (the core county in the Cincinnati Consolidated Metropolitan Statistical Area [CMSA]). In
this article, we aim to answer the following four research questions:
1. To what extent has the HCVP shifted to the suburbs between 2000 and 2005? That is, to what
extent has the distribution of HCVP clients changed between the central city and the remainder
of the metropolitan area?
2. To what extent has the HCVP helped to deconcentrate poverty? Specifically, to what degree has
the HCVP shifted from high- to low-poverty census block groups?
3. To what extent has the administration of the HCVP led to a decreased propensity for recipients
to cluster spatially? That is, how prevalent is the tendency for HCVP recipients to live in high
HCVP density clusters (that is, HCVP hot spots)? Is the tendency to cluster most apparent in
hot housing markets in which HCVP recipients have the fewest opportunities to find affordable
rental housing?
4. To what extent have these hot spots changed between 2000 and 2005? For particular
metropolitan areas, where have the 2000 hot spots disappeared and where have new hot spotse have the 2000 hot spots disappeared and e have new hot spots
emerged by 2005?
This article helps to clarify whether HUD’s current focus on vouchers without restrictions on
geographical destination and without intensive counseling is leading to a shift of households to
low-poverty neighborhoods where the density of HCVP households is low.
2

The U.S. Census Bureau divides the country into four regions—Northeast, Midwest, South,
and West. When we started this research project, our goal was to select two metropolitan areas
from each region based on three criteria: (1) that each metropolitan area should contain at least
two million people, (2) that each should contain a large proportion of African Americans and
1
HUD’s emphasis on poverty deconcentration is reflected in HUD’s Section Eight Management Assessment System. HUD
provides five points to housing agencies that can demonstrate that an increasing proportion of Housing Choice Voucher
Program households with children are moving to low-poverty census tracts. (See HUD, 2001.)
2
Here we are distinguishing between the regular operation of the Housing Choice Voucher Program and two major HUD
demonstration programs, the Gautreaux Assisted Housing Mobility Program in Chicago and the Moving to Opportunity for
Fair Housing Demonstration.

??Cityscape
Measuring the Deconcentration of Housing Choice Voucher Program
Recipients in Eight U.S. Metropolitan Areas Using Hot Spot Analysis
Hispanics, and (3) that the two selected for each region should include a so-called “hot” housing
market (that is, where it is necessary to have a relatively high income to afford an apartment) and a
so-called “cool” one. Exhibit 1 shows the eight metropolitan areas that we ultimately selected with
respect to total population size and demographic and housing characteristics. Exhibit 2 shows how
the eight metropolitan areas differ in the three criteria. For example, both New York and Baltimore
have similarly high proportions of African Americans, but New York’s housing market is much
hotter than Baltimore’s as shown by the higher income required to afford a two-bedroom apartment
at or below the Fair Market Rent (FMR) standard.
3
3
We originally chose Philadelphia as the comparison metropolitan area for New York. Philadelphia is a Moving to Work
site. For several years, the Philadelphia Housing Authority has been permitted to skip reporting its Housing Choice Voucher
Program data to HUD. We replaced Philadelphia with Baltimore. Although technically Baltimore is a southern city, in
actuality it resembles Philadelphia and other northeastern cities for spatial structure (that is, like Philadelphia, Baltimore has
a historically important downtown core), a declining manufacturing base, and a large African-American population.
Exhibit 1
Study Areas
Sources: Regional boundary data were derived from 2004 ESRI data set; metropolitan boundary data were provided by HUD
in 2006

?? Policy Issues in Public and Assisted Housing
X. Wang, Varady, and Y. Wang
Exhibit 2 Socioeconomic and Housing Characteristics of the Eight Metr
opolitan Ar
eas
Metr
opolitan
Ar
ea
Region
2000
Population
(in millions,
U.S. Census)
Ethnic
Minorities
a

FMR for
T
wo-
Bedr
oom
Apartment
b

($)
Income Requir
ed
T
o Af
for
d
T
wo-Bedr
oom
Apartment at or
Below FMR
Standar
d
b
($)
Median
Household
Income in
1999
(in $, U.S.
Census)
African-
American Residents
(%)
Hispanic
Residents
(%)
Households
Below
Poverty
(%)
New Y
ork
Northeast
12.1
High African-American/
high Hispanic
1,189
45,320
50,795
20.78
21.74
15.49
Baltimor
e
Northeast
2.6
High African-American/
high Hispanic
941
37,640
49,938
27.21
2.02
9.72
Miami
South
2.3
High African-American/
high Hispanic
1,018
40,720
38,632
20.07
57.32
18.08
Houston
South
4.2
High African-American/
high Hispanic
768
30,720
44,761
17.45
29.90
12.34
Chicago
Midwest
8.3
High African-American/
high Hispanic
935
37,400
51,046
18.75
17.12
9.67
Cincinnati
Midwest
1.6
High African-American/
high Hispanic
668
26,720
44,914
12.97
1.08
10.06
Los Angeles
W
est
9.5
High African-American/
high Hispanic
1,269
50,760
45,903
9.63
44.58
15.13
Phoenix
W
est
3.3
High Hispanic
782
31,280
44,752
3.58
25.13
9.88
F
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??Cityscape
Measuring the Deconcentration of Housing Choice Voucher Program
Recipients in Eight U.S. Metropolitan Areas Using Hot Spot Analysis
Methods
We conducted our spatial analysis in three steps. First, we developed a database for the selected
metropolitan areas using ArcGIS, a commercial Geographic Information System (GIS) software
package. Exhibit 3 summarizes the data layers included in the databases. Second, we produced
maps describing the distribution of HCVP recipients for the metropolitan areas. Third, we identi-
fied hot spots in each metropolitan area in 2000 and 2005 and summarized census block group
data by hot spots.
Exhibit 3
Data Layer Data Source
Sources for Data Layers Used
2000 and 2005 individual HCVP recipient points HUD HCVP recipient data
Census block group polygons ESRI compiled from TIGER file
Metropolitan polygons ESRI compiled counties from TIGER file
County polygons ESRI compiled from TIGER file
Central city boundary polygons HUD
Major highway polylines ESRI compiled from TIGER file
ZIP boundary polygons ESRI compiled from TIGER file
Major surface water polylines ESRI compiled from TIGER file
Census block group level attribute data: % African
Americans, % Hispanics, % households below poverty,
median household income, number of rental units
HUD
HCVP = Housing Choice Voucher Program.
We used the longitude/latitude location of HCVP recipients to create an ArcGIS point feature
layer for 2000 and 2005 HCVP recipients. For HCVP recipient records without longitude/latitude
data, we geocoded them using ZIP Code location; that is, we placed them on the center of the
corresponding ZIP Code. We included these records only in the analysis of the distribution of
HCVP recipients with respect to central city versus suburbs and not in the hot spot analyses. The
HCVP recipient records that lacked longitude/latitude and ZIP Code data were excluded from all
analyses. Exhibit 4 summarizes the data for each metropolitan area.
4
After overlaying the census
data with the 2000 and 2005 HCVP recipient data, we produced a series of dot maps to describe
the distribution of HCVP recipients in each metropolitan area. For space reasons, we do not
include or discuss these maps in this article.
4
Note that in 2000, more than 4,000 Housing Choice Voucher Program records for Chicago were missing location data, a
much larger number than for any other metropolitan area.

?0 Policy Issues in Public and Assisted Housing
X. Wang, Varady, and Y. Wang
Exhibit 4 HCVP Information for the Eight Metr
opolitan Ar
eas
Metr
opolitan
Ar
ea
Counties
Y
ear
T
otal
HCVP
Recor
ds
HCVP Recor
ds
Geocoded With
x, y Coor
dinates
HCVP Recor
ds
Geocoded With
ZIP Code
HCVP Recor
ds
Not Gecoded Because of Missing Information
New Y
ork
NY
: Br
onx, Kings, Nassau, New Y
ork,
Putnam, Queens, Richmond, Rockland, Suf
folk, W
estchester
2000
66,865
59,645
6,416
804
2005
132,427
126,355
6,014
58
Baltimor
e
MD: Anne Arundel, Baltimor
e, Carr
oll,
Harfor
d, Howar
d, Queen Anne’
s
2000
10,290
9,915
309
66
2005
21,175
20,979
170
26
Miami
FL: Miami-Dade
2000
13,005
12,547
437
21
2005
21,130
20,758
370
2
Houston
TX: Chambers, Fort Bend, Harris, Liberty
,
Montgomery
, W
aller
2000
9,803
9,341
428
34
2005
16,198
15,785
403
10
Chicago
IL: Cook, DeKalb, DuPage, Grundy
, Kane,
Kendall, Lake, McHenry
, Will
2000
46,462
39,775
2,586
4,101
2005
57,190
54,083
3,099
8
Cincinnati
KY
: Boone, Campbell, Gallatin, Grant,
Kenton, Pendleton; OH: Br
own, Clermont,
Hamilton, W
arr
en; IN: Dearbor
n, Ohio
2000
9,125
8,639
463
23
2005
15,330
14,892
431
7
Los Angeles
CA: Los Angeles
2000
51,736
51,277
429
30
2005
76,134
75,648
485
1
Phoenix
AZ: Maricopa, Pinal
2000
8,708
8,602
92
14
2005
11,811
11,628
140
43
H
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V
P

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6

??Cityscape
Measuring the Deconcentration of Housing Choice Voucher Program
Recipients in Eight U.S. Metropolitan Areas Using Hot Spot Analysis
We used hot spot analysis to identify areas with a high density of voucher recipients. For each met-
ropolitan area, we created an HCVP-recipient density raster grid using the floating grid technique
described in our 2005 Housing Studies article (Wang and Varady, 2005).
5
From the 2000 HCVP
density, we calculated the highest density in a metropolitan area. Hot spot areas include any area
with a density greater than half of this highest density. Then we defined 2005 hot spot areas using
the same density threshold value.
6
Exhibit 5 summarizes the raster cell size, the search radius,
7
and
the hot spot threshold values for each metropolitan area. Note that we identified the hot spot areas
by analyzing the density distribution for each metropolitan area independently. This approach
enabled us to look at HCVP density in tandem with the population density for that particular
metropolitan area. In other words, it would be expected that the HCVP density would be highest in
New York, Chicago, and Los Angeles, where the population density is highest. Because this study
focuses on variations in HCVP concentration among these different metropolitan areas and the
changes in HCVP density between 2000 and 2005, we have not examined the link between HCVP
density and the density of below-FMR units, the density of project-based assistance, or population
density as a whole. Future research should be directed at these statistical relationships.
5
Previously, hot spot analysis has been used in criminological and epidemiological research (Harries, 1999).
6
The literature on spatial analysis provides no operational definition of the term “hot spots.” In fact, Harries (1999) argues
that no single, absolute definition may be possible. Choosing a threshold level for identifying hot spots is as much an art as
a science. For our 2005 Housing Studies article (Wang and Varady, 2005), we experimented with different threshold values.
The 50-percent figure proved best for estimating and describing spatial patterns. Atkinson and Unwin (2002) provide
support for our approach, stating that subjective judgment based on a range of density surfaces is a method that is as good
as any.
7
The search radius is the distance used to define a circle for calculating each cell’s Housing Choice Voucher Program
recipient density.
Exhibit 5
Metropolitan
Area
Cell Size
(feet)
Search Radius
(miles)
Maximum Density
Hot Spot
Threshold
2000 2005
Hot Spot Parameters
New York 500 0.50 3,275 6,778 1,637
Baltimore 500 0.50 1,149 1,766 575
Miami 500 0.30 1,558 2,264 779
Houston 500 0.75 296 532 148
Chicago 500 0.50 2,177 2,050 1,088
Cincinnati 500 0.50 579 979 290
Los Angeles 500 0.50 2,720 3,566 1,360
Phoenix 500 1.00 192 191 96

?? Policy Issues in Public and Assisted Housing
X. Wang, Varady, and Y. Wang
After overlaying the 2000 and 2005 hot spots, we divided the census block groups for each metro-
politan area into four categories:
1. Ongoing hot spot areas: census block groups that included or touched hot spots in both 2000
and 2005.
2. Disappearing hot spot areas: census block groups that included or touched hot spots in 2000 only.
3. Emerging hot spot areas: census block groups that included or touched hot spots in 2005 only.
4. Non-hot spot areas: census block groups that did not include or touch a hot spot in either 2000
or 2005.
We compared these four categories of census block groups for the proportion of poverty house-
holds, African-American households, and Hispanic households.
Census Block Group Analysis
In this section, we first compare the eight metropolitan areas with respect to changes in HCVP
recipient densities. We then compare the eight with respect to the distribution of HCVP recipients
between the central city and the rest of a metropolitan area. Finally, we examine the extent to
which the administration of the HCVP is linked to poverty and minority deconcentration.
Changes in Overall Voucher Density
In all eight metropolitan areas, the number of voucher recipients increased substantially between
2000 and 2005. The greatest increase in absolute terms was in New York (an increase of 66,308
households), but the largest percentage increase was in Baltimore (106.9 percent). The Cincinnati
metropolitan area experienced a 68.3-percent increase. The smallest increase in absolute numbers
was in Phoenix; however, because of the small number of HCVP households in Phoenix in 2000,
the percentage increase, 35 percent, was similar to the change in Chicago.
The wide variation in overall voucher densities among the metropolitan areas, in general, is con-
sistent with differences in overall household density. In 2000, the highest voucher density, at about
27 households per square mile, was in New York; the second highest density, at 13 households
per square mile, was in Los Angeles; and the lowest voucher density, at less than 1 household per
square mile, was in Phoenix.
The New York metropolitan area, with the largest increase in HCVP recipients, also had the greatest
increase in density between 2000 and 2005, doubling from 27 households per square mile to
55 households per square mile. In Baltimore, the HCVP density also doubled during this period,
from 3.9 to 8.0 households per square mile, but the density level in 2005 was much lower than in
New York. With the exception of Phoenix, all the other metropolitan areas experienced fairly large
percentage increases in overall voucher density. In Phoenix, the overall level of voucher density
remained at less than 1 household per square mile.

??Cityscape
Measuring the Deconcentration of Housing Choice Voucher Program
Recipients in Eight U.S. Metropolitan Areas Using Hot Spot Analysis
Houston stood out as the highest of the eight metropolitan areas with respect to the proportion
of HCVP households, with 5.4 percent in 2000 and 9.0 percent in 2005. Phoenix had the lowest
proportion, at less than 1 percent, while the other six cities varied between 1 and 3 percent.
Changes in the Distribution of HCVP Households Between the Central City and
Its Suburbs
The eight metropolitan areas vary considerably in the distribution of HCVP households between
the central city and the rest of the metropolitan area (exhibit 6). In 2000, slightly less than 80
percent of HCVP recipients in the New York metropolitan area lived in New York City. In sharp
contrast, only about 20 percent of HCVP recipients in the Miami region lived in the city of Miami.
Between 2000 and 2005, almost no change occurred in the city/suburban distribution of HCVP
recipients in Miami and Los Angeles. The biggest change took place in Baltimore, where the
proportion of HCVP households living in the city of Baltimore rose from 34 to 49 percent. In con-
trast, the proportion of HCVP households living in the central city decreased slightly in Phoenix,
Chicago, and Houston but increased slightly in Cincinnati and New York.
In general, the concentration of HCVP households in a central city reflects the city-suburban
distribution of all households in that metropolitan area; however, our eight metropolitan areas
Exhibit 6
Proportions of Total and HCVP Households Living in the Central City
Source: 2000 Census and Housing Choice Voucher Program data provided by HUD in 2006
2000 and 2005 Central City Proportion of HCVP Households
Compared With the 2000 Central City Proportion of
Total Households in Each Metropolitan Area

?? Policy Issues in Public and Assisted Housing
X. Wang, Varady, and Y. Wang
showed some variation in distribution patterns. In Chicago and Cincinnati, the proportion of
HCVP households living in the central city was much higher than it was for all metropolitan area
households. In Houston, which differed from the other seven metropolitan areas, the proportion of
HCVP households living in the central city was lower than it was for all metropolitan households.
We also suspected that the concentration of HCVP households in the central city also reflected
the concentration of rental units there. As predicted, for all of the eight metropolitan areas, the
proportion of rental units in the central city was substantially higher than for the metropolitan
area as a whole. For example, in the city of Cincinnati, the incidence of renting (54.3 percent) was
75 percent higher than it was for the metropolitan area as a whole (31.2 percent). In Miami and
Chicago, the incidence of renting in the city was 50 percent higher than it was for the metropolitan
area as a whole.
Poverty and Minority Deconcentration
The presumed linkage between HCVP household concentrations and poverty concentrations variedhousehold concentrations and poverty concentrations varied
between cities. We defined “higher poverty” level census block groups as ones where 20 percent ore defined “higher poverty” level census block groups as ones where 20 percent or
more of the households were below the poverty line.households e below the poverty line.
8
In New York, in 2000, three-fifths of HCVP
households lived in higher poverty census block groups; the comparable figure for Phoenix was groups;s; the comparable figure for Phoenix was
less than one-third. No one pattern of change occurred between 2000 and 2005. In five metro-
politan areas, the proportion of HCVP households living in higher poverty areas declined, with bigeas declined, with big
drops occurring in Phoenix, Houston, and Chicago. The proportion increased slightly in New York, Houston, and Chicago.. The proportion increased slightly in New York
and Los Angeles while it decreased a little in Miami and Cincinnati. The maps (not included here) decreased a little in Miami and Cincinnati. The maps (not included here)
show that, except for Phoenix, almost all the higher poverty areas contain some HCVP households. except for Phoenix, almost all the higher poverty areas contain some HCVP households.
Not surprisingly, there was a link between where HCVP recipients and African-American families
are located. This scenario was most evident in Chicago, where two-thirds of HCVP households
lived in census block groups where 30 percent or more of the residents were African American. groupss e 30 percent or more of the esidents were African American.
Relatively little change occurred, however, between 2000 and 2005 in the extent to which HCVP
households were concentrated in heavily African-American areas. The most significant increase wasThe most significant ease was
in Cincinnati, where the proportion rose from 45 to 50 percent.e the proportion rose om 45 to 50 percent.
The connection between HCVP concentrations and Hispanic populations was most apparent in
Miami and Los Angeles, where in 2000 nearly three-fourths of HCVP households were located inLos Angeles, where inn 2000 nearly ee-fourths-fourthsourthsths of HCVP households were located in
census block groups where Hispanics made up 30 percent or more of the total population. Because population.. Because
Cincinnati had only a small Hispanic presence (no census block groups were 30 percent or more only a small Hispanic presencecee (nono census block groups 30 percent or mor
Hispanic), no meaningful correlation was found between HCVP recipients and Hispanic concentra-), no meaningful correlation was found between HCVP recipients and Hispanic concentra-
tion. Between 2000 and 2005, a fairly large drop occurred in the extent to which Phoenix HCVP. Between 2000 and 2005, a fairly large drop occurred in the extent to which Phoenix HCVP
households lived in heavily Hispanic areas. Meaningful but less noticeable decreases were observedlived in heavily Hispanic areas. Meaningful but less noticeable eases e observed
in Miami and Houston and even smaller changes were calculated in the other three metropolitan areas.
8
Most recent research has defined high-poverty areas as census tracts where 40 percent or more of the households live
below the poverty line.

??Cityscape
Measuring the Deconcentration of Housing Choice Voucher Program
Recipients in Eight U.S. Metropolitan Areas Using Hot Spot Analysis
Hot Spot Analysis
Our hot spot analysis was carried out in three steps. First, we computed the cell-based HCVP re-
cipient densities for the eight metropolitan areas for 2000 and 2005. Next, we compared the eight
areas’ hot spots with respect to the size of the hot spots, their spatial distribution, and changes
between 2000 and 2005. Finally, we overlaid the hot spots on census block group data and maps,
thereby enabling us to compare ongoing, disappearing, emerging, and non-hot spot areas.
Changes in Density
After dividing the nonzero HCVP density cells into four quartiles based on the 2000 densities,
we calculated the mean density for each quartile and mapped the results. (Because of space
limitations, these maps are not included in this article.) In general, fairly small differences occurred
among the eight metropolitan areas with respect to mean HCVP density for the first three quartiles.
Wide variation occurred, however, among the eight for the highest density quartile. Although the
mean density for the New York metropolitan area in 2000 was 300 HCVP households per square
mile in the highest density quartile, the mean density in the highest density quartile in Phoenix
was only 48 households per square mile.
We used the 2000 density threshold values to group the 2005 density cells into four classes. A
comparison of the 2000 and 2005 results showed virtually no change for the first three classes but
large increases for the fourth density class. The biggest density increase, from 300 to 498 households
per square mile, or 66 percent, occurred in New York. Baltimore experienced the second largest
percentage increase, 46 percent, rising from 103 to 150 households per square mile. Cincinnati
and Los Angeles also experienced meaningful increases, but Miami’s and Chicago’s increases in
density were negligible. Houston and Phoenix experienced decreases in HCVP density of 17 and 6
percent, respectively.
Overall Description of Hot Spots
Exhibits 7 through 14 show the location of hot spots for the eight metropolitan areas in 2000
and 2005. Exhibit 15 compares the metropolitan areas for changes in the proportion of HCVP
households residing in hot spots. In the New York metropolitan area, most hot spots are in New
York City. The large increase in HCVP households in New York between 2000 and 2005 was
accompanied by a five-fold increase in the size of the area encompassed by hot spots (from 2.9
to 15.7 square miles).
9
Furthermore, the growth in the size of hot spots in New York was accom-
panied by an increase in the mean density of HCVP households in those hot spots (increasing 25
percent from 2,193 households per square mile to 2,743 households per square mile). As a result,
New York stands out from the other metropolitan areas based on the high proportion of HCVP
households living in hot spots (41 percent). New York’s hot spots were entirely concentrated in
New York City in both 2000 and 2005. In 2000, the city hot spots were in northern Manhattan,
southern Bronx, and northern Brooklyn. Between 2000 and 2005, hot spots spread over more of
these three boroughs.
9
Comparisons across metropolitan areas based on hot spot results must be made cautiously; see the concluding section of
this article.

?? Policy Issues in Public and Assisted Housing
X. Wang, Varady, and Y. Wang
Exhibit 7
Hot Spots in New York
Sources: Metropolitan and city boundary data provided by HUD in 2006; census block, highway, and stream data obtained
from the 2004 ESRI data set

??Cityscape
Measuring the Deconcentration of Housing Choice Voucher Program
Recipients in Eight U.S. Metropolitan Areas Using Hot Spot Analysis
Exhibit 8
Hot Spots in Baltimore
Sources: Metropolitan and city boundary data provided by HUD in 2006; census block, highway, and stream data obtained
from the 2004 ESRI data set

?? Policy Issues in Public and Assisted Housing
X. Wang, Varady, and Y. Wang
Exhibit 9
Hot Spots in Miami
Sources: Metropolitan and city boundary data provided by HUD in 2006; census block, highway, and stream data obtained
from the 2004 ESRI data set

??Cityscape
Measuring the Deconcentration of Housing Choice Voucher Program
Recipients in Eight U.S. Metropolitan Areas Using Hot Spot Analysis
Exhibit 10
Hot Spots in Houston
Sources: Metropolitan and city boundary data provided by HUD in 2006; census block, highway, and stream data obtained
from the 2004 ESRI data set

?0 Policy Issues in Public and Assisted Housing
X. Wang, Varady, and Y. Wang
Exhibit 11
Hot Spots in Chicago
Sources: Metropolitan and city boundary data provided by HUD in 2006; census block, highway, and stream data obtained
from the 2004 ESRI data set

??Cityscape
Measuring the Deconcentration of Housing Choice Voucher Program
Recipients in Eight U.S. Metropolitan Areas Using Hot Spot Analysis
Exhibit 12
Hot Spots in Cincinnati
Sources: Metropolitan and city boundary data provided by HUD in 2006; census block, highway, and stream data obtained
from the 2004 ESRI data set

?? Policy Issues in Public and Assisted Housing
X. Wang, Varady, and Y. Wang
Exhibit 13
Hot Spots in Los Angeles
Sources: Metropolitan and city boundary data provided by HUD in 2006; census block, highway, and stream data obtained
from the 2004 ESRI data set

??Cityscape
Measuring the Deconcentration of Housing Choice Voucher Program
Recipients in Eight U.S. Metropolitan Areas Using Hot Spot Analysis
Exhibit 14
Hot Spots in Phoenix
Sources: Metropolitan and city boundary data provided by HUD in 2006; census block, highway, and stream data obtained
from the 2004 ESRI data set
Baltimore also experienced large increases in the number of hot spots and the area covered by
them. In 2000, the Baltimore metropolitan area contained only two small hot spots, both outside
the city of Baltimore in Baltimore County, one to the west and one to the east of the city. Between
2000 and 2005, the size of hot spots in Baltimore rose from 0.3 to 2.7 square miles. Most new hot
spots were in the city of Baltimore, particularly in sections of east, west, and southwest Baltimore.
By 2005, the city had eight hot spots and the county had five. Baltimore’s hot spots are in neigh-
borhoods populated heavily by African Americans and low-income households.
Topography appears to play a role in accounting for variations in the number of hot spots in the
two midwestern metropolitan areas. Cincinnati is a hilly city, which results in relatively well-
defined communities and that, in turn, leads to spatially concentrated HCVP populations and many
hot spots (44 in the metropolitan area in 2005, up from 15 in 2000). The Cincinnati metropolitan
area’s hot spots are mostly in the northern sector of Hamilton County, and most of those are within
the city limits. Three older Kentucky cities across the Ohio River from Cincinnati (Covington,

?? Policy Issues in Public and Assisted Housing
X. Wang, Varady, and Y. Wang
Newport, and Dayton) have hot spots. Three-fourths of the growth of Cincinnati’s hot spots over
the 5 years took place in the city of Cincinnati, with the biggest change being the emergence of
new hot spots to the northeast and northwest of downtown. The area northeast of downtown is
located in Cincinnati’s African-American ghetto; the one that is northwest of downtown and on
the western side of the industrial Mill Creek Valley is in a racially changing area containing garden
apartments built in the 1950s, 1960s, and 1970s.
In contrast, Chicago has few hot spots, and the hot spot distribution remained relatively stable
between 2000 and 2005. The small number of hot spots may reflect the fact that HCVP density is
uniformly high in large swaths of the West Side and South Side, where many HCVP recipients live.
This uniformity would depress the number of hot spots. In 2005, five of the seven Chicago hot
spots were located on the South Side, one on the western edge of the city, and another to the north
of downtown. Chicago’s hot spots are in more heavily African-American and higher poverty areas.
In both southern metropolitan areas—Miami and Houston—the size of the hot spots increased
between 2000 and 2005; however, the total area encompassed by the hot spots was far larger in
Houston than in Miami. In 2000, a major Miami hot spot was located to the west of Interstate 95
(I-95), but many small hot spots also were dispersed throughout Dade County. Although many
areas identified as hot spots in 2000 remained as hot spots in 2005, several new hot spots emerged
Exhibit 15
Metropolitan
Area
Year
Total HCVP
Households
HCVP Households
(Number)
Within Hot Spots
(Percent)
HCVP Households in Hot Spots
New York
2000 59,645 5,475 9
2005 126,355 51,297 41
Baltimore
2000 9,915 470 5
2005 20,979 3,300 16
Miami
2000 12,547 1,686 13
2005 20,758 3,660 18
Houston
2000 9,341 1,632 17
2005 15,785 3,715 24
Chicago
2000 39,770 3,304 8
2005 54,078 2,639 5
Cincinnati
2000 8,639 1,623 19
2005 14,892 5,517 37
Los Angeles
2000 51,276 1,483 3
2005 75,648 4,429 6
Phoenix
2000 8,602 1,132 13
2005 11,628 1,132 10
HCVP = Housing Choice Voucher Program.
Sources: 2000 Census block boundary data from 2004 ESRI data set; 2000 Census and HCVP data provided by HUD in 2006

??Cityscape
Measuring the Deconcentration of Housing Choice Voucher Program
Recipients in Eight U.S. Metropolitan Areas Using Hot Spot Analysis
to the west of the city along U.S. Highway 27. Almost all hot spots were in areas with high propor-
tions of Hispanic and low-income households.
Houston’s hot spots were spread throughout the city, but a few were located in the suburbs as well.
In general, the hot spots were concentrated in heavily African-American and high-poverty areas.
Between 2000 and 2005, a new hot spot developed near downtown Houston, and the hot spot in
the south loop expanded. Although more than two-thirds (69 percent) of Houston’s hot spots were
located outside the central city, only about two-fifths (44 percent) of Miami’s were.
In 2000, Los Angeles’ hot spots were concentrated in south and central Los Angeles. A few
more hot spots appeared over the next 5 years, but, with one exception, all were located inside
these same two districts. In general, the hot spots in Los Angeles were located in areas with high
proportions of African-American and low-income households. The one new hot spot that emerged
between 2000 and 2005 was located south of I-405 in a heavily Hispanic area.
In 2005, nearly nine-tenths (86 percent) of the hot spots in the Los Angeles metropolitan area were
concentrated in the city of Los Angeles, while only about one-fourth (27 percent) of the hot spots
in Phoenix were inside the central city. Other hot spots in the Phoenix metropolitan area were in
the eastern suburban cities (Tempe, Scottsdale, and Mesa) and to the west of Phoenix.
Changing Hot Spots
For each metropolitan area, we classified census block groups into one of four categories:
(1) ongoing hot spot areas—that is, contiguous census block groups that fully or partially enclosed
a hot spot in both 2000 and 2005; (2) disappearing hot spot areas—that is, contiguous census
block groups that fully or partially enclosed a hot spot in 2000 but not in 2005; (3) emerging hot
spot areas—that is, contiguous census block groups that fully or partially enclosed a hot spot in
2005 only; and (4) non-hot spot areas—that is, the collection of census block groups that did not
fully or partially include a hot spot in either 2000 or 2005. For purposes of simplicity, we use the
term “ongoing hot spots” rather than “ongoing hot spot census block groups” (and so forth).
Exhibit 16 shows by metropolitan area the breakdown of census block groups between hot spot
and non-hot spot areas. In Los Angeles and Chicago, only about 1 percent of the census block
groups enclosed hot spots. In contrast, in New York and Cincinnati, more than 10 percent of the
census block groups contained or touched hot spots. In the other four metropolitan areas, between
6 and 8 percent of the census block groups contained or touched hot spots.
Exhibit 17 shows the breakdown of the hot spots by category. As shown, no consistent pattern
among the metropolitan areas is evident. Baltimore differed from the other metropolitan areas
because virtually all (93 percent) of the hot spots were emerging ones. New York, Cincinnati, and
Los Angeles had a high, but not overwhelming, proportion of emerging hot spots, but those loca-
tions also had a meaningful proportion (about one-quarter) of ongoing hot spots. In Miami and
Houston, the number of hot spots was split equally between emerging and ongoing hot spots. In
Phoenix, the hot spots were roughly evenly distributed among all three categories (ongoing, disap-
pearing, and emerging). In Chicago, most (73 percent) hot spots were ongoing ones.

?? Policy Issues in Public and Assisted Housing
X. Wang, Varady, and Y. Wang
Exhibit 16
Metropolitan
Area
Census Block Groups
Total Non-Hot Spots Hot Spots
(number) (number) (percent) (number) (percent)
Proportion of Hot Spot Census Block Groups
New York 9,103 8,036 88.3 1,067 11.7
Baltimore 1,893 1,775 93.8 118 6.2
Miami 1,221 1,117 91.5 104 8.5
Houston 2,331 2,192 94.0 139 6.0
Chicago 5,970 5,893 98.7 77 1.3
Cincinnati 1,291 1,093 84.7 198 15.3
Los Angeles 6,395 6,336 99.1 59 0.9
Phoenix 2,229 2,099 94.2 130 5.8
Sources: 2000 Census block boundary data from 2004 ESRI data set; 2000 Census and Housing Choice Voucher Program
data provided by HUD in 2006
Exhibit 17
Metropolitan Area
Hot Spot Census Block Groups
Ongoing Disappearing Emerging
(number) (percent) (number) (percent) (number) (percent)
Three Categories of Hot Spot Census Block Groups
New York 232 22 0 0 835 78
Baltimore 3 3 5 4 110 93
Miami 53 51 8 8 43 41
Houston 55 40 15 11 69 50
Chicago 56 73 14 18 7 9
Cincinnati 54 27 10 5 134 68
Los Angeles 14 24 0 0 45 76
Phoenix 40 31 40 31 50 38
Sources: 2000 Census block boundary data from 2004 ESRI data set; 2000 Census and Housing Choice Voucher Program
data provided by HUD in 2006
If the HCVP successfully promoted poverty and minority deconcentration,
10
we would expect to
see two results: (1) compared to ongoing hot spots, disappearing hot spots would tend to be in
high-poverty and high-minority areas; and (2) compared to ongoing hot spots, emerging hot spots
would tend to be in low-poverty and low-minority areas.
Exhibit 18 shows the socioeconomic indicators for the metropolitan areas’ hot spots and non-hot
spots. In Miami, the proportion of low-income households in disappearing hot spots meets expec-
tations—they are far higher than in ongoing ones (33 and 24 percent, respectively). The results in
10
We emphasize again that poverty deconcentration is not the main goal of the Housing Choice Voucher Program.

??Cityscape
Measuring the Deconcentration of Housing Choice Voucher Program
Recipients in Eight U.S. Metropolitan Areas Using Hot Spot Analysis
Chicago are mixed. As expected, the proportion of HCVP households in poverty is lower in emerg-
ing than in ongoing hot spots (14 percent versus 65 percent). In contrast to expectations, however,
the percentage of HCVP households in poverty areas is also lower in disappearing hot spot areas
(32 percent) than in ongoing hot spots (65 percent). In Houston, emerging hot spots had higher
proportions of households below the poverty level than did ongoing hot spots.
The results dealing with race and ethnicity were similarly inconclusive. As expected, Miami’s disap-
pearing hot spots had higher proportions of African Americans than ongoing or emerging hot spots
Exhibit 18
Metropolitan
Area
Ongoing
Hot Spots (%)
Disappearing
Hot Spots (%)
Emerging
Hot Spots (%)
Non-Hot Spots
(%)
Socioeconomic Indicators for the Non-Hot Spot and Three Categories of Hot Spot
Census Block Groups
Percentage of Households in Poverty
a
New York 39 NA 31 12
Baltimore 19 22 20 8
Miami 24 33 23 14
Houston 33 27 38 1
Chicago 65 32 14 10
Cincinnati 25 14 23 7
Los Angeles 34 NA 28 15
Phoenix 16 18 15 9
Percentage of African Americans
New York 33 NA 36 19
Baltimore 71 42 61 25
Miami 12 30 9 21
Houston 50 25 44 16
Chicago 87 70 77 18
Cincinnati 48 27 44 8
Los Angeles 62 NA 18 10
Phoenix 6 6 5 4
Percentage of Hispanics
New York 59 NA 45 17
Baltimore 2 3 3 2
Miami 76 60 79 55
Houston 32 56 28 30
Chicago 3 8 4 17
Cincinnati 1 2 2 1
Los Angeles 24 NA 32 45
Phoenix 32 40 37 24
NA = not available.
a
We used the 2000 Census definition that identified households below the poverty level.
Sources: 2000 Census block boundary data drawn from the 2004 ESRI data set; 2000 Census and Housing Choice Voucher
Program data provided by HUD in 2006

?? Policy Issues in Public and Assisted Housing
X. Wang, Varady, and Y. Wang
did. At the same time, Miami’s disappearing hot spots had relatively low proportions of Hispanics.
Houston exhibited the opposite pattern. There, disappearing hot spots had higher proportions of
Hispanics, but low proportions of African Americans. In Los Angeles, the results are consistent
with the deconcentration hypothesis; that is, the proportion of African Americans in emerging hot
spot areas was much lower than in ongoing ones (18 percent versus 62 percent). The Los Angeles
pattern was not evident in any of the other metropolitan areas.
Conclusions
Using hot spot analysis, this article has sought to expand the limited literature available on the spatial
distribution of households participating in HUD’s HCVP. To perform this hot spot analysis, we created
two HCVP-recipient density raster grids for eight metropolitan areas, two in each region of the United
States. We defined HCVP hot spots for each metropolitan area as the aggregation of grid cells with
an HCVP density greater than half of the highest 2000 HCVP density in the metropolitan area.
In general, the results should dampen expectations concerning the potential effect of the HCVP on
poverty deconcentration. First, minimal evidence suggested that HCVP was shifting to the suburbs.
Although the proportions of HCVP recipients living in the central city decreased between 2000 and
2005 in Phoenix, Houston, and Chicago, they increased in the other five metropolitan areas.
Second, little indicated that the HCVP was promoting poverty or minority deconcentration. The
proportion of HCVP households in high-poverty and high-minority (African-American or His-
panic) census block groups remained stable during the 2000-to-2005 period. Furthermore, there
was no support for our hypothesis that disappearing hot spots would have relatively high-poverty
and minority-population rates while rates in emerging hot spots would be relatively low.
Third, no evidence emerged to show a decline in HCVP clustering. Although HCVP recipients
were becoming less concentrated in hot spots in Chicago and Phoenix, the opposite was true in the
other metropolitan areas, especially in New York, Cincinnati, and Baltimore.
Fourth, the results also failed to show that the hotter the housing market, the greater the degree of
concentration of HCVP households in hot spots. The concentration of HCVP recipient hot spots in
the hottest housing market, New York, was comparable to the degree of concentration in the cool-
est housing market, Cincinnati. Both levels of concentration far exceeded that in Chicago, another
hot-market area.
Finally, the results show that growth in the HCVP between 2000 and 2005 has affected clustering
patterns in different metropolitan areas in different ways. In New York and Cincinnati, the growth
of the HCVP population has led to a large increase in the number of hot spots, the density in these
hot spots and the areas they encompass, and the characteristics of the population living in the
hot spots. In contrast, Chicago and Los Angeles continue to have few hot spots that cover only
relatively small areas.
The fact that many HCVP households live in central cities and that HCVP clustering continues
should not be surprising. HCVP housing opportunities are limited to available affordable housing; that
is, to properties renting below Fair Market Rent or at the somewhat higher payment standard. If

??Cityscape
Measuring the Deconcentration of Housing Choice Voucher Program
Recipients in Eight U.S. Metropolitan Areas Using Hot Spot Analysis
these units are concentrated in particular areas, housing agencies can do little to move households
to other areas. Other factors, such as proximity to friends, churches, and public transportation, also
contribute to the continued concentration of poverty and to the continued concentration of HCVP
households. Unless the distribution of affordable housing opportunities changes, the clustering of
HCVP clients and their concentration in high-poverty, high-minority areas will continue.
Implications for HCVP Hot Spot Research
We caution against comparing the number of hot spots in one metropolitan area with those in
another metropolitan area. A metropolitan statistical area (MSA) with high HCVP densities may
not have many hot spots if the HCVP households are evenly distributed within large districts
such as Chicago’s South Side and West Side. On the other hand, an MSA with much lower HCVP
densities may contain a large number of hot spots if HCVP households are confined to relatively
clearly defined communities based on topography or housing patterns (for example, Cincinnati).
The most appropriate use of hot spot analysis is for examining changes in the location of hot spots
for particular metropolitan areas over time. For example, are more of them emerging outside the
central city?
Furthermore, hot spot results are sensitive to density distribution and, in particular, to extremely
high densities. In Baltimore, Chicago, and Los Angeles, the highest densities were 25 to 32 times
as high as the mean densities; the ratios in the other five metropolitan areas were between 12 and
20 times as high as the mean densities. Consequently, fewer hot spots were identified in the three
high-density metropolitan areas.
The number of hot spots should not be confused with the number of HCVP households in the
metropolitan area. The latter affects the density value for the whole metropolitan area, not the
spatial distribution within the metropolitan area. For example, some hot spots may be so close to
each other that they merge into a bigger hot spot. In other metropolitan areas, hot spots may be
very distant from each other. The policy implications of these two patterns are quite different.
Hot spot results are very much affected by the threshold value chosen. In our study, we used the
value that was half of the highest 2000 HCVP density for that particular metropolitan area. Other
methods may be used to define the threshold value, such as using three standard deviations above
the mean density. Density is calculated from the number of HCVP households in the area’s vicinity.
Clearly, hot spot analysis is as much an art as it is a science.
Our experience demonstrates that it is more important to use the hot spot method to focus
attention on certain areas rather than highlight the actual hot spot boundaries. Some hot spots
effectively identify the areas of HCVP household concentration. In other cases, however, the actual
boundary may not appear reasonable because, for example, it may cut through an apartment
complex rather than include the entire development. It is important to recognize that density is
calculated for each cell and that the resulting density statistic is a function of the number of HCVP
recipients in the vicinity of the cell. Take, for example, a high HCVP density apartment complex
with a scattering of HCVP households in the vicinity of the complex. The dispersal pattern of
HCVP households outside the apartment complex could affect the actual shape of the hot spot.

?0 Policy Issues in Public and Assisted Housing
X. Wang, Varady, and Y. Wang
Hot spot analysis is most likely to be useful as a research and planning tool if it is combined with
field observation The latter could help in identifying different types of hot spots (for example, 1950s
and 1960s garden apartment complexes, newer low-income housing tax credit developments, or
single-family detached home neighborhoods containing houses for rent). Field observation is a
necessary prerequisite for developing programs that address the unique clustering-related issues
pertinent to these different types of neighborhoods.
Authors
Xinhao Wang is a professor of planning at the University of Cincinnati.
David Varady is a professor of planning at the University of Cincinnati.
Yimei Wang is a graduate student at the University of Cincinnati.
References
Atkinson, P.J., and D.J. Unwin. 2002. “Density and Local Attribute Estimation of an Infectious
Disease Using MapInfo,” Computers and Geosciences 28: 1095–1105.
Frey, William H. 2006. Diversity Spreads Out: Metropolitan Shifts in Hispanic, Asian and Black
Populations Since 2000. The Living Cities Census Series (March). Washington, DC: The Brookings
Institution. Available at http://www.brook.edu/metro/pubs/20060307_Frey.pdf.
Harries, Keith. 1999. Mapping Crime: Principle and Practice. Washington, DC: U.S. Department of
Justice.
National Low Income Housing Coalition (NLIHC). 2005. Out of Reach. Washington, DC: National
Low Income Housing Coalition. Available at http://www.nlihc.org/oor/oor2006/data.cfm?getstate=o
n&getmsa=on&msa=401&state=AZ.
U.S. Department of Housing and Urban Development (HUD). 2003. Housing Choice Voucher
Location Patterns: Implications for Participant and Neighborhood Welfare. Washington, DC:
U.S. Department of Housing and Urban Development. Available at http://www.huduser.org/
publications/pdf/Location_Paper.pdf.
———. 2001. Housing Choice Voucher Handbook. Washington, DC: U.S. Department of Housing
and Urban Development.
———. 1998.1998.. A Picture of Subsidized Households in 1998. Washington, DC: U.S. Department of
Housing and Urban Development. Available at http://www.huduser.org/publications/pubasst/
picture.html.
Wang, Xinhao, and David P. Varady. 2005. “Using Hot Spot Analysis to Study the Clustering of
Section 8 Housing Voucher Families,” Housing Studies 20 (1): 29–48.

??Cityscape: A Journal of Policy Development and Research • Volume 10, Number 1 • 2008
U.S. Department of Housing and Urban Development • Office of Policy Development and Research
Cityscape
Use of Flat Rents in the
Public Housing Program
Meryl Finkel
Ken Lam
Abt Associates Inc.
Abstract
The 1998 Quality Housing Work Responsibility Act (QHWRA) requires public housing
agencies (PHAs) to offer the option of a flat rent (as opposed to an income-based rent)
to residents of public housing. Flat rents are based on market rents and, therefore, the
tenant rent does not vary with income. The U.S. Department of Housing and Urban
Development (HUD) expected that by having the option of paying a flat rent, public
housing residents would not be discouraged from working and increasing their income
because their rent would not increase if their income increased. Similarly, QHWRA’s
flat-rent option was also expected to avoid creating disincentives for continued residency
by families that are attempting to become economically self-sufficient.
HUD implemented the provision on flat rents in 1999. As of the end of 2005, about
105,000 families (of the more than 1.2 million public housing households) were
identified on HUD’s data system as paying either flat rents or ceiling rents.
This article uses extracts from HUD’s Office of Public and Indian Housing Information
Center data system to provide some basic information on the use of flat rents in public
housing, including the types of PHAs, places, and families that have selected a flat rent,
and changes that have taken place in these properties and for these families coincident
with the use of flat rents.
The article shows that, although nearly all PHAs have at least some flat-rent units,
the proportion of flat-rent units in each PHA is generally small. Households paying
flat rents have much higher incomes compared with other public housing residents.
Similarly, a much higher percentage of households paying flat rents reported that most
of their income was from wages compared with other public housing households. Thus,
flat rents appear to be succeeding in allowing residents in these units to increase their
income through employment and to remain in their units even as their income increases.
Rents in units where residents are paying flat rents are substantially higher than in
other public housing units. At the same time, households paying flat rents are virtually
always paying less than 30 percent of their income for rent. In other words, flat rents

?? Policy Issues in Public and Assisted Housing
Finkel and Lam
Background
The 1998 Quality Housing Work Responsibility Act (QHWRA) required public housing agencies
(PHAs) to offer the option of a flat rent (as opposed to an income-based rent) to residents of public
housing. Flat rents are based on market rents and, therefore, the tenant rent does not vary with
income. By having this option, the U.S. Department of Housing and Urban Development (HUD)
expected that public housing residents would not be discouraged from working and increasing
their income because their rent would not increase if their income increased. Similarly, QHWRA’s
flat-rent option was also expected to enable working families, as they become more self-sufficient,
to continue to live in public housing, thereby providing possible positive role models to other
public housing residents.
Before the implementation of QHWRA, HUD had permitted the use of ceiling rents, a provision
that allowed PHAs to place a cap on the amount of income-based rent that could be charged for
public housing. Ceiling rents were cost based, not market based. HUD’s regulations implementing
flat rents indicated that PHAs were permitted to retain ceiling rents that were authorized and
established before October 1, 1999, for 3 years ending September 30, 2002. After that date, PHAs
were allowed to continue to charge ceiling rents, but with several conditions. First, the ceiling rents
had to be equal to the flat-rent (and, presumably, market-based) amounts.
1
Second, the ceiling rent
had to be offered as an annual choice to families and had to be equal to at least the PHA’s minimum
rent amount.
2

According to HUD’s Office of Public and Indian Housing Information Center (PIC) data system,
as of December 2005, about 105,000 families were identified as paying either flat rents or ceiling
rents. More than one-third of these families were residents of units operated by the New York City
1
The existence of ceiling rents can be very beneficial for families with multiple income changes during the year. Although
a family is given the option of selecting a flat rent only once a year, the ceiling rent can go into effect at any time during the
year when the family’s income changes. For example, if a family’s income increases after the family declines the option of
selecting a flat rent for that year and the family’s income-based rent now exceeds the flat rent it would have paid, the ceiling
rent can go into effect immediately and protect the family by capping its rent at the level it would have paid had it selected
a flat rent.
2
Regardless of whether it is a ceiling rent, a flat rent, or an income-based rent, the family must pay at least the minimum
rent amount as determined in the public housing agency’s Admissions and Continued Occupancy Policy.
Abstract (continued)
offer benefits to both the residents and the housing agencies. Residents pay less than they
would under an income-based rent scenario and the PHAs receive a higher rent than
they would from regular public housing tenants. Properties with flat-rent units have a
higher degree of income mixing than other properties do. This finding is as expected
because households in units with flat rents have higher incomes than most other public
housing households do.

??Cityscape
Use of Flat Rents in the Public Housing Program
Housing Authority.
3
Besides these aggregate statistics, however, very little was known about the
type of households and PHAs that have used flat rents.
This article uses annual extracts of data from HUD’s PIC system to answer some basic questions
about the use of flat rents in public housing: What types of PHAs are adopting flat rents in large
numbers? What types of households are choosing flat rents? How has household income changed
coincident with the use of flat rents? How have turnover and income changed in properties with
concentrations of flat rents? The PIC data provide household-level observations on a rich array of
programmatic, tenant demographic, and locational variables. Household identification numbers are
available so that household information from different years can be linked to study changes over time.
Research Questions and Data Sources
The goal of this article is to describe the following aspects of the use of flat rents in public housing:
• Number and characteristics of PHAs using flat rents.
• Characteristics of households in units with flat rents.
• Changes in the use of flat rents by families during the 2003-through-2005 period.
• Assessments of flat rents relative to local rents and relative to income-based rents.
• Dispersion of flat rents across PHAs and a comparison of tract poverty in census tracts with
concentrations of flat rents.
• Changes in wages and turnover in properties with flat rents compared with other properties.
• Income mixing in properties with flat rents compared with other properties.
We used a December 2005 extract from the PIC data system to describe the characteristics of PHAs,
households, and locations of flat-rent units at a certain time. From this data file, we extracted the
subset of households reported to be using flat rents at that time. We used cross-tabulations to
examine the characteristics of those households and their housing agencies.
To describe changes in the use of flat rents over time, we used the annual PIC data system files for
2003 through 2005 and linked them by household identification number for all households that
ever paid flat rents.
4

To compare properties that had clusters of flat-rent residents with other properties, we used all
household records on the 2005 PIC system. To document changes in wages and turnover in these
so-called “cluster” properties relative to other properties required using a linked longitudinal file
for all households in any PHA that had any households with flat rents.
3
Rental Integrity Monitoring reviews that Abt Associates Inc. conducted for HUD indicate that true market-based flat rents
have never been implemented in New York City and that the income-based ceiling rents had not been increased from 1998
through 2006.
4
Although HUD implemented the provision on flat rents in 1999, all Office of Public and Indian Housing Information
Center records for 2000 through 2002 had missing data for the field labeled “flat rent”; thus, the analysis includes only
2003-through-2005 data.

?? Policy Issues in Public and Assisted Housing
Finkel and Lam
A key challenge in this study was identifying households paying flat rents and households pay-
ing ceiling rents among the general public housing population paying income-based rents in the
PIC system.
5
Rent information for public housing households is reported on page 8 as line items
10(a) through 10(u) of HUD Form-50058. According to regulations, PHAs are required to offer
every household the option of paying a flat rent. PHAs are supposed to report the flat-rent amount
for each unit on line item 10(b) regardless of the household’s decision to use flat rents or income-
based rents. Thus, a household record with a flat-rent amount does not imply that the household
has selected to pay a flat rent. By the same token, a household record with a ceiling-rent amount
does not imply that the household has selected to pay ceiling rents. Our examination of the 2000-
through-2005 PIC system data indicates that, until 2003, item 10(b) is never populated and, from
2003 through 2005, only 11 to 16 percent of the household records include a flat-rent amount. As
a result, the work for this analysis is limited to the 2003-through-2005 period. Exhibit 1 shows the
prevalence of flat rents and ceiling rents reported in the public housing stock during the 2000-
through-2005 period. Line item 10(u) explicitly identifies whether a household is paying a flat rent
versus an income-based rent.
5
Originally, HUD intended for agencies to update their flat rents annually to ensure that they remain market based;
however, the final regulation is silent on how often the flat rents must be updated. Some agencies may not have recalibrated
their flat rents since implementation.
Exhibit 1
Year
2000 2001 2002 2003 2004 2005
The Prevalence of Flat-Rent and Ceiling-Rent Amounts Reporting in the Public
Housing Stock, 2000–05
Number of household records
reporting neither a flat-rent nor
ceiling-rent amount
499,643 516,985 463,605 495,783 571,999 589,220
(Percent) 56 51 54 52 55 53
Number of household records
reporting a ceiling-rent amount
398,971 504,573 391,933 351,005 326,255 348,737
(Percent) 44 49 46 37 31 31
Number of household records
reporting a flat-rent amount
0 0 0 88,461 115,652 143,925
(Percent) 0 0 0 9 11 13
Number of household records
reporting both a flat-rent and
ceiling-rent amount
0 0 0 25,590 29,451 33,383
(Percent) 0 0 0 3 3 3
Total 898,614 1,021,558 855,538 960,839 1,043,357 1,115,265
(Percent) 100 100 100 100 100 100
Note: The presence of a household record with a flat-rent or ceiling-rent amount does not imply that the household is being
charged the flat (or ceiling) rent.
Source: Office of Public and Indian Housing Information Center system (PIC system)

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Use of Flat Rents in the Public Housing Program
For this analysis, we used an algorithm that HUD developed to identify which households are actu-
ally paying flat rents, ceiling rents, or income-based rents. The algorithm, described in appendix A,
uses data from lines 10(b) and 10(u) and other data elements from the HUD form.
Exhibit 2 shows the distribution of public housing units by rent type during the 2003-through-
2005 period. In 2005, more than 10 percent of the public housing residents were paying either
flat rents or ceiling rents. As discussed earlier, beginning October 1, 2002, all PHAs were required
to adjust their ceiling rents to the level required for flat rents. In the remainder of this article, we
combine these two categories of public housing units and call them flat-rent units.
As indicated earlier, the New York City Housing Authority did not implement market-based flat
rents as required by HUD regulations, and it did not increase its income-based ceiling rents during
the 1998-to-2006 period. Thus, households in that agency that reported paying ceiling rents are
not being charged market-based rents. It is likely other housing agencies may be following the
same practice.
It is also worth noting that income information for households paying flat rents may not have been
updated in the PIC system during the annual reexamination process. We understand that some
agencies did not know that action/transaction code 12 (flat-rent annual update) existed and that
they may have used code 2 (annual reexamination) without updating the income information.
Conversely, agencies that used transaction code 12 may have updated income information because
the agency had to calculate annually the income-based rent before offering the family an informed
choice between rent types.
Another methodological challenge for this study was in determining empirically when a property
has a cluster of flat-rent residents and when a census tract has a so-called “concentration” of flat-
rent units. To make this determination, we first examined the distribution of residents reported to
Exhibit 2
Year
2003 2004 2005
Number of Public Housing Households by Rent Type, 2003–05
Number of households paying flat rent 37,663 43,774 50,574
(Percent) 4.6 5.3 5.8
Number of households paying ceiling rent 47,189 47,995 48,959
(Percent) 5.8 5.8 5.6
Number of households paying income-based rent 723,316 719,636 766,997
(Percent) 88.3 87.6 87.5
Number of households for which rent type is unknown 10,914 10,075 10,386
(Percent) 1.3 1.2 1.2
Total 819,082 821,480 876,916
(Percent) 100 100 100
Notes: Calculated using HUD’s rent determination algorithm. Records with transaction/action type of “end-of-participation”
are excluded.
Source: Office of Public and Indian Housing Information Center system (PIC system)

?? Policy Issues in Public and Assisted Housing
Finkel and Lam
be using flat rents by project, across the PHAs, and at the distribution of percents of public housing
units with flat rents by tract. Using these empirical distributions, we determined a set of alternative
cutoffs to define concentrations or clusters.
Study Findings
This section presents the findings on aspects of the use of flat rents.
Number and Characteristics of PHAs Using Flat Rents
Exhibit 3 shows the number of PHAs with any flat-rent units and the characteristics of those PHAs
compared with PHAs that have no such units. Nearly all PHAs (88.6 percent) have at least some
flat-rent units. The following points summarize our findings on the number and characteristics of
PHAs using flat rents:
• The distribution of PHAs with flat rents by region is similar to the overall distribution of PHAs.
• Smaller PHAs, however, are less likely to have flat-rent units than are larger PHAs.
• High-performing PHAs, based on Public Housing Assessment System
6
scores, are more likely to
have flat-rent units than are lower-performing PHAs.
Although most PHAs have some flat-rent units, the proportion of flat-rent units is generally small.
Flat-rent units account for less than one-quarter of all PHA units in more than 80 percent of PHAs
with flat-rent units (including 45.5 percent of PHAs with fewer than 10 percent of units). (See
exhibit 4 for details.)
Overall, flat rents are paid for 99,533, or 11.5 percent, of all public housing units. Exhibit 5
presents information on the units with flat rents compared with other public housing units. The
exhibit shows the following results:
• The New York/New Jersey region accounts for only 21 percent of all public housing units but
for more than one-third (37 percent) of all flat-rent units. (Most flat-rent units in this region are
in New York City, which, with 32,179 flat-rent units, accounted for about one-third of all flat-
rent units nationwide). In contrast, the Southeast/Caribbean region accounts for 29 percent of
all public housing units but only 23 percent of flat-rent units.
• More than one-third of flat-rent units nationwide are in PHAs with more than 6,500 units
(again, this number is mostly driven by New York City’s 32,179 flat-rent units).
• Similarly, 65 percent of the flat-rent units nationwide are in high-performing PHAs (again, this
number is driven by New York City’s high-performer status).
6
HUD’s Public Housing Assessment System categorizes the overall performance of each of the nation’s public housing
agencies (PHAs) by integrating assessments from four aspects of performance: (1) financial condition, (2) physical
condition, (3) management, and (4) resident satisfaction. PHAs are categorized as high performers, standard performers,
substandard performers, or troubled performers based on a combination of the scores from the four aspects of performance.

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Use of Flat Rents in the Public Housing Program
PHA = public housing agency. PHAS = Public Housing Assessment System.
Sources: Office of Public and Indian Housing Information Center system (PIC system); PHAS scores
Exhibit 3
PHA Has
Flat-Rent Units
PHA Has
No Flat-Rent Units
All PHAs
Characteristics of PHAs With Any Flat-Rent Units and PHAs That Have No Flat-Rent
Units
Number of PHAs 2,782 322 3,140
Percent of total PHAs 88.6 10.3 100
(percent)
PHA location: HUD region
New England 4.7 10.9 5.3
New York/New Jersey 5.3 5.3 5.3
Mid-Atlantic 5.8 3.1 5.6
Southeast/Caribbean 26.2 19.6 25.5
Midwest 16.9 17.5 17.0
Southwest 21.9 24.6 22.2
Great Plains 11.7 5.9 11.1
Rocky Mountain 3.4 7.2 3.8
Pacific 2.5 2.8 2.5
Northwest 1.6 3.1 1.7
Total 100.0 100.0 100.0
PHA size category (units)
1 to 249 72.4 92.5 74.5
250 to 499 14.8 6.2 13.9
500 to 1,249 8.2 1.3 7.5
1,250 to 6,599 4.1 0.0 3.7
6,600 or more 0.4 0.0 0.4
Total 100.0 100.0 100.0
PHA performance (PHAS) category
High performer 48.4 37.3 47.2
Standard performer 43.7 49.2 44.2
Substandard—financial 4.1 6.3 4.3
Substandard—management 0.1 0.0 0.1
Substandard—physical 1.8 3.1 2.0
Troubled performer 2.0 4.1 2.2
Total 100.0 100.0 100.0
Average PHAS score 86.9 84.0 86.6

?? Policy Issues in Public and Assisted Housing
Finkel and Lam
Exhibit 5
Flat-Rent Units
Other Public
Housing Units
All Public
Housing Units
Characteristics of Units With Flat Rents and Other Public Housing Units—2005
Number of housing units
99,533 766,977 866,510
Percent of all units
11.5 88.5 100
(percent)
PHA location: HUD region
New England 3 7 7
New York/New Jersey 37 19 21
Mid-Atlantic 7 9 9
Southeast/Caribbean 23 30 29
Midwest 11 14 14
Southwest 10 10 10
Great Plains 4 4 4
Rocky Mountain 1 2 2
Pacific 4 5 5
Northwest 0 1 1
Total
100 100 100
PHA size category (units)
1 to 249 23 20 20
250 to 499 13 15 15
500 to 1,249 14 17 17
1,250 to 6,599 15 25 24
6,600 or more 35 23 25
Total
100 100 100
PHA performance (PHAS) category
High performer 65 47 49
Standard performer 31 46 45
Substandard—financial 2 3 3
Substandard—management 0 0 0
Substandard—physical 2 2 2
Troubled performer 1 2 2
Total
100 100 100
PHA = public housing agency. PHAS = Public Housing Assessment System.
Source: Office of Public and Indian Housing Information Center system (PIC system)
Exhibit 4
Percent of Flat-Rent Units
Among All PHA Units
Number of PHAs Percent of PHAs
Distribution of PHAs With Flat-Rent Units, by Proportion of Flat-Rent Units—2005
0 to 10 1,267 45.5
11 to 25 1,009 36.3
26 to 50 441 15.9
51 to 75 53 1.9
76 or more 12 0.4
Total 2,782 100.0
PHA = public housing agency.
Source: Office of Public and Indian Housing Information Center system (PIC system)

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Use of Flat Rents in the Public Housing Program
Characteristics of Households in Units With Flat Rents
Exhibit 6 presents the characteristics of households in units with flat rents. The following list
summarizes the characteristics:
• Households paying flat rents are less likely to include a resident who is disabled compared with
households in other public housing units. (Only 12 percent of households in flat-rent units
reported a head of household who was disabled compared with 22 percent of households in
other public housing units.)
Exhibit 6
Flat-Rent Units
Other Public
Housing Units
All Public
Housing Units
Characteristics of Households in Units Paying Flat Rents and Other Public Housing
Units—2005 Data
Number of housing units
99,553 766,977 866,510
Percent of all units
11.5 88.5 100
Age of household head (average)
51.3 50.5 50.6
(percent)
Disability status of household head

Yes 12 22 21
No 88 78 79
Total
100 100 100
Race/ethnicity of household head
Non-Hispanic White 32 33 33
Non-Hispanic African American 46 43 44
Hispanic 19 21 21
Other 2 3 3
Total
100 100 100
Presence of children in household
Yes 39 41 41
No 61 59 59
Total
100 100 100
Total household income (average)
$28,150 $9,426 $11,659
Household income as percent of area
median
52 17 21
Source of income
Most of income from wages
Yes 68 25 30
No 32 75 70
Total
100 100 100
Most of income from welfare
Yes 1 12 11
No 99 88 89
Total
100 100 100
Source: Office of Public and Indian Housing Information Center system (PIC system)

?00 Policy Issues in Public and Assisted Housing
Finkel and Lam
7
We excluded 4.6 percent of the household records in which the total household income is zero or greater than $90,000
because these levels of income most likely result from coding and reporting errors in the Office of Public and Indian
Housing Information Center system.
8
The higher incomes of households paying flat rents are influenced by the fact that a large percentage of these units are
in New York City. Incomes and rents tend to be higher in New York City than in other places. As shown in appendix B,
however, incomes of households paying flat rents outside New York City are also substantially higher than incomes of other
public housing residents.
9
Program exit rates are similar for households paying flat rents and households in other public housing. Of households
paying other rents in 2003, 14.5 percent ended participation by 2004 and 15.3 percent had an unrecorded exit. Of
households paying other rents in 2003, 16.8 percent ended participation by 2005 and 21.8 percent had an unrecorded
exit. For comparison, as shown in exhibits 7a through 7c, of households paying flat rents in 2003, 13.4 percent ended
participation by 2004 and 13.8 percent had an unrecorded exit. Of households paying flat rents in 2003, 15.3 percent
ended participation by 2005 and 22.8 percent had an unrecorded exit.
• The racial composition and average age were similar in flat-rent units and other public housing
units, as was the probability of having children in the unit.
• Consistent with the goals of flat rents to promote employment and increased income, substantial
differences were evident between the incomes of households paying flat rents and the incomes
of other public housing households. Households paying flat rents had, on average, much higher
incomes in absolute dollars (an average of $28,150 per household compared with $9,426 in
other units) and relative to the local area median (an average of 52 percent of area median
compared with 17 percent in other units).
• Residents paying flat rents were more likely to receive most of their income from employment
compared with residents of other public housing. Of the households paying flat rent, 68 percent
reported that most of their income was from wages compared with only 25 percent of residents
in other public housing, and only 1 percent of households had most of their income from
welfare compared with 12 percent of other public housing households.
7, 8
Changes in the Use of Flat Rents by Families During the
2003-Through-2005 Period
To estimate changes in the use of flat rents, we identified households paying flat rents in 2003
and then tracked the rent type and income information of these households in 2004 and 2005.
As described in the Research Questions and Data Sources section, we could do this for only the
2003-through-2005 period.
Exhibits 7a through 7c show that households often move in and out of paying flat rents. The
following list summarizes the information in the three exhibits.
• About 60 percent of households paying flat rents in 2003 continued to do so in 2004, and fewer
than half (47.5 percent) were still paying flat rents in 2005.
• About 13 percent of households paying flat rents in 2003 were paying other (typically income-
based) rents in 2004, as were 14.5 percent in 2005.
• Most households that were no longer paying flat rents in 2005 had left the program.
9
• Apparently, the main reason households switch from flat rents to nonflat rents is because their
income decreases. Exhibit 7b shows that 78 percent of households that moved from flat rents to
other rents experienced a decrease in income, although about 20 percent experienced an increase.

?0?Cityscape
Use of Flat Rents in the Public Housing Program
• In comparison, most households that continued to pay flat rents experienced an increase in
income over time. For example, as shown in exhibit 7c, nearly 58 percent of the households
that paid flat rents in 2003 and 2004 had an increase in income in 2004. More than 70 percent
of households that were still paying flat rents in 2005 experienced an increase in income relative
to 2003. This increase in income may indicate that flat rents appear to be accomplishing their
legislative intent of enabling higher income residents to remain in public housing as their
income increases.
Exhibit 7a
2004 Status 2005 Status
Number of Units Percent Number of Units Percent
2004 Status and 2005 Status of 84,852 Households Paying Flat Rents in 2003
Paying flat rent 50,506 59.5 40,279 47.5
Paying other rent 11,186 13.2 12,307 14.5
Paying unknown rent type 131 0.2 403 0.5
End of participation 11,365 13.4 13,005 15.3
Unrecorded program exit
a
11,664 13.8 18,858 22.2
Total 84,852 100.0 84,852 100.0
a
Households were classified as “unrecorded program exits” if they were in the Office of Public and Indian Housing Information
Center file in 2003 but not in the later years and if they were not identified as having ended participation.
Source: Office of Public and Indian Housing Information Center system (PIC system)
Exhibit 7b
From 2003 to 2004 From 2003 to 2005
Number of Units Percent Number of Units Percent
Change in Household Income for Households Paying Flat Rents in 2003 and
Switching to Other Rents in 2004 and 2005
Increase 2,040 19.1 2,582 21.8
Equal 319 3.0 69 0.6
Decrease 8,328 77.9 9,172 77.6
Total 10,687
a
100.0 11,823
b
100.0
a
Income information is missing, out of range, or zero for 499 of the 11,186 households that paid other rents in 2004.
b
Income information is missing, out of range, or zero for 484 of the 12,307 households that paid other rents in 2005.
Source: Office of Public and Indian Housing Information Center system (PIC system)
From 2003 to 2004 From 2003 to 2005
Number of Units Percent Number of Units Percent
Exhibit 7c
Change in Household Income for Households Paying Flat Rents in 2003 and Staying
With Flat Rents in 2004 and 2005
Increase 28,974 57.7 27,954 70.4
Equal 11,077 22.1 3,991 10.0
Decrease 10,124 20.2 7,779 19.6
Total 50,175
a
100.0 39,724
b
100.0
a
Income information is missing, out of range or zero for 331 of the 50,506 households that paid flat rents in 2004.
b
Income information is missing, out of range, or zero for 555 of the 40,279 households that paid flat rents in 2005.
Source: Office of Public and Indian Housing Information Center system (PIC system)

?0? Policy Issues in Public and Assisted Housing
Finkel and Lam
Assessment of Flat Rents Relative to Local Rents and Relative to
Income-Based Rents
Exhibit 8 compares rents in flat-rent units and other public housing units with prevailing local
market rents (as proxied by local Fair Market Rents [FMRs])
10
and with income-based rents
(defined as 30 percent of income). We would expect rents in flat-rent units to be similar to market
rents because they are based on market rents and to be higher than rents in other public housing
units because, presumably, market rents are higher than the income-based rents that public
housing residents generally pay.
The following list summarizes the information in exhibit 8:
• Consistent with the expectation that market rents are higher than income-based rents, rents
in units where residents were paying flat rents, averaging $413 per month, were substantially
higher than rents in other public housing units, which averaged $200 per month.
11
• Although we expected rents in units with flat rents to be close to market-rent levels, in fact,
rents in both units with flat rents and other rents were well below comparable market levels (as
proxied by the local FMR). More than half (51 percent) the units with flat rents had rents below
50 percent of the FMR, as did 88 percent of other units. (The fact that flat rents are generally
below the local FMR implies that the FMR is not a good proxy for market rents for public
housing units. Although the FMR is a metropolitan statistical area-wide or nonmetropolitan
countywide measure of rents across the jurisdiction, many public housing units are located in
the low-rent portions of the jurisdiction).
• Households paying flat rents were virtually always paying less than 30 percent of their income
for rent, as shown by the ratio of the flat rent to income-based rent, which is below 1 for 99
percent of flat-rent units. Most (62 percent) were paying less than 75 percent of the comparable
income-based rent. As expected, households in other public housing units typically paid the
income-based rent, defined as 30 percent of income.
12

10
HUD establishes Fair Market Rents (FMRs) for metropolitan areas or nonmetropolitan counties in the country. FMRs
represent HUD’s estimates of the 40th (or, in some cases, the 50th) percentile of recently rented nonluxury apartments in an
area. FMRs are used to determine the amount of the federal subsidy for participants in the tenant-based Section 8 program.
11
As shown in appendix B, rents in units where residents were paying flat rents were substantially higher than in other
public housing units, both in New York City (average flat rent $510) and in other locations (average flat rent $366).
12
About 7 percent of households paying “other rents” paid more than 30 percent of income. According to HUD regulations
(24 CFR 960.253), public housing tenants must pay at least the minimum rent amount as determined in the public housing
agency’s Admissions and Continued Occupancy Policy. For these households, it is most likely that the minimum rent
amount is greater than 30 percent of income. The requirement to pay a minimum rent may explain a portion of the cases
with rents above 30 percent of income. Minimum rent, however, is capped at $50, and the average rent and adjusted annual
income for “other rent” households paying more than 30 percent of income for rent are $403 and $7,979, respectively.
Thus, it is likely that, at least in part, this finding reflects errors in the data.

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Use of Flat Rents in the Public Housing Program
13
The poverty rate is missing for about 11 percent of the units because of missing geocoding information.
Exhibit 8
Flat-Rent Units
Other Public
Housing Units
All Public
Housing Units
Rents in Units With Flat-Rent Units and Other Public Housing Units
Number of housing units 99,553 766,977 866,510
(dollars)
Rent level charged
Mean 413 200 224
Median 416 174 185
Standard deviation 159 137 155
Percentile statistic
10th percentile 225 50 50
25th percentile 296 116 133
75th percentile 495 251 290
90th percentile 619 367 436
(percent)
Rent to FMR ratio
Median 49 27 30
> 1 1 0 0
0.76 to 1 12 2 3
0.51 to 0.75 36 11 14
0.26 to 0.5 49 41 42
< 0.26 2 47 41
Rent to income-based rent ratio
Median 68 100 100
> 1.01 1 7 5
1.00 to 1.01 3 92 81
0.76 to 0.99 34 0 4
0.51 to 0.75 40 0 5
0.26 to 0.5 21 0 3
< 0.26 1 0 0
FMR = Fair Market Rent.
Note: R-square for the current formula is 0.787; 0.927 for the Administration’s proposal.
Source: Office of Public and Indian Housing Information Center system (PIC system)
Dispersion of Flat Rents Across PHAs and Comparison of Tract Poverty in
Census Tracts With Concentrations of Flat Rents
Exhibit 9 shows the census tract poverty rate for each public housing unit.
13
The exhibit shows
that units with flat rents in general are located in tracts with similar poverty rates as other public
housing units.
This finding indicates that households paying flat rents in New York City tend to live in high-
poverty tracts. (Three-fourths of the households paying flat rents are in tracts with a poverty rate

?0? Policy Issues in Public and Assisted Housing
Finkel and Lam
Exhibit 9
Number of Units Tract Poverty Rate (percent) Percent of Units
Distribution of Poverty Rate by Household Rent Type
Flat-rent units 92,207 Average 31
0 to 9 7
10 to 19 24
20 to 29 23
30 or more 46
Other public housing units 677,465 Average 30
0 to 9 10
10 to 19 24
20 to 29 22
30 or more 44
Source: Office of Public and Indian Housing Information Center system (PIC system)
above 75 percent, and the average poverty rate is 40 percent.) This finding is offset by the fact that
households paying flat rents in other locations tend to be in higher income tracts than other public
housing residents. The average poverty rate of census tracts for households paying flat rents in
locations outside New York City is 25 percent (compared with 30 percent for households living in
other public housing outside the New York City area), and only 31 percent of households paying
flat rents in locations outside New York City live in tracts with poverty rates of more than 30
percent (compared with 44 percent of other public housing residents).
14

The next portion of this analysis focuses on differences in poverty rates in tracts with concentra-
tions of flat-rent units versus other tracts with public housing units.
We defined a concentration of flat-rent units in the following ways:
1. Among tracts with any flat-rent units, the average number was 13 flat-rent units; they accounted
for, on average, 19 percent of the tract’s public housing units. Thus, one way to define a tract
with a cluster of flat-rent units is to identify any tract with at least 13 flat-rent units that account
for at least 19 percent of the public housing units in the tract. This definition includes 925 tracts
with 51,637 flat-rent units (or 56 percent of flat-rent units).
2. We could arbitrarily say that a tract with a concentration of flat-rent units must have at least
10 flat-rent units, accounting for at least 10 percent of the tract’s public housing units. This
definition includes 1,740 tracts with 69,156 flat-rent units (or 75 percent of flat-rent units).
3. Similar to the second definition, we could set a higher threshold to define a concentration; for
example, we could require that the tract have at least 20 flat-rent units, accounting for at least
20 percent of the tract’s public housing units. This definition includes 614 tracts with 47,026
flat-rent units (or 51 percent of flat-rent units).
14
See exhibit B-3 in appendix B.

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Use of Flat Rents in the Public Housing Program
We compared tracts with high concentrations of flat-rent units with tracts with high concentrations
of public housing units with other rents (using the same definitions of concentration as was used
for the flat-rent concentration).
15
As shown in exhibit 10, all three of these definitions yield similar
findings regarding concentrations of poverty.
15
Note that tracts with high concentrations of public housing units account for nearly all public housing units (95 to 98 percent,
depending on the definition of concentration used) but only for about two-thirds to three-fourths of tracts, depending on
the definition. These findings mean that a large number of tracts have a very small number of public housing units.
Census Tracts With
a High Concentration
of Flat-Rent Units
Census Tracts With
a High Concentration of
Other Public Housing Units
Census Tracts With
a High Concentration
of Flat-Rent Units
Census Tracts With
a High Concentration of
Other Public Housing Units
Exhibit 10
Census Tracts With a High Concentration of Flat-Rent Units Versus Other Census Tracts With Public
Housing Units
Definition #1: A census tract must have at least 13 flat-rent units, and the flat-rent units must account for
at least 19 percent of the total public housing units in that tract.
Comparison of Tract Poverty Rate
Number of census tracts 925 7,247
Number of flat-rent units or other units covered 51,637 655,965
Percent of flat-rent units or other units covered 56 97
Poverty rate (percent)
Average 25 23
0 to 9 9 17
10 to 19 36 35
20 to 29 25 23
30 or more 30 25
Total 100 100
Definition #2: A census tract must have at least 10 flat-rent units, and the flat-rent units must account for
at least 10 percent of the total public housing units in that tract.
Number of census tracts
Number of flat-rent units or other units covered
Percent of flat-rent units or other units covered
Poverty rate (percent)
Average
0 to 9
10 to 19
20 to 29
30 or more
Total
1,740 7,793
69,156 661,925
75 98
(percent)
25 23
9 18
36 35
26 22
29 25
100 100

?0? Policy Issues in Public and Assisted Housing
Finkel and Lam
Census Tracts With
a High Concentration
of Flat-Rent Units
Census Tracts With
a High Concentration of
Other Public Housing Units
Definition #3: A census tract must have at least 20 flat-rent units, and the flat-rent units must account for
at least 20 percent of the total public housing units in that tract.
Number of census tracts
Number of flat-rent units or other units covered
Percent of flat-rent units or other units covered
Poverty rate (percent)
Average
0 to 9
10 to 19
20 to 29
30 or more
Total
614 6,334
47,026 641,425
51 95
(percent)
28 23
5 15
31 34
27 23
37 27
100 100
Exhibit 10
Comparison of Tract Poverty Rate (continued)
Note: Of the overall public housing units, 11 percent are excluded from this analysis due to missing geocoding information.
Source: Office of Public and Indian Housing Information Center system (PIC system)
16
To identify the tract-level characteristics, we needed to geocode the properties to obtain their census tract. Necessary
information was missing for about 11 percent of all properties.
The following list summarizes our analysis of tract poverty levels:
• Tracts with high concentrations of flat-rent units have slightly higher poverty rates than census
tracts that include high concentrations of other public housing units. This finding might be
driven in part by the fact that smaller, rural PHAs do not have flat rents and some PHAs did not
provide geocodable address information.
16
• According to each of these definitions, less than 10 percent of tracts with high concentrations of
flat-rent units have low poverty rates (below 10 percent poverty). In contrast, 18 percent or less
of tracts with high concentrations of other public housing units are low poverty tracts.
• At the other extreme, depending on the definition used, anywhere from 29 to 37 percent of tracts
with high concentrations of flat-rent units had poverty rates greater than 30 percent. This finding
compares to about 25 percent of tracts with high concentrations of other public housing units.
These findings seem to be contrary to expectations. Because households paying flat rents have
higher incomes, we might expect concentrations of flat-rent units in lower poverty tracts; however,
the data show that tracts with high concentrations of flat-rent units tend to have higher poverty
rates than other tracts with public housing units have. This finding may be a result of the fact that
when HUD introduced flat rents, the Department suggested that a very low flat rent in a less-than-
desirable development could be a strategy for attracting and keeping higher income families and,
thus, stabilizing the community. It also may be driven in part by the fact that flat-rent units in New
York City tend to be in high-poverty tracts. The high concentration of flat-rent units in New York
City could be affecting this result.

?0?Cityscape
Use of Flat Rents in the Public Housing Program
Changes in Wages and Turnovers in Properties With Flat Rents Compared With
Other Properties
Exhibit 11 compares properties with clusters of flat-rent units and other public housing develop-
ments relating to tenant turnover rates and wage increases. We might hypothesize that allowing
residents to pay flat rents would enable residents to stay in their units as their income increases,
thus increasing tenure (and decreasing turnover). Turnover rate is measured as a portion of house-
holds that moved in within the previous year. Similarly, we might expect higher wage increases in
these properties because residents have no negative incentives associated with increased income.
As with defining concentrations of flat-rent units in census tracts, we used three alternative defini-
tions of clusters of flat-rent units in a property.
Exhibit 11
Developments With a Cluster of Flat-Rent Units Versus Other Public Housing Units
Definition #1: A development must have at least 10 flat-rent units, and the flat-rent units must account for
at least 10 percent of the units in the development.
Comparison of Wage Increase and Turnover
Number of developments
Number of flat-rent units covered
Percent of flat-rent units covered
Percent of wage increase (2003 to 2005)
Percent of tenants moved in within past year
Developments With
a Cluster of Flat-Rent Units
Other Public Housing
Developments
2,071 11,039
67,607 31,926
68 32
6 4
14 13
Definition #2: A development must have at least 10 flat-rent units, and the flat-rent units must account for
at least 15 percent of the units in the development.
Number of developments
Number of flat-rent units covered
Percent of flat-rent units covered
Percent of wage increase (2003 to 2005)
Percent of tenants moved in within past year
Developments With
a Cluster of Flat-Rent Units
Other Public Housing
Developments
1,685 11,425
60,893 38,640
61 39
6 3
14 12
Definition #3: A development must have at least 20 flat-rent units, and the flat-rent units must account for
at least 20 percent of the units in the development.
Number of developments
Number of flat-rent units covered
Percent of flat-rent units covered
Percent of wage increase (2003 to 2005)
Percent of tenants moved in within past year
Developments With
a Cluster of Flat-Rent Units
Other Public Housing
Developments
568 12,542
43,099 56,434
43 57
7 4
18 13
Source: Office of Public and Indian Housing Information Center system (PIC system)

?0? Policy Issues in Public and Assisted Housing
Finkel and Lam
1. A property with at least 10 flat-rent units, accounting for at least 10 percent of the units in the
property.
2. A property with at least 10 flat-rent units, accounting for at least 15 percent of the units in the
property.
3. A property with at least 20 flat-rent units, accounting for at least 20 percent of the units in the
property.
To conduct the analysis, we identified projects with a cluster of flat rents in 2003 and tracked the
tenant wage and turnover status of these properties in 2005. The following points summarize our
findings:
• As exhibit 11 shows, according to all three definitions of clusters of flat-rent units, we find that
tenant wages in these properties increased more than in other public housing developments
between 2003 and 2005. Tenant wages increased by about 6 to 7 percent, compared with a
3- to 4-percent increase in other public housing developments.
• Turnover rates in these properties, however, were higher than in other developments, averaging
about 14 to 18 percent compared with a 12- to 13-percent turnover rate in other public
housing developments. (The data are for a development as a whole, and not specifically for the
households paying flat rents.)
Income Mixing in Properties With Flat Rents Compared With Other Public
Housing Properties
A commonly used measure of income mixing is the coefficient of variation of a project’s tenant
income distribution, defined as the standard deviation of the income distribution divided by the
mean household income in the property, expressed as a percentage. The larger the percentage,
the more income mixing a property has. For example, HUD’s data file, “A Picture of Subsidized
Households in 1998,” uses the same measure to assess income mixing across projects in HUD’s
assisted housing programs.
Exhibit 12 shows the average coefficient of variation for properties with any flat-rent units, for
properties with clusters of flat-rent units, and for other public housing properties. As with defining
concentrations of flat-rent units in census tracts, we used three alternative definitions of clusters of
flat-rent units in a property.
1. A property with at least 10 flat-rent units, accounting for at least 10 percent of the units in the
property. This definition includes 1,685 properties with 60,893 flat-rent units (or 61 percent of
flat-rent units).
2. A property with at least 10 flat-rent units, accounting for at least 15 percent of the units in the
property. This definition includes 2,071 properties with 67,607 flat-rent units (or 68 percent of
flat-rent units).
3. A property with at least 20 flat-rent units, accounting for at least 20 percent of the units in the
property. This definition includes 568 properties with 43,099 flat-rent units (or 43 percent of
flat-rent units).

?0?Cityscape
Use of Flat Rents in the Public Housing Program
Regardless of the definition of a cluster used, properties with flat-rent units have a higher degree
of income mixing than other properties. This outcome is as expected because households in units
with flat rents have higher incomes than most other public housing residents.
Exhibit 12
Cluster Definition #1: A development must have at least 10 flat-rent units, and the flat-rent units must
account for at least 10 percent of the units in the development.
Comparison of Income Mixing
Number of developments
Number of flat-rent units covered
Percent of flat-rent units covered
Coefficient of variation of the income distribution
average (percent)
Developments With
a Cluster of Flat-Rent Units
Other Public Housing
Developments
2,071 11,039
67,607 31,926
68 32
65 59
Cluster Definition #2: A development must have at least 10 flat-rent units, and the flat-rent units must
account for at least 15 percent of the units in the development.
Number of developments
Number of flat-rent units covered
Percent of flat-rent units covered
Coefficient of variation of the income distribution
average (percent)
Developments With
a Cluster of Flat-Rent Units
Other Public Housing
Developments
1,685 11,425
60,893 38,640
61 39
65 59
Cluster Definition #3: A development must have at least 20 flat-rent units, and the flat-rent units must
account for at least 20 percent of the units in the development.
Number of developments
Number of flat-rent units covered
Percent of flat-rent units covered
Coefficient of variation of the income distribution
average (percent)
Developments With
a Cluster of Flat-Rent Units
Other Public Housing
Developments
568 12,542
43,099 56,434
43 57
68 60
Note: Coefficient of variation (CV) is defined as the standard deviation divided by the mean. The larger the CV, the more
income mixing there is.
Source: Office of Public and Indian Housing Information Center system (PIC system)
Developments With Any Flat-Rent Units Versus Developments Without Any Flat-Rent Units
Number of developments
Number of flat-rent units covered
Percent of flat-rent units covered
Coefficient of variation of the income distribution
average (percent)
Developments With
a Cluster of Flat-Rent Units
Other Public Housing
Developments
9,925 3,185
99,533 0
100 0
63 52

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Finkel and Lam
Summary and Conclusions
This study used extracts from HUD’s PIC data system from 2003 through 2005 to describe the
characteristics of PHAs and households that use flat rents. Following are some key findings:
PHAs Using Flat Rents
• Nearly all PHAs have at least some flat-rent units; however, the proportion of flat-rent units
is generally small. Flat-rent units account for less than 10 percent of units in nearly half of all
PHAs with flat-rent units.
• Overall, flat rents are paid for about 100,000, or 11.5 percent, of all public housing units.
New York City, with 32,179 flat-rent units, accounted for about one-third of all flat-rent units
nationwide.
Characteristics of Households in Units With Flat Rents
• As expected, households paying flat rents had much higher incomes compared with other
public housing residents. Similarly, a much higher percentage of households paying flat rents
reported that most of their income was from wages compared with other public housing
households. Thus, flat rents appear to be succeeding in allowing residents in these units to
increase incomes through employment and to remain in their units even as their incomes
increase.
• The higher incomes of households paying flat rents are influenced to some extent by the fact
that a large fraction of flat-rent units are in New York City, where incomes and rents tend to be
higher than in other places. Incomes of households paying flat rents outside New York City,
however, are still substantially higher than incomes of other public housing residents.
Changes in the Use of Flat Rents by Families During the 2003-Through-2005
Period
• Households often move in and out of paying flat rents. Less than half of the households paying
flat rents in 2003 continued to do so in 2005.
• It appears that the main reason households switch from flat rents to other rents is because their
income decreases. More than three-quarters of households moving from flat rents to other rents
experienced a decrease in income. In comparison, most households that continued to pay flat
rents experienced an increase in income over time. This finding may be an indication that flat
rents are accomplishing their legislative intent of enabling higher income residents to remain in
public housing as their incomes increase.
Assessment of Flat Rents Relative to Local Rents and Relative to Income-Based
Rents
• Rents in units where residents were paying flat rents were substantially higher than rents in
other public housing units. (As in the case of income, this fact is partially influenced by the large
fraction of flat-rent units located in New York City; but even outside New York City, flat rents
are much higher than rents in other public housing units.)

???Cityscape
Use of Flat Rents in the Public Housing Program
• Rents in both units with flat rents and other rents were well below comparable market levels (as
proxied by the local FMR). More than half the rents in units with flat-rent units had rents below
50 percent of the FMR, as did nearly 90 percent of other units.
• Households paying flat rents were virtually always paying less than 30 percent of their income
for rent. Most were paying less than 75 percent of the comparable income-based rent. As expected,
households in other units typically paid the income-based rent, defined as 30 percent of income.
Dispersion of Flat Rents Across PHAs and Comparison of Tract Poverty in
Census Tracts With Concentrations of Flat Rents
• Units with flat rents are generally located in tracts with similar poverty rates as other public
housing units. This finding indicates that households paying flat rents in New York City tend to
live in high-poverty tracts, which is offset by the fact that households paying flat rents in other
locations tend to be in higher income tracts than other public housing residents.
• Tracts with high concentrations of flat-rent units tend to have higher poverty rates than
tracts with high concentrations of other public housing units. This finding is in contrast with
expectations. Because households paying flat rents have higher incomes, we might expect
concentrations of flat-rent units in lower poverty tracts. This counterintuitive finding may have
resulted when flat rents were introduced; HUD suggested that a very low flat rent in a less-than-
desirable development could be a strategy for attracting and keeping higher income families
and, thus, stabilizing the community. It also may be driven in part by the fact that flat-rent units
in New York City are in high-poverty tracts. The high concentration of flat-rent units in New
York City could be affecting this result.
Changes in Wages and Turnovers in Properties With Flat Rents Compared With
Other Properties
• We might expect higher wage increases in properties with high concentrations of flat-rent units
because residents have no negative incentives associated with increased income. In fact, we do
find that wages in these properties increased more than in other public housing developments
between 2003 and 2005.
• Similarly, we might expect lower turnover rates in properties with concentrations of flat rents
(because households paying flat rents do not have an incentive to move). The data, however,
show that turnover, measured as the portion of households that moved in within the previous
year, was slightly higher than in other developments. (The data are for a development as a
whole and not specifically for the flat-rent households.)
Income Mixing in Properties With Flat Rents Compared With Other Public
Housing Properties
• Properties with flat-rent units have a higher degree of income mixing than other properties.
This finding is as expected because households in units with flat rents have higher incomes than
most other public housing residents have.

??? Policy Issues in Public and Assisted Housing
Finkel and Lam
Appendix A. Rent Determination Algorithm
Definition
The following line items are reported on page 8 of HUD Form-50058:
10a = Total Tenant Payment (TTP) (generally 30 percent of adjusted income minus utility allowance).
10b = flat-rent amount.
10c = ceiling-rent amount.
10d = lower of TTP or ceiling rent.
10u = Type of rent code—an indicator flagging whether a household selects to pay a flat rent or an
income-based rent.
Determination of Flat-Rent Units
Household records are determined as paying flat rents if any of the following conditions is true:
• Type of action = 12, and flat-rent amount > 0.
• Type of rent code = F, and flat-rent amount > 0.
• Type of rent code = blank, and flat-rent amount > 0, and flat-rent amount < TTP, and flat-rent
amount = lower rent amount (that is, line item 10d).
Determination of Ceiling-Rent Units
Household records are determined as paying ceiling rents if the following condition is true:
• Ceiling rent amount > 0 and TTP > or = ceiling rent and ceiling rent = lower rent amount.
Determination of Income-Based Rent Units
Household records are determined as paying a nonceiling income-based rent if any of the following
conditions is true:
• The record has not already been flagged as flat-rent units or ceiling-rent units, and lower rent
amount > 0, and type of rent code = I.
• The record has not already been flagged as a flat-rent unit, ceiling-rent unit, or income-based
rent, and TTP = lower rent amount, and lower rent amount > 0.

???Cityscape
Use of Flat Rents in the Public Housing Program
Appendix B. Supplementary Tables
Exhibit B-1
Flat-Rent Units
in New York City
Flat-Rent Units
Elsewhere
All Flat-Rent
Units
Characteristics of Households in Units Paying Flat Rents in New York City and
Elsewhere—2005 Data
Number of housing units 32,179 67,354 99,533
Percent of all units 32.3 67.7 100
Age of household head (average) 52.0 51.0 51.3
(percent)
Disability status of household head
Yes 5 14 12
No 95 86 88
Total 100 100 100
Race/ethnicity of household head
Non-Hispanic White 5 45 32
Non-Hispanic African American 55 42 46
Hispanic 37 11 19
Other 3 2 2
Total 100 100 100
Presence of children in household
Yes 38 39 39
No 62 61 61
Total 100 100 100
(dollars)
Total household income (average) 37,606 23,663 28,150
(percent)
Household income as percent of area
median
69 46 52
Source of income
Most of income from wage
Yes 82 61 68
No 18 39 32
Total 100 100 100
Most of income from welfare
Yes 0 1 1
No 100 99 99
Total 100 100 100
Source: Office of Public and Indian Housing Information Center system (PIC system)

??? Policy Issues in Public and Assisted Housing
Finkel and Lam
Exhibit B-2
Flat-Rent Units
in New York City
Flat-Rent Units
Elsewhere
All Flat-Rent
Units
Rent Comparisons in Units With Flat Rents in New York City and Elsewhere
Number of housing units 32,179 67,354 99,533
Percent 32.3 67.7 100
(dollars)
Rent level charged
Mean 510 366 413
Median 495 333 416
Standard deviation 77 167 159
Percentile statistic
10th percentile 421 200 225
25th percentile 495 259 296
75th percentile 495 430 495
90th percentile 619 575 619
(percent)
Rent to FMR ratio
Median 46 59 49
> 1 0 1 1
0.76 to 1 0 18 12
0.51 to 0.75 1 52 36
0.26 to 0.5 99 26 49
< 0.26 0 3 2
Rent to income-based rent ratio
Median 62 70 68
> 1.01 0 2 1
1.00 to 1.01 1 4 3
0.76 to 0.99 28 36 34
0.51 to 0.75 41 40 40
0.26 to 0.5 29 17 21
< 0.26 1 1 1
FMR = Fair Market Rent.
Note: Income-based rents are defined as 30 percent of adjusted income.
Source: Office of Public and Indian Housing Information Center system (PIC system)

???Cityscape
Use of Flat Rents in the Public Housing Program
Acknowledgments
This work was funded under Contract Abt C-225, Task Order 6, which was a subcontract from
the QED Group that had a prime contract with the U.S. Department of Housing and Urban
Development.
Authors
Meryl Finkel is a senior associate with Abt Associates Inc.
Ken Lam is an associate with Abt Associates Inc.
Exhibit B-3
Number of Units
Tract Poverty Rate
(percent)
Percent of Units
Distribution of Poverty Rate by Household Rent Type in Flat-Rent Units in New York
City and Elsewhere
Flat-rent units in New York City 32,029 Average 40
0 to 9 1
10 to 19 6
20 to 29 18
30 or more 75
Flat-rent units elsewhere 60,178 Average 25
0 to 9 10
10 to 19 34
20 to 29 25
30 or more 31
All flat-rent units 92,207 Average 31
0 to 9 7
10 to 19 24
20 to 29 23
30 or more 46
Source: Office of Public and Indian Housing Information Center system (PIC system)

??? Policy Issues in Public and Assisted Housing
Finkel and Lam

???Cityscape: A Journal of Policy Development and Research • Volume 10, Number 1 • 2008
U.S. Department of Housing and Urban Development • Office of Policy Development and Research
Cityscape
Household Life Cycle and
Length of Stay in Housing
Assistance Programs
Alvaro Cortes
Ken Lam
David Fein
Abt Associates Inc.
Abstract
In 1998, public housing agencies (PHAs) were given considerable discretion to select
tenants on the basis of local PHA preferences rather than on old federal preferences for
households experiencing housing-related hardships. Many PHAs have adopted other
categorical preferences. As a result, the demographic profile and household composition
of public housing tenants have changed. These changes have important implications for
the U.S. Department of Housing and Urban Development’s Housing Choice Voucher
Program (HCVP), because past research has found that household characteristics and
location factors significantly affect a household’s length of stay in the program. The
study described in this article uses administrative data to explore the factors associ-
ated with a household’s length of stay in the HCVP. The study focuses on the degree to
which the presence of children of varying ages affects a household’s length of stay in the
program and the degree to which older children, as a potential source of childcare, may
mitigate a longer duration of housing assistance. The study also explores the degree to 
which the disability status of the household head or children affects a household’s length
of stay in the program. The study’s main finding is that the presence of an infant or a
toddler increases a household’s length of stay in the HCVP, after controlling for an array
of household and location characteristics, but the presence of other children in the same
household attenuates this effect. Conversely, the study finds that the presence of teenag-
ers, especially male teenagers, magnifies this effect.

??? Policy Issues in Public and Assisted Housing
Cortes, Lam, and Fein
Introduction
The U.S. Department of Housing and Urban Development’s (HUD’s) Housing Choice Voucher
Program (HCVP)—formerly called the Section 8 Rental Certificate Program—was created in 1974;
since then, it has become a primary federal vehicle for providing housing assistance to low-income
households. In 2005, the program served more than 1.9 million households.
1
For many years, the
program was targeted to households that were experiencing various housing-related hardships,
such as being involuntary displaced, living in substandard housing, being homeless, and paying
more than 50 percent of household income for rent. Public housing agencies (PHAs) across the
country were charged with administering the program and were required by law to allocate 90
percent of their vouchers (and certificates) to households that were confronting housing-related
hardships (Devine et al., 2000).
In 1998, federal preferences for households with housing-related hardships were repealed under
the Quality Housing and Work Responsibility Act (QHWRA), and PHAs were given considerable
discretion in selecting tenants on the basis of local PHA preferences. For example, PHAs may select
people from their waiting lists who are enrolled in training programs or who are already working,
although PHAs that adopt a preference for working tenants must also give an equal preference
to heads of households who are elderly and people with disabilities. PHAs are simultaneously
expected to allocate 75 percent of their vouchers to households with adjusted incomes that do not
exceed 30 percent of the area median income (AMI). Research conducted in 2000 suggests that
very few PHAs nationally (12 percent) base their program preferences exclusively on the old federal
preferences, and most PHAs have adopted other categorical preferences (Devine et al., 2000). These
preference decisions, in turn, have altered the composition of households served by the HCVP
nationwide.
Changes in household composition have important implications for the HCVP. Recent research us-
ing administrative data on HCVP participants suggests that household and location characteristics
affect a household’s length of stay in the program (Ambrose, 2005; Freeman, 2005; Olsen, Davis,
and Carrillo, 2005). Some household characteristics (for example, being elderly or disabled) are
associated with longer lengths of stay in the program and others (for example, higher income and
younger age groups) are associated with shorter lengths of stay. This evidence suggests that PHAs
that alter their tenant compositions on purpose may unknowingly be affecting the amount of time
that households use the voucher and, thus, potentially limiting the number of vouchers available
to serve households on the PHAs’ waiting lists. Conversely, changing tenant composition may
lead to quicker exits from the program, which, in turn, would free up vouchers for other needy
households. Therefore, to understand how vouchers are used nationwide, we must first understand
which household characteristics affect HCVP attrition rates.
The study described in this article analyzes administrative data from HUD to explore household
characteristics that are associated with a household’s length of stay in the HCVP. Although a
number of such characteristics have been analyzed previously, this article focuses on the degree
to which the presence of children of varying ages is related to a household’s length of stay in the
1
The total includes 11,221 certificates and 1,922,654 vouchers.

???Cityscape
Household Life Cycle and Length of Stay in Housing Assistance Programs
program and the degree to which older children, as a potential source of childcare, may mitigate a
longer duration of assistance for households with infants and toddlers. The study also explores the
degree to which the disability status of the household head or children affects a household’s length
of stay in the program. Previous research has found disability status to be strongly associated with
attrition rates in housing assistance programs. To understand better the unique constraints for
households with a disabled head of household or child with disabilities, we compare characteristics
associated with length of stay in the program across three types of heads of households: nonelderly
heads of households with a child or children; nonelderly, disabled heads of households with a
child or children; and nonelderly heads of households with at least one disabled child.
Literature Review: A Brief Summary
An emerging body of literature explores the length of time that a household receives housing
assistance and the factors that influence the length of time. Research suggests that attrition rates in
assisted housing are associated with multiple factors, including tenants’ socioeconomic characteris-
tics, changes in household composition, housing market conditions and the availability of afford-
able housing options, and the year of entry into assisted housing. Particular attention has been
given to characteristics that describe the life cycle of a program participant, such as year of entry
and age composition, and several metropolitanwide economic factors, such as wages, demand for
labor, and rent structures.
Using the Longitudinal Occupancy, Demography, and Income file,
2
Olsen, Davis, and Carrillo (2005)
conclude that elderly status and disability status of the head of household are by far the two largest
influences on a household’s decision to leave the HCVP. Heads of households who have disabilities
are about 37 percent less likely to exit the tenant-based HCVP, and heads of households who
are elderly are about 23 percent less likely to exit compared with otherwise similar households.
Other household characteristics, such as race and size of the household, played a much smaller
role in explaining differences in attrition rates. The study also found that a $100-per-month lower
local payment standard was associated with a 3-percent higher rate of program exit and a $100-
per-month higher minimum tenant contribution to rent was associated with a 13-percent higher
program attrition rate.
Ambrose (2005) used a random sample of households from HUD’s Multifamily Tenant Character-
istics System (MTCS) and Tenant Rental Assistance Certification System (TRACS) and found that
individual characteristics are very important to understanding a program participant’s exit from the
tenant-based HCVP, a public housing program, or a project-based housing program. Elderly heads
of households are 48, 56, and 59 percent, respectively, less likely to exit from these programs;
disabled heads of households are 54, 76, and 57 percent, respectively, less likely to exit. Gender,
race, income, housing composition, housing location, and city size were also found to be statisti-
cally significant factors in determining the likelihood of a participant’s leaving assisted housing.
The effects of local economic conditions on program exits varied by program type.
2
The file contains data from HUD’s Multifamily Tenant Characteristics System and Tenant Rental Assistance Certification
System for 1995 through 2002.

??0 Policy Issues in Public and Assisted Housing
Cortes, Lam, and Fein
Freeman (2005) used data from the New York City Housing and Vacancy Survey to explore
whether household composition is related to the receipt of housing assistance. Freeman found that
the likelihood of exiting housing assistance is highest in the earliest years of a housing assistance
stay and that the most significant predicators of ending a housing assistance stay were residing in
public housing; being White, young, and nondisabled; and having no children. The vacancy rate of
the local housing market was also a predicator. The study also suggested that recipients of housing
assistance were less likely to be married and less likely to get married over time, but little evidence
was found that housing assistance contributed to the dissolution of partnerships. In addition,
recipients of housing assistance have more children, but, after they receive housing assistance, they
are less likely to have additional children.
Bahchieva and Hosier (2001) used administrative data from the New York City Housing Authority
to explore lengths of stay in public housing. The authors found that lengths of stay were influenced
by demographic characteristics, income level and sources, and housing characteristics. The highest
exit rates were among participants with incomes exceeding 80 percent of AMI, single people,
young or very old heads of households, White heads of households, and non-Hispanic immigrants.
The authors also found that tenants in smaller apartments and higher crime neighborhoods had
higher exit rates, and previously homeless people had the highest probability of exiting during
the early years of tenure. Most striking, the median length of stay derived from a survival analysis
is more than 42 years, and more than one-quarter of the lengths of stay are more than 55 years.
These very long lengths of stay likely are associated with the very tight housing market in New
York City, which greatly restricts mobility options among low-income households.
Before these recent studies, most research relied on surveys that asked program participants to
self-report their housing assistance status, although evidence suggests that such self-reporting may
be unreliable (Shroder, 2002). Freeman (1998) used data from the Panel Study of Income Dynamics
to examine the dynamics of residents in public housing and found that demographic, location,
and cultural factors—including where residents grew up, the educational attainment of heads of
households, and whether a child grew up in a household with both parents—influence lengths of
stay, or durations, in public housing. Freeman concludes that quicker exits from public housing
are associated with various factors: growing up in a two-parent family; being non-Hispanic; having
more than a primary school education (1–8 years of school); having additional work experience;
being divorced; residing in an area with a higher vacancy rate and more affordable housing units;
and living in the Northeast or Midwest. In addition, Freeman did not find evidence supporting the
notion that participants who use housing assistance for longer periods of time are less likely to exit,
sometimes referred to as the “duration dependence phenomenon.” The duration dependence phe-
nomenon suggests that program participants become accustomed to living with housing assistance
and making ends meet and, thus, are less likely to exit the program (Bane and Ellwood, 1994).
Susin (1999) used data from the Survey of Income and Program Participation to examine the
effect of employment, earnings, and household composition on the length of housing subsidy.
The study’s main finding is that employment and earnings are modestly associated with an exit
from subsidized housing, and up to 56 percent of exits are associated with household composition
changes (for example, the birth of a child or a marriage). Susin also found that other household
characteristics—such as having a high school degree, higher earnings and income, and welfare

???Cityscape
Household Life Cycle and Length of Stay in Housing Assistance Programs
receipt—and location factors, such as a local area’s median rent and the state vacancy rate—were
important predictors of exiting from subsidized housing.
Conceptual Framework for Understanding the Effect of Children on
a Household’s Length of Stay in the HCVP
Past research underscores the important relationship between households’ life cycles and length of
stay in assisted housing, but little is known about the independent effect of children on the length
of stay. On initial reflection, the relationship between the presence of children and length of stay in
the program is not obvious.
Associations between the presence of children and the length of time households receive housing
assistance seem especially likely when households include either infants or toddlers together with
teenagers. The presence of infants or toddlers may lead some households to remain in the housing
program if the heads of households cannot find adequate daycare and, consequently, are unable
to obtain gainful employment to pay rent in the private market. Among participants in the HCVP,
lack of good-quality childcare has been found to be one of the most important barriers to finding
employment and, thus, becoming self-sufficient (Reed, Pashup, and Snell, 2005; Turnham et al., 2006).
The presence of teenagers ages 13–17 may influence the exit of households from housing assistance
in different ways, albeit very limited evidence exists describing this potential association. On one
hand, the presence of teenagers may increase the likelihood of exits from the HCVP. Research on
welfare recipients suggests that program-induced increases in maternal employment, especially
in low-wage labor markets, may lead some teenagers to assume more household and adult-like
responsibilities, such as caring for younger children in the household, housecleaning, shopping,
cooking, or employment (Burton, 1997). Female teenagers are particularly more likely to assume
household duties (Dodson and Dickert, 2004). Although this research focuses on the effect of
maternal employment on teenagers, it also raises the possibility of an inverse dynamic: heads
of households may be more likely to become employed if a teenager is available to help care for
younger children in the household. Expressed differently, the presence of teenagers—especially
female teenagers—may help a household become self-sufficient if the head of the household can
leave the children with the teenager and find gainful employment, rather than stay home to care
for the children.
On the other hand, teenagers often present more challenges to households. Households that have
teenagers who are getting into trouble in school or in the neighborhood may be prompted to
move to another community to change the teenager’s environment. Some of these moves may be
portability moves (that is, the household continues to receive housing assistance in another PHA’s
jurisdiction) and other moves may involve exits from the program altogether if the household has
sufficient resources. In some cases, households may have greater incentives to become self-sufficient
and exit housing assistance if they believe they can improve their teenagers’ environment and, in
turn, their outcomes as adults. Previous research that focused on recipients of Temporary Assis-
tance for Needy Families who also received tenant-based rental assistance suggests that recipients’
decisions to move are influenced greatly by their desire to improve their children’s well-being
(Turnham et al., 2006).

??? Policy Issues in Public and Assisted Housing
Cortes, Lam, and Fein
Alternatively, some households with at-risk teenagers may feel overwhelmed by the stresses
brought on by teenagers and decide to focus on stabilizing their family life before considering other
important choices (for example, seeking employment, asking for a raise, or uprooting the family
and moving elsewhere). As Turnham et al. (2006) suggest, families in the HCVP are confronted
by an array of stressful circumstances at home and in their neighborhoods—paying rent and
utilities, coping with the absence of a male partner/spouse, avoiding drug and criminal activity in
the neighborhood, providing for their children—and many families triage their living situation by
focusing on one issue at a time. Families may choose to focus on the well-being of their teenagers
before considering other issues that, in turn, could affect their self-sufficiency outcomes and ability
to exit the HCVP.
Some households with teenagers may be less willing to exit housing assistance if they prefer to
keep teenagers rooted in their social settings, especially the teenagers’ school and friends, and the
housing assistance enables them to stay where they are. Studies that have explored household
outcomes following moves to different communities—aided by housing assistance—demonstrate
that some households continue to send their teenage children back to the schools located in
previous neighborhoods (Orr et al., 2003). If housing assistance is helping a household maintain
a teenager’s routine, the housing assistance may be providing a disincentive for households to
become self-sufficient and exit housing assistance.
The potential effects of young children (ages 6–12) on the length of time a household stays in
the program are less understood or studied. Young children may delay a household’s exit from
housing assistance for some of the same reasons associated with infants and toddlers. On the
other hand, children approaching their teens may be asked by the head of household to babysit
younger children in the household while the head of household is working or taking other steps to
become self-sufficient (for example, enrolling in a job training program, going to a job interview, or
completing a certificate or degree program).
The Data
This analysis uses data from three data sources: (1) administrative data collected in HUD’s MTCS/
Public and Indian Housing Information Center (PIC) system between 1997 and 2005, (2) the 2000
Decennial Census, and (3) the Low-Income Housing Tax Credit database.
The MTCS/PIC database from 1997 to 2005 contains nearly 14 million records—3.3 million
certificate records and 10.5 million voucher records. The total number of records increases steadily
from about 1.1 million in 1997 (about 807,000 certificates and 274,000 vouchers) to 1.9 million
in 2005 (approximately 11,000 certificates and 1.88 million vouchers). The universe of records
for the analysis consists of households in the MTCS/PIC data file that began receiving assistance in
1997 or later. Some of those households may have experienced housing assistance before 1997, but
at some point they exited the program and then reentered between 1997 and 2005. We addressed
three key data issues to construct the MTCS/PIC analysis file: (1) truncated records, (2) discon-
tinuities in the longitudinal data file, and (3) unrecorded or unknown exits from the program.
3

After we addressed these issues and identified new program entrants between 1997 and 2004,
3
Further information on how these issues were addressed is available from the authors.

???Cityscape
Household Life Cycle and Length of Stay in Housing Assistance Programs
we selected three types of heads of households: (1) nonelderly heads of households with a child or
children; (2) nonelderly, disabled heads of households with a child or children; and (3) nonelderly
heads of households with at least one disabled child.
4
In this study, we used all records associated
with these head-of-household types.
Exhibit 1 shows the total number of records in the analysis file by head-of-household type and year
of entry. Overall, the final analysis file contains nearly 760,000 records. The overwhelming majority
of households (84 percent) are nonelderly heads of households with a child or children; some of
the records (13 percent) are nonelderly, disabled heads of households with a child or children; and
a few (3 percent) are nonelderly heads of households with at least one disabled child.
We used census data in the analysis to control for location factors that may influence households’
lengths of stay in the HCVP. The census data provide indicators of level of urbanization (central
city, suburb, and nonmetropolitan), census division of the United States, metropolitan civilian
unemployment rate, and metropolitan statistical area/primary metropolitan statistical area (MSA/
PMSA) housing vacancy rate. We also use census data to determine whether the PHA has a high
mean rent in the metropolitan area. We sorted data from Census 2000 Summary File 3 by census
tract and merged the information to the MTCS/PIC data file using the 2000 Census tract identifier.
Under a separate HUD contract, Abt Associates assembled a database with low-income housing tax
credit (LIHTC) units placed in service between 1995 and 2004. The database includes information
on a multitude of variables, including project location, number of total units, and number of
tax credit units. Census tract identifiers also are attached to each record. We merged the LIHTC
database to the MTCS/PIC data file to identify and control for the availability of LIHTC units in the
same census tract as the household receiving housing assistance.
Exhibit 1
Year
Nonelderly Heads of
Households With
a Child or Children
Nonelderly,
Disabled Heads of
Households With
a Child or Children
Nonelderly Heads of
Households With
at Least One
Disabled Child
Total Household
Records
Number of Households in the HCVP Analysis File by Head-of-Household Type,
1997–2004
1997 75,415 11,966 2,933 90,314
1998 72,460 11,353 2,772 86,585
1999 70,495 12,034 2,831 85,360
2000 70,516 11,119 2,976 84,611
2001 105,921 15,395 4,504 125,820
2002 92,271 13,292 3,717 109,280
2003 87,389 12,237 3,470 103,096
2004 63,305 8,510 2,676 74,491
Total 637,772 95,906 25,879 759,557
HCVP = Housing Choice Voucher Program.
4
We constructed variables to identify each head-of-household type.

??? Policy Issues in Public and Assisted Housing
Cortes, Lam, and Fein
Using these data sources, we constructed several key variables needed to (1) address central study
questions about the effects of children’s age composition and of household members’ disability
status on HCVP length of stay; and (2) represent a series of covariates that we used to control for
rival explanations (but which are also of interest in their own right). The covariates used included
the age of the householder, availability of units funded by the Low-Income Housing Tax Credit
program, race and ethnicity, income and sources of income, and other key household and location
characteristics.
5
(The full list of covariates appears in exhibits 2, 3, and 4.)
The variables associated with the effect of children on program lengths of stay are particularly
noteworthy. To measure the effects of children on a household’s length of stay in the program, we
constructed the following variables: the total number of children in the household (a continuous
variable) and a series of dummy variables indicating whether the household included the presence
of children in a series of specific age-gender categories.
To test hypotheses about caring for younger children in the household, we created a series of variables
representing the interaction of dummy variables indicating the presence of infants (ages 0–3) and
toddlers (ages 4–5) and variables indicating the presence of older boys and girls, both young children
and teenagers. For variables on older boys and girls, we included dummy variables distinguishing
male young children ages 6–12, female young children ages 6–12, male teenagers ages 13–17, and
female teenagers ages 13–17.
The interaction terms allow us to explore the significance of spacing between children of different
ages and to test if varying age groups are associated statistically with exits from the HCVP. The
interaction terms also describe how the presence of young children and teenagers influences the
potential effects of infants and toddlers on exits. For example, the hypothesis regarding caring for
younger children in the household would be supported by a finding that households with infants
stay on assistance longer than those without infants, but this effect is smaller when the household
also includes the presence of a teenager. The next section describes our multivariate analysis
strategy in detail.
The Methodology
We use a piecewise-exponential duration model to explore the relationship between household
compositions—specifically, the presence of children of different ages—and length of stay, or
duration, in the HCVP.
6
The model assumes that the exact timing of an event (that is, a household’s
exit from the HCVP) is known. The exact timing of the event is indicated by the “effective date of
action” variable in the MTCS/PIC database. This model accounts for right-censoring and accom-
modates time-varying covariates.
5
We also attempted to control for local rent structures by including two variables: the household’s income in proportion
to area median income and gross rent in proportion to Fair Market Rent. These variables were highly correlated with a
metropolitan area’s vacancy rate in the regression equation, which is problematic because parameters in duration models
are estimated by an iterative numerical method called “maximum likelihood.” When variables that are highly correlated are
included in a model, the likelihood function does not converge and coefficient estimates cannot be produced.
6
We used SAS software, as described in Allison (1995).

???Cityscape
Household Life Cycle and Length of Stay in Housing Assistance Programs
We used two regression models in the study. The first model contains the dummy variables that in-
dicate whether a child of a particular age group is present, plus an array of household and location
characteristics. The model does not include the child-interaction variables. The full model adds the
child interaction variables to the first model. Both models are estimated separately for each type of
head of household (that is, nonelderly heads of households with a child or children; nonelderly,
disabled heads of households with a child or children; and nonelderly heads of households with at
least one disabled child) and for the total universe of households.
Formally, the model is written:
log[h
i
(t)] = X
i1
b
1
+ X
i2
b
2
+ X
i3
b
3
+ X
i4
b
4
+ X
i5
b
5
(equation 1)
where
h
i
(t) represents the duration of household i in the program at time period t;
X
i1
represents 0-1 dummy variables indicating various entry years (cohorts);
X
i2
is a set of variables accounting for the number and spacing of children of different ages in the
household;
X
i3
is a vector of household demographic variables;
X
i4
includes the set of geographic covariates;
X
i5
is a series of period-specific intercepts for the households; and
b
1
, b
2
, b
3
, b
4
,

and b
5
are vectors of regression coefficients.
To interpret the magnitude of the regression coefficients, we transformed (or exponentiated) the
parameter estimates reported by SAS software into a survival time ratio (Hosmer and Lemeshow,
1999). For a dummy (0/1) explanatory variable, the time ratio (that is, e
b
) provides the estimated
ratio of the expected survival time of the two groups, where the reference group is the group coded
as 0 in the dummy variable. For instance, assume that the estimated coefficient for the dummy
variable for wage income is negative 0.363. The time ratio, e
-0.363
, is equal to 0.70 and suggests
that a working household’s length of stay in the program is 70 percent of the length of stay among
households that are not in the labor force, after controlling for other covariates. For a continuous
variable, the formula 100(e
b
-1) yields the percentage increase in the expected survival time for a
one-unit increase in the explanatory variable.
Parameter estimates associated with the child-related variables suggest whether and how HCVP
exit probabilities are affected by the presence of children in a household. To test hypotheses about
the effects of combinations of infants, toddlers, and older children, we add a series of multiplicative
terms to a base model including only dummy variables for each main age group.
To control for other household characteristics and local labor and housing market conditions, the
model includes numerous covariates (see exhibits 2, 3, and 4). Most of the covariates are measured
as of the year of the tenant’s program entry. Our variables capturing numbers of children in different
age groups are time-varying, however, to reflect changes in age groups resulting from new arrivals
in the household (for example, births) and departures (for example, young adults leaving home).

??? Policy Issues in Public and Assisted Housing
Cortes, Lam, and Fein
Including other covariates (apart from the number and age of children) helps to control for other
factors that influence probabilities of exit from the HCVP, which could affect estimates of child-age
effects.
In addition, the model includes a set of geographic variables to account for location effects. The
number of LIHTC units in the census tract controls for the availability of alternative affordable
rental units in the neighborhood. The unemployment and housing vacancy rate measures control
for the greater likelihood that households in worse labor markets and households in tighter rental
markets are more likely to remain in the HCVP. The dummy variables for central city, suburb,
and rural location, plus the census division variables, control for fixed effects in these geographic
locations.
Results
Exhibits 2, 3, and 4 provide descriptive profiles of the three types of heads of households in the
study: nonelderly heads of households with a child or children; nonelderly, disabled heads of
households with a child or children; and nonelderly heads of households with at least one disabled
child. Exhibit 2 lists household characteristic variables, exhibit 3 lists the child-related variables,
and exhibit 4 lists the location variables that are included in the analysis.
Household Characteristics
Exhibit 2 lists household characteristics for all households in the analysis file and for each
head-of-household type. Overall, the three head-of-household types share many of the following
characteristics:
• Small (averaging slightly more than three people in the household).
• Young (average age of household head is 31).
• Headed by a minority (60 percent of household heads are African American or Hispanic).
• Headed by a single person (91 percent of households do not have a spouse present).
• Poor (average annual income is $10,100, which is about 20 percent of the AMI).
We note a few key differences across the head-of-household types. Nonelderly heads of households
with a child or children have significantly higher wage incomes compared with other head-of-
household types. Nonelderly heads of households with a child or children earn about $6,300
annually, which is about five times greater than the wage income of nonelderly, disabled heads of
households with a child or children ($1,209) and almost twice the amount of nonelderly heads of
households with at least one disabled child ($3,585).
Also, nonelderly, disabled heads of households with a child or children are older than their
counterparts. These head-of-household types are about 38 years old, and nearly two-thirds of these
heads of households are adults ages 35 or older. By comparison, nonelderly heads of households
with a child or children and nonelderly heads of households with at least one disabled child
typically are adults 30 and 32 years old, respectively, and less than one-third of these heads of
households are adults 35 years or older.

???Cityscape
Household Life Cycle and Length of Stay in Housing Assistance Programs
Exhibit 2
V
ariable
Household Characteristics
All
Households
Nonelderly
Heads
of Households With a Child or
Childr
en
Nonelderly
,
Disabled Heads
of Households With a Child or
Childr
en
Nonelderly Heads
of Households
With at Least One Disabled
Child
T
est of
Dif
fer
ence
in Means
Acr
oss
Gr
oups
Mean
Stdev
Mean
Stdev
Mean
Stdev
Mean
Stdev
Descriptive Statistics for Households in the HCVP
, Household Characteristic V
ariables
AgeHH
Age of household head
30.90
8.78
29.82
8.11
37.93
9.97
31.66
7.80
***
FamSize
T
otal number of people in the household
3.26
1.25
3.25
1.23
3.21
1.27
3.74
1.43
***
Adult1824
Pr
esence in household of adults ages 18–24
34
48
37
48
20
40
25
44
***
Adult2534
Pr
esence in household of adults ages 25–34
43
49
44
50
34
47
51
50
***
Adult35p
Pr
esence in household of adults ages 35 or older
33
47
28
45
64
48
33
47
***
White
Head of household is non-Hispanic White
37
48
36
48
44
50
28
45
***
Black
Head of household is non-Hispanic African American
45
50
45
50
40
49
55
50
***
Hispanic
Head of household is Hispanic
15
36
15
36
12
33
15
35
***
Other
Head of household is other race
3
18
3
18
4
20
3
16
***
Female
Head of household is female
91
29
92
27
82
38
94
24
***
nMarried
No spouse is pr
esent in household
90
29
91
28
85
36
93
25
***
Homeless
Head of household was pr
eviously homeless
3
18
3
18
3
18
4
19
Income
T
otal household annual income
10,093
6,308
10,011
6,469
10,339
5,067
11,135
6,449
***
Asset
T
otal household asset amount
47
886
44
790
70
1,392
46
684
W
age
T
otal household wage income
5,553
7,355
6,290
7,550
1,209
3,883
3,585
6,328
***
adjAMI
Household income in pr
oportion to AMI
20
12
20
12
21
10
22
12
***
adjRent
Gr
oss r
ent in pr
oportion to FMR
101
137
101
103
100
20
119
526
***
Number of households
759,557
637,772
95,906
25,879
AMI = ar
ea median income. FMR = Fair Market Rent. HCVP = Housing Choice V
oucher Pr
ogram. Stdev = standar
d deviation.
Notes: *** significance at the 1-per
cent level; ** significance at the 5-per
cent level; * significance at the 10-per
cent level.
(per
cent)
(US$)
(number)

??? Policy Issues in Public and Assisted Housing
Cortes, Lam, and Fein
The nonelderly heads of households with at least one disabled child are often African American
(55 percent), which is about 10 and 15 percentage points greater than the proportion associated
with nonelderly heads of households with a child or children and nonelderly, disabled heads of
households with a child or children, respectively. In addition, these head-of-household types are
paying about 20 percent more than Fair Market Rent (FMR).
7
Nonelderly heads of households with
a child or children and nonelderly, disabled heads of households with a child or children typically
are paying FMRs.
Child-Related Characteristics
Exhibit 3 shows the descriptive statistics for the child-related variables, and the variable means
across household groups are statistically significant (.01) for nearly every variable.
8
As discussed
earlier in this article, the child-related characteristics are as follows: “infant” refers to a child ages
0–3, “toddler” refers to a child ages 4–5, “young child” refers to a child ages 6–12, and “teenager”
refers to a child ages 13–17.
Overall, each household has about two children. Of the households in the study, 50 percent have
one or more infants (ages 0–3), 50 percent have one or more young children (ages 6–12), 25
percent have toddlers (ages 4–5), and 25 percent have teenagers (ages 13–17). Also, about 10
percent of households have an infant and a male teenager, and an additional 10 percent have an
infant and a female teenager.
Nonelderly heads of households with at least one disabled child differ from the other head-of-
household types in several important ways. These heads of households are much more likely to
have young children (67 percent) than are nonelderly heads of households with a child or children
(50 percent) and nonelderly, disabled heads of households with a child or children (53 percent).
Moreover, these head-of-household types are consistently more likely to have different child-group
pairings, particularly pairings that include young children. For example, about 16 percent of
nonelderly heads of households with at least one disabled child have an infant and a young male
child and 15 percent of these households have an infant and a young female child, 5 to 10 percent-
age points above the corresponding rates in other head-of-household types.
Geographic Characteristics
Exhibit 4 provides the descriptive statistics for the geographic variables. Although statistically
significant at the .01 or .05 level for most of these variables, the differences in means across
head-of-household types are very small. Overall, households in the study are residing in areas
7
Housing assistance payments distributed by HUD are limited by Fair Market Rents (FMRs) that are established by HUD.
In the Housing Choice Voucher Program, the FMR is the basis for determining the “payment standard amount” used to
calculate the maximum monthly subsidy for an assisted family. For more information, see HUD’s Proposed Fair Market
Rents for Fiscal Year 2008 for the Housing Choice Voucher Program and Moderate Rehabilitation Single Room Occupancy
Program; Notice (Docket No. FR–5152–N–01), available at http://www.huduser.org/datasets/fmr/fmr2008p/fy2008p_
preamble_complete.pdf.
8
Because households can have multiple children in different age categories, the variables indicating the presence of children
in different age groups, such as Kids03, Kids45, Kids612, and Kids1317, are not mutually exclusive. Expressed differently,
the households can be observed in multiple child-age groupings.

???Cityscape
Household Life Cycle and Length of Stay in Housing Assistance Programs
Exhibit 3
V
ariable
Child-Related Characteristics
All
Households
Nonelderly
Heads
of Households With a Child or
Childr
en
Nonelderly
,
Disabled Heads
of Households With a Child or
Childr
en
Nonelderly Heads
of Households
With at Least One Disabled
Child
T
est of
Dif
fer
ence
in Means
Acr
oss
Gr
oups
Mean
Stdev
Mean
Stdev
Mean
Stdev
Mean
Stdev
Descriptive Statistics for Households in the HCVP
, Child-Related V
ariables
nKids
Number of childr
en in household
2.04
1.13
2.05
1.12
1.87
1.10
2.56
1.32
***
Kids03
a
Pr
esence in household of infants ages 0–3
50
50
54
50
31
46
47
50
***
Kids45
a
Pr
esence in household of toddlers ages 4–5
26
44
27
45
18
39
32
47
***
Kids612
a
Pr
esence in household of young childr
en ages
6–12
51
50
50
50
53
50
67
47
***
Kids1317
a
Pr
esence in household of teenagers ages 13–17
26
44
23
42
41
49
30
46
***
Kids03*Kids612m
Pr
esence in household of infants ages 0–3 and male young childr
en ages 6–12
10
30
10
30
6
25
16
37
***
Kids03*Kids612f
Pr
esence in household of infants ages 0–3 and female young childr
en ages 6–12
10
30
10
30
7
25
15
36
***
Kids03*Kids1317m
Pr
esence in household of infants ages 0–3 and male teenagers ages 13–17
2
14
2
14
2
15
4
19
***
Kids03*Kids1317f
Pr
esence in household of infants ages 0–3 and female teenagers ages 13–17
3
17
3
17
3
17
5
22
***
Kids45*Kids612m
Pr
esence in household of toddlers ages 4–5 and male young childr
en ages 6–12
8
27
8
2
6
23
13
34
***
Kids45*Kids612f
Pr
esence in household of toddlers ages 4–5 and female young childr
en ages 6–12
8
27
8
27
6
24
12
32
***
Kids45*Kids1317m
Pr
esence in household of toddlers ages 4–5 and male teenagers ages 13–17
2
13
2
12
2
13
3
17
***
Kids45*Kids1317f
Pr
esence in household of toddlers ages 4–5 and female teenagers ages 13–17
2
13
2
13
2
12
2
16
*
Number of households
759,557
637,772
95,906
25,879
HCVP = Housing Choice V
oucher Pr
ogram. Stdev = standar
d deviation.
a
These age categories ar
e not mutually exclusive—households may appear in one or mor
e categories—and thus the per
centages may not total 100 per
cent.
Notes: *** significance at the 1-per
cent level; ** significance at the 5-per
cent level; * significance at the 10-per
cent level.
(number) (number)(per
cent)

??0 Policy Issues in Public and Assisted Housing
Cortes, Lam, and Fein
Exhibit 4
V
ariable
Location Characteristics
All
Households
Nonelderly
Heads
of Households With a Child or
Childr
en
Nonelderly
,
Disabled Heads
of Households With a Child or
Childr
en
Nonelderly Heads
of Households
With at Least One Disabled
Child
T
est of
Dif
fer
ence
in Means
Acr
oss
Gr
oups
Mean
Stdev
Mean
Stdev
Mean
Stdev
Mean
Stdev
Descriptive Statistics for Households in the HCVP
, Location V
ariables, 2000
V
acancy
Metr
opolitan ar
ea r
ental vacancy rate, 2000
9
6
9
6
9
6
9
7
*
Lihtc
Number of LIHTC units in census tract, 2000
119
139
120
137
113
142
125
165
Unemp
Metr
opolitan ar
ea unemployment rate
6
2
6
2
6
2
6
2
***
OwnOcc
Metr
opolitan ar
ea owner
-occupancy rate
53
21
53
21
54
21
51
22
***
City
Household located in central city
49
50
49
50
49
50
53
50
**
Suburb
Household located in suburban ar
ea
30
46
30
46
30
46
31
46
Rural
Household located in rural or nonmetr
opolitan ar
ea
21
41
21
41
22
41
16
37
***
PovRate
Metr
opolitan ar
ea poverty rate
20
12
20
12
20
12
22
13
***
NEglnd
In the New England census division
6
23
6
23
7
25
5
22
**
MAtlntc
In the Middle Atlantic census division
12
33
12
33
12
33
14
35
ENCetrl
In the East North Central census division
15
36
15
35
17
37
16
37
***
WNCetrl
In the W
est North Central census division
9
28
9
29
7
25
6
23
***
SAtlntc
In the South Atlantic census division
16
37
16
37
16
37
18
38
ESCetrl
In the East South Central census division
7
26
7
26
8
27
8
27
**
WSCetrl
In the W
est South Central census division
15
36
15
36
12
33
15
36
***
Mtn
In the Mountain census division
6
23
5
23
6
25
4
20
***
Pac
In the Pacific census division
14
35
14
35
15
36
14
34
Number of households
759,557
637,772
95,906
25,879
HCVP = Housing Choice V
oucher Pr
ogram. LIHTC = low-income housing tax cr
edit. Stdev = standar
d deviation.
Notes: *** significance at the 1-per
cent level; ** significance at the 5-per
cent level; * significance at the 10-per
cent level.
(per
cent)
(per
cent)
(number)(number)

???Cityscape
Household Life Cycle and Length of Stay in Housing Assistance Programs
with similar characteristics: moderate-poverty neighborhoods (20-percent poverty rate) that are
typically located in central cities (49 percent) and have a fair number of LIHTC units (119 units).
These neighborhoods are in metropolitan areas with 9-percent vacancy rates, 6-percent unemploy-
ment rates, and 53-percent owner-occupied units, on average. Households are geographically
distributed, much like the national population, with larger concentrations of households in the
South and West.
Descriptive Analysis by Exit Status
Exhibit A-1 in appendix A presents the household, child-related, and geographic characteristics
separately for households that exited the HCVP and for households that never exited (that is,
stayers) during the study period (1997 to 2004). Nearly all of the characteristics associated with
exiters and stayers are statistically different at the .01 level, and a few merit particular attention.
• Race. Exiters are substantially more likely to be non-Hispanic Whites compared with stayers.
About 47 percent of exiters are non-Hispanic Whites, and 28 percent of stayers are non-
Hispanic Whites. In particular, African Americans constitute a large proportion of stayers (53
percent) but a much smaller proportion of exiters (37 percent).
• Gender. Although most households in the study are headed by females, stayers have more
female heads of household than exiters have. About 88 percent of exiters are households headed
by females, compared with 93 percent of stayers.
• Income. Both exiters and stayers are highly disadvantaged economically. Interestingly, exiters
have slightly lower total income and wage income compared with stayers. The average annual
income among exiters is about $10,050, and more than half of that amount ($5,573) is from
wage income. In contrast, the average annual income among stayers is about $10,789, and
about $6,121 is associated with wage income.
• Number of Children. Exiters have slightly fewer children than stayers. Exiters average fewer
than two children per household, whereas stayers have more than two children per household.
• Age of Children. Exiters are more likely to have teenagers than are stayers, and stayers are
more likely to have infants, toddlers, and young children. Stayers are also slightly more likely to
have different types of child pairings. For example, 2 percent of stayers have both toddlers and
teenagers together in the household compared with 1 percent of exiters.
• Vacancy Rates. Exiters are more likely to reside in metropolitan areas with larger vacancy rates
than are stayers. The vacancy rate among households that exited the HCVP is about 9.2 percent
compared with 8.6 percent among stayers.
• Central City Location. Exiters are less likely to live in central cities (43 percent) compared
with stayers (56 percent).
• Poverty Rate. Although both exiters and stayers live in poor neighborhoods, exiters lived in
neighborhoods with poverty rates that are about 2 percentage points lower than those of stayers.
The neighborhood poverty rate among exiters is 19 percent compared with 21 percent among
stayers.

??? Policy Issues in Public and Assisted Housing
Cortes, Lam, and Fein
Duration of Assistance and Exit Rates by Head-of-Household Type
Exhibit 5 presents the length of program stays (in years) by head-of-household type, and exhibit 6
provides survival curves for each head-of-household type. To account for censoring of the data that
occurs when household records are observed for differing amounts of time, we use the Kaplan-
Meier product-limit method to estimate lengths of stay and graph the survival curves (Kaplan and
Meier, 1958). For example, a household that entered in 1997 can be observed for up to 8 years,
but a household that entered in 2003 is observed for only 2 years.
The median length of stay for all households in the HCVP is nearly 3 years, with substantially
longer stays for households with at least one disabled child than for other households. The median
length of stay among nonelderly heads of households with a child or children is about 2.8 years,
which is nearly two-thirds of the median (4.4 years) associated with nonelderly heads of house-
holds with at least one disabled child.
Exhibit 6 graphically displays survival curves for each head-of-household type. Each line repre-
sents the fraction of households that had not yet exited from the HCVP at successive lengths of
stay after the point of original entry. The statistic (log-rank test) that tests for equality of survival
functions indicates that the survival curves associated with the three head-of-household types
are significantly different (.01 level). The log-rank test statistic compares the observed number of
exits from the HCVP with the number expected in each head-of-household type under the null
hypothesis of no survival difference among the three head-of-household types.
Both nonelderly heads of households with a child or children and nonelderly, disabled heads of
households with a child or children experience a substantial fall in program participation after the
first year in the HCVP. Fewer than 90 percent of those households remain in the program for 1 year.
By the second year since program entry, only about 60 percent of those head-of-household types
remain in the program. For both of those head-of-household types, the proportion remaining in
the program steadily decreases over time, and the rate of decrease is slightly larger for nonelderly
heads of households with a child or children. Fewer than 20 percent of those head-of-household
types remain in the HCVP by the ninth year since program entry.
In contrast, the slope of the curve associated with nonelderly heads of households with at least
one disabled child is less steep, particularly between the first and third years since program entry.
Exhibit 5
Percentile
All
Households
Nonelderly Heads of
Households With a
Child or Children
Nonelderly,
Disabled Heads of
Households With
a Child or Children
Nonelderly Heads of
Households With
at Least One
Disabled Child
Length of Program Stay (in Years) in the HCVP by Head-of-Household Type, 1997–
2004 Entering Cohorts
25th 1.45 1.44 1.47 1.87
50th 2.89 2.83 3.00 4.40
75th 6.58 6.44 6.84 8.59
HCVP = Housing Choice Voucher Program.

???Cityscape
Household Life Cycle and Length of Stay in Housing Assistance Programs
Survival Function
About 90 percent of these households remain in the HCVP after 1 year since program entry,
71 percent remain after 2 years since program entry, and around 60 percent are receiving housing
assistance after 3 years since program entry. Approximately one-quarter of the households remain
in the HCVP after 9 years since program entry.
The survival curves suggest that many households exit after the first year of program participation,
but the curves do not disaggregate the exit rates by entry cohort. For example, households that
entered before 1998 (the year QHWRA was passed) may exit at different rates than households
that entered more recently.
Exhibit 7 shows the cumulative exit rates by year of entry and years since entry for each head-
of-household type, 1997–2004. Although very few households exit the program after 1 year of
participation, a dramatic increase in exit rates is noticeable as participants stay in the HCVP for
longer periods of time.
For example, among nonelderly heads of households with a child or children who entered in
1997, only 1 percent exited after 1 year of participation, but 43 percent exited by the second year
of participation (a 42-percentage-point increase). The cumulative exit rate continues to increase
with each additional year in the program, although the magnitude of the increases tapers gradually
from 19 percentage points between years 2 and 3 to about 2 percentage points between years 7
and 8. By the eighth year of program participation, 84 percent of nonelderly heads of households
with a child or children who entered in 1997 have exited from the program. Similar trends are
Exhibit 6
Survival Function by Head-of-Household Type
HCVP = Housing Choice Voucher Program.
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
Pr
oportion of HCVP Households Remaining
0 1 2 3 4 5 6 7 8 9
Nonelderly heads of households with a child or children
Nonelderly, disabled heads of households with a child or children
Nonelderly heads of households with at least one disabled child
Years Since Program Entry

??? Policy Issues in Public and Assisted Housing
Cortes, Lam, and Fein
Nonelderly heads of households with a child or children
Number of years
since entry (percent)
1 1.0 1.1 2.5 2.1 2.8 1.4 1.4 1.4
2 43.0 41.7 37.4 29.6 36.6 24.3 25.0
3 62.1 59.3 52.8 49.5 50.5 40.6
4 72.2 68.0 65.7 60.0 60.6
5 78.2 75.7 72.5 67.9
6 83.4 80.2 77.7
7 86.4 83.7
8 88.8
Total
households
(number)
75,415 72,460 70,495 70,516 105,921 92,271 87,389 63,305
Nonelderly, disabled heads of households with a child or children
Number of years
since entry (percent)
1 0.4 0.5 1.6 1.3 1.5 1.0 1.1 1.2
2 33.1 36.2 32.7 26.9 33.2 25.8 27.0
3 51.2 52.8 48.1 46.0 47.5 43.5
4 62.7 62.4 61.2 57.6 59.0
5 69.3 71.4 69.2 66.7
6 76.0 76.6 75.2
7 80.2 81.2
8 83.8
Total
households
(number)
11,966 11,353 12,034 11,119 15,395 13,292 12,237 8,510
Nonelderly heads of households with at least one disabled child
Number of years
since entry (percent)
1 0.4 0.8 1.8 1.1 1.6 0.9 0.9 1.1
2 30.8 26.8 25.6 19.3 22.5 16.6 19.3
3 46.4 40.7 37.7 33.5 33.6 30.2
4 56.9 49.6 48.3 44.0 43.3
5 64.1 57.8 55.8 53.2
6 70.5 64.0 63.6
7 74.7 68.9
8 78.6
Total
households
(number)
2,933 2,772 2,831 2,976 4,504 3,717 3,470 2,676
Exhibit 7
Year of Entry
1997 1998 1999 2000 2001 2002 2003 2004
Cumulative Exit Rates in the HCVP by Year of Entry and Years Since Entry for Each
Head-of-Household Type, 1997–2004
HCVP = Housing Choice Voucher Program.

???Cityscape
Household Life Cycle and Length of Stay in Housing Assistance Programs
observed for other head-of-household types and entry cohorts, although nonelderly heads of
households with a child or children have the highest exit rates for each year of participation and
nonelderly heads of households with at least one disabled child have the lowest exit rates for each
year of participation; nonetheless, these findings are consistent with Freeman’s (2005) conclusions
that the likelihood of exiting housing assistance is highest in the earliest years of an assisted
housing stay. These findings also suggest that most households with a child or children, including
households with a nonelderly, disabled head of household or at least one disabled child, are not
long-term users of the HCVP.
Exit rates diminish steadily across entry cohorts, except for a slight increase in exit rates among
households that entered in 2001. For example, although 31 percent of nonelderly heads of
households with at least one disabled child that entered in 1997 exited after 2 years of program
participation, only 19 percent of households that entered in 2003 exited after 2 years in the
program. Similarly, 57 percent of nonelderly heads of households with at least one disabled
child that entered in 1997 exited after 4 years of program participation, but only 43 percent of
households that entered in 2001 exited after 4 years in the program. These trends are observed for
all head-of-household types. Thus, cohorts that entered recently are less likely to exit the program
when compared with older entry cohorts. This finding may be associated with tighter housing
markets over time and fewer affordable housing options. It may also suggest that the characteristics
of more recent cohorts are different from those of older cohorts, and these differences are related to
longer lengths of stay in the HCVP.
Exhibit 8 focuses on the composition of households entering the HCVP with children of different
ages by entering cohort, and evidence suggests that more recent cohorts are different from past
cohorts. The proportion of households with infants has steadily increased across entry cohorts,
from about 46 percent in 1997 to 54 percent in 2004 (or an 8-percentage-point increase). The
Exhibit 8
Household Type
a
Entering Cohort
1997 1998 1999 2000 2001 2002 2003 2004
Percentage of Households Entering the HCVP With Children of Different Ages, by
Entering Cohort
Households with infants
ages 0–3
45.9 47.1 49.2 51.6 51.3 52.1 51.8 53.5
Households with toddlers
ages 4–5
28.1 28.9 27.6 26.0 26.5 25.3 26.2 26.8
Households with young
children ages 6–12
55.2 53.4 52.7 51.3 50.3 49.4 49.4 48.0
Households with
teenagers ages 13–17
27.3 27.3 24.9 23.7 24.9 25.0 26.6 24.9
Total households 90,314 86,585 85,360 84,611 125,820 109,280 103,096 74,491
HCVP = Housing Choice Voucher Program.
a
Household types are not mutually exclusive—households may be observed in more than one household type—and thus the
column percentages may not total 100 percent.
(percent)
(number)

??? Policy Issues in Public and Assisted Housing
Cortes, Lam, and Fein
increase in the proportion of households with infants has occurred simultaneously with a decrease
in households with children of other ages, especially households with young children ages 6–12.
From 1997 to 2004, the proportion of households with children in this age group decreased by
about 7 percentage points.
Changes in the proportion of households with children of different age groups entering the HCVP
could influence exit rates from the program if the presence of children of different ages is associ-
ated with program exits. The results from the multivariate analysis address this relationship.
Results From the Multivariate Analysis
Exhibits 9 and 10 show the results from the piecewise-exponential duration models.
9
Exhibit 9
provides the estimates from the first model, which includes dummy variables for the presence of
children in different age groups and genders, and an array of household and location characteris-
tics. Exhibit 10 summarizes the results from the full model, which include the various household
and location characteristics, the child-dummy variables from the first model, and the series of
child-interaction terms. All the estimates from the full model appear in exhibit B-1 in appendix B.
The results reported in exhibit 9 are consistent with previous research on the effect of household
and location characteristics on attrition rates. Among all household types, several of the following
household and location characteristics are associated with lengths of stay in the HCVP:
• Race. African-American households have lengths of stay in the HCVP that are 51 percent longer
than those of White households (the omitted reference category), and Hispanics have lengths of
stay that are 28 percent longer than those of Whites. The longer lengths of stay associated with
minorities are observed across all household types, although the effect on nonelderly, disabled
heads of households with a child or children is less severe. Nonelderly, disabled heads of
households who are African American and have a child or children stay about 27 percent longer
than White, nonelderly disabled heads of households with a child or children.
• Gender. Households headed by females have lengths of stay that are 18 percent longer than
households headed by males. The effect of gender on length of stay is consistent across all
household types.
• Homeless Status. The expected length of stay among people who were previously homeless
is about 3 percent shorter than the length of stay among people who were previously not
homeless.
• Income. Among all household types, a $1,000 increase in annual income is associated with
a 1-percent decrease in a household’s length of stay in the program. The effect of income on
length of stay is more pronounced among nonelderly heads of households with at least one
disabled child. For these households, a $1,000 increase in annual income is associated with
a 3-percent decrease in length of stay. Wage income had a marginal effect on length of stay.
9
Because all household records were used in the analysis, even small differences in the estimates will be statistically
significant; thus, it is important to focus on the size of the estimates.

???Cityscape
Household Life Cycle and Length of Stay in Housing Assistance Programs
Exhibit 9
V
ariable
Characteristics
All
Households
Nonelderly
Heads of Households
With a Child
or Childr
en
Nonelderly
, Disabled
Heads of Households
With a Child
or Childr
en
Nonelderly
Heads of Households
With at Least One
Disabled Child
Coef
f.
Time Ratio
Coef
f.
Time Ratio
Coef
f.
Time Ratio
Coef
f.
Time Ratio
Estimates Fr
om the Piecewise-Exponential Duration Model With Child-Dummy V
ariables
Panel A: Household Characteristics AgeHH
Age of household head
– 0.0048
0.9952
***
– 0.0015
0.9985
**
– 0.0107
0.9893
***
– 0.0009
0.9991

FamSize
T
o
t
a
l

n
u
m
b
e
r

o
f

p
e
o
p
l
e

i
n

t
h
e

h
o
u
s
e
h
o
l
d
– 0.0742
0.9284
***
– 0.0784
0.9246
***
– 0.0597
0.9420
***
– 0.1215
0.8856
**
Adult1824
Pr
esence in household of adults ages 18–24
– 0.0459
0.9551
*
– 0.0368
0.9639
***
– 0.0564
0.9452
*
0.0952
1.0999

Adult2534
Pr
esence in household of adults ages 25–34
– 0.0103
0.9897

– 0.0151
0.9850

– 0.0387
0.9620

0.1112
1.1176

Adult35p
Pr
esence in household of adults ages 35 and older
0.0562
1.0578
***
0.0264
1.0268

0.0706
1.0731
**
0.0499
1.0511

Black
Head of household is non-Hispanic
African American
0.4139
1.5126
***
0.4391
1.5514
***
0.2391
1.2700
***
0.4398
1.5524
***
Hispanic
Head of household is Hispanic
0.2530
1.2879
***
0.2583
1.2947
***
0.2522
1.2869
***
0.2936
1.3412
***
Other
Head of household is other race
0.1761
1.1925
***
0.1572
1.1703
***
0.2818
1.3255
***
0.1944
1.2145

Female
Head of household is female
0.1661
1.1807
***
0.1704
1.1858
***
0.1585
1.1717
***
0.1862
1.2047
***
Homeless
Head of household was pr
eviously
homeless
– 0.0311
0.9694
*
– 0.0320
0.9685
*
– 0.1250
0.8825
***
0.2140
1.2386
*
Income
a
T
otal household annual income
– 0.0093
0.9908
***
– 0.0051
0.9949
***
– 0.0155
0.9846
***
– 0.0298
0.9707
***
W
age
a
T
otal household wage income
0.0060
1.0061
***
0.0023
1.0023
***
0.0164
1.0166
***
0.0320
1.0325
***
H2
Nonelderly
, disabled head of
household with a child or childr
en
0.1741
1.1902
***

H3
Nonelderly head of household with at
least one disabled child
0.2403
1.2716
***

??? Policy Issues in Public and Assisted Housing
Cortes, Lam, and Fein
V
ariable
Characteristics
All
Households
Nonelderly
Heads of Households
With a Child
or Childr
en
Nonelderly
, Disabled
Heads of Households
With a Child
or Childr
en
Nonelderly
Heads of Households
With at Least One
Disabled Child
Coef
f.
Time Ratio
Coef
f.
Time Ratio
Coef
f.
Time Ratio
Coef
f.
Time Ratio
Exhibit 9 Estimates Fr
om the Piecewise-Exponential Duration Model With Child-Dummy V
ariables (continued)
Panel B: Child-Related Characteristics nKids
Number of childr
en in household
0.0912
1.0954
***
0.0979
1.1028
***
0.0280
1.0284

0.1128
1.1195
*
Kids03
Pr
esence in household of infants ages 0–3
0.0942
1.0988
***
0.0941
1.0986
***
0.1498
1.1616
***
0.1770
1.1937
***
Kids45
Pr
esence in household of toddlers ages 4–5
0.0855
1.0893
***
0.0744
1.0772
***
0.2003
1.2217
***
0.1247
1.1328
**
Kids612m
Pr
esence in household of male young childr
en ages 6–12
0.1030
1.1085
***
0.0860
1.0898
***
0.2395
1.2706
***
0.1073
1.1133
**
Kids612f
Pr
esence in household of female young childr
en ages 6–12
0.1117
1.1182
***
0.0932
1.0977
***
0.2555
1.2911
***
0.1106
1.1170
**
Kids1317m
Pr
esence in household of male teenagers ages 13–17
– 0.0353
0.9654
***
– 0.0389
0.9618
***
0.0261
1.0264

– 0.0500
0.9512
**
Kids1317f
Pr
esence in household of female teenagers ages 13–17
– 0.0307
0.9698
***
– 0.0381
0.9626
***
0.0365
1.0371

– 0.0366
0.9641

???Cityscape
Household Life Cycle and Length of Stay in Housing Assistance Programs
V
ariable
Characteristics
All
Households
Nonelderly
Heads of Households
With a Child
or Childr
en
Nonelderly
, Disabled
Heads of Households
With a Child
or Childr
en
Nonelderly
Heads of Households
With at Least One
Disabled Child
Coef
f.
Time Ratio
Coef
f.
Time Ratio
Coef
f.
Time Ratio
Coef
f.
Time Ratio
Exhibit 9 Estimates Fr
om the Piecewise-Exponential Duration Model With Child-Dummy V
ariables (continued)
Panel C: Location Characteristics Lihtc
b
Number of LIHTC units in census tract
0.0051
1.0051
*
0.0065
1.0066
**
– 0.0089
0.9912

0.0288
1.0293
*
Unemp
Metr
opolitan ar
ea unemployment rate,
2000
– 2.2991
0.1003
***
– 2.6280
0.0722
***
– 0.3878
0.6785

1.8120
6.1225

V
acancy
Metr
opolitan ar
ea r
ental vacancy rate,
2000
– 0.6362
0.5293
***
– 0.6322
0.5314
***
– 0.5756
0.5624
***
– 0.6707
0.5113
***
City
Household located in central city
0.0688
1.0712
***
0.0736
1.0764
***
0.0270
1.0274

0.0732
1.0760
*
Rural
Household located in rural or
nonmetr
opolitan ar
ea
– 0.2592
0.7717
***
– 0.2657
0.7666
***
– 0.2002
0.8185
***
– 0.2973
0.7428
***
NEglnd
In the New England census division
0.1566
1.1695
***
0.1589
1.1722
***
0.1809
1.1982
***
0.0484
1.0495

MAtlntc
In the Middle Atlantic census division
0.1323
1.1414
***
0.1417
1.1522
***
0.0952
1.0999
***
0.0016
1.0016

ENCetrl
In the East North Central census
division
– 0.1159
0.8905
***
– 0.1164
0.8902
***
– 0.0672
0.9350
**
– 0.1541
0.8572
**
WNCetrl
In the W
est North Central census
division
– 0.2872
0.7503
***
– 0.2943
0.7451
***
– 0.1684
0.8450
***
– 0.3837
0.6814
***
SAtlntc
In the South Atlantic census division
– 0.1514
0.8595
***
– 0.1518
0.8592
***
– 0.1168
0.8898
***
– 0.1626
0.8500
*
ESCetrl
In the East South Central census
division
– 0.2073
0.8127
***
– 0.2036
0.8158
***
– 0.1629
0.8497
***
– 0.4109
0.6630
***
WSCetrl
In the W
est South Central census
division
– 0.3030
0.7386
***
– 0.3070
0.7356
***
– 0.2010
0.8179
***
– 0.3389
0.7126
***
Mtn
In the Mountain census division
– 0.1588
0.8531
***
– 0.1631
0.8495
***
– 0.0604
0.9414

– 0.3857
0.6800
***
Number of households
759,557
637,772
95,906
25,879
Coef
f. = coefficient. LIHTC = low-income housing tax cr
edit.
a
For the piecewise-exponential duration model, the income and wage variables wer
e adjusted (divided) by a factor of 1,000.
b
For the piecewise-exponential duration model, the LIHTC variable was adjusted (divide
d) by a factor of 100.
Notes: *** significance at the 1-per
cent level; ** significance at the 5-per
cent level; * significance at the 10-per
cent level.

??0 Policy Issues in Public and Assisted Housing
Cortes, Lam, and Fein
• Household Type. Nonelderly, disabled heads of households with a child or children and
nonelderly heads of households with at least one disabled child stay 19 and 27 percent longer,
respectively, than nonelderly heads of households with a child or children stay.
• Central-City Location. Households in central cities stay 7 percent longer than households
in suburban jurisdictions (the omitted reference category). The effect of urban location on
length of stay is consistent across all household types, although the longer length of stay in the
HCVP for households in central cities is less pronounced among nonelderly, disabled heads of
households with a child or children.
• Census Division. Households in the New England and Middle Atlantic census divisions stay
16 and 14 percent longer, respectively, compared with households in the Pacific division (the
omitted reference category). The effect of census division on length of stay is consistent across
all household types.
The presence of children is associated strongly with lengths of stay in the HCVP, after controlling
for an array of household and location characteristics, as shown in exhibit 9.
• Number of Children. Among all households, each additional child in a household is associated
with a 10-percent increase in the household’s expected length of stay. This effect is observed
across all head-of-household types, although it is less pronounced among nonelderly, disabled
heads of households with a child or children.
• Infants (ages 0–3). The presence of infants in a household increases lengths of stay among
all households by about 10 percent when compared with households without the presence
of infants. The effect of the presence of infants on lengths of stay is greater among nonelderly,
disabled heads of households with a child or children (16 percent longer) and nonelderly heads
of households with at least one disabled child (19 percent longer).
• Toddlers (ages 4–5). Among all households, the presence of toddlers increases lengths of stay
by about 9 percent when compared with households without toddlers. The effect of toddlers on
lengths of stay is especially pronounced among nonelderly, disabled heads of households with a
child or children (22 percent longer).
• Young Children (ages 6–12). The presence of young children, regardless of gender, has large
effects on lengths of stay in the program. Households with young female children stay about
12 percent longer than households without these children, and households with young male
children show a similar effect on lengths of stay (about 11 percent longer). The effect of young
children (male and female) on lengths of stay is especially acute for nonelderly, disabled heads
of households with a child or children; those households have expected lengths of stay that are
27 and 29 percent longer than those of similar households without these children.
• Teenagers (ages 13–17). The presence of male or female teenagers in a household lowers
expected lengths of stay by about 3 percent for most head-of-household types. The effect of
teenagers on lengths of stay is again significantly different for nonelderly, disabled heads of
households with a child or children; those households stay for about 3 percent longer than
similar households without teenagers.

???Cityscape
Household Life Cycle and Length of Stay in Housing Assistance Programs
These results suggest that lengths of stay in the program are modestly longer when households
include the presence of children under age 13 and are slightly shorter when they include teenagers.
The results also indicate that these effects are more pronounced among nonelderly, disabled heads
of households with a child or children. Depending on the severity of the disability, disabled heads
of household may not have many opportunities for gainful employment, and many have fixed
incomes from mainstream income supports, such as Supplemental Security Income. The presence
of children in these head-of-household types may put further strain on household finances and,
thus, limit the household’s ability to pay rent in the private market, which can lead to longer
lengths of stay in the HCVP.
Exhibit 10 summarizes key results indicating how relative survival times (or lengths of stay) for
households with the presence of infants and toddlers change when households also include older
children in varying ages and gender groups. The first column repeats the estimated overall effect
of infants and toddlers from exhibit 9. The second column shows the ratio of effects of infants and
toddlers when older children of various ages and genders also are present in the household. As
noted earlier, we estimated the latter ratios by adding interactions between the infant and toddler
dummy variables and the dummy variables for each of the other age-gender groups. (See exhibit
B-1 for full results from this model.)
For example, row 1, column 2 shows that average lengths of stay are 10 percent longer overall
in households with the presence of an infant compared with those without an infant. Column 4
shows that this effect is slightly lower (5 percent) in households that also have the presence of a
male young child age 6–12 than in those that do not, and column 5 indicates that the difference
is statistically significant. Findings in the first panel of exhibit 10 apply to all household types and
suggest the following relationships:
• The presence of young children ages 6–12 in the same household with an infant or a toddler
attenuates slightly the effect that infants and toddlers have on lengths of time in the program.
The effects are somewhat larger for toddlers (about .90 to .91) than for infants (.95 to .97) and
are about the same for male and female young children ages 6–12.
• The presence of teenagers, especially male teenagers, magnifies the lengthening of stays
associated with infants and toddlers. For example, when a male teenager is present, the ratio
of length of stay associated with an infant is 1.09 times larger than when no male teenager is
present, and the ratio of length of stay for a toddler is 1.15 times larger.
• Effects are multiplicative; the presence of older boys and girls from multiple categories would
have an even larger combined effect. For example, the increased length of stay associated with
the presence of a toddler ages 4–5 is .90 * .90 = .81 times lower when households include both
male and female young children ages 6–12.
The results for all households suggest that the presence of young children or teenagers in the same
household with infants and toddlers affects the lengths of stay in the program. The descriptive
statistics in exhibit 3 indicate that more households in the HCVP have an infant or a toddler and
a young child than households with an infant or a toddler and a teenager. The lower prevalence
of teenagers in households with infants or toddlers across all voucher households substantially
constrains the negative effects of teenagers.

??? Policy Issues in Public and Assisted Housing
Cortes, Lam, and Fein
Exhibit 10
Overall Effect of
Infant and Toddler
a
Ratio of Effects of Infant and Toddler
When Household Does and Does Not Include
at Least One Older Child in Specified Age-Sex Groups
T-test
Summary of Child-Related Interaction Effects on Lengths of Stay in the HCVP
All households
Infant ages 0–3 + 10%***
Male young child ages 6–12 .95 ***
Female young child ages 6–12 .97 ***
Male teenager ages 13–17 1.09 ***
Female teenager ages 13–17 .98
Toddler ages 4–5 + 9%***
Male young child ages 6–12 .91 ***
Female young child ages 6–12 .90 ***
Male teenager ages 13–17 1.15 ***
Female teenager ages 13–17 1.06 **
Nonelderly heads of households with a child or children
Infant ages 0–3 + 10%***
Male young child ages 6–12 .97 ***
Female young child ages 6–12 .97 ***
Male teenager ages 13–17 1.06 ***
Female teenager ages 13–17 .98 ***
Toddler ages 4–5 + 8%***
Male young child ages 6–12 .93 ***
Female young child ages 6–12 .91 ***
Male teenager ages 13–17 1.14 ***
Female teenager ages 13–17 1.05 ***
Nonelderly, disabled heads of households with a child or children
Infant ages 0–3 + 16%***
Male young child ages 6–12 .84 ***
Female young child ages 6–12 .93 *
Male teenager ages 13–17 1.13 *
Female teenager ages 13–17 1.01
Toddler ages 4–5 + 22%***
Male young child ages 6–12 .79 ***
Female young child ages 6–12 .84 ***
Male teenager ages 13–17 1.19 **
Female teenager ages 13–17 1.10
Nonelderly heads of households with at least one disabled child
Infant ages 0–3 + 19%***
Male young child ages 6–12 .90 *
Female young child ages 6–12 .99
Male teenager ages 13–17 1.38 ***
Female teenager ages 13–17 .91
Toddler ages 4–5 + 13%***
Male young child ages 6–12 .80 ***
Female young child ages 6–12 .94
Male teenager ages 13–17 1.12
Female teenager ages 13–17 1.10
HCVP = Housing Choice Voucher Program.
a
The overall effect of an infant or a toddler is equal to the time ratios reported in exhibit 9.
Notes: *** significance at the 1-percent level; ** significance at the 5-percent level; * significance at the 10-percent level.

???Cityscape
Household Life Cycle and Length of Stay in Housing Assistance Programs
Findings in the remaining panels of exhibit 10, which apply to the three head-of-household types
in the study, suggest the following conclusions:
• The effects of child-pairings on nonelderly, disabled heads of households with a child or
children are consistently more pronounced in comparison with the other household types.
For example, the effects associated with infants ages 0–3 and male young children ages 6–12
(.84) or with infants ages 0–3 and female young children ages 6–12 (.93) are larger than those
associated with nonelderly heads of households with a child or children (.97 for both genders)
and nonelderly heads of households with at least one disabled child (.90 and .99, respectively).
Also, nonelderly, disabled heads of households with a child or children are the only households
in the study to show negative effects from a female teenager with an infant (1.01).
• The effect of male teenagers on lengths of stay is most severe among infants in a household
with a nonelderly head of household with at least one disabled child. When a male teenager is
present in these head-of-household types, the ratio of lengths of stay associated with an infant is
1.38 times larger than when no male teenager is present.
• The multiplicative effect associated with households that include both male and female young
children ages 6–12 is greatest in households with nonelderly, disabled heads of households with
a child or children. For these head-of-household types, the increased length of stay associated
with the presence of toddlers ages 4–5 is .79 * .84 = .66 times lower when households include
both male and female young children ages 6–12.
These results raise interesting questions about the relationship between household composition
and the ability of single heads of households to exit from the HCVP. Why do infants and toddlers
lead to longer lengths of stay? Why do young children attenuate the effects associated with infants
and toddlers on expected lengths of stay? And, finally, why do teenagers exacerbate the effects
associated with infants and toddlers? In the absence of qualitative information about a household’s
decisionmaking process, answers to these questions are speculative.
As suggested earlier, the presence of infants and toddlers may lead some households to stay in the
housing program until they can secure adequate daycare, which, in turn, provides an opportunity
to find gainful employment. If those households are unable to find appropriate daycare for their
infants or toddlers, then longer lengths of stay would be expected.
Reasons for the attenuating effects of young children (ages 6–12) on household lengths of stay
are less clear. Responsible young children may be asked to babysit for the younger children in the
household while the head of the household is taking incremental steps to become self-sufficient
(for example, enrolling in a job-training program, going to a job interview, or completing a
certificate or degree program), but the head of the household may be limited in his or her ability to
pursue different types of self-sufficiency opportunities. For example, the head of a household may
not feel comfortable leaving a young child with an infant or a toddler while the household head
is working full time, but perhaps the household head does feel comfortable leaving the children
while he or she attends a 2-hour General Educational Development (GED) class. The head of
household potentially could become self-sufficient more quickly by attending GED classes during
the day and working in the evening, but responsibilities for the children prevent the household

??? Policy Issues in Public and Assisted Housing
Cortes, Lam, and Fein
head from pursuing these activities simultaneously. Thus, young children in the household may
facilitate the ability of a household head to become self-sufficient to the extent that the household
head can relinquish his or her responsibilities for a short time, but not enough to overcome the
exit-delays associated with having infants and toddlers in the household.
In addition, the results do not support the hypothesis that teenagers—male or female—help a
household become self-sufficient by allowing the head of household to leave the infants or toddlers
with the teenager and find gainful employment. On the contrary, the presence of teenagers,
especially male teenagers, magnifies the stay-lengthening effect of the presence of infants and
toddlers. Perhaps some households with at-risk teenagers are overwhelmed by the stresses brought
on by the teenager, and, as a result, they decide to focus on stabilizing their household life before
considering other important choices, such as seeking employment, asking for a raise, or uprooting
the household and moving elsewhere. Or perhaps some households with teenagers prefer to keep
their teenagers rooted in their social settings, and the housing assistance enables them to stay
where they are. These hypotheses merit closer study and could be the subject of future research.
The results also suggest that PHAs that have large concentrations of households with a child or
children, especially households with infants or toddlers only or households with infants, toddlers,
and teenagers, should expect longer lengths of stay when compared with other household types.
The longer lengths of stay in the program likely are affected by the location of PHAs because
the results also suggest that location characteristics are associated with longer lengths of stay.
Indeed, exhibit 11 shows the location and rent costs of PHAs in 2005 with the highest percent-
age of households with a child or children (under age 6)—weighted by the number of housing
units—compared with other PHAs. PHAs with the highest percentage of households with young
children are those whose proportion of households with young children is within the highest
quartile among all PHAs. Also, households with infants and toddlers are defined as households
with one or more children age 5 or younger.
We find a large concentration of PHA units with infants and toddlers (ages 5 or under) in central
cities (56 percent), and about the same concentration for other areas (53 percent). Results shown
in exhibit 9 suggest that households in central cities stay in the program about 7 percent longer
than households in suburban jurisdictions, and, thus, the confluence of young children and central
city locations is expected to lead to even longer lengths of stay. Few households participating in the
HCVP (about 10 percent) are located in census divisions that were associated with longer lengths
of stay; that is, the New England and Middle Atlantic divisions.
In addition, about one-quarter of PHA households participating in the HCVP reside in areas where
the PHAs’ average rent is more than the areas’ FMRs. Past research suggests that higher cost areas
are associated with longer lengths of stay (Susin, 1999), and increases in the monthly value of the
local payment standard are also associated with lower rates of program exit (Olsen, Davis, and
Carrillo, 2005). Although we were unable to control for local rent structures in the piecewise-expo-
nential duration model because of multicollinearity issues, past research suggests that households
in these higher cost markets are expected to stay in the program longer than households in lower
cost markets.

???Cityscape
Household Life Cycle and Length of Stay in Housing Assistance Programs
Conclusion
This study analyzed administrative data from HUD to explore the factors associated with a
household’s length of stay in the HCVP. Analyses summarize relationships between length of stay
and various demographic, economic, and geographic characteristics of households. The study
placed particular emphasis on differences in characteristics and program exit rates across three
types of heads of households (nonelderly heads of households with a child or children; nonelderly,
Exhibit 11
Characteristic
PHAs With Highest Percentage of
Households With Young Children
b
Other PHAs
Location and Rent Costs Among PHAs With the Highest Percentage of Households
With Infants and Toddlers Compared With Other PHAs, 2005
a
Number of PHAs 604 1,816
Metropolitan location
Central city 56.2 53.4
Suburb 23.6 32.0
Nonmetropolitan area 20.2 14.7
Total 100.0 100.0
Census division
New England 5.2 9.1
Middle Atlantic 4.5 18.8
East North Central 11.9 14.1
West North Central 8.8 6.5
South Atlantic 22.1 14.6
East South Central 10.8 4.4
West South Central 23.9 8.3
Mountain 5.5 5.1
Pacific 7.3 19.1
Total 100.0 100.0
Rent-to-FMR ratio
c

Average ratio 0.957 0.953
> 1 25.5 23.0
1 to 0.85 68.2 69.2
0.85 to 0.75 6.1 7.3
< 0.75 0.2 0.5
Total 100.0 100.0
FMR = Fair Market Rent. PHA = public housing agency.
a
The data in the table are weighted by number of housing units.
b
PHAs with the highest percentage of households with young children are those whose proportion of households with young
children is within the highest quartile among all PHAs. Households with infants and toddlers are defined as households with
one or more children age 5 or younger.
c
PHA data are aggregated to calculate mean gross rents each year, and mean rents among PHAs are compared with their
respective metropolitan area’s FMR to generate the rent-to-FMR ratio.
(percent)
(percent)

??? Policy Issues in Public and Assisted Housing
Cortes, Lam, and Fein
disabled heads of households with a child or children; and nonelderly heads of households with at
least one disabled child) and on the effect of the presence of children (and their number, ages, and
spacing) on exit rates from the program.
Overall, we found that exit rates from the HCVP vary somewhat across head-of-household types.
Households with a child or children have the highest rates of exit for each year of program
participation, and nonelderly heads of households with at least one disabled child have the lowest
rates of exit for each year of program participation. Exit rates by number of years of participation
strongly suggest that most households with a child or children are not long-term participants in the
HCVP. Half of all households exit the program after less than 3 years of program participation, and
exit rates increase precipitously after 1 year of program participation. Depending on the head-of-
household type and entry cohorts, as many as 43 percent of households exited the program by the
second year of participation; however, exit rates diminish steadily across entry cohorts. Cohorts
that entered recently are much less likely to exit the program after 1 or more years in the program
when compared with cohorts who entered several years ago. This finding raises the possibility that
characteristics related to a household’s length of stay in the program may be somewhat different for
cohorts who recently entered the program when compared with cohorts entering several years ago.
We also find that households with infants are increasingly common among new entrants in the
HCVP. The proportion of households with infants has increased steadily across entry cohorts, which
has occurred simultaneously with a decrease in households with children of other ages, particularly
households with young children (ages 6–12). From 1997 to 2004, the proportion of households
with young children at entry has decreased by about 7 percentage points. This finding is particu-
larly important because the piecewise-exponential duration model suggests that the presence of
children in a household strongly affects the household’s expected length of stay in the HCVP.
The study’s main findings on the effect of the presence of children in the household suggest that
the presence of an infant or a toddler increases a household’s length of stay in the HCVP, after
data are controlled for an array of household and location characteristics. Moreover, the estimates
associated with different-age child-pairings demonstrate that the presence of young children
ages 6–12 in the same household that has infants and toddlers attenuates slightly the effect that
infants and toddlers have on lengths of stay, and the effect is about the same for the presence of
male and female young children ages 6–12. We also find that the presence of teenagers, especially
male teenagers, magnifies the lengthening of stays associated with infants and toddlers, but the
smaller proportion of households with teenagers and younger children in the study population
constrains the negative effects of teenagers on lengths of stay. These findings are consistent across
all household types, although the effects are particularly acute among nonelderly, disabled heads of
households and nonelderly heads of households with at least one disabled child.
Overall, the study results have implications for policy decisions regarding the use of self-sufficiency
programs, time limits on program participation, and tenant selection policies. The impetus for
incorporating self-sufficiency programs into housing assistance programs is the belief that many
participants are long-term program users who become dependent on government-subsidized hous-
ing. The study’s results find little support for the notion that households, including households
with a nonelderly, disabled head of household and households with at least one disabled child, stay

???Cityscape
Household Life Cycle and Length of Stay in Housing Assistance Programs
in the HCVP for long periods of time. On the contrary, about half of HCVP participants exited the
program after 3 years of participation and about three-quarters exited after 6 years, although exit
rates varied by entry cohort. These findings also suggest that time limits on program participation
may be unnecessary.
The findings suggest that self-sufficiency programs that traditionally have focused on promoting
positive outcomes among heads of households also should consider the needs of both infants or
toddlers and teenagers. Access to adequate childcare among program participants may attenu-
ate the effects that infants and toddlers have on lengths of stay if these services allow the head
of household to seek employment, enroll in training or education, or otherwise become more
self-sufficient. The need for adequate childcare has consistently been cited in research literature as
a key barrier to households’ self-sufficiency outcomes (Turnham et al., 2006). In addition, incor-
porating counseling services or after-school programs for at-risk teenagers may assuage the effects
that teenagers (male or female) have on a household’s length of stay in the program when younger
children are present. These services may lessen the stress brought on by an at-risk teenager and
help discipline the teenager, which, in turn, may allow the head of household to focus on other
aspects of life, such as opportunities to become more self-sufficient.
The results also suggest that PHA policies may significantly affect lengths of stay among households
with a child or children if the policies affect the characteristics of households entering the program.
Evidence presented in this study suggests that recent entry cohorts are more likely to have infants
or toddlers in the household and less likely to have older children, especially young children ages
6–12. This shift in household composition has occurred concomitantly with lower exit rates and
longer expected lengths of stay among recent cohorts. This phenomenon raises several important
questions about the forces that might be underpinning this shift. Are PHAs’ tenant-selection prefer-
ences directly or indirectly promoting this shift? Is the management of waiting lists affecting the
selection of applicants by age group of children in households and, thus, leading to longer lengths
of stay in the program? Is the fungibility in income eligibility requirements between a PHA’s public
housing program and tenant-based HCVP resulting in a higher concentration of poor households
(many with children) in the HCVP and, thus, leading to longer lengths of stay? Addressing these
critical questions is fundamental to understanding how vouchers are used by program participants
and how vouchers will turn over in the future.

??? Policy Issues in Public and Assisted Housing
Cortes, Lam, and Fein
Panel A: Household Characteristics AgeHH
Age of household head
31.23
9.29
30.81
8.59
***
FamSize
T
otal number of people in the household
3.19
1.22
3.28
1.26
***
Adult1824
Pr
esence in household of adults ages 18–24
36
48
34
47
***
Adult2534
Pr
esence in household of adults ages 25–34
41
49
44
50
***
Adult35p
Pr
esence in household of adults ages 35 or older
34
48
32
46
***
White
Head of household is non-Hispanic White
47
50
28
45
***
Black
Head of household is non-Hispanic African American
37
48
53
50
***
Hispanic
Head of household is Hispanic
13
34
16
36
***
Other
Head of household is other race
3
18
3
17
***
Female
Head of household is female
88
33
93
26
***
nMarried
No spouse is pr
esent in household
88
32
92
27
***
Homeless
Head of household was pr
eviously homeless
3
17
4
20
***
Income
T
otal household annual income
10,050
6,217
10,789
6,632
***
Asset
T
otal household asset amount
56
1,037
19
558
***
W
age
T
otal household wage income
5,573
7,229
6,121
7,816
***
adjAMI
Household income in pr
oportion to AMI
21
12
20
11
***
adjRent
Gr
oss r
ent in pr
oportion to FMR
102
173
100
17
***
Exhibit A-1
V
ariable
Household Characteristics
Exiters
Stayers
Dif
fer
ence in
Means T
est
Mean
Stdev
Mean
Stdev
Descriptive Statistics for the Household Characteristic V
ariables, by Exit Status
Appendix
A
(per
cent)
(US$)
(per
cent)

???Cityscape
Household Life Cycle and Length of Stay in Housing Assistance Programs
Panel B: Child-Related Characteristics nKids
Number of childr
en in household
1.94
1.09
2.10
1.17
***
Kids03
Pr
esence in household of infants ages 0–3
50
50
51
50
***
Kids45
Pr
esence in household of toddlers ages 4–5
24
43
28
45
***
Kids612m
Pr
esence in household of male young childr
en ages 6–12
34
61
41
65
***
Kids612f
Pr
esence in household of female young childr
en ages 6–12
35
61
41
65
***
Kids1317m
Pr
esence in household of male teenagers ages 13–17
18
44
16
43
***
Kids1317f
Pr
esence in household of female teenagers ages 13–17
19
45
17
44
***
Kids03*Kids612m
Pr
esence in household of infants ages 0–3 and male young childr
en ages 6–12
9
28
11
31
***
Kids03*Kids612f
Pr
esence in household of infants ages 0–3 and female young childr
en ages 6–12
8
28
11
32
***
Kids03*Kids1317m
Pr
esence in household of infants ages 0–3 and male teenagers ages 13–17
2
14
2
16
***
Kids03*Kids1317f
Pr
esence in household of infants ages 0–3 and female teenagers ages 13–17
3
17
3
17
*
Kids45*Kids612m
Pr
esence in household of toddlers ages 4–5 and male young childr
en ages 6–12
7
25
8
28
***
Kids45*Kids612f
Pr
esence in household of toddlers ages 4–5 and female young childr
en ages 6–12
7
25
8
27
***
Kids45*Kids1317m
Pr
esence in household of toddlers ages 4–5 and male teenagers ages 13–17
1
11
2
13
***
Kids45*Kids1317f
Pr
esence in household of toddlers ages 4–5 and female teenagers ages 13–17
1
12
2
14
***
Exhibit A-1
V
ariable
Household Characteristics
Exiters
Stayers
Dif
fer
ence in
Means T
est
Mean
Stdev
Mean
Stdev
Descriptive Statistics for the Household Characteristic V
ariables, by Exit Status (continued)
(per
cent)

??0 Policy Issues in Public and Assisted Housing
Cortes, Lam, and Fein
Panel C: Location Characteristics V
acancy
Metr
opolitan ar
ea r
ental vacancy rate, 2000
9.2
6.4
8.6
6.2
***
Lihtc
Number of LIHTC units in census tract
39.63
93.91
46.47
108.99
***
Unemp
Metr
opolitan ar
ea unemployment rate, 2000
6
2
6
1
***
OwnOcc
Metr
opolitan ar
ea owner
-occupancy rate, 2000
56.4
19.8
50.5
22.1
***
City
Household located in central city
43
49
56
50
***
Suburb
Household located in suburban ar
ea
29
45
31
46
***
Rural
Household located in rural or nonmetr
opolitan ar
ea
29
45
13
34
***
PovRate
Metr
opolitan ar
ea poverty rate
19.1
11.3
21.0
12.6
***
NEglnd
In the New England census division
5
21
6
23
***
MAtlntc
In the Middle Atlantic census division
10
29
16
37
***
ENCetrl
In the East North Central census division
14
35
15
36
***
WNCetrl
In the W
est North Central census division
11
32
6
24
***
SAtlntc
In the South Atlantic census division
16
36
17
38
***
ESCetrl
In the East South Central census division
8
28
7
25
***
WSCetrl
In the W
est South Central census division
16
37
13
33
***
Mtn
In the Mountain census division
6
25
5
22
***
Pac
In the Pacific census division
13
34
15
35
***
Number of households
372,182
387,375
Exhibit A-1
V
ariable
Household Characteristics
Exiters
Stayers
Dif
fer
ence in
Means T
est
Mean
Stdev
Mean
Stdev
Descriptive Statistics for the Household Characteristic V
ariables, by Exit Status (continued)
(per
cent)
AMI = ar
ea median income. FMR = Fair Market Rent. LIHTC = low-income housing tax cr
edit. Stdev = standar
d deviation.
Notes: *** significance at the 1-per
cent level; ** significance at the 5-per
cent level; * significance at the 10-per
cent level.
(per
cent)

???Cityscape
Household Life Cycle and Length of Stay in Housing Assistance Programs
Exhibit B-1
V
ariable
Characteristics
All
Households
Nonelderly Heads of
Households With
a Child or Childr
en
Nonelderly
, Disabled
Heads of Households
With a Child or Childr
en
Nonelderly Heads of
Households With
at Least One
Disabled Child
Coef
f.
Time Ratio
Coef
f.
Time Ratio
Coef
f.
Time Ratio
Coef
f.
Time Ratio
Estimates Fr
om the Full Piecewise-Exponential Duration Model
Panel A: Household Characteristics AgeHH
Age of household head
– 0.0045
0.9955
***
– 0.0012
0.9988
– 0.0104
0.9897
***
– 0.0006
0.9994

FamSize
Number of people in the
household
– 0.0725
0.9301
***
– 0.0764
0.9264
***
– 0.0597
0.9420
***
– 0.1277
0.8801
**
Adult1824
Pr
esence in household of adults ages 18–24
– 0.0497
0.9515
***
– 0.0399
0.9609
***
– 0.0646
0.9374
**
0.0958
1.1005

Adult2534
Pr
esence in household of adults ages 25–34
– 0.0092
0.9909
– 0.0144
0.9857
– 0.0324
0.9681
0.1236
1.1316

Adult35p
Pr
esence in household of adults ages 35 or older
0.0550
1.0566
***
0.0253
1.0256
0.0708
1.0733
**
0.0594
1.0612

Black
Head of household is non-
Hispanic African American
0.4145
1.5136
***
0.4399
1.5525
***
0.2386
1.2695
***
0.4427
1.5569
***
Hispanic
Head of household is Hispanic
0.2530
1.2879
***
0.2584
1.2949
***
0.2504
1.2845
***
0.2956
1.3440
***
Other
Head of household is other race
0.1750
1.1913
***
0.1564
1.1693
***
0.2755
1.3172
***
0.2084
1.2316
*
Female
Head of household is female
0.1663
1.1809
***
0.1708
1.1862
***
0.1584
1.1716
***
0.1791
1.1962
***
Homeless
Head of household was
pr
eviously homeless
– 0.0319
0.9686
*
– 0.0325
0.9681
*
– 0.1264
0.8813
***
0.2095
1.2330
*
Income
a
T
otal household annual income
– 0.0094
0.9907
***
– 0.0052
0.9948
***
– 0.0155
0.9846
***
– 0.0297
0.9707
***
W
age
a
T
otal household wage income
61.0000
1.0061
***
0.0023
1.0023
***
0.0162
1.0164
***
0.0318
1.0323
***
H2
Nonelderly
, disabled head of
household with a child or childr
en
0.1741
1.1902
***

H3
Nonelderly head of household
with at least one disabled child
0.2403
1.2716
***

Appendix B

??? Policy Issues in Public and Assisted Housing
Cortes, Lam, and Fein
V
ariable
Characteristics
All
Households
Nonelderly Heads of
Households With
a Child or Childr
en
Nonelderly
, Disabled
Heads of Households
With a Child or Childr
en
Nonelderly Heads of
Households With
at Least One
Disabled Child
Coef
f.
Time Ratio
Coef
f.
Time Ratio
Coef
f.
Time Ratio
Coef
f.
Time Ratio
Exhibit B-1 Estimates Fr
om the Full Piecewise-Exponential Duration Model (continued)
Panel B: Child-Related Characteristics nKids
Number of childr
en in household
0.0881
1.0921
***
0.0947
1.0993
***
0.0240
1.0243
0.1072
1.1132
*
Kids03
Pr
esence in household of infants ages 0–3
0.1217
1.1294
***
0.1190
1.1264
***
0.2098
1.2334
***
0.2219
1.2484
***
Kids45
Pr
esence in household of toddlers ages 4–5
0.1349
1.1445
***
0.1153
1.1222
***
0.2989
1.3484
***
0.2367
1.2670
***
Kids612m
Pr
esence in household of male young childr
en ages 6–12
0.1316
1.1406
***
0.1087
1.1148
***
0.2982
1.3474
***
0.1696
1.1849
***
Kids612f
Pr
esence in household of female young childr
en ages 6–12
0.1397
1.1500
***
0.1199
1.1273
***
0.2962
1.3447
***
0.1321
1.1412
***
Kids1317m
Pr
esence in household of male teenagers ages 13–17
– 0.0470
0.9541
***
– 0.0506
0.9506
***
0.0318
1.0323
– 0.0754
0.9273

Kids1317f
Pr
esence in household of female teenagers ages 13–17
– 0.0470
0.9541
***
– 0.0506
0.9506
***
0.0318
1.0323
– 0.0754
0.9273

Kids03*Kids612m
Pr
esence in household of infants ages 0–3 and male young childr
en ages 6–12
– 0.0487
0.9525
***
– 0.0353
0.9653
***
– 0.1740
0.8403
***
– 0.1101
0.8957
*
Kids03*Kids612f
Pr
esence in household of infants ages 0–3 and female young childr
en ages 6–12
– 0.0356
0.9650
***
– 0.0322
0.9683
**
– 0.0769
0.9259
*
– 0.0098
0.9903

K
i
d
s
0
3
*
K
i
d
s
1
3
1
7
m
Pr
esence in household of infants ages 0–3 and male teenagers ages 13–17
0.0816
1.0851
***
0.0576
1.0593
**
0.1195
1.1269
*
0.3251
1.3842
***
Kids03*Kids1317f
Pr
esence in household of infants ages 0–3 and female teenagers ages 13–17
– 0.0189
0.9813
– 0.0251
0.9752
0.0134
1.0135
– 0.0896
0.9143

???Cityscape
Household Life Cycle and Length of Stay in Housing Assistance Programs
Exhibit B-1 Estimates Fr
om the Full Piecewise-Exponential Duration Model (continued)
Kids45*Kids612m
Pr
esence in household of toddlers ages 4–5 and male young childr
en ages 6–12
– 0.0990
0.9057
***
– 0.0739
0.9288
***
– 0.2414
0.7855
***
– 0.2191
0.8033
***
Kids45*Kids612f
Pr
esence in household of toddlers ages 4–5 and female young childr
en ages 6–12
– 0.1089
0.8968
***
– 0.0992
0.9056
***
– 0.1719
0.8420
***
– 0.0657
0.9364
K
i
d
s
4
5
*
K
i
d
s
1
3
1
7
m
Pr
esence in household of toddlers ages 4–5 and male teenagers ages 13–17
0.1383
1.1483
***
0.1316
1.1407
***
0.1723
1.1881
**
0.1095
1.1158
Kids45*Kids1317f
Pr
esence in household of toddlers ages 4–5 and female teenagers ages 13–17
0.0592
1.0610
**
0.0486
1.0498
*
0.0974
1.1024
0.0953
1.1000
V
ariable
Characteristics
All
Households
Nonelderly Heads of
Households With
a Child or Childr
en
Nonelderly
, Disabled
Heads of Households
With a Child or Childr
en
Nonelderly Heads of
Households With
at Least One
Disabled Child
Coef
f.
Time Ratio
Coef
f.
Time Ratio
Coef
f.
Time Ratio
Coef
f.
Time Ratio
Panel C: Location Characteristics Lihtc
b
Number of LIHTC units in census
tract
0.0052
1.0052
*
0.0066
1.0066
**
– 0.0088
0.9913
0.0284
1.0288
*
Unemp
Metr
opolitan ar
ea unemployment
rate, 2000
– 2.2969
0.1006
***
– 2.6243
0.0725
***
– 0.3901
0.6770
1.8326
6.2501
V
acancy
Metr
opolitan ar
ea r
ental vacancy
rate, 2000
– 0.6326
0.5312
***
– 0.6283
0.5335
***
– 0.5831
0.5581
***
– 0.6802
0.5065
***
City
Household located in central city
0.0685
1.0709
***
0.0734
1.0762
***
0.0281
1.0285
0.0720
1.0747
*
Rural
Household located in rural or
nonmetr
opolitan ar
ea
– 0.2592
0.7717
***
– 0.2658
0.7666
***
– 0.1983
0.8201
***
– 0.2976
0.7426
***
NEglnd
In the New England census
division
0.1566
1.1695
***
0.1590
1.1724
***
0.1791
1.1961
***
0.0610
1.0629
MAtlntc
In the Middle Atlantic census
division
0.1322
1.1414
***
0.1416
1.1521
***
0.0975
1.1024
***
0.0056
1.0056

??? Policy Issues in Public and Assisted Housing
Cortes, Lam, and Fein
V
ariable
Characteristics
All
Households
Nonelderly Heads of
Households With
a Child or Childr
en
Nonelderly
, Disabled
Heads of Households
With a Child or Childr
en
Nonelderly Heads of
Households With
at Least One
Disabled Child
Coef
f.
Time Ratio
Coef
f.
Time Ratio
Coef
f.
Time Ratio
Coef
f.
Time Ratio
Exhibit B-1 Estimates Fr
om the Full Piecewise-Exponential Duration Model (continued)
Coef
f. = coefficient. LIHTC = low-income housing tax cr
edit.
a
For the piecewise-exponential duration model, the income and wage variables wer
e adjusted (divided) by a factor of 1,000.
b
For the piecewise-exponential duration model, the LIHTC variable was adjusted (divide
d) by a factor of 100.
Notes: *** significance at the 1-per
cent level; ** significance at the 5-per
cent level; * significance at the 10-per
cent level.
ENCetrl
In the East North Central census
division
– 0.1156
0.8908
***
– 0.1161
0.8904
***
– 0.0668
0.9354
**
– 0.1446
0.8654
*
WNCetrl
In the W
est North Central census
division
– 0.2872
0.7504
***
– 0.2942
0.7451
***
– 0.1673
0.8459
***
– 0.3823
0.6823
***
SAtlntc
In the South Atlantic census
division
– 0.1519
0.8591
***
– 0.1522
0.8588
***
– 0.1179
0.8888
***
– 0.1544
0.8570
**
ESCetrl
In the East South Central census
division
– 0.2076
0.8126
***
– 0.2037
0.8157
***
– 0.1625
0.8501
***
– 0.4075
0.6653
***
WSCetrl
In the W
est South Central census
division
– 0.3027
0.7388
***
– 0.3068
0.7358
***
– 0.2014
0.8176
***
– 0.3314
0.7179
***
Mtn
In the Mountain census division
– 0.1589
0.8531
***
– 0.1633
0.8493
***
– 0.0569
0.9447
– 0.3890
0.6777
***
Number of Households
759,557
637,772
95,906
25,879

???Cityscape
Household Life Cycle and Length of Stay in Housing Assistance Programs
Acknowledgments
The authors gratefully acknowledge financial support from the U.S. Department of Housing and
Urban Development (HUD), Office of Policy Development and Research, under a contract with
QED Group LLC. The authors also thank Barbara Haley of HUD and Jill Khadduri of Abt Associ-
ates Inc. for their comments on drafts of this article.
Authors
Alvaro Cortes is an associate in the Social and Economic Policy Division, Abt Associates Inc.
Ken Lam is an associate in the Social and Economic Policy Division, Abt Associates Inc.
David Fein is a senior associate in the Social and Economic Policy Division, Abt Associates Inc.
References
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Institute, Inc.
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Programs,” Cityscape 8 (2): 69–93.
Bahchieva, Raisa, and Amy Hosier. 2001. “Determinants of Tenure Duration in Public Housing:
The Case of New York City,” Journal of Housing Research 12 (2): 307–348.
Bane, Mary Jo, and David Ellwood. 1994. Welfare Realities. Cambridge, MA: Harvard University
Press.
Burton, L.M. 1997. “Ethnography and the Meaning of Adolescence in High-Risk Neighborhoods,”
Ethos 25: 208–217.
Devine, Deborah, Barbara A. Haley, Lester Rubin, and Robert W. Gray. 2000. The Uses of
Discretionary Authority in the Tenant-Based Section 8 Program. Washington, DC: U.S. Department of
Housing and Urban Development, Office of Policy Development and Research.
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Decade of Qualitative Research,” Journal of Marriage and the Family 66: 318–332.
Freeman, Lance. 2005. “Does Housing Assistance Lead to Dependency? Evidence From HUD
Administrative Data,” Cityscape 8 (2): 115–133.
———1998. “Interpreting the Dynamics of Public Housing: Cultural and Rational Choice
Explanations,” Housing Policy Debate 9 (2): 323–353.
Hosmer, David W., Jr., and Stanley Lemeshow. 1999. Applied Survival Analysis: Regression Modeling
of Time to Event Data. New York: John Wiley.

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Kaplan, Edward L., and Paul Meier. 1958. “Nonparametric Estimation from Incomplete
Observations,” Journal of the American Statistical Association 53: 457–481.
Olsen, Edgar O., Scott E. Davis, and Paul E. Carrillo. 2005. “Explaining Attrition in the Housing
Voucher Program,” Cityscape 8 (2): 95–113.
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Jeffrey B. Liebman, and Jeffrey R. Kling. 2003. Moving to Opportunity for Fair Housing Demonstration
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Development, Office of Policy Development and Research.
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Cityscape 8 (2): 219–239.
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Inc.

???Cityscape: A Journal of Policy Development and Research • Volume 10, Number 1 • 2008
U.S. Department of Housing and Urban Development • Office of Policy Development and Research
Cityscape
Are Census Variables Highly
Correlated With Housing
Choice Voucher Holders’
Perception of the Quality of
Their Neighborhoods?
Larry Buron
Satyendra Patrabansh
Abt Associates Inc.
Abstract
This article presents the results of a study examining voucher holders’ ratings of their
neighborhoods on the U.S. Department of Housing and Urban Development’s Housing
Choice Voucher Program Customer Satisfaction Survey. Specifically, the study examines
whether these ratings were internally consistent and whether they were highly correlated
with any census neighborhood variables often used as measures of neighborhood quality.
We found that the voucher holders’ neighborhood ratings were consistent with their
answers to more specific survey questions about the attributes of their neighborhoods
but only weakly correlated with census-based measures of neighborhood quality.
Internal consistency was demonstrated by the strong correlation between neighborhood
ratings and voucher holders’ perceptions of crime problems and physical disorder in
their neighborhoods. The comparison with census-based measures of the neighborhood
showed that, although a very systematic correlation exists between the neighborhood
rating and census measures of the neighborhood, the correlation was not very strong for
any of the census variables tested. The variable with the strongest correlation (percent-
age of female-headed households with children) explained less than 5 percent of the
variation in the neighborhood rating. Furthermore, combining multiple census variables
into a neighborhood quality indicator increased the explanatory power by only a small
amount.

??? Policy Issues in Public and Assisted Housing
Buron and Patrabansh
Introduction
To measure the customer satisfaction of Housing Choice Voucher Program (HCVP) recipients,
the U.S. Department of Housing and Urban Development (HUD) surveyed housing voucher
holders by mail (the HCVP Customer Satisfaction Survey), asking recipients about their housing
and their neighborhoods. Focusing on one neighborhood question that asked respondents to
rate their neighborhoods on a scale of 1 to 10, this study first examines whether voucher holders’
neighborhood ratings were consistent with their responses to other survey questions about their
neighborhoods. It then compares voucher holders’ neighborhood ratings with census variables that
measure attributes of their neighborhoods and explores whether there is a census-based indicator
of neighborhood quality that is highly correlated with voucher holders’ neighborhood ratings. If
such a census-based indicator can be derived, then it can be used as a proxy for voucher holders’
satisfaction with the quality of the neighborhoods where they use their vouchers.
Data
This study uses data from a national mail survey of HCVP recipients, conducted in 2000, 2001,
and 2002, and the decennial census in 2000.
HCVP Customer Satisfaction Survey
The HCVP Customer Satisfaction Survey collects respondents’ perceptions of the quality of their
housing and neighborhoods. The survey questions were tested in two large pilot studies. The pilot
studies sampled more than 5,000 households of various composition types and demographic
categories in 11 counties of various sizes in Illinois, Indiana, and Missouri.
Those pilot studies had high response rates of 76 percent in the first pilot and 58 to 74 percent in
the second pilot, depending on the survey delivery method and the housing program. In both pilot
studies, residents’ ratings on the quality of their housing were compared with an evaluation of their
units by professional inspectors; the degree of agreement was high. Of the 64 dichotomous items
compared, 38 percent had rates of agreement of 90 percent or higher, and another 23 percent had
rates of agreement between 80 and 90 percent. Agreement rates for the 20 nondichotomous items
tested were 80 percent or more for 80 percent of the items. In addition, inspectors’ assessments
of the same unit conducted at two different times agreed as much as residents’ and inspectors’
assessments. These results suggest that the survey responses on the dimensions of housing quality
covered in the survey are as consistent and accurate as could be obtained using professional
inspectors.
Although the survey validation issues analyzed in the pilot studies focused on housing quality
rather than on neighborhood quality, the results indicate that survey respondents took the survey
seriously and tried to provide meaningful answers. These results give us confidence that their
responses to the neighborhood quality questions also are meaningful.
In addition to testing the validity of survey responses, the pilot studies tested two types of survey
distribution methods: centralized distribution by mail compared with distribution by public
housing agency (PHA) staff. The centralized, mail-delivered distribution was more successful. It

???Cityscape
Are Census Variables Highly Correlated With Housing Choice
Voucher Holders’ Perception of the Quality of Their Neighborhoods?
had a higher response rate and also provided a higher degree of confidentiality, both of which
contributed to the quality and reliability of resident responses to the HCVP Survey.
The HCVP Survey data for this study are from the annual surveys conducted between 2000 and
2002. The data set contains 887,689 records of all the households to which surveys were mailed in
those 3 years. The overall response rate to the HCVP Survey, conducted in 2000, 2001, and 2002,
was 51.7 percent, with a total of 459,298 responses.
1
The HCVP Survey asks voucher recipients to rate their neighborhoods on a scale of 1 to 10, with 1
being the worst rating and 10 being the best rating. The distribution of the neighborhood ratings
is shown in exhibit 1. The neighborhood ratings were generally high, with nearly one-fourth of the
respondents rating their neighborhoods a 10 (the highest possible rating) and 70 percent rating
their neighborhoods a 7 or above. A small portion (3.7 percent) of the respondents did not rate
their neighborhoods, leaving 442,399 records for analysis.
The neighborhood section of the HCVP Survey also asked voucher recipients to assess if each
of the three neighborhood attributes listed in the next paragraph was “A Big Problem,” “Some
Problem,” or “Not a Problem.” We used these neighborhood variables for checking whether
1
The responses by year number 173,362 in 2000, 166,844 in 2001, and 119,092 in 2002.
Exhibit 1
Distribution of HCVP Survey Respondents’ Overall Neighborhood Ratings
HCVP = Housing Choice Voucher Program.
Note: The sample includes all survey respondents who provided a neighborhood rating (n = 442,399).
Source: HCVP Customer Satisfaction Survey, 2000, 2001, and 2002

??0 Policy Issues in Public and Assisted Housing
Buron and Patrabansh
respondents’ answers were consistent with their overall neighborhood ratings. That is, we used
these variables to check the internal (within the same respondent’s survey) consistency of their
answers. Some respondents either did not respond to these neighborhood questions or responded
that they did not know. Percentages of missing and “Don’t Know” responses among those who
rated their neighborhood are noted in parentheses.
1. Crime or drugs. (20.7 percent)
2. Vacant or rundown homes or stores. (11.7 percent)
3. Trash or junk on nearby streets, sidewalks, or properties. (6.1 percent)
Exhibit 2 shows the breakdown of missing rates among the neighborhood variables by the overall
neighborhood rating. The missing rates of the neighborhood variables were not evenly distributed
across the overall neighborhood rating. Respondents who rated their neighborhoods highest
(8 to 10) were generally the least likely to have missing information on the other questions.
The differences were small in the mean neighborhood rating between the groups that answered
these neighborhood questions and the groups that did not answer or answered “Don’t Know.” The
groups that answered all three neighborhood questions rated their neighborhoods 7.5 on average.
The groups that did not answer the neighborhood questions or answered “Don’t Know” rated their
neighborhoods from 7.0 to 7.4 on average. The differences were statistically significant but small
and, therefore, did not meet the minimum size effect of a 1-point difference in the neighborhood
rating that we determined was meaningful.
We established a minimum size effect to identify a difference that would be meaningful because
statistical significance tests were not useful with a sample of more than 400,000 respondents. Even
very small differences that did not reflect substantive differences would be statistically significant
with a sample size this large. We based our determination of a 1-point difference in the overall
neighborhood rating as a meaningful size effect based on research literature that defines a “medium
effect size” as half a standard deviation of the variable of interest.
2
In the overall neighborhood
2
See Cohen (1988), who defines a medium effect size as an effect that is at least one-half the size of the standard deviation.
Exhibit 2
Missings and Don’t Knows for
HCVP Survey Neighborhood Variables
Neighborhood Rating
1–4
(N = 44,667)
5–7
(N = 142,723)
8–10
(N = 255,009)
Missing Rate by Neighborhood Rating
Crime and drugs in neighborhood 14.5 25.3 19.3
Trash or junk on nearby streets, sidewalks, or
properties
6.9 7.4 5.2
Vacant or rundown homes or stores in neighborhood 16.0 13.0 9.3
HCVP = Housing Choice Voucher Program.
Note: The sample includes all survey respondents who provided a neighborhood rating (n = 442,399).
Source: HCVP Customer Satisfaction Survey, 2000, 2001, and 2002
(percent)

???Cityscape
Are Census Variables Highly Correlated With Housing Choice
Voucher Holders’ Perception of the Quality of Their Neighborhoods?
rating, we used the difference of 1 point in the 10-point neighborhood rating scale as a meaningful
effect size because it was roughly half the standard deviation of the neighborhood rating, or 2.2.
For example, a difference in the average neighborhood rating of an 8 for HCVP Survey respondents
identifying crimes and drugs as a big problem in the neighborhood and a 7 for the group identify-
ing it as some problem would exactly meet the minimum criterion to be a meaningful difference.
In addition to variables from the neighborhood section of the HCVP tenant survey, we also selected
four “Yes/No” questions from the survey sections on “area outside of the home” and “sanitation
and safety” for the internal consistency checks. We added these variables because they also seem
to measure neighborhood conditions. Very few of the survey respondents were missing responses
to these questions; therefore, we did not analyze these missing data further. The variables used for
the internal consistency check are listed below; the percentages of missing responses are shown in
parentheses.
1. Is there enough light for safety (outside your home)? (1.6 percent)
2. Does the garbage service pick up each week? (1.5 percent)
3. Did you see a rat anywhere in your building or outside around the grounds this week? (1.4 percent)
4. In the last 3 months, has your mail been stolen or tampered with? (1.8 percent)
Decennial Census 2000 Data
This study uses census data at the tract level from the 2000 Census to compare with voucher
holders’ neighborhood ratings from the survey. The census variables chosen for this study were
based on a review of recent literature that used census variables as measures of neighborhood
quality. We then merged the selected census tract-level data variables with the HCVP Survey data
using the census tract identifiers.
Geocodes were missing for 5.5 percent of the HCVP Survey respondents who rated their neighbor-
hoods. As a result, census tract-level variables could not be attached to those records, leaving
418,308 records for analysis. The difference in the average neighborhood rating between records
with geocodes and records without geocodes was only 0.3 points, which is well below the 1-point
effect size we used as a minimum threshold for a significant substantive difference. The differences
in other HCVP Survey neighborhood variables were also very small and well below half the
standard errors of those variables. From this difference, we concluded that missing geocodes would
not bias the analysis of census data for respondents to the HCVP Survey.
A Comparison With Other Survey Responses About the Neighborhood
To check the consistency of voucher holders’ overall neighborhood ratings to other questions about
their neighborhoods, we used the other three questions from the neighborhood section of the
HCVP Survey and four selected questions from the survey sections on “area outside of the home”
and “sanitation and safety.”
Exhibit 3a shows the percentage of respondents who reported “A Big Problem” on the three specific
neighborhood quality questions by their overall rating of the neighborhood (in rating groups 1 to 4,
5 to 7, and 8 to 10).

??? Policy Issues in Public and Assisted Housing
Buron and Patrabansh
The percentage of respondents reporting “A Big Problem” for each of these neighborhood quality
variables was substantially higher, the lower the neighborhood rating.
3
For each of the three
variables, respondents who gave their neighborhood a low rating (between 1 and 4) were ap-
proximately 4 times more likely to report the quality variable was “A Big Problem” compared with
respondents who gave their neighborhoods a middle rating (between 5 and 7); they were 20 to
30 times more likely to report the issue as “A Big Problem” than were respondents who gave their
neighborhoods a high rating (between 8 and 10). For example, 50 percent of the respondents
who gave their neighborhoods a low rating reported crime and drugs were “A Big Problem” in
their neighborhoods compared with 12 percent of respondents who gave their neighborhoods a
middle rating and 1.6 percent of respondents who gave their neighborhoods a high rating. Clearly,
perceptions of big problems with these specific neighborhood issues are consistent with the overall
neighborhood ratings.
We also examined differences in overall neighborhood ratings across respondents who reported an
item in the neighborhood section of the survey as “A Big Problem,” those who reported it as “Some
Problem,” and those who reported it as “Not a Problem” in their neighborhoods. Exhibit 3b shows
these results.
Respondents reporting “Not a Problem” consistently rated their neighborhoods higher than those
who reported “Some Problem,” who in turn rated their neighborhoods higher than those who
reported “A Big Problem.” The differences in average neighborhood ratings between respondents
citing various levels of problems were statistically significant and greater than our 1-point mini-
mum size effect criterion.
The last column of exhibit 3b shows the Pearson correlation coefficient between recipients’
responses to each individual question in the neighborhood section and their overall rating of
3
All chi-squared statistics testing the relationship between the overall neighborhood rating and each specific neighborhood
survey item were statistically significant.
Exhibit 3a
Percentage of Respondents Reporting “A Big Problem” on Neighborhood Questions
by Overall Neighborhood Rating Category
Crime and drugs in neighborhood 50.5 12.0 1.6
Trash or junk on nearby streets, sidewalks, or properties 31.1 7.9 1.6
Vacant or rundown homes or stores in neighborhood 15.9 3.1 0.6
Notes: The sample includes all survey respondents who provided a neighborhood rating (n = 442,399). Complete responses
number 350,771 for the crime and drugs variable, 415,561 for the trash or junk variable, and 393,044 for the vacant or
rundown buildings variable.
Source: Housing Choice Voucher Program Customer Satisfaction Survey, 2000, 2001, and 2002
Problems in Neighborhood
Percent Reporting “A Big Problem”
by Neighborhood Rating
1–4
(N = 44,667)
5–7
(N = 142,723)
8–10
(N = 255,009)

???Cityscape
Are Census Variables Highly Correlated With Housing Choice
Voucher Holders’ Perception of the Quality of Their Neighborhoods?
their neighborhoods. According to the Pearson correlation coefficients, all neighborhood problem
variables were strongly and negatively correlated to the overall neighborhood ratings, confirming
that respondents with more neighborhood problems gave their neighborhoods lower ratings. The
correlation of –0.62 between the crime and drugs variable and the overall neighborhood rating was
especially strong, suggesting that the perception of crime and drugs strongly influenced the overall
neighborhood rating; it explained almost 40 percent of the variation in the neighborhood rating.
4
We also selected four “Yes/No” questions from the survey sections on “area outside of the home”
and “sanitation and safety” for our internal consistency checks. The results were similar to the
HCVP Survey neighborhood variable results shown earlier, but the relationships were not as
strong. As exhibit 4a shows, respondents who gave their neighborhoods a low rating were about
twice as likely as respondents who gave their neighborhoods a middle rating and about four
times as likely as respondents who gave their neighborhoods a high rating to report these issues
as problems. The one exception was with the weekly garbage pickup variable, which did not vary
much by neighborhood rating.
We also computed the Pearson correlation coefficient and average neighborhood rating for
responses to each of the four selected variables from the survey sections on the area outside of the
home and on sanitation and safety, as shown in exhibit 4b. The differences in average neighbor-
4
The square of the correlation coefficient is the same as the R-square of a regression of neighborhood rating on the
neighborhood item of interest.
Exhibit 3b
Problems in Neighborhood
Average Neighborhood
Rating
(percent)
Pearson Correlation
Coefficient
Average Neighborhood Rating and Pearson Correlation Coefficient by Response to
Problem-in-Neighborhood Questions
Crime and drugs in neighborhood
“A Big Problem” 4.2
“Some Problem” 6.3 – 0.62
“Not a Problem” 8.5
Trash or junk on nearby streets, sidewalks, or properties
“A Big Problem” 4.6
“Some Problem” 6.1 – 0.49
“Not a Problem” 8.1
Vacant or rundown homes or stores in neighborhood
“A Big Problem” 4.4
“Some Problem” 5.7 – 0.39
“Not a Problem” 7.9
Notes: The sample includes all survey respondents who provided a neighborhood rating (n = 442,399). Complete responses
number 350,771 for the crime and drugs variable, 415,561 for the trash or junk variable, and 393,044 for the vacant or
rundown buildings variable.
Source: Housing Choice Voucher Program Customer Satisfaction Survey, 2000, 2001, and 2002

??? Policy Issues in Public and Assisted Housing
Buron and Patrabansh
Other HCVP Survey Variables That Are
Potential Neighborhood Indicators
Percent Reporting Stated Issue
by Neighborhood Rating
1–4
(N = 44,667)
5–7
(N = 142,723)
8–10
(N = 255,009)
Exhibit 4a
Percentage of Respondents Reporting a Problem on Other HCVP Survey Questions
by Overall Neighborhood Rating Category
Outside of home
Not enough light for safety 30.2 16.2 7.9
Garbage service does not pick up each week 7.5 4.8 4.5
Sanitation and safety
Rats in building or outside around grounds this week 21.0 9.9 4.7
Mail stolen or tampered with in last 3 months 18.1 7.9 3.8
Exhibit 4b
Other HCVP Survey Variables That Are
Potential Neighborhood Indicators
Average
Neighborhood
Rating
(percent)
Pearson Correlation
Coefficient
Average Neighborhood Rating and Pearson Correlation Coefficient by Response to
Other HCVP Survey Questions
Outside of home
Not enough light for safety
Yes 6.2 – 0.22
No 7.7
Garbage service does not pick up each week
Yes 7.2 – 0.03
No 7.5
Sanitation and safety
Rats in building or outside around grounds this week
Yes 6.1 – 0.19
No 7.6
Mail stolen or tampered with in last 3 months
Yes 6.0 – 0.17
No 7.6
HCVP = Housing Choice Voucher Program.
Notes: The sample includes all survey respondents who provided a neighborhood rating (n = 442,399). Complete responses
number 435,388 for the external light variable, 435,583 for the garbage collection variable, 436,324 for the rats variable, and
434,401 for the mail-tampering variable.
Source: HCVP Customer Satisfaction Survey, 2000, 2001, and 2002
HCVP = Housing Choice Voucher Program.
Notes: The sample includes all survey respondents who provided a neighborhood rating (n = 442,399). Complete responses
number 435,388 for the external light variable, 435,583 for the garbage collection variable, 436,324 for the rats variable, and
434,401 for the mail-tampering variable.
Source: HCVP Customer Satisfaction Survey, 2000, 2001, and 2002

???Cityscape
Are Census Variables Highly Correlated With Housing Choice
Voucher Holders’ Perception of the Quality of Their Neighborhoods?
Exhibit 5
HCVP Survey Variables That Are
Potential Neighborhood Indicators
Pearson Correlation
Coefficient
Summary of Internal Consistency Checks
Problems with crime and drugs in neighborhood – 0.62
Problems with trash or junk on nearby streets, sidewalks, or properties – 0.49
Problems with vacant or rundown homes or stores in neighborhood – 0.39
Not enough light for safety outside of home – 0.22
Rats in building or outside around grounds this week – 0.19
Mail stolen or tampered with in last 3 months – 0.17
Garbage service does not pick up each week – 0.03
HCVP = Housing Choice Voucher Program.
Notes: The sample includes all survey respondents who provided a neighborhood rating (n = 442,399). Complete responses
number 350,771 for the crime and drugs variable, 415,561 for the trash or junk variable, 393,044 for the vacant or rundown
buildings variable, 435,388 for the external light variable, 436,324 for the rats variable, 434,401 for the mail-tampering
variable, and 435,583 for the garbage collection variable.
Source: HCVP Customer Satisfaction Survey, 2000, 2001, and 2002
hood ratings between respondents who cited problems and those who did not cite problems were
statistically significant and greater than 1 point (except for problems with garbage pickup).
According to the Pearson correlation coefficients, the selected variables were negatively and
moderately correlated to the overall neighborhood ratings, confirming that respondents who
reported problems rated their neighborhoods lower. In other words, the selected variables seemed
to play a role in respondents’ overall neighborhood ratings. The only exception was the variable
about weekly garbage pickup. Respondents who reported that the garbage service did not pick
up their garbage weekly also rated their neighborhoods only 0.3 point lower than respondents
who reported regular weekly garbage pickup. The correlation between the weekly garbage pickup
variable and the overall neighborhood rating was almost nonexistent at only –0.03, which led us to
conclude that respondents’ ratings of the variable on weekly garbage pickup had virtually no role
in respondents’ overall neighborhood ratings.
From our analysis, we conclude that respondents’ overall neighborhood ratings are internally
consistent with their responses to other questions about attributes of their neighborhood. The role
of these neighborhood attributes in respondents’ overall neighborhood ratings, however, range
from strong (crime and drugs variable) to almost none (weekly garbage pickup variable). Exhibit 5
summarizes the findings by ordering the HCVP Survey variables from the strongest to weakest
relationship with the overall neighborhood rating.
A Comparison of Neighborhood Ratings and Census-Based
Measures of Neighborhood Quality
The census tract poverty rate is the most widely used neighborhood quality indicator from census
data. We calculated the percentage of voucher holders in each poverty rate category by the voucher
holders’ rating of their neighborhood. The results in exhibit 6 indicate that the voucher hold-

??? Policy Issues in Public and Assisted Housing
Buron and Patrabansh
ers’ neighborhood ratings moderately correlate with the census tract poverty rate. For example,
respondents who gave their neighborhoods high ratings were more than twice as likely to live in
census tracts with poverty rates below 10 percent as were respondents who gave their neighbor-
hoods poor ratings (32.7 percent compared with 15.8 percent, respectively). Conversely, respond-
ents who rated their neighborhoods as high were half as likely to live in a census tract with poverty
rates above 30 percent as were respondents who rated their neighborhoods as low (10.1 percent
compared with 22 percent, respectively).
This pattern was also clear when we looked at average overall neighborhood ratings by respondents
in census tracts with various poverty rates (also shown in exhibit 6). The respondents’ average
neighborhood rating dropped systematically from 8 to 6.8 as we moved from respondents living in
low-poverty census tracts to respondents living in high-poverty census tracts; however, the difference
in the average neighborhood rating between the respondents in the lowest and highest poverty
census tracts was only slightly above our 1-point minimum effect size. This narrow variation in the
average overall neighborhood rating between respondents living in a census tract with differing
poverty rates was corroborated by the Pearson correlation coefficient of –0.18 between the poverty
rate and the overall neighborhood rating, shown in exhibit 7. These results suggest that, even
though the census tract poverty rate was correlated with the respondents’ overall neighborhood
rating, it alone did not explain most of the differences in neighborhood ratings.
Exhibit 7 shows the correlation between respondents’ overall neighborhood ratings and a host of
census variables that have been reported in the research literature as measures of neighborhood
quality. The relationships among all the census tract neighborhood variables and the respondents’
overall neighborhood ratings were in the expected direction; however, none of the correlations
were very strong. The strongest correlation was only –0.22. In other words, not one census variable
on its own captured much of what determines the respondents’ overall neighborhood ratings.
We also computed the averages of all census variables for each of the 10 groups of respondents
rating their neighborhoods on a scale from 1 to 10. Exhibit 7 shows the average of each census
Exhibit 6
Census Tract-Level
Poverty Rate
Percent in Poverty Category by Neighborhood Rating
1–4
(N = 42,597)
5–7
(N = 135,815)
8–10
(N = 239,896)
Average
Comparison of Neighborhood Ratings and Census Tract-Level Poverty Rate
Categories
Less than 10% 15.8 22.2 32.7 8.0
10–19.99% 36.3 40.0 40.3 7.5
20–29.99% 25.9 22.4 17.0 7.1
30–39.99% 14.5 10.4 6.7 6.8
40% or more 7.5 4.9 3.4 6.8
Total 100.0 100.0 100.0 7.5
Note: The sample includes survey respondents who provided a neighborhood rating and could have their record matched to
census data at the census tract level (n = 418,308).
Sources: Housing Choice Voucher Program Customer Satisfaction Survey, 2000, 2001, and 2002; 2000 Decennial Census

???Cityscape
Are Census Variables Highly Correlated With Housing Choice
Voucher Holders’ Perception of the Quality of Their Neighborhoods?
Exhibit 7
Selected Census Variables
Correlation to
Neighborhood Rating
Average by
Neighborhood Rating
Sign Coefficient 1 5 10
Comparison of Neighborhood Ratings and Census 2000 Variables
Households with female heads and own
children under 18 years old (percent)
– 0.2160 13 11 8
Households with female heads (percent) – 0.2077 21 17 14
People with income twice or more the
poverty level (percent)
+ 0.1867 52 57 64
People 15 years old or older who are married
females (percent)
+ 0.1836 23 25 27
Families with related children under 18 years
old (percent)
– 0.1827 59 57 53
Households receiving public assistance
income (percent)
– 0.1798 7 6 4
Housing units that are owner occupied
(percent)
+ 0.1780 49 53 61
People with income lower than the poverty
level (percent)
– 0.1780 24 20 16
Housing units without vehicles (percent) – 0.1720 20 16 12
People who are non-Hispanic Whites
(percent)
+ 0.1671 49 60 71
People who are minorities (percent) – 0.1671 51 40 29
Households with minority heads (percent) – 0.1646 47 36 25
Civilian people 16 years old or older who are
unemployed (percent)
– 0.1620 6 5 4
Civilian people 16 years old or older In
managerial, professional, and technical
employment (percent)
+ 0.1552 23 25 28
People who are non-Hispanic Blacks
(percent)
– 0.1541 31 21 13
Median household income (dollars) + 0.1500 29,658 32,044 36,390
People 25 years old or older without a high
school diploma (percent)
– 0.1437 30 27 23
People who are 9 years old or younger
(percent)
– 0.1391 16 15 14
People 25 years old or older with a college
degree or more education (percent)
+ 0.1289 19 21 25
People 16 to 19 years old who are high
school dropouts (percent)
– 0.1273 16 14 11
Civilian uninstitutionalized people 5 years old
or older who are disabled (percent)
– 0.1259 25 24 22
People 16 to 19 years old who are in school
(percent)
+ 0.1217 72 73 77
Households with heads under 35 years old
(percent)
– 0.1137 26 26 23
Median gross rent to median value of owner-
occupied housing (capitalization rate)
(percent)
– 0.1106 0.68 0.65 0.59

??? Policy Issues in Public and Assisted Housing
Buron and Patrabansh
tract neighborhood variable for respondents who gave their neighborhoods the lowest ratings (1),
middle ratings (5), and highest ratings (10). The consistency in the way the averages of the census
tract neighborhood variables changed from one respondent groups’ neighborhood ratings to
another was remarkable. This pattern is more clearly visible in the complete table in the appendix.
5

Although a consistent relationship exists between the census variables and the neighborhood
ratings, the differences in the average value of the census variables across the overall neighborhood
ratings were small. The small difference indicates that each census variable on its own was not
capturing much of what determined respondents’ neighborhood ratings.
Nevertheless, several variables stood out. The percentage of female-headed households with
children had the strongest correlation with overall neighborhood ratings. In fact, the correlation
of the single-mother variable to the overall neighborhood rating was stronger than the correlation
between the neighborhood poverty rate and the overall neighborhood rating. In addition, the
overall neighborhood rating also had a slightly stronger correlation with the prevalence of receipt
of public assistance and share with income greater than two times the poverty level in the neigh-
borhood than with the neighborhood poverty rate.
From our analysis, we conclude that the neighborhood rating is weakly correlated with the external
census measures of neighborhood quality. We say “weakly” because none of the census variables
have a very strong relationship with the responding voucher holders’ neighborhood ratings.
Nonetheless, across a wide spectrum of census measures of neighborhood quality, a consistent
pattern is evident that respondents’ higher neighborhood ratings are associated with higher census
measures of neighborhood quality.
Exhibit 7
Selected Census Variables
Correlation to
Neighborhood Rating
Average by
Neighborhood Rating
Sign Coefficient 1 5 10
Comparison of Neighborhood Ratings and Census 2000 Variables (continued)
Housing units (with heads 15 years old or
older) that households moved into more
than 5 years ago (percent)
+ 0.1091 47 48 51
Housing units built since 1980 (percent) + 0.1052 21 22 27
Median value of owner-occupied housing
units (dollars)
+ 0.0857 87,775 93,413 107,219
Median gross monthly rent (dollars) + 0.0506 508 522 542
Housing units that are vacant (percent) – 0.0442 10 9 9
Note: The sample includes survey respondents who provided a neighborhood rating and could have their record matched to
census data at the census tract level (n = 418,308).
Sources: Housing Choice Voucher Program Customer Satisfaction Survey, 2000, 2001, and 2002; 2000 Decennial Census
5
The appendix shows the values for every neighborhood rating from 1 to 10. The appendix also includes several additional
variables that had low correlations with neighborhood ratings and, thus, were not included in exhibit 7.

???Cityscape
Are Census Variables Highly Correlated With Housing Choice
Voucher Holders’ Perception of the Quality of Their Neighborhoods?
6
The primary sources of census variables measuring neighborhood quality were from Devine et al. (2003), Feins and
Patterson (2005), Galster, Hayes, and Johnson (2005), Holin et al. (2003), and Newman and Schnare (1997).
7
The square of the correlation coefficient (in exhibit 8) is the percentage of the variation in the neighborhood rating that
is explained by the census variable; that is, the square of the correlation coefficient is the same as the R-square from a
regression of the neighborhood rating on that census variable and an intercept term.
Deriving a Census-Based Measure of Neighborhood Quality
Because none of the individual census variables were strongly correlated with responding voucher
holders’ overall neighborhood ratings, we attempted to derive a neighborhood quality index from
census variables that would be strongly correlated with neighborhood quality. As a first step to
building a census-based measure of neighborhood quality, we compiled a list of 55 census variables
that were used as neighborhood quality indicators in the research literature and grouped them
into 10 categories, such as household type and income.
6
Using the decennial Census 2000 data,
we created census variables quantifying census tract-level percentages (for example, percentage
of persons in the census tract with incomes below the poverty level). After preparing the data,
we selected a 25-percent random subsample of HCVP Survey respondents for use in establishing
a census-based neighborhood quality indicator. The subsample contains 104,580 HCVP Survey
respondent records.
Because the studies of neighborhood quality we reviewed had closely related variables, our com-
piled list contained duplicate or similar variables. We eliminated obvious duplicates (for example,
“percent White” was kept but “percent minority” was not). Then we chose between two or more
census variables measuring very similar attributes—often derived from the same census count
data—based on their correlation with the HCVP Survey respondents’ neighborhood ratings and
with each other. As a result, we pared the list from 55 variables to 35 variables, shown in exhibit 8
in descending order of their correlation with the HCVP Survey respondents’ neighborhood ratings.
The census variable with the strongest correlation coefficient is shown first; the variable with the
next highest correlation coefficient is shown second, and so on.
Exhibit 8 shows that six census variables—the percentage of households headed by a female with
own children under 18 years old in particular—have a higher correlation with the HCVP neighbor-
hood ratings than do the poverty rates. The percentage of households headed by a female with own
children explained 4.7 percent of the variation in the HCVP neighborhood rating compared with
3.1 percent explained by the poverty rate.
7
This finding that some census variables have a higher
correlation with the voucher holders’ neighborhood ratings suggests that building a census-based
index of neighborhood quality might result in a more accurate census-based neighborhood quality
measure than using the poverty rate alone.
Preliminary Regression Analysis
We started our analysis with ordinary least square (OLS) regressions of the HCVP Survey respondents’
neighborhood ratings on the poverty rate alone and then on all 35 census variables. Exhibit 9 shows
selected regression fits. All of the regression fits were poor, but the combination of all 35 census
variables explained 6.9 percent of the variation in the HCVP Survey respondents’ neighborhood
rating, a little more than twice the variation explained by the poverty rate alone (3.1 percent).

??0 Policy Issues in Public and Assisted Housing
Buron and Patrabansh
Exhibit 8
Selected Census Variables
(as Percent)
Correlation to HCVP
Neighborhood Rating
Sign
Correlation
Coefficient
Selected Census Variables
Households with female heads and own children under 18 years old – 0.2160
Tract median household income relative to county median household income + 0.1892
People with income twice or more the poverty level + 0.1867
People 15 years old or older who are married females + 0.1836
Families with related children under 18 years old – 0.1827
Households receiving public assistance income – 0.1798
People with income lower than the poverty level (poverty rate) – 0.1780
Housing units that are owner occupied + 0.1780
Housing units without vehicles – 0.1720
People who are non-Hispanic Whites + 0.1671
Civilian people 16 years old or older who are unemployed – 0.1620
Civilian people 16 years old or older in managerial, professional, and technical
employment
+ 0.1552
People who are non-Hispanic Blacks – 0.1541
People 25 years old or older without a high school diploma – 0.1437
People who are 9 years old or younger – 0.1391
People 25 years old or older with a college degree or more education + 0.1289
People 16 to 19 years old who are high school dropouts – 0.1273
Civilian uninstitutionalized people 5 years old or older who are disabled – 0.1259
People 16 to 19 years old who are in school + 0.1217
Households with heads under 35 years old – 0.1137
Median gross rent to median value of owner-occupied housing (capitalization rate) – 0.1106
Housing units (with heads 15 years old or older) that households moved into more
than 5 years ago
+ 0.1091
Housing units built since 1980 + 0.1052
Households with heads older than 65 years old + 0.0944
Tract median value of owner-occupied housing relative to county value of
owner-occupied housing
+ 0.0857
Housing units built before 1940 – 0.0834
People who are 10 to 19 years old – 0.0796
People who are citizens + 0.0708
People who are Hispanics – 0.0535
Civilian people 16 years old or older who are in the labor force + 0.0470
Housing units that are vacant – 0.0442
Housing units in single-family structures (1–4 units) + 0.0385
Housing units that households moved into between 1995 and 1998 + 0.0383
Housing units without plumbing – 0.0319
Households with wage or salary income + 0.0048
HCVP = Housing Choice Voucher Program.
Note: The sample includes a random one-fourth of 418,308 survey respondents who provided neighborhood ratings and
could match their data to census data at the census tract level (n = 104,850).
Sources: HCVP Customer Satisfaction Survey, 2000, 2001, and 2002; 2000 Decennial Census

???Cityscape
Are Census Variables Highly Correlated With Housing Choice
Voucher Holders’ Perception of the Quality of Their Neighborhoods?
8
The exploratory factor analysis we conducted is similar to the one reported in Galster, Hayes, and Johnson (2005). We
included factors with eigenvalues higher than 1.5 and performed both Varimax and Promax rotations. We selected the
Varimax rotation because the interfactor correlations in the Promax rotation were low and factor loadings were similar in
both rotations using a cutoff of 0.45.
9
We created six distinct factors using the standardized values of the 35 census variables and their weight coefficient
determined through the factor analysis. When we regressed the Housing Choice Voucher Program neighborhood rating
to the 6 factors, the fit was weaker than the ordinary least squares regression using all 35 census variables. This result was
expected, considering the reduction in the number of dependent variables from 35 census variables to 6 factors.
We then changed the continuous independent variables into categorical variables based on
percentiles, deciles, quintiles, and quartiles, and we performed regressions again for each of
these methods of categorizing the census variables; however, these categorical variables could not
improve the regression fit. In other words, categorizing each census variable into as many as 100
categories each (that is, the percentiles) did not improve the explanatory power of the regression.
We also performed ordered logistic regressions to investigate other functional forms, but ordered
logistic regressions fit worse than OLS regressions. Exhibit 9 shows the R-square from a typical
ordered logistic regression.
Paring Down Census Variables
Because of their poor fit, none of the regression models we tried could predict the HCVP Survey
respondents’ neighborhood ratings well. Another way to improve the accuracy of predicting the
HCVP ratings was to categorize census variables and then create appropriate interaction terms, but
the list of 35 census variables needed to be pared further to effectively explore this method. First,
we conducted an exploratory factor analysis to identify census variables representing different
neighborhood dimensions.
8
We could identify six distinct factors and a handful of census variables
that highly influenced these factors.
9
We used the results of the exploratory factor analysis plus correlation and distribution analyses
to calculate a 6-point scale to further pare down the number of census variables. Every census
Exhibit 9
Independent Variables R
2
Regressions of HCVP Survey Respondents’ Neighborhood Rating
OLS regressions
Poverty rate 0.0309
Percentiles of poverty rate 0.0367
All 35 census variables 0.0693
Percentiles of all 35 census variables 0.0696
Ordered logisitic regressions
Poverty rate 0.0218
HCVP = Housing Choice Voucher Program. OLS = ordinary least square.
Note: The sample includes a random one-fourth of 418,308 survey respondents who provided neighborhood ratings and
could have their records matched to census data at the census tract level (n = 104,850).
Sources: HCVP Customer Satisfaction Survey, 2000, 2001, and 2002; 2000 Decennial Census

??? Policy Issues in Public and Assisted Housing
Buron and Patrabansh
variable received a point for each of the six criteria that it met. Exhibit 10 describes these criteria.
The maximum number of possible points a variable could receive was 6, and we selected census
variables that received 3 or more points. A handful of the selected variables still measured similar
aspects of the neighborhood, as indicated by the factor analysis; thus, we excluded some of those
variables if either criterion A or D was not satisfied.
After this selection process, we retained the following 11 census variables:
• Percentage of people living below the poverty level.
• Percentage of people with income two or more times the poverty level.
• Percentage of households headed by a female with own children under 18 years old.
• Percentage of housing units that are owner occupied.
• Ratio of census tract median household income to county median household income.
• Percentage of people 16 years or older who are unemployed.
• Percentage of people who are White.
• Percentage of people 16 years or older in professional jobs.
• Percentage of people 25 or older without a high school diploma.
• Percentage of housing units without vehicles.
• Percentage of households receiving public assistance income.
We then performed an OLS regression of the HCVP neighborhood rating to these 11 census
variables to test the fit and discovered that the percentage of people with incomes twice or more
the poverty level and the percentage of households receiving public assistance income were
Exhibit 10
Point Qualifying Criteria for Census Variables
Qualifying Criteria for Further Selection of Census Variables
A 1 if the correlation coefficient with HCVP neighborhood rating is 0.15 or above
(15 variables qualified)
B 1 if the correlation with HCVP neighborhood rating is the highest in the category, such as
housing type or race
(10 variables qualified, 1 in each category)
C 1 if R
2
is 0.5 or above when the variable is regressed with the factor it loads
(22 variables qualified)
D 1 if the variable has the highest R
2
in regresssion with the factor it loads
(6 variables qualified)
E 1 if the difference in average for HCVP neighborhood ratings 4 and 10 were the highest
(10 variables qualified)
F 1 if the percentage point difference in the distribution of HCVP neighborhood ratings 4 and 10
was greater than 20 when binary variable was created using the sample mean as the average
(8 variables qualified)
Total A + B + C + D + E + F
HCVP = Housing Choice Voucher Program.

???Cityscape
Are Census Variables Highly Correlated With Housing Choice
Voucher Holders’ Perception of the Quality of Their Neighborhoods?
diminishing the effect of the poverty rate on the HCVP neighborhood rating without improving the
fit (R-square was 0.0611). In other words, those two variables were not adding explanatory power;
therefore, we excluded those two variables, retaining nine census variables for creating categories
and interaction terms.
Creation of Census-Based Measures
We tested various ways of categorizing the nine selected census variables and creating interaction
terms. The most promising method we found was to create binary variables that could be added to
create interaction terms.
First, we created standardized binary census variables to indicate more favorable neighborhood
attributes, designated as 1. We used the following six cutoffs for each variable: (1) sample averages
to indicate neighborhoods that were better than the sample average, (2) national averages to
indicate neighborhoods that were better than the national average, (3) sample medians to indicate
neighborhoods that were better than half or more of the sample neighborhoods, (4) sample 25th
percentiles to indicate neighborhoods that were better than 25 percent of the sample neighbor-
hoods, (5) sample 75th percentiles to indicate neighborhoods that were better than 75 percent of
the sample neighborhoods, and (6) sample 10th percentiles to indicate neighborhoods that were
better than 10 percent of the sample neighborhoods.
The distributions of HCVP neighborhood rating appeared somewhat similar across broad catego-
ries (0 to 9 “good” attributes) of the scores, with minimal but consistent variation across scores.
That is, the distributions of various scores for each of the 10 HCVP neighborhood ratings varied
but were somewhat clumped in the middle (4 or 5 “good” attributes). We were able, however, to
exploit the distributions of the various scores and collapse the HCVP neighborhood ratings based
on the location of the median score. The exact cutoffs varied somewhat for each type of score (that
is., based on 25th percentile, based on 75th percentile, and so on) but the most sensible rule was
to combine HCVP neighborhood ratings into four categories: 1 to 4, 5 or 6, 7 or 8, and 9 or 10.
We then added the binary census variables from the methods described above to create six
score variables with the maximum possible value of 9 and the minimum possible value of 0. We
compared each of the resulting variable scores with the HCVP Survey respondents’ neighborhood
ratings. The relationships were consistent between each of the six census scores and the HCVP
Survey respondents’ neighborhood ratings (that is, the census variable scores indicating more good
attributes correlated consistently with the likelihood that respondents rated their neighborhoods
higher); however, the relationship was not strong. This observation means that none of the six
census scores met the objective of providing a census-based indicator of neighborhood quality that
was highly correlated with the voucher holders’ neighborhood ratings.
We also tested whether using the variable cutoffs as the 10th percentile values (better than only
10 percent of the neighborhoods) for the group of survey respondents with HCVP neighborhood
ratings of 9 or 10 could be exploited to make a better prediction for the survey respondents with
HCVP neighborhood ratings of 1 to 4. That is, we tried to identify the factors that would separate
the worst rated neighborhoods from the best rated neighborhoods. We used a stepwise approach:

??? Policy Issues in Public and Assisted Housing
Buron and Patrabansh
We selected the census variable with the highest difference in distributions between the highest
and lowest groups of HCVP neighborhood ratings at each stage. This stepwise selection was unsuc-
cessful in distinguishing between the highest and lowest groups of HCVP neighborhood ratings in
any significant way. After the fourth stage, the difference in prediction between the groups of HCVP
neighborhood ratings of 9 or 10 and 1 to 4 was less than 14 percentage points, and this difference
grew by less than one-half a percentage point with the fourth variable added.
Finally, because the six census scores we created from the binary census variables were lacking in
variation in the distribution—most of the observations had four or five positive attributes—we
decided to add some of these scores together to create new scores. The goal was to create a new
census score that had more variation and a stronger correlation with the voucher holder’s neighbor-
hood rating. After studying the correlations between combinations of the census scores and the
HCVP neighborhood ratings, we picked the sum of the scores based on the 25th, 50th, and 75th
percentiles of the sample for the cutoffs as our best census-based measure of neighborhood quality.
This census score ranged from 0 to 27; in this score, neighborhoods above the 75th percentile re-
ceived 3 points for a positive attribute, neighborhoods between the median and the 75th percentile
received 2 points for a positive attribute, and neighborhoods between the 25th percentile and the
median received 1 point for a positive attribute.
Results: Comparison of Poverty Rate and Combined Census-Based
Measure of Neighborhood Quality To Predict Housing Choice
Voucher Holders’ Neighborhood Ratings
For a comparison, we first tested how the poverty rate predicted the HCVP neighborhood ratings.
We grouped the poverty rate into four categories: 30 percent or more, between 20 and 29.99 percent,
between 10 and 19.99 percent, and below 10 percent. We also grouped the neighborhood ratings
into the following four categories:
• 1 to 4.
• 5 or 6.
• 7 or 8.
• 9 or 10.
The results are shown in exhibit 11. As can be seen in the poverty rate row of the exhibit, the
poverty rate was a correct predictor in 33 percent of all neighborhood ratings, including—
• 22 percent of HCVP neighborhood ratings of 1 to 4.
• 24 percent of HCVP neighborhood ratings of 5 or 6.
• 41 percent of HCVP neighborhood ratings of 7 or 8.
• 34 percent of HCVP neighborhood ratings of 9 or 10.
The most common poverty rate category for each group of neighborhood ratings was 10 to 19.99
percent. In other words, under the maximum likelihood criteria, an HCVP neighborhood rating of
7 or 8 will be picked every time instead of the correct HCVP neighborhood rating.

???Cityscape
Are Census Variables Highly Correlated With Housing Choice
Voucher Holders’ Perception of the Quality of Their Neighborhoods?
Using the categories of the 0-to-27 census score as shown in exhibit 11, on the row “Census score
(optimal—each HCVP rating category predicted equally accurately),” the prediction rate could
be optimized so that each category of the HCVP neighborhood rating was predicted with similar
accuracy. This census score correctly predicted 32 percent of all neighborhood ratings, including—
• 33 percent of HCVP neighborhood ratings of 1 to 4.
• 29 percent of HCVP neighborhood ratings of 5 or 6.
• 32 percent of HCVP neighborhood ratings of 7 or 8.
• 33 percent of HCVP neighborhood ratings of 9 or 10.
In addition, the same prediction rates would hold even when the maximum likelihood was used as
the criteria.
We were also able to create categories of the census score that predicted the HCVP neighborhood
rating slightly better than the poverty rate, as shown in exhibit 11, row “Census score (maximum
overall accuracy)”; however, a neighborhood quality index should not be judged only by the higher
overall prediction rate. Given the distribution of the HCVP neighborhood rating, an index that
Exhibit 11
Census Neighborhood Quality Indicator
HCVP Neighborhood Rating Overall
Correct
Prediction
1–4 5–6 7–8 9–10
Correct Prediction of HCVP Neighborhood Rating
Poverty rate
30% or more
21.9
16.6 11.6 9.5
33.26
20–29.99% 25.9
24.0
19.3 16.4
10–19.99% 36.1 39.4
40.9
39.7
Less than 10% 16.1 20.0 28.2
34.4
Total 100.0 100.0 100.0 100.0
Census score (optimal—each HCVP rating category
predicted equally accurately)
0–5
33.3
25.8 17.0 12.3
31.75
6–12 28.4
28.8
26.2 22.1
13–19 24.6 28.1
31.9
32.9
20–27 13.8 17.3 24.9
32.7
Total 100.0 100.0 100.0 100.0
Census score (maximum overall accuracy)
0–3
24.3
17.8 11.1 7.6
34.43
4–9 25.9
24.2
19.8 15.5
10–18 33.0 36.8
39.4
38.8
19–27 16.7 21.2 29.7
38.1
Total 100.0 100.0 100.0 100.0
HCVP = Housing Choice Voucher Program.
Notes: The sample includes a random one-fourth of 418,308 survey respondents who provided neighborhood ratings and
could have their record matched to census data at the census tract level (n = 104,850). The bold numbers are the percentage
of that neighborhood-rating category that is accurately categorized by the census neighborhood quality indicator.
Sources: HCVP Customer Satisfaction Survey, 2000, 2001, and 2002; 2000 Decennial Census
(percent)
(percent)
(percent)

??? Policy Issues in Public and Assisted Housing
Buron and Patrabansh
always picks a neighborhood rating of 9 or 10 is correct more than 40 percent of the time and an
index that selects 7 to 10 is correct 70 percent of the time. On the other hand, these indices have
zero prediction power for the lower neighborhood ratings; thus, optimizing the balance in predict-
ing all levels of neighborhood quality should be preferred over maximizing the overall prediction.
Despite a slight improvement over the poverty rate, our census score is not a strong proxy for the
HCVP neighborhood rating because it can predict the correct HCVP neighborhood rating only
one-third of the time.
The reason for these results can be better understood by examining exhibits 12a and 12b. In
exhibit 12a, we graphed the distribution of poverty rate for the Housing Choice Voucher holders’
neighborhood rating (HCV NR) categories 1 through 4 (darker line) and the HCV NR categories
9 and 10 (lighter line). The two distributions had most of their areas under the curves in com-
mon, as shown by the area labeled “Overlap Area”; hence, the poverty rate is not very effective in
distinguishing between the lowest and highest categories of neighborhood rating. The first vertical
line separated the lowest poverty group (less than 10 percent) to its left. More than half of the area
under the lighter line (HCV NR 9 or 10) was also under the darker line (HCV NR 1 through 4).
The second vertical line separated the highest poverty group (greater than or equal to 30 percent).
Exhibit 12a
Distribution of Poverty Rate for HCVP Neighborhood Rating Categories 1 to 4
and 9 or 10
HCV NR = Housing Choice Voucher holders’ neighborhood rating. HCVP = Housing Choice Voucher Program.
Note: The sample includes a random one-fourth of 418,308 survey respondents who provided neighborhood ratings and
could match their data to census data at the census tract level (n = 104,850).
Sources: HCVP Customer Satisfaction Survey, 2000, 2001, and 2002; 2000 Decennial Census

???Cityscape
Are Census Variables Highly Correlated With Housing Choice
Voucher Holders’ Perception of the Quality of Their Neighborhoods?
About one-half of the area under the darker line (HCV NR 1 through 4) was also under the lighter
line (HCV NR 9 or 10). The commonality in the middle area between the two vertical lines is even
more severe.
In Exhibit 12b, we graphed the distribution of our census score for the HCV NR categories 1
through 4 (darker line) and the HCV NR categories 9 and 10 (lighter line) The two distributions
had most of their areas under the curves in common, but the area of commonality is slightly
reduced compared with the poverty rate. The first vertical line separated the lowest census score
group (less than or equal to 5) to its left. Less than half of the area under the lighter line (HCV NR
9 or 10) was also under the darker line (HCV NR 1 through 4). The second vertical line separated
the highest census score group (20 through 27). Less than half of the area under the darker line
(HCV NR 1 through 4) was also under the lighter line (HCV NR 9 or 10). This distribution was a
modest improvement over the poverty rate.
Exhibit 12b
Distribution of Census Score for HCVP Neighborhood Rating Categories 1 to 4 and 9
or 10
HCV NR = Housing Choice Voucher holders’ neighborhood rating. HCVP = Housing Choice Voucher Program.
Note: The sample includes a random one-fourth of 418,308 survey respondents who provided neighborhood ratings and
could match their data to census data at the census tract level (n = 104,850).
Sources: HCVP Customer Satisfaction Survey, 2000, 2001, and 2002; 2000 Decennial Census

??? Policy Issues in Public and Assisted Housing
Buron and Patrabansh
Conclusion
We did not find a census-based housing quality measure that is highly correlated with voucher
holders’ ratings of the quality of their neighborhoods. The census tract poverty rate—the most
common census measure of neighborhood quality in the research literature—does almost as well
by itself as the more complex measures that draw on multiple census variables.
At this point, the question arises regarding whether the voucher holder respondents are rating
their neighborhoods or rating something else, such as their housing or their living situation.
The neighborhood ratings were highly correlated with voucher holders’ responses about specific
neighborhood attributes, such as problems with crime, trash on the streets, and vacant lots. This
consistent relationship suggests that the neighborhood ratings do indeed reflect the respondents’
perceptions of their neighborhoods.
We believe two other reasons more likely explain why the census variables we tested are not highly
correlated with voucher holders’ neighborhood ratings. First, the neighborhood that voucher
holders are rating may not coincide with a census tract. The neighborhood may be smaller or
larger or be an area that crosses census tract boundaries. Additional research could test whether
census variables at the block level are more correlated with voucher holders’ ratings. Second,
perhaps the census variables we tested are not the attributes that drive voucher holders’ ratings.
We tested a comprehensive list of census variables, and, thus, it may be neighborhood attributes
that are not captured by the census. With the high correlation between voucher holders’ ratings of
their neighborhoods and their indicated perception of crime, neighborhood measures that use the
crime rate might be more strongly correlated with neighborhood ratings. Based on the research of
Galster, Hayes, and Johnson (2005), neighborhood measures based on home loan applications, the
size of loans, and business activity may be more appropriate indicators than census-based variables
of neighborhood quality. Followup conversations or cognitive testing would be efficient ways
to understand whether the census variables we tested are not strongly correlated with voucher
holders’ ratings because the census tract is not the geography they are basing their neighborhood
ratings on or because the census data does not capture the factors that are determining their
neighborhood ratings.

???Cityscape
Are Census Variables Highly Correlated With Housing Choice
Voucher Holders’ Perception of the Quality of Their Neighborhoods?
Household type Households with female heads and own childr
en
under 18 years old
– 0.2160
13
12
12
12
11
11
10
9
9
8
Households with female heads
– 0.2077
21
19
19
18
17
17
16
15
14
14
People 15 years old or older who ar
e married
females
0.1836
23
24
24
24
25
25
25
26
27
27
Families with r
elated childr
en under 18 years old
– 0.1827
59
58
58
58
57
56
55
54
53
53
Households with own childr
en under 18 years old
– 0.0619
35
34
34
34
34
33
33
33
33
32
Age People who ar
e 9 years old or younger
– 0.1391
16
16
15
15
15
15
15
14
14
14
Households with heads under 35 years old
– 0.1137
26
26
27
27
26
26
25
25
24
23
Households with heads older than 65 years old
0.0944
21
21
21
21
21
22
22
22
23
23
People who ar
e 10 to 19 years old
– 0.0796
15
15
15
15
15
15
14
14
14
14
Ethnicity/race People who ar
e non-Hispanic Whites
0.1671
49
54
57
58
60
62
65
68
70
71
People who ar
e minorities
– 0.1671
51
46
43
42
40
38
35
32
30
29
Households with minority heads
– 0.1646
47
41
39
38
36
34
31
28
26
25
People who ar
e non-Hispanic Blacks
– 0.1541
31
26
24
23
21
20
17
15
13
13
People who ar
e not Whites, Blacks, Hispanics,
Asians, or Native Americans
– 0.0641
2
2
2
2
2
2
2
2
2
2
People who ar
e Hispanics
– 0.0535
15
15
14
14
14
13
12
12
11
11
People who ar
e not Whites, Blacks, or Hispanics
– 0.0214
5
5
5
5
5
5
5
5
5
5
People who ar
e Native Americans
– 0.0110
1
1
1
1
1
1
1
1
1
1
People who ar
e Asians
0.0022
2
2
2
2
2
2
2
2
3
2
Exhibit
A-1
Census V
ariables
A
verage by Neighborhood Rating
Corr
elation
1
2
3
4
5
6
7
8
9
10
A
verages of Decennial Census 2000 V
ariables by Overall Neighborhood Rating
Sample Size
7,107
5,404
12,150
17,939
49,136
32,486
54,199
82,320
55,424
102,155
Appendix
(per
cent)
(per
cent)
(per
cent)

??0 Policy Issues in Public and Assisted Housing
Buron and Patrabansh
Census V
ariables
A
verage by Neighborhood Rating
Corr
elation
1
2
3
4
5
6
7
8
9
10
A
verages of Decennial Census 2000 V
ariables by Overall Neighborhood Rating (continued)
Education People 25 years old or older who ar
e high school
dr
opouts
– 0.1704
19
17
17
17
16
16
15
14
14
14
People 16 to 19 years old who ar
e high school
dr
opouts
– 0.1273
16
15
15
15
14
13
13
12
11
11
People 16 to 19 years old who ar
e in school
0.1217
72
72
73
73
73
74
75
76
77
77
People 25 years old or older with some college
education or mor
e
0.1205
38
40
41
41
41
42
44
45
46
45
People 25 years old or older without a high school
diploma
– 0.0825
12
11
11
10
10
10
9
9
9
9
People 25 years old or older with some college
education
0.0309
19
20
20
20
20
20
21
21
21
20
People 25 years old or older with a high school
diploma
– 0.0062
31
32
32
32
32
32
32
32
32
32
Immigration People who ar
e citizens
0.0708
93
93
93
93
94
94
94
94
95
95
People who ar
e noncitizens
– 0.0708
7
7
7
7
6
6
6
6
5
5
People who ar
e natural-bor
n citizens
0.0494
90
90
90
90
90
91
91
91
92
92
People who ar
e for
eign bor
n
– 0.0494
10
10
10
10
10
9
9
9
8
8
People who ar
e for
eign-bor
n citizens
0.0011
3
3
3
3
3
3
3
3
3
3
Institutionalization Civilian uninstitutionalized people 5 years old or
older who ar
e disabled
– 0.1259
25
24
24
24
24
23
23
22
22
22
People who ar
e institutionalized
0.0072
2
2
2
2
2
2
2
2
2
2
(per
cent)
(per
cent)
(per
cent)
Exhibit
A-1

???Cityscape
Are Census Variables Highly Correlated With Housing Choice
Voucher Holders’ Perception of the Quality of Their Neighborhoods?
Census V
ariables
A
verage by Neighborhood Rating
Corr
elation
1
2
3
4
5
6
7
8
9
10
A
verages of Decennial Census 2000 V
ariables by Overall Neighborhood Rating (continued)
Labor for
ce
Civilian people 16 years old or older who ar
e
unemployed
– 0.1620
6
5
5
5
5
5
5
4
4
4
Civilian people 16 years old or older in managerial,
pr
ofessional, and technical employment
0.1552
23
24
24
24
25
25
26
27
28
28
Males 16 years old or older who ar
e not working at
least part time mor
e than 26 weeks
– 0.0786
11
11
11
10
10
10
10
10
10
10
Civilian people 16 years old or older who ar
e in the
labor for
ce
0.0470
60
61
61
61
61
62
62
62
63
62
Income People with income twice or mor
e the poverty level
0.1867
52
55
55
56
57
58
61
62
64
64
Households r
eceiving public assistance income
– 0.1798
7
7
7
6
6
6
5
5
4
4
People with income lower than the poverty level
– 0.1780
24
22
21
21
20
19
18
17
16
16
Housing units without vehicles
– 0.1720
20
18
17
17
16
15
14
13
12
12
Median household income
0.1500
29,658
30,878
31,330
31,541
32,044
32,879
34,119
35,083
36,446
36,390
Households with wage or salary income
0.0048
74
75
75
75
75
75
75
75
75
75
Housing costs Median gr
oss r
ent to median value of owner
-
occupied housing (capitalization rate)
– 0.1106
0.68
0.67
0.66
0.66
0.65
0.64
0.62
0.61
0.59
0.59
Median value of owner
-occupied housing units
0.0857
87,775
91,230
92,375
92,101
93,413
96,539
100,696
102,769
107,370
107,219
Median gr
oss monthly r
ent
0.0506
508
521
522
522
522
530
538
540
548
542
Median monthly r
ent
0.0222
430
446
443
446
442
450
457
457
461
455
(per
cent)
Exhibit
A-1
(per
cent)
(per
cent)
(per
cent)
(US$)(US$)

??? Policy Issues in Public and Assisted Housing
Buron and Patrabansh
Housing market Housing units that ar
e owner occupied
0.1780
49
50
51
51
53
54
55
57
60
61
Housing units (with heads 15 years old or older) that
households moved into mor
e than 5 years ago
0.1091
47
47
47
47
48
48
48
49
50
51
Housing units built since 1980
0.1052
21
22
22
22
22
22
24
25
27
27
Housing units built since 1970
0.0990
38
39
39
39
39
39
41
43
45
45
Housing units built befor
e 1940
– 0.0834
33
32
33
33
32
32
31
29
28
27
Housing units that ar
e vacant
– 0.0442
10
10
9
9
9
9
9
9
8
9
Housing units in single-family structur
es (1–4 units)
0.0385
73
74
74
74
74
75
75
75
76
76
Housing units that households moved into between
1995 and 1998
0.0383
21
22
22
22
22
22
23
23
23
23
Housing units without plumbing
– 0.0319
1
1
1
1
1
1
1
1
1
1
Census tract matches
95
95
96
95
95
95
95
95
94
93
Note: The sample includes all survey r
espondents who pr
ovided neighborhood ratings and could match their data to census data at census tract level (n = 418,308).
Sour
ces: Housing Choice V
oucher Pr
ogram Customer Satisfaction Survey
, 2000, 2001, and 2002; 2000 Decennial Census
Census V
ariables
A
verage by Neighborhood Rating
Corr
elation
1
2
3
4
5
6
7
8
9
10
A
verages of Decennial Census 2000 V
ariables by Overall Neighborhood Rating (continued)
(per
cent)
Exhibit
A-1

???Cityscape
Are Census Variables Highly Correlated With Housing Choice
Voucher Holders’ Perception of the Quality of Their Neighborhoods?
Acknowledgments
The authors gratefully acknowledge support from the U.S. Department of Housing and Urban
Development (HUD), Office of Policy Development and Research, for this work. We thank both
Barbara Haley of HUD and Jill Khadduri of Abt Associates for their comments on drafts of this study.
Authors
Larry Buron is an economist and a senior associate at Abt Associates Inc.
Satyendra Patrabansh is an associate at Abt Associates Inc.
References
Cohen, Jacob. 1988. Statistical Power Analysis for the Behavioral Sciences. Hillsdale, NJ: Lawrence
Erlbaum Associates.
Devine, Deborah J., Robert W. Gray, Lester Rubin, and Lydia B. Taghavi. 2003. “Housing Choice
Voucher Location Patterns: Implications for Participants and Neighborhood Welfare.” Washington,
DC: U.S. Department of Housing and Urban Development.
Feins, Judith D., and Rhiannon Patterson. 2005. “Geographic Mobility in the Housing Choice
Voucher Program: A Study of Families Entering the Program, 1995–2002,” Cityscape 8 (2): 21–47.
Galster, George, Chris Hayes, and Jennifer Johnson. 2005. “Identifying Robust, Parsimonious
Neighborhood Indicators,” Journal of Planning Education and Research 24: 265–280.
Holin, Mary Joel, Larry Buron, Gretchen Locke, and Alvaro Cortes. 2003. “Interim Assessment of
the HOPE VI Program Cross-Site Report.” A report to the U.S. Department of Housing and Urban
Development.
Newman, Sandra J., and Ann B. Schnare. 1997. “‘… And a Suitable Living Environment’: The
Failure of Housing Programs to Deliver on Neighborhood Quality,” Housing Policy Debate 8 (4):
703–740.

??? Policy Issues in Public and Assisted Housing

???Cityscape: A Journal of Policy Development and Research • Volume 10, Number 1 • 2008
U.S. Department of Housing and Urban Development • Office of Policy Development and Research
Cityscape
Housing in the Nation’s
Micropolitan Areas:
A First Look
Robert E. Lang
Karen A. Danielsen
Virginia Polytechnic Institute and State University
Abstract
Micropolitan area is a newly defined unit of analysis for examining housing affordabil-
ity. Before the creation of micropolitan areas in 2003, U.S. counties were categorized as
either metropolitan or nonmetropolitan. The category of micropolitan area allows for a
more detailed analysis of housing affordability conditions in areas with populations less
than metropolitan areas but more than nonmetropolitan areas. Variables examined in
this analysis of micropolitan areas include demographic and housing characteristics. A
policy section highlights how the findings from this analysis may be applied to micropoli-
tan geography.
Introduction
This article is a first-ever look at housing conditions and affordability in the nation’s micropolitan
areas. Micropolitan area is a newly defined Census Bureau (Census) geography term introduced in
June 2003 (OMB, 2003) and updated in December 2003, November 2004, and December 2005.
1

Before the creation of micropolitan areas, all counties in the United States were designated as either
metropolitan or nonmetropolitan; use of the term micropolitan area offers a gradation between these
two endpoint areas in terms of urban qualities.
2

1
This new designation uses 2000 Census data as a reference point. In 2000, the new core-based statistical areas (CBSAs)
replaced metropolitan statistical areas (MSAs), consolidated metropolitan statistical areas (CMSAs), and primary metropoli-
tan statistical areas (PMSAs). CBSAs define both micropolitan and metropolitan areas. Because of the unusual timing in the
release of this definition, many researchers remain unaware that the metropolitan definitions have shifted away from the
traditional CMSAs and PMSAs.
2
Depending on the location of principal cities and commuting relationships between counties, all counties are now desig-
nated as metropolitan (in a metropolitan statistical area), micropolitan (in a micropolitan statistical area), or noncore (not in
either type of statistical area). As such, noncore counties can be assumed to be sparsely populated and not connected with
any surrounding urbanization.

??? Refereed Papers
Lang and Danielsen
The Census Bureau currently identifies 573 micropolitan areas, accounting for 690 out of 3,141
U.S. counties. These places represent a significant new area of study because more than 28.3 million
people live in micropolitan areas, which amounts to more than one-fifth of U.S. counties and one-
tenth of the nation’s total population. Because micropolitan areas are a transition category between
city and country, micropolitan areas have only two-thirds as many housing units in Census-defined
urban areas compared with the nation as a whole (51.6 and 77.6 percent, respectively).
3
Micro-
politan areas also have roughly double the percentage of Census-defined rural housing units as the
entire United States.
The introduction of micropolitan areas significantly redefines census geography. With this new
definition, more than half the land in the continental United States is now officially designated as
“core based”; that is, it is designated either micropolitan or metropolitan by the Census Bureau.
With the addition of micropolitan areas, much more U.S. land area falls into urban areas, or core-
based areas, rather than into noncore-based areas. In 1890, America’s Census-designated frontier
closed as settlement swept into remote corners of the nation. As defined by the Census Bureau, the
United States became majority urban by 1920 for the first time in its history. At mid-20th century,
more than half of the U.S. population lived in metropolitan areas. By the 1970 Census, the United
States had become a suburban-dominated nation, with more than half of all metropolitan residents
living outside central cities.
4
Now a new milestone has been reached: as of 2000, noncore-based
areas cover less than half the continental United States.
As might be expected, the academic literature on the subject is very limited because of the newness
of the micropolitan area definition (Frey et al., 2004; Lang and Dhavale, 2004, 2006). Nevertheless,
this new geography already has been embraced elsewhere. The media, local governments, and
policymakers have started adopting and using the micropolitan area designation, and businesses,
government agencies, and planners are working with a new geography. Publications took notice;
Site Selection Magazine, for example, started a list of “Top Micropolitans” in which to locate busi-
nesses (Starner, 2005).
Because micropolitan areas are so new, this article begins by examining micropolitan definitions
and geography. Micropolitan geography is also a key determinate of housing conditions, including
affordability. The article then turns to the methods used to examine micropolitan housing condi-
tions and affordability, followed by an overview of general housing conditions in micropolitan
areas. The next section then addresses the least and most affordable micropolitan areas. The article
concludes by analyzing some policy implications raised by the data analysis.
Micropolitan Geography
Similar to metropolitan areas, micropolitan areas are constructed from counties containing the
population center and any surrounding counties with commuting relationships with the central
3
The Census Bureau (Census) defines rural places using population density; therefore, a nonmetropolitan or noncore
designation is not synonymous with rural. In this article, we use the terms “urban” and “metropolitan” interchangeably, as
well as “rural” and “nonmetropolitan.” Every use of the Census-designated definition of urban or rural is explicitly noted in
the text.
4
For a fuller discussion of these key transitions, see Lang, Popper, and Popper (1997, 1995) and Katz and Lang (2003).

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Housing in the Nation’s Micropolitan Areas: A First Look
Cityscape
county or counties. The only difference is in the size of the city; the micropolitan principal cities
5

include populations of 10,000 to 49,999, while the populations of metropolitan principal cities are
greater than 50,000. A statistical area may be anchored by more than one principal city.
Micropolitan areas can be populous regions but without large cores, while metropolitan areas may
have large cores that are surrounded by little additional population. An area is defined as metropolitan
or micropolitan based on the size of its center rather than its total population (Frey et al., 2004).
This definition raises important research questions about how to determine what is urban based on
previous notions of urbanization. The traditional view holds that an original large-core city anchors
subsequent suburbanization and creates a metropolitan area. The big micropolitan areas reverse
this standard pattern because they grew to a metropolitan scale without a large central city.
Some of the largest micropolitan areas are more than just overgrown small towns—they appear
to be exemplars of a new decentralized or even countrified city. Most research on decentralized
cities (for example, edge cities and edgeless cities) examines the places that have grown next to
traditional cores, such as the Tysons Corner area outside Washington, D.C. (Garreau, 1991; Lang,
2003). Yet the suburban growth in large micropolitan areas is not outside anything because there is
no real center to be outside of—and no urban to be a sub of. In this way, the micropolitan growth
represents a new metropolitan form with an expansive periphery and a relatively small core.
Micropolitan areas fall between metropolitan and rural areas in their urban qualities (Lang and
Dhavale, 2004, 2006). They lack the large central city (more than 50,000 residents) that the Office
of Management and Budget requires as a criterion for being a metropolitan area. By contrast,
micropolitan areas have central cities that compare with modest-sized towns.
6
As with metropoli-
tan areas, micropolitan areas are quite diverse. As the term micropolitan implies, these places are
generally, but not always, less populous than metropolitan areas; however, large micropolitan areas
can exceed small metropolitan areas in total population. In fact, the largest micropolitan area (Tor-
rington, CT) outranks 103 of the smallest metropolitan areas (out of 276); therefore, micropolitan
areas and metropolitan areas substantially overlap.
We can measure the suburb-to-center city relationship in both metropolitan and micropolitan
areas. The ratio of center city populations to their suburbs has been tracked back to 1910 (U.S.
Census Bureau, 2002). In the first decade of the 20th century, central cities dominated the metropolis,
accounting for three-fourths of all people in the region. By 2000, the roles had been reversed, with
just 37.7 percent of metropolitan residents living in central cities. In comparison, an analysis of
5
Changes to how the Census Bureau classifies places in 2003, however, have eliminated the category “central cities” (Frey
et al., 2004; OMB, 2003). The new definitions relabel all of what were previously classified as central cities, plus some other
places, as “principal cities,” demonstrating the Census Bureau’s awareness that important cities need not be central to their
metropolitan regions. In fact, a principal city need not even be an incorporated place. For example, Paradise, Nevada, (a
principal city in the Las Vegas/Paradise, NV Metropolitan Statistical Area) is only a “Census-designated place” carved out of
unincorporated Clark County, Nevada (Lang and Dhavale, 2003).
6
The Office of Management and Budget definition for a micropolitan area is “at least one urban cluster of at least 10,000
but less than 50,000 in population,” although more than 50,000 residents can live in the entire micropolitan statistical area.
As with metropolitan areas, micropolitan areas are constructed from counties containing the population center and from
those that have commuting relationships with the central county. (Available at http://www.census.gov/population/www/
estimates/aboutmetro.html.)

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Lang and Danielsen
micropolitan areas reveals that their central cities are even more modest relative to their suburbs:
only 31.6 percent of micropolitan area residents live in the area’s core (Lang and Dhavale, 2004).
Micropolitan areas reflect U.S. regional differences. Metropolitan and micropolitan areas substantially
fill in the eastern half of the nation. The only big spaces left in the East without micropolitan areas
are the upper Great Lakes, northern Maine, and the central Appalachian Mountains. A state such as
Vermont, which has few metropolitan counties, is full of micropolitan ones. In southeastern states
from Mississippi to North Carolina, substantial micropolitan zones now complement metropolitan
areas. So extensive is the urban coverage of the East that if a driver traveled the entire length of In-
terstate 95 from Maine to Florida, he or she would pass through only five noncore-based counties.
By contrast, significant stretches of the West are without urban places. The Great Plains and the
northern Rockies have large rural gaps between their micropolitan areas and metropolitan areas;
however, the Interstate highways that pass through the Rockies and Great Plains often support
multiple micropolitan areas along their lengths.
The differences between East and West also hint at the role that micropolitan areas play in regional
development. In the East, micropolitan areas seem to be small-scale urban fillers between bigger
metropolitan areas, while in the West, micropolitan areas may be central places that anchor economic
development across a broad area. This difference could affect conditions such as housing afford-
ability in micropolitan areas. For example, micropolitan areas in the East that are essentially exurbs
to large metropolitan areas may face future housing cost pressures as their metropolitan neighbors
continue to sprawl. Micropolitan areas currently number 49 in the Northeast, 235 in the South,
206 in the Midwest, and 64 in the West. Exhibit 1 shows the distribution of micropolitan areas.
Exhibit 1
Micropolitan Regional Distribution

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Housing in the Nation’s Micropolitan Areas: A First Look
Cityscape
Methods
The Census Bureau identifies 573 micropolitan areas. This analysis of housing conditions
and affordability focuses on the 567 micropolitan areas in the coterminous United States. The 
micropolitan areas in Alaska, Hawaii, and Puerto Rico are excluded because preliminary analyses
show these places to be outliers. For example, some very remote micropolitan areas in Alaska are
actually closer to Seattle, Washington, than to Anchorage, Alaska. In Hawaii, three entire islands
are micropolitan areas. To compare micropolitan areas and their housing conditions, this analysis
concentrates on the continental United States.
In the first part of the analysis, we focus on three demographic dimensions of micropolitan
areas—size, growth, and location—thus establishing a baseline of micropolitan geography. In
the second part of the analysis, we examine the relationship between affordability and housing
conditions in the micropolitan areas.
Data Sources
The definitions of the micropolitan areas come directly from the Census Bureau (OMB, 2003), as
do the population data (U.S. Census Bureau, 2004). The housing information and variables come
from the 2000 Census long-form compilation. The data are obtained at the county level for two
reasons: (1) counties are the building blocks for micropolitan areas and (2) the Census Bureau
currently is not reporting data at the micropolitan level. Because some micropolitan areas are
composites of multiple counties, we used a weighted average to obtain micropolitan-level data.
Population size and growth are the first micropolitan characteristics we discuss to establish a basic
micropolitan area typology. We use location as a remoteness indicator that measures the distance
between the center of a micropolitan area and the center of a big metropolitan area.
7
Big refers to
metropolitan areas with more than 1 million residents, which describes the top 50 U.S. regions
ranging in size from Richmond, Virginia, with a population of slightly more than 1 million, to New
York City, with a population of more than 21 million.
8
The big 50 metropolitan areas, which alone account for more than half of the U.S. population, are
home to key transportation infrastructure, such as hub airports. Distance from these metropolitan
areas, which are also the nation’s economic engines, puts remote micropolitan areas at a locational
disadvantage. The 25 most remote micropolitan areas are at least 275 miles from a large metropoli-
tan area, which means their residents must drive 4 or more hours to reach big-city services and
amenities. As discussed in the next section, micropolitan areas near large metropolitan areas tend
to be larger and faster growing, while the more remote places are often smaller and slower growing.
This finding is consistent with demographer Calvin Beale’s work that established remoteness as a
key indicator of metropolitan development (Beale, 1990). Remoteness (and what it implies about
access) is a key concept in rural geography, and remoteness should apply to this new small-scale
urban form.
7
The remoteness measure was done by calculating the distance from the centroids of micropolitan areas to the centroids
of metropolitan areas in ArcView 3.3. For a longer discussion of the remoteness measurement see Lang (2002) and Lang,
Popper, and Popper (1997).
8
Because this study focuses on the United States, San Juan, Puerto Rico, was replaced with the next largest city, Richmond,
Virginia.

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Lang and Danielsen
Micropolitan Affordability Index
The Micropolitan Affordability Index (MAI) calculated and used in this article applies assump-
tions similar to those used in the National Association of Home Builders-Wells Fargo Housing
Opportunity Index (HOI). The HOI computes the portion of homes in each metropolitan area
and region that are affordable to a family with a median household income.
9
The MAI computes a
similar index, with the focus being solely on micropolitan areas and homeownership. The MAI is
the percentage of households that can purchase a median-priced home; the HOI and MAI provide
different but complementary statistics about the affordability of housing. The HOI shows the share
of homes sold that could be purchased with a median income. The MAI indicates the percentage of
households that can afford a median-priced home for each micropolitan area.
Because the micropolitan areas are a brand new geography, data for these places are not nearly
as developed as for metropolitan areas, which have existed as a census category for more than
half a century. No trade group data sources are available on micropolitan housing similar to those
produced for metropolitan areas by national organizations such as the NATIONAL ASSOCIATION
OF REALTORS
®
or the National Association of Home Builders. The data for the micropolitan housing
came from the Census Bureau’s long-form compilations. These data were collected on April 1, 2000.
Although the housing data obviously are not current, they nonetheless show the historic relative
condition of micropolitan housing. These data are important for framing the basic housing costs
and characteristics in these areas.
When possible, the data types and underlying assumptions for the MAI closely match the HOI.
The formula assumes a 30-year, fixed-rate loan for 90 percent of the sale price with a 10-percent
downpayment. The interest rate used to calculate payments was 7.25 percent—an average interest
rate in 1999.
10
The rate was obtained from the Federal Housing Finance Board Monthly Interest
Rate Survey, which is an average of the effective interest rates offered for conventional single-family
mortgages during that period. We obtained median real estate taxes from the Census Bureau,
as well as the categorized gross incomes of micropolitan households and median house values,
which we used to approximate the cost of purchasing a new home. The monthly payment amount
incorporates principal, interest, and real estate taxes. Property and mortgage insurance are not
included in the total.
The MAI formula takes both total loan amount and household income into account. We computed
a 30-year loan schedule for a median monthly payment based on the costs of homeownership—
loan interest, principal, and taxes—for each micropolitan area. We used the payment amount
to determine a required yearly salary necessary to pay less than 28 percent of gross income for
housing. As with the HOI, the MAI considers housing affordable when no more than 28 percent
of gross income is spent on housing. We then tallied the total number of households earning more
than the amount of that required yearly threshold. We compared this result to the total number of
9
For a more complete background on the Housing Opportunity Index and a description of its methodology, see http://
www.nahb.org/generic.aspx?sectionID=135&genericContentID=533. This index is a rough measure of affordability, and it
does not include other costs associated with mortgages, such as mortgage insurance. In addition, the census data used to
derive this data do not have detailed housing costs available for analysis.
10
Although the decennial census was taken in April 2000, the collected median income and housing data are from 1999.

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Housing in the Nation’s Micropolitan Areas: A First Look
Cityscape
households, arriving at the percentage of households in each micropolitan area capable of buying
a median-priced home. Thus, the MAI is the percentage of households in each micropolitan area
that can afford the median cost of housing considering the incomes actually available. The HOI
is slightly different because it determines the percentage of housing available to a family with the
median income.
Micropolitan Housing Conditions—The Top Ten Micropolitan Areas by Type
As discussed in Lang and Dhavale (2004, 2006), micropolitan areas vary greatly on three key
demographic variables—size, growth rate, and remoteness. These variables enable researchers
to capture the essence of micropolitan areas and understand some of the forces that might be at
work. This analysis takes those basic variables further and examines other variables that influence
housing affordability. We examine the relationship between housing affordability, as calculated by
the MAI, and demographic and housing variables associated with affordability—housing type, age,
and size. Because of the difficulty of conceptualizing trends in hundreds of places, we use top 10
lists to illustrate some of the most and least affordable micropolitan areas.
In total, the micropolitan areas contain 12,944,559 housing units, or 11.2 percent of the nation’s
housing stock. Exhibit 2, which summarizes micropolitan housing statistics, shows that micropolitan
areas have a higher percentage of single-family detached units than the United States as a whole.
Micropolitan areas also have more than double the percentage of manufactured homes than the
nation as a whole (15 and 7.6 percent, respectively). The micropolitan housing stock is roughly
comparable in age to the national housing stock; the median age for both hovers around 15 years,
with a median building date of 1971. Micropolitan area housing size as distributed is comparable
with U.S. averages in number of bedrooms and total number of rooms per dwelling (14.5 percent
of micropolitan homes and 16.9 percent of national homes have four or more bedrooms; 6.8 percent
of micropolitan homes and 7.7 percent of national homes have nine or more total rooms). The
micropolitan areas slightly lag the nation in the percentage of owner-occupied units.
Exhibit 2

Affordable
Housing
(MAI)
(percent)
Owner-
Occupied
Housing
Units
(percent)
Detached
Single-
Family
Homes
(percent)
Manu-
factured
Housing
Units
(percent)
Median
Manu-
factured
Home
Value
(US$)
Median
Home
Value
(US$)
Median
Year
Built
Nine or
More
Rooms
(percent)
U.S. and Micropolitan Area Housing Summary
Micropolitan area 63.0 63.0 68.8 15.0 30,900 78,461 1969 6.8
United States 58.5 66.2 60.3 7.6 31,200 111,800 1971 7.7
MAI = Micropolitan Affordability Index.
The Least and Most Affordable Micropolitan Areas
The following top 10 lists, which show micropolitan areas that are most and least affordable, based on
MAI, are useful analyses of hundreds of micropolitan areas, and they illustrate some key findings.

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Lang and Danielsen
Affordability is a component of house price, household income, and a monthly payment consisting
of principal, interest, taxes, and insurance. The micropolitan median house value (as of 2000) is
about 70 percent that of the United States house value ($78,461 and $111,800, respectively). In
2000, the $34,234 median income in the micropolitan areas was about 80 percent of the U.S. me-
dian income of $41,994, which translated into more affordable housing in the micropolitan areas.
Affordability in micropolitan areas ranges from a low of 16 percent in the Jackson, WY-ID area to
a high of 81.6 percent in the Borger, TX area. In other words, fewer than one in six households in
the Jackson, WY-ID area could afford a median-priced home compared with more than four in five
households in the Borger, TX area.
Least Affordable Micropolitan Areas. In line with national trends, 9 of the 10 least affordable
micropolitan areas are in the West (see exhibit 3). Many of these micropolitan areas are mostly
vacation spots—the Jackson, WY-ID area, near the Grand Teton National Park; the Key West, FL
area, on the Florida Keys; and the Edwards and Silverthorne, CO area, in Colorado ski country.
High-cost micropolitan areas vary in remoteness from major metropolitan areas (varying from the
Truckee-Grass Valley, CA area 40 miles outside Sacramento to the Jackson, WY-ID area 242.2 miles
from Salt Lake City). The remote micropolitan areas also tend to be smaller; only three micro-
politan areas exceed the average population size of all the 567 micropolitan areas. Although less
populous, remote micropolitan areas are the least affordable of all the micropolitan areas and are
booming. None of the least affordable micropolitan areas lost population; six of the least affordable
micropolitan areas experienced double-digit growth from 1990 to 2000, and four grew more than
50 percent. In this top 10 list of least affordable micropolitan areas, the population growth could
have a disproportionate influence on housing affordability by bidding up housing costs and mak-
ing homes less affordable.
Exhibit 3
Micropolitan Area
Population
2000
Change
1990–
2000
(percent)
Region
Owner-
Occupied
Housing
Units
(percent)
Manu-
factured
Housing
Units
(percent)
House-
holds That
Can Afford
Housing
(percent)
Median
Home
Value
(US$)
The Top 10 Least Affordable Micropolitan Areas*
Jackson, WY-ID 24,250 66.0 West 44.5 6.6 16.0 301,099
Silverthorne, CO 23,548 82.8 West 22.2 2.3 19.9 268,800
Edwards, CO 49,471 77.1 West 44.9 10.1 20.9 271,083
Key West, FL 79,589 2.0 South 42.4 19.0 32.8 195,700
Brookings, OR 21,137 9.4 West 61.0 26.1 35.9 125,000
Truckee-Grass Valley, CA 92,033 17.2 West 63.1 7.3 36.1 199,300
Heber, UT 15,215 50.8 West 58.3 4.2 37.3 186,800
Eureka-Arcata-Fortuna, CA 126,518 6.2 West 52.8 9.8 38.7 128,500
Taos, NM 29,979 29.7 West 55.0 21.3 39.0 124,900
Astoria, OR 35,630 7.0 West 47.9 8.3 39.3 138,800
U.S. average 60.2 7.6 58.5 111,800
* Micropolitan area names as designated in 2006.

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Housing in the Nation’s Micropolitan Areas: A First Look
Cityscape
In other least-affordable micropolitan areas, two scenarios emerge. One scenario produces relative-
ly modest home values, but taxes are high. Micropolitan areas in this category are the Brookings,
OR area, the Eureka-Arcata-Fortuna, CA area, the Taos, NM area, and the Astoria, OR area. The
other least affordable micropolitan area scenario produces hefty home prices, all close to double or
triple the U.S. median, higher median taxes, and somewhat higher incomes than the United States
as a whole. These micropolitan areas include the Jackson, WY-ID area, the Silverthorne, CO area,
and the Edwards, CO area.
Only three of the least affordable micropolitan areas have manufactured housing percentages
exceeding the micropolitan average, but this pattern does not seem to be connected to income.
These 10 least affordable micropolitan areas have lower rates of homeownership or owner-occupied
housing units—most likely related to the resort-driven nature of their economies. Vacation housing
and desirability of location, which often come paired with environmental constraints, also drive up
the price of housing costs.
Most Affordable Micropolitan Areas. Also similar to national housing cost trends, the most
affordable micropolitan areas cluster in the South and Midwest, with eight in Texas alone (see
exhibit 4). Except for the Mineral Wells, TX area and the Lamesa, TX area, the most affordable
micropolitan areas are losing population, some with double-digit declines. All these places are
smaller than the micropolitan area average and, except for one, are more remote than the typical
micropolitan area. Many of these areas also are among the micropolitan areas that are the smallest
and most quickly losing population (McGranahan and Beale, 2002); however, only 2 of the 10
most affordable micropolitan areas have a percentage of manufactured housing exceeding the
micropolitan average; 4 are less than the U.S. average.
Micropolitan Area
Population
2000
Change
1990–
2000
(percent)
Region
Owner-
Occupied
Housing
Units
(percent)
Manu-
factured
Housing
Units
(percent)
House-
holds That
Can Afford
Housing
(percent)
Median
Home
Value
(US$)
Exhibit 4
The Top 10 Most Affordable Micropolitan Areas*
Borger, TX 23,857 – 7.1 South 67.4 11.9 81.6 43,100
Andrews, TX 13,004 – 9.3 South 67.9 17.9 80.4 38,900
Pecos, TX 13,137 – 17.1 South 63.0 12.5 79.5 23,400
Parsons, KS 22,835 – 3.6 Midwest 65.3 6.3 79.2 44,300
Snyder, TX 16,361 – 12.2 South 59.8 8.9 77.9 45,000
Mineral Wells, TX 27,026 7.9 South 54.1 19.9 77.8 46,900
Pampa, TX 23,631 – 5.5 South 64.4 5.7 77.8 37,560
Vernon, TX 14,676 – 2.9 South 57.7 6.5 77.1 46,300
Coffeyville, KS 36,252 – 6.6 Midwest 62.0 7.8 77.1 47,500
Lamesa, TX 14,985 4.4 South 63.1 7.0 76.7 41,000
U.S. average 60.2 7.6 58.5 111,800
* Micropolitan area names as designated in 2006.

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Lang and Danielsen
The median house values in the most affordable micropolitan areas are well below the national
average. For instance, several micropolitan areas are about one-half as much as the national average
at the high end of the group in exhibit 4 and some micropolitan areas reach to one-third or less of
the national average. Likewise, the taxes are one-fourth or one-third of the median value for micro-
politan areas. The median household income in the most affordable micropolitan areas is moderate,
although none of the areas equal the U.S. national average The data show that a combination of
low housing costs and average income makes money go further in these micropolitan areas.
Micropolitan Areas With the Highest House Values. In 2000, median housing values in
micropolitan areas ranged from a low of $23,400 in the Pecos, TX area to a high of $301,099 in
the Jackson, WY-ID area. The West has 9 of the 10 micropolitan areas with the highest median
housing values (see exhibit 5). Of the 9 micropolitan areas in the West, 5 appear on the top 10 list
of the least affordable micropolitan areas (see exhibit 3).
The Los Alamos, NM area, home to a large U.S. Department of Defense weapons laboratory, ranked
fourth in micropolitan area house values as of 2000, yet its housing was reasonably affordable. This
relative affordability may be a result of the moderately high wages paid to government employees
who dominate the area’s workforce. Conversely, the resort-driven economies of the three micro-
politan areas ahead of Los Alamos have more low-wage jobs and, thus, fewer people can afford its
home prices. Much of the demand for housing in these micropolitan areas comes from outsiders
seeking vacation property.
Micropolitan Area
Population
2000
Change
1990–
2000
(percent)
Region
Owner-
Occupied
Housing
Units
(percent)
Manu-
factured
Housing
Units
(percent)
House-
holds That
Can Afford
Housing
(percent)
Median
Home
Value
(US$)
Exhibit 5
The Top 10 Micropolitan Areas With the Most Expensive Homes*
Jackson, WY-ID 24,250 66.0 West 44.5 6.6 16.0 301,099
Edwards, CO 49,471 77.1 West 44.9 10.1 20.9 271,083
Silverthorne, CO 23,548 82.8 West 22.2 2.3 19.9 268,800
Los Alamos, NM 18,343 1.3 West 74.3 5.5 64.0 213,000
Truckee-Grass Valley, CA 92,033 17.2 West 63.1 7.3 36.1 199,300
Key West, FL 79,589 2.0 South 42.4 19.0 32.8 195,700
Heber, UT 15,215 50.8 West 58.3 4.2 37.3 186,800
Durango, CO 43,941 36.1 West 57.1 16.6 44.9 174,500
Gardnerville Ranchos, NV 41,259 49.3 West 64.1 8.7 40.7 174,200
Oak Harbor, WA 71,558 18.9 West 60.1 10.9 50.7 168,400
U.S. average 60.2 7.6 58.5 111,800
* Micropolitan area names as designated in 2006.
Micropolitan Areas With the Lowest House Values. All 10 micropolitan areas with the
lowest median housing values are in the South (see exhibit 6); Texas alone has 9, and 4 of these
low-house-value micropolitan areas also appear on the most affordable micropolitan areas list
(see exhibit 4).

???
Housing in the Nation’s Micropolitan Areas: A First Look
Cityscape
The lowest value micropolitan areas are smaller places that lost population in the 1990s and lie
very far from major metropolitan areas. These small, remote micropolitan areas have only average
homeownership rates, a finding that suggests a mismatch between housing costs and income. The
median income in all these micropolitan areas is below the micropolitan area median and only
three-fourths of the U.S. average. Mortgage payments and taxes in these areas are approximately
one-half of the micropolitan area median and one-third of the U.S. average. Median house values
are one-third to one-half of the micropolitan area median and one-fifth to one-third of the U.S.
average. Rio Grand City stands out from this group because of its growth rate—it lies along the
Mexican border next to McAllen, Texas, and has seen a recent boom in immigration.
Policy Implications
This research has many possible implications, in part, because micropolitan areas are a new census
category. Micropolitan areas differ in housing conditions somewhat from the nation as a whole.
In some ways, micropolitan areas represent a housing success story because, in general, they have
more affordable housing. Considering the relative affordability of these communities, policymakers
should explore ways to strengthen the communities in areas such as economic development and
housing preservation.
Certainly, micropolitan areas have major differences, many derived from regional location and
degree of remoteness. Remote micropolitan areas may be affordable, but often they lack economic
development. Micropolitan areas in the path of large urban sprawl may be developing rapidly;
however, they also may need housing preservation programs.
The designation “micropolitan area” addresses a longstanding concern among rural advocates
that many smaller—although important—cities fall below the Census Bureau’s metropolitan area
Micropolitan Area
Population
2000
Change
1990–
2000
(percent)
Region
Owner-
Occupied
Housing
Units
(percent)
Manu-
factured
Housing
Units
(percent)
House-
holds That
Can Afford
Housing
(percent)
Median
Home
Value
(US$)
Exhibit 6
The Top 10 Micropolitan Areas With the Least Expensive Homes*
Pecos, TX 13,137 – 17.1 South 63.0 12.5 79.5 23,400
Raymondville, TX 20,082 13.4 South 64.2 12.5 63.8 33,500
Sweetwater, TX 15,802 – 4.8 South 58.5 6.3 70.2 35,800
Rio Grande City-Roma, TX 53,597 32.3 South 65.1 10.2 54.3 35,900
Pampa, TX 23,631 – 5.5 South 64.4 5.7 77.8 37,560
Big Spring, TX 33,627 4.0 South 58.2 7.9 75.9 38,500
Andrews, TX 13,004 – 9.3 South 67.9 17.9 80.4 38,900
Alice, TX 39,326 4.4 South 66.9 16.5 72.7 40,400
Lamesa, TX 14,985 4.4 South 63.1 7.0 76.7 41,000
Middlesborough, KY 30,060 – 4.6 South 60.8 24.8 59.6 41,700
U.S. average 60.2 7.6 58.5 111,800
* Micropolitan area names as designated in 2006.

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Lang and Danielsen
category. These advocates lobbied the Census Bureau to find a means to capture such places,
which resulted in the micropolitan label (Mahtesian, 2005). The micropolitan label also allows for
a more sophisticated differentiation between what was rural (nonmetropolitan) and is now deep
rural (noncore). The Census Bureau’s previous nonmetropolitan designation was too broad to be
synonymous with rural areas. The remaining noncore counties that fall below the micropolitan
level can now be seen as truly rural.
The Census Bureau now officially recognizes a new category of statistical area, which could entitle
what were formally nonmetropolitan places to apply for metropolitan-based federal and state
housing aid. It may also mean that micropolitan areas need to forgo rural housing assistance. At
this point, the jury is still out on the way micropolitan areas may go. The Census Bureau creates
new categories based only on its read of human geography. The Census Bureau, however, makes
no recommendations for how a new category such as micropolitan areas should relate to public
policy issues such as federal aid. The leadership in micropolitan areas must determine the nature of
the areas’ needs and lobby state and federal agencies accordingly. For now, most micropolitan areas
remain nonmetropolitan in self-identity, but that condition should change in the next several years
as more of these areas come to understand the implications of their new designation.
11

The Booming Western and Stagnating Southern Micropolitan Areas. The housing policy
implications of the findings in this study range widely, depending on location. Apparently, large re-
gional differences exist in housing opportunity, with the two extremes being resort communities of
the West and remote rural parts of the South. Western micropolitan areas have affordability prob-
lems, while Southern micropolitan areas face potential abandonment issues. In both cases, housing
opportunity is bound up with regional development trends. In some instances, housing could play
a role in addressing larger issues such as maintaining sustainable environments and reinvigorating
economic development.
The resort towns in the West have an acute housing affordability problem borne of a mostly
low-wage economy and high home costs. Many of the houses in these micropolitan areas are for
affluent second-home buyers, whose salaries are derived from professional and managerial jobs in
big metropolitan areas. Most locals, who rely on a tourist economy, simply cannot compete in this
housing market and sometimes need to commute from great distances to their jobs.
Although affordability may be primarily a housing problem, other competing interests weigh
heavily on these areas. Much of the micropolitan West is picturesque and ecologically fragile. Most
urban residents, including many from outside the region, want to see this land preserved and
developed only as tourist destinations. Yet most locals prefer that these places be used for more
than tourism, which would include extractive industries such as mining (Rengert and Lang, 2001).
A booming mining or energy economy would have two effects. This type of economy generally
pays higher wages than tourism and may also drive off vacation home development and big city
homebuyers. Both of these effects could produce more affordable housing, but they also may result
11
Very few micropolitan county officials even realized that the U.S. Census Bureau had recategorized the region they
administered based on anecdotal reactions to Robert Lang’s plenary address at the National Association of Counties
legislative affairs meeting in March 2004.

???
Housing in the Nation’s Micropolitan Areas: A First Look
Cityscape
in a less sustainable use of the land. Failing to address the housing affordability problem in western
micropolitan areas could produce a conflict between locals and outsiders over the fate of these
areas and change the direction of their economic development.
The problem in parts of the rural South, especially the Great Plains sections of Texas, is too little
demand for housing. The housing there is affordable, resulting partly from the stagnating regional
economy. Significant population loss occurred during the 1990s in areas such as Pecos, Borger,
and Andrews, TX. These micropolitan areas have had an especially difficult time retaining recent
high school and college graduates who seek opportunity elsewhere. Part of the efforts aimed at
economic revitalization could be to profile housing opportunities.
Future research on micropolitan areas presents a challenge because of the Census Bureau’s
shift from the census long form to the American Community Survey (ACS). The absence of the
long-form data will make it problematic to study the smallest micropolitan areas; however, the
ACS should have enough cases to continue to track trends in larger micropolitan areas. The large
micropolitan areas offer researchers an opportunity to study housing trends at the smallest urban
level. Without the long form, it will be harder to cobble together data on less populous micropoli-
tan areas; perhaps the ACS could conduct a special micropolitan analysis at least once each decade
to fill this gap.
Acknowledgments
The authors thank Dawn Dhavale for compiling and analyzing the statistics in this article and
acknowledge the Fannie Mae Foundation for providing a generous grant to fund this research.
Authors
Robert E. Lang is a professor in the department of Urban Affairs and Planning at Virginia Polytech-
nic Institute and State University (Virginia Tech) and is the director of the Metropolitan Institute at
Virginia Tech.
Karen A. Danielsen is an assistant professor in the department of Urban Affairs and Planning at
Virginia Tech.
References
Beale, Calvin L. 1990. “A Characterization of Types of Metropolitan Areas.” In A Taste of the Country:
A Collection of Calvin Beale’s Writings, edited by Peter A. Morrison. University Park, PA, and London:
The Pennsylvania State University Press: 59–75.
Frey, William H., Jill H. Wilson, Alan Berube, and Audrey Singer. 2004. “Tracking Metropolitan
America into the 21st Century: A Field Guide to the New Metropolitan and Micropolitan Definitions.”
In Living Cities Census Series. Washington, DC: Brookings Institution.
Garreau, Joel. 1991. Edge City: Life on the New Frontier. New York: Anchor.

??? Refereed Papers
Lang and Danielsen
Katz, Bruce, and Robert E. Lang, eds. 2003. Redefining Cities and Suburbs: Evidence from Census 2000,
vol. I. Washington, DC: Brookings Institution Press.
Lang, Robert E. 2004. “Micropolitan Areas: A Brand New Geography.” Plenary address to the
National Association of Counties Annual Legislative Meeting, Washington, DC, March 1. Available
at http://www.mi.vt.edu.
———2003. Edgeless Cities: Exploring the Elusive Metropolis. Washington, DC: Brookings Institution
Press.
———2002. “Open Spaces, Bounded Places: Does the American West’s Arid Landscape Yield
Dense Metropolitan Growth?” Housing Policy Debate 13 (4): 755–778.
Lang, Robert E., and Dawn Dhavale. 2006. “Micropolitan America: A Brand New Geography.” In
Redefining Cities and Suburbs: Evidence from Census 2000, vol. III, edited by Alan Berube, Bruce Katz,
and Robert E. Lang. Washington, DC: Brookings Institution Press: 237–258.
———. 2004. “Micropolitan America: A Brand New Geography,” Metropolitan Institute at Virginia
Tech Census Note 04: 01 (March). Alexandria, VA: Metropolitan Institute at Virginia Tech.
______. 2003. “Reluctant Cities? Exploring Big Unincorporated Census Designated Places,” Metro-
politan Institute at Virginia Tech Census Note 03: 01 (July). Alexandria, VA: Metropolitan Institute at
Virginia Tech.
Lang, Robert E., Deborah Epstein Popper, and Frank J. Popper. 1997. “Is There Still a Frontier?
The 1890 Census and the Modern West,” Journal of Rural Studies 13 (4): 377–386.
———. 1995. “Progress of the Nation: The Settlement History of the Enduring American Fron-
tier,” The Western Historical Quarterly 23 (3): 289–307.
Mahtesian, Charles. 2005. Political World The Mighty Micropolis. Government Executive.Com.
Available at http://www.govexec.com/features/0205-01/0205-01politworld.htm.
McGranahan, David, and Calvin Beale. 2002. “Understanding Rural Population Loss,” Rural
America Winter 17 (4): 2–11.
Office of Management and Budget (OMB). 2003. OMB Bulletin No. 03-04 (June 6).
Rengert, Kristopher M., and Robert E. Lang. 2001. “Cowboys and Cappuccino: The Emerging
Diversity of the Rural West,” Fannie Mae Foundation Census Note 01: 04 (May). Washington, DC:
Fannie Mae Foundation.
Starner, Ron. 2005. “Top Micropolitans,” Site Selection Magazine (March): 16–20.
U.S. Census Bureau. 2004. Census 2000 Summary File 1 (SF 1) 100-Percent Data. Available at
http://factfinder.census.gov/.
———. 2002. Demographic Trends in the 20th Century. Washington, DC: U.S. Census Bureau.

???Cityscape: A Journal of Policy Development and Research • Volume 10, Number 1 • 2008
U.S. Department of Housing and Urban Development • Office of Policy Development and Research
Cityscape
Using American Community
Survey Data in HUD’s Income
Limits and Fair Market Rents
Kurt Usowski
Lynn Rodgers
Marie Lihn
Peter Kahn
U.S. Department of Housing and Urban Development
Abstract
The U.S. Department of Housing and Urban Development (HUD) is required to base its
Income Limits and Fair Market Rents (FMRs) on the “most recent data available.” The
release of the 2005 American Community Survey (ACS) data posed several challenges
for including these survey data in the estimation of these program parameters. This
article discusses how HUD uses 2005 ACS data to establish Income Limits and FMRs.
HUD encourages researchers to evaluate HUD’s current use of the data and provide
suggestions for incorporating the use of ACS data for smaller areas, when available, into
these estimates.
Introduction
HUD is required by law to develop annually Income Limits for use in determining the eligibility
of applicants for its assisted housing programs. Major active HUD-assisted housing programs that
rely on Income Limits to determine eligibility are the Public Housing program, Section 8 Housing
Assistance Payments program, Section 202 Supportive Housing for the Elderly, and Section 811
Supportive Housing for Persons with Disabilities. Many other federal and state housing, lending, or
Policy Briefs
The Policy Briefs department summarizes a change or trend in national policy
that may have escaped the attention of researchers. The purpose is to stimulate the
analysis of policy in the field while the policy is being implemented and thereafter.

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Usowski, Rodgers, Lihn, and Kahn
Policy Briefs
other programs with income-based standards for participation incorporate, by statutory or regula-
tory reference, HUD’s Median Family Incomes (MFIs)
1
and Income Limits.
Fair Market Rents (FMRs) are also required to be developed annually and published in final form,
after providing an opportunity to comment, by October 1st of each year. These estimates are used
primarily for HUD’s Housing Choice Voucher Program, the Moderate Rehabilitation Single Room
Occupancy Program, and the HOME rental assistance programs.
HUD calculates Income Limits and FMRs using the Office of Management and Budget’s (OMB’s)
current geographic area definitions, with some exceptions. Currently, HUD develops these esti-
mates annually for 2,575 geographic areas—530 metropolitan areas and 2,045 nonmetropolitan
counties.
Description of the American Community Survey
Beginning with the 2010 Census, the long-form sample survey, heretofore the “gold standard” of
socioeconomic survey data, will no longer be conducted. The Census Bureau developed the Ameri-
can Community Survey (ACS) to replace the decennial census long-form survey and to provide
more timely information on the social, economic, and housing characteristics of the population in
areas smaller than census regions, census divisions, or states. Starting in 2006, the Census Bureau
implemented its plan to release ACS data annually for areas with a population of 65,000 or more.
For areas with a population ranging between 20,000 and 65,000, the Census Bureau will release
estimates based on 3 years of aggregated survey data, with the first such release in 2008. Updates
of the 3-year estimates will be published annually in subsequent years. The same approach will
be used for areas with a population of less than 20,000, but these releases will require aggregating
data for 5 years to produce estimates. The first 5-year estimates for these areas will be released as
early as 2010, and annual releases will follow. Partly because the data are more current and partly
because decennial census long-form data no longer will be collected, HUD will rely increasingly on
ACS data to produce its Income Limits and FMRs.
The ACS and the decennial census long-form survey use similar questions and similar data collection
methods (that is, both are mailed surveys with extensive nonresponse followup). Despite these
similarities, the ACS differs from the long-form survey in important respects that affect its use for HUD
purposes. Among the most important differences are those (detailed below) concerning timeliness
of data, measurement of variables, and the size and related statistical imprecision of the ACS.
• The ACS provides updated information throughout the decade because ACS data are collected
continuously. In contrast, the long-form data were collected only once each decade, and that
data became increasingly outdated as the decade progressed.
• The ACS is conducted on a continuous, rolling basis throughout the year; therefore, survey
responses do not correspond to a particular date. The long-form responses are as of the census
date, typically April 1; the lack of a fixed as-of date has implications for the as-of date assumed
1
Most financial institutions and programs refer to the Area Median Income. This term is equivalent to HUD’s Median
Family Income.

?0?Cityscape
Using American Community Survey Data in HUD’s Income Limits and Fair Market Rents
for ACS-based calculations. The as-of date HUD has assigned for ACS 2005-based rents is
June 30, 2005, the midpoint of 2005. The Census Bureau inflates income data to the annual
average Consumer Price Index (CPI) for the ACS reference year. ACS MFI estimates thus
correspond to “annual 2005” estimates, which HUD then updates to December by using the
December CPI.
• The annual ACS has slightly more than one-tenth as many completed surveys as the decennial
long-form survey, which surveyed approximately one out of every six households.
• The ACS reports data using different reporting periods for different-sized areas. For areas with
a population of less than 65,000, the Census Bureau considers annual estimates to be below
publication standards; therefore, it will release only 3-year moving average estimates for areas
with a population ranging from 20,000 to 65,000 and 5-year moving averages for areas with
a population below 20,000. The Census Bureau has addressed the relative imprecision of ACS
estimates by publishing 90-percent confidence intervals around all ACS estimates. In contrast,
long-form data releases do not include estimates of confidence intervals because normally they
are small.
• The smaller ACS annual samples mean that ACS estimates have larger estimated margins of error
(MoEs) than the comparable estimates from the long-form decennial census data. The ACS seeks
to provide estimates that are close to the true population values for the variables measured. The
likely accuracy of these estimates depends partly on sample sizes and partly on the distribution
of values for a variable. The MoE, when added to and subtracted from the survey estimate,
provides an indication of the range around a survey estimate, or the confidence interval, within
which the true population value is likely to be found. For example, the 90-percent confidence
interval for an estimate is the range around an estimate that provides a 90-percent likelihood of
the true population value. ACS 1-year survey results, even for the largest areas, are inherently
less reliable than 2000 Census results, as the following examples illustrate.
• MoEs for 90-percent confidence intervals around 2000 Census median incomes for
metropolitan areas as estimated by HUD range from 0.3 percent to 9 percent and average
1.5 percent.
• In the 2000 Census, 91 percent of metropolitan area MFI estimates have MoEs of 2.5 percent
or less.
• MoEs around 2005 ACS MFI estimates for metropolitan areas with a population of 65,000 or
more range from 0.7 percent to just under 20 percent and average 6.4 percent.
• Less than 10 percent of 2005 ACS MFI estimates have MoEs of less than 2.5 percent.
• Estimates for areas smaller than census tracts (for example, block groups) will not be released in
the official ACS tables. In contrast, long-form estimates provide block group data.
• The nature of several ACS-collected data items is altered. For example, the time period
considered for the concept of income is changed significantly. The decennial census, taken in
April, asked about income in the past calendar year, meaning that the 2000 Census actually
provided annual 1999 income data. The ACS, for which data are collected throughout the year,

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Usowski, Rodgers, Lihn, and Kahn
Policy Briefs
asks for income from the preceding 12 months, meaning that the 2005 ACS collects income
information spanning 2 years, from January 2004 through December 2005. Incomes reported
in ACS surveys are adjusted for inflation by the Census Bureau, using the CPI, to make them
equivalent to annual incomes for the survey year.
2
• Another change that affects both Income Limits and FMRs is the definition of residency. The
ACS defines residency as “current residence.” Use of this definition means a housing unit is a
survey household’s current residence if (1) the household that is currently living or staying in the
unit is expected to stay more than 2 months, (2) the household in the unit is staying for less than
2 months but has no other place to live or stay, or (3) the household usually lives at the sample
address but is away for a short period of time. In contrast, the long-form survey used a “usual
residence” definition (that is, the place where a household lives and sleeps most of the year).
This difference has a potentially significant effect on measured incomes and rents in areas where
households typically reside in vacation or second homes for more than 2 months.
Estimating Income Limits Using ACS Data
MFI estimates serve as the basis for Income Limit calculations. The following definitions apply to
HUD’s Income Limit groups:
• Very low-income families—families whose incomes do not exceed 50 percent of the area MFI.
This is the principal income limit definition, subject to adjustment as described in the following
text, from which others are generally derived.
• Low-income families—families whose incomes do not exceed 80 percent of the area MFI.
• Extremely low-income families—families whose incomes do not exceed 30 percent of the area MFI.
Exceptions to these arithmetic relationships between MFIs and Income Limits occur when family
incomes or housing-cost-to-income relationships are unusually high or low. HUD updates the MFI
by using ACS income data and then calculates Income Limits based on the MFI.
HUD calculates MFIs by FMR area, using the Census Bureau definition of “family”
3
as the first step
in the process of establishing Income Limits. The 2000 Census provides base income estimates for
1999.
The 2005 ACS data became available in late 2006 and were incorporated into HUD’s fiscal year
(FY) 2007 MFI estimates and Income Limits, released on March 20, 2007. HUD sought to make as
much use of the 2005 ACS data as was statistically justified. The base income was still set at 1999
from the 2000 Census and the 2005 ACS data were used to update the 1999 income to 2005.
MFI estimates have significantly larger MoEs than decennial census estimates of MFIs and often
2
Income, Earnings, and Poverty Data From the 2005 American Community Survey, page 2, accessed at census.gov/prod/2006pubs/
acs-02.pdf.
3
The Census Bureau definition of family is a householder with one or more other people living in the same household who
are related to the householder by birth, marriage, or adoption. The definition of family excludes one-person households and
multiperson households of unrelated individuals.

?0?Cityscape
Using American Community Survey Data in HUD’s Income Limits and Fair Market Rents
produce lower estimates compared with forward-trended 2000 census data; therefore, HUD has
implemented ACS results with some caution.
The following major steps outline the calculation of FY 2007 MFIs and Income Limits:
• Aggregate 2000 Census income distributions by FMR area and estimate 1999 MFIs based on
these data.
4

• For update factors, take 1999 MFI estimates to December 2005:
• For areas with a population of 65,000 or more, a weighted average of (a) the change in local area
MFI from 1999 (2000 Census) to 2005 (local 2005 ACS) and (b) the change in state MFI
from 1999 (state 2000 Census estimates) to 2005 (state 2005 ACS estimates) is calculated.
The weight assigned to the change in state MFI (b) is five times the local “margin of error
ratio” (MoER), or one, whichever is smaller. The MoER is defined as the margin of error for
the 90-percent confidence interval of the 2005 ACS local estimate divided by the 2005 ACS
estimate of local MFI. The weight assigned to the change in local median family income from
the ACS (a) is the larger of 1 minus 5 times the MoER or zero.
5
• For areas with a population of less than 65,000, Income Limits are developed in several steps.
First, census and ACS survey data are used to develop national- and state-level estimates of
change in MFIs. Then, the Bureau of Labor Statistics’ (BLS’s) local area wage data are used to
develop an indicator of relative income change within states; but they are adjusted so that
when summed to the state level they produce the same change as the ACS. Based on research
on the relationship between state median income growth, local average wage growth, and
local median income growth between the 1990 and 2000 Censuses, local 2000 Census-based
MFI estimates are updated by HUD using a combination of ACS state median income and
local BLS average wage data until more localized ACS data begin to be available.
• Because of delays in the availability of BLS and ACS data, estimates are trended to produce a
current estimate. All estimates are trended from December 2005 to April 2007 (1.25 years) with
a trending factor of 3.5 percent per year, which is based on the average change in MFIs between
the past two decennial censuses.
• For the outlying territories,
6
which currently lack BLS or ACS coverage, the 1999 income data
from the 2000 Census are updated to 2005 using the national ACS income change.
4
Underlying 2000 Census income distribution tables have not changed from fiscal year 2006. They are posted at www.
huduser.org.
5
Because the largest margin of error rate (MoER) in the fiscal year (FY) 2005 American Community Survey (ACS) local data
is approximately 0.2, the factor of 5 ensures that the local ACS estimates with the largest MoERs exert almost no influence
on the FY 2007 Median Family Income (MFI) estimates. In cases in which HUD’s special tabulations of MFIs have MoERs
larger than in Census-published areas, HUD effectively excludes their use by capping the value of 5 times MoER at 1.
6
The areas without ACS coverage are American Samoa, Guam, the Northern Mariana Islands, and the U.S. Virgin Islands.
Puerto Rico is covered by the ACS-equivalent Puerto Rico Community Survey.

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Usowski, Rodgers, Lihn, and Kahn
Policy Briefs
Estimating Fair Market Rents Using ACS Data
Fair Market Rents are based on a gross-rent concept. Gross rent includes the costs of all major
utilities, whether they are included in contract rent or paid directly by the family. All utility costs
are included except telephone, cable or satellite television, and Internet services. HUD seeks to set
FMRs at levels that will ensure availability of a sufficient supply of rental housing to program par-
ticipants. To accomplish this objective, HUD must set FMRs both high enough to permit a selection
of units and neighborhoods and low enough to serve as many low-income families as possible.
The level at which HUD sets FMRs is expressed as a percentile point within the rent distribution of
standard-quality, recent-mover, rental housing units.
7
HUD currently uses the 40th percentile rent,
the dollar amount below which 40 percent of standard-quality, recent-mover, rental-housing units
are rented.
8
In its computation, HUD is required to exclude nonmarket rental housing; therefore,
HUD excludes all units that fall below a specified rent level derived from HUD public housing rent
data as likely to be either assisted housing or some other form of nonmarket rent.
HUD incorporated the 2005 ACS data into the FMR calculations for the FY 2008 proposed FMRs,
published July 12, 2007. Some of the same concepts that HUD developed during the production
of Income Limits were also used for the publication of FMRs. HUD also took into account the
significantly smaller sample available for generating FMRs. Because FMR estimates are based on
only rents for two-bedroom, standard-quality, recent-mover, market-rate rental units, sample sizes
are often small. To explicitly consider this factor in the calculations, HUD uses information from
both the survey MoE and the sample size to determine when and to what extent local ACS data
should be used in FMR rent calculations. The Census Bureau requires 3 unweighted sample cases
or 50 weighted sample cases for publication of any cell in HUD special tabulations; however, HUD
believes these requirements are too liberal. For example, a single area could have five sample cases
with very similar rent values. The MoE for this survey result would be very small, but HUD would
still reject these data as possibly nonrepresentative.
HUD used data from the 2005 ACS survey largely to replace the accumulated 2001-through-2005
FMR update factors from various sources HUD used to estimate FY 2007 FMRs. HUD continues
to use random digit dialing (RDD) telephone surveys performed between 2001 and 2005 in FMR
calculations in limited circumstances.
9
When both the FMR standard-quality, recent-mover, market-
rate rental units, sample size is 200 or more and the MoE is small, HUD has rebenchmarked FMR
areas using the annual ACS rent estimates; these FMR areas have been rebenchmarked to 2005
7
Standard-quality rental housing units have the following attributes as derived from possible responses on the American
Community Survey (ACS) questionnaire: “Occupied rental units paying cash rent,” “Specified renter on 10 acres or less,”
“With full plumbing,” “With full kitchen,” “Unit built before 2005,” and “Meals not included in rent.” For the 2005 ACS,
recent movers have moved into the unit in 2004 or 2005.
8
Most Fair Market Rent (FMR) areas are based on a 40th-percentile rent. Certain areas, however, are assigned 50th-percentile
FMRs, which were established by a rule published on October 2, 2000, that also established the eligibility criteria used to
select areas that would be assigned 50th-percentile rather than the normal 40th-percentile FMRs. (See 24 CFR 888.113.)
9
Random digit dialing telephone surveys rely on computer-assisted technology to randomly select the phone number that
will be called. These surveys collect recent mover rents by metropolitan area.

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Using American Community Survey Data in HUD’s Income Limits and Fair Market Rents
for the FY 2008 proposed FMRs. For the FY 2009 FMRs, HUD may rebenchmark these and some
additional FMR areas to 2006 using ACS data.
To produce FY 2008 proposed FMRs, HUD first calculates 2005-equivalent rents for all FMR areas
using update factors, then evaluates valid local area ACS results against these 2005-equivalent
rents, and then updates and trends these rents to April 2008, the midpoint of FY 2008, using
standard FMR update procedures. HUD calculates update factors using decennial census data and
2005 ACS data. All update factors reflect the change in standard-quality, two-bedroom median
rents between the 2000 decennial census and the 2005 ACS at the smallest level of geography for
which at least 200 survey cases are available in the 2005 ACS data. HUD uses four different levels
of aggregation to measure rent changes from 2000 to 2005. These levels of aggregation can be
separated into two geographic categories: the first category is state based, and the second category
is metropolitan-area based.
Of the two varieties of state-based updated factors, the first, and most basic, factor measures the
change in median rents using all observations available for a given state. The second state-based
update factor is calculated from a subset of state observations. HUD derives the subset by removing
the observations in metropolitan areas with valid ACS surveys (that is, ACS surveys with 200
or more standard-quality, two-bedroom observations). HUD uses this update factor to measure
the change in median rents without the effect of rent changes in the portion of the state already
covered by ACS metropolitan surveys.
HUD also generates update factors for two types of metropolitan area definitions, Core Based
Statistical Areas (CBSAs) and FMR areas. CBSAs are unmodified OMB-defined metropolitan areas.
FMR areas are either OMB-defined metropolitan areas or HUD-defined subareas of CBSAs, as
defined in the proposed FY 2006 and proposed FY 2007 FMR preambles.
To generate the 2005 FMR base calculation (which is not the same as the published FY 2005
two-bedroom FMR), HUD uses the update factor, which varies by the level of geography used.
With one exception, the update factor HUD uses is the update factor for the smallest geographic
area that also contains 200 or more survey observations. Subareas for FMR areas without valid
local surveys receive either the CBSA- or state-level update factor, based on which factor moves its
estimate closer to the CBSA rent value. The actual decision process, which is somewhat involved, is
described in detail in the online FMR documentation systems referenced at the end of this article.
After HUD generates the 2005 FMR base calculation using the decennial census-based rent and
the relevant ACS-based update factor, it evaluates local area recent-mover ACS results. ACS recent-
mover rent estimates are used only to provide a new 2005 FMR base calculation when the FMR
area has more than 200 recent-mover cases and when the rent result from these recent-mover cases
is statistically different from the 2005 FMR base calculation.
HUD used local area and regional CPI inflation factors to take the rent estimates from June 2005 to
December 2006 and used the standard HUD annual trending of 3 percent for 1.25 years to project
the FMR estimate from the end of 2006 to April 2008.

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Usowski, Rodgers, Lihn, and Kahn
Policy Briefs
Benefits and Challenges of Incorporating 2005 ACS Data in
Income Limit Calculations
HUD has no choice but to use the ACS data. It is all that will be available from now on, and it
represents the most current data available. HUD is using the 1-year data with caution because of
the greater MoEs, but the state ACS data derived from the fully implemented 2005 ACS have a
much greater degree of reliability than previous (2000-to-2004) test-ACS state MFI estimates. In
the FY 2007 HUD MFI estimates, HUD is using direct comparisons between the state estimates
from the 2000 Census and the 2005 ACS to calculate state-level changes, rather than using a
combination of CPS-to-CPS (the Census Bureau’s Current Population Survey) and ACS-to-ACS
changes and applying them to 2000 Census estimates.
Eventually, when the 5-year aggregated data become available, HUD will be able to eliminate the
use of BLS data. The BLS data are measuring wages, which, although an important component, is
not the same as measuring family income, so the elimination of this updating measure will improve
the income estimates.
The new procedure has the effect of producing a number of downward adjustments to state
median family income estimates due to inherent differences between forward-trended 2000 Census
estimates and the ACS estimates. One source of this difference may be that the flow nature of the
income reference period for ACS respondents results in the failure to capture real income growth
experienced by sampled families after they were surveyed. That is, if all families surveyed by the
2005 ACS were asked in December 2005 about their income during the previous 12 months rather
than throughout the course of the year, the resulting MFI estimate would be higher to the extent
families had experienced real income growth during 2005. HUD anticipates that as local ACS MFI
estimates become available for smaller areas, they will also reflect the negative differential between
2000 Census and ACS MFI estimates, which is why HUD implemented this change in estimation
methodology with the first use of the full ACS. HUD mitigates declines in its MFI estimates by
using a “hold harmless policy” for Income Limits.
In implementing 2005 ACS data, HUD faced two primary challenges. First, only estimates for areas
with a population of 65,000 or more are available. Second, even when estimates of local median
income are available, the smaller sample sizes of the ACS relative to the decennial census mean that
ACS survey estimates are not as reliable. Decennial census estimates were also subject to sampling
error, but the ACS develops estimates from annual samples using fewer surveys, which are more
likely to vary due to sampling error.
HUD’s objective was to minimize the possibility of publishing income estimates in which the
annual change is more a reflection of the variation in estimation errors than a reflection of changes
in underlying economic conditions. To meet this objective, HUD developed a formula for incor-
porating 2005 ACS local median income estimates into its FY 2007 MFI estimates that explicitly
considers the MoEs in the local ACS results. The formula HUD developed gives lower weight to the
potentially less accurate ACS estimates with large MoEs, thus limiting the influence of local ACS
estimates in these areas on the HUD MFI estimates. Conversely, the formula gives heavier weight
to ACS local median income estimates with small MoEs, enabling the ACS estimate to be the
dominant component of HUD estimates in these areas.

?0?Cityscape
Using American Community Survey Data in HUD’s Income Limits and Fair Market Rents
Benefits and Challenges of Incorporating 2005 ACS Data in Fair
Market Rent Calculations
The decennial census served as the benchmark for FMRs. To eliminate trending of up to 14 years,
HUD developed a survey methodology—RDD telephone surveys—that would rebenchmark areas
between census periods. Based on telephone interviewing, the RDD survey has become much more
costly and difficult to conduct over time, and, because the ACS provides better sample data, the
use of the ACS data provides a benefit to HUD in reducing the need for RDD surveys in most large
metropolitan areas.
A challenge in using ACS data in the production of FMRs is inherent in the definition of FMR.
HUD calculates FMRs for standard-quality, two-bedroom, recent-mover, market-rate, rental units.
This definition of FMR means that HUD must eliminate large portions of the survey sample to
provide a rental unit distribution that can generate FMRs using the following process:
• Remove approximately 65 percent of housing units because they are owned, not rented.
• From the 35 percent remaining renter housing sample, remove 60 percent of rental units
because they are not two-bedroom units.
• Eliminate an additional 5 percent of the sample because those units do not meet standard-
quality and market-rate housing requirements.
In total, HUD can use only about 10 percent of the ACS sample in the calculation of FMRs.
Although the use of 3-year and 5-year data will improve sample sizes for the calculation of FMRs,
the term “recent mover” will have no meaningful definition. Traditionally, FMRs have been based
on recent-mover rents, a consideration that rents for new tenants often are higher than those for
long-term residents. In the long-form decennial census data, the term “recent mover” generally was 

defined as a renter who moved into a unit within the past 15 months; however, this type of renter
cannot be captured consistently in ACS data. For example, in an area where estimates are based on
5-year data (2005 through 2009 and released in 2010), a tenant who moved in during February
2004 and was surveyed in January 2005 would count as a recent mover, but a tenant who moved
in during November 2007 and would be surveyed in December 2009 would not be classified as a
recent mover.
The small ACS sample size means that few large areas have enough recent-mover rent responses to
be considered probable as fully representative. HUD’s ability to obtain and use recent-mover rents
from the ACS as the basis of its FMRs will most likely require the development of recent-mover
bonuses based on larger area data.
Conclusion
HUD cannot ignore the income and rent data available from the ACS, and it must continue to
find ways to incorporate these data for smaller areas. HUD’s measured approach to incorporating
1-year data reflects the stability of the fully implemented ACS. Although changes for FY 2007
Income Limits and FY 2008 FMRs reflect some significant changes resulting from differences in the

?0?
Usowski, Rodgers, Lihn, and Kahn
Policy Briefs
2000 Census and 2005 ACS data, significant differences are not expected to continue in the next
year’s publication. In its examination of 2006 ACS income data for the FY 2008 publication, HUD
sees no significant fluctuations. HUD also expects FMRs to be fairly stable.
HUD will require additional research to provide answers on how best to proceed with the 3-year
and 5-year data. For example, will HUD effectively rebenchmark Income Limits and FMRs an-
nually after these data become available? For Income Limits, even with the hold harmless policy,
does effective annual rebenchmarking of MFIs mean there will be greater annual fluctuations?
For FMRs, would problems continue in measuring small areas with tight markets? Annual ACS
data would enable HUD to adjust bedroom intervals (the difference in gross rents by number of
bedrooms) for FMRs; however, would this annual adjustment cause too much variation in FMRs?
Last, as previously mentioned, the big issue for FMRs is how to continue to use the concept of
recent mover.
Researchers wishing to use MFIs, Income Limits, and/or FMRs should be fully cognizant of the
origins of these series and the fact that computational methodology changes over time. Although
HUD strives to maintain statistical rigor and accuracy in these series, they were designed to be
program operating parameters and not a purely statistical release like Census data. Researchers
are advised to limit their use of MFIs, Income Limits, and FMRs to studies of the HUD programs
for which they were designed unless, after careful consideration of the estimation methodology,
investigators are satisfied that the series can contribute to their research objectives.
Authors
Kurt Usowski is Acting Deputy Assistant Secretary for Economic Affairs in the Office of Policy
Development and Research at the U.S. Department of Housing and Urban Development.
Lynn Rodgers is a senior economist in the Economic Market Analysis Division at the U.S.
Department of Housing and Urban Development.
Marie Lihn is a senior economist in the Economic Market Analysis Division at the U.S. Department
of Housing and Urban Development.
Peter Kahn is an economist in the Economic Market Analysis Division at the U.S. Department of
Housing and Urban Development.
Additional Reading
Researchers may further investigate Income Limits and Fair Market Rents (FMRs) by accessing
useful information at the following websites:
• Interactive documentation system that explains the calculation of both 2007 Median Family
Income (MFI) estimates and 2007 Income Limits: http://www.huduser.org/datasets/il/il2007_
docsys.html. The direct link to the MFI-only system: http://www.huduser.org/datasets/il/index_
mfi.html.


?0?Cityscape
Using American Community Survey Data in HUD’s Income Limits and Fair Market Rents
• Interactive documentation system that explains the calculation of FMRs from 2005 through
2008: http://www.huduser.org/datasets/fmr.html.
• Information about HUD’s use of the American Community Survey in its calculation of FMRs for
fiscal year (FY) 2008 is accessible directly through the FY 2008 documentation system: http://
www.huduser.org/datasets/fmr/fmrs/index.asp?data=fmr08.
• Extensive discussion of the development of current HUD FMR areas: http://www.huduser.org/
datasets/fmr/fmr2006P/Preamble_FY06_FMRP.pdf. Discussion of further modifications: http://
www.huduser.org/datasets/fmr/fmr2007P/FY2007P_Preamble.pdf.

??0 Policy Briefs

???Cityscape: A Journal of Policy Development and Research • Volume 10, Number 1 • 2008
U.S. Department of Housing and Urban Development • Office of Policy Development and Research
Cityscape
HUD-Assisted Housing 101:
Using “A Picture of Subsidized
Households: 2000”
Lydia B. Taghavi
U.S. Department of Housing and Urban Development
Abstract
“A Picture of Subsidized Households” is a series of reports showing aggregated data for
various U.S. Department of Housing and Urban Development programs and at different
geographic levels. This article demonstrates one of the uses of these data for research,
including data limitations.
Data Shop
Data Shop, a department of Cityscape, presents short papers or notes on the uses of data
in housing and urban research. Through this department, PD&R introduces readers
to new and overlooked data sources and to improved techniques in using well-known
data. The emphasis is on sources and methods that analysts can use in their own
work. Researchers often run into knotty data problems involving data interpretation
or manipulation that must be solved before a project can proceed, but they seldom get
to focus in detail on the solutions to such problems. If you have an idea for an applied,
data-centric note of no more than 3,000 words, please send a one-paragraph abstract to
David.A.Vandenbroucke@hud.gov for consideration.
Introduction
One objective of the Office of Policy Development and Research (PD&R) at the U.S. Department of
Housing and Urban Development (HUD) is to provide access to HUD program data to researchers
and policy analysts. This provision is accomplished in a way that supports research and, hence,
policy formulation, without revealing information about individual people. As part of this effort,
PD&R has compiled a series of comprehensive information on subsidized households from HUD’s
major data collection systems. In the 1990s, this compilation resulted in printed publications

???
Taghavi
Data Shop
called “A Picture of Subsidized Households” (Picture). Previous publications include Picture as of
the 1970s, 1996, 1997, and 1998. Paul Burke (a former staff member in PD&R) conceptualized
and created these data summaries. The current Picture 2000 uses the same concepts and general
layout for presentation as the original summaries. A few items have been revised or deleted and
some new items were added. Although all the reports are available for downloading from the web,
“A Picture of Subsidized Households: 2000” (Picture 2000) is the first one with a web-based query
and download tool.
1
The updates and improvements to this data system are the responsibility of
the Program Monitoring and Research Division within PD&R.
In this article, we first describe the scope of the data and then provide illustrative examples of how
researchers might use the database and discuss some limitations of its use.
Scope of the Data
The goal of Picture is to provide basic information for a researcher (or anyone interested in housing
policy) to be able to sketch the characteristics of participants in the HUD-subsidized housing
program, know some information about the public housing agencies (PHAs) or contracts/projects,
and gain knowledge about the neighborhoods where the participants lives.
The report (and accompanying database) includes household data, aggregated by program at
various geographic levels. The programs are Public Housing, Housing Choice Voucher Program
(HCVP, formerly called Section 8 Certificates or Vouchers), Moderate Rehabilitation (Mod Rehab),
Project-based Section 8—New Construction and Substantial Rehabilitation, Section 236, Below
Market Interest Rate, Section 202 Supportive Housing for the Elderly, and Section 811 Supportive
Housing for Persons with Disabilities. The geographic levels are national, metropolitan statistical
area (MSA), state, city, census tract, and PHA.
2
Summaries at the MSA and city levels are new in
2000 and will be available in future issues of Picture.

The data included in Picture are primarily characteristics of the participants in HUD’s rental
subsidy program. These characteristics include social and economic categories such age, race, rent,
and income; household type, such as elderly, disabled, and families with children; and certain
characteristics of the housing unit and, where appropriate, neighborhood. In general, the summary
characteristics are provided as a percent and averages with the denominator as the number of re-
ported households. In addition to providing the tenant data, the report also includes some limited
data related to projects or PHAs, such as total units, percent occupied, number reported, percent
reported, and spending. Project-level summaries are available for Multifamily Assisted Housing
Programs and Public Housing. The geographic summaries are provided at the national, state,
county, and census tract levels. In addition to including geographic location, the report provides
some neighborhood characteristics, such as poverty, minority, and single-family home ownership.
1
See www.huduser.org/picture2000/index.html for a more complete description of the data elements in Picture 2000.
2
Individual households in the Housing Choice Voucher Program and Moderate Rehabilitation program provide addresses
as part of their report to HUD. Addresses for Public Housing and Multifamily Assisted Housing Programs were derived from
project addresses. These addresses are geocoded to determine the metropolitan statistical area, city, county, and census tract
location of the household.

???
HUD-Assisted Housing 101: Using “A Picture of Subsidized Households: 2000”
Cityscape
These neighborhood characteristics are obtained directly from Census 2000 (SF3) at the census tract
level. A weighted version of these characteristics is calculated for other geography levels.
3
Data in
Picture 2000 represent households reported in the 18-month period ending December 31, 2000.
Uses and Limitations of the Data
Picture provides easy access to information about the size of HUD’s assisted programs and about
the participants. Although Picture does not make all information about tenants available, it
provides key factors for policy analysis, according to what PD&R believes to be key factors. The
underlying data come from HUD’s administrative data systems, which are designed to capture
information about all assisted tenants. In HUD’s database system, each household that receives a
subsidy is required to report at least annually. In fact, only about 92 percent of active households
had information reported as of December 2000.
Beginning with Picture 2000, users can obtain the data through a web-based query tool. This
access enables researchers to select the programs, demographic characteristics, and summary levels
of interest. The results of the query can be viewed as a web-based report or viewed and saved as
a comma-delimited file available for downloading and further statistical analysis.
4
In addition, the
entire database (at several summary levels) and the data documentation can be downloaded from
the website.
The following examples illustrate the kinds of results available from Picture 2000. This article
focuses on two tenant characteristics—age (elderly or not) and income—and one neighborhood
characteristic—poverty rate. These examples are not meant to analyze fully the data in Picture;
they merely illustrate the kinds of questions that Picture data easily answer. We hope these
examples stimulate interest in the dataset as a tool for policy analysis.
At the national level, as of December 2000, of the 4.88 million HUD-subsidized units available,
87 percent are occupied. Exhibit 1 shows some of the characteristics to be found in the data. For
example, one tenant characteristic is being classified as elderly (62 years or older). Nationwide,
31 percent of HUD-assisted housing programs consist of elderly households; however, more
than 80 percent of Mod Rehab and HCVP consist of nonelderly households. Elderly households
represent 59 percent of Section 8 New Construction/Substantial Rehabilitation and 36 percent
of the category All other multifamily assisted. The category New Construction/Substantial
Rehabilitation includes the program assisted through Section 202/8, and the All other multifamily
assisted category includes households assisted through the program Section 202/PRAC.
3
The weights are the number of occupied units for Multifamily Assisted Housing and Public Housing and for the Housing
Choice Voucher Program (HCVP) at the public housing agency and state levels. The weight for the HCVP at the city and
metropolitan statistical area levels is the number of reported households.
4
Instructions for downloading and using the comma-delimited file are provided as a link on the Picture 2000 web page:
www.huduser.org/picture2000/index.html.

???
Taghavi
Data Shop
Exhibit 1
Pr
ogram
Number of Subsidized
Units
A
vailable*
Per
cent
Occupied
Number
Reported
Per
cent
Reported
Per
cent
Elderly
(62 or Older)
Spending
per
Month**
($)
Income Categories (per
cent)
Per
cent
Extr
emely
Low Income
Less Than
$5,000
$5,000
to
$9,999
$10,000
to
$14,999
$15,000
to
$19,999
$20,000
or
Mor
e
A Pictur
e of Subsidized Households: 2000, National Summary Data
U.S. total, all
HUD pr
ograms
4,881,081
87
3,903,506
92
31
421
17
45
21
10
7
70
Public Housing
1,282,099
89
1,056,174
93
32
426
21
45
18
8
8
69
Housing Choice
V
oucher
Pr
ogram
1,817,360
82
1,497,040
100
17
427
16
43
21
11
9
73
Moderate
Rehabilitation
111,392
65
57,367
79
16
449
32
44
13
6
4
82
New
Construction/ Substantial Rehabilitation
877,830
84
724,129
98
59
497
11
52
25
9
4
69
Section 236
440,329
96
276,799
66
35
263
16
39
23
13
9
63
All other
multifamily assisted
352,337
98
291,997
85
36
324
20
43
20
9
8
70
* Number of units under contract for federal subsidy and available for occupancy
.
** A
verage spending per unit per month.

???
HUD-Assisted Housing 101: Using “A Picture of Subsidized Households: 2000”
Cityscape
Picture also enables researchers to easily determine other important tenant characteristics. For
example, exhibit 1 shows that 70 percent of the households reported in all HUD-subsidized
programs are extremely low-income households. At least 55 percent of these households have an
annual household income of less than $10,000.
The smallest geography that Picture provides is the census tract summary. At this geography level,
the number of units available and occupied are coded as “not applicable.” The unit-based programs
such as Multifamily Assisted Housing and Public Housing can provide unit information at the
census tract level.
5
Before presenting some examples of analysis at the census tract level, we need to note a few data
limitations. First, if the number of tenants reported for a particular program in a single tract is less
than 11 households, all the household characteristics are suppressed (coded as “–4”). This action is
taken to protect the identities of HUD’s clients when information is released to the public.
Second, some of our data records do not provide sufficient address information to determine the
census tract with complete confidence. The geocoding process results in a small percentage of records
without a specific census tract. For those records, we provide state and county but no census tract
information. At the census tract summary level, the Picture 2000 database has more than 69,000
records while 66,304 census tracts are in the nation. The extra records represent those summaries
of households with no identifiable census tract. These records are included to maintain the overall
total for each program.
The census tract summaries lend themselves to illuminating analyses. The following examples
illustrate working with these census tract summaries. These types of analyses were done by Devine
et al. (2002).
Most important, the census tract data in Picture enables analysts to map the location of assisted
housing in their community.
6
Maps often present a clearer message about the programs than can be
derived from tabular data. One such example is shown in exhibit 2. The map, developed by Seth
Marcus in the Program Monitoring and Research Division, shows the location of various forms of
assisted housing in Baltimore, Maryland. In this case, the census tract summaries of HCVP tenants
are shown in relation to the locations for public housing and project-based assistance projects. The
presence of several clusters of housing assistance is readily apparent in the map.
In addition to being mappable, census tract summary data can also provide useful tabular analyses.
Using the census tract summaries for all HUD programs combined, it is possible to analyze,
for example, the distribution of assisted households by location and by neighborhood poverty
concentration. Exhibit 3 shows that about one-third of HCVP households in metropolitan areas
reside in neighborhoods with 10- to 19-percent poverty, while 48 percent of HCVP households live
5
The Housing Choice Voucher Program (HCVP) and Moderate Rehabilitation (Mod Rehab) do not lend themselves to
calculating units available and occupied at the census tract level. For HCVP and Mod Rehab, the lowest level of geography
for these variables is the public housing agency.
6
The Picture 2000 website, www.huduser.org/picture2000/index.html, contains additional details about downloading and
using the geographic information in Picture 2000.

???
Taghavi
Data Shop
HUD-Funded projects
Public Housing
Chesapeake
Bay
Under 20%
20% to 30%
30% to 40%
40% or higher
0 1
Miles
S. Marcus, 2007
2
S. 236
S8 NC/SR
Tracts with
250+ Vouchers
2000 Census Tract Poverty Rate
in similar poverty neighborhoods in nonmetropolitan areas. In the metropolitan suburban areas,
about 40 percent live in neighborhoods with less than 10-percent poverty. In central cities, higher
proportions of households live in areas of high poverty compared with households in the suburbs
and nonmetropolitan areas.
Exhibit 4 presents the distribution of households headed by elderly tenants in the HCVP. The num-
ber of households headed by elderly tenants was calculated based on the percent and number of
reported households. Those census tracts with fewer than 11 reported households were considered
zero elderly households. About 2 percent of households headed by elderly tenants were lost using
this process. For the HCVP, the proportion of households headed by elderly tenants and living in
Exhibit 2
Poverty Rate and Location of Subsidized Housing in Baltimore, Maryland

???
HUD-Assisted Housing 101: Using “A Picture of Subsidized Households: 2000”
Cityscape
Exhibit 3
Poverty
Rate Total
Metropolitan
Non-
metropolitan
Total
Metropolitan
Central City Suburb
Number % Number % Number % Number % Number %
Distribution of HCVP Households by Location and Neighborhood Poverty Rate,
Picture 2000
All tracts 1,397,717 100 1,189,650 100 725,722 100 463,967 100 208,028 100
0-9% 301,449 21.6 264,885 22.3 80,380 11.1 184,534 39.8 36,535 17.6
10-19% 489,217 35 389,309 32.7 212,901 29.3 176,418 38 99,898 48
20-29% 325,749 23.3 278,399 23.4 209,912 28.9 68,487 14.8 47,350 22.8
30-39% 177,809 12.7 160,353 13.5 135,315 18.6 25,038 5.4 17,456 8.4
40% or more 103,493 7.4 96,704 8.1 87,214 12 9,490 2 6,789 3.3
HCVP = Housing Choice Voucher Program.
Exhibit 4
Poverty
Rate
Total Elderly Central City Suburb Nonmetropolitan
Number % Number % Number % Number %
Distribution of HCVP Households Headed by Elderly Tenants by Location and
Neighborhood Poverty Rate, Picture 2000
All tracts 216,859 100 108,291 100 77,803 100 30,765 100
0-9% 50,042 23.1 12,937 11.9 31,649 40.7 5,456 17.7
10-19% 81,438 37.6 34,313 31.7 31,078 39.9 16,047 52.2
20-29% 47,917 22.1 30,440 28.1 11,159 14.3 6,319 20.5
30-39% 23,903 11 18,753 17.3 2,884 3.7 2,266 7.4
40% or more 13,558 6.3 11,847 10.9 1,033 1.3 678 2.2
HCVP = Housing Choice Voucher Program.
high-poverty areas of central cities is at least double the proportion of those who live in nonmetro-
politan areas and suburbs of similar poverty concentration. Only 17 percent of HCVP households
in the Section 8 program are headed by elderly tenants.
Exhibit 5 provides the neighborhood poverty rate distribution for nonelderly HCVP tenants. The
results are quite similar, with a slightly greater percentage of households headed by nonelderly
tenants living in the highest poverty census tracts.
The data for HCVP reported households provided at the census tract level can be aggregated to
MSA, city, state, and national levels but not to the PHA level. Picture does not provide the PHA
code with the HCVP census tract summary because a few tracts with tenants are from more than
one PHA. For Public Housing, project-level summaries are provided, enabling researchers to
aggregate information to the PHA level.
A similar analysis can be done for all HUD programs combined because this summary is available
at the census tract level. Exhibits 6, 7, and 8 present the same analysis as in the previous tables

???
Taghavi
Data Shop
Exhibit 6
Poverty
Rate Total
Metropolitan
Non-
metropolitan
Total
Metropolitan
Central City Suburb
Number % Number % Number % Number % Number %
Distribution of All HUD-Assisted Households by Location and Neighborhood Poverty
Rate, Picture 2000
All tracts 3,693,445 100 3,081,981 100 2,037,708 100 1,044,316 100 611,464 100
0-9% 691,259 18.7 595,010 19.3 188,357 9.2 406,686 38.9 96,249 15.7
10-19% 1,128,900 30.6 848,092 27.5 467,583 22.9 380,519 36.4 280,808 45.9
20-29% 812,047 22 664,593 21.6 512,076 25.1 152,517 14.6 147,454 24.1
30-39% 528,378 14.3 471,493 15.3 405,297 19.9 66,196 6.3 56,885 9.3
40% or more 532,861 14.4 502,793 16.3 464,395 22.8 38,398 3.7 30,068 4.9
Exhibit 7
Poverty
Rate
Total Central City Suburb Nonmetropolitan
Number % Number % Number % Number %
Distribution of All HUD-Assisted Households Headed by Elderly Tenants by Location
and Neighborhood Poverty Rate, Picture 2000
All tracts 1,154,164 100 598,611 100 363,653 100 191,900 100
0-9% 275,018 23.8 66,694 11.1 169,564 46.6 38,760 20.2
10-19% 369,713 32 148,773 24.9 127,875 35.2 93,065 48.5
20-29% 236,770 20.5 153,619 25.7 43,465 12 39,686 20.7
30-39% 141,655 12.3 113,155 18.9 14,706 4 13,794 7.2
40% or more 131,008 11.4 116,371 19.4 8,043 2.2 6,594 3.4
Exhibit 5
Poverty
Rate
Total Nonelderly Central City Suburb Nonmetropolitan
Number % Number % Number % Number %
Distribution of HCVP Households Headed by Nonelderly Tenants by Location and
Neighborhood Poverty Rate, Picture 2000
All tracts 1,184,929 100 618,598 100 388,128 100 178,204 100
0-9% 253,759 21.4 67,975 11 154,362 39.8 31,422 17.6
10-19% 408,925 34.5 178,907 28.9 145,720 37.5 84,299 47.3
20-29% 278,145 23.5 179,606 29 57,396 14.8 41,142 23.1
30-39% 154,032 13 116,644 18.9 22,175 5.7 15,214 8.5
40% or more 90,068 7.6 75,466 12.2 8,475 2.2 6,127 3.4
HCVP = Housing Choice Voucher Program.

???
HUD-Assisted Housing 101: Using “A Picture of Subsidized Households: 2000”
Cityscape
but for tenants in all HUD-assisted programs. These exhibits show that the overall poverty rates
tend to be slightly higher for all programs combined than for the HCVP alone. This article is not
intended to explain why this would be the case but simply to illustrate how a researcher might use
Picture to gather basic information about the tenants. Further analysis of which particular program
participants are more likely to live in high-poverty tracts is left to the reader.
Conclusion
Picture 2000 provides researchers and policy analysts with ready access to information about
the size of assisted housing programs and the characteristics of tenants at a variety of levels. This
article is intended to stimulate interest in using this tool for research and policy analysis. We have
shown just a few simple examples of how researchers might use Picture. We hope you find these
examples useful. Many additional data elements are available for download using the web-based
query tool in Picture. We hope you will explore the data set and find it valuable. A new edition of
Picture, using data for 2004, is being prepared for release in early 2008. Soon after the release of
Picture 2004, we will begin working on Picture 2006 to bring the data set reasonably up to date.
After those two editions are completed, we intend to solicit suggestions from users on how we
can improve the Picture series. In the meantime, readers may e-mail questions or comments to
helpdesk@huduser.org.
Acknowledgments
I thank my colleagues, David E. Chase and Seth Marcus, for comments on the draft and assistance
with creating the map, respectively.
Author
Lydia B. Taghavi is a social science analyst in the Program Monitoring and Research Division, Office
of Policy Development and Research, U.S. Department of Housing and Urban Development.
Exhibit 8
Poverty
Rate
Total Central City Suburb Nonmetropolitan
Number % Number % Number % Number %
Distribution of All HUD-Assisted Households Headed by Nonelderly Tenants by
Location and Neighborhood Poverty Rate, Picture 2000
All tracts 2,542,477 100 1,439,980 100 682,302 100 420,195 100
0-9% 418,250 16.5 122,117 8.5 238,404 34.9 57,730 13.7
10-19% 760,024 29.9 319,048 22.2 252,931 37.1 188,044 44.8
20-29% 575,484 22.6 358,551 24.9 109,099 16 107,833 25.7
30-39% 386,794 15.2 292,190 20.3 51,501 7.5 43,104 10.3
40% or more 401,925 15.8 348,074 24.2 30,367 4.5 23,483 5.6

??0
Taghavi
Data Shop
References
Devine, Deborah J., Robert W. Gray, Lester Rubin, and Lydia B. Taghavi. 2002. Housing Choice
Voucher Location Patterns: Implications for Participant and Neighborhood Welfare. Washington, DC:
U.S. Department of Housing and Urban Development, Office of Policy Development and Research,
Division of Program Monitoring and Research.

???Cityscape: A Journal of Policy Development and Research • Volume 10, Number 1 • 2008
U.S. Department of Housing and Urban Development • Office of Policy Development and Research
Cityscape
Ten Years of Smart Growth
Regina C. Gray
U.S. Department of Housing and Urban Development
Correction
I thank Martin Klingmeyer for bringing to my attention an error in my article entitled “Ten Years
of Smart Growth: A Nod to Policies Past and a Prospective Glimpse Into the Future” (Cityscape,
Volume 9, Number 1, 2007, page 112), in which I st