Unknown

by Unknown

A Journal of Policy
Development and Research
U.S. Department of Housing and Urban Development
Office of Policy Development and Research
Cityscape
Residential Mobility:
iMplications foR faMilies and coMMunities
VoluMe 14, nuMbeR 3
•
2012

The goal of Cityscape is to bring high-quality original research on housing and community develop-
ment issues to scholars, government officials, and practitioners. Cityscape is open to all relevant
disciplines, including architecture, consumer research, demography, economics, engineering, ethnog -
raphy, 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 (HUD). Subscriptions are available at
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PD&R welcomes submissions to the Refereed Papers section of the journal. Our referee process is
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Opinions expressed in the articles are those of the authors and do not necessarily reflect the views
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iiiCityscape
Contents
Symposium
Residential Mobility: Implications for Families and Communities
Guest Editor: Cynthia Guy
Guest Editor’s Introduction ........................................................................................................ 1
Residential Mobility and the Reproduction of Unequal Neighborhoods .................................. 9
by Patrick Sharkey
Pathways to Integration: Examining Changes in the Prevalence of Racially
Integrated Neighborhoods ........................................................................................................ 33
by Ingrid Gould Ellen, Keren Horn, and Katherine O’Regan
Residential Mobility and Neighborhood Change: Real Neighborhoods Under
the Microscope .......................................................................................................................... 55
by Claudia Coulton, Brett Theodos, and Margery A. Turner
Tracking Mobility at the Household Level ............................................................................... 91
by Kate Bachtell, Ned English, and Catherine Haggerty
Moving and Staying in Los Angeles Neighborhoods: Money Matters, but So Does
Family Composition ................................................................................................................ 115
by William A.V. Clark
Public Housing Transformation and Crime: Making the Case for Responsible Relocation .... 137
by Susan J. Popkin, Michael J. Rich, Leah Hendey, Chris Hayes, Joe Parilla, and George Galster
Addressing Residential Instability: Options for Cities and Community Initiatives ............ 161
by G. Thomas Kingsley, Audrey Jordan, and William Traynor
Mobility, Mixing, and Neighborhood Change: A British Perspective ................................... 185
by Ade Kearns
Refereed Papers
Reducing Worst Case Housing Needs With Assisted Housing ............................................. 189
by Kirk McClure
Departments
Data Shop
Using Administrative Data for Spatial and Longitudinal Analysis of the Housing
Choice Voucher Program ......................................................................................................... 195
by Eric Schultheis, Gregory Russ, and Carolina Lucey
Graphic Detail
Concentrated Out-Migration ................................................................................................... 207
by Ron Wilson

iv Residential Mobility: Implications for Families and Communities
Contents
Impact
The Impact of Limiting Sellers Concessions to Closing Costs ............................................. 211
by Alastair McFarlane
SpAM
Using Dual Kernel Density Estimation To Examine Changes in Voucher Density
Over Time ................................................................................................................................ 225
by Ron Wilson
Foreign Exchange
Comparative Analysis of Best Practices of Sustainable Communities:
Adelaide, Australia Case Study .............................................................................................. 235
by Alven H. Lam and Brianne M. Mullen

1Cityscape: A Journal of Policy Development and Research • Volume 14, Number 3 • 2012
U.S. Department of Housing and Urban Development • Office of Policy Development and Research
Cityscape
Guest Editor’s Introduction
Cynthia Guy
The Annie E. Casey Foundation
Residential mobility—residents’ movement from one housing unit to another—could be either a
positive or a negative phenomenon for families and neighborhoods. At the family level, residential
mobility can reflect positive changes in individual or household circumstances. Moving up and out
in search of better homes, better schools, and more advantageous neighborhoods has long been
a rite of passage for the American middle class. However, residential mobility can also indicate
household instability and insecurity, particularly in cases in which low-income families churn
through a series of short-term, short-distance relocations (Crowley, 2003). For those families who
lack sufficient financial resources and are disconnected from the informal support networks that
can play a crucial role in weathering emergencies, frequent moves magnify the difficulty of dealing
with day-to-day challenges such as childcare and transportation.
Similarly, at the neighborhood level, mobility has different consequences—or no consequences—
depending on the characteristics and balance of in-mover and out-mover households. In some cases,
such as the classic gentrification scenario, the replacement of low-income residents with better re-
sourced households may lead to increases in neighborhood safety, better amenities, and improve-
ments in public services (Lerman and McKernan, 2007). Conversely, an exodus of economically
advantaged households and their replacement with lower income households may precipitate the
overall neighborhood decline associated with greater concentrations of poverty (Galster, 2012;
Jargowsky, 1997; Turner and Kay, 2006). In some cases, neighborhood quality remains in a social
and economic “steady state,” despite high rates of housing unit turnover, because residents of similar
social and economic circumstances replace those who exit (Andersson and Brama, 2004).
Residential Mobility, Poverty, and Public Policy
Residential mobility becomes a critical issue for public policy when it is associated with poverty
and disadvantage. Analysis by income quintile of Current Population Survey mobility data from
1998 to 2011 demonstrates a sustained, consistent relationship between low income and high
mobility (see exhibit 1). Every step down the income scale corresponds to a rise in mobility rates.
In 2011, mobility rates stood at 17.5 percent for the lowest income quintile compared with a na -
tional rate of 11.5 percent (Theodos, 2012). Thus, although the current economic downturn has
led to an overall decline in residential mobility to the lowest levels since 1948 (Frey, 2011), mobil-
ity remains significantly greater among low-income populations. This pattern reflects an overall

2
Guy
Residential Mobility: Implications for Families and Communities
Exhibit 1
Income

quintile
Movers (Per
cent)
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Mobility Rates by Income Quintile Lowest quintile
22.2
22.4
21.8
19.2
19.8
19.2
23.3
18.5
19.5
19.2
17.7
18.6
18.7
17.5
Second quintile
19.1
17.8
18.5
17.4
17.2
16.1
19.9
16.6
15.8
15.7
14.2
15.3
15.0
13.7
Middle quintile
15.7
15.7
16.0
14.8
14.9
14.4
15.6
14.2
14.2
13.3
11.8
11.9
12.6
10.4
Fourth quintile
12.0
12.5
12.8
11.4
11.7
11.5
12.3
11.1
10.8
9.9
9.1
9.6
9.4
8.6
Highest quintile
10.7
11.3
11.6
10.1
10.8
10.1
9.9
9.0
8.6
8.5
7.2
7.7
6.8
7.2
T
otal
15.8
15.7
15.8
14.3
14.6
14.0
13.6
13.7
13.6
13.0
11.8
12.3
12.3
11.5
Notes: Calculated in adjusted 2005

dollars. Lowest quintile cutoff: $21,705.88. Second quintile cutoff: $40,223.61. Middle quintile cutoff: $62,879.34. Fourth q
uintile cutoff: $97,416.01.
Source: Current Population Sur
vey tabulations (Theodos, 2012)

Guest Editor’s Introduction
3Cityscape
decline in opportunities for voluntary relocation to better jobs and homes (Frey, 2009), partially
offset by residential churning and evictions because of job losses and landlord foreclosures among
households that lack savings and assets (Cohen and Wardrip, 2011; Cunningham and McDonald,
2012; Pettit and Comey, 2012).
Low-income residential mobility raises a host of challenges for policy responses to poverty and
disadvantage at the family and neighborhood levels. Student mobility, much of which is related to
residential churning, stymies efforts to improve educational outcomes for low-income populations.
Lack of continuity in instruction, higher absence rates, and lack of accountability for student prog-
ress are all closely connected to student transience (Cohen and Wardrip, 2011).
Residential mobility also poses a challenge to place-based initiatives and community-change efforts
designed to improve household and individual outcomes by saturating disadvantaged neighbor-
hoods with services and opportunities. Increasingly, policymakers and program operators are rec-
ognizing the difficulties of serving a target population that does not stay put long enough to benefit
fully from place-based interventions (Kubisch et al., 2010). Some practitioners directly address
this challenge by making affordable housing a key dimension of place-based interventions, but the
evidence shows that, even among recipients of significant assistance, housing turnover continues to
be an issue (Lubell, Shroder, and Steffen, 2003; Thompson, 2007).
Programs designed intentionally to channel and promote mobility as a means of improving family
well-being—recently bolstered by long-term findings on positive health effects (Ludwig et al., 2011)—
continue to produce disappointing employment and education effects for all but the small share of
families who manage to spend significant time in high-opportunity neighborhoods (Turner et al.,
2012). Moreover, these programs might have negative consequences for neighborhoods on the
receiving end if relocatees concentrate in vulnerable and declining neighborhoods.
Finally, residential mobility poses particular challenges to the evaluation of place-based initiatives.
Programs that seek to improve community economic conditions by increasing the economic suc-
cess of residents may have difficulty demonstrating positive outcomes at the neighborhood level,
because successful households move up and out. As MDRC’s Jobs Plus experiment demonstrated,
“resident move-out rates greatly influence how earnings effects for individuals can translate into
development-level effects” (Blum et al., 2005: 12).
The Symposium
The goal of this symposium is to present policy-relevant research and research-based discussions
of residential mobility and its implications for families and neighborhoods. In the effort to advance
the science around this topic, the symposium features innovative analytical methods, rich but
underused data resources, and discussions of technical challenges and advances in the study of
residential mobility.
The articles in this symposium represent creative and insightful uses of a range of data sources and
analytical methods. One article, based on administrative data (public housing authority records
and crime reports), presents the results of highly complex statistical modeling techniques. Another
article makes innovative use of the most recent decennial census in combination with the Urban

4
Guy
Residential Mobility: Implications for Families and Communities
Institute’s Neighborhood Change Database, a longitudinal file of decennial census data for 1970
through 2000 remapped to census 2000 tract boundaries. Two articles make use of exceptionally
rich and sophisticated longitudinal research surveys conducted in two major American cities: The
Project on Human Development in Chicago Neighborhoods
1
and the Los Angeles Family and
Neighborhood Survey.
2
Three articles use a program survey, the Making Connections cross-site
survey,
3
that produced multiple waves of cross-sectional data on 10 urban neighborhoods and
longitudinal data on a sample of their original residents.
Patrick Sharkey examines the role of residents’ “cognitive maps” in channeling residential mobility
into patterns that reproduce urban inequality, and he analyzes the population dynamics underly-
ing the entrenched patterns of segregation so prevalent in northeastern and midwestern cities. His
research draws on a wide range of data sources, including the Project on Human Development in
Chicago Neighborhoods, to make a significant contribution to our understanding of the demand
side of housing policy. Sharkey’s analysis raises important considerations for policymakers and
practitioners in crafting housing mobility programs that can help address segregation and inequal-
ity in American cities.
Ingrid Gould Ellen, Keren Horn, and Katherine O’Regan bring a deep and broad perspective to
the issue of residential mobility, analyzing two decades of census data with a focus on shifts in
neighborhood-level racial integration. Their analysis shows that, although most metropolitan
neighborhoods continue to be racially segregated, the past 20 years have exhbited a long-term and
accelerating trend toward racial integration at the national level. Ellen et al. examine the pathways
of integration, finding, for example, that, for the vast majority of neighborhoods, integration results
from the in-movement of minority households into predominantly white tracts. Using a variety of
tract-level demographic and economic data, they analyze the characteristics of neighborhoods that
become integrated and the characteristics of neighborhoods that remain integrated. In so doing,
the authors shed new light on the role of residential mobility in defining the racial map of our
increasingly diverse society.
Claudia Coulton, Brett Theodos, and Margery A. Turner explore residential mobility from the
perspective of both neighborhoods and families. Using survey data from the 10-site Making Con-
nections community initiative, they analyze the “push and pull” factors underlying the mobility
decisions of the neighborhoods’ mover, stayer, and newcomer households. The authors also
examine the net effects of residential mobility on neighborhood-level socioeconomic change over
time. Their analysis of Making Connections survey data demonstrates the need for policymakers,
funders, and practitioners to take residential mobility and neighborhood dynamics into account
when designing and evaluating place-based interventions.
Kate Bachtell, Ned English, and Catherine Haggerty focus on the methodological dimensions
of mobility research, based on the National Opinion Research Center’s work in designing,
1
Data access information is available at http://www.icpsr.umich.edu/icpsrweb/PHDCN/daa.jsp.
2
Data access information is available at http://lasurvey.rand.org/data/.
3
Data access information is available at http://mcstudy.norc.org/data-access/.

Guest Editor’s Introduction
5Cityscape
conducting, and analyzing the longitudinal and cross-sectional Making Connections survey. The
authors explain their retrospective approach for linking individual household members across
successive survey waves and describe a two-dimensional approach to the analysis of mobility, an
approach that considers both residential movement and change in household composition. Their
work advances the field both substantively and methodologically by providing a more nuanced
and complex analysis of residential mobility, one that encompasses both the relocation of intact
households and internal household dynamics.
William A.V. Clark analyzes the relationship between households’ socioeconomic status (SES) and
their neighborhood choices. Drawing on the rich data resources of the Los Angeles Family and
Neighborhood Survey together with census data, Clark compares the characteristics of destination
neighborhoods with those of neighborhoods of origin for different socioeconomic groups, then
analyzes the characteristics of households that move “up,” “down,” or remain in the same or socio-
economically similar neighborhoods. Clark also explores the subjective dimensions of residential
mobility by examining neighborhood satisfaction among movers to higher SES neighborhoods
compared with that of movers to destination neighborhoods of the same or lower SES. Clark
makes a significant contribution by expanding the analysis of residential mobility to include the
full socioeconomic spectrum of neighborhoods and households to address fundamental questions
about the relationship between residential mobility and social mobility.
Susan J. Popkin, Michael J. Rich, Leah Hendey, Chris Hayes, Joe Parilla, and George Galster draw
on housing voucher records and crime data to analyze the relationship between local crime rates
and the large-scale relocation of public housing residents resulting from the sweeping transfor-
mation of public housing that took place in Chicago and Atlanta starting in the 1990s. Basing
their findings on sophisticated statistical modeling, the researchers demonstrate that destination
neighborhoods with a low density of relocated households experienced no change in crime rate
attributable to relocation. For destination neighborhoods with a medium-to-high density of
relocatees, however, their model shows that the overall decline in neighborhood crime rates was
shallower than would otherwise have been the case. This study constitutes a major methodological
advance in its use of complex modeling to quantify the effects of housing policy. Its substantive
contribution is twofold. The article presents credible, objective research findings on the nature
and magnitude of the neighborhood effects of public housing resident relocation—findings that
establish an effect at greater concentrations of relocatees but nevertheless counteract popular
misconceptions that link relocatees to perceived neighborhood crime waves. In addition, these
findings have important implications for housing policy, highlighting the need to prioritize the
deconcentration of poverty as an objective of housing voucher programs.
G. Thomas Kingsley, Audrey Jordan, and William Traynor address the policy and programmatic
implications of residential mobility for community-based initiatives. After reviewing Making
Connections survey data on the prevalence and patterns of residential instability in 10 low-income
neighborhoods, Kingsley et al. identify and describe a set of household- and community-focused
strategies for preventing involuntary residential churning and for continuing to serve mobile
families within the context of place-based interventions. They examine household-focused strate-
gies based on lessons from a range of homelessness prevention and service programs. Their review
of community-based strategies centers on the experience and principles of network organizing, an

6
Guy
Residential Mobility: Implications for Families and Communities
innovative approach that can deliver formal and informal supports to families at risk of involuntary
mobility and provide a mechanism to link residentially unstable households back to community-
based resources and opportunities.
Ade Kearns of the University of Glasgow provides the international commentary for this sym-
posium. While exploring the common ground in U.S. and European policy research related to
residential mobility, he focuses on three key areas: (1) the question of what constitutes mobility;
(2) the increasing research interest in the processes of mobility, particularly in the decisionmaking
process; and (3) the broadening of the residential mobility research agenda to include a range of
different populations and units of geography.
Conclusion
Each article in this symposium makes its own contribution to the research literature around
residential mobility and its implications for families and neighborhoods. Looking across the
articles, some common themes, which may be particularly important for policy and program
development, also clearly emerge. Some articles point out a disturbing connection between nega-
tive mobility (churning among low-income families and downward residential mobility) and the
disruption of the family unit itself. Recognizing the connection between residential stability and
family stability, this symposium underscores the need to coordinate housing policy and supportive
services targeted to vulnerable families. Programs such as the U.S. Department of Housing and
Urban Development’s (HUD’s) Choice Neighborhoods and the U.S. Department of Health and
Human Services’ Housing and Child Welfare Demonstration indicate promising movement in
this direction at the federal level. Considering the community-level implications of residential
mobility, place-based work clearly must be reconceptualized in a way that recognizes that the
target population is a moving target. Programs that promote the development of social networks
as enduring links to place-based resources constitute an innovative response to resident mobility.
We can expect further progress as federal initiatives such as HUD’s Choice Neighborhoods and the
U.S. Department of Education’s Promise Neighborhoods pursue the commitment to longitudinal
tracking of target-population outcomes. Longitudinal tracking data can provide policymakers
and practitioners with the information necessary to address the challenge of serving mobile fami-
lies—whose success or failure will not show up in the kind of cross-sectional neighborhood data
available from traditional sources such as the census—through place-based interventions. Finally,
the findings in this symposium challenge policymakers to develop strategies to channel residential
mobility into moves that work to the benefit of families and neighborhoods through interventions
such as mobility counseling, fair housing enforcement, inclusionary zoning, voucher portability,
and other interventions designed to maximize access to opportunity neighborhoods and minimize
the reconcentration of poverty.

Guest Editor’s Introduction
7Cityscape
Acknowledgments
The guest editor thanks G. Thomas Kingsley and Claudia Coulton for their role in originating
the idea for this symposium and providing outreach to potential authors. The guest editor also
gratefully acknowledges the reviewers and discussants who participated in the Residential Mobility
Authors’ Conference.
Guest Editor
Cynthia Guy is associate director for policy research at The Annie E. Casey Foundation.
References
Andersson, Roger, and Asa Brama. 2004. “Selective Migration in Swedish Distressed Neighbour-
hoods: Can Area-Based Urban Policies Counteract Segregation Processes?” Housing Studies 19 (4):
517–539.
Blum, Howard, James A. Riccio, and Nandita Verma, with Johanna Walter. 2005. Promoting Work
in Public Housing: The Effectiveness of Jobs-Plus Final Report. New York: MDRC.
Cohen, Rebecca, and Keith Wardrip. 2011. Should I Stay or Should I Go? Exploring the Effects of
Housing Instability and Mobility on Children. Washington, DC: Center for Housing Policy.
Crowley, Sheila. 2003. “The Affordable Housing Crisis: Residential Mobility of Poor Families and
School Mobility of Poor Children,” Journal of Negro Education 72 (1): 22–38.
Cunningham, Mary, and Graham McDonald. 2012. Housing as a Platform for Improving Education
Outcomes Among Low-Income Children. Washington, DC: Urban Institute.
Frey, William H. 2011. “Census Data Show Americans Are Still Stuck at Home.” Available at http://
www.brookings.edu/up-front/posts/2011/11/17-migration-census-frey.
———. 2009. “The Great American Migration Slowdown: Regional and Metropolitan Dimen-
sions.” The Brookings Institution Metropolitan Policy Program. Washington, DC: The Brookings
Institution.
Galster, George C. 2012. “The Mechanism(s) of Neighbourhood Effects: Theory, Evidence, and
Policy Implications.” In Neighbourhood Effects Research: New Perspectives, edited by Maarten van
Ham, David Manley, Nick Bailey, Ludi Simpson, and Duncan Maclennan. Dordrecht, The Nether-
lands: Springer: 23–56.
Jargowsky, Paul A. 1997. Poverty and Place: Ghettos, Barrios, and the American City. New York: Rus-
sell Sage Foundation.
Kubisch, Anne C., Patricia Auspos, Prudence Brown, and Tom Dewar. 2010. Voices From the Field
III: Lessons and Challenges From Two Decades of Community Change Efforts. Washington, DC: The
Aspen Institute Roundtable on Community Change.

8
Guy
Residential Mobility: Implications for Families and Communities
Lerman, Robert, and Signe-Mary McKernan. 2007. Promoting Neighborhood Improvement While
Protecting Low-Income Families. Washington, DC: Urban Institute.
Lubell, Jeffrey, Mark Shroder, and Barry Steffen. 2003. “Work Participation and Length of Stay in
HUD-Assisted Housing,” Cityscape 6 (2): 207–223.
Ludwig, Jens, Lisa Sanbonmatsu, Lisa Gennetian, Emma Adam, Greg J. Duncan, Lawrence F. Katz,
Ronald C. Kessler, Jeffrey R. Kling, Robert C. Whitaker, and Thomas McDade. 2011. “Neighbor-
hoods, Obesity, and Diabetes—A Randomized Social Experiment,” The New England Journal of
Medicine 365 (16): 1509–1519.
Pettit, Kathryn L.S., and Jennifer Comey. 2012. The Foreclosure Crisis and Children: A Three-City
Study Research Report. Washington, DC: Urban Institute.
Theodos, Brett. 2012. Analysis of Current Population Survey, 1998–2011. Unpublished paper.
Washington, DC: Urban Institute.
Thompson, Dianne T. 2007. “Evaluating Length of Stay in Assisted Housing Programs: A Method-
ological Note,” Cityscape 9 (1): 217–237.
Turner, Margery, and Deborah R. Kay. 2006. “How Does Family Well-Being Vary Across Different
Types of Neighborhoods?” Washington, DC: Urban Institute.
Turner, Margery Austin, Austin Nichols, and Jennifer Comey. 2012. Benefits of Living in High-
Opportunity Neighborhoods: Insights From the Moving to Opportunity Demonstration. Washington, DC:
Urban Institute.

9Cityscape: A Journal of Policy Development and Research • Volume 14, Number 3 • 2012
U.S. Department of Housing and Urban Development • Office of Policy Development and Research
Cityscape
Residential Mobility and the
Reproduction of Unequal
Neighborhoods
Patrick Sharkey
New York University
Abstract
Housing assistance policy has shifted away from project-based assistance toward tenant-
based assistance. This shift in approach reflects a common assumption that, if families
have the option to find homes on their own in the private market, they will seek out better
quality homes in racially diverse neighborhoods with lower levels of poverty. This article
presents evidence to qualify this assumption by highlighting the limits of residential mobil -
ity in reducing, in any substantive way, the degree of racial and ethnic inequality in urban
America. Two empirical observations form the basis of the argument. The first observa-
tion is that residential mobility typically serves to reproduce urban inequality instead of
disrupting it. The second is that urban inequality is resilient: even when individuals or
families make moves that disrupt patterns of racial and ethnic inequality, the changes
such moves induce are undermined by system-level processes that serve to reproduce in -
equality in the urban landscape. As a result, changes in families’ neighborhood environ -
ments arising from residential mobility are often temporary and are diluted by subsequent
changes occurring around families. The article concludes with a discussion of implications
for housing assistance policy.
Introduction
During the past two decades, there have been several high-profile federal housing programs and
policies that reflect a shift away from project-based assistance toward tenant-based assistance
(Orlebeke, 2000). The number of families receiving vouchers for rental assistance through the
Section 8 program has grown steadily, the Moving to Opportunity (MTO) for Fair Housing dem-
onstration launched with great fanfare to assess whether mobility out of public housing projects
could transform families’ lives, and the HOPE VI Program demolished some of the most notorious

10 Residential Mobility: Implications for Families and Communities
Sharkey
highrise public housing projects across the country (Cisneros and Engdahl, 2009; Goering and
Feins, 2003). This shift in approach has been driven at least in part by the widespread sentiment
that the deterioration of highrise public housing projects has contributed to the problems associ-
ated with concentrated urban poverty and racial segregation in America’s cities. It may also be
driven by an underlying assumption that, if families have the option to find homes on their own in
the private market, they will seek out better quality homes in racially diverse neighborhoods with
lower poverty levels.
This article does not challenge this assumption—in fact, a good deal of evidence indicates that
families receiving housing vouchers live in neighborhoods with lower levels of concentrated poverty
and crime than families receiving project-based assistance (Devine et al., 2003; Lens, Ellen, and
O’Regan, 2011; McClure, 2008; Newman and Schnare, 1998). Rather, this article presents an argu -
ment about the limits of residential mobility in reducing, in any substantive way, the degree of
racial and ethnic inequality in urban America. Two empirical observations form the basis of the
argument. The first observation is that residential mobility typically serves to reproduce urban
inequality instead of disrupting it. Residential moves are made within the highly stratified resi-
dential landscapes found in most American cities, and most moves lead families into aggregate
flows of mobility that reinforce the larger structure of racial and ethnic inequality in the city or
metropolitan area as a whole. Structural constraints, arising from the supply of affordable housing
in an area and the resources that families bring to the housing market, are obvious explanations for
this pattern. Although I acknowledge these structural constraints, in this article I focus attention
on the less obvious cognitive constraints that help to explain why families rarely make moves that
disrupt the larger patterns of racial and ethnic inequality. To be perfectly clear, the term cognitive
constraints has nothing to do with the cognitive skills or abilities of individuals or groups; instead,
the term, as used here, captures the constraints on residential mobility arising from individuals’
perceptions and understandings of which communities are possible or realistic residential destina-
tions. Cognitive constraints affect the housing choice process for all groups, but the consequences
of such constraints are not equal.
The second observation is that urban inequality is resilient. Even when individuals or families
make moves that disrupt patterns of racial and ethnic inequality, system-level processes that serve
to reproduce inequality in the urban landscape often undermine the changes such moves induce.
As a result, change in families’ neighborhood settings arising from residential mobility is often
temporary, and it is reversed or diluted by subsequent change occurring around families.
The Structure of Residential Mobility
Evidence From the Project on Human Development in Chicago Neighborhoods
The first part of this article provides descriptive evidence on the relationship between residential
mobility and neighborhood change. I begin in Chicago before expanding outward to consider na-
tional patterns. Much of the evidence I review is based on Sampson and Sharkey’s (2008) analysis
of neighborhood attainment trajectories, which drew on data from the Project on Human Develop-
ment in Chicago Neighborhoods (PHDCN). The PHDCN is a longitudinal study that tracked a

11Cityscape
Residential Mobility and the Reproduction of Unequal Neighborhoods
sample of families with children living in a representative set of Chicago neighborhoods as of 1995.
The analyses I discuss in this section are based on data from the 0-, 3-, 6-, 9-, 12-, and 15-year-old
age cohorts of the PHDCN Longitudinal Cohort Study; these analyses exclude the 18-year-old
cohort, because many members of this group lived independently at the first wave of the survey,
but I present separate analyses focusing on the 18-year-old cohort subsequently. The study gave
children and caregivers in the PHDCN sample extensive interview assessments at three interview
waves and followed them wherever the family moved (in the United States) over a 7-year period
extending to 2002. This feature of the data allows for the decomposition of change in families’
neighborhood environments arising from residential moves and from change in the composition of
the neighborhood residents surrounding a family. The extensive data available on caregivers and
their children allow for an assessment of the degree to which the child or caregiver’s individual
characteristics, or the family’s changing conditions, help to account for trajectories of change in the
families’ neighborhoods arising from residential mobility.
Exhibit 1 displays trajectories of change in families’ neighborhood conditions, which are based
on a set of multilevel growth curve models in which the time points at which families were
interviewed are nested within individuals, allowing for the description of change in neighborhood
characteristics and adjusting for stable and time-varying characteristics or circumstances of the
family (see Sampson and Sharkey, 2008, for details on the models and the covariates included).
This exhibit shows trajectories of change in families’ neighborhood economic status—as measured
by the median income in a family’s neighborhood—separately for families who remained in the
same neighborhood over the course of the survey, families who moved to a new neighborhood
within Chicago, and families who moved to a new neighborhood outside Chicago’s city limits. In
each case, slopes of change are allowed to vary by race and ethnicity.
The racial and ethnic hierarchy in Chicago’s neighborhoods is immediately visible from exhibit 1.
Particularly notable is the persistent gap in neighborhood median income across racial and ethnic
groups. This gap in neighborhood economic status is present among families who remain in their
origin neighborhood, among families who move within the city, and among families who leave
Chicago. In each case, White families live in the most affluent neighborhoods, followed by members
of other ethnic groups (primarily Asian Americans) and then by Latinos and African Americans,
respectively. This racial and ethnic hierarchy persists over the course of the study, and it persists
no matter where families move.
A second observation is that change in neighborhood economic status is driven almost entirely by
residential mobility. Families who remain in the same neighborhood over the course of the study
experience virtually no change in neighborhood economic status, and families who move within
the city find themselves in slightly more affluent neighborhoods over time. Only when families exit
Chicago, however, do they experience substantial change in the neighborhood environment. After
adjusting for any changes in the economic circumstances of the family, moves out of Chicago are
found to bring about gains in neighborhood median income of more than $10,000 for all racial
and ethnic groups.
Similar findings emerge in analyses of change in neighborhood racial composition. Families who
remain in the city experience minimal change in neighborhood racial composition over the course
of the study, but when African Americans move beyond Chicago’s city limits, they move into

12 Residential Mobility: Implications for Families and Communities
Sharkey
30
35
40
45
50
55
60
65
70
1995 1997/1998 2002
Median Income (thousands)
30
35
40
45
50
55
60
65
70
Median Income (thousands)
30
35
40
45
50
55
60
65
70
Median Income (thousands)
(a) Stayers
(b) Movers within Chicago
(c) Movers outside Chicago
1995 1997/1998 2002
White
Other
African American Latino
1995 1997/1998 2002
Survey Time Points
Exhibit 1
Trajectories of Change in Neighborhood Median Income, by Mobility Status and
Destination: PHDCN Families With Children
PHDCN = Project on Human Development in Chicago Neighborhoods.
Source: Adapted from Sampson and Sharkey (2008)

13Cityscape
Residential Mobility and the Reproduction of Unequal Neighborhoods
neighborhoods that are much more integrated than their origin neighborhoods. The probability of
making this transition out of Chicago varies by race and ethnicity, however. After conditioning on
stable and time-varying characteristics of families, the odds of African-American or Latino families
moving out of Chicago are only about 40 percent as high as the odds for White families.
This first set of results leads to two conclusions about change in families’ neighborhood environ-
ments. First, trajectories of neighborhood attainment are greatly constrained by the rigid structure
of economic segregation and of racial and ethnic segregation within Chicago. As a result, moves
made within the city lead to minimal change in families’ neighborhood environments. Second,
although moving out of Chicago leads to substantial change in families’ neighborhoods, the likeli-
hood of exiting the city is conditioned by race and ethnicity.
The result is a system of neighborhood inequality that is reproduced by the mobility of different
groups within it. This system becomes visible through the analysis of flows of movement across
different “types” of neighborhoods characterized by location (within or outside Chicago), by the
dominant racial or ethnic group within the neighborhood (predominantly White, African Ameri-
can, Latino, or mixed, meaning none of these groups composes a majority of residents in the neigh-
borhood), and by economic status (poor or nonpoor, with poor neighborhoods defined as those
within the poorest quartile of neighborhood median income in Chicago). Exhibit 2 shows flows of
movement across community subtypes. An arrow represents a flow of mobility if at least 5 percent
of families in the origin neighborhood subtype undertake the transition to the new subtype, and
circular loops represent flows of mobility that lead families from one type of neighborhood to a
new neighborhood of the same type.
The dominant flows of families shown in exhibit 2 serve to reproduce the structure of racial
and economic stratification within Chicago and the surrounding metropolitan area, rather than
to disrupt it. Flows linking communities within Chicago and communities outside the city are
few, and, by and large, they represent movement from nonpoor communities within the city to
predominantly White, nonpoor communities outside Chicago. Within the city limits, the dominant
flows depict a pattern of circulation between communities of similar economic status and similar
racial and ethnic composition. This pattern is particularly pronounced within the set of predomi-
nantly African-American communities. Although movement from racially mixed neighborhoods
into predominantly African-American communities occurs, not a single flow of migration leads out
of African-American neighborhoods into neighborhoods that are mixed or that feature a majority
of residents from other racial or ethnic groups. Instead, exhibit 2 reveals a pattern of circulation
within the majority-African-American neighborhoods of Chicago.
Among the diverse set of communities found in Chicago, this pattern of circulation is unique to
the city’s African-American communities. Considerable exchange of families takes place between
communities that are racially mixed, predominantly Latino, or predominantly White, whether
these communities are poor or nonpoor. The flow of families across these communities reflects
the high number of neighborhoods within the city undergoing a continuous process of transition,
in which the community’s population shifts from one dominant ethnic group to another, or, in
some cases, reflects population change in neighborhoods that remain stably integrated over time.
African-American neighborhoods are largely separate from these flows of migration, and they are
distinguished by the absence of connections to other types of communities throughout the city.

14 Residential Mobility: Implications for Families and Communities
Sharkey
In Chicago Outside Chicago
Legend
Size of arrow weighted based on
percentage making the transition.
Moves within same neighborhood type.
6
9
56
15
9
7
7
10
7
6
16
15
7
5
7
6
18
7
20
20
17
5
13
Mixed
poor
poor
White
poor
Latino
npoor
Latino
poor
Latino
npoor
5
White
npoor
Mixed
npoor
African
American
African
American
poor
African
American
npoor
African
American
npoor
Mixed
npoor
Latino
poor
White
npoor
6
Mixed
poor
Evidence From Moving to Opportunity
The flows of mobility shown in exhibit 2 are based on data from a representative sample of
families within Chicago. The implications of these findings for housing policy become clearer
when examining similar mobility flows among a sample of Chicago families living in public hous-
ing that participated in one of the more ambitious social experiments of our time, the Moving
to Opportunity (MTO) program. MTO is a social experiment in which public housing residents
in five cities who volunteered for the program were randomly assigned to one of three groups:
(1) an experimental group that received vouchers that the family could use to relocate only in
Exhibit 2
Flows of Mobility Among White, African-American, Latino, and Mixed Neighborhoods,
by Poverty and Destination in or Outside Chicago: PHDCN Families With Children
npoor = nonpoor. PHDCN = Project on Human Development in Chicago Neighborhoods.
Notes: All flows represent the percentage of families in the origin neighborhood making a transition, either within (mover-
stayers) or to another neighborhood type. Arrows are not shown for transitions under 5 percent. Numbers attached to the
arrows indicate the percentage of individuals making the transition.
Source: Adapted from Sampson and Sharkey (2008)

15Cityscape
Residential Mobility and the Reproduction of Unequal Neighborhoods
low-poverty neighborhoods, (2) a Section 8 group that received traditional Section 8 vouchers with
no restrictions on where the family could locate, or (3) a control group that received no vouchers
(see Briggs, Popkin, and Goering, 2010, and Goering and Feins, 2003, for details on the interven-
tion and its history).
Analyzing mobility patterns within the Chicago sample of MTO families, Sampson (2008) gener-
ated a remarkable map comparing flows of mobility among families in the MTO experimental
group with those of families in the control group, which is reproduced in exhibit 3. As the exhibit
shows, the MTO sample, which was almost entirely African American, moved from the South Side
neighborhoods where they originated to a set of neighborhoods across the south and west of the
city, with only trivial numbers of families venturing into any of the other communities throughout
Chicago. Families in both the experimental and control groups fanned out across the city in almost
identical paths, creating aggregate flows of migration that are difficult to distinguish. The families
in the experimental group did relocate into neighborhoods with lower poverty levels, but these
destination neighborhoods were often contiguous to the higher poverty neighborhoods of the
Exhibit 3
Flows of Movement Among the Control Group and the Experimental Group in the
MTO Chicago Site
Control Group Families
(Total = 232)
Concentrated
Disadvantage
2000
Low
Medium
High
Low
Medium
High
Concentrated
Disadvantage
2000
Experimental Group Families
(Random sample, 234 of 460 total)
MTO = Moving to Opportunity.
Note: Communities are shaded by the level of concentrated disadvantage as of 2000, and the size of arrows is weighted by
the volume of movement.
Source: Adapted from Sampson (2008)

16 Residential Mobility: Implications for Families and Communities
Sharkey
control group (Sampson, 2008). They located in neighborhoods that were similarly segregated by
race and that offered similar quality schools, and they located in neighborhoods that were chang-
ing in different ways than the destination neighborhoods of the control group. All of the destina-
tion communities in Chicago were experiencing a decline in concentrated disadvantage during this
period, but the destination neighborhoods of the experimental group were improving at a slower
pace than the destination neighborhoods of the control group.
Sampson’s (2008) analysis revealed the way that all moves made by families in the Chicago MTO
sample were conditioned by the larger community structure of residential Chicago. Even holding
a voucher that required them to move into neighborhoods with relatively low poverty levels,
families in the experimental group moved along spatial pathways that were indistinguishable from
those of the control group, they moved within the subset of predominantly African-American
neighborhoods in the city, and they moved into neighborhoods that were on a trajectory of change
that would make any differences between the neighborhoods of the control and experimental
groups fade away over time. In the city of Chicago, the rigid structure of neighborhood racial and
economic inequality overwhelmed the policy intervention; even with the capacity to “move to op-
portunity,” MTO participants made residential moves that served to reproduce the larger structure
of urban inequality (Sampson, 2008; see also Sampson, 2012).
Before moving on to consider potential explanations for these findings, it is important to acknow-
ledge that the patterns of mobility uncovered in Chicago may be very different from patterns in other
cities across the country, particularly the newer cities in the sunbelt regions of the South and West,
which feature less entrenched patterns of racial, ethnic, and economic segregation. For instance,
Clark (2008) provided similar evidence on the changes that MTO induced in all five cities in which
it was carried out: Baltimore, Boston, Chicago, Los Angeles, and New York. The overarching con-
clusion from Clark’s analysis was that the intervention produced minimal long-term changes in
African-American families’ neighborhoods, which is consistent with the argument made here. Note
also, however, that the most pronounced changes in the geographic location and neighborhood
environments of African-American MTO families were found in Los Angeles; a map showing the
origin and destination locations of participating families from this city looks very different from
the map produced from the Chicago sample. This difference does not imply that the findings from
Chicago are irrelevant for families in other cities but, rather, that one cannot assume the patterns
derived from a city like Chicago are identical to patterns in other cities (Small, 2007). I explore this
issue in more depth in the following section, as I widen my perspective to the nation as a whole.
Structural and Cognitive Constraints on Mobility
To understand why residential moves tend to reproduce, rather than disrupt, patterns of inequal-
ity, one must first consider the wide array of factors that influence where families live. Residential
decisions are influenced by families’ preferences for their neighborhood’s composition and the
amenities, risks, and resources that it offers, and residential decisions are constrained by families’
circumstances (for example, life-cycle stage, family size and structure, income and assets) and the
supply of affordable, quality housing. I refer to constraints arising from the interaction of the sup-
ply of affordable housing and the economic resources that individuals bring to the housing market
as structural constraints.

17Cityscape
Residential Mobility and the Reproduction of Unequal Neighborhoods
Less obvious than these structural constraints on residential mobility are the set of cognitive con-
straints on housing decisions and the way that these two sets of constraints interact and operate in
tandem to influence housing and neighborhood choices. By cognitive constraints, I mean individu-
als’ mental perceptions and understandings of which communities are possible residential destina-
tions. Shroder (2002) captured a similar idea with the term psychological constraints. Individuals’
ideas about possible residential destinations may be based on familiarity (or lack of familiarity)
with an area, a sense of whether the individual would “fit” in the community, perceptions of the
history of the community, or a range of other factors that affect the individual’s understanding
about whether a given community is a realistic residential destination.
Perhaps the clearest example of cognitive constraints comes from research on “community blind
spots” that Krysan and Bader (2009) conducted. In an innovative survey of Chicago adults con-
ducted in 2004, Krysan and Bader selected 41 specific communities in and around Chicago and
asked adult respondents to look at a map and identify which of these communities they “don’t
know anything about.” The researchers then analyzed how the prevalence of community blind
spots varies by the racial and ethnic composition of the community and by the race and ethnicity
of the respondent, after adjusting for the respondent’s social and economic status and the distance
between the identified community and the respondent’s own community.
Their findings revealed that all groups have incomplete information about the communities in and
around Chicago. Latinos had the largest number of community blind spots, followed by Whites
and African Americans, who had similar numbers of blind-spot communities. Not surprisingly,
respondents were more likely to know nothing about a community if the community’s residents
were predominantly members of a different racial or ethnic group; a large proportion of African
Americans knew little about several all-White communities in the greater Chicago area, and a
large proportion of Whites knew little about the predominantly African-American neighborhoods
of Chicago and its surrounding suburbs. Of particular interest, the study found that Whites were
more likely to know nothing about racially and ethnically mixed neighborhoods in and around
Chicago; in some cases this was true even for mixed neighborhoods in which Whites represent a
majority of the neighborhood population.
Krysan and Bader’s study revealed very clearly that information on the full range of communities
within an urban area is limited for all groups, and the existence of community blind spots is
not limited to any segment of the urban populace. Within the context of an urban landscape in
which community advantage and disadvantage are stratified by race and ethnicity, however, the
consequences of these blind spots vary by group, even if the prevalence does not. The pattern of
community blind spots suggests that African Americans making decisions about residential moves
are likely to be limited to a choice set of communities dominated by racial and ethnic minorities.
In most cities across the nation, this choice set includes the communities that have been the object
of consistent disinvestment over time, communities with greater risks and fewer economic op-
portunities (Dreier, Mollenkopf, and Swanstrom, 2001). Although Whites are similarly limited in
their knowledge of the full range of potential destination communities, their choice set includes
predominantly White communities that are commonly the most advantaged within an urban area.
In focusing on community blind spots, I do not intend to suggest that cognitive constraints on
mobility decisions are driven primarily by biased or incomplete information about potential

18 Residential Mobility: Implications for Families and Communities
Sharkey
communities that are held by residential movers. Perceptions of specific neighborhoods or entire
sections of an urban area are influenced by a combination of direct or indirect experiences, individual
and collective memories, and community reputations that persist over time. It is a mistake to think
that the legacy of racial and ethnic violence in urban America has been wiped clean from the memo-
ries of America’s urban dwellers, or that racial and ethnic discrimination is a thing of the past.
1
For instance, excellent evidence demonstrates very clearly that discrimination remains prevalent in
America’s residential markets and that it affects every aspect of individuals’ search for housing. The
evidence comes from a series of experimental audits of the real estate industry conducted under
the auspices of the U.S. Department of Housing and Urban Development (Turner and Ross, 2005;
Turner et al., 2002). The Housing Discrimination Study 2000 showed that, in 17 to 25 percent of
cases, African Americans and Latinos were “consistently” treated unfavorably when compared with
their White counterparts, meaning “whites were more likely to find out about available houses
and apartments, more likely to be given the opportunity to inspect these units, more likely to be
offered favorable financial terms, more likely to be steered toward homes for sale in predominantly
white neighborhoods, and more likely to receive assistance and encouragement in their housing
search” (Turner and Ross, 2005: 86).
Equally important as the presence of racial and ethnic discrimination may be the perception of how
individuals from different racial and ethnic backgrounds would be received in various communi-
ties across a metropolitan area. As an example, a survey of individuals in the Atlanta metropolitan
area found that nearly 90 percent of African-American respondents believed that Whites commonly
used discriminatory practices in the housing market, and a majority of both White and African-
American respondents believed that Whites in the northern, largely White suburbs of Atlanta would
be upset if an African-American family moved into the neighborhood (Thompson, 2001). The per -
ception of racial animosity in these communities is widespread, and this perception is highly likely
to affect African Americans’ decisions about whether to relocate to these suburbs, even in the ab-
sence of any personal experience with racism, racial discrimination, or informal hostility in these
communities. For housing voucher holders, perceptions about whether landlords would accept
their vouchers may be equally important (Briggs, Popkin, and Goering, 2010).
The overarching point is that individuals’ cognitive maps of the metropolitan areas surrounding
them play a large role in leading families to choose neighborhoods in ways that reproduce racial
and ethnic inequality in urban areas. The supply of affordable housing, families’ preferences, and
families’ economic resources are certainly important in generating urban inequality, but historical
and current racial discrimination, racial tension, and racial violence—or the perception that there
is the potential for discrimination or hostility—matter as well.
Hints of the importance of such perceptions are evident in a study predicting which families leased
up as part of the MTO intervention. Shroder (2002) considered the characteristics of families,
services provided, and the local housing markets in developing a model to better understand
lease-up patterns for MTO experimental group families, to whom MTO offered vouchers that
they could use only in low-poverty neighborhoods, and for Section 8 group families, who could
1
Massey and Denton (1993) provided the most comprehensive history of the formal and informal policies that generated
and maintained racial inequality in American cities through the 1980s.

19Cityscape
Residential Mobility and the Reproduction of Unequal Neighborhoods
use their vouchers anywhere. In addition to finding that the constraint for voucher use reduced
takeup substantially, Shroder found that families’ self-reported “uncertainty” about whether they
would like their new neighborhoods was strongly predictive of whether the family ultimately used
the voucher. As noted in the article: “Metro vacancy rates and household size—standard features
of an economic model—have some power to explain lease-up, but ‘satisfaction,’ ‘uncertainty,’
and ‘discomfort’ measures often have more predictive power at the individual level than standard
economic indicators like the hourly wage” (Schroder, 2002: 336). This passage implies that it is
not only the supply of housing and the resources of families that determine where a family ends
up, but it is also the family’s perceptions about what life would be like in different residential com-
munities and which of these communities represents a realistic destination.
The interaction of structural and cognitive constraints on mobility is illuminated more explicitly
in a study of mobility decisions among low-income families holding housing choice vouchers in
Mobile, Alabama. In their interviews with minority voucher holders, DeLuca, Rosenblatt, and Wood
(2012) found that respondents knew very little about the large number of predominantly White
communities in the greater Mobile area and had only vague ideas about the types of housing that
might be available in these communities. They found also that low-income families typically do not
plan or research their residential moves for long periods of time but, rather, make moves in response
to acute changes in their personal lives or housing circumstances (see also Coulton, Theodos, and
Turner, 2009). The imposition of time limits to find an apartment through the Housing Choice
Voucher Program quickens the pace of decisionmaking; families are under pressure to find a suitable
apartment in a satisfactory community before their time to maintain their housing voucher runs out.
The type of “reactive” mobility that was documented in this study limits the degree to which
moves can be planned in advance and elevates the importance of cognitive perceptions of potential
communities in the residential search process. Combined with the structural constraints associated
with finding decent, affordable housing with limited financial resources, the presence of cognitive
constraints comes close to ensuring that most individual moves will reproduce the larger structure
of urban inequality.
The Resilience of Urban Inequality
Evidence From the Project on Human Development in Chicago Neighborhoods
To this point in the article, I have described the way in which residential moves typically align with
larger flows of mobility that tend to reproduce racial and ethnic patterns of urban inequality. Not
all moves fit this pattern, however. A common finding in several recent studies is that residential
moves that lead families beyond the borders of their origin city, county, or metropolitan area are
the most likely to generate substantial change in families’ residential environments (Keels et al.,
2005; Sharkey, 2012). Moves that lead to long-range geographic mobility are the only type of
moves that commonly disrupt patterns of urban inequality, particularly if such moves lead families
out of highly segregated metropolitan areas.
Evidence for this observation comes from multiple studies focusing on residential mobility and
neighborhood change, two of which drew on data from the PHDCN. As discussed previously,

20 Residential Mobility: Implications for Families and Communities
Sharkey
Sampson and Sharkey (2008) found that the only group of Chicago families experiencing sub-
stantial change in their neighborhood environments are families who move outside the city limits,
whether they move into Chicago’s suburbs or well beyond. Families who move beyond the city
limits experience substantial improvements in neighborhood economic status, and African-American
families who leave Chicago move into neighborhoods with much less severe racial segregation.
The “leveling” of racial inequality attributable to mobility out of Chicago is even more pronounced
among young adults moving out of the family home. Analyzing neighborhood change in the 18-
year-old cohort of the PHDCN, Sharkey (2012) found that gaps in neighborhood poverty between
African-American and White young adults originating in Chicago are reversed among those who
leave the city when they exit the parental home. The top panel of exhibit 4 shows that White young
0
5
10
15
20
25
30
1995
(a) Neighborhood poverty
(b) Neighborhood percentage African American
1997/1998 2002
Neighborhood Poor (%)
Survey Wave
1995 1997/1998 2002
Survey Wave
White—left home
White—left Chicago
African American—left home
African American—left Chicago
Latino—left home
Latino—left Chicago
0
10
20
30
40
50
60
70
80
90
Neighborhood African American (%)
Exhibit 4
Trajectories of Change in Neighborhood Poverty and Percent African American
Among Homeleavers Who Remain in Chicago and Homeleavers Who Exit Chicago,
by Race/Ethnicity: PHDCN 18-Year-Old Cohort
PHDCN = Project on Human Development in Chicago Neighborhoods.
Source: Adapted from Sharkey (2012)

21Cityscape
Residential Mobility and the Reproduction of Unequal Neighborhoods
adults who leave Chicago when they exit the parental home end up in neighborhoods with higher
poverty levels than Latino and African-American homeleavers who make the same transition out
of Chicago. This finding is partially explained by the movement of young adults to diverse college
campuses, but this is only part of the story. College attendance does not explain declines in neigh -
borhood poverty among African-American young adults who leave Chicago, and college attendance
explains only part of the increase in neighborhood poverty among White homeleavers. Even White
young adults who do not attend college typically move to neighborhoods with higher poverty rates
if they exit Chicago when leaving the parental home.
Despite the change in neighborhood economic status among Whites who leave the city when they
form independent households, this group does not enter into more racially integrated neighbor-
hoods. As shown in the bottom panel of exhibit 4, all groups of White and Latino young adults
continue to live in neighborhoods with minimal presence of African Americans regardless of whether
they leave the city. However, African Americans who leave home and exit Chicago experience
substantial declines in neighborhood racial segregation. After controlling for a range of individual
and family characteristics, Sharkey (2012) found that African-American young adults who leave
Chicago when forming independent households find themselves in racially integrated communities
that are, on average, less than 50 percent African American.
Evidence From the Panel Study of Income Dynamics
The stark change in neighborhood conditions brought about by moves outside of Chicago raises
the question of whether such findings are unique to this city, which continues to be distinguished
by the severity of racial and economic stratification across its neighborhoods. To assess this ques-
tion, Sharkey (2012) analyzed similar trajectories of change among young adults who leave home
in a nationally representative sample from the Panel Study of Income Dynamics (PSID). Although
the change induced by geographic mobility among the national sample is not as dramatic as the
change among young adults leaving Chicago, the patterns of change are quite similar, particularly
when the sample is limited to young adults originating in highly segregated urban areas.
2
Whites
who exit highly segregated urban areas when they form independent households live in neighbor-
hoods with a greater representation of African Americans and higher poverty rates, whereas African
Americans who leave segregated metropolitan areas relocate into neighborhoods where the propor-
tion of African-American residents and the poverty rate are substantially lower. The similarity
between these patterns and those reported from the PHDCN suggests that this is a general pattern
of change associated with exiting the residential structure of extremely segregated urban areas.
One might consider the changes in individuals’ neighborhood environments that arise from long-
range geographic mobility as a counterexample to the previous argument about the way that resi -
dential mobility tends to reproduce urban inequality. This conclusion is premature, however.
Although geographic mobility can serve to reduce racial and ethnic gaps in neighborhood economic
status and segregation, urban inequality is resilient. There are several explanations for this resilience.
The first explanation is that the types of long-range moves that cross municipal boundaries and
disrupt urban inequality are much less commonly made by racial and ethnic minorities than they
are by Whites. Sampson and Sharkey (2008) found that, among all Chicago families, Whites are
the most likely to move beyond the city limits, followed by African Americans and Latinos. After

22 Residential Mobility: Implications for Families and Communities
Sharkey
adjusting for a full range of stable and time-varying family characteristics, the odds of African
Americans and Latinos moving out of Chicago were found to be roughly 40 percent as high as
the odds of Whites leaving the city. The same racial and ethnic gaps in long-range mobility are
found among the older cohort of 18-year-olds in Chicago, and among families in the PSID national
sample (Sharkey, 2012). Coming to a full explanation for why there is such variation in the degree
of long-range migration across different racial and ethnic groups is challenging, and it is beyond
the scope of this article. Potential hints come from the literature on community blind spots and
discrimination reviewed previously, on attachments to place, and on the role of spatial family
and kin networks that may act to limit long-range migration among racial and ethnic minorities
(Altman and Low, 1992; Shroder, 2002; Spilimbergo and Ubeda, 2004). The central point for the
purposes of this article is that the types of moves that disrupt racial and ethnic inequality in urban
neighborhoods are less commonly made by racial and ethnic minorities.
A second characteristic of moves that disrupt racial and ethnic inequality is that these moves com-
monly lead families into neighborhoods in the process of transition. To understand the implica-
tions of this observation for trends in urban inequality, it is necessary to shift from a perspective
that focuses on individual trajectories of change and to a perspective that focuses on the dynamics
of change in the families’ destination neighborhoods. In an extension of the analysis of change in
the neighborhood environments of young adults who exit Chicago when they leave the parental
home, Sharkey (2012) described changes in the young adults’ destination neighborhoods over the
course of the 1990s. Results showed that moves out of Chicago led to substantial changes in young
adults’ neighborhood environments, but they also revealed that young adults from different racial
and ethnic groups were moving into neighborhoods that were changing in very different ways. The
average change in the poverty rate in the destination neighborhoods of Whites and Latinos was
negligible, but poverty rose by an average of 3 percentage points in the destination neighborhoods
of African-American young adults over the decade. During the 1990s, the destination neighbor-
hoods of Whites, African Americans, and Latinos all experienced growth in the African-American
population and a decline in the White population, but the degree of change varied markedly. The
amount of change in the racial composition of Whites’ destination neighborhoods was minimal.
A more pronounced change took place in the destination neighborhoods of Latinos, but nothing
approaching that found in the destination neighborhoods of African Americans, where the popula-
tion of African-American residents rose by an average of 16 percentage points and the population
of White residents declined by an average of 20 percentage points.
These results provide strong evidence to suggest that African Americans (and, to a lesser extent,
Latinos) who leave home and leave Chicago enter neighborhoods with growing concentrations of
minority populations. Whereas the individual trajectories of African-American homeleavers who
exit Chicago show steep declines in racial segregation, figures describing change in the destination
neighborhoods of homeleavers suggest a process of resegregation, in which the destination neigh-
borhoods of African Americans who leave Chicago are transforming into racially and ethnically
segregated neighborhoods that resemble the segregation found within Chicago. This evidence
2
Because of the small sample sizes for Latinos and other ethnic groups in the PSID, the sample for the national analysis in
Sharkey (2012) was limited to African Americans and Whites.

23Cityscape
Residential Mobility and the Reproduction of Unequal Neighborhoods
is consistent with what Sampson (2008) found among families who moved as part of the MTO
demonstration. Although families in the experimental group did move into neighborhoods with
relatively low poverty rates, they moved into neighborhoods in which poverty was on the rise.
Followup evaluations of MTO reveal that, roughly 10 to 15 years after the program was imple-
mented, there were only minor differences in the neighborhood poverty rates of families in the
experimental group and the control group (Ludwig et al., 2011).
A third explanation for the resilience of urban inequality is that residential moves are not made in
isolation. Moves that disrupt patterns of racial and ethnic inequality are commonly undermined
by subsequent mobility in individuals’ destination neighborhoods. As a consequence, the change
in neighborhood environment that occurs as a result of moves beyond the boundaries of highly
segregated cities is often temporary change that fades over time.
Evidence for this phenomenon emerges most clearly in analyses of young adults who exit highly
segregated urban areas when they leave the parental home and form independent households
(Sharkey, 2012). Results discussed previously from Chicago and from the nation as a whole show
that young adults who move beyond the boundaries of highly segregated urban areas experience a
substantial leveling of racial inequality, with African Americans in particular moving into more in-
tegrated neighborhoods with less poverty. When the national sample of young adults is followed
further into adulthood, however, these stark changes in individuals’ neighborhood environments
begin to fade and to reverse.
Exhibit 5 displays trajectories of neighborhood change for African-American and White young
adults derived from growth-curve models covering an extended period of early adulthood. The
dashed lines show trajectories of change for young adults who move to a different county when
forming their own households, and solid lines represent trajectories of change for those who
remain in the same county. All figures are based on models that adjust for a set of covariates that
capture key aspects of individuals’ economic status and life-cycle changes during this period of
the life course.
The top panel of the exhibit shows that African Americans who exit highly segregated urban
areas experience a pronounced drop in neighborhood poverty during early adulthood, and all
groups of Whites experience rising neighborhood poverty over the same period. These trends
shift as the sample moves further into adulthood, however. Whereas Whites experience slight
declines in neighborhood poverty as they age beyond 25, the pattern of declining neighborhood
poverty among African Americans flattens and reverses as they age further into adulthood. In early
adulthood, a clear movement toward racial equality emerges among young adults who exit highly
segregated metropolitan areas, but the long-term trend suggests a reproduction of racial gaps in
neighborhood poverty as African-American and White young adults move further into adulthood.
The bottom panel of exhibit 5 displays the same results using the neighborhoods’ percentage of
African Americans as the dependent variable. Although Whites remain in neighborhoods with
minimal African-American presence no matter where they reside, African Americans who exit
highly segregated metropolitan areas enter neighborhoods that are much less segregated than
those from which they came. Again, however, the longer term trend is one of resegregation; the
percentage of African-American neighbors gradually rises as African Americans age further into

24 Residential Mobility: Implications for Families and Communities
Sharkey
adulthood. By the time they are in their 30s, African-American adults who had moved into neigh-
borhoods that were relatively integrated when forming their own households find themselves back
in neighborhoods similar to those in which they started: neighborhoods that are mostly African
American. Whites experience a very modest increase in the percentage of African Americans in
their neighborhoods over the course of young adulthood but continue to live in neighborhoods
with less than 10 percent African-American residents, on average, throughout this period. Thus,
although the period of early adulthood shows a leveling of racial inequality in neighborhood eco-
nomic status among young adults who exit highly segregated urban areas, a longer term pattern of
resilient racial inequality emerges.
0
5
10
15
20
25
30
35
40
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
Neighborhood Poor (%)
Age
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
Age
White—same county White—different county
African American—same county
African American—different county
0
10
20
30
40
50
60
70
80
Neighborhood African American (%)
(a) Neighborhood poverty
(b) Neighborhood percentage African American
Exhibit 5
Trajectories of Change in Neighborhood Poverty and Percent African American
From Age 17 to 35, by Race: PSID Young Adult Sample Originating in High-
Segregation MSAs
MSA = metropolitan statistical area. PSID = Panel Study of Income Dynamics.
Source: Adapted from Sharkey (2012)

25Cityscape
Residential Mobility and the Reproduction of Unequal Neighborhoods
How is it that young adults who select out of extremely segregated areas when establishing inde-
pendent households return to segregated neighborhoods when they are further into adulthood? To
pursue this question, Sharkey (2012) analyzed the change occurring in young adults’ neighbor-
hood environments after they have left their parental home and selected a new neighborhood
outside their origin city. Change in the neighborhood environment after the transition out of the
family home is decomposed into change occurring in the young adult’s initial “spell” of residence
in the destination neighborhood and change occurring from additional residential moves.
Exhibit 6 shows average levels of change in the racial composition of young adults’ destination
neighborhoods over the duration of their initial spell in the new neighborhood. The exhibit shows
that the average destination neighborhood of African Americans who exit highly segregated metro-
politan areas undergoes a process of demographic change during their time in the neighborhood.
Whereas the racial composition in the destination neighborhoods of Whites changes very little (see
the top and bottom lines in the exhibit), in African Americans’ neighborhoods, the average propor-
tion of White residents drops steadily and the proportion of African-American residents rises.
Exhibit 7 complements this analysis by plotting selected change in neighborhood racial composition
arising from the first residential move after the initial spell of residential independence. Trends
of change for African Americans run in the opposite direction from those found in exhibit 6—
residential moves lead African Americans into neighborhoods with slightly lower percentages of
African-American and higher percentages of White residents than the neighborhoods from which
they moved. Moves made by Whites do not alter the racial composition of their neighborhood
substantially, although they do lead to neighborhoods with slightly lower percentages of African-
American residents and higher percentages of White residents.
0
10
20
30
40
50
60
70
80
90
100
0 1 2 3 4 5 6
Neighborhood Racial Composition (%)
Year of Spell
White—percent White White—percent African American
African American—percent White African American—percent African American
Exhibit 6
Changes in Neighborhood Racial Composition During Young Adults’ First Independent
Residential Spell: PSID Young Adult Sample Originating in High-Segregation MSAs
MSA = metropolitan statistical area. PSID = Panel Study of Income Dynamics.
Note: Sample limited to young adults who move to a different county when forming independent households.
Source: Adapted from Sharkey (2012)

26 Residential Mobility: Implications for Families and Communities
Sharkey
0
10
20
30
40
50
60
70
80
90
100
Neighborhood Racial Composition (%)
Before move After move
White—percent White White—percent African American
African American—percent White African American—percent African American
Exhibit 7
Changes in Neighborhood Racial Composition Arising From Young Adults’ First
Residential Move After the First Independent Residential Spell: PSID Young Adult
Sample Originating in High-Segregation MSAs
MSA = metropolitan statistical area. PSID = Panel Study of Income Dynamics.
Note: Sample limited to young adults who move to a different county when forming independent households.
Source: Adapted from Sharkey (2012)
Together, these two exhibits suggest that one important explanation for the persistence of racial
inequality in neighborhood environments, even among young adults who have selected out of
segregated environments, is a phenomenon that Sharkey (2012) referred to as unselected change.
Unselected change refers to change in the neighborhood environment that occurs around indi-
viduals or families and that runs counter to the preferences of the individual, as inferred by his
or her decision to relocate into the neighborhood. The idea relates closely to a strand of research
that considers how the preferences of different groups of individuals interact to create aggregate
patterns of racial segregation (Bruch and Mare, 2006; Clark, 2007; Schelling, 1971). The central
lesson from this research is that to understand neighborhood change one must move beyond an
exclusive focus on individual choices and instead consider systems of interrelated decisions made
by individuals responding to the change that is occurring around them (see also Crowder and
South, 2008; Quillian, 1999). In this example, African-American young adults who exit severely
segregated metropolitan areas and select into racially integrated neighborhoods find themselves in
neighborhoods that are undergoing a gradual demographic shift toward resegregation.
The pattern of unselected change suggests that the reproduction of neighborhood inequality from
childhood to adulthood is not attributable only to the decisions of White and African-American
young adults to live in segregated neighborhoods but, rather, to the decisions of those around
them to exit or enter such neighborhoods. The analysis of change arising from the second move of
African Americans reinforces this idea. Like the move out of the family home, African-American
young adults again move into more integrated environments when they decide to relocate for a
second time. In other words, selected change appears to lead African Americans into relatively
integrated environments, whereas unselected change leads to increasing segregation around
African-American young adults.

27Cityscape
Residential Mobility and the Reproduction of Unequal Neighborhoods
Implications for Housing Policy
The focus of this article on residential choice is motivated by a long-term shift in the approach
of federal housing policy. As a result of the shift away from project-based assistance, low-income
families receiving housing assistance are increasingly navigating the private housing market on
their own when making decisions about where to live. This concluding section does not consider
the merits of this shift in approach, nor does it consider the full range of policy approaches that
might reduce neighborhood inequality. Many excellent studies have provided more comprehensive
discussions of housing policy, with explicit focus on the supply side of the housing market and on
the structure of federal housing programs (Briggs, Popkin, and Goering, 2010; Dolbeare, 2001;
Grigsby and Bourassa, 2004; Katz and Turner, 2001; Quigley, 2011; Quigley and Raphael, 2004).
My focus in this concluding section is more modest. Taking the shift in the approach of housing
policy as given, I offer several suggestions for how mobility policies or programs might be altered
in order to increase the probability that families are able to make residential moves that generate
meaningful change.
These concluding suggestions derive from two overarching findings in the empirical evidence I
have reviewed. The first finding is that residential moves made by low-income families tend to
reinforce, rather than disrupt, patterns of urban inequality. Although individual residential choices
are undoubtedly influenced by the availability of affordable housing in different parts of a given
metropolitan area, the evidence reviewed in this article reveals the ways in which the structure of
economic and racial segregation within urban areas interacts with individuals’ economic resources
and perceptions of the city to constrain the residential moves of low-income families in ways that
reproduce urban inequality. The second finding is that urban inequality is resilient, meaning that
change arising from residential moves is undermined by long-term patterns of unselected change.
Despite this pattern, substantial evidence indicates that moves that cross city and county bounda -
ries have the greatest capacity to bring about substantive change in families’ neighborhood envi-
ronments, but these moves are exceedingly uncommon.
Collectively, these findings indicate that any housing program or policy that relies on families navi-
gating the private housing market on their own is unlikely to reduce neighborhood inequality in
a meaningful way. Housing assistance programs that rely on residential mobility require extensive
intervention into the process of housing choice to improve the likelihood that families are able
to make moves, if they so choose, into neighborhoods that are less disadvantaged than the most
common destinations of public housing recipients. Intensive assistance in the process of finding
a neighborhood and a home is crucial to facilitating the type of residential moves that have the
potential to reduce neighborhood disadvantage among recipients of housing assistance. I would
argue that this mechanism is the only one by which tenant-based housing assistance can be used to
confront urban inequality.
The most obvious form of such intensive assistance is housing counseling and support in the hous-
ing search. Many housing experts have called for more intensive counseling for housing assistance
recipients, but altering the form of such support may also be important. For instance, instead of
supplying voucher holders with a list of available units throughout the city, housing counselors
might provide families with a “default” set of two or three units available in different communities

28 Residential Mobility: Implications for Families and Communities
Sharkey
within the city (see also Briggs, Popkin, and Goering, 2010). Altering the “choice architecture” of
voucher holders in this way may lead to substantial changes in the destinations of housing recipi-
ents without reducing their freedom to move wherever they wish.
The Gautreaux Assisted Housing Program in Chicago provides an example of an extreme version
of this approach, because families participating in this program were offered specific units located
throughout the Chicago metropolitan area based on their position on a waiting list (Rubinowitz
and Rosenbaum, 2000). Unlike most residential mobility programs, the residential moves that
arose from Gautreaux took families across the entire Chicago metropolitan area and brought about
a change in families’ neighborhood environments that persisted over time (Keels et al., 2005). I
would not argue for a policy that assigns a specific unit to a family but, rather, a policy that pro-
vides a set of default units in several different communities across a city or metropolitan area from
which a family could choose.
A more general principle might be that families should be provided the information, support, and
resources necessary to make the types of moves that bring them into less disadvantaged parts of the
city, or out of their origin city altogether, and that disrupt the structure of residential stratification
within the metropolitan area: the types of moves that are rare among non-White, low-income fami-
lies. The specific policies that would be most effective in achieving this goal are subject to debate.
Providing more resources for housing counseling or more aggressive targeting of discrimination
among landlords may be most effective (Goering, 2007). Establishing a “mobility bank” (Ludwig
and Raphael, 2010) that provides credit for families lacking the information and resources to make
long-range, risky moves is another creative alternative. Altering the structure of the housing search
process so that families are provided with a default set of units is a third option, and many others
undoubtedly exist. The central point of this article is that the most common current approach,
which relies on families to navigate the private market largely on their own, has limited capacity
to generate meaningful change in families’ neighborhood environments. As a consequence, the
dominant form of low-income housing assistance is unlikely to generate substantive change in the
structure of neighborhood inequality in our nation’s cities.
Acknowledgments
The author thanks Sandra Newman for her excellent comments on the original draft of the article;
the participants in the Symposium on Residential Mobility held at the Urban Institute in April
2012; symposium guest editor, Cindy Guy; Claudia Coulton; Tom Kingsley; and Robert Sampson,
who provided comments on an earlier version of the article.
Author
Patrick Sharkey is an associate professor of sociology at New York University.

29Cityscape
Residential Mobility and the Reproduction of Unequal Neighborhoods
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32 Residential Mobility: Implications for Families and Communities

33Cityscape: A Journal of Policy Development and Research • Volume 14, Number 3 • 2012
U.S. Department of Housing and Urban Development • Office of Policy Development and Research
Cityscape
Pathways to Integration:
Examining Changes in
the Prevalence of Racially
Integrated Neighborhoods
Ingrid Gould Ellen
Keren Horn
Katherine O’Regan
New York University
Abstract
Few researchers have studied integrated neighborhoods, yet these neighborhoods offer
an important window into broader patterns of segregation. In this article, we explore
changes in racial integration in recent decades using decennial census tract data from
1990, 2000, and 2010. We begin by examining changes in the prevalence of racially
integrated neighborhoods and find that the share of metropolitan neighborhoods that are
integrated increased significantly during this period, from slightly less than 20 percent to
slightly more than 30 percent. We then shed light on the pathways through which these
changes have occurred. We find both a small increase in the number of neighborhoods
becoming integrated for the first time during this period and a more sizable increase in
the share of integrated neighborhoods that remained integrated. Finally, we offer insights
about which neighborhoods become integrated in the first place and which remain stably
integrated over time.
Introduction
Although many scholars track patterns of racial segregation in metropolitan areas, very few have
focused attention on racially integrated communities. This lack of attention might be a conse-
quence of the popular view in the United States that racial integration is extremely rare and, when
it occurs, it is only temporary. In the years after World War II, many urban neighborhoods quickly
changed from all White to all Black. Schelling (1972) coined the term tipping to describe this rapid
change and helped explain it using a simple model of racial preferences. His model assumes that

34
Ellen, Horn, and O’Regan
Residential Mobility: Implications for Families and Communities
White residents will continue to live in a community only as long as the Black population remains
below their individual tolerance thresholds. As the most prejudiced White residents leave, the
proportion of Black residents will rise above the tolerance threshold of the next most prejudiced
White group, until the neighborhood population becomes all Black.
Equipped with Schelling’s simple model and the empirical reality of rapid racial transition in the
postwar era, most researchers have, until recently, viewed integration as a rare exception to the
norm of racial homogeneity. Even the researchers who have studied integrated neighborhoods
have tended to focus their case studies on communities that self-consciously work to maintain
their diversity (Keating, 1994; Nyden, Maly, and Lukehart, 1997; Saltman, 1990). The implied
message of these studies is that without such robust, ongoing efforts to maintain integration, stably
diverse communities would not exist.
Although our metropolitan areas remain highly segregated by race, racially integrated neighbor-
hoods grew considerably more common between 1980 and 2000 (Easterly, 2009; Ellen, 2007,
2000; Farrell and Lee, 2011; Fasenfest, Booza, and Metzger, 2004; Friedman, 2008; Logan and
Zhang, 2010; Rawlings, Harris, and Turner, 2004). Moreover, previous research suggests many of
these recently integrated neighborhoods were not just temporarily mixed in the process of moving
from all White to all minority, but they remained integrated for years (Ellen, 2007, 2000, 1998;
Logan and Zhang, 2010; Rawlings, Harris, and Turner, 2004). This literature does not extend past
2000, however. We do not know what has happened to the prevalence or stability of integrated
neighborhoods more recently.
Our goal in this article is to fill this gap using decennial census data from 1990, 2000, and 2010.
We start by examining recent changes in the number and share of neighborhoods that are racially
integrated. We then offer some evidence about the pathways through which these changes have
occurred. Finally, we examine the characteristics of the racially homogenous neighborhoods that
become integrated in the first place and those of the integrated neighborhoods that remain stably
integrated over time.
Background and Literature Review
Although researchers have focused more on racial segregation, a number of papers offer insights
into our questions. In this section, we review existing evidence about pathways to integration, the
characteristics of homogenous neighborhoods that become integrated, and the attributes of the
integrated neighborhoods that remain stably integrated over time.
Pathways to Racial Integration
We can draw from the existing literature on segregation to identify some hypotheses about why
racially integrated neighborhoods might increase in number. Note that integration can increase
through two basic pathways: (1) more neighborhoods can become integrated, or (2) a greater
number of existing integrated neighborhoods can remain integrated over time. At a macro level,
three factors might lead to either changes in the number of neighborhoods becoming integrated
or shifts in the stability of neighborhoods after they become integrated: (1) demographic trends,
(2) shifts in income differences across racial groups, and (3) changes in racial attitudes.

Pathways to Integration:
Examining Changes in the Prevalence of Racially Integrated Neighborhoods
35Cityscape
First, in terms of demographic trends, an increase in the share of the population that belongs to a
particular minority group will lead to more integration (at least up to the point at which the group
is no longer a minority). For example, if the population moves from all White to 80 percent White,
the potential for integration surely increases. A larger minority population, however, also provides
the potential for minority groups to become more segregated, because their numbers are substan-
tial enough to create concentrated minority neighborhoods (South, Crowder, and Pais, 2011).
White households might also begin to feel less comfortable living in integrated neighborhoods as
the overall number of non-White residents in their city or region grows.
Other evidence supports the belief that integration will grow as the non-White population diversifies.
Surveys have suggested that White residents are more comfortable sharing neighborhoods with
Asian and Hispanic residents than they are sharing neighborhoods with Black residents (Bobo and
Zubrinsky, 1996), and other research has suggested that the segregation of non-Black minorities
is less persistent and easier to explain (Bayer, McMillan, and Rueben, 2004).
1
Overall population
growth and the accompanying new housing might also facilitate the emergence of integration,
because newer communities do not have the same legacy of racial segregation or history of discrimi-
natory housing practices (Farley and Frey, 1994; Logan, Stults, and Farley, 2004; South, Crowder,
and Pais, 2011).
2
Second, to the extent that income differences among racial groups contribute to racial segregation
(Bayer, McMillan, and Rueben, 2004; Harsman and Quigley, 1995), reductions (or increases) in
such gaps should lead to increases (or reductions) in the prevalence of integration. Although the
difference between the median incomes of non-White and White households has barely changed
in the past few decades,
3
the increased number of middle- and high-income minority households
has allowed for more integration. In other words, the distributions of income by race, and thus
the type of housing and neighborhoods accessible and attractive to different racial groups, might
overlap more than they did in the past.
4
Third, shifts in racial attitudes might lead to shifts in neighborhood preferences (Clark, 1991; Harris,
1999). Considerable evidence suggests that White households have grown more open to living in
integrated neighborhoods over time (Bruch and Mare, 2006; Farley, Fielding, and Krysan, 1997).
Similarly, as racially integrated neighborhoods grow in number, more White residents might start
to view integrated communities as viable options, creating something of a virtuous cycle.
1
Some researchers have argued that White residents feel more comfortable sharing their neighborhoods with Black
residents when the non-White population as a whole is more diverse (Frey and Farley, 1996).
2
Similarly, greater fragmentation of the metropolitan area might provide more opportunities to segregate (Farley and Frey,
1994; South, Crowder, and Pais, 2011).
3
The ratio of Black to non-Hispanic White household income was unchanged between 1972 and 2000, and the ratio of
Hispanic to non-Hispanic White household income declined only slightly, from 0.74 to 0.69 (DeNavas-Walt, Proctor, and
Smith, 2011).
4
The creation of a larger middle class among minority groups could also provide an opportunity for greater segregation
through the development of middle-class minority neighborhoods (Bayer, Fang, and McMillan, 2011).

36
Ellen, Horn, and O’Regan
Residential Mobility: Implications for Families and Communities
Which Neighborhoods Become Integrated?
Few researchers have studied the creation or emergence of integrated communities, but some of
the same factors that explain shifts in the prevalence of integration over time (demographic trends,
shifts in income differences, and changes in racial attitudes) are also helpful in predicting variation
across space. We would expect to see a larger number of integrated communities emerge in areas
with more rapidly growing populations (minority populations in particular), with more similar
incomes across racial groups, and with more racially tolerant populations.
In addition, given the research suggesting that minority households are more open to moving into
largely White neighborhoods than White households are to moving into largely minority neighbor-
hoods, we expect largely White neighborhoods to become integrated more commonly than largely
minority neighborhoods (Bruch and Mare, 2006).
Finally, the characteristics of the housing market in a metropolitan area might also influence the
share of neighborhoods in that area that become integrated. For example, racially homogenous
neighborhoods might be more likely to move toward integrated communities when rapid price
appreciation pushes White households to look beyond homogenous White neighborhoods, which
could encourage the integration of largely minority areas and potentially create opportunities for
middle-income minority households to enter previously White neighborhoods.
Which Integrated Neighborhoods Remain Integrated Over Time?
The body of research studying the question of which integrated neighborhoods are likely to stay
that way is small. Again, previous research has suggested demographic trends, income differences,
and racial attitudes as factors explaining differences across areas in the stability of integration.
Empirical evidence has found White-Black integrated tracts in metropolitan areas with fewer Black
households (Ellen, 2000) and those in cities where White households have more tolerant racial
attitudes (Card, Mas, and Rothstein, 2008) to be more stable. Researchers have also found evidence
that the underlying growth of the minority population in a city or metropolitan area affects stability
insofar as integrated neighborhoods are likely to tip to largely minority more frequently when
the minority population is growing (Denton and Massey, 1991; Ellen, 2007; Ottensmann, Good,
and Gleeson, 1990). Interestingly, researchers have found little evidence that the mean income or
poverty level of a neighborhood affects stability (Ellen, 2000; Galster and Keeney, 1993; Logan and
Schneider, 1984; Logan and Stearns, 1981; Steinnes, 1977; White, 1984).
As for other factors, Ellen (2000) posited a theory of race-based neighborhood stereotyping, sug-
gesting that White households (and also some non-White households) tend to assume integrated
neighborhoods will unravel and experience the type of structural decline that White households
associate with largely minority areas. Ellen found some empirical support in that the White popu-
lation loss was less in neighborhoods that White households expected to remain integrated in
the future (those farther from the central area of Black residence and those that experienced only
modest growth in the minority population in the previous decade).
5
She also found that, despite
5
Lee and Wood (1991) also found evidence that distance to the nearest tract with minority concentration is positively
correlated with racial stability.

Pathways to Integration:
Examining Changes in the Prevalence of Racially Integrated Neighborhoods
37Cityscape
the lower mobility rates of homeowners, White population loss in integrated tracts during the 1980s
was greater in neighborhoods with higher homeownership rates, perhaps because White homeowners,
due to their financial stake in the community, are more sensitive to worries about the trajectory of
conditions in a neighborhood than are White renters (Ellen, 2000).
Definitions
No single definition is widely accepted for the term integrated neighborhood. Drawing on previous
literature and taking into account recent demographic changes, we derive a set of definitions of
neighborhood types. To define categories, we use constant thresholds across the United States,
rather than relative thresholds that vary depending on the racial composition of the individual
metropolitan area. We make this choice because our definitions aim to capture the experience of
residents in the neighborhood; that is, whether its composition is such that residents experience
meaningful integration in their surrounding community. A neighborhood that is 98 percent White
and 2 percent minority might be relatively diverse in an essentially all-White metropolitan area,
but it cannot be considered a meaningfully integrated community.
To start, all neighborhoods are categorized as either integrated or not, then further classified by the
race and ethnicity of the groups with a significant presence. We define integrated neighborhoods
as those shared by a significant number of non-Hispanic White residents (to whom we refer simply
as White in this article) and a significant number of individuals belonging to at least one minority
racial group.
6
We require the presence of White residents because White remains the dominant
race in our society, and historically it is White individuals who have excluded or have avoided
living near members of minority groups. Thus, although a community with Black, Hispanic, and
Asian residents might be highly diverse, we do not consider it to be integrated. Rather, we classify
it as mixed minority. For computational ease, we divide the non-White population into three
mutually exclusive groups: Black, Hispanic, and Asian/other. Most individuals in the Asian/other
category are Asian, but the category also includes non-Hispanic individuals who identify as a mem-
ber of a racial group other than Black, White, or Asian, such as Native American.
7
We group these
individuals into a single racial group to keep the number of neighborhood categories manageable.
Specifically, we identify nine different neighborhood types. Four are integrated: (1) White-Black,
(2) White-Hispanic, (3) White-Asian/other, and (4) White-mixed minority. Five are nonintegrated:
(1) White, (2) Black, (3) Hispanic, (4) Asian/other, and (5) Mixed minority.
To be counted as significantly present in a neighborhood, a group must comprise at least 20 percent
of the population. Thus, an integrated White-Black neighborhood is one in which at least 20 percent
of the population is White, in which at least 20 percent is Black and in which the Hispanic and
Asian/other minority groups each comprise less than 20 percent of the population. A predominantly
White neighborhood is one in which none of the three minority racial groups comprises 20 percent
6
For expositional ease, we refer to race and ethnicity grouping as race.
7
In all three decades, between 84 and 88 percent of the Asian/other population is Asian.

38
Ellen, Horn, and O’Regan
Residential Mobility: Implications for Families and Communities
or more of the population. Technically, this definition means that a neighborhood that is 43 percent
White, 19 percent Black, 19 percent Hispanic, and 19 percent Asian/other would be identified as
predominantly White. In fact, however, of the predominantly White neighborhoods, all are majority
White and most are overwhelmingly White; in 1990, the median predominantly White neighbor-
hood was 93 percent White, and 90 percent of White neighborhoods were at least 79 percent White.
Similarly, the populations of all predominantly minority tracts overwhelmingly comprised that
single minority group. The median predominantly Black neighborhood in 1990 was 94 percent
Black, and 90 percent of predominantly Black neighborhoods were more than 80 percent Black.
This definition of integration, like any definition, is arbitrary to some degree, but, when we experi -
mented with different thresholds and definitions, we found that our key results were highly robust.
For example, we experimented with using a lower threshold to capture non-White presence and a
higher threshold to capture White presence, given the difference in their overall population shares,
and the overall trends remained the same.
Although we are using the term race in this article to indicate both race and Hispanic ethnicity, the
census asks respondents a separate question about Hispanic origin. We classify individuals who
self-identify as Hispanic and Black as Black but code all other self-identified Hispanic individuals
as Hispanic. Another complicating factor is that, starting in 2000, the census enabled individuals
to self-identify as belonging to multiple racial groups. We use the bridging method, which Jeffrey
Passel at the Urban Institute developed, to categorize these multiracial respondents (Tatian, 2003).
Specifically, we categorize anyone who selects Black as one of his or her racial groups as Black
(essentially applying the one-drop-of-blood rule). We categorize anyone who lists Asian and not
Black as Asian. We consider anyone who self-identifies as White and does not also list Black, Asian,
or Pacific Islander to be White.
Data
Following the practice of most previous research, we use census tracts to proxy for neighborhoods. 
Census tracts include, on average, about 4,000 people, and most include between 2,500 and 8,000
people. Some researchers have argued that census tracts are too large to approximate neighbor-
hoods and have advocated studying segregation at the block level instead (Farley and Frey, 1994;
Jargowsky, 1997). To be sure, census tracts are not perfect representations of neighborhoods, and
presumably fewer neighborhoods would appear integrated if we used a smaller level of geography.
Nonetheless, census tracts are probably closer in size to what most people view as a neighborhood
than are individual blocks, and far more data are available at the tract level than at the block level.
This study relies on the Neighborhood Change Database (NCDB), developed by GeoLytics and the
Urban Institute for data on 1990 and 2000 census tracts, and on decennial census data from 2010.
The NCDB draws on census tract data from the 1970, 1980, 1990, and 2000 censuses and covers
all census tracts in the United States. In addition to including individual files for each of these four
census years, the NCDB also includes a longitudinal file of census tracts with fixed boundaries,
which remaps 1970, 1980, and 1990 census tract data to 2000 census tract boundaries. We use
this data set because it is particularly useful for examining changes in the composition of census

Pathways to Integration:
Examining Changes in the Prevalence of Racially Integrated Neighborhoods
39Cityscape
tracts that are not the result of boundary alterations. We limit our analysis to census tracts in met-
ropolitan areas, and we omit any census tracts with fewer than 200 residents or more than one-half
of its population living in group quarters in either 1990 or 2000.
We rely on the weights that the Census Bureau correspondence file provides to link the 2010
census tract data to 2000 tract boundaries. To account for errors in matching data, we omit tracts
that experienced extremely large reductions or increases in population between 2000 and 2010.
Specifically, we rank neighborhoods according to population change and omit the top and bottom
1 percent. In total, our sample includes 49,074 tracts spread across 331 metropolitan areas.
Prevalence of Racial Integration
Our first questions are simply how many neighborhoods are racially integrated, and how has that
number changed in the past two decades? As exhibit 1 shows, 30 percent of metropolitan census
tracts (or slightly more than 14,600 census tracts) in the United States were racially integrated in
2010, according to our definitions. Of these tracts, most were White-Black or White-Hispanic;
these two types of neighborhoods together accounted for about three-fourths of all integrated neigh -
borhoods. Exhibit 1 also shows that integration has become more common in the past 20 years.
In 1990, slightly less than 20 percent of metropolitan census tracts were racially integrated. That
share then rose to 25 percent in 2000 and 30 percent in 2010.
8
Not all types of integrated neigh-
borhoods have seen the same rate of growth. The proportion of neighborhoods shared by Black
and White residents grew between 1990 and 2000 but remained similar between 2000 and 2010,
whereas the proportions of other types of integrated neighborhoods increased steadily in both
8
We obtain nearly identical percentages in each decade when weighting by population.
Exhibit 1
Overall Share (%)
1990 2000 2010
Racial Composition of Census Tracts, 1990–2010
Integrated
White-Black 9.1 10.1 10.1
White-Hispanic 7.7 10.0 12.9
White-Asian/other 1.4 2.5 3.4
White-mixed minority 1.5 2.6 3.8
Total integrated 19.7 25.2 30.3
Nonintegrated
White 69.6 60.5 52.3
Black 6.0 6.9 6.9
Hispanic 2.3 3.7 5.5
Asian/other 0.3 0.5 0.6
Mixed minority 2.2 3.4 4.5
Total nonintegrated 80.4 74.8 69.8
N = 49,074.
Sources: 2010 Decennial Census; Neighborhood Change Database

40
Ellen, Horn, and O’Regan
Residential Mobility: Implications for Families and Communities
decades. To some extent, this growth might simply reflect the underlying growth in Hispanic and
Asian populations. The proportion of Hispanic residents (defined as share Hispanic) in all tracts
in our sample nearly doubled between 1990 and 2010, rising from 10.2 to 18.3 percent of the
population, and the share of Asian/other residents (share Asian/other) rose from 4.0 to 7.5 percent.
When examining who lives in integrated neighborhoods, we find that White households are much
less likely than minority households to live in such communities. In 2010, only 24 percent of White
households lived in integrated neighborhoods compared with 39 percent of Black households,
42 percent of Hispanic households, and 44 percent of Asian/other households. White households,
however, have experienced a larger increase in integration than minority households. The percentage
of White residents (share White) living in integrated tracts rose from 14 percent in 1990 to 24 per-
cent in 2010, whereas the share of Black residents (share Black) living in integrated tracts rose from
34 to 39 percent and the share Hispanic in integrated tracts inched up from 40 to 42 percent.
In terms of regions, exhibit 2 shows that, perhaps not surprisingly, integration was most common
in the West; indeed, by 2010, 41.3 percent of census tracts in the West Census Region were racially
integrated. The Midwest, with only 20.1 percent of census tracts classified as integrated, was the
least integrated region. Notably, despite the great variation in the extent of racial integration, the
prevalence of integration increased in all four regions between 1990 and 2010, and the relative
ranking remained the same.
Exhibit 2 also shows the shares of integrated neighborhoods for central-city and suburban neigh-
borhoods for 1990, 2000, and 2010. In each year, a greater share of central-city neighborhoods
were integrated than suburban neighborhoods. In 2010, for example, 36.5 percent of central-city
neighborhoods were racially integrated compared with just 25.7 percent of suburban neighbor-
hoods. Integration became more common in both suburban and central city areas during our study
period, however.
Share Integrated (%)
1990 2000 2010
Exhibit 2
Share of Tracts Integrated, by Census Region and City/Suburb, 1990–2010
Census region
Northeast 12.4 17.2 21.0
Midwest 11.5 15.9 20.1
South 24.8 30.7 36.5
West 28.1 35.0 41.3
City/suburb
Central city 26.4 32.5 36.5
Suburb 14.8 19.9 25.7
Sources: 2010 Decennial Census; Neighborhood Change Database

Pathways to Integration:
Examining Changes in the Prevalence of Racially Integrated Neighborhoods
41Cityscape
Pathways to Integration
As we noted previously, an increase in the prevalence of integrated neighborhoods can occur in
two ways: a larger share of homogeneous neighborhoods might become integrated, or a larger
share of integrated neighborhoods might remain integrated.
9
We find that both of these channels to
integration increased between the 1990s and the 2000s. Exhibit 3 shows that four of our five types
of nonintegrated neighborhoods were more likely to become integrated between 2000 and 2010
than they were between 1990 and 2000. Whereas most increases were fairly modest, the increase
for largely Black neighborhoods was dramatic. Although only 5.5 percent of these neighborhoods
became integrated between 2000 and 2010, this share was up from only 1.8 percent in the 1990s.
In absolute numbers, 173 largely Black neighborhoods became integrated between 2000 and 2010,
up from just 54 census tracts between 1990 and 2000. Interestingly, most of the increase came
from neighborhoods in the South.
Despite this shift, predominantly White neighborhoods remained far more likely to become
integrated than did largely minority neighborhoods. Between 2000 and 2010, 15.0 percent of
predominantly White neighborhoods became integrated compared with only 5.5 percent of Black
neighborhoods, 3.4 percent of Hispanic neighborhoods, 6.4 percent of Asian/other neighborhoods,
and 4.9 percent of mixed-minority neighborhoods. Thus, contrary to media attention on the
entry of young White residents into a few urban, minority neighborhoods, integration still results
overwhelmingly from the in-movement of minority households to largely White neighborhoods.
Indeed, of all newly racially integrated neighborhoods in 2010, 93 percent were White neighbor-
hoods in 2000.
10
The regional variation exhibit 4 presents is striking. Between 2000 and 2010, 18.8 percent of
nonintegrated neighborhoods in the West became integrated compared with 16.3 percent in
the South, 9.3 percent in the Northeast, and only 8.6 percent in the Midwest. Such transitions
Exhibit 3
Share Integrated (%)
1990–2000 2000–2010
Transitions to Integration, by Nonintegrated Census Tract Type
White 14.1 15.0
Black 1.8 5.5
Hispanic 3.7 3.4
Asian/other 5.0 6.4
Mixed minority 2.2 4.9
Total nonintegrated 12.6 12.9
Sources: 2010 Decennial Census; Neighborhood Change Database
9
In addition, the number of neighborhoods might change, and more new neighborhoods might be integrated from the
start. Note, we have also performed our analysis when controlling for the number of neighborhoods and found the same
pattern; new census tract designations do not drive the increased prevalence of integrated neighborhoods.
10
This share was actually down from 2000, when 97.5 percent of all newly racially integrated neighborhoods were White
neighborhoods in 1990.

42
Ellen, Horn, and O’Regan
Residential Mobility: Implications for Families and Communities
increased slightly from the 1990-to-2000 period, an increase that primarily occurred in the
Midwest and South. Despite these differences, in all four regions, more than 90 percent of newly
integrated neighborhoods in 2010 were White neighborhoods in 2000.
We next turn to the stability of integrated neighborhoods over this period. Exhibit 5 shows the
share of neighborhoods in each of our four categories of integrated neighborhoods that remained
integrated at the end of the decade. The first column shows the results for the 1990-to-2000
decade and the second column for the 2000-to-2010 decade.
Two key observations emerge from this exhibit. First, integrated neighborhoods appear fairly
stable in each decade. We see that the overwhelming majority of neighborhoods that began each
decade integrated ended the decade integrated. Second, a noticeably larger share of each category
of integrated neighborhood remained integrated between 2000 and 2010 than between 1990 and
2000. Of White-Black neighborhoods in 1990, 78.6 percent remained integrated 10 years later.
That share rose to 82.6 percent between 2000 and 2010. White-Hispanic neighborhoods showed
a similar pattern. Between 1990 and 2000, 78.8 percent remained integrated, whereas between
2000 and 2010, 82.7 percent remained integrated. The increases over this period were even more
striking for White-Asian/other and White-mixed-minority neighborhoods, with the share of the
former remaining integrated rising from 82.8 to 89.4 percent and the share of the latter remaining
integrated rising from 50.0 to 64.0 percent.
Despite this substantial increase in stability, White-mixed-minority neighborhoods were consider-
ably less likely to remain integrated than were other integrated neighborhoods, probably because
the baseline proportion of White residents in White-mixed-minority neighborhoods is considerably
lower than in other integrated neighborhoods. In 2000, the average White-mixed-minority neigh-
borhood was 34 percent White, whereas the average White-Black neighborhood was 51.8 percent
Exhibit 4
Share Integrated (%)
1990–2000 2000–2010
Transitions to Integration, by Census Region
Northeast 9.2 9.3
Midwest 7.7 8.6
South 15.3 16.3
West 18.8 18.8
Sources: 2010 Decennial Census; Neighborhood Change Database
Exhibit 5
Share Integrated (%)
1990–2000 2000–2010
Stability of Integration, by Integrated Census Tract Type
White-Black 78.6 82.6
White-Hispanic 78.8 82.7
White-Asian/other 82.8 89.4
White-mixed minority 50.0 64.0
Total integrated 76.8 81.6
Sources: 2010 Decennial Census; Neighborhood Change Database

Pathways to Integration:
Examining Changes in the Prevalence of Racially Integrated Neighborhoods
43Cityscape
White. Hence, it takes a much smaller decline in the White population for a typical White-mixed-
minority neighborhood to transition to an all-minority neighborhood than for a White-Black
neighborhood to transition.
To avoid this asymmetry, and in recognition that the primary avenue through which integrated
neighborhoods transition to nonintegrated is through declines in share White, exhibit 6 shows
changes in share White across decades for each of our integrated neighborhood categories. Column 1
shows that share White fell in all types of integrated tracts in both decades, but the loss in share
White was less in each type of integrated neighborhood between 2000 and 2010 than during the
1990s. The mean decrease in share White in White-Black tracts, for example, was 8.2 percentage
points between 2000 and 2010 compared with 10.9 percentage points during the 1990s. Even
when accounting for changes in the overall rate of share White decrease in metropolitan areas,
integrated tracts appear to have been more stable between 2000 and 2010 than they were during
the 1990s.
11
The remaining three columns in exhibit 6 provide more detailed information on the distribution of
neighborhoods within each of our categories by the change in share White. The numbers show that,
in both decades, a substantial majority of all types of integrated tracts experienced a 5-percentage-
point-or-greater decline in share White and were thus at risk of not remaining integrated over time.
Once again, however, a lower proportion of integrated neighborhoods experienced a significant
loss in share White between 2000 and 2010 than during the 1990s.
In summary, we see an increase in both pathways to racial integration between the 1990s and
the 2000s. The shift over time was more dramatic, however, for the proportion of neighborhoods
remaining integrated than it was for the proportion of neighborhoods becoming integrated.
Exhibit 6
Mean Percentage-Point
Change in Share White
Tract Distribution, by Percentage-Point
Change in Share White (%)
Decrease ≥ 5
Increase or
Decrease < 5
Increase ≥ 5
Change in Share White, by Integrated Census Tract Type
1990 to 2000
White-Black – 10.9 66.3 24.8 8.8
White-Hispanic – 14.0 80.1 14.4 5.6
White-Asian/other – 12.8 80.9 16.2 2.9
White-mixed minority – 12.0 80.4 14.1 5.5
2000 to 2010
White-Black – 8.2 61.7 29.0 9.3
White-Hispanic – 10.4 78.2 16.1 5.7
White-Asian/other – 9.8 76.7 20.9 2.4
White-mixed minority – 8.1 71.6 21.4 7.0
Sources: 2010 Decennial Census; Neighborhood Change Database
11
The overall loss in share White in our metropolitan census tracts was very similar over the two decades, dropping from a
6.8-percentage-point decline during the 1990s to a 6.5-percentage-point decline during the 2000s.

44
Ellen, Horn, and O’Regan
Residential Mobility: Implications for Families and Communities
Which Neighborhoods Become Integrated?
As previously stated, almost no existing research examines the question of which neighborhoods
become integrated in the first place. We first focus on the predominantly Black neighborhoods that
became integrated in the 2000s, because this group of nonintegrated tracts experienced the largest
increase in the share becoming integrated. Exhibit 7 compares the 2000 baseline characteristics of
largely Black neighborhoods in which the share White increased by at least 5 percentage points
over the subsequent decade (thus moving toward integration) with those in which the share White
did not increase.
12
Note that when we examined changes in the absolute numbers of White residents,
we found that the White population actually grew significantly in the integrating tracts; these tracts
did not simply lose Black residents. Perhaps surprisingly, virtually no baseline difference emerged
in the proportion of Black residents across the two types of tracts. Perhaps even more surprisingly
Exhibit 7
Percentage-Point Change in Share White, 2000–2010
Increase ≥ 5
Increase or
Decrease < 5
Characteristics of Predominantly Black Census Tracts, by Subsequent Racial Change
Baseline neighborhood characteristics
Share Black (%) 86.3 88.9
Poverty rate (%) 30.7 25.2
Share with college degree (%) 14.1 12.7
Homeownership rate (%) 33.8 49.9
Share with children (%) 31.9 38.0
Share foreign born (%) 11.4 9.2
Median household income ($) 31,438 38,081
Contemporaneous neighborhood changes
(2000–2005/2009)
Poverty rate (percentage-point change) – 1.6 + 1.5
Share with college degree (percentage-point
change)
+ 10.5 + 2.2
Median household income ($ change) + 2,786 – 2,630
Baseline MSA characteristics
Share of tracts in central city (%) 93.8 81.8
Minority-White segregation index 0.60 0.61
Contemporaneous MSA changes (2000–2010)
Overall population (percentage-point change) + 7.1 + 5.5
Minority population (percentage-point change) + 21.3 + 19.3
House prices (2000–2006) (percentage-point
change)
+ 43.2 + 44.4
Total census tracts 384 2,990
MSA = metropolitan statistical area.
Sources: 2010 Decennial Census; Neighborhood Change Database
12
The results are largely the same for the 1990s.

Pathways to Integration:
Examining Changes in the Prevalence of Racially Integrated Neighborhoods
45Cityscape
(given racial differences in income), the neighborhoods that moved toward integration started
the decade with higher poverty rates and lower median household incomes. Middle-class Black
neighborhoods, in other words, are not the Black neighborhoods most likely to diversify. Indeed,
the largely Black neighborhoods in which the share White increased had average poverty rates of
31 percent, above a typical threshold used to identify high-poverty neighborhoods. This finding
suggests that the growth of the Black middle-class does not explain the increased integration of
Black neighborhoods.
We find more support for other theories about the entry to integration. In particular, the Black
neighborhoods that moved toward integration had lower homeownership rates, consistent with
the notion of race-based neighborhood stereotyping, which posits that White renters feel they face
less risk than White owners in entering a largely Black community (Ellen, 2000). The communities
that became more integrated also had fewer families with children, perhaps suggesting that White
households are more open to entering largely Black neighborhoods when those neighborhoods
(or those households) have fewer children. The neighborhoods where White populations grew
also tended to experience gains in median income and the share of residents with college degrees,
suggesting an economic as well as a racial transition (exhibiting patterns of transition typically
associated with gentrification).
In terms of broader metropolitan features, the Black neighborhoods where share White grew were
typically in more rapidly growing metropolitan areas (including growing minority populations) but
not specifically in areas with greater housing appreciation. The Black neighborhoods that became
more White were, however, more likely to be in central cities than were other Black neighborhoods.
Given that so many more integrated neighborhoods began as largely White, studying the charac-
teristics of the largely White neighborhoods that became integrated is arguably more important.
Exhibit 8 compares the 2000 baseline characteristics and selected contemporaneous changes of
largely White neighborhoods in which share White decreased by at least 5 percentage points in
the subsequent decade (thus moving toward integration) with those in which share White did not
decrease.
13
In this case, we see few notable differences across the two groups of neighborhoods.
Largely White tracts that experienced a loss in share White were slightly less White and had slightly
lower median household incomes at baseline, but otherwise the two groups exhibited few clear dif -
ferences, other than a somewhat lower homeownership rate and shorter distance to largely minority
neighborhoods. Notably, the two groups of neighborhoods also exhibited little difference in economic
trajectory; the contemporaneous income changes in the neighborhoods in which share White de -
creased were almost identical to those in the neighborhoods in which share White did not decrease.
Consistent with the preceding predictions, White tracts that experienced reductions in share White
and moved toward integration tended to be in metropolitan areas experiencing greater overall and
minority population growth and greater increases in housing costs than other White tracts. In addi -
tion, White tracts that experienced a decline in share White were more commonly in the central city.
13
Note that the growth in the absolute size of minority populations was a significant driver in the share White decrease. The
actual magnitude of the White population in these neighborhoods barely declined, whereas overall population growth was
quite high.

46
Ellen, Horn, and O’Regan
Residential Mobility: Implications for Families and Communities
Which Integrated Neighborhoods Remain Integrated Over
Time?
Another key question that remains unanswered is which integrated neighborhoods remain inte-
grated over time? To answer this question, exhibit 9 compares the 2000 baseline characteristics
of (a) White-Black and (b) White-Hispanic
14
integrated census tracts in which the share White
decreased by at least 5 percentage points between 2000 and 2010 (thus moving toward becoming
largely minority) with those of two other types of integrated neighborhoods: (1) those with stable
White population shares, and (2) those that experienced at least a 5-percentage-point increase in
share White (thus moving toward becoming largely White).
15
Exhibit 8
Percentage-Point Change in Share White, 2000–2010
Increase ≥ 5
Increase or
Decrease < 5
Characteristics of Predominantly White Census Tracts, by Subsequent Racial Change
MSA = metropolitan statistical area.
Sources: 2010 Decennial Census; Neighborhood Change Database
Baseline neighborhood characteristics
Share White (%) 83.9 91.7
Poverty rate (%) 7.4 7.0
Share with college degree (%) 30.4 31.8
Homeownership rate (%) 69.1 75.1
Share with children (%) 29.5 30.2
Share foreign born (%) 8.8 5.3
Median household income ($) 65,798 68,418
Distance to nearest minority tract (miles) 8.5 12.6
Contemporaneous neighborhood changes
(2000–2005/2009)
Poverty rate (percentage-point change) + 1.9 + 1.2
Share with college degree (percentage-point
change)
+ 2.9 + 3.4
Median household income ($ change) – 2,215 – 1,088
Baseline MSA characteristics
Share of tracts in central city (%) 34.0 23.0
Minority-White segregation index 0.55 0.57
Contemporaneous MSA changes (2000–2010)
Overall population (percentage-point change) + 8.7 + 5.8
Minority population (percentage-point change) + 31.4 + 27.6
House prices (2000–2006) (percentage-point
change)
+ 67.0 + 54.0
Total census tracts 15,891 13,782
14
We focus on these two categories of integrated neighborhoods because they are the most common.
15
The results were fairly similar when we replicated the analysis for integrated tracts in 1990.

Pathways to Integration:
Examining Changes in the Prevalence of Racially Integrated Neighborhoods
47Cityscape
Exhibit 9
Percentage-Point Change in Share White, 2000–2010
Decrease ≥ 5
Increase or
Decrease < 5
Increase ≥ 5
Characteristics of Integrated Census Tracts (1 of 2)
(a) White-Black tracts
Baseline neighborhood characteristics
Share White (%) 52.1 53.0 43.9
Share Black (%) 38.6 39.5 45.8
Share Hispanic (%) 5.6 4.4 6.2
Share Asian/other (%) 3.8 3.1 4.1
Share foreign born (%) 8.4 6.4 9.0
Poverty rate (%) 14.2 18.0 21.9
Share with college degree (%) 20.8 21.7 30.0
Black-to-White median household income ratio 0.87 0.73 0.62
Share White with children (%) 25.9 26.4 19.6
Share Black with children (%) 44.9 36.7 30.8
Homeownership rate (%) 57.1 56.0 40.5
Distance to nearest minority tract (miles) 4.2 8.0 4.4
Lag neighborhood changes (1990–2000)
Share Black (percentage-point change) + 14.6 + 2.7 – 1.3
Contemporaneous neighborhood changes
(2000–2005/2009)
Poverty rate (percentage-point change) + 3.4 + 1.2 – 1.3
Share with college degree (percentage-point
change)
+ 1.5 + 3.7 + 9.5
Median household income ($ change) – 10,387 – 7,403 – 1,348
Baseline MSA characteristics
Share of tracts in central city (%) 54.4 53.2 80.5
Minority-White segregation index 0.57 0.56 0.56
Contemporaneous MSA changes (2000–2010)
Overall population (percentage-point change) + 9.4 + 7.8 + 8.3
Minority population (percentage-point change) + 30.5 + 27.4 + 27.2
House prices (2000–2006) (percentage-point
change)
+ 47.7 + 50.1 + 49.8
Total census tracts 3,060 1,441 461
(b) White-Hispanic tracts
Baseline neighborhood characteristics
Share White (%) 48.5 45.4 42.1
Share Black (%) 6.5 5.8 6.5
Share Hispanic (%) 39.2 42.6 45.4
Share Asian/other (%) 5.8 6.2 6.0
Share foreign born (%) 22.5 27.6 30.4
Poverty rate (%) 14.6 18.5 20.5
Share with college degree (%) 17.4 22.6 28.5
Hispanic-to-White median household income ratio 0.88 0.78 0.74
Share White with children (%) 32.1 26.5 20.1
Share Hispanic with children (%) 57.1 50.3 45.5
Homeownership rate (%) 56.9 44.0 32.6
Distance to nearest minority tract (miles) 4.7 5.5 3.0

48
Ellen, Horn, and O’Regan
Residential Mobility: Implications for Families and Communities
We begin by contrasting integrated neighborhoods that experienced a loss in share White with
other integrated neighborhoods. Notably, integrated neighborhoods experiencing a loss in share
White did not have a larger share of non-White residents at the start of the decade than neighbor-
hoods where the White population was stable. Indeed, for White-Hispanic neighborhoods, the
share Hispanic was lower. Contrary to Schelling’s canonical model, then, the loss of White popu-
lation share in these neighborhoods does not appear to have been triggered by the size of the
minority population reaching some tipping point. In the White-Hispanic tracts, we also see that
tracts with more stable White populations had a larger foreign-born population than tracts that
lost White households. White residents do not seem to be avoiding immigrants.
The overall differences in socioeconomic status were small and mixed. Tracts with stable or grow-
ing White population shares tended to have higher poverty rates but more residents with college
degrees. Whereas income differences for White residents across these types of tracts were small,
minority incomes were much higher in tracts that experienced a loss in share White. In other
words, contrary to theoretical predictions, the tracts in which White and non-White residents had
more divergent incomes were more likely to see stable or growing White population shares. The
non-White residents in these neighborhoods might have lived in pockets that were less affluent
and cut off from the White part of the community. Alternatively, differences in demand from
Exhibit 9
Percentage-Point Change in Share White, 2000–2010
Decrease ≥ 5
Increase or
Decrease < 5
Increase ≥ 5
Characteristics of Integrated Census Tracts (2 of 2)
Lag neighborhood changes (1990–2000)
Share Hispanic (percentage points) + 15.2 + 9.7 + 2.4
Contemporaneous neighborhood changes
(2000–2005/2009)
Poverty rate (percentage-point change) + 1.8 – 0.5 – 2.5
Share with college degree (percentage-point
change)
+ 1.7 + 5.1 + 12.1
Median household income ($ change) – 7,825 – 4,265 – 1,764
Baseline MSA characteristics
Share of tracts in central city (%) 46.5 61.5 84.2
Minority-White segregation index 0.53 0.55 0.58
Contemporaneous MSA changes (2000–2010)
Overall population (percentage-point change) + 11.2 + 7.7 + 6.5
Minority population (percentage-point change) + 28.0 + 20.0 + 18.6
House prices (2000–2006) (percentage-point
change)
+ 99.7 + 88.2 + 94.4
Total census tracts 3,847 792 278
MSA = metropolitan statistical area.
Sources: 2010 Decennial Census; Neighborhood Change Database

Pathways to Integration:
Examining Changes in the Prevalence of Racially Integrated Neighborhoods
49Cityscape
middle-class minority residents may have explained the differences in growth patterns. Middle-class
minority residents may have been especially attracted to the integrated neighborhoods in which
minority residents had higher incomes (and thus minority-white income gaps were smaller).
16
As predicted, past growth in the minority population was highly correlated with losses in share
White in the most recent decade, perhaps because White households had little faith that those
neighborhoods would remain integrated over time. The tracts that experienced a loss in share White
were also closer to largely minority tracts than those integrated neighborhoods that remained racially
stable. This closer distance to largely minority tracts might have either contributed to fears of insta-
bility among White residents or increased the desirability of such neighborhoods for minorities.
The results are generally consistent with the idea that households with a greater stake in the com -
munity will be more wary of integration, although perhaps surprisingly more so for White-Hispanic
tracts. Tracts that experienced a loss in share White were generally those in which a greater share
of housing units were owner occupied and a greater share of households had children. Within
White-Black tracts, the real difference was that these shares were noticeably lower in the small
number of tracts that actually saw a gain in share White.
The differential in the share of households with children held for both White and non-White
households. The fact that the minority households living in tracts that retained or gained share
White also had fewer children might suggest that White households are less comfortable with
integration when more of their minority neighbors have children, or suggest that the White house-
holds who are open to integration are less interested in living in neighborhoods with services and
amenities geared to children.
As for the broader city or metropolitan area, integrated neighborhoods that experienced a loss in
share White were in metropolitan areas with more rapidly growing minority and total populations.
Counter to expectations, we see no meaningful difference in the baseline metropolitan racial segrega -
tion across these types of integrated tracts and no difference in the degree of house price appreciation.
The patterns of growth in the small share (7 percent) of neighborhoods that were integrated in
2000 and experienced a gain of more than 5 percentage points in share White (during a decade
when the average change in share White in metropolitan tracts was a decrease of 6.5 percentage
points) seem to suggest classic gentrification. First, they began the decade with larger minority
population shares and higher poverty rates than either of the other two groups of integrated tracts.
Second, these tracts saw reductions in poverty levels and large increases in the proportion of college-
educated residents. Third, these neighborhoods experienced both large increases in the absolute
size of the White population and declines in the absolute size of the minority population. Finally,
more than 80 percent of integrated neighborhoods that saw a gain in White population share were
located in central cities compared with only about one-half of those that lost or retained White
residents. Further, these tracts were much closer to a largely minority tract (also a sign of central
location) than were the integrated tracts that maintained their racial composition.
16
This pattern is consistent with Bayer, Fang, and McMillan (2011), who found that, as the share of Black residents with
higher levels of education increases in a metropolitan area, a greater share of these residents is more likely to choose to live
in middle-income Black neighborhoods.

50
Ellen, Horn, and O’Regan
Residential Mobility: Implications for Families and Communities
Conclusion
We address three distinct research questions in this article. First, we explore how prevalent racially
integrated neighborhoods have become from 1990 through 2010. We find significant growth in
the presence of integrated neighborhoods during this period, with the share of metropolitan tracts
that are integrated increasing from slightly less than 20 percent to slightly more than 30 percent.
Second, we examine the pathways through which integration has increased. We find during this
period both a small increase in the number of neighborhoods becoming integrated for the first time
and a more sizable increase in the share of integrated neighborhoods that remained integrated.
Although we observe a particularly substantial increase in the share of Black neighborhoods that
became integrated in the 2000s, the overall share remains small. Thus, belying the growing atten-
tion to the gentrification of largely Black neighborhoods, this path to integration remains lightly
tread. Examining the stability of racial integration, we find that integration appeared fairly stable
in both decades, but a larger share of each category of integrated neighborhood remained stable
during the 2000s than in the 1990s.
Third, we shed some light on the types of neighborhoods that have become integrated and have re-
mained so over time. Focusing on the types of Black neighborhoods that attracted White residents,
we find not middle-class minority neighborhoods but, rather, neighborhoods that initially had
higher poverty rates and lower levels of income. Also, these neighborhoods had lower homeowner-
ship rates, a finding that is consistent with the theory of race-based neighborhood stereotyping,
because renters are less likely to worry about a community’s future. Finally, the largely Black
neighborhoods that attracted White residents began with fewer families with children, suggesting
that White households might be more open to sharing neighborhoods with non-White neighbors
when those neighbors do not have children. As for the predominantly White neighborhoods that
moved toward integration, we find few evident patterns.
Finally, exploring the types of integrated neighborhoods that saw stable or growing White popula-
tion shares, we find, perhaps surprisingly, that they were those in which White and non-White
residents had more divergent incomes. In addition, again consistent with the notion of race-based
neighborhood stereotyping, the integrated tracts that retained (for White-Hispanic tracts) or
attracted (both White-Hispanic and White-Black tracts) White residents appear to be those inte-
grated neighborhoods that households believed would remain stable in the future based on past
trends. The integrated tracts that saw stable or growing White population shares were also those
with residents that likely had a lesser stake in the quality of a community’s services (notably renters
and households without children), again consistent with neighborhood racial stereotyping.
In the most recent decade, a small share of integrated tracts actually experienced a meaningful gain
in share White. Although the numbers remain small, this pattern suggests that some integrated
neighborhoods might unravel by becoming more White. These tracts were overwhelmingly in
central cities and closer to largely minority neighborhoods, perhaps suggesting a pattern of gentri -
fication. Indeed, the integrated neighborhoods that saw an increase in share White also saw reduc-
tions in poverty levels and increases in the number of college-educated residents.

Pathways to Integration:
Examining Changes in the Prevalence of Racially Integrated Neighborhoods
51Cityscape
In sum, although our cities and metropolitan areas remain highly segregated by race, a growing
number of neighborhoods are integrated and remain so over time. This article has also provided
some stylized facts on the types of neighborhoods that are becoming and remaining integrated over
time, but more work is needed to understand these pathways.
Acknowledgments
The authors thank Stephanie Rosoff for her outstanding research assistance and colleagues at the
Furman Center for Real Estate and Urban Policy for many useful discussions. The authors also
appreciate the helpful comments from Kirk White and other participants in Cityscape’s Symposium
on Residential Mobility, co-hosted by the Annie E. Casey Foundation and the Urban Institute.
Authors
Ingrid Gould Ellen is a professor of public policy and urban planning at New York University,
Wagner School of Public Service.
Keren Horn is an assistant professor of economics at the University of Massachusetts Boston.
Katherine O’Regan is an associate professor of public policy at New York University, Wagner
School of Public Service.
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54 Residential Mobility: Implications for Families and Communities

55Cityscape: A Journal of Policy Development and Research • Volume 14, Number 3 • 2012
U.S. Department of Housing and Urban Development • Office of Policy Development and Research
Cityscape
Residential Mobility and
Neighborhood Change:
Real Neighborhoods
Under the Microscope
Claudia Coulton
Case Western Reserve University
Brett Theodos
Margery A. Turner
Urban Institute
Abstract
Residential mobility is a process that changes lives and neighborhoods. Efforts to build
strong communities are unavoidably caught up with this dynamic but have insufficient
understanding of its complexities. To shed light on the underlying forces of residential
mobility, this study uses a unique panel survey from the Casey Foundation’s Making
Connections initiative targeting poor neighborhoods in 10 cities.
The study classified households in the 10 cities as movers, newcomers, or stayers,
and it evaluated the push and pull factors related to their mobility decisions. Cluster
analysis revealed discernible types based on life cycle, household economic factors, and
neighborhood attachment. The study also investigated the effect of residential mobility
on neighborhood composition, finding that neighborhood change was primarily due to
differences between movers and newcomers rather than changes for stayers. Combining
information on the mix of household types with the components of neighborhood change,
the study suggests these neighborhoods functioned in quite different ways that are
relevant to family well-being and community development.

56
Coulton, Theodos, and Turner
Residential Mobility: Implications for Families and Communities
Introduction
Americans change residences frequently and mobility rates are higher among low-income house-
holds, renters, and younger families. Households sometimes move to improve their housing situa-
tions or their neighborhood surroundings. Low-income households, however, may make frequent
moves because of economic or social distress. Residential mobility not only affects individual
households, but it may also affect neighborhoods as a whole. Place-based initiatives that attempt to
improve outcomes for individuals and strengthen neighborhoods face challenges in such dynamic
and fluid environments. Despite the importance of neighborhood change and mobility, however,
limited research has disaggregated how neighborhoods change for those households that remain in
the neighborhood and from the mix of those households that leave and join.
To shed new light on these processes, this article draws on a unique panel survey conducted as
part of the Making Connections initiative, a decade-long effort of the Annie E. Casey Foundation
that focused on target neighborhoods in 10 cities: Denver, Des Moines, Hartford, Indianapolis,
Louisville, Milwaukee, Oakland, Providence, San Antonio, and White Center (outside Seattle). The
target neighborhoods offer a unique and valuable window on the dynamics of low-income, mostly
minority neighborhoods nationwide.
This article consists of three components. The first component explores the characteristics and
changing circumstances of movers, newcomers, and stayers, identifying distinctly different groups
of households that reflect different reasons for moving or staying in place. The second component
focuses on how residential mobility contributed to changes over time in the socioeconomic com-
position of the Making Connections neighborhoods, essentially dividing neighborhood change
into changes contributed by households that stayed in the neighborhood versus changes caused by
differences between those who joined and those who left. The final component draws on these pat-
terns to suggest five stylized models of neighborhood performance, each of which has implications
for the well-being of low-income families and for community-change efforts.
Background
The recognition that place matters (Ellen and Turner, 1997) has led to several generations of
community-change initiatives that attempt to address conditions thought to negatively affect fami-
lies and children in poor neighborhoods. Often led by philanthropy and engaging both public and
private partners, these initiatives embody a range of strategies intended to benefit residents directly
through improved services and indirectly through strengthening social connectedness or access
to resources (Kubisch et al., 2010). Both the service-reform and community-building aspects of
community-change initiatives assume some degree of residential stability in their target areas. For
residents to benefit from improved services and conditions in their neighborhoods, they presum-
ably must have access to these programs for some minimum period of time. Likewise, for capacity
building to result in a community that can mobilize to achieve the common good, it needs some
stability in emerging leaders and networks. Thus, excessive residential mobility can be a challenge
to the theories of change and assumptions underlying community-change initiatives (Kubisch et
al., 2010; Silver, Weitzman, Mijanovick, and Holleman, 2012).

Residential Mobility and Neighborhood Change: Real Neighborhoods Under the Microscope
57Cityscape
It is important that residential mobility be appreciated in the context of community-change initia-
tives, however, for both its positive and negative aspects. Residential mobility can reflect improve-
ments in a family’s circumstances, such as buying a home for the first time, moving to be close to a
new job, or trading up to a better quality housing unit or neighborhood. It can also be a symptom
of instability and insecurity, with many low-income households making short-distance moves
because of problems with landlords, creditors, or housing conditions. Similarly, staying in place
sometimes reflects a family’s security, satisfaction, and stability with its home and neighborhood
surroundings, but in other cases it may reflect that a family lacks the resources to move to better
housing or to a preferred neighborhood (Gramlich, Laren, and Sealand, 1992; South, Crowder,
and Chavez 2005). Moreover, residential turnover can be a source of neighborhood vitality and
progress. Any such one-dimensional views of residential mobility within communities may mask
important and valuable variants regarding the functions of neighborhoods (Robson, Lymperopou-
lou, and Rae, 2008), potentially useful information to guide community-change initiatives.
Factors Related to Household Moves
Many push and pull factors affect a household’s decision to relocate and influence the move’s timing
and location. Changing household circumstances, such as employment or family composition, may
make the current housing unit or location less tenable or satisfactory. In addition, deterioration in
the current housing unit or the surrounding area may further the desire to move. The household
may also be attracted to other housing units or neighborhoods for various reasons that contribute
to the decision to relocate. At the same time, however, the household may experience forces that
make them resistant to a move, including attachment to their current house or neighborhood and
relationships that would be disrupted by a move; they may also face physical, economic, or social
barriers to achieving a desirable living situation elsewhere. Such complexities have generated several
complimentary conceptual frameworks to explain both the intention to move and the actual moving.
A commonly used theoretical framework for understanding residential mobility is a disequilibrium
model. In this model, a decision to move occurs when the current living arrangements become
suboptimal. Absent such disequilibrium, the household will stay put, because it incurs adjustment
costs and other losses when moving. What is optimal relates to the housing unit’s characteristics,
its location, and the neighborhood surroundings relative to the household’s needs and preferences
(subject to cost and income constraints). Housing that may have been optimal can become sub-
optimal due to changes in household composition or circumstances, housing or neighborhood
quality, and household income or the cost of housing. Theory has also drawn a distinction among
the household’s experience of housing dissatisfaction, the intent to move, and the household’s
actual relocation (Speare, 1974). The decision about whether to move can be seen as weighing
satisfaction with current housing relative to the anticipated satisfaction with alternatives. From
this point of view, a combination of push and pull factors determines if, when, and where the
household moves, subject to various constraints or barriers to mobility.
A complimentary framework, the life-course perspective, views residential mobility as one of many
related aspects of human development. From this point of view, moving or staying is related to
other life events such as marriage or divorce; birth of children; children leaving home or attending
college; change of employer, income, or assets; and retirement. Several studies have found that

58
Coulton, Theodos, and Turner
Residential Mobility: Implications for Families and Communities
these life events are potential triggers of mobility (Clark, 2005; Clark and Withers, 1999). These
events can result in dissatisfaction with the current house, such as when a growing family needs
more space, or may change the household’s aspirations, such as when a better job leads to increased
status expectations. Moreover, homeownership or residential stability may become more or less
salient at particular stages of life, such as marriage, birth of a child, or retirement. These life events
tend to be correlated with demographic characteristics, such as age, gender, race or ethnicity, so-
cioeconomic status, and so forth, and these characteristics are also associated with the probability
of residential mobility.
Neighborhood attachment and social ties may deter residential mobility or affect the distance
that a household moves. Positive feelings toward the neighborhood and strong social connections
have been found to keep households in place longer, and these effects have a stronger limiting
effect on residential mobility among low-income compared with high-income families. (Dawkins,
2006). Attachment to the neighborhood may also affect where households move and how they
adjust to their new surroundings. A study of Seattle movers found that households moving a
shorter distance (that is, staying in the same census tract) showed higher post-move neighborhood
attachment. Also, households that moved for family reasons showed lower attachment to their
new neighborhood than did households that moved to improve their housing or neighborhood
surroundings (Bolan, 1997).
Although most of the literature has focused on explaining the likelihood that households will
move, some of the literature addresses the concern that some households face barriers to effective
residential mobility. In particular, racial segregation and racial inequities may undermine the
probability that people of color can move to satisfactory housing and neighborhoods. A study of
structural barriers to residential mobility found that after life-cycle factors and neighborhood and
housing satisfaction were held constant, African-American households in the United States had
a lower probability of moving than White households. Although neighborhood dissatisfaction
predicted residential movement among Whites, it was the opposite among African Americans, with
African-American homeowners who judged their neighborhoods to be only fair as compared with
excellent less likely to move than Whites who expressed similar dissatisfaction (South and Deane,
1993). This pattern suggests that many African-American households may remain in unsatisfactory
housing or neighborhoods due to social and economic barriers to movement. Moreover, studies
demonstrate African Americans are less likely than any other ethnic group to move to better
neighborhoods, despite gains in education and income that permit other groups to move up and
out (Logan et al., 1996; Sharkey, 2008).
Although residential mobility can be a path to greater opportunity and satisfaction, concern exists
that many low-income families move not to better their circumstances but due to unstable housing
arrangements, and that such moves may have negative consequences. Some studies suggest that
frequent moving during childhood undermines educational attainment (Wood et al., 1993), but
other studies have found little or no effect after other risk factors are taken into account (Murphey,
Bandy, and Moore, 2012). Nevertheless, relocating may disrupt social ties and undermine a fam-
ily’s social capital (Briggs, 1997), and it has a particularly disruptive effect on children when par-
ents provide only modest emotional support and involvement (Hagan, MacMillan, and Wheaton,
1996). The quality of the new neighborhoods may buffer the effect of a move as well. For example,

Residential Mobility and Neighborhood Change: Real Neighborhoods Under the Microscope
59Cityscape
teenagers who moved into distressed neighborhoods had higher dropout rates than those who had
lived there a longer time (Crowder and South, 2003), but teenagers who moved from poverty areas
to middle-class neighborhoods established positive ties in their new locations (Pettit, 2004).
Effect of Mobility on Neighborhoods
Residential mobility affects not only individual families, but it may also change the neighborhood
as a whole. In particular, very high residential turnover can contribute to the erosion of social con-
trol and social capital. Studies have shown a negative effect of residential turnover on a neighbor-
hood’s collective efficacy, and this loss has been linked to problems such as crime and delinquency
(Morenoff, Sampson, and Raudenbush, 2001; Sampson and Raudenbush, 1997). Moreover, high
residential turnover may itself promote further mobility, as suggested by the link found between
residents’ desire to move and the perceptions that neighborhood residents move frequently or are
not “close knit” (Clark and Ledwith, 2006; Lee, Oroposa, and Kanan, 1994).
If the characteristics and well-being of newcomers differ from those of movers, mobility can
change a neighborhood’s demographic or socioeconomic mix, which in turn can reposition the
neighborhood with institutions, resources, and the marketplace (Bruch and Mare, 2006). For
example, differential mobility into and out of a neighborhood might result in an increasing share
of minority residents or new immigrants, rising homeownership rates or incomes, or a growing
share of childless residents. The evolving profile of a neighborhood’s population can further affect
investments by both individuals and institutions through social and political processes that are
reinforcing and evolve over time (Temkin and Rohe, 1996). But selective mobility can also main-
tain a neighborhood’s status quo, despite changes in individual residents’ well-being. For example,
if the more successful residents leave a distressed neighborhood and are replaced by others who
are less well off, the neighborhood will remain distressed, even though individual households from
the neighborhood improved their economic status (Andersson and Bråmå, 2004).
The realities of residential mobility and neighborhood change make evaluating community-change
initiatives difficult. Interventions may improve services for neighborhood residents or create em-
ployment and other opportunities, but needy families might not remain in the same neighborhood
long enough to benefit. Alternatively, families may take advantage of the neighborhood’s enhanced
services and opportunities, and then move because they have benefited. In addition, larger struc-
tural forces in the surrounding housing market or economy may cause more affluent families to
move into a neighborhood, improving its profile without producing any gains in the well-being of
low-income residents. The process of selective mobility is complicated, however, because it is not
simply a collection of individual decisions but is also a process that is influenced by macro forces,
including public policy, housing markets, economic shifts, and racial segregation.
Study Design and Methods
The Making Connections neighborhoods, like neighborhoods in general, experience considerable
residential mobility. At the same time, however, they are neighborhoods in which many residents
stay in place. Using two waves of household surveys, this study segments the population of movers,
stayers, and newcomers into clusters that indicate whether their mobility behavior reflects positive

60
Coulton, Theodos, and Turner
Residential Mobility: Implications for Families and Communities
or negative transitions. In addition, it partitions changes in the socioeconomic composition of
the neighborhood into the changing poverty status of stayers versus differences in poverty status
between movers and the newcomers that replace them. These two perspectives on neighborhood
dynamics are drawn on to suggest qualitative differences in how neighborhoods are functioning in
this community-change initiative.
Data Sources
The data for this study come from the Making Connections survey that provides information about
representative samples of households in the initiative’s 10 target neighborhoods.
1
Data come from
two waves of surveys, with wave 1 conducted between 2002 and 2004 (depending on the neigh-
borhood) and wave 2 conducted between 2005 and 2007. At wave 1, interviews were conducted
at a random sample of residential addresses in each neighborhood. Then, at wave 2, researchers
returned to the same addresses, interviewing the current occupants, regardless of whether they
were the same residents as at wave 1. If the household living at a sampled address had moved by
the time of the second survey and if the original household had children, it was contacted and
interviewed at its new address.
2
At both waves, survey questions covered a wide range of topics,
including employment, income, hardship, community engagement, satisfaction with neighborhood
services, and perceptions of neighborhood quality, safety, and social cohesion. This approach makes
it possible to measure changes in the composition of the neighborhoods as well as changes in the
location and well-being of families with children who lived in these neighborhoods at baseline.
Study Sites
The Making Connections neighborhoods are not a nationally representative sample and all are
relatively disadvantaged. Shown in exhibit 1, however, neighborhoods vary considerably in their
demographic and economic composition. At the time of wave 1, 39 percent of households in these
neighborhoods fell below the federal poverty level, but the 10 neighborhoods were not equally
poor. Four neighborhoods had poverty rates above 40 percent at the beginning of the study, with
the Louisville neighborhood at the extreme with 57 percent. White Center had the lowest poverty
rate at 19 percent.
The survey neighborhoods also vary widely in racial and ethnic composition. In the Des Moines,
Indianapolis, and White Center neighborhoods, most households were non-Hispanic White.
3

1
These neighborhoods were selected (and their boundaries defined) in partnership with local policymakers and practitioners,
and, as a consequence, they vary in size and composition. These areas are larger than traditionally defined neighborhoods.
The median size is 4.9 square miles, with a median population of 30,598. The Making Connections neighborhoods do not
always correspond to what might be considered natural neighborhood boundaries, and three target areas are composed
of multiple, noncontiguous neighborhoods. Although these areas may differ from what are traditionally perceived to be
neighborhoods, we use the term neighborhood to describe them for readability and consistency with the wider literature.
2
Because the Making Connections initiative focuses on the well-being of families with children, childless households that
moved between survey waves were not reinterviewed at their new addresses.
3
Households have been classified as non-Hispanic White, non-Hispanic African American, Hispanic, Asian, or other
ethnicity. For the remainder of this report, the term White refers to non-Hispanic Whites and the term African American
refers to non-Hispanic African Americans.

Residential Mobility and Neighborhood Change: Real Neighborhoods Under the Microscope
61Cityscape
The residents of Louisville and Milwaukee neighborhoods were predominantly African American,
but the residents of the San Antonio neighborhood were predominantly Hispanic. Hartford, Prov-
idence, and Denver had substantial populations of both African Americans and Hispanics. The
White Center and Oakland neighborhoods reflect the greatest racial and ethnic diversity, including
Whites, African Americans, Hispanics, Asians, and other ethnic groups.
Poverty and race are correlated with other indicators of well-being: quality work, health benefits,
educational opportunities, and economic success. The survey neighborhoods, in general, have low
homeownership rates (34 percent), low college completion (12 percent), a low share of households
with working adults (63 percent), and low incomes (only 28 percent of households earn above
$30,000).
The Making Connections neighborhoods also have high rates of residential mobility, as shown in
exhibit 1. In the 3 years between survey waves, more than one-half (57 percent) of the households
from the survey neighborhoods moved out of their original housing units.
4
The 3-year mobility
rates ranged from a low of 43 percent (in San Antonio) to a high of 65 percent (in Milwaukee).
In all but two neighborhoods, more than one-half of the households moved.
4
In wave 2 of the Making Connections survey, interviewers returned to the same sample of residential addresses that they
interviewed at wave 1. If the focus child from the wave 1 interview was no longer living at that address (and was not yet
more than 18 years of age), the wave 1 household was classified as a mover and was interviewed at its new address. The
household currently living at the original sample address was classified as a newcomer, even though it is possible that some
members of the wave 1 household still remained.
Exhibit 1
Site
Poverty
Rate
Percent
White
a
Percent
Black
a
Percent
Hispanic
Percent
Asian
a

and
Other
a
Percent
Home-
owner
Percent
College
Graduate
or Higher
Percent
Employed
Adult in
House-
hold
Percent
Turnover
Between
W1 and
W2
Demographic and Economic Characteristics of Residents by Neighborhood
Denver 38.2 40.1 14.3 36.4 9.3 35.4 27.3 64.3 56.4
Des Moines 32.6 51.7 27.6 9.7 11.0 50.9 12.3 69.1 50.9
Hartford 46.3 5.4 53.4 36.0 5.3 12.5 8.5 56.4 63.4
Indianapolis 33.6 60.2 27.2 8.5 4.0 41.0 6.9 66.6 59.3
Louisville 57.2 16.0 78.8 2.2 3.1 22.2 8.1 47.3 63.6
Milwaukee 49.3 10.7 76.1 4.7 8.5 29.9 9.8 57.4 65.4
Oakland 35.0 10.5 25.1 28.2 36.2 17.6 14.8 67.6 59.8
Providence 39.0 14.1 24.8 47.2 13.9 25.9 14.6 63.6 56.4
San Antonio 42.4 5.9 1.8 84.9 7.4 54.0 3.8 64.6 42.7
White Center 19.2 54.4 8.6 14.9 22.1 51.3 18.2 74.5 47.3
Average 39.3 26.9 33.8 27.3 12.1 34.1 12.4 63.1 56.5
W1 = wave 1. W2 = wave 2.
a
Non-Hispanic.
Note: Racial, education, and employment characteristics are for survey respondents. Poverty, homeownership, and earnings
characteristics are for survey households.
Source: Making Connections neighborhood-change data, wave 1

62
Coulton, Theodos, and Turner
Residential Mobility: Implications for Families and Communities
Based on these indicators, some illustrative contrasts among the neighborhoods can be identified.
The Making Connections neighborhood in Louisville epitomizes a severely distressed urban neigh -
borhood, with 57 percent of households below the poverty level and only 14 percent earning
more than $30,000. This neighborhood is mostly composed of renters, including a large share of
subsidized housing; only 22 percent of households own their homes. Only 8 percent of the survey
respondents have a college degree, and less than one-half are in working households (47 percent).
Hartford and Milwaukee are only slightly less disadvantaged than Louisville along most of these
same dimensions. San Antonio’s Making Connections neighborhood is also deeply poor (42 percent
of households below the poverty level), with only 19 percent of households earning more than
$30,000. But it is a more stable neighborhood, with a large share of homeowners (54 percent) and
moderate employment (65 percent), although little formal education (46 percent of residents have
no high school degree).
In Denver, Oakland, and Providence, poverty rates are still high (35 percent or more), but the
neighborhoods appear considerably less distressed. About two-thirds of the households in these
neighborhoods have an employed adult. Denver’s neighborhood also includes a considerable
number of relatively well-off households. Specifically, 36 percent earn more than $30,000 and
27 percent have college degrees. Poverty rates in the Making Connections neighborhoods of Des
Moines and Indianapolis are somewhat lower, although still above 30 percent. Both have high
homeownership rates and high rates of employment, but they have few college graduates and few
households earning more than $30,000.
Finally, the White Center neighborhood differs from all the other neighborhoods; it is much less
poor. Only 19 percent of households have incomes below the poverty level, and more than one-
half (57 percent) earned more than $30,000. Relatively large shares of residents are homeowners
(51 percent), college graduates (18 percent), and employed (75 percent).
Cluster Analysis of Movers, Newcomers, and Stayers
We anticipated that some households may be making positive moves to better housing or neigh-
borhoods, some may be moving because changes in family size or composition require a different
housing unit, and some may be moving involuntarily, due to a crisis or economic insecurity. Also,
some households that stayed may be satisfied with their house and neighborhood, but others may
be dissatisfied but unable to move due to barriers. Similarly, some newcomers may be drawn to a
place to improve their circumstances, but others may face limited housing options or be relocating
under duress. Because the literature suggests many factors that influence moving, the identification
of types requires a method that can uncover differences among households along many dimensions
simultaneously. We use cluster analysis to explore whether identifiable groups of movers, newcomers,
and stayers exist based on factors influencing their mobility and how much they are bettering or
worsening their residential situations. A mover is defined as a household that moved out of its housing
unit between wave 1 and wave 2, a stayer is a household that was in the same housing unit at both
waves,
5
and a newcomer is a household that was in its housing unit at wave 2 but not at wave 1.
5
Because the Making Connections survey did not reinterview childless households that moved between survey waves, our
analysis of movers is limited to families with children.

Residential Mobility and Neighborhood Change: Real Neighborhoods Under the Microscope
63Cityscape
The variables used in the cluster analysis were chosen based on the literature cited in the text. We
identified variables in the Making Connections survey that represented factors that could affect
the chances that a household would move or stay in its housing unit between the two waves of the
survey. Exhibit 2 lists these variables and their definitions. Newcomers were interviewed only in
wave 2, so the cluster analysis for newcomers involves a more limited set of variables.
Exhibit 2
Variable Definition
Movers
With
Children
New-
comers
Stayers
Variables for Cluster Analysis (1 of 3)
Demographic
Age Respondent's age at time of survey—continuous W1 W2 W1
Age of youngest
child
Age of youngest child W2 W2 W2
Joined spouse Respondent's spouse or partner was not present in
household at wave 1 but is present at wave 2—Yes/No
W1 to
W2
change
Left or lost
spouse
Respondent's spouse or partner was present in
household at wave 1 but is not present at wave 2—
Yes/No
W1 to
W2
change
Spouse or
partner
present
Respondent's spouse or partner was present in
household at time of survey—Yes/No
Change in
number of
children
Children left or entered the household; this is the
difference in the number of children in the household
between wave 2 and wave 1—continuous
W1 to
W2
change
Number of
children
The number of children present in the household—
continuous
W2 W1
Employment, income, and distress
Got job Respondent and/or spouse not employed at wave 1
and respondent and/or spouse employed at wave 2—
Yes/No
W1 to
W2
change
Lost job Respondent and/or spouse employed at wave 1 and
respondent and/or spouse not employed at wave 1
at wave 2—Yes/No
W1 to
W2
change
Employed Childless movers and newcomers: respondent and/
or spouse employed at time of survey. Stayers:
respondent and/or spouse employed at wave 1 and
at wave 2—Yes/No
W2 W1
and
W2
Not employed Childless movers and newcomers: respondent and/
or spouse not employed at time of survey. Stayers:
respondent and/or spouse not employed at wave 1
and wave 2—Yes/No
W2 W1
and
W2
Income Total household income—continuous W2 W2 W2
Difficulty paying
housing costs
Household had difficulty paying its housing costs
and/or utilities for the household were disrupted by
nonpayment of bills—Yes (if either or both are true)/
No to both
W1 W2 W1

64
Coulton, Theodos, and Turner
Residential Mobility: Implications for Families and Communities
Exhibit 2
Variable Definition
Movers
With
Children
New-
comers
Stayers
Variables for Cluster Analysis (2 of 3)
Homeownership and housing subsidy (public housing or voucher)
Became a
homeowner
Respondent rented housing unit at wave 1 and was a
homeowner or was in the process of homebuying at
wave 2—Yes/No
W1 to
W2
change
Became a renter Respondent was a homeowner or was in the process of
homebuying at wave 1 and was a renter at wave 2—
Yes/No
W1 to
W2
change
Homeowner Respondent was a homeowner or was in the process of
homebuying—Yes/No
W2 W1
Kept subsidized
housing
Household received subsidy for housing cost in wave 1
and wave 2—Yes/No
W1 to W2
change
Lost subsidized
housing
Household received housing subsidy in wave 1 but did
not at wave 2—Yes/No
W1 to W2
change
Gained
subsidized
housing
Household did not receive subsidy at wave 1 but
received subsidy at wave 2—Yes/No
W1 to
W2
change
Never had
subsidized
housing
Household did not receive subsidy at either wave 1
or 2—Yes/No
W1 to
W2
change
Subsidized
housing
Household had subsidized housing at the time of the
survey—Yes/No
W2 W1
Neighborhood services and future
Perceived safe
neighborhood
Difference in combined scale based on how safe
respondent feels in neighborhood between wave 2
and wave 1: neighborhood is safe for children, safe in
home at night, safe in neighborhood during the day,
would help someone asking for directions, children go 

trick or treating, most criminal activity is committed by
people who live outside the neighborhood—ordinal
W1 to
W2
change
W2 W1
School
satisfaction
Difference in satisfaction with child's school for respon-
dents with children between wave 2 and wave 1
(focus child responses used in wave 2)—ordinal
W1 to
W2
change
W2 W1
Neighborhood attachment
Neighborhood
involvement
Combined scale variable at time of survey based on
neighborhood-attachment variables: respondent
attends neighborhood events, respondent volunteers
in neighborhood, respondent gets together with
neighbors to resolve community problems—ordinal
W1 W2 W1
Know child’s
friends
Does respondent know none, some, or most of their
child's friends, for respondents with children at time of
survey (focus child responses used in wave 2)—ordinal
W1 W2 W1
Years in
neighborhood
Combined years and months lived in the neighborhood
at wave 1—continuous
W1 W1

Residential Mobility and Neighborhood Change: Real Neighborhoods Under the Microscope
65Cityscape
Demographic variables consist of proxies for life-cycle events that may trigger a move or that cap-
ture stages in the life cycle that are associated with the chances of moving. For example, although
households with children are generally more stable, gaining or losing children may trigger the need
for more or less space. Similarly, the addition or departure of a spouse or partner may influence
the desire or ability to relocate.
Employment, income, and distress variables relate to employment and income. Change in
employment status may trigger a move, either due to location of the job or its effect on income.
Financial hardships may bring on a housing crisis, but financial improvements may make a move
to a better situation possible.
Homeownership and housing subsidy (public housing or voucher) variables used in the
analysis show that homeowners and households with subsidies are expected to move less often,
but changing tenure is a possible reason for a move.
Neighborhood services and future variables measure perceived neighborhood quality. Dissatis-
faction with neighborhood quality could serve as a push factor for movers, but a positive view of
the neighborhood might be a pull factor for newcomers and stayers.
Neighborhood attachment measures anticipate that attachment might be strongest among stayers
and that movers who went only short distances or who were forced to move for other reasons
might also show high attachment.
Exhibit 2
Variable Definition
Movers
With
Children
New-
comers
Stayers
Variables for Cluster Analysis (3 of 3)
Neighborhood conditions (external measures)
Poverty rate of
census tract
Change between the 1999 poverty rate of wave 2
tract and the 1999 poverty rate of the wave 1 tract—
continuous
W1 to W2
change
Percent minority
of census tract
Change between the 1999 percent minority of wave 2
tract and the 1999 percent minority of the wave 1
tract. Percent minority is determined by subtracting by
the percent of white population in 1999 from the total
population—continuous
W1 to W2
change
Median home
loan amount
of census tract
Change in the median home loan amount between the
2005 wave 2 tract to the 2002 wave 1 tract. Median
home loan amount is defined as the median mortgage
amount for home purchase loans—continuous
W1 to W2
change
Move distance
Distance of
move
Log of distance of move W1 to W2
change
W1 = wave 1. W2 = wave 2.
Sources: Making Connections cross-site data, waves 1 and 2; 2000 census; Home Mortgage Disclosure Act

66
Coulton, Theodos, and Turner
Residential Mobility: Implications for Families and Communities
Neighborhood conditions—measures from the American Community Survey and Home Mort-
gage Disclosure Act data—distinguish movers who improved their neighborhood circumstances
from those who did not.
Move distance as a variable distinguishes movers who remained nearby from those who moved
farther away. Theoretical considerations and availability of data influenced how each cluster model
differed in some respects in the waves of data available and the variable specification. This measure
captures the distance households moved between wave 1 and wave 2. The variable is specified as
the log distance of the move, in miles.
The statistical procedure adopted to analyze this set of variables is cluster analysis. Cluster analysis
is an exploratory data analysis procedure that classifies cases into a smaller number of mutually
exclusive groups based on their similarity on a set of measures. Several algorithms are available for
clustering, but all rely on mathematical measures of distances among the cases on the variables.
The method used in this analysis is a nonhierarchical cluster technique known as k-means and
relies on Euclidean distances. The technique was chosen for this study because it is suitable for
variables that are continuous or categorical. After standardizing the input variables using the
Jaccard coefficient, we conducted this analysis using the FASTCLUS procedure in SAS. In cluster
analysis, cases with shorter distances on the set of variables are grouped together. The cluster
analysis was conducted separately for moves, newcomers, and stayers. We determined the number
of clusters by looking for the maximum value of the pseudo-F statistic and the minimum of the R
2

(Finch, 2005). We also evaluated how the clusters differed on each variable used in the analysis to
describe distinctive characteristics of the cluster.
Components of Neighborhood Change Analysis Methods
Using data from waves 1 and 2 of the Making Connections survey, we developed a new method
used to determine the components of neighborhood change. This analysis is not focused on changes
for people per se but on changes in a place as influenced by changes for (and of) people. We divide
neighborhood change in the neighborhood poverty rate into its three components. Stayers—the
households that remained at the same home—contribute to changes in neighborhood poverty by
switching from being poor to nonpoor, or the reverse, between the two survey waves. Mobility
contributes to changes in neighborhood poverty when those exiting and entering the neighborhood
are differentially poor. Finally, a shift in the relative share of the residents who are stayers or movers
changes each groups’ contribution to neighborhood poverty.
To do so, we restricted the Making Connections sample to cases in which an interview was com-
pleted in a housing unit at both waves, or in which a housing unit was not occupied or did not
exist at one of the waves and an interview was completed at the other wave. By these criteria, we
excluded 311 cases, leaving a sample of 5,980 at wave 1 across all 10 neighborhoods. In running
sensitivity tests on the restricted sample and comparing it with the full sample, we found minimal
statistical differences between them. In the end, we included only 9 of the 10 Making Connections
neighborhoods in this analysis; in Hartford, the neighborhood boundaries were changed between
the two survey waves, so that the sample is too small to reliably measure changes for those who
moved or stayed within the redefined boundaries.

Residential Mobility and Neighborhood Change: Real Neighborhoods Under the Microscope
67Cityscape
The restricted sample enabled us to classify wave 1 and wave 2 respondents into two categories:
those who stayed and those who moved. We further subdivided movers into those who left the
neighborhood (movers) and those who joined it (newcomers). At wave 1, our sample includes
stayers and movers, and at wave 2, the panel includes stayers and newcomers, where the newcomers
live in units either vacated by wave 1 movers or vacant or not existent at wave 1. For the Making
Connections neighborhoods, we separate the change in neighborhood poverty into its three com-
ponents. Changes in poverty for stayers, as a result of mobility, and due to changes in the relative
size of these groups, are additive; they may move in the same direction or they may offset each
other. In calculating the change in poverty among stayers, we determine the share of stayers who
improved (fell below the federal poverty level at wave 1 but were above it at wave 2), the share
who worsened (were above the federal poverty level at wave 1 but were below it at wave 2), and
the share whose poverty status did not change.
In measuring the change in poverty due to mobility, we calculate differences in the characteristics
of movers (at wave 1) and newcomers (at wave 2) for each housing unit present at both waves.
Where a housing unit was present at either survey wave but vacant or nonexistent at the other, we
also include the household’s poverty status in our calculations. By definition, each neighborhood
has the same number of stayers at both waves. But in each of the 10 cases, the number of newcomers
and movers were not the same, meaning the neighborhood’s population was different at wave 2
than at wave 1. This difference in population had to be accounted for when we calculated the com -
ponents of change. For example, in a neighborhood with fewer newcomers than movers, population
declines. As a result at wave 2, stayers represent a larger proportion of the neighborhood than they
did at wave 1. Therefore, stayers contribute to neighborhood change by changing their personal
circumstances, and also by increasing their share of the neighborhood’s population.
Using this information, we defined the following terms accordingly:
P
1
= Neighborhood poverty rate at wave 1.
P
2
= Neighborhood poverty rate at wave 2.
s
1
= Poverty rate of stayers at wave 1.
s
2
= Poverty rate of stayers at wave 2.
m
1
= Poverty rate of movers at wave 1.
m
2
= Poverty rate of newcomers at wave 2.
t
s1
= Stayers in the neighborhood at wave 1.
t
s2
= Stayers in the neighborhood at wave 2.
t
m1
= Movers in the neighborhood at wave 1.
t
m2
= Newcomers in the neighborhood at wave 2.
We defined each group’s share of population as
w
s1
= t
s1
/ (t
s1
+

t
m1
) = Share of wave 1 population that are stayers. (1)
w
s2
= t
s2
/ (t
s2
+

t
m2
) = Share of wave 2 population that are stayers.
w
m1
= t
m1
/ (t
s1
+

t
m1
) = Share of wave 1 population that are movers.
w
m2
= t
m2
/ (t
s2
+

t
m2
) = Share of wave 2 population that are newcomers.

68
Coulton, Theodos, and Turner
Residential Mobility: Implications for Families and Communities
Neighborhood poverty is
P
1
= w
s1
s
1
+ w
m1
m
1.
(2)
P
2
= w
s2
s
2
+ w
m2
m
2.
The change in neighborhood poverty is
P
2
- P
1
= w
s2
s
2
+ w
m2
m
2
- w
s1
s
1
- w
m1
m
1.
(3)
The change in poverty for stayers is
∆s
12
= s
2
– s
1.
(4)
The difference in poverty between movers and newcomers is
∆m
12
= m
2
– m
1.
(5)
The change in each group’s share of the population is
∆w
s1s2
= w
s2
– w
s1.
(6)
∆w
m1m2
= w
m2
– w
m1.
Substituting equations 4, 5, and 6 into equation 3 yields
P
2
- P
1
= (w
s1
+∆w
s1s2
)s
2
+ (w
m1
+∆w
m1m2
)m
2
– w
s1
(s
2
-∆s
12
) - w
m1
(m
2
-∆m
12
). (7)
Rearranging and canceling terms produces the equation
P
2
- P
1
= w
s1
∆s
12
+ w
m1
∆m
12
+ ∆w
s1s2
s
2
+ ∆w
m1m2
m
2.
(8)
These final terms measure the three components of neighborhood change. The first term (w
s1
∆s
12
)
is the contribution of change in poverty among stayers (holding their population share constant
at wave 1). The second term (w
m1
∆m
12
) is the change in neighborhood poverty attributable to the
difference between movers and newcomers (holding their population share constant at wave 1).
Combined, the final two terms are the change in neighborhood poverty resulting from changes in
population ratios (∆w
s1s2
s
2
+ ∆w
m1m2
m
2
).
Findings
Types of Residential Mobility
Previewing our findings, the study showed three discernible types of movers, newcomers, and
stayers in the Making Connections neighborhoods. One of the types in all instances reflected
households in distress. Their residential situations were dictated more by economic exigencies or
family stress than by choice. Another type could be characterized as positive in their residential
choices, whether they were staying in satisfactory places or moving to better situations. Finally, in
all instances we identified a type for which life stage and household composition were predomi-
nant factors in their residential location. These patterns are consistent with the expectation that
households move or stay put for various reasons, and that simple mobility rates belie differences.

Residential Mobility and Neighborhood Change: Real Neighborhoods Under the Microscope
69Cityscape
Movers With Children
For movers with children, three clusters were identified (see exhibit 3). The largest cluster, labeled
Churning movers, accounted for 46 percent of the mover sample. The families in this cluster tend
to be young and are adding children to their households. They have very low incomes (median
$14,000), are mostly renters who had not lived in their old house very long (median 2 years), and
were the least involved of any cluster in their neighborhood. These families moved short distances
(median 1.7 miles) and did not gain much in terms of neighborhood amenities and satisfaction.
They started out in poor neighborhoods that they viewed as somewhat unsafe and not very positive
Exhibit 3
Churning
Movers
Nearby-Attached
Movers
Up-and-Out
Movers
Weighted
Average
Selected Characteristics of Households in the Movers With Children Cluster
Cases in cluster (%) 46 24 30 100
Life-cycle factors
Respondent age (mean) 28.0 40.9 32.4 32.5
Age of youngest child (mean) 3.57 10.38 5.79 5.88
Change number of children (mean) 0.53 – 0.27 0.21 0.24
Added adult to household (%) 9 5 18 11
Lost adult from household (%) 16 16 6 13
Employment and income
Employed, W1 (%) 70 69 78 72
Household income, W2 (median $) 14,000 15,000 28,000 16,000
Gained a job (%) 14 12 13 13
Lost a job (%) 14 17 5 12
Difficulty paying housing costs, W1 (%) 42 43 35 40
Homeownership and housing subsidy
Homeowner, W1 (%) 8 29 23 18
New homebuyer (%) 10 9 26 15
Shifting to rental (%) 2 19 7 8
Gained subsidy (%) 12 16 7 12
Lost subsidy (%) 13 12 16 14
Neighborhood quality
Safety rating, W1 (mean) 4.33 4.74 3.72 4.24
Change in safety (mean) 0.16 – 0.27 1.93 0.59
Neighborhood good for children, W1 (%) 62 66 34 55
New neighborhood better for children (%) 14 17 63 30
Neighborhood attachment
Neighborhood involvement, W1 (mean) 0.57 1.30 0.72 0.79
Years in neighborhood, W1 (median) 2.0 7.5 3.0 3.0
Neighborhood conditions (census tract)
Change in poverty rate (mean) – 4.78 – 6.46 – 22.33 – 10.53
Change in percent minority (mean) – 6.8 – 6.49 – 38.42 – 16.36
Increase in housing prices (median $) 23,500 26,000 45,000 31,000
Distance of move
Distance in miles (median) 1.66 1.14 5.77 2.17
Source: Making Connections cross-site data, waves 1 and 2

70
Coulton, Theodos, and Turner
Residential Mobility: Implications for Families and Communities
for their children, and they gained little by moving. This pattern suggests that these households
may be frequent movers whose moves are a response to financial stress or problems in their rental
housing arrangements.
The second mover cluster is labeled as Nearby-attached movers, constituting 24 percent of the sample.
The families in this cluster are middle aged and have declined in household size. They have very
low incomes (median $15,000). Unlike churning households, however, more of them were home -
owners at wave 1, had lived in their homes for a very long time (median 7.5 years), and were highly
involved in their original neighborhoods. These families moved the shortest distances (median
1.1 miles), with some (19 percent) shifting from homeowner to rental tenure. Their relocation did
not appreciably affect their neighborhood distress or satisfaction, but they reported somewhat less
neighborhood participation following their move. Thus, nearby attached movers had been stable
involved residents whose moves may have been dictated more by life-cycle factors than by a desire
to leave their house or neighborhood. In fact, they have not moved far nor have they changed very
much in their feelings about the place.
The last cluster, Up-and-out movers, comprised 30 percent of the sample. These movers are young
families but are more likely to be gaining an adult in the household than are churning movers. They
have moderate incomes (median $28,000), had not lived in their old house very long (median
3 years), and were the most dissatisfied with the old neighborhood. These families moved much
farther (median 5.8 miles), with more families becoming homeowners than in other clusters. They
are more satisfied and optimistic about their new neighborhoods, which are substantially less poor
and less predominantly minority, and which have higher (and rising) house values. In summary,
up-and-out movers seem to have moved a long distance to improve their housing and neighbor-
hood satisfaction. They had the financial wherewithal to make such moves possible.
Newcomer Households
Next, we focus on households moving into homes and apartments in the Making Connections
neighborhoods. For these households, the Making Connections survey provides only wave 2
information; we do not know where these households lived before or how their circumstances
changed.
6
We can explore factors that may have pushed or pulled them into their current location,
however, including age and number of children, employment and income, housing tenure and
subsidy (voucher or public housing) status, affordability problems, and perceptions of the neigh-
borhood and attachment to it. Cluster analysis yields three categories of newcomers distinguished
on the factors shown in exhibit 4.
The first cluster is labeled dissatisfied renter newcomers and accounts for 36 percent of the newcomer
sample. In this cluster, nearly all households are renters (96 percent). They are young families with
young children (mean age of adults is 30.8 and children is 3.7). They have low incomes (median
$12,000) and have difficulty affording their housing. About one-fifth (22 percent) receive housing
subsidies and about two-thirds have an employed member in the household. These families are
6
As discussed previously, we do not have wave 1 addresses for these newcomers; some may have lived nearby and
considered themselves in the same neighborhood.

Residential Mobility and Neighborhood Change: Real Neighborhoods Under the Microscope
71Cityscape
very dissatisfied with the neighborhood and have not become very involved in it since their move.
This pattern is consistent with being pushed to move by circumstances rather than attracted to
their new residence by a positive feeling about the neighborhood or the achievement of a stable
housing situation. Their profile suggests that they may move again quickly due to further disrup-
tion or dissatisfaction.
The second cluster, low-income retired newcomers, comprises 24 percent of the newcomer sample.
This cluster is composed of predominately older households with very low employment rates
(9 percent) and very low incomes (median $7,500). A large proportion of newcomers in this
cluster have housing subsidies (35 percent) and most of the households in this cluster are renters
(81 percent). Many report that they have trouble paying for their housing costs (33 percent). Despite
their financial difficulties, they are positive about the neighborhood and are moderately involved.
This cluster seems to represent households that already felt positively toward the neighborhood
and changed residences due to reaching retirement and requiring lower housing costs or more
housing assistance. Households in this newcomer group are likely to remain settled unless their
personal situations change or they can find more affordable or subsidized housing elsewhere.
Exhibit 4
Dissatisfied
Renter
Newcomers
Low-Income
Retired
Newcomers
Positive
Newcomers
Weighted
Average
Selected Characteristics of Households in the Newcomers Cluster
Cases in cluster (%) 36 24 40 100
Life-cycle factors
Respondent age (mean) 30.8 53.1 36.6 38.4
Age of youngest child (mean) 3.65 10.51 7.01 5.52
Number of children (mean) 1.91 0.30 0.78 1.08
Adults in household (mean) 1.64 1.80 1.52 1.63
Employment and income
Employed (%) 67 9 97 66
Household income (median $) 12,000 7,500 30,000 15,000
Difficulty paying housing costs (%) 51 33 21 35
Homeownership and housing subsidy
Homeowner (%) 4 19 37 21
Housing subsidy (%) 22 35 2 17
Neighborhood quality
Safety rating (mean) 3.62 4.85 5.04 4.48
Neighborhood good for kids (%) 26 72 81 59
Neighborhood attachment
Neighborhood involvement (mean) 0.47 0.72 0.87 0.69
Years in neighborhood (median) 1 2 2 2
Neighborhood conditions (census tract)
Poverty rate (mean) 36.44 34.27 29.95 33.30
Percent minority (mean) 78.02 74.31 69.77 73.80
Source: Making Connections cross-site data, waves 1 and 2

72
Coulton, Theodos, and Turner
Residential Mobility: Implications for Families and Communities
Positive newcomers are 40 percent of the sample. This third cluster is made up of working house-
holds (97 percent are employed) in their middle child-rearing years. They have relatively high
incomes (median $30,000), are the most likely of the newcomer households to be homeowners
(37 percent), and are the least likely to have difficulty with housing affordability. They are very
optimistic about the neighborhood and participate in it. Households in this cluster are likely to
become engaged with their new community and to remain stable as long as their housing remains
optimal. Those with rising incomes may move on, however, as they become ready for homeowner-
ship or as their housing needs and preferences shift.
Stayer Households
Finally, we turn to the households that stayed at their original addresses. For these households,
the survey provides two waves of information about both families with children and childless
households, including a wide range of factors that might have made them want to stay as well as
factors that might have limited their options for leaving. Again, we find three identifiable clusters
of stayers as shown in exhibit 5.
Dissatisfied stayers comprise 22 percent of the newcomer sample. This cluster is the youngest of
the stayer clusters (the mean age of adult members is 38.9), although stayers as a group are older
than movers. Most of these families have an adult who is working (79 percent), but their incomes
Exhibit 5
Dissatisfied
Stayers
Long-Term
Older Stayers
Positive
Stayers
Weighted
Average
Selected Characteristics of Households in the Stayers Cluster
Cases in cluster (%) 22 31 47 100
Life-cycle factors
Respondent age (mean) 38.9 63.8 41.3 47.7
Age of youngest child (mean) 6.73 9.21 8.08 7.83
Number of children (mean) 0.14 – 0.16 – 0.04 0.00
Adults in household, W1 (mean) 1.57 1.71 1.42 1.54
Employment and income
Employed, W1 (%) 79 20 95 69
Household income, W2 (median $) 20,000 10,000 30,000 20,000
Difficulty paying housing costs, W1 (%) 39 14 19 22
Homeownership and housing subsidy
Homeowner, W1 (%) 39 56 68 58
Housing subsidy, W1 and W2 (%) 17 18 3 11
Neighborhood quality
Safety rating, W1 (mean) 3.36 4.83 5.12 4.64
Neighborhood good for children, W1 (%) 15 80 93 72
Neighborhood attachment
Neighborhood involvement, W1 (mean) 0.76 0.81 0.89 0.83
Years in neighborhood (median) 6 24 10 11
Neighborhood conditions (census tract)
Poverty rate, W1 (mean) 32.49 32.14 28.88 30.68
Percent minority, W1 (mean) 77.26 82.10 77.91 79.07
Increase in housing prices (median $) 15,500 10,500 10,500 11,000
Source: Making Connections cross-site data, waves 1 and 2

Residential Mobility and Neighborhood Change: Real Neighborhoods Under the Microscope
73Cityscape
are only low to moderate (median $20,000). Most of these households are renters (61 percent) and
likely to be having difficulty paying housing costs. They have lived in the neighborhood the short-
est time (median 6 years) and, out of all stayers, are the least positive about it. If they continue to
remain in their current residence, it is likely because of barriers to movement rather than a stable
and satisfactory situation.
The second cluster, labeled long-term older stayers, accounts for 31 percent of the sample. The
households in this cluster are a bit older than those in the other clusters (mean age of adults 63.7),
seldom include working adults (only 20 percent employed), and have very low incomes (median
$10,000). Yet, more than one-half of these households own their homes and few are having dif-
ficulty with housing costs. They have lived in the neighborhood for many years (median 24 years)
and are satisfied with it. Although it seems likely that these stayers will remain in place, their fixed
incomes and advancing age may make them somewhat vulnerable.
Finally, positive stayers comprise the largest cluster at 47 percent of all stayers. These households
tend to be middle-aged (mean age of adults 41.3) families who are working (95 percent are
employed) and have the highest incomes (median $30,000) of the three stayer groups. Most are
homeowners (68 percent), and the median number of years living in the neighborhood is 10.
These households participate most in their neighborhood and are the most optimistic about it.
This cluster is likely to continue to be involved and remain in their residence as long as they
remain satisfied with their housing and surrounding neighborhood.
Differences in Cluster Mix by Making Connections Sites
Now we turn to the question of how this classification of households regarding their residential
mobility status characterizes the Making Connections neighborhoods. The mix of movers,
new comers, and stayers is shown by site in exhibits 6, 7, and 8. For example, the Denver
Exhibit 6
Types of Movers by Making Connections Neighborhood
Source: Making Connections cross-site data, waves 1 and 2
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Denver
Churning movers Nearby-attached movers Up-and-out movers
Des
Moines
Hartford Indianapolis Louisville Milwaukee Oakland Providence San
Antonio
White
Center

74
Coulton, Theodos, and Turner
Residential Mobility: Implications for Families and Communities
Exhibit 7
Exhibit 8
Types of Newcomers by Making Connections Neighborhood
Types of Stayers by Making Connections Neighborhood
Source: Making Connections cross-site data, waves 1 and 2
Source: Making Connections cross-site data, waves 1 and 2
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Denver
Denver
Dissatisfied renter newcomers
Dissatisfied stayers
Low-income retired newcomers
Long-term older stayers
Positive newcomers
Positive stayers
Des
Moines
Des
Moines
Hartford
Hartford
Indianapolis
Indianapolis
Louisville
Louisville
Milwaukee
Milwaukee
Oakland
Oakland
Providence
Providence
San
Antonio
San
Antonio
White
Center
White
Center
neighborhood has a large component of long-term older stayers and the percentage of dissatisfied
stayers is low. Denver is also low on nearby-attached movers and low-income retiree newcomers
while being in the middle range on other clusters. This pattern suggests the core of a stable older
population in the Denver neighborhood, with little influx of older newcomers. In general, the posi-
tive newcomers exceed the dissatisfied ones. Churning movers exceed the nearby-attached movers
by about two to one, however, reflecting considerable churning among in the younger low-income
population in the Denver neighborhood.

Residential Mobility and Neighborhood Change: Real Neighborhoods Under the Microscope
75Cityscape
In Des Moines, three clusters stand out: up-and-out movers, positive stayers, and positive new-
comers. Des Moines is also low on churning movers and low-income retiree newcomers. This pat-
tern suggests that the Des Moines neighborhood is a positive attraction for many households but is
also a place movers leave behind to improve their situations. Oakland and Providence have similar
profiles to Des Moines, although somewhat less positive. In Oakland and Providence slightly more
movers are churning, and more stayers are dissatisfied than in Des Moines.
The Hartford Making Connections neighborhood is characterized by large proportions of dissatis-
fied newcomers, dissatisfied stayers, and churning movers. The small proportion in the up-and-out
cluster suggests that few are moving on to better housing or neighborhoods. Few of the newcomer
households fall into the low-income retirees, suggesting that younger distressed families are the
bulk of those relocating to the neighborhood. Milwaukee’s mix of movers and newcomers is
similar to Hartford’s. More positive stayers and fewer negative stayers are in Milwaukee than in
Hartford, however.
Louisville stands out in the high proportion of stayers and newcomers in the low-income older
clusters. Also, Louisville’s movers tend more than the other neighborhoods to remain nearby-
attached movers. Few are up-and-out movers and few of the households that stay or move in are
doing so for positive reasons. This pattern suggests that many households in the Louisville Making
Connections neighborhood are there mainly because housing is affordable and that many are long-
term residents with a connection to the neighborhood. The mix of movers, newcomers, and stayers
in Indianapolis is similar to Louisville, with the exception that Indianapolis has a higher proportion
of positive newcomers.
San Antonio is unique among the neighborhoods in that its movers mostly remain nearby and are
seldom bettering their situation by moving out. Nevertheless, San Antonio also has a large group
of positive stayers who are remaining in place and are satisfied with the neighborhood. The new-
comer mix in San Antonio is unremarkable compared with the other neighborhoods, with a nearly
equal mix of newcomers in the positive and dissatisfied clusters. This pattern suggests that the San
Antonio Making Connections neighborhood is one with a core of long-term residents, but many of
them frequently change housing units within the general neighborhood.
The White Center neighborhood is low on up-and-out movers and high on positive stayers and
positive newcomers. It appears, therefore, that residents who are being drawn to the neighborhood
are seeking its positive qualities and not moving away for better situations. The neighborhood is
also high on churning movers, however, suggesting that an element of frequent moving also exists
among residents with unstable living situations.
Components of Neighborhood Change
In this section, we focus on the Making Connections neighborhoods and turn to the question of
how residential mobility shapes the overall composition of the neighborhoods. We illustrate this
dynamic by calculating separately the components of change in neighborhood poverty rates because
concentrated urban poverty has received a great deal of policy attention and poverty reduction is a
common goal in community-change initiatives. Across the Making Connections neighborhoods,
changes in poverty rates occurred primarily through mobility, not because of changing circumstances

76
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Residential Mobility: Implications for Families and Communities
for stayers. Reductions in neighborhood poverty occurred in one of two ways: through a sizable
departure of poor residents, or through an influx of better off households. For neighborhoods
where stayers saw reductions in the prevalence of poverty, these improvements were not sufficient
to produce neighborhood gains. The biggest increases in neighborhood poverty rates occurred
where poverty increased both among stayers and as a result of mobility.
At the start of this study, the Making Connections neighborhoods ranged from moderately to severely
distressed, with an average poverty rate in 2002 or 2003 of 35 percent. Of the nine neighborhoods
analyzed, four saw statistically significant changes in the poverty rate. Of these nine neighborhoods,
three experienced reductions in poverty, with the biggest reductions occurring in some of the poorest
communities: Louisville (-10.8 percentage points), Milwaukee (-7.5 percentage points), and Denver
(-5.2 percentage points). San Antonio experienced an increase in poverty of 6.3 percentage points.
To determine the role that mobility played in these poverty trends, we calculate the components of
change for each site using the technique outlined in the methods section of this article. The results
appear in exhibit 9. For each city, the first column is the change in neighborhood poverty attributable
to changes in stayers’ poverty status. The second is the change due to differences between movers’
and newcomers’ poverty rates. The third column is the contribution of shifts in the neighborhood’s
population (and the shares of residents who are stayers or who move between the two survey waves).
These three components sum to the total neighborhood change in poverty, which is shown in
parentheses.
Exhibit 9
The Components of Change in Neighborhood Poverty
Note: Net percentage point change shown in parentheses.
Source: Making Connections neighborhood-change data, waves 1 and 2
Denver (– 5.1%)
Des Moines (– 2.6%)
Indianapolis (– 3.3%)
Louisville (– 11.1%)
Milwaukee (– 8.0%)
White Center (– 2.0%)
Percentage Point Change in Poverty
Changes for stayers Difference between movers and newcomers Changes due to population shifts
Oakland (+ 2.1%)
Providence (+ 4.2%)
San Antonio (+ 5.7%)
– 12 – 11 – 10 – 9 – 8 – 7 – 6 – 5 – 4 – 3 – 2 – 1 0 1 2 3 4 5 6 7

Residential Mobility and Neighborhood Change: Real Neighborhoods Under the Microscope
77Cityscape
Summarizing our findings, the decline in Denver’s neighborhood poverty rate was driven by the
arrival of better off residents. In Louisville and Milwaukee, on the other hand, declining poverty
rates were driven by the departure of poor residents. In Des Moines and White Center, although
the poverty rate remained essentially unchanged, poverty fell slightly among households that
stayed in the neighborhood. Poverty in Indianapolis did not change for any group. Somewhat
higher poverty rates among newcomers than among movers were not enough to notably shift
Oakland’s overall poverty rate. Providence saw modest increases in poverty from both stayers
becoming poorer and from poor newcomers replacing some nonpoor movers. Finally, in San
Antonio, neighborhood poverty rates rose due to increasing poverty among stayers and to higher
poverty among newcomers than among movers.
None of the Making Connections neighborhoods saw economic improvements among stayers that
were sufficient to produce a statistically significant net reduction in poverty rates. This economic
situation is because of the high rates of mobility these neighborhoods experienced and because it is
difficult for households to leave poverty (Cellini, McKernan, and Ratcliffe, 2008; Rank and Hirschl,
2001; Stevens, 1999). None of the neighborhoods that experienced rising poverty rates did so
solely due to mobility or changing circumstances for stayers&mdash;both trends worsened together. A
shifting share of the neighborhood’s population (that is, the share that was made up of stayers or
movers/newcomers) generally had little effect on neighborhood poverty. We explore these findings
below—grouping sites that experienced improving, unchanging, or worsening poverty conditions.
One Making Connections neighborhood—Denver—improved, because newcomers were better off
than movers. As shown in exhibit 9, the poverty rate declined 5.1 points. This reduction in poverty
was entirely attributable to mobility, with newcomers more than 9 percentage points less poor than
movers, a sizable shift. Between 2003 and 2006, more than one-half of the Denver neighborhood’s
residents left (56 percent) and were replaced by newcomers, with no net change in population
(exhibit 2). Residents who remained in the neighborhood from 2003 to 2006 were, on average, no
more or less poor.
Declining neighborhood poverty can be produced simply through the departure of poor residents,
a scenario that some may consider a Pyrrhic victory and others consider a necessary deconcentra-
tion of poverty. Both the Louisville and Milwaukee neighborhoods reflect this pattern. Looking at
Louisville to illustrate this phenomenon, we see that the poverty rate fell dramatically, dropping
more than 11 percentage points in 3 years (exhibit 9). Yet, this improvement was entirely attribut-
able to the departure of some poor households. More than 63 percent of households in Louisville
left the neighborhood and many of these residents were not replaced by newcomers—the neigh-
borhood’s population declined 17.3 percentage points (exhibit 2). Further driving the changes,
newcomers had a substantially lower poverty rate than movers (13.3 percentage points). With a
poverty rate approaching 50 percent, however, they were still severely disadvantaged. A sizeable
share of Louisville residents relocated between the two survey waves as a result of the HOPE VI
program. Public housing revitalization led to poverty reduction in Louisville, but the Milwaukee
neighborhood also saw the departure of poor residents, not as a result of a federal program.
Households that remained in the Louisville and the Milwaukee communities experienced no
improvements in their poverty rates.

78
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Residential Mobility: Implications for Families and Communities
Unlike the previously described neighborhoods, five Making Connections neighborhoods did not
demonstrate changes in poverty rates, although one group of residents may have experienced a
greater or lesser likelihood of being poor. For these neighborhoods, changes among or between
individual groups were not sufficient to generate a net change. Because the net poverty rates for
these communities did not change, relying on these aggregate figures alone may mask divergent
outcomes for the different groups.
In two neighborhoods, Des Moines and White Center, stayers were somewhat less poor at wave 2,
an important outcome in assessing community change efforts. Yet this change did not improve the
overall neighborhood. Oakland also showed no net change in neighborhood poverty. But in this
case, it was stayers who were unchanged, and newcomers were 5.0 percentage points less poor
than movers. These components resulted in a 2.1-percentage-point increase in Oakland’s poverty.
Poverty rates in Indianapolis were not substantially different for stayers, neither as a result of
mobility nor shifts in the neighborhood’s population. In Providence, poverty increased modestly
for stayers and as a result of mobility. These two factors resulted in a 4.1-percent increase in
neighborhood poverty, but this change is not statistically significant.
As opposed to improving, neighborhood poverty worsened in only one manner. The poverty rate
increased in San Antonio, driven by a worsening situation among stayers and due to mobility.
Poverty among stayers rose by 5.5 percentage points from 2003 to 2006—a change that resulted
in neighborhood poverty increasing by 3.2 percentage points. At the same time that stayer house-
holds experienced greater poverty, the community absorbed even more poor migrants and lost
households that were better off. Those who joined the neighborhood had a poverty rate 7.5 points
higher than those who left.
In sum, across all Making Connections neighborhoods, this analysis shows few communities with
poverty-rate reductions among stayers, a core indicator of neighborhood health and vitality. But in
neighborhoods where poverty declined among stayers, that gain would be overlooked by focusing
simply on neighborhood change. The magnitude of change among stayers is smaller than change
as a result of mobility. The fates of stayers and movers were linked in surprisingly few neighbor-
hoods—only in worsening neighborhoods did they change in the same direction. Given the high
rates of mobility and the greater likelihood that movers and newcomers were differentially poor,
mobility was a larger influence in changing neighborhoods. Mobility contributed to neighborhood
improvement in several cases, even if gains were not experienced by stayers. Also, in no neighbor-
hoods did mobility alone drive neighborhood poverty-rate increases, although, where poverty
increased, poor newcomers added to an already deteriorating situation for stayers.
Discussion of Functional Differences in Low-Income
Neighborhoods
In this section, we apply the preceding findings about the mix of movers, newcomers, and stayers
and the components of neighborhood change to offer some insights on how different low-income
neighborhoods may be functioning for the families who live in them (and move through them).

Residential Mobility and Neighborhood Change: Real Neighborhoods Under the Microscope
79Cityscape
We begin by proposing five stylized models. Two of these stylized types—incubator and launch
pad—function in positive ways for their low-income residents, two types—neighborhood of choice
and comfort zone—are mixed, and one—trap—essentially fails low-income families.
If a neighborhood is an incubator, mobility rates will be low. Stayers would be attached and posi-
tive about the neighborhood and newcomers would be positive about it as well. If, on the other
hand, a neighborhood is a launch pad, mobility rates will be higher. Successful families would be
moving out while needier families moved in.
If a neighborhood is a launch pad, mobility rates will be high. Many movers would be transitioning
up and out, but those who stayed would be attached and positive, and newcomers (although poor)
would be positive about what the neighborhood had to offer.
If a neighborhood is a neighborhood of choice, mobility rates will be moderate and neighborhood
outcomes would be improving. These gains, however, should reflect the well-being of neighbor-
hood newcomers, with lesser improvements reflected in stayers’ well-being. In addition, although
newcomers should be very positive about the neighborhood, many movers are likely dissatisfied
and disconnected.
If a neighborhood is a comfort zone, mobility rates will be low and outcomes for long-term resi-
dents or the neighborhood as a whole would have little or no improvement. In this way, comfort
zones are like traps. In a comfort zone, however, many stayers would be strongly attached and
many newcomers would be satisfied with their neighborhood circumstances.
Finally, if a neighborhood is a trap, mobility rates will be moderate and neighborhood outcomes
would either remain unchanged or decline over time, reflecting static or worsening conditions
among stayers. Short-distance churning moves may be common, although long-distance opportu-
nity moves would be infrequent. Movers, newcomers, and stayers would all be dissatisfied about
their neighborhood circumstances.
Patterns of mobility and neighborhood change in most of the Making Connections neighborhoods
roughly align with these stylized models. The White Center neighborhood and possibly Indian-
apolis appear to be functioning as incubators. Des Moines and Oakland look like launch pads.
Denver can be best described as a neighborhood of choice. San Antonio and Providence appear
to be functioning as comfort zones for low-income households struggling under tough economic
circumstances. Louisville, Milwaukee, Hartford, and possibly Indianapolis all have attributes that
correspond with traps.
Despite this alignment with a typology of neighborhood functions, the full picture in every Making
Connections neighborhood is more complex and messy. All exhibit characteristics that differ from
their stylized models. And none unambiguously functions in the same way for all of its residents.
For example, even in an incubator neighborhood, some residents feel trapped or dissatisfied and
some movers appear to be churning. Likewise, even in a trap neighborhood, some families are able
to move up and out. In the following sections, we focus in turn on five Making Connections neigh-
borhoods that most closely match the stylized neighborhood models (see exhibit 10), highlight the
complexities and contradictions within these neighborhoods, and suggest possible implications for
community-change strategies.

80
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Residential Mobility: Implications for Families and Communities
An Incubator for Many, but Instability Persists Among Poor Renters
The White Center Making Connections neighborhood has many features suggestive of being an
incubator. Stayers are experiencing modest declines in poverty and most are positive and attached
to the neighborhood. The population is growing, but mobility is not driving the decline in poverty;
the poverty rate among newcomers is essentially the same as among movers. Few movers appear to
be up-and-out movers, and most newcomers are positive newcomers.
Exhibit 10
Site Mobility
Components of
Neighborhood Change
Characteristics of
Movers, Newcomers, and Stayers
Overall
Among
Stayers
Due to
Mobility
Family
Movers
New comers Stayers
Stylized Models of How Neighborhoods Function for Residents
Incubator
White Center Low No change Improving
modestly
No change High
churning;
low up-
and-out
High
positive;
low
dissatisfied
renters
High attached
positive; low
long-term
older
Launch pad
Des Moines Low No change Improving
modestly
No change High
up-and-
out; low
churning
High
positive
High attached
positive
Neighborhood of choice
Denver Interme-
diate
Improving No change Newcomers
better off
than movers
High up-
and-out
High
positive;
but also
dissatisfied
renters
High long-
term older
Comfort zone
San Antonio Low Worsening Worsening Newcomers
worse off
than movers
High
churning
and
nearby-
attached;
low up-
and-out
All three
types of
newcomers
High
positive; low
dissatisfied
Trap
Louisville High Improving No change Movers
much worse
off than
stayers or
newcomers
High
nearby-
attached
High low-
income
retirees;
low positive
High long-
term older;
low positive

Residential Mobility and Neighborhood Change: Real Neighborhoods Under the Microscope
81Cityscape
White Center differs from the stylized model of an incubator in one respect, however. Some mov-
ers—in fact, a substantial share of movers (slightly more than one-half)—are churning movers.
This category of mover has lived in the neighborhood for only a short time, is not strongly attached
to it, moves only a short distance, and is not any more satisfied or optimistic about the new loca-
tion. Thus, although White Center may be functioning as an incubator for many of its residents, it
also exhibits residential churning for some families. Further analysis suggests that these churning
movers are mostly young working families, often single parents, who rent homes and apartments.
They are considerably more likely than White Center’s stayers to be minorities or immigrants.
Because so many of the households moving into White Center are positive newcomers, one might
wonder whether the neighborhood is experiencing gentrification. Poverty rates among newcomers
are essentially the same as among movers, however. Further analysis shows that even the positive
newcomers have lower average incomes than most stayers. Newcomers are also more likely to be
minorities or immigrants than the neighborhood’s stayers. The positive newcomers are much less
likely to have children than stayers, however, which may suggest an influx of singles and childless
couples to the White Center neighborhood.
What strategies make sense under these circumstances? White Center already offers substantial
assets that attract and retain residents who are positive about the neighborhood and attached to it.
And the well-being of those who stay in the neighborhood is rising. Community initiatives should
build on these assets and expand their reach so that more families can benefit. In particular, the
large share of churning movers need targeted help to achieve greater stability. One strategy might
be to target low-income families who rent homes and apartments in the neighborhood, reaching
out to draw them into available services and activities, and expanding rental assistance, including
short-term emergency assistance to help families remain in place longer. In addition, resident
engagement and community-building efforts might explicitly work to engage the neighborhood’s
newcomers, including childless singles and couples. Many of these households appear very posi-
tive about the neighborhood and seem prepared to get involved and contribute to it. But these
newcomers are by no means affluent; they too need help connecting to neighborhood-based
services and supports.
A Launch Pad, Although Many Residents May Be Happy To Stay
Des Moines’s Making Connections neighborhood exhibits dynamics that match the model of a
launch pad neighborhood (exhibit 10). In particular, many movers are up-and-out movers and
few are churning movers. In addition, many newcomers are positive, although they do not appear
to be substantially poorer than the households they are replacing. In addition, the flow of movers
out of the Des Moines neighborhood is smaller than one might expect for a launch pad, the stayers’
well-being appears to be improving, and many stayers are attached and positive.
In fact, Des Moines’ up-and-out movers appear similar to positive stayers in many respects. Most
of both groups are renters, most are minorities, and most are native born. The up-and-out movers
are somewhat more likely than the positive stayers to rent and somewhat more likely to be native
born. So Des Moines may actually be functioning as a launch pad for some residents and an
incubator for others.

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Residential Mobility: Implications for Families and Communities
Not all Des Moines residents are experiencing positive change, however. In particular, low-income
immigrants appear to be less well served by the neighborhood. Churning movers have much lower
incomes and are more likely to be immigrants than either the up-and-out movers or the stayers.
Also, dissatisfied newcomers are more likely to be immigrants than are the positive newcomers.
These findings suggest the need to build on existing neighborhood assets, but to explicitly extend
them to reach immigrants living in and coming to the neighborhood. Currently, these households
appear substantially less engaged, less positive about what the neighborhood has to offer, and less
stable. Because they are immigrants and are more likely to move frequently, they may be left out
of community-building and resident-support networks. In addition, community-based work in
such a neighborhood might help them retain connections with the up-and-out movers, effectively
extending the network of engagement and support beyond the neighborhood boundaries.
A Neighborhood of Choice, but Few Gains for Low-Income Residents
Denver’s Making Connections neighborhood appears to be a neighborhood of choice. As shown in
exhibit 10, the neighborhood’s poverty rate is declining but not due to any gains among stayers.
Among stayers, the poverty rate remains unchanged, but newcomers to the neighborhood are much
less likely to be poor than movers. Movers include both up-and-out movers and churning movers.
Denver is home to a core of attached elderly stayers, however, as well as many positive stayers.
Denver’s positive newcomers have substantially higher incomes than any group of stayers or
movers. They also have small households on average, with few children. The positive newcomers
are more likely to be White, less likely to be Hispanic, and less likely to be immigrants than are
movers. The neighborhood as a whole is not undergoing dramatic racial/ethnic change, but the
differences between newcomers and movers will gradually make the neighborhood more affluent,
with more White households and fewer children.
Finally, although roughly one-half of the newcomers to the Denver neighborhood are positive
newcomers, a smaller but still substantial share is dissatisfied renters. These newcomers are much
poorer, less likely to be working, more likely to be African American or Hispanic, and more
likely to be immigrants. Moreover, Denver has a large group of long-term older stayers. These
households have children, high rates of joblessness, and very low incomes. They are more likely
to be African American or Hispanic than either the more affluent, positive stayers or the positive
newcomers. Thus, at the same time that Denver is a neighborhood of choice, it also continues to
serve a large population of needy households and can be characterized as moving toward a more
diverse income mix.
Such neighborhood dynamics challenge community-based strategies to engage the positive new -
comers, so that they may become active participants in making improvements that benefit residents
who are still struggling economically. Many positive newcomers express strong attachment to the
neighborhood and optimism about its future. Actively reaching out to them and involving them
in ongoing community-building activities and social networks may enable a neighborhood like
Denver’s to capitalize on its resources and influence to the benefit of the neighborhood as a whole.
Yet potential gains from these positive newcomers must be balanced against the risks of future dis-
placement and efforts may be needed to preserve affordable housing and stabilize the income mix.

Residential Mobility and Neighborhood Change: Real Neighborhoods Under the Microscope
83Cityscape
A Comfort Zone, Despite Worsening Economic Outcomes
The Making Connections neighborhood in San Antonio corresponds closely to the model of a com-
fort zone. As exhibit 10 illustrates, economic outcomes are deteriorating in the neighborhood as a
whole, both because stayers are getting poorer and because newcomers are worse off than movers.
Although the rate of mobility is low, the neighborhood’s total population is declining. A substantial
share of movers is classified as churning movers, but the share of nearby-attached movers is also
high, and most stayers are either positive and attached or long-term older stayers. In other words,
many residents appear to be attached to and positive about the San Antonio neighborhood,
although their economic outcomes are deteriorating.
Most of the San Antonio neighborhood’s residents are Hispanic, and the Hispanic residents appear
most likely to be attached to and positive about their neighborhood. More specifically, nearly all
the neighborhood’s stayers are Hispanic, as are the nearby-attached movers. In contrast, nearby-
disconnected movers are nearly all African American.
These findings suggest that the San Antonio Making Connections neighborhood may function as a
comfort zone for its Hispanic residents, although larger structural factors in the economy prevent
much economic advancement. Hispanic residents appear to have established strong social net-
works and community activities that they enjoy and value. These neighborhood assets, however,
may not be capable of compensating for low wages and insecurity in the entry-level labor market.
These dynamics suggest that community-based initiatives may not always be able to tackle the
larger barriers undermining residents’ well-being, and that they need to work in concert with larger
policy-change strategies designed to address structural challenges of employment and income.
A Trap, Despite a Big Drop in the Neighborhood Poverty Rate
The Making Connections neighborhood in Louisville suffers from a higher rate of poverty than
any other neighborhood. Although the poverty rate dropped 11 points during a 3-year period, the
Louisville neighborhood continues to suffer from severe distress, and its dynamics correspond in
many troubling respects to the hypothesized characteristics of an isolated neighborhood (exhibit 10).
The neighborhood is losing population (in part because a large public housing development was
demolished and will ultimately be redeveloped), and the decline in poverty is attributable to the
loss of public housing residents. The poverty rate among stayers remained unchanged. Moreover,
few movers are up-and-out movers, few newcomers are positive newcomers, and few stayers are
positive stayers.
Despite this generally discouraging picture, a large share of the neighborhood’s movers remains
nearby and appears attached to the community. These nearby-attached movers appear slightly
better off than the churning movers; they are a little older, are more likely to be employed, and
have fewer children. Although the share of nearby-attached movers is high compared with that of
the other Making Connections neighborhoods, in Louisville the much needier churning movers
outnumber this group. The Louisville neighborhood is also home to a large group of older attached
stayers. These families have lived in the community for a long time and are strongly attached to it.
In Louisville, these older stayers typically have children but do not work and are extremely low
income.

84
Coulton, Theodos, and Turner
Residential Mobility: Implications for Families and Communities
Given its current dynamics, the Louisville neighborhood might be experiencing a continuing
downward spiral of poverty, disinvestment, and distress. At the same time, it might be a good
candidate for equitable redevelopment strategies. Presumably, demolition of the public housing
project is a first step toward developing new, higher quality housing that serves a wider mix of
incomes (including public housing residents). Because the neighborhood has lost population, it
should have room to grow by attracting new residents, without risk of displacement. The challenge
will be to provide higher quality housing and neighborhood amenities that attract moderate- and
middle-income households, while also providing the service and support that current residents
need to increase their employment and earnings prospects. In particular, the neighborhood’s long-
term older stayers and its churning movers are extremely needy. A mixed-income redevelopment
strategy probably would not help these families unless it is accompanied by tangible supports for
both adults and children.
Summary, Limitations, and Implications for Community
Initiatives
The cross-neighborhood survey conducted as part of the Casey Foundation’s Making Connections
initiative provided a unique opportunity to explore the dynamics of residential mobility from the
perspectives of both neighborhoods and families. Several important limitations of the study exist
as well. The study neighborhoods are not a nationally representative sample but were deliberately
selected for a community-change initiative and may differ from other low-income neighborhoods
in important ways. Moreover, the neighborhood boundaries used in this analysis were defined by
the stakeholders in the sites and do not necessarily agree with residents’ neighborhood definitions
(Coulton, Chan, and Mikelbank, 2011). In addition, the analysis of types of movers had to exclude
households without children because no data exist on their move destinations. These households
may have differed from movers with children in their reasons for moving or the outcomes of their
relocation.
This analysis reinforces findings from past research about high rates of residential mobility, but it
also offers new insights on patterns of mobility and their implications for neighborhood change in
low-income communities located in 10 very different sites. Across all 10 neighborhoods, we found
high rates of residential mobility. More than one-half of the households that lived in the neighbor-
hoods at the time of the first survey wave had moved to a new address 3 years later. Although
this finding is by no means new, its significance is frequently overlooked by community-based
initiatives and local practitioners. Efforts to improve the well-being of families and children by
strengthening conditions in poor neighborhoods cannot simply assume that families will remain in
one place long enough to fully benefit. Many of the Making Connections movers remained nearby,
however. These nearby movers may retain social connections from their original residential loca-
tion and may still participate in activities and services there. This finding highlights an opportunity
for community-based initiatives to continue serving families who move but remain nearby.
Moreover, our findings suggest that many of these nearby movers may need ongoing help. Nearly
one-half of the Making Connections families who moved were classified as churning movers;
they appear to be moving frequently, renting in different locations without establishing strong

Residential Mobility and Neighborhood Change: Real Neighborhoods Under the Microscope
85Cityscape
neighborhood attachments. These families tend to be young and have very low incomes. This
finding highlights the potential importance of housing assistance to community-based work. By
reaching out to engage churning movers and helping them remain in place longer or by helping
them move to opportunity neighborhoods, local initiatives could improve outcomes for these
vulnerable families and their children.
A move, however, does not always signal problems. For a substantial minority of families, resi-
dential mobility represents a positive choice. Across the Making Connections neighborhoods, 3 of
every 10 movers were up-and-out movers, often becoming homeowners in better neighborhoods
where they were more satisfied and optimistic. In some cases, these up-and-out movers may
simply be escaping from a bad environment; in other cases, their moves may reflect the success of
community-based services and supports that have helped them obtain the resources they needed
to advance.
High rates of residential mobility mean that measuring gross changes in neighborhood outcomes
can be misleading. A decline in a neighborhood’s poverty rate or an increase in its employment rate
does not necessarily mean that the well-being of individual residents has improved. In fact, we find
that neighborhood change is often the result of mobility—differences between the characteristics of
movers and newcomers. In contrast, changes among stayers over a 3-year period, in general, are
small. Efforts to strengthen neighborhoods should acknowledge both the slow pace of change among
stayers and the role played by the continuous flow of households into and out of neighborhoods.
That outcomes improved only slowly, if at all, among families who stayed in the Making Con-
nections neighborhoods does not mean that they stayed unwillingly—unable to escape to better
neighborhoods. In fact, across the 10 Making Connections neighborhoods, close to one-half of
all stayers were attached to their neighborhood and positive about their future. A smaller share of
stayers was unambiguously dissatisfied with their neighborhoods, remaining in place primarily
because they lacked viable alternatives.
Although it is instructive to classify low-income neighborhoods based on stylized models—incuba-
tor, launch pad, neighborhood of choice, comfort zone, and trap—the evidence from Making
Connections teaches us that reality is far more complex. Although each Making Connections
neighborhood roughly corresponds to one of these models, none of them performs in the same
way for all their residents. All 10 have both up-and-out movers and churning movers, all 10 have
both attached and dissatisfied stayers, and all 10 have both positive and dissatisfied newcomers. In
other words, each neighborhood may be working in different ways for different residents. The goal
of community-based initiatives should be to strengthen a neighborhood’s performance for all its
residents: supporting up-and-out movers while reducing churning, supporting the attached stayers
while improving the choices available to dissatisfied stayers, and engaging with both positive and
dissatisfied newcomers to draw them into neighborhood networks and supports.
In particular, residential churning appears to pose a significant challenge in all neighborhood
types. This finding suggests that addressing “housing instability” should receive more attention
in efforts to improve low-income neighborhoods. Vulnerable families need help along many
dimensions (from job training to mental health services), but recent evidence on programs that
serve chronically homeless people shows that addressing the housing instability first can make it

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Coulton, Theodos, and Turner
Residential Mobility: Implications for Families and Communities
easier to deal with other challenges (Bratt, 2008; Lipton et al., 2000; Sussman, 2005; Tsemberis
et al., 2004). Expanding the availability of high-quality affordable housing, preserving the current
stock of moderately priced rentals (most of which receive no subsidy), and helping families apply
for and use available housing assistance can all contribute to greater housing stability and reduce
churning. In addition, programs that provide short-term emergency assistance to prevent eviction,
foreclosure, or a forced move could help vulnerable families remain in place even when long-term
housing assistance is scarce.
The evidence from the Making Connections neighborhoods also argues for flexible and fluid
definitions of neighborhood boundaries. Instead of focusing exclusively on households living
within a defined geography, neighborhood-based services and supports should provide continuity
for nearby movers, so that families can remain part of the community and receive uninterrupted
services even if they have to change their address. Similarly, community-building efforts should
sustain connections with families who move, including those moving up and out, to broaden the
social networks for those who choose to stay and for those relocating nearby.
Evidence and analysis from the Making Connections neighborhoods demonstrate convincingly
that the dynamics of residential mobility and neighborhood change pose critical challenges for
community-change initiatives. Policymakers and practitioners should avoid the mistake of seeing
neighborhoods as static areas within which a population of residents waits for services, supports,
or opportunities. Instead, community-based interventions must focus on the characteristics and
needs of households moving through a neighborhood and of those of longer term residents. Also,
it may be unrealistic for every neighborhood initiative to create an incubator for all residents.
Neighborhoods can also serve their residents well by offering a launch pad to better environments
and opportunities. Understanding how a neighborhood is functioning today may help in defining
realistic goals for improving its performance over time.
Acknowledgments
The authors thank the Annie E. Casey Foundation for its support and give particular credit to
Cindy Guy. This report would not have been possible without the partnership of the Making
Connection site teams and the National Opinion Research Center’s survey collection efforts. They
also thank Jessica Cigna, Kerstin Gentsch, and Michel Grosz of the Urban Institute for providing
research assistance. Finally, the authors thank reviewers who provided helpful comments on an
earlier draft of this article: Mary Achatz, George Galster, Paul Jargowsky, G. Thomas Kingsley, and
Sandra Newman.
Authors
Claudia Coulton is the Lillian F. Harris Professor of Urban Research and Social Change at Case
Western Reserve University.
Brett Theodos is a research associate at the Urban Institute.
Margery A. Turner is vice president for research at the Urban Institute.

Residential Mobility and Neighborhood Change: Real Neighborhoods Under the Microscope
87Cityscape
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90 Residential Mobility: Implications for Families and Communities

91Cityscape: A Journal of Policy Development and Research • Volume 14, Number 3 • 2012
U.S. Department of Housing and Urban Development • Office of Policy Development and Research
Cityscape
Tracking Mobility at the
Household Level
Kate Bachtell
Ned English
Catherine Haggerty
NORC at the University of Chicago
Abstract
Considerable literature supports the desirability of studying individuals in the context
of their immediate social unit, the household. Focused studies of household composition
reveal that households in economically disadvantaged populations with low homeowner-
ship rates are particularly likely to experience additions, subtractions, and substitutions
among members. This article examines the methodological challenges associated with
defining, tracking, and explaining mobility at the household level. We describe a retro -
active approach for linking individual household members across waves that was em-
ployed for the Making Connections survey, a cross-sectional and longitudinal study of
10 low-income urban communities. Our method involved comparing individuals at three
different points in time using a combination of probabilistic matching software, data
queries, and human review. The process produced personal identifiers that could be
integrated with the household-level data to identify changes beyond numerical shifts
in household size. We use the combined data to examine mobility across a gradient of
stability in household composition. Our work advances past studies in two ways. First,
our definition of adding or losing individuals is calculated based on the presence or
absence of a specific person, rather than numerical change in the number of adults and
children in the household. Second, we demonstrate a more comprehensive and nuanced
understanding of household mobility by examining various types of change in household
composition—gaining, losing, or replacing individuals, or being repopulated entirely
with new occupants—in combination with physical relocation during a 6-year period.
A series of maps compares the patterns of residential movement and household composi-
tion change within a specific territory.

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Bachtell, English, and Haggerty
Residential Mobility: Implications for Families and Communities
Introduction
Considerable literature supports the desirability of studying individuals in the context of their
immediate social unit, the household. This approach has only recently been embraced by demo g-
raphers and other users of population and survey data in studies of residential mobility. In the
opening to a special edition of Population, Space and Place, Cooke (2008: 1) applauded the emergence
of a broader, more transdisciplinary perspective regarding family migration in the last decade,
which “emphasizes the broader social and economic implications of family migration processes.”
He observed that this shift, which geographers initiated, later spread to include economists and
sociologists. Cooke suggests that much of the prevailing literature mischaracterizes migration as
the behavior of an individual instead of being a family process. This lingering tendency, despite
the increasing diversification of family types and new negotiations of work and domestic life in
the United States (Geist and McManus, 2008; Thistle, 2006), bears marked consequences for the
study of families living in poverty. Focused empirical work on household composition reveals that
households in economically disadvantaged populations with low homeownership rates are par-
ticularly likely to experience additions, subtractions, and substitutions among members. Analysts
have been constrained in their ability to systematically explore temporal changes in household
composition using longitudinal data by relying on summary variables measuring major changes in
the life course (for example, a birth), numerical change in household size (the “head count”), or
marriage and divorce (du Toit and Haggerty, 2012). Thus, migration tends to be characterized as
the movement of “an intact family unit from one location to another,” even in the emerging family
migration studies (Cooke, 2008: 6).
This article examines the methodological challenges associated with defining, tracking, and explain -
ing mobility at the household level. We attempt to build on the migration scholarship exploring
the interaction of family events with migration events (see De Jong and Graefe, 2008) by outlining
a methodological approach that further refines the former down to the level of the individual. We
describe a retroactive technique that was employed after two waves of data collection for the Making
Connections survey, a cross-sectional and longitudinal study of 10 low-income urban communities.
Our method involved linking all individuals within the household at different points in time using
a combination of probabilistic matching software, data queries, and human review. The process
produced personal identifiers that could be integrated with the household-level data to identify
changes beyond numerical shifts in household size. We used the combined data to examine resi -
dential mobility across a gradient of stability in household composition. Our work advances past
studies in two ways. First, our definition of adding or losing individuals is calculated based on the
presence or absence of a specific person, rather than on numerical change in the number of adults
and children in the household. Second, we demonstrate a more comprehensive and nuanced under-
standing of household mobility by examining various types of change in household composition—
gaining, losing, or replacing individuals, or being repopulated entirely with new occupants—in
combination with physical relocation during a 6-year period. A series of maps compares the patterns
of residential movement and household composition change within a specific territory.

Tracking Mobility at the Household Level
93Cityscape
Literature Review
Residential mobility, which has been shown to affect economic and social well-being, has been
studied intensely by demographers, sociologists, economists, and geographers. Relocation data
may be analyzed to help situate the experiences and perceptions of individuals reported in a survey
in a particular geographic context, nested within his or her household. From the perspective of
survey operations, changes in residential address increase the complexity and expense of panel
studies. For responding households, however, a residential move is symptomatic of a major life
change, complete with the challenges associated with adjusting to a new set of neighbors and deci-
sions about where to go for basic services and amenities. Scholars across disciplines have debated
the potential motivations for migration but seem to agree that the risks of instability associated
with relocation in the short term (if not also in the long term) merit focused study. In his seminal
article, “Bowling Alone,” Putnam (1995: 75) wrote, “Mobility, like frequent re-potting of plants,
tends to disrupt root systems, and it takes time for an uprooted individual to put down new roots.”
Space/Place
Numerous studies on space/place, residential mobility, and race and ethnic relations have demon -
strated that where one lives has consequences for social and economic well-being. Massey and
Denton’s landmark book, American Apartheid: Segregation and the Making of the Underclass (1998),
convinced many American social scientists that residential segregation limits opportunity and
perpetuates the impoverishment of African Americans. Mary Pattillo-McCoy’s (1999, 2007) eth-
nographic work in middle-class Black communities reveals how neighborhood context shapes
individual experiences of race and class. These authors draw heavily on the foundations of the Chi-
cago School of Sociology, centered at the University of Chicago in the early 20th century, which
emphasized using ecological approaches to investigate the dynamics of social boundaries across
space. Another body of scholarship has focused on the Gautreaux Assisted Housing Program (1976
to 1998), which inspired the relocation of public housing residents from racially segregated areas
with concentrated poverty to communities with greater affluence and better resources. Research by
Rubinowitz and Rosenbaum (2000) suggests that moving to suburban neighborhoods positively
affects educational and employment outcomes among youths whose families moved during the
1980s as part of the Gautreaux Program. These studies have established the need for systematic
investigation of the neighborhood-, household-, and individual-level factors that constrain or
promote geographic mobility.
Factors Influencing Residential Mobility
Different populations move at different rates. Between 2007 and 2008, only 12 percent of Ameri-
cans changed residences—the lowest percentage on record (Cohn and Morin, 2008). Following
the economic downturn of 2008, the 2009 rate was slightly higher: 15 percent of Americans were
found to have moved in the previous year. This higher rate raises questions about the effect of
the downturn among families living in disadvantaged neighborhoods, which generally experience
higher rates of mobility (Coulton et al., 2009).
Attempts to isolate the factors influencing residential mobility have produced mixed results. In terms
of socioeconomic status, recent reports attribute declines in migration partly to a rise in dual-career

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Bachtell, English, and Haggerty
Residential Mobility: Implications for Families and Communities
households but observe that movers today—particularly those who move to a new town or city—are
likely to be college-educated and/or pulled by job prospects (Cohn and Morin, 2008). Conversely,
South and Crowder (1998b) found that, although higher incomes are associated with a lower
propensity to end up in a poorer neighborhood among single mothers who move, none of the
following variables produced a statistically significant influence: education, employment status,
and receipt of benefits from the now-defunct Aid to Families with Dependent Children (AFDC).
Another area of interest pertains to life-cycle characteristics. South and Crowder (1998a) found
that residential mobility is lower among married and older people, homeowners, and families with
children. Longitudinal data from the Panel Study of Income Dynamics (PSID) provide evidence
that divorce among parents increases the probability that children will move to a new neighborhood,
and that receiving neighborhoods tend to be significantly poorer for children with divorced parents
than for children with stable two-parent families (South et al., 1998). Cohn and Morin’s analysis of
reasons for living at a current residence supports the expectation that family stability and “staying
put” will be correlated; they indicate from their review data from a 2008 Pew Research survey of
2,260 adults that stayers most often report “the tug of family and connections” (Cohn and Morin,
2008: 3). Other social factors, including the race and ethnicity of both the household and neigh-
borhood, have been found to affect migration patterns even after adjusting for other characteristics
(South and Crowder, 1998a). The sum of these findings suggests that mobility comprises a compli-
cated set of behaviors that are influenced by a number of primary and intermediary factors.
Administrative Challenges for Longitudinal Studies
Mobility is a major concern for collecting survey data, because it threatens to introduce coverage
bias in the results. Tracking individuals from known to unknown locations brings logistical
challenges and additional cost (Ansolabehere and Schaffner, 2010; Marshall and Bush, 2010).
Survey data collection at the household level proves especially challenging for longitudinal studies,
because changes may occur due to births, deaths, marriages, divorces, children leaving home,
imprisonment, and countless other situations (Duncan and Hill, 1985; Marshall and Bush, 2010).
To define the “household” as a specific combination of individuals is thus inappropriate, leaving
most with the option of prioritizing one individual and considering those found to be living under
the same roof to comprise a household. Many studies begin by selecting the “head of household”
as the respondent and tracking the head and his or her cohabitants over time and space. This
definition provides a clean methodological solution but can lead to ill-fitting interpretations in the
event that some household members remain constant while others leave, join, or are replaced, and
the dynamics among individuals shift. An additional factor pertains to differences in the interpreta-
tion of the question, “Who (else) is living here?” (Marshall and Bush, 2010; Martin, 1999). For
example, in the Making Connections survey, interviewers were instructed to clarify that individuals
who were incarcerated at the time of the interview should be included in the household but adult
children living in noninstitutional housing away at school, such as an apartment, should not be
included. These guidelines are intended to ensure systematic data collection, but they may not be
entirely in sync with every respondent’s understanding of his or her household. It is possible that
individuals who play an important positive or negative role in the respondent’s support network
might be omitted under these definitions, or that someone who contributes little or not at all to the
daily experiences of the household may be included.

Tracking Mobility at the Household Level
95Cityscape
Knowing about changes in household composition is important when seeking to understand
patterns of change within a population (Laurie and Sullivan, 1991). Some panel surveys build into
the initial study design a method for producing unique personal identifiers so that, as the data are
collected, matches are established and subsequent individual links are possible across waves (for
example, National Survey of Families and Households; National Longitudinal Survey of Youth,
1997; Living in Ireland Survey). Marshall and Bush (2010) point out that, although identifying
households as the unit of analysis in cross-sectional research is straightforward, this straightfor-
ward approach is not the case in longitudinal studies. The inevitable changes in household compo-
sition pose a considerable challenge to following households over time. The Panel Study of Income
Dynamics (Duncan and Hill, 1985) found that, after 1 year, 22 percent of households had some
compositional change, after 5 years compositional change grew to 55 percent, and after 15 years it
grew to 88 percent (Laurie and Sullivan, 1991). “A system (for creating unique personal identifiers)
that is foolproof and straightforward is critical, so as to avoid any possibility that data can become
mixed from individual to individual over different waves” (Marshall and Bush, 2010: 67).
Scientific and operational variations, as well as some similarities, exist among studies that track
household composition. The National Survey of Families and Households uses a national multi-
stage area probability sample. The survey collected data about the composition of families, family
background, and the relationship of household members to each other, including marriage, separa-
tion, and divorce histories and adoption, child custody arrangements, and stepfamily relations.
To facilitate the examination of family household composition and changes over time, the study
assigned a unique household identifier to each member of a household. The sample for the Living
in Ireland Survey was drawn from voter registration records; however, all members of a household
in which a sample member lived were assigned a unique identifier, and, in subsequent waves, an
attempt was made to reinterview all household members. This list included those who moved and
those who were newly added to an existing household. The movers kept their original identifiers,
and all the people living in their housing unit were interviewed and assigned a unique identifier.
The National Longitudinal Survey of Youth 1997 (NLSY97) sample is designed to represent U.S.
residents in 1997 who were born during the years 1980 through 1984. The household roster
section collected information on all members of a respondent’s household, and the nonresident
roster portion gathered data on those members of the respondent’s immediate family (for example,
parents, siblings, spouse, or children) who lived elsewhere. For each household resident or
nonresident relative, those rosters collected demographic information, marital status, educational
attainment, and employment status. All household members were assigned a unique household
identifier. Finally, the initial sample for the Panel Study of Income Dynamics (PSID) consisted
of two independent samples: a cross-sectional, national sample (based on stratified multistage
selection of the civilian noninstitutional population of the United States) and a national sample of
low-income families. Both samples are probability samples. Although these studies have significant
differences in sample design and survey content, each keeps track of household members to allow
for the measurement of changes in household composition over time.

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Residential Mobility: Implications for Families and Communities
Current Study
In this article, we describe the methodological challenges associated with defining, tracking, and
explaining residential mobility at the household level. Our data are derived from the Making Con -
nections survey, a study of a multisite initiative designed to improve conditions in low-income urban
communities. We endeavor to improve on past studies by approaching mobility as a dynamic family
process. We use Geographic Information System (GIS) software to track the movement of respond -
ents within and around low-income neighborhoods in U.S. cities from 2002 to 2011. We combine
the geographic data with measures of household composition change to illustrate and argue for a
two-dimensional approach to studying mobility.
Data
The data for this analysis are derived from the Making Connections survey (http://mcstudy.norc.
org), a longitudinal and cross-sectional study conducted in 10 low-income neighborhoods across
the United States. The survey aimed to gather household- and community-level data to inform
program planning and was part of a larger initiative funded by the Annie E. Casey Foundation. The
neighborhoods are located in inner-city areas within metropolitan Denver, Colorado; Des Moines,
Iowa; Hartford, Connecticut; Indianapolis, Indiana; Louisville, Kentucky; Milwaukee, Wisconsin;
Oakland, California; Providence, Rhode Island; San Antonio, Texas; and Seattle, Washington. The
demographic and socioeconomic characteristics of the survey neighborhoods vary considerably. For
example, the Denver neighborhood is home to large Latino and foreign-born populations with
predominate origins in Mexico and Vietnam. Most are renting and display high residential mobility,
including considerable long-distance movement (Bachtell and Latterner, 2011). San Antonio is
distinct in that it is almost exclusively Hispanic (90 percent), and respondents report low levels of
education (45.6 percent of respondents had less than a high school diploma) and low household
incomes (with a median of $18,000). Despite these differences, the sites share three main similarities:
they all are located in urban sectors of metropolitan areas, are economically disadvantaged, and are
the focus of local community outreach efforts funded in part by the Annie E. Casey Foundation.
NORC gathered baseline survey data between 2002 and 2004 in the 10 sites listed in the previous
section, and a first followup effort (wave 2) was completed between 2005 and 2007 at each site.
Between 2008 and 2011, NORC completed a second round of followup interviews (wave 3) in
7 of the 10 sites. Interviews for the Making Connections neighborhood surveys were executed in
roughly 3-year intervals using a paper-and-pencil questionnaire. The main questionnaire topics
include neighborhood connections, community involvement, civic engagement and volunteerism,
employment, and income and assets. In addition, a separate set of questions were devoted to the
experiences of children living in the household, including topics about childcare arrangements,
schooling, participation in extracurricular activities, and health.
The Making Connections survey design is unique because it combines cross-sectional and longitu-
dinal (panel) methodologies. In each wave, NORC employed area probability sampling techniques
to select a random set of addresses representative of each target neighborhood. In households with
children, interviewers randomly selected one child to be the focal child and chose the parent or
guardian who knew the most about the focal child to be the respondent (meaning the selection of
the adult respondent was not random, although that of the focal child was random). In adult-only

Tracking Mobility at the Household Level
97Cityscape
households, the focal child selection process was omitted and one adult was randomly chosen to
be the respondent. In waves 2 and 3, interviewers revisited these sampled addresses in person or
by telephone with the goal of collecting data with the current occupants, whether or not they were
interviewed in the past. Many times, the occupants had not changed. Other times, new people
were found to have moved in. NORC also subsampled new addresses at the start of each followup
effort to include buildings that were constructed since the previous wave. This methodology yields
a cross-sectional snapshot of neighborhood residents at different points in time. Making Connec-
tions is also longitudinal, because NORC (1) reinterviewed families who remained at sampled
addresses within target neighborhoods and (2) tracked families with children who moved to a new
address either inside or outside of the neighborhood.
A primary benefit of the Making Connections data is that detailed information regarding occupants
was gathered in a household roster at three points in time, including the age, sex, and relation-
ship of each person to a focal child in all waves. In waves 2 and 3, interviewers also gathered the
relationship of each person to the respondent. A few limitations must also be acknowledged. First,
the 7 sites included in our analysis are not representative of poor urban communities nationwide.
The Annie E. Casey Foundation, with assistance from local leaders, selected the neighborhoods
used for the survey based on the presence of existing grassroots or community organizations whose
missions were consistent with the objectives of the Making Connections initiative. It is possible
that disadvantaged communities in other cities may demonstrate different patterns of mobility.
Similarly, as with any longitudinal study, unmeasured differences due to sample attrition may
exist; that is, differences may exist among households that did not participate in later waves of the
survey that are not captured in our data.
A final difference between Making Connections and other studies that measure household change
in composition, which may also be considered a limitation, pertains to the method by which
people were linked across waves. Unlike the National Survey of Families and Households, Living
in Ireland Survey, and others mentioned previously, the Making Connections survey design did
not include a feature to assign personal identifiers at the time of each interview. After the third
wave of data were collected, the research team discussed the importance of understanding house-
hold composition down to the individual level and decided to undertake a retrospective matching
of people across all three waves of the survey.
Methods
Matching People
In the spring of 2010, NORC developed a detailed process for reviewing the household rosters from
all waves completed to date and matching individuals over time. We began the people-matching
process by building a dataset, which included information about every individual found in an in-
terviewed household in every site in each of the three waves. We then used Link Plus—probabilistic
record linkage software—to compare the names of individuals across waves, identified matches, and
generated a unique personal identifier (“PERSONID” hereafter) with a comprehensive list of individ -
uals found in each wave. The PERSONID was set to equal the original household ADDRESSID—
an 8-digit number associated with a sampled address inside the Making Connections target neigh-
borhoods—followed by a unique alphabetical letter. We set the parameters for matching based first

98
Bachtell, English, and Haggerty
Residential Mobility: Implications for Families and Communities
on character strings within the first name field, then two numeric fields: age (adjusted for a stand-
ard number of years in between waves of data collection) and sex (dummy coded, with 0 = female
and 1 = male). The software reports a confidence score for each match—that is, an indication of
how likely it is that the individual matched as a pair across records actually represents the same
person. Exhibit 1 provides an example of the PERSONID assignment for one fictional linked house -
hold, identified with the ADDRESSID of the original household (91105820). In the example, two
of the three individuals remain present in all three waves—Clara and Edward, Jr. (91105820A and
91105820C, respectively). The father, Edward, was present in waves 1 and 2 but absent in wave 3.
Note that each individual preserves his or her PERSONID across waves.
Exhibit 1
PERSONID Letter ADDRESSID Wave MOVEID First Name Age Sex Roster
Example of PERSONID Assignment by Link Plus Software for 91105820
a
91105820A A 91105820 1 NA Clara 42 F Adult
91105820B B 91105820 1 NA Edward 41 M Adult
91105820C C 91105820 1 NA Edward, Jr. 11 M Child
91105820A A 91105820 2 Stayer Clara 46 F Adult
91105820B B 91105820 2 Stayer Edward 46 M Adult
91105820C C 91105820 2 Stayer Edward, Jr. 14 M Child
91105820A A 91105820 3 Stayer Clara 49 F Adult
91105820C C 91105820 3 Stayer Edward, Jr. 17 M Child
NA = not applicable.
a
Names and other information have been edited to protect participants’ confidentiality.
Roughly one-half of households with children in the Making Connections sample were found to
have moved between each wave. In these instances, the original households were deemed to have
“spawned” a “new household,” because interviews were attempted both at the new residence of the
focal child selected in the previous wave (the “mover”) and with the new occupants of the focal child’s
previous address (the “new household”). Movers and new households inherit the ADDRESSID asso -
c iated with the original household (91105820 in this case), but the fourth digit of their ADDRESSID
is adjusted to signal the movement of the focal child and the change in occupancy at the sampled
address (without the focal child), respectively. The labels associated with this fourth digit, the
MOVEID, are shown in the fifth column. In exhibit 2, we continue the example of the linked
household from exhibit 1 but demonstrate the identification of the Rodriquez family as “movers”
and the spawning of a new household in wave 3. In this alternative scenario, data were collected in
the final wave of the survey from both the Rodriquez family at their new home and an elderly man
named John who had moved into the Rodriquez’s previous home. Note that the link between all
individuals in all waves (what makes this a “linked household”) is established with the first eight
digits of the PERSONID, the ADDRESSID of the original household.
Further details about the sample design and spawning logic are available at http://mcstudy.norc.
org/study-design/.
A small team of coders then imported the output from Link Plus into a customized Microsoft
Access database for review and editing. NORC designed a form to display the roster data from
all waves for each household affiliated with a given baseline address on one screen (a “linked

Tracking Mobility at the Household Level
99Cityscape
household” hereafter). To minimize the risk of misrepresenting a given household as a stable unit
or missing an important change among specific members over time, NORC reviewed 100 percent
of the linked households.
We isolated patterns among the tough-to-code records and made adjustments to streamline the
review process. We increased the confidence threshold for accepting matches identified by Link
Plus. False positives proved to be fairly common among very large households and those in which
several family members had similar-sounding names (for example, “Marcela” and “Marcy”) or an
adult and child shared a name (for example, Frank and Frank, Jr.). Minor spelling and keying
errors contributed to the false positive rate as well. These challenges were exacerbated among
Hispanic households by inconsistent recording of matriarchal and patriarchal surnames. After an
initial review, between 5 and 10 percent of the linked households were selected for a secondary
review. A final set of quality checks was performed using a series of Microsoft Access queries and
Microsoft Excel.
The end product was a person-level data set in which every individual ever found in a surveyed
household across all waves occupied a row. The ADDRESSID was included at each wave to provide
a link between a given individual and the household survey data to which it should be associated.
Also included were fields indicating the individual’s age, sex, and position on the household roster
at each wave. These fields facilitated the linkage between the person- and household-level survey
data and facilitated the aggregation of the former, which would be required for developing more
sophisticated measures of household composition change.
Geographic Analysis
We constructed an address-level dataset to investigate geographic patterns of movement among
households that participated in the final wave of the survey. The 7 sites represented are Denver, Des
Moines, Indianapolis, Louisville, Providence, San Antonio, and White Center (near Seattle). The
dataset contains addresses from four different sources. Three of the sources are derived from the
household address reported by the respondent at the end of the wave 1, wave 2, and wave 3
Exhibit 2
PERSONID Letter ADDRESSID Wave MOVEID First Name Age Sex Roster
Example of PERSONID Assignment by Link Plus Software With Spawning for
91105820
a
91105820A A 91105820 1 NA Clara 42 F Adult
91105820B B 91105820 1 NA Edward 41 M Adult
91105820C C 91105820 1 NA Edward, Jr. 11 M Child
91105820A A 91105820 2 Stayer Clara 46 F Adult
91105820B B 91105820 2 Stayer Edward 46 M Adult
91105820C C 91105820 2 Stayer Edward, Jr. 14 M Child
91105820A A 91125820 3 Mover Clara 49 F Adult
91105820B B 91125820 3 Mover Edward 49 M Adult
91105820C C 91125820 3 Mover Edward, Jr. 17 M Child
91105820D D 91145820 3 New HH John 75 M Adult
HH = household. NA = not applicable.
a
Names and other information have been edited to protect participants’ confidentiality.

100
Bachtell, English, and Haggerty
Residential Mobility: Implications for Families and Communities
interviews. The fourth source could include up to six additional addresses that represent the places
where the respondent lived in between waves 2 and 3 of the survey (or more precisely, since
Thanksgiving of 2005 and the date of the wave 3 interview). These six addresses were reported ret-
rospectively during the wave 3 interview and are thus referred to as the “retrospective addresses.”
Each set of addresses was then interactively geocoded using MapMarker Plus 24.1. The combined
dataset includes 15,077 addresses from the wave 1, 2, and 3 series and 2,966 retrospective
addresses, for a total of 18,043 addresses. The coordinates from the 18,043 addresses were then
transposed in SAS to create a file with longitude and latitude coordinates for each address by
the wave 3 case identifier. This approach resulted in a dataset with nine sets of coordinates for
each wave 3 household, including a set of coordinates for each of the three waves and up to six
retrospective addresses.
In the interest of maintaining a clear methodological focus, in this article we include maps showing
mobility in only one site, San Antonio. A few unique characteristics of West Side, the San Antonio
survey neighborhood, are worth noting. First, West Side is the largest of the 10 Making Connections-
defined survey neighborhoods; it covers 24.4 square miles compared with an overall median of
4.9 miles for the 10 sites (see Coulton et al., 2009: appendix A). Second, the population is almost
exclusively Hispanic (with predominate origins in Mexico) and U.S. born. At the city level, Hispanics
comprise most of the population, 63 percent, and 46 percent of people age 5 and older speak a
language other than English at home (2010 census). These characteristics are greatly exaggerated
in West Side, where 89 percent of wave 3 survey respondents self-identified as Hispanic or Latino.
Third, the San Antonio site has a high rate of homeownership: 61.3 percent of residents living in
census tracts within the survey neighborhood were homeowners, based on American Community
Survey 2005–2009 tract-level estimates. Many families who would be priced out of suburban areas
are able to afford homes in West Side because of the availability of an ample stock of small, single-
family bungalows built before the 1970s. The diverse social, economic, and spatial characteristics
of the other 6 sites may yield different patterns of movement that merit separate study.
After geocoding the addresses, we used MapInfo Professional 11.0 to create points out of the coor-
dinates and lines to connect each of the points for each household. This approach resulted in the
creation of 45,664 lines, which were then combined into a single file with 2,979 lines representing
the moves of individual households over 6 years. Exhibit 3 depicts an example based on real data
of a household that was found at three different addresses during the study period. The small dots
represent the physical location of the residence at each wave and the person figures inside the
houses represent the household composition. Those figures appearing in black represent people
who were part of the original household at wave 1. Those figures appearing in dark gray represent
individuals added following wave 1 and those figures appearing in light gray are previous members
of the household who were lost or who were not present in a later wave. This example demonstrates
the importance of linked-person identifiers for measuring compositional change because, if we
were to rely solely on variables indicating the number of children in the household, those present
at waves 1, 2, and 3 would appear to have remained stable. The linked-person data make it possible
to detect the replacement of one child from the original household with a new child at wave 3. Aside
from these “mover” households, 2,729 households reported no change of address during the 6-year
period and thus are represented in proceeding maps with a single point and no connecting lines.

Tracking Mobility at the Household Level
101Cityscape
WAVE 1
WAVE 2
WAVE 3
Original household member
New member added
Prior member lost
Neighborhood boundary
Residential move
Highway
Major road
Household Composition Change Analysis
In addition to constructing and analyzing the geographic data, we constructed a series of flags using
the linked-person data for the 7 sites that participated in wave 3. The flags enable us to distinguish
among households with stable membership over time; those that experienced one or more additions,
subtractions, or substitutions of an individual; and those in which the initial residents were entirely
replaced by a new set of occupants at up to two points in time (waves 2 and 3). We also calculated
continuous measures summarizing the total number of adults and children who were added to and
subtracted from the household between waves 1 and 2, 2 and 3, and 1 and 3. These calculations
reflect not simply numerical change in the household size, but also the presence or absence of each
individual household member in each wave. The maps and tables in the following section report
findings from the combined geographic and household composition change data.
Findings
In the following discussion, we use “residential movement” to refer to the geographic relocation of
a household from one address to another. We use “household composition change” to account for
the addition or subtraction of at least one member between two points in time. We argue that this
Exhibit 3
Example of Linking Households to Track Residential Movement and Composition
Change

102
Bachtell, English, and Haggerty
Residential Mobility: Implications for Families and Communities
latter phenomenon represents an important, if often neglected, aspect of mobility—the transfer
of individuals into and out of households. Note that our definition of compositional change is re-
stricted to the addition, subtraction, or replacement of one or more individuals. We do not include
natural changes, including the birth of a child to the respondent, minor children moving from the
child roster to the adult roster in a later wave, or teenagers age 15 or older leaving the household.
The exhibits in this section display geographic data from sample members in San Antonio. Tables
are labeled to distinguish between results for all 7 sites and those only for San Antonio. The results
reported in tables are unweighted. Unless otherwise noted, we restrict our analyses to households
with one or more children. We again use this distinction to maintain a clean methodological focus
on the different measurements of mobility. Adult-only households were not tracked to new locations
under the Making Connections sample design. By examining only households with children, we
ensure that all observations will have had the same opportunity to change residences and be followed
over time. We also omit the retrospective addresses collected at wave 3, representing places where
the respondent had lived since Thanksgiving of 2005 to allow for more straightforward comparisons
between the geographic and household composition data available for each household. Although
the respondents were asked to report up to six retrospective moves, the detailed household roster
information was gathered only at the time of the wave 1, 2, and 3 surveys.
Residential Movement Among Sample Members
Residential movement among Making Connections households is frequent, as is commonly
observed in populations with high percentages of lower income minority groups and renters. Data
from the 2008 Current Population Survey suggest that the percentage of individuals who moved
in the previous year was more than five times higher among occupants of rental units compared
with those in owner-occupied units (Ruggles et al., 2010). In administering the wave 2 Making
Connections survey, NORC found that more than one-half (55 percent) of all households and 61
percent of households with children had moved since wave 1 (unweighted). These moves tended
to be short distance: the median distance for all completed child “movers” at wave 2 was 2.4 miles,
and 32.6 percent remained in the target Making Connections neighborhoods. The findings in sites
like Louisville reflect the situation of residents living in public housing developments undergo-
ing relocation as part of the HOPE VI Program’s relocation effort. The demolition of several of
these developments contributed to a movement rate of 67 percent among all households and 78
percent among households with children. In addition, individual sample members—particularly
children—sometimes returned to previous addresses after moving away. In between waves 2
and 3, 43 percent of respondents in Denver, Des Moines, Indianapolis, San Antonio, and White
Center (Seattle) reported having moved at least once in the retrospective address series (Bachtell
and Latterner, 2011). San Antonio had the lowest percentage of respondents reporting at least one
retrospective address among 5 sites. Only 37.4 percent of San Antonio respondents had moved
in the past 3 years, and, among those who had moved, relocation from outside the county was
extremely rare (2.4 compared with 11.5 percent among the 5 sites). Also, despite the proximity
of San Antonio to Mexico and the predominance of Hispanics in the survey sample, transnational
migration accounted for less than 1 percent of the retrospective moves reported by San Antonio
respondents in wave 3.

Tracking Mobility at the Household Level
103Cityscape
Exhibit 4 displays all residential moves among households with children during the 6-year period,
including potential relocation at waves 2 and 3, for households in San Antonio. Each gray line
beginning and ending with a small dot represents the path between one address and another for a
given household. These lines are connected to show multiple moves made by the same household.
The survey neighborhood boundary is shown with black dashes, and highways and major roads
are shown with double black and thick gray lines, respectively. This map makes evident the
frequent relocation of households within the survey neighborhood, West Side.
0 2.5 5
miles
Neighborhood boundary
Residential move
Highway
Major road
Exhibit 4
Residential Moves Among San Antonio Households With Children Over a 6-Year Period
Exhibit 5 reveals that composition changes were far more commonly observed than residential
moves among San Antonio households with children over a 6-year period (81.4 and 46.7 percent,
respec tively). As mentioned previously, our definition of compositional change does not include
natural changes, including the birth of a child to the respondent, a minor child moving from the child
roster to the adult roster in a later wave, or a teenager age 15 or older leaving the household. An
important caveat is that differences in the sample sizes for the various household panels (households
with children, households without children, movers, stayers, and so on) may account for some of
the magnitude of the compositional change percentages, particularly in the wave 1-wave 3 comparisons.
It is possible that those households that could not be located or refused to participate in followup
waves of the survey were somehow different from those that were successfully interviewed as part of
the wave 1–2, wave 2–3, and wave 1–2–3 panels in terms of household composition. In exhibit 5,
we also report the incidence of compositional change among households without children to put

104
Bachtell, English, and Haggerty
Residential Mobility: Implications for Families and Communities
in context the mobility patterns of this subgroup relative to the full sample. We find that house holds
with children in West Side are far more likely to undergo compositional changes than adult-only
households. Returning to only families with children in exhibit 6 (and in subsequent exhibits),
we find that 44.1 percent of San Antonio households moved one or more times during the 6-year
period. Of those households, 26.0 percent moved only once and 18.2 percent moved two times.
Exhibit 6
Number of Moves N %
Residential Movement Among San Antonio Households With Children Over a 6-Year
Period
None 366 55.9
One 170 26.0
Two 119 18.2
Total 655 100.0
Note: Includes households that were interviewed in wave 3 and any combination of waves 1 and 2.
Exhibit 5
Type of Change
Households With Children Households Without Children
N % N %
Residential Movement and Composition Change Among San Antonio Households
a

Over a 6-Year Period
Total at wave 1 533 100.0 132 100.0
Residential movement 249 46.7 NA
b
NA
Composition change 434 81.4 67 50.8
NA = not applicable.
a
Includes only households that were interviewed at wave 1 and wave 3.
b
As part of the sample design, only households with children were followed to new locations in followup waves of the survey.
Residential Movement and Household Composition Change
In the next series of maps, exhibits 7 and 8, we demonstrate the distinction between residential
movement and household composition change using spatial markers. We again display residential
moves among San Antonio residents with children and add a layer to show the locations at which
households experienced a change in composition. Exhibit 7 displays the two distinct aspects of
mobility over a 3-year period, between waves 1 and 2. Exhibit 8 extends the period to 6 years,
between waves 1 and 3.
Exhibit 8 reveals that, although residential movement in San Antonio is concentrated within the
survey neighborhood, over the 6-year period we see both a slight increase in families experiencing
household composition change (87.1 percent between waves 1 and 3 compared with 81.2 percent
between waves 1 and 2; see exhibit 9) and the dispersion of the addresses at which this change
occurs among households in the wave 1–3 panel. More families who moved at least once during
the 6-year period are also experiencing a “shuffling” of individuals in and out of the household. As
shown in exhibit 9, families who move are on average 9.4 percent more likely to have undergone
a compositional change across the three periods (wave 1 to 2, wave 2 to 3, and wave 1 to 3) than
families with no moves.

Tracking Mobility at the Household Level
105Cityscape
0 2.5 5
Neighborhood boundary
Residential move
Household composition
change
Highway
Major road
Exhibit 7
Residential Moves and Household Composition Change Among San Antonio
Households With Children Between Waves 1 and 2 (3-year interval)
Before performing the linkage of people to create the PERSONID data, the mobility of Making
Connections households was tracked using a “MOVEID” variable that was captured at the time of
each interview. The MOVEID data enable us to evaluate the effect of adding household composi-
tion change as a second component of mobility beyond residential movement. Households were
classified as “stayers,” “movers,” “new households” (or “in-movers”), “aged-out movers,” and “fresh
cases.” Stayers can be described as the original household (although certain compositional changes
are possible) at a sampled address. These classifications were depicted in the example shown in
exhibit 1. Movers were identified when the family associated with a previous wave focal child
moved to a new address, and the focal child remained under age 18 (see example in exhibit 2).
Conversely, aged-out movers were identified when the family associated with a previous wave
focal child moved to a new address and the focal child turned age 18 or older. New households
(or “in-movers”) represent different people living at a sampled address than those who had lived
there in the previous wave (again, see example in exhibit 2). Finally, fresh cases include the oc-
cupants of recently constructed or renovated buildings. The important distinction for the present
analysis is between stayers and movers. All households identified in the latter group will have
undergone residential movement, defined by the physical location of the focal child at each wave.
They may also have experienced a departure of one or more household members, an addition
of a new person or set of people, or both a departure and an addition, but these changes are not
captured in the MOVEID. As reported in exhibit 10, using this one-dimensional approach, we find

106
Bachtell, English, and Haggerty
Residential Mobility: Implications for Families and Communities
Miles
0 2.5 5
Neighborhood boundary
Residential move
Household composition
change
Highway
Major road
Exhibit 8
Residential Moves and Household Composition Change Among San Antonio
Households With Children Between Waves 1 and 3 (6-year interval)
Exhibit 9
Change Over Time Period
No Moves One or More Moves
N % N %
Household Composition Change Among San Antonio Households With Children
Over a 6-Year Period
Waves 1 to 2
Total at wave 2 273 100.0 234 100.0
Composition change between waves 1 and 2 178 65.2 190 81.2
Waves 2 to 3
Total at wave 3 344 100.0 258 100.0
Composition change between waves 2 and 3 223 64.8 171 66.3
Waves 1 to 3
Total at wave 3 284 100.0 249 100.0
Composition change between waves 1 and 3 217 76.4 217 87.1

Tracking Mobility at the Household Level
107Cityscape
that 62.6 percent of households with children in our analytic sample from 7 sites between waves 1
and 2 and 64.6 percent of households with children between waves 2 and 3 are classified as having
experienced mobility. In the second row, we report the percentage of households with children who
are classified as mobile after we combine this information with a measure of composition change.
Households that changed residences, changed individuals, or changed both are counted in the
second row. Of households with children between waves 1–2 and 2–3, 79.9 and 78.3 percent,
respectively, are found to be mobile, using this two-dimensional approach. A binomial test con-
firms that the differences between these two proportions in each period are statistically significant.
When employing the one-dimensional definition of mobility, we fail to account for an additional
17.3 and 13.6 percent of households between waves 1–2 and 2–3, respectively, that experienced a
change in composition.
Exhibits 11 and 12 demonstrate the differences in the aggregate sum of residential movement and
household composition change over 6 years at the census tract level. In exhibit 11, tracts are shaded
to show the percentage of households with children that relocated one or more times during this
period. In exhibit 12, tracts are shaded to show the percentage of households with children that
underwent any form of compositional change. Households experiencing each type of change were
added and then divided by the total number of sampled households residing in a given census
tract at wave 1. The increase in the tracts with percentages in the highest category (75 percent or
more) and the overall “darkening” of the neighborhood in exhibit 12 support the depiction of
households as dynamic collections of individuals and highlight the degree of individual turnover
occurring among families in West Side.
Exhibit 13 presents the type of compositional change over time among households with children.
The columns to the right isolate the change among only stayers and movers, as defined by the
MOVEID variable that was maintained during data collection to assess the movement history of
each household. Note again that the sample design for Making Connections requires that house-
holds include one or more children to qualify as a mover. These two groups, stayers and movers,
are combined in the “All” column. We do not report changes between waves 1 and 3 because the
Exhibit 10

Waves 1-2 Waves 2-3
N % N %
Comparison of One and Two-Dimensional Approaches to Mobility—Percentage of
Households With Children Defined as Experiencing Mobility (7 sites)
One-dimensional—using only residential movement to
define mobility
1,817 62.6 2,354 64.6
Two-dimensional—using residential movement and
composition change to define mobility
2,320 79.9*** 2,851 78.3***
Missed mobility when employing one-dimensional approach 503 17.3 497 13.6
***p < 0.001.
Notes: The numbers for waves 1-2 and waves 2-3 are 2,904 and 3,643, respectively. Significance testing was performed using
a one-sample binomial test to assess whether the proportion of households defined as mobile under the two-dimensional
approach significantly differs from the proportion defined as such under the one-dimensional approach (0.626 and 0.646 for
waves 1-2 and 2-3, respectively).

108
Bachtell, English, and Haggerty
Residential Mobility: Implications for Families and Communities
012
Miles
75 to 100 (0)
50 to 75 (14)
25 to 50 (10)
0 to 25 (1)
Neighborhood boundary
Highway
Major road
Exhibit 11
Percent of San Antonio Households With Children Experiencing Residential Moves
Over a 6-Year Period: Aggregate for Census Tract Based on Wave 1 Address
0
Miles
12
75 to 100 (19)
50 to 75 (7)
25 to 50 (0)
0 to 25 (0)
Neighborhood boundary
Highway
Major road
Exhibit 12
Percent of San Antonio Households With Children Experiencing Household Compo-
sition Change Over a 6-Year Period: Aggregate for Census Tract Based on Wave 1
Address

Tracking Mobility at the Household Level
109Cityscape
MOVEID values were generally assigned by comparing the location of the focal child at the current
wave with the wave immediately before. Percentages in this exhibit do not add up to 100 percent
within each period by group (all, stayers, and movers) because the categories of compositional
change are not mutually exclusive. For example, it is possible for a household to have added
some adults and lost some adults, added some adults but lost some children, or both. Recall that
these results are based on linked person-level data rather than numerical change in household
size. When examining both groups, we find that 47.9 and 44.8 percent experienced a change in
household composition between waves 1–2 and 2–3, respectively. The most common transfer
involves the loss of one or more adults. Of households with children, 26.9 and 26.2 percent lost
an adult between waves 1–2 and 2–3. Conversely, nearly one-fourth (24.2 percent) gained an
adult after the first wave and 23.6 percent did so after the second wave. Transfers of children were
less common, with 14.2 and 12.6 percent of households gaining and 9.0 and 7.9 percent losing a
child, respectively.
Unlike our expectation for the mover subgroup, our general expectation for the stayer subgroup
going into this analysis was that the individuals residing in these households should be relatively
stable over time. By definition, these households are those in which at least the focal child from
the previous wave remained present at the followup wave in question. While acknowledging that
migrations of other members around these key individuals could occur, we might still hypothesize
Exhibit 13
Change Over Time Period
All
c
Stayers Movers
d
N % N % N %
Type of Composition Change Between Waves
a
Among Households With Children by
MOVEID Subgroup
b
(7 sites)
Waves 1 to 2
No change 819 52.1 584 55.5 235 45.1
Added one or more adults 381 24.2 228 21.7 153 29.4
Added one or more children 224 14.2 153 14.5 71 13.6
Lost one or more adults 423 26.9 234 22.2 189 36.3
Lost one or more children 141 9.0 72 6.8 69 13.2
Total 1,573 NA 1,052 NA 521 NA
Waves 2 to 3
No change 1,332 55.2 651 60.6 681 50.8
Added one or more adults 570 23.6 208 19.4 362 27.0
Added one or more children 305 12.6 144 13.4 161 12.0
Lost one or more adults 632 26.2 214 19.9 418 31.2
Lost one or more children 190 7.9 57 5.3 133 9.9
Total 2,415 NA 1,075 NA 1,340 NA
NA = not applicable.
Note: The total number for all households, stayers, and movers is not equal to the sum of the five preceding rows within each
time period because categories are not mutually exclusive. Similarly, percentages do not add up to 100.
a
Adjusted for new births and teenagers/adult children ages 18 to 30 moving out.
b
As indicated by the MOVEID value for the later wave in each comparison.
c
This includes stayers and movers (as indicated by the MOVEID value for the later wave in each comparison).
d
Households that changed residences between waves 1 and 2 but remained in place between waves 2 and 3 are considered
movers.

Tracking Mobility at the Household Level
111Cityscape
Discussion
In past works, analysts have frequently used household size as a proxy for household composition.
In this article, we attempted to demonstrate the importance of considering specific changes in
household composition—that is, shifts in the presence of individuals over time—in studies of
mobility. Our research among households with children in 7 low-income communities suggests
that nearly one-half experienced some change in composition during a 6-year period (exhibit 13).
Employing a two-dimensional approach that combines residential movement and compositional
change significantly increases the percentage of households identified as having experienced mobil-
ity between waves, by a difference of 17.3 percent between waves 1–2 and 13.6 percent between
waves 2–3. Our findings also raise substantive questions about the relationships among individuals
in households experiencing compositional change compared with those that remain a consistent
unit over time. We intend to explore these questions in future analyses by examining mobility
among various types of families, including single parents, two-parent families, and extended
(multigenerational) families.
The ideal methodological technique for tracking household members in longitudinal studies is
to assign personal identifiers at each wave and to adjust for additions, subtractions, and substitu-
tions of individuals in real time. Our work demonstrates that such identifiers can be assigned
retroactively, although this option is contingent on the availability of identifying information such
as the name, age, and sex of each individual, as well as variables indicating the relationship of each
individual to at least one other person in the household (for example, the respondent and/or focal
child). The linked PERSONID data allow for a more sophisticated, two-dimensional approach to
tracking mobility at the household level that we think better captures the shifting nature of real lives.
Acknowledgments
This research was supported with a grant from the Annie E. Casey Foundation and from the
Center for Excellence in Survey Research at NORC at the University of Chicago. The authors are
grateful to Bob Goerge for providing the idea to retrospectively match household members across
survey waves and for comments that greatly improved this article. The authors thank Cindy
Guy for her encouragement and support of the work on which this article is based. The mobility
research undertaken by Claudia Coulton, Brett Theodos, and Margery Turner was an important
precursor to and inspired this investigation into the movement of individuals in and out of Making
Connections households. Participants at a convening of potential contributors to this volume and
other scholars hosted at the Urban Institute in April 2012 provided useful suggestions. Becki Cur-
tis created the maps included in this article and Jizhou Fu assisted with the analysis; both provided
excellent research support. The authors owe deepest gratitude to the Making Connections survey
respondents. Without their participation, the authors would have had no data to inform this
research. The findings and conclusions expressed in this article are solely those of the authors and
do not represent the views of the Annie E. Casey Foundation or NORC.

112
Bachtell, English, and Haggerty
Residential Mobility: Implications for Families and Communities
Authors
Kate Bachtell is an associate project director at NORC at the University of Chicago.
Ned English is a geographer at NORC at the University of Chicago.
Catherine Haggerty is a project director at NORC at the University of Chicago.
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115Cityscape: A Journal of Policy Development and Research • Volume 14, Number 3 • 2012
U.S. Department of Housing and Urban Development • Office of Policy Development and Research
Cityscape
Moving and Staying in
Los Angeles Neighborhoods:
Money Matters, but So Does
Family Composition
William A.V. Clark
University of California, Los Angeles
Abstract
In this article, I use data from the Los Angeles Family and Neighborhood Study to
examine the residential selections that households and individuals make when they
change residences and, in particular, the relationship between their choices and their
socioeconomic status. I evaluate outcomes across neighborhoods grouped into deciles
and quintiles of advantage and disadvantage, where the neighborhoods are allocated
to groupings of advantage and disadvantage based on the first factor of a principal
components analysis.
Resources—income, homeownership, and education—play important roles in neighbor-
hood selection and can also affect the decision to move. Commonly accepted, and as
demonstrated in this study, households on the whole move short distances within cities,
and, thus, where an individual originates has an important effect on his or her ability
to positively change his or her neighborhood status. The research shows that family
composition and ethnicity can constrain how much of a change in outcome is possible
with a move and highlights the difficulty of neighborhood or household interventions
intended to improve outcomes after a move. Modest evidence points to an increase in
satisfaction when households move up the hierarchy of the sociospatial scale.

116
Clark
Residential Mobility: Implications for Families and Communities
Introduction
Interest is increasing among researchers as to what role places can play in the outcomes of families
and individuals. It seems reasonable to expect that where a person lives can influence a wide
variety of outcomes such as access to schools, health care, and jobs, hence the continuing research
interest in the role of neighborhoods and communities in the urban fabric. Although an extensive
literature addresses mobility across low-income and poverty neighborhoods and whether or not
households in poor neighborhoods can escape those environments, the broad spectrum of neigh-
borhoods has received less attention. Often, the focus is on movers and less is known about stayers
or those who move within similar kinds of neighborhoods. Thus, it is useful to put the mobility
across low-income neighborhoods into a wider perspective, while at the same time not losing inter-
est in the problems of low-income movers. The research in this article aims to broaden the interest
from deprived neighborhoods to the whole range of socioeconomic statuses within the urban
fabric, and to contrast the outcomes at the different ends of the spectrum of income and education.
A significant body of research has established that residential mobility is a function of age, tenure
(homeowner or renter), family status (income level, education level), the demand for living space,
and changes in household composition. Less developed is the outcome of residence change.
Although it is generally assumed that people move to improve, in many cases mobility is not vol-
untary and people do not always gain from residential changes. These questions then arise: Which
households make gains in neighborhood quality? Do families who move make, at the least, subjec-
tive gains after moving? Specifically, the article examines a set of questions about neighborhood
outcomes and individual levels of satisfaction from the Los Angeles Family and Neighborhood
Survey (LAFANS).
It is common knowledge that cities are divided by socioeconomic status and that the division has
a spatial pattern. It is this pattern that is summarized in neighborhoods, leading to the question:
How do people sort themselves into these spatial units? The research in this article is about that
sorting process—about the outcomes of residential relocation within the structure of the city.
The survey asks these questions: (1) Which of the families who move are able to locate to better
neighborhoods and which are not? (2) What are the differences between those who stay in their
neighborhood, those who move within a similar neighborhood type, and those who move to more
or less advantaged neighborhoods? (3) Are families who move and make gains in neighborhood
quality more satisfied than those who move but do not make neighborhood quality gains? (4) What
evidence shows that moving improves neighborhood quality?
Previous Studies of Mobility and Neighborhoods
The research in this article is set within a rich body of previous investigations of mobility and
neighborhood sorting. A limited review of what is now a very large literature can usefully be or-
ganized around studies of mobility and residential sorting, specific studies of entering and leaving
deprived neighborhoods, and studies of household neighborhood intervention. Within the latter,
the Moving to Opportunity (MTO) program tried to measure the outcomes for individual families
and provides an opportunity to view the difficult issue of translating findings into contributions to
solving the problems of poverty.

Moving and Staying in Los Angeles Neighborhoods:
Money Matters, but So Does Family Composition
117Cityscape
Mobility and Sorting in the Urban Mosaic
The creation of neighborhoods is not a random process but is embedded in the preferences people
reveal in their wish to live near similar households in terms of income, composition (presence
of children, for example), and ethnicity. A set of analytic and simulation studies established the
relevance of these sorting mechanisms and the grouping of like individuals into spatially defined
areas (neighborhoods) from which similar behaviors and common outcomes are observed (Bruch
and Mare, 2006; Clark, 1991, 1992; Clark and Fossett, 2009; Fossett, 2006; Schelling, 1971). The
differential choices of movers and stayers are important, and these choices are an essential element
of the creation of spatial stratification (Clark and Morrison, 2012; Sampson and Starkey, 2008).
Then, if the residential sorting process leads to a widening of differences between neighborhoods,
some places will experience a more rapid descent socioeconomically than others and generate
characteristics that may initiate threshold effects on social behavior of the associated residents
(Meen, 2006). At the same time, some neighborhoods may experience increases in socioeconomic
status or, at the least, the maintenance of present levels of high socioeconomic status. In this sense,
neighborhood outcomes (both positive and negative) can result directly from residential mobility,
as extensive reviews of the literature show (Dietz, 2002; Durlauf, 2004).
Choices or the lack of choices have been related to family and household resource characteristics,
especially income, assets as measured by home ownership, and social capital (education). The
choices up the hierarchy of neighborhoods tend to be related to higher education levels, profes-
sional occupations, ownership, and income (Clark, 2007; Clark and Dieleman, 1996; Clark and
Rivers, 2012; Sharygin, 2010). Measures of income and socioeconomic status are also associated
with movements in and out of deprived neighborhoods, although race plays a role as well (Bolt et
al., 2008; South et al., 2005). Whites are more likely to choose largely White tracts (Clark, 2009),
but it is notable in a national study that more than one-fifth (21 percent) of African-American
households, 51 percent of Asian households, and 23 percent of Hispanic households move to
tracts that are 70 percent or more White (Clark and Rivers, 2012). Clearly, considerable fluidity ex-
ists in the choice processes and outcomes in terms of racial and ethnic composition. Many of these
households that move to White areas are in fact moving to areas that overall are more advantaged,
not because they are White per se, but because, in general, Whites have been able to secure more
advantaged neighborhoods. Overall, the residential mobility studies clearly show that individuals
do adjust their neighborhood location to fit with changes in income as well as to accommodate
changing preferences for family and ethnic composition over the life course.
Research on mobility in the context of family behavior shows that, indeed, those families who
can leave unsatisfactory locations are more satisfied. As part of the Making Connections initiative,
Coulton et al. (2009) found that 30 percent of their movers were up-and-out movers who often
became homeowners in better neighborhoods. At the same time, those households that cannot
make such transitions are often vulnerable households that “need help along many dimensions”
(Coulton, 2009: 28). Thus, as Cheshire et al. (2003) found, mobility often leads to an increase in
the average level of deprivation of the area of exit. Mobility behavior is also intertwined with the
composition of the household. When vulnerable households are affected by unforeseen changes,
housing stress and downward housing career moves are often the outcome.

118
Clark
Residential Mobility: Implications for Families and Communities
The Role of Selective Mobility
A small, important literature looks specifically at the propensity to enter and to leave areas ranked
by levels of deprivation. These studies are variants of the question posed in the previous section—
who gets on and moves out and who moves in to replace the households that are able to relocate.
Although, in most cases, the focus is on only the most deprived neighborhoods, several studies,
in both the United States and Europe, documented that selectivity matters in the ability to escape
deprivation and, even though resources matter, minority status increases the difficulty of leaving
(Bolt and Von Kempen, 2003; South and Crowder, 1997; South, Crowder, and Pais, 2011). Other
research also documents that selection occurs across communities even when a policy commitment
to social integration exists; for example, the planning process in the Netherlands. Studies show
people leave some neighborhoods and choose others, with the mobility decision often being trig -
gered by the presence of minority populations (Bolt et al., 2008; Van Ham and Feijten, 2008).
Obviously, the response to neighborhood composition is then embedded in the selectivity process.
The focus on deprived neighborhoods draws on the notion that social networks and place attach-
ment in such situations shape young people’s attitudes toward education and work opportunities.
Thus, the notion that deprived areas serve as conditioning communities in creating an underclass
population becomes a basis for intervention to either help disadvantaged populations to selectively
move, or to provide place-based assistance to improve the neighborhood. Given the selective nature
of mobility, however, it is difficult to affect these place-based interventions Evidence reveals that,
“net migration flows act to maintain the gap between deprived areas and the average and, as a result,
work to undermine efforts to regenerate deprived neighborhoods” (Bailey and Livingston, 2008:
948). In addition, Sharkey (2012) showed that unselected change (that is, a change in neighbor-
hood conditions after a move into a new neighborhood) can undo the gains of moving up.
Clearly this process is complicated. It is a process in which mobility occurs against a changing
backdrop and with changes in the household and family as well. The changes in the backdrop
have been examined recently in the context of the decline in housing values, the foreclosure crisis,
and the implications for mobility. On the one hand, foreclosure may have stimulated mobility and
created neighborhood changes (Sharygin, Ellen, and Lacoe, 2010), while on the other hand, the
sudden decline in home values has locked homeowners into their locations and made moves that
much more difficult (Ferreira, Gyourko, and Tracy, 2010). They show that about a 12-percent
decline exists in mobility with every $1,000 increase in negative equity. This finding is especially
troubling for African-American households that often stretched their finances to become home-
owners in the middle of the 2000s (Clark, 2011). All of these findings raise the issue of how to
intervene to bring about substantive change for families, and what is the probability of success if
some manner of intervention is made.
Mobility and Policy
Interest in using mobility to provide opportunities for disadvantaged households has existed for
two decades. Some research suggested that vouchers to aid relocation to suburban areas would
increase job opportunities for low-income populations and solve some of issues of residence in
inner-city neighborhoods with problems of substance abuse, poor schools, and crime. Beginning
with the Gautreaux studies, some researchers suggested that vouchers to move out to suburban

Moving and Staying in Los Angeles Neighborhoods:
Money Matters, but So Does Family Composition
119Cityscape
locations have provided gains for families who can successfully relocate (Rosenbaum, 1993, 1995).
The MTO studies also argued for real gains from relocation (Goering, 2005; Briggs, 2005; Orr et
al., 2003). Others suggest more caution on the outcomes of these interventions (Clark, 2008; Im-
brocsio, 2012; Varady, 2003). Although it may be possible to disperse some individual households,
whether using voucher programs as a policy intervention to change the distribution of poverty is
successful is far from clear (Sampson, 2008). Overall, it is likely to take a lot of individual moves
and money to affect any substantial deconcentration of the poor (Goetz, 2003).
When examining the mobility of the households between those who received help to move and
those who were the control group (received no help to move), this study found that the unaided 
groups in some cities achieved residence in low-income neighborhoods to the same extent as those
who had help. Moreover, those who moved with help often moved to neighborhoods like the ones
they came from and, in some cases, moved back to their old neighborhoods. Households vote
with their feet, so to speak, and decisions by governments are always embedded in the dynamic
demography of the city (Tiebout, 1956). Income and asset levels are central elements of the choice
process and, as will become clear in the empirical section of the article, it is difficult to determine
how to change the choice process without fundamentally changing income levels.
This critique is not designed to ignore the fact that some households benefited from the MTO inter -
vention. Overall, initial gains were made for nearly all moving households in the MTO program;
these gains, however, could simply not be sustained for most households (Clark, 2008). The inter -
vention takes place, as mentioned previously, in the context of the sociospatial structure of the
city, which is a moving target because cities continue to change when new immigrants arrive and
when established households leave. More change probably occurs in Los Angeles neighborhoods
from immigration than could be influenced by government intervention. Behavioral changes will
continue to affect the metropolitan structure. Understanding the bases for choice and selection may
provide the environment for creating the connections to community and providing the gains for
disadvantaged households.
Analysis Format, Data, and Methods
This study draws the data for analysis from the LAFANS and an analysis of census data of neighbor -
hood characteristics. It uses the data from the first wave of interviews and examines the 994 mover
households from the LAFANS data in the context of all households in the survey. It is possible to
track the movers across tracts in Los Angeles County and to match the households and families to
their neighborhoods, identified in this study as census tracts. It is possible to assess their progress
across neighborhoods that are defined by levels of advantage and disadvantage. This specific analysis
uses matrices of neighborhoods that are grouped into deciles and quintiles of advantage and disad-
vantage and examines the mobility behavior of families and individuals across these combinations
of geographic units.
The deciles and quintiles of advantage and disadvantage are created from census tract data for Los
Angeles County. Tracts are assigned to deciles based on their factor scores from the first factor of a
principal components analysis that uses nine variables from the 2000 census. The variables used
to create the scores are broadly similar to those used in other studies of neighborhood advantage
and disadvantage, including studies in the United Kingdom (Noble et al., 2004) and New Zealand

120
Clark
Residential Mobility: Implications for Families and Communities
(White et al., 2008). These measures are designed to capture the demography of the underlying
urban structure, the levels of poverty and deprivation, and the socioeconomic status of the tracts.
1

Thus, this study measures the proportion of single-headed families, the levels of education, levels
of unemployment, and whether they had access to vehicles, among other variables.
2
Experiments
that use other sets of variables and a more parsimonious list do not materially alter the position
of tracts across the first principal component. The underlying assumption of creating a neighbor-
hood index is that these areas provide varying contexts, from good to not so good, for individual
households that live in these areas and that may try to use their resources to improve their level
of advantage by moving and by moving up the hierarchy. This index, however, does not capture
either the larger picture of urban sustainability or the externalities of crime and disorder, although
it is likely that these externalities are associated with the index as it has been constructed here.
Using the index, the movements of individuals and households are tracked through the levels of
advantage and disadvantage. The moves are in the interval 2000 to 2002, which is close to the time
of the 2000 census measures. The second wave of LAFANS data will require attention to change in
these neighborhoods over time, but, for this analysis, any single change in an individual neighbor-
hood is unlikely to change that neighborhood’s ranking. The map of the neighborhoods illustrates
the common urban distribution of advantaged neighborhoods in more suburban locations and
a greater distribution of deprivation in the inner-city neighborhoods of Los Angeles (exhibit 1).
The map is presented in quintiles with a gray scale, but a decile map in color is accessible on the
Cityscape website at http://www.huduser.org. The population flows across the levels of deciles and
quintiles are presented in a series of matrices, and then these population flows are modeled using
multinomial logit models pertaining to choice on the diagonal line, either above the diagonal line
(more advantaged) or below the diagonal line (less advantaged).
The findings use a framework from a national study of household moves across tracts using the
Panel Study of Income Dynamics (Clark and Rivers, 2012). In that study, and in this specific city
study, the aim is to build a picture of exactly how much dynamism is in neighborhood selection
and where movers end up as a result of their move. Also relevant is not simply who moves, but
who stays in a similar neighborhood, and how households and individuals who move, locally and
otherwise, compare with those who do not move. Unlike the national study, this study measures
the extent to which households and individuals express varying levels of satisfaction with their move.
This article presents findings on the following variables:
1. Matrices of movement across neighborhood deciles and quintiles.
2. The intersection of income, education, and tenure for movers across quintiles.
3. The intersection of expressed satisfaction levels by mobility outcomes.
4. Models of mover choices across quintiles.
1
To clarify, I use the word neighborhood for the tract in which the respondents live and use U.S. Bureau of the Census data
for census tracts to create the deciles and quintiles of advantage and disadvantage.
2
The variables in the factor score index are percent single family, percent linguistically isolated, percent high school, percent
unemployed, percent public assistance, percent below poverty, percent high-density housing occupation, percent no
vehicle, and percent median household income.

Moving and Staying in Los Angeles Neighborhoods:
Money Matters, but So Does Family Composition
121Cityscape
The use of both deciles and quintiles for presentation and analysis is necessitated by sample size
constraints and the opportunity to provide more details in some aspects of the presentation.
Findings
To build the picture of how people and households choose within a complex urban structure
and to illustrate how these choices create and perpetuate residential patterns of advantage and
disadvantage, the article examines both aggregate movements and movements by race and ethni-
city. It also examines the intersection of the choices and the underlying resources available to the
household.
1. Matrices of Choice
The matrix of choices across the matrix of deciles of advantage and disadvantage shows both
concentration and dispersal (exhibit 2). As expected, there is a significant probability of moving
on the diagonal line (that is, within the same decile) or to deciles that are one cell above or below
the diagonal line. Slightly more than 38 percent of all movers remain on the diagonal line. Overall,
37.4 percent of movers make gains in status and 24.3 percent lose a level in the hierarchy when
they move. The mobility behavior and selection in Los Angeles reflect the overall likelihood of
moving very short distances. In general, in residential mobility, the moves are short, often not
Exhibit 1
Advantaged and Disadvantaged Neighborhoods in Los Angeles County, 2000
Source: 2000 Census
Legend
1 Least advantaged
2
3
4
5
6
7
8
9
10 Most advantaged

122
Clark
Residential Mobility: Implications for Families and Communities
breaking neighborhood ties, but the moves in Los Angeles appear to be even more limited. Nearly
20 percent moved within the same census tract; overall, 36 percent of the moves were less than
1 mile away, and another 13 percent were less than 2 miles away. Such short-distance moves are
unlikely to break the ties with the decile of origin, and considerable continuity can be expected in
neighborhood outcomes.
The results for the mobility data at the quintile level naturally show a greater concentration directly
on the diagonal line (exhibits 3–6). For all movers, 51.3 percent begin and end in the same quin-
tile on the diagonal line, 30 percent gain a level, and 19 percent lose a level. The conditional row
probabilities emphasize the likelihood of staying in the highest and lowest quartiles, but it is the
breakdown of moves across ethnic and racial groups that add to understanding relocation behavior.
The data samples are modest for some groups, but it is significant that, across all groups, if a mover
is in the highest quintile, he or she has an extremely high probability of staying in that quintile.
Overall, White households have the highest probability of moving up in status. Less than 50
percent of White households remain on the diagonal line, nearly 33 percent gain a level, and 20
percent lose a level. In contrast, more than 52 percent of Hispanic households stay on the diagonal
line, 29 percent gain a level, and only 17 percent lose a level. The fact that Hispanic households
either maintain or gain status is testimony to their increasing gains in socioeconomic status, in gen-
eral. This type of move is significantly different from the moves by African-American households,
where more than 56 percent remain on the diagonal line, only 19 percent gain a level, and more
households are moving down in quintile status than are moving up. White households, even those
with their origin in the lowest quintile, show significant probabilities of being able to access higher
level quintiles, but this probability is much less for Hispanic and African-American households;
more than 70 percent of African-American households and 64 percent of Hispanic households that
began in the lowest quintile remained there after their move. In addition, although nearly one-third
of White households are able to move from the lowest to the highest quintile, nearly no Hispanic
or African-American households can experience this outcome.
Exhibit 2
Least Advantage Status Destination Decile Wave Most
Total
1 2 3 4 5 6 7 8 9 10
Matrix of Changes in Neighborhood Decile for Household Moves
(weighted responses)
1 42.8 20.3 15.2 4.3 7.2 5.3 1.1 .8 1.3 0 98.3
2 12.0 44.8 16.5 12.7 2.0 2.8 .9 1.5 .5 9.9 103.5
3 1.6 11.5 61.4 1.2 6.9 8.0 10.1 4.7 6.2 7.3 118.8
4 4.5 1.2 10.7 27.1 1.7 5.2 1.6 14.7 6.1 4.3 77.1
5 1.5 11.7 18.5 3.9 25.4 10.2 12.0 7.3 1.4 10.2 102.0
6 3.6 1.8 10.7 10.5 4.0 27.8 13.6 21.3 22.2 0 115.0
7 .5 .6 2.0 11.4 10.2 2.3 42.0 11.5 16.7 10.6 107.8
8 .1 1.6 4.0 3.1 1.5 6.7 25.5 28.4 3.8 22.3 97.0
9 .8 0 0 0 4.6 4.1 1.6 12.5 26.6 28.2 78.5
10 0 0 .3 .4 2.7 5.2 8.9 17.5 5.3 55.6 95.8
Total 67.4 93.6 139.2 75.6 66.3 77.5 117.3 120.2 90.2 148.3 994.4
Note: Because the numerical values sum to about 1,000, they can also be interpreted as percentages.
Origin decile

Moving and Staying in Los Angeles Neighborhoods:
Money Matters, but So Does Family Composition
123Cityscape
Exhibit 3
Destination
Total
1 2 3 4 5
Destination
Total
1 2 3 4 5
Weighted Distribution (a) and Conditional Row Probabilities (b) of All Household
Choices Across Quintiles (low/high)
1 120 49 17 4 12 202
2 19 100 22 31 24 196
3 19 44 67 54 34 218
4 3 20 21 107 53 205
5 1 1 17 41 116 174
Total 161 214 144 237 238 994
1 .594 .242 .086 .021 .058 100.0
2 .096 .512 .111 .158 .122 100.0
3 .085 .200 .310 .249 .156 100.0
4 .014 .100 .101 .524 .261 100.0
5 .005 .004 .095 .233 .664 100.0
Total .162 .215 .145 .239 .240 100.0
(a) (b)
Origin quintile Origin quintile
Exhibit 4
Destination
Total
1 2 3 4 5
Destination
Total
1 2 3 4 5
Weighted Distribution (a) and Conditional Row Probabilities (b) of White Household
Choices Across Quintiles (low/high)
1 7 2 9 1 9 27
2 4 7 4 9 13 37
3 6 8 48 35 21 118
4 1 5 10 63 36 114
5 0 1 11 36 76 174
Total 18 22 81 143 156 419
1 .259 .088 .325 .009 .319 100.0
2 .097 .191 .102 .255 .355 100.0
3 .052 .064 .407 .296 .182 100.0
4 .005 .041 .085 .551 .312 100.0
5 .000 .003 .090 .290 .616 100.0
Total .004 .005 .194 .342 .371 100.0
(a) (b)
Origin quintile Origin quintile
Exhibit 5
Destination
Total
1 2 3 4 5
Destination
Total
1 2 3 4 5
Weighted Distribution (a) and Conditional Row Probabilities (b) of Hispanic
Household Choices Across Quintiles (low/high)
1 72 29 6 2 3 111
2 9 68 10 11 2 100
3 7 22 8 9 9 55
4 0 6 8 12 8 35
5 0 0 0 0 6 6
Total 88 125 33 34 27 307
1 .642 .263 .054 .017 .026 100.0
2 .092 .680 .103 .106 .019 100.0
3 .133 .391 .154 .165 .157 100.0
4 .006 .175 .239 .348 .233 100.0
5 .000 .000 .000 .006 .933 100.0
Total .287 .406 .108 .112 .088 100.0
(a) (b)
Origin quintile Origin quintile
Exhibit 6
Destination
Total
1 2 3 4 5
Destination
Total
1 2 3 4 5
Weighted Distribution (a) and Conditional Row Probabilities (b) of African-American
Household Choices Across Quintiles (low/high)
1 28 9 1 1 1 40
2 6 12 6 0 3 27
3 1 11 4 1 1 18
4 2 9 1 10 1 23
5 1 0 0 0 16 17
Total 38 41 11 12 22 125
1 .717 .225 .024 .023 .011 100.0
2 .218 .462 .213 .000 .023 100.0
3 .075 .605 .203 .057 .060 100.0
4 .087 .373 .047 .447 .046 100.0
5 .046 .000 .000 .000 .954 100.0
Total 100.0
(a) (b)
Origin quintile Origin quintile

124
Clark
Residential Mobility: Implications for Families and Communities
An examination of the two top quintiles as a measure of continuing concentration of outcomes
provides a real contrast between the White sample, where approximately 50 percent of households
moved into or within the top quintiles, and the Hispanic (8.5 percent) and African-American (21.6
percent) samples. To put these selections into context, the national data suggest that only 31.0 per-
cent of White households, 5.3 percent of African-American households, and 9.9 percent of Hispanic
house holds move within the top quartiles (Clark and Rivers, 2012). White households clearly are
concentrating more in their selections in Los Angeles than they do nationally, but African-American
households are nearly four times more likely to be in, or moving into, the top deciles than African-
American households nationally. Hispanic households in Los Angeles are similar to their national
averages. How can these numbers be interpreted? The numbers suggest that two forces are competing
in Los Angeles: one is creating greater concentration and another is reflecting the greater fluidity
for minorities who have greater resources. The finding is consistent with previous research that
showed very different results (from the data for Baltimore, Chicago, and New York) for the MTO
sample that moved in the Los Angeles metropolitan area (Clark, 2008).
The focus of this study, however, is not only about the ethnic makeup of those who gain and
lose; it is also about their associated socioeconomic characteristics. This analysis was conducted
for movers within quintiles, stayers within quintiles, and movers who were above and below the
diagonal line and who were disaggregated by the nature and direction of the move.
2. The Intersection of Race and Socioeconomic Status and the Implications for
Mobility
Movers and stayers within quintiles are not that different in lower status neighborhoods, although,
in general, movers have higher incomes, are more likely to have college degrees or attended col-
lege, and are more likely to be homeowners (exhibit 7).
Of course, the differences in outcomes are greatest when a household moves up the status quintiles,
which is expected (median income is one of the variables in the index). Indeed, incomes are nearly
five times higher for movers in the most advantaged quintiles, and they have a nearly linear increase
across the distribution of quintiles. This pattern holds fairly well across all racial and ethnic groups.
The outcomes in homeownership reflect, of course, the differences in income. Differences in education
levels, specifically the proportion with some college or a complete college education, demonstrate
the importance of education in creating the basis for homeownership and upward mobility. Over-
all, the differences are much more striking over the distribution of quintiles than they are over the
differences in race and ethnicity (exhibit 8).
It is important to note that the small sample sizes do not negate the overall conclusion—that
socio economic status for households is closely associated with the place of residence and movement
into more advantaged areas. Overall, homeowners prevail in high-status areas and renters dominate
low-status areas. Demonstrated in the quintile matrices, White households dominate the higher status
quintile, but some Hispanic and African-American households do live in these most favored areas.
3

Still, the number of households is a very small fraction of the populations in those high-status areas.
3
In the quintile matrices, the data are reported as weighted results. The data for the socioeconomic characteristics are
unweighted. The weighting produced unreliable estimates on income and homeownership.

Moving and Staying in Los Angeles Neighborhoods:
Money Matters, but So Does Family Composition
125Cityscape
Exhibit 7
Quintile
Households
(n)
Family Income
($)
Have College Degree
(%)
Homeowner
(%)
Movers Stayers Movers Stayers Movers Stayers Movers Stayers
Stayers and Movers Within Quintiles, by Race and Ethnicity (unweighted)
All Households
1 253 627 18,119 14,109 21.7 17.7 15.8 13.0
2 90 432 24,384 23,964 28.9 28.0 34.1 29.7
3 42 276 36,620 28,577 54.8 40.2 52.4 45.8
4 79 251 69,823 52,008 81.0 66.1 64.1 51.7
5 73 213 88,100 75,568 84.9 75.6 82.2 76.1
White
1 9 39 21,358 16,149 66.7 59.0 0.0 14.3
2 5 42 35,500 44,329 60.0 76.2 60.0 55.0
3 21 82 41,710 30,966 61.9 53.7 52.4 54.4
4 56 122 78,730 63,667 85.7 76.2 64.3 60.2
5 49 147 92,581 77,236 95.9 78.9 85.7 78.3
Hispanic
1 193 500 18,929 14,136 10.9 9.0 15.0 11.0
2 75 317 23,654 20,368 24.0 15.1 29.7 23.5
3 11 147 28,273 24,850 27.3 23.1 63.6 46.0
4 11 68 63,750 35,789 63.6 50.0 63.6 51.5
5 6 24 82,717 63,242 50.0 58.3 66.7 60.9
African American
1 42 65 10,603 12,213 54.8 52.3 19.0 26.7
2 5 44 33,333 33,032 80.0 65.9 75.0 45.2
3 5 15 * 30,357 * 93.3 * 33.3
4 3 21 * 40,308 * 81.0 * 26.3
5 4 8 * 37,500 * 62.5 * 42.9
* Small sample sizes.
Exhibit 8
Mover-to-Stayer Differences Across Quintiles
100,000 100 100
80,000 80 80
60,000 60 60
40,000 40 40
20,000 20 20
0
1
Income ($)
College educated (%)
Homeowner (%)
1 12
Neighborhood quintile Neighborhood quintile Neighborhood quintile
2 23 3 34 4 45 5 5
0 0
Movers Stayers

126
Clark
Residential Mobility: Implications for Families and Communities
An analysis of the movers who change quintiles provides greater detail about the role resources play
in making gains or suffering losses in neighborhood quality. The study examines all gains (moves
from the lowest quintile to quintiles 3 to 5, from quintile 2 to quintiles 3 to 5, from quintile 3 to
quintiles 4 to 5, and from quintile 4 to quintile 5) and all losses (moves from quintile 2 to quintile
1 and from quintiles 3 to 5 to quintiles 1 to 2). In effect, the study does not consider the very lowest
exchanges; it examines income, education levels, and homeownership status for White, Hispanic, and
African-American households that gain or lose a level, defined in the previous section. Does socio-
economic status matter in the available choices, especially for minority households? Clearly, it does
(exhibit 9).
Across all income, education, and homeownership levels, the number of moves made from lower to
higher quintiles are about twice the moves made from higher to lower quintiles (exhibit 10). The
exceptions to this observation are education levels for White households and homeownership levels
for African-American households. The biggest contrasts are for Hispanic households—those moving
up are nearly five times more likely to have a college education and four times more likely to be
homeowners. White households that make the transition to the highest quintile, in general, are likely
to be homeowners, have higher levels of college education, and have significantly higher incomes.
Exhibit 9
Race or
Ethnicity
Advantage Moves Disadvantage Moves
House-
holds
(n)
Family
Income
($)
Have
College
Degree
(%)
Home-
owner
(%)
House-
holds
(n)
Family
Income
($)
Have
College
Degree
(%)
Home-
owner
(%)
Aggregate Moves to More and Less Advantaged Quintiles
All 189 61,513 71.4 69.1 124 28,119 30.6 26.2
White 89 72,204 75.3 71.9 15 43,400 70.0 33.3
Hispanic 59 49,595 55.9 71.2 78 25,586 10.3 18.4
African American 14 43,846 78.6 38.5 23 19,762 56.5 39.1
Note: Advantage moves are defined as from the lowest quintile to quintiles 3 to 5, from quintile 2 to quintiles 3 to 5, from
quintile 3 to quintiles 4 to 5, and from quintile 4 to quintile 5. Disadvantage moves are defined as from quintile 2 to quintile 1
and from quintiles 3 to 5 to quintiles 1 to 2.
Exhibit 10
Characteristics of Households That Move Up and Move Down Across
Neighborhoods in Los Angeles
100,000 100
100
80,000 80 80
60,000 60 60
40,000 40
40
20,000 20 20
0
3 > 4/5 3 > 4/5 3 > 4/5
Increase Increase IncreaseDecrease Decrease Decrease
Income ($)
College educated (%)
Homeowner (%)
4 > 5 4 > 5 4 > 5
Status Status Status
3 > 1/2 3 > 1/2 3 > 1/22 > 1 2 > 1 2 > 1
0 0

Moving and Staying in Los Angeles Neighborhoods:
Money Matters, but So Does Family Composition
127Cityscape
Exhibit 11
Move
a
Households
(n)
Family Income
($)
Have College Degree
(%)
Homeowner
(%)
Moves Up or Down From the Middle Quintiles to More and Less Advantaged Quintiles
Advantage moves
3 to 4–5 57 43,245 73.7 66.7
4 to 5 47 101,650 95.1 87.2
Disadvantage moves
3 to 1–2 61 30,166 29.5 23.3
2 to 1 45 18,992 20.0 18.2
a
Among quintiles.
A specific analysis of moves made up and down from the middle quintiles provides some of the most
useful data for understanding the selection process and its outcomes. The moves from the 4th quintile
to the 5th quintile typically involve high-earner homeowners with college educations. The moves
into the bottom quintile are composed of low-income and less educated homeowners and renters
(exhibit 11). When the data are graphed, the differences between the uniformly high values on
income, education, and tenure for those who gain a level and the much more varied outcomes for
those who lose a level are striking (exhibit 12). In one way, this outcome parallels the structure of
the quintiles, which reflect income differences as well as other socioeconomic characteristics. A
detailed decomposition of the movers provides further understanding of what underlies a house-
hold’s move up, and especially, a household’s move down the hierarchy. What is the composition of
the movers, especially of those moving at the bottom of the advantage and disadvantage structure?
Exhibit 12
Differences Between Moves Above and Below the Middle Quintile
100,000
100,000
100
100
100
100
80,000
80,000
80
80
80
80
60,000
60,000
60
60
60
60
40,000
40,000
40
40
40
40
20,000
20,000
20
20
20
20
0
0
All All All
All All All
Income ($)
Income ($)
College educated (%)
College educated (%)
Homeowner (%)
Homeowner (%)
White White White
White White White
Move up Move up Move up
Move down Move down Move down
Hispanic Hispanic Hispanic
Hispanic Hispanic Hispanic
African
American
African
American
African
American
African
American
African
American
African
American
0
0
0
0

128
Clark
Residential Mobility: Implications for Families and Communities
The decomposition of movers into their family structures and additional details on their socioeco-
nomic characteristics help reveal the processes of choice and who is likely to choose or be con strained
to lose a level in the neighborhood hierarchy (exhibit 13). The results add considerable detail to
the focus on resources, per se. Nearly one-half of those individuals who drop from quintile 2 to 1
are divorced, are divorced with children, or have never been married or had children; nearly all are
renters; and only a few have some college education. It is a similar story for moves from quintile 3
to 1. The structure of the table is striking, with increasing numbers with incomes of more than
$50,000—the resource effect; a changing distribution of age—the life-cycle effect; and the role of
education—the knowledge effect, knowing how to negotiate in an increasingly complicated world,
as the moves to more advantaged neighborhoods take place. The analysis reveals that marginal
households with only tenuous links to their communities suffer from downward mobility. For the
moves made to the highest quintile, lower incidences of household dissolution and children in
never-married households exists. The breakdown of household structure is being played out in
neighborhood choice at the lower end of the advantage and disadvantage scale.
Exhibit 13
Move
a
Percent
Minority
Percent
College
Educated
Percent
Renter
Percent
Income
≥ $50,000
Percent
Divorced
Percent
Never
Married
Percent
Age
< 35
Individual Relocation Moves Between Quintiles
2 to 1 98.4 19.0 81.0 6.3 17.5 27.0 55.6
3 to 1 87.1 35.5 83.9 12.9 16.1 22.6 38.7
3 to 2 86.7 24.0 62.2 20.0 8.8 13.3 35.6
3 to 4–5 53.4 72.6 37.0 45.6 13.7 17.8 24.7
4 to 5 35.0 88.0 10.0 86.8 10.0 3.3 18.3
a
Among quintiles.
Some national data reveal that younger households lose a level in the hierarchy to enter the home -
owner market, but this trend does not appear in this Los Angeles study. It is clear that younger
renters are the movers in the lowest quintiles, but, in general, they are not entering the homeowner
market. That being said, some African-American households become homeowners in the lowest
deciles. This trend may be an outcome of the push to homeownership created when the U.S. Depart -
ment of Housing and Urban Development required Fannie Mae to dedicate 50 percent of its busi-
ness to low- and moderate-income families. Certainly this allotment increased homeownership for
lower income households, although they now are dealing with the associated debt burdens and
declining house values. In general, however, the moves from the middle quintile to the lowest quintiles
exhibit traces of family breakup or instability often associated with lower education levels. It is im-
portant not to stereotype these processes, as recently occurred with the Coming Apart study (Murray,
2012). Still, the issue of household composition and the difficulty in sustaining family stability,
and consequently improving residential locations, are clear. As in the Coulton et al. (2009) study,
households that run into social problems have higher likelihoods of slipping down the social scale.
3. Satisfaction With Mobility
For those households that gain a level in status and have better outcomes, what is the intersection
with levels of satisfaction for moving above the diagonal line versus remaining on the diagonal

Moving and Staying in Los Angeles Neighborhoods:
Money Matters, but So Does Family Composition
129Cityscape
line or moving below the diagonal line? Data from the LAFANS enable us to look at the cross-
classification of several characteristics of neighborhoods in contrast with the neighborhood gains
that were observed from the mobility behavior. The data enable cross-classification of overall
satis faction and neighborhood safety, and they reveal whether the neighborhood is close knit and
whether the neighbors share the same values. Each of these outcomes can be ranked by where
gain intersects satisfaction on a four-point scale. For example, it is possible to examine overall
satisfaction, when analyzed in terms of those who make a gain, those who stay the same (but have
no change in satisfaction), or those who make a selection that puts them below the diagonal, and
to examine their outcomes on whether or not they were very satisfied, satisfied, dissatisfied, or very
dissatisfied with the outcome (exhibit 14).
Exhibit 14
Mobility Outcomes and Responses to Neighborhood Characteristics
(a) Overall Satisfaction
a
(b) Neighborhood Safety
b
(c) Close-Knit Neighborhood
c
(d) Neighbors Share Same Values
d
Moves
Very Satisfied Satisfied Dissatisfied Very Dissatisfied
(n) (%) (n) (%) (n) (%) (n) (%)
Advantage 126 43.2 149 50.9 16 5.5 1 0.3
Same 139 27.9 274 54.9 58 11.6 10 2.0
Disadvantage 27 15.0 118 65.6 26 14.4 6 3.3
Moves
Completely Safe Fairly Safe
Somewhat
Dangerous
Dangerous
(n) (%) (n) (%) (n) (%) (n) (%)
Advantage 109 37.2 143 48.8 27 9.2 14 4.8
Same 125 25.0 258 51.7 94 18.8 23 4.6
Disadvantage 31 17.2 92 51.0 40 22.2 17 9.4
Moves
Strongly Agree Agree Disagree Strongly Disagree
(n) (%) (n) (%) (n) (%) (n) (%)
Advantage 42 14.3 147 50.2 79 27.0 9 3.1
Same 44 8.8 260 52.1 154 30.9 33 6.6
Disadvantage 4 2.2 92 51.1 74 41.1 1 0.6
Moves
Strongly Agree Agree Disagree Strongly Disagree
(n) (%) (n) (%) (n) (%) (n) (%)
Advantage 21 7.2 156 53.2 57 19.5 19 6.5
Same 19 3.8 245 49.1 157 31.5 14 2.8
Disadvantage 1 0.6 60 33.3 92 51.1 6 3.3
a
Chi square for 3x3 table (collapse dissatisfied) 49.8 pr > .0001.
b
Chi square for 3x3 table (collapse dangerous) 35.1 pr > .0001.
c
Chi square for 3x3 table (collapse disagree) 22.9 p > .0001.
d
Chi square for 3x3 table (collapse disagree) 47.5 pr > .0001.
Notes: Advantage (n = 293) is a move above the diagonal, same (n = 499) is a move on the diagonal, and disadvantage (n = 180)
is a move below the diagonal of the matrix of moves. The response “unsure or neutral” is omitted for overall satisfaction, close-
knit neighborhood, and neighbors share same values, but the number can be computed by subtraction from the total.

130
Clark
Residential Mobility: Implications for Families and Communities
The four matrices of gains, when cross-referenced with satisfaction, safety, neighborhood con-
nections, and neighborhood values, provide convincing evidence that those who move above the
diagonal line (that is, those households that make gains) are also significantly more satisfied, feel
safer, and agree that they live in close-knit neighborhoods with people who have similar values,
versus those who move below the diagonal line (exhibit 14). Regarding overall satisfaction, those
households that moved above the diagonal line are nearly three times more likely to be very satis -
fied than those that moved below the diagonal line and are even more satisfied than those that
moved on the diagonal line. This powerful evidence makes a connection among moving, improv-
ing outcomes, and outcomes of satisfaction. Similarly, those households that made gains are twice
as likely to feel completely safe and many more times as likely to agree that they live in a close-knit
neighborhood and one that shares the same values than those households that do not. Collapsing
those tables to the simple 3-by-3 gain and loss versus satisfaction generates statistically significant
results. The chi-square values are all greater than 0.001.
What are the conclusions from this analysis of mobility behavior and neighborhood satisfaction
outcomes? Moving matters, and moving brings gains in general life satisfaction for many movers.
Those who stay often are not especially dissatisfied, but the movers who lose a level are less satis-
fied. It is a reiteration of the general view that those who can move, move out and move up, which
of course leaves those who are less advantage behind. Still, many households that move below the
diagonal line are still somewhat satisfied or feel that they are fairly safe. Of course, their satisfaction
is not totally unexpected because, in many cases, the household will have chosen that neighbor-
hood. It will be only with the second wave of data that longer term satisfaction can be evaluated.
It is only for the response to the question of whether households feel they are in a neighborhood
where neighbors share the same values that we see strong discrepancies in feelings about their
neighborhood. These results matter, because they reveal something about how people are reacting
to the outcomes from the mobility behavior. In essence, mobility does not always work out. In
these instances, people make the best of the situation. However, in the end, the matching of values
may be one of the most important indicators of future mobility. If individuals and households can
find places where they feel at home, sharing the same values is certainly a presumption of a lower
probability of future mobility.
4. Models of Choice
A series of multinomial logit models were constructed to further explore the associations with
census tract choice that can be observed in the matrices of moves. The study examines the vari-
ables that are associated with moves above and below the diagonal line, using the diagonal line as
the reference category. It examines total number of moves made and the choices made by White,
African-American, and Hispanic households, each analyzed separately.
The model for all moves, including variables that measure the tract proportion of African-American
and Hispanic households, is significant and confirms the discussion of the roles of income, education,
and tenure in the quintile outcomes (exhibit 15). Age is significant, as are education and tenure, but
clearly tenure is substituting for income, because when income is used as an independent measure,
it is not significant. The race and ethnic variables are not significant. When the same model is exam -
ined for moves above and below the diagonal line, with the diagonal line being used as a reference

Moving and Staying in Los Angeles Neighborhoods:
Money Matters, but So Does Family Composition
131Cityscape
category for White, African-American, and Hispanic movers, education dominates in influence
over outcomes (exhibit 16). Indeed, it is the only variable that is significant across all three groups.
In addition, for Hispanic households, tenure is a significant measure of moving above the diagonal
line and marital status is marginally significant at level .10 for both White and African-American
households, as is income for African-American households.
A preliminary interpretation of the findings from the advantage and disadvantage moves suggests
that status, as measured by education, is a critically important measure of the choices that matter
across neighborhoods in metropolitan Los Angeles. Income lurks in the background for African-
American and Hispanic households, although it is only marginally significant in both cases. Family
status is also a background variable for White and African-American households’ ability to make
more positive moves. That tenure is not important when the data are broken down by ethnicity
emphasizes the much lower ownership levels of African-American and Hispanic households
overall. The results reveal two forces that were discussed in previous sections. First, the income
effect seems to be greatest for the higher quintiles—that is, the increase in income across quintiles
is not linear but increases rapidly in the two highest quintiles. Second, the strong findings on the
influence of education level achieved upon positive outcome, reflect and include the status differ-
ences that are highlighted for families who are moving down the neighborhood hierarchy because
of family trauma.
Exhibit 15
Variable Chi Square Pr > Chi Square
Multinomial Models of Advantage and Disadvantage Moves Across the Mobility Matrix
Intercept 24.14 .0001
Age 7.21 .0272
Married family 3.21 .2004
Family income 2.35 .3095
College educated 7.65 .0219
Homeowner 16.16 .0003
African American 0.25 .8808
Hispanic 4.37 .1127
Notes: An advantage move is a move above the diagonal, and a disadvantage move is a move below the diagonal of the
matrix of moves. The diagonal is the reference category.
Exhibit 16
Variable
White African American Hispanic
Chi Square
Pr >
Chi Square
Chi Square
Pr >
Chi Square
Chi Square
Pr >
Chi Square
Multinomial Models of Advantage and Disadvantage Moves Across the Mobility
Matrix by Race and Ethnicity
Intercept 3.74 .1539 0.49 .7811 23.62 < .0001
Age 5.34 .0692 0.05 .9740 2.37 .3065
Married family 5.25 .0724 5.22 .0736 1.65 .4377
Family income 0.69 .7067 4.88 .0870 4.45 .1081
College educated 9.50 .0087 15.45 .0004 12.83 .0016
Homeowner 2.35 .3089 2.43 .2960 6.07 .0482

132
Clark
Residential Mobility: Implications for Families and Communities
Observations and Conclusions
A great deal of selectivity is occurring across neighborhoods in metropolitan Los Angeles, and that
selectivity is tending to reinforce patterns of separation—patterns that have long been in place in
neighborhoods across the urban area. The evidence of the tendency to reinforce patterns comes
from the robust probabilities of selection on the diagonal line and across all levels of socioeconomic
status, but—and it is a very important caveat—at the same time, there is substantial fluidity in the
mobility outcomes in the Los Angeles metropolitan area, and there is considerable evidence from
this study of people moving to improve. The proportions of households that move up vary by race
and ethnicity but, among Whites and Hispanics, one-third and one-fifth of movers, respectively,
make gains in their neighborhood status. It is a positive view of opportunities in the urban mosaic,
a view that there are opportunities to access better neighborhoods and that those with resources
are able to do so. Moreover, the levels of satisfaction for those households that can access high-
quality neighborhoods are greater than for those that find themselves below the diagonal line of the
matrix of moves. In particular, substantial numbers of Hispanic movers are able to increase both
their socioeconomic status and, by extension, their greater levels of residential integration, defined
as living in census tracts with larger proportions of White residents.
To the extent that education and income are intertwined (that is, that people with a higher level of
education are more likely to have higher incomes), a persuasive argument can be made that money
matters in the choices that are available to households in Los Angeles neighborhoods. Money and
resources matter more than most people want to acknowledge. Given that money matters so much,
what are the available options to bring policy to bear on the mobility and moving patterns in large
urban areas?
As others have noted, it would take a lot of money and a lot of moves to solve poverty. This as-
sumption returns the debate to—How is it possible to intervene in society and the urban fabric? As
Coulton and colleagues (2009) noted, the critical challenge may be to figure out how to help those
who are falling down the hierarchy. In this sense, it picks up an issue that was discussed previously
in this article, regarding moves to the lowest status quintiles. Social issues are clearly an important
part of solving the problems for households that run into the problems of surviving and improving
in modern urban society. Perhaps the most troubling aspect of the downward urban mobility is
the negative outcomes for children. Those movers and their children who move to inner-city,
challenged neighborhoods have fewer resources, and their children are not doing as well as those
leaving inner-city areas in Los Angeles.
Can places or people’s outcomes, or both, be improved? Recent discussions of this exact problem
again juxtapose the very different approaches of groups with different agendas and juxtapose
those who have place effects at the forefront of their approach to the problem with those who are
more interested in individual outcomes. Nearly two decades ago, a vigorous debate ensued about
whether to invest in places or people—the place prosperity versus the people prosperity debate.
This debate may now be subsumed by the increasing importance of issues of equity and fairness
and by the question of whether a developed society can continue to ignore the high levels of
inequality that are at the heart of the issues and outcomes that this article reveals in microcosm.

Moving and Staying in Los Angeles Neighborhoods:
Money Matters, but So Does Family Composition
133Cityscape
Author
William A.V. Clark is distinguished research professor at the University of California, Los Angeles.
Acknowledgments
The author thanks the Los Angeles Family and Neighborhood Survey for making the data available.
(See Pebley, Anne, and Narayan Sastry. Los Angeles Family and Neighborhood Study Computer
file ICPSR22940-v3. Ann Arbor, MI: Inter-university Consortium for Political and Social Research,
2009-11-09, doi:10.3886/ICPSR22940. Now available from the RAND Corporation, Santa Monica.)
The author also thanks Narayan Sastry, who offered insights and suggestions that improved the
analysis, and Claudia Sharygin and Cindy Guy, who critiqued the article and helped improve the
presentation. In addition, the author thanks the Andrew W. Mellon Foundation for research support.
This article was originally presented at the Cityscape Symposium on Residential Mobility, Washington,
D.C., April 2012.
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136 Residential Mobility: Implications for Families and Communities

137Cityscape: A Journal of Policy Development and Research • Volume 14, Number 3 • 2012
U.S. Department of Housing and Urban Development • Office of Policy Development and Research
Cityscape
Public Housing Transformation
and Crime: Making the Case
for Responsible Relocation
Susan J. Popkin
Urban Institute
Michael J. Rich
Emory University
Leah Hendey
Chris Hayes
Urban Institute
Joe Parilla
The Brookings Institution
George Galster
Wayne State University
Abstract
The research in this article examines the effect on crime rates of public housing trans-
formation in Atlanta and Chicago, focusing on the neighborhoods receiving households
relocated with housing vouchers. Modeling the complex relationship between voucher
holder locations and crime, using quarterly data, our analysis found that crime rates fell
substantially in neighborhoods with public housing demolition, whereas destination neigh-
borhoods experienced a much lesser effect than popular accounts imply. Nevertheless, on
average, negative effects emerge for some neighborhoods with modest or high densities
of relocated households compared with conditions in areas without relocated households.
Overall, we estimate small net decreases citywide in violent crime over study periods
during which crime declined significantly. These findings suggest a need for thoughtful
relocation strategies that support both assisted residents and receiving communities.

138
Popkin, Rich, Hendey, Hayes, Parilla, and Galster
Residential Mobility: Implications for Families and Communities
Introduction
Chicago and Atlanta are very different cities but, in the 1990s, both faced serious problems with
their public housing—distressed, high-crime developments that were damaging residents’ lives and
contributing to neighborhood decline. By the end of the decade, both cities’ housing authorities
had used federal HOPE VI
1
grants to launch ambitious citywide transformation efforts, with the
goal of demolishing their worst developments and replacing them with new, mixed-income com-
munities. Transforming public housing meant relocating thousands of households while the new
housing was constructed, a process that often took years and required developing new services to
support residents through the process. As part of the relocation effort, many former public housing
residents in both cities received housing choice (Section 8) vouchers (HCV) and moved to private-
market housing; most opted to keep their vouchers and stay in their new neighborhoods rather
than return to the new mixed-income communities.
Not surprisingly, the nation’s two largest public housing transformation efforts—the Chicago Housing
Authority’s (CHA’s) Plan for Transformation and the Atlanta Housing Authority’s (AHA’s) Olympic
Legacy Program and Quality of Life Initiative (QLI)—generated a variety of concerns, and many
affordable housing advocates focused on how former residents fared during the relocation process
(Bennett, Smith, and Wright, 2006; Keating, 2001; NHLP et al., 2002). Local politicians and press
accounts in these cities and others have also raised questions about whether households receiving
vouchers bring crime and disorder to their new communities (Dumke, 2011; Medina, 2011).
A 2008 Atlantic Monthly article sparked a media controversy by claiming that HOPE VI—specifi-
cally, relying on vouchers to relocate residents in private rental housing—was to blame for rising
crime in Memphis (Rosin, 2008). The article drew a grim picture of rapidly increasing crime in
previously safe Memphis communities and then used an analysis that associated crime incidents
with the movement of voucher recipients to make the case that HOPE VI was responsible for these
problems. The article ignited a national debate about the effect of housing vouchers on crime, with
many researchers and advocates arguing that the Atlantic Monthly’s analysis was too simplistic,
blaming voucher holders unfairly for broader trends (Briggs and Dreier, 2008). Until recently,
however, no systematic efforts have tried to understand whether empirical evidence supports these
fears or if they simply represent negative stereotypes of public housing residents.
Using a panel data set of administrative records from each housing authority and reported Part I
crimes at the census tract level for more than 30 quarters in Chicago and Atlanta, our research
examines the relationship between crime and relocation from public housing using advanced
modeling techniques. The three questions we explore in this article are (1) the degree to which the
entrance into a neighborhood of relocated voucher households has a significant effect on crime;
(2) whether any detected effect varies according to thresholds in the concentration of relocated
households; and (3) the degree to which the transformation efforts affected overall crime, looking
at tracts where public housing was demolished and at destination neighborhoods for relocated
households.
1
HOPE VI stands for Housing Opportunities for People Everywhere. Begun in 1992, it funded the demolition and rehabili-
tation of public housing around the country. For more information on the program, see Popkin et al. (2004).

Public Housing Transformation and Crime: Making the Case for Responsible Relocation
139Cityscape
This article begins a discussion of the possible way that public housing transformation might
influence neighborhood crime rates and an overview of the transformation efforts in Atlanta and
Chicago. We describe the data used, the methodology employed, and the challenges we faced in
trying to answer our research questions. We review the results of the analysis on destination neigh-
borhoods first and then describe how results are a part of an analysis on the citywide net effects of
the transformation efforts.
The relationship between crime rates and public housing households relocating into the private
market is complex. Crime declined dramatically in both cities throughout the 2000s, even in
neighborhoods that received many relocated households. Furthermore, the transformation efforts
led to substantial decreases in crime in neighborhoods
2
where the CHA and the AHA demolished
public housing communities. This decline contributed to a small but significant net decrease in
violent crime across all Chicago neighborhoods and a small decrease in violent crime and property
crime in Atlanta neighborhoods. The picture is not entirely positive, however. The transformation
contributed to slightly more property crime overall in Chicago, and some neighborhoods in both
cities have experienced problems associated with concentrations of relocated households. After the
number of relocated households reached a certain threshold, crime rates, on average, decreased
less than they would have if no former public housing residents had moved in. We conclude the
article with a discussion of the policy implications of these findings and suggest that future reloca-
tion efforts need to learn from Chicago’s and Atlanta’s experiences, particularly the responsible
relocation strategies both housing authorities developed as they learned more about residents’
needs (AECF, 2008).
3
How Could Public Housing Transformation Affect Crime?
Over the past two decades, housing assistance in the United States has undergone a profound
transformation (Turner, Popkin, and Rawlings, 2009). The $6 billion HOPE VI Program facilitated
the demolition of hundreds of distressed inner-city public housing developments and enabled
housing authorities to replace them with a combination of new, mixed-income communities and
vouchers. Underlying this transformation was the hope that public housing residents would benefit
both socially and economically from living in more diverse, higher opportunity neighborhoods
(Joseph, Chaskin, and Webber, 2007). Although not every public housing revitalization project
has realized all these hopes, a large body of research shows that former residents are generally liv-
ing in better housing in safer neighborhoods where they experience less stress and anxiety (Briggs,
Popkin, and Goering, 2010; Popkin, Levy, and Buron, 2009; Turner, Popkin, and Rawlings, 2009).
Public housing transformation also intends to improve neighborhoods. Removing distressed public
housing properties that cause blight may allow for new development, increase property values, and
2
For purposes of this analysis, we define neighborhoods as census tracts. Throughout this article, we use the terms neigh-
borhood, census tract, and tract interchangeably. These terms are not in reference to Chicago’s 77 community areas, which
are much larger, typically containing about nine census tracts each.
3
Responsible relocation provides relocation counseling and other direct services to ensure that residents receive appropriate
relocation benefits and have the opportunity to move to better neighborhoods than those they are leaving.

140
Popkin, Rich, Hendey, Hayes, Parilla, and Galster
Residential Mobility: Implications for Families and Communities
attract more affluent residents (Turner, Popkin, and Rawlings, 2009). Although large-scale reloca-
tion of public housing families is controversial, however, the question of how such moves might
affect destination communities has received relatively little attention from researchers, despite
real concerns about the potential for creating new concentrations of poverty (Galster et al., 2003).
Only one major study rigorously explored how voucher holders living in a community might
affect crime rates, and it found no evidence to support a link between the presence of voucher
holders and increased crime (Ellen, Lens, and O’Regan, 2011). That study, however, looked only
at traditional
4
voucher holders (who are not generally former public housing residents), used an-
nual data on voucher holders, and did not explicitly examine the question of the potential effect of
large-scale public housing relocation.
5
We have several reasons to expect that large-scale public housing demolition and relocation might
affect crime in destination communities more than the presence of traditional voucher holders.
6

First, relocating public housing residents for redevelopment could disrupt their social networks
(Hagedorn and Rauch, 2007), increasing their risk for either perpetrating or becoming victims of
crime in their new neighborhoods (Haynie and South, 2005; Sharkey and Sampson, 2010). Second,
new residents moving into a neighborhood could disrupt the community’s collective efficacy—the
degree of mutual trust and social cohesion that acts as a protective factor for residents—thereby
making the residents of these neighborhoods less safe (Sampson, Raudenbush, and Earls, 1997).
Third, some public housing residents or their associates could simply bring crime with them, essen tially
displacing problems like drug trafficking and gang activity from one neighborhood to another.
7
Transforming Public Housing in Chicago and Atlanta
Atlanta and Chicago have undertaken the two most prominent public housing transformation
efforts in the nation, initiatives that have been both widely lauded and extremely controversial. In
both cities, the most visible change has been replacing notorious developments like Robert Taylor
4
Throughout the article, we refer to those households that receive a housing choice voucher subsidy but that did not
relocate from public housing as “traditional” or “regular” voucher holders.
5
Research is thin on the differences between housing choice voucher households and public housing households, but
evidence indicates that public housing households are more likely to include elderly people and less likely to include
children.
6
The connection between public housing and crime is complex, but the two are clearly related. For example, research
shows a moderate-to-strong positive relationship between the location of subsidized housing in cities and crime hotspots
(Galster et al., 2002, McNulty and Holloway, 2000; Roncek, Bell, and Francik, 1981; Suresh and Vito, 2007), and that
public housing might impose negative crime externalities on surrounding neighborhoods (Sandler, 2011). Opportunities
for involvement in gang violence and drug sales, among other kinds of offending, are more readily available to youth who
reside in public housing developments than to those who live elsewhere (Popkin et al., 2000; Venkatesh, 2000). Public
housing residents also experience elevated levels of criminal victimization relative to their nonpublic housing counterparts
(DeFrances and Smith, 1998; DeKeseredy et al., 2003; Griffiths and Tita, 2009; Holzman, Hyatt, and Dempster, 2001;
Kling, Liebman, and Katz, 2005).
7
Evidence across multiple U.S. cities is mixed (Kleinhans and Varady, 2011; Suresh and Vito, 2009; VanZandt and Mhatre,
2009), although the most rigorous research suggests this phenomenon is not occurring as a result of HOPE VI demolition
(Cahill, Lowry, and Downey, 2011; Santiago, Galster, and Pettit, 2003).

Public Housing Transformation and Crime: Making the Case for Responsible Relocation
141Cityscape
Homes in Chicago and Techwood Homes in Atlanta with new, mixed-income housing that re-
flected the current thinking on how to provide affordable housing without creating concentrations
of poverty.
8
Chicago has been one of the country’s housing policy bellwethers, and efforts there have received
considerable national attention. The CHA’s Plan for Transformation began in 1999, when the agency
announced its goal to replace or rehabilitate 25,000 units of public housing.
9
As in many cities, re-
location proved the most challenging aspect of the transformation initiative. First, with more than
16,000 households to relocate, the sheer magnitude of the problem was daunting. Second, many
CHA residents faced numerous barriers that made relocation particularly challenging. Because of
the terrible conditions in CHA family developments, tenants who had better options had left long
ago, leaving behind a population dominated by extremely vulnerable families (Popkin et al., 2000).
Third, like most housing authorities, the CHA had little experience providing supportive services
and certainly had not previously attempted a large-scale relocation.
10
The challenges only intensi-
fied over time, as families who were easier to relocate moved, leaving the CHA with a population
increasingly dominated by the most vulnerable households (Popkin, 2010). The agency ultimately
overcame these challenges. Using the funding and regulatory flexibility that the U.S. Department of
Housing and Urban Development’s (HUD’s) Moving to Work (MTW) program provided, the CHA
built a robust resident services department; by 2011, the CHA had completed work on more than
85 percent of its planned units (Popkin et al., 2010).
CHA residents had three relocation options; they could (1) move to new mixed-income housing,
(2) live in rehabilitated public housing, or (3) use a voucher to rent a private-market unit. By 2008,
approximately 6,400 former public housing households had relocated to the private market with
vouchers. The limitations of the voucher program—that rents must fit HUD’s guidelines for afford -
ability and landlords must be willing to comply with program rules and regulations—meant, however,
that voucher rental units tended to concentrate in lower income, heavily minority areas (Cunning-
ham and Droesch, 2005). Although the CHA offered residents relocation assistance and mobility
counseling to encourage them to move to opportunity areas that offered better schools and services,
and although those who chose vouchers could move to any unit that met housing quality and rent
payment standards, many chose to stay in familiar areas on the city’s South and West Sides (Popkin,
Levy, and Buron, 2009). Furthermore, one benefit of tenant-based vouchers is that recipients have
the freedom to move, and many residents moved several times after leaving public housing.
Although the CHA did not launch its Plan for Transformation until 1999, the AHA was an early
leader in the national movement to replace distressed public housing developments with market-
quality communities. In 1996, the Atlanta Blueprint called for using a HOPE VI grant to revitalize
the Techwood-Clark Howell Homes, the nation’s oldest public housing development, marking an
8
For an overview of the CHA’s history and the Plan for Transformation, see Popkin (2010).
9
The CHA’s Plan for Transformation included providing relocation and self-sufficiency services to the existing lease-
compliant households living in public housing as of October 1, 1999 (known as original 10/1/99 residents), to help them
relocate (CHA, 2011).
10
The agency’s long history of mismanagement and broken promises compounded its problems with relocation (Bennett et
al., 2006; Popkin and Cunningham, 2005; Venkatesh et al., 2004).

142
Popkin, Rich, Hendey, Hayes, Parilla, and Galster
Residential Mobility: Implications for Families and Communities
important point in the evolution of the HOPE VI program nationally. When the first phase of
Centennial Place—in reference to the Centennial Olympic Games that Atlanta hosted that year—
opened in summer 1996, it was the nation’s first mixed-income development that included pub-
licly assisted housing. Also in 1996, the AHA unveiled its Olympic Legacy Program, the agency’s
effort to bring to scale the mixed-income revitalization model for traditional public housing. The
AHA was able to build on the momentum from the Centennial Place revitalization and leverage
additional local investment to support replacing three additional public housing developments
with mixed-income communities.
11
After nearly a decade of experience in turning distressed public
housing into mixed-income, mixed-use developments, the AHA launched its final and even more
ambitious effort to fully transform public housing in the city of Atlanta. As with the CHA, the AHA’s
participation in the MTW program, which began in July 2003, made possible the legal and regula-
tory framework for that effort. Among the key initiatives were a number of policy changes in AHA
leasing standards and practices and the adoption of a set of strategies intended to enable families
to use their vouchers in a broader range of neighborhoods. In addition, like the CHA, the AHA
introduced in 1998 a 5-year, family-focused coaching and counseling program to provide compre-
hensive assistance to tenants throughout and after the relocation process.
AHA’s QLI, launched in 2007, aimed to demolish nearly all of the city’s remaining family public
housing developments and to replace those units with new mixed-income communities. Just as the
CHA discovered, the AHA found that the families still needing to be relocated during these later
phases of the transformation initiative were more vulnerable and required more substantial support.
The AHA’s comprehensive supportive services, launched as part of its expanded relocation strategy,
were available to relocated families for up to 5 years. Relocated households that received this com-
prehensive support reported substantial improvements in their quality of life (Rich et al., 2010). By
2010, the AHA no longer owned or operated any large-scale family public housing developments.
To underscore the magnitude of the transformation, in 1996, more than 70 percent of AHA assisted
households lived in conventional public housing; by 2011, nearly 70 percent of AHA residents
had vouchers, another 15 percent lived in new mixed-income housing, and the rest lived in other
mixed-income properties throughout the city with project-based rental assistance. In the course of
this transformation, about 10,000 households relocated, and most, by far, used vouchers to move
to the private market.
Data
Our analysis draws on several data sources: data on voucher holders and relocated households
from housing authority administrative records in Atlanta and Chicago, crime incident reports, and
census data.
11
Overall, the AHA included 10 family public housing projects in the Olympic Legacy Program, with 7 receiving assistance
through the HOPE VI Program.

Public Housing Transformation and Crime: Making the Case for Responsible Relocation
143Cityscape
Housing Authority Administrative Data
Using HUD Form 50058
12
data obtained from the AHA and the CHA, we tracked voucher holders
longitudinally, from January 2002 through December 2009 in Atlanta and from October 1999
through December 2008 in Chicago, and we created a data set with the number of voucher holder
households aggregated to the census tract level for each quarter.
13
We were able to distinguish
voucher holder households that had relocated from public housing developments from those
participating in the regular Section 8 program.
14
Using the number of households in a tract (a
description of the calculation follows), we generated separate rates for relocated voucher holder
households per 1,000 households and for regular voucher holder households per 1,000 house-
holds. We refer to the former group as “relocated households per 1,000 households” and the latter
as “regular voucher holders per 1,000 households” in the remainder of this article. To answer
our question about the effect voucher holders have on crime in their destination neighborhoods,
we removed from the analysis sample tracts with substantial demolition of public housing units
(because, by definition, these areas would not be receiving communities) and those that are non-
residential.
15
These data do not include households relocated from public housing that did not take
a housing choice voucher, such as those that relocated to mixed-income developments.
Crime and Population Data
We obtained the quarterly tract-level crime data containing Part I crime
16
and gun crime
17
reports
for our study period. We also separately tabulated and included with our data reports of crimes
that involved a gun. The Chicago estimates used tract-level data from the 2000 and 2010 decennial
censuses to create intercensal population and household estimates for each quarter in Chicago,
whereas the Atlanta estimates used 2000 census data and population estimates that the Atlanta
Regional Commission calculated. The final analysis sample in Chicago contained observations for
813 tracts over 37 quarters, and the Atlanta sample contained observations for 121 tracts over
12
Form 50058 is a module of HUD’s Inventory Management System/Public and Indian Housing Information Center, a
system that stores information on families who participate in public housing or Section 8 rental subsidy programs (http://
portal.hud.gov/hudportal/HUD?src=/program_offices/public_indian_housing/systems/pic).
13
The Chicago study period begins with the fourth quarter of 1999 to include the start date of the Plan for Transformation
and the significant improvements in data quality after the HUD takeover of the CHA. Although Atlanta’s public housing
transformation began in the early 1990s, data on relocated public housing households were not available before 2002, so
the Atlanta study period begins then.
14
For Chicago, we matched public housing residents on the CHA’s 10/1/99 list to the data and created flags to indicate
if a voucher holder was part of the Plan for Transformation. For Atlanta, we derived data from the AHA’s housing choice
voucher and relocation administrative databases, which included a field indicating whether voucher holders received their
vouchers as a result of public housing transformation.
15
This excluded group includes tracts with 100 or more public housing units demolished: 18 total tracts in Atlanta and 30
total tracts in Chicago.
16
Part I violent crime includes aggravated assault, forcible rape, murder, and robbery. Part I property crime includes arson,
burglary, larceny, and motor vehicle theft.
17
Gun crimes (those involving handguns or other firearms) include homicide, sexual assault, robbery, battery, ritualism, and
assault.

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Popkin, Rich, Hendey, Hayes, Parilla, and Galster
Residential Mobility: Implications for Families and Communities
32 quarters. We chose quarters as the unit of time for this analysis because we sought to accumu-
late sufficient numbers of reported crimes of various types and thereby avoid substantial numbers
of observations with zero counts.
Exhibit 1 shows the summary statistics over the analysis period for our dependent and indepen-
dent variables of interest. We did not model gun crime in Atlanta. The two cities’ average quarterly
violent crime rates are similar: 4.9 crimes per 1,000 people in Chicago and 4.4 in Atlanta. Property
crime rates are substantially higher in Atlanta than in Chicago. The average population in a census
tract is roughly the same between cities, with a slightly higher average in Atlanta, about 3,800
compared with about 3,400 in Chicago. On average, the density of relocated households is very
similar between the two cities, but Atlanta has higher densities of regular voucher households.
To give a sense of how these variables change from quarter to quarter in Chicago, the average
absolute change in the voucher holder rates is 0.39 for relocated households and 1.6 for regular
voucher households. For crime counts, the average change is 4.19 for violent crime, 9.78 for
property crime, and 2.35 for gun crime.
Exhibit 1
Variable
Chicago Atlanta
Mean
Standard
Deviation
Mean
Standard
Deviation
Descriptive Statistics of Dependent and Independent Variables for Analysis Samples
Part I violent crimes 12.3 13.2 14.5 11.9
Part I violent crimes per 1,000 population 4.9 5.9 4.4 3.8
Part I property crimes 41.3 37.3 75.4 60.3
Part I property crimes per 1,000 population 17.5 28.6 21.9 22.9
Gun crimes 4.7 5.8 NA NA
Gun crime per 1,000 population 1.8 2.4 NA NA
Population 3,382 2,525 3,788 2,080
Relocated HH per 1,000 HH 3.5 7.0 3.3 6.7
Regular HCVP HH per 1,000 HH 31.2 40.8 50.2 63.3
Number of Observations 30,081 3,296
HCVP = Housing Choice Voucher Program. HH = households. NA = data not available.
Source: Analysis of 50058 data from Chicago and Atlanta Housing Authorities, Chicago and Atlanta Police Departments and
the U.S. Census Bureau
Methodology
The question of whether relocated households cause crime in their new neighborhoods appears
straightforward superficially. Substantial analytical challenges make answering it very difficult,
however, because of three potential problems—selection bias, endogeneity bias, and spatial
autocorrelation—that violate the basic statistical assumption about the independence of errors
associated with observations.
Efforts To Confront Statistical Challenges
Selection bias occurs when one or more unmeasured (uncontrolled in the model) neighborhood
characteristics causally affect both crime and where voucher holders decide to live. This problem

Public Housing Transformation and Crime: Making the Case for Responsible Relocation
145Cityscape
can bias the estimated coefficients; the amount of bias depends on the strength of the correlation
between the voucher holders’ residential selections and the unmeasured variables. In this case,
several neighborhood characteristics—availability of affordable housing, layout of streets, archi-
tectural character of buildings, access to mass transit, and presence and design of public spaces
and facilities—will likely affect both how many voucher holders move into the neighborhoods
and how much crime will occur there. To minimize selection bias, we estimate a fixed-effects
model in which the dummy variable we specify for each tract serves as a summary proxy for all the
aforementioned, unmeasured characteristics.
Endogeneity bias arises if crime rates and voucher holder concentration are mutually causal. Voucher
holder concentration might indeed affect the crime rate in a neighborhood, for any or all of the
reasons noted previously. The causation might also work in reverse, however. Landlords in neigh-
borhoods with rising crime rates, who face falling property values and skyrocketing vacancies,
might respond by recruiting voucher holders more aggressively. Concurrently, rents might fall
in these areas so that they become more economically attractive destinations in which voucher
holders can save out-of-pocket contributions to rent payments. This circular pattern of causation
can bias the coefficients of the endogenous crime and voucher concentration variables, with the
strength of the bias depending on the degree of reverse causation. Our use of quarterly data helps
address some issues with endogeneity bias because it precisely estimates the sequence of voucher
holders and crime. We count voucher holders in a tract if they are present at the beginning of the
quarter and count crimes during each quarter.
18
Although this approach does not address endo-
geneity completely, it reduces potential feedback bias if the market does not respond quickly to
changes in crime—that is, if landlords take several quarters or longer to reduce rents in response
to crime increases.
19
Spatial autocorrelation occurs when observations with similar values cluster across geographic
space. Clusters of this sort undoubtedly occur with the phenomenon of crime, for which spatial
spillovers have long been considered the norm. Statistical analyses that do not correct for spatial
dependency can have unstable parameter estimates and yield unreliable significance tests. We
make this correction by employing a spatial lag variable in our model, defined using the tract
centroids and an inverse distance decay function, α = 1, with a 2-mile cutoff (Hipp, 2010).
18
The voucher population in a tract is highly correlated quarter to quarter. A test of lagged or future voucher holder rates in
the same model with the current rate violates assumptions about the independence of errors.
19
We tested a specification of the model employing the lagged (one quarter) value of tract median sales prices for single-
family homes and condominiums as a control, but the additional term did not appreciably affect our results and caused
a number of tracts to drop from the analysis because of missing data. With Chicago data, we also experimented at length
with instrumental variables (IVs) as a way of confronting endogeneity bias. Unfortunately, the number of relocated voucher
households was zero for most observations and very small in nearly every other observation, thus rendering the predictive
power of our first-stage residential location models very poor. Because of this weak instrument in the second stage, we
were reluctant to report those results. We stress, however, that our IV estimates supported the conclusions we report here
regarding the effect of relocated voucher households on neighborhood crime rates. Thus, we have some confidence that our
conclusions have not been seriously distorted by endogeneity bias.

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Popkin, Rich, Hendey, Hayes, Parilla, and Galster
Residential Mobility: Implications for Families and Communities
Specification of the Crime Models
We estimate negative binomial, fixed-effects panel models of crime counts as a function of relocat-
ed households per 1,000 households.
20
Our base model treats this key variable as continuous; our
threshold model breaks the rate into various categories to test for nonlinear effects. As a control for
the aggregate concentration of all vouchers, we enter the rate of regular voucher holders per 1,000
households in the models. We also control for the citywide crime trend and seasonality of criminal
activity by including dummy variables for each quarter and omitting the first quarter. Tract fixed
effects and the spatial lag of the given crime dependent variable, as explained previously, complete
the specification.
Following recent convention in criminological research, we employ a negative binomial specification
(Hipp and Yates, 2009; Osgood, 2000). This specification handles crime counts instead of crime
rates and includes population as a separate explanatory variable with its coefficient constrained to 1.
Constraining the coefficient for population enables us to interpret the estimates in relation to crime
rates per capita instead of counts. A negative binomial estimation is preferable to standard ordinary
least squares (OLS) regression on crime rates because it accounts for skewed crime rate distributions,
particularly in areas with small populations like census tracts, where small increases in crime counts
can produce dramatic changes in crime rates (Osgood, 2000). This estimation uses the Poisson
distribution for counts and includes an error term with a gamma distribution, the latter to allow
for overdispersion caused by dependence between crime events.
Given the critical policy importance of identifying whether a threshold of voucher holder concentra-
tion that triggers crime exists, we also estimate a threshold model. Based on the literature, we expect
that threshold points might exist below which voucher holders have no effect on crime (Galster,
Tatian, and Smith, 1999). Because the distribution of voucher households in neighborhoods is
highly skewed, and because many neighborhoods have no voucher households, we use separate
categorical dummy variables for various threshold levels of both relocated and regular voucher
households per 1,000 households, with zero voucher holders as the omitted category. We created
threshold categories using the quartiles for tracts with nonzero values of relocated and regular
voucher households separately over four quarters at the end of the study period in Chicago, the
fourth quarter of 2007 through the third quarter of 2008. Atlanta tracts fell into similar threshold
categories, so we applied the Chicago threshold categories to the Atlanta data for comparability.
Finally, we experimented with a variety of models that enabled the potential effect of relocated
households on crime to differ according to the neighborhood context, as measured by variables
operationalized with 2000 census tract-level data. Given that our models include tract fixed effects,
these contextual variables enter the model as interactions with the relocated household rates vari-
able. We tried a wide variety of variables that attempted to measure the degree of collective efficacy
and pre-existing concentrations of poverty in the neighborhood. Unfortunately, none of these
20
A test of the model using the count of relocated households instead of the rate per 1,000 households did not produce any
substantive differences in the coefficients estimated.

Public Housing Transformation and Crime: Making the Case for Responsible Relocation
147Cityscape
experiments provided sufficiently consistent, meaningful, or robust results to report here. The
Atlanta research team is continuing to experiment with other tract-level data sources that might
serve as proxies for collective efficacy.
21
Our model estimates the marginal effect of relocated households on crime in destination neighbor-
hoods during the transformation efforts, but it does not tell us what effect relocation under these
efforts had on crime in neighborhoods where public housing was demolished. To predict how
many crimes would have been reported in these neighborhoods in the absence of the demolition
of the public housing developments, we estimated an OLS model for the public housing tracts for
each crime type, using data from 1991 through 2008 for Chicago and from 1997 through 2009 for
Atlanta (n = 30 in Chicago; n = 18 in Atlanta). In each tract, actual crime counts were set to missing
after the start date for the relocation of households in preparation for the first building demolition.
22

We used the coefficients from the terms in this model, including controls for time, tract fixed effects,
and indicators for season, to produce an expected crime count in these neighborhoods in the ab-
sence of public housing demolition.
23
Subtracting the expected number from the actual number of
crimes gives us the change in crime because of the public housing transformation and demolition
in these tracts.
For the other residential, or destination, neighborhoods, we divided the number of actual crimes in
each tract by the appropriate coefficient from the threshold model to estimate what crime would
have occurred in these neighborhoods if no households had relocated there and the public housing
transformation had not occurred. By aggregating the results across tracts and over the study periods
of what expected crime would have been without the transformation efforts in the public housing
demolition tracts and the destination neighborhoods, we arrive at a citywide net effect of the efforts.
Results
Overall, our negative binomial fixed-effect models
24
of crime in Atlanta or Chicago suggest a more
complex relationship between crime and public housing transformation than has been implied
in the popular media. The control variables indicated that, as expected, higher counts of similar
crimes within a 2-mile radius were associated with higher counts of that crime in the particular
neighborhood.
25
Most tract fixed effects proved statistically significant, suggesting substantial,
21
Residential churning within a tract might also contribute to decreased collective efficacy. We do not have a reliable
method of measuring all residential movement at the census tract level, either annually or quarterly. If residential churning
within tracts is consistent over time, however, the inclusion of tract fixed effects in the model would account for it.
22
The relocation of households in public housing units took place over an extended period. We assumed that relocation
began at least 1 year before the “notice to proceed” date for the demolition of each building (obtained from the CHA).
23
Regression results for this model are available from the authors on request. We excluded one tract in Chicago with public
housing demolition (818.00) from this model for property crime only because of its unusually high volume of property
crime and crime trend over time. We used a linear extrapolation for this tract to estimate the quarters after demolition
occurred. This procedure did not alter our conclusions overall.
24
We estimated parameters using the NBREG procedure in STATA MP 11.
25
Details are available on request; they are omitted from exhibit 2 for brevity.

148
Popkin, Rich, Hendey, Hayes, Parilla, and Galster
Residential Mobility: Implications for Families and Communities
persistent crime differentials among census tracts in Chicago and Atlanta, but we do not discuss
them here because they offer no important insights. The results of central interest, however, indi-
cate that greater concentrations of relocated households were associated with higher crime rates of
all types investigated; this relationship manifested itself only after surpassing a threshold occurring
in a minority of tracts.
Violent, Property, and Gun Crime Effects on Destination Neighborhoods
The estimation of our base model specification shows that higher rates of relocated households
in a neighborhood (census tract) in both Atlanta and Chicago are associated with higher violent
crime rates during that quarter (exhibit 2). In these negative binomial models, because tract
population has been logged and its coefficient constrained to 1, we can interpret the coefficient on
relocated households per 1,000 households as the percentage increase in crimes per capita for each
additional relocated household per 1,000 households in a quarter. The coefficient of 0.00769 for
violent crime in the Chicago base model indicates a 0.77-percent increase in per capita crime for
each additional relocated household per 1,000 households. We found similar results in Atlanta,
where an additional relocated household per 1,000 households is associated with a 0.72-percent
increase in per capita crime. The associated effect on crime for relocated households is slightly less
for property crime in both cities and slightly more for gun crime in Chicago.
Exhibit 2
Dependent Variable
(Crime Count)
Violent Property Gun
Chicago Atlanta Chicago Atlanta Chicago
Base Models All Residential Tracts (Except Public Housing Demolition Tracts)
Relocated voucher holder
HH per 1,000 HH
0.00769*** .00717*** 0.00657*** 0.00477*** 0.00926***
(0.000539) (0.00128) (0.000450) (0.000936) (0.000795)
Regular voucher holder
HH per 1,000 HH
0.000725*** – 0.00035 0.000766*** 0.0002562 0.000831***
(0.000152) (0.000245) (0.000132) (0.000172) (0.000227)
Spatial lag of crime 0.0374*** 0.0212*** 0.0150*** 0.00713*** 0.0887***
(0.000754) (0.00254) (0.000239) (0.00053) (0.00189)
Constant – 6.010*** – 7.339*** – 5.697*** – 5.073*** – 7.130***
(0.0485) (0.119) (0.0444) (0.0631) (0.0792)
Observations 30,081 3,296 30,081 3,296 30,081
HH = households.
** p < 0.05. *** p < 0.01.
Notes: All models include population, tract fixed effects, and indicators for each quarter as described in the text. Standard
errors included in parentheses.
Effect by Density of Relocated Households
The base models show small effects associated with relocated households on average across census
tracts, but we expected that variation across neighborhoods was possible based on the density
of relocated households. The findings from our threshold models, shown in exhibit 3, indicate a
much smaller effect of public housing transformation on destination neighborhood crime rates
than the popular accounts we discussed in the beginning of this article imply. Nevertheless, they

Public Housing Transformation and Crime: Making the Case for Responsible Relocation
149Cityscape
Dependent Variable
(Crime Count)
Violent Property Gun
Chicago Atlanta Chicago Atlanta Chicago
suggest negative effects for some neighborhoods when relocated households take up residence in
them. Using neighborhoods with at least one relocated household, we defined four categories of
relocated household density: very low-density areas have more than 0 to 2 relocated households
per 1,000 households; low-density areas have more than 2 to 6; moderate-density areas have
more than 6 to 14; and high-density areas have more than 14.
In Chicago, for instance, a neighborhood with a low density of relocated households at the begin-
ning of the quarter has a (statistically) significantly higher rate of violent and property crimes per
capita (5 percent) during that quarter than a neighborhood without relocated households, all
Exhibit 3
Threshold Models All Residential Tracts (Except Public Housing Demolition Tracts)
Very low density – 0.0100 – 0.0389 0.00517 – 0.0308 – 0.0301**
Relocated HHs per 1,000 HHs:
> 0 to 2
(0.00823) (0.0236) (0.00594) (0.0164) (0.0124)
Low density 0.0465*** 0.0354 0.0449*** 0.00551 0.025
Relocated HHs per 1,000 HHs:
2 to 6
(0.00978) (0.0274) (0.00747) (0.0197) (0.0146)
Moderate density 0.126*** 0.106*** 0.0918*** 0.0573** 0.119***
Relocated HHs per 1,000 HHs:
6 to 14
(0.0119) (0.0331) (0.00925) (0.0236) (0.0177)
High density 0.193*** 0.190*** 0.168*** 0.0865*** 0.209***
Relocated HHs per 1,000 HHs:
14+
(0.0145) (0.0432) (0.0114) (0.0306) (0.0216)
Very low density – 0.00530 0.0223 0.0103 0.0373 0.0163
Regular voucher holders per
1,000 HHs: > 0 to 5
(0.0170) (0.0407) (0.00915) (0.0224) (0.0297)
Low density – 0.00855 0.0979 0.0135 0.0347 0.00432
Regular voucher holders per
1,000 HHs: 5 to 22
(0.0196) (0.0565) (0.0111) (0.0333) (0.0335)
Moderate density 0.0316 0.0868 0.0139 0.0389 0.0812**
Regular voucher holders per
1,000 HHs: 22 to 64
(0.0229) (0.0659) (0.0142) (0.0401) (0.0382)
High density 0.0770*** 0.0739 0.0550*** 0.0142 0.139***
Regular voucher holders per
1,000 HHs: 64+
(0.0249) (0.0704) (0.0164) (0.0437) (0.0407)
Spatial lag of crime 0.0367*** 0.0217*** 0.0149*** 0.00708*** 0.0882***
(0.000753) (0.00257) (0.000240) (0.000537) (0.00189)
Constant – 5.967*** – 7.347*** – 5.665*** – 5.081*** – 7.125***
(0.0527) (0.121) (0.0464) (0.0650) (0.0868)
Observations 30,081 3,296 30,081 3,296 30,081
HCVP = Housing Choice Voucher Program. HH = household.
** p < 0.05. *** p < 0.01.
Note: All models include population, tract fixed effects, and indicators for each quarter as described in the text. Standard
errors included in parentheses.

150
Popkin, Rich, Hendey, Hayes, Parilla, and Galster
Residential Mobility: Implications for Families and Communities
other things being equal.
26
Relocated households have no effect on gun-related crime in Chicago
or either property or violent crime in Atlanta until they reach a moderate density. A neighborhood
with a moderate density of relocated households compared with a similar neighborhood with no
relocated households has a violent-crime rate, on average, 11 percent higher in Atlanta and 13
percent higher in Chicago. Compared with a similar neighborhood with no relocated households,
a neighborhood with a high density of relocated households has 21 percent higher violent-crime
rates in both Atlanta and Chicago.
Aggregate Effect in Destination Neighborhoods
Crime generally decreased during the study periods in the residential neighborhoods without
public housing demolition in Atlanta and Chicago. The solid line in exhibit 4 represents the actual
number of crimes reported. The dashed line shows how much crime we predict would have oc-
curred in these neighborhoods if no households had relocated there and public housing transfor-
mation had not occurred. We used the estimates from the previously described threshold models
to calculate the dashed line. The difference between the two lines is our estimate of the effect of the
relocated households on crime in these neighborhoods.
26
To the extent that neighborhoods with higher pre-existing crime rates attracted more relocated households because
vacancies were higher, rents were lower, or landlords were more heavily recruiting there, our estimates will overstate the
true effect of these households on subsequent crime rates.
Exhibit 4
Annual Numer of Violent Crimes in Destination Tracts (1 of 2)
Source: Urban Institute analysis of data from the Chicago Police Department and the Chicago Housing Authority
60,000
Chicago
52,500
45,000
37,500
30,000
2001 2003 2005 2007 20092000 2002 2004 2006 2008
With public housing transformation Without public housing transformation

Public Housing Transformation and Crime: Making the Case for Responsible Relocation
151Cityscape
Our estimates of the effects of relocated households on crime in the destination neighborhoods
vary depending on the density of relocated households; these estimates suggest that overall crime
reports in the destination neighborhoods would have been 2.8 and 5.5 percent less, respectively,
for violent crime in Atlanta and Chicago if public housing transformation had not occurred. With-
out relocated households in these neighborhoods, property crime would have been 1.1 percent
less in Atlanta and 2.8 percent less in Chicago. Gun crime in Chicago would have been 4.3 percent
less in destination neighborhoods.
Aggregate Effect in Neighborhoods With Public Housing Demolition
In the Chicago neighborhoods where public housing was demolished, violent crime decreased
more than 60 percent compared with our estimate of crime if housing transformation had not
occurred. Property crime declined 49 percent and gun crime declined 70 percent between 2000
and 2008. In Atlanta, violent crime declined 13 percent and property crime declined 9 percent
between 2002 and 2009 in neighborhoods with public housing demolition.
Aggregate Effect of Public Housing Transformation Citywide
By combining our analyses of destination and public housing demolition neighborhoods, we can
estimate the aggregate effect across each study period of the transformation efforts on crime in Chi-
cago and Atlanta. As in many American cities, crime declined in both Chicago and Atlanta during
Exhibit 4
Annual Numer of Violent Crimes in Destination Tracts (2 of 2)
Source: Emory University analysis of data from the Atlanta Police Department and the Atlanta Housing Authority
8,000
Atlanta
7,000
6,000
5,000
4,000
2001 2003 2005 2007 20092000 2002 2004 2006 2008
With public housing transformation Without public housing transformation

152
Popkin, Rich, Hendey, Hayes, Parilla, and Galster
Residential Mobility: Implications for Families and Communities
the study period. In both cities, however, tearing down public housing and relocating residents
with vouchers meant a modest, statistically significant reduction in violent crime overall.
27
Over
the period from 2000 to 2008, the CHA’s Plan for Transformation is associated with a 1.0-percent
net decrease in violent crimes reported and a 0.3-percent increase in property crimes reported,
independent of other factors affecting crime rates. The demolition of CHA housing had a greater
effect on gun crime, which was more heavily concentrated in public housing; reports of gun crime
decreased, on net, 4.4 percent citywide. In Atlanta, the effects of public housing transformation
from 2002 through 2009 yielded a 0.7-percent net decrease in violent crimes and a 0.5-percent
decrease in property crimes.
28
Although the overall effect on crime in both cities was generally positive, as with any major social
policy intervention, CHA and AHA efforts generated positive effects in some places and negative
effects in others. Both cities experienced significant and lasting crime declines in neighborhoods
where they tore down public housing and in many neighborhoods where former public housing
residents moved. In a relatively few areas in Chicago and Atlanta that received more than a few
relocated households, however, crime decreased less than it would have if no former public hous-
ing residents had moved in.
The analyses also examined the effect of traditional voucher holders—those who were not relocated
from public housing—on crime (exhibits 2 and 3). Traditional voucher holders have much smaller
effects on crime rates than do relocated households, and a much higher density of traditional
voucher holders is necessary before we see any effect at all. For violent crime in Chicago, compared
with a similar neighborhood with no traditional voucher holders, the density of traditional voucher
holders in the neighborhood has no effect on crime until it exceeds 64 households per 1,000 house -
holds, which is nearly five times greater than the high-density threshold for public housing reloca -
tion vouchers. Violent crime per capita in Chicago neighborhoods with a high density of traditional
voucher holders is about 8 percent higher, on average, than a neighborhood with no voucher
holders.
29
In Atlanta, no statistically significant threshold effects emerged at any level for traditional
voucher holders in regards to property or violent crime.
These findings raise the question of how many and how often census tracts have densities of relo-
cated households that are associated with higher crime rates. Because households move, census
tracts might shift among our four density categories over the course of the study period. Also, be -
cause we based these thresholds on cumulative voucher holders in a neighborhood, as more public
housing relocation vouchers enter the private market over the study period, the number of census
tracts with moderate and high densities of relocation households is more likely to be greater. During
the study period, most Chicago tracts (52 percent) had no (33 percent) or very low (19 percent)
densities of relocated households—the categories for which no effects on crime exist. Another
27
Note that this analysis of public housing transformation includes only former public housing residents relocated with
Section 8 vouchers and does not include analysis of residents living without vouchers in rehabilitated public housing or
mixed-income developments.
28
Because of data limitations, we were unable to perform the analysis for gun crime in Atlanta.
29
We found that traditional voucher holders were associated with a very small effect on property crime per capita in
Chicago (an average of less than 0.1 percent more), but we could not identify thresholds for the effect.

Public Housing Transformation and Crime: Making the Case for Responsible Relocation
153Cityscape
one-third of tracts had relocated households at the density levels associated with effects on crime
(low, moderate, and high) for most of the study period. In the remaining 15 percent of tracts, the
density of relocated households was also at these levels but not for most of the study period.
Similarly, most of Atlanta’s census tracts fell into the lowest relocated household categories. From
2002 through 2009, about one-half of the Atlanta census tracts included in the analysis had either
no public housing transformation relocation households (21 percent) or a very low or low density
of relocation households (25 percent), in which the effects of public housing transformation on
crime were not statistically significant. Only about 13 percent of the census tracts in Atlanta had
moderate or high densities of relocated households during most of the study period, whereas 41
percent had moderate or high densities of relocated households for less than one-half of the study
period, with most of these tracts reaching that threshold level during the final four quarters of the
study period.
Finally, the tracts in both cities that experienced the greatest effect on crime associated with relo -
cated households were neighborhoods that were already vulnerable, with high poverty and crime
rates before the arrival of public housing relocation households. In other words, our story is not
the popular version of previously stable communities spiraling into decline because public housing
residents moved in, but rather a story of poor families moving into areas that were already strug-
gling. In Chicago tracts where at least a low density of relocated households persisted for at least
one-half of the study period, the median income was $31,400 and the poverty rate was 31 percent
(citywide figures were $38,600 and 20 percent, respectively). In 2000, the violent crime rate in
these tracts was 29.6 per 1,000 people compared with 16.6 per 1,000 people for Chicago overall.
The tracts that received relocated households only at the lowest category are much less vulnerable.
In these tracts, the median income (on average) was $50,858, the poverty rate was 15 percent, and
the violent crime rate was 8.8 per 1,000 people. In 2008, the proportion of the city that experienced
the effects on crime associated with relocated households included 12 percent of tracts with a low
density of relocated households, 16 percent with a moderate density, and 14 percent with a high
density. Of the remaining tracts, 41 percent did not contain any relocated households and 17 per-
cent had a very low density of relocated households, so the effects were not statistically significant.
In Atlanta, the census tracts classified as having a moderate or high density of relocated households
for more than one-half of the study period had a median income of only $26,000 and a 32-percent
poverty rate (citywide figures were $37,200 and 24 percent, respectively). The violent crime rate
in these tracts in 2002 was 29.7 per 1,000 people; the rate for Atlanta overall was 22.7 per 1,000
people. By comparison, in the tracts that had relocated households but never at sufficient densities
to classify them into the two highest threshold categories at any point during the study, the median
income (on average) was $56,090 and the poverty rate was 22.6 percent. The violent crime rate
in these tracts was 22.6 per 1,000 people, equivalent to the overall rate for Atlanta. By the end of
2009, 14 percent of tracts in the city had a moderate density and 37 percent had a high density of
relocated households. Of the remaining tracts, where we found no effect on crime, 22 percent had
no relocated households, 15 percent had a very low density, and 12 percent had a low density of
relocated households.

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Residential Mobility: Implications for Families and Communities
Policy Implications
Untangling the relationship between public housing transformation and crime trends is extremely
challenging. Neighborhoods with higher pre-existing crime rates are also more likely to be afford-
able and accessible to voucher holders because they have more vacancies, lower rents, and more
landlords actively recruiting them (Popkin and Cunningham, 2000). The econometric techniques
we developed for this research provide the best estimation possible of the effect of large-scale pub-
lic housing relocation on crime trends in the neighborhoods where relocated households move.
This analysis shows a similar pattern in both Chicago and Atlanta: not the simplistic relationship
implied by media accounts, but rather a complex picture of declining crime rates in both cities, a
small net decrease in violent crime citywide associated with the transformation efforts, and effects
in some neighborhoods—those that received more than a few relocated households—that suggest
that crime would have been less there had no public housing transformation occurred. Overall, our
findings show that most neighborhoods in both cities were able to absorb public housing reloca-
tion voucher households without any adverse effect on neighborhood conditions.
This research raises many questions, most notably why the presence of even relatively small clus-
ters of relocated households in destination neighborhoods is associated with statistically significant
differences in crime rates during that quarter, on average, compared with tracts without any reloca-
tion voucher holders, whereas the presence of traditional voucher holders seems to have little to
no effect. In a historical context, public housing developments suffered extreme violent crime and
drug trafficking rates; many households had members tied to gangs or the drug trade (Popkin,
2010; Popkin et al., 2000). Some former households might have brought problem behaviors—or
associates—with them or, conversely, might have become targets in their new communities
because of gang turf issues.
30
Ethnographic research might help shed light on how relocated house-
holds affect neighborhood dynamics.
Regardless of the mechanism, a crucial policy implication from this research is the need for respon-
sible relocation strategies—like those both Chicago and Atlanta now employ—that offer former
residents a real choice of housing and neighborhoods and that provide long-term support after
those residents leave public housing.
31
Other housing authorities planning large-scale redevelop-
ment should learn from the experiences of these two cities about how to support former residents
in moving to a wider range of communities and how not to create new concentrations of poverty in
other vulnerable communities.
30
We do not have empirical evidence on which to stake this claim, which would require linking addresses of crime victims
and crime perpetrators to addresses of voucher holders. We are attempting to do this follow-up work in Chicago, although
we may encounter considerable data limitations in the completeness of reported unit numbers for multifamily residences.
We also acknowledge that the housing authorities do perform background checks on households before enrolling them in
public housing or the voucher program.
31
This recommendation is consistent with a wide range of research showing how concentrations of disadvantaged
households adversely affect neighborhoods (Galster, Cutsinger, and Malega, 2008; Galster et al., 2003). How to best
prevent such reconcentration has been discussed at considerable length (Briggs, Popkin, and Goering, 2010; Galster et al.,
2003; Grigsby and Bourassa, 2004; Katz and Turner, 2008, 2001; Pendall, 2000; Popkin and Cunningham, 1999; Popkin,
Cunningham, and Burt, 2005; Turner and Williams, 1998).

Public Housing Transformation and Crime: Making the Case for Responsible Relocation
155Cityscape
These strategies include—
• Comprehensive supportive services for relocated households before and after relocation.
• Mobility counseling to ensure that residents make informed choices about their housing and
neighborhood options.
• Financial incentives, such as raising allowable Fair Market Rent levels, to voucher holders and
potential landlords in desirable areas.
32
Other types of strategies that HUD or local housing authorities should consider are—
• Direct leasing and brokerage for connecting voucher holders to market-rate rental housing and
subsidized developments in a wider range of neighborhoods.
• Performance incentives for housing authorities, rewarding those that help voucher holders move
outside disadvantaged neighborhoods and that avoid creating new concentrations of poverty.
• Prohibitions on the use of vouchers in certain neighborhoods that already have high
concentrations of assisted housing and requirements that they be used only in more
opportunity-rich neighborhoods.
• Requirements for all landlords to participate in the voucher program on request.
• Intensified fair housing enforcement aimed at expanding choices for minority voucher holders
and families with children.
• Coordination with local law enforcement to ensure that patrol officers and narcotics and
gang units are aware of the neighborhoods receiving relocated households and take action in
preventing any violence that might result.
Promoting opportunity and choice will not be sufficient, however, to address the needs of many
relocated households—the families who endured the worst of the gang violence, drug trafficking,
and management neglect that characterized the nation’s most distressed public housing. The sub-
stantial differences in crime effects between relocated households and traditional voucher holders
underscore the unique challenges of long-term public housing residents and suggest that observers
should not apply these findings regarding relocated households to the general voucher holder
population. Many of these residents—who are, after all, moving involuntarily—require much more
intensive support throughout the search, relocation, and postmove process than most housing
authorities have provided to date. Other research on CHA families has found that many of these
residents have never lived anywhere other than public housing and lack the skills and experience
necessary to negotiate the private market (Popkin, 2010). The Chicago Family Case Management
Demonstration showed that providing intensive, wraparound services—more intensive than the
comprehensive services that the CHA offers to all residents—to vulnerable families is feasible, even
after relocation (Popkin et al., 2010). The costs of these services were not insignificant, but they
were not more expensive than standard place-based services. Furthermore, the benefits in terms of
stable households could be significant for both former public housing residents and the communi-
ties to which they move.
32
Both the AHA and the CHA were able to use the flexibilities that MTW afforded to institute these reforms.

156
Popkin, Rich, Hendey, Hayes, Parilla, and Galster
Residential Mobility: Implications for Families and Communities
Acknowledgments
The authors thank Wesley Skogan for lending his insights to the methodology and report. For
their critical review of the authors’ methodology, the authors thank John Hipp, Lance Hannon,
Jens Ludwig, and Akiva Liberman. At the Urban Institute, the authors acknowledge the thoughtful
guidance given by Nancy La Vigne and Marge Turner and the graphic design by Timothy Meko.
At Emory University, the authors thank Moshe Haspel, Lance Waller, Jeffrey Switchenko, Michael
Leo Owens, and Elizabeth Griffiths (now at Rutgers) for their important contributions. They ac-
knowledge the John D. and Catherine T. MacArthur Foundation, Annie E. Casey Foundation, and
Chicago Housing Authority (CHA) for their generous support of this work. Finally, the authors
acknowledge the CHA and Atlanta Housing Authority (AHA) for their collaboration, transparency,
and commitment to evidence-driven policymaking. The findings and conclusions presented are
those of the authors alone, and do not necessarily reflect the opinions of their funders; the CHA;
the AHA; or the Urban Institute, its trustees, or its funders.
Authors
Susan J. Popkin is Senior Fellow and Director of the Program on Neighborhoods and Youth at the
Urban Institute.
Michael J. Rich is an associate professor of political science and Director of the Office of University-
Community Partnerships at Emory University.
Leah Hendey is a research associate at the Urban Institute.
Chris Hayes is a research associate at the Urban Institute.
Joe Parilla is a senior policy and research assistant at The Brookings Institution.
George Galster is the Clarence Hilberry Professor of Urban Affairs in the Department of Urban
Studies and Planning at Wayne State University.
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161Cityscape: A Journal of Policy Development and Research • Volume 14, Number 3 • 2012
U.S. Department of Housing and Urban Development • Office of Policy Development and Research
Cityscape
Addressing Residential
Instability: Options for Cities
and Community Initiatives
G. Thomas Kingsley
Urban Institute
Audrey Jordan
William Traynor
Lawrence CommunityWorks
Abstract
Recent research based on surveys of low-income neighborhoods in 10 cities, part of the
Annie E. Casey Foundation’s Making Connections initiative, confirms that overall rates
of residential mobility in such neighborhoods are high but also shows that the overall rate
is made up of very different types of moves with dramatically different implications. Per-
haps most important is the finding that a large share of all moves are churning moves—
frequent, usually short-distance moves by vulnerable families. Research has shown this
kind of mobility to be associated with negative education and health outcomes for young
children.
After summarizing key findings from the Making Connections initiative, this article
reviews policy and programmatic options that might address these outcomes. It finds
considerable relevance at the citywide level in new approaches to homelessness preven-
tion being considered. It also identifies actions that can be taken at the community level.
The article focuses, in particular, on how the network organizing approach might be
mobilized toward this end.
Introduction
Researchers have known for some time that the rate of residential mobility among low-income
families is high. In 2011, 17.5 percent of households in the lowest income quintile moved com-
pared with only 11.5 percent of the nation’s households, on average (Theodos, 2012).

162
Kingsley, Jordan, and Traynor
Residential Mobility: Implications for Families and Communities
Knowledge of high movement rates among low-income people, however, has not led to consensus
regarding what, if anything, policymakers should try to do about mobility. Those who manage
community-improvement initiatives typically find the subject disturbing. How are they to build
strong social networks and social capital if many residents are likely to soon move away? Others,
however, see opportunity in mobility: the possibility of devising policy approaches that result in
more families escaping the effects of concentrated poverty.
Surveys conducted in low-income neighborhoods in 10 cities that were a part of the Annie E. Casey
Foundation’s Making Connections initiative, however, offer a fundamentally different understanding
(Coulton, Theodos, and Turner, 2012; Coulton, Theodos, and Turner, 2009). Total mobility, in
fact, comprises very different types of moves with dramatically different implications, some good
and some bad.
The surveys confirm that the mobility rate in distressed neighborhoods is indeed high; 28 percent
of families with children move each year. Surprisingly, however, most movers (20 of the 28 percent)
do not actually “move away.” Rather, they relocate in or near their original neighborhood, remaining
“within reach” of the community. The number of cases in which residents actually leave the neigh-
borhood is comparatively small—about 8 percent per year—and it is difficult to argue that either
the scale or the nature of that mobility is problematic.
A large share of the shorter moves, however, do represent a problem. This share of moves appears
to be a product of residential instability, a churning kind of mobility; in many cases, they are moves
made by vulnerable families likely to be near the edge of homelessness. To be clear, families in this
situation were by no means dominant in any of the Making Connections neighborhoods. Nonethe-
less, reducing this type of mobility seems to be a challenge that policymakers ought to consider
how to address.
The opening sections of this article summarize the basic findings about residential mobility and
explain why residential instability is a serious problem. The remainder of the article explores
policy and programmatic options. After briefly framing possible policy responses related to posi-
tive mobility, the article focuses on how to address residential instability. One section looks at
relevant citywide systems, emphasizing approaches that have evolved to deal with homelessness;
in particular, it addresses the logic behind the shift from the initial shelter-dominated responses to
the concept of homelessness prevention. The final section explores how to address the issue at the
neighborhood level in the context of community-based improvement initiatives.
Mobility in Low-Income Neighborhoods
The Annie E. Casey Foundation’s Making Connections initiative began in 2000 and operated in
selected neighborhoods (most often groups of neighborhoods) in 10 cities for most of the decade.
The initiative collected survey data in three waves: at the beginning, middle, and end of the 2000
decade, at approximately 3-year intervals. The findings reported in this article are based on first-
and second-wave survey data.
1
1
Information about the Making Connections initiative and details about the surveys can be found at http://www.aecf.org/
MajorInitiatives/MakingConnections.aspx. Also see http://mcstudy.norc.org/.

Addressing Residential Instability: Options for Cities and Community Initiatives
163Cityscape
As of the 2000 census, the populations of the Making Connections neighborhoods averaged
40,500 per city. Three of the areas—in Hartford, Louisville, and Milwaukee—were in the extreme
poverty range (rates of 40 percent or more). Four more areas—in Denver, Oakland, Providence,
and San Antonio—were in the high poverty range (rates of 30 to 40 percent). The remaining three
areas—in Des Moines, Indianapolis, and White Center, Washington (just south of Seattle)—were
in the moderate range (15 to 30 percent), but even these neighborhoods are clearly more distressed
than the average neighborhood in metropolitan America.
The 10 neighborhoods also differed from each other notably in racial and ethnic composition. The
populations of all were predominantly minority, but only two (in Louisville and Milwaukee) were
predominantly African American and one (in San Antonio) was predominantly Hispanic. The oth-
ers were mixed. In three of the mixed neighborhoods (in Hartford, Oakland, and Providence), the
foreign born comprised more than one-half of the population (by contrast with 6 percent or less in
Louisville, Indianapolis, and Milwaukee).
Coulton, Theodos, and Turner (2012) analyzed in detail the data on mobility between the first two
waves of Making Connections surveys. Exhibit 1 annualizes and presents overall levels of mobility
from that analysis.
2
The data indicate that the average household mobility rate (share of households
that move per year) was 24 percent over this period, but rates were quite different for different
types of households: 28 percent for households with children compared with only 20 percent for
childless households (a group that includes many elderly couples and singles who tend to move
less frequently, on average, than younger households).
2
We annualized the data on moves between survey waves assuming a period of exactly 3 years. The actual periods between
interviews varied modestly from that number, but those variations were not enough to affect the numbers presented here.
Exhibit 1
Movers per Year (%)
Total
Households
Without Children
Households
With Children
Household Mobility in Making Connections Sites
Average 24 20 28
Denver 24 22 27
Des Moines 21 18 23
Hartford 27 20 30
Indianapolis 26 21 33
Louisville 31 26 40
Oakland 26 27 25
Providence 24 22 25
San Antonio 17 10 23
White Center 20 17 22
Correlation to poverty 0.60 0.23 0.64
Source: Making Connections cross-site surveys, first and second waves

164
Kingsley, Jordan, and Traynor
Residential Mobility: Implications for Families and Communities
The correlation between mobility rates and poverty rates across sites for households with children
was fairly strong (0.64); that is, it was consistent with the CPS data we noted previously showing
that lower income households move more often. That correlation was not as strong, however, for
childless households (0.23).
Annual mobility rates for households with children varied from a low of 22 percent in White Cen-
ter to a high of 40 percent in Louisville. The Louisville rate was likely above normal in this period
because of the relocation of families from about-to-be-demolished public housing in the HOPE VI
Program. The next highest rate was 33 percent, in Indianapolis.
Coulton, Theodos, and Turner (2012) recognized that households decide to move for various
reasons; normally, they seek a balance between the positives associated with a new home and
neighborhood and the negatives associated with inadequacies they see in their current structure
and/or location. Some families move of their own volition to a better place, but some are forced
by circumstances to move (for example, because the loss of a job reduces their income) and might
have to move to lower quality locations.
Accordingly, the authors conducted a cluster analysis to see if they could identify meaningfully
different groups of movers based on characteristics that might affect their mobility decisions and
the way the move might improve or worsen their residential situations. This analysis across sites
led to the identification of three basic types of movers among families with children.
3
• Churning movers. Households with very low incomes (median $14,000), mostly renters
who had not lived in their unit very long before the most recent move (median 2 years). They
generally viewed their neighborhood as unsafe and not good places to raise children. They
moved only short distances (median 1.7 miles), benefiting little from the moves in terms of
neighborhood amenities and satisfaction. Their moves were most often in response to financial
stress or problems with their rental housing arrangements.
• Nearby attached movers. Households, also with low incomes (median $15,000) and moving
very short distances (median 1.1 miles), that had lived in their homes much longer before
the move (median 7.5 years) and were more likely to be homeowners before moving. They
were more involved in neighborhood activities before the move, and the indications are that
the moves were because of life-cycle factors rather than a desire to leave their old house or
neighborhood. In general, they had positive views of their neighborhood and their new unit
after the move.
• Up-and-out movers. Households with much higher incomes (median $28,000) who had not
lived in their old house very long (median 3 years) and were the most dissatisfied with their
original Making Connections neighborhood. They moved by far the longest distances (median
5.8 miles) and were more satisfied and optimistic about their new neighborhoods, where lower
shares of the population were low income and minority and where home prices were high and
increasing.
3
Appendix C of Coulton, Theodos, and Turner (2009) explains the details of the cluster analysis. The cluster analysis for
movers could be conducted only for households with children since only they were tracked and interviewed in the second
survey wave.

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On an annual basis, 8 percent of families with children made up-and-out moves, 7 percent made
nearby attached moves, and 13 percent made churning moves (exhibit 2). Therefore, the up-and-
out movers, the only group that normally moves to locations a long distance from the original
neighborhood, accounted for only 30 percent of the total moves.
Two groups that typically moved within or near their original neighborhood accounted for the re -
maining 70 percent. The nature of the moves for these two groups was strikingly different, however.
Nearby attached moves (25 percent) appeared to be fairly positive and normal adjustments within
the neighborhood; for example, people moving because they want a larger or better apartment. Churning
movers (45 percent), however, in general, were negative about their moves. For them, the circum-
stances suggested mobility caused by vulnerability rather than the seeking of some positive result.
The variations across sites in the shares that made up-and-out moves were not strongly correlated
with site poverty rates. As one might expect, correlations with poverty were stronger for the nearby
attached and the churning movers.
Exhibit 2
Movers per Year (%)
All Moves Up-and-Out Nearby Attached Churning
Types of Moves, by Households With Children
Average 28 8 7 13
Denver 27 10 5 12
Des Moines 23 9 5 9
Hartford 30 9 8 13
Indianapolis 33 10 8 15
Louisville 40 12 11 16
Oakland 25 9 4 12
Providence 25 7 6 12
San Antonio 23 4 7 11
White Center 22 5 5 12
Correlation to poverty 0.64 0.43 0.70 0.53
Source: Making Connections cross-site surveys, first and second waves
The Challenge of Residential Instability
Although we do not have information on the move histories for the churning movers, given what
we know about them, the term residential instability would seem to fit their situations. That condi-
tion certainly appears problematic. Cunningham, Harwood, and Hall (2009:1) noted a “growing
body of evidence showing that residential instability (for example, frequent moves, doubling up,
homelessness) is associated with poor academic outcomes among children” and reviewed that
literature. In another literature review, Cohen and Wardrip (2011:4) stated that “Hyper-mobility
can present special challenges to children’s well-being, both through direct effects on children (for
example, the disruption of being uprooted, the difficulty of catching up with classmates at school)
and as mediated through their parents (for example, the parents’ preoccupation with the move and
the forces behind it could reduce their ability to be supportive to their children).”

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Previous research has associated residential instability with a number of negative outcomes, such as
educational problems, including low reading scores and low school completion rates. Explanatory
factors include disruptions in instruction, excessive absenteeism, and disruption of peer networks
(Hango, 2006; Kerbow, Azcoita, and Bell, 1996; Pribesh and Downey, 1999; Rafferty, Shinn, and
Weitzman, 2004; Rhodes, 2005; Rumberger, 2003; Tucker, Marx, and Long, 1998).
Other studies have associated it with disruptions in access to healthcare services and other, broader
physical and mental health problems (Jelleyman and Spencer, 2008; Pettit, Kingsley, and Coulton,
2003). A host of other negative outcomes range from behavioral problems in adolescence to longer
term effects, have also been documented (Hagan, MacMillan, and Wheaton, 1996; Jelleyman and
Spencer, 2008; McCoy-Roth, Mackintosh, and Murphey, 2012; Moore, Vandivere, and Ehrle,
2000; Pettit, 2004).
All these factors create stress for the child in what is likely to be an increasingly troubling family
environment, and stress itself has negative effects on physical and mental health. One or two moves
for a young child, particularly when they result in a better neighborhood, can of course be ben-
eficial. In addition, Hendershot (1989) showed that the right kind of parental support can reduce
the negative consequences of high mobility. The evidence overall, however, suggests that reducing
family residential instability and hypermobility for children is a policy objective worth seeking.
4
Two facts about churning movers are important in considering how best to address this issue. First,
churning movers were not concentrated in only one or two Making Connections neighborhoods
but, rather, were significant in all of them (ranging from 9 percent of all households per year in Des
Moines to 16 percent in Louisville). Therefore, residential instability is almost certain to represent a
nontrivial issue in all neighborhoods likely to be candidates for community-improvement programs.
Second, although nearly all have low incomes, churning movers appear varied in terms of their
probable need for services and support. To demonstrate this point, we compare the representation
of families across the different categories of Making Connections mover groups, in terms of their
degree of vulnerability, along the following continuum.
• Most vulnerable. Low-income households (incomes below 200 percent of the poverty threshold)
in which no adult had a full-time job at the time of either the first or the second survey wave
(a sign that they are likely to have severe or multiple problems that reduce their capacity).
• Other vulnerable. Low-income households in which one or more of the following barriers
pertained: (1) the survey respondent lacked a high school degree, (2) the respondent had a
permanent disability, or (3) any of the children in the household had poor health or a disability.
4
Murphey, Bandy, and Moore (2012) examined outcome indicators for children in the 2007 National Survey of Children’s
Health who were younger than age 6 and had moved five or more times since birth. They found that “frequent mobility by
itself was associated with few effects once other child/family characteristics were taken into account” (Murphey, Bandy, and
Moore, 2012: 4). They said that frequent mobility might be a marker for other characteristics (for example, poverty and
single-parenthood) that might be the main drivers of bad outcomes for children. This finding suggests that the problems we
have been discussing will not be fixed only by reducing hypermobility, but it does not suggest that reducing hypermobility
(in conjunction with other forms of family strengthening) does not remain a valid objective. Frequent moves under pressure
still create stress for all family members.

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• Other low-income. Low-income households that do not meet the criteria for the most
vulnerable or other vulnerable categories.
• Higher income. Households with incomes greater than 200 percent of the poverty threshold.
Exhibit 3 shows distributions of the survey sample across sites and across these groups and categor -
ies of movers. Two findings are important. First, household types vary considerably within each
of these categories; we find nontrivial numbers of households in each of these groups in every col-
umn. Second, however, the distributions are markedly different from each other. The fact that the
most vulnerable families account for a notably larger share of churning movers than of any other
category is most relevant to this discussion. The most vulnerable and other vulnerable households
together account for 46 percent of churning movers compared with 39 percent of all households.
Although we cannot be precise about the numbers, this finding suggests that churning movers are
indeed likely to include many multiproblem families—families who might require fairly intensive
services for a long period of time to become stable. Still, more than one-half of churning movers
are in the less troubled categories whose needs for service and support to overcome residential
instability might be more modest.
Of interest is that the most vulnerable and other vulnerable groups together also account for a large
share (45 percent) of nearby attached movers, but a smaller share (30 percent) of up-and-out mov-
ers. By contrast, higher income households account for 29 percent of up-and-out movers but only
8 percent of churning movers. This suggests that, although trying to retain some community ties
to up-and-out movers in their new locations may well be worthwhile, their ongoing service needs
may be considerably less extensive, on average, than those of the other groups.
Exhibit 3
2002/03 Status
All
Households
Movers (annual)
Total Up-and-Out
Nearby
Attached
Churning
Mover Clusters by Vulnerability and Income Status, as of First Wave
All households 100 28 8 7 13
Low-income (< 200% of poverty)
Most vulnerable 13 15 6 22 18
Other vulnerable 26 25 24 23 28
Other low-income 40 44 42 42 46
Total low-income 80 84 71 86 92
Higher income (> 200% of poverty) 20 16 29 14 8
Total 100 100 100 100 100
Source: Making Connections cross-site surveys, first and second waves

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Residential Mobility Overall—How Should Policymakers
Respond?
The complex picture of residential mobility that the Making Connections data paint calls for new
ways of thinking about policy. Seeking responses that attempt simply to either expand or dimin-
ish mobility overall now seems inappropriate. Distinctly different types of mobility—good and
bad—exist, and policymakers should tailor responses to fit the circumstances of each type. In the
following review, we note what appear to be appropriate directions for policy in each case, and
then, in the rest of the article, we explore policy options for addressing the most troubling case: the
residential instability of churning movers.
Up-and-Out Moves and Nearby Attached Moves
Two types of moves that Coulton, Theodos, and Turner (2012) identified appear largely positive
for the families involved. Most up-and-out movers moved to new homes in new neighborhoods,
presumably meeting one or more of a number of possible personal objectives; for example, to be
near to a new job, to enable their children to attend a better school, or to become homeowners in
a neighborhood with higher and rising property values. Nearby attached movers did not move to
new neighborhoods, but they found a new housing unit nearby that they felt would be a better fit
for them. Both groups, in general, were happy with the results of their moves regarding both hous-
ing and neighborhood characteristics.
Is policy intervention related to such movement needed? Perhaps not, in general, but a case exists
that, for low-income households, supportive counseling would facilitate positive mobility. The
type of counseling that would be relevant would offer advice on how to make sensible choices
about mobility options and how to deal effectively with actors in the real estate market. Counseling
to help renters who want to become homeowners is well established, but many renters could
probably use guidance in navigating the process of moving from one rental unit to another. In
both situations, practitioners advocate offering this sort of guidance in conjunction with broader
counseling on family financial management and asset building.
Would such counseling expand the share of residents who move to lower poverty neighborhoods;
that is, those who make up-and-out moves to what are now often termed opportunity neighborhoods?
The answer no doubt depends on what the counseling includes. Many living in low-income neigh-
borhoods might limit their choice to neighborhoods with which they are familiar unless provided
with exposure to a wider range of places. Cunningham and Sawyer (2005) studied the mobility
of households that relocated with housing vouchers as part of Chicago’s massive public housing
demolition program in the early 2000s. This program offered search assistance and connected
interested voucher holders with landlords in low-poverty neighborhoods. The authors found that
program enrollees who received mobility services were “significantly more likely to move to op-
portunity neighborhoods” (Cunningham and Sawyer, 2005:6. They also suggested, however, that
much remains to be learned about what makes mobility counseling programs effective.
Considerable debate has surrounded mobility versus neighborhood improvement approaches to
dealing with the problems of concentrated poverty, and this article is not the place to review them

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fully.
5
This debate widely recognizes that barriers to full mobility because of racial discrimination
and other factors remain enormous and that reducing those barriers must remain a high priority
for policy. Consistent with that objective, we find it difficult to fault the idea of providing counsel-
ing to help residents of low-income neighborhoods become more informed about the mobility
opportunities open to them with a reasonable assessment of their costs and benefits.
Churning Moves
Because of the damage it can do in the short term, however, the other type of mobility we have
discussed—the churning move—seems an important case for policy intervention, and we explore
that type more fully here. The main purpose of the remainder of this article is to identify and
review programmatic approaches to diminishing residential instability and its harmful effects.
First, however, it is necessary to discuss the most obvious solution and why it alone is not suf-
ficient at this point. For the residentially unstable, clearly, the main barrier to securing decent
housing on a stable basis is insufficient income. Means-tested programs that offer to pay rent
directly, or supplement income to enable the family to do so, are therefore central to the results of
all of these efforts. U.S. Department of Housing and Urban Development (HUD) housing vouchers,
for example, have proven effective at preventing homelessness and rapidly restoring housing to
those who do experience homelessness (Khadduri, 2008).
The problem, however, is that HUD assistance programs are woefully underfunded in relation to
the need. As of 2005, only 5.5 million (31 percent) of the 18.0 million eligible households with
housing problems nationwide actually received HUD assistance (Turner and Kingsley, 2008). The
economic recession and housing crisis that have occurred since then have been accompanied by a
further reduction in the housing stock available and affordable to the lowest income groups (HUD,
2011), and housing assistance budgets are now tightly constrained. In fact, concerns exist that
future budgets for housing assistance might not be sufficient to support as many households as
they do now (Rice and Sard, 2012). Although continued pressure to increase the national housing
assistance budget is clearly important, it is difficult to be optimistic about much expansion in the
near term.
What is the appropriate policy response in this environment? Looking for approaches to helping
residentially unstable families that are designed to use available assistance resources (HUD and
other) with as much efficiency as possible appears to make sense. A scan of the types of social
service and community building programs that now operate in American cities suggested that
two areas seem promising in this regard and warranted further exploration in the last two major
sections of this article:
• Homelessness prevention services for families. Virtually the only programs in the social
service sphere whose mandate relates directly to residentially instability are those concerned
with homelessness. In this section, we note how this field has evolved. As it has shifted
emphasis to homelessness prevention, it has prioritized finding more efficient and effective
5
Recognition is growing of the benefits of seeing these approaches as complementary rather than in opposition (see, for
example, Pastor and Turner, 2010).

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forms of service delivery. These forms recognize the importance of varying the nature and level
of service to match families’ circumstances and seem highly relevant to this inquiry. Although
directly comparable descriptive data are not available, what is known about homeless and near-
homeless families suggests strong similarities to the churning mover populations in Making
Connections neighborhoods (Culhane et al., 2007; Shinn et al., 1998).
• Community networks: strengthening families and links to service. Citywide organizations
are likely to continue taking on the central roles in homelessness prevention and related
services. Such agencies, however, often have a harder time meaningfully connecting with low-
income families than do grassroots neighborhood organizations. We focus on one community
approach—network organizing—that seems a particularly promising adjunct to city systems in
addressing residential instability.
In the next two sections, we review relevant activity in these two areas and consider how to learn
more about them to better harness their potential.
Addressing the Needs of Churning Movers Through
Homelessness Prevention Services
To help understand the strategies and programmatic approaches emerging in this area, we begin
by briefly reviewing the history of how America’s response to homelessness has evolved over the
past few decades.
Initial Responses to Homelessness
For some time after the problem of homelessness emerged and spread in U.S. cities in the 1980s,
responses focused on serving those who had lost their housing by expanding emergency shelter
capacity, then transitional housing. Most homeless then were adult males, many with chronic
problems (for example, substance abuse and mental illness).
The view was widespread that people who experienced homelessness needed to overcome their
personal problems before they were ready for permanent housing. Accordingly, many shelter sys -
tems were organized in a continuum of care approach that, as Culhane, Metraux, and Byrne (2011:
301) noted, “re-creates community based services systems inside the homelessness system, and
often functions to extend people’s homeless spells through service rich transitional housing;”
programs that can extend periods of homelessness for up to 2 years.
Although single men were dominant among the homeless at the outset, residentially unstable
families with children (the group that is the central concern of this article) are now a sizeable and
expanding part of the total. In 2008, 30 percent of all people using homeless shelters were mem-
bers of homeless families (474,000 individuals, approximately 300,000 of whom were children,
according to HUD, 2008).
Most who enter the shelter system stay for only a short time, but some subgroups remain for
considerably longer periods. Nationwide, the median length of stay for single adults is 18 days.
The median is longer for families (30 days), but even most of their stays are quite short; 23 percent
leave within 1 week, 76 percent leave within 3 months (Cunningham, 2009; HUD, 2008).

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This system grew significantly over the years. HUD (2008) reported that 1.6 million people used
homeless shelters in a 1-year period, that 211,000 emergency shelter beds existed nationwide, and
that an equal number of beds existed in transitional housing facilities.
Still, as early as the late-1990s, criticisms of this system arose. Shelters were often stressful
places to stay even for a short time, and the whole system was expensive. As noted previously,
the transitional housing that developed as a part of the homeless system incorporated housing
management activities and social services that duplicated other mainstream systems operating in
most communities, and, as such, reasonable concerns arose about comparative costs. Interest grew
in the idea that preventing homelessness among large groups in this population in the first place
might be both far less painful and more cost effective.
Furthermore, University of Pennsylvania research sharply contradicted the view that the homeless
were not ready for permanent housing. This research showed that a small subgroup of about 10
percent of single, homeless adults with serious personal problems used 50 percent of shelter ser-
vices and that the cost of instead providing permanent supportive housing to them initially would
be more than offset by savings in public services thereafter (for example, reduced emergency room
visits, mental health services, and jail stays; see Culhane, Metraux, and Hadley, 2002; Kuhn and
Culhane, 1998). For this population, therefore, preventing homelessness might not be possible,
but dealing with it in a much more cost-effective manner by providing permanent housing first
does appear possible.
Homeless families are less likely to suffer from the personal and social barriers that affect so many
unaccompanied single adults (Burt and Cohen, 1989), but, while perhaps not yielding the same
cost-effectiveness advantage, the housing first approach is being widely advocated for them as well,
hopefully avoiding longer term entanglement with the shelter system.
Addressing Degrees of Residential Instability Through Preventive Services
In general, homelessness prevention aims to divert households that are either homeless or at risk of
homelessness from the shelter system and to expedite rapid exit from that system for those already
in it. As early as 2000, the National Alliance to End Homelessness put forward a plan that recog-
nized these themes (Cunningham, 2009). Burt, Pearson, and Montgomery (2005) then developed
a comprehensive framing of the approach, emphasizing both efficiency and effectiveness (also see
the discussion in Culhane, Metraux, and Byrne, 2011). They suggested the following three levels of
prevention.
• Primary. Preventing new cases of homelessness and stopping people from ever becoming
homeless.
• Secondary. Intervening early during the first spell of homelessness to help people leave
homelessness and not return.
• Tertiary. Providing services to assist those with serious barriers; those who without help would
probably remain homeless for a long period of time.

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This approach would seem to fit the data on the variety of circumstances among churning movers
presented previously. At one end of the spectrum are households with multiple problems that are
likely to require considerable support (housing and services) to become stable. At the other end
are people with more capacity, whose residential instability, we can reasonably assume, might have
been triggered by one-time problems that are probably easier to fix at a lower cost.
Primary prevention efforts to reduce residential churning have been under way in a number of
cities for some time (see Burt, Pearson, and Montgomery, 2005; Cunningham, 2009; Shinn,
Baumohl, and Hopper, 2001). Approaches include—
• Mediation with current housing providers. In some cases, residential stability might be
promoted and homelessness avoided at very low cost simply by having an independent party
help by talking things over with the landlord or family with whom the at-risk household has
been staying and modifying some aspect of their arrangements (at least to secure additional
time). One local group that provides this type of service is the Eastside Housing Opportunity
Program in St. Paul (Mohr and Mueller, 2008).
• Cash assistance to cover rent or mortgage arrears. When mediation alone is unable to do so,
a comparatively small amount of one-time cash assistance might cover deficiencies in the near
term and avoid evictions; that is, for households that do not need sizeable income support over
the long term.
• Legal services to prevent evictions. Programs that offer mediation in housing courts after
landlords have filed for eviction have also been able to promote residential stability and preserve
tenancy. For example, Burt, Pearson, and Montgomery (2007) reported that as a result of such
mediation services, 69 percent of the cases filed against families in the Hennepin County,
Minnesota housing court were settled without eviction, and the family retained housing.
• Discharge planning and programs to ameliorate domestic conflicts. Former prisoners
released back into the community and youths aging out of foster care face higher-than-
average risk of residential instability leading to homelessness. Domestic conflicts in the
family environment also heighten the risk. Programs to identify individuals and families in
these situations early, then provide counseling and help in finding affordable housing, could
presumably reduce incidence.
It turns out, however, that reliably identifying households that will become homeless ahead of time
is much more difficult than might be thought. Of the residentially unstable households that have
characteristics that make them seem to be at high risk of homelessness, a very large share somehow
manage to avoid it. Thus, providing assistance to all families in a defined risk group would result
in spending more than is needed to avoid homelessness. Using data on characteristics and case
histories of homeless families in New York, Shinn et al. (1989) defined what seemed to be a
reasonable high-risk group and found that serving that group would imply providing assistance to
six households for every one that ultimately became homeless.
One response to this problem would be waiting to assist only those who have actually become
homeless (see the discussion of secondary treatment in the following paragraph) or those who
exhibit clear indications that homelessness is imminent (for example, receiving an eviction notice).

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By no means has any study demonstrated, however, that efforts to identify and deal with likely
residential instability at an earlier stage cannot be made more effectively. The goal is not only to
avoid homelessness but also to put families on a path that leads to self-sufficiency (stability in a
broader sense) over time. Doing that would require considerable strengthening of outreach and
referral networks. This strengthening could entail beefing up the 311 systems that now exist in
many cities and making homelessness prevention a greater priority for established referral net-
works (for example, churches and community nonprofits) that attempt to connect those in need to
appropriate services. It would then require highly efficient targeting of prevention resources. (We
discuss the way community networks might help with both referrals and strengthening families
directly in the next section.)
Secondary treatment, in a prevention context, emphasizes getting families who appear at the door
of the shelter system back into stable housing outside of that system as rapidly as possible. Rapid
rehousing has in fact become an accepted programmatic theme in many systems nationwide. This
approach uses techniques identified previously: negotiating and providing modest cash assistance
as needed to enable the family to return on a stable basis to the apartment they have just left.
Alternatively, it can mean referrals and help in securing other housing opportunities in the private
market (often with subsidies) or in publicly subsidized projects.
The field increasingly recognizes the need for some type of up-front diagnostic function; that is,
some way to assess the nature and severity of a family’s particular needs and circumstances as a
basis for guiding them to an appropriate mix of housing opportunities, services, and supports.
6

Because so many shelter users leave after only a night or two, systems often wait a few days before
administering interviews. Regular staff might be able to make these assessments for the more
capable among the residentially unstable, but trained case managers will usually be needed to work
out a realistic course for the more distressed, multiproblem families.
A prevention approach triggers tertiary treatment in a very different way than in the past. In
traditional shelter systems, lengthy stays in the shelter were often the key indicator that initiated
a more intensive service regime for a multiproblem individual or family. In the new approach,
staff conduct a diagnosis (discussed previously) early, hopefully leading not only to rapid rehous-
ing, but to rapid rehousing linked to a sensible mix of services designed to promote residential
stability by addressing the households’ specific needs. Housing professionals often assume that the
right solution for most multiproblem cases is an apartment in a supportive housing development
(decent housing with needed services provided on site). The Chicago Family Case Management
Demonstration, however, has shown that in many cases the approach can work as well for families
with vouchers living in independent apartments with services brought to the property from outside
(Popkin et al., 2010; Theodos et al., 2011).
6
Theodos et al. (2011) identify and review a number of formal assessment tools that have been developed to try to help
practitioners perform this function more effectively and reliably.

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Assessing Directions for Promoting Residential Stability
Through Homelessness Prevention Services
Although progress has been uneven, movement away from a shelter-dominated homelessness
system and toward a more prevention-oriented approach to promoting residential stability is now
evident in a number of cities. Important support for this orientation has come at the federal level.
First, HUD’s 2008 appropriation included a $25 million demonstration of the Rapid Re-housing
for Families Program. Then, the 2009 American Recovery and Reinvestment Act allocated $1.5
billion for a new Homelessness Prevention and Rapid Re-housing Program (HPRP) that aims both
to prevent individuals and families from becoming homeless and to move those that are already
homeless into permanent housing on a stable basis as rapidly as possible. Furthermore, a 2009
reauthorization made important changes to the McKinney-Vento Act in support of prevention;
among other things, changing the name of the Emergency Shelter Grants to the Emergency
Solutions Grant Program, with more emphasis on prevention and rapid rehousing (as Culhane,
Metraux, and Byrne, 2011, have explained).
Very little hard information is yet available on how this shift is working in practice, so this article
cannot assess the progress. Substantial new information will likely be available over the coming
year, however, as two major federally funded research efforts reach completion. First, Abt As-
sociates is conducting a $1.25 million evaluation of the Rapid Re-housing for Families Program,
involving all 23 grantees in the demonstration. Second, the Urban Institute is conducting the
Homelessness Prevention Study, which began in 2010 and entails, among other things, site
research on HPRP implementation in 17 communities.
In addition, an evaluation is nearing completion of what is probably the most extensive local
implementation of the prevention approach to date: the HomeBase initiative in New York City.
HomeBase operates through a network of eight community service providers in designated
high-need areas throughout the city. The providers offer an array of prevention-oriented services
to households at risk of homelessness, including benefits advocacy, mediation, employment assist-
ance, legal referrals, and financial assistance. The evaluation involved the monitoring of outcomes
for approximately 400 eligible households that HomeBase randomly assigned to treatment and
control groups.
Also keep in mind that the trend toward prevention could motivate broader systems changes
beyond what these initial studies will show. Culhane, Metraux, and Byrne, (2011), in fact, saw the
evolution of ideas about a prevention approach leading to a dramatic transformation in the way the
United States addresses residential instability and homelessness. This transformation would entail
streamlining and revising the operating philosophies of many shelter systems, including the elimi-
nation of duplicative services and other functions the shelters now perform that mainstream social
service and human development agencies should ideally handle better. Culhane, Metraux, and
Byrne, recognize, however, that this transformation will require challenging expansions of capacity
and changes of approach for many of those mainstream agencies as well. Trying to fund this vision
via federal homelessness subsidies alone would clearly be inappropriate; rather, the mainstream
agencies’ funds would need to expand to cover the costs of ongoing housing and services for needy

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individuals whom the homelessness system hands off to them. Addressing these issues successfully
will be difficult unless the nation expands resources targeted at these issues, but this vision seems
to suggest a way to more effectively use the resources that are available.
Community-Based Approaches To Promoting Residential
Stability
The previous section addressed an emerging approach to homelessness prevention that would
make addressing residential instability one task of an expanded and better integrated social and
human development services delivery system. Although the existing literature is not fully clear
about which institutions should take on which tasks, it seems to assume central roles for the public
bureaucracies (and the nonprofits that work for them) traditionally assigned those responsibilities.
In most cities, however, observers know those systems to be strained financially and in other ways.
Many factors that have traditionally inhibited integration—factors that perpetuate silos have not yet
been eliminated.
In this environment, exploring the roles that grassroots neighborhood organizations might play
in addressing the mobility issues we have discussed also seems worthwhile. These organizations
would not serve as an alternative to the citywide systems but in a collaborative mode, taking on
some tasks that might be difficult for public systems to perform and assisting on others in a man-
ner that would enhance overall effectiveness.
Observers often consider community-level groups better suited than public bureaucracies for tasks
like strengthening social networks and building social capital in neighborhoods, and these activi-
ties might be critical. Many among the churning movers are likely to be socially isolated families
who lack confidence, have few trusted friends to help them, and do not know how to access the
services they need and opportunity more broadly. Prospects for effectively addressing residential
instability might be quite different in a neighborhood where social networks are strong than in one
where they are not.
Accordingly, this section first focuses on one advanced approach to achieving these goals: network
organizing. We then look at more specific tasks that neighborhood-level groups might take on to
address residential mobility issues in this context.
Lawrence CommunityWorks: A Case Study in Strengthening Informal and
Formal Supports for Families at Risk of Residential Instability
The term community organizing is most often associated with developing resident leadership and
other activities focused on achieving political ends. Network organizing, by contrast, gives more pri-
ority to basic interpersonal relationships and strengthening family capacity. It refers to the “process
of getting involved” and argues that “community building has to build habits of engagement to
replace deeply embedded habits of detachment that dominate place” (Traynor, 2008:10).
The work of a Lawrence, Massachusetts community development corporation, Lawrence Com-
munityWorks (LCW), is an example of this approach. LCW began in an early-1980s struggle to

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Residential Mobility: Implications for Families and Communities
build affordable housing in the North Common area of Lawrence. LCW’s mission is to (1) foster
individual and neighborhood empowerment and leadership by organizing residents to develop
politically, economically, and socially; (2) produce and preserve safe, decent, and affordable housing
for low- and moderate-income families; (3) create programs and facilities that build the educational
and economic assets of neighborhood young people, adults, and families; and (4) build a sustained
institutional infrastructure for community revitalization through strategic local, regional, and
national partnerships.
Since 2000, developing and implementing the principles of network organizing in its community,
functioning as a membership network, has been one area of focus for LCW. A basic premise of LCW
network organizing is that all members have value and assets to contribute, and all members have
needs that the activities and programs in the LCW network meet. Participating residents are not
treated as clients; they are members who at times gain knowledge and skills and at other times
provide mutual assistance to other members, volunteer, or take up a leadership position within
the network. As a membership organization, LCW is designed to increase the formal and informal
social supports available to participating residents by increasing the flow of information, the op-
portunity, and the connections that families have with each other. Network organizing, as LCW
practices it, focuses on creating the space and opportunity for formal and informal engagement and
interaction among member residents (what LCW calls “bumping and sparking”) and building the
habits, devices, skills, and awareness that enable members to take constructive action based on the
connections they make and knowledge they gain through that “bumping and sparking.”
One strategy for strengthening social networks practiced by LCW is “NeighborCircles,” in which
“LCW-trained facilitators convene a group of neighbors in a given area for a series of dinner
discussions focused on getting to know each other, identifying common challenges, ... and devel-
oping discrete, manageable projects for tackling those challenges” (Traynor and Andors, 2005:5).
LCW has assisted more than 500 families using this approach. Another strategy is to house many
concurrent activities for people with varied needs and interests in a common space. Most LCW
networking functions are based in a former mill building subdivided to house a variety of concur-
rent activities; for example, a meeting to talk about a proposed zoning change, a sewing club, a
Scholastic Assessment Test preparation class for teens, a seminar on managing personal finances, a
session to plan a neighborhood cleanup campaign, a job-training course, and so on. The premise is
that people engaged in one activity will develop an interest in other activities being offered and get
to know a broad range of their neighbors in the process (Traynor and Andors, 2005).
LCW intends its network development strategies to endow members with the connections neces-
sary to give and receive the kind of formal and informal help that enables families to thrive and
averts the kinds of crises that can precipitate severe social and economic problems, including resi-
dential churning. An added value of these strategies is that LCW designs them to build members’
skills and habits to participate in—or recreate—the network environment, even if they choose or
are forced to relocate.
LCW has not attempted to establish a full array of programmatic responses to deal directly with
the issue of residential instability, but it has put a number of the basic mechanisms in place. The
network-organizing approach is designed so that, when confronted with a crisis that might force

Addressing Residential Instability: Options for Cities and Community Initiatives
177Cityscape
them to move—a loss of income (temporary or long term) that prevents them from honoring their
lease obligations, a health emergency, an impending eviction for any reason, and so on—LCW
member families have the diverse and trusted connections that can enable them to find advice and
help. The network can provide some kinds of help directly. Alternatively, friends and staff in the
network know who to contact for outside assistance if that is what is required.
Specific Community-Based Activities To Address Residential Instability
LCW is one of a number of neighborhood-level organizations that provide services that might assist
residentially unstable families. Based on a review of such work, a list of six activities that community-
based groups could take on to more directly address this issue follows. In general, we recognize
that the core staff that operate community initiatives in individual neighborhoods are already
stretched thin. Nonetheless, large numbers of churning moves are costly to projects that aim to
promote stability. Some efforts to reduce the number of moves are likely to prove cost effective.
• Establish or broaden outreach. Community groups are likely able to strengthen outreach
related to residential instability at a fairly low cost. This activity entails only mounting
effective methods to make neighborhood residents aware of the problem and the right people
or organizations to contact for further assistance. The organization leading a community-
improvement initiative would communicate with residents directly through its own channels
(meetings, newsletters, and so on), but it would also engage other neighborhood organizations
in the outreach process. Putting schools on the lookout for signs of impending or actual
homelessness among their students and having them inform the community organization, so
referrals can be handled appropriately, is especially important.
• Establish or strengthen referral functions. The most basic approach would be for the lead
community organization (after the referral has been made) to proactively link families about to
lose their homes to mediators in citywide homelessness prevention systems (services ranging
from mediation in pending evictions to help in applying for assisted housing, as discussed
previously).
Depending on the initiative, however, this work might be handled internally; that is, one or
more internal community staff members might be trained in how to mediate these cases and, in
addition to making referrals, handle some direct work themselves. This approach could work
easily in an environment like LCW, where residents recognize the existence of trusted channels
through which they can talk about problems and get help. Some other existing neighborhood
programs already emphasize housing stability in this way. As noted, the Eastside Housing Op-
portunity Program in St. Paul is one example (Mohr and Mueller, 2008).
• Establish or strengthen workforce development, financial management, and housing and
mobility counseling. LCW has recognized the potential power of colocating these functions
in the recent work of its Family Asset Building department in establishing a Homeownership
Center. Others are following similar paths. The Annie E. Casey Foundation has pioneered the
development of Centers for Working Families in a number of cities. This approach involves
“bundling access to a full range of essential economic supports in a convenient location” (Center
for Working Families, 2010:3). The services these centers offer include workforce and career

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Residential Mobility: Implications for Families and Communities
development; income and work supports; and counseling on financial services, family financial
management, and asset building. The services aim to be “seamlessly integrated” so as to more
effectively support family economic success overall (Center for Working Families, 2010). The
Local Initiatives Support Corporation (LISC) has adopted this approach (now calling them
“Financial Opportunity Centers”) and operates 32 of them in Chicago and other cities that are a
part of its Building Sustainable Communities initiative (LISC, 2010; http://www.lisc.org/section/
ourwork/sc). Adding services specifically related to addressing residential instability seems a
natural fit, particularly given the importance of housing expenses to families’ financial well-
being.
• Provide affordable housing in the community, with some earmarked for residentially
unstable families. Virtually all comprehensive community initiatives already have programs
to expand the supply of affordable housing in their neighborhoods. LCW’s efforts in this
regard have been extensive. This function involves expanding housing first options within the
community. In this effort, staff might set aside, as vacancies permit, a prescribed number of units
in the developments they build or rehabilitate for churning movers from the neighborhood.
An example of a community-based program that focuses on providing housing units for the
homeless, or near homeless, in an individual neighborhood in this manner is Project H.O.M.E.
in Philadelphia (http://www.projecthome.org/about).
• Maintain links and services to outmovers. As noted, LCW encourages members who move
out of the community to remain in the network by keeping them informed about network
activities, inviting them to network convenings, and so on. Benefits exist in both directions. The
outmovers can continue to contribute ideas, leadership, contacts, and other types of support
to the network, and the network is still there for them if they have problems down the line.
Other community organizations would do well to adopt this philosophy. Plans to assist more
vulnerable residents who might be forced to move outside the community warrant special
attention. Efforts might well be needed in these cases to craft handoffs from a set of service
providers in the old neighborhood to a new set in the new one.
• Collaborate with and become stronger advocates for the reform and strengthening of the
relevant citywide programs. Residents of low-income neighborhoods depend on a host of
social, human development, and homelessness prevention services that citywide agencies and
nonprofits operate. The leaders of individual neighborhood initiatives cannot be expected to
deliver such services themselves, yet a significant number of the residents of their communities
are likely to require them. For many, these services are key to the reduction of residential
instability. Considering their own objectives, community initiatives will likely find it much
in their interests to partner with these agencies, facilitate their work in the community, and
advocate for actions that will strengthen them overall.
Assessing Directions in Community-Based Approaches To Promoting
Residential Stability
Neighborhood improvement initiatives are now under way in many U.S. cities, at probably the
most extensive scale in our history (Kubisch et al., 2010). A substantial share of these efforts rely

Addressing Residential Instability: Options for Cities and Community Initiatives
179Cityscape
on some form of community organizing, and the LCW network organizing approach, although
still rare, has been initiated in several other locations.
7
Its attractive features lead us to expect its
application will spread.
Regardless of the form of organizing, however, our review of the recent literature on community
initiatives yielded almost no explicit recognition of the problem of residential instability, let alone
plans to address it.
8
This article has presented data, however, suggesting that the problem is likely
to be serious in all low-income neighborhoods and pointed out a number of ways grassroots
groups could help deal with it, consistent with their missions and without major effects on work-
loads. Organizations (national and local) responsible for community initiatives should consider
adding this issue to their agendas explicitly and to begin experimenting with actions such as we
have outlined to diminish residential instability and its effects in their neighborhoods. Foundations
should provide funding to national community development intermediaries (such as LISC or
Enterprise Community Partners) to document these activities and their results.
Finally, it is important to recognize that the research community has made little progress in
formally evaluating programs, like LCW’s network organizing, that attempt to strengthen social
networks in low-income communities. The reason is that such programs are enormously difficult
to evaluate reliably, because they are hardly ever precisely enough defined or controlled in imple-
mentation, making it extraordinarily difficult to conduct a randomized trial (see the discussion
in Connel et al., 1995). Despite the difficulty, we believe that more empiricism would pay off
handsomely at this point.
For LCW, one might survey a random sample of Lawrence residents and compare the circum-
stances of those who are LCW members with those with similar characteristics who are not. A
step down from that would be surveying a random sample of LCW members only, asking in some
depth about their experiences with LCW.
Conclusion
The research reviewed in this article presents a view of the dynamics of low-income neighborhoods
that contrasts with the conventional wisdom. To be sure, overall mobility rates are ubiquitously high.
The implications are mixed, however. One group, those who actually move a long distance, is fairly
small. A second group moves for mostly natural reasons (for example, they need a bigger house),
but its members often have positive ties to their original neighborhood and choose to move nearby.
A third group, however, represents a more urgent concern. The members of this group also move
to locations in or near their original neighborhoods, but most are moving under pressure. They are
7
LCW has a Network Organizing Department (NOD) that is responsible for increasing the practice-based learning around
network-centric approaches both internally and with community organizations elsewhere. The NOD has so far engaged
in training, technical assistance, documentation assistance, and learning exchanges with organizations in two cities—
Cleveland and Seattle—and two states—Maryland and Mississippi.
8
One prominent exception is Project H.O.M.E. in Philadelphia, cited previously (http://www.projecthome.org/about). That
more is being done along these lines than has been documented is, of course, quite possible.

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the churning movers who lack the income or capacity to secure decent housing in a stable manner
on their own. Stress and disruption related to their moves are likely to have negative effects on the
education and health of their children.
Across social service programs, new approaches being implemented in the name of homelessness
prevention, although they warrant more evaluation, seem promisingly effective in addressing this
sort of residential instability. These efforts try to catch problems early and assign levels and types
of interventions that match the nature and extent of the problems at hand, family by family.
A number of steps also appear to be available to those who operate community initiatives that
could help restore residential stability for families in this group. Fortifying social networks within
the community (as illustrated by the network-organizing model) seems especially valuable. Doing
so would both directly build the capacities of the most vulnerable families and facilitate their
connection to needed supports. Other steps more specifically involve supportive actions by the
internal staff, actions that seem consistent with the themes of comprehensive community improve-
ment. Others involve forming closer working ties to, and advocating for more support for, citywide
programs in homelessness prevention and related services. Again, however, these steps warrant
more testing and evaluation.
Some practitioners have viewed residential mobility in distressed neighborhoods as a serious
overall threat to community building. This research suggests that it does not have to be. Local
stakeholders (citywide and at the community level), however, do need to make explicit efforts to
address harmful effects that can occur.
Acknowledgments
The authors thank Mary Cunningham of the Urban Institute and Cindy Guy of the Annie E. Casey
Foundation for their thoughtful comments on earlier drafts of this article.
Authors
G. Thomas Kingsley is a senior fellow with the Metropolitan Housing and Community Policies
Center at the Urban Institute.
Audrey Jordan is the director of Partnerships, Innovation and Evaluation at Lawrence Community-
Works.
William Traynor is the executive director of Lawrence CommunityWorks.
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185Cityscape: A Journal of Policy Development and Research • Volume 14, Number 3 • 2012
U.S. Department of Housing and Urban Development • Office of Policy Development and Research
Cityscape
Mobility, Mixing, and
Neighborhood Change:
A British Perspective
Ade Kearns
University of Glasgow
The articles in this symposium highlight three important areas of inquiry related to residential
mobility—what constitutes mobility, the processes of mobility, and the effects of mobility on dif-
ferent groups and locations. In each of these areas, the challenge of understanding becomes more
difficult as research reveals the complexity of the underlying processes. Further, our consideration
of these challenges helps to identify the integral link between residential mobility studies and
research on neighborhood effects (Hedman and van Ham, 2012).
The first area of inquiry is about what constitutes mobility. This area may seem straightforward but
is complicated by two dimensions additional to that of the household move. First, the composi-
tion of the household may change before, during, or after a move, so saying who has moved and
who has not is not simple. Allied with this factor is the reality that households may retain close
connections to their previous neighborhood after a move, so that the key factors of neighborhood
exposure or dosage (Galster, 2012) and place attachment, both functional and psychological
(Bailey, Livingston, and Kearns, 2012), are difficult to describe or measure in investigations of
neighborhood effects.
The second area of inquiry concerns the increasing research interest, in both the United States and
Europe, in the processes of mobility: How do households arrive at decisions to move? How do
households make choices about when and where to go, with or without the help of others? How
do counseling and support services provided to poorer households, both before and after a move,
influence the human, social, and economic outcomes. The psychological dimensions to these
processes—particularly the interactions between variations in personality, underlying individual
values, experiences during mobility, and cognitive processes related to an understanding of the
move and its likely consequences—are currently underresearched. People are much more complex
and dynamic than our usual means of describing them. We are only beginning to appreciate that
residents’ attitudes to the restructuring of their old neighborhoods affect their views about their
own relocation; for example, “Was it worth it? Will regeneration be fully accomplished?”
The third area of inquiry examines the research agenda in relation to the effects and outcomes of
residential mobility, which has broadened to include both disadvantaged and more advantaged
groups; both movers and nonmovers; both origin and destination neighborhoods; and both

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Residential Mobility: Implications for Families and Communities
urban-level and neighborhood effects, such as those on patterns of spatial inequality across cities.
Here, in both the United States and Europe, the evidence on issues such as negative spillover, or
waterbed, effects arising from the reclustering of those relocated in other areas (for example, effects
on property values, crime, school performance, and local community conflict) has yet to catch up
with the theory (Kleinhans and Varady, 2011). To these areas of inquiry, we should add an interest
in the potential societal-level effects of mobility—certainly if it results in more residential mixing—
on political attitudes; for example, attitudes regarding the causes and desirability of social diversity
and (in)equality, which may or may not be very evident to people, depending on where they live.
The advent of integrated neighborhoods, either produced through policy intervention or arising
through organic, or market, processes of mobility, raises interesting questions about their effects.
Whether looking at economically or racially integrated neighborhoods, much inquiry has been
concerned with whether, and to a lesser extent how, more disadvantaged individuals might gain
from “rubbing shoulders” with more advantaged neighbors; for example, in terms of their orienta-
tion toward education or awareness of employment opportunities—so-called social-interactive
mechanisms (Galster, 2012). Less research has focused on the effects of residential mixing on the
more advantaged group, in situations both where it is the majority and the minority. In the case of
economically integrated, or mixed income, communities, effects have also been hypothesized on the
economic and political vitality of the communities concerned, the former having been examined
occasionally and found wanting, and the latter having been assessed rarely (see Sautkina, Bond,
and Kearns, 2012, for a review of U.K. evidence).
As a result of occasional disturbances between ethnic groups in specific towns or cities (for example,
in the United Kingdom), policymakers have proposed racially integrated neighborhoods and schools
as means to overcome so-called “parallel lives” (Phillips, 2006) and thus reduce the potential for
local conflict, presumably through greater tolerance as a result of contact (Allport, 1954), although
the mechanisms involved are usually unstated. This approach, however, has tended to focus on the
behavior (that is, potential mobility) of the minority group(s) rather than asking questions of the
host majority group; the majority are not expected or asked to move and integrate, yet it is they
who often effectively protect their own “enclaves”—although, being predominantly White areas,
they are not called that. This example effectively illustrates how policy and research attention that
is centered only on the identified mover group may result in a lack of attention to the influence of
the nonmovers and potential receivers, whose effects on mobility and its consequences also require
research.
Research is also welcome on the psychology of mobility and mixing, as in the role played by “cog-
nitive constraints” on moving, reported in this symposium. In relation to mobility that produces
more mixing, interesting connections remain to be explored between community- and societal-level
effects. From a well-being perspective, one would wish to know whether the psychosocial pathway
between inequality and health—due to stress caused by subordination, lack of control, and status
anxiety (Kawachi and Kennedy, 1999)—operates at a neighborhood as well as at a societal level.
In other words, is relative deprivation (Runciman, 1966) a potential outcome of any mobility that
results in social mixing? More research on the mental health and well-being outcomes of mobility
would help provide an answer to this question.

Mobility, Mixing, and Neighborhood Change: A British Perspective
187Cityscape
Beyond studying effects on individuals, an interest in mobility and its consequences extends to the
issue of whether integrated or mixed neighborhoods also have societal-level effects on social and
political attitudes (which is possibly more likely in societies with interventionist or welfare states).
Previous research has argued that societal inequality reduces empathy between groups so that
people have to fend for themselves more (Wilkinson and Pickett, 2009), which raises two interest-
ing questions for neighborhood research. First: Can residential mixing serve to change social and
political attitudes over time (for example, on questions of the causes of poverty and the desirability
of social assistance) or, even more fundamentally, by lowering people’s anxiety about difference
(Sennett, 2012)? Can other groups become more accepted as a legitimate and welcome presence?
Second: Could such effects arise from the co-presence of groups within neighborhoods, or do they
also require social interaction, or “contact,” and, if so, of what type (Pettigrew and Tropp, 2005)?
Studying residential mobility is complex and fascinating in itself, but extending that interest to its
effects on not only individuals but also on neighborhoods, cities, and societies brings home the
importance of where we live for how we live in the modern world.
Acknowledgments
The author acknowledges the Scottish Government, National Health Service (NHS) for Scotland,
Glasgow Housing Association, Glasgow Centre for Population Health, and NHS Greater Glasgow
& Clyde for funding the GoWell Programme, of which the author’s research on relocation and
mobility forms a part. See www.gowellonline.com.
Author
Ade Kearns is professor of urban studies at the University of Glasgow.
References
Allport, Gordon. 1954. The Nature of Prejudice. Cambridge, MA: Addison-Wesley.
Bailey, Nick, Mark Livingston, and Ade Kearns. 2012. “Place Attachment in Deprived Neighbour-
hoods: The Impacts of Population Turnover and Social Mix,” Housing Studies 27 (2): 208–231.
Galster, George. 2012. “The Mechanism(s) of Neighbourhood Effects: Theory, Evidence, and Policy
Implications.” In Neighbourhood Effects Research: New Perspectives, edited by M. van Ham, D. Manley,
N. Bailey, L. Simpson, and D. Maclennan. London, United Kingdom: Springer: 23–56.
Hedman, Lina, and Maarten van Ham. 2012.”Understanding Neighbourhood Effects: Selection
Bias and Residential Mobility.” In Neighbourhood Effects Research: New Perspectives, edited by
Maarten van Ham, David Manley, Nick Bailey, Ludi Simpson, and Duncan Maclennan. London,
United Kingdom: Springer: 79–99.
Kawachi, Ichiro, and Bruce P. Kennedy. 1999. “Income Inequality and Health: Pathways and
Mechanisms,” Health Services Research 34: 215–227.

188
Kearns
Residential Mobility: Implications for Families and Communities
Kleinhans, Reinout, and David Varady. 2011. “Moving Out and Going Down? A Review of Recent
Evidence on Negative Spillover Effects of Housing Restructuring Programs in the United States and
the Netherlands,” International Journal of Housing Policy 11 (2): 155–174.
Pettigrew, Thomas F., and Linda R. Tropp. 2005. “Allport’s Intergroup Contact Hypothesis: Its
History and Influence.” In On the Nature of Prejudice: Fifty Years After Allport, edited by John F. Dovidio,
Peter Glick, and Laurie A. Rudman. Oxford, United Kingdom: Blackwell Publishing: 262–277.
Phillips, Deborah. 2006. “Parallel Lives? Challenging Discourses of British Self-Segregation,”
Environment and Planning D: Society & Space 24 (1): 25–40.
Runciman, W. Garry. 1966. Relative Deprivation and Social Justice: A Study of Attitudes to Social
Inequality in 20th Century England. London, United Kingdom: Routledge Kegan Paul.
Sautkina, Elena, Lyndal Bond, and Ade Kearns. 2012. “Mixed Evidence on Mixed Tenure Effects:
Findings From a Systematic Review of UK Studies, 1995–2009,” Housing Studies 27 (6): 748–782.
Sennett, Richard. 2012. Together: The Rituals, Pleasure, and Politics of Cooperation. New Haven, CT:
Yale University Press.
Wilkinson, Richard, and Kate Pickett. 2009. The Spirit Level: Why More Equal Societies Almost
Always Do Better. London, United Kingdom: Allen Lane.

189Cityscape: A Journal of Policy Development and Research • Volume 14, Number 3 • 2012
U.S. Department of Housing and Urban Development • Office of Policy Development and Research
Cityscape
Reducing Worst Case
Housing Needs With
Assisted Housing
Kirk McClure
University of Kansas
Abstract
This research note seeks to answer this question: When units of assisted housing are
added to a metropolitan market, is there a commensurate reduction in the number of
households with worst case housing needs (WCN)? WCN are defined as unassisted
renter households with very low incomes (VLIs) that pay more than 50 percent of in-
come on housing or live in severely inadequate housing conditions or experience both.
Previous work estimated the relationship among extremely low-income renter families
with children, finding a reduction in WCN of 76 households for each 100 additional
assisted units in the market. This work was replicated with more recent data drawn
from the larger population of VLI renter households. It found a reduction in WCN of
68 households per 100 units of assistance. There appears to be a threshold in the relation -
ship. In markets in which less than 45 percent of the VLI renter households are assisted,
the count of WCN households is reduced by an estimated 92 households per 100 assisted
units added. If the percentage of assisted households is greater than 45 percent, no reduc -
tion in WCN is found with the introduction of additional assisted units.
Introduction
This research note addresses this question: If assisted housing units are added to a housing market,
is there a commensurate reduction in the count of renter households with worst case housing needs
(WCN)? WCN form a benchmark that is used to assess the need for housing assistance across the
nation. The benchmark is estimated by the U.S. Department of Housing and Urban Development
(HUD) as the count of poor renter households that suffer from severe housing affordability condi -
tions. This count is limited to renters with incomes that are less than 50 percent of the Area Median
Income (AMI). This income level is referred to as very low-income (VLI). These VLI renters are

190 Refereed Papers
McClure
deemed to have WCN if they do not receive federal housing assistance and (1) pay more than
one-half of their monthly income for rent and utilities or (2) live in severely inadequate housing
conditions or (3) experience both (Steffen et al., 2011).
To properly budget scarce resources and quantify the expected results of investments in assisted
housing, HUD and Congress want and need to know the responsiveness of housing markets to
interventions through the provision of assisted housing. The assisted housing can take the form of
project-based assisted housing or tenant-based vouchers. Can the government expect a one-for-one
reduction in WCN with the introduction of an assisted unit or a voucher, or does some lesser level
of response indicate that not all assisted housing goes to households with WCN?
Previous Work
Khadduri, Shroder, and Steffen (2003) examined this topic in the context of welfare reform.
Because of their interest in welfare reform, the population they studied was composed of families
with children. The families they studied had incomes placing them at less than 30 percent of the
AMI for families. Income at this level is referred to as extremely low-income (ELI). The authors,
who based their paper on data from 44 metropolitan areas (exhibit 1), illustrated the relationship
graphically and found a strong negative relationship.
Khadduri, Shroder, and Steffen (2003) did not model the relationship between WCN and assisted
housing. Using their data, however, an estimate can be generated (model 1 in exhibit 2). The
model finds a regression coefficient of -0.76 between the percentage of ELI families with children
and assisted housing as a percentage of the same group. The coefficiant is, as expected, negative
and significant. Their work suggests that adding 100 assisted housing units to a metropolitan
market results in 76 fewer poor renter households with WCN.
80
70
60
50
40
30
20
15 20 25 30 35 40 45 50 55 60 65
ELI families with children with WCN (%)
ELI families with children receiving housing assistance (%)
Exhibit 1
Percent of ELI Families With Children With WCN and Percent of ELI Families With
Children Receiving Housing Assistance for Metropolitan Areas, 1989 Through 1996
ELI = extremely low-income. WCN = worst case housing needs.
Source: Khadduri, Shroder, and Steffen (2003)

Reducing Worst Case Housing Needs With Assisted Housing
191Cityscape
The model generated from the Khadduri, Shroder, and Steffen (2003) data suggests a less than one-
for-one reduction in the number of WCN households with the addition of assisted housing units.
Perhaps the less than one-for-one reduction in WCN households should be expected. Hardiman
et al. (2010) found that about 85 percent of the households benefiting from HUD-funded housing
assistance have VLIs. This 85-percent figure provides an expected value for the estimated coefficient
between assisted housing and WCN. The admission requirements for the various HUD-funded
housing assistance programs vary. Each program serves people with low incomes, but those served
are not exclusively VLI or ELI renters. In addition, although most households that receive housing
assistance will be from the VLI population, not all will come from housing with severely inadequate
conditions or housing that costs more than 50 percent of their household income, conditions that
define WCN households. Steffen et al. (2011) found that only 51 percent of all unassisted VLI renters
have WCN.
Many VLI renters do not suffer from WCN, and the households that are admitted to housing
assistance programs may or may not be drawn from those with WCN. Public housing authorities
(PHAs) have some discretion as they administer the public housing program and the Housing
Choice Voucher Program (HCVP), two of the largest forms of housing assistance for VLI renters.
These scarce housing resources are allocated to eligible households that enter their names onto the
PHA’s waiting lists for housing assistance. Households can move up the list as a function of both
federal and local preferences. The highest priority tends to be given to households displaced by
natural disaster or government action. Although some priority is given to households whose mem-
bers are employed, it appears that most households receiving housing assistance have few, if any,
resources (Leopold, 2012). Given this process, it seems likely that as housing assistance becomes
available, the recipient households would probably be drawn from the population of unassisted
VLI renters who are likely to have high housing cost burdens. This probability makes it reasonable
to expect the reduction in WCN to be closer to 85 per 100 new units of housing assistance. This
process also means, however, that not every incremental assisted unit will become occupied by a
household drawn from the population of WCN.
Model 1 Model 2 Model 3 Model 4
Dependent variable
Percent of
ELI families
with children
with WCN
Percent of
VLI renter
households
with WCN
Percent of
VLI renter
households
with WCN
Percent of
VLI renter
households
with WCN
Number of cases 44 30 24 6
Cases All All
Percent with
WCN < 45
Percent with
WCN > 45
Exhibit 2
Models Explaining Percent of Households With WCN as a Function of Percent of
Households Assisted
Coefficient for
percent assisted
– 0.756 – 0.678 – 0.918 – 0.346
t score 10.539 5.565 3.526 1.214
Significance 0.001 0.001 0.002 0.292
Adjusted R squared 0.719 0.508 0.332 0.087
ELI = extremely low-income. VLI = very low-income. WCN = worst case needs.

192 Refereed Papers
McClure
Recent Data From the American Housing Survey
To establish an estimate of the relationship between WCN and assisted housing, data are taken
from both HUD and from the American Housing Survey (AHS). Data on WCN are available from
the AHS but not from the decennial census or the American Community Survey (ACS). The census
and the ACS examine housing cost hardship, but they do not provide data on the incidence of
severely deficient housing, a component of WCN. Thus, analysis in this research note is restricted
to the 30 metropolitan areas for which recent AHS data are available.
The counts of assisted housing are derived from HUD data. The counts include public housing,
Section 8 new construction/substantial rehabilitation units, Section 236 units, HCVP households,
and a few older HUD multifamily programs with units that are still under contract. The data do
not include the units that are subsidized under the Low-Income Housing Tax Credit Program,
because the program tends to assist households that are less poor, such as households with 30 to
60 percent of AMI.
Using AHS and HUD data illustrated in exhibit 3, a simple bivariate model is estimated (model 2
in exhibit 2). The dependent variable is the percentage of VLI renter households with WCN, a
larger population than was used in the previous study. This population reflects the universe from
which WCN households are drawn. The independent variable is assisted housing as a percentage
of VLI renter households.
65
60
55
50
45
40
35
30
VLI renter households with WCN (%)
25 30 35 40 45 50 55 60
VLI renter households receiving housing assistance (%)
Exhibit 3
Percent of VLI Renter Households With WCN and Percent of VLI Renter Households
Receiving Housing Assistance for Metropolitan Areas, 2002 Through 2007
VLI = very low-income. WCN = worst case housing needs.
Sources: U.S. Department of Housing and Urban Development, Office of Policy Development and Research, Assisted
Housing Data, 2008; American Housing Survey Metropolitan Data, 2002, 2004, and 2007

Reducing Worst Case Housing Needs With Assisted Housing
193Cityscape
The coefficient estimated from these more recent data is lower than found with the previous study.
This model suggests that 100 units of additional assisted housing will result in a reduction of WCN
of only 68 households. There appears to be a threshold in the relationship between WCN and
assisted housing, however. If assisted housing is introduced into a market in which less than 45
percent of the VLI renter households are assisted, the percentage of VLI renters with WCN appears
to be very responsive. The count of WCN households is reduced by an estimated 92 households
per 100 assisted units added. If the percentage of VLI renters who are assisted is greater than 45
percent, however, no reduction can be found with the introduction of additional assisted units
(models 3 and 4 in exhibit 2).
Conclusion
The relationship between the incidence of WCN and the incidence of assisted housing is negative.
We would expect this relationship to be close to 85 households per 100 additional assisted hous-
ing units. This expectation is reasonable because 85 percent of the households that benefit from
HUD-funded housing assistance have VLIs and those households selected for entry into housing
assistance programs tend to be those VLI households with a high housing cost burden. The small
amount of available data does not permit construction of models with controls that could help im-
prove our understanding of the relationship. Inspection of the relationship indicates that a thresh-
old exists, however. In the metropolitan areas studied, those with lower levels of assisted housing
seem to be very responsive to additional assisted housing. These markets experience higher levels
of VLI households with WCN. Alternatively, markets with higher levels of assisted housing do not
show any significant relationship, but these markets are few in number.
This finding suggests that markets are most responsive to the introduction of additional assisted
housing if they suffer from lower levels of housing assistance for their renter populations. Where
a reduction of WCN is sought, additional assisted housing will generate the greatest effect if it is
introduced into markets with these low levels of assisted housing.
Author
Kirk McClure is a professor in the Department of Urban Planning at The University of Kansas.
References
Hardiman, David L., Carolyn Lynch, Marge Martin, Barry L. Steffen, David A. Vandenbroucke, and
Yung Gann David Yao. 2010. Worst Case Housing Needs 2007: A Report to Congress. Washington,
DC: U.S. Department of Housing and Urban Development, Office of Policy Development and
Research.
Khadduri, Jill, Mark Shroder, and Barry Steffen. 2003. “Can Housing Assistance Support Welfare
Reform?” In A Place To Live, a Means To Work: How Housing Assistance Can Strengthen Welfare Policy,
edited by Barbara Sard and Amy Bogdon. Washington, DC: Fannie Mae Foundation: 23–62.

194 Refereed Papers
McClure
Leopold, Josh. 2012. “The Housing Needs of Rental Assistance Applicants,” Cityscape 14 (2):
275–298.
Steffen, Barry L., Keith Fudge, Marge Martin, Maria Teresa Souza, David A. Vandenbroucke, and
Yung Gann David Yao. 2011. Worst Case Housing Needs 2009: A Report to Congress. Washington,
DC: U.S. Department of Housing and Urban Development, Office of Policy Development and
Research.

195Cityscape: A Journal of Policy Development and Research • Volume 14, Number 3 • 2012
U.S. Department of Housing and Urban Development • Office of Policy Development and Research
Cityscape
Using Administrative Data
for Spatial and Longitudinal
Analysis of the Housing
Choice Voucher Program
Eric Schultheis
Massachusetts Institute of Technology
Gregory Russ
Carolina Lucey
Cambridge Housing Authority
Data Shop
Data Shop, a department of Cityscape, presents short articles or notes on the uses of
data in housing and urban research. Through this department, the Office of Policy Devel -
opment and Research 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.
Abstract
Place and time are important dimensions of the administration of and policy behind
the Housing Choice Voucher Program (HCVP). Spatial and longitudinal analyses of the
HCVP are rare, however. In part, this scarcity is because of the lack of widely available,
microscale spatial and temporal HCVP data. This article introduces a process that re -
searchers and public housing authorities (PHAs) can use to generate a spatially located,
person-period data set of participant households in the HCVP, using off-the-shelf software
and administrative data that HUD requires PHAs to collect via Form HUD-50058.
This spatially located, person-period data set enables researchers and PHAs to conduct
a variety of longitudinal and microscale spatial analyses not possible using untransformed
50058 data or other widely available data sources.

196
Schultheis, Russ, and Lucey
Data Shop
Introduction
Where do Housing Choice Voucher Program (HCVP) households live? How has the spatial pattern
of where they live changed over time? Using currently available HCVP data, these two seemingly
simple questions are surprisingly difficult to answer, at least at the subcity scale. These two questions
are directly relevant, however, to ongoing research and administration questions about the HCVP,
such as whether HCVP participants live in “high-opportunity” neighborhoods.
Public housing authorities (PHAs) capture and store a wealth of data about client demographics
and spatial location. In most cases, these data are collected in administrative databases designed to
support program operations and that comply with various HUD reporting requirements. One such
data source, Form HUD-50058, provides comprehensive secondary, household-level, program-
participant data. The 50058 data include household-level information for all participants in the
public housing program, HCVP, and Section 8 Moderate Rehabilitation program.

In exhibit 1, we
summarize the information available from the 50058 data.
This article outlines a process to transform the 50058 data into a spatially located, person-period
data set using off-the-shelf software and free tools.
1
The resulting data set enables researchers to
Exhibit 1
Section (selected) Contents (selected)
A Summary of the Data Captured by Form HUD-50058
Agency Agency name; PHA code; program
Action Action type; effective date; action correction (y/n)
Household Head of household; household size; demographic information about all
household members: name, age, sex, relation, disability, race, ethnicity,
and citizenship
Background at admission Date entered waiting list; ZIP Code before admission; homeless before
admission (y/n)
Unit to be occupied on
effective date of action
Unit address; number of bedrooms; date of last HQS inspection; structure
type; year structure built
Assets Owner of asset; asset type; asset cash value; income from asset; total
household assets
Income Income by household member; income after exclusions; annual household
income
Expected income per year Amount of permissible deductions by type and household member;
household unreimbursed medical expenses; dependent allowance;
unreimbursed childcare costs; adjusted annual household income
Total tenant payment Total tenant payment; most recent total tenant payment; qualify for
minimum rent hardship (y/n)
Tenant-based vouchers Number of bedrooms on voucher; qualify as hard-to-house family (y/n);
utility allowance; housing assistance payment to owner
HQS = Housing Quality Standards. PHA = public housing authority. y/n = Binary response of “yes” or “no.”
Source: Form HUD-50058
1
A person-period format has one record for each household for each temporal unit. This format is particularly well suited
for a variety of longitudinal analyses (Singer and Willett, 2003).

Using Administrative Data for Spatial and Longitudinal Analysis
of the Housing Choice Voucher Program
197Cityscape
conduct a variety of longitudinal, microscale spatial analyses that they can use to explore admin-
istration- and policy-relevant questions about how the HCVP functions across space and time.
2
In this article, we first outline the process for transforming the 50058 data into a spatially located,
person-period data set. Second, we propose a method to determine an HCVP household’s program
status from its certification event history and to identify several reliability issues associated with the
50058 data. Third, we briefly describe two analyses of the HCVP, an exploratory spatiotemporal
data analysis project and a stock-and-flow mapping project
3
that the Cambridge Housing Authority
(CHA), in Cambridge, Massachusetts, performed with these data.
Transforming the 50058 Data Into a Spatially Located,
Person-Period Data Set
The 50058 data can support a broad range of heretofore unexplored policy research questions that
require temporal and spatial data. The 50058 data must be transformed into a spatially located,
person-period data set to support such research, however. In this section, we outline how to spa-
tially locate the 50058 data and how to reshape the 50058 data into a person-period data structure.
Spatially Locating the 50058 Data
The 50058 data can be spatially located because it includes the addresses of program participants.
The process of converting address data to geographic coordinates (typically latitude and longitude)
is called geocoding (for example, Longley et al., 2010).
4
Despite advances in geocoding methods, inaccurate or improperly formatted address data pose a
substantial challenge in the case of the 50058 data. As with most address data captured for admin-
istrative purposes, the 50058 data’s structure does not facilitate geocoding.
5
The likelihood that
PHAs did not establish or enforce guidelines aimed at normalizing the address capture-and-storage
processes magnifies this difficulty. The likelihood that the 50058 address data, like most adminis-
trative data, are rife with typographical errors is a further complication.
6
Commercial geocoding engines deploy various methods to geocode addresses that (1) are in non-
standardized and nonnormalized formats, (2) contain common typographical errors, or (3) both.
2
Use of the 50058 data for research often raises issues of PHA client confidentiality. These issues range from not inadver-
tently disclosing client addresses in map visualizations to the handling and use of PHA clients’ personal data. If a PHA part -
ners with university-affiliated researchers, the universities’ institutional review boards can help the PHA and researchers
design protocols to ensure that PHA clients’ personal information is sufficiently protected.
3
Johnson and Nelson (1998) provide a useful introduction to stock-and-flow mapping for those unfamiliar with this form
of cartographic representation.
4
Alternatively, this process is sometimes referred to as “address matching” (Demers, 2008).
5
The 50058 data likely store addresses in a single attribute field. Address data stored in multiple fields are frequently easier to
geocode. For instance, street address data might be stored in separate fields such as street number, street name, and street type.
6
Such typographical errors could be minimized if agencies implemented data input interfaces that used “lookup” tables or
autocomplete functionality. In the authors’ experience, PHA data entry systems rarely implement such functionalities.

198
Schultheis, Russ, and Lucey
Data Shop
Although such geocoding engines offer ease of use, they have several drawbacks. First, they can be
cost prohibitive. Second, the methods behind commercial geocoding engines are typically a “black
box,” to protect proprietary algorithms. Thus, although commercial engines likely could geocode
the 50058 data addresses as is, they are less than ideal. We sought an alternate engine to geocode
the 50058 data addresses and settled on a minimal-cost geocoding service that the Geographic In -
formation System (GIS) Research Laboratory at the University of Southern California (USC WebGIS)
developed (Goldberg and Wilson, 2012).
7
Using the USC WebGIS service necessitated conducting some preprocessing of the 50058 data
addresses before geocoding. We determined that the minimal cost and overall transparency of the
USC WebGIS service compared with those of commercial services outweighed this preprocessing
burden. Before geocoding, we normalized the 50058 data addresses using a combination of lookup
tables in Microsoft Access and the “find” and “replace” functions in Microsoft Excel.
8, 9
Although tedious, the address normalization process can be implemented relatively quickly. We
devoted approximately 10 hours of staff time to normalize the addresses of HCVP households,
spanning a 7-year period.
10
In the 50058 data, we observed a 15-percent increase in the geocoding
success rate after implementing basic address normalization techniques. Using the addresses, our
normalization process, the USC WebGIS geocoding engine, and minimal manual geocoding, we
achieved good-quality geocodes for more than 98 percent of the 50058 data addresses. In exhibit 2,
we summarize our geocoding success rates.
Exhibit 2
Year
HCVP
Households
HCVP
Household
Addresses
Addresses
Geocoded
Match Rate
(Percent)
Matched Using
USC WebGIS
Geocoding Engine
Matched
Using Manual
Geocode
Results of Geocoding HCVP Household Residence Addresses From the 50058 Data
2004 2,876 3,111 3,074 98.8 3,000 74
2005 2,832 3,078 3,036 98.6 2,957 79
2006 2,809 3,038 2,985 98.2 2,885 100
2007 2,952 3,140 3,082 98.1 2,963 119
2008 2,894 3,079 3,033 98.5 2,886 147
HCVP = Housing Choice Voucher Program. USC WebGIS = Geographic Information System Research Laboratory at the
University of Southern California.
7
To geocode more than 2,500 addresses, a user must register as a partner with USC WebGIS. Additional information about
USC WebGIS usage rules is available at https://webgis.usc.edu/About/UsageCosts.aspx.
8
We used lookup tables to list the variable spellings of road names in the 50058 data to create a new data set, wherein we
assigned a given address component (that is, street name, city, county, or state) a single spelling. Using this process, we
were able to correct most typographical errors in the 50058 data addresses. In addition, we used Microsoft Excel’s “find”
and “replace” functions to normalize the format of the 50058 data addresses by removing address data irrelevant to the
geocoding process (such as apartment numbers).
9
Detailed technical notes about our address normalization and reshaping process are available on request from the authors.
The technical documentation includes a detailed listing of the Excel functions and Structured Query Language queries used.
10
Because of reliability issues at the ends of the data set, we were able to use only 5 years of the data.

Using Administrative Data for Spatial and Longitudinal Analysis
of the Housing Choice Voucher Program
199Cityscape
Reshaping the 50058 Data Into a Person-Period Format
We reshaped the 50058 data into a person-period format.
11
In exhibit 3, we present, for one HCVP
household, the 50058 data in the original event-level structure and also in a person-period struc-
ture. The person-period structure supports various longitudinal analyses, and we argue that it is, in
general, a more useable format.
Many statistical software packages (for example, R and STATA) can reshape data from an event-
level to a person-period structure. We used Microsoft Access to reshape the 50058 data, which had
several advantages. First, our approach enabled collaboration between CHA database administra-
tors fluent in Structured Query Language and policy staff fluent in Access’ graphical user interface.
Second, we designed the reshaping process iteratively and tested the logic of our design by viewing
the results of intermediate queries.
12
Third, the iterative design and implementation afforded us the
opportunity to identify, investigate, and correct issues in the 50058 data during the reshaping process.
11
See footnote 1 for the definition of a person-period data structure.
12
Inspecting intermediate steps of the reshaping process would be substantially more difficult if we used R or STATA.
Exhibit 3
Tenant ID Certification Effective Date (2003) Income
Sample HCVP Household Data in Event-Level (a) and Person-Period (b) Structures
(a) Event-Level Structure
(b) Person-Period Structure
1 January 1 150
1 April 28 200
1 December 1 120
Tenant ID Period (2003) Last Certification Effective Date (2003) Income
1 January 1 January 1 150
1 February 1 January 1 150
1 March 1 January 1 150
1 April 1 January 1 150
1 May 1 April 28 200
1 June 1 April 28 200
1 July 1 April 28 200
1 August 1 April 28 200
1 September 1 April 28 200
1 October 1 April 28 200
1 November 1 April 28 200
1 December 1 December 1 120
Issues Related to the Transformed 50058
Determining an HCVP household’s program status for each period in the transformed 50058 data
is one of the more difficult aspects of our process. In all likelihood, an HCVP household’s program
certification type (from which one can determine HCVP status) will be recorded inconsistently in
the 50058 data. We assume that inconsistent recording of certification type is widespread across
PHAs and thus describe, in some detail, our method of interpolating HCVP status.
HCVP = Housing Choice Voucher Program.

200
Schultheis, Russ, and Lucey
Data Shop
When certification event type is missing or inconsistently recorded, determining an HCVP house-
hold’s program status is difficult. For instance, looking at one certification event that lacks certifi-
cation type information, one cannot tell if the certification event is a program start certification, an
annual recertification, or a program termination certification. Most analyses using the 50058 data
will require that an HCVP household be assigned a program status for every period in the trans-
formed 50058 data. For instance, mapping HCVP households at a given point in time requires
knowing whether a particular HCVP household was a program participant at that given time.
We interpolated an HCVP household’s program status using a set of assumptions grounded in
HCVP requirements and CHA staff expertise. In particular, we assumed that, absent contrary evi-
dence in the 50058 data, an HCVP household would have a certification event every 12 months,
in accordance with federal law.
13
In addition, we assumed that an HCVP household reported major
changes to its composition or income via an interim recertification, as required by the same federal
regulation. Applying these rules produced unreliable results in the first and last years of the 50058
data analyzed. Because of this unreliability, despite having the 50058 data covering the period
from January 1, 2003, through December 31, 2009, we excluded data from 2003 and 2009 (that
is, the data set’s edges).
For every month in the period analyzed, we assigned HCVP households one of six program statuses:
Not Yet in Program, Start of Program Participation, Program Participant, Final Certification, Termination
Ghost, or No Longer Program Participant.
14
We used the following decision rules to interpolate an
HCVP household’s program status from its certification event history.
• Not Yet in Program: All periods before an HCVP household’s first certification.
• Start of Program Participation: The first full period after an HCVP household’s first certification
became effective.
• Program Participant: All periods between an HCVP household’s first and last certifications.
• Final Certification: The first full period after an HCVP household’s last certification.
• Termination Ghost: The 12 months (periods) after an HCVP household’s final certification. If PHAs
consistently recorded program terminations, this status would be unnecessary. Given the quality
of the 50058 data, however, we knew only that an HCVP household had its last certification on
a particular date. We had no knowledge of when its program participation terminated. If an HCVP
household had no subsequent certifications, based on the assumption that an HCVP household
should have an annual recertification every 12 months, we assigned that HCVP household Term i -
nation Ghost status for 12 months after its last certification. This status indicates that we were
unsure of the HCVP household’s status in the HCVP, because the program termination date was
not properly recorded.
13
“Family Income and Composition: Regular and Interim Examinations,” 24 CFR Part 982.516. 59 FR 36682. July 18, 1994.
14
Before we excluded records from 2003 and 2009, we included two additional household statuses to indicate instances in
which we were unable to ascertain an HCVP household’s program participation status because of edge effects. For instance,
if an HCVP household’s first certification in the 50058 data occurred in 2003, we were unable to determine whether that
certification was the HCVP household’s first or whether its first certification actually occurred at some previous time outside
the 50058 data’s temporal span.

Using Administrative Data for Spatial and Longitudinal Analysis
of the Housing Choice Voucher Program
201Cityscape
• No Longer Program Participant: All periods after the Termination Ghost status ended. This status
indicates our relative certainty that an HCVP household was no longer participating in the HCVP.
We provide the following example to clarify the application of these decision rules. Imagine an
HCVP household that had three certifications. Its first certification occurred on January 1, 2006, its
second on June 1, 2006, and its third and final on June 1, 2007.
• For all periods before January 1, 2006, we assign the status Not Yet in Program.
• For the period of January 1, 2006, we assign the status Start of Program Participation.
• For all periods after January 1, 2006, but before June 1, 2007, we assign the status Program
Participant.
• For the period of June 1, 2007, we assign the status Final Certification.
• For the 12 months (periods) after June 1, 2007, we assign the status Termination Ghost.
• Beginning on the period of July 1, 2008, and for all subsequent periods, we assign the status No
Longer Program Participant.
In exhibit 4, we provide a visual representation of the assignment of program participation status
to the example HCVP household.
Although our process results in a more useable data set, the resulting product is only as reliable as
the underlying data. Researchers have explored the reliability of administrative data in other con-
texts, but the reliability of administrative housing data, such as the 50058 data, has been explored
insufficiently (see, for example, Boehmer et al., 2002; Lee et al., 2005). As such, researchers and
PHAs should be cautious about relying solely on administrative data to test hypotheses or evaluate
agency policy.
Exhibit 4
Visual Representation of Status Assignment for an Example HCVP Household
2005 Jan. Feb. Mar. Apr. May June July Aug. Sep. Oct. Nov. Dec.
Jan. Feb. Mar. Apr. May June July Aug. Sep. Oct. Nov. Dec.
Jan. Feb. Mar. Apr. May June July Aug. Sep. Oct. Nov. Dec.
Jan. Feb. Mar. Apr. May June July Aug. Sep. Oct. Nov. Dec.
Not Yet
in Program
Start of
Program
Participation
Program
Participant
Final
Certification
Termination
Ghost
No Longer
Program
Participant
2006
2007
2008
Legend
HCVP = Housing Choice Voucher Program.

202
Schultheis, Russ, and Lucey
Data Shop
Examples of Analyses Enabled by the Transformed
50058 Data
CHA used the transformed 50058 data to explore several issues related to changing spatial patterns
of HCVP household residences. Exhibits 5 and 6 are maps excerpted from an exploratory spatial
data analysis (ESDA) project that CHA conducted. Exhibit 5 is a kernel density map of where HCVP
households reside in Cambridge, Massachusetts, at five different temporal cross sections. The over-
all spatial distribution of HCVP households in Cambridge does not change. We observe a progres-
sive decrease in the count and density of HCVP households in Cambridge, however, as we move
from earlier to later periods. This phenomenon is easier to read from the tabular element of exhibit 5,
indicated on the map with a dashed callout line.
Exhibit 6 is an overlay map of rental-unit density in Cambridge and the residences of HCVP house -
holds on June 1, 2008. We observe that the spatial distribution of HCVP households roughly con-
forms to the density of Cambridge rental units. The exception to this observation is that few HCVP
households reside in the more affluent northeastern portions of Cambridge.
Exhibit 5
Kernel Density Map of HCVP Households That Reside in Cambridge
HCVP households per square kilometer
0–160
161–550
551–1,300
1,301–2,900
0 0.5 1 2 Kilometers
(1) Date
(2) HCVP households
(3) HCVP households in Cambridge
(4) Percent of HCVP households in Cambridge
(1) (2) (3) (4)
June 1, 2004 2,497 2,682 93
June 1, 2005 2,378 2,672 89
June 1, 2006 2,228 2,595 86
June 1, 2007 2,143 2,602 82
June 1, 2008 2,021 2,609 77
June 1, 2004
June 1, 2007
June 1, 2005
June 1, 2008
June 1, 2006
Legend
HCVP = Housing Choice Voucher Program.
Sources: City of Cambridge, Cambridge Housing Authority, July 14, 2011

Using Administrative Data for Spatial and Longitudinal Analysis
of the Housing Choice Voucher Program
203Cityscape
One working hypothesis that emerged from the ESDA was that HCVP households were increas-
ingly moving out of Cambridge because of relatively higher rental costs compared with those of
neighboring cities. CHA tested the salience of this hypothesis by conducting stock-and-flow map-
ping of HCVP households between 2004 and 2008.
15
This stock-and-flow mapping highlighted
the “regionalization” of CHA’s HCVP activities and the existence of a substantial number of HCVP
household moves out of Cambridge.
16
CHA’s stock-and-flow mapping of the 50058 data provided
the agency with empirically based and persuasive analysis that challenged the parochial stance of
limiting PHA policy inquiry to intra-PHA matters and an agency’s formal jurisdictional bounds.
Exhibit 6
Map of 2008 HCVP Household Residences and Cambridge Rental Unit Density
0–982
983–2,804
2,805–5,207
5,208–8,525
8,526–14,730
14,731–27,441
HCVP = Housing Choice Voucher Program.
Notes: Point symbols of HCVP household residences dispersed in circle pattern to prevent overlapping symbols. HCVP
households residing in Cambridge (total HCVP households) = 2,021 (2,609). Number of Cambridge rental units (2000 census)
= 29,616.
Sources: 2009 census, Summary File 1; City of Cambridge, Cambridge Housing Authority, July 21, 2011
Rental units per
square kilometer
15
The result of this analysis was a finding that most of the decline in the number of HCVP households residing in Cambridge
resulted from the net effect of program starts and ends in neighboring cities. This finding focused CHA’s research on
understanding why new HCVP households are increasingly leasing up outside Cambridge.
16
Unlike some PHAs, CHA has the option to continue to administer vouchers outside of its jurisdictional bounds. This
option stems from Massachusetts law and CHA’s participation in the Moving to Work demonstration project.

204
Schultheis, Russ, and Lucey
Data Shop
Cambridge
Sommerville
Boston
Wrentham
Wellsley
Newton
Milton
Quincy
Canton
Randolph
Maynard
Waltham
Lexington
Winchester
Watertown
Belmont
Arlington
North Reading
Medfrod
Chelsea
Revere
Lynn
Malden
Everett
1 to 5 internal moves
107 internal moves
1 household
2 to 4 households
5 to 10 households
11 to 20 households
Fewer Households More Households
107
107
Cities where particpant households lived on June 1, 2008
6 to 15 internal moves
Bedford
Melrose
Dedham
Stoneham
Brookline
Hull
Woburn
Salem
Norwood
Walpole
Melrose
Wakefield
Burlington
Arlington
Belmont
Burlington
Lexington
Quincy
Randolph
Revere
Stoneham
Everett
Outside metro
Waltham
Watertown
Woburn
Exhibit 7 is a stock-and-flow map of HCVP households for the 2008 calendar year. In exhibit 7, a
circle represents a city where at least one CHA HCVP household resides. We graduated each circle’s
size to reflect differences in the number of HCVP households that reside in the city. The various
lines connecting city symbols represent the number of HCVP households that moved from one city
to another during the period analyzed. We graduated each line’s thickness to represent differences
in the number of HCVP households that moved between two cities. The directional arrow points
to the city to which HCVP households moved. The line’s origin is at the city of departure.
Exhibit 7
HCVP Household Flows, June 1, 2007 to June 1, 2008

Using Administrative Data for Spatial and Longitudinal Analysis
of the Housing Choice Voucher Program
205Cityscape
Conclusion
The transformed 50058 data enable researchers and PHAs to conduct a variety of microscale spatial
analyses of the HCVP. In addition to supporting spatial analyses, the transformed 50058 data support
a variety of household-level, longitudinal statistical analyses. Both of these types of analyses offer the
potential to deepen our understanding of the HCVP and to better evaluate how it is administered.
Acknowledgments
The authors thank the Doctoral Public Policy Fellowship Program of the Rappaport Institute for
Greater Boston, whose support made possible the project on which we based this article. They also
thank the staff at the Cambridge Housing Authority, in particular Tito Evora, for their ongoing
support and willingness to collaborate on research endeavors.
Authors
Eric Schultheis is a doctoral student in the Department of Urban Studies and Planning at the
Massachusetts Institute of Technology.
Gregory Russ is executive director of the Cambridge Housing Authority.
Carolina Lucey is senior program manager for administration and policy at the Cambridge Housing
Authority.
References
Boehmer, Ulrike, Nancy R. Kressing, Dan R. Berlowitz, Cindy L. Christiansen, Lewis E. Kazis, and
Judith A. Jones. 2002. “Self-Reported vs. Administrative Race/Ethnicity Data and Study Results,”
Research and Practice 92 (9): 1471–1473.
Demers, Michael N. 2008. Fundamentals of Geographic Information Systems. Hoboken, NJ: Wiley.
Goldberg, D.W., and J.P. Wilson. 2012. “USC WebGIS Services.” https://webgis.usc.edu (accessed
April 4).
Johnson, Harry, and Elizabeth S. Nelson. 1998. “Using Flow Maps To Visualize Time-Series Data:
Comparing the Effectiveness of a Paper Map Series, a Computer Map Series, and Animation,”
Cartographic Perspectives 30: 47–64.
Lee, Douglas S., Linda Donovan, Peter C. Austin, Yanyan Gong, Peter P. Liu, Jean L. Rouleau, and
Jack V. Tu. 2005. “Comparison of Coding of Heart Failure and Comorbidities in Administrative
and Clinical Data for Use in Outcomes Research,” Medical Care 43 (2): 182–188.
Longley, Paul A., Mike Goodchild, David J. Maguire, and David W. Rhind. 2010. Geographic Infor-
mation Systems and Science. Hoboken, NJ: Wiley.
Singer, Judith D., and John B. Willett. 2003. Applied Longitudinal Data Analysis. New York: Oxford
University Press.

206
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207Cityscape: A Journal of Policy Development and Research • Volume 14, Number 3 • 2012
U.S. Department of Housing and Urban Development • Office of Policy Development and Research
Cityscape
Concentrated Out-Migration
Ron Wilson
U.S. Department of Housing and Urban Development
Graphic Detail
Geographic Information Systems (GIS) organize and clarify the patterns of human activ -
ities on the earth’s surface and their interaction with each other. GIS data, in the form of
maps, can quickly and powerfully convey relationships to policymakers and the public.
This department of Cityscape includes maps that convey important housing or commu-
nity development policy issues or solutions. If you have made such a map and are willing
to share it in a future issue of Cityscape, please contact ronald.e.wilson@hud.gov.
A primary question about the Housing Choice Voucher Program (HCVP) is, “Do participants move
far from their previous addresses when they relocate?” Most participants stay within the HCVP for
an average of 4 years, but a small percentage of participants stay much longer. Using CrimeStat 3.3,
I analyzed 2,891 HCVP participants whose Social Security numbers matched between 2000 and
2010 in the Baltimore metropolitan region to identify the mobility patterns of long-term participants.
1
Across the region, most long-term HCVP participants did not move far from their previous ad-
dress. Of those who relocated, 19 percent (556) moved slightly more than 1 mile and 50 percent
moved less than 3 miles. Most participants (2,720, or 94.1 percent) moved to another tract,
however, 24 percent (688) relocated at distances of more than 6 miles.
I identified a subgroup of participants from four tracts with a large out-migration-to-in-migration
difference.
2
One was in Baltimore city and the rest were exurban tracts near Essex, Randallstown,
and Severn (see exhibit 1).
3
From these tracts, 132 HCVP participants (4.6 percent) relocated to
other tracts.
1
I use two datasets in this analysis: the 2000 census geography and the 2010 Public and Indian Housing Information
Center from the U.S. Department Housing and Urban Development.
2
I selected these tracts by first subtracting destination counts from origin counts in a tract, and then identifying tracts with
extremely negative net migration—the first percentile.
3
The respective numbers of out-migrants were 65 (49.2 percent) for the tract near Essex, 32 (24.2 percent) for the tract
near Randallstown, 29 (22.0 percent) for the tract near Severn, and 6 (4.5 percent) for the tract in Baltimore city.
The views expressed in this article are those of the author and do not represent the official positions or
policies of the Office of Policy Development and Research or the U.S. Department of Housing and Urban
Development.

208
Wilson
Graphic Detail
Exhibit 1 reveals three mobility patterns from these four tracts that are different than mobility pat-
terns for the rest of the long-term HCVP participant population in the region. First, although some
participants from these four tracts relocated to the same tracts, many moved significantly longer
distances—indicated by the black lines—than their counterparts from other tracts; 34 participants
(25.7 percent) moved at least 3.9 miles, 66 (50.0 percent) moved more than 6.9 miles, and 33
(25.0 percent) relocated at distances of more than 10.3 miles. Second, most participants dispersed
into Baltimore city or the immediate vicinity. Third, many participants relocated to areas with high
voucher holder concentrations—indicated by the gray circles—except those who moved out of the
tract near Severn.
These results suggest a systematic reason for these single-tract, high-volume exits and the subsequent
similarities in the relocation patterns. Further analysis requires other data sources.
Exhibit 1
Census Tracts That Showed Significant Out-Migration of HCVP Participants Between
2000 and 2010 in the Central Baltimore Metropolitan Region (equal-interval
classification)
High-volume
relocation exits
Destination direction
and counts
Total relocations
Counts
1–4
5–8
9–11
12–14
2–12
13–23
24–33
34–43
HCVP = Housing Choice Voucher Program.

Concentrated Out-Migration
209Cityscape
Author
Ron Wilson is a social science analyst in the Office of Policy Development and Research at the U.S.
Department of Housing and Urban Development and an adjunct faculty member of the Geographic
Information Systems program at the University of Maryland, Baltimore County.
References
Levine, Ned. 2010. CrimeStat: A Spatial Statistics Program for the Analysis of Crime Incident Locations,
v 3.3. Houston: Ned Levine & Associates; Washington, DC: National Institute of Justice.

210
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Graphic Detail

211Cityscape: A Journal of Policy Development and Research • Volume 14, Number 3 • 2012
U.S. Department of Housing and Urban Development • Office of Policy Development and Research
Cityscape
The Impact of Limiting
Sellers Concessions to
Closing Costs
Alastair McFarlane
U.S. Department of Housing and Urban Development
Impact
A regulatory impact analysis must accompany every economically significant federal rule
or regulation. The Office of Policy Development and Research performs this analysis for
all U.S. Department of Housing and Urban Development rules. An impact analysis is a
forecast of the annual benefits and costs accruing to all parties, including the taxpayers,
from a given regulation. Modeling these benefits and costs involves use of past research
findings, application of economic principles, empirical investigation, and professional
judgment.
Summary of Impact Analysis
The National Housing Act requires the U.S. Department of Housing and Urban Development
(HUD) to adjust program standards and practices to operate the Mutual Mortgage Insurance Fund
(MMIF) on a self-sustaining basis. In a recent revised notice, “Federal Housing Administration Risk
Management Initiatives: Revised Seller Concessions” (HUD, Office of the Assistant Secretary for
Housing, 2012), the Federal Housing Administration (FHA) placed a ceiling on the closing cost
concessions that sellers can make to borrowers. The set of actions outlined in the revised notice
will reduce the FHA&rsquo;s net losses resulting from high rates of insurance claims. The total gain to
FHA is expected to range from $60 to $70 million annually. The additional social benefits from
preventing foreclosures, which are positively associated with seller concessions, are estimated at
$25 million. The combined compliance cost for homebuyers and sellers could range from $21 to
$97 million and depends a great deal on the rate of capitalization of concessions into sales prices.
The views expressed in this article are those of the author and do not represent the official positions or
policies of the Office of Policy Development and Research or the U.S. Department of Housing and Urban
Development.

212
McFarlane
Impact
Need for Policy Change
The immediate purpose of the policy change is to achieve the statutorily mandated 2-percent mini-
mum capital reserve ratio. The broader purpose, however, is to preserve both the historical role of
the FHA in providing a home financing vehicle during periods of economic volatility and HUD’s
social mission of helping underserved borrowers. FHA loans are in greater demand as a result of
the failure or withdrawal of private investors from the mortgage market. FHA’s share of new single-
family mortgages was about 17.0 percent (33.0 percent for all home purchase mortgages) in fiscal
year (FY) 2010, up from 3.4 percent in FY 2007. The dollar volume of insurance written jumped
from $77 billion in FY 2007 to $319 billion in FY 2010. The growth in the MMIF portfolio
over that 3-year period coincided with a set of difficult economic conditions, namely, continued
housing price declines and increasing unemployment levels. Together, these external conditions
increased the risk of additional losses to FHA.
An independent actuarial study (IFE, 2009) showed that the MMIF capital ratio had fallen to less
than its statutorily mandated 2-percent threshold. The study reported that FHA would likely sus-
tain significant losses from mortgage loans made before 2009 because of the high concentration of
seller-funded downpayment assistance mortgage loans and declining real estate values nationwide.
The capital ratio of the MMIF remains at 0.5 percent, less than the critical level (HUD, 2010).
Description of the Revised Notice for Reducing Seller
Concessions
The revised notice
1
revises the proposed cap on the amount of seller concessions that can be con-
sidered as offsets to actual closing costs rather than as inducements to purchase.
2
Seller concessions
include any payment toward a borrower’s closing costs and other fees by any other party with an
interest in the transaction, including the seller, builder, developer, mortgage broker, lender, or settle -
ment company. This notice makes the following changes to seller concessions on FHA mortgages:
• It limits the acceptable uses of seller concessions to (1) payments toward borrower closing
costs, (2) prepaid items, (3) discount points, (4) the FHA upfront mortgage insurance premium,
and (5) any interest rate buydown. Under this revised definition, seller payment supplements
for homeowners or condominium association fees, mortgage interest payments, and mortgage
payment protection plans are unacceptable to FHA.
1
FR-5404-F-04.
2
The proposed notice of July 15, 2010 (HUD, Office of the Assistant Secretary for Housing, 2010), consisted of three risk-
management initiatives directed at underwriting (McFarlane, 2010). The revised notice and this impact analysis address
seller concessions specifically, whereas the regulatory impact analysis for the proposed notice focused primarily on guidelines
for credit scores and loan-to-value ratios.

The Impact of Limiting Sellers Concessions to Closing Costs
213Cityscape
• It reduces the amount of seller concessions permitted as offsets to actual closing costs to 3 per -
cent
3
or $6,000,
4
whichever is greater, but not to exceed the borrower’s actual costs. This reduc-
tion in concession allowances does not apply to HUD homes or the Neighborhood Stabilization
Program, for which the allowance remains at 6 percent.
5
Default Risk and Seller Concessions
Sellers do not generally provide concessions without increasing the price of housing by a fraction
of the seller concessions. The increase in the sales price to the homebuyer, however, does not
mean that the property is worth more. The increase in the loan-to-value (LTV) ratio raises FHA’s
exposure to default risk. Consider the role of seller concessions on loan size in the following il-
lustrative example.
Suppose that someone wishes to buy a $350,000 home and that the required downpayment is
at least 3.5 percent (the minimum downpayment FHA allows), or $12,250. Suppose that closing
costs are $14,132, comprising a fixed cost of $4,000 and a variable cost of 3.0 percent of the size
of the loan.
6, 7
If the potential buyer has only $12,250 of cash on hand to close the deal, the seller
could assist the buyer by paying the closing costs.
The concession is not a simple donation, however; for every 1 dollar of seller concession, the seller
may elect to raise the price of the home by 1 dollar. In this particular example, the final sales price
to the buyer would be $364,132 for a home with a market value of $350,000 and a 100-percent
capitalization rate. The final LTV ratio would be 100.5 percent, rather than 96.5 percent. The LTV
ratio increases because the market value does not change, but the price paid by the buyer increases.
A higher LTV ratio increases the risk of default and eventual foreclosure. To prevent excessive risk,
conventional mortgage lenders have capped allowances for seller concessions at 3 percent of the
sales price on loans with LTV ratios similar to those of FHA loans.
In general, default and foreclosure typically arise when (1) homeowners have negative equity (the
value of the home is less than the outstanding principal balance on the mortgage) and (2) home-
owners experience a reduction in income and wealth because of job loss or an event such as a
medical emergency that renders making monthly mortgage payments difficult (HUD PD&R, 2010).
This difficult situation is often known as a double trigger.
3
The percentage is based on the lesser of sales price and appraised value.
4
To address potential future increases in closing costs, the $6,000 cap this notice establishes will be indexed to a measure of
inflation. The dollar limit may increase annually and at the same percentage rate as the FHA national loan limit floor.
5
In mathematical terms, the limitation on concessions is—Minimum of [closing cost, maximum of ($6,000, 3 percent of
property value)], where property value = minimum of (sales price, appraised value).
6
Total closing costs vary by a wide set of parameters such as the loan amount, sales price, and interest rate. In this article,
I choose to characterize closing costs as varying with loan amount.
7
The interaction among the variables is slightly more complex, especially if any of the variables depend on the sales price of a
home. See HUD PD&R (2011a) for a more indepth treatment.

214
McFarlane
Impact
Most observers argue that the level of negative equity must exceed some threshold before default
and foreclosure become a possibility (Bhutta, Dokko, and Shan, 2010). Strategic default is uncom-
mon for several reasons (HUD PD&R, 2010). Households always need shelter, and defaulting
induces search costs and the disruption of established patterns of living. Families also often expect
that housing values will rebound and thus interpret a relatively small negative equity situation as
temporary. Nonetheless, raising the LTV ratio increases the exposure to risk. For example, Bhutta,
Dokko, and Shan (2010) found that the median borrower does not strategically default but rather
defaults only after equity falls to -62 percent of the home’s value, yielding a 162-percent LTV ratio.
On balance, prevailing evidence suggests that negative equity alone (except in extreme cases) is
unlikely to trigger foreclosure.
The effect of seller concessions on the riskiness of a loan varies over the life of the loan. In the short
run, seller concessions reduce the borrower’s housing expense because of the large lump sum
pay ment made to the borrower (Cotterman, 1992). Over time, however, the net effect of seller
concessions will be to increase the probability of default and foreclosure. To confirm this finding,
Cotterman (1992) found that FHA’s default experience was worse on loans with seller contributions.
Woodward (2008) found that foreclosures are more frequent when seller contributions are higher.
In the revised notice (HUD, Office of the Assistant Secretary for Housing, 2012), HUD provided
statistical data illustrating a greater incidence of home loss for borrowers who received seller con-
cessions in excess of 3 percent. The notice provided further evidence of the relationship between
default risk and seller concessions, for loans originated in 2009. The defining metric is a failure
rate, which includes all loans that resulted in an insurance claim (by March 31, 2011), are pres-
ently in foreclosure processing, or have gone through the foreclosure process but the insurance
claim has not yet been filed or processed. Such failures are directly associated with losses to FHA
insurance operations.
8
Credit risk, within each loan amount category, is highest for loans with larger closing costs and
with larger concessions.
9
Credit risk rises faster and higher for loan amounts greater than $240,000
than for lower loan amounts, because closing costs and concessions each exceed 3 percent of prop-
erty value. Exhibit 1 (Table F of the revised notice) shows that, although the lowest risk loans are
those in the greatest loan amount category (greater than $360,000) when no concessions are pres-
ent, the highest risk is for the same category of loans when concessions are more than 4 percent of
property value and especially when they are more than 5 percent of property value.
8
HUD recognizes that not all loans for which a foreclosure process begins will result in the loss of the borrower’s home and a
claim payment from FHA. The various rates at which foreclosure actions have been initiated, however, do provide a valid
measure for differentiating credit risk across groups of loans.
9
See tables E, F, and G of HUD, Office of the Assistant Secretary for Housing (2012).

The Impact of Limiting Sellers Concessions to Closing Costs
215Cityscape
Economic Impact of Regulatory Change
Reducing the number of claims to FHA by limiting risky loans will contribute to restoring FHA’s
fiscal integrity and meeting the required 2-percent capital ratio. Quantifying the benefit of doing so
involves the problematic tasks of measuring the extent to which this notice increased FHA’s prob-
ability of survival and multiplying this change in probability by an estimate of the public benefit of
FHA endorsement activities. Other effects of the notice are easier to measure.
A government agency’s net revenue increase is usually treated as a transfer, because governments
traditionally raise revenue through taxes and fees. In the case of the seller concessions restriction,
the increase in FHA revenue occurs as the result of more rigorous underwriting practices that re-
duce the number of claims. FHA cannot alter the price it charges, but it can control costs through
risk management practices. The lower costs can be considered a benefit to FHA. The gain to FHA
is an eventual transfer to other parties. Reducing the number of the riskiest loans will enable FHA
to insure more loans at the same cost or return excess revenues to the U.S. Treasury. Additional
social benefits of reducing foreclosures are not captured in the estimated gain to FHA.
Compliance costs associated with this regulation include a higher loan cost for affected borrowers
and possibly a higher cost for affected homesellers. First, borrowers who experience a reduction in
seller concessions also experience an increase in the cost of their loans. Second, sellers who offer
concessions as an incentive to potential buyers will face a compliance cost.
Expected Impact of the Notice on the Number and Value of Loans
The revised notice is not expected to reduce the number of mortgages originated. The regulatory
impact analysis (RIA) of the July 15, 2010 proposed notice, however, assumed that risk management
initiatives would exclude some households from the benefits of homeownership. In the proposed
version of the notice (HUD, Office of the Assistant Secretary for Housing, 2010), the denial of access
to homeownership was primarily because of the introduction of a minimum credit score and maxi-
mum LTV ratio for FHA single-family mortgage insurance. A ceiling on seller concessions, which
Exhibit 1
Loan Amount
($ thousands)
Seller Concessions (% of Property Value
a
)
0
a
≤ 1 2 3 4 5 6 > 6 All
To-Date Percentage Failure Rates by Loan Amount and Seller Concession Rates,
2009 Loan Originations
≤ 180 0.72 1.12 1.01 1.03 1.18 1.41 1.58 2.15 1.04
181–240 0.62 0.67 0.73 0.87 1.04 1.09 1.45 1.71 0.79
241–360 0.70 0.79 0.82 1.03 1.48 1.85 1.51 2.27 0.88
> 360 0.58 0.91 0.76 1.15 1.53 2.24 6.70 0.00
b
0.93
All 0.66 0.77 0.80 0.94 1.14 1.32 1.66 2.02 0.89
a
Any amount up to $500 is considered 0. Other categories represent amounts greater than the next lower limit, up to the
percentage listed. Rows add to 100 percent.
b
Only 19 loans are in this cell.
Notes: Foreclosure action is completed and a claim filing is pending. Data as of March 31, 2011.
Source: U.S. Department of Housing and Urban Development, Office of Evaluation

216
McFarlane
Impact
is the subject of the revised notice, likely will not have a significant effect on mortgage origination.
Some commenters on the July 15, 2010 proposed notice disagreed with the proposition that limit-
ing seller concessions would not diminish access to homeownership. These commenters wrote that
many FHA borrowers require the seller’s contribution to proceed with the purchase of the home.
HUD recognizes that an across-the-board reduction in concession allowances could have a large
negative impact on the ability of low- and moderate-income households to purchase moderately
priced homes (lower loan amounts). As Tables A, B, C, and D from the revised notice show, how-
ever, the impact of any change to seller concessions would not be great. For example, 80 percent
of all loans have seller concessions not greater than 3 percent of home value. Table A of the notice,
however, highlights how fixed-cost factors (for example, appraisals, title services, inspections, and
flood and lien certifications) tend to create percentage amounts that are greatest for small-balance
loans. More than 70 percent of loans of up to $180,000 have closing costs in excess of 3 percent of
the property value.
The $6,000 limitation is generous to borrowers with loan amounts of up to $180,000. In that loan
value range, $6,000 is greater than the 90th percentile threshold of all borrower closing costs (see
Table D of the final notice). Thus, less than 10 percent of borrowers with loan amounts of less than
$180,000 would have concession allowances that are less than their actual closing costs. For borrowers
in the next loan amount category ($180,000 to $240,000), $6,000 nearly reaches the 75th percentile
of closing costs. The binding limit for borrowers with loan amounts of $195,000 or greater is not
$6,000. In that higher range, 3 percent of the property value is greater than $6,000 and becomes
the amount to compare with actual closing costs to determine maximum allowable concessions.
Exhibit 2 shows the actual effects of the proposed limitation when applied to the 2009 and 2010
loan originations this analysis uses.
10
Overall, the limitation would have affected just 13.4 per-
cent of home purchase loans. The dollar amount of the resulting excess contributions appears in
exhibit 2. For the lowest loan amount group, the median effect is less than $1,000; for the highest
loan amount group, it is more than $4,000.
Exhibit 2
Loan
Amount
($ thousands)
Loans
Affected
(N)
Loans
Affected
(%)
Median Reductions at Various Percentiles,
Affected Loans Only ($)
5th 25th 50th 75th 95th
Proposed Concessions Limitation, Effects by Loan Size Category, 2009–10 FHA-
Insured Loans
≤ 180 43,592 9.7 86 480 988 1,670 3,018
181–240 114,726 15.3 116 664 1,434 2,562 4,900
241–360 30,499 15.0 150 1,001 2,247 4,106 8,160
> 360 8,819 12.7 327 1,850 4,138 7,541 14,635
FHA = Federal Housing Administration.
10
See tables J and K of HUD, Office of the Assistant Secretary for Housing (2012).

The Impact of Limiting Sellers Concessions to Closing Costs
217Cityscape
Of borrowers receiving an FHA guarantee, 82 percent make the 3.5-percent minimum required
downpayment. HUD must therefore ensure that the notice adjusts allowable mortgage amounts
appropriately for what may actually be inducements to purchase. This notice may or may not affect
borrowers who make more than the minimum 3.5-percent downpayment.
Although HUD does not expect a ceiling on concessions to reduce the number of mortgages origi -
nated, I do expect a decrease in the dollar amount of the affected mortgage loans. The median
reduction in concessions ranges from $86 for the bottom 5th percentile of the smallest category of
loans to $14,635 for the top 95th percentile of the largest category of loans. Exhibit 2 illustrates
that the median reduction in concessions for the most common loan amount category ($181,000
to $240,000) is $1,434. Median reductions for the higher categories of loan amounts are $2,247
and $4,138.
Gain to FHA From More Rigorous Underwriting
The effects on FHA of the revised notice will be to shift borrowers into groups with lower failure
rates on average and decrease the number of loans with a greater share of concessions. For
example, most loans characterized by seller concessions equal to 6 percent of value will move to a
lower seller concessions category, except for the lower value loans, which are allowed a ceiling of
$6,000. In general, the proportion of loans with seller concessions will increase 3 percent or less.
Combining the projected change in the distribution of loans with the distribution of the failure rate
of loans can predict how the notice will affect failure rates of loans. The overall failure rate would
fall from 0.89 to 0.86 percent. From this average decline, it is possible to deduce the decline in the
affected group, which represents 13.4 percent of the total. If the failure rate of the unaffected group
did not change, then the failure rate of the affected group would have to fall by 0.22 percentage
points ((0.86-0.89)/0.134). HUD measured the failure rate of the affected group at 1.10 percent
before the implementation of the notice. The post-notice failure rate would be 0.88 percent, which
represents a 25-percent decline of failure rates. This result is consistent with Cotterman (1992),
who finds that seller concessions increase default rates by 25 to 60 percent.
The effect of the revised notice will be to reduce the default rate and thus decrease the claim rate,
which results in significant transfers to FHA. HUD bases its budget accounting on forecasts of
claim and prepayment rates calculated using the forecasting model from the independent actuarial
study of the MMIF (IFE, 2010). The actuarial models rely on 30 years of actual FHA experience
and are calibrated to produce loan-performance outcomes using forecasts of future economic
conditions. The following equation represents the expected net claim expense associated with any
given loan in any given year:
Expected claim amount = claim rate x loss rate x unpaid loan balance. (1)
The notice will affect the claim rate and loan balance for the group of borrowers affected by the
notice. The claim rate is the number of claims during a particular period divided by the total num-
ber of loans endorsed when an annual insurance cohort was underwritten. The most recent budget
forecasts an 8.11-percent cumulative claim rate for all mortgages (IFE, 2010).

218
McFarlane
Impact
The loss rate is the net loss after property sale recoveries, as a percentage of the unpaid balance on
the defaulting loan. Exhibit E-1 of the actuarial review (IFE, 2010) provides a time series of loss
rates. Loss rates were as low as 32 percent at the beginning of the 2000s but reached 61 percent by
2009. Integrated Financial Engineering’s estimates of the loss rate for 30-year mortgages originated
in 2011 range from 32 percent in the first year to 48 percent in the 30th year (IFE, 2010).
The revised notice has two expected consequences: (1) the balance of the affected loans will decline
as allowable seller concessions decline, and (2) the claim rate on affected loans will fall as the LTV
ratio declines. In the RIA of the notice (HUD PD&R, 2011a), PD&R calculated that the loan balance
will decline from $176,500 to $175,000
11
and that the cumulative claim rate over 30 years of the
affected loans will decline from 10.04 to 8.03 percent.
12
PD&R calculated that the present value of
expected claims will fall to $2,764, the present value of premium revenue will fall to $5,785, and
the net expected revenue to FHA will rise to $3,022. The notice results in an approximately $645
gain per loan to FHA when the discount rate is 3 percent.
An increase of net revenue from the subject loans provides a direct benefit to the financial status
of the MMIF. Over time, this increase of FHA revenue might also lead to transfers to remaining
FHA-insured borrowers through lower premium rates. The annual aggregate benefits would be ap-
proximately $69 million when the size of the group affected is 107,200 (13.4 percent of 800,000
FHA loans endorsed annually). When the social discount rate
13
is 7 percent, the per-loan transfer
is $562 and the aggregate transfers are $60 million.
Benefit of Regulatory Change: Preventing Foreclosures
One indirect benefit of the notice would be to diminish the resource losses from foreclosures.
Fore closures cause sizeable losses, which borrowers, lenders, property markets, and local govern-
ments bear. An estimate of the loss from a foreclosure net of all transfers can contribute to a more
comprehensive benefit-cost analysis. The estimate of the deadweight loss from a foreclosure could
include transaction costs, some portion of the distress discount on the sale of foreclosed property,
the negative effect of foreclosure on the value of surrounding properties, the loss to local govern-
ment, and some loss in the welfare of borrowers.
The lender may incur significant losses from a foreclosure, which is given by
Lender loss = loan balance + interest costs –
sales price of foreclosed property + transaction costs. (2)
11
The average FHA purchase loan originated in 2010 was $175,000 (HUD PD&R, 2011b). Assume a perfect capitalization
and an impact on seller concessions of $1,500. A change in seller concessions of $1,500 is approximately equal to the median
decline of $1,434 for home values of $181,000 to $240,000, a category that corresponds to the loan size of $175,000. Using
$175,000 as our original loan balance and adding $1,500 in capitalized concessions yields $176,500.
12
The post-notice claim rate of the affected loan is expected to be 0.99 (0.88/0.89) of the current claim rate. Given that the
cumulative claim rate over 30 years is 8.12 percent, this change will represent a decline from 10.04 to 8.03 percent.
13
To compute the net present value of the impact of a notice, it is necessary to discount future economic impacts. The higher
the discount rate, which is an estimate that provides the time value of money, the lower the present value of future impacts.

The Impact of Limiting Sellers Concessions to Closing Costs
219Cityscape
Loss severity typically ranges from 30 to 50 percent of the current loan amount, depending on the
state of the market and the size of the loan. For example, an average loss severity ratio (40 percent)
and a standard loan size ($175,000) would yield a lender loss of $70,000. A large part of the lender
loss comprises pure transfers, however. The goal of this discussion is to separate the real costs of a
foreclosure from transfers in which one party’s loss is another’s gain.
The Regulatory Impact Analysis of this notice (HUD PD&R, 2011a) finds that the total deadweight
loss is $40,730 (approximately three-fifths of the lender loss). For one cohort, the decline in fore -
closures is only 10 in the first year, with a peak of 320 in the fourth year, to total 1,280 foreclosures
avoided over a 30-year period. The discounted present value over 30 years of the avoided external-
ities is $47 million at 3 percent and $41 million at 7 percent. The per-loan public benefit averages
$437 at a 3-percent social discount rate or $385 at a 7-percent social discount rate over the
107,200 loans affected.
Reduction in Borrowers’ Welfare: Increasing the Cost of Homeownership
Seller concessions enable the borrower to allocate more of his or her funds toward the downpay-
ment instead of for closing costs. Seller concessions alleviate some burden of upfront costs and
facilitate an intertemporal transfer for the borrower. Thus, seller concessions permit the borrower
to leverage the purchase of housing. The multiplier effect of seller concessions on housing con-
sumption will increase with the maximum required LTV ratio. Not all borrowers, however, will
find seller concessions advantageous. The increase in the size of the loan, and possibly the mort-
gage interest rate (as the probability of default increases with the size of the loan), may outweigh
the benefit of reducing upfront costs. Overall, the limitation on seller concessions is estimated to
affect only 13.4 percent of the home purchase loans analyzed. Exhibit 2 shows the dollar amount
of the resulting excess contributions.
The regulation will distort the consumption of borrowers affected by the ceiling. A borrower who
otherwise would have accepted a higher level of seller concessions will lose from a ceiling that
restricts the amount of seller concessions that he or she may accept. Although seller concessions
come at the price of a larger loan and higher mortgage payments, the assistance provides an oppor -
tunity that the borrower would not have without the concessions. A restriction on the size of seller
concessions will affect borrowers who both (1) have a strong preference for housing versus non -
housing consumption and (2) discount the future heavily. The optimal level of housing expenditure
may be greater than what the borrower can afford without significant debt because of a household’s
demand for public services, home size, or desired length of commute. Such households will be
more likely to demand debt to finance their housing consumption. Households that the regulation
does not affect either spend less on housing or save to finance a greater portion of the upfront costs
of a home purchase.
One means of estimating the magnitude of the impact of a ceiling on concessions would be a cost-
effectiveness approach: to estimate the present value of the cost (mortgage payments and upfront
costs of the loan) of providing the same level of housing at different levels of seller concessions. A
reduction in seller concessions will increase the upfront costs but reduce the loan balance and thus
reduce future mortgage payments. Whether the borrower loses will depend to a large extent on the

220
McFarlane
Impact
size of a borrower’s discount rate relative to the mortgage interest rate. Borrowers with low discount
rates are not likely to take advantage of seller concessions unless they are severely liquidity constrained.
For a borrower with a 7-percent subjective discount rate and a 30-year fixed-rate mortgage at 5 per -
cent,
14
however, decreasing seller concessions by $1,500 will raise the present value of the cost of
the mortgage loan by approximately $200.
15
The aggregate loss to buyers would thus be $21 million
(13.4 percent X 800,000
16
X $200). The cost of reducing seller concessions would be lower when
the mortgage interest rate is higher because the corresponding reduction in mortgage payments
would be lower. The recent history of interest rates of less than 5 percentage points (see HUD PD&R,
2011b) would suggest that the previous estimate of 5 percent is a reasonable one (that is, closer
to what a borrower would pay than the Office of Management and Budget’s (OMB’s) estimated
7-percent private cost of capital).
Buyers have at least three potential responses to minimize the harm from reducing seller conces-
sions: (1) reduce or postpone housing purchase, (2) find a less expensive loan, and, similarly,
(3) ensure that they receive the full benefit of the seller concessions they receive. Thus, the $270
estimated loss per borrower loss is a maximum.
Cost of Regulatory Change: Effects of Restrictions on the Seller
The simplest way of thinking of a seller concession is as a transfer from the seller (or lender) to the
borrower. For the purposes of an economic impact analysis, however, that characterization may not
be completely accurate. Although the seller concession is literally a transfer from the seller to the
buyer, it is also a mutually beneficial transfer. Seller concessions, when sellers use them properly,
will make the difference between closing a home sale and losing one. In other words, an experienced
seller will set seller concessions so that the expected loss from losing the sale is equal to the conces -
sion itself. The financial loss to the seller would be because of the opportunity costs of additional
days on the market. Cotterman (1992) found that from one-half to three-fourths of seller conces-
sions are capitalized into the sales price of a home, suggesting that the uncapitalized portion repre-
sents the reduced holding cost from a more rapid sale. If the seller benefits from having the option
to offer contributions to closing costs, then the seller loses from the reduction of this flexibility.
17
Imperfect capitalization of seller concessions reflects situations in which sellers benefit from offer-
ing the concession. I summarize the effect as follows:
Market impact of reduced concessions = cost to borrowers + cost to sellers, (3)
14
See Exhibit D-1 of IFE (2010) for a forecast of mortgage interest rates.
15
Present value of loan cost = downpayment + closing costs - seller concessions + net present value of mortgage payments.
16
FHA predicts FY 2010 purchase loan volume to be 1.1 million. The FHA has recently forecast purchase loan volume in
subsequent years to be approximately 800,000. This revised number is less than the prediction of 1.3 million found in the
Actuarial Review (IFE, 2010). The first-time homebuyer tax credit affected previous estimates.
17
The seller can just as easily reduce the asking price to sell a house. For example, a study of a policy change in Massa-
chusetts that prevented relisting found that, after the policy change, sales prices in Massachusetts went down by around
$11,000 relative to prices in Rhode Island but that the average number of days on the market shortened by 18 (Tucker,
Zhang, and Zhu, 2009).

The Impact of Limiting Sellers Concessions to Closing Costs
221Cityscape
where the “cost to borrowers” is equal to the increased cost of a loan as a result of concessions
(taking into account the capitalization rate), and the increased cost to sellers is equal to
(1 – capitalization rate) X the reduction in seller concessions.
The share of transfers and efficiency loss will depend on the state of the market. For example, in
the preceding analysis, I assume a perfect capitalization of seller concessions. A slack real estate
market would lead to imperfect capitalization at, suppose, a 75-percent rate. Exhibit 3 illustrates
that the benefits to borrowers of seller concessions would be greater. Instead of a $200 borrower
loss per loan from a $1,500 reduction in concessions, the loss in transfers would be $530. Addi-
tional efficiency losses of $375 ($1,500x(1-0.75)) per loan would accrue to sellers.
18
One advantage to a loan originator of offering the closing cost concession is that the seller conces -
sion is not as transparent as a price reduction. Seller contributions to closing costs appear to be
another source of confusion and friction in the mortgage market; for each $100 the seller contrib-
utes, borrowers benefit in terms of reduced loan costs of roughly $70 from depositories and
large mortgage banks but closer to $40 when dealing with brokers (Woodward, 2008). Perfectly
informed borrowers who are diligent comparison shoppers should experience a $100 reduction
in loan costs for every $100 the seller contributes. Thus, when lenders contribute to seller closing
costs, the loss to the borrower will not be not as great as the preceding net present value calcula-
tion of $200 assumes; perhaps only one-half as much. Indeed, greater borrower attention to the
tradeoff between seller concessions and other loan costs could ensure that the borrower realizes the
full benefit of the seller concession.
18
The estimated combined effect on sellers and borrowers will be different from the change in concessions when the social
discount rate used to calculate present value (3 and 7 percent, as provided by OMB) is different from the real mortgage
interest rate paid by the borrower.
Exhibit 3
Impact
Capitalization Rate of Seller Concessions Into Sales Price
100 Percent 75 Percent
Discount Rate Discount Rate
3 Percent 7 Percent 3 Percent 7 Percent
Per
Loan
($)
All
Loans
($ millions)
Per
Loan
($)
All
Loans
($ millions)
Per
Loan
($)
All
Loans
($ millions)
Per
Loan
($)
All
Loans
($ millions)
Regulatory Impact of the Reduction of Seller Concessions
Gain to FHA 645 69 562 60 650 70 567 61
Benefit of foreclosures
prevented
230 25 203 22 230 25 203 22
Cost to borrowers 200 21 200 21 530 57 530 57
Cost to sellers 0 0 0 0 375 40 375 40
FHA = Federal Housing Administration.
Notes: Assumes a 5-percent mortgage interest rate, a $1,500 reduction in seller concessions, and 107,200 loans. Assumes
all homebuyers the notice affects to have 7-percent discount rates. Only the gain to FHA and benefit of foreclosures prevented
vary with the discount rate.

222
McFarlane
Impact
Summary of Economic Impact
The advantage of limiting seller concessions on FHA loans will be to decrease the LTV ratio on
affected loans and thus reduce the default risk for FHA. FHA will reduce the expected losses
stemming from high rates of insurance claims that are paid on loans with high levels of seller
concessions. The lower costs can be considered a benefit to FHA or the taxpayers. My estimates of
the net gain to FHA (and subsequent transfer to the U.S. Treasury) range from $60 to $70 million,
depending on the discount rate (see exhibit 3).
Reducing foreclosures has additional social benefits. Some deadweight costs (for example, distress
discount related to property damage and transaction fees) would already be included in FHA
severity loss estimate. Other deadweight costs, however (such as neighborhood externalities and
costs to local government) are social costs separate from the severity loss estimate. PD&R has esti-
mated the per-loan external costs at $230 (at a 3-percent discount rate) and $203 (at a 7-percent
discount rate).
Costs associated with this regulation include a higher loan cost for affected borrowers and pos-
sibly a higher cost for affected homesellers. First, borrowers who experience a reduction in seller
concessions also experience an increase in the upfront and interest costs of their loans. The loss to
borrowers can vary depending on assumptions concerning the capitalization of seller concessions
into home prices. When only a fraction of the seller concessions are capitalized, borrowers derive
greater benefit from seller concessions and thus lose more when concessions are limited. When
the market is tight, the loss to borrowers using seller concessions is $200 per loan. The cost to the
borrower rises to $530 per loan when the capitalization rate is 75 percent.
Sellers who offer concessions as an incentive to potential buyers will incur costs. The size of the
cost will depend on whether it is a buyers’ or sellers’ market, which the capitalization rate of
concessions will indicate. In a healthy market, the sellers’ cost of compliance will be close to non-
existent because incentives are not necessary to sell the home. In a tight market, the prospective
homebuyer recognizes that good homes go quickly. If borrowers want to leverage home purchases
through seller concessions, they will have to accept the sellers’ terms. In a slower market, however,
a seller would be willing to share some costs of seller contribution. The cost to a seller could be
$375 per loan. Combined, the two costs are $905 per loan in a slack market.
None of the aggregate effects meets the OMB threshold of economic significance (more than $100
million), although some come close. Moreover, these estimates are highly stylized: behavioral
responses would reduce the impact. Currently, the market is very slack and so the bottom half of
exhibit 3 would represent the impact of the notice. Over the next few years, the real estate market
will possibly become more vigorous, in which case, the top half of exhibit 3 would be more repre-
sentative of the impact of the notice.

The Impact of Limiting Sellers Concessions to Closing Costs
223Cityscape
Acknowledgments
The author thanks Chuck Capone of the U.S. Department of Housing and Urban Development,
Office of Evaluation, for providing the tables concerning the characteristics of Federal Housing
Administration loans and seller concessions. He also thanks Bill Reeder, Ross Rutledge, Mark
Shroder, and Ed Szymanoski for suggestions.
Author
Alastair McFarlane is director of the Economic Development and Public Finance Division at the
U.S. Department of Housing and Urban Development, Office of Policy Development and Research.
References
Bhutta, Neil, Jane Dokko, and Hui Shan. 2010. The Depth of Negative Equity and Mortgage Default
Decisions. Washington, DC: Federal Reserve Board of Governors.
Cotterman, Robert F. 1992. Final Report: Seller Financing of Temporary Buydowns. Washington, DC:
U.S. Department of Housing and Urban Development.
Integrated Financial Engineering, Inc. (IFE). 2010. Actuarial Review of the Federal Housing Adminis-
tration Mutual Mortgage Insurance Fund (Excluding HECMs) for Fiscal Year 2010. Report prepared for
the U.S. Department of Housing and Urban Development. Washington, DC: U.S. Department of
Housing and Urban Development.
———. 2009. Actuarial Review of the Federal Housing Administration Mutual Mortgage Insurance
Fund (Excluding HECMs) for Fiscal Year 2009. Report prepared for the U.S. Department of Housing
and Urban Development. Washington, DC: U.S. Department of Housing and Urban Development.
McFarlane, Alastair. 2010. “The Impacts of More Rigorous FHA Underwriting Guidelines,”
Cityscape 12 (3): 143–158.
Tucker, Catherine, Juanjuan Zhang, and Ting Zhu. 2009. Days on Market and Home Sales. NET
Institute Working Paper #09-16. New York: Networks, Electronic Commerce, and Telecommuni-
cations Institute.
U.S. Department of Housing and Urban Development (HUD). 2010 (November 15). Annual Report
to Congress Regarding the Financial Status of the FHA Mutual Mortgage Insurance Fund Fiscal Year
2010. Washington, DC: U.S. Department of Housing and Urban Development.
U.S. Department of Housing and Urban Development (HUD), Office of the Assistant Secretary
for Housing-Federal Housing Commissioner. 2012. “Federal Housing Administration (FHA) Risk
Management Initiatives: Revised Seller Concessions,” [Docket No. FR-5572-N-01]. Federal Register
77 (36): 10695–10707. Washington, DC: U.S. Government Printing Office.

224
McFarlane
Impact
———. 2010. “Federal Housing Administration Risk Management Initiatives: Reduction of Seller
Concessions and New Loan-to-Value and Credit Score Requirements,” [Docket No. FR-5404-N-01].
Federal Register 75 (135): 41217–41225. Washington, DC: U.S. Government Printing Office.
U.S. Department of Housing and Urban Development (HUD), Office of Policy Development and
Research (PD&R). 2011a. Regulatory Impact Analysis of Federal Housing Administration Risk Manage-
ment Initiatives: Revised Seller Concessions. FR-5572-N-01.
———. 2011b. U.S. Housing Market Conditions: 2nd Quarter 2011. Washington, DC: U.S. Depart-
ment of Housing and Urban Development, Office of Policy Development and Research.
———. 2010 (January). Report to Congress on the Root Causes of the Foreclosure Crisis. Washington,
DC: U.S. Department of Housing and Urban Development, Office of Policy Development and
Research.
Woodward, Susan. 2008. A Study of Closing Costs for FHA Mortgages. Report prepared with
the Urban Institute for the U.S. Department of Housing and Urban Development, Office of
Policy Development and Research. Washington, DC: U.S. Department of Housing and Urban
Development, Office of Policy Development and Research.
Additional Reading
Apgar, William C., and Mark Duda. 2005. Collateral Damage: The Municipal Impact of Today’s
Mortgage Foreclosure Boom. Report prepared for the Homeownership Preservation Foundation.
Minneapolis: Homeownership Preservation Foundation.
Brueckner, Jan K. 1986. “The Downpayment Constraint and Housing Tenure Choice: A Simplified
Exposition,” Regional Science and Urban Economics 16: 519–525.
Bunce, Harold, Alastair McFarlane, William J. Reid, and Kurt Usowski. 2009. “The Impact of
Mortgage Disclosure Reform Under RESPA,” Cityscape 11 (2): 117–136.
Campbell, John Y., Stefano Giglio, and Parag Pathak. 2011. “Forced Sales and House Prices,”
American Economic Review 101 (5): 2108–2131.
Immergluck, Daniel, and Geoff Smith. 2006. “The External Costs of Foreclosure: The Impact of
Single-Family Mortgage Foreclosures on Property Values,” Housing Policy Debate 17 (1): 57–80.
Union Bank of Switzerland. 2008. “Severity: Where Does It Come From?” UBS Mortgage Strategist,
August 12.

225Cityscape: A Journal of Policy Development and Research • Volume 14, Number 3 • 2012
U.S. Department of Housing and Urban Development • Office of Policy Development and Research
Cityscape
Using Dual Kernel Density
Estimation To Examine
Changes in Voucher Density
Over Time
Ron Wilson
U.S. Department of Housing and Urban Development
SpAM
SpAM (Spatial Analysis and Methods) presents short articles on the use of spatial
statistical techniques for housing or urban development research. Through this depart-
ment of Cityscape, the Office of Policy Development and Research introduces readers to
the use of emerging spatial data analysis methods or techniques for measuring geographic
relationships in research data. Researchers increasingly use these new techniques to
enhance their understanding of urban patterns but often do not have access to short
demonstration articles for applied guidance. If you have an idea for an article of no more
than 3,000 words presenting an applied spatial data analysis method or technique, please
send a one-paragraph abstract to ronald.e.wilson@hud.gov for review.
Abstract
The measurement of participants in the Housing Choice Voucher Program across time is
an important analytical step toward understanding their settlement patterns, particularly
whether they concentrate or deconcentrate. Many analyses of voucher-holder settlement
patterns employ some areal unit in which counts are divided by unit area to calculate
a density. This approach has methodological problems and produces less-than-accurate
results because it does not directly measure the locations of voucher holders. In this article,
I show how to apply a technique, known as Dual Kernel Density Estimation, to measure
directly the concentration of voucher-holder locations to produce more accurate results
about where voucher holders have concentrated and deconcentrated over time.
The views expressed in this article are those of the author and do not represent the official positions or
policies of the Office of Policy Development and Research or the U.S. Department of Housing and Urban
Development.

226
Wilson
SpAM
Introduction
Many housing and urban development problems are inextricably tied to place. Because of this link
to place, certain questions often arise. Will foreclosures concentrate and spread to neighboring ar-
eas through falling house prices? Will tax increases in one county send residents to nearby counties
to shop or relocate? Will crime displace to adjacent neighborhoods in the event of a concerted ef-
fort to break up a concentration of incidents? Answers to such questions require the measurement
of spatial relationships between places that classical statistical techniques are not capable of meas-
uring. In this premier article of SpAM, I demonstrate how to use a spatial smoothing technique to
identify changing patterns of voucher-holder concentration between two points in time.
Housing researchers are often concerned about the concentration of voucher holders. A typical
approach to measuring voucher-holder density change is by comparing areal densities (events per
acre or per square mile) at two different times using already defined political or administrative
units (for example, nations, states, counties, or census tracts). Chances are high that measuring
change with these units will produce less-than-accurate results, because it does not directly
measure the locations of voucher holders. In this article, I show how to apply a more accurate
technique, Levine’s Dual Kernel Density Estimation (DKDE), using the locations of housing choice
voucher holders in the Charlotte-Mecklenburg, NC metropolitan region for purposes of illustra-
tion. For a more detailed exposition, see chapter 8 in Levine (2010).
The Housing Choice Voucher Program (HCVP) enables low-income families to relocate to neigh-
borhoods of their choice. In 2010 alone, approximately 2.1 million families received assistance
through the HCVP.
1
One common concern about the relocation freedom that HCVP offers is that
participants will concentrate in certain neighborhoods. Research has shown that voucher holders
often relocate to neighborhoods comparable with those in which they lived before receiving as-
sistance (Freeman and Botien, 2002; Huartung and Henig, 1997; McClure, 2010; Pendall, 2000;
Varady, Walker, and Wang, 2001; Wang, Varady, and Wang, 2008).
Moving Beyond Measuring Density Calculations With
Areal Units
Many voucher-holder location analyses use census tracts to measure density. In a typical calcula-
tion of densities from areal units, a count of observations within the unit is divided by the unit
area. This approach has two main problems. First, the aggregation of observations to the areal units
forces an incorrect assumption that voucher-holder locations are evenly spread across the unit; the
larger the census tract, the more unrealistic the assumption becomes. Second, the variation in cen-
sus tract shapes will arbitrarily influence the unit within which an observation falls; this method
may split up groups of voucher-holder locations.
2
1
Public and Indian Housing Tenant-Based Rental Assistance: 2012 Summary Statement and Initiatives. http://portal.hud.
gov/hudportal/documents/huddoc?id=Tenant_BR_Assis_2012.pdf.
2
These problems are symptoms of the Modifiable Areal Unit Problem, which has adverse consequences for data analysis
because the unit of geography changes, but the observation data do not. See Openshaw (1994).

Using Dual Kernel Density Estimation To Examine Changes in Voucher Density Over Time
227Cityscape
With DKDE, single kernel density surfaces are created by interpolating estimates from a geographi-
cally distributed set of observations. Estimates are calculated by overlaying a grid system across a
geography in which the distance from each cell to every observation within a specified distance is
measured and weighted. Measuring and weighting is achieved by using a mathematical function
to create a symmetrical distance decay curve (kernel) that decreases from the cell origin. Exhibit 1
depicts the interpolation process.
3
Each observation within the bandwidth is measured and weighted based on how close it is to the
cell origin and where it corresponds on the distance decay curve. This process is completed for each
cell in the grid to produce an overall density surface of the geographic distribution of locations.
Using DKDE to measure density change has several advantages. First, voucher-holder locations are
weighted based on proximity to each other. Second, measurement is standardized using the same
mathematical function across the entire geography. Third, an overall geographic density surface
with gradients of continuous estimates is produced that summarizes clusters of points for easier
interpretation of patterns. Finally, our use of DKDE protects privacy by representing voucher
density as an areal estimate rather than as a collection of points.
In CrimeStat 3.3, five mathematical kernel functions weight distance decay differently.
4
A function
should be selected based on a theoretical, empirical, or other substantive reason for its use. I selected
the negative exponential function for this analysis, because the close-proximity weighting it empha -
sizes characterizes two substantive aspects of voucher-holder locations. The research cited previ-
ously suggested that (1) voucher holders tend to live in places where other voucher holders are in
close proximity and (2) voucher holders often do not move far from where they previously lived.
5
3
Exhibit 1 depicts the normal quartic function, but the same principles apply to the negative exponential function and the
other functions in CrimeStat 3.3.
4
Those functions are (1) normal, (2) quartic, (3) negative exponential, (4) triangular, and (5) uniform. For a description of
each operation, see pages 8.3 through 8.9 in Levine (2010).
5
The author’s unpublished work (Wilson, 2012) on vouchers in the Baltimore area shows that the median moving distance
from previous locations is about 3 miles.
Exhibit 1
The Kernel Density Estimation Process
Source: Adapted from Fischer, Leitner, and Staufer-Steinnocher (2001)
Cell origin
Observation
Bandwidth
Geography
Kernel

228
Wilson
SpAM
The negative exponential function creates a distance decay curve that forms a very narrow peak
at the cell origin, which rapidly and monotonically decreases up to a specified distance. Locations
closer to the cell are weighted more highly than those farther away; locations outside the specified
distance are excluded.
6
The negative exponential function is formally defined as
g
i
(x
j
) =
{∑
A·e
-K·d
ij
}
∈h
.
(1)
In this function, x
j
is a set of voucher-holder locations affecting our estimate of the areal density in
cell i; g
i
(x
j
) is the density estimate; h is a search distance threshold (bandwidth) that encompasses
the subset of voucher-holder locations within the bandwidth (that is, value = 0 if distance > h);
A is a rescaling constant that ensures the density estimates sum to the number of locations;
7
K is
an exponent constant set to 3, producing a steep decrease in the decay curve; and d is a distance
between the center of cell i and a location j within h. The estimate g
i
(x
j
) will be larger when
voucher-holder locations j are near cell i and smaller when voucher-holder locations j are farther
away from cell i.
One important factor in calculating the density estimate is the bandwidth size of the search
distance for identifying locations to measure. Two types of bandwidths—adaptive or fixed—can
be employed. An adaptive bandwidth, which is a changing search distance used to identify a
specified number of locations within its radius, is used when a need arises to account for changes
in the distribution of locations due to variation in the underlying geography; for example, popula-
tion density. A fixed bandwidth, which uses a constant search distance from each cell origin and
identifies a varying number of observations within its radius, is used to detect concentrations of
observations within a specified distance.
The type of bandwidth chosen affects the calculation of density estimates and is more important
than the type of distance decay (kernel) function selected, because bandwidth size will determine
how much smoothing will occur. With a larger bandwidth, estimates will generally be weighted
and distributed more evenly. Large bandwidths produce generalized density surfaces that identify
subregional clusters. Smaller bandwidths consolidate estimates and produce a more discretely
weighted and abruptly changing density surface that creates multiple noncontiguous local clusters.
As with the kernel function, the bandwidth size should be determined using a theoretical, empiri-
cal, or other substantive reason for its specification. In this analysis, I used a fixed bandwidth to
identify condensed groupings of voucher holders in close proximity to one another. The specified
distance was 2.5 miles, which was based on a distance analysis.
8
The DKDE technique creates two separate density grids for each distribution to represent the indi-
vidual geographic distribution of observations before the grids are combined. The first distribution
is voucher-holder locations in 2010 and the second distribution is voucher-holder locations in
6
This exclusion of observations is true for all the mathematical functions in CrimeStat 3.3, except for the normal function.
The normal function includes all observations across the geography.
7
A is initially set to 1 and is iteratively adjusted until the estimates sum to the number of locations. Exhibit 2 shows the
totals in this illustration.
8
This distance was derived using the Ripley’s K technique, in which the level of significant clustering dissipates. (Analysis
available from the author.)

Using Dual Kernel Density Estimation To Examine Changes in Voucher Density Over Time
229Cityscape
2000. Estimates between corresponding cells of the two grids are combined with any one of six
different mathematical operations.
9
I selected the relative difference operation for this analysis to
identify density changes between 2000 and 2010. This operation subtracts the density estimates in
2000 (secondary file) from the estimates in 2010 (primary file), producing divergent values greater
and less than 0 to depict areas of concentration and deconcentration of vouchers.
Measuring Concentration Change in the Charlotte-
Mecklenburg, NC Metropolitan Region
The number of voucher holders in the Charlotte-Mecklenburg, NC metropolitan region increased
significantly over the past decade, with most jurisdictions showing double-digit percentage
increases (see exhibit 2).
10
Exhibit 3 shows the density change output of the DKDE to reveal patterns of group concentration
and deconcentration. I used a standard deviation classification to thematically map the patterns
to show divergence from cells with little or no change in densities. Values greater than 1 standard
deviation from the mean (> 12.26) are depicted in dark gray, indicating increased density (con-
centration) and that fewer voucher holders appear per square mile in the second period than in
the first. Values less than 1 standard deviation from the mean (< -10.99) are depicted in light gray,
indicating decreased density (deconcentration) and that more voucher holders appear per square
mile in the second period than in the first.
Exhibit 3 reveals a wide dispersal of local and subregional voucher-holder residential patterns over
the past decade. Many areas of marked concentration and deconcentration occurred around the
larger towns of Concord, Gastonia, Kannapolis, Monroe, Rock Hill, and Salisbury.
9
Those operations are (1) ratio, (2) log ratio, (3) absolute difference, (4) relative difference, (5) absolute sum, and (6) relative
sum. For a description of each operation, see pages 8.27 through 8.29 in Levine (2010).
10
The voucher-holder point location data come from the U.S. Department Housing and Urban Development’s 2010 Public
and Indian Housing Information Center for the years 2000 and 2010.
Exhibit 2
Jurisdiction
Particpants in 2000 Participants in 2010 Change
N Percent N Percent N Percent
HCVP Participant Changes by County in the Charlotte-Mecklenburg, NC
Metropolitan Region, 2000 to 2010
Cabarrus County, NC 416 7.7 497 5.6 81 19.5
Charlotte-Mecklenburg County, NC 2,380 43.8 4,983 56.1 2,603 109.4
Gaston County, NC 924 17.0 1,215 13.7 291 31.5
Lincoln County, NC 252 4.6 290 3.3 38 15.1
Rowan County, NC 579 10.7 755 8.5 176 30.4
Union County, NC 233 4.3 293 3.3 60 25.8
York County, SC 648 11.9 856 9.6 208 32.1
Total 5,432 8,889 3,457 63.6
HCVP = Housing Choice Voucher Program.

230
Wilson
SpAM
Exhibit 3 also shows the concentration patterns are primarily situated around the town centers of
these towns. The patterns around Charlotte show an arc of concentration around the downtown
area, from the northwest to the southeast. Those concentrations happen to correspond with
areas that had the largest increases in new housing over the past 10 years. Several clusters of
deconcentration also emerge in the downtown area of Charlotte, which contains the city’s oldest
neighborhoods. Similar patterns of deconcentration appear in the central areas of Concord, Gasto-
nia, Salisbury, and Rock Hill, but not in Kannapolis or Monroe. The technique succinctly depicts
density changes at the neighborhood level, in both shape and size that would have been lost by
using census tracts or other predefined administrative units.
The research cited previously often found that voucher holders tend to concentrate in impoverished
neighborhoods. To explore this finding, census tracts with greater than 20 percent poverty (shown
in alternating black and white lines) were added to the map
11
and overlaid with the densities to
show how geographically specific clusters are in comparison with the areal units. The map in
Exhibit 3
Differential Density Change in HCVP Participant Densities in the Charlotte-Mecklenburg,
NC Metropolitan Region, 2000 to 2010 (standard deviation classification)
– 838.235 to – 34.237 (deconcentration)
– 34.236 to – 22.614
– 22.613 to – 10.991
– 10.990 to 12.256 (no change)
12.257 to 23.879
23.880 to 35.502
35.503 to 1,443.706 (concentration)
State boundary
Metropolitan counties
2000 and 2010
Relative Differences in Densities
11
Poverty data come from the 2005–2009 American Community Survey 5-year estimates.
HCVP = Housing Choice Voucher Program.

Using Dual Kernel Density Estimation To Examine Changes in Voucher Density Over Time
231Cityscape
exhibit 4 shows several clusters of voucher-holder concentration and deconcentration that cut
across the boundaries of high-poverty census tracts, illustrating the benefit of the DKDE output
with respect to identifying specific areas of concentration within census tracts.
Had the densities been calculated using census tracts, the concentration and deconcentration
patterns depicted would have been less precise. Using census tract boundaries can also segment
groups of voucher-holder locations and reduce the chances of the group being identified as a co-
herent cluster. For example, several clusters of concentration and deconcentration are split across
multiple census tract boundaries in Concord, Gastonia, and central Charlotte. In some instances
in which only a small portion of a cluster is within a census tract, any micropatterns of voucher-
holder locations would have been dilluted.
These results extend the change rates reported in exhibit 2 by showing exactly where voucher
holders have concentrated and deconcentrated beyond the change rates. Although significant
growth in voucher holders occurred outside central Charlotte, growth also took place around
many of the smaller town centers in the metropolitan region. Considerable levels of voucher-
holder concentration remain, however, in areas with higher poverty rates.
Exhibit 4
Differential Density Change in HCVP Participant Densities in Central Charlotte,
North Carolina, 2000 to 2010 (standard deviation classification)
– 838.235 to – 34.237 (deconcentration)
– 34.236 to – 22.614
– 22.613 to – 10.991
– 10.990 to 12.256 (no change)
12.257 to 23.879
23.880 to 35.502
35.503 to 1,443.706 (concentration)
State boundary
Metropolitan counties
Poverty tracts (> 20%)
2000 and 2010
Relative Differences in Densities
HCVP = Housing Choice Voucher Program.

232
Wilson
SpAM
Extensions of the DKDE and Resulting Output
The DKDE output is not limited to visualizing clusters. At the very least, the grid cells could be
coded and aggregated to areal units to create ratios between cells that have high estimates and
cells that do not to produce a more accurate level of density. This approach provides a marked
improvement in the precision of density as opposed to generalized densities with areal units.
More importantly, the density grids can be overlaid with other local-level geographic data to explore
microrelationships. For example, clusters from DKDE could be matched with parcel data to examine
relevant local factors, such as property type, housing amenities, assessment values, land use, and
code violations. Voucher-holder locations could also be analyzed in conjunction with the distribu-
tion of businesses to determine how extensively participants concentrate in areas with accessible
jobs or good-quality services. Local-level data may even be used with other mathematical functions
from DKDE analysis of voucher-holder locations to reveal additional spatial relationships.
Estimates from the DKDE can also be converted into change rates. Visualizing change rates will
enhance the understanding of the cluster patterns. To create a change rate for the cells in the surface,
the density values for the earlier period can be created with a Single Kernel Density Estimate (SKDE)
using the same settings as the DKDE. After the single-density surface is created, it can be joined
with the DKDE surface in a Geographic Information System, and rate change can be calculated for
each cell by taking the difference estimates from the DKDE analysis, dividing the estimates by the
SKDE estimates, and multiplying the quotient by 100 to produce a rate.
Many geographic analyses are still limited in precision because of the continued use of areal units.
Researchers with access to point-level data can use a technique like DKDE directly with point loca-
tions to more precisely depict the levels and changes of geographic microactivity of concentration
and deconcentration.
Acknowledgments
The author thanks Ned Levine from Ned Levine and Associates, Jay Lee from Kent State University,
and Fahui Wang from Louisiana State University for providing valuable comments toward improv-
ing this article.
Author
Ron Wilson is a social science analyst in the Office of Policy Development and Research at the U.S.
Department of Housing and Urban Development and an adjunct faculty member of the Geographic
Information Systems program at the University of Maryland, Baltimore County.

Using Dual Kernel Density Estimation To Examine Changes in Voucher Density Over Time
233Cityscape
References
Fischer, Manfred M., Michael Leitner, and Petra Staufer-Steinnocher. 2001. “Spatial Point Pattern
Analysis: Some Useful Tools for Analysing Locational Data.” In Geographischer Jahresbericht aus
Österreich, Vol. 58, edited by H. Wohlschlägl. Vienna, Austria: Vienna University, Institute for
Geography and Regional Research: 49–66.
Freeman, Lance, and Hilary Botein. 2002. “Subsidized Housing and Neighborhood Impacts: A
Theoretical Discussion and Review of the Evidence,” Journal of Planning Literature 16 (3): 359–378.
Huartung, John M., and Jeffrey R. Henig. 1997. “Housing Vouchers and Certificates as a Vehicle
for Deconcentrating the Poor: Evidence From the Washington DC Metropolitan Area,” Urban
Affairs Review 32 (3): 403–419.
Levine, Ned. 2010. “Chapter 8.” In CrimeStat: A Spatial Statistics Program for the Analysis of Crime
Incident Locations, v 3.3. Houston: Ned Levine & Associates; Washington, DC: National Institute of
Justice.
McClure, Kirk. 2010. “The Prospects for Guiding Housing Choice Voucher Households to High
Opportunity Neighborhoods,” Cityscape 12 (3): 101–122.
Openshaw, Stan. 1994. The Modifiable Areal Unit Problem. London, United Kingdom: Geo Books.
Pendall, Rolf. 2000. “Why Voucher and Certificate Users Live in Distressed Neighborhoods,”
Housing Policy Debate 11 (4): 881–910.
Varady, David P., Carole C. Walker, and Xinhao Wang. 2001. “Voucher Recipient Achievement of
Improved Housing Conditions in the U.S.: Do Moving Distance and Relocation Services Matter?”
Urban Studies 38 (8): 1273–1304.
Wang, Xinhao, David Varady, and Yimei Wang. 2008. “Measuring the Deconcentration of Housing
Choice Voucher Program Recipients in Eight U.S. Metropolitan Areas Using Hot Spot Analysis,”
Cityscape 10 (1): 65–90.
Wilson, Ron. 2012. Long-Term Mobility Patterns of Housing Choice Voucher Recipients in the
Baltimore Metropolitan Region, 2000 to 2010. Unpublished manuscript.
Additional Reading
Miller, Henry J. 2004. “Tobler’s First Law and Spatial Analysis,” Annals of the Association of American
Geographers 94 (2): 284–289.
Wang, Fahui. 2006. Quantitative Methods and Applications in GIS. Boca Raton, FL: Taylor & Francis.

234
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235Cityscape: A Journal of Policy Development and Research • Volume 14, Number 3 • 2012
U.S. Department of Housing and Urban Development • Office of Policy Development and Research
Cityscape
Comparative Analysis of
Best Practices of Sustainable
Communities: Adelaide,
Australia Case Study
Alven H. Lam
Brianne M. Mullen
U.S. Department of Housing and Urban Development
Abstract
Asia Pacific Economic Cooperation member economies produce 55 percent of the world’s
gross domestic product and 64 percent of global greenhouse gas emissions. Many cities in
this region are implementing sustainable development policies and practices to balance
economic growth, quality of life, and environmental protection. As part of a research
project examining best practices of sustainable communities in the Asia-Pacific region,
we chose Adelaide, Australia, as one of six case study cities. This article introduces the
larger project and summarizes the Adelaide case study. We also present key lessons learned
during the research process and suggest next steps for further exploration of this topic.
Foreign Exchange
Foreign Exchange, a department of Cityscape, reports on what the U.S. Department of
Housing and Urban Development’s Office for International and Philanthropic Innovation
has learned about new departures in housing and development policy in cities and suburbs
throughout the world that might have value if applied in U.S. communities. If you have
a recent research report or article of fewer than 2,000 words to share in a forthcoming
issue of Cityscape, please send a one-paragraph abstract to stewart.g.sarkozy-banoczy@
hud.gov.
The views expressed in this article are those of the author and do not represent the official positions or
policies of the Office of Policy Development and Research or the U.S. Department of Housing and Urban
Development.

236
Lam and Mullen
Foreign Exchange
Introduction
The Asia-Pacific region has experienced rapid urbanization in recent years, as well as the environ-
mental impacts that accompany such growth. Asia Pacific Economic Cooperation (APEC) member
economies produce 55 percent of the world’s gross domestic product, and they also generate 64
percent of global greenhouse gas emissions. Major cities are implementing sustainable develop-
ment policies and practices to balance economic growth, quality of life, and environmental health.
We conducted a comparative policy study to identify best practices of sustainable APEC cities and
review those strategies with the objectives of (1) identifying innovative policy tools or institutional
structure to implement the sustainable community agenda, (2) reviewing the impact of these sus-
tainable community projects on economic development and job creation, and (3) examining the
applicability of the identified best practices in the United States and developing countries.
Adelaide, Australia Case Study
Adelaide, Australia, was one of six case study cities—including Tokyo, Japan; Yokohama, Japan;
Tianjin Yujiapu, China; and Seoul, South Korea—that we chose for the research project based on
their strong policy agendas for sustainability and for their economic importance to the sustainable
community effort worldwide. We examined each city’s policies in the areas of energy, urban transit,
land use planning, and green building. This article summarizes the Adelaide case study.
Adelaide is the capital city of the state of South Australia, with a population of 1,200,000 that
is growing quickly (Australian Bureau of Statistics, 2011). South Australia has a parliamentary
government based on the model of the United Kingdom, and the Adelaide City Council manages
day-to-day governance in cooperation with state entities (McDougall and Vines, 2006). The case
study research identifies the following four primary program and policy interventions as most
noteworthy in the sustainability agenda in Adelaide.
1. National-Local Policy Framework for Climate Change Policy: Adelaide Green
City Sector Agreement
Adelaide established an intergovernmental framework to develop and implement the city’s sus-
tain able policies. In April 2010, the Adelaide City Council and the Minister for Sustainability
and Climate Change for South Australia entered into the Adelaide Green City Sector Agreement,
forming a joint commitment to “respond to climate change by pursuing development of the
City of Adelaide as an environmentally sustainable city” (ACC and Government of South
Australia, 2010: 1). South Australia set a target to reduce greenhouse gas (GHG) emissions by
at least 60 percent of 1990 levels by 2050; this agreement is a means of collaboration between
the two governments to achieve that goal.
2. Energy Management Action Plan
The Environmental Sustainability Strategy 2009–2012, the Adelaide City Council’s guiding docu -
ment for achieving sustainability, envisions Adelaide as an “energy efficient City that maximises
the use of renewable energy and local renewable energy generation” (ACC, 2009: 19). The

Comparative Analysis of Best Practices of Sustainable Communities:
Adelaide, Australia Case Study
237Cityscape
Energy Management Action Plan 2011—2014 carries out these objectives with specific strategies
(ACC, 2011b). For example, solar panels were installed on the iconic Rundle Lantern and Cen -
tral Market buildings, and the Adelaide Sustainable City Incentives Program provides incentives
to residents and community organizations to install solar panels and solar hot water systems.
Adelaide also participates in the CitySwitch Green Office program, a national partnership be-
tween businesses and local governments to reduce GHG emissions that office tenants produce
and to promote office energy efficiency (CitySwitch, 2011).

In Adelaide, at least 43 offices have
committed to participate in the program, representing about 18 percent of commercial office
space in the city.

In addition to establishing these priorities, the city participates in South Aus-
tralia’s GreenPower program, which sets a goal of buying renewable energy for 50 percent of
the state government’s electricity needs and at least 20 percent of the city government’s needs
by 2014 (Zeppel, 2011).
3. Sustainable Transport, Walking, and Cycling
In 2008, the South Australian state government began an unprecedented, decade-long invest-
ment in Adelaide’s public transport with a $2.6 billion investment to transform the city’s trans-
portation network into a vibrant, state-of-the-art system providing faster, greener, and more
efficient services for train, tram, and bus commuters (Government of South Australia, 2007).

This investment is making Adelaide one of the country’s most livable and sustainable cities,
constituting the largest single state government
investment in public transport in Australia.
Milestones of this project include 400 addi -
tional buses on the network linking local areas
to dedicated rail corridors and high-frequency
bus corridors; extended tram lines and addi-
tional trams; train line upgrades, including
electric trains; and a “smart” ticketing system.
These upgrades contribute to the South Austra-
lia’s Strategic Plan target of increasing the use of
public transport to 10 percent of metropolitan
weekday passenger vehicle distance traveled
by 2018 (SA, 2011).
As part of the overall investment, cycling
and walking are important sustainable
transportation options. Moving Adelaide:
Inte grated Movement Strategy 2012–22: Draft
for Consultation (ACC, forthcoming) is the
overarching strategic framework for achieving
a pedestrian- and cyclist-friendly city (exhibit
1).

In addition, the city’s Bicycle Action Plan
2011–13 (ACC, 2011a) aims to strengthen op-
portunities for people to cycle to, from, within,
and around the city and its park lands.
Exhibit 1
Moving Adelaide: Integrated Movement
Strategy 2012–22 (draft cover)
Source: City of Adelaide, Australia

238
Lam and Mullen
Foreign Exchange
4. Compact Development
The Adelaide Green City Sector Agreement (ACC and Government of South Australia, 2010) also
focuses on green buildings and urban design. One strategic direction is to support increased
population density and activity in the city and reduce the need to travel. Ongoing priority projects
to support this direction include (1) encouraging environmentally sustainable medium- to high-
density residential and mixed-use developments, including through the Land Management
Corporation and through zero-carbon neighborhoods, and (2) facilitating the Development Plan
Adelaide (City) (Government of South Australia, 2012) amendments that support increased en-
vironmentally sustainable development. The Adelaide City Council developed a Guide to Mixed
Use Development (ACC, 2008) to demon strate how to successfully achieve mixed use in the city.
Policy Comparison and Applicability
Although it is not possible to examine every measure in Adelaide at this time, and although this
article does not permit deeper discussion, it is clear that many of the strategies employed in Adelaide
to increase the city’s sustainable growth already parallel current initiatives in the United States,
although others could be further adapted. For example, Adelaide has taken many steps toward
promoting renewable energy, such as installing solar panels on public buildings and providing
incentives to the community for solar panels and hot water systems. Local governments across
the United States offer incentives for solar installations, including property tax incentives, rebate
programs, and loan programs, among others (NC State, 2012). Another parallel policy is the
implementation of mixed-use development. Adelaide is actively promoting its Guide to Mixed Use
Development for city developers and planners to use (ACC, 2008). Similar design manuals and
the promotion of these principles are evident in U.S. cities such as Colorado Springs, Colorado
(Colorado Springs, 2004); Fort Worth, Texas (Fort Worth, 2005); and Germantown, Tennessee
(Briley, 2007), among many others.
Several initiatives employed in Adelaide are well suited for implementation in the United States.
For example, Adelaide is taking countless steps to promote cycling and walking as sustainable trans -
portation options. Strategies include safely accommodating pedestrian movement and mandatory
safety specifications for on-street cycle lanes. (See exhibit 2.)
The U.S. Federal Highway Administration disseminates standards for bicycle traffic control devices,
such as provisions for signs, pavement marking, and signals, but these standards are limited in their
consideration of cyclists’ safety (DOT/FHWA, 2009).

Adopting strict regulations for bike lane design
and safety would be beneficial and promote this sustainable, economical mode of transportation in
U.S. cities.

Comparative Analysis of Best Practices of Sustainable Communities:
Adelaide, Australia Case Study
239Cityscape
Lessons Learned
The full paper for this research project, including case studies for all the cities, acts as a preliminary
best practices reference for implementing sustainable community strategies. The larger products and
present conclusions of this research include innovative individual strategies for promoting sustain -
ability, as well as several broader or crosscutting policies. Key lessons learned include (1) establishing
a governance framework with national and regional support for local policies is vital to the success
of a city’s mission to increase sustainability; (2) developing policy or financial incentive programs
are an important tool, not only for promoting strategies for reducing emissions, but also for fostering
a strong foundation for future initiatives through public-private collaboration; (3) implementing
transit-oriented development is a strategic approach to achieving compact and mixed land use,
transportation efficiency, and sustainability goals; (4) enhancing and improving public-private
partnerships are key to leveraging resources and creating consensus within communities; (5) in-
creasing implementation of energy strategies with promotion of renewable energy and carbon trad-
ing or green power purchasing can have long-term effects in cities of any size; and (6) monitoring
and indicator systems are important for assessing the successes and challenges of each individual
strategy and supporting knowledge sharing.
Among these larger sustainable policy measures are some specific creative actions that U.S. cities
should consider: (1) integrating pedestrian planning and transit—walkability in city planning,
(2) integrating green building and transportation in GHG-reduction strategies, (3) establishing
a smartphone bike-sharing system, and (4) instituting measurable indicators for monitoring.
Policy measures that would be most effective for the cities in the emerging economies include
(1) enhancing a policy implementation framework (national policy, local regulations, and action
plans); (2) enhancing a planning framework to incorporate high-density, mixed-use development
with transit, public-private sharing profits at station area—land value to finance infrastructure; and
(3) establishing baseline and monitoring indicators.
Exhibit 2
Example of Street Space Reallocation
Source: Moving Adelaide: Integrated Movement Strategy 2012—22 (ACC, forthcoming)
footpath footpath
parallel
parking
traffic
lane
traffic
lane
median
strip
traffic
lane
traffic
lane
parallel
parking
bicycle
lane
bicycle
lane
buf
fer
buf
fer

240
Lam and Mullen
Foreign Exchange
Next Steps
The full paper for this research, which the U.S. Department of Housing and Urban Development
produced, describes case study findings, best practices analysis, and U.S. applicability in detail; it
can be used as a preliminary best practices guide for localities (Lam and Mullen, 2012). A con densed
version of the paper was presented at the World Bank Research and Knowledge Symposium in Bar-
celona, Spain, in October 2012, and will be presented at other research and professional seminars
and conferences. Through this dissemination, academic collaboration and avenues for continued
research will be identified.
Although this type of best practices identification and analysis is valuable for developing sustain-
ability strategies in U.S. cities, it simply sets a vague foundation for the planning process. Potential
gaps in the research occur due to limited availability of information via city or national government
resources and a general lack of monitoring and comparative studies on international sustainable
development strategies. This research should be expanded to include analyses of the long-term
benefits, challenges, and other effects of individual strategies in the case study communities. Also,
further research is necessary to determine the specific applicability of strategies in U.S. cities—this
research entails examining each strategy closely to determine its environmental, economic,
developmental, and other effects and for identifying specific cities for implementation and the
conditions that best suit each individual strategy.
Authors
Alven H. Lam is the director of international research in The Office for International and Philan-
thropic Innovation at the U.S. Department of Housing and Urban Development, Office of Policy
Development and Research.
Brianne M. Mullen is an international graduate research fellow at the U.S. Department of Housing
and Urban Development.
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