A Journal of Policy

Development and Research

Youth homelessness

Volume 20, number 3 • 2018

U.S. Department of Housing and Urban Development Office of Policy Development and Research

Managing Editor: Mark D. Shroder

Associate Editor: Michelle P. Matuga

Advisory Board

Dolores Acevedo-Garcia

Brandeis University

Ira Goldstein

The Reinvestment Fund

Richard K. Green

University of Southern California

Mark Joseph

Case Western Reserve University

Matthew E. Kahn

University of California, Los Angeles

C. Theodore Koebel

Virginia Tech

Jens Ludwig

University of Chicago

Mary Pattillo

Northwestern University

Carolina Reid

University of California

Patrick Sharkey

New York University

Cityscape

A Journal of Policy

Development and Research

Youth Homelessness

Volume 20, Number 3 • 2018

U.S. Department of Housing and Urban Development

Office of Policy Development and Research

The goal of  Cityscape is to bring high-quality original research on housing and community development issues to scholars, government officials, and practitioners.  Cityscape  is open to all relevant disciplines, including architecture, consumer research, demography, economics, engineering, ethnography, finance, geography, law, planning, political science, public policy, regional science, sociology, statistics, and urban studies.

Cityscape  is published three times a year by the Office of Policy Development and Research (PD&R) of the U.S. Department of Housing and Urban Development (HUD). Subscriptions are available at no charge and single copies at a nominal fee. The journal is also available on line at 

huduser.gov/periodicals/cityscape.html.

PD&R welcomes submissions to the Refereed Papers section of the journal. Our referee process is double blind and timely, and our referees are highly qualified. The managing editor will also respond to authors who submit outlines of proposed papers regarding the suitability of those proposals for inclusion in  Cityscape. Send manuscripts or outlines to cityscape@hud.gov.

Opinions expressed in the articles are those of the authors and do not necessarily reflect the views and policies of HUD or the U.S. government.

Visit PD&R’s website, huduser.gov, to find this report and others sponsored by PD&R. Other services of HUD USER, PD&R’s Research and Information Service, include listservs, special interest and bimonthly publications (best practices, significant studies from other sources), access to public use databases, and a hotline (1–800–245–2691) for help with accessing the information you need.

Contents

Symposium

Youth Homelessness ...................................................................................................................... 1

Guest Editor: Matthew H. Morton

Guest Editor’s Introduction

Youth Homelessness: Research Insights for Coordinated Community Response .................... 3

Homelessness and Housing Experiences among LGBTQ Young Adults in Seven U.S. Cities .. 9

by Jama Shelton, Jonah DeChants, Kim Bender, Hsun-Ta Hsu, Diane Santa Maria, Robin Petering, 

Kristin Ferguson, Sarah Narendorf, and Anamika Barman-Adhikari

“Nothing is for free...”: Youth Logics of Engaging Resources While Unstably Housed ......... 35

by Gina M. Samuels, Christine Cerven, Susanna R. Curry, and Shantá R. Robinson

Linking the TAY-VI-SPDAT Tool to Housing Placements and Outcomes for Youth 

Experiencing Homelessness ...................................................................................................... 69

by Eric Rice, Monique Holguin, Hsun-Ta Hsu, Matthew Morton, Phebe Vayanos,  

Milind Tambe, and Hau Chan

What’s Next? A Grounded Theory of the Relationship Between Ontological Security,  

Mental Health, Social Relationships and Identity Formation for Young Adults in  

Supportive Housing  .................................................................................................................. 87

by Benjamin F. Henwood, Brian Redline, Sara Semborski, Harmony Rhoades, Eric Rice,  

and Suzanne L. Wenzel

Predictors of Running Away from Out-of-Home Care: Does County Context Matter?  ......... 101

by Amy Dworsky, Fred Wulczyn, and Lilian Huang

Developing a Coordinated Youth Housing Stability Program for Juvenile Courts ............... 117

by Sarah Cusworth Walker, Esteban Valencia, Asia Bishop, Michael Irons, and Arina Gertseva

Reflections from Canada: Can Research Contribute to Better Responses  

to Youth Homelessness?  .......................................................................................................... 139

by Stephen Gaetz

International Commentary: Some Reflections on the Policy History of Youth  

Homelessness in Australia ...................................................................................................... 147

by David MacKenzie

Cityscape

iii

Contents

Refereed Papers ................................................................................................................... 157

From Foreclosure to Eviction: Housing Insecurity in Corporate-Owned  

Single-Family Rentals  ............................................................................................................. 159

by Elora Lee Raymond, Richard Duckworth, Benjamin Miller, Michael Lucas, and Shiraj Pokharel

School Performance of Schools Assigned to HUD-Assisted Households .............................. 189

by Brent Mast

Departments ......................................................................................................................... 223

Affordable Design

2018 Innovation in Affordable Housing Student Design and Planning Competition:  

Whittier Falls in Dover, New Hampshire ............................................................................... 225

by Regina C. Gray

Data Shop

The Housing and Children’s Healthy Development Study .................................................... 237

by Sandra Newman and Tama Leventhal

Street Vending in the United States: A Unique Dataset from a Survey of Street  

Vendors in America’s Largest Cities ....................................................................................... 245

by Dick M. Carpenter II

Policy Briefs

Performance of Small Public Housing Authorities: Opportunities and Barriers .................. 257

by Andrew J. Greenlee, Han Bum Lee, and Paul E. McNamara

Graphic Detail

Intersecting Opportunity Zones with Vacant Business Addresses ........................................ 277

by Alexander Din

Evaluation Tradecraft

Talking to Landlords ............................................................................................................... 281

by Philip M.E. Garboden and Eva Rosen

iv Volume 20 Number 3

Symposium

Youth Homelessness

Guest Editor: Matthew H. Morton

Cityscape: A Journal of Policy Development and Research •  Volume 20, Number 3 • 2018

Cityscape

1

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

2

Youth Homelessness

Guest Editor’s Introduction

Youth Homelessness: Research 

Insights for Coordinated 

Community Response

Matthew H. Morton

Chapin Hall at the University of Chicago

This symposium brings together research from different methods and perspectives to inform coordinated community responses to youth homelessness. Coordinated response efforts have played important roles with other populations experiencing homelessness (for example, veterans and chronic homeless adults). In most communities, however, the concept is more nascent for addressing homelessness among youth and young adults, and systems and services need to be attuned to young people’s circumstances and preferences.

Homelessness is not unique to youth and young adults, but it does have unique implications for this developmental period of the lifecycle (Gaetz et al., 2013). Neuroscience and developmental research have shown that brain structure changes into early adulthood and that adolescence and young adulthood may be a particularly sensitive period for socioemotional development (Dumontheil, 2016; Fuhrmann, Knoll, & Blakemore, 2015).

High brain plasticity and sensitivity to social cues during youth present both opportunities and vulnerabilities. With positive, stable environments and social supports, adolescence and young adulthood offer a highly formative period for gaining skills and experiences that pave pathways into healthy and productive adulthood. On the other hand, such a significant developmental period becomes a vulnerability in the context of homelessness, instability, and exposure to related trauma. For example, Australian researchers found that the longer young people experienced homelessness, the harder it was for them to escape as they became further entrenched in street culture and the social risks associated with instability in the absence of safe and stable housing and positive connections (Johnson and Chamberlain, 2008). They also found that youth homelessness was the single most common pathway into adult homelessness, underscoring the importance of early intervention at this stage (Chamberlain and Johnson, 2013).

Recent U.S. efforts have expanded our understanding of homelessness among youth and young adults and provided new opportunities to make greater headway. These include significant research undertakings as well as policy actions supporting more coordinated, systems-level actions to address youth homelessness.

Cityscape: A Journal of Policy Development and Research •  Volume 20, Number 3 • 2018

Cityscape

3

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Morton

Voices of Youth Count, for example, is a national research initiative led by Chapin Hall at the University of Chicago on youth homelessness, from which results have recently started emerging. 

Among other research components, it included a nationally representative population-based survey to capture prevalence and incidence of homelessness among youth and young adults, ages 13 to 25.

The researchers estimated that at least 1 in 30 adolescent minors (ages 13-17), and 1 in 10 young adults (ages 18-25), experience some form of homelessness during a 1-year period (Morton, Dworsky, and Samuels 2017; Morton et al., 2018). While these experiences vary in risk, frequency, and duration, they nonetheless reflect a broader and more hidden challenge than previously documented. Furthermore, the research underscored substantial inequities beneath the surface of the problem.

American Indian and Alaska Native, Black, and Hispanic youth, and young people who identify as lesbian, gay, bisexual, transgender, or queer (LGBTQ), were far more likely than non-Hispanic White youth who identified as cisgender and heterosexual to face homelessness. The evidence points to the need to understand and address systemic inequities that underlie much of our nation’s youth homelessness challenge. Relatedly, young people who lack a high school diploma and those with histories of child welfare and/or juvenile or criminal justice systems involvement—where the same racial and ethnic minorities are also disproportionately represented—are at much greater risk for experiencing homelessness (Morton, Dworsky, and Samuels 2017).

Since 1974, when Congress first passed what is now known as the Runaway and Homeless Youth Act, federal policy has formally acknowledged the need to support young people experiencing homelessness. Among other things, this legislation came to include a set of federally funded programs administered by the U.S. Department of Health and Human Services (HHS) to help address youth homelessness, including the street outreach program, the short-term basic centers program for younger youth, the longer-term transitional living and maternity group home programs for older youth, and a national switchboard for runaway youth to seek help. Later, the Every Student Succeeds Act of 2015 expanded on the McKinney-Vento Act’s Education for Homeless Children and Youth program to provide additional supports for students experiencing homelessness to enroll in and attend school, complete their high school education, and continue on to higher education.

Recent policy actions have placed increasing emphasis on coordinated responses to the 

complex challenge of youth homelessness. In 2012, the U.S. Interagency Council on 

Homelessness (USICH) amended the national plan to end homelessness to include a specific Federal Framework to End Youth Homelessness, outlining steps that need to be coordinated across federal agencies to advance the goal of ending youth homelessness by 2020. More recently, USICH released updated  Criteria and Benchmarks for Achieving the Goal of Ending Youth Homelessness (USICH, 2018). The document outlines essential elements and metrics for communities’ collective efforts to address youth homelessness, underscoring the importance of pivoting from program-based approaches to coordinated, cross-agencies and cross-systems identification and service delivery models better suited to match the scale and complexity of the problem.

4

Youth Homelessness

Youth Homelessness: Research Insights for Coordinated Community Response Further, over the last 2 years, the U.S. Department of Housing and Urban Development (HUD) has administered the Youth Homelessness Demonstration Program (YHDP), which involves grant funds and technical assistance awarded through a competitive selection process to communities for advancing innovative, coordinated responses intended to dramatically reduce the number of youth who experience homelessness in those communities. HUD has also provided funding for technical assistance to a number of communities across the country to devise and implement 100-day challenges—brief, coordinated, catalytic efforts—to build momentum and make measurable progress in addressing youth homelessness locally.

Taken together, emerging national evidence on the scale, scope, and characteristics of youth homelessness, coupled with new federal direction and resources elevating collective, cross-systems efforts to address the challenge, present a unique moment for our country. While much more is needed, communities have unprecedented evidence, guidance, and (in some cases) resources as impetus to craft and execute coordinated strategies to make faster progress toward ending youth homelessness.

This  Cityscape Symposium responds to this national moment.

We invited new and relevant research that, in a variety of ways, and from different perspectives, could inform communities’ efforts to devise coordinated responses to addressing this problem. In addition, we invited international commentaries from leading subject matter experts in Australia and Canada to give reflections that would allow readers to consider the findings from this symposium’s collection of studies in the context of how other countries have come to understand and address the issue of youth homelessness.

Each research paper was subject to double-blind peer-review by at least three scholars with relevant expertise. All manuscripts were recommended by reviewers for acceptance conditional on varying degrees of revisions, and the guest editor reviewed all final manuscripts for satisfactory integration of, or response to, reviewers’ feedback. We greatly appreciate the time and valuable inputs from 19 

reviewers, which contributed to a stronger symposium.

The first two papers in the symposium (Shelton et al. and Samuels et al.) help to deepen and anchor our understanding of youth homelessness with respect to the roles of young 

people’s complex identities. As discussed previously, a growing evidence base underscores that homelessness does not afflict everyone equally, even when we control for income or poverty. 

For communities and policy-makers to make serious strides toward ending youth homelessness, there needs to be a stronger understanding of, and grappling with, the systemic inequities that disproportionately push youth of color and LGBTQ youth into homelessness.

Shelton and colleagues draw lessons from a seven-city survey of LGBTQ young adults experiencing homelessness. Given the targeted and relatively large sample, the study offers unique insights into understudied subpopulations, including bisexual and transgender young people specifically, and the intersections of race and ethnicity with sexual orientation and gender identity. For example, while being kicked out or asked to leave the family or foster home was the most commonly cited cause of homelessness among LGBTQ young people as a whole, this was particularly common for Cityscape

5

Morton

transgender young people. Further, transgender young adults commonly faced a double burden of discrimination related to both gender identity and sexual orientation. Similarly, LGBTQ young people who identified as Black or Hispanic reported high levels of discrimination related to both their LGBTQ-identity and to their race or ethnicity. These findings underscore the importance of understanding how intersecting identities can shape and compound young people’s experiences of exclusion in their day-to-day lives.

Through qualitative and quantitative methods based on indepth interviews in five communities with young people experiencing homelessness, Samuels and colleagues explored young people’s experiences with navigating services and supports in their communities. The analysis delivers a nuanced understanding of “youth logics of engagement” with formal and informal resources that are informed by weighing perceived emotional, psychological, and relational risks associated with specific services against the risks of continued homelessness. Selective engagement of services was the most common form of engagement among youth in the sample, and identities like being LGBTQ 

played into young people’s decisions about whether and when to engage available resources.

The research reveals how young people face multiple layers of discrimination related to different marginalized identities in the contexts of their families, communities, employment, and housing markets. It is important that coordinated responses to youth homelessness involve hiring, training, and rewarding staff across the continuum of services in accordance with their ability to ensure that young people feel safe, affirmed, and well-supported with respect to all of their identities—especially those identities that are frequently subject to discrimination and stigmatization.

The subsequent two papers (Rice et al. and Henwood et al.) include new evidence for systems providing housing-based interventions to young people experiencing homelessness. Rice and colleagues directly examine a key tenant of system-level crisis response: coordinated entry and assessment for youth experiencing homelessness and seeking housing and support. Based on intake assessment and longitudinal administrative data from 16 communities in 10 states, the study explored communities’ use of scores from a common risk assessment tool—the TAY-VI-SPDAT: Next Step Tool for homeless youth—and how well the scores predicted young people’s risk of remaining homeless or returning to homelessness once housed. Overall, the authors find that the instrument successfully identifies young people with higher odds of returning to homelessness without formal housing intervention, as well as promising evidence of even young people assessed as high-risk remaining out of the local homelessness system once placed into housing and service programs.

Henwood and colleagues’ qualitative study offers a more vivid understanding of the changes that occur as young people participate in one of these types of housing programs, permanent supportive housing. The research reveals how supportive housing fosters a sense of “ontological security” characterized by a sense of well-being that stems from a sense of constancy, control, and routine. This, in turn, enables young people to engage in positive identity formation and cultivation of different types of social relationships. The research also points to opportunities for supportive housing interventions to better mitigate the risk of some participants still feeling insecurities about the future and social isolation.

6

Youth Homelessness

Youth Homelessness: Research Insights for Coordinated Community Response The last two papers of the series (Dworsky et al. and Walker et al.) investigate two broader public systems that are critical to coordinated efforts to ending youth homelessness: child welfare (Dworsky et al.) and juvenile justice (Walker et al.). While a robust, youth-sensitive crisis response system is critical to effective and efficient  outflow of youth from homelessness, this, by itself, does little to curb  inflow  into homelessness.

To this end, Dworsky and colleagues analyzed a large, multi-state foster care data archive and found that a sizeable share (17 percent) of youth who entered foster care as adolescents ran away during their first out-of-home spell. In turn, running away from foster care is a well-documented risk factor for more entrenched forms of future homelessness. Equity concerns reemerge, with Black and Hispanic youth having been more likely than non-Hispanic White youth to run away from care. Moreover, adolescents who had greater instability in foster care placements, and those placed in congregate care settings, were at greater risk for running away. While these analyses are observational in nature (we cannot assume causality between placement instability or congregate care arrangements and running away, though the associations are clear), the findings point to the potential for avoidance of these situations for youth in foster care helping to mitigate young people’s risk of running away and, ultimately, falling into homelessness.

Walker and colleagues took a mixed methods approach that explored both administrative and qualitative data. These researchers used juvenile court data from Washington state and qualitative data drawn from stakeholder meetings to better understand the decision-making circumstances related to addressing the risk of justice-involved youth for homelessness. Here again, we observe high rates of running away or being kicked out of the home, with 20 to 50 percent of juvenile court-involved youth reporting at least one episode. The qualitative research elevates tensions for a juvenile justice system addressing issues that can be seen as outside of its mandate, as well as needs and opportunities to improve identification of youth experiencing, or at risk for, homelessness, and connecting those young people with intensive family interventions to mitigate risks.

In their international commentaries, both Gaetz (Canada) and MacKenzie (Australia) speak to how the symposium papers’ findings resonate or differ from their own country contexts. Critically, they both highlight the pivoting of national research and policy frameworks toward a greater emphasis on prevention of youth homelessness in coordinated efforts involving multiple public systems.

This symposium’s papers collectively draw attention to the many facets and complexities of comprehensive coordinated responses to the challenge of youth homelessness. They make it clear that the problem will not be solved by quick fixes or simplistic interventions.

The research, however, should also inspire a sense of optimism. Shelton et al. and Samuels et al. both find resilience among even the most vulnerable young people and willingness to engage supports when delivered in ways that counter the discrimination and stigma they face elsewhere in society and value their individual agency. Rice et al. reveal promising results for housing-based resources and the most commonly used assessment tool to help allocate them for youth. Henwood et al. illuminate the important sense of security and well-being that housing stability makes possible for young people during a key developmental stage. Articles by Dworsky et al. and Walker et al. both point to broader public systems beyond homelessness and housing systems that have Cityscape

7

Morton

the potential to make major contributions to curbing the inflow of youth into homelessness. This would reduce the burden on crisis response systems in the future while helping young people to get onto a path to thriving earlier. Gaetz and MacKenzie both elucidate country examples of how political paradigms and will, influenced by research and advocacy over time, can in fact move toward a big picture, comprehensive approach to ending youth homelessness that includes a strong focus on upstream actions.

Much remains to be done, but the efforts and investments are worth it for young people, their families, and the communities in which they live. This symposium should be leveraged as a starting point for national and local dialogue on what we know, and what we still need to know, to advance momentum for coordinated responses to ending youth homelessness.

References

Chamberlain, C., and G. Johnson. 2013. “Pathways into adult homelessness,”  Journal of Sociology 49 

(1): 60–77.

Dumontheil, I. 2016. “Adolescent brain development,”  Current Opinion in Behavioral Sciences 10: 39–44. doi: https://doi.org/10.1016/j.cobeha.2016.04.012.

Fuhrmann, D., L.J. Knoll, and S.J. Blakemore. 2015. “Adolescence as a Sensitive Period of Brain Development,”  Trends in Cognitive Sciences 19 (10): 558–566. doi: https://doi.org/10.1016/j.

tics.2015.07.008

Gaetz, S., B. O’Grady, K. Buccieri, J. Karabanow, and A. Marsolais, eds. 2013.  Youth Homelessness in Canada: Implications for Policy and Practice. Toronto: Canadian Homelessness Research Network Press.

Johnson, G., and C. Chamberlain. 2008. “From youth to adult homelessness,”  Australian Journal of Social Issues 43 (4): 563–582.

Morton, M.H., A. Dworsky, and G.M. Samuels. 2017.  Missed opportunities: Youth homelessness in America. National Estimates.  Chicago, IL: Chapin Hall at the University of Chicago.

Morton, M.H., A. Dworsky, J.L. Matjasko, S.R. Curry, D. Schlueter, R. Chávez, and A.F. Farrell. 

2018. “Prevalence and Correlates of Youth Homelessness in the United States,”  Journal of Adolescent Health 62 (1): 14–21. doi:10.1016/j.jadohealth.2017.10.006.

United States Interagency Council on Homelessness (USICH). 2018.  Criteria and Benchmarks for Achieving the Goal of Ending Youth Homelessness.  Washington, DC.

8

Youth Homelessness

Homelessness and Housing 

Experiences among LGBTQ Young 

Adults in Seven U.S. Cities

Jama Shelton

Silberman School of Social Work, Hunter College

Jonah DeChants

University of Denver

Kim Bender

University of Denver

Hsun-Ta Hsu

University of Missouri School of Social Work

Diane Santa Maria

University of Texas Health Science Center at Houston

Robin Petering

University of Southern California

Kristin Ferguson

Arizona State University

Sarah Narendorf

University of Houston

Anamika Barman-Adhikari

University of Denver

by Jama Shelton, Jonah DeChants, Kim Bender, Hsun-Ta Hsu, Diane Santa Maria, Robin Petering, Kristin Ferguson, Sarah Narendorf, and Anamika Barman-Adhikari

Cityscape: A Journal of Policy Development and Research •  Volume 20, Number 3 • 2018

Cityscape

9

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Shelton, DeChants, Bender, Hsu, Santa Maria, Petering, Ferguson, Narendorf, and Barman-Adhikari Abstract

Research demonstrates the challenges faced by lesbian, gay, bisexual, transgender, queer, and questioning (LGBTQ) young adult(s) (YA) experiencing homelessness, including preliminary evidence regarding the unique barriers and circumstances of the subpopulations within the broader category of LGBTQ. Few research efforts have investigated the differential experiences between identity and racial subgroups within the population of LGBTQ YA experiencing homelessness, however. This study uses a seven-city sample of 442 LGBTQ YA experiencing homelessness to examine the homelessness and housing experiences of LGBTQ YA—including specific experiences of marginalized and understudied subgroups—and compare these experiences across racial subgroups. Analyses revealed LGBTQ YA most commonly experienced homelessness because they were kicked out/asked to leave the home of their parents, relatives, foster or group homes. This experience was more common among transgender YA. Other differential experiences related to duration of homelessness, discrimination, and stress were reported across subgroups. This study fills a critical gap in the literature by identifying differential experiences of subgroups within the LGBTQ YA homeless population that can better inform program and policy interventions designed to prevent and end homelessness among YA.

Background and Purpose

LGBTQ YA are disproportionately represented in the population of youth experiencing 

homelessness in the United States (Choi et al., 2015; Durso and Gates, 2012; Lankenau, et al., 2005; Maccio and Ferguson, 2015; Quintana et al., 2010; Van Leeuwen et al., 2006). A recent report from Voices of Youth Count estimates that LGBTQ YA have a 120 percent increased risk of experiencing homelessness compared to cisgender and heterosexual YA (Morton et al., 2017). Likewise, youth of color, specifically Black youth, are at heightened risk of experiencing homelessness and are overrepresented both in the general population of youth experiencing homelessness (Morton, et al., 2017) and the population of LGBTQ youth experiencing 

homelessness (Choi et al., 2015; Maccio and Ferguson, 2016).

Structural barriers and systemic oppression affect the experiences of LGBTQ and youth of color experiencing homelessness. They frequently face barriers to housing and employment, as they are subjected to care rooted in heterosexism and cisgenderism, as well as widespread discrimination and misunderstanding from service providers and their service using peers (Abramovich, 2016; Shelton, 2015; Cochran et al., 2002; Gangamma et al., 2008; Gattis, 2013).  Heterosexism refers to the systematic marginalization of lesbian, gay, and bisexual people and the structural favoring of heterosexual people and relationships (Ansara and Hegarty, 2012).  Cisgenderism can be understood as the belief system that produces transphobia (Pyne, 2011). This prejudicial ideology delegitimizes the inherent knowledge people possess of their own genders and their own bodies (Ansara and Berger, 2016) and presumes that all people are cisgender. Black LGBTQ YA experiencing homelessness must also contend with systemic racism and its subsequent effects, such as racial profiling, police and community harassment, and racial microaggressions (Gattis and Larson, 2017). Of concern, youth-serving systems (that is, housing, healthcare, education, employment) 10 Youth Homelessness

Homelessness and Housing Experiences among LGBTQ Young Adults in Seven U.S. Cities often lack the ability to recognize and respond to the needs of YA whose lives are impacted by the multiple and layered stigmas resulting from racism, classism, heterosexism, cisgenderism, and transbias (Olivet and Dones, 2016).

Despite this growing literature concerning LGBTQ YA homelessness, a great deal of work remains. Much of the recent LGBTQ YA homelessness research examined the needs of the LGBTQ 

population as a broad group, often masking the variability of the experiences of the subgroups within. Studies have compared LGBTQ YA and non-LGBTQ YA or lesbian, gay, bisexual, and heterosexual YA (Cochran et al., 2002; Corliss, et al., 2011; Gangamma et al., 2008; Gattis, 2013; Walls, Hancock, and Wisneski, 2007). More recent work has begun to examine distinct subpopulations of LGBTQ YA experiencing homelessness, including transgender YA (Shelton and Bond, 2017; Shelton, 2015); Latino gay and bisexual male YA (Castellanos, 2016); and Black lesbian, gay, bisexual, and transgender (LGBT) YA (Gattis and Larson, 2017; Gattis and Larson, 2016). Still, few studies have adopted an intersectional lens toward understanding how multiple marginalized identities contribute to YA experiences of housing and homelessness.

This study contributes to the existing literature in several ways. First, it provides an up-to-date account of the homeless experiences of LGBTQ YA. Additionally, multicity data collection extends previous research situated in single cities or regions. Representing one of the largest samples of LGBTQ YA experiencing homelessness to date, this study enables an examination of specific subpopulations. Examining the characteristics of understudied subgroups within the population of LGBTQ YA experiencing homelessness provides an intersectional understanding of the ways in which race/ethnicity, gender identity, and sexual orientation interact with the experience of homelessness—a critical step to informing policy and programmatic interventions aimed at addressing YA homelessness. This study uses a seven-city sample of 442 LGBTQ YA experiencing homelessness to examine the homelessness and housing experiences of LGBTQ YA, as well as how these experiences differ among particularly marginalized and understudied subgroups experiencing homelessness (that is, bisexual and transgender identifying YA). The study also examines how experiences of homelessness compare across racial/ethnic subgroups within the LGBTQ, bisexual, and transgender YA samples.

Literature Review

A growing body of literature details the variabilities of LGBTQ YA experiencing homelessness. 

Research has demonstrated the disproportionate representation of LGBTQ YA in the population of YA experiencing homelessness, estimating that LGBTQ YA make up 20–40 percent of the overall homeless YA population (Choi et al., 2015; Durso and Gates, 2012; Lankenau et al., 2005; Maccio and Ferguson, 2015; Quintana et al., 2010; Van Leeuwen et al., 2006). LGBTQ YA experience homelessness at earlier ages (Moon et al., 2000) and remain homeless or unstably housed longer than their heterosexual and cisgender counterparts (Choi et al., 2015). One of the primary pathways into homelessness for all YA is family conflict (Cull, Platzer, and Balloch, 2006; Gaetz, 2014; Karabanow, 2004). A commonly cited reason for homelessness among LGBTQ YA is family conflict related to or exacerbated by sexual and/or gender identity (Shelton and Bond, 2017; Choi et al., 2015; Durso and Gates, 2012; Rew et al., 2005; Whitbeck et al., 2004). It is important, Cityscape 11

Shelton, DeChants, Bender, Hsu, Santa Maria, Petering, Ferguson, Narendorf, and Barman-Adhikari however, to not overlook the social and economic conditions and structural factors that produce and maintain housing instability and homelessness (Shelton and Bond, 2017; Castellanos, 2016). 

To focus solely on family characteristics and individual risk ignores the systematic oppression and stigmatization at play in the lives of marginalized YA. Additional reasons for homelessness among LGBTQ YA noted in the literature include verbal abuse, parental substance use, aging out of child welfare systems, and a lack of affordable housing (Choi et al., 2015; Gangamma et al., 2008).

Evidence indicates that, once homeless, LGBTQ YA are at heightened risk for experiencing a range of negative physical, mental, and behavioral health outcomes. For example, compared to their heterosexual and cisgender counterparts, LGBTQ YA experiencing homelessness report higher rates of substance abuse, engagement in the sex industry, mental health symptoms, and victimization (Cochran et al., 2002; Corliss, et al., 2011; Gangamma et al., 2008; Gattis, 2013; Walls, Hancock, and Wisneski, 2007). The risks for LGBTQ YA experiencing homelessness have been well documented. These studies provide a crucial understanding of the differential experiences of LGBTQ YA and non-LGBTQ YA experiencing homelessness. The studies have informed best practice and policy recommendations for effectively serving LGBTQ YA experiencing homelessness (Cray, Miller, and Durso, 2013; Ferguson and Maccio, 2012; Keuroghlian, Shtasel, and Bassuk, 2014; Page, 2017; Wilber, Ryan, and Marksamer, 2006).

Research to date has also increased governmental awareness of and investment in addressing homelessness among LGBTQ YA. For example, the United States Interagency Council on 

Homelessness Framework to End Youth Homelessness acknowledges LGBTQ youth as a 

subpopulation warranting attention given their disproportionate representation and unique needs (United States Interagency Council on Homelessness, 2013). These research-informed policy and practice advances are critical for adequately addressing homelessness among YA.

Minimal research has detailed the within-group differences of LGBTQ YA experiencing 

homelessness, however. They are not a homogenous group. The needs and experiences of one subgroup within the homeless LGBTQ YA population do not necessarily reflect the needs and experiences of another subgroup. For example, cisgender YA with a minority sexual orientation may have vastly different experiences than transgender YA. Likewise, the experiences and resulting needs of LGBTQ YA of color are different from those of White LGBTQ YA. Although transgender people can also possess a minority sexual orientation, conflating their experiences with lesbian, gay, bisexual, queer, and questioning (LGBQ) people ignores a salient dimension of their identity. 

Therefore, including transgender YA in research on sexual minorities rather than as a distinct category of inquiry renders their gender identity-related experiences invisible. Further, examining the experiences of LGBTQ YA without including a race/ethnicity-based analysis can mask the experiences of LGBTQ YA of color.

In a survey of homeless youth service providers, Choi et al. (2015) sought to identify similar and unique experiences of cisgender LGBQ YA and transgender YA experiencing homelessness. 

Several distinctions emerged. First, service providers were asked to compare the physical and mental health status of LGBQ, heterosexual, transgender, and cisgender YA they serve (response options included much worse, somewhat worse, about the same, somewhat better). Respondents reported that the physical health status of LGBQ YA experiencing homelessness was about the same 12 Youth Homelessness

Homelessness and Housing Experiences among LGBTQ Young Adults in Seven U.S. Cities as heterosexual YA and that transgender YA were in worse physical health than their cisgender counterparts were. Similarly, providers reported that the mental health status of the LGBTQ YA they served was worse than the mental health status of their heterosexual and cisgender peers. 

Respondents were more likely to report worse mental health status for transgender YA.

While providers reported LGBTQ YA experienced longer durations of homelessness than their heterosexual and cisgender counterparts, they were more likely to report longer periods of homelessness for the transgender YA they serve. This finding is important, as longer durations of homelessness have been identified as a threat to resilience among YA experiencing homelessness (Cleverley and Kidd, 2011). Longer durations also are associated with higher levels of sexual risk behaviors, including engaging in sex while using substances and using contraceptives less consistently. Longer duration also negatively affects motivation to adopt and maintain HIV 

protecting behaviors (Collins and Slesnick, 2011; Rew et al., 2008). Additionally, longer durations of homelessness resulted in greater difficulty exiting homelessness among a sample (N=1,677) of Australian people who first experienced homelessness when they were 18 years old or younger (Johnson and Chamberlain, 2008).

Trauma history was another area in which experiences differed between cisgender LGBQ YA and transgender YA. Researchers found statistically significant differences across seven of nine indicators of past trauma, with survey respondents reporting that a higher proportion of transgender YA (compared to cisgender LGBQ YA) had histories of harassment and bullying, intimate partner violence, family rejection, physical, sexual or emotional abuse, mental health issues, sexual exploitation, and alcohol or substance abuse (Choi et al., 2015). Recent literature also identified unique challenges faced by transgender YA and Latino gay and bisexual male YA experiencing homelessness (Shelton and Bond 2017; Shelton, 2015; Castellanos, 2016). 

These findings point to differential experiences among subgroups of LGBTQ YA experiencing homelessness that warrant further investigation. Such disaggregation is necessary for the development of effective homelessness prevention and family reconnection efforts (Shelton, 2015; Castellanos, 2016).

Burgeoning research has focused on specific subgroups within the population of LGBTQ YA experiencing homelessness across intersections of race, sexual orientation, and gender identity. 

Intersectional approaches consider the ways in which multiple social categories collectively shape an individual’s experiences of oppression, power, and privilege (Crenshaw, 1991). An intersectional understanding of the ways in which race/ethnicity, gender identity, and sexual orientation interact with the experience of homelessness is a critical step in informing interventions aimed at addressing YA homelessness. Race, gender identity, and sexual orientation do not operate as mutually exclusive categories; rather, they operate as “reciprocally constructing phenomena that in turn shape complex social inequalities” (Collins, 2015: 2). In their investigation of microaggressions and mental health among Black youth experiencing homelessness, Gattis and Larson (2017) underscore the importance of comprehensively addressing how subtle, pervasive forms of heterosexism, gender normativity, and racism affect the mental health of YA experiencing homelessness.

Other research has examined the pathways into homelessness through an intersectional lens. 

Begun and Kattari (2016) found that transgender people of color were more likely to experience Cityscape 13

Shelton, DeChants, Bender, Hsu, Santa Maria, Petering, Ferguson, Narendorf, and Barman-Adhikari housing instability than their White counterparts. Specifically, respondents who identified as Black or African-American, American Indian or Alaskan Native, biracial or multiracial were significantly more likely than their White counterparts to have experienced homelessness or sought temporary sleeping arrangements due to their gender identity (Begun and Kattari, 2016). Though the study did not focus specifically on YA (participant ages ranged from 18 to 98, with a mean age of 36.7), findings demonstrate the increased barriers encountered by transgender people of color in comparison to their White counterparts.

Findings from these studies underscore the importance of disaggregating the experiences of LGBTQ 

YA of different races and gender identities to identify service-related barriers and to support their specific needs and reasons for homelessness. Castellanos (2016) suggests that such disaggregation is necessary for the development of effective homelessness prevention and intervention efforts.

This study describes the reported reasons LGBTQ YA experience homelessness, characteristics of homelessness (that is, age at first homelessness, total length of time homeless, and current living situation), and stress and coping (that is, experiences of discrimination, difficulty finding resources, desire for help, positive coping strategies).

Methods

Interdisciplinary homeless YA researchers from around the country developed a national research collaborative called REALYST (http://www.realyst.org) between universities and homeless youth-serving organizations to examine and compare risk and resilience characteristics of YA experiencing homelessness (aged 18–26) across seven cities in the United States. This national study was conducted in 2016–17 in Denver, Houston, Los Angeles, New York City, Phoenix, San Jose, and St. Louis. Data were collected using tablets to deliver a self-administered survey. The collaborative developed and used a standardized study protocol and assessment tool—the Homeless Youth Risk and Resilience Survey—across all study sites.

Research settings

To broaden our understanding of YA homelessness in various regions, an initial cohort of seven cities (located within distinct U.S. Census areas and with a lead university investigator and host organization in each city) were selected. Using a cross-sectional study design, study investigators in each university collaborated with agencies serving YA experiencing homelessness in each city. Participating agencies were non-profit organizations offering a range of services including shelter, transitional housing, street outreach, and drop-in services to YA experiencing or at risk of experiencing homelessness. Human subjects’ approval was received by each investigator’s university. Each investigator independently funded data collection at its site, including the purchase of participant incentives and support for local research assistants.

Sample and recruitment

A standardized protocol for recruiting and screening potential participants was used across research sites. Using purposive sampling, researchers and trained research assistants recruited 14 Youth Homelessness

Homelessness and Housing Experiences among LGBTQ Young Adults in Seven U.S. Cities approximately 200 unique English speaking YA who were experiencing homelessness and seeking services at host agencies in each city. Sites intentionally sampled from different service outlets (for example, drop-in centers, shelters, transitional housing programs) to capture the varied experiences and characteristics of YA accessing a range of services. All YA accessing services during the data collection period were asked to participate in an eligibility screener. Due to challenges in consenting minors at host agencies and an interest in the YA developmental stage, the eligibility screener assessed if potential participants were within the required age range (18–26 years old). 

The screener also assessed whether potential participants were considered homeless or unstably housed, defined as spending the prior night on the streets, in a location not meant for human habitation, in a shelter, in an apartment provided through a temporary housing voucher, or staying temporarily with friends, acquaintances, or family where they could not stay for more than 30 

days. Informed consent documents were reviewed with eligible participants. Interested participants consented to the study by clicking a box on the tablet-delivered survey.

Data collection

After YA consented to participate in the study, an anonymous person identification code was generated for each participant that allowed for assessment of duplication across data collection sites within and across cities. Next, YA completed the REALM-SF (Murphy et al., 1993) screener for health literacy, which was modified to reflect topics and words that would come up in the survey. If YA scored between 1 and 3 (out of 9) on the REALM-SF, they were encouraged to have the survey read aloud to them by the researchers in a private setting. Those with scores higher than 3 were asked to complete the self-administered survey independently. Study staff were available to assist participants as needed throughout the survey implementation. Self-administering reduced concern for social desirability associated with face-to-face disclosure of sensitive information (Phillips et al., 2010). The tablet displayed a slide bar showing the participant’s progress throughout the survey and included reminders of anonymity. The survey took approximately 50 minutes to complete. Participants received a $10–20 gift card (depending on site) to a local store for completing the survey.

Measures

The survey included demographic questions, including age and race/ethnicity. Response options for race/ethnicity included: White or Caucasian (not Hispanic or Latino), Black or African-American (not Hispanic or Latino), Hispanic or Latino, American Indian, Asian or Pacific Islander, Multiracial/Mixed Race, and Other. Participants were asked to select a single category they felt best described their racial and ethnic identity. Data for this analysis included only participants who self-identified as White or Caucasian (not Hispanic or Latino), Black or African-American (not Hispanic or Latino), or Hispanic or Latino. Participants who identified themselves as multiracial/mixed race were not included, since the survey did not inquire about the specific identities that made up their multiracial identity.

Sexual orientation and gender identity were measured using questions previously tested among a geographically and racially diverse population of LGBT and non-LGBT health clinic patients (Cahill et al., 2014). Response options for sexual orientation included gay or lesbian; straight, that is, not gay; bisexual; something else (please specify); and I don’t know/questioning. For this Cityscape 15

Shelton, DeChants, Bender, Hsu, Santa Maria, Petering, Ferguson, Narendorf, and Barman-Adhikari study, all youth who reported that their sexuality was something other than straight or heterosexual were included in the LGBQ subsample. Gender identity was measured using a two-part gender identity question. The first question asked respondents their current gender identity. Respondents could select multiple responses from the following options: Male; Female; Transgender Male/Trans Man/Female-to-Male; Transgender Female/Trans Female/Male-to-Female; Genderqueer, neither exclusively male nor female; Additional Gender Category (or other); Decline to Answer, please explain why. The second question asked respondents to choose the sex assigned on their original birth certificate (Male or Female). For the purpose of this study, youth were coded as transgender if they 1) reported a gender identity other than “male” or “female” or 2) reported a “male” or 

“female” current gender identity that did not match the sex assigned on their birth certificate. The transgender subsample thus includes youth with a diversity of transfeminine, transmasculine, and non-binary gender identities.

The survey also inquired about reasons for homelessness. Participants could select from 18 

categories: I was kicked out/asked to leave my family home, my foster home, my relative’s home, my group home; I ran away from my family home, my foster family home, my relative’s home, my group home; I aged out of the foster care system; I aged out of the juvenile justice system; I couldn’t pay rent; I had no place to go when I got out of jail/prison; I had no place to go when I got out of the hospital; I left a situation of domestic violence; I left a gang or a neighborhood with gang violence; My family does not have a stable place to stay; I had no place to stay when I moved here; or Other.

The survey queried youth about a set of homelessness characteristics, including the age of their first homelessness episode, duration of homelessness (How long have you been without a stable place to stay/homeless in this most recent episode/this time?), and current housing situation. 

Housing situations were sorted into three groups: 1) couch surfing, or staying with friends, family, strangers, or sexual partners for an undetermined period of time; 2) housed, or currently staying at an institution such as a shelter or transitional housing program; and 3) outside, or currently sleeping in a public place such as a park, abandoned building, or on public transportation. 

Duration of homelessness was recoded into three categories: short-term homelessness (less than 6 

months), medium-term homelessness (6 months to 2 years) and long-term homelessness (greater than 2 years).

Finally, participants were also asked about the stressors experienced while homeless and the forms of coping they used. This included forms of discrimination as measured by the Everyday Discrimination Scale (Milburn et al., 2010), which asks how often participants experience discrimination (such as being treated with less courtesy than others, people acting as if they are afraid of you, or being threatened or harassed) in their day-to-day life. Choices were never, less than once a year, a few times a year, at least once a week, or always. Participants were also asked to identify the reasons for the discriminatory experiences, selecting from the following options: your ancestry or national origin, gender, gender identity/gender expression, race, your age, religion, height, weight, sexual orientation, housing status (that is, being homeless or without a stable place to live), education or income level, or some other aspect of your physical appearance.

Use of positive coping strategies was measured using items from the Coping Scale (Kidd and Carol, 2007). That method considers whether youth never, rarely, sometimes, or often use specific 16 Youth Homelessness

Homelessness and Housing Experiences among LGBTQ Young Adults in Seven U.S. Cities strategies to deal with problems. Strategies youth might use include concentrate on what to do and how to solve the problem, go to someone I trust for support, try to value myself and not think so much about other people’s opinions, realize that I am strong and can deal with whatever is bothering me, and use my spiritual beliefs/belief in a higher power. These items were subsequently recoded for analysis to report the frequency and percentage of youth who sometimes or often engaged in each of five positive coping strategies.

Desire for help with housing was assessed using a five-point Likert scale. The single item asked YA how strongly they agreed with the statement “I need help in dealing with my housing situation.” 

This was subsequently recoded to report the frequency and percentage of youth who agreed or strongly agreed with this statement.

Stress finding certain resources on the streets was assessed using items from the Rew Stress of the Streets Scale (Rew et al., 2016). The survey asked how much (none at all, a little, more than a little, a lot) participants felt stress in the previous month about finding enough food to eat, a place to sleep, a place to bathe or shower, a place to wash clothes, work, or a way to earn money. 

Participants rated each item as either none at all, a little, more than a little, or a lot.

Data analysis

To explore differences in homeless experiences among homeless LGBTQ YA subgroups, we 

conducted the following analyses. First, we used descriptive statistics (that is, frequencies, percentages, means, and standard deviations) to characterize the homelessness and housing experiences of the full sample of LGBTQ YA. Second, we used the same descriptive statistics to describe specific subgroups, with particular focus on YA who identified as transgender and bisexual. This allowed for an examination of traditionally understudied and potentially more marginalized subgroups within the population of LGBTQ YA experiencing homelessness. Finally, the full sample and subgroups (bisexual YA and transgender YA) were described through the intersection of race/ethnicity. Specifically, descriptive statistics and bivariate analysis (that is, chi square and independent t-tests) were used to describe and compare the homelessness and housing experiences of Black, Latino, and White YA within the full LGBTQ sample and the transgender and bisexual subsamples.

Results

Descriptive characteristics of the sample

The full sample consisted of 442 LGBTQ YA experiencing homelessness. For gender identity, the sample identified as 43 percent female (n=189), 32.7 percent male (n=144), and 24.3 percent gender minority (n=107). As for sexual orientation, the sample identified as 46 percent bisexual YA (n=205), 27.6 percent gay or lesbian, 13.3 percent something else (n=59), 7.7 percent straight (n=34), and 4.3 percent questioning (n=19). Participants averaged 20.9 years old (SD=2.1). A racially diverse sample, 82 percent were YA of color (n=361). Specifically, 30 percent identified as Black (n=133), 21.9 percent as mixed race (n=97), 16 percent as Latino (n=72), 18 percent as White (n=80), and 13.3 percent as something else (n=59).

Cityscape 17

Shelton, DeChants, Bender, Hsu, Santa Maria, Petering, Ferguson, Narendorf, and Barman-Adhikari Housing and homelessness experiences among LGBTQ youth and YA

Reasons for homelessness.  YA survey respondents were asked to identify why they became homeless by selecting all applicable reasons from a list of 18 possible answers. A substantial number of LGBTQ YA reported being kicked out/asked to leave their previous living arrangement. 

Specifically, the majority reported being kicked out/asked to leave their family home by their parents (70 percent) or another relative (25 percent). Other LGBTQ YA reported being kicked out of child welfare placements, such as foster homes (18 percent) or group homes (11 percent). In addition to being kicked out, many reported running away from their parents’ house (38 percent), another relative’s home (16 percent), a foster home (12 percent), or a group home (10 percent). 

More LGBTQ YA reported aging out of foster care (18 percent) than juvenile justice services (8 

percent). Poverty played a clear role in reasons for homelessness. Many respondents said they became homeless after they could no longer afford rent (35 percent) or because their family became homeless (18 percent). Nearly a third (29 percent) reported that they became homeless due to domestic violence, although it is unclear if this violence was from intimate partners or from parental or other familial figures or if the participant was a direct victim of domestic violence or a witness to it. Additionally, 31 percent of respondents reported that they became homeless after moving to a new city and having nowhere to live.

Characteristics of homelessness. The average age at which LGBTQ YA reported first experiencing homelessness was 17, although this mean should be considered within the context of a limited study inclusion criteria of ages 18–26 at the time of data collection. Nearly a third (30 percent) of LGBTQ YA respondents reported being homeless fewer than 6 months; 38 percent reported being homeless for 6 months to 2 years, and 32 percent reported being homeless more than 2 years. 

Respondents reported a variety of current living situations. Over half of LGBTQ respondents (56 

percent) stayed at an institutional setting such as a shelter, hospital, or transitional housing program the previous night. A quarter (25 percent) reported they were currently staying outside, in a park or abandoned building, or sleeping on public transportation. Nearly a fifth (18 percent) reported they were couch surfing or temporarily staying with family, friends, relatives, or sexual partners.

Stressors.  YA respondents experienced stress while homeless. LGBTQ YA were particularly stressed about earning money (64 percent) and being unable to find work (58 percent). Nearly half of the sample also reported stress over meeting their own basic needs, including finding a place to sleep (48 percent), food to eat (45 percent), a place to wash their clothes (44 percent), and a place to shower or wash themselves (42 percent).

Many LGBTQ YA reported experiencing discrimination while homeless. Approximately a third of respondents reported experiencing discrimination due to their gender (36 percent) or their gender identity or expression (30 percent). Many experienced discrimination due to their sexual orientation (41 percent) or their race (39 percent). Identity categories were not the only source of discrimination, as 45 percent of LGBTQ YA respondents perceived that they were experiencing discrimination due to their housing status.

The majority of LGBTQ YA reported engaging in positive coping strategies to deal with their problems. Strategies included concentrating on solving the problem (75 percent), recognizing 18 Youth Homelessness

Homelessness and Housing Experiences among LGBTQ Young Adults in Seven U.S. Cities one’s own strength and resiliency (74 percent), and valuing one’s self over worrying about others’ 

opinions (70.2 percent). Coping strategies less common but still endorsed by more than half this group were going to someone trusted for support (59.5 percent) and relying on spirituality or belief in a higher power (58.9 percent). Two-thirds of LGBTQ YA (66 percent) also reported a desire for help with obtaining housing. (Note: this scale only asks about engaging in positive coping strategies—not negative ones).

Housing and homelessness experiences among bisexual and transgender subgroups

Our second research question examined the experiences of particularly marginalized and understudied subgroups among the population of LGBTQ YA experiencing homelessness, 

namely bisexual and transgender YA. These two subgroups were examined specifically because of their increased vulnerability and limited representation in previous studies of LGBTQ youth homelessness. Exhibit 1 provides descriptive results for the full sample, as well as for the bisexual and transgender subsamples. Because these subgroup categories are not mutually exclusive (for example, one can be a bisexual as well as transgender), statistical comparisons were not run between the full LGBTQ sample and the transgender and bisexual subsamples. Some patterns are worth noting, however, in describing the transgender and bisexual subgroups.

Exhibit 1

Comparison of Homelessness Experiences Across Full LGBTQ Sample, Transgender Subsample, and Bisexual Subsample (N=442)

LGBTQ

Transgender

Bisexual

n=442

n=107

n=205

Freq (%)

Freq (%)

Freq (%)

Reason for homelessness

Kicked out

Family home

223 (70.3)

58 (75.3)

98 (66.7)

Foster home

35 (18)

11 (26.2)

14 (14.6)

Relative’s home

53 (25.2)

14 (31.1)

22 (21)

Group home

20 (10.9)

4 (10.8)

8 (8.7)

Ran away

Family home

86 (38.2)

23 (46.9)

41 (36.3)

Foster home

23 (12.2)

8 (20)

8 (8.4)

Relative’s home

30 (15.8)

8 (20.5)

14 (14.4)

Group home

18 (9.8)

5 (13.5)

8 (8.5)

Aged out of foster care

34 (17.9)

10 (25)

15 (15.5)

Aged out of juvenile justice

14 (7.7)

3 (8.3)

9 (9.5)

Can’t pay rent

74 (35.2)

20 (43.5)

31 (29.8)

Nowhere to go after prison

15 (8.3)

7 (19.4)

2 (2.2)

Exiting hospital

18 (9.9)

4 (10.8)

10 (10.5)

Domestic violence

62 (29.4)

17 (36.2)

31 (29.2)

Left gang

11 (6.2)

2 (5.6)

5 (5.6)

Family homelessness

35 (17.5)

7 (17.5)

19 (18.4)

Moved and had no place to live

67 (31.2)

21 (43.8)

22 (21.4)

Cityscape 19

Shelton, DeChants, Bender, Hsu, Santa Maria, Petering, Ferguson, Narendorf, and Barman-Adhikari Exhibit 1

Comparison of Homelessness Experiences Across Full LGBTQ Sample, Transgender Subsample, and Bisexual Subsample (N=442)

LGBTQ

Transgender

Bisexual

n=442

n=107

n=205

Freq (%)

Freq (%)

Freq (%)

Characteristics of homelessness

Duration of homelessness

Short term (<6 months)

132 (29.9)

35 (32.7)

60 (29.3)

Medium term (6 months–2 years )

168 (38.1)

37 (34.6)

76 (37.1)

Long term (>2 years)

141 (32)

35 (32.7)

69 (33.7)

Living situation

Couch surfing

81 (18.3)

14 (13.1)

35 (17.1)

Housed

247 (55.9)

70 (65.4)

115 (56.1)

Outside

111 (25.1)

22 (20.6)

53 (25.9)

Mean (SD)

Mean (SD)

Mean (SD)

Age first homeless

17.22 (3.55)

17.8 (3.05)

17 (3.65)

Stress and coping

Discrimination

Gender

136 (35.8)

44 (49.4)

52 (29.1)

Gender identity/expression

112 (29.5)

54 (60.7)

31 (17.3)

Race

148 (38.9)

31 (34.8)

59 (33)

Sexual orientation

156 (41.1)

53 (59.6)

47 (26.3)

Housing status

171 (45)

35 (39.3)

88 (49.2)

Stress in finding resources

Place to sleep

210 (48.3)

46 (43.8)

103 (50.7)

Food to eat

197 (45.4)

48 (46.2)

99 (48.8)

Shower

183 (42.4)

33 (31.7)

91 (44.8)

Wash clothes

190 (44.2)

33 (32.4)

101 (50)

Earning money

279 (64.4)

76 (73.1)

135 (66.5)

Work

251 (58.1)

58 (55.2)

125 (61.9)

Desire for help with housing

289 (66.3)

71 (67.6)

135 (66.5)

Coping strategies

Concentrated and problem solve

327 (75.0)

74 (70.5)

154 (75.9)

Go to someone for support

257 (59.5)

68 (65.4)

133 (65.8)

Value self over others’ opinions

304 (70.2)

69 (66.3)

135 (66.8)

Recognize own strength

320 (73.9)

74 (70.5)

144 (71.3)

Rely on spirituality

259 (59.8)

61 (58.1)

121 (59.9)

Mean (SD)

Mean (SD)

Mean (SD)

Transgender YA.  Compared to the full LGBTQ sample, transgender YA reported higher frequencies of running away or being kicked out of their family home, foster home, or relative’s home. Transgender YA more often reported becoming homeless due to an inability to pay rent or having nowhere to go after leaving prison or moving to a new city.

20 Youth Homelessness

Homelessness and Housing Experiences among LGBTQ Young Adults in Seven U.S. Cities Higher percentages of transgender YA reported experiencing discrimination due to their gender, gender identity, or gender expression. Compared to the full LGBTQ sample, higher percentages of transgender YA also reported discrimination due to their sexual orientation, indicating that although sexual orientation and gender identity are two separate entities, many transgender YA have minority sexual orientations. Interestingly, transgender YA were no more likely, compared to the full LGBTQ sample, to report stress in finding a place to shower or to sleep while experiencing homeless. This is surprising, given the difficulty many transgender people face when trying to access sex-segregated services, such as restrooms or dormitories. Transgender YA reported coping strategies quite similar to the broader LGBTQ sample, with slightly more seeking support from someone they trust (65.4 percent) and slightly fewer concentrating on problem solving (70.5 

percent). Transgender YA reported a similar desire for help obtaining housing (68 percent) as the full sample of LGBTQ YA.

Bisexual YA.  Bisexual YA in the sample did not report many differences from the full sample of LGBTQ YA as a whole. They were less likely to report discrimination due to sexual orientation, perhaps because they may be in opposite-sex relationships or be less open about their sexual orientation. They were also less likely to have become homeless due to leaving prison or because they moved and had no place to live. Most other characteristics were strikingly similar to those found in the full LGBTQ sample.

Housing and homelessness experiences across Black, Latino, and White members 

of the LGBTQ, bisexual, and transgender subgroups

Exhibit 2 shows statistical comparison of racial groups within the LGBTQ, bisexual, and transgender subsamples. When possible, statistical tests examined differences between racial groups in both the full LGBTQ sample and the transgender and bisexual subsamples. As for reasons for homelessness, White and Latino LGBTQ YA were significantly more likely to report becoming homeless because they could not pay rent than their Black LGBTQ peers (p < 0.05).

Cityscape 21

Shelton, DeChants, Bender, Hsu, Santa Maria, Petering, Ferguson, Narendorf, and Barman-Adhikari 2 𝙓

2.63

0.25

2.31

2.96

4.59

1.83

0.08

2.69

2.93

0.84

4.9

2.73

1.99

4.27

3.42

3.74

0.85

4 (16)

2 (8)

7 (28)

2 (8)

4 (16)

2 (8)

1 (4)

White 

n=37

eq (%)

5 (19.2)

3 (11.5)

3 (12.5)

4 (15.4)

4 (14.8)

4 (15.4)

3 (11.5)

3 (11.5)

Bisexual

Fr

18 (62.1)

11 (39.3)

2 (25)

4 (40)

9 (60)

0 (0)

Latino 

n=35

eq (%)

2 (22.2)

2 (22.2)

1 (12.5)

2 (22.2)

3 (33.3)

1 (12.5)

7 (53.8)

1 (12.5)

4 (46.2)

1 (14.3)

4 (36.4)

2 (22.2)

Bisexual

Fr

19 (82.6)

0 (0)

0 (0)

Black 

n=68

eq (%)

5 (17.2)

9 (25.7)

1 (3.8)

2 (7.4)

5 (16.7)

1 (3.8)

3 (10.7)

2 (7.4)

6 (20.7)

1 (3.7)

8 (26.7)

4 (13.8)

6 (19.4)

Bisexual

Fr

36 (70.6)

10 (32.3)

2 𝙓

0.59

0.78

0.2

2.29

0.49

1.07

1.3

1.07

2.31

1.07

3.57

0.9

0.8

0.35

1.07

1.35

0.34

2 (25)

2 (25)

5 (50)

0 (0)

6 (60)

2 (25)

4 (40)

3 (30)

5 (50)

White 

Trans

n=17

eq (%)

3 (37.5)

1 (12.5)

1 (14.3)

1 (12.5)

1 (12.5)

2 (22.2)

1 (12.5)

Fr

11 (84.6)

2 (40)

2 (40)

0 (0)

3 (50)

0 (0)

1 (25)

0 (0)

1 (25)

0 (0)

0 (0)

0 (0)

3 (50)

0 (0)

0 (0)

3 (60)

Latino 

Trans

n=21

eq (%)

5 (62.5)

Fr

12 (85.7)

9 (75)

0 (0)

0 (0)

0 (0)

0 (0)

0 (0)

1 (20)

0 (0)

Black 

Trans

n=18

eq (%)

1 (16.7)

2 (28.6)

2 (33.3)

2 (28.6)

1 (16.7)

1 (16.7)

2 (33.3)

1 (16.7)

3 (42.9)

Fr

2 𝙓

4.14

0.15

1.62

2.4

1.94

2.93

0.35

1.6

0.8

1.63

0.7

6.72*

0.16

2.29

1.34

0.05

3.11

rans Subsample, and Bisexual Subsample (n=285)

9 (20)

6 (13)

White 

eq (%)

4 (8.9)

2 (4.5)

LGBTQ

n=80

6 (13.3)

5 (11.1)

7 (15.6)

5 (10.9)

8 (17.8)

7 (14.9)

9 (18.4)

Fr

44 (68.8)

10 (20.8)

18 (36.7)

27 (49.1)

13 (26.5)

16 (31.4)

4 (20)

1 (5.9)

9 (36)

1 (6.3)

4 (20)

Latino 

eq (%)

LGBTQ

n=72

3 (16.7)

7 (31.8)

2 (11.1)

2 (11.1)

3 (15.8)

3 (16.7)

2 (11.1)

2 (11.8)

8 (38.1)

Fr

41 (85.4)

13 (46.4)

13 (48.1)

9 (17)

4 (8)

Black 

eq (%)

72 (75)

2 (4.3)

18 (31)

3 (6.1)

3 (6.1)

3 (6.1)

2 (4.1)

1 (2.1)

11 (20)

LGBTQ

n=133

6 (11.5)

Fr

11 (20.4)

19 (29.7)

14 (26.4)

13 (23.6)

10 (17.9)

e

oss Racial Categories within LGBTQ Sample, T

ent

s home

s home

e to go after prison

oup home

oup home

Family home

Foster home

Relative’

Gr

Family home

Foster home

Relative’

Gr

Exhibit 2

Comparison acr

Reason for homelessness

Kicked out

Ran away

Aged out of foster car

Aged out of juvenile justice

Could not pay r

Nowher

Exiting hospital

Domestic violence

Left gang

Family homelessness

Moved and had no place to live

22 Youth Homelessness

Homelessness and Housing Experiences among LGBTQ Young Adults in Seven U.S. Cities 2 𝙓

1.87

3.74

t

.44

3.45

3.04

0.12

1.76

10.98*

8.44*

10.52**

17.14 

(4.12)

8 (23.5)

4 (11.8)

5 (14.7)

8 (23.5)

White 

n=37

eq (%)

8 (21.6)

6 (16.2)

15 (44.1)

Bisexual

Fr

15 (40.5)

14 (37.8)

17 (45.9)

14 (37.8)

Mean (SD)

9 (29)

8 (25.8)

8 (25.8)

Latino 

n=35

eq (%)

6 (17.1)

4 (11.4)

9 (25.7)

13 (41.9)

18 (58.1)

Bisexual

Fr

13 (37.1)

16 (45.7)

22 (62.9)

Mean (SD)

16.6 (3.77)

``

17.24 

(2.99)

14 (25)

Black 

n=68

eq (%)

9 (13.2)

11 (19.6)

26 (46.4)

32 (57.1)

Bisexual

Fr

28 (41.2)

26 (38.2)

14 (20.6)

15 (22.1)

44 (64.7)

Mean (SD)

2 𝙓

6.64

1.46

0.43

1.94

t

.59

1.4

0.78

7.16*

8.6*

1.04

17.12 

(4.39)

3 (17.6)

8 (47.1)

White 

Trans

n=17

eq (%)

5 (29.4)

6 (35.3)

6 (35.3)

2 (11.8)

3 (17.6)

10 (58.8)

11 (64.7)

14 (82.4)

Fr

12 (70.6)

Mean (SD)

9 (50)

1 (4.8)

7 (38.9)

7 (38.9)

6 (33.3)

6 (33.3)

Latino 

Trans

n=21

eq (%)

2 (28.6)

7 (33.3)

8 (38.1)

7 (33.3)

Fr

13 (61.9)

Mean (SD)

18.05 (2.5)

18.11 

(1.84)

6 (50)

6 (50)

7 (58.3)

Black 

Trans

n=18

eq (%)

6 (33.3)

1 (5.6)

3 (16.7)

3 (16.7)

7 (58.3)

8 (66.7)

Fr

11 (61.1)

11 (61.1)

Mean (SD)

2 𝙓

8.38

0.18

t

.04

3.29

1.67

3.51

1.42

9.84**

10.7**

10.96**

rans Subsample, and Bisexual Subsample (n=285)

17.03 

(3.99)

White 

eq (%)

28 (35)

44 (55)

LGBTQ

n=80

9 (11.3)

25 (33.8)

24 (32.4)

18 (24.3)

35 (47.3)

40 (54.1)

Fr

18 (22.5)

34 (42.5)

27 (33.8)

Mean (SD)

17.18 

(3.63)

Latino 

eq (%)

18 (25)

LGBTQ

n=72

27 (44.3)

20 (32.8)

21 (34.4)

20 (32.8)

27 (44.3)

Fr

28 (38.9)

26 (36.1)

10 (13.9)

42 (58.3)

20 (27.8)

Mean (SD)

27 (25)

Black 

eq (%)

20 (15)

LGBTQ

n=133

17.12 (3)

33 (30.6)

52 (48.1)

39 (36.1)

51 (47.2)

Fr

49 (36.8)

54 (40.6)

30 (22.6)

36 (27.1)

76 (57.1)

Mean (SD)

ession

oss Racial Categories within LGBTQ Sample, T

elative, friend, 

, hotel)

, r

eet, park, abandoned

, foster

Short term (<6 months)

Medium term (6 months–2 years)

Long term (>2 years)

Couch surfing (staying with 

family

sexual partner

Housed (Shelter/institution/own

apartment/transitional housing)

Outside (str

building, public transportation)

ess and Coping

Discrimination

Gender

Gender identity/expr

Race

Sexual orientation

Housing status

Exhibit 2

Comparison acr

Characteristics of homelessness

Duration of homelessness

Living situation

Age first homeless

Str

Cityscape 23

Shelton, DeChants, Bender, Hsu, Santa Maria, Petering, Ferguson, Narendorf, and Barman-Adhikari 2 𝙓

2.89

0.52

3.05

1.82

3.63

1.87

1.94

.566

t

5.57~

.877

2.19

1.07

White 

n=37

eq (%)

13 (35.1)

18 (43.2)

14 (37.8)

14 (37.8)

26 (70.3)

Bisexual

Fr

23 (62.2)

21 (56.8)

12 (75.0)

14 (93.3)

11 (68.8)

13 (81.3)

73 (61.3)

Mean (SD)

28 (80)

Latino 

n=35

eq (%)

19 (54.3)

18 (51.4)

11 (31.4)

17 (48.6)

Bisexual

Fr

26 (74.3)

25 (71.4)

29 (82.9)

23 (65.7)

24 (68.6)

25 (71.4)

19 (54.3)

Mean (SD)

Black 

n=68

eq (%)

33 (48.5)

31 (45.6)

33 (48.5)

35 (51.5)

42 (61.8)

Bisexual

Fr

41 (61.2)

46 (67.6)

50 (73.5)

42 (61.8)

41 (60.3)

43 (63.2)

44 (64.7)

Mean (SD)

2 𝙓

2.21

5.21

2.49

1.43

3.72

3.36

2.3

t

4.82~

8.35*

.633

.451

1.11

8 (47.1)

4 (23.5)

6 (35.3)

White 

Trans

n=17

eq (%)

11 (64.7)

14 (82.4)

7 (41.2)

Fr

13 (76.5)

14 (82.4)

14 (82.4)

16 (94.1)

11 (64.7)

13 (76.5)

Mean (SD)

11 (55)

8 (40)

9 (42.9)

Latino 

Trans

n=21

eq (%)

12 (57.1)

18 (85.7)

Fr

10 (47.6)

18 (85.7)

11 (52.4)

13 (61.9)

11 (55.0)

14 (66.7)

12 (57.1)

Mean (SD)

Black 

Trans

n=18

eq (%)

6 (33.3)

5 (27.8)

4 (22.2)

4 (22.2)

11 (61.1)

9 (50.0)

8 (44.4)

Fr

10 (55.6)

12 (66.7)

14 (77.8)

12 (66.7)

13 (72.2)

Mean (SD)

t

2 𝙓

1.16

2.08

0.27

0.35

2.75

0.29

0.99

3.43

3.14

1.68

2.21

2.39

rans Subsample, and Bisexual Subsample (n=285)

34 (43)

White 

eq (%)

LGBTQ

n=80

38 (48.1)

30 (38.5)

31 (39.7)

52 (65.8)

Fr

48 (60.8)

50 (63.3)

66 (83.5)

54 (68.4)

60 (75.9)

63 (79.7)

42 (53.2)

Mean (SD)

44 (62)

Latino 

eq (%)

LGBTQ

n=72

37 (51.4)

35 (49.3)

28 (38.9)

31 (43.7)

53 (73.6)

Fr

51 (70.8)

52 (72.2)

41 (56.9)

48 (67.7)

53 (73.6)

37 (51.4)

Mean (SD)

Black 

eq (%)

LGBTQ

n=133

60 (45.1)

53 (40.2)

55 (41.7)

58 (43.6)

82 (62.1)

Fr

77 (58.3)

90 (67.7)

98 (73.7)

75 (56.8)

91 (68.4)

93 (70.5)

81 (61.4)

Mean (SD)

ces

ength

oss Racial Categories within LGBTQ Sample, T

esour

e for help with housing

ash clothes

ning money

oblem solve

ork

ess in finding r

Place to sleep

Food to eat

Shower

W

Ear

W

Desir

Coping strategies

Concentrate and 

pr

Go to someone for support

Value self over others’ opinions

Recognize own str

Rely on spirituality

Exhibit 2

Comparison acr

Str

~ = p<.1*=p<.05; ** = p<.01

24 Youth Homelessness

Homelessness and Housing Experiences among LGBTQ Young Adults in Seven U.S. Cities Related to characteristics of homelessness, among bisexual YA, significant racial/ethnic differences in the duration of homelessness (p < 0.05) occurred, with Black YA more likely to report having been homeless for less than 6 months and Latino YA more likely to report having been homeless for more than 2 years. There were also significant racial/ethnic differences in where YA reported staying at the time of the survey. Black LGBTQ YA reported higher instances of couch surfing, or staying for an indefinite amount of time with friends, family, strangers, or sexual partners, than White or Latino LGBTQ YA (p < 0.01). White LGBTQ YA (p < 0.01) and White bisexual YA (p < 0.05) reported higher frequencies of staying outside, in a public place or an abandoned building, or sleeping on public transportation than Black or Latino LGBTQ YA and Black or Latino YA, respectively.

When it came to discrimination and coping, Black and Latino YA reported higher frequencies of racial discrimination compared to their White peers within the three examined groups (the full LGBTQ sample, transgender, and bisexual subgroups). White transgender YA were more likely to report discrimination due to sexual orientation than transgender Black or Latino YA (p < 0.05). 

For coping strategies, White youth were significantly more likely than Black or Latino YA to go to someone they trusted for support. This strategy was true among transgender YA (p<.05) and marginally true among bisexual YA (p<.10). No other significant differences were found, with about half to three-quarters of YA in each intersectional group reporting each positive coping strategy.

Some other differences were not statistically significant but nonetheless indicated patterns of racial/

ethnic differences. For example, across all groups (the full LGBTQ sample and the transgender and bisexual subsamples), Latino YA reported higher frequencies of being kicked out of their family home than White or Black YA. Latino bisexual YA reported higher frequencies of running away from their family home than Black or White bisexual YA. Latino YA (in the full LGBTQ sample and the transgender and bisexual subsamples) were more likely to report stress related to finding a place to sleep and reported a desire for help with housing at a higher frequency than their Black and White counterparts.

Discussion and Implications

This study provides a current account of the homeless experiences of LGBTQ YA. Representing one of the largest samples of LGBTQ YA experiencing homelessness to date, this study examined specific subpopulations as well, including Black and Latino LGBTQ YA, transgender YA, and bisexual YA. Each of these subpopulations experiencing homelessness has received scant attention in the literature, and LGBTQ YA experiencing homelessness are rarely examined outside of their risks in comparison to non-LGBTQ YA. As such, this study fills two critical gaps in the literature. 

First, the study describes the population of LGBTQ YA experiencing homelessness in more detail across multiple regions of the United States. Second, the study identifies differential experiences of subgroups within the population that can better inform program and policy interventions designed to prevent and end homelessness among YA.

LGBTQ YA identified varied pathways into homelessness. Consistent with the literature (Shelton and Bond, 2017; Choi et al., 2015; Durso and Gates, 2012; Rew et al., 2005; Whitbeck et al., 2004), the majority of study participants reported being kicked out or asked to leave their parents’ 

homes, their relatives’ homes, or foster and group homes. Transgender YA were more likely to Cityscape 25

Shelton, DeChants, Bender, Hsu, Santa Maria, Petering, Ferguson, Narendorf, and Barman-Adhikari report being kicked out/asked to leave their homes. This may indicate lower levels of awareness, understanding, and acceptance of transgender identities within communities and families compared to gay, lesbian, and bisexual identities—mirroring societal attitudes at large. Though the survey did not inquire about the specific reasons they were kicked out/asked to leave, findings highlight the need for policy and programmatic homeless prevention strategies targeting the families of LGBTQ YA and the systems within which they are involved.

Findings also highlight additional factors leading to homelessness for LGBTQ YA, particularly the role of poverty. Just over one-third (35 percent) of respondents became homeless because they could no longer afford to pay rent, emphasizing the importance of short-term rental assistance and affordable housing options as homelessness prevention strategies for LGBTQ YA. An additional 18 percent reported becoming homeless because their family became homeless. Additionally, 31 

percent of respondents reported becoming homeless after relocating to a new city and having no place to live. One possible explanation of this finding could be due to LGBTQ YA moving from less accepting environments to urban centers in search of a more LGBTQ-inclusive environment. 

Additional research can further investigate the reasons associated with such moves.

The role of poverty in LGBTQ YA homelessness has implications for policy and practice. From a practice perspective, programs often presume that YA experiencing homelessness are in need of a range of microlevel interventions related to individual skill building, symptom management, or behavioral modification. While this may be true for some YA, interventions based solely on this presumption may not be effectively engaging and serving YA whose experience of homelessness was precipitated by a financial crisis. Individualized assessment reasons for homelessness should help discern the types of interventions most suitable for YA. Communities around the country are implementing this practice through coordinated entry and assessment. If YA are not deemed highly vulnerable during the assessment process, however, they are not often prioritized for services. 

This makes conceptual sense—to service those most in need—but it leaves out those who would most benefit from minimal intervention. Findings point to the need for exploring and identifying potential policy and programmatic solutions such as short-term rental assistance, universal basic income, and affordable housing options for YA experiencing homelessness.

Durations of homelessness were nearly evenly distributed between short-term homelessness (<6 

months), medium-term homelessness (6 months to 2 years), and long-term homelessness (>2 

years) when examined among the entire sample of LGBTQ YA, as well as among the subsample of transgender youth and the subsample of bisexual youth. Respondents were slightly more likely to report medium-term homelessness. Examining durations of homelessness among subgroups revealed significant differences among bisexual youth. Black bisexual YA were more likely to report having been homeless for less than 6 months, while Latino bisexual YA were more likely to report having been homeless for more than 2 years. Further investigation is warranted, as reasons for differential durations of homelessness were not explored. A possible explanation for future exploration is the cultural resource of kinship structures in Black communities (Wilson, 1989). 

For example, the full sample of Black LGBTQ YA in this study reported higher instances of couch surfing or staying for an indefinite amount of time with friends, family, strangers, or sexual partners than the full sample of White or Latino LGBTQ YA. The availability of kinship networks may have contributed to the shorter periods of homelessness experienced by YA in this study. Conversely, 26 Youth Homelessness

Homelessness and Housing Experiences among LGBTQ Young Adults in Seven U.S. Cities the extended durations of homelessness reported by Latino YA could be due, in part, to the lack of similar kinship structures.

While family is an integral part of Latino culture (Perez and Romo, 2011), family members may not be accessible to Latino YA experiencing homelessness. In this study, higher percentages of Latino YA reported being kicked out of their family homes than Black or White YA. Accessing family support may not be an option for this group. Family members of Latino YA may not be accessible for other reasons. For example, Latino YA who left their country of origin without their families or were separated by their families due to immigration policies may not have access to familial support systems. The survey did not inquire about immigration status or immigration experiences, however, so we could not explore this theory. Though not statistically significant, Latino YA (LGBTQ, transgender, and bisexual) reported a desire for help with housing at a higher frequency than their Black and White counterparts did. This finding, as well as the durations of homelessness, raises questions about the efficacy of YA homeless service organizations in engaging and retaining Latino LGBTQ YA in supportive services that could be investigated in future research.

Earning money was the biggest stress identified by all of the participants, followed by finding work. 

Transgender people in general report high rates of employment discrimination due to their gender identity or expression. Their unemployment rate is three times that of the general population (James et al., 2016). Unemployment and underemployment can make finding and maintaining stable housing incredibly difficult for YA, who may face discrimination from landlords due to age and lack of previous housing histories, credit histories, or other sources of external support that might make them desirable tenants.

Further, LGBTQ people lack universal protection from housing discrimination, and people of color often encounter racial discrimination on the housing market. In a recent study, nearly a quarter of transgender people surveyed (N=27,715) reported experiencing housing discrimination related to their gender identity. Transgender women of color were more likely to report housing discrimination, and participants who reported being kicked out of their family’s homes due to their gender identity were almost twice as likely to report experiencing housing discrimination at the time of the survey (James et al., 2016). Given the frequency with which transgender YA in this study reported being kicked out or asked to leave their homes and the increased likelihood of experiencing housing discrimination among transgender adults ejected from their homes (James et al., 2016), it is important to identify programmatic and policy strategies for supporting transgender YA in maintaining safe and stable housing.

In addition to stress related to financial stability, LGBTQ YA reported experiencing discrimination related to their sexual orientation, gender identity, race, and housing status. Racial discrimination and discrimination related to housing status have been associated with depressive symptoms among Black YA (Gattis and Larson, 2016). In this study, Black and Latino YA were more likely to report discrimination due to their race or ethnicity. This supports previous findings (Gattis and Larson, 2017) that LGBTQ YA of color must contend with homophobia/transphobia and systemic racism and the subsequent effects as they navigate homelessness and housing instability. This finding highlights the oppressive structural dynamics of heterosexism, cisgenderism, and racism that inform the daily experiences of LGBTQ YA experiencing homelessness. It is incumbent upon Cityscape 27

Shelton, DeChants, Bender, Hsu, Santa Maria, Petering, Ferguson, Narendorf, and Barman-Adhikari policy makers, organizational leaders, and YA homelessness advocates to extend their intervention efforts beyond individual supports and services to include structural interventions aimed at dismantling systems and institutions rooted in centuries of racist housing policy.

Study findings support the call for further research investigating the intersection of race/ethnicity and LGBTQ identities among vulnerable populations (Institute of Medicine, 2011). People of color are disproportionately impacted by homelessness (Jones, 2016), and LGBTQ YA of color were overrepresented in the current study. Future research should continue to disaggregate the experiences of subgroups within the population of LGBTQ YA experiencing homelessness. As the intersections of race/ethnicity, gender identity, and sexual orientation contribute to differential experiences of homelessness among YA, these intersections should be considered in the 

development of future research design, policy, and programmatic interventions. It is imperative to uphold existing policies regarding the collection of sexual and gender identity data and to make guidance available where such policies do not yet exist.

Limitations

Consider certain limitations when interpreting the study findings. The cross-sectional study design limits the ability to identify causal relationships. The purposive sampling strategy in this study limits the generalizability of the study findings. YA participants were all service seeking, and it is not clear whether young people more disconnected from services would report similar rates of experiences as reported here. YA were sampled strategically from seven distinct geographic regions to gather data that reflects the experiences of a diverse sample of LGBTQ YA experiencing homelessness. Though regionally diverse, data were collected from urban environments within each region. Findings, therefore, may not reflect the experiences of LGBTQ YA in rural and suburban locales. The survey was provided only in English, which may have excluded the experiences of YA who were not English speaking, thus findings may not reflect the experiences of non-English speaking LGBTQ YA who may experience further marginalization and less access to services.

Also, because YA under age 18 were excluded from participating, we did not assess the experiences and needs of minors experiencing homelessness among this potentially more vulnerable group of young people. Findings indicate differential experiences among Latino LGBTQ YA, so future efforts should include opportunities for participation among Spanish speaking YA. Additionally, the current analyses do not include multiracial YA. Their exclusion is a study limitation, as some groups of multiracial YA experience discrimination based on their race/ethnicity. Further, the race/ethnicity categories do not align with the measurements used in the U.S. Census, limiting comparisons across samples in other existing datasets. Although this study extends previous research efforts by using standardized sampling and data collection methods across seven locations, the sample is not nationally representative. Additionally, this study relied on self-reporting, with no method of objective verification. Though the survey was fully self-administered to reduce social desirability of face-to-face disclosure of sensitive information, the possibility of inaccurate reporting exists nonetheless.

28 Youth Homelessness

Homelessness and Housing Experiences among LGBTQ Young Adults in Seven U.S. Cities Conclusion

The purpose of this study was to examine the homelessness and housing experiences of LGBTQ 

YA, identify how these experiences differ among particularly marginalized and understudied subgroups of LGBTQ YA (bisexual and transgender identifying YA), and examine how experiences compare across racial subgroups within the LGBTQ, bisexual, and transgender YA samples. 

Findings highlight differential experiences among subgroups of LGBTQ YA experiencing 

homelessness and support the disaggregation of understudied and multiply marginalized LGBTQ 

YA to address their needs more adequately. For example, in the full LGBTQ sample and the subsamples, higher percentages of Black YA reported experiencing racial discrimination, and higher percentages of Black transgender YA reported discrimination related to their housing status. 

Prior research finds racial discrimination and discrimination related to housing status are associated with depressive symptoms among Black YA (Gattis and Larson, 2016). The role of discrimination should be considered when developing programs and policies to support the mental health of Black YA experiencing homelessness.

Study findings also highlight differential experiences of transgender YA, including higher rates of aging out of foster care and higher rates of being kicked out of or running away from family and foster care settings than the full LGBTQ sample and the subsample of bisexual YA. Transgender YA were almost twice as likely to have been kicked out of or run away from foster care settings, suggesting a potential lack of trans-affirming foster parents and supportive foster care settings.

Significant differences were found in durations of homelessness between Black and Latino bisexual YA in this study. Latino YA were more likely than their Black and White peers to report stress related to finding a place to sleep and a desire for help with housing. These findings indicate that programmatic interventions may not be successfully engaging Latino YA. Despite its limitations, this study provides a foundation from which other researchers may further investigate the specific causes of and potential solutions for addressing the differential experiences found among LGBTQ 

YA experiencing homelessness.

References

Abramovich, Alex. 2016. “Understanding how policy and culture create oppressive conditions for LGBTQ2S youth in the shelter system,”  Journal of Homosexuality 64 (11): 1484–1501.

Ansara, Y. Gavriel, and Peter Hegarty. 2012. “Cisgenderism in Psychology: Pathologizing and misgendering children from 1999 to 2008,”  Psychology & Sexuality 3: 137–160.

Ansara, Y. Gavriel, and Israel Berger. 2016. Cisgenderism.  The Wiley Blackwell Encyclopedia of Gender and Sexuality Studies. Hoboken, NJ: Wiley-Blackwell: 1–3.

Begun, Stephanie, and Shanna K. Kattari. 2016. “Conforming for survival: Associations between transgender visual conformity/passing and homelessness experiences,”  Journal of Gay and Lesbian Social Services 28 (1): 54–66.

Cityscape 29

Shelton, DeChants, Bender, Hsu, Santa Maria, Petering, Ferguson, Narendorf, and Barman-Adhikari Cahill, Sean, Robbie Singal, Chris Grasso, Dana King, Kenneth Mayer, Kellan Baker, and Harvey Makadon. 2014. “Do Ask, Do Tell: High Levels of Acceptability by Patients of Routine Collection of Sexual Orientation and Gender Identity Data in Four Diverse American Community Health Centers,”  PLoS ONE  9 (9): e107104.

Castellanos, H. Daniel. 2016. “The role of institutional placement, family conflict, and homosexuality in homelessness pathways among Latino LGBT youth in New York City,”  Journal of Homosexuality 63 (5): 601–632.

Choi, Soon Kyu, Bianca D.M. Wilson, Jama Shelton, and Gary Gates. 2015.  Serving our youth 2015: The needs and experiences of LGBTQ youth experiencing homelessness. Los Angeles, CA: Williams Institute with the True Colors Fund.

Cleverley, Kristin, and Sean A. Kidd. 2011. “Resilience and suicidality among homeless youth,” 

Journal of Adolescence 34: 1049–1054.

Cochran, Bryan N., Angela J. Stewart, Joshua A. Ginzler, and Ana Mari Cauce. 2002. “Challenges faced by homeless sexual minorities: Comparison of gay, lesbian, bisexual, and transgender homeless adolescents with their heterosexual counterparts,”  American Journal of Public Health 92 

(5): 773–777.

Collins, Jennifer, and Natasha Slesnick. 2011. “Factors associated with motivation to change HIV 

risk and substance use behaviors among homeless youth,”  Journal of Social Work Practice in the Addictions 11 (2): 163–180.

Collins, Patricia Hill. 2015. “Intersectionality’s definitional dilemmas,”  Annual Review of Sociology 41: 1–20.

Corliss, Heather L., Carol S. Goodenow, Lauren Nichols, and S. Bryn Austin. 2011. “High burden of homelessness among sexual minority adolescents: Findings from a representative Massachusetts high school sample,”  American Journal of Public Health 101 (9): 1683–1689.

Cray, Andrew, Katie Miller, and Laura E. Durso. 2013. Seeking shelter: The unmet needs of LGBT 

homeless youth. Washington, DC: Center for American Progress. https://www.americanprogress.

org/wp-content/uploads/2013/09/LGBTHomelessYouth.pdf.

Crenshaw, Kimberle. 1991. “Mapping the margins: Intersectionality, identity politics, and violence against women of color,”  Stanford Law Review 43 (6): 1241–1299.

Cull, Mark, Hazel Platzer, and Sue Balloch. 2006.  Out on my own: Understanding the experiences and needs of homeless lesbian, gay, bisexual and transgender youth. Brighton and Hove: Health and Social Policy Research Centre. http://homelesshub.ca/resource/out-my-own-understanding-

experiences-and-needs-homeless-lesbian-gay-bisexual-and

Durso, Laura E., and Gary Gates. 2012.  Serving our youth: Findings from a national survey of service providers working with LGBT youth who are homeless or at risk of becoming homeless. Los Angeles, CA: Williams Institute with True Colors Fund and The Palette Fund.

30 Youth Homelessness

Homelessness and Housing Experiences among LGBTQ Young Adults in Seven U.S. Cities Ferguson-Colvin, Kristin M. and Elaine M. Maccio. 2012.  Toolkit for Practitioners/Researchers Working with Lesbian, Gay, Bisexual, Transgender, and Queer/Questioning (LGBTQ) Runaway and Homeless Youth (RHY). National Resource Center for Permanency and Family Connections Silberman School of School of Social Work. http://www.hunter.cuny.edu/socwork/nrcfcpp/info_

services/download/LGBTQ%20HRY%20Toolkit%20September%202012.pdf.

Gaetz, Stephen. 2014.  Coming of age: Reimagining the response to youth homelessness in Canada. 

Toronto: The Canadian Homelessness Research Network Press.

Gangamma, Rashmi, Natasha Slesnick, Paula Toviessi, and Julianne Serovich. 2008. “Comparison of HIV risks among gay, lesbian, bisexual and heterosexual homeless youth,”  Journal of Youth and Adolescence 37: 456–464.

Gattis, Maurice N. 2013. “An ecological systems comparison between homeless sexual minority youths and homeless heterosexual youths,”  Journal of Social Services Research 39 (1): 38–49.

Gattis, Maurice N., and Andrea Larson. 2017. “Perceived microaggressions and mental health in a sample of Black youths experiencing homelessness,”  Social Work Research 41 (1): 7–17.

———.2016. “Perceived racial, sexual identity, and homeless status-related discrimination among Black adolescents and young adults experiencing homelessness: Relations with depressive symptoms and suicidality,”  American Journal of Orthopsychiatry 86 (1): 79–90.

Institute of Medicine. 2011.  The health of lesbian, gay, bisexual, and transgender people: Building a foundation for better understanding. Washington, DC: National Academies Press.

James, Sandy E., Jody L. Herman, Susan Rankin, Mara Keisling, Lisa Mottet, and Ma’ayan Anafi. 

2016. The report of the 2015 U.S. Transgender survey. Washington, DC: National Center for Transgender Equality.

Johnson, Guy, and Chris Chamberlain. 2008. “From youth to adult homelessness,”  Australian Journal of Social Issues 43 (4): 563–582.

Jones, Marian Moser. 2016. “Does race matter in addressing homelessness? A Review of the literature,”  World Medical and Health Policy 8 (2): 139–156.

Karabanow, Jeff. 2004.  Being young and homeless: Understanding how youth enter and exit street life. New York: Peter Lang.

Keuroghlian, Alex S., Derri Shtasel, and Ellen L. Bassuk. 2014. “Out on the street: A public health and policy agenda for lesbian, gay, bisexual, and transgender youth who are homeless,”  American Journal of Orthopsychiatry 84 (1): 66–72.

Kidd, Sean A., and Michelle R. Carol. 2007. “Coping and suicidality among homeless youth,” 

Journal of Adolescence 30 (2): 283–296.

Cityscape 31

Shelton, DeChants, Bender, Hsu, Santa Maria, Petering, Ferguson, Narendorf, and Barman-Adhikari Lankenau, Stephen E., Michael C. Clatts, Dorinda Welle, Lloyd A. Goldsamt, and Marya Gwadz. 

2005. “Street careers: homelessness, drug use, and sex work among young men who have sex with men (YMSM),”  The International Journal of Drug Policy 16: 10–18.

Maccio, Elaine M., and Kristin M. Ferguson-Colvin. 2016. “Services to LGBTQ runaway and homeless youth: Gaps and recommendations,”  Children and Youth Services Review 63: 47–57.

Milburn, Norweeta G., Philip Batterham, George Ayala, Eric Rice, Rosa Solorio, Kate Desmond, Lynwood Lord, Javier Iribarren, and Mary Jane Rotherman-Borus. 2010. “Discrimination and mental health problems among homeless minority young people,”  Public Health Reports 125 (1): 61–67.

Moon, Martha W., William McFarland, Timothy Kellogg, Michael Baxter, Mitchell H. Katz, Duncan MacKellar, and Linda A. Valleroy. 2000. “HIV risk behavior of runaway youth in San Francisco,” 

Youth & Society 32 (2): 184–201.

Morton, Matthew H., Amy Dworsky, Jennifer L. Matjasko, Susanna R. Curry, David Schleuter, Raul Chavez, and Anne F. Farrell. 2017. “Prevalence and correlates of youth homelessness in the United States,”  Journal of Adolescent Health 62: 14–21.

Murphy, Peggy W., Terry C. Davis, Sandra W. Long, Robert H. Jackson, and Barbara C. Decker. 

1993. “Rapid estimate of adult literacy in medicine (REALM): a quick reading test for patients,” 

Journal of Reading 37 (2): 124–130.

Olivet, Jeff, and Marc Dones. 2016. “Intersectionality and race: How racism and discrimination contribute to homelessness among LGBTQ youth. In  At the intersections: A collaborative report on LGBTQ youth homelessness, edited by Christa Price, Coco Wheeler, Jama Shelton, and Meghan Maury, New York: True Colors Fund and the National LGBTQ Task Force.

Page, Michelle 2017. “Forgotten youth: Homeless LGBT youth of color and the Runaway and Homeless Youth Act,”  Northwestern Journal of Law and Social Policy 12 (2): 17–45.

Perez, Beatrix, and Harriett Ro. 2011. “‘Couch surfing’ of Latino foster care alumni: Reliance on peers as social capital,”  Journal of Adolescence 34: 239–248.

Phillips, Anna Elizabeth, Gabriella B. Gomez, Marie-Claude Boily, and Geoffrey P. Garnett. 2010. 

“A systematic review and meta-analysis of quantitative interviewing tools to investigate self-reported HIV and STI associated behaviours in low-and middle-income countries,”  International Journal of Epidemiology 39 (6): 1541–1555.

Pyne, Jake. 2011. “Unsuitable bodies: trans people and cisnormativity in shelter services,” 

Canadian Social Work Journal 28 (1): 129–138.

Quintana, Nico Sifra, Josh Rosenthal, and Jeff Krehely. 2010.  On the streets: The Federal response to gay and transgender homeless youth. Washington, DC: Center for American Progress. 

https://www.americanprogress.org/issues/lgbt/reports/2010/06/21/7983/on-the-streets/

32 Youth Homelessness

Homelessness and Housing Experiences among LGBTQ Young Adults in Seven U.S. Cities Rew, Lynn, Matthew Grady, Tiffany A. Whittaker, and Katherine Bowman. 2008. “Interaction of duration of homelessness and gender on adolescent sexual health indicators,”  Journal of Nursing Scholarship 40 (2): 109–115.

Rew, Lynn, Tara Powell, and Sanna Thompson. 2016. “Stress on the streets: Validation of a measurement,”  Journal of Social Distress and the Homeless 25 (1): 11–18.

Rew, Lynn, Tiffany A. Whittaker, Margaret A. Taylor-Seehafer, and Lorie R. Smith. 2005. “Sexual health risks and protective resources in gay, lesbian, bisexual, and heterosexual homeless youth,” 

Journal for Specialists in Pediatric Nursing 10 (1): 11–19.

Shelton, Jama. 2015. “Transgender youth homelessness: Understanding programmatic barriers through the lens of cisgenderism,”  Children and Youth Services Review 59: 10–18.

Shelton, Jama, and Lynden Bond. 2017. “‘It just never worked out’: How transgender and gender expansive youth understand their pathways into homelessness,”  Families in Society: The Journal of Contemporary Social Services 98 (4): 284–291.

United States Interagency Council on Homelessness. 2013. Framework to End Youth 

Homelessness: A Resource Text for Dialogue and Action. https://www.usich.gov/resources/

uploads/asset_library/USICH_Federal_Youth_Framework.pdf

Van Leeuwen, James M., Susan Boyle, Stacy Salomonsen-Sautel, D. Nico Baker, J.T. Garcia, A. 

Hoffman, and C. Hopfer. 2006. “Lesbian, gay, and bisexual homeless youth: An eight-city public health perspective,”  Child Welfare League of America 85 (2): 151–170.

Walls, N. Eugene, Pam Hancock, and Hope Wisneski. 2007. “Differentiating the social service needs of homeless sexual minority youths from those of non-homeless sexual minority youths,” 

Journal of Children and Poverty 13 (2): 177–205.

Whitbeck, Les B., Xiaojin Chen, Dan R. Hoyt, Kimberly A., and Kurt D. Johnson. 2004. “Mental disorder, subsistence strategies, and victimization among gay, lesbian, and bisexual homeless and runaway adolescents,”  The Journal of Sex Research 41 (4): 329–342.

Wilber, Shannan, Caitlin Ryan, and Jody Marksamer. 2006. Best Practice Guidelines: Serving LGBT 

Youth in Out-of-Home Care. Washington, DC: Child Welfare League of America. 

http://www.nclrights.org/wp-content/uploads/2013/07/bestpracticeslgbtyouth.pdf.

Wilson, Melvin N. 1989. “Child development in the context of the Black extended family,” 

American Psychologist 44 (2): 380–385.

Cityscape 33

34 Youth Homelessness

“Nothing is for free. .”: Youth  

Logics of Engaging Resources  

While Unstably Housed

by Gina M. Samuels, 

Christine Cerven, Susanna 

Gina M. Samuels

R. Curry, and Shantá R. 

University of Chicago, School of Social Service Administration

Robinson

Christine Cerven

Chapin Hall at the University of Chicago

Susanna R. Curry

California State University-Sacramento, Division of Social Work

Shantá R. Robinson

University of Chicago, School of Social Service Administration

Disclaimer: The substance and findings of the work are dedicated to the public. The authors are solely responsible for the accuracy of the opinions, statements, and interpretations contained in this publication, and these do not necessarily reflect the views of the government or any of Chapin Hall’s partners.

Abstract

This article presents findings from a national study of 215 youth, ages 13 to 25, experiencing housing instability in five U.S. counties. Drawing on life-course interviews, a housing timeline tool, and background survey data, we explored the factors associated with their use and rejection of both formal and informal resources. Using inductive conceptual methods of analysis, we created a model of “youth logics of engagement,” illustrating three factors that shaped how youth interpreted the costs versus benefits of using available resources. The three interrelated factors were (1) identity protection, (2) accumulated experience, and (3) personal agency. We feature four vignettes as examples that highlight how these three factors drive logics—processes of evaluating the pros and cons—of engaging resources in ways that are both shared and individually unique across all 215 participants. Our findings support the need to expand our attention beyond youth’s physical risks, to include risks and costs that are emotional, psychological, and relational. Youth’s management of these often-hidden elements of risk sometimes increased their exposure to physical risk as a consequence of rejecting or avoiding resources that might compromise their emotional, psychological, or relational well-being. 

Key Terms: Help-seeking, Identity, Positive youth development, Risk management, Qualitative research, Resilience, Self-reliance, Youth decision making, Youth homelessness, Service use

Cityscape: A Journal of Policy Development and Research •  Volume 20, Number 3 • 2018

Cityscape 35

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Samuels, Cerven, Curry, and Robinson

Introduction

The most recent national estimates suggest nearly 4.2 million young people, 1 in every 10 18- 

to 25-year olds and 1 in every 30 13- to 17-year olds, has experienced some form of housing instability within a period of 1 year (Morton, Dworsky, and Samuels, 2017). This statistic alone is concerning. It becomes more alarming, however, when one considers the host of adversities that typically characterize the developmental contexts of these young people prior to their homelessness (Bender et al., 2015; Davies and Allen, 2017). Youth who experience housing instability often describe early adverse childhood contexts mired in intergenerational poverty; parental struggles with addiction and/or mental health conditions; family instability; and chronic family conflict including abuse, neglect, and violence (Haber and Toro, 2004; Laird, 2007).

Youth experiencing unaccompanied homelessness (that is, on their own without a parent or guardian) represent a unique population in which to explore help-seeking and engagement. By definition, these young people are assumed to be disengaged from the informal support of parents and extended family; resources that are normative and critical to healthy child development and achievement even into early adulthood (Arnett, 2000). Because these youth have accumulated experience with a host of adversities both while homeless and prior to homelessness (Davies and Allen, 2017; Keuroghlian, Shtasel, and Bassuk, 2014; Mallett, Rosenthal, and Keys, 2005) many have also been exposed to formal service systems (for example, the child welfare system). 

Some evidence supports the idea that some youth can be reticent to engage formal services as trustworthy or reliable sources of help (Malow et al., 2007; Stewart et al., 2010). Although the experience of unaccompanied youth homelessness creates an acute need for supports, it may also strengthen one’s existing belief that engaging even needed resources is risky, and as such, may be more harmful than helpful.

This study investigates youth perspectives on risks of engaging resources, specifically elevating how young people framed why they use or reject resources. We take a resilience approach to understanding youth’s behavior as protective attempts to anticipate and mitigate a host of risks or negative costs relationally, emotionally, physically, and psychologically. Our article contributes to the literature on youth resilience specifically, and positive youth development generally, by focusing on youth’s processes of meaning making. Our findings illuminate their personal agency and power in activating resilience as they manage the risks they perceived were inherent in using both informal and formal resources and supports. We use the word “logics” to label the process of how youth made meaning of, and evaluated risks and benefits attached to, existing supports and resources. The title “Nothing is for free” acknowledges their overwhelming endorsement of a belief (grounded in lived experience) that asking for and receiving help often comes with personal, relational, emotional, and psychological costs that might outweigh the gains.

Background and Significance

Not all youth are at equal risk of becoming homeless. Young adults who transition to adulthood from foster care are at a higher risk of becoming homeless during early adulthood (Dworsky, Napolitano, and Courtney, 2013). Gay, lesbian, bisexual, transgender and queer (LGBTQ) youth have a 120 percent higher risk for becoming homeless compared with heterosexual and cisgender 36 Youth Homelessness

“Nothing is for free...”: Youth Logics of Engaging Resources While Unstably Housed youth (Morton, Dworsky, and Samuels, 2017), comprising an estimated 20-40 percent of the youth homeless population in the U.S. (Kipke and Unger, 1997; Quintana, Rosenthal, and Krehely, 2010; Morton, Dworsky, and Samuels, 2017). Most research associates their increased risk for homelessness with the severe rejection, homophobia, and transphobia they often endure within their own families (Durso and Gates, 2012). Recent estimates also suggest that Latin@ youth and African-American youth are at higher risk than White youth for homelessness at 33 percent and 83 percent respectively (Morton, Dworsky, and Samuels, 2017). Perhaps most stunning, this same report indicated that youth who had less than a high school diploma were at a 346 percent higher risk than their high school-graduated peers to have experienced homelessness in the past year. These findings suggest the condition of unaccompanied youth homelessness is a symptom of many structural problems and failings in the ability of society’s basic systems and institutions to be equally safe, supportive, and growth fostering for all young people (Lippy et al., 2017).

Once homeless, youth are further exposed to a host of increased threats to their literal survival and to their emotional and physical health (Bender et al., 2015). Young people face increased risks for sexual exploitation, victimization, abuse, substance use, pregnancy, and incarceration (Bender et al., 2010; Ferguson et al., 2012; Thompson et al., 2008). Research has identified sub-groups of youth who face higher levels of vulnerability while homeless, including women (Ensign and Panke, 2002), LGBTQ youth (Abramovich, 2013; Herman, 2013; Keuroghlian, Shtasel, and Bassuk, 

2014), youth of color (Gattis and Larson, 2016; 2017), and youth who hold multiple stigmatized identities by race, gender, or sexuality, such as transgender young women of color (Ensign and Panke, 2002; Page, 2017). Taken together, homelessness is not only a critical public health concern, it is a serious developmental threat to the young people who must survive and attempt escaping threats to their basic physical safety and survival. Their need for supports and help are undeniably high. Understanding how and under what conditions youth decide to seek and engage a resource for that support is an understudied but critical element of their achieving and retaining stability and wellbeing into adulthood.

Exploring Factors and Processes of Resilience

Since the early 1970s, scholars have sought to explore the developmental and behavioral effects of experiencing trauma, adversity, and developmental disruptions that place the wellbeing and health of individuals, families and entire communities at risk (Masten, 2018). Risk as pathology and dysfunction were major themes in early scholarly research as both an overarching theoretical construct and an analytical lens (Greene, 2014; Jenson and Fraser, 2016; Masten, 2018; Zimmerman, 2013). Starting in the late 1980s, the focus expanded to resilience, defined as a person’s capacity to withstand, rebound, mitigate, or “adapt successfully to disturbances that threaten system function, viability, or development” (Masten, 2014: 6). Today, most scholars consider one’s resilience as a dynamic interplay between personal and environmental processes and characteristics (Lerner et al., 2013; Masten, 2018). Resilience is normative to all human beings and is multifaceted, context driven, developmental, and mutually reinforcing (Greene, 2014).

In this article, we take a similar approach to understanding and defining resilience as mutually reinforcing interactions, behaviors, and systems of meaning that indicate “adaptive significance” 

(Lerner et al., 2013: 1). Although most studies of youth homelessness focus solely on the risks Cityscape 37

Samuels, Cerven, Curry, and Robinson

youth face, a literature on resilience and strength among youth navigating homelessness is emerging (Cleverley and Kidd, 2011; Lindsey, Kurtz, Jarvis, Williams, and Nackerud. 2000; Kidd and Davidson, 2007; Perron, Cleverley, and Kidd, 2014). Within and beyond the field of homelessness, related research is sought to focus on identity as a critical but often hidden dimension of risk and resilience particularly for minoritized and stigmatized young people (Abramovich, 2017; Forrest-Bank, Nicotera, Anthony & Jenson, 2015) and among youth 

experiencing family disruption (Bender et al., 2007; Kools, 1997; Perron et al., 2014). Here, we examine “youth logics of engagement”−behaviors and systems of meaning making that facilitate both risk and resilience.

Intersectionality

In this study, we explicitly engage theories of intersectionality as a theoretical tool for exploring and articulating risk and resilience as tied to social identities that are oppressed or privileged. 

First introduced by third-wave feminist writing (Hill Collins and Bilge, 2016), the idea of intersectionality is typically used to underscore how a person’s multiple oppressed and/or privileged statuses intersect in mutually reinforcing ways (Hutchinson, 2001; Samuels and Ross-Sheriff, 2007). For example, not all women experience womanhood or sexism in the same way, and women with class, cisgender, race, and/or sexual identity privilege can enact oppression on other women without those privileged statuses. Intersectionality is certainly relevant for young people navigating homelessness, a stigmatized status. Young transgender women of color often report experiencing simultaneous and compounded stigmas tied to racism, homophobia, and transphobia in society and while seeking services (Abramovich, 2017; Quintana, Rosenthal, and Krehely 2010; Price, Wheeler, Shelton, and Maury, 2016). Throughout, this article expands beyond single categories and typologies of experience, to examine intersecting identities and social statuses that shape differences in individual behavior, experience, and assessments of risk.

Young People and “Help-seeking”

Help-seeking is typically defined as the act and process of identifying and using formal or informal relationships and resources to address a problem or personal struggle (Rickwood, Deane, and Wilson, 2007). However, help-seeking as leading to actual use of (that is, engaging) a resource is a complex and relational process involving an interpretive awareness of the problem and recognizing the need for help, the ability to identify a potential solution, the actual accessibility and availability of a resource, and a willingness to disclose information and one’s need for help to another (Kauer, Mangan, and Sanci, 2014). Given the vulnerability of many youth and emerging adults (Arnett, 2000), particularly those navigating homelessness, it is essential for stakeholders to understand the barriers to engaging potentially critical resources.

Research outside of homelessness has sought to explain the general reluctance of young people to seek a formal resource when it appears necessary to others (Pryce, Napolitano, and Samuels, 2017; Rutman and Hubberstey, 2016). Reported reasons range from lack of awareness and perceiving too many barriers to access to concern about provider characteristics (Gulliver, Griffiths, and Christensen, 2010) and negative attitudes toward help-seeking in general (Rickwood, Deane, 38 Youth Homelessness

“Nothing is for free...”: Youth Logics of Engaging Resources While Unstably Housed and Wilson, 2007). Research also finds that anticipating providers’ stigma, prejudice, and discrimination can cause clients to avoid formal services (Abramovich, 2013 2017; Page, 2017; Scott, McMillen, and Snowden, 2015; Stotzer, Silverschanz, and Wilson, 2013). On the other hand, positive attitudes or trust toward professionals and social encouragement are found to facilitate help-seeking (Rickwood, Deane, and Wilson, 2007).

Patterns of Help-Seeking among Youth Experiencing Homelessness

Research to date suggests that many youth experiencing homelessness, like youth in general, underutilize available formal services (DeRosa et al., 1999; Kipke and Unger, 1997). In particular, estimates of the proportion of homeless youth using shelters range from as low as 7 percent to a high of only 40 percent (Ha et al., 2015; Carlson et al., 2006; DeRosa et al., 1999). In general, reasons youth report rejecting formal resources include strict rules, their distrust of adults, or lack of physical safety in shelter facilities (DeRosa et al., 1999; Pedersen, Tucker, and Kovalchik, 2016).

Some research indicates that youth reject resources because they value being self-reliant (Barker, 2014; Garrett et al., 2008; Ha et al., 2015). These studies suggest that negative experiences with institutions, adults, and parents can cause youth to mistrust their support (Barker, 2014; Kidd, 2003; Kurtz et al., 2000; Samuels, 2008; Thompson et al., 2006). Other youth report experiences with professionals that left them feeling dehumanized and disrespected, causing them to avoid engaging formal resources in general (Christiani et al., 2008; Ha et al., 2015).

The Role of Identity

A small but critical body of scholarship suggests that sexual identity, gender identity, and racial/

ethnic identity separately and together may also influence one’s perception and use of resources. 

For example, LGBTQ youth sometimes report a preference to sleep on the streets because shelters are often sources of homophobic or transphobic violence and discrimination (Abramovich, 2013; 2017). Many report rejection and stigma in their families of origin to such a degree that it has threatened or destroyed their sense of unconditional family belonging (Robinson, 2018). Although many LGBTQ youth often prefer LGBTQ-attuned services, few resources are attuned to the varied needs and substantial diversity within this population (Page, 2017; Shelton, 2015, 2016; Stotzer et al., 2013). This finding echos research in other fields that highlight identity safety (Gamarel et al., 2014), the centrality of a new or tenuous identity (Gunn and Samuels, in press; Shade et al., 2012), or identity stigma (Abramovich, 2013, 2017; Gunn and Samuels, in press; Forrest-Bank et al., 2015) as critical to the meaning youth make of the risks as they experience social service systems and professionals (Feinstein, 2015).

One’s identities tied to race, ethnicity, and culture, and experience of discrimination and stereotype threat may also influence help-seeking while homeless. Studies suggest that African-American youth, may be less likely than other youth to identify as homeless, and as a result, may avoid resources labeled as such (Winetrobe et al., 2017). Relatedly, Hickler and Auerswald (2009) found that although White youth rejected shelters due to strict rules and safety issues, African-American youth rejected shelters because they refused to identify as “homeless,” a label implying that they had failed. Identity management of stigmatized or discredited statuses and identities in general is a seriously understudied, but likely an important, element of help-seeking.

Cityscape 39

Samuels, Cerven, Curry, and Robinson

Our study sought to build on and expand the existing research on help-seeking and resilience to explore youth perspectives on what factors shape their use or avoidance of  both formal and informal resources in their environments. Few studies have examined risk management as multidimensional or created a comprehensive conceptual model that explains why youth may reject resources they believe they need. Even fewer analyses have contributed a conceptualization of the help-seeking processes that highlight hidden dimensions of risk that youth are managing separately or together (Liang et al., 2005). A deep understanding of how youth make meaning of the emotional, psychological, and relational dimensions of risk, and the factors that inform these processes of discernment overtime, is an important contribution of this paper.

Method

This study was part of a larger national research and policy initiative to end unaccompanied youth homelessness in the U.S., Voices of Youth Count (VoYC). VoYC involved a multi-component research design including (1) a national household survey, (2) point-in time counts and brief surveys of homeless and unstably housed youth, (3) a survey of service providers, (4) an evidence review, (5) a policy and fiscal review, and (6) in-depth interviews. The target population for VoYC 

was youth ages 13 to 25 who ran away, were homeless, or were unstably housed without a parent or caregiver. The VoYC initiative used a broad definition of homelessness, consistent with the most inclusive federal definitions, by including different kinds of sleeping arrangements involving the lack of a safe and stable alternative, including the streets, shelters, motels, couch surfing, as well as a host of other contexts not intended for permanent residence (for example, waiting rooms, stairwells, cars, abandoned buildings). In this article, we present findings from the In-Depth Interview component (hereafter referred to as the IDI).

The IDI is a mixed-method study designed to highlight youth-driven insight into the causes, conditions, and consequences of the diverse experiences of running away, homelessness, and being unstably housed. In the following, we briefly outline the study’s methods for site selection, recruitment, data collection, and analysis. The Institutional Review Board at the University of Chicago approved all procedures associated with this study, including a waiver of the requirement for parental consent for minor youth.

Site Selection and Recruitment

Drawing on VoYC’s 22 randomly selected partner sites, the IDI used purposive methods (that is, intentional rather than random) to select and partner with 5 of these 22 counties. Selection criteria included diversity in geography, urbanicity, and homeless youth service infrastructure as well as unique local factors such as proximity to a national border, climate, and regional demographics. Ultimately, we partnered with one small and four more urban counties: Cook County, Illinois; Philadelphia County, Pennsylvania; San Diego County, California; Travis County, Texas; and Walla Walla County, Washington. The goal was to interview 40 youth at each site (See exhibit 1 for sample demographics).

Each county included a local team of interviewers, transcribers, and lead agencies that served as a home base for field staff. Recruitment strategies included using information from focus groups held at each locale with youth and providers about “hot spots” where homeless youth hang out, posting flyers, 40 Youth Homelessness

“Nothing is for free...”: Youth Logics of Engaging Resources While Unstably Housed online announcements, as well as directly contacting youth on the streets. We also used peer-driven methods (that is, snowball sampling), asking youth who completed interviews to share our contact information with others who we may not easily encounter. Most youth were recruited via agency referrals (n=50), direct recruitment of youth at an agency (n=48), street-based recruitment (n=28), peer referrals from study participants (n=36), and schools (n=9). We ultimately interviewed 215 youth (See exhibit 1 within the results section for sample demographics).

Data Collection

Data were collected from July 2016 through March 2017. All youth were informed about the study, their rights, and the voluntary nature of their participation. We received Internal Review Board (IRB) approval to collect verbal assent from youth under 18 and verbal consent for the youth 18 

and over. Interviews were audio recorded and lasted, on average, 1.5 hours. Participants received a $25.00 Visa gift card and a local service/resource guide that we created for this study. Youth shared their current age (not birthdate) and selected their own pseudonyms for use during interviews and within all reports. The IDI’s research design was comprised of four interwoven data collection methods and included narrative interviews, a housing timeline tool, a background survey, and interviewer reflection logs. All IDI components could be completed in either Spanish or English.

Interviews began by asking the youth: “If you were to think of your experience with housing instability as a story, where does your story begin?” The interviewer then used the “Housing Timeline Tool” to document the young person’s story of housing instability over time and throughout the interview, probed around any changes in six key domains of interest: jobs/

employment, family, friends/peers/intimate partners, school/education, use of formal and informal supports, and health/well-being. Participants also completed a survey on an iPad asking them to self-report identities tied to race, gender and sexuality, formal service use, government benefit use, education, and adversities experienced both while stably and unstably housed. All data were uploaded to NVivo Pro11, a qualitative software program.

Analysis

Analyses largely followed interpretive and Constructivist Grounded Theory Method (CGTM) approaches (Charmaz, 2006) and involved a three-phase year-long process. Briefly, phase I involved reviewing and comparing the survey data with the timelines and narrative interview data to create a single integrated database of the demographic and variable-based data (for example, history of foster care, preferred gender identity). Phase II involved cycles of reading and coding the narrative data together as a group. Over the course of Phases I and II, the group met weekly and used both descriptive and constant comparison techniques (Charmaz, 2006) to develop, revise, and finalize a codebook comprised of stable thematic codes including action-oriented codes (for example, facilitating informal resource use) and conceptual codes (for example, styles of engagement, statements of “youth logics”). All 215 transcripts were coded, and 25 percent of transcripts were double coded to ensure rigor and thorough coding.

In Phase III, we refined conceptual categories and tested the relationship between thematic concepts using constant comparison, axial coding, and dimensional analysis techniques, all Cityscape 41

Samuels, Cerven, Curry, and Robinson

typical of CGTMs of analyses (Charmaz, 2006; Leech and Onwuegbuzie, 2011). These analytic methods are, in part, intended to test “hypotheses,” account for both confirming and disconfirming evidence across the entire sample and to ultimately build a model or conceptual framework that is explanatory across the data. For example, we asked the question, “Under what conditions do youth reject/engage a service? Why?” “What are the exceptions to this rule?” By actively seeking out exceptions and negative cases, these techniques move analyses beyond descriptive themes toward explanations that are responsive to the diversity within a sample.

In our analyses, this process produced three conditions—identity protection, prior experience, and personal agency—as explanatory of the differences and dynamic similarities that we observed across the sample in their engagement and rejection of services. It also resulted in identifying three broad styles of engagement: full engagement, selective engagement, disengagement. Use of these interpretive and conceptual methods of analysis also resulted in our choice to label the styles of engagement and articulate a process, rather than label the youth or numerically categorize typologies of youth. This decision means that the model and processes it depicts do not indicate types of youth (that is, engagers or disengagers). Rather, findings support the idea of  styles of engaging that are driven by common factors used in combination as youth consider a resource. 

Individual youth used all three engagement styles across their trajectories.

Finally, typical of Grounded Theory Methods, and specifically traditions that engage dimensional analyses (Bowers and Schatzman, 2009; Kools et al., 1996), we portray our findings visually (see exhibit 2). As such, this model is a comprehensive depiction of our analyses of all 215 interviews. 

However, to illustrate variance and diversity, even within a single youth, we present four vignettes as examples that highlight how the three factors of identity, accumulated experience, and personal agency shaped different interpretations of risk and, in turn, distinct patterns of engaging and rejecting both formal and informal resources.

Ensuring Rigor and Trustworthiness

We made use of several established methods within qualitative and interpretive research traditions to ensure a systematic and rigorous research process throughout (Hays et al., 2016; Sandelowski, 1993). We regularly involved key stakeholders and critical external reviews at each stage of this study including its conception, design, data collection, analysis, and findings. The research team also met a minimum of once a week throughout the year-long analysis process to discuss and critique emerging themes and concepts. Research team members, including interviewers, completed reflexive memoing and used consensus methods, which ensured consistency and 

systematic interpretations in coding all 215 transcripts. Finally, audit trails recorded key decision points, and we returned to the field to meet with key stakeholders to debrief emerging analyses and more final-stage reporting of our findings.

Results

The goal was to interview approximately 40 youth in each of the 5 sites for a total sample of 200. 

We were able to interview 215 young people (see exhibit 1). Most participants (86 percent) were age 18 or older. Slightly more than one-half identified as either Black/African-American (31 percent) or 42 Youth Homelessness

“Nothing is for free...”: Youth Logics of Engaging Resources While Unstably Housed White (23 percent), and 21 percent identified as multiracial. Most young people reported gender identities as either male (52 percent) or female (41 percent).

Youth were able to report their sexual identities on a spectrum. Although 58 percent identified as exclusively heterosexual/straight, 38 percent did not. Among those, 11 percent identified as bisexual and 10 percent as exclusively gay or lesbian. Nearly one-fourth reported being a parent and an additional 8 percent of youth (n=18) indicated that they or their partner were currently pregnant.

Exhibit 1

Characteristics of Participants (N=215)

Number

Percent

Age (in years)

13 to 17

31

14.4

18 to 21

112

52.1

22 to 25

72

33.5

Race/ethnicity

White

50

23.2

Black/African American

67

31.2

Latin@

30

14.0

American Indian or Alaskan Native

6

2.8

Asian

1

0.5

Multiracial

44

20.5

Other

4

1.8

Don’t know

1

0.5

Refused

7

3.2

Missing

5

2.3

Gender identity

Female

87

40.5

Male

112

52.1

Transgender M-F

8

3.7

Transgender F-M

4

1.8

Genderqueer/nonconforming

2

0.9

Other

1

0.5

Refused

0

0.0

Missing

1

0.5

Sexual orientation

100 percent heterosexual

125

58.1

Mostly heterosexual

16

7.4

Bisexual

24

11.2

Mostly gay/lesbian

8

3.7

100 percent gay/lesbian

21

9.8

Not sexually attracted to either males or females/asexual

1

0.5

Other

6

2.8

Don’t know

5

2.3

Refused

5

2.3

Missing

4

1.9

Cityscape 43

Samuels, Cerven, Curry, and Robinson

We also asked young people about their educational experiences, completion of high school, and involvement in the formal workforce. A slight majority of youth (60 percent) had already completed high school or a GED. However, only one-third of youth were formally employed. 

Relatedly, the term  disconnected youth refers to 16- to 24-year-olds who are neither working nor in school. Based on this definition, 46 percent of IDI participants would be considered disconnected.

We asked youth about their lifetime use of a select list of resources as well as government benefits. 

Among government benefits available, food stamps (63 percent) were the most commonly used, followed by Medicaid (33.5 percent) and WIC (16 percent). Over half (58 percent) indicated receiving subsidized lunch at school and less than half (44.5 percent) said they received transportation assistance. Only 8 percent reported receiving food vouchers. Nearly 44 percent of participants said that they did not use any of the services listed in exhibit 2.

Exhibit 2

Government Benefit Use (N =203)*

Currently Receiving

Ever Received

Number

Percent

Number

Percent

Food stamps/SNAP (Supplemental Nutrition 

90

44.3

128

63.1

Assistance Program)

TANF (Temporary Assistance for Needy Families)

8

3.9

17

8.4

Medicaid

48

23.6

68

33.5

State Children’s Health Insurance Program (S-CHIP)

3

1.5

7

3.4

WIC (Special Supplemental Nutrition Program for 

21

10.3

32

15.8

Women, Infants, and Children)

Housing Assistance (Section 8, public housing)

6

3.0

16

7.9

Supplemental Security Income (SSI)

11

5.4

16

7.9

Social Security Survivor’s Benefits

2

1.0

7

3.4

Social Security Disability Insurance (SSDI)

2

1.0

4

1.9

Unemployment insurance or worker’s compensation

0

0.0

2

1.0

Veteran’s benefits

0

0.0

0

0.0

*Participants could select multiple responses.

Youth reported receiving mental health services more than any other category of formal resource use. However, 40 percent indicated that they never used formal services for any of the listed reasons in exhibit 3.

Exhibit 3

Reasons for Service Receipt (N = 211)*

Number

Percent

Physical disability or developmental disability

19

 9.0

Alcohol or drug use

33

15.6

HIV/AIDS and related health issues

5

2.4

Mental health

81

38.4

None of the above

92

43.6

*Participants could select multiple responses.

44 Youth Homelessness

“Nothing is for free...”: Youth Logics of Engaging Resources While Unstably Housed These statistics, however, provide an incomplete story of their experiences with formal services or their need for them. They also hide how formal service use is shaped by existing or unavailable informal resources. The following section shares findings from the 215 narrative interviews and timelines to illustrate why youth used or rejected a much broader array of resources, and what shaped how they engaged that resource. We explore those factors and present their interrelationships within a model of engagement (See exhibit 2).

Exploring Youth Logics of Engagement

“I didn’t enroll in a shelter. I had too much pride. I just slept on the streets ...”  

Angel, Travis County

 “I’ve never tried to find anyone as a support because people have their own agendas and I understand that and, I can do things alone.” Kyle, San Diego County

 “Never depend on nobody … I’m on my OWN!” Paris, Cook County

Like Kyle, Angel, and Paris, youth often rejected both formal and informal resources, even when they were available. An important part of our analysis was to understand why. First, however, we define the three different ways youth engaged resources and these include disengagement, selective engagement, and full engagement. As we define each, we emphasize that these are not  types of youth but rather patterns in the way they engaged a particular source of assistance. Any individual youth may, as youth often did, display all three. Some youth also changed styles over the course of their housing instability. The four case examples illustrate that fluidity and diversity.

We intentionally use the word resource to include both formal and informal sources and kinds of assistance. It is a term that does not assume its receipt is experienced by youth as supportive or as helpful. We ultimately present our findings based on analysis of all 215 interviews in the form of a conceptual model (exhibit 2) and provide four youths’ stories to illustrate the complexity in how this process unfolded for youth differently over time.

Defining the Engagement Styles

As youth contemplated the available resources in their local and social environments, they faced choices about using them. This section defines the three ways in which youth engaged resources: Disengagement, Selective Engagement, and Full Engagement.

Disengagement

“I just wanted to stay out on the street ’cuz I don’t trust people and everybody.” Selena, Walla Walla County

 “… My mom raised me to take nothing and that nothing is for free.” D, San Diego County On one end of the spectrum, sometimes certain services or resources were rejected and avoided. 

When youth reported this style toward a particular resource, they often referenced past experiences of service systems (or their family systems) that left them less open to, or trusting of, help-seeking Cityscape 45

Samuels, Cerven, Curry, and Robinson

or help-receiving in general. This was the only pattern of engagement where some youth attempted to use this style exclusively and fully desired to disengage from all resources (formal and informal). 

The desire to disengage was explained by needing to affirm a high degree of self-reliance, blaming their own “pride,” or an insistence on doing things independently or, “on my own.” Even in these cases, however, young people still engaged some resources when externally forced to, due to harsh weather, an arrest, a pregnancy, or because their literal survival depended on it.

Although certainly youth with chronic stories of homelessness used disengagement styles, sometimes young people new to homelessness were exposed to a resource they had been avoiding, and the positive experience caused them to engage. For example, Alicia had been dead set against ever using shelters. As she continued to be homeless, however, an outreach worker encouraged her to go, “To be honest,  it’s a lot better than I thought … no one wants to be in a shelter, but it’s some place (pause) like-the irony of it, it’s some place you can call  home. Because like, you know, you don’t really have anywhere to go, but it’s like somewhere you stay for a while until you get all your stuff together.” Although few youth reported a complete change of heart (that is, change of logic), new experiences that were positive, particularly tied to formal resources, did indeed sometimes shift their levels of openness, even if selective, to engage a specific resource.

Selective Engagement

“I mean anything is better than being out on the street. But if it’s not geared for LGBT people, I can’t do it. Cause I’m just-uh-I just can’t not be myself.” London, Philadelphia County Selective engagement was by far the most common style of engaging. Selective engagement refers to a pattern of using specific criteria or conditions to engage or disengage on a case-by-case basis. 

This method resulted in either conditionally engaging an array of formal or informal services or being selective within a category in choosing one resource over another. For example, sometimes youth like London might only go to a particular shelter if it specifically targeted LGBTQ youth. 

Other times, youth would only engage a resource if important relationships could be retained or preserved (for example, shelter allows baby to stay with them, or will also accept a partner or friend). When these conditions were not met, youth rejected the resource often choosing to stay on the streets instead.

Sometimes selective engagement affected the length of time a young person would engage. For example, when couch surfing, it was very common for youth to only stay for a short period of time, not wanting to wear out their welcome or be a “burden.” This was especially true when youth couch surfed at the home of a relative, friend, or intimate partner’s family. For example, Ashanti (one of the youth we will explore in a following vignette) explained why she stopped staying at the family home of her boyfriend. Referring to the friend’s mother she said, “It wasn’t  her fault, I got kicked out and stuff. I didn’t wanna feel a burden to her, like take advantage… it’s not her responsibility to sit here and give me a home.”

Full Engagement

“I’m thinking that every week they just gonna give us stuff so I’m like, ‘alright!’ So I was there! 

But when I started going there I soon learned that they help you get into college, they help you 46 Youth Homelessness

“Nothing is for free...”: Youth Logics of Engaging Resources While Unstably Housed do resumes, they help you get jobs. They do all this stuff and I was like, “What?!?” I’m going here! I’m gonna come here!” Alanna, Philadelphia County

“I’m gonna take advantage of every damn thing they’re giving me! I’m gonna use it.”  

Dillinger, Cook County

Full engagement refers to a style where a young person deeply connects either to a trusted single resource or to a constellation of resources. It is important to remind readers that even young people who fully engaged one resource may choose to fully disengage or selectively engage another. 

Exhibiting this style took several forms. Sometimes youth used an array of informal and formal resources and rotated between them. This often meant youth would have a complex schedule of using shelter resources some days, couch surfing other days, the streets, possibly going to a relative’s home to do laundry and having a meal, and accessing other formal services in between. 

Other times, like Alanna and Dillinger, this style involved youth attaching (when available) to a single agency that provided many services.

For youth in the smaller county, where formal resources were limited, styles of full engagement more heavily involved informal networks including peers, strangers, occasionally parents, and even 

“trap houses” (that is, buildings and apartments that were eventually overrun by drug use and unstably housed youth and adults cycling in and out). This lack of formal housing resources often shaped their involuntary engagement with formal resources and systems (for example, juvenile justice). As Natalie described, however, this was still a welcomed substitute for disengaging shelters, “I’m grateful to be here (in juvenile detention) … I have a bed to sleep, I’m safe here … I have nowhere safe to go when I leave … this is like a second home to me.”

The following section will now explore the “why” behind these engagement patterns. Our analyses suggest each are deeply informed by perceiving and managing risks through the lens of three underlying factors: identity protection, accumulated lived experience, and personal agency (that is, one’s sense of control and autonomy). These factors shaped how youth weighed the risks and benefits of engaging the actual resources in their environments.

Managing Risks and Benefits: Identity Protection, 

Accumulated Experience and Personal Agency

As youth considered their available options and access to resources, their decision-making processes were overshadowed by a need to manage risk against the benefits. All young people in our study had, to varying degrees, prior experiences of receiving or being offered assistance from peers, adults, and/or professionals. Participants also shared a history of navigating complex and chronically stressed, toxic, and even traumatic relationships with parents or adult family members. 

Based on our survey data, youth reported experiencing discrimination and stigma within their own families (n=100) and being physically harmed by someone while stably housed (n=62) more than while on the streets (n=41). Content analyses of their narrative interviews included histories of foster care (n=82), parental struggles with addictions and mental illnesses (n=74), and chronic family conflict that often led to youth’s rejection by their own parents (n=65). Understandably, most remained leery of the hidden or explicit costs of receiving “help” from others. If someone Cityscape 47

Samuels, Cerven, Curry, and Robinson

offers a place to stay, what will they want in exchange? Was returning home to a mother addicted to drugs, or whose boyfriend is homophobic, riskier than sleeping on the streets? Was disclosing one’s homelessness to a teacher worth risking a call to child protective services? These were among the commonly articulated risks that young people in our study mentioned as they considered making use of a resource.

As youth differed in weighing the possible risks against the gains, so too did they vary in their individual degrees of openness to a resource and help in general. Not all youth had to navigate the same kinds of risks. Our analysis identified three factors that were commonly featured across all interviews and shaped their assessments of risks and gains of engaging resources: identity protection, past experience, and personal agency. We briefly define these concepts, present their interrelationship within a model (exhibit 2) and then offer four case examples to illustrate how these factors show up uniquely and complexly in the stories of young people’s engagement styles with both formal and informal resources.

Identity Protection

Although all 215 youth had identities that mattered to them, some youth held identities that they felt needed extra protection or were at risk for discrimination, stigma, or invalidation. This situation was overwhelmingly true for the youth in our study who identified as gender minorities (transgender youth), and as sexual minorities—in particular youth who identified as gay or lesbian. As London’s previous quote illustrated, an agency’s reputation for being a safe space for 

“LGBT people” was often a filter through which they assessed risk versus benefits. Youth also felt protective of or had to manage risk around other identities and statuses, however (for example, being a new parent, family belonging, and citizen status). Some of our following vignettes will highlight the ways in which youth weighed the benefits of engaging a resource at the expense of an identity that was stigmatized, marginalized, or discredited.

Accumulated Experience

Despite their young ages, participants also had acquired lived experiences that factored significantly into how they perceived the risk or gains attached to the people and resources in their environments. 

The emotional and relational residue, both positive and negative, that these lived experiences deposited were important reference points for all 215 young people. Specifically, accumulated experience contributed to a youth’s level of openness or trust that help would indeed be helpful. 

For some, like Selena who previously self-described as distrustful of “people and everybody” this could easily reinforce one’s reticence to engage a formal or informal support. However, other youth interpreted their experiences like Dillinger, who despite an equally negative lived experience of adults and services, remained open to the potential gains from using some resources. In the following vignettes, readers will hear youth reference their accumulated experiences as they weigh the risk and benefits and explain why they rejected or used one resource versus another.

Personal Agency

Finally, youth varied in their sense of personal agency—how they made use of and understood their own power to act, resist, and create change in their worlds. Again, for Dillinger who remained 48 Youth Homelessness

“Nothing is for free...”: Youth Logics of Engaging Resources While Unstably Housed open, his personal agency contributed to, and was affirmed by, actively engaging resources. This generated a corresponding positive experience for doing so and sense of personal power and control. For others like Selena who were less open, it caused her to steadfastly avoid shelters and acquire a resulting experience of avoiding the risk she feared. Youth also varied in the degree to which they believed their personal agency was further threatened by receiving help; that their pride, autonomy, and control (that is, personal agency) would be at risk and weakened by engaging a particular resource.

Exhibit 4, in the following, illustrates how these three factors fueled a process of youths’ logics of engagement in weighing risks and benefits of a given resource. In combination, these three factors were essential parts of youth’s toolkits, their logics for navigating not only housing instability but daily life. We recognize youth may likely carry many other concerns for harm with them as they move through their environments and assess risk. These three, however, were the most frequently mentioned among all 215 participants as they made meaning of their options. The four youth vignettes that follow are used to illustrate the diversity in how these factors show up uniquely within the logics of individual young people’s stories over time.

Exhibit 4

Youth Logics of Engagement—Identity, Experience and Personal Agency

Although youth in our sample were distinct in their understanding of risks versus benefits in engaging, what drove their engagement were the shared factors of identity protection, accumulated experience, and personal agency (exhibit 2). As mentioned previously, even for young people who expressed preferences to disengage from all resources, over time they were forced, or strongly encouraged, to make use of a resource. Jax, the first young person we feature, is an example of this. Other youth had complex and individually unique combinations of selective/engagement and disengagement across informal and formal resources, as well as intersecting identities and statuses. Ashanti, Brad, and Jamal (as well as Jax) represent that diversity. We present the vignettes to challenge the idea of “types of youth” and rather, that youth make meaning of the risks and Cityscape 49

Samuels, Cerven, Curry, and Robinson

benefits and use or reject resources to varying degrees based on factors articulated within this model that are often hidden to others but that mattered deeply to youth.

Putting It All Together: Youth Logics of Engagement in 

Individual Context

In the following, we provide four examples: Jax, Ashanti, Brad, and Jamal. These young people were selected to illustrate the diversity across these three factors, and each demonstrate how these factors matter in their processes of engaging and rejecting resources in general, and across informal and formal resources specifically. Each young person’s story also illustrates different expressions of the three factors (identity, experience, personal agency) that in turn, shaped their styles of engagement with the resources available to them. We provide these vignettes to illustrate that all youth, like these four, were both unique in their individual stories and ultimate patterns of engagement over time, and yet, all 215 stories have the common thread of managing risks through the lens of three shared factors: identity protection, accumulated experiences, and personal agency. 

Each of the four stories portray a degree of diversity in how these three factors manifest, but also demographic diversity in place of residence, gender identity, sexual identity, foster care experience, and mental/physical health. We italicize  identity protection, accumulated experience,  and  personal agency, as we narrate their stories to help emphasize their presence within the young persons’ logic of engagement over time.

“Jax”

Disengaged informal resources, selectively engaged one formal resource

Jax was an 18-year-old heterosexual male living in Travis County. Born in México, he and his family arrived to the U.S. undocumented. In addition to the strong confidence Jax exuded throughout his interview, his expression of  personal agency was projected by the tattoo he pridefully displayed, 

“TRUST NOBODY.” This extreme sense of  personal agency paired with his general distrust of others has caused him to reject adoption, and to turn down educational opportunities, “I just didn’t wanna depend on anybody no more and kind of just be independent.”

Jax has actually been independent most of his life, however; his is an  accumulated lived experience of loss and sense of rejection that shows up throughout his story. His mother abandoned the family when Jax was six. His father often left Jax and his older siblings alone for weeks at a time while he was away working. Eventually, Jax’s father was deported when Jax was 12. Parentless and undocumented in the U.S., Jax and his remaining brother spent most of their time fully disengaged from school to avoid being discovered and reported as undocumented. To avoid arrest, his brother ran away to México leaving Jax at 14, alone in the family trailer. Avoiding formal services, he rotated between staying at the trailer, couch surfing at friends, and living on the streets. A friend’s dad offered to help Jax find a job and go back to school but Jax refused. A cousin also reached out and invited Jax to come live with them and re-enroll in high school. He again rejected this resource and opportunity. Months later, exhausted, he moved to a small nearby town to work. Shortly thereafter, however, he bought drugs to commit suicide, “I tried killing myself … I was done … I just didn’t see no point in life no more … I didn’t see why God took everything from me like that.” 

50 Youth Homelessness

“Nothing is for free...”: Youth Logics of Engaging Resources While Unstably Housed Police eventually discovered Jax and took him to the hospital. Once stable, he entered foster care. 

Although the case plan was to obtain his paperwork for citizenship, Jax believes the paperwork fell through the cracks after his caseworker left. In an effort to provide support, his foster parents offered to adopt him. Jax also rejected this, “they were good … there was nothing wrong with them. They wanted to adopt me hard. I’d be like, ‘No, no!’ … They tried a lot.”

Despite this history of disengagement, strongly rooted in his own  accumulated experiences of rejection, Jax is currently selectively engaged in a transitional living placement (TLP). He does this only because it preserves his relationship with his fiancé. It also protects a newly emerging  identity as a father, “I don’t have family, you know, and I have my own family you know with my girl and our baby … no drug use, no alcohol use. Everything is good. She’s my happiness, you know?” This selective engagement is made possible only because the TLP allows him to be in close contact with his fiancé who lives in the same town in her own foster placement. He also indicates that most of the staff affirm his  emerging parent identity, “They think I’m gonna be a really good father.” Jax has read, “eight books for babies and stuff, and I’m trying to prepare myself … and I had sympathy symptoms … I’m the one that has the nausea!” Just as the tape recorder is turned off, he discloses happily that the name he has chosen to use as his own during this interview, “Jax,” is the name they plan to give the baby.

“Ashanti”

Disengaged informal and formal resources, selectively engaged shelter, fully engaged school Ashanti, from Cook County, identified as a heterosexual, 16-year-old African-American female with a lifetime of unstable housing tied to her mother’s mental illness and both parents’ drug addictions— accumulated experiences that led her to grow up fast and depend on herself. As she notes of her mother’s reliability, “depending on my mama is like depending on a brick wall.” 

Her story of family is also one of experiencing rejection and abandonment. She was “tormented” 

by her sister, and told she was “adopted.” By the time Ashanti was 6, her mother had been institutionalized and, by age 10, her father moved Ashanti and her siblings in with his own parents. But even at her grandmother’s, she slept on a couch and was largely on her own. At 14, her grandmother kicked Ashanti out when she became pregnant. She says she felt “betrayed, lost (and) hurt.” For the following week, she snuck in and out of her grandmother’s house through an open basement window until one night, that window was locked. From then on, she cycled between sleeping on the streets and on the city train. Ashanti believes the stress of it all caused her miscarriage. Throughout, Ashanti remained engaged with school but never told them of her homelessness, “… it’s not a story that everybody should know … cause once you tell one grown person, it just spread around …” Thus, she  protected her identity as “not a bum” in as many settings as she could. As she explained, “… I’m not a BUM, … Being a bum, I wouldn’t care about nothing. 

But I care about everything that’s happening to me.”

Ashanti’s  accumulated lived experiences also included therapy when she was diagnosed with “bipolar depression.” This left her feeling therapists lacked genuine care and were only “in it for money,” 

so she limited her use of school for education only. School was a place riddled with other risks. 

Despite her serious need for shelter and supports, she rejected formal services, and chose to hide her pregnancy, miscarriage, and homelessness to avoid teachers’ reports to the police or to the local Cityscape 51

Samuels, Cerven, Curry, and Robinson

child welfare agency. In fact, she remained fully disengaged from formal services until a friend strongly encouraged using a drop-in center. Her positive experience there led to her trusting a recommendation for a youth shelter where she was staying at the time of the interview.

Ashanti’s  personal agency, has always been strong, mostly out of necessity, “I’m doing it on my  own, cause in this WORLD, … don’t nobody got me like how I got me.” At one point, she engaged a friend from school as a resource for informal housing. Eventually, this friend’s mom kicked both girls out because of their partying. Still rejecting formal services, they slept in an abandoned house for the entire summer. She reported being assaulted several times while sleeping on public transit. 

Ashanti’s strong sense of  personal agency and her own need to not be a burden, however, caused her to stay on the streets instead of joining her friend when that friend was allowed to return home.

Ashanti dreams of finishing high school, earning a scholarship, and attending a historically Black college or university in the South. In ending her interview, she also was clear about only engaging people and resources that affirmed and protected her identity as a caring person with self-dignity, 

“… I’m not a bum, I’m less fortunate … I care about my body, I care about how I present myself, I care about how I talk to people, I care about my education, I care about all that. Like, I take all that to the heart.”

“Brad”

Disengaged most formal services, selectively engaged with informal networks

Brad identified as a White heterosexual male who currently lives in Walla Walla County, Washington. He began his story at age 17 by naming his mom’s addiction to methamphetamines and family homelessness as the beginning of his own instability. “I lost my place when I was 17 

with my mom … my mom got really bad into drugs and so we were just bouncing from, you 

know, tweaker houses to park benches …” Brad and his younger brother were removed from their mother’s care because of her drug use. Brad’s  accumulated experience included cycling through five foster care placements until he was returned home. Brad noted foster care as mostly a positive experience that gave him a respite from his mom’s struggles with addiction and enabled him to reengage with school. When he returned home, however, his mom relapsed into drug use, they again became homeless, and he dropped out of school.

When asked if he ever sought help, Brad explained, “I was always afraid to tell anybody because … I didn’t want my mom to … get in trouble or have somebody come in and take her to some facility or something.” Brad also explains that his negative  accumulated experiences with counseling services in foster care made him doubt the benefits of seeking help now, “… My counselors never really lasted 

… they’d be like, ‘Oh well, this is our last appointment cause we’re no longer being paid for it.’ … 

I’d just realize, ‘Oh yeah, it’s all about money so I don’t really want to sit and talk to you anyways.”

Brad described mainly coping on his own. The  personal agency, independence, and autonomy his life has required of him, however, does not always produce pride, but instead, a sense of loneliness from which he seeks distraction, “I think about all the [expletive] that I’ve been through … I’d sit and pity myself sometimes ... It’s when I’m alone that it starts getting bad … so I always try to keep myself occupied.” At the time of his interview, Brad was connected to his dad, and his dad’s girlfriend had hired him to work in her seasonal landscaping business. She helped him to get 52 Youth Homelessness

“Nothing is for free...”: Youth Logics of Engaging Resources While Unstably Housed his ID and re-engage in school to complete his GED. At the time of his interview, Brad was still unstably housed and was still spending most nights on the streets. He made minimal selective use of a local church’s meals and their health services and sometimes went to the hospital for “panic attacks.” Brad was ambivalent about ending his homelessness and talked at length about its benefits including enabling an  identity, personal agency and supported lifestyle of not feeling “confined”—a sense of unbridled freedom that he identified with and “liked too much.” Although he appreciates the stability of times when he has been housed, he explains being stable included risks to his own  identity as independent and unconstrained, “It took me a little while to transition into not being homeless again ... I felt confined when I lived in a place … But then I got used to it again and like now I can kinda see it from both –both angles.” Brad’s  personal agency has certainly caused him to reject formal and at times, informal, housing resources. He asserted the key to ending his homelessness was based on individual motivation and  personal agency, “I think to achieve the stability you would … need to want it.”

“Jamal”

Fully engaged formal services, selectively engaged informal networks

Jamal was a 21-year-old African-American male living in Philadelphia County. Jamal began his story of instability when he first came out as gay at the age of 14. His family’s early awareness and discrimination toward his identity brewed for 3 years until it resulted in Jamal’s first episode of unaccompanied homelessness at age 17. Jamal was never kicked out for being gay, but he left a home that was certainly a source of stigma and discrimination because of this identity. As Jamal recalled, “My mom, when she found out that I was gay, she didn’t really have a big problem with it. 

She did accept me, took me in, like with open arms. My dad, he was a little on edge about it, but he finally came around. But um my older brothers and like my grandmother were … against it … 

My grandma she would claim it was a phase or … it was like a disgrace or disgust to her … One of my older brothers … stopped speaking to me.”

The emotional and literal cutoffs from his grandmother and brothers made Jamal feel like he no longer had a home. He said these years were like “hell.” From the ages of 15 to 17, in attempts to  protect his identity, he cycled between couch surfing at a cousin’s house. When his cousin died, Jamal, then 17, was forced to live full-time with his grandmother again. Despite being unaware of local resources, his strong  personal agency inspired initiating a departure from his grandmother’s home to couch surf with a friend and thus, escape the “hell” he endured in his grandmother’s home.

Eventually, he told the friend that he was gay. That friend told him about a local agency that served LGBTQ youth. Jamal was elated to discover this resource and safe space that  affirmed an identity that was unprotected in his own home, “I gained family and friends there … I’d rather see them more than my friends, my brother’s friends, and him any day!” After this awareness, he fully engaged with and trusted this provider. Jamal made use of all their resources, “They gave me resources and staff to talk to … [who] still help me out … to this day.” With Jamal’s lack of previous negative experience of formal resources, and continued  accumulated positive experience with the provider, he trusted their recommendations to fully engage with other services that were not specifically targeting LGBTQ youth.

Cityscape 53

Samuels, Cerven, Curry, and Robinson

As Jamal spent less and less time at his grandmother’s and more time couch surfing and at shelters, he continued to think of his mother as a support system. Although she could only provide limited emotional support from a distance as she remained at his grandmother’s house. However, she insisted, and he accepted, that he was welcomed there, “She was very inviting. My mom used to always tell me if I ever had a boyfriend or a friend … she’d rather us be there in the house safe than to be out any other place that is unsafe.” Jamal did not return home, however.

Instead, he graduated from high school and engaged a job training program. At the time of his interview, Jamal had learned he was accepted to a transitional living program and was already working three part-time jobs. Jamal was also engaged in therapy sessions and was completing a life skills course. He considered an invitation to live with a friend who was also transitioning out of homelessness, but after contemplating his  past experience living in tight quarters with his brothers and his grandmother, he opted to live on his own and thwart the risk of potential tension or turmoil. As Jamal ended his interview, he expressed his strong  personal agency, his now protected and  affirmed identity, paired with openness to make change in his life. He offered the following wisdom to other youth who might be going through similar struggles, “And regardless of anything that may come your way, you still have the ability to fight it. Like whether it’s with help, by yourself, with friends, family, coworkers, like anything … know that there’s someone out here …

that can relate to you. So, you’re never in this world alone by yourself going through just one thing 

… never give up trying to make a better you.”

Discussion

This article examined the ways in which unstably housed young people make decisions 

about engaging the resources available to them. The four examples illustrate several lessons in understanding the conceptual model and the larger analysis of our sample. First, a person’s identity and sense of self matters. When young people had an identity that needed affirming, nurturing, or protecting, that reality helped to illuminate a unique set of risks and gains with regard to resource use. The vignettes illustrate this theme that ran across all our participants’ stories. For example, Jamal’s identity as a young gay man and Jax’s experience as a young expectant father both required identity protection. Each found a resource where those identities could grow and develop. These resources also gave access to important relationships with adults and peers who validated those identities. Conversely, other youth were searching for an affirming resource. Ashanti wanted affirmation of her sense of self as caring and having dignity, and “not a bum.” These identity related factors were critical to youth’s choices to engage, and then stay engaged, or to avoid, service providers. In fact, positive experiences drove engagement for Jamal with other formal resources that were not solely targeting LGBTQ youth. Our analysis suggests that understanding how young people identify, and perceive risks to those core identities, is critical to understanding patterns of engagement with informal and formal resources.

Second, youth’s accumulated experiences shaped their openness or hesitance in believing that certain sources of help are actually helpful rather than harmful. Although all the young people highlighted in this article had accumulated some experiences with formal resources, Brad was the most disengaged from, and least open to, formal services. His negative experiences of service 54 Youth Homelessness

“Nothing is for free...”: Youth Logics of Engaging Resources While Unstably Housed providers are shared by many other youth in this study, and experientially grounded their doubts that any gains would result by seeking out formal services. Still, Brad was not disconnected from all resources and did make use of some limited informal support from his dad and step-mom. 

Ashanti’s story reminds us that change is possible. Through a trusted friend (informal resource), she is connected to a formal resource in the form of a shelter. That shelter becomes a novel and positive experience of support from a formal resource she previously avoided. For Jax, his history of rejection in his family of origin (an informal resource) shadowed his own interpretation of the risks and gains presented by the potential adoptive family as a trusted new informal resource. Thus, he rejected it. Conversely, Jamal was the rare youth example who lacked a childhood experience of formal services. His first contact, through his friend, was exclusively positive and quite transformative. As he accumulated this new experience, it only fueled deeper levels of engagement with service providers. His story is instructive about the importance of positive experiences when receiving first-time formal service provision. This did not generalize to his interpretation of risk for informal resources, however. His experiences in his family of origin caused him to be reticent to engage the resource of a roommate for financial help with a place to stay.

Finally, our findings remind us that personal agency can often derive from extreme self-reliance in childhood, caused by outright rejection, or simply by having parents whose protective presence is compromised by poor mental health or addiction among many other factors. This circumstance sometimes fueled the courage to engage a new resource through a trusted friend’s recommendation; other times, it facilitated a rejection of a resource. To illustrate this theme of rejection in our analyses, the cases of Jax and Brad are particularly instructive. Time and again, Jax disengages the informal resources in his social network. They are too risky. This sentiment highlights the critical importance of his only informal resource, his fiancé and future child, an identity and relationship he works hard to protect and that likely offers a critical counterpoint to his prior experience of family. Brad’s version of self-reliance causes him to reject formal services, and he still wrestles with the attraction of the freedom and unconfined lifestyle gained by homelessness; it is affirming to his sense of independence and self-reliance.

In Ashanti’s story, her personal agency affirms a positive sense of competence that results in similar rejections of help but enables her to remain engaged in school for its educational benefits and later, to agree to her friend’s suggestion of using a drop-in center. This in turn facilitates her trust to more deeply engage formal services as the drop-in staff recommend a shelter. Finally, Jamal’s story extends our understanding of the interaction of a strong sense of personal agency and resource engagement. His story suggests that youth can fully engage in resources even when they uphold a value of personal agency. Like other youth, Jamal’s personal agency compelled him to leave his family home at 17, convinced in his ability to find a better more nurturing and affirming place. 

Although he remained reticent of some informal resources (for example, the roommate), he was fully open and trusting of help from professionals at the agency for LGBTQ homeless youth.

Limitations

Several limitations are important to note. First, this study uses point-in-time data with young people experiencing homelessness. Although our trajectory method helped to illuminate their use Cityscape 55

Samuels, Cerven, Curry, and Robinson

and rejection of resources both current and in the past, we do not know how it unfolds into the future. Relatedly, we are also not able to speak to how these logics inform the actual relationships youth have with providers and informal resources overtime. We especially lack data from family, friends, and peers—important members of youth’s informal resource network. Future research should use more ethnographic and case study methods with a smaller sample of youth to also understand how factors like identity, their accumulated experience, and personal agency shape and are shaped by new or unique experiences with both formal and informal resource use.

Second, this study was not designed to be a nationally representative sample of youth experiencing homelessness. In particular, because we used recruitment methods that included agency-based recruitment, many of the youth in our sample were already connected in some way to formal resources. It is possible that our sample represents a disproportionately high number of these youth than exist in the general population of youth experiencing homelessness. As such, we may have a less robust and diverse set of findings about processes related to experiences of young people who are fully disengaged than were youth in our sample. That said, youth’s frequent rejection of resources, avoidance and use of both formal and informal resources across our sample, suggest that our findings still speak to youth patterns and processes of disengagement.

Finally, youth in this study are experiencing a specific developmental moment of adolescence and emerging adulthood. For many young people in the U.S., this developmental stage is characterized by increasing levels of independence and autonomy from parents and heightened exploration, risk taking and a reliance on one’s social and family networks of choice. These youth represent a distinct sub-population of emerging adults who have experienced independence and autonomy earlier than youth in the general population. However, our sample of younger adolescents (that is, 13 to 16) was too small to conduct age comparisons in order to understand potential developmental differences within the group. This field would be advantaged by pursuing developmental approaches (Nott and Vuchinich, 2016) and research that is responsive and relevant to this population’s experiences of independence, family, and autonomy during adolescence and early adulthood.

Implications for Literature and Research

The breadth and depth of the interviews provided a unique opportunity to develop a more complex understanding of the concept of engagement and processes that shape styles in the use and rejection of a broad spectrum of resources. This analysis produced our  Model of Youth Logics of Engagement and suggests future research should broaden its consideration to the full array of resources and relationships present within the social ecologies of youth. No youth across their trajectory of instability remained disconnected and disengaged from every type of resource. 

Consequently, future studies of engagement and help-seeking should measure and explore both formal and informal resources overtime and together. Our findings suggest these are interconnected rather than separate features of support that shift and change across a young person’s trajectory of homelessness. As in prior studies of identity, self-reliance and independence among homeless youth (Barker, 2014; Garrett et al., 2008; Ha et al., 2015), and in populations of systems, involved youth (Cunningham and Diversi, 2013; Havlicek and Samuels, 2018; Kools, 1997; Mulkerns and Owen, 2008; Samuels and Pryce, 2008); our findings reinforce how identities shape the type, frequency, and depth of connection to resources. Future research however is needed to explore 56 Youth Homelessness

“Nothing is for free...”: Youth Logics of Engaging Resources While Unstably Housed how these different identities that youth protected (that is, minoritized and stigmatized identities, parent identities) uniquely intersect identities and self-concepts tied to resilience and personal agency in ways that inhibit or facilitate use of a resource. Specifically, youth’s sense of personal agency varied, and when extremely anchored in individualistic and self-reliant conceptions, often caused serious delays or outright rejection of important resources. Youth also mobilized personal agency in ways that opened up new opportunities to affirm an identity that was marginalized in their immediate environments. Future research should take seriously the varied ways in which identities and self-concepts intersect and interact to foster risk and resilience in young people.

Our findings reinforce other studies that have illustrated aspects of disengagement that are simultaneously self-protective while engendering risks and vulnerabilities (Gunn & Samuels, in press; Kools, 1997; Samuels, 2008). Disengagement was indeed sometimes resilient and protective of youth’s immediate emotional and psychological safety (for example, leaving an abusive or stigmatizing family). It also posed other short- and long-term risks, however (for example, homelessness). The meaning youth made of their use or rejection of a resource often resulted in outcomes that were mutually reinforcing of their resilience and abilities to be self-protective against future harm. Yet these same outcomes increased their vulnerability and validated continued disconnection or rejection of needed supports to end homelessness. Future research on resilience and risk must explore the ways in which a single behavior or system of meaning can indicate simultaneous resilience and risk across different contexts. Pursuing holistic and multidimensional measures of risk and resilience can illuminate these dualities and paradoxes in ways that may inform more effectively supporting young people as they learn to enact their resilience in ways that are not ultimately self-defeating.

This study’s findings complicate the tendency within service research to theorize engagement as a static condition of a person as engaged or disengaged (Becker et al., 2018; Chacko et al., 2016). 

Rather youth’s engagement involved ongoing and dynamic decision-making processes in which they engaged, selectively engaged, and disengaged a host of known resources simultaneously. 

More fluid and multidimensional concepts of engagement that use youth perspectives outside of narrowly measuring youth’s use of a single source of support are needed.

Finally, the purpose of a Grounded Theory Method study is theory-building research: to produce conceptual and theoretical explanations that derive from ground-up, inductive analyses. Although continued refinement of the core concepts in our youth logics of engagement model is certainly needed, the salience of many of our model’s factors are individually well-substantiated in scholarship across a range of fields and populations. Protection or negotiation of a discredited identity leading to selective engagement styles has been discussed in prior research generally (Gunn and Samuels, in press), and among foster youth (Kools, 1997; Samuels and Pryce, 2008). Identity safety and relational health has been examined among LGBTQ youth (Gamarel et al., 2014), and youth experiencing the juvenile justice system (Feinstein, 2015; Squatriglia, 2008). Therefore, our findings are not new. They do operationalize, into a model, complementary findings from a range of fields, including foster care and juvenile justice, and extend the relevance of individual findings to a broader group of young people. The young people in our study had extremely similar (and often literally overlapping) childhood histories, family backgrounds, and adversities that caused contact with social services and formal resources.

Cityscape 57

Samuels, Cerven, Curry, and Robinson

One potential next step for theory development in this area could be to test various aspects of the model separately or together. This could further refine conceptual constructs, as well as strengthen its potential for generalizability. Although most theory-testing activities are thought to belong to experimental and positivist methods of science, constructivist and interpretive methods often tied to Grounded Theory traditions also provide avenues for future research to test and challenge the relationships set forth in our model. Any of these scientific methods could include exploring the role of identities that are privileged rather than stigmatized, or changes in the three factors that cause a change in engagement around a specific resource. Other possibilities could include testing the role of trusted peers and adults as potential mediators to both one’s awareness and openness to using a resource, or as moderators of risk to sustain engagement with resources. Future research should also examine the long-term outcomes for youth’s well-being and housing stability as it relates to any of the model’s engagement styles.

Implications for Practice

Perhaps one of the most important implications of this study is the need for practices that are more explicitly attuned to the dual presence of strength and vulnerability in youth experiences and coping behaviors while homeless. Our work supports prior research that has illuminated similar risks among youth who are homeless (Auerswald and Eyre, 2002) including the role of stigmatized and marginalized identities (Abramovich, 2017), youth’s autonomy and self-reliance deriving from prior experiences (Barker, 2014, 2016; Garrett et al., 2008; Ha et al., 2015), and their ongoing exposure to, and experience with, formal and informal resources (Kurtz et al., 2000; Christiani et al, 2008). This study joins that body of work in calling for practice models and outreach approaches that take seriously the many ways in which youth anticipated ongoing risk and harm from nearly all sources of “help.” These youth, need practitioners that are attuned to their accumulated experiences of adults within families and service systems that have caused harm relationally. Youth were responsive to sources of help that explicitly facilitated their trust and healing from relational and complex trauma (including family-based stigma and discrimination) and that honored positive but often invalidated identities and aspects of who they are or were wanting to become.

Our findings complicate the constrained narrative common in practice research claiming that a person’s resistance, ambivalence or avoidance of a resource is an exclusively problematic or an ill-conceived stance to be overcome or managed within practice relationships (Miller and Rollnick, 2012; Westra and Norouzian, 2018). Ultimately, we find young adults in this study relied on complex logics that considered intersecting needs and concerns in deciding when, how, how much, and with whom to engage. As our title suggests, many young people engaged the philosophy that “nothing is for free.” This meant all forms of help from any source, including family, always incurred some sort of emotional, relational, or psychological cost or debt. Our findings suggest that their logics structured decision-making processes that privileged a need to avoid or minimize such costs, and consequently, manage a set of risks that were often hidden to service providers, adults, and even informal sources of support. Our practices must find ways of illuminating and respecting these costs and risks and supporting youth’s abilities to navigate them successfully.

58 Youth Homelessness

“Nothing is for free...”: Youth Logics of Engaging Resources While Unstably Housed Our findings call for practice and organizational structures that attend to youth’s developmental needs to maximize their control, personal agency, and their sense of positive identity. These needs are normative to their developmental stage (Arnett, 2000), but are needs that may be more acutely present among this group of young people (Nott and Vuchinich, 2016). Many youth in our study, however, perceived resources (including family) as disempowering, a risk to their sense of personal agency, or a threat to invalidate an important identity. When these risks lessened and their positive development was nurtured, youth were more apt to engage. Our work reinforces scholarship that highlights practice approaches that explicitly identify personal agency as a critical element of socially just practice in general (Alford, 2009; Benjamin and Campbell, 2015), and for engaging youth experiencing housing instability in particular (Abramovich and Shelton, 2017; Christiani et al., 2008; Nott and Vuchinich, 2016). Such power-enabling practices must include youth at the center of decision making about their own lives and draw on their resilience and unique strengths as starting places for engagement. Youth often hid or downplayed their needs to avoid parents getting in trouble, avoid reports to child welfare systems, or retain their own independence. Outreach and other services must also rethink access requirements in engaging young people and reconsider ways to provide basic resources to them without having to fully assess and investigate youth in ways that may feel intrusive, risk harming or losing an important relationship, or invalidate or discredit an identity.

Understanding youth’s needs for trauma-informed supports is part of effective engagement and intervention (Bronstein, 1996; Davies and Allen, 2017; Davies et al., 2014; DeRosa et al., 1999). 

Although the idea of mistrust among youth in the literature emerges from chronic homelessness (Barker, 2014; Garrett et al., 2008), for many of these youth, mistrust in adults was first born out of their traumatic childhood experiences with parents and other adults. Our research reinforces findings elsewhere that youth’s accumulated experiences in foster care, juvenile justice, or simply in homes struggling with chronic adversity shape how young people assess and determine risk (Bender et al., 2018; Kools, 1997). Participants noted parents who struggled with addictions, poverty, mental health conditions, families who experienced homelessness, or their own removal from home (for example, through foster care). It is not surprising that emerging in the field is a strong call for trauma-informed practices with this population of young people (Davies and Allen, 2017). Our work supports this movement in practice as of critical importance to these youth’s stability and well-being long term. Often missing from most trauma-informed models, however, are practices that explicitly interrupt and directly address intrafamilial stigma and discrimination as both a societal and relational injustice (Samuels, 2018).

Youth of color and LGBTQ youth are overrepresented among those experiencing unaccompanied youth homelessness, and this was true within our study sample. Consequently, the typical youth experiencing homelessness is often negotiating multiple intersecting stigmatized identities. They are also navigating both overt forms of stigma and discrimination as well as microaggressions (Sue, 2010) in their families, schools, and communities. Our findings strongly support the need for trauma-specific approaches that include supporting young people in healthy identity development tied to statuses that are marginalized and stigmatized, including homelessness. Youth in our study were learning to anticipate, avoid, and cope with overt and covert discrimination and stigma. 

When agency staff affirmed and nurtured youth’s positive identity work, it often facilitated deeper engagement by the youth. Our findings fully support the small but growing trend in work with Cityscape 59

Samuels, Cerven, Curry, and Robinson

minoritized youth experiencing homelessness that calls for use of intersectional models for practice (Abramovich and Shelton, 2017, Baines, 2011; Hyde, 2005; Zufferey, 2017) and methods of research (Lavizzari, 2015) that are anti-oppressive, holistic, and identity affirming. These models offer a shift in understanding the role of power, and cycles of oppression tied to structural and interpersonal factors, that are deeply relevant to the lived experience of all youth in this study. 

They offer a person-centered-in-context frame from which to assess unique needs of youth collaboratively and guide work that is relationally just and restorative (Gal, 2015).

Finally, agencies could also address these developmental needs by designing intake assessments (including ones similar to our own narrative timeline tool) that explore, collaboratively with youth, the meaning they have made of their accumulated experiences (rather than collecting lists of experiences), the meaning of their identity (rather than assuming the meanings of labels), and how youth understand help-seeking as affirming or threatening to their personal agency or self-reliance. Indeed, relational practices that foster mutual engagement could facilitate more tailored and effective interventions for youth (Dang and Miller, 2014). In turn, these improved practices could promote youth’s greater trust and involvement in the services they need to achieve stability and reach developmental milestones successfully. This development of trust, an instrumental factor in any engagement process, may increase youth’s willingness to consider a greater spectrum of supports across the life course (Toro, Dworsky, and Fowler, 2007).

Conclusion

This analysis provided important youth-centered insight and highlights factors that shaped youth’s patterns of engaging or avoiding resources. These findings suggest the need to rethink how providers message their resources to youth in general, but in particular, attune to youth’s need to manage often hidden risks. Our findings suggest that work with even young adults must continue to be developmentally informed and consider not only youth’s physical safety and basic needs, but their unique needs for resources that nurture resilience, psychological and emotional security, and trust. Youth deepened their engagement when resource providers offered relationships that affirmed their identities and personal agency. Such relationships could provide important counterweights to their common experience of adults and services as untrustworthy and harmful. We highlight the need for an increased focus in practice on identity and personal agency as critical, but often hidden, elements of youth resilience and risk. This article is a call to the field to engage young people in ways that affirm their healthy identity development, model relationally just and restorative experiences, and inspire their own capacities to enact resilience within their communities and social worlds, and ultimately, to thrive.

Acknowledgments

The authors thank the young people and community partners without whom none of this learning could have been achieved. This research is the result of a collaboration involving the Voices of Youth Count (VoYC) Policy and Research Team from Chapin Hall at the University of Chicago and many partners. The VoYC initiative is made possible through a grant from U.S. Department of Housing and Urban Development Office of Policy Development and Research (PD&R, grant 60 Youth Homelessness

“Nothing is for free...”: Youth Logics of Engaging Resources While Unstably Housed no. RP-16_IL-001) and generous support from Chapin Hall, Arcus Foundation (grant no. G-PGM 

1511-1561), Ballmer Group Philanthropy, Campion Foundation, Casey Family Programs (grant no. FY14-1060), Dr. Inger Davis, Elton John AIDS Foundation (grant no. 5202-00-00), Melville Charitable Trust (grant no. 2015-015), and Raikes Foundation (grant no. 281).

Authors

Gina M. Samuels is an Associate Professor at the University of Chicago, School of Social Service Administration and can be contacted by email at gmsamuels@uchicago.edu.

Christine Cerven is a Researcher at Chapin Hall at the University of Chicago.

Susanna R. Curry is an Assistant Professor at California State University-Sacramento, Division of Social Work.

Shantá R. Robinson is an Assistant Professor at the University of Chicago, School of Social Service Administration.

References

Abramovich, Alex. 2017. “Understanding how policy and culture create oppressive conditions for LGBTQ2S youth in the shelter system,”  Journal of Homosexuality 64 (11): 1484–1501.

Abramovich, Alex, and Jama Shelton, eds. 2017.  Where Am I Going to Go? Intersectional Approaches to Ending LGBTQ2S Youth Homelessness in Canada & the U. S.  Toronto, ON: Canadian Observatory on Homelessness Press.

Abramovich, Alex. 2013. “No fixed address: Young, queer, and restless.” In  Youth Homelessness in Canada: Implications for Policy and Practice, edited by S. Gaetz, B. O’Grady, K. Buccieri, J. Karabanow, and A. Marsolais. Toronto, ON: Canadian Homelessness Research Network Press: 387–404.

Alford, John. 2009.  Engaging Public Sector Clients: From Service Delivery to Co-Production. New York: Palgrave Macmillan.

Arnett, Jeffrey J. 2000. “Emerging adulthood: A theory of development from the late teens through the twenties,”  American Psychologist 55 (5): 469–480.

Auerswald, Colette L., and Stephen L. Eyre. 2002. “Youth homelessness in San Francisco: A life cycle approach,”  Social Science & Medicine 54 (10): 1497–1512.

Baines, Donna. 2011.  Doing Anti-Oppressive Practice: Social Justice Social Work, 2nd ed. Halifax, Nova Scotia: Fernwood.

Barker, Justin. 2016. “A habitus of instability: Youth homelessness and instability,”  Journal of Youth Studies 19 (5) 665–683.

Cityscape 61

Samuels, Cerven, Curry, and Robinson

———.“Alone together: The strategies of autonomy and relatedness in the lives of homeless youth,” 

Journal of Youth Studies 17 (6) 763–777.

Becker, Kimberly D., Maya Boustani, Resham Gellatly, and Bruce F. Chorpita. 2018. “Forty years of engagement research in children’s mental health services: Multidimensional measurement and practice elements,”  Journal of Clinical Child and Adolescent Psychology 47 (1): 1–23.

Bender, Kimberly, Stephanie Begun, Rebecca Durbahn, Kristin Ferguson, and Nick Schau. 

2018. “My own best friend: Homeless youth’s hesitance to seek help and strategies for coping independently after distressing and traumatic experiences,”  Social Work in Public Health 33 (3): 149.

Bender, Kimberly, Kristin Ferguson, Sanna Thompson, Chelsea Komlo, and David Pollio. 2010. 

“Factors associated with trauma and posttraumatic stress disorder among homeless youth in three US cities: The importance of transience,”  Journal of Traumatic Stress 23 (1): 161–168.

Bender, Kimberly, Sanna Thompson, Kristin Ferguson, Jamie Yoder, and Anne DePrince. 2015. 

“Risk detection and self-protection among homeless youth,”  Journal of Research on Adolescence 25 

(2): 352–365.

Bender, Kimberly, Sanna Thompson, Holly McManus, Janet Lantry, and Patrick M. Flynn. 2007. 

“Capacity for survival: Exploring strengths of homeless street youth,”  Child and Youth All Forum 36 

(1): 25–42.

Benjamin, Lehn M., and David C. Campbell. 2015. “Nonprofit performance: Accounting for the agency of clients,”  Nonprofit and Voluntary Sector Quarterly 44 (5): 988–1006.

Bowers, Barbara, and Leonard Schatzman. 2009. “Dimensional analysis.”  In Developing Grounded Theory: The Second Generation, Developing Qualitative Inquiry Series, edited by J. Morse, P. Stern, J. 

Corbin, B. Bowers, K. Charmaz, and A. Clarke. Walnut Creek, CA: Left Coast Press, 86–125.

Bronstein, Laura. 1996. “Intervening with homeless youths: Direct practice without blaming the victim,”  Child Adolescent Social Work Journal 13 (2): 127–138.

Carlson, Jennifer L., Eiko Sugano, Susan G. Millstein, and Colette L. Auerswald. 2006. “Service utilization and the life cycle of youth homelessness,”  Journal of Adolescent Health 38 (5): 624–627.

Chacko, Anil, Scott A. Jensen, Lynda S. Lowry, Melinda Cornwell, Alyssa Chimklis, Elizabeth Chan, Daniel Lee, Brenda Pulgarin. 2016. “Engagement in behavioral parent training: Review of the literature and implications for practice,”  Clinical Child and Family Psychology Review 19 (3): 204–215.

Charmaz, Kathy. 2006.  Constructing Grounded Theory: A Practical Guide Through Qualitative Analysis. 

London: Sage Publications.

Christiani, Ashley, Angela L. Hudson, Adeline Nyamathi, Malaika Mutere, and Jeffrey Sweat. 2008. 

“Attitudes of homeless and drug-using youth regarding barriers and facilitators in delivery of quality and culturally sensitive health care,”  Journal of Child and Adolescent Psychiatric Nursing 21 (3): 154–163.

62 Youth Homelessness

“Nothing is for free...”: Youth Logics of Engaging Resources While Unstably Housed Cleverley, Kristin, and Sean A. Kidd. 2011. “Resilience and suicidality among homeless youth,” 

Journal of Adolescence 34 (5): 1049–1054.

Cunningham, Miranda J., and Marcelo Diversi. 2013 “Aging out: Youths’ perspectives on foster care and the transition to independence,”  Qualitative Social Work 12 (5): 587–602.

Dang, Michele T., and Elizabeth Miller. 2014. “Characteristics of natural mentoring relationships from the perspectives of homeless youth,”  Journal of Child Adolescent Psychiatric Nursing 26 (4): 1–13.

Davies, Benjamin R., and Nicholas B. Allen. 2017. “Trauma and homelessness in youth: 

Psychopathology and intervention,”  Clinical Psychology Review 54: 17–28.

Davies, Rosemary L., Pauline Heslop, Steve Onyett, and Tony Soteriou. 2014. “Effective support for those who are “hard to engage”: A qualitative user-led study,”  Journal of Mental Health 23 (2): 62–66.

DeRosa, Christine J., Susanne B. Montgomery, Michelle D. Kipke, Ellen Iverson, Joanne L. Ma, and Jennifer B. Unger. 1999. “Service utilization among homeless and runaway youth in Los Angeles, California: Rates and reasons,”  The Journal of Adolescent Health 24 (3): 190–200.

Durso, Laura E., and Gary J. Gates. 2012.  Serving Our Youth: Findings from a National Survey of Services Providers Working with Lesbian, Gay, Bisexual and Transgender Youth Who Are Homeless or At Risk of Becoming Homeless. Los Angeles, CA: The Williams Institute Fund with the True Colors Fund and the Palette Fund.

Dworsky, Amy, Laura Napolitano, and Mark Courtney. 2013. “Homelessness during the transition from foster care to adulthood,”  American Journal of Public Health 103 (S2): 318–323.

Ensign, Josephine, and Aileen Panke. 2002. “Barriers and bridges to all: Voices of homeless female adolescent youth in Seattle, Washington, USA,”  Journal of Advanced Nursing 37 (2): 166–172.

Feinstein, Rachel A. 2015. “White privilege, juvenile justice, and criminal identities: a qualitative analysis of the perceptions and self-identification of incarcerated youth,”  Contemporary Justice Review 18 (3): 313–333.

Ferguson, Kristin, Kimberly Bender, Sanna Thompson, Bin Xie, and David Pollio. 2012. “General strain predictors of arrest history in homeless youths from four U.S. cities,”  Journal of Juvenile Justice 1: 1–19.

Forrest-Bank, Shandra S., Nicole Nicotera, Elizabeth K. Anthony, and Jeffrey M. Jenson, 2015. 

“Finding their Way: Perceptions of risk, resilience, and positive youth development among adolescents and young adults from public housing neighborhoods,”  Children and Youth Services Review 55: 147-158.

Gal, Tali. 2015. “Restorative Child Protection,”  Social Science Research Network: 1–57.  

http://dx.doi.org/10.2139/ssrn.2694399.

Gamarel, Kristi E., Ja’Nina J. Walker, Lillian Rivera, and Sarit A. Golub. 2014. “Identity safety and relational health in youth spaces,”  Journal of LGBT Youth 11: 289–315.

Cityscape 63

Samuels, Cerven, Curry, and Robinson

Garrett, Sharon B., Darrel H. Higa, Melissa M. Phares, Peggy L. Peterson, Elizabeth A. Wells, and John S. Baer. 2008. “Homeless youths’ perceptions of services and transitions to stable housing,” 

Evaluation and Program Planning 31 (4): 436–444.

Gattis, Maurice N., and Andrea Larson. 2016. “Perceived racial, sexual identity, and homeless status-related discrimination among black adolescents and youth adults experiencing 

homelessness: Relations with depressive symptoms and suicidality,”  American Journal of Orthopsychiatry 86 (1): 79–90.

Gattis, Maurice N., and Andrea Larson. 2017. “Perceived microaggressions and mental health in a sample of Black youths experiencing homelessness,”  Social Work Research 41 (1), 7–17.

Greene, Roberta R. 2014. “Resilience as effective functional capacity: An ecological-stress model,” 

Journal of Human Behavior in the Social Environment 24: 937–950.

Gulliver, Amelia, Kathleen M. Griffiths, and Helen Christensen. 2010. “Perceived barriers and facilitators to mental health help-seeking in young people: A systematic review,”  BMC Psychiatry 10 

(113): 1–9.

Gunn, Alana, and Gina Miranda Samuels. (in press.) Promoting recovery identities among mothers with histories of addiction: Strategies of family engagement.  Family Process.

Ha, Yoonsook, Sarah C. Narendorf, Diane Santa Maria, and Noel Bezette-Flores. 2015. “Barriers and facilitators to shelter utilization among homeless young adults,”  Evaluation and Program Planning 53: 25–33.

Haber, Mason G., and Paul A. Toro. 2004. “Homeless among families, children, and adolescents: An ecological-developmental perspective,”  Clinical Child Family Psychology Review 7 (3): 123–164.

Havlicek, Judy, and Gina Miranda Samuels. 2018. “The Illinois state foster youth advisory board as a counterspace for well-being through identity work: Perspectives of current and former members,” 

Social Service Review 92 (2): 241–289.

Hays, Danica G., Chris Wood, Heather Dahl, and Andrea Kirk-Jenkins. 2016. “Methodological Rigor in Journal of Counseling & Development Qualitative Research Articles: A 15-Year Review,” 

Journal of Counseling & Development 94 (2): 172–183.

Herman, Jody L. 2013. “Gendered restrooms and minority stress: The public regulation of gender and its impact on transgender people’s lives,”  Journal of Public Management & Social Policy 19 (1): 65.

Hickler, Benjamin, and Colette L. Auerswald 2009. “The worlds of homeless White and African American youth in San Francisco, California: A cultural epidemiological comparison,”  Social Science 

& Medicine 68 (5): 824–831.

Hill Collins, Patricia, and Sirma Bilge. 2016.  Intersectionality: Key Concepts. Malden, MA: Polity Press.

Hutchinson, Darren L. 2001. “Identity Crisis: ‘Intersectionality,’ ‘Multidimensionality,’ and the Development of an Adequate Theory of Subordination,”  Michigan Journal of Race and Law 6: 285–317.

64 Youth Homelessness

“Nothing is for free...”: Youth Logics of Engaging Resources While Unstably Housed Hyde, Justeen. 2005. “From home to street: Understanding young people’s transitions into homelessness,”  Journal of Adolescence 28: 171–183.

Jenson, Jeffrey M., and Mark W. Fraser, eds. 2016.  Social Policy for Children and Families: A Risk and Resilience Perspective. Thousand Oaks, CA: Sage Publications.

Kauer, Sylvia D., Cheryl Mangan, and Lena Sanci. 2014. “Do online mental health services improve help-seeking for young people? A systematic review,”  Journal of Medical Internet Research 16 (3): e66.

Keuroghlian, Alex S., Derri Shtasel, and Ellen L. Bassuk. 2014. “Out on the street: A public health and policy agenda for lesbian, gay, bisexual, and transgender youth who are homeless,”  American Journal of Orthopsychiatry 84 (1): 66–72.

Kidd, Sean A. 2003. “Street youth: Coping and interventions,”  Child and Adolescent Social Work Journal 20 (4): 235–261.

Kidd, Sean A., and Larry Davidson. 2007. “‘You have to adapt because you have no other choice:’ 

The stories of strength and resilience of 208 homeless youth in New York City and Toronto,”  Journal of Community Psychology 35 (2): 219–238.

Kipke, Michele D., and Jennifer B. Unger. 1997 “Street youth, their peer group affiliation and differences according to residential status, subsistence patterns, and use of services,”  Adolescence 32 

(127): 655–670.

Kools, Susan 1997. “Adolescent identity development in foster care,”  Family Relations 46 (3): 263–271.

Kools, Susan, Marianne McCarthy, Roberta Durham, and Linda Robrecht. 1996. “Dimensional analysis: Broadening the conception of grounded theory,”  Qualitative Health Research 6 (3): 312–330.

Kurtz, P. David, Elizabeth W. Lindsey, Sara Jarvis, and Larry Nackerud. 2000. “How runaway and homeless youth navigate troubled waters: The role of formal and informal helpers,”  Child and Adolescent Social Work Journal 17 (5): 381–402.

Laird, Gordon. 2007.  Shelter – Homelessness in a growth economy: Canada’s 21st century paradox. 

Report prepared for the Sheldon Chumir Foundation for Ethics in Leadership. Calgary, Alberta: Sheldon Chumir Foundation for Ethics in Leadership & Gordon Laird.

Lavizzari Anna. 2015.  Intersectionality Research. Report prepared for the European Union, Council of Europe European Youth Foundation and the Government of the Netherlands. Brussels, Belgium: The International LGBTQI Youth and Student Organisation.

Leech, Nancy L., and Anthony J. Onwuegbuzie. 2011. “Beyond Constant Comparison Qualitative Data Analysis: Using NVivo,” School  Psychology Quarterly 1: 70.

Lerner, Richard M., Jennifer P. Agans, Miriam R. Arbeit, Paul A. Chase, Michelle B. Weiner, Kristina L. Schmid, and Amy Eva Alberts Warrant. 2013. “Resilience and Positive Youth Development: A Relational Developmental Systems Model.” In  Handbook of Resilience in Children, edited by S. 

Goldstein, and R. Brooks. Boston, MA: Springer: 293–308.

Cityscape 65

Samuels, Cerven, Curry, and Robinson

Liang, B., L. Goodman, P. Tummala-Narra, and S. Weintraub. 2005. “A Theoretical Framework for Understanding Help-Seeking Processes Among Survivors of Intimate Partner Violence,”  Journal of Community Psychology 36 (1/21): 71–84.

Lindsey, Elizabeth W., P. David Kurtz, Sara Jarvis, Nancy R. Williams, and Larry Nackerud. 2000. 

“How runaway and homeless youth navigate troubled waters: Personal strengths and resources,” 

Child and Adolescent Social Work Journal 17 (2): 115–141.

Lippy, Carrie, Sydney Pk, Emily Hsieh, Shannon Perez-Darby, and Connie Burk. 2017.  King County Youth of Color Needs Assessment: Final report on the experiences, strengths, and needs of homeless 

& unstably housed youth of color.  Seattle, WA: The NW Network of Bi, Trans, Lesbian, and Gay Survivors of Abuse.

Mallett, Shelley, Doreen Rosenthal, and Deborah Keys. 2005. “Young people, drug use and family conflict: Pathways into homelessness,”  Journal of Adolescence 28 (2) 185–199.

Malow, Robert M., Trace Kershaw, Heather Sipsma, Rhonda Rosenberg, and Jessy G. Dévieux. 

2007. “HIV preventive interventions for adolescents: A look back and ahead,”  Current HIV/AIDS 

Reports 4 (4): 173–180.

Masten, Ann S. 2018. “Resilience theory and research on children and families: Past, present, and promise,”  Journal of Family Theory & Review 10 (1): 12–31.

———.2014. “Global perspectives on resilience in children and youth,”  Child Development 85 (1): 6–20.

Miller, William R., and Stephen Rollnick. 2012.  Motivational interviewing: Helping people change, 3rd ed. New York: Guilford Press.

Morton, Matthew H., Amy Dworsky, and Gina Miranda Samuels. 2017.  Missed opportunities: Youth homelessness in America.  National estimates. Chicago, IL: Chapin Hall at the University of Chicago.

Mulkerns, Heather, and Carol Owen. 2008. “Identity development in emancipated young adults following foster care,”  Smith College Studies in Social Work 78 (4): 427–449.

Nott, Brooke D, and Sam Vuchinich. 2016. “Homeless adolescents’ perceptions of positive development: A comparative study.  Child and Youth Care Forum 45 (6): 865–886.

Page, Michelle. 2017. “Forgotten youth: Homeless LGBT youth of color and the Runaway and Homeless Youth Act,”  Northwestern Journal of Law & Social Policy 12 (2): 16–45.

Pedersen, Eric R., Joan S. Tucker, and Stephanie A. Kovalchik. 2016. “Facilitators and barriers of drop-in center use among homeless youth,”  Journal of Adolescent Health 59 (2): 144–153.

Perron, Jeff L., Kristin Cleverley, and Sean A. Kidd. 2014. “Resilience, loneliness, and psychological distress among homeless youth,”  Archives of Psychiatric Nursing 28 (4): 226–229.

Price, Christa, Coco Wheeler, Jama Shelton, and Meghan Maury, 2016.  At the intersections: A collaborative resource on LGBTQ Youth Homelessness. True Colors Fund and the National LGBTQ Task Force.

66 Youth Homelessness

“Nothing is for free...”: Youth Logics of Engaging Resources While Unstably Housed Pryce, Julia, Laura Napolitano, and Gina Miranda Samuels. 2017. “Transition to adulthood of former foster youth: Multilevel challenges to the help-seeking process,”  Emerging Adulthood 5 (5): 311–321.

Quintana, Nico S., Josh Rosenthal, and Jeff Krehely. 2010.  On the Streets: The Federal Response to Gay and Transgender Homeless Youth. Washington, DC: Center for American Progress.

Rickwood, Debra, Frank Deane, and Coralie Wilson. 2007. “When and how do young people seek professional help for mental health problems?”  Medical Journal of Australia 187 (7): S35–S39.

Robinson, Brandon A. 2018. “Conditional families and lesbian, gay, bisexual, transgender, and queer youth homelessness: Gender, sexuality, family instability, and rejection,”  Journal of Marriage and Family 80 (2): 383–396.

Rutman, Deborah and Carol Hubberstey, 2016 “Is anybody there? Informal supports accessed and sought by youth from foster care,”  Children and Youth Services Review, 63: 21-27.

Samuels, Gina Miranda. 2008.  A Reason, a Season, or a Lifetime: Relational Permanence among Young Adults with Foster Care Backgrounds. Chicago, IL: University of Chicago, Chapin Hall Center for Children.

Samuels, Gina Miranda. 2018. “When homelessness starts at home: The ambiguous loss of 

home.” Keynote, Neurosequential Model Annual Conference, Child Trauma Academy. Banff, 

Alberta Canada.

Samuels, Gina Miranda, and Julia M. Pryce. 2008. “‘What doesn’t kill you makes you stronger:’ 

Survivalist self-reliance as resilience and risk among young adults aging out of foster care,”  Children and Youth Services Review 30 (10): 1198–1210.

Samuels, Gina Miranda, and F. Ross-Sheriff. 2007. “Identity, oppression, and power: Feminism and intersectionality theory,”  Affilia: Journal of Women and Social Work 23: 5–9.

Sandelowski, Margarete. 1993. “Rigor or rigor mortis: The problem of rigor in qualitative research revisited,”  Advanced in Nursing Science 16: 1–8.

Scott, Lionel D., J. Curtis McMillen, and Lonnie R. Snowden. 2015. “Informal and formal help-seeking among older Black male foster care youth and alumni,”  Journal of Child and Family Studies 24 (2): 264–277.

Shade, Kate, Susan Kools, Howard Pinderhughes, and Sandra J. Weiss. 2012. “Choosing 

fatherhood: How teens in the justice system embrace or reject a father identity,”  Journal of Community Health Nursing 29 (2): 91–105.

Shelton, Jama. 2016. “Reframing risk for transgender and gender-expansive young people experiencing homelessness,”  Journal of Gay & Lesbian Social Services 28 (4): 277–291.

———.2015. “Transgender youth homelessness: Understanding programmatic barriers through the lens of cisgenderism,”  Children and Youth Services Review 59: 10–18

Cityscape 67

Samuels, Cerven, Curry, and Robinson

Squatriglia, Heather. 2008. “Lesbian, gay, bisexual and transgender youth in the juvenile justice system: Incorporating sexual orientation and gender identity into the rehabilitative process,”  Journal of Law 14 (3): 793–818.

Stewart, Miriam, Linda Reutter, Nicole Letourneau, Edward Makwarimba, and Krista Hungler. 

2010. “Supporting Homeless youth: Perspectives and preferences,”  Journal of Poverty 14 (2): 145–165.

Stotzer, Rebecca L., Perry Silverschanz, and Andre Wilson. 2013. “Gender identity and social services: Barriers to all,”  Journal of Social Service Research 39: 63–77.

Sue, Derald Wing. 2010.  Microaggressions in Everyday Life. Hoboken, NJ: Wiley.

Thompson, Sanna J., Kimberly A. Bender, Carol M. Lewis, and Rita Watkins. 2008. “Runaway and pregnant: Risk factors associated with pregnancy in a national sample of runaway/homeless female adolescents,”  Journal of Adolescent Health 43 (2): 125–132.

Thompson, Sanna J., Holly McManus, Janet Lantry, Liliane Windsor, and Patrick Flynn. 2006. 

“Insights from the street: Perceptions of services and providers by homeless young adults,” 

Evaluation and Program Planning 29 (1): 34–43.

Toro, Paul, Amy Dworsky and Patrick Fowler. 2007.  Toward understanding homelessness: The 2007 

National Symposium on Homelessness Research, Washington, DC: U.S. Department of Housing and Urban Development and U.S. Department of Health and Human Services.

Westra, Henny A, and Nikoo Norouzian. 2018. “Using motivational interviewing to manage process marker of ambivalence and resistance in cognitive behavioral therapy,”  Cognitive Therapy and Research 42 (2): 193–203.

Winetrobe, Hailey, Harmony Rhoades, Eric Rice, Norweeta Milburn, and Robin Petering. 2017. “‘I’m not homeless, I’m houseless:’ identifying as homeless and associations with service utilization among Los Angeles homeless young people,”  Journal of Social Distress and the Homeless 26 (1): 16–24.

Zimmerman, Marc A. 2013. “Resiliency Theory: A Strengths-Based Approach to Research and Practice for Adolescent Health,”  Health Education & Behavior: The Official Publication of the Society for Public Health Education 40 (4): 381–383.

Zufferey, Carole. 2017.  Homelessness and Social Work: An Intersectional Approach. New York: Routledge.

68 Youth Homelessness

Linking the TAY-VI-SPDAT  

Tool to Housing Placements  

and Outcomes for Youth 

Experiencing Homelessness

Eric Rice

University of Southern California

Monique Holguin

University of Southern California

Hsun-Ta Hsu

University of Missouri

Matthew Morton

Chapin Hall at the University of Chicago

Phebe Vayanos

University of Southern California

Milind Tambe

University of Southern California

Hau Chan

University of Southern California

by Eric Rice, Monique Holguin, Hsun-Ta Hsu, Matthew Morton, Phebe Vayanos, Milind Tambe, and Hau Chan

Abstract

Youth homelessness has reached a concerning level of prevalence in the United States. Many communities have attempted to address this problem by creating coordinated community responses, typically referred to as Coordinated Entry Systems (CES). In such systems, agencies within a community pool their housing resources in a centralized system. Youth seeking housing are first assessed for eligibility and vulnerability and then linked to appropriate housing resources. The most widely adopted tool for assessing youth vulnerability is the Transition Age Youth-Vulnerability Index-Service Prioritization Decision Assistance Tool (TAY-VI-SPDAT): Next Step Tool (NST) for homeless youth. To date, no evidence has been amassed to support the value of using this tool or its proposed scoring schematic to prioritize housing resources. 

Similarly, there is little evidence on the outcomes of youth whose placements are determined by the tool.

Cityscape: A Journal of Policy Development and Research •  Volume 20, Number 3 • 2018

Cityscape 69

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Rice, Holguin, Hsu, Morton, Vayanos, Tambe, and Chan

Abstract (continued)

This article presents the first comprehensive and rigorous evaluation of the connection between vulnerability scores, housing placements, and stability of housing outcomes using data from the Homeless Management Information System (HMIS) collected between 2015 and 2017 from 16 communities across the United States. The two primary aims are (1) to investigate the degree to which communities are using the tool’s recommendations when placing youth into housing programs, and (2) to examine how effectively NST scores distinguish between youth in greater need of formal housing interventions from youth who may be able to self-resolve or return to family successfully. High vulnerability scores at intake were associated with higher odds of continued homelessness without housing intervention, suggesting the tool performs well in predicting youth that need to be prioritized for housing services in the context of limited resources. The majority of low scoring youth appear to return home or self-resolve and remain stably exited from homelessness. Youth placed in permanent supportive housing (PSH) had low recorded returns to homelessness, regardless of their NST score. Youth with vulnerability scores up to 10 who were placed in rapid rehousing (RRH) also had low returns to homelessness, but success was much more variable for higher-scoring youth.

The Voices of Youth Count study found youth homelessness has reached a concerning prevalence level in the United States; one in 30 teens (13 to 17) and one in 10 young adults (18 to 25) experience at least one night of homelessness within a 12-month period, amounting to 4.2 million persons a year (Morton et al., 2018). Many communities have attempted to address this problem by creating coordinated community responses, typically referred to as Coordinated Entry Systems (CES). In such systems, most agencies within a community pool their housing resources in a centralized system called a Continuum of Care (CoC). A CoC is a regional or local planning body that coordinates housing and services funding—primarily from the U.S. Department of Housing and Urban Development (HUD)—for people experiencing homelessness. Youth seeking housing are first assessed for eligibility and vulnerability and then those youth identified as having the greatest need are linked to appropriate housing resources. The most widely adopted tool for assessing youth vulnerability is the Transition Age Youth-Vulnerability Index-Service Prioritization Decision Assistance Tool (TAY-VI-SPDAT): Next Step Tool (NST) for homeless youth, which was developed by OrgCode Consulting, Corporation for Supportive Housing (CSH), Community 

Solutions, and Eric Rice (Orgcode Consulting, 2015).

This article presents the first comprehensive evaluation of the connection between vulnerability scores, housing placements, and stability of housing outcomes using data from the Homeless Management Information System (HMIS) collected between 2015 and 2017 from 16 communities in the United States. The two primary goals of the article are (1) to understand to what extent communities are using the OrgCode recommendations when placing youth into housing programs, and (2) to understand to what extent NST scores are effectively differentiating those youth who have greater needs for permanent supportive housing (PSH) and rapid rehousing (RRH) interventions from those youth who may successfully self-resolve or return to their family. As these results come from administrative data, we do not perceive this article as a formal test of validity of the NST tool, 70 Youth Homelessness

Linking the TAY-VI-SPDAT Tool to Housing Placements  

and Outcomes for Youth Experiencing Homelessness

nor do we see it as a formal evaluation of the effectiveness of PSH and RRH as housing interventions for youth. Rather, we see this article as a valuable first look into how communities have been using the NST to prioritize housing for youth and how successful or not youth have been in a variety of exits from homelessness in the context of CoCs that have adopted this tool.

Background

In almost all communities in the United States, the number of youth experiencing homelessness exceeds the capacity of the housing resources available to them (Morton et al., 2018). This situation leaves communities with the predicament of trying to decide who to prioritize for the precious few spots available in housing programs. Many cities have attempted to address this problem through the creation of CES. For adults, the use of CES and assessment tools has a longer history (Padgett, Henwood, and Tsemberis, 2016). In the context of adult homelessness, tools for assessing vulnerability have focused on assessing factors that are associated with premature mortality (Hwang et al., 1998; Juneau Economic Development Council, 2009; Swanborough, 2011) or with higher system costs (Economic Roundtable, 2011). Youth under the age of 25, however, are less likely to experience health-related premature mortality and thus potentially less prone to incurring system costs, relative to chronically homeless adults (Winetrobe, et al., 2015). Therefore, new assessment tools have been developed in recent years that reflect the needs and realities of youth who are homeless. The TAY Triage Tool, developed by the CSH and Rice (Rice, 2013), and the NST are the most widely used tools to assist homeless youth (Orgcode Consulting, 2015). Recently, Rice (2018) has provided an extensive description of the development of these two tools and the differences between them (Rice, 2018). Notably, the NST incorporates the six items that make up the TAY 

Triage Tool as the two tools are not entirely distinct. This article focuses on examining how 16 

communities have used the NST for assessing vulnerability and as a guide for housing prioritization.

The NST is a set of 28 multiple-choice, dichotomous, and frequency-type questions to measure a youth’s vulnerability based on his/her history of housing and homelessness risks, socialization, daily functions, and wellness. Example questions include: “Is your current lack of stable housing because of violence at home between family members?” and “Have you threatened to, or tried to, harm yourself or anyone in the last year?”

Youth responses to the NST are cumulatively scored from 0 to 17; the higher the score, the higher the assessed vulnerability. For those youth who score 0 to 3, the recommendation is that no moderate or high intensity services be provided at that time. For the youth who score 4 to 7, the recommendation is for time-limited housing supports with moderate intensity. For youth scoring 8 or higher, the NST recommends assessment for long-term housing with high service intensity. 

Currently, there is no research to validate the specific cutoff scores recommended by the NST tool. 

These assessments are merely recommendations by the developers, and one of the goals of this article is to explore the appropriateness of these cutoff scores.

The Current Approach to Youth Housing

HUD offers many mandates, guidelines, and best practice recommendations to communities on housing youth (HUD, 2015; HUD, 2016). In most CoCs, housing agencies within a community Cityscape 71

Rice, Holguin, Hsu, Morton, Vayanos, Tambe, and Chan

pool their housing resources in a centralized system. First, a youth experiencing homelessness enters a centralized intake location (for example, designated emergency shelters, street outreach, or drop-in centers) to sign up for housing support. There, they are assessed for housing eligibility and vulnerability/risk. All of this information is entered into the HMIS. Then, based on these assessments, a case manager or a team of housing navigators decide how a youth is to be prioritized for housing, considering the options available. The youth is then placed on a waiting list until appropriate housing becomes available in the community. Typically, if a young person comes in with a higher risk assessment score than a previously assessed youth, that young person is placed higher on the waitlist; it is, in other words, not a first-come-first-serve system by design.

In many communities, based on the recommendations provided in the NST documentation, 

youth who score 8 to 17 are designated “high risk” and prioritized for PSH, a resource-intensive, non-time-limited housing program which includes “wraparound” social services (staff support and treatment offered as needed) for youth to assist them in remaining stably housed (Padgett, Henwood, and Tsemberis, 2016). Youth who score in the mid-range (4 to 7) are typically referred to less intensive services, which often appears to be operationalized as RRH, a short- or medium-term rental subsidy program having various social services attached, though there can be considerable variability in the duration of subsidies and the extent and quality of associated services. Some youth who score low (less than 4) often do not ever receive housing resources through the CoC. The NST scoring recommendations are not a hard-and-fast set of rules, thus, we are interested in assessing to what extent communities follow the recommended scoring system.

More importantly, however, the goal of this article is to provide greatly needed evidence to elucidate the pathway from assessment, to housing placement, to outcomes in housing stability (at least in the short-term). There is an overwhelming desire on the part of communities to house youth, and HUD wants community housing systems to be systematic, data-driven, and grounded in research (HUD, 2015; HUD, 2016). Despite this goal, save for a few exceptions (Focus Strategies, 2017), the current housing allocation system for youth has not been evaluated. 

If we are to understand the value of the NST (or other vulnerability assessment tools), we must understand whether linking high-scoring and mid-scoring young people to particular housing interventions, such as PSH or RRH, actually increases their chances of becoming stably housed. 

Likewise, it is important to understand whether low-scoring persons (less than 4) are able to successfully exit homelessness without community-provided housing interventions.

This article presents several important pieces of information. First, we explored how many youth who were assessed exited homelessness into different types of housing (for example, PSH, RRH, return to Family, or Self-Resolved), and we examine how these exits varied by NST score. Second, we investigated how NST scores were related to stably exiting local homelessness systems for at least 180 days, again looking into variation by type of housing. Finally, we conducted a series of logistic regressions to determine whether specific NST items can help further differentiate those youth who successfully exited homelessness systems for at least 180 days from the ones who did not. We see these results as potential “red flags” for CoCs who could then provide additional services to youth with particular experiences who may need additional assistance in remaining stably exited from homelessness.

72 Youth Homelessness

Linking the TAY-VI-SPDAT Tool to Housing Placements  

and Outcomes for Youth Experiencing Homelessness

Methods

Data Set

The current data set is an administrative collection acquired by OrgCode on May 1, 2017, from the HMIS database of 16 communities in the United States. These data were collected by communities in the context of assessment for eligibility for housing programs from youth experiencing homelessness, between January 2015 and May 2017. The data set consists of 10,922 youth experiencing homelessness. These records were accessed, anonymized, and provided to the authors by Iain De Jong of Orgcode. The data were collected by service providers in the 16 communities and entered into the HMIS data system.

Variables

The data set includes several key variables which we treat as independent variables. Demographic variables include the youth’s age, gender, LGBTQQI2 (Lesbian, Gay, Bisexual, Transgender, Questioning, Queer, Intersex and 2-Spirit) status, race/ethnicity, and type of community. Each record also contains responses to each of the questions asked in the NST tool as well as the overall calculated NST score.

These data include four key exits from homelessness. Two are housing program exits: RRH and PSH. 

The data did not capture placements in other housing programs, such as transitional housing, nor did it capture supportive services offered with or without housing program placements. These data also include key exits from homelessness that reflect little assistance from the CoC: some youth experiencing homelessness went to live with their family members (“Family” exit type) or were able to find housing themselves or possibly with non-housing support services (“Self-Resolved” exit type). 

These are the four main homelessness exits we focus on: RRH, PSH, Family, and Self-Resolved.

The first recorded exit in the data set was January 2, 2015, and the last recorded exit was March 29, 2017. There were also exits from homelessness to boarding homes, incarceration, veterans’ 

programs such as Supportive Services for Veteran Families (SSVF), youth who are still pending in the system, and youth who have been lost to the system (“unknown”). There were also a small number of youth (n=68) who died, but they have been removed from these analyses as a deeper investigation of this outcome is needed. Furthermore, the number of youth (n=211) who were incarcerated have also been removed from these analyses considering this situation is a markedly different issue and one also in need of a deeper investigation. Due to small sample sizes for boarding homes (n=8) and veterans’ programs (n=54), both exits were removed from the analyses. 

Three time points are included in this data set: (1) the date of initial assessment with the NST, (2) the date of exit from homelessness (if the youth exited homelessness), (3) the date of return to homelessness (if the youth returned to homelessness and engaged with services linked to the HMIS system such as emergency shelter). Most importantly, for each youth in the data, there are fields specifying whether the youth was still living in the initial housing exit. The first assessment date was January 4, 2015, and the last assessment date was February 20, 2017. The first recorded exit from homelessness was January 24, 2015, and the last recorded exit was March 19, 2017. 

The first recorded return to homelessness was October 9, 2015, and the last recorded return to Cityscape 73

Rice, Holguin, Hsu, Morton, Vayanos, Tambe, and Chan

homelessness was April 13, 2017. Many youth were still housed by the end of the observation period, which was May 1, 2017.

Data Analysis

We account for time in two ways in these analyses. The data was downloaded on May 1, 2017. 

For our investigation of exits from homelessness and stability of exit, we want to provide a minimum time window of 180 days of observation. For our examination of housing exits, we removed those youth who were assessed after November 2, 2016, so as to allow for at least 180 

days for youth to be observed attempting to exit homelessness. For our examination of stability of exits, we removed all youth from the data set who did not exit to PSH, RRH, Family, or Self-Resolved. Moreover, we removed any youth who exited homelessness after November 2, 2016, so as to allow for at least 180 days of observation to determine their housing stability. We coded a youth as stably exited from homelessness if they had remained out of the homelessness system for at least 180 days post exit (note “exit” refers to an exit from the homelessness system, and it also marks  entry into a housing program for those youths placed into RRH or PSH). We present frequency distributions for demographic characteristics in exhibit 1. We present the distribution of homelessness exits by NST score.

To examine how scores and other variables predict stable exits from homelessness for different exit types, we conducted multivariate logistic regression models. The modelling strategy was based on procedures described by Hosmer and Lemeshow (2000) and proceeded as follows: the larger sample was broken into four sub-samples, those youth who exited to PSH, RRH, Family, and Self-Resolved. In each sub-sample, bivariate associations between stable exits, the overall NST score, and each unique item collected in the NST was assessed. Associations which were statistically significant at the p is less than .10 level were retained for the next step. Then multivariate logistic regressions including all significant bivariate associations were run. Subsequently, items were removed one at a time until the final models only containing variables with associations at the p is less than .05 level remained. The overall NST score variable was still constructed by using the full set of items. We retained demographic covariates regardless of statistical significance.

Exhibit 1

Frequency Distributions for Demographic Characteristics ( n=10,922)

n

%

Demographics

Race/Ethnicity

 Black

3,382

31

 Hispanic

1,656

15.2

 White

5,212

47.7

 Asian

333

3.1

 Hawaiian/Pacific Islander

20

0.2

 Native American

319

2.9

Gender

 Female

2,429

22.2

 Male

8,487

77.8

74 Youth Homelessness

Linking the TAY-VI-SPDAT Tool to Housing Placements  

and Outcomes for Youth Experiencing Homelessness

Exhibit 1

Frequency Distributions for Demographic Characteristics ( n=10,922)

n

%

Sexual Orientation

 LGBQQI2

3,319

30.4

 Heterosexual

7,603

69.6

Mean

SD

19.1

2.66

n

%

Age

 17 or younger

3,303

30.2

 18 or older

7,619

69.8

Types of Community

 Rural

1,591

14.6

 Suburban

2,046

18.7

 Urban

7,285

66.7

Where do you sleep most frequently

 Car

766

7

 Couch

665

6.1

 Outdoors

798

7.3

 Shelter

7,188

65.8

 Transitional Housing

1,505

13.8

Homelessness exits1

 Family

1,250

12.6

 PSH

574

5.8

 RRH

2,872

28.8

 Self-Resolved

1,140

11.5

 Boarding Home

7

0.1

 Deceased

45

0.5

 Incarcerated

211

2.1

 Pending

2,717

27.3

 SSVF

54

0.5

 Unknown

1,087

10.9

 Stably Housed for 180+ days2

4,361

88.8

Notes:  1. Among those youth who were assessed by November 2, 2016. (n=9957).

2. Among those youth who exited to PSH, RRH, Family, or Self-Resolved by November 2, 2016, or earlier.

Results

We present frequency distributions of demographic characteristics in exhibit 1. We present the distribution of housing exits by NST scores in exhibit 2 and represent these numbers graphically in exhibit 3. Exhibit 4 presents the percentage of youth who remained stably housed for at least 180 

days and the distribution of successful exits by NST score. These numbers are represented graphically in exhibit 5. Exhibits 6 to 9 present the results of the multivariate logistic regression models.

As reflected in exhibit 1, we can see CoCs’ across the country housed large numbers of youth. 

Of the 9,957 youth in the sample who were assessed at least 180 days prior to the observation window closing, slightly more than one-fifth were able to exit homelessness by either finding Cityscape 75

Rice, Holguin, Hsu, Morton, Vayanos, Tambe, and Chan

housing on their own (Self-Resolve) or returning home with family members (Family). Regarding the other two housing exits in the data set, more youth exited into RRH (28.8 percent) than PSH (5.8 percent). The majority of youth (88.8 percent) were stably exited for at least 180 days following this first exit from homelessness.

Exhibit 1 also presents the demographic profile of the youth in the full sample. The mean age was 19.1. More specifically, 3,303 youth were under 18 years old (30.2 percent) and 7,619 youth were 18 and over (69.8 percent). Slightly less than one-half of the sample was White (47.7 percent) followed by Black (31 percent) and Hispanic (15.2 percent) youth. Asians, Hawaiian/Pacific Islanders and Native Americans accounted for 6.2 percent of the sample. The majority of youth were male. Nearly one-third (30.4 percent) of the sample identified as LGBQQI2. The majority of the youth came from urban communities (66.7 percent) and slept most frequently in shelters (65.8 

percent) followed by transitional housing (13.8 percent). Notably, 2,229 youth reported sleeping most frequently in a car, couch, or outdoors (20.4 percent).

Exhibit 2 presents the distribution of youth by NST score for all housing exits with the exception of boarding homes, veterans’ programs (SSVF), and death exits. This distribution is also graphically presented in exhibit 3. Most communities reserved PSH for those youth who scored 8 or higher on the NST. Only 15 youth (0.2 percent) who scored less than 8 had an exit to PSH. RRH was most frequently given to youth with an NST score of 5 to 7. Only 14 youth (0.7 percent) who scored 4 

or lower had an exit to RRH. Some high scoring youth (8 or higher), however, had an exit to RRH 

(579, or 20.2 percent of all RRH exits).

Moreover, it is useful to examine how the OrgCode scoring system maps onto actual exits from homelessness. Of the 768 youth who scored 0 to 3 (considered low scores by OrgCode), 253 (32.9 

percent) self-resolved, 261 (34.0 percent) returned to family, 1 (0.1 percent) was placed in PSH, 4 

(0.5 percent) into RRH, and 249 (32.4 percent) were either lost to the housing system or are still attempting to exit. Among the 6,550 youth scoring 4 to 7 (medium scores), 2,289 (34.9 percent) were placed in RRH, 14 (0.2 percent) were placed in PSH, 875 (13.4 percent) self-resolved, 901 

(13.8 percent) returned to family, and 2,451 (37.4 percent) were either lost or still awaiting a housing intervention. Among the 2,532 youth designated at high risk (NST 8 or higher), there were 559 (22.1 percent) PSH exits, 579 (22.9 percent) RRH exits, 88 (3.5 percent) family exits, 12 

(0.4 percent) self-resolved exits, and 1,104 (43.6 percent) were either lost or pending housing by the close of the observation period.

Exhibit 2

Distribution of Housing Exits by NST Score (Among youth assessed by November 2, 2016) (n= 9,850) NST 

Self-

PSH

RRH

Family

Incarcerated Unknown

Pending

Total

Score

Resolved

1

0

0

9

7

0

2

3

21

2

0

0

55

61

0

28

27

171

3

1

4

189

193

0

80

109

576

4

5

10

471

462

0

214

111

1273

5

3

620

315

223

4

184

515

1864

6

2

899

67

143

9

118

672

1910

7

4

760

22

73

7

121

516

1503

76 Youth Homelessness

Linking the TAY-VI-SPDAT Tool to Housing Placements  

and Outcomes for Youth Experiencing Homelessness

Exhibit 2

Distribution of Housing Exits by NST Score (Among youth assessed by November 2, 2016) (n= 9,850) NST 

Self-

PSH

RRH

Family

Incarcerated Unknown

Pending

Total

Score

Resolved

8

65

397

10

53

12

83

306

926

9

107

157

0

19

36

107

163

589

10

134

19

2

8

44

59

116

382

11

131

5

0

7

50

47

100

340

12

66

1

0

1

21

28

47

164

13

39

0

0

0

15

13

22

89

14

10

0

0

0

6

1

7

24

15

7

0

0

0

6

2

3

18

16

0

0

0

0

0

0

0

0

17

0

0

0

0

0

0

0

0

Note: Boarding homes, veteran’s program (SSVF), and death exits were dropped. 

Exhibit 3

Distribution of Housing Exits by NST Score.

As exhibit 4 and exhibit 5 show, as NST scores increased, the number of youth who successfully remain housed for 180 or more days decreased. That is to say, higher scoring youth were more likely to return to homelessness after an initial exit from homelessness. This finding is consistent across all four exit types. PSH was associated with more stable exits from homelessness systems for almost every youth: 95 percent of youth scoring 8 or 9 did not reenter the homelessness system for 180 or more days, and more than 90 percent of youth who scored 10 to 13 did not reenter the homelessness system for 180 days or more. Although there is a slight dip in successful exits from homelessness systems among youth placed into RRH at a score of 4, these data come from Cityscape 77

Rice, Holguin, Hsu, Morton, Vayanos, Tambe, and Chan

only 10 youth. When examining youth scoring 5 to 7, more than 90 percent remained out of the homelessness system for at least 180 days. Moreover, more than 80 percent of the youth who scored 8 and 9 who were given RRH did not reenter the homelessness system for at least 180 days. 

Family exits appear to be more successful for lower scoring youth. Among youth scoring 1 to 3, Exhibit 4

Percentage of Youth Who Remained Stably Housed for at Least 180 Days, and the Distribution of Successful Exits by NST Score. (n=4913) (Among youth who exited by November 2, 2016).

NST Score

PSH %

RRH %

Family %

Self-Resolved %

1

83.3

83.3

2

97.9

94.6

3

100.0

100.0

93.2

96.8

4

100.0

71.4

89.0

95.1

5

100.0

91.7

79.5

84.2

6

100.0

91.4

82.8

83.6

7

100.0

90.6

60.9

63.2

8

98.2

87.3

59.6

57.1

9

94.7

80.6

46.7

10

91.2

61.1

33.3

100.0

11

93.5

33.3

0.0

12

90.9

0.0

0.0

13

96.3

14

75.0

15

50.0

16

17

Exhibit 5

Percentage of Youth Who Remained Stably Housed for at Least 180 days, and the Distribution of Successful Exits by NST Scores. (Among youth who exited by November 2, 2016).

78 Youth Homelessness

Linking the TAY-VI-SPDAT Tool to Housing Placements  

and Outcomes for Youth Experiencing Homelessness

more than 93 percent remained stably with family for at least 180 days; at a score of 7, this rate dropped precipitously to 61 percent and continued to decline with higher scores. Self-resolved exits showed a similar pattern. Among youth who score 1 to 3, 95 percent or more remained stably exited from homelessness systems for at least 180 days. At a score of 7, however, this rate dropped to only 63 percent and declined further with higher scores.

The multivariate models presented in exhibits 6 to 9 provide additional information about the NST score’s association with stable exits from homelessness systems for at least 180 days as well as on specific items from the NST that are positively and negatively associated with stable exits. 

In all four models, there is a significant negative association between NST score and stable exits from homelessness systems, which can also be seen graphically in exhibit 5. Exhibit 6 indicates that, other than NST scores, only the duration of the last homelessness episode was significantly associated with stable exits into PSH. Exhibit 7 suggests there is a 65-percent reduction in the odds of remaining stably housed in RRH if a youth exited from transitional living programs into RRH compared to those youth from the street. Exhibit 8 suggests that racial/ethnic minority youth, relative to White youth, experienced a 43 percent reduction in the odds of successfully remaining with family for 180 or more days. Moreover, youth who experienced conflict around sexual or gender identity had a 54 percent reduction in the odds of remaining stably exited from the homelessness system, and youth with pregnancy histories experienced a 52 percent reduction in the odds of remaining stably exited. Exhibit 9 suggests that youth under the age of 18 experienced an 81 percent reduction in the odds of remaining stably self-resolved for 180 days or more. In addition, relative to rural communities, both urban and suburban dwelling youth experienced a reduction in the odds of successfully self-resolving, with a 63 percent reduction for suburban youth and a 57 percent reduction for urban youth.

Exhibit 6

Multivariable Logistic Regression Models of Stable Exits to PSH for at Least 180 days.

PSH Exit (n=479)

β

S.E.

OR

95% CI

Demographics

17 or Younger

1.20

0.75

3.32

[0.76, 14.57]

Female

-0.21

0.38

0.81

[0.33, 1.99]

LGBTQQI2

0.06

0.38

1.06

[0.50, 2.23]

Minority

-0.35

0.38

0.70

[0.33, 1.49]

Homeless History

Duration of last 

0.00

0.00

1.00

[1.00, 1.00]     *

homeless episode

Trauma History/Risk

Final Acuity Score

-0.33

0.11

0.72

[0.58,0.90]     **

Pseudo R2

 0.07

Note: *p is less than .05; **p is less than .01

Cityscape 79

Rice, Holguin, Hsu, Morton, Vayanos, Tambe, and Chan

Exhibit 7

Multivariable Logistic Regression Models of Stable Exits to RRH for at Least 180 days.

RRH Exit (n=2436)

β

S.E.

OR

95% CI

Demographics

17 or Younger

-0.16

0.16

0.85

[0.62, 1.17]

Female

0.29

0.19

1.34

[0.93, 1.94]

LGBTQQI2

0.08

0.16

1.08

[0.92, 1.53]

Minority

-0.08

0.14

0.92

[0.79, 1.47]

Living Situation

Couch

-0.78

0.40

0.46

[0.21, 1.01]

Outdoors

0.37

0.48

1.45

[0.57, 3.69]

Shelter

0.56

0.29

1.75

[0.99, 3.12]

Transitioning Housing

-1.06

0.31

0.35

[0.19, 0.64]     **

Trauma History/Risk

Final Acuity Score

-0.30

0.06

0.74

[0.66, 0.84]     ***

Pseudo R2

0.09

Note: *p is less than .05; **p is less than .01; ***p is less than .001

Exhibit 8

Multivariable Logistic Regression Models of Stable Exits to Family for at Least 180 days.

Family Exits (n=1031)

β

S.E.

OR

95% CI

Demographics

17 or Younger

0.81

0.16

0.81

[0.55, 1.18]

Female

0.89

0.18

0.89

[0.61, 1.32]

LGBTQQI2

1.23

0.27

1.23

[0.80. 1.90]

Minority

0.57

0.1

0.57

[0.40, 0.81]     **

Trauma History/Risk

Conflicts around gender identity/

0.46

0.15

0.46

[0.24, 0.86]     *

sexual orientation

Pregnancy History

0.48

0.17

0.48

[0.24, 0.96]     *

Final Acuity Score

0.64

0.03

0.64

[0.58, 0.71]     ***

Pseudo R2

0.12

Note: *p is less than .05; **p is less than .01; ***p is less than .001

80 Youth Homelessness

Linking the TAY-VI-SPDAT Tool to Housing Placements  

and Outcomes for Youth Experiencing Homelessness

Exhibit 9

Multivariable Logistic Regression of Stable Self-Resolved Exits for at Least 180 days.

Self-Resolved Exit (n=967)

β

S.E.

OR

95% CI

Demographics

17 or Younger

0.19

0.05

0.19

[0.12, 0.31]     ***

Female

0.71

0.19

0.71

[0.42, 1.19]

LGBTQQI2

0.74

0.2

0.74

[0.44, 1.26]

Minority

1.19

0.29

1.19

[0.74, 1.92]

Community Types

Suburban

0.37

0.15

0.37

[0.17, 0.83]     *

Urban

0.43

0.16

0.43

[0.21, 0.90]     *

Trauma History/Risk

Final Acuity Score

0.53

0.06

0.53

[0.43, 0.66]     ***

Pseudo R2

0.16

Note: *p is less than .05; **p is less than .01; ***p is less than .001

Discussion

To our knowledge, these findings are the first data that link NST assessment scores to particular exits from homelessness and include longitudinal information on returns to homelessness systems. 

There are several important findings which emerge from this analysis. First, most communities appear to be approximately using OrgCode’s recommended thresholds for housing interventions. 

That is, PSH placements were largely extended to youth scoring 8 or higher, RRH to youth scoring 5 to 71 , and neither PSH nor RRH were provided to many youth scoring 4 or less. It is not necessarily surprising these 16 communities are following the Orgcode recommended scoring system, given HUD’s encouragement of communities to use such tools (HUD, 2015; HUD, 2016).

The proportion of youth who succeeded in a given housing exit declined as NST scores increased. 

Looking across all four main types of housing exits provides a picture of the capacity for NST 

score to not only predict vulnerability, but also to predict who is likely to do better in different types of exits from homelessness. Youth who scored 4 or less, if they exited to family or self-resolved, had a high likelihood of success—measured, in this case, as no recorded returns to the local homelessness system for at least 180 days. Youth who scored 5 to 9 generally did well in RRH for at least a six-month period. Almost all youth receiving PSH had low recorded returns to homelessness within at least six months, even at very high scoring levels. Unfortunately, these data do not include any information about specific social services attached to PSH or RRH, nor on the duration of RRH rental assistance. Such information would be useful to further understand the factors contributing to successful or unsuccessful housing placements.

RRH generally appears to be a useful housing intervention for those youth with an NST score of less than 10. We believe more experimentation with higher scoring youth may be warranted—

using a more rigorous impact evaluation design and longer-term follow-up periods—particularly 1 OrgCode guidance is for moderate-intensity time-limited housing and services to be provided to young people scoring 4 to 7.

Cityscape 81

Rice, Holguin, Hsu, Morton, Vayanos, Tambe, and Chan

in communities where PSH is not readily available but RRH is available. Perhaps a progressive engagement approach may be warranted, wherein youth scoring 10 or higher are first given RRH 

and then PSH if they are unsuccessful with RRH. Furthermore, additional experimentation with the lengths of RRH rental subsidy, approaches to youth and landlord engagement, and supportive services may help to refine RRH programming to meet the needs of youth with different levels and types of vulnerability.

Many low scoring youth appear able to exit homelessness systems either on their own as “Self-Resolved” or to “Family”. The percentage of youth who successfully remained self-resolved or with family dropped dramatically as their NST score increased. Clearly, higher scoring youth need more assistance. Notably, however, 27.3 percent of the youth who scored less than 4 did not exit homelessness. It is possible evidence-based family reunification services, such as the Support to Reunite, Involve, and Value Each Other (STRIVE) project (Milburn et al., 2012), may increase the viability of family housing outcomes for this group of youth. Presumably, some of these youth do not have a safe or viable family to be the focus of reunification. For some youth who are unable to return to home or self-resolve quickly, RRH, transitional housing, or other forms of assistance may be an appropriate intervention, even for youth with an NST score less than 4. Importantly, transitional living programs or transitional housing—funded, for example, by the U.S. Department of Health and Human Services (HHS) for youth experiencing homelessness—could be a useful resource for some young people requiring housing and youth-centric services and supports for a defined period of time, but, because such programs were not included in the data as an exit type (because they are not funded and considered by HUD to be an exit from homelessness), we were unable to evaluate and compare results for this kind of intervention.

These success rates by score, and the distribution of scores, may provide added insight into how communities could consider adjusting guidance around the current scoring system, particularly considering our understanding that many communities may want to reserve PSH for youth scoring 10 or higher. PSH is very expensive relative to RRH and other time-limited housing programs with moderate-intensity services. Yet, 87 percent of youth who scored 8, and 81 percent who scored 9, and were given RRH did not reenter the homelessness system for at least 180 days. Only 11 

percent of youth in the sample scored 10 or higher, and perhaps these youth should be prioritized for limited and resource-intensive PSH.

It is worth noting that, across these communities, 15 percent of the population scored 8 or 9, which means, rather than needing to find PSH for 25 percent of youth, we may be able to focus PSH on the 11 percent or so who score 10 or higher and who are less likely to succeed without more intensive resources. Comparing the success of PSH to that of RRH and/or other time-limited housing interventions with youth scoring 8 to 9 on NST, along with more rigorous evaluation and longer-term follow-up periods, would be a useful area for future experimentation.

Moreover, although lower scores were associated with lower likelihood of remaining pending or unknown in the homelessness system, 27 percent of the youth who scored less than 4, nonetheless, never exited homelessness systems during the observation period. We ought to provide more intensive supports and/or housing interventions (perhaps RRH or maybe transitional living) for this subset of low scoring youth who are unable to return home or self-resolve in a short period of time. 

82 Youth Homelessness

Linking the TAY-VI-SPDAT Tool to Housing Placements  

and Outcomes for Youth Experiencing Homelessness

An extension of youth-centric RRH to lower and higher scoring youth, with a concurrent contraction to 10+ for most PSH placements, would likely present cost savings to communities in the long run and allow for serving a larger number of youth more quickly. Additional research is needed to determine whether there are predictors of low scoring youth who are unable to self-resolve or return to their family without some type of housing intervention. Furthermore, prospective evaluation and a cost analysis of our proposed changes in the scoring guidance are needed.

While our findings suggest the scores are generally meaningful predictors of young people’s risk of remaining homeless or returning to homelessness systems, the scores by themselves still represent limited information and offer a blunt basis for good decision making with individual youth. To this end, the logistic regression results provide an understanding of “red flags” for specific youth who may need additional services in order to help them succeed in given exit types. For example, we believe the result of the regressions for family exits indicate minority youth, and youth who have conflict with family about sexual orientation or gender identity, are in greater need of better-suited family reunification services and/or other support services to help sustain their family exits or to achieve housing stability outside of the family.

While this study is based on an unprecedented longitudinal administrative data set linking intake assessment scores and variables to service placements and outcomes across multiple communities, it also has several limitations that signal areas for data improvements and future research. First, stable housing outcomes are identified as youth either still being in a program or exiting to a stable housing situation and NOT subsequently returning to the local homelessness system (for example, to a shelter in the CoC) for at least six months and being recorded in the Homeless Management Information System. Youth who became homeless again but did not 

reenter the local homelessness system would have been falsely coded as a successful outcome. 

It is likely some number of youth do return to homelessness in ways that are not recorded in HMIS, or return to the local homelessness system later than the duration of the data set’s observation period, but we have no way of knowing how many. Second, we need more data and larger sample sizes to better understand how RRH works particularly for youth who score 10 or higher. Third, there is no information about the types or quality of services delivered to youth or the frequency of contact these youth had with personnel in the housing system. Even youth that were indicated as having “self-resolved” exits could have received non-housing services that were vital to their exits from homelessness.

Fourth, the data do not include all types of housing programs in which youth could be 

placed. Perhaps most importantly in this respect, the data do not include exits to transitional living programs that are primarily funded by HHS and other non-HUD funding streams. In future research, it would be very useful to compare results associated with different housing interventions—for example, between RRH and transitional housing for youth with different characteristics and degrees of vulnerability—so that communities could make the most informed decisions about their inventories of housing programs for youth.

Fifth, as these results are administrative data, we only have information on youth who had contact with a specific CoC. Many youth experience homelessness but do not come into contact with the local CoC. Similarly, if youth came into contact with the CoC but left that community, or Cityscape 83

Rice, Holguin, Hsu, Morton, Vayanos, Tambe, and Chan

became homeless again but did not reengage with the local CoC, we have no information on their subsequent outcomes.

Sixth, this study was not designed as an impact evaluation. We can examine administratively-recorded housing stability over time associated with a few broad types of exits/programs, but there was no prospective control group, and youth were not assigned randomly to different interventions. 

As such, results associated with housing programs like RRH and PSH could be biased by 

unobserved characteristics. For example, selection bias could emerge if case managers allocated RRH spaces to a youth scoring a 7 who she/he thought more likely to succeed in the program and not to a youth scoring a 7 who she/he thought less likely to succeed in the program. The results are promising but should encourage more rigorous evaluation of such housing programs to better understand their effectiveness and under what circumstances.

Finally, there are gaps in both the NST tool and the outcomes data available that limit the depth of the analysis possible. For example, as a triage tool, the NST focuses on risk factors but lacks information on young people’s strengths and assets, which could play important roles in informing appropriate service connections or predicting housing stability. Additionally, beyond a simple HMIS-based indicator of housing stability (not returning to the local homelessness system for at least 180 days), it would be useful to capture and analyze information on a broader range of outcome areas—such as those proposed by the U.S. Interagency Council on Homelessness’s (USICH)  Framework to End Youth Homelessness, which advises systems and services to also target and collect data on education and employment, positive social connections, and social-emotional wellbeing outcomes (USICH, 2013).

Policy Recommendations

Ultimately, as youth service providers contend with the abhorrent reality of how to prioritize and place young people in the precious few spots available in housing programs, this study elucidates the importance of using an evidence-informed triage tool like the NST to assess vulnerability to facilitate more efficient and informed prioritization decisions at the local level. Furthermore, PSH 

is associated with very low likelihood of returns to homelessness for any youth, regardless of NST 

score, emphasizing the compelling promise of this housing model. At the same time, PSH is a relatively expensive form of intervention, and less intensive housing interventions may be more cost-effective for lower acuity youth. To this end, the results suggest RRH may be a useful solution for many youth with an NST score less than 10. Communities may feel more assured about connecting these youth with time-limited and less service-intensive based programs like RRH if they have a decision aid indicating a likelihood to succeed in such programs. By using RRH for less at risk youth, the limited and more costly PSH spaces can be maintained for youth with a higher vulnerability score.

Nonetheless, further evaluation with longer-term follow-up periods is necessary regarding the effectiveness of this model for high-scoring youth; providers should continue to be cautious when deciding on which youth to assign to RRH. Greater attention to case management services that specifically address family reunification and other “diversion” and prevention services is recommended for those youth with an NST score of 4 or less. These evaluations may also include 84 Youth Homelessness

Linking the TAY-VI-SPDAT Tool to Housing Placements  

and Outcomes for Youth Experiencing Homelessness

identification of youth who will be unable to self-resolve or return to family and who should be considered for RRH—or other programs not captured in these data, such as transitional housing—

after a period of time.

Acknowledgments

Iain De Jong with OrgCode, Inc. provided the data for this research. Megan Blondin and Megan Gibbard Kline provided important feedback on the draft manuscript. This work was generously supported by a grant from Schultz Family Foundation.

References

Economic Roundtable. 2011.  Crisis Indicator: Triage Tool for identifying homeless adults in crisis. 

Los Angeles, CA: Author. http://www.economicrt.org/wp-content/uploads/2011/08/Crisis_

Indicator_2011.pdf.

Focus Strategies. 2017. Children’s Hospital Los Angeles Youth Coordinated Entry System (CES) Final Evaluation Report. https://focusstrategies.net/wp-content/uploads/2016/09/Updated-

Final-Youth-CES-Report-042017.pdf.

Hosmer, David W. and Stanley Lemeshow. 2000. “Special Topics,”  Applied Logistic Regression, Second Edition: 260–351.

Hwang, Stephen W., Joan M. Lebow, Michael F. Bierer, James J. O’Connell, E. John Orav, and Troyen A. Brennan. 1998. “Risk Factors for Death in Homeless Adults in Boston,”  Archives of Internal Medicine 158 (13): 1454–1460.

Juneau Economic Development Council. 2009.  Vulnerability Index: Prioritizing the street homeless population by mortality risk. http://www.jedc.org/forms/Vulnerability%20Index.pdf.

Milburn, Norweeta G., Francisco Javier Iribarren, Eric Rice, Marguerita Lightfoot, Rosa Solorio, Mary Jane Rotheram-Borus, Katherine Desmond, Alex Lee, Kwame Alexander, and Katherine 

Maresca. 2012. “A Family Intervention to Reduce Sexual Risk Behavior, Substance Use, and Delinquency among Newly Homeless Youth,”  Journal of Adolescent Health 50 (4): 358–364.

Morton, Matthew H., Amy Dworsky, Jennifer L. Matjasko, Susanna R. Curry, David Schlueter, Raul Chavez, and Anne F. Farrell. 2018. “Prevalence and Correlates of Youth Homelessness in the United States,”  Journal of Adolescent Health 62 (1): 14–21.

Orgcode Consulting. 2015.  Transition Age Youth – Vulnerability Index – Service Prioritization Decision Assistance Tool (TAY-VI-SPDAT): Next Step Tool for Homeless Youth. http://ctagroup.org/wp-content/

uploads/2015/10/Y-SPDAT-v1.0-Youth-Print.pdf

Padgett, Deborah, Benjamin F. Henwood, and Sam J. Tsemberis. 2016.  Housing First: Ending Homelessness, Transforming Systems, and Changing Lives.  Oxford: Oxford University Press, USA.

Cityscape 85

Rice, Holguin, Hsu, Morton, Vayanos, Tambe, and Chan

Rice, E. 2013. “The TAY Triage Tool: A Tool to Identify Homeless Transition Age Youth Most in Need of Permanent Supportive Housing.”  New York: The Corporation for Supportive Housing 

http://www.csh.org/wp-content/uploads/2014/02/TAY_TriageTool_2014.pdf.

Rice, E. 2018. “Assessment Tools For Prioritizing Housing Resources for Youth Who Are Homeless” 

in  Mental Health & Addiction Interventions for Youth Experiencing Homelessness: Practical Strategies for Front-line Providers, edited by Kidd, Slesnick, Frederick, Karabanow, and Gaetz. Canadian Observatory on Homelessness Press: 303–313.

____. 2018.  Assessment Tools for Prioritizing Housing Resources for Youth Who are Homeless. 

http://www.homelesshub.ca/sites/default/files/Ch4-1-MentalHealthBook.pdf.

Swanborough, Theresa. 2011. “Homeless Vulnerability to What?”  Parity 24 (1): 31.

U.S. Department of Housing and Urban Development (HUD). 2015.  Coordinated entry policy brief. 

http://www.hudexchange.info/resources/documents/Coordinated-Entry-Policy-Brief.pdf. 

Washington, DC: U.S. Department of Housing and Urban Development.

____. 2016. Coordinated entry and youth FAQs. https://www.hudexchange.info/resources/

documents/Coordinated-Entry-and-Youth-FAQs.pdf. Washington, DC: U.S. Department of Housing and Urban Development.

U.S. Interagency Council on Homelessness. 2013. Framework to End Youth Homelessness: A Resource Text for Dialogue and Action. Washington, DC: USICH.

Winetrobe, H., E. Rice, H. Rhoades, and N. Milburn. 2015. “Health Insurance Coverage and Healthcare Utilization Among Homeless Young Adults in Venice, CA,”  Journal of Public Health 38 

(1), 147–155.

86 Youth Homelessness

What’s Next? A Grounded Theory 

of the Relationship Between 

Ontological Security, Mental Health, 

Social Relationships, and Identity 

Formation for Young Adults in 

Supportive Housing

Benjamin F. Henwood

Brian Redline

Sara Semborski

Harmony Rhoades

Eric Rice

Suzanne L. Wenzel

University of Southern California

Suzanne Dworak-Peck School of Social Work

Abstract

This qualitative study of 29 young adults (aged 18–25) living in permanent supportive housing (PSH) resulted in a grounded theory that shows how PSH generally provides a sense of  ontological security  

for young adults—much like for older adults—who are also experiencing significant developmental change processes. Simply stated, ontological security refers to a concept of well-being in the world that is rooted in a sense of order in one’s social and material environment. Thematic analyses indicated that the presence of markers of  ontological security  (for example, constancy, routine, control) positively affected participants’ mental health and well-being, which helped with positive identity construction. An increase in ontological security also related to residents’ social environment and participants’ ability to improve on social relationships, which supported improved mental health and sense of self. Most young adults in by Benjamin F. Henwood, Brian Redline, Sara Semborski, Harmony Rhoades, Eric Rice, and this study regarded living in PSH as “a chance to start my life” and considered the question of “What’s Suzanne L. Wenzel

next?” within a normative developmental trajectory. Counterexamples that demarcate the limits of these thematic findings are included in the grounded theory model, including some experiences of social isolation and struggles with mental health associated with less positive orientations toward “what’s next.” 

Cityscape: A Journal of Policy Development and Research •  Volume 20, Number 3 • 2018

Cityscape 87

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Henwood, Redline, Semborski, Rhoades, Rice, and Wenzel

Introduction

For adults who have experienced long-term homelessness, PSH using a housing first approach—

or the provision of immediate access to low-barrier, affordable housing along with wrap-around services—has been effective at ending homeless (U.S. Interagency Council on Homelessness, 2010) and providing a secure base for identity construction (Padgett, 2007). PSH has also been advanced as a solution to youth homelessness (Dworsky et al., 2012; Gaetz, 2014), yet there has been relatively limited research on whether such programs promote healthy development, including identity construction (Henwood, Redline, and Rice, 2018; Kozloff et al., 2016a, 2016b; Munson et al., 2017). The transition from youth to young adulthood is an important period for healthy biopsychosocial development, which is disrupted by experiences of homelessness and related adverse childhood experiences (Catalano et al., 2004; Wood et al., 2018) and can have adverse health consequences throughout life (Felitti et al., 1998; Mackelprang et al., 2014). 

In a 12-month prevalence study using a nationally representative sample, Morton et al., (2018) estimated that 3.48 million young adults aged 18–25 experience homelessness in the United States. 

Higher homelessness rates occur among lesbian, gay, bisexual, and transgender young adults; underserved racial and ethnic minority young adults; and young adults with limited education and/or who are parenting. Given the heterogeneity within this population, a one-size-fits-all housing model for young adults is unlikely, whereas helping young people successfully transition out of homelessness will likely require an array of support services and housing options. Although PSH is considered the clear solution for chronically homeless adults (U.S. Interagency Council on Homelessness, 2010), it is just one of several housing models—including transitional housing, rapid rehousing, and host homes—being advanced for young adults experiencing homelessness (Curry and Abrams, 2015; Curry and Petering, 2017; Maccio and Ferguson, 2016). 

Although few studies have examined the implementation of PSH specifically for youth, initial research suggests that while it effectively ends homelessness (Kozloff et al., 2016a), PSH programs may not meet the needs of transition-age youth due to design or implementation flaws (Gilmer, 2016). Research also suggests implementation of PSH for youth may differ from PSH for adults because more youth self-refer. Housing for youth is often transitional and involves roommates; services likely focus on education and gaining meaningful employment than on health services (Gilmer et al., 2013). In one of the few studies focused on the experiences of youth or young adults living in PSH, Munson et al., (2017) found that residents received mixed messages about the need to become independent while following restrictive program rules—a consistent theme in other research on transitional housing programs for youth (Curry and Abrams, 2015). How do housing models for youth (such as PSH and transitional housing) differ, and how do those differences affect youth development? 

In this qualitative and descriptive study, we expand on youth experiences in housing programs by using ontological security—or well-being in the world that is rooted in a sense of constancy in one’s social and material environment (Giddens, 1990; Laing, 1965)—as a sensitizing framework to examine the perspectives of formerly homeless young adults who are living in PSH. Padgett (2007), who found that adults living in apartments of their own experienced conditions conducive to the development of ontological security, was the first to apply the concept to the field of 88 Youth Homelessness

What’s Next? A Grounded Theory of the Relationship Between Ontological Security, Mental Health, Social Relationships, and Identity Formation for Young Adults in Supportive Housing homelessness. As outlined by Dupuis and Thorns (1998), these conditions include experiencing housing as (1) a place of constancy, (2) where daily routines can be enacted/carried out, (3) where people feel “most in control in their lives,” and (4) as a place “around which identities are constructed.” Padgett (2007) also found that the presence of ontological security prompted important questions about “what’s next?” for adults whose lives and personal goals had long been disrupted by homelessness. Prior to its application to homeless services, the majority of studies of ontological security focused on the concept’s utility in exploring how aspects of the housing or residential environment—particularly home ownership—can foster ontological security (Cairney and Boyle, 2004; Kearns et al., 2000; Vigilant, 2005). In this study, we seek to understand whether PSH provides ontological security for young adults undergoing significant developmental change processes and how young adults view the presence or absence of ontological security as affecting their development.

Methods

In the following section, we describe recruitment and data collection efforts along with a description of the data analytic approach.  We note that the authors’ institutional review board approved all study procedures.

Participants and data collection

During June 2014, 29 young adults (18–25 years old) living in four PSH buildings in the Los Angeles area were recruited using convenience sampling methods (fliers posted at housing sites and word-of-mouth recruitment from onsite agency staff). All participants provided verbal informed consent and completed the study in English; no participant names were collected. Participants completed an interviewer-administered survey and semi-structured qualitative interview that lasted approximately 1.5 hours. Youth received $25 for participation. Interviewer-administered survey items asked about demographics, housing characteristics, and mental health. Qualitative interviews focused on participants’ experiences in PSH, including discussion of their housing program, unit, and neighborhood. Qualitative interviews also focus on how relationships with family, friends, or providers may have been impacted by moving into PSH; improvements or challenges experienced since being housed; and how PSH has affected their lives. Interviewers had previous experience working on a federally funded study of homeless youth and had training on the importance of establishing trust and building rapport. Qualitative interviews were audio recorded, transcribed verbatim, and entered into ATLAS.ti software for data management and analysis. 

Data analysis

Interview transcripts were analyzed using a grounded theory approach that uses constant comparative analysis and outlines procedures for coding qualitative data (Charmaz, 2006; Strauss and Corbin, 1990). This involved reading and re-reading all transcripts and then having investigators independently code and then co-code transcripts. For this study, two authors co-coded all transcripts. Sensitizing concepts representing domains of ontological security (for example, privacy and daily routines) were included as part of the initial codebook, along with codes representing the overall domains included in the interview guide (for example, current Cityscape 89

Henwood, Redline, Semborski, Rhoades, Rice, and Wenzel

living situation, housing challenges, typical day). Open coding of transcripts generated additional codes (such as goals, life skills, and outlook on life) that were applied to all transcripts. During the coding process, discrepancies resolved through consensus to develop an initial set of themes. 

Negative case analysis, in which transcripts were reviewed specifically to find counterexamples to our generalized themes, was also conducted. A final analysis phase determined the relationship between themes, which resulted in a grounded theory. The first three authors discussed and finalized individual themes, negative case examples, and the final grounded theory, which the entire team reviewed and approved.

Results

In the following, we present the demographic characteristics and self-reported service utilization of the sample followed by an emergent grounded theory model. Themes that explain the model are also described.  

Sample characteristics

As shown in exhibit 1, youth in this study were in PSH for an average of nearly 18 months, were 23 years-old on average, and were mostly male (62 percent). Slightly more than 41 percent were African-American, followed by Latinx (24 percent), mixed or other race (21 percent), and White (14 percent). Nearly 68 percent identified as heterosexual, followed by 21 percent gay or lesbian and 7 percent bisexual. Most youth reported a high school (62 percent) or more than high school education (17 percent); 28 percent were currently in school (50 percent of those were taking college courses), and 45 percent reported that they had continued their education since they had been in housing. Most youth reported they were currently working full or part-time (41 percent) or looking for work (45 percent; employment categories are not mutually exclusive). About 45 

percent of respondents reported that they had a history of foster care involvement. More than half (55 percent) had been arrested previously, 48 percent had been incarcerated previously, and 21 

percent reported an incarceration experience prior to their 18th birthday. Seventeen percent of respondents reported that they had at least one biological child.

Exhibit 1

Demographic Characteristics and Service Utilization (n=29)

N (%) / mean (Standard Deviation)

Months in housing

17.84 (11.08)

Age

23.28   (1.83)

Gender

Male

18 (62.07)

Female

11 (37.93)

Race/Ethnicity

African-American

12 (41.38)

Latinx

 7 (24.14)

White

 4 (13.79)

Mixed or other race

 6 (20.69)

90 Youth Homelessness

What’s Next? A Grounded Theory of the Relationship Between Ontological Security, Mental Health, Social Relationships, and Identity Formation for Young Adults in Supportive Housing Exhibit 1

Demographic Characteristics and Service Utilization (n=29)

N (%) / mean (Standard Deviation)

Sexual orientation identity

Gay/lesbian

  6 (21.43)

Bisexual

  2  (7.14)

Heterosexual

19 (67.86)

No preference

  1  (3.57)

Education

  6 (20.69)

High school

18 (62.07)

>High school

  5 (17.24)

School since housed*

13 (44.83)

Currently in school

  8 (27.59)

Current school level (of those in school)

High school/GED1 

1 (12.50)

College

4 (50.00)

Trade/technical school

3 (37.50)

Employment (not mutually exclusive)

Working full or part-time

12 (41.38)

Unemployed, not looking for work

  6 (20.69)

Un/under-employed and looking for work

13 (44.83)

Student

  9 (31.03)

Odd jobs

  1   (3.45)

History of foster care

13 (44.83)

Ever arrested

16 (55.17)

Ever incarcerated

14 (48.28)

Incarcerated <18 years old

  6 (20.69)

Incarcerated >18+ years old

11 (37.93)

Benefits

Supplemental Security Income

  5 (17.24)

General Relief

15 (51.72)

Unemployment

  1   (3.45)

Supplemental Nutrition Assistance Program

20 (68.97)

HUD/Section 8

  8 (27.59)

Temporary Assistance for Needy Families

  1   (3.45)

None

  3 (10.34)

Services

Therapy or counseling

19 (65.51)

Food assistance

15 (51.72)

Job help

15 (51.72)

Medical 

13 (44.83)

Condoms/birth control

11 (37.93)

Housing Arrangement

Living alone

12 (41.38)

Living with roommate(s)

15 (51.72)

Living with child(ren)

  2   (6.90)

1 GED = General Equivalency Diploma

Cityscape 91

Henwood, Redline, Semborski, Rhoades, Rice, and Wenzel

The most common financial benefit that youth reported receiving was the Supplemental Nutrition Assistance Program (69 percent), followed by General Relief (52 percent). Since moving into housing, 66 percent of youth reported receiving counseling or therapy, 52 percent assistance with free food or meals, 52 percent job services, 45 percent help with medical/health care, and 38 

percent access to condoms or birth control. A little more than half of respondents reported that they lived in a housing unit with a roommate (52 percent), while 41 percent reported living alone, and 7 percent were living with their child(ren). 

Grounded theory

Exhibit 2 displays a grounded theory model of the relationship between ontological security, mental health, social relationships, and identity formation based on the experiences of young adults living in PSH. As shown, moving into PSH generally brought with it Dupuis and Thorns’ four traditional markers of ontological security. Those markers are to have a place: (1) of social and material constancy; (2) where daily routines can be enacted and carried out; (3) “where people feel most in control in their lives because they are free from surveillance;” and (4) “around which identities are constructed” 

(Dupuis and Thorns, 1998: 29). As described in theme 1, the presence of these markers of ontological security generally had positive impact on participants’ mental health and well-being, which helped with positive identity construction. In theme 2, we note how increased ontological security also relates to residents’ social environment and participants’ ability to improve upon different types of social relationships (familial, social, romantic, and with service providers), which supported improved mental health and sense of self. In the third theme, we discuss how most young adult PSH residents also took up Padgett’s (2007) emergent theme of “what’s next?” Importantly, however, young adults often saw it as, “a chance to start my life” (SP022 ), leaving their past behind and moving to the next stage of life given an improved sense of self-identity. This report describes each theme with elucidating and supporting quotes. Exhibit 2 includes counterexamples that demarcate the limits of each generalized theme by the possible experience of young adults being more socially isolated and continuing to struggle with mental health in PSH, resulting in a less positive outlook in terms of “what’s next.” 

Exhibit 2

Grounded theory model of the relationship between ontological security, mental health, social relationships, and identity formation based on the experiences of young adults living in PSH.

2 Quotes are labeled throughout using participant identification numbers (that is, SP02 is study participant #02).

92 Youth Homelessness

What’s Next? A Grounded Theory of the Relationship Between Ontological Security, Mental Health, Social Relationships, and Identity Formation for Young Adults in Supportive Housing Theme 1: Improved mental health and positive identity through increased ontological security. Participants’ descriptions of their experiences since moving into PSH were replete with markers of ontological security. When asked how life has changed, SP07 responded in a way that makes clear how PSH enables him to carry out daily routines and have a safe space that he controls. He said, “Well, first of all I have a roof over my head and shower so that makes me happy. 

Being able to cook is another thing because I love to cook and bake. So being able to have my own food and stuff like that, I love that … But I think the most valuable thing that I love about having my apartment is that I have my own personal space where I can just shut the door and have everybody outside the world leave me the hell alone. I think that’s the most valuable thing to me.”

Some participants were explicit about the positive impact PSH has had on mental health. SP16 

said, “My mental health has changed a lot. I’ve been a lot healthier and more stable. My happiness has changed a lot. And it has to do with my mental health.” 

Others described the specific effects of PSH on their well-being. SP23 explained, “I’ve noticed just brain chemistry, like my mind works better. I’m able to think more clearly. I don’t have as many paranoid thoughts as I used to. I still have sleeping issues, but I’m able to sleep better; it’s more consistent. I worry a lot less.”

The consistency in environment and ability to carry out daily routines was important. SP03 

explained, “I have to keep organized and I think this apartment helps me keep organized because a lot of times my memory comes and goes and I’ve actually been able to kind of like better that since I’ve been here because now I have a calendar everywhere and I can put everything everywhere to where every room I go to I can always know what did I need to do, what did I forget. I can make lists. I can listen to music. There’s no one else I’m disturbing and I can just be free.”

SP09 reflected on life before and after accessing PSH, saying, “You no longer bounce around shelter to shelter, you’re no longer having curfews like you’re in prison and having chores, like in a halfway house, and having to answer to people, and be treated like you’re some criminal because you don’t have any support and you’re homeless. They treat all homeless people the same, like you’re all crazy, like you’re all on drugs, or alcoholics, or can’t handle money, or don’t want to get a job, or ignorant, or stupid, or lazy, all those stereotypes. So you don’t have to deal with that anymore.”

For some, having a place of their own was novel. As SP15 explained, “It’s just from being the streets, like, just like straight like, even though I knew what I was going to get into with my other roommates, it was just joyfulness, like knowing that you have somewhere, you have your like all your own keys and you can go in your own room when you never had that all your life. So it’s like awesome, like you know? It’s one of my first summers in all my life that I’ve had my own room, you know?”

Participants generally agreed that having an apartment instilled a sense of ontological security that ultimately helped with identity construction and their ability to express their personality. As SP03 expressed, “I like the fact because you get to see your house build. You get to see your house growing. And especially when it comes to being just human, you get to see your personality spread throughout the room.”

Cityscape 93

Henwood, Redline, Semborski, Rhoades, Rice, and Wenzel

Theme 2: Social dimensions of ontological security.  Markers of ontological security brought about by the physical home environment that PSH provides can also influence the social environment and residents’ capacity for relational growth. In fact, most participants discussed how having an apartment influenced their relationships. For example, SP01 said, “Now I have a place that I can call my own. And I can tell my friends or girlfriend or family members to come over and spend time with me. So it’s definitely good to have a place that people can come over and spend time with me and stuff like that, instead of having to meet up somewhere public or whatever the situation is.”

SP06 said, “I’m more willing to have friends due to the fact now I know like okay they have a place to come over. They don’t have to kick it with me in my car or at the shelter. Because you kind of feel like that you won’t be respected as much because of your living situation so yeah I made more friends.”

Some participants discussed getting rid of bad influences or old friends for new and better influences. As SP16 described, “I got better friends now. And mostly, I see [provider name], my caseworker, I see him as a friend. My roommate, he’s one of my closest friends. And just some neighbors, they’re friends of mine, I dropped a lot of friends I was with because I wasn’t doing good with them. And that’s when I saw I was in a better position, so like I wasn’t, as much as I, and I tried, as much as I wanted to help, I knew I really couldn’t. So I just had to, you know, tell them, 

‘hey, you can’t come back’ and so I just kind of changed my friends completely.” 

SP14 explained it differently by saying, “I said I was making a lot of bad friends, but you know, they weren’t really friends, it was just they were using me because I would share my money and my things.”

Some participants, however, did not feel like having an apartment necessarily improved their social relationships. SP17 said, “I’m not going to lie, it feels good and happy to have your own spot, but all that I got from when I was homeless and in a shelter, now that I have my own place, technically, I’m still alone. Even though I have a roommate and stuff and I have [the housing program], it’s like you feel alone because you don’t have your family or stuff like that.” 

The people who felt isolation even after moving into an apartment also often talked about struggling with their mental health. SP17, for example, went on to say, “So depression, insomnia, sometimes not being able to sleep, has stayed the same for me. And then the mentality of being homeless again is hard to really get rid of.” 

Some participants also expressed concern about the social environment they experienced within the PSH program. As SP07 recounted, “I get harassed here by some residents. Some of the residents that were causing the issues have left. They got evicted or they moved away or whatever. And so that was better, starting to get better, but there are still a couple residents here who are very disruptive and they harass multiple residents for no reason.”

While ongoing struggles with mental illness and/or newfound acquaintances were important counterexamples, most PSH residents identified how improved social relationships improved their mental health and well-being and their sense of self. SP015 explained how housing has improved his relationships, self-confidence, and ability to stay out of jail, saying, “It’s not losing 94 Youth Homelessness

What’s Next? A Grounded Theory of the Relationship Between Ontological Security, Mental Health, Social Relationships, and Identity Formation for Young Adults in Supportive Housing touch, it’s losing negative touch … Because if you’re already negative, and you surround yourself with negative people it’s going to be a negative outlook. But if you’re negative and you surround yourself with positive maybe you can be more positive to yourself … My life improved a lot in the positive way whereas that before when I was in the homeless shelter, straight up … I just had no self-confidence. I was like, you know, I’m broke, I ain’t got nothing, so I’m going to do criminal stuff. So that was the stuff I knew what to do at that point. So when I finally got my housing I realized that I have housing now so I don’t have to do that no more. So I have to better keep my housing and better myself. So as far getting my housing it just stopped me from going back to jail. And that’s been like my whole life, I always went to jail because I didn’t have no house, so I always had to steal food. I’d steal something to give to someone so I could stay at their house for a night, like you know what I mean?”

Theme 3: “What’s next” developmentally?  Padgett’s (2007) finding that older adults brought up 

“what’s next?” questions after finally getting into one’s own apartment was also applicable to our younger adult sample. Participants described a feeling that with an apartment, “It felt like … this is the chance for me to start my life” (SP02). For many, questions about the future were framed as part of a specific developmental stage. SP14 said, “When I was homeless I felt the future seemed bleak, I didn’t know what was going to happen to me … finally it’s just I got somewhere to stay, so now I feel like it’s possible, soon I’ll be driving my own car, I’ll be saving up for my own house, and I’ll find somebody and settle down, it just seems positive.”

Similarly, SP01 explained, “So this is just that next level. Just like getting a car. It’s that next level in life. You’re thinking differently. You have more time to do things because you can get places quicker. It just puts you in a different mindset. A more responsible mindset and a more mature mindset. Just having all these responsibilities. Having to take care of it in order to keep it. So definitely put me in a different mindset.”

When asked about his thoughts on moving forward, SP16 said, “I see myself as being a great dad. 

I see myself as, I don’t know, just, I want to be a dad, you know? And I know I can do it now. I just need to get a couple more things. It’s really just getting more work. And just raising my son. 

But I don’t plan on getting into trouble. I’m just planning down the road, you know, playing with grandkids. And, you know, this [PSH agency] is a place that helped.”

Despite programs referring to their service model as “permanent” supportive housing, a few participants aged 24–25 noted they were concerned for their future. SP07 explained, “I’m 25 now so I only have one more year before I don’t know what the hell’s going to happen. You know, I only have one more year before they decide, oh well you know what? She’s 26 and this is for Section 8. So Section 8 can just suddenly decide not to pay for me to stay here any longer. And then I wouldn’t have a place to stay and I’d be homeless again. So and there’s no services for them to help you find another place to live or get on other housing lists or whatever because then there’s those waiting lists. And then on top of that now since I’ve past the 24 mark, they don’t consider me TAY 

[transition age youth] anymore so I’ve gotta find adult housing and that’s even harder than youth housing and so there’s a whole bunch of other issues. So it’s kind of frustrating and stressful for me because I don’t know 100 percent what my next year is going to be like.”

Cityscape 95

Henwood, Redline, Semborski, Rhoades, Rice, and Wenzel

Discussion 

The concept of ontological security, which was useful in capturing the experience of older adults living in PSH who had experienced a lifetime of trauma (Padgett, 2007; Padgett et al., 2012), also proved useful in organizing the experience of younger adults living in PSH. In this study, the relationship between markers of ontological security, mental health, and social relationships suggest that PSH has the potential to help young adults develop a more positive identity from which to consider their future. In fact, the “what’s next?” question was closely tied to participants’ 

sense of moving to the next developmental life stage such as going to school, getting a car, or buying a house. In general, “emerging adulthood” is also marked by a move away from a family of origin, away from adolescent peer groups, and toward more stable young adult relationships, including strong romantic attachments that can lead to marriage or long-term partnerships (Arnett 2000; Arnett 2001). Findings from this study suggest PSH can help young adults’ 

thinking return to a more “normative” young adult developmental process, resulting in network shifts and a focus on future stability and relationships. This shift in thinking could replace what researchers of homeless youth development often say, which is that homelessness derails youth from “normal” developmental trajectories, exacerbating the social distance from parents and family, and increasing the engagement in deviant peer networks (Milburn et al., 2009). 

Our study findings indicate the benefits of having a home may include improved mental health and social relationships for young adults and may have implications for the timing of screening and brief mental health interventions for homeless youth transitioning into housing programs (Harpin et al., 2016). Improved social relationships found in young adults in PSH underscore the impact of the built environment on social relationships and suggest that both the physical and social environment may relate closely to the concept of ontological security (Giddens, 1990; Laing, 1965). It is difficult to determine the extent to which young adults distance themselves from past acquaintances, as is the case with older adults (Rhoades et al., 2018), or move on as part of a more normative developmental stage that occurs for young people (Wood et al., 2018). The findings from this study differ from Padgett’s (2007) sample of older adults who had experienced chronic homelessness and whose next steps tended to focus on making up for lost opportunities. This hopefully suggests young adults who have experienced homelessness can transition into a more normative developmental trajectory through housing interventions such as PSH, rather than becoming another cohort of adults that experience prolonged homelessness (Culhane et al., 2013).

Notable exceptions to our thematic findings, known as negative cases, are also important since not everyone experiences ontological security and improved social relationships, which can be influenced by how PSH is implemented (for example, living alone or with roommates) or the larger neighborhood context and how one’s demographic characteristics fit in with the community. The fact that young adults who experience homelessness also vary in needs is an important consideration, especially when trying to determine the right mix of services. Future research can examine more fully how young adults’ mental health, social networks, and health risk behaviors change as they transition from homeless to PSH (Rhoades et al., 2018), which may depend on the extent to which they experience ontological security. 

96 Youth Homelessness

What’s Next? A Grounded Theory of the Relationship Between Ontological Security, Mental Health, Social Relationships, and Identity Formation for Young Adults in Supportive Housing Strengths and limitations

This study employed many strategies of rigor for qualitative research, including having a robust sample size to ensure rich qualitative data, immersion in the data, co-coding and consensus-driven findings, and negative case analysis (Padgett, 2011). Study limitations include a cross-sectional design rather than having prolonged engagement, and we interviewed young adults living in PSH 

but do not know how people were selected to be enrolled in these PSH programs. We also did not investigate how these PSH programs were implemented, which could vary and affect participant experiences (Gilmer et al., 2013). There was also important discussion around parenting and mental health that did not “earn” its way into more generalized themes (Charmaz, 2006) but may be important considerations for program implementation. Finally, while the data were robust enough to support a grounded theory, the extent to which this theory helps capture experiences across housing programs for young adults is unknown. 

Conclusion

Although more research should focus on client outcomes for young adults in PSH, findings from this study remind us it is important to understand how people experience programs. These experiences may influence longer-term outcomes that result from young adults being engaged during a critical developmental phase, which often is not considered when evaluating program outcomes. Ontological security could ultimately prove to be an important factor that contributes to the success of interventions aimed at helping young adults experiencing homelessness. 

Acknowledgments

This work was supported by the National Institutes of Health (1R01MH110206).

Authors

Benjamin F. Henwood, PhD, LCSW is an associate professor at the University of Southern California, Suzanne Dworak-Peck School of Social Work and can be reached by email at 

bhenwood@usc.edu.

Brian Redline, B.A. is the project manager at the University of Southern California, Suzanne Dworak-Peck School of Social Work.

Sara Semborski, LCSW is a Ph.D. student at the University of Southern California, Suzanne Dworak-Peck School of Social Work.

Harmony Rhoades, Ph.D. is a research associate professor at the University of Southern California, Suzanne Dworak-Peck School of Social Work.

Eric Rice, Ph.D. is an associate professor at the University of Southern California, Suzanne Dworak-Peck School of Social Work.

Cityscape 97

Henwood, Redline, Semborski, Rhoades, Rice, and Wenzel

Suzanne L. Wenzel, Ph.D. is a professor at the University of Southern California, Suzanne Dworak-Peck School of Social Work.

These authors can be reached at the University of Southern California, Suzanne Dworak-Peck School of Social Work, 1150 S. Olive Street, Los Angeles, CA 90015, United States of America.

References

Arnett, Jeffrey Jensen. 2000. “Emerging adulthood: A theory of development from the late teens through the twenties,”  American Psychologist 55 (5): 469.

Arnett, Jeffrey Jensen. 2001. “Conceptions of the transition to adulthood: Perspectives from adolescence through midlife,”  Journal of Adult Development 8 (2): 133–143.

Cairney, John, and Michael H. Boyle. 2004. “Home ownership, mortgages and psychological distress,”  Housing Studies 19 (2): 161–174.

Catalano, Richard F., M. Lisa Berglund, Jean AM Ryan, Heather S. Lonczak, and J. David Hawkins. 

2004. “Positive youth development in the United States: Research findings on evaluations of positive youth development programs,”  The Annals of the American Academy of Political and Social Science 591 (1): 98–124.

Charmaz, Kathy. 2006.  Constructing grounded theory: A practical guide through qualitative research.  London: Sage Publications Ltd.

Culhane, Dennis P., Stephen Metraux, Thomas Byrne, Magdi Stino, and Jay Bainbridge. 2013. “The age structure of contemporary homelessness: evidence and implications for public policy,”  Analyses of Social Issues and Public Policy 13 (1): 228–244. 

Curry, Susanna R., and Laura S. Abrams. 2015. “They Lay Down the Foundation and Then 

They Leave Room for Us to Build the House”: A Visual Qualitative Exploration of Young Adults’ 

Experiences of Transitional Housing,”  Journal of the Society for Social Work and Research 6 (1): 145–172. doi:10.1086/680188.

Curry, Susanna R., and Robin Petering. 2017. “Resident Perspectives on Life in a Transitional Living Program for Homeless Young Adults,”  Child and Adolescent Social Work Journal 34 (6): 507–515.

Dupuis, Ann, and David C. Thorns. 1998. “Home, home ownership and the search for ontological security,”  The Sociological Review 46 (1): 24–47.

Dworsky, Amy, Keri-Nicole Dillman, Robin M. Dion, Brandon Coffee-Borden, and Miriam Rosenau. 

2012.  Housing for youth aging out of foster care: A review of the literature and program typology. U.S. 

Department of Housing and Urban Development, Office of Policy Development & Research (April).

Felitti, Vincent J., Robert F. Anda, Dale Nordenberg, David F. Williamson, Alison M. Spitz, Valerie Edwards, Mary P. Koss, and James S. Marks. 1998. “Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults,”  American Journal of Preventive Medicine 14 (4): 245–258.

98 Youth Homelessness

What’s Next? A Grounded Theory of the Relationship Between Ontological Security, Mental Health, Social Relationships, and Identity Formation for Young Adults in Supportive Housing Gaetz, Stephen. 2014. “A Safe and Decent Place to Live: Towards a Housing First framework for youth.” Toronto: Canadian Homelessness Research Network.

Giddens, Anthony. 1990.  The Consequences of Modernity. Oxford: Polity Press.

Gilmer, Todd P. 2016. “Permanent supportive housing for transition-age youths: Service costs and fidelity to the housing first model,”  Psychiatric Services 67 (6): 615–621.

Gilmer, Todd P., Victoria D. Ojeda, Sarah Hiller, Ana Stefancic, Sam Tsemberis, and Lawrence A. 

Palinkas. 2013. “Variations in Full Service Partnerships and Fidelity to the Housing First Model,” 

American Journal of Psychiatric Rehabilitation 16 (4): 313–328.

Harpin, Scott B., Amber Murray, Michael Rice, Kendall Rames, and Christine Gilroy. 2016. 

“Feasibility of a Mental Health Screening and Brief Intervention Shelter Intake Procedure for Runaway/Homeless Youth,”  Journal of Adolescent Health 58 (2): S53–S54.

Henwood, Benjamin F., Brian Redline, and Eric Rice. 2018. “What do homeless transition-age youth want from housing interventions?”  Children and Youth Services Review 89: 1–5.

Kearns, Ade, Rosemary Hiscock, Anne Ellaway, and Sally Macintyre. 2000. “‘Beyond Four Walls’. 

The Psycho-social Benefits of Home: Evidence from West Central Scotland,”  Housing studies 15 (3): 387–410.

Kozloff, Nicole, Carol E. Adair, Luis I. Palma Lazgare, Daniel Poremski, Amy H. Cheung, Rebeca Sandu, and Vicky Stergiopoulos. 2016a. ““Housing first” for homeless youth with mental illness.” 

Pediatrics: e20161514.

Kozloff, Nicole, Vicky Stergiopoulos, Carol E. Adair, Amy H. Cheung, Vachan Misir, Greg Townley, Jimmy Bourque, Michael Krausz, and Paula Goering. 2016b. “The unique needs of homeless youths with mental illness: baseline findings from a Housing First trial,”  Psychiatric Services 67 

(10): 1083–1090.

Laing, R.D. 1965.  The divided self. An existential study in madness and sanity. London: Pelican Press.

Maccio, Elaine M., and Kristin M. Ferguson. 2016. “Services to LGBTQ runaway and homeless youth: Gaps and recommendations,”  Children and Youth Services Review 63: 47–57.

Mackelprang, Jessica L., Scott B. Harpin, Joseph A. Grubenhoff, and Frederick P. Rivara. 2014. 

“Adverse outcomes among homeless adolescents and young adults who report a history of 

traumatic brain injury,”  American Journal of Public Health 104 (10): 1986–1992.

Milburn, Norweeta G., Eric Rice, Mary Jane Rotheram-Borus, Shelley Mallett, Doreen Rosenthal, Phillip Batterham, Susanne J. May, Andrea Witkin, and Naihua Duan. 2009. “Adolescents exiting homelessness over two years: The risk amplification and abatement model,”  Journal of Research on Adolescence 19 (4): 762–785.

Cityscape 99

Henwood, Redline, Semborski, Rhoades, Rice, and Wenzel

Morton, Matthew H., Amy Dworsky, Jennifer L. Matjasko, Susanna R. Curry, David Schlueter, Raúl Chávez, and Anne F. Farrell. 2018. “Prevalence and correlates of youth homelessness in the United States,”  Journal of Adolescent Health 62 (1): 14–21.

Munson, Michelle R., Victoria Stanhope, Latoya Small, and Kendall Atterbury. 2017. ““At times I kinda felt I was in an institution”: Supportive housing for transition age youth and young adults,” 

Children and Youth Services Review 73: 430–436.

Padgett, Deborah K. 2007. “There’s no place like (a) home: Ontological security among persons with serious mental illness in the United States,”  Social Science & Medicine 64 (9): 1925–1936.

Padgett, Deborah K. 2011.  Qualitative and mixed methods in public health. Thousand Oaks: Sage Publications.

Padgett, Deborah K., Bikki Tran Smith, Benjamin F. Henwood, and Emmy Tiderington. 2012. “Life course adversity in the lives of formerly homeless persons with serious mental illness: context and meaning,”  American Journal of Orthopsychiatry 82 (3): 421–430.

Rhoades, Harmony, Hsun-Ta Hsu, Eric Rice, Taylor Harris, Wichada LaMotte-Kerr, Hailey Winetrobe, Benjamin Henwood, and Suzanne Wenzel. 2018. “Social network change after moving into 

permanent supportive housing: Who stays and who goes?” Manuscript submitted for publication.

Strauss, Anselm, and Juliet Corbin. 1990.  Basics of grounded theory. Thousand Oaks: Sage Publications.

U.S. Interagency Council on Homelessness. 2010.  Opening Doors: Federal strategic plan to prevent and end homelessness. U.S. Interagency Council on Homelessness.

Vigilant, Lee Garth. 2005. “‘I Don’t Have Another Run Left With It’: Ontological Security in Illness Narratives of Recovering on Methadone Maintenance,”  Deviant Behavior 26 (5): 399–416.

Wood, David, Tara Crapnell, Lynette Lau, Ashley Bennett, Debra Lotstein, Maria Ferris, and Alice Kuo. 2018. “Emerging adulthood as a critical stage in the life course.” In  Handbook of Life Course Health Development, Springer, Cham: 123–143. 

Additional Reading 

Aledort, Nina, Yusyin Hsin, Susan Grundberg, and Jim Bolas. 2011. “More than a roof over their heads: A toolkit for guiding transition age young adults to long-term housing success.”  The New York City Children’s Plan Young Adult Housing Workgroup (March). http://ccsinyc.org/wp-content/

uploads/2011/06/YoungAdultHousingToolkit1.pdf.

Roos, Leslie E., Natalie Mota, Tracie O. Afifi, Laurence Y. Katz, Jino Distasio, and Jitender Sareen. 

2013. “Relationship between adverse childhood experiences and homelessness and the impact of axis I and II disorders,”  American Journal of Public Health 103 (S2): S275–S281.

100 Youth Homelessness

Predictors of Running Away from 

Out-of-Home Care: Does County 

Context Matter?

Amy Dworsky 

Fred Wulczyn 

Lilian Huang

Chapin Hall at the University of Chicago

by Amy Dworsky, Fred Wulczyn, and Lilian Huang

Abstract

Running away is a relatively common experience, especially among youth in out-of-home care.  This report uses child-level placement data from the Multistate Foster Care Data Archive (FCDA; n=53,610) to examine the incidence of running away during the first out-of-home care spell among youth who entered out-of-home care as adolescents. We estimate a three-level logistic regression model that includes youth characteristics, placement history characteristics, county characteristics, and a measure of state policy. Consistent with prior studies, we find that the odds of running away vary by gender, race/

ethnicity, age, and placement type. Our results also suggest that county context (that is, population density and socioeconomic disadvantage) matters, although additional research to better understand these relationships is needed. Additionally, we find some evidence that having a screening or risk assessment process for youth entering out-of-home care may reduce the incidence of running away. 

Introduction

Running away is a relatively common experience, especially among youth in out-of-home care. 

Although a majority of youth who run away from out-of-home care are only absent from their placement for a short period of time, some are gone for a month or more, including some who never return (Courtney et al., 2005; Courtney and Zinn, 2009; Finkelstein et al., 2004; Nesmith, 2006). Additionally, at least some evidence suggests that youth who run away while in out-of-home care are at greater risk of experiencing homelessness after they “age out” (Dworsky, Napolitano, and Courtney, 2013).

Taken together, this research suggests that preventing youth from running away from out-of-home care could reduce the size of the population of youth who experience homelessness. Key to 

Cityscape: A Journal of Policy Development and Research •  Volume 20, Number 3 • 2018

Cityscape 101

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Dworsky, Wulczyn, and Huang

developing effective prevention strategies is a better understanding of the factors associated with running away. The aim of the present study is to identify those factors so that effective prevention strategies can be developed.

We begin with a brief summary of what we know about youth who run away from out-of-home care. Next, we describe the data and methodology we used and present the results of our analysis. 

We conclude with a discussion of our findings and their implications for policy and practice.

How common is it for youth to run away from out-of-home care?

How common it is for youth in out-of-home care to run away is difficult to say. Approximately 1 percent of the 437,465 children in out-of-home care at the end of Federal Fiscal Year 2016 

were absent from their placement after having run away (U.S. Department of Health and Human Services, 2017). This percentage is based on children of all ages, and children typically do not begin to run away until their adolescent years. Moreover, any point-in-time measure will underestimate the percentage of youth in out-of-home care who have ever run away from their placement. Unfortunately, states are not required to report the number of youth in out-of-home care who run away because running away is not among the core outcome measures used by the U.S. Department of Health and Human Services to track state child welfare system performance.

Studies of youth who run away from out-of-home care are another source of information. Estimates of the percentage of youth who ever run away vary widely, ranging from as low as 23 percent to as high as 71 percent (for example, Biehal and Wade, 2000; Fasulo et al., 2002; Nesmith, 2006). 

Some of that variation is due to differences in how running away is defined, the type of estimate (lifetime prevalence vs. point-in-time), the sampling procedures used (Witherup et al., 2008), and the jurisdictions included in the study.

Who runs away from out-of-home care?

A number of studies have examined the relationship between the characteristics of youth in out-of-home care and their likelihood of running away. These studies consistently found that older children are more likely to run away than younger children (Courtney et al., 2005; Courtney and Zinn, 2009; Nesmith, 2006; Finkelstein et al., 2004) and that females are more likely to run away than males (Courtney et al., 2005; Courtney and Zinn, 2009; Day and Riebschleger, 2007; Nesmith, 2006).

Some of these studies also found a relationship between the likelihood of running away and race/ethnicity. African-American and Hispanic youth in out-of-home care are more likely to run away than their peers who are White (Courtney et al., 2005; Courtney and Zinn, 2009; Day and Riebschleger, 2007). This difference is consistent with other well-documented racial disparities in the trajectories of youth once they have been placed in out-of-home care. For example, African-American youth generally exit care at a slower rate, have lower rates of reunification, and have higher rates of reentry than youth who are White (Hill, 2006; Hines et al, 2007).

Research on youth who run away from out-of-home care has also examined how factors related to the experiences of youth in out-of-home care that might increase their risk of running away. One 102 Youth Homelessness

Predictors of Running Away from Out-of-Home Care: Does County Context Matter?

such factor is instability. Some studies have found a positive relationship between the number of placement changes youth experience and their risk of running away (Clark et al., 2008; Courtney and Zinn, 2009).

Another factor is placement type. Out-of-home care placement options exist along a continuum that ranges from the home of relatives to non-relative foster homes to congregate care settings, including shelters, group homes, and residential treatment facilities. Although the placement of children in congregate care has declined in recent years, congregate care remains an integral part of the continuum of placement options, especially for adolescents (Butler and McPherson, 2007; Leichtman, 2006; U.S. Department of Health and Human Services, 2015). Studies have found that the likelihood of running away from care is higher among youth placed in congregate care (that is, shelters, group homes, and residential treatment facilities) than among youth placed in foster homes (Clark et al., 2008; Courtney et al., 2005; Courtney and Zinn, 2009; Eisengart, Martinovich, and Lyons, 2008; Karam and Robert, 2013). Some evidence indicates that placement in a relative foster home as opposed to a non-relative foster home can substantially reduce the risk of running away (Courtney and Zinn, 2009).

Why do youth run away from out-of-home care?

Qualitative studies involving interviews with youth who ran away from foster families and the adults who care for or work with them suggest that the reasons youth run away from out-of-home care are varied (Clark et al., 2008; Skyles, Smithgall, and Howard, 2007). They include wanting to regain control over their lives or express their feelings (Courtney et al., 2005; Karam and Robert, 2013), a desire to maintain relationships with family or friends (Kerr and Finlay, 2006), and as a response to been victimizing or feeling unsafe (Nesmith, 2006; Courtney et al., 2005).

What do we know about state policies related to running away from out-of-home care?

Child welfare systems must operate in accordance with broad federal laws, but states are given wide latitude when it comes to operationalizing those federal mandates through state policy and regulation (Vesneski, 2011). The U.S. Department of Health and Human Services maintains a digest of state laws and regulations pertaining to child maltreatment, child welfare, and adoption.1 The child welfare section covers topics related to foster care, including case planning, court hearings, permanency, guardianship, relative care, and termination of parental rights. Running away from foster care is conspicuously absent from the list. In fact, there is no central depository for state policies related to running away from foster care. This may help explain why so little is known about how those policies vary across states and whether that variation is related to differences in rates of running away.

Research Questions

Our study addresses four main research questions:

1.  What is the incidence of running away during a first out-of-home care spell?

1 https://www.childwelfare.gov/topics/systemwide/laws-policies/state/

Cityscape 103

Dworsky, Wulczyn, and Huang

2.  What effect do demographic or placement history characteristics have on odds of running away?

3.  What effect do contextual factors, measured at the county level, have on the odds of running away?

4.  Are there between-county differences in the effect of placement type on the odds of running away?

Methodology

Data Sources

Administrative Data

The primary source of data for this analysis is the Multistate Foster Care Data Archive (FCDA), a longitudinal database maintained by the Center for State Child Welfare Data, which is housed within Chapin Hall at the University of Chicago. The FCDA contains foster care placement records for approximately 3 million children in out-of-home care from 21 states.2 The placement records include dates of entry, placement changes, and exit, as well as exit reasons (including running away) and various child characteristics (age, gender, and race/ethnicity). The data are fully harmonized and integrated at the county level with a wide range of census data, including measures of urbanicity and socioeconomic disadvantage.

Runaway Policy Data

Because there is no digest of state policies related to youth who run away from out-of-home care placements, we created a taxonomy of state policies related to running away. To create this taxonomy, we searched for relevant documents for each FCDA state using the Child Welfare Information Gateway’s State Guides and Manuals Search website and the Child Welfare Information Gateway’s State and Tribal Child Welfare Law and Policy website. The latter provides links to state statutes and regulations, administrative code, and agency policies related to child protection, adoption, child welfare, legal guardianship, and youth services. We also requested documents from state child welfare administrators in the 21 states that are FCDA members. The resulting taxonomy included policies related to the definition of running away from care; policies related to preventing youth from running away from care; child welfare agency response when youth run away from care; follow-up measures when youth return to care after running away; post-runaway placement; and oversight (for example, caregiver reimbursement, action plan reviews, and tracking runaways).

Sample

The sample for this analysis includes 53,610 youth in 21 FCDA states who (1) entered foster care for the first time between January 1, 2009 and December 31, 2011, as observed through December 31, 2015 and (2) had at least one out-of-home care spell that began when they were between 13 

and 17 years old.3 Youth who were in care for four or fewer days are excluded. The 2009–2011 

2 For more information about the FCDA, please visit http://fcda.chapinhall.org.

3 An out-of-home care spell is a continuous time in out-of-home care that begins on the date a child enters and ends on the date a child exits. A child may experience one or multiple placements during a single spell and may experience multiple spells over time.

104 Youth Homelessness

Predictors of Running Away from Out-of-Home Care: Does County Context Matter?

timeframe was selected for two reasons. First, we wanted to minimize censoring. Only 3 

percent of the youth in our sample were in care at the end of the observation period. Had we included youth who entered care more recently, we could not have observed whether they ran away before their 18th birthday. Second, we wanted to minimize the possibility that any policy changes that could affect the incidence of running away occurred.

Dependent Variables

Our dependent variable is whether youth ran away from their placement during their first spell in out-of-home care.4 Youth are generally identified as running away when they caregivers report that they are absent from placement without permission. If a youth ran away during that first out-of-home care spell, our dependent variable is coded 1. If a youth did not run away during that first spell, our dependent variable is coded 0.

Independent Variables

Our independent variables include youth characteristics, placement history characteristics, county characteristics, and a measure of state policy. Youth characteristics include gender (female or male), race/ethnicity (African-American, White, Hispanic, or other)5, and age at first entry into out-of-home care. Placement history characteristics include the year in which youth first entered out-of-home care (2009, 2010, or 2011), placement type at the time youth ran away (foster home, kinship care, congregate care, or other), and level of care changes (whether the youth experienced a step up, a step down, or both). A step up is a placement change that involves moving from a family setting (for example, foster home, home of relative) to a congregate care setting (for example, group home, residential care facility). 

Conversely, a step down is a placement change that involves moving from a congregate care setting to a family setting. Although youth in care longer have more opportunities to run away, the model did not include length of time in care because length of time in care and age at entry are highly correlated.

County characteristics include the population density and level of socioeconomic disadvantage of the county where the youth was living when the first placement occurred. Our measure of population density is based on the six-level scheme used by the National Center for Health Statistics to classify U.S. counties and county equivalents (Ingram and Franco, 2014). We collapsed these six levels into three: urban core, urban collar, and rural. Urban core counties are large central metro counties in metropolitan statistical areas (MSAs) with a population of at least 1 million that (1) contain the entire population of the MSA’s largest principal city, (2) have their entire population contained in the MSA’s largest principal city, or (3) include a principal city with a population of at least 250,000. Urban collar counties are large fringe metro counties in MSAs with a population of at least 1 million that did not qualify as large central metro counties, medium metro counties in MSAs with a population of 250,000 to 999,999, or small metro counties in MSAs with a population of less than 250,000. Rural counties are counties in 4 Although some states extend out-of-home care beyond age 18, we only consider whether youth ran away before their 18th birthday.

5 Other includes Asian, Native American, other races/ethnicities, and unknown.

Cityscape 105

Dworsky, Wulczyn, and Huang

micropolitan statistical areas and nonmetropolitan counties that did not qualify as micropolitan.6

Socioeconomic disadvantage was measured using four county-level indicators collected as part of the 2010 national census data: the child poverty rate, the percentage of adults without a high school diploma or high school equivalency degree (for example, GED), the percentage of single parent households, and the unemployment rate. Every county is coded as better than (indicator = 

0) or worse than (indicator = 1) the average for the state in which it is located on each of the four indicators. The four indicators are then summed to create an index, the values of which range from 0 to 4. Higher scores indicate more disadvantage. A county with a score of 0 would be low on socioeconomic disadvantage because it is below the state average on each indicator. Conversely, a county with a score of 4 would be high on socioeconomic disadvantage because it is above the state average on each of the indicators.

We included these county-level factors in our model for two reasons. First, prior research found these factors are related to the placement of youth in congregate care (Wulczyn, Alpert, Martinez, and Weiss, 2015) and that placement in congregate increases the likelihood of running away. Urban counties use more congregate care than non-urban counties, and economically disadvantaged counties are less likely to place children in group care than areas classified as better off but counties that are both urban and low socioeconomic status use more congregate care than other counties.

Our measure of state policy is a variable that indicates whether there is a screening or assessment process for youth entering out-of-home care to determine their risk for running away. Six of the 21 

states in our sample do have a screening or assessment process. Three of those six states use data from the Child and Adolescent Needs and Strengths (CANS), an assessment tool developed to support level of care decision-making and service planning by providers of children’s services (Lyons, 2009).7

Analytic Strategy

We estimated a three-level logistic regression model with county random effects and state fixed effects. The model predicted the likelihood that youth ran away from care from their first out-of-home care spell. The parameter estimates represent the rate of change in the “log odds” of the dependent variables associated with a change in the independent variable. Although this interpretation is not particularly intuitive, the parameter estimates can be converted into “odds ratios” by exponentiating the coefficients. An odds ratio significantly greater than 1.0 means an increase in the value of the independent variable is associated with an increase in estimated odds the outcome will occur. An odds ratio significantly less than 1.0 means an increase in the value of the independent variable is associated with a decrease in estimated odds the outcome will occur.

The model includes youth characteristics (that is, gender, race, and age at entry), placement history characteristics (that is, entry cohort, level of care changes, and placement type), county characteristics (population density and level of socioeconomic disadvantage), and the state policy 6 A micropolitan statistical area is one or more adjacent counties or county equivalents with at least one urban core area with a population of at least 10,000 but less than 50,000, plus adjacent territory that has a high degree of social and economic integration with the core as measured by commuting ties.

7 For more information about Child and Adolescent Needs and Strengths, see http://praedfoundation.org/tools/the-child-and-adolescent-needs-and-strengths-cans/.

106 Youth Homelessness

Predictors of Running Away from Out-of-Home Care: Does County Context Matter?

indicator. The model also includes a random intercept for county that captures variation due to differences between counties and a random slope for last placement type, which captures variation in the effect of last placement type between counties.

Results

Sample Characteristics

A total of 53,610 youth between 13 and 17 years old entered a first out-of-home care spell from January 1, 2009 through December 31, 2011. Exhibit 1 shows their demographic characteristics. 

White youth (39 percent) comprise the largest percentage of the sample, but African-American youth (28 percent) and Hispanic youth (25 percent) were overrepresented relative to the general population. Females (56 percent) made up a larger proportion than males (44 percent). Two-thirds of the youth were between 14 and 16 years old the first time they entered foster care, and nearly half came from an urban core county. Seventeen percent of these youth (n=8,109) ran away at least once during their first out-of-home care spell.

Exhibit 1

Sample Characteristics (N = 53,610)

Characteristics

Frequency

Percentage

Race/Ethnicity

African-American

15,011

28.0

White

20,908

39.0

Hispanic 

13,510

25.2

Other

4,181

7.8

Gender

Female

30,182

56.3

Male

23,428

43.7

Age at first entry to care

13

10,360

19.3

14

11,427

21.3

15

12,559

23.4

16

11,737

21.9

17

7,527

14.0

Socioeconomic disadvantage

0 (Least disadvantaged)

12,656

23.6

1

6,423

12.0

2

7,749

14.5

3

11,811

22.0

4 (Most disadvantaged)

14,551

27.1

Missing

420

0.8

Urbanicity

Rural 

7,573

14.1

Urban collar

24,574

45.8

Urban core

17,541

32.7

Missing

3,922

7.3

Cityscape 107

Dworsky, Wulczyn, and Huang

Estimates from Logistic Regression Model

Exhibit 2 presents parameter estimates and odds ratios from the logistic model. With respect to demographic characteristics, gender, race, and age all matter. The odds of running away are lower for males than for females, higher for youth who are African-American or Hispanic than for youth who are White, and higher for youth who entered care when they were older than age 13 than for youth who entered care when they were 13 years old. Placement history also matters. The odds of running away are lower for youth who entered care in 2011 than for youth who entered care in 2009 and higher for youth moved between levels of care than for those whose did not. Additionally, compared to youth placed in a traditional foster home, the odds of running away were higher for youth placed in congregate care and lower for youth placed in kinship care or other care types.

Although county characteristics made a difference, population density had a clearer effect than level of socioeconomic disadvantage. The odds of running away are higher for youth from urban core counties and youth from collar counties that surround those urban core counties than for youth from rural counties. Compared to youth from counties with the lowest level of socioeconomic disadvantage (that is, the most advantaged counties), the odds of running away were higher for youth from counties with the highest level of socioeconomic disadvantage and for youth from counties in which the level of disadvantage was neither a particularly high nor particularly low. The odds of running away were also lower in states with a screening or assessment process for determining the risk for running away among youth entering out-of-home care.

Finally, parameter estimates for both the random intercept for county and the random slope for last placement type were statistically significant. The random intercept effect indicates that unmeasured differences between counties accounted for a significant amount of the variation in the odds of running away. The random slope effect indicates that the effect of last placement type on the odds of running away varies significantly between counties.

Exhibit 2

Estimates from Logistic Regression Model*

Covariate

Estimate

S.E.

p

Odds Ratio

Intercept

-2.6800

0.0716

<.0001

0.069

Entry cohort

2009 entry cohort

2010 entry cohort

-0.0271

0.0214

0.2055

0.973

2011 entry cohort

-0.0514

0.0220

0.0197

0.950

Gender

Female

Male

-0.2681

0.0184

<.0001

0.765

Race/ethnicity

White

African-American

0.2728

0.0246

<.0001

1.314

Hispanic

0.2711

0.0261

<.0001

1.311

Other

0.0980

0.0364

0.0071

1.103

108 Youth Homelessness

Predictors of Running Away from Out-of-Home Care: Does County Context Matter?

Exhibit 2

Estimates from Logistic Regression Model*

Covariate

Estimate

S.E.

p

Odds Ratio

Age at entry

Age 13

Age 14

0.4298

0.0335

<.0001

1.537

Age 15

0.7007

0.0318

<.0001

2.015

Age 16

0.8509

0.0315

<.0001

2.342

Age 17

0.7133

0.0335

<.0001

2.041

Level of care change

No level of care 

change

Any level of care 

0.1555

0.0206

<.0001

1.168

change

Placement type

Foster care

Congregate care

0.3775

0.0454

<.0001

1.459

Kinship care

-1.4270

0.0353

<.0001

0.240

Other

-1.0972

0.0491

<.0001

0.334

County socioeconomic disadvantage

0 (Least 

disadvantaged)

1

0.0771

0.0464

0.0962

1.080

2

0.1142

0.0446

0.0105

1.121

3

0.0272

0.0421

0.5183

1.028

4 (Most 

0.4007

0.0413

<.0001

1.493

disadvantaged)

Urbanicity

Rural

Urban Core

1.0671

0.0467

<.0001

2.907

Urban Collar

0.4277

0.0374

<.0001

1.534

Screening for runaway risk

No

Yes

-0.2545

0.0289

<.0001

0.775

Covariance 

Parameter 

Subject

Estimate

S.E.

Pr > Z

Estimates

Intercept

county

0.3322

0.0558

<.0001

Last placement type

county

0.1585

0.0359

<.0001

*Reference groups are italicized.

Discussion and Implications

We found that 17 percent of the adolescents who entered out-of-home care for the first time from 2009 through 2011 ran away at least once during their first spell. The results of our multivariate analysis suggest that the likelihood of running away is not the same for all youth. First, females are more likely to run away than males. This result is consistent with prior studies (Courtney et al., 2005; Courtney and Zinn, 2009; Day and Riebschleger, 2007; Fasulo et al., 2002; Nesmith, 2006). The reasons for this difference are unclear, but one possibility is that services designed to Cityscape 109

Dworsky, Wulczyn, and Huang

prevent youth in out-of-home care from running away are less effective for adolescent girls than adolescent boys. Another is that caregivers are more likely to report adolescent girls as being away from their placement without permission than adolescent boys because they are perceived as more vulnerable. This gender difference merits additional attention.

Second, African-American and Hispanic youth are more likely to run away than youth who are White. This finding aligns with prior research on racial and ethnic disparities in child welfare outcomes. It also suggests that African-American and Hispanic youth are more likely to experience the adverse outcomes for which youth who run away are at risk. In this way, African-American and Hispanic youth are further disadvantaged relative to their White counterparts by the very system that is supposed to protect them and promote their well-being. Policymakers and child welfare administrators have an obligation to address this disparity.

Third, youth were more likely to run away from congregate care, but less likely to run away from kinship care, than to run away from traditional foster homes. This finding is consistent with prior research and reinforces longstanding concerns about the negative consequences of congregate care placement (Dishion, Nelson, Winter, and Bullock, 2004; Hawkins-Rodgers, 2007) and suggests that states could potentially reduce the incidence of running away by limiting the use of congregate care. Although congregate care use varies widely across and within states, it has been declining overall. This trend may continue because the Family First Prevention Services Act (FFPSA), enacted as part of the Budget Act of 2018, places new restrictions on eligibility for Title IV-E maintenance (that is, room and board) payments for children placed in group care.8

Fourth, youth who moved between levels of care are more likely to run away than youth who did not. This finding is consistent with prior studies that found a positive relationship between placement stability (measured by the number of placement changes) and reduced risk of running away (Clark et al., 2008; Courtney and Zinn, 2009).9 Although youth can change placements without moving between levels of care, they cannot move between levels of care without changing placements. One explanation for the greater likelihood of running away among youth who experienced a change in level of care is that youth run away from out-of-home care when the type of care in which they are placed is not meeting their treatment and service needs. If this explanation is correct, then states could potentially reduce the incidence of running away by improving how the needs of youth are assessed when they enter care and how youth are matched to placement types based on those assessments. Future research should examine whether the direction of the change in level of care matters. In other words, is the effect of moving from a level of care that is less restrictive to one that is more restrictive the same as the effect of moving from a level of care that is more restrictive to one that is less restrictive?

Our results also point to significant differences in the likelihood of running away based on the characteristics of the county from which the youth were removed. Youth who came from urban 8 FFPSA limits federal Title IV-E maintenance payments to 14 days unless the child is placed in a “qualified residential treatment program” and the placement is necessary to meet the child’s behavioral or emotional health treatment or service needs.

9 It was not possible to include both the number of placement changes and changes in level of care in the model due to collinearity.

110 Youth Homelessness

Predictors of Running Away from Out-of-Home Care: Does County Context Matter?

counties (both core and collar) were more likely to run away than those who came from rural counties. Why the likelihood of running away is greater for urban youth compared to rural youth is unclear from our data. One possibility is that congregate care placements, as well as African-American and Hispanic youth, tend to be concentrated in urban as opposed to rural areas (Wulczyn, Alpert, Martinez, and Weiss, 2015), but youth from urban counties were more likely to run away than youth from rural counties even after controlling for race and placement type. 

Another possibility is that youth from urban counties have more opportunity to run away because there are more places for them to go and more resources for them to use (for example, programs for runaway or homeless youth). Understanding the source of this urban-rural difference will require more research.

The differences we found in the likelihood of running away between youth from counties with different levels of socioeconomic disadvantage are more difficult to explain. Youth from the most disadvantaged counties were more likely to run away than youth from the least disadvantaged counties. This could reflect a lack of services or other resources for youth from counties that are the most socioeconomically disadvantaged. Less obvious is why youth from moderately disadvantaged counties were more likely to run away than youth from the least disadvantaged counties but youth from somewhat more or somewhat less disadvantaged counties were not. 

This could reflect measurement error in the variables used to construct the index of county socioeconomic disadvantage, or the relatively simple taxonomy of socioeconomic disadvantage we created for this exploratory work. Additional research might help explain this relationship.

Importantly, we found that youth in out-of-home care are less likely to run away if their state has a screening or risk assessment process than if their state does not. This finding suggests states could potentially reduce the incidence of running away from out-of-home care by instituting a screening or assessment process to identify high-risk youth. Yet, what appears to be a policy effect might be an effect of unmeasured differences between states in which youth are screened or assessed and states in which youth are not. For example, states that have a policy may invest more resources in runaway prevention than states with no such policy. Future studies should explore current screening or assessment procedures to learn whether systematic differences between states with and without a policy could account for these results.

Finally, we found that a significant amount of the variation in running away from out of home care is due to between-county differences not captured by our covariates. We also found significant differences between counties in how placement type affects the likelihood of running away. This could reflect differences in how different types of care are used. Our study was the first to explore these county-level contextual effects on running away, and our findings suggest that a more thorough analysis of them is needed.

Limitations

Readers should consider these findings in the context of our study limitations. First, our analysis does not control directly for mental or behavioral health problems that may affect the type of care in which youth are placed and whether they run away. For example, a higher prevalence of mental or behavioral health problems among youth placed in congregate care could explain why youth Cityscape 111

Dworsky, Wulczyn, and Huang

are more likely to run away from congregate care than from foster homes. Although we recognize the value of including measures of mental and behavioral health in an analysis of running away, assessment data are not captured in the FCDA. Future research should examine whether similar placement effects are observed when data that include measures of mental and behavioral health are used.

Second, we limited our sample to youth who entered out-of-home care as adolescents for two reasons. First, youth in out-of-home care typically do not run away until their adolescent years. 

Second, youth in out-of-home care who entered during adolescence are developmentally different from youth in out-of-home care who entered as children, and developmental differences could affect both the incidence and the predictors of running away. Future research could focus on the impacts of developmental differences on running away.

Third, our analysis looked only at whether youth ran away during their first out-of-home care spell. 

We did not analyze what happened during subsequent out-of-home care spells if youth exited and reentered because predicting whether youth ever ran away requires a more complicated model that accounts for prior experiences in out-of-home care. Future research should include analyses of running away that extend beyond the first out-of-home care spell.

Fourth, we examined whether youth ran away during their first out-of-home care spell but not the rate at which they ran. Although understanding how various factors affect the rate at which youth run away is important and could have implications for prevention, modeling the risk of running away is complicated when the proportionality assumption is violated—as is likely when the sample includes youth from 21 different states and hundreds or thousands of counties. Modeling the rate at which youth in out-of-home care first ran away when the proportionality assumption is violated is a challenge for future research.

Fifth, our analysis was limited to youth from the 21 states that are members of the FCDA. Although these states are socially, economically, and geographically diverse, the factors that predict running away among youth in out-of-home care may be different in these states than in the 29 states that are not FCDA members.

Finally, we know states define running away differently. Differences may also exist both between and within states in the consistency with which youth who run away are reported. Our analysis does not account for potential differences in reporting practices, which could affect the reliability of our data.

Conclusion

Running away from out-of-home care can have serious negative consequences. It can adversely affect young people’s emotional and social development (Biehal and Wade, 2000; Skyles, Smithgall, and Howard, 2007; Courtney et al., 2005), disrupt their education and acquisition of life skills (Shirk and Stangler, 2004; Skyles, Smithgall, and Howard, 2007), and limit the formation of social support networks and positive relationships with caring adults (Choca et al., 2004; Christenson, 2002; Clark and Crosland, 2009; Nesmith, 2006). Running away also puts youth in out-of-home 112 Youth Homelessness

Predictors of Running Away from Out-of-Home Care: Does County Context Matter?

care at risk for criminal victimization, sexual exploitation, physical and mental health problems (for example, sexually transmitted diseases, malnutrition, substance abuse), and delinquent behavior (Courtney et al., 2005; Nesmith, 2006; Hyde, 2005; Clark et al., 2008; Finkelstein et al., 2004). By preventing youth in out-of-home care from running away, we can avert these undesirable outcomes and reduce the number of youth who experience homelessness.

Acknowledgments

The authors would like to thank the W.T. Grant Foundation, which provided funding for this work, and the states that support the Center for State Child Welfare Data.

Authors

Amy Dworsky is a Research Fellow at Chapin Hall at the University of Chicago and can be emailed at adworsky@chapinhall.org.

Fred Wulczyn is a Senior Research Fellow at Chapin Hall at the University of Chicago.

Lilian Huang is a Project Assistant at Chapin Hall at the University of Chicago.

References

Biehal, Nina, and Jim Wade, J. 2000. “Going missing from residential and foster care: Linking biographies and contexts,”  British Journal of Social Work 30: 211–225.

Butler, Linda, and Peter McPherson. 2007. “Is residential treatment misunderstood?” J ournal of Child Family Studies  16: 465–472.

Choca, Miryam, Jedediah Minoff, Lyn Angene, Michele Byrnes, Lois Kenneally, DeWayne Norris, Deanne Pearn, and Marina Rivers. 2004. “Can’t do it alone: Housing collaborations to improve foster youth outcomes,”  Child Welfare 83: 469–492.

Christenson, Sandra. 2002.  Families, educators, and the family-school partnership: Issues or opportunities for promoting children’s learning competence?  Paper presented at the annual meeting of the invitational conference on the future of school psychology, Indianapolis, Indiana.

Clark, Hewitt, and Kimberly Crosland. 2009. “Social and life skills development: Preparing and facilitating youth for transition into young adult roles.” In  Achieving permanence for older children and youth in foster care, edited by Benjamin Kerman, Madelyn Freundlich, and Anthony Maluccio. New York: Columbia University Press.

Clark, Hewitt, Kimberly Crosland, David Geller, Michael Cripe, Terresa Kenney, Bryon Neff, and Glen Dunlap. 2008. “A functional approach to reducing runaway behavior and stabilizing placements for adolescents in foster care,”  Research on Social Work Practice 18: 429–441.

Cityscape 113

Dworsky, Wulczyn, and Huang

Courtney, Mark., Ada Skyles, Gina Miranda, Andrew Zinn, Eboni Howard, and Robert Goerge. 

2005.  Youth who run away from substitute care. Chicago, IL: Chapin Hall Center for Children at the University of Chicago.

Courtney, Mark, and Andrew Zinn. 2009. “Predictors of running away from out-of-home care,” 

Children and Youth Services Review 31: 1298–1306.

Day, Angelique, and Joanne Riebschleger. 2007. “Circumstances and suggestions of youth who run from out-of-home care,”  Michigan Child Welfare Law Journal 11: 20–30.

Dishion, Thomas, Sarah Nelson, Charlotte Winter, and Bernadette Bullock. 2004. “Adolescent friendship as a dynamic system: Entropy and deviance in the etiology and course of male antisocial behavior,”  Journal of Abnormal Child Psychology 32: 651–663.

Dworsky, Amy, Laura Napolitano, and Mark Courtney. 2013. “Homelessness during the transition from foster care to adulthood,”  American Journal of Public Health 103: 318–323.

Eisengart, Julie, Zoran Martinovich, and John Lyons. 2008. “Discharge due to running away from residential treatment: Youth and setting effects,”  Residential Treatment for Children Youth 24: 327–343.

Fasulo, Samuel, Theodore Cross, Peggy Mosley, and Joseph Leavey. 2002. “Adolescent runaway behavior in specialized foster care,”  Children and Youth Services Review 24: 623–640.

Finkelstein, Marni, Mark Wamsley, Dan Currie, and Dorren Miranda. 2004.  Youth who chronically AWOL from foster care.  New York, NY: Vera Institute of Justice.

Hawkins-Rodgers, Yolanda. 2007. Adolescents adjusting to a group home environment: A 

residential care model of re-organizing attachment behavior and building resiliency.  Children and Youth Services Review, 29: 1131–1141.

Hill, Robert. 2006. “Synthesis of Research on Disproportionality in Child Welfare: An Update,” 

Casey-CSSP Alliance for Racial Equity in the Child Welfare System.

Hines, Alice, Peter Lee, Kathy Osterling, and Lauri Drabble. 2007. “Factors predicting family reunification for African American, Latino, Asian and White families in the child welfare system,” 

Journal of Child and Family Studies 16: 275–289.

Hyde, Justeen. 2005. “From home to street: Understanding young people’s transitions into homelessness,”  Journal of Adolescence, 28: 171-183.

Ingram Deborah, and Sheila Franco. 2014. “2013 NCHS urban–rural classification scheme for counties. National Center for Health Statistics,”  Vital Health Statistics 2 (166): 1–73.

Karam, Rana, and Marie Robert. 2013. “Understanding runaway behavior in group homes: What are runaways trying to tell us?”  Journal of Community Positive Practices 13: 69–79.

Kerr, Julie, and Judy Finlay. 2006.  Youth running from residential care: “The Push” and “The Pull.”  

Ontario, Canada: Office of Child and Family Service Advocacy.

114 Youth Homelessness

Predictors of Running Away from Out-of-Home Care: Does County Context Matter?

Leichtman, Martin. 2006. “Residential treatment of children and adolescents: Past, present, and future,”  American Journal of Orthopsychiatry 76: 285–294.

Lyons, John. 2009.  Communimetrics: A theory of measurement for human service enterprises. New York: Springer.

Nesmith, Andrea. 2006. “Predictors of running away from family foster care,”  Child Welfare 85: 585–609.

Shirk, Martha, and Gary Stangler 2004.  On Their Own: What Happens to Kids When They Age Out of the Foster Care System?  New York: Basic Books.

Skyles, Ada, Cheryl Smithgall, and Eboni Howard, 2007.  School engagement and youth who run away from care: The need for cross-system collaboration. Chicago: Chapin Hall at the University of Chicago.

U.S. Department of Health and Human Services. 2017. The AFCARS Report Preliminary FY1 2016 

Estimates as of Oct 20, 2017 No. 24. Washington, DC: USDHHS.

———. 2015.  A National Look at the Use of Congregate Care in Child Welfare. Washington, DC: USDHHS.

Vesneski, William. 2011. “State law and the termination of parental rights,”  Family Court Review 49: 364–378.

Witherup, Luanne, Timothy Vollmer, Carole Van Camp, Han-Leong Goh, John Borrero, Kristin Mayfield. 2008. “Baseline measurement of running away among youth in foster care,”  Journal of Applied Behavior Analysis 41: 305–318.

Wulczyn, Fred, Lily Alpert, Zach Martinez, and Ava Weiss. 2015.  Trends in congregate care use: A report from the Multistate Foster Care Data Archive. Chicago: Chapin Hall Center for Children at the University of Chicago.

Cityscape 115

116 Youth Homelessness

Developing a Coordinated Youth 

Housing Stability Program for 

Juvenile Courts

Sarah Cusworth Walker

University of Washington School of Medicine

Esteban Valencia

University of Washington School of Medicine

Asia Bishop

University of Washington

Michael Irons

Snohomish County Juvenile Court

Arina Gertseva

Washington State Center for Court Research

by Sarah Cusworth Walker, Esteban Valencia, Asia Bishop, Michael Irons, and Arina Gertseva Abstract

Homeless youth engaging in street survival behaviors are at higher risk of justice involvement.  Advocates for reducing youth homelessness have called on the juvenile justice system and allied system partners to minimize the legal consequences of these behaviors and to improve systemic responses to identifying and reducing homelessness. The current study used a community-based participatory approach to develop a model for reducing homelessness from within the juvenile justice system. This mixed methods study leveraged a research-practice partnership between university researchers and a midsized county court in Washington State to examine data from local juvenile filings in 2017, (n=555), statewide juvenile court data from 2016-17, (n=6,791/6,866), and qualitative data from workgroup meetings. Prevalence statistics suggest 20-50 percent of the youth filed in juvenile court had at least one prior episode of running away or being kicked out of the home. Key qualitative findings included tensions around the role of probation in addressing youth homelessness, the need for better methods of identification, and a lack of intensive family-based services targeted at preventing housing instability. The resulting juvenile court based model for addressing youth homelessness is presented and lessons learned from the research-practice partnership are discussed. 

Cityscape: A Journal of Policy Development and Research •  Volume 20, Number 3 • 2018

Cityscape 117

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Walker, Valencia, Bishop, Irons, and Gertseva

Background

Youth homelessness and juvenile justice involvement intersect in a number of complex 

ways. Youth who are homeless over an extended period of time are significantly more likely to have contact with the justice system as well as other unfavorable outcomes, including higher levels of violent victimization and drug use (Ferguson et al., 2011; Kaufman and Widom, 1999; Stein et al., 2009; Yoder et al., 2014). Using a homeless sample, Yoder et al. 

(2014) found youth who engaged in a greater number of survival behaviors were 2.6 times more likely to be arrested. This study is consistent with a finding by McCarthy and Hagan (2005) in which youths’ perception of danger while homeless was significantly associated with criminal activities, including theft, drug selling, and prostitution. Yoder et al. (2014) described these illegal actions as “survival behaviors” to distinguish them from other theoretical frameworks that assume criminal behaviors are primarily driven by deficits in thinking and problem-solving.

Justice involvement itself can also act as an active barrier to stable housing (Quirouette et al. 

2016). For youth transitioning out of detention or incarceration, for example, the legal status of having a criminal record can severely limit opportunities for securing independent housing (Mears and Travis 2004). Having a criminal record can also act as a barrier for youth trying to move back in with their family after release, particularly when the families’ housing unit policy prohibits felons from residing on the premises (Snyder 2004). Longer term incarceration of more than a year also disrupts preexisting social networks, leaving youth with lower social capital (for example, diminished relationships or connections with extended family members) upon release. This disruption leaves youth heavily reliant on public systems to provide basic housing and needed resources for successful transition back to the community. When beds are not available in the youths’ communities of origin (Tam et al., 2016), youth are often required to search for housing elsewhere. With fewer social networks, youth are less likely to remain in stable placements.

Youth dually involved in the child welfare and juvenile justice system face compounded risks for poor social connections and homelessness. One study by Shah et al. (2017) found youth with justice system contact were 1.5 times more likely to become homeless after aging out of the foster care system. Finally, a less studied but important intersection is the role the justice system plays in identifying youth currently housed but at risk of later housing instability. This risk appears to be particularly salient for youth arrested due to home conflict, one of the most common precipitants of youth homelessness (Administration on Children, Youth and Families 

[ACYF], 2016). At least 50 percent of youth homelessness appears to be directly preceded by a family conflict resulting in the youth running away or told to leave the home (ACYF, 2016).

There are multiple and often intersecting consequences for youth at risk of homelessness and justice involvement. These various intersections make a compelling case for the need of increased coordination between the juvenile justice system and youth housing systems in order to reduce youth homelessness and promote general youth well-being. The complex and reciprocal nature of this relationship is likely to require a multicomponent approach (Britton and Pilnik, 2018).

118 Youth Homelessness

Developing a Coordinated Youth Housing Stability Program for Juvenile Courts Prevalence and Causes of Youth Homelessness

A staggeringly high number of youth in the United States will experience homelessness before the age of 17. A recent national study estimated 1 in 10 young adults (18-25 years) and 1 in 30 

adolescents (13-17 years) will experience some form of homelessness over the course of a year (Morton, Dworsky, and Samuels, 2017). Studies over the last twenty years suggest this number is stable and may be modestly growing. For instance, trends found an estimated 1.6 million runaway youth in 1998 (Ringwalt, Greene, and Robertson, 1998), to a possible 1.7 million youth in 2013 (Fernandes-Alcantara, 2016). The prevalence of homelessness also disproportionately impacts certain highly vulnerable subpopulations. For example, a recent national study found that lesbian, gay, bisexual, trans, and queer (LGBTQ) identified youth are 120 percent more likely to have episodes of homelessness compared to non-LGBTQ identified youth (Morton, Dworsky, and Samuels, 2017), and comprise 20-40 percent of the youth homeless population. Research also suggests youth with trauma histories are more likely to become homeless. One study by Bender et al. (2014) found 93 percent of homeless youth experienced some form of maltreatment prior to leaving home, which is disproportionately high compared to the general population experiencing childhood maltreatment (7-9 percent; Fantuzzo, Perlman, and Dobbins, 2011; Sullivan and Knutson, 2000).

The majority of youth homelessness appears to result from unstable or conflictual home environments. A 2016 study by the Administration of Children, Youth, and Families conducted with over 600 homeless youth found the majority of youth first become homeless when they are asked to leave home by a parent or caregiver (51 percent). Extrusion from the home was followed by being unable to find a job (25 percent), being physically abused or beaten (24 percent), or as a result of a caretaker’s substance abuse problems in the home (23 percent). Only 30 percent of the surveyed sample thought they had the option of returning home. Recent studies estimate the average length of homelessness can last nearly two years (ACYF, 2016) with less than one-fourth (23 percent) of youth experiencing short-term homelessness (that is, less than one month; Milburn et al., 2012).

Risks Associated with Youth Homelessness

Homelessness poses significant health risks to youth, compounding any prior mental and physical health challenges. Nearly two thirds of youth will be victimized while homeless, including physical or sexual assault (33 percent), being threatened with a weapon (41 percent), or robbed (41 percent; ACYF, 2016; Kipke et al., 1997; Rotheram-Borus, Rosario, and Koopman, 1991). A little over one-fourth of youth report “being sexual” in exchange for a place to spend the night (ACYF, 2016). Runaway and homeless youth are at a greater risk of depression, substance use, and conduct problems compared to housed youth (Chen et al., 2006).

The Juvenile Justice System’s Role in Addressing Youth Homelessness

A number of recent reports include policy and practice suggestions for improving the justice and public health response to youth homelessness (Columbia Legal Services, 2015; Morton, Dworsky, and Samuels, 2017; Britton and Pilnik, 2018; Pilnik et al., 2017). A recent report developed by the Coalition for Juvenile Justice outlines Ten Principles for Change, designed to support communities Cityscape 119

Walker, Valencia, Bishop, Irons, and Gertseva

to improve housing stability for justice-involved youth (Pilnik et al., 2017). These principles largely focus on reducing or minimizing justice system contact for youth entering the justice system, and on accessing stable housing for youth exiting the justice system. For example, the report recommends jurisdictions should avoid charging youth for survival behaviors, such as prostitution or squatting in abandoned buildings (Pilnik et al., 2017). The recommendations include repealing laws prohibiting loitering, camping, and the like; removing truancy as a chargeable offense; prohibiting confinement for unstable housing; and eliminating court fines. Similarly, the report outlines the different actions multiple systems can take in ensuring youth are not released from justice settings into homelessness (Pilnik et al., 2017). These recommendations include more expansive transition planning, coordinated school reenrollment efforts, and maintaining open child welfare cases through justice placement. The report also acts as a resource guide with links to over 100 different resources focusing on training, policy, direct service examples, and research reports.

A report from the National Council of Juvenile and Family Court Judges proposes three specific strategies courts can take to prevent youth homelessness (Britton and Pilnik, 2018). These strategies include (1) prevention with coordinated transition/re-entry planning, (2) prevention with effective legal representation, and (3) prevention with sound judicial leadership.

Recommendations include better identification of risks and improved coordination among systems during dependency and at-risk youth hearings to prevent future housing instability and behaviors that lead to criminal offenses.

While the risk of justice contact is high for homeless youth, there is relatively little known about the prevalence of homelessness among youth arrested or charged with juvenile crimes. We could not find a credible source or study showing the percentage of youth with housing needs among a juvenile population. This prevalence is important to understand because justice systems have a number of competing mandates and performance goals, for example, reducing racial/ethnic disparities, improving gender and culturally responsive services, and addressing trauma and behavioral health needs to name a few. Implementing the systemic changes recommended by the previous policy reports are likely to be more successful to the degree that homelessness is identified as a significant issue for justice-involved youth or can be aligned with other initiatives addressing similar needs.

The Current Study

The recent reports from Pilnik et al. (2017) and Britton and Pilnik (2018) provide valuable guidance and principles for systems to consider when addressing youth homelessness. The current project attempts to translate some of these principles via a research-practice partnership with one juvenile court in Washington State and is the first phase of a larger study to develop and evaluate court-based models that improve the identification and service referral process for youth at risk of homelessness. Principles of community-based participatory research (Israel et al., 1998; Bess et al., 2009) were applied in this process, ensuring that the developed model reflects the values and system operations of the local setting. This report presents the formative process for developing the model, key local and state data used to inform the model, and discusses the findings and potential application of the model for other jurisdictions.

120 Youth Homelessness

Developing a Coordinated Youth Housing Stability Program for Juvenile Courts Methods

Program team.  To ensure program development was tailored to the system operations of the local court and allied partners, as well as the needs of youth and families, a Development Workgroup was established. To form this workgroup, the court probation manager convened an internal team to brainstorm all of the known systems partners that intersected with homelessness (exhibit 1). 

Stakeholders identified in this first meeting were solicited to participate in a second meeting, which included representatives from the juvenile court, the Washington State Department of Social and Health Services, the local school district, two organization specializing in sexual exploitation and abuse, and a drug abuse task force. At subsequent meetings, workgroup participants were encouraged to identify other important stakeholder contacts, leading to additional invitations to a member of the county drug use taskforce, the director of special programs and services at the school district, and a state level administrator for youth homelessness.

Exhibit 1

Workgroup Participants

Agency

Title

Cocoon House

Program Manager

Juvenile Court

Program Manager

Probation Supervisor

Probation Counselor

Assistant Court Administrator

Juvenile Justice Fellow

Department of Social And Health Services (State)

Program Quality Assurance Specialist

School District

McKinney Vento Facilitator

Special Programs Director

Juvenile Detention Instructor

Sexual Exploitation Intervention Network

Commercially Sexually Exploited Youth Coordinator

Providence Intervention Center for Assault and Abuse

Human Trafficking & Advocate Specialist

Drug Abuse Taskforce

Program Coordinator

The Development Workgroup was the primary driver of model development. An Intervention Mapping process (Bartholomew, Parcel, and Kok, 1998) was used to develop a list of objectives for the program and selected strategies to meet those objectives using the results from the qualitative data analysis, findings from the local and state administrative data, and findings from the academic research literature The theory of Social Development (Hawkins and Weis, 1985) was applied in developing the intervention piece of the program model given the importance of the intrapersonal and ecological factors on youth housing, such as family environment, poverty, and youth development. The discussion was also informed by the juvenile justice and homelessness reduction principles from Pilnik et al. (2017), including (1) current methods of identifying homelessness at the point of justice contact, (2) existing referral mechanisms for connecting youth to services, (3) adequacy of existing services to prevent or address housing instability, and (4) laws and regulations impacting the provision of services.

Finally, the investigators presented research-based approaches to improving family communication and healthy youth development using the prevention to intervention framework (Tolan, Guerra, Cityscape 121

Walker, Valencia, Bishop, Irons, and Gertseva

and Kendall, 1995). The facilitator of the study team presented the matrix of existing family-based services shown to build family resiliency for reducing conflict and/or addressing youth substance use or delinquency. Using this list as a starting point for discussion, the group identified areas of the county with and without family-based services. The most populated city of the county had the most resources already in place to serve families through phone consultation, parenting groups, or intensive case management. The northeastern part of the county was identified as lacking any known family support services, with other parts of the county having limited resources. The group noted that few of the research-based programs on the list were specifically available in the county, but that available programs appeared to cover similar components.

The investigators facilitated the meetings and captured the discussion through handwritten notes and recordings. In between workgroups, the investigators would follow up on key questions posed by members and bring possible solutions back to the group for further discussion. The workgroup met four times over eight months to provide time between sessions for the investigators to bring additional program and policy information, data, and academic research findings for the group to consider. The discussion was informed by a program review conducted by the study team that highlighted programs shown to be effective in preventing and intervening with family conflict.

Setting.  The juvenile court in the study is a midsized court extending across semi-urban, suburban, and rural areas. The 2017 county population included 59, 225 adolescents ages 12-17 

(Washington State Office of Financial Management, 2017). The majority of these youth were White (75.33 percent) followed by Hispanic (13.71 percent), Asian (9.81 percent), and Multiracial youth (9.08 percent). Black (3.25 percent), American Indian / Alaskan Native (1.19 percent), and Native Hawaiian / Pacific Islander youth (0.68 percent) comprised a smaller proportion of the population. 

In 2017, the county juvenile court filed 882 criminal offense cases. The court has been a member of the Juvenile Detention Alternatives Initiative (Annie E Casey Foundation) since 2012 and instituted a number of policies and practices to reduce the number of youth detained for minor and moderate offenses. In 2014, the court had the second lowest rate of detained youth in the state (9.1 per 1,000 youth from general population) and the fourth lowest rate of case filing (11.1 per 1,000 youth from general population; Gilman, 2016).

Qualitative Analysis

Handwritten notes and recordings were captured from each meeting and themes were coded and summarized to inform subsequent meetings. We present qualitative data from the first meeting, as this meeting was similar to a focus group with the investigators facilitating discussion based on questions related to perceived needs, existing resources, potential barriers, and values related to youth housing and juvenile court operations. Notes from the meeting were taken by two of the study personnel and combined into a single set of notes. The notes were then coded for themes using directed content analysis (Hsieh and Shannon, 2005), which were analyzed within the topic areas (needs, existing resources, and gaps) identified in the framework used in the workgroup discussion. Information captured in subsequent meetings related to these themes was used to corroborate the information needs and emerging themes identified in the first meeting and to develop a prototype model with greater specificity. This process of triangulation (using multiple sources of information to cross-check) helped establish trustworthiness and credibility of the 122 Youth Homelessness

Developing a Coordinated Youth Housing Stability Program for Juvenile Courts findings (Miles, Huberman, and Saldaña, 2014), a process analogous to establishing validity and reliability in quantitative research.

Quantitative Prevalence Data

Quantitative data to estimate local and state housing instability prevalence came from the Positive Achievement Change Tool (PACT; Hamilton, van Wormer, and Barnoski, 2015), a case management risk and needs tool used by juvenile courts in Washington State to guide service planning. The PACT is completed in two phases. A shortened, “prescreen” version of the form is administered to all court-referred youth and some diverted youth for classification into low, moderate, or high risk for recidivism. Only youth scoring at moderate or high received the full assessment. State data was requested from the Washington State Center for Court Research to inform the group about the overall need for a specific emphasis on housing for justice-involved youth, and to estimate the number of youth likely to need intensive housing support as housing status is only available through the full assessment. This process included two data extracts. 

The first data extract was taken from the PACT pre-screen, to examine how local data compared to state data on presumed indicators of housing instability risks for 2016 (n=6,791) and 2017 

(n=6,866). The second extract used the full PACT assessment to examine the percent of current housing instability among youth assessed as moderate or high risk for recidivism for 2016 

(n=4,307) and 2017 (n=4,296).

Local data on indicators of housing instability risk were examined to provide monthly estimates of how many youth could be expected to be flagged as at risk for housing instability. The data included all cases (which could include duplicates) receiving the PACT prescreen between February 2016 through February 2017, n=555. The prevalence of local data items presumed to indicate risk for housing instability were also compared to the state findings as a check on generalizability of the developed model for other jurisdictions. These indicators include previous runaway incidents, previous out of home placement, and level of conflict in the home.

Results

Themes from the meetings highlighted the perceived main sources of youth homelessness, the limitations of existing ways to identify housing needs, and ideas for leveraging existing supports and addressing needs.

Housing and services gaps.  Workgroup discussion of service model gaps primarily focused on the lack of long term housing for 12- to 15-year-olds, and on the challenges of finding appropriate, family-based services to prevent homelessness. In the county, limited long term housing was available for transition-aged youth (18-24) and adults. Long term housing for youth under age 18 was only available through child welfare services. Accessing child welfare dollars and beds was restricted to a finding of dependency after substantiated parental abuse or neglect, which was not the case for many of the homeless youth known to the workgroup members (five mentions).

Workgroup members also reflected on their experiences with parents who “are done” by the time a youth comes in contact with the justice system, making reunification after a detention Cityscape 123

Walker, Valencia, Bishop, Irons, and Gertseva

stay difficult (six mentions). Workgroup members noted that families will become frustrated with the perceived inadequacy of available justice responses to home conflict and youth intractability. 

One workgroup member shared that “it’s very frustrating for the families when services engage with lower level ideas and families have already tried it.” The services available through the justice center, however, in collaboration with the county-based youth shelter were perceived as adequate for youth with lower justice involvement (for example, low-risk youth). The shelter representative noted that an outreach employee attended court on Mondays to identify and refer families who were in need of brief family support and that this process was working well for some families.

Workgroup members also noted ways in which court processes created additional barriers for homeless youth. For example, court summons are mailed and the workgroup members discussed how these notices were likely not reaching youth who are unstably housed, creating greater legal consequences: “Warrants, how many kids show up to court because they don’t get the notice, or they get it but they’re so all over the place they can’t prioritize it? So there’s a court outcome due to the status of [homelessness].”

Identifying homelessness. The workgroup members noted two significant challenges with identifying youth homelessness from within the juvenile court. The first challenge was the lack of items relating to homelessness in existing screenings and assessments. The court uses the PACT 

as a validated criminal risk assessment to guide service decisions. A shortened, screener version of the tool is administered by probation counselors to all youth charged with a non-divertible crime following a law enforcement referral. The screener contains items about past runaway instances but not items about current homelessness. The full assessment, administered with youth who score as moderate or high risk on the screener, contains a question about whether the youth was currently under adult supervision, but the workgroup members had concerns about whether probation counselors had a shared understanding about how to score this item. For example, “[We] can’t rely on PACT because it doesn’t have a good indicator of stability of youth’s housing status at the moment of recording.” The group also agreed to adopt the McKinney-Vento definition of homelessness used for school-based assessment. The McKinney-Vento definition considers “unaccompanied” to be not in “the physical custody of a parent or guardian” (42 U.S.C. § 1143a(6)).

The second challenge related to concerns about introducing a new assessment tool and new job responsibilities for court employees. One of the probation counselors commented, “What’s going through my mind is ‘does that mean that [Probation Counselors] are going to have another assessment to administer’?” This comment lined up with concerns expressed by other workgroup members about implementation of any new tools or roles, as existing probation staff had inconsistent views on the probation counselor’s responsibility to address homelessness as a part of supervision. The workgroup also noted that the detention school was run by the educational school district, and instructors had access to youth administrative files, including homeless status (McKinney-Vento). One workgroup member noted, however, that prior requests to look up this information while a youth was in detention had been complicated by a similar confusion about whether this assessment fell under anyone’s specific job responsibilities.

Leveraging existing service providers. The workgroup members also noted a number of existing resources and services that could be leveraged to create better connections between 124 Youth Homelessness

Developing a Coordinated Youth Housing Stability Program for Juvenile Courts systems (five mentions), including existing community-based expertise working with homeless youth and the court’s existing relationships with the youth shelter for diversion services: “So an option can be that when kids are enrolled in school at the detention center, school staff should identify who are homeless...for kids who have already been identified [as homeless] 

... [we] connect with services when they reach detention.” The workgroup also recommended that child welfare and detention alternatives (community-based alternatives to detention stays, largely as a response to probation violations) be included in planning as other important systems to have involved in the workgroup.

Summary

The meetings provided an opportunity for the members to share ideas and expertise about existing challenges and resources and begin to map out some potential approaches to improving system coordination. The major themes from the first meeting highlighted (1) challenges with identifying youth homelessness and risk of homelessness from within the court using existing tools and human resources, (2) the inadequacy of the existing service options for youth and families at an elevated level of need including long term housing for adolescents and intensive family-based services, and (3) the need for child welfare and other community-based justice service providers to be involved in planning.

Prevalence of Youth Homelessness

For the local data, exhibit 2, The Prevalence of Youth Housing Indicators, displays the frequencies of selected items for youth who received the PACT screen. A minority of the youth, about 10 percent, had at least one previous out of home placement in foster care, mental health treatment, or a state justice facility. The percent of youth with assessed runaway history was also relatively low compared to the total assessed group: 22 percent had at least one previous runaway episode, and 7 percent of the assessed population had more than five runaway episodes. Youth displaying consistently hostile behaviors at home, presumed to be at risk for being kicked out by parents, reached 11 percent of the assessed sample.

While the presence of these indicators was relatively low in the overall population, the number of youth with at least one of the above indicators reached 175 youth a year (not accounting for possible duplicates) when runaway history was set to at least two prior episodes. Divided by 12 

months, the court could expect about 14 referrals a month if these items were considered “flags” 

for potential housing instability or risk. Including the indicator for consistent youth hostility in the home could add another 60 youth a year, for an estimated 19-20 “flagged” youth per month from court-referred youth alone. The court also processes about 20 at risk youth (ARY) cases a year, increasing the estimated monthly referrals to 22-24 cases. The workgroup was not able to access detention data for the planning phase, but estimated another five referrals monthly from detention and diversion/non PACT screened youth. This information led the workgroup to estimate approximately 30 referrals per month for a housing coordinator to assess, triage, develop case plans, and coordinate follow up with indicated services.

Cityscape 125

Walker, Valencia, Bishop, Irons, and Gertseva

Exhibit 2

Prevalence of Youth Housing Instability Indicators in One Juvenile Court, February 2016–February 2017, n = 555

Indicator

N

%

History of out-of-home placement

No out-of-home placement exceeding 30 days

501

90.3

1 out-of-home placement

33

6.0

2 out-of-home placements

8

1.4

3 or more out-of-home placements

13

2.3

History of running away/kicked out

No history of running away or being kicked out

434

78.2

1 instance of running away/kicked out

33

6.0

2 to 3 instances of running away/kicked out

42

7.6

4 to 5 instances of running away/kicked out

9

1.6

Over 5 instances of running away/kicked out

37

6.7

Parental authority & control

Minor usually obeys and follows rules

359

64.7

Sometimes obeys or obeys some rules

136

24.5

Consistently disobeys and/or is hostile

60

10.8

As shown in exhibit 3, The Prevalence of Homelessness for Justice-Involved Youth, (n=6,791/6,866) the prevalence of any runaway history in the screening sample (n=6,791/6,866) decreased slightly from 42 

percent in 2016 to 41 percent in 2017. Items indicating current housing instability among the population of youth who scored as moderate or high risk on the screening tool and received the full assessment were also examined (n=4,307/4,296). The prevalence of current runaway status in 2016 (39 percent of the sample) was slightly lower than the prevalence of runaway history for the same year. In 2017, however, the prevalence of both indicators was equivalent among the sample (41 percent). The full assessment also includes a question about whether the youth was currently under adult supervision. The recorded number of youth who were unaccompanied by an adult was unexpectedly low given these other numbers, with only 2 percent identified in both 2016 and 2017. This item defines adult supervision as 

“someone who is responsible for the minor’s welfare, either legally or with parental consent,” and allows three response options for no adult supervision: (1) Living with peers without adult supervision; (2) living alone without adult supervision; and (3) transient without adult supervision.

Exhibit 3

Prevalence of Homelessness Risk for Justice-Involved Youth in Washington State, 2016–2017

2016

2017

Indicator

n

%

n

%

All court referred youth

6,791

6,866

History of runaway/kicked out

2,842

41.9

2,781

40.5

History of runaway/kicked out or living 

2,854

42.0

2,792

40.7

without adult supervision

Youth at moderate – high recidivism risk

4,307

4,296

Currently a runaway/kicked out

1,648

39.1

1,708

40.5

Currently without adult supervision

92

2.1

81

1.9

Note. Within group percentages are displayed. Data produced from responses to Positive Achievement Change Tool (PACT), the standard juvenile court risk assessment in Washington State.

126 Youth Homelessness

Developing a Coordinated Youth Housing Stability Program for Juvenile Courts Taken together, the prevalence data suggests one-fourth to one-half of juveniles referred to court will have at least one indicator of unstable housing, either from past or current episodes of running away or being kicked out of their home. The statewide data also suggests that the majority of these youth end up in some other situation in adult care if the assessments are being conducted accurately and represent a shared understanding of what constitutes reasonable and sustainable adult supervision.

Youth Housing Stability Program for Juvenile Courts

Data from the qualitative and quantitative analyses were reviewed along with the principles identified from the Intervention Mapping exercise and the prevention services’ literature to develop the Youth Housing Stability program model for juvenile courts as shown in exhibit 4. 

The workgroup members reviewed the major gaps and resources identified from the previous meeting and the prevalence data to develop a working model to improve identification, system coordination, and services to reduce youth homelessness through prevention and intervention services. The results model specified the need for five major components: (1) agency wide awareness training, (2) referral criteria, (3) housing coordination, (4) prevention services, and (5) housing services.

Exhibit 4

Components, Objectives, and Content for a Youth Housing Stability Program for Juvenile Courts Component

Target Population

Objective

Content

Awareness 

All court divisions (diversion,  Engage court staff in 

Definitions of youth 

Training

probation, ARY, detention)

supporting a new direction 

homelessness

in practice

Overview of existing services

Set expectations about 

Signs and risk for 

referring youth based on 

homelessness

routine data flags

Existing screening items 

Educate staff about 

requiring referral

common signs of 

housing instability for 

discretionary referrals

Referral 

All court divisions (diversion,  Identify youth across 

PACT prescreen items:  

probation, ARY, detention)

the continuum of court 

2 or more runaway 

involvement

episodes; any out-of-home 

Create court wide 

placement; highest level of 

expectations for  

hostility at home.

referring youth

At-Risk-Youth (ARY):  

All petitions

Detention: 2 or more runaway 

episodes; all domestic 

violence assault holds; 

current McKinney Vento.

Cityscape 127

Walker, Valencia, Bishop, Irons, and Gertseva

Exhibit 4

Components, Objectives, and Content for a Youth Housing Stability Program for Juvenile Courts Component

Target Population

Objective

Content

Housing Stability 

All court referred youth  

Centralized coordination  

Conducts agency wide 

Coordinator

and families

of services

awareness trainings

Brings expertise on housing  Follows up on court referrals 

and family-based prevention  to conduct a housing 

to court operations

stability assessment

Works flexibly with court staff  Develops case plans

to support housing as one 

Monitors case plans through 

component of a case plan

completion of services 

(for prevention) or after 

confirming contact with 

community-based case 

management (for unstably 

housed youth)

Prevention 

Youth assessed as low to 

Provide a continuum of care  Low need: Brief family 

Services

high risk for instability but 

for families based on need

support through telehealth, 

currently housed under 

Save costs and time with a 

phone coaching, education 

adult supervision in a family  stepped care model

and information about 

that is currently housed

community resources.

Build resiliency in youth 

and families to promote 

Moderate need: Selective 

youth development

family-based prevention 

services, 5-7 weeks of 

curriculum, practice and 

coaching.

High need: In home support 

using intensive family 

intervention, for example, 

Wraparound, family systems 

therapy models.

Housing 

Youth unhoused at  

Provide youth with 

Court Housing Coordinator 

Intervention

the time of assessment

immediate shelter

refers to existing community 

Plan for long term housing

case management to support 

long term housing stability.

Build youth resiliency  

and life skills

Training. The workgroup identified agency wide training as a needed component of the model in order to address the challenge of inconsistent awareness and perceived responsibility to address homelessness among current court and probation staff. As identified by the workgroup, the training would need to be offered to all probation and diversion staff and focus on flags for homelessness not available in the existing assessment tools, the benefits of addressing homelessness for reducing youth recidivism, and any new procedures the court adopts to assist with system coordination.

Data flags using routine data. Given the challenges of instituting an entirely new screening tool on top of existing paperwork and responsibilities, the workgroup focused on how to use existing indicators to flag youth for referral to a central coordinator for further assessment. The workgroup identified the indicators on the prescreen assessment as noted above, as well as indicators from detention (McKinney Vento data), the ARY non-criminal court, and for youth with warrants for failing to appear in court. Court-referred youth included all youth with two or more instances 128 Youth Homelessness

Developing a Coordinated Youth Housing Stability Program for Juvenile Courts of running away, current or past foster care status, and the highest score possible (3) on an item measuring levels of home conflict. For ARY youth, the workgroup recommended that all be referred to the program for assessment. For detained youth, all youth with an active McKinney Vento indicator, all youth detained for an assault, and all youth with more than one runaway episode would be referred. Because of various screening practices for youth on diversion, the recommendations varied. For diverted youth receiving the PACT screen, the same indicators would apply as for youth referred to court. For youth not receiving the PACT screen, the diversion staff would be trained on common indicators of family stress and housing risk to facilitate referrals to a housing coordinator. Combined, these various indicators would likely identify 15-20 youth a month who could be referred to a housing coordinator for follow up consideration.

System coordinator. The workgroup felt a dedicated job position was necessary to avoid underserving youth who could benefit from further assessment if the responsibility to provide comprehensive housing and services coordination otherwise fell to the probation counselors. 

Further, this dedicated position would ensure that referrals would not be limited to only youth on probation and eligibility could be opened up as needed. The workgroup also felt that the coordinator should come from a community agency rather than the court so that the youth could continue to have contact with the individual past the point of justice contact, if necessary. The coordinator’s job would be to locate youth referred by court staff, conduct a housing assessment, and develop a support plan that includes leveraging available resources and services to keep youth in the most stable, home-like situation available. Potential possibilities could include connecting the family with effective family support services, coordinating short and long-term housing, providing or arranging for transport, coordinating with schools to preserve enrollment, and advocating for the youth in relevant social service systems. The coordination would prioritize transitioning the youth and family to longer term case management services and would not be expected to last more than two to three months per case.

Community services.  The workgroup identified a number of community services already in place that were providing supportive services to families. For the model, the workgroup proposed additional family-based services to assist with high levels of family conflict and “host homes” as a potential solution to the problem of housing 13- to 16-year-olds long term. Host homes are an emerging innovation for housing youth who are not involved in the dependency system but are no longer residing with their family of origin. Volunteers from the community offer to host youth for a period of time, and may or may not receive a stipend depending on the arrangement. To date, the contractual agreements for these homes are managed directly through counties or funding agencies. 

In Washington State, host homes are not licensed or regulated. The workgroup also identified the need to obtain more information about research on host homes’ safety and effectiveness.

Finally, the workgroup discussed the need for family-based services to prevent youth from being kicked out or running away when reunification or prevention was an option. The group discussed needing to “right-size” the family program to the level of the family’s need in order to address the original concern that some families need more services than are currently provided or offered. 

The program model, therefore, aimed to build a feasible system level intervention for coordinating communication and referral across service systems while articulating the program principles necessary for effectively preventing and intervening to improve youth housing stability.

Cityscape 129

Walker, Valencia, Bishop, Irons, and Gertseva

Consequently, the workgroup proposed a “stepped care” model of intervention. In this model, youth are assessed and triaged into one of five paths: no need, low need, moderate need, high need, or currently unhoused. Each path specifies a set of appropriate services given the level of need and theory-driven approaches to reduce risk and support long term housing stability and youth development. These services include, at the low need level, brief family stabilizing interventions including information about community resources and parent phone coaching. At the moderate level of need, families would be referred to in-person group sessions based on evidence-based principles of family-based prevention science. These models (for example, Strengthening Families, Guiding Good Choices) build communication skills and positive relationships between parents and adolescents. 

At the high level of need, families would be referred to more intensive in-home supports including Functional Family Therapy (Sexton and Turner, 2011) or Wraparound services (Bruns et al., 2010). 

At each level of care, families would be assessed for whether more intervention services were needed, with families moving up the hierarchy of intensity as indicated.

Summary

The Community-Based Participatory Research (CBPR) approach adopted by the team resulted in a model that is expected to be feasible to implement and meets the needs of the court and youth serving agencies to effectively meet the housing needs of youth. A number of important findings emerged from the juvenile court workgroup process. The initial meeting identified the court’s current challenges with accurately identifying youth who have housing instability risks and highlighted the tension involved in proposing a new area of focus for existing court staff and probation roles. As capacity already existed in the community to work with homeless youth, the workgroup identified system coordination as a key feature of improving outcomes for justice-involved youth, along with some enhancements for community services to (1) increase the geographical spread of family-based supports, (2) provide more intensive family-based services when indicated, and (3) provide long term housing for younger adolescents when reunification was not possible. Data from local and state court-based assessments showed that current or past housing instability is a concern for 25-50 percent of the justice-referred population. A key finding from the workgroup process was that adequately addressing youth housing was not something that could be accomplished with existing court resources. It will require a new position dedicated to managing referrals, assessment, triage, and light case management, as well as the funding or connection to existing community resources based on effective principles of family-based prevention, intervention and housing services.

Discussion

This study adds to the literature by reporting the prevalence of housing instability within a justice-involved population, highlighting the tensions involved in court and probation roles for addressing the complex needs of youth, identifying a data-informed service coordination model grounded by practice-based expertise, and providing a program review of existing family-based programs designed to improve family communication and promote youth well-being.

While studies of homeless youth show high rates of justice contact, no studies have yet examined the prevalence of homelessness within a justice-involved youth population. Data presented in this 130 Youth Homelessness

Developing a Coordinated Youth Housing Stability Program for Juvenile Courts report supports the assumptions made in other reports that housing instability is a prevalent issue for justice-involved youth (Pilnik et al., 2017). A little over 20 percent of the local sample and 40-60 percent of the state sample had previous running away or being kicked out episodes. Somewhat surprisingly, the incidence of being housed without any adult supervision was very low (2 percent) for the youth receiving the full PACT assessment. The low number requires some scrutiny and may reflect interpretations of the item (for example, youth in detention may be counted as under adult supervision) or reporting issues (for example, youth may inaccurately report they are in a shelter or with an adult) and should be viewed as information needing additional exploration.

The workgroup process highlighted the challenges facing multiple areas of justice reform as existing staff are continuously pushed to incorporate more holistic and developmentally-friendly approaches into their work (Mulvey, 2014). As a profession, probation began as an alternative to incarceration and existed outside of the court system (Matthews and Hubbard, 2007). In its formative years, probation was akin to supportive case management and mentoring. As the effectiveness of this approach became apparent for cost savings and outcomes, courts began to adopt the model in-house, contingent on the probationers’ compliance with the court orders. This approach had the benefit of vastly expanding the use of probation as an alternative to incarceration but also, by bringing the model under the supervision of the court, shifted the emphasis away from supportive case management to compliance-focused supervision. Consequently, probation officers in adult and juvenile contexts can variously see themselves as carrying out orders from the court and/or conducting case management services. Even under the case management model, courts that adopt a service-oriented framework are advised to invest in services that will reduce the risk of youth reoffending with a heavy focus on individual capacities, such as problem-solving, anger-management, substance use, and family-focused interventions. Court services are not intended to address all areas of youth medical, physical and housing needs due to concerns about funding resources, cost-benefit, and widening the net of justice involvement in cases where service access is only possible after a youth is charged with an offense (Nadel et al., 2018).

Cross-system coordination is a key feature of working effectively with youth who are justice-involved as many youth have current involvement in at least one other social system (such as foster care, mental health; Farineau, 2016). Effective and promising strategies include models of team-based coordination such as multidisciplinary teams (Arciaga, 2007; Arredondo et al. 2001; Hochstadt and Harwicke, 1985) and Wraparound services (Howell et al., 2004; Maschi et al., 2008; Pullmann et al., 2006), ecological interventions facilitated by a highly trained coach therapist such as MultiSystemic Therapy (Timmons-Mitchell et al., 2006; Schaeffer and Borduin, 2005), and navigator services such as Parent for Parent (Law et al., 2001; Singer et al., 1999). The selection of a particular model to improve service coordination for a particular outcome, in this case homelessness, should be guided by the proposed benefits of bringing multiple professional partners together versus the efforts and costs of doing so, the scope of the coordination (short or long term), and the scope of the services provided (comprehensive vs. focused). In the present case, the workgroup selected a navigator model to help youth and families bridge the different service systems because the county already supports Intensive case management for youth homelessness and Wraparound services for youth with intensive mental health needs. The county identified their local need was to be more consistent in connecting to these services from the juvenile court.

Cityscape 131

Walker, Valencia, Bishop, Irons, and Gertseva

Finally, interventions focused on homeless youth are rarely evaluated on their ability to reduce days of homelessness (Altena, Brilleslijper-Kater, and Wolf, 2010). For example, there are no family-based interventions that have outcomes relating to improved youth housing stability, specifically, even for programs targeting runaway youth (Altena, Brilleslijper-Kater, and Wolf, 2010; Rice et al., 2012; Slesnick and Prestopnik, 2005). As noted by Slesnick and Prestopnik (2005), researchers have focused on this population as an intervention group at increased risk of poor health outcomes (for example, HIV and/or substance abuse) and have largely focused on understanding the impacts on health outcomes rather than homelessness. Many family-based programs have demonstrated effectiveness in improving family functioning, reducing conflict, and improving youth well-being, which suggests these benefits would likely extend to increased housing stability, particularly in preventing a homelessness event. Family-based programs, however, need more examination to assess their full effectiveness. In the current study, the county workgroup identified available family-based resources to support the prevention of youth homelessness. At the same time, all of the services were locally developed and their consistency with research-based models of intervention or independent effectiveness for supporting improved family functioning and youth well-being are not yet known.

Conclusions

The present study examines the process of developing a juvenile-court based model for addressing youth homelessness. As a result of this process, the court found housing instability events, such as runaway episodes or out-of-home placements, were fairly common, but no consistent processes were already in place to address these issues. A key finding was concern about using existing court staff, such as probation counselors, to act as system navigators for youth who needed support with basic needs, such as housing. The workgroup did, however, recommend agency wide awareness training to support identification and referral to a housing coordinator. Next steps will include implementing and evaluating the model for expected improvements in identification, service referral, the prevention of homelessness events, and securing long term housing.

Acknowledgments

This work is being supported by the Raikes Foundation, the Block-Levitt Foundation, the Office of Youth Homelessness Prevention and Protection, and the Washington State Department of Commerce. 

We are grateful for the support of Carl McCurley at the Washington State Center for Court Research for his involvement in the project as well as the members of the community workgroup.

Authors

Sarah Cusworth Walker, Ph.D., is a Research Associate Professor in the Department of Psychiatry and Behavioral Sciences at the University of Washington School of Medicine, 2815 Eastlake Ave E 

Ste. 200, Seattle, WA 98115, and can be emailed at secwalkr@uw.edu.

132 Youth Homelessness

Developing a Coordinated Youth Housing Stability Program for Juvenile Courts Esteban Valencia, BS, is a Research Analyst in the Department of Psychiatry and Behavioral Sciences at the University of Washington School of Medicine.

Asia Bishop, MSW, is a Predoctoral Research Associate at the School of Social Work and a Research Analyst Lead in the Department of Psychiatry and Behavioral Sciences at the University of Washington.

Michael Irons, MA, is the Program Manager at the Snohomish County Juvenile Court.

Arina Gertseva, Ph.D., is the Senior Research Associate at the Washington State Center for Court Research.

References

Administration on Children Youth and Families. 2016.  Administration for Children and Families, Family and Youth Services Bureau: Street Outreach Program. Washington, D.C.: Administration on Children, Youth and Families.

Altena, Astrid M., Sonja N. Brilleslijper-Kater, and Judith R. L. M. Wolf. 2010. “Effective Interventions for Homeless Youth: A Systematic Review,”  American Journal of Preventative Medicine 38 (6): 637–645.

Arciaga, Michelle. 2007. “Multidisciplinary Gang Intervention Teams,”  National Youth Gang Center Bulletin 3: 1–7.

Arredondo, David E., Kurt Kumli, Larry Soto, Enrique Colin, Jill Ornellas, Judge Raymond J. 

Davilla, Judge Leonard P. Edwards, and Judge Eugene M. Hyman. 2001. “Juvenile Mental Health Court: Rationale and Protocols,”  Juvenile and Family Court Journal 52 (4): 1–20.

Bartholomew, L. K., G. S. Parcel, and Gerjo Kok. 1998. “Intervention mapping: A Process for Developing Theory- and Evidence-based Health Education Programs,”  Health Education & Behavior 25 (5): 545–563.

Bender, Kimberly A., Sanna J. Thompson, Kristin M. Ferguson, Jamie R. Yoder, and Leah Kern. 

2014. “Trauma Among Street-Involved Youth,”  Journal of Emotional and Behavioral Disorders 22 (1): 53–64.

Bess, K. D., I. Prilleltensky, D. D. Perkins, and L. V. Collins. 2009. “Participatory Organizational Change in Community-Based Health and Human Services: From Tokenism to Political Economy,” 

American Journal of Community Psychology 43 (1-2): 134–148.

Britton, Linda, and Lisa Pilnik. 2018. “Preventing Homelessness for System-Involved Youth,” 

Juvenile and Family Court Journal 69 (1): 19–33.

Cityscape 133

Walker, Valencia, Bishop, Irons, and Gertseva

Bruns, Eric J., Janet S. Walker, Michelle Zabel, Marlene Matarese, Kimberly Estep, Deborah Harburger, Madge Mosby, and Sheila A. Pires. 2010. “Intervening in the Lives of Youth with Complex Behavioral Health Challenges and their Families: The Role of the Wraparound Process,” 

American Journal of Community Psychology 46 (3-4): 314–331.

Chen, Xiaojin, Lisa Thrane, Les B. Whitbeck, and Kurt Johnson. 2006. “Mental Disorders, Comorbidity, and Postrunaway Arrests among Homeless and Runaway Adolescents,”  Journal of Research on Adolescence 16 (3): 379–402.

Columbia Legal Services. 2015.  Falling through the Gaps: How a Stay in Detention Can Lead to Youth Homelessness. Seattle, WA: Columbia Legal Services.

Fantuzzo, John W., Staci M. Perlman, and Erica K. Dobbins. 2011. “Types and Timing of Child Maltreatment and Early School Success: A Population-Based Investigation,”  Children and Youth Services Review 33 (8): 1404–1411.

Farineau, H. M. 2016. “An Ecological Approach to Understanding Delinquency of Youths in Foster Care,”  Deviant Behavior 37 (2): 139–150.

Ferguson, Kristin, Kimberly Bender, Sanna Thompson, Elaine Maccio, Bin Xie, and David Pollio. 

2011. “Social Control Correlates of Arrest Behavior among Homeless Youth in Five U.S. Cities,” 

Violence and Victims 26 (5): 648–668.

Fernandes-Alcantara, A. L. 2016.  Runaway and Homeless Youth: Demographics and Programs. 

Washington, D.C.: Congressional Research Services.

Gilman, Amanda. 2016. “Juvenile Incarceration in Washington State” [PowerPoint Presentation], Olympia, WA: Washington State Center for Court Research.

Hamilton, Z., J. van Wormer, and R. Barnoski. 2015. “PACT Validation and Weighting Results: Technical Report,” Olympia, WA: Washington Association of Juvenile Court Administration.

Hawkins, J., and Joseph Weis. 1985. “The Social Development Model: An Integrated Approach to Delinquency Prevention,”  Journal of Primary Prevention 6 (2): 73–97.

Hochstadt, Neil J., and Neil J. Harwicke. 1985. “How Effective Is the Multidisciplinary Approach? 

A Follow-Up Study,”  Child Abuse & Neglect 9: 365–372.

Howell, James C., Marion R. Kelly, James Palmer, and Ronald L. Mangum. 2004. “Integrating Child Welfare, Juvenile Justice, and Other Agencies in a Continuum of Services,”  Child Welfare 83 (2): 143–156.

Hsieh, Hsiu-Fang, and Sarah Shannon. 2005. “Three Approaches to Qualitative Content Analysis,” 

Qualitative Health Research 15 (9): 1277–1288.

Israel, Barbara A., Amy J. Schulz, Edith A. Parker, and Adam B. Becker. 1988. “Review of Community-Based Research: Assessing Partnership Approaches to Improve Public Health,”  Annual Review of Public Health 19: 173-202.

134 Youth Homelessness

Developing a Coordinated Youth Housing Stability Program for Juvenile Courts Kaufman, Jeanne G., and Cathy Spatz Widom. 1999. “Childhood Victimization, Running Away, and Delinquency,”  Journal of Research in Crime and Delinquency 36 (4): 347–370.

Kipke, M. D., T. R. Simon, S. B. Montgomery, J. B. Unger, and E. F. Iversen. 1997. “Homeless Youth and Their Exposure to and Involvement in Violence while Living on the Streets,”  Journal of Adolescent Health 20 (5): 360–367.

Law, Mary, Susanne King, Debra Stewart, and Gillian King. 2001. “The Perceived Effects of ParentLed Support Groups for Parents of Children with Disabilities,”  Physical & Occupational Therapy in Pediatrics 21 (2-3): 29–48.

Maschi, Tina, Schnavia Smith Hatcher, Craig S. Schwalbe, and Nancy Scotto Rosato. 2008. 

“Mapping the Social Service Pathways of Youth to and through the Juvenile Justice System: A Comprehensive Review,”  Children and Youth Services Review 30: 1376–1385.

Matthews, Betsy, and Dana Hubbard. 2007. “The Helping Alliance in Juvenile Probation: The Missing Element in the ‘What Works’ Literature,”  Journal of Offender Rehabilitation 45: 105–122.

McCarthy, Bill, and John Hagan. 2005. “Danger and the Decision to Offend,”  Social Forces 83 (3): 1065–1096.

Mears, Daniel P., and Jeremy Travis. 2004. “Youth Development and Reentry,”  Youth Violence and Juvenile Justice 2 (1): 3–20.

Milburn, Norweeta G., Francisco Javier Iribarren, Eric Rice, Marguerita Lightfoot, Rosa Solorio, Mary Jane Rotheram-Borus, Katherine Desmond, Alex Lee, Kwame Alexander, Katherine Maresca, Karen Eastmen, Elizabeth Mayfield Arnold, and Naihua Duan. 2012. “A Family Intervention to Reduce Sexual Risk Behavior, Substance Use, and Delinquency among Newly Homeless Youth,” 

Journal of Adolescent Health 50 (4): 358–364.

Miles, Matthew B., A. M. Huberman, and Johnny Saldaña. 2014.  Qualitative Data Analysis: A Methods Sourcebook. 3rd ed. Thousand Oaks, CA: SAGE.

Morton, M. H., A. Dworsky, and G. M. Samuels. 2017.  Missed Opportunities: Youth Homelessness in America. National Estimates.  Chicago, IL: Chapin Hall at the University of Chicago.

Mulvey, E. P. 2014. “Using Developmental Science to Reorient Our Thinking About Criminal Offending in Adolescence,”  Journal of Research in Crime and Delinquency 51 (4): 467–479.

Nadel, Melissa R., George Pesta, Thomas Blomberg, William D. Bales, and Mark Greenwald. 2018. 

“Civil citation: Diversion or Widening the Net?”  Journal of Research in Crime and Delinquency 55 (2): 278–315.

Pilnik, L., D. Bardine, L. Furr, M. Maury, M. Sickmund, N. Smoot, and J. Szanyi. 2017.  Addressing the Intersections of Juvenile Justice Involvement and Youth Homelessness: Principles for Change. 

Washington, D.C.: Coalition for Juvenile Justice.

Cityscape 135

Walker, Valencia, Bishop, Irons, and Gertseva

Pullmann, Michael D., Jodi Kerbs, Nancy Koroloff, Ernie Veach-White, Rita Gaylor, and Dede Sieler. 2006. “Juvenile Offenders with Mental Health Needs: Reducing Recidivism Using 

Wraparound,”  Crime & Delinquency 52 (3): 375–397.

Quirouette, Marianne, Tyler Frederick, Jean Hughes, Jeff Karabanow, and Sean Kidd. 2016. 

“‘Conflict with the Law’: Regulation & Homeless Youth Trajectories toward Stability,”  Canadian Journal of Law and Society 31 (3): 383–404.

Rice, Eric, Eve Tulbert, Julie Cederbaum, Anamika Barman Adhikari, and Norweeta G. Milburn. 

2012. “Mobilizing Homeless Youth for HIV Prevention: A Social Network Analysis of the 

Acceptability of a Face-to-Face and Online Social Networking Intervention,”  Health Education Research, 27 (2): 226–236.

Ringwalt, Christopher L., Jody M. Greene, and Marjorie J. Robertson. 1998. “Familial Backgrounds and Risk Behaviors of Youth with Thrownaway Experiences,”  Journal of Adolescence 21 (3): 241–252.

Rotheram-Borus, M. J., M. Rosario, and C. Koopman. 1991. “Minority Youths at High Risk: Gay Males and Runaways,”  Adolescent stress: Causes and consequences: 181–200.

Schaeffer, Cindy M., and Charles M. Borduin. 2005. “Long-Term Follow-Up to a Randomized Clinical Trial of Multisystemic Therapy with Serious and Violent Juvenile Offenders,”  Journal of Consulting and Clinical Psychology 73 (3): 445–453.

Sexton, Thomas, and Charles W. Turner. 2011. “The Effectiveness of Functional Family Therapy for Youth with Behavioral Problems in a Community Practice Setting,”  Couple and Family Psychology: Research and Practice 1 (S): 3–15.

Shah, Melissa Ford, Qinghua Liu, J. Mark Eddy, Susan Barkan, David Marshall, David Mancuso, Barbara Lucenko, and Alice Huber. 2017. “Predicting Homelessness among Emerging Adults Aging Out of Foster Care,”  American Journal of Community Psychology 60 (1-2): 33–43.

Singer, George H., Janet Marquis, Laurie K. Powers, Lynn Blanchard, Nancy Divenere, Betsy Santelli, Judith G. Ainbinder, and Michael Sharp. 1999. “A Multi-Site Evaluation of Parent to Parent Programs for Parents of Children with Disabilities,”  Journal of Early Intervention, 22 (3): 217–229.

Slesnick, Natasha, and Jillian L. Prestopnik. 2005. “Ecologically Based Family Therapy Outcome with Substance Abuse Running Away Adolescents,”  Journal of Adolescence 28 (2): 277–298.

Snyder, Howard N. 2004. “An Empirical Portrait of the Youth Reentry Population,”  Youth Violence and Juvenile Justice 2 (1): 39–55.

Stein, Judith A., Norweeta G. Milburn, Jazmin I. Zane, and Mary Jane Rotheram-Borus. 2009. 

“Paternal and Maternal Influences on Problem Behaviors among Homeless and Runaway Youth,” 

American Journal of Orthopsychiatry 79 (1): 39–50.

Sullivan, Patricia M., and John F. Knutson. 2000. “Maltreatment and Disabilities: A Population- 

based Epidemiological Study,”  Child Abuse & Neglect: The International Journal 24 (10): 1257–1273.

136 Youth Homelessness

Developing a Coordinated Youth Housing Stability Program for Juvenile Courts Tam, C. C., B. Freisthler, S. R. Curry, and L. S. Abrams. 2016. “Where Are the Beds? Housing Locations for Transition Age Youth Exiting Public Systems,”  Families in Society 97 (2): 111–119.

Timmons-Mitchell, Jane, Monica B. Bender, Maureen A. Kishna, and Clare C. Mitchell. 2006. “An Independent Effectiveness Trial of Multisystemic Therapy with Juvenile Justice Youth,”  Journal of Clinical Child & Adolescent Psychology 35 (2): 227–236.

Tolan, Patrick H., Nancy G. Guerra, and Phillip C. Kendall. 1995. “A Developmental-Ecological Perspective on Antisocial Behavior in Children and Adolescents: Toward a Unified Risk and Intervention Framework,”  Journal of Consulting and Clinical Psychology 63 (4): 579–584.

Washington State Office of Financial Management, Forecasting Division. 2017. “Small Area Demographic Estimates by Age, Sex, Race and Hispanic Origin: County-Level Special Age Groups” 

[version 20171222_R04_VM]. Olympia, WA: Office of Financial Management, Forecasting Division.

Yoder, Jamie Rae, Kimberly Bender, Sanna J. Thompson, Kristin M. Ferguson, and Badiah Haffejee. 

2014. “Explaining Homeless Youths’ Criminal Justice Interactions: Childhood Trauma or Surviving Life on the Streets?”  Community Mental Health Journal 50 (2): 135–144.

Cityscape 137

138 Youth Homelessness

Reflections from Canada: Can 

Research Contribute to Better 

Responses to Youth Homelessness?

Stephen Gaetz

York University, Toronto, Canada

by Stephen Gaetz

Abstract

As with the United States, youth homelessness in Canada is a seemingly intractable problem; in the past, Canada has mainly looked to the United States for how to address the system. Moreover, the Canadian response has not been robustly driven by research and evidence. In the last few years, much has changed in terms of how we are responding to youth homelessness in Canada in policy and practice. This change includes an increase in the influence, uptake and impact of research. In this commentary, key issues shaping the national dialogue on youth homelessness in Canada are discussed. The research articles from this volume are used to illustrate and highlight some of the key challenges associated with these key issue areas, to point to where research can have an impact, and to identify where some clear gaps in knowledge exist. More opportunities to increase international collaboration on youth homelessness research stands to enhance the influence of research on solutions to homelessness.

Historically, Canada has in many ways taken its lead from the United States on how to address the problem of homelessness. On the positive side, this has included the broad adoption of Housing First, community systems planning, Homelessness Management Information Systems (HMIS) 

data management, and coordinated entry, for instance. However, it has also meant that we have almost completely ignored prevention, that we have allowed the “politics of scarcity” to shape how we think about prioritization, outcomes, and performance indicators, and in some ways have considered a response to youth homelessness as an afterthought—something we can deal with more seriously once we have made much more progress on addressing chronic homelessness (particularly amongst adults). At the level of policy and practice in both countries, I would argue that historically very little of what we do regarding youth homelessness is adequately informed by research evidence, in spite of claims otherwise.

Cityscape: A Journal of Policy Development and Research •  Volume 20, Number 3 • 2018

Cityscape 139

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Gaetz

The last 5 years have seen some important shifts in how we think about and respond to youth homelessness in Canada. First, broader international engagement—in particular with Australia and Europe (through FEANTSA and the European Observatory on Homelessness)—has greatly 

expanded our thinking about the nature of the problem and what to do about it. Second, higher orders of government (federal, provincial, and territorial) have begun to take youth homelessness seriously. For instance in the new Canadian Federal strategy, Reaching Home, communities are expected to incorporate a strategy on youth homelessness in systems plans, and prevention is being prioritized. Third, the arrival of A Way Home Canada, a national coalition to prevent and end youth homelessness (which has since inspired the creation of A Way Home America as well as similar movements in Scotland, Belgium, and many individual communities and states), has had a huge impact on policy, planning, and practice, in helping encourage a shift from a crisis response to youth homelessness, to one that focuses on prevention and sustainable exits.

Finally, there is research. In Canada, research has advanced our understanding of youth homelessness, contributed to conceptual shifts on how to respond to the problem, and increased our understanding of what works, for whom and in what contexts. While all of these shifts have not yet resulted in the broader systems transformation we are looking for, at least they are helping us point in the right direction.

Research can and should have an impact on how we think about and respond to homelessness. A key challenge that impedes creating real solutions to youth homelessness is that, while we know much about its causes and conditions, we know much less about how to prevent it, and how to produce better outcomes for youth who have experienced homelessness. The different papers in this volume are helpful contributions to our knowledge and illuminate many of the issues for which we need to increase our understanding in order to inform better policy and practice, leading to more positive outcomes for young people. In addition, issues raised in these articles speak to many challenges and concerns we have in Canada regarding how we are, and how we should be, responding to youth homelessness. In the following are some of these key issues.

Prevention

Although the language of prevention is often used in Canada and the United States to discuss responses to homelessness, little evidence supports the idea that we are actually doing much to prevent the problem. In 2017, the Canadian Observatory on Homelessness released  A New Direction: A Framework for Homelessness Prevention (Gaetz and Dej, 2017) providing a definition and typology. The prevention of  youth homelessness refers to legislation, policies, interventions, and practices that reduce the likelihood that an unattached young person between the ages of 13 

through 24 will experience homelessness. Moreover, it means “providing those who have been homeless with the necessary resources and supports to stabilize their housing, enhance integration and social inclusion, and ultimately reduce the risk of the recurrence of homelessness” (Gaetz and Dej, 2017: 35). The shift to prevention is supported by an emerging body of literature from Canadian (City of Toronto, 2016; Distasio et al., 2014; Forchuk et al., 2008) and international (Busch-Geertsema and Fitzpatrick, 2008; Mackie, 2015; Mackie, Thomas, and Bibbings, 2017; Maher and Allen, 2014; Pleace and Culhane, 2016; Shinn et al., 2013) sources that demonstrate 140 Youth Homelessness

Reflections from Canada: Can Research Contribute to Better Responses to Youth Homelessness?

that prevention strategies have a positive impact on reducing homelessness. However, a paucity of research demonstrates effective policy and program interventions that address youth homelessness specifically, outside of Australia (MacKenzie, 2018; MacKenzie and Thielking, 2013; Australian Government, 2013).

We have argued that the prevention of youth homelessness should be a priority in Canada. The first national survey on youth homelessness found that 42 percent had their first experience of homelessness prior to the age of 16 and that this cohort typically experienced multiple episodes and worse health and mental health outcomes (Gaetz et al., 2016). In Canada we don’t really do anything to help young teens who experience homelessness, and they are largely invisible to the homelessness serving system (which typically does not provide support until a person is 16 or sometimes 18 years old). This is a serious flaw in policy and practice in Canada, meaning that in practical terms we are waiting too long to provide young people with assistance. In a very real sense, we are waiting until such young people age, become more ill, and experience more trauma before we deem them worthy of support. We need to fix this problem.

Two articles in this volume speak to prevention by pointing to the need to provide better supports for young people leaving public systems. Dworsky, Wulczyn, and Huang (2018) have conducted research on young people who run away from “out-of-home care,” and identified that personal characteristics such as gender, race/ethnicity and age, as well as contextual factors such as placement type and community context (population density and poverty) are predictors of running away. They suggest that with this knowledge, effective screening would enable targeting of supports for those at risk of running away. It could be argued that such targeting could also inform better transitional planning and supports for those who age out of care. Walker et al. 

present their findings on an evaluation of a research practice partnership in the development of a Coordinated Youth Housing Stability Program. Their analysis of state-wide court data found that across jurisdictions, 20 to 50 percent of all youth in juvenile court had at least one prior episode of homelessness. Their qualitative analysis pointed to the “tensions around the role of probation in addressing youth homelessness, the need for better methods of identification, and a lack of intensive family-based services targeted at preventing housing instability” (Walker et al., 2018). Both studies point to the need for other systems and institutions to play a larger role in the prevention of youth homelessness.

Addressing the Needs of Key Sub-Populations

The diversity of youth experiencing homelessness and the intersectionality of different forms of exclusion are clearly important to consider within any strategy to address youth homelessness (Gaetz et al., 2016; Abramovich and Shelton, 2018). In our national survey on youth homelessness, we identified that Indigenous youth, who make up less than 5 percent of the Canadian population, make up almost a third (30.6 percent) of the population of youth experiencing homelessness (Gaetz et al., 2017). Moreover, LGBTQ youth are also overrepresented, accounting for 30 percent of the youth homelessness population, a figure that is consistent with what Rice et al. report in this volume. Family rejection, inadequate social services, institutional erasure, homophobic and transphobic violence, and discrimination in shelters and Cityscape 141

Gaetz

housing programs make it difficult for LGBTQ youth to secure safe and affirming places to live (Abramovich, 2016; Abramovich and Shelton, 2018). In their analysis of LGBTQ youth who experience homelessness, Shelton et al., found they most commonly experienced homelessness because: “they were kicked out/asked to leave the home of their parents, relatives, foster or group homes; this was even more common among transgender (young adults).” Their study identified some interesting intersectional differences in experiences and supports based on race/ethnicity and experience of poverty, and they suggest the need for more research to disaggregate the differential experiences (and needs) of subpopulations of LGBTQ youth. Given that homophobia and transphobia are not only causes of youth homelessness, but also frame the experience of being homeless and accessing supports, it is imperative that, from a policy, program, practice, and training perspective, we address discrimination in a proactive way.

Related to this, Samuels, et al., in their study of how and why unstably housed youth access support included a discussion of the experiences of LGBTQ youth. Their fascinating study identified a number of factors associated with the use and rejection of both formal and informal resources—including the experience of discrimination, level of trust and resilience—that had an impact on accessing services. Understanding service avoidance is an important area of research, given the underlying assumptions we often make regarding the reliability of Point-in-Time counts and By-Name lists (which track who is homeless and accessing services in a community) to adequately capture and reflect the extent of homeless when we do not clearly have a handle on who avoids “touching the system” to access services. As Rice et al. point out, “many youth experience homelessness who do not come into contact with the Continuum of Care.”

The Politics of Scarcity and the Need to Revisit What 

Outcomes We Are Looking For

The finite amount of resources available to the sector has a profound impact on how we think about, discuss, and make decisions regarding prioritization, who gets what support, and what outcomes the sector is responsible for. This is what we refer to as “The Politics of Scarcity” and considerable implications result from it. Vulnerability is often constructed purely in terms of chronicity and medical risk factors as opposed to other important factors relevant to youth homelessness, such as risk of sexual exploitation, trafficking, gang violence, and so on. It means that important policy and planning directions such as the prevention of youth homelessness are avoided, the argument sometimes made that first we must house all those who experience chronic homelessness, without a solid understanding of how and why people transition to that status. 

It means resistance to expanding the definition of youth homelessness to include the hidden homelessness, without understanding the complex pathways youth experience on their way to the streets. It means that many of our prioritization methods (and assessment tools) unwittingly commit low acuity youth to an expectation that they bootstrap themselves out of homelessness with the unintended consequence that many young people have to wait until things get really bad before we help them.

This leads to the question, from a policy, practice, and research perspective, regarding exactly what outcomes we are looking for when young people exit homelessness either from their own volition, 142 Youth Homelessness

Reflections from Canada: Can Research Contribute to Better Responses to Youth Homelessness?

or with necessary supports. Again, research needs to challenge our underlying assumptions. In Canada and the United States, the key performance indicator we tend to look to is simply whether someone is stably housed (or not) when they exit homelessness. The question we need to explore when analyzing the work is what exactly stability means, and whether these outcomes are sufficient.

Rice et al., in a thoughtful analysis of data from TAY-VI-SPDAT, discuss outcomes for low acuity youth, many of whom are described as returning home or self-resolving their housing situation. 

I question whether we really do have a good understanding of what happens in these cases. How many young people will use a romantic relationship to exit homelessness, even if it is unsafe and unhealthy, because the other option is worse? How many die, given what we know about high rates of suicidality for youth who experience homelessness? How many move to other jurisdictions? 

How many simply go underground, because their experience of services and supports was not deemed helpful? We definitely need more research in this area to better inform whether and how we support young people new to the streets. Given that 27 percent of low scoring youth did not exit homelessness, we need to consider how to better assess the needs of this group. Rice et al.’s conclusion that offering Rapid Rehousing support and/or more attention to nurturing family and natural supports for this group is a good idea; expecting young people to bootstrap themselves out of homelessness is not.

What do we know about the outcomes for youth who have longer experiences of homelessness who are now housed? Henwood et al. explore the outcomes of the implementation of supportive housing for youth. They report very positive findings for young people who receive Permanent Supportive Housing, including ontological security, improved mental health and well-being, and positive identity development. This is an important finding given that we actually know very little about what housing stability actually  means when young people exit homelessness, whether by their own volition or with some form of support. Almost no research has been done on health, well-being, and inclusion outcomes for young people receiving rapid rehousing, for instance. In contrast to what Henwood et al. found, much of the research that does exist on the outcomes for youth exiting homelessness demonstrates results that are certainly compelling, but not in a positive way. Simply being housed is not a positive indicator of well-being, recovery, safety, healthy living, labour force participation, nor social inclusion.

Several Canadian studies point to this conclusion. Kozloff et al. in an analysis of data from the At Home/Chez Soi study, found that for young adults (aged 18 through 24) housing outcomes were similar to the adult cohort, but in terms of quality of life indicators the results were not so positive. 

An analysis of the outcomes for a study in Toronto and Halifax followed 51 young people for 12 

months as they transitioned from homelessness (Kidd et al., 2016; Karabanow et al., 2018). This study showed that while young people made an incredible commitment, they also faced significant structural barriers resulting in social isolation, challenges in maintaining housing stability and finding employment, and a decline in hope. Thulien et al. (2017) in Toronto describes in detail the factors that undermine housing stability and well-being, including the lack of affordable housing, limited social capital, inadequate education, and limited labour force participation, which leads to poverty-level income, an inability to formulate long-range plans, ongoing feelings of outsiderness, and the constant fear of becoming homeless again.

Cityscape 143

Gaetz

Mayock, Corr, and O’Sullivan. (2014) have conducted considerable research on youth transitions to housing in Dublin, Ireland, including a qualitative study tracking the housing trajectories of 40 young people for 6 years. On a more positive note, almost all young people who exited homelessness returned to education or vocational training; most young people identified significant challenges including financial hardship and establishing positive social relationships; and few were able to maintain independent housing, with most either moving back home or into transitional housing (it should be noted that family support was a positive predictive factor). Of significance is the importance of supporting rapid exits from homelessness. “Those young people who ‘got out’ 

early were likely to ‘stay out,’ even if a number did return to homelessness temporarily for a period” 

(Mayock and Corr, 2013: 65).

The key conclusion here is that we need to consider exactly what outcomes—beyond housing stability—we should be looking for if we want to enhance well-being and inclusion, and reduce the longer term risk of a return to homelessness. Longitudinal research on what happens to young people when they exit homelessness, as well as more systematic evaluations of interventions designed to support young people to leave the streets, are needed to enhance our understandings of how to better support young people. As long as our efforts to support youth are based on unquestioned (and perhaps unfounded) assumptions about our interventions and the lives of young people after they exit homelessness, I am not sure we can truly make the claim that we are doing the right thing.

One thing is certain—much can be gained from international collaboration on research on youth homelessness. Understanding of how issues are framed, understood, and responded to in different national contexts helps to shed light on the problem at home and on what needs to be done to effectively respond. David MacKenzie’s commentary on the response to youth homelessness in Australia is a great example of what can be learned from other nations. As a Canadian, the research presented in this volume is immensely helpful in advancing my own thinking on a range of issues. 

More opportunities to engage in international sharing and collaboration on youth homelessness research stands to enhance the influence of research on solutions to homelessness.

References

Abramovich, Alex. 2016. “Understanding how policy and culture create oppressive conditions for LGBTQ2S youth in the shelter system,”  Journal of Homosexuality 64 (11): 1484–1501.

Abramovich, Alex, and Jama Shelton, eds. 2018.  Where Am I Going to Go? Intersectional Approaches to Ending LGBTQ2S Youth Homelessness in Canada & the U.S.  Toronto: Canadian Observatory on Homelessness.

Australian Government. 2013.  Reconnect departmental review. Phase 2 evaluation: Departmental review of the Reconnect program.  Canberra: Australia: Department of Families, Housing, Community Services and Indigenous Affairs.

144 Youth Homelessness

Reflections from Canada: Can Research Contribute to Better Responses to Youth Homelessness?

Busch-Geertsema, Volker, and Suzanne Fitzpatrick. 2008. “Effective homeless prevention? 

Explaining reductions in homelessness in Germany and England,”  European Journal of Homelessness 2: 69–95.

City of Toronto. 2016.  Evictions prevention framework. Toronto: City of Toronto, Shelter, Support and Housing Administration.

Distasio, Jino, Scott McCullough, Matthew Havens, and Zoe St. Aubin. 2014.  Holding on!: Supporting successful tenancies for the hard to house. Winnipeg: Institute of Urban Studies.

Dworsky, Amy, Fred Wulczyn, and Lilian Huang. 2018. “Predictors of Running Away from Out-of-Home Care: Does County Context Matter?”  Cityscape  20 (3): 110–115.

Forchuk, Cheryl, S.K. MacClure, M. Van Beers, C. Smith, R. Csiernik, J. Hoch, and E. Jensen. 

2008. “Developing and testing an intervention to prevent homelessness among individuals discharged from psychiatric wards to shelters and ‘no fixed address,’”  Journal of Psychiatric Mental Health Nursing 15 (7): 569–575.

Gaetz, Stephen and Erin Dej. 2017.  A New Direction: A Framework for Homelessness Prevention.  

Toronto: Canadian Observatory on Homelessness Press.

Gaetz, Stephen, Bill O’Grady, Sean Kidd, and Kaitlin Schwan. 2016.  Without a home: The national youth homelessness survey.  Toronto: Canadian Observatory on Homelessness Press.

Karabanow, Jeff, Kidd, Sean, Frederick, Tyler, and Hughes, Jean. 2018.  Homeless youth and the search for stability.  Waterloo: Wilfrid Laurier University Press.

Kidd, Sean, Frederick, Tyler, Karabanow, Jeff, Hughes, Jean, Naylor, Ted, and Barbic, Skye. 2016. “A mixed methods study of recently homeless youth efforts to sustain housing and stability,”  Child and Adolescent Social Work Journal 33 (3): 207–218.

MacKenzie, David. 2018.  Interim Report: The Geelong Project, 2016-2017. Melbourne, Australia: Swinburne University, Swinburne Institute for Social Research.

MacKenzie, David. and Monica Thielking. 2013.  The Geelong project: A community of schools and youth services model for early intervention. Melbourne: Swinburne University, Swinburne Institute for Social Research.

Mackie, Peter. 2015. “Homelessness prevention and the Welsh legal duty: Lessons for international policies,”  Housing Studies 30 (1): 40–59.

Mackie, Peter, Ian Thomas, and Jennie Bibbings. 2017. “Homelessness prevention: Reflecting on a year of pioneering Welsh legislation in practice,”  European Journal of Homelessness 11 (1): 81–107.

Maher, Catherine, and Mike Allen. 2014. “What is preventing us from preventing homelessness? A review of the Irish national Preventative Strategy,”  European Journal of Homelessness 8 (2): 119–135.

Cityscape 145

Gaetz

Mayock, Paula, and Mary Louise Corr. 2013 Young people’s homeless and housing pathways: key findings from a 6-year qualitative longitudinal study. Dublin: Department of Children and Youth Affairs.

Mayock, Paula, Mary Louise Corr, and E. O’Sullivan. (2014). “Moving on, not out: When young people remain homeless,”  Journal of Youth Studies 16 (4): 441–459.

Pleace, Nicholas, and Dennis Culhane. 2016.  Better than the cure?: Testing the case for enhancing prevention of single homelessness in England.  London: Crisis.

Shinn, Marybeth, Andrew Greer, Jay Bainbridge, Jonathan Kwon, and Sara Zuiderveen. 2013. 

“Efficient targeting of homelessness prevention services for families,”  American Journal of Public Health 103 (S2): S324–S330.

Thulien, Naomi, Gastaldo, Denise, Hwang, Stephen, and McCay, Elizabeth. 2017. “The elusive goal of social integration: A critical examination of the socio-economic and psychosocial consequences experienced by homeless young people who obtain housing,”  Canadian Journal of Public Health 109 

(1): 89–98.

Walker, Sarah Cusworth, Esteban Valencia, Asia Bishop, Michael Irons, and Arina Gertseva. 2018. 

“Developing a Coordinated Youth Housing Stability Program for Juvenile Courts,”  Cityscape 20 (3): 117–137.

146 Youth Homelessness

International Commentary: Some 

Reflections on the Policy History of 

Youth Homelessness in Australia

David MacKenzie

University of South Australia

Swinburne University

by David MacKenzie

Abstract

Youth homelessness in Australia was recognised early on as a social problem area prior to other Western countries, such as the United States and Canada. This article traces the policy history of youth homelessness since the 1980s and finds that, despite vigorous community-based youth advocacy, three official inquiries on youth homelessness and a royal commission-like independent people’s inquiry in 2008, public policy prominence does not necessarily mean policy priority. There were advances. The Reconnect Program launched in 1997 was the first early intervention program for young people at-risk of homelessness or recently homeless, but until recently further implementation of early intervention and a youth-specific and youth-appropriate housing sector remained under-developed. Some lessons can be drawn as the U.S. research and policy development on youth homelessness gains momentum. Using an Advocacy Coalition Framework perspective for policy formation analysis, what has been missing is a sophisticated government engagement and media communications strategy, as well as the deeper and stronger community-based advocacy coalitions that have begun to assemble around the system reform Community of Services and Schools (COSS) model of early intervention.

The Social Construction of Youth Homelessness

Despite many similarities in the structural changes since the 1960s among the United States, Canada, Australia, and the United Kingdom that have led to an increase in homelessness as well as a more diverse population including more young people and families (Rossi, 2013), Australia has been notable for the early prominence given to “youth homelessness” as an identifiable social problem focus.

All social problems including “homelessness” are socially constructed, and the definitional debates and decision-making take place in the realm of politics and policy-making processes. Social 

Cityscape: A Journal of Policy Development and Research •  Volume 20, Number 3 • 2018

Cityscape 147

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

MacKenzie

researchers play a part in these debates and in the policy processes, but as one amongst many stakeholders, including the major service agencies, advocacy and lobby groups, government bureaucracies, political parties, and politicians (Best, 2017).

Describing policy-making as a process tends to be descriptive rather than a theory of policy change and framing policy-making as social construction does not explain how the dynamics of policy-making processes for particularly complex social problems are played out. A promising and increasingly influential theoretical model of policy formation is the Advocacy Coalition Framework (ACF) developed by Sabatier and Jenkins-Smith (1988, 1999). The salient value of their analytical model is that it captures the elements involved in complex, contested areas of policy change and provides for conflict and political claims-making by various actors/claims-makers/stakeholders organised loosely or tightly in coalitions advancing different strategies, claims, and proposals. 

The ACF model analysis of policy-related events and activities recognises that the achievement of major reform generally involves playing a long game (at least a decade on average). The Advocacy Coalition Framework has the dual value of being a sophisticated explanatory model but also a theory of practice.

From a program delivery perspective, service delivery definitions are required to identify who is eligible to receive assistance for homeless people; from a research perspective, operational definitions are required to determine who will be counted as homeless when estimating the size of the homeless population; and from a policy and planning perspective, definitions are framed to 

“target groups” authorised as a focus for planning and program delivery. Apart from debates about the concept of homelessness, for all practical purposes, different definitions are required for a range of purposes. In social problems discourses, the size of the population has often been controversial and contested, with advocates tending to opt for larger estimates while governments tend to favour more conservative figures. As Joel Best (2012) reminds us, social statistics are social constructions as well dependent on the definition used and how counting is undertaken. During the 1980s and 1990s in the United States, estimates of the homeless population were highly contested (Roleff, 1996) and even after the HUD street and shelter counts, the issue of whether homeless young people were adequately counted has remained controversial.

In Australia, from the time youth homelessness was first brought to public attention in the seventies, a number of notable milestones has occurred. But, as Archbishop Peter Hollingsworth (1993), a major leader in the welfare sector during the 1970s, reflected: “… the great difference between the 1960s and the 1990s is that (youth) homelessness was viewed as an individual problem affecting a few. It was never defined as a societal problem of serious proportions.” During the 1980s, grassroots community advocacy around the problems of homeless young people was vigorous, accompanied by a steady output of media coverage of “street kids.” Perhaps in response, the first Australian Government inquiry was the Senate Standing Committee on Social Welfare’s Report on Youth Homelessness in 1982. The report complained that the existing estimates of the number of homeless youth were “unreliable.” The Senate report had very little public impact, but it did serve to draw the issue of youth homelessness to the attention of policymakers.

The main government response was the consolidation of several state and Commonwealth 

homelessness and housing programs into a joint Commonwealth-State program known as 

148 Youth Homelessness

International Commentary: Some Reflections on the Policy History of Youth Homelessness in Australia the Supported Accommodation and Assistance Program (SAAP) in 1985. The definition of 

homelessness that found its way into the SAAP was originally developed by the National Youth Coalition for Housing (NYCH). For the purpose of support and accommodation under SAAP, 

“a person is homeless if and only if he or she has inadequate access to safe and secure housing” 

(Chamberlain, Johnson, and Robinson, 2014: 74). SAAP stood as the signature national program response for homeless Australians for 25 years. The diversity of service responses was one of its strengths. In Australia, beginning in the early 1990s, the large capital city shelters were redeveloped into more adequate and supportive private environments for homeless residents. 

Early on it was recognized that homelessness was increasingly being experienced by a diversity of groups—women escaping domestic violence, families, and of course, young people. The SAAP 

definition was a broad service delivery definition that co-existed alongside a research-based cultural definition, also a broad definition encompassing a range of situations of temporary shelter as well as rough sleeping (Chamberlain and MacKenzie, 1992; MacKenzie, 2012). Nevertheless, the focus for SAAP-funded services remained largely oriented to crisis accommodation and “chronic homelessness.” Again, advocacy from the youth sector was formative in the formulation of the SAAP definition and a significant proportion of youth services was part of SAAP (about 34 percent of 1300 agencies in 2005–2006). On the other hand, the transition of young people from crisis accommodation to affordable social or private rental housing or supportive housing, which hardly existed, has remained a continuing problem.

By comparison, the U.S. response to homelessness for a long time has largely focused on crisis responses—street outreach, homelessness shelters, and transitional accommodation. A number of competing definitions frame homelessness, which presents a public policy challenge (see Federal Definitions). However, more recently, “prevention” has begun to enter U.S. policy discourse with policy papers from the United States Inter-agency Council on Homelessness (USICH, 2015) discussing prevention and a HUD-funded Youth Homelessness Demonstration Program that 

includes community-based prevention initiatives.

The Burdekin Report

The Human Rights and Equal Opportunity Commission’s (HREOC) 1989 Inquiry into Youth 

Homelessness served to bring “youth homelessness” into the national consciousness (Fopp, 2003). The HREOC was established in 1986 as a statutory authority under an act of parliament. 

Brian Burdekin was the foundation Human Rights Commissioner and his Inquiry into youth homelessness was the first Inquiry of the newly formed Human Rights Body. Over 9 months, 20 

hearings were held; with evidence from 300 witnesses and 160 written submissions; visits were made to 20 youth refuges and services, and the Inquiry commissioned 7 special reports, including one that estimated the extent of homelessness in Australia. The HREOC report,  Our Homeless Children, was wide-ranging and thorough (HREOC, 1989).

As with all social problems, media coverage plays an important role. In the case of the Burdekin report the huge amount of media stimulated community interest (Fopp, 1989). The quoted figure from a special report was 50,000 to 70,000 homeless youth, although the Commissioners opted for a more conservative estimate of 20,000 to 25,000; media reports tended to relay for the higher Cityscape 149

MacKenzie

figure. The contrary estimates stimulated a spirited debate about numbers and further research (Chamberlain and MacKenzie, 1992; Fopp, 1993). This led to the  Counting the Homeless project (Chamberlain and MacKenzie, 2003) which produced estimates of homelessness, including youth homelessness, that were accepted by all stakeholders including government.

In the United States context, the  Voices of Youth Count is a particularly notable national initiative that has set out to “more clearly define the size of the (homeless youth) population”, as well as express the diverse experiences of young people experiencing homelessness while taking this knowledge out to a “broad national community dedicated to ending youth homelessness” (Voices of Youth Count, 2018; Youth.gov). The size of a social problem population matters greatly in the social policy process.

Despite raising public awareness, the Burdekin Report reinforced the dominant public typification of homeless youth as “street kids”. The report highlighted that young people leaving care or who had been in care and Indigenous young people were particularly vulnerable groups requiring appropriate support. The symposium papers by Samuels et al. and Shelton et al. both address equity issues and how race, ethnicity, sexual orientation, and gender identities intersect to produce a higher risk for homelessness and different experiences while homeless. Broadly similar parallels exist in the Australian context, despite historical differences, for special need sub-groups such as Indigenous Australians, LGBTQI youth, and young people leaving care.

The Federal Labor Government responded with a $100 million Social Justice Package for Young Australians over 4 years. About one-half of this went on improving social welfare benefits for homeless youth, providing a small number of innovative health services and a few new accommodation services, but a significant amount went on pilot projects that were not recurrently funded or replicated.

In response to conservative criticism of a new government benefit designed specifically for homeless youth (the Youth Homeless Allowance), the House of Representatives conducted another parliamentary review, producing its  Report on Aspects of Youth Homelessness (The Morris Report, 1995). The core insight offered by this Inquiry was that “early intervention is probably the one area of public policy that could deliver the greatest returns in terms of social cohesion through the reduction in family breakdown and long-term welfare dependency.” Morris argued that an early intervention strategy was needed and that “schools become the focal point for early intervention.”

The Federal Government changed soon after in 1996. Most of the Morris Report’s 

recommendations were not acted on, but what did happen was that the incoming Howard Liberal Government set up its own taskforce, a  Prime Ministerial Taskforce on Youth Homelessness, chaired by Major David Eldridge from the Salvation Army. The Taskforce report clearly proposed “early intervention” and fielded a pilot program of 26 pilot projects to explore how early intervention might be done using family mediation and reconciliation approaches. This was an important innovation in policy and service provision for the homelessness sector and the first explicit early intervention program in the homelessness sector, possibly a world first. The Reconnect program was launched in 1997. By 2003, at 100 sites, Reconnect was deployed to work with at-risk young people and their families and to address incipient homelessness. The program was rolled 150 Youth Homelessness

International Commentary: Some Reflections on the Policy History of Youth Homelessness in Australia out in stages from 1998 to 2003, to allow time for workers and agencies adapt to the new early intervention practices.

During the 1980s, youth homelessness attracted a lot of community advocacy but relatively little research. Following the Burdekin Report, advocacy continued but at the same time, small cadre of university-based researchers formed an ongoing research effort (Chamberlain and MacKenzie, 1992, 1995, 1998; Mallett et al., 2009; Neil and Fopp, 1994; Fopp, 2009). A sobering understanding is that achieving public policy prominence for youth homelessness was not the same as a policy priority and even when it appeared that youth homelessness was due for major investment, changes of government at critical points in time limited what actually happened. But also, when it came to decisions about resources, the established large charitable organisations that were the main providers in homelessness services appear to have time and again been more effective in getting resource allocations in their favour. The resilient media typification of homelessness as “rough sleeping” seems to have been a relevant issue. Research on the costs of youth homelessness has consistently shown that when early intervention prevents a young person becoming homeless and/or completes secondary schooling the cost saving is significant for the community over the longer term. The two policy imperatives from this work have been prevention and early intervention and rapid rehousing.

Focused on the U.S. context, Rice et al. in this symposium series provides interesting data from a study of prioritization decisions for entry into the crisis response system given limited resources and the need for triage so that youth with the greatest vulnerability receive a priority housing intervention. This addresses the management of “outflow” from the crisis system. There is no similar approach in Australia. Young people who remain in the crisis system and do not return home at an earlier stage generally require a housing solution. The introduction of intake points in some jurisdictions and communities are attempts to prioritise entry into the crisis system but this approach has not been implemented system-wide nationally. However, ending youth homelessness depends on dramatically reducing the “inflow” into the crisis system. This is the challenge of early intervention in both the United States and Australia.

Reflecting on the fate of youth homelessness policy and program responses from 1987 to 2007, a general observation would be that youth homelessness in Australia has had a public prominence not matched by the government decisions about resources for homelessness youth services and housing options for young people.

The Road Home (2008)

The Federal Government changed at the end of 2007 and the Rudd Labor Government declared that homelessness would be one of its highest priorities. The National Youth Commission (NYC) inquiry into Youth Homelessness in 2007–2008 and its report,  Australia’s Homeless Youth, together with the ethnographic feature documentary,  The Oasis, was an important milestone in revivifying a focus on youth homelessness. The Inquiry was not funded by any Australian government but constructed as a community process adhering strictly to the practices and standards of an official inquiry. Hearings were held in all jurisdictions, collecting evidence from 319 people and 92 written submissions, producing a 400-page final report with 80 recommendations and a graphic booklet Cityscape 151

MacKenzie

with a Roadmap of 10 key reform propositions. The new Labor Minister Tanya Plibersek and the former HREOC Commissioner Brian Burdekin spoke at the launch. The subsequent Government’s 2008 White Paper,  The Road Home, drew liberally on the NYC’s advice. The White Paper proposed a strong strategic framework linked to the long-term objective of halving homelessness. As one of the three core strategies, the metaphor of “turning off the tap” colourfully expressed the idea of “early intervention.” The important of mainstream institutions and programs in the early intervention policy frame was raised but not given much in the way of detail despite advocacy around “schools as sites for early intervention” since the mid-1990s. The symposium papers by Dworsky et al. and Walker et al. papers discuss how youth homelessness could be more effectively prevented by engaging earlier with broader service systems such as child welfare/foster care (Dworsky) and juvenile justice (Walker). In Australia, slow progress has been made in constructing in leaving care support programs but not as systematically and systemically as would be needed to effect significant measurable improvement in the official statistics.

What assessment can be made about the period from 2008 to 2018? The government invested an initial down-payment in service development and repair and social housing, but amid endemic political turmoil in the Government and the Global Financial Crisis (GFC), thereafter, implementation in this high priority area faltered. The implementation of “The Road Home” 

strategies for Australian homelessness policy remained focused on the most obvious and visible aspects of homelessness like the “Streets to Home” response for rough sleepers and “Housing First” approaches for the chronic homeless. Considerable resources have gone into these approaches, but virtually nothing on early intervention. Also, apart from several special youth foyer projects, very little of the GFC-driven investment in social housing seems to have benefited young people exiting homelessness.

However, during this same decade, determined research and development to trial early intervention as a place-based local service system reform—the “community of services and schools” model or COSS Model, also known as The Geelong Project after the first and most developed trial site, has been conducted. This effort was not driven by government but through an alliance between researchers and practitioner leaders (MacKenzie and Thielking, 2014). Some key innovations are a reliable process for whole of school population screening for risk prior to crises, a flexible dynamic tiered practice framework, longitudinal monitoring and a sophisticated embedded outcomes measurement regime linked to practice. Rather than an add-on program, the COSS Model represents a collective impact reform of local service provision connecting youth services with the universal institution of secondary schools (MacKenzie, 2018). Practitioners and youth agencies have expressed a groundswell of interest; a small number of operational pilots in Australia and Canada; government funding is beginning to flow; and researchers in Australia, Canada and the United States have an active interest in forming an international collaboration around “early intervention.”

Some Reflections on Policy Formation and Change

Bipartisanship has generally been underpinned by a degree of stability in the funding for homelessness services in Australia, while never providing anywhere near the level required to seriously redress the problem. Although governments and bureaucracies can be criticised for a lack 152 Youth Homelessness

International Commentary: Some Reflections on the Policy History of Youth Homelessness in Australia of political will or continuing established but not highly effective programs, the homelessness sector has been actively reformist, proposing creative initiatives and systemic solutions. One issue is the dominance of crisis-oriented practice and agencies. Then, agency-focused thinking that seeks “innovation” primarily to enhance the business of the agency rather than a systems approach which begins with the totality of the community is a problem. Departmental leadership, which could be expected to take a systems perspective, too often yields to political expediency, short-term program responses, or a plethora of “pilot” projects that never get scaled up.

In the 1990s, early intervention was not initially embraced even by the youth homelessness sector. Some were anxious that the new program would be funded by cost shifting from 

expensive crisis 24/7 services to cheaper early intervention models. But, that did not happen when Reconnect was funded. In the sector, the main pushback against early intervention came from agencies that predominately work with chronically homeless individuals. A 

similar pushback has occurred in the United States against moves to broaden the scope of the homelessness response and shift modestly toward early intervention and prevention (Shinn and Baumohl, 1999).

Over nearly three decades (1980–2008), the Australian experience with youth homelessness as a social problem has been marked by a promising early start, vigorous advocacy by community organisations and youth advocacy coalitions, the launch of the Reconnect program in 1997, three major official inquiries, and one independent people’s inquiry, a plenitude of media coverage and a continuous stream of research on youth homelessness in 1990s. The concept of early intervention found its way into high-level policy documents such as the Australian Government 2008 White Paper,  The Road Home, under “turning off the tap.” But, as has been argued previously, government resourcing for youth homelessness fell short of what might have been expected from its public policy prominence. Thinking about this period from an Advocacy Coalition Framework perspective, what was missing was a sophisticated bipartisan strategy for engaging long-term with government around implementable social policy solutions.

By contrast, since 2008, the developmental work around early intervention focused on a place-based service system reform model. The coalition between the university research team and the Geelong community stakeholders became a community-wide Geelong advocacy coalition. 

The research focused on development issues and the measurement of outcomes, and the COSS 

Model/ Upstream Project Australia leadership does have a sophisticated strategy for engaging with government at all levels as well as media communication to broader constituencies of interest. In Australia (circa 2018), the scaleup of the COSS Model to effect local system change and reduce youth homelessness significantly seems to have begun.

Author

David MacKenzie is an associate professor at the University of South Australia and Swinburne University, Chair of Youth Development Australia Ltd, and Director of the Upstream Project Australia.

Cityscape 153

MacKenzie

References

Best, Joel. 2017. Images of issues: Typifying contemporary social problems. London and New York: Routledge.

———. 2012. Damned lies and statistics: Untangling numbers from the media, politicians, and activists. Berkeley: University of California Press.

Chamberlain, Chris, and David MacKenzie. 2003.  Counting the homeless 2001. Canberra: Australian Bureau of Statistics.

———. 1998.  Youth Homelessness: Early Intervention & Prevention. Erskinville, New South Wales: Australian Centre for Equity through Education.

———. 1995. “Temporal Dimensions of Youth Homelessness,”  Australian Journal of Social Issues 29 

(1): 1–25.

———. 1992. “Understanding Contemporary Homelessness: Issues of definition and Meaning,” 

Australian Journal of Social Issues 29 (4): 274–297.

Chamberlain, Chris, Guy Johnson, and Catherine Robinson, eds. 2014.  Homelessness in Australia: An Introduction. Sydney: NewSouth Publishing.

Fopp, Rodney. 2009. “Metaphors in homelessness discourse and research: Exploring ‘pathways,’ 

‘careers’ and ‘safety nets,’”  Housing, Theory and Society 26 (4): 271–291.

———. 2003. “After Burdekin - a Brief Evaluation 14 Years On.”  Parity 16 (7): 13–14.

———. 1993. “More heat than houses,”  Youth Studies 12 (2:): 45–50.

—–—. 1989. “Press coverage of the Burdekin Report,”  Youth Studies Australia 8 (4): 10–14.

Hollingsworth, Peter. 1993. “Foreword.” In  Youth Homelessness: Courage and Hope, edited by Helen Sykes. Melbourne: Melbourne University Press.

House of Representatives Standing Committee on Community Affairs. 1995.  Report on Aspects of Youth Homelessness. Canberra: Australian Government Publishing Service [The Morris Report].

Human Rights and Equal Opportunity Commission. 1989.  Our Homeless Children. Canberra; Australian Government Publishing Service [The Burdekin Report].

Jordan, Alan. 1973.  Inner City Homeless Men in Melbourne. MA Thesis, La Trobe University.

MacKenzie, David, and Chris Chamberlain. 2003.  Homeless Careers: Pathways in and out of homelessness. Melbourne: Counting the Homeless 2001 Project.

MacKenzie, David, and Monica Thielking. 2014.  The Geelong Project: A community of schools and youth services model for early intervention. Canberra: Department of Family, Housing, Community Services and Indigenous Affairs (FAHCSIA).

154 Youth Homelessness

International Commentary: Some Reflections on the Policy History of Youth Homelessness in Australia MacKenzie, David. 2018.  The Geelong Project: Interim Report 2016-2017. Melbourne: Swinburne University.

———. 2012. “Homelessness: Definitions.” In  International Encyclopedia of Housing and Home, edited by S. Smith, M. Elsinga, L. O’Mahony, O. Eng, S. Wachter, and S. Fitzpatrick, S. San Diego: Elsevier.

Mallett, Shelley, Doreen A. Rosenthal, Deb Keys, and Roger Averill. 2009.  Moving out, moving on: Young people’s pathways in and through homelessness. London and New York: Routledge.

Neil, Cecily Clare, and Rodney Fopp. 1994.  Homelessness in Australia: Causes and consequences. Vol. 

1. Australian Housing and Urban Research Institute by arrangement with Victorian Ministerial Advisory Committee on Homelessness and Housing.

Roleff, Tamara, ed. 1996.  The Homeless: Opposing Viewpoints. San Diego, CA: Greenhaven Press.

Rossi, Peter. 2013,  Down and Out in America: The Origins of Homelessness. Chicago: University of Chicago Press.

Sabatier, Paul, and Hank Jenkins-Smith. 1999. “The Advocacy Coalition Framework: An 

Assessment.” In  Theories of the Policy Process, edited by Paul Sabatier. Boulder, Co: Westview Process.

———. 1988. “An advocacy coalition model of policy change and the role of policy-oriented learning therein,”  Policy Sciences 21: 129–168.

Shinn, Marybeth, and Jim Baumohl. 1999. “Rethinking the prevention of homelessness.” In Practical Lessons: The 1998 Symposium on Homelessness Research.

United States Inter-agency Council on Homelessness (USICH). 2015.  Preventing and Ending Youth Homelessness: A Coordinated Community Response. 18 September. https://www.usich.gov/tools-for-

action/coordinated-community-response-to-youth-homelessness.

Voices of Youth Count, Chapin Hall at the University of Chicago. http://voicesofyouthcount.org.

Youth.Gov.  Federal Definitions. https://youth.gov/youth-topics/runaway-and-homeless-youth/

federal-definitions.

Cityscape 155

156 Youth Homelessness

Refereed Papers

Refereed papers that appear in  Cityscape  have undergone a thorough and 

timely double-blind review by highly qualified referees. The managing editor 

reviews submitted manuscripts or outlines of proposed papers to determine 

their suitability for inclusion in this section. To submit a manuscript or 

outline, send an e-mail to cityscape@hud.gov.

Cityscape: A Journal of Policy Development and Research •  Volume 20, Number 3 • 2018

Cityscape 157

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

158 Refereed Papers

From Foreclosure to Eviction: 

Housing Insecurity in Corporate-

Owned Single-Family Rentals 

Elora Lee Raymond

Georgia Institute of Technology

Richard Duckworth

U.S. Department of Agriculture

Benjamin Miller

Emory University

Michael Lucas

Atlanta Volunteer Lawyers Foundation

Shiraj Pokharel

Georgia State University

by Elora Lee Raymond, Richard Duckworth, Benjamin Miller, Michael Lucas, and Shiraj Pokharel Abstract

In this research, we examine evictions in post-foreclosure single-family rentals in Atlanta, GA, placing eviction-driven housing insecurity in the broader context of rising middle-class precarity and institutional change in housing markets. 

To understand the evictions rate in Atlanta and investigate how corporate ownership relates to housing insecurity, we use a unique dataset: parcel-level eviction records scraped from the Fulton County Georgia Magistrate Court’s website. We then matched these records with tax assessors and deeds data, as well as block group data on tenant characteristics from the American Community Survey. We document a high, spatially concentrated evictions rate. More than 20 percent of all rental households received an eviction notice in 2015, and 5.6 percent of tenants received a judgment or were forcibly removed from their homes. 

Evictions are spatially concentrated; in some zip codes, over 40 percent of all rental households received an eviction notice and over 15 percent of all households received a judgment or were forcibly removed. 

We then examine the relationship between post-foreclosure single-family rentals, large corporate landlords that invested in bank-owned homes, and eviction rates. In a cross-sectional regression of single-family rentals, we find that overall, post-foreclosure homes are 58 percent more likely to have an eviction filing than single-family rentals with no foreclosure history. Foreclosure-driven housing insecurity of the late 2000s has been followed by eviction-driven housing insecurity. We find that large corporate owners

Cityscape: A Journal of Policy Development and Research •  Volume 20, Number 3 • 2018

Cityscape 159

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Raymond, Duckworth, Miller, Lucas, and Pokharel

Abstract (Continued)

of single-family rentals, which we define as firms with more than 15 single-family rental homes in Fulton County, are 68 percent more likely than small landlords to file eviction notices even after controlling for past foreclosure status, property characteristics, tenant characteristics, and neighborhood. 

We use dummy variables to identify large institutional investors in single-family rentals like Invitation Homes and American Homes 4 Rent and find that these firms have uniquely high eviction rates. Depending on the firm, institutional investors were between 11 percent and 205 percent more likely to file for eviction than mom-and-pop firms, even after controlling for property, tenant, and neighborhood characteristics. 

Introduction

During the foreclosure crisis, around 5 to 6 percent of households in the United States exited homeownership, contributing to both the supply and demand for single-family rental homes. 

The foreclosure crisis was the culmination of a long period of institutional change in housing and mortgage markets, in which moderate- and middle-income households were exposed to 

increasing levels of housing precarity (Dwyer and Phillips Lassus, 2015). Broad changes in mortgage markets, including deregulation, technological change, innovation in product offerings, and the rising importance of non-bank mortgage lenders the 1990s and 2000s had the composite effect of shifting risk of foreclosure away from government institutions and financial firms and onto households. Homeowners that had previously been sheltered from precarity were exposed to increasing housing insecurity.1 In this article, we examine the phenomena of evictions among single-family rentals, many of which were formerly foreclosed homes, as another episode in institutional change in housing markets, and another example of growing housing insecurity among moderate- and middle-income households.

Since the real estate and financial crisis of the early 2000s, homeownership has fallen to 62.9 

percent, a 51-year low. More households are renting for a variety of reasons: home price instability; demographic shifts; changing tastes among millennials, delayed household formation and widening wealth and income inequality; and rapid change in the financial institutions that undergird mortgage markets, leading to the credit tightness that characterizes the post-crisis mortgage markets (Acolin, Goodman, and Wachter, 2016; Goodman, Pendall, and Zhu, 2015; Immergluck, 2018).

In response to the post-crisis decline in demand for homes and the glut of bank-owned properties, the government made some effort to stabilize neighborhoods and help struggling homeowners with neighborhood stabilization programs and direct governmental assistance around financial education and refinance and loan modification programs (Immergluck, 2011). Another part of 1 The majority of subprime borrowers in the 1990s and 2000s were existing homeowners who obtained high-risk refinance loans that terminated in foreclosure; for example, 67.1 percent of subprime loans were refinances in 2004; 57.3 in 2006 (Immergluck, 2011). A sizable portion of subprime borrowers had prime credit but still received high-risk, high-price subprime loans, which were associated with high foreclosure rates in subsequent years (Foote, Gerardi, Goette, and Willen, 2008; Immergluck, 2011).

160 Refereed Papers

From Foreclosure to Eviction: Housing Insecurity in Corporate-Owned Single-Family Rentals government response involved facilitating the shift of single family homes from owner-occupied into rental housing stock in the private real estate market. From 2009 to 2015, the number of single-family rentals grew by 2.8 million, from 11.8 million to 14.6 million; over two-thirds of these rentals were in the 50 largest metropolitan areas (Census, 2005-2009, 2011-2015; Immergluck, 2018). In part responding to encouragement by the government, private sector institutional investors realized an opportunity and poured cash into an illiquid housing market. 

From 2011 to 2013, institutional investors and hedge funds bought an estimated 350,000 bank-owned homes (Eisfeldt and Demers, 2014). Those purchases were focused on newer single-family homes in Sunbelt cities like Atlanta where increased demand during the housing bubble of the early 2000s had led to an explosion in new construction and where the long-term market outlook was rosy.

Investors bought with a variety of profit strategies that ultimately influenced property management decisions. Some bought to quickly resell; others to rent for the short term and resell; in other cases, to manage properties long term as scattered-site rental properties. Research in the last 5 

years has tried to understand what sort of landlords these corporations would be (Eisfeldt and Demers, 2014; Fields, Kohli, and Schafran, 2016; Green Street Advisors, 2016; Immergluck, 2013; Immergluck and Law, 2014a; Lambie-Hanson, Herbert, Lew, and Sanchez-Moyano, 2015; Mallach, 2014). What sort of strategies would this new breed of landlord pursue, and would these strategies lead to safe, affordable housing, or would they further contribute to housing insecurity?

Housing insecurity, sometimes referred to as housing instability, describes the condition where a household or family has a residence, but faces uncertainty about their ability to retain that residence due to lack of tenure security, affordability, poor housing conditions, or threats of harassment (Cox, Henwood, Rice, and Wenzel, 2017). Families with high levels of housing insecurity may move frequently, suffer eviction, or otherwise be at increased risk of homelessness. 

In this research, we focus on rates of legal eviction filings as a key measure of housing insecurity, although housing insecurity typically refers more broadly to a household’s overall lack of security about shelter.

Affordability is a key component of eviction and housing insecurity (Cox et al., 2017). As homeownership has declined and renting has increased, demand for rentals has caused urban rents to increase sharply (Desmond, 2018; Immergluck, Carpenter, and Lueders, 2017). The number of households that are cost burdened has climbed, rental housing insecurity has increased, and documentation of an ensuing high rate of evictions in U.S. cities is increasing, partly due to tenants’ 

inability to afford higher rents (Desmond, 2016).

Eviction rates are concerning because residential displacement has been linked to a variety of adverse outcomes for individuals and neighborhoods. Evictions can result in forfeiture of property and lead to stays in homeless shelters, and is often quickly followed by subsequent moves. Eviction is associated with higher rates of depression, illness, and job loss. Eviction is also thought to lead to underperforming schools and poor student outcomes (Desmond, Gershenson, and Kiviat, 2015; Desmond and Kimbro, 2015; Desmond and Shollenberger, 2015). Even an eviction filing that is resolved can mar a tenant’s credit record and bar them from renting elsewhere or accessing public assistance. At the neighborhood level, high eviction rates are associated with poor housing Cityscape 161

Raymond, Duckworth, Miller, Lucas, and Pokharel

conditions, high rates of school turnover, and neighborhood and community instability (Desmond, 2012; Desmond and Shollenberger, 2015).

Despite the importance of evictions as a cause of poor outcomes among tenants, evictions are still poorly understood. In part, this lack of insight is due to a lack of quantitative data on evictions. 

This research seeks to examine evictions in the frame of a shifting institutional context for housing, in which moderate- and middle-income households are renting at higher rates and face higher levels of housing insecurity in the wake of the foreclosure crisis (Dwyer and Phillips Lassus, 2015; Immergluck, 2011). We investigate the relationship between landlord characteristics and housing insecurity, asking whether institutional investors that bought post-foreclosure single-family homes are associated with higher housing insecurity.

To understand the prevalence of evictions and how corporate ownership relates to eviction rates, we use a unique data set: 2015 eviction court records scraped from the Fulton County, Georgia Magistrates Court website that the authors geocoded and matched with tax assessors and deeds data at the parcel level, as well as with American Community Survey block group data, to proxy for tenant socioeconomic status. With this data set, we are able to link ownership characteristics with eviction rates, while controlling for property, tenant, and neighborhood characteristics.

In our data, we find an alarmingly high, spatially concentrated evictions rate in Fulton County. 

In Fulton County, GA, in 2015, we found an average of 107 eviction notices filed each day, for a yearly total equal to 22 percent of all rental households (Census, 2011-2015). Eviction filings in Fulton County were concentrated in multifamily properties/tenants, with an eviction filing rate of around 28 percent. About 7 percent of single-family renters received an eviction filing in 2015. By contrast, according to Princeton’s Eviction Lab dataset, the national eviction filing rate in 2015 was 6 percent (Desmond et al., 2018b). Overall, like several other Southeastern cities, Atlanta’s eviction judgment rate was extremely high, but was not among the highest, ranking 38th in the nation. 

Within Georgia, Fulton County had the 10th highest eviction filing rate, which was nearly half that of neighboring Clayton County, GA, in which 44 percent of all households faced eviction in 2015 

(Desmond et al., 2018b).

Using a logistic regression model to predict the probability of an eviction filing, we investigate the relationship between large landlords, institutional investors, and housing insecurity among single-family rentals. We observe strong and significant effects associated with landlord size and type that are robust to multiple model specifications. Large corporate owners in the single-family rental business are 68 percent more likely than small landlords to evict tenants, even after controlling for property, household, and neighborhood factors. Finally, we find that institutional investors like Colony American Homes2 and American Homes 4 Rent were many times more likely to file evictions than small landlords, even after controlling for property, tenant, and neighborhood characteristics.

Although in some urban submarkets they have a large market share, institutional investors in single-family rentals remain a small percentage of the overall single-family rental market. Some 2 Since 2015, the timeframe of this study, strong consolidation of institutional investor-backed corporate landlords resulted in Colony American Homes merging with Starwood Waypoint in 2016 to form Colony Starwood Homes, later rebranded as Starwood Waypoint Homes, which, in 2017, was absorbed by Blackstone’s Invitation Homes.

162 Refereed Papers

From Foreclosure to Eviction: Housing Insecurity in Corporate-Owned Single-Family Rentals industry experts argue that the innovation of securitization in single-family rental markets, as well as subsequent multi-borrower investment offerings, represent breakthroughs in this sector, allowing what was once a deconcentrated and local industry to efficiently access capital markets, allowing for future growth (Schwarz and Ferris, 2015). Estimates place the ultimate value of this market anywhere from $20 billion to $300 billion (Yalamanchili, 2016). Regardless of whether this sector grows or remains stable at this size, institutional investment in single-family rentals is a continuation of the financialization of mortgage and housing markets, in which deeper ties are established between capital markets and single-family rental homes (Fields, 2018). This research finds that institutional change in housing and mortgage markets—the segue of foreclosed homes from owner-occupied properties to corporate owned rentals, the entrance of institutional investors into this market, and the layering of finance through single family rental securitization offerings (SFRS)—have resulted in increasing housing insecurity and precarity for moderate- and middle-income renters.

Literature Review

Following the foreclosure crisis, around 5 percent of U.S. homeowners exited homeownership, and roughly 2 million homes that had previously been owner-occupied lay vacant, or became rentals. 

During the recovery, institutional investors invested in the single-family rental market, at first using their own equity, but ultimately drawing on a broader segment of financial market investors by securitizing single-family rentals and obtaining financing from Fannie Mae and, more recently, Freddie Mac.

The embrace of single-family rentals by global institutional investors, and incorporation into secondary mortgage markets through mortgage securitization and subsidy by the Government Sponsored Enterprises (GSEs) is part of a broader process of liberalization and institutional change in U.S. housing markets. Streeck and Thelen (2005) describe liberalization as the expansion of market relations within and between nation states, characterized by retrenchment and deregulation by government, the withdrawal of social safety nets, followed by growing market pressures and distributional conflict. Hacker (2008) describes this process of liberalization as one which transfers risk from societies to households and individuals. Hacker’s “great risk shift” is a process of governmental deregulation and secular change in labor markets which leaves households increasingly responsible for providing social insurance around health, retirement, and unemployment.

In the housing sector, institutional change in mortgage markets during the 1990s and 2000s shifted the risk of foreclosure away from government institutions and financial firms and onto households. 

American families became accustomed to using leveraged homeownership as a stable strategy of procuring shelter and accumulating wealth (Pattillo, 2013). However, during the foreclosure crisis, homeowners were exposed to increased and unanticipated housing insecurity. The foreclosure crisis has been described as a culmination of rising housing insecurity, in which moderate- and middle-income households were exposed to unprecedented levels of housing precarity (Dwyer and Phillips Lassus, 2015). Governmental response to the foreclosure crisis has been characterized as “too little, too late” (Immergluck, 2013), and an important component of governmental policy involved the facilitation of private market response. Although the entrance of institutional Cityscape 163

Raymond, Duckworth, Miller, Lucas, and Pokharel

investors into single-family rental markets is relatively small in relation to the overall size of the rental housing market, the gradual withdrawal of governmental supports for homeowners and facilitation of a market response in the single-family rental sector is emblematic of liberalization of housing markets. Furthermore, institutional change under liberalization is a process which, though incremental, can be transformative. Small initial changes can lead to large effects (Streeck and Thelen, 2009; Streeck and Thelen, 2005).

In this article, we examine housing insecurity 10 years after the foreclosure crisis began, focusing on post-foreclosure single-family rentals and the eviction practices of landlords, large and small, who manage these properties.

The Rise of Institutional Investor Investment in Single-

Family Rentals

Since the 1930s, homeownership has been a core institution in the United States, creating the basis for a property-owning society and stabilizing a system in which public goods like schools and access to services and jobs are allocated by location (Hays, 2012). Single-family homeownership in particular was the preferred structure type for policies promoting suburbanization, segregation, and homeownership from the 1940s onward (Jackson, 1987; Rothstein, 2017). However, the institution of single-family homeownership is changing. Currently, homeownership rates are at the low point of a volatile cycle, and the GSEs that underpin U.S. mortgage and housing markets are under pressure to reform and change. Leading up to the crisis, deregulation and technological innovation saw the rise of private label securitization, risk-based pricing, the growth of shadow banking, and rapid rises in homeownership (Newman, 2009). During the crisis, subprime 

mortgage lending and private label securitization ground to a halt; GSEs Fannie Mae (FNMA) and Freddie Mac (FHLMC) went into conservatorship; fiscal and monetary policies went into effect to help troubled homeowners and inject liquidity into secondary markets; and the passage of the Dodd-Frank Wall Street Reform and Consumer Protection Act created a new residential mortgage regulatory body, the Consumer Finance Protection Bureau (CFPB), and restructured the residential mortgage lending business (Immergluck, 2011).

In the decade following the foreclosure crisis, the GSEs, U.S. Treasury, and the Federal Reserve coordinated to innovate structured transactions that would connect capital markets to landlords who would convert foreclosed homes into single-family rentals. These structured transactions were designed to facilitate the transition of hundreds of thousands of bank-owned homes, also known as real estate owned (REO), from the GSEs and private financial institutions back into the hands of landlords and homeowners. In 2012, the Federal Housing Finance Agency (FHFA), conservator of the GSEs, issued a pilot program to develop structured transactions that could be used to sell its REO in bulk. The private market followed by developing and standardizing financial instruments to allow broader market investment into the process of converting foreclosed homes into single-family rentals (Fields et al., 2016; Schwarz and Ferris, 2015; Yalamanchili, 2016). 

Single-family rental housing, traditionally the purview of mom-and-pop landlords (Mallach, 2010), increasingly caught the attention of large financial firms, as did the potential for securitizing single-family rentals. Nationwide, institutional investors purchased an estimated 350,000 homes 164 Refereed Papers

From Foreclosure to Eviction: Housing Insecurity in Corporate-Owned Single-Family Rentals from 2011 through 2013, and these were spatially concentrated in cities like Atlanta with high numbers of bank-owned homes and the prospect of future home price appreciation (Eisfeldt and Demers, 2014; Fields et al., 2016; Yalamanchili, 2016). Industry discussion about securitization of single-family rentals continued in the wake of the FHFA pilot structured transaction; Blackstone’s Invitation Homes issued the first single-family rental securitization in 2013. GSE support of institutional investors in single-family rentals continues. In 2017, Fannie Mae guaranteed a 10-year, interest-only $1 billion loan to Blackstone’s Invitation Homes. At the outset of 2018, Freddie Mac followed suit, investing $11 million of a $1 billion pilot program to back institutional investment in affordable single-family homes.

Sunbelt cities like Atlanta have been particularly attractive to institutional investors in single-family homes (Fields et al., 2016; Immergluck, 2013, 2018). In the late 2000s, Atlanta had a glut of residential mortgage foreclosures, which occur when a mortgage lender forecloses on a homeowner due to a delinquent residential mortgage. The Atlanta region also had a glut of construction foreclosures, which occurred when banks that had lent to construction firms foreclosed on newly built homes when the construction firm became delinquent (Raymond, 2017). Atlanta’s residential mortgage and construction foreclosure crisis presented appealing investment opportunities for firms wishing to invest in residential real estate. The geography of the foreclosure crisis and the timing of home price rises and investor entry into the Atlanta market governed where large investors bought homes. Four local factors stand out as important: the glut of brand new homes in construction foreclosure concentrated in the suburbs; swaths of residential mortgage foreclosures concentrated in older, in-town neighborhoods; the expectation that Atlanta’s long-term home prices and economic health were bright; and lastly, high levels of racial and income segregation that structured the housing market recovery (Immergluck, 2018; Raymond, Wang, and Immergluck, 2016).

This research contributes to the literature evaluating whether the entrance of institutional investors, and conversion of single-family owner-occupied homes to single-family rentals represents continuation of rising housing insecurity for moderate-income households during the recovery. The conversion of single-family homes from owner-occupied to rentals in moderate- and middle-income communities could improve access to desirable locations for renting households. 

Historically, the spatial concentration of owner-occupied housing stock in high-income neighborhoods has been a barrier to entry for many desirable neighborhoods. In some cities, the rise of investor-owned foreclosed homes has generated new opportunities for low-income renters. 

Pfeiffer and Lucio (2015) find that Section 8 voucher holders in Phoenix living in investor-owned homes are more likely to live in low-poverty neighborhoods when compared to other voucher holders. Conversely, Kim and Cho (2016) study the post-foreclosure trajectory of REO homes in Orange County, FL and find that post-REO properties are more likely to be renter occupied in high minority neighborhoods, presenting affordable rental opportunities but possibly also reinforcing racial and ethnic segregation.

Neighborhood characteristics contribute to household-level housing insecurity, and so the spatial distribution of landlord’s rental properties may affect their average eviction rates. This analysis compares housing insecurity between institutional investor landlords and mom-and-pop landlords. In addition to controlling for neighborhood characteristics, it’s important to understand whether institutional investor landlords were systematically investing in Cityscape 165

Raymond, Duckworth, Miller, Lucas, and Pokharel

disadvantaged neighborhoods in which eviction rates might be systematically higher. In the next section, we describe the spatial patterns of investment into Atlanta’s REO homes by institutional investors and other investor-owners.

REO Investors in Fulton County, GA

During the early 2000s, Atlanta builders flooded the market with new homes as mortgage firms originated cheap prime purchase mortgages, subprime purchases, and cash out refinances (Fishbein and Bunce, 2000; Immergluck, 2013). As the foreclosure crisis unfolded, REO properties became spatially concentrated in the suburbs in Clayton, Gwinnett, and Henry Counties, alongside construction foreclosures in new developments in these more peripheral locations. These newly constructed, suburban properties were often favored by institutional investors (Fields et al., 2016). 

REO properties were also concentrated in historically Black neighborhoods in the southwest of the city (Immergluck, 2013).

Small investors were important buyers in early years, purchasing 40 percent of foreclosed properties from 2005 to 2009 (Immergluck, 2013). While small investors were a large proportion of buyers of REO homes from 2009 to 2012, it wasn’t until the 2013 rise in home prices in other Sunbelt cities that large institutional investors began buying Atlanta properties in earnest or consolidating small investors’ holdings by buying their portfolios (Herbert, Lew, and Sanchez-Moyano, 2013). Herbert et al. (2013) find few purchases by large investors prior to 2012. At that time, large investors’ purchases were concentrated in moderate- to middle-income neighborhoods in the suburbs outside of Fulton County and the City of Atlanta. These buyers only made purchases in Fulton County after 2012.

The timing of the entry of large institutional investors into the REO purchases coincides with a shift in the price and neighborhood characteristics of the properties being sold. From 2005 to 2009, homes with the weakest home prices and highest risk loans went into foreclosure; those homes were concentrated in lower middle-income, high-minority areas. As the subprime crisis progressed into the foreclosure crisis from 2010 onwards, properties from less distressed and middle-income neighborhoods went into foreclosure (Immergluck and Law, 2014a, 2014b). From this, we would expect large corporate investors to have invested in less distressed, higher income neighborhoods than smaller purchasers. Studies of foreclosure sales in Fulton County and Los Angeles confirm that as time progressed, foreclosure sales became more dispersed and increasingly common in less distressed, racially segregated neighborhoods (Ellen, Madar, and Weselcouch, 2015; Molina, 2016).

Overall, because of the timing of entry into the REO-to-rental market and the preferences institutional investors had for new homes, we do not expect they will be more heavily invested in disadvantaged neighborhoods than small firms who bought in the 2009 to 2012 era.

Eviction and Housing Insecurity

What causes eviction? Often the cause is nonpayment of rent. This nonpayment can happen because of high housing cost burdens, in which tenants have moved into a property they have difficulty affording. The number of renters with high housing cost burdens has reached record 166 Refereed Papers

From Foreclosure to Eviction: Housing Insecurity in Corporate-Owned Single-Family Rentals levels in the United States. Over 21 million households spend more than 30 percent of their income on rent; 11 million of those spend more than 50 percent, which is considered severely cost burdened. Much of the increase in households reporting housing insecurity can be attributed to soaring rents as demand for rental housing climbs (Joint Center for Housing Studies of Harvard University, 2016). Desmond (2012) describes the rising gap between wages and rents for the lowest wage workers, which, combined with declining federal housing assistance, has resulted in tenants have housing burdens that are upwards of 80 percent of their income.

Eviction can be the result of a disturbance to tenant income. The rise of unpredictable scheduling and work hours can make income fluctuations more common. Kalleberg (2012) describes the rise of precarious work, in which employer flexibility erodes employee security. The percentage of employees working as contractors, for less than full-time, or as waged employees with flexible scheduling has increased. The percentage of employees that are full time, with a constant weekly salary and benefits has decreased. In this way, pay is unpredictable, sick leave is uncompensated, and employees—not employers—bear the risk of slowdowns in demand and downturns. Rising labor precarity has been attributed to the decline of unions and manufacturing employment and the rise of the services economy, as well as the rise of financialization of the economy, in which downsizing and layoffs are more common (Desmond and Gershenson, 2016). When tenants live paycheck to paycheck, life events such as missed work due to illness or car repairs, can cause a tenant to be late with rent.

Maintenance issues are a common source of tension between landlords and tenants, and in states like Georgia where tenants do not have the right to deduct repairs from rent, the common practice of tenant withholding maintenance expenses from rent, or withholding rent to protest a severe maintenance issue can trigger eviction. Depending on a landlord’s strategy for profit, they may be incentivized to take maintenance and upkeep more or less seriously. The literature describes an array of landlord strategies and behaviors in post-foreclosure properties. Mallach (2010) describes two landlord strategies: “milkers” and “holders.” Milking focuses on rental income more than resale value. Landlords extract highest rents with the least investment, allowing building condition to deteriorate, then dump the property on the market or the municipality. Holders seek profits through home price appreciation, place more importance on maintenance, and may keep properties vacant until prices rise. Investor location can affect maintenance of properties as well, with local landlords being more willing to invest in properties where they are likely to capture spillovers, either through other properties or because they themselves live nearby; whereas non-local owners may be more likely to “milk” properties (Mallach, 2010).

Landlord factors influencing housing insecurity also include property management incentives. 

Interviews with Atlanta landlords suggests that landlords who worked with property managers sometimes had higher turnover as property managers sought to maximize fee revenue by selecting tenants who would turn over quickly (Herbert et al., 2013; Immergluck, 2013). Eviction filings can also be part of landlords’ routine rent collection strategy. This does not always result in displacement. A common feature of low-income tenant relationships with landlords is that rent is short, late, or deducted due to necessary repairs and maintenance. In these cases, routine eviction filings are part of a rent collection/late fee strategy on the part of the landlord. They are not used to evict tenants, but rather filed then dismissed to increase revenue. In some cases, some landlords Cityscape 167

Raymond, Duckworth, Miller, Lucas, and Pokharel

or third-party property management firms make a meaningful portion of their income on ancillary charges like late fees and eviction filing fees. In 2017, Invitation Homes attributed a $2 million increase in overall revenue to the implementation of a standard lease that automated delinquency tracking and other ancillary fees, which led to a 22 percent increase in revenue from ancillary fees. 

Starwood Waypoint attributed its impressive revenue gains from 2015 to 2016 to a combination of new acquisitions and automation of ancillary fees like tenant chargebacks, late fees, eviction fees, and withholding security deposits (Abood, 2018; Starwood, 2017).

Housing insecurity due to eviction is of great concern because of the long list of negative consequences for households, landlords, and for neighborhoods. At worst, evicted families and individuals face homelessness (Crane and Warnes, 2000). Desmond and Shollenberger (2015) find that if households are able to find another home after an eviction, their moves are characterized by greater increases in neighborhood poverty and crime as compared to voluntary moves. This scramble to secure a need as basic as shelter, often with short notice, compels households to accept more dangerous environments with less opportunity. These evictions also cause families to accept substandard housing conditions (Desmond et al., 2015). Dissatisfaction with the poor living conditions households are forced into by an eviction often leads to another move. Compounding residential insecurity harms children and communities (Desmond, An, Winkler, and Ferriss, 2013; Desmond et al., 2015; Desmond and Kimbro, 2015).

Eviction also leads to negative consequences for health and income. Renters who experience the stressful and time-consuming experience of a forced move are more likely to lose their jobs (Desmond and Gershenson, 2016). Evicted mothers experience higher levels of parenting stress, depression, and poorer physical health, in addition to greater material hardship. These effects continue for years after the eviction (Desmond and Kimbro, 2015). The stress associated with evictions has been connected with a higher suicide rate (Fowler, Gladden, Vagi, Barnes, and Frazier, 2015). High rates of eviction also impair neighborhood well-being. Some research supports the finding that residential turnover leads to loss of social cohesion/neighborliness, which can create opportunities for violent crime (Morenoff, Sampson, and Raudenbush, 2001; Sampson, Raudenbush, and Earls, 1997).

The Legal Process of Eviction

Evictions and housing displacement can occur rapidly in Georgia as the state has a swift eviction process that typically lasts less than a month. Hatch (2017) divides state landlord-tenant law into three clusters: protectionist, which favors tenants; pro-business, which favors landlords, and contradictory, which is a mixture. Hatch (2017) classifies Georgia as a pro-business state, with few landlord-tenant laws overall and a higher proportion of statutes benefiting landlords in their landlord-tenant law.

This research on housing insecurity relies on the paper trail that a legal eviction process generates as it moves through the courts; that data documents many of the moments in which a tenant can be displaced. The legal process of an eviction can follow many pathways of which many result in a tenant being forced to relocate. Exhibit 1 depicts some ways a case can proceed. The legal process of an eviction is distinct from events on the ground, and only partially captures the 168 Refereed Papers

From Foreclosure to Eviction: Housing Insecurity in Corporate-Owned Single-Family Rentals number of tenants who leave under pressure from their landlord, thus many displacements are not captured in the data. Some landlords use the eviction process as an ancillary to collections efforts, and service of an eviction notice, (or in legal terms, a dispossessory filing) does not necessarily end in the landlord obtaining a writ of possession, but rather collection of fees and rent due. Tenants may also leave immediately after an eviction notice is served so that displacement occurs without any further paperwork being filed, representing displacement that would not be captured in a data set of court records.

Exhibit 1

Simplified Eviction Legal Process for Fulton County, GA

Source: Atlanta Volunteer Lawyers Foundation

As shown in exhibit 1 in the box shaded black, evictions begin when a landlord files a dispossessory or eviction notice. From there, intermediate steps are shaded in gray, and potential final outcomes are shaded in white. Regardless of the outcome of the eviction case, once an eviction notice is filed, unless the eviction is dismissed, this event is reflected on a tenant’s record and is visible to future landlords who pursue background checks. After a filing, the tenant has 7 days to answer. If the tenant fails to answer, the court issues a default judgment, and the tenant is subject to forcible eviction. If, the tenant does answer, they are granted a hearing. The hearings take place at the Magistrate Court in three different sessions. In dispossessory courts one and two, the tenant’s answer has raised a potential defense. The first court is for cases in which neither the tenant nor the landlord has a lawyer. In the second court, one or both parties have a lawyer. A handful of lawyers represent the vast majority of landlords in these cases. Most of these cases are sent to mediation for settlement. In dispossessory court four, the tenant’s answer was deemed by the court Cityscape 169

Raymond, Duckworth, Miller, Lucas, and Pokharel

to be insufficient for a defense. The fourth court is remarkable because all tenants who appear are evicted en masse.

At the hearing the judge may issue an order and judgment or the two parties may resolve the dispute themselves (Thaler, 2016). This resolution results in a consent judgment, agreement, or order. A judgment or order can be used to garnish wages, establish terms of payment, establish that a tenant must leave, or all three. Once a judgment or order is obtained, the landlord can go to the court and for a $20 filing fee, obtain a writ of possession that allows marshals to forcibly remove a tenant from their home. The writ leads to a tenant being ejected or vacated. If part of the judgment involves monetary conditions, once the tenant has completed payment, the landlord should file a Satisfaction of Judgment. At any point in this process, the landlord may dismiss the eviction case or the tenant may decide to leave the rental property.

However, not all landlords go to the trouble of filing a dismissal (Thaler, 2016). This failure to file a dismissal can have effects on the credit record of the tenant and means that the resulting paper-trail can indicate evictions in cases where the case was dismissed. When an eviction notice is filed, it shows up on tenants’ credit records and can make it difficult for them to access public assistance and rent private housing in the future. Whether or not there is subsequent displacement, an eviction notice in and of itself can be an adverse event for the tenant (Desmond and Shollenberger, 2015).

The goal of this research is to understand whether the new phenomenon of post-foreclosure single-family homes shifting into rental markets and the emergence of large corporate landlords managing scattered site rentals, has resulted in increased evictions and higher levels of housing insecurity. This research connects to three strands in the housing literature. We follow in the footsteps of other research into the trajectory of REO homes in the wake of the foreclosure crisis; we continue to investigate the ramifications of the rise of the large investor landlord in the scattered site, single-family rental space; and we contribute to a growing understanding of the phenomenon of high rates of evictions, in this case, examining patterns of evictions among a moderately well-off segment of renters—those renting single-family homes.

Design and Conceptual Framework

This research examines housing insecurity in Fulton County, GA by seeking to answer three questions.

1.  What was the prevalence of eviction in Fulton County, GA in 2015?

2.  Did tenants living in properties owned by large corporate landlords have more housing insecurity than other single-family rental tenants?

3.  Did tenants living in properties owned by institutional investors have more housing insecurity than other single-family rental tenants?

We instrumentalize housing insecurity as the probability of a landlord filing for eviction. This form of housing insecurity is caused by four types of factors: ownership characteristics; tenant characteristics; property characteristics such as housing quality; and neighborhood characteristics like employment rates, access to jobs, neighborhood change like gentrification, demographics, and income.

170 Refereed Papers

From Foreclosure to Eviction: Housing Insecurity in Corporate-Owned Single-Family Rentals Exhibit 2

Conceptual Framework

Housing 

Ownership 

Property 

Tenant 

Neighborhood 

+

+

+

Insecurity

Characteristics

Characteristics

Characteristics

Characteristics

The intent of this paper is to determine whether landlord characteristics like size or institutional investor backing have an effect on housing insecurity. We expect that property characteristics will affect eviction rates, with newer and higher quality properties having fewer eviction rates, as there are fewer maintenance issues, which are a major cause of conflict between landlords and tenants.

We also expect that tenant characteristics will affect housing insecurity. Using census block group data, we impute tenant characteristics, measuring household income, race, gender, education, and rents to control for tenant characteristics. This technique is commonly used in the public health literature (Geronimus and Bound, 1998; Geronimus, Bound, and Neidert, 1996; Greenwald, Polissar, Borgatta, and McCorkle, 1994; Kaufman, 2017; Krieger, 1992; Soobader, LeClere, Hadden, and Maury, 2001). There are some caveats to be noted with regard to this approach. In two influential papers, Geronimus et al. (1996) and Geronimus and Bound (1998) found weaker associations between socioeconomic status and outcome variables when aggregate variables were used as compared to individual measures. Unlike this study, they used census tract and zip-code level aggregates which are at a higher geography and typically less homogenous than block groups. 

Summarizing the methodological literature, Kaufman (2017) still recommends the use of aggregate data, arguing that individual measures fail to capture the latent variable of socioeconomic status and that accounting for location allows for a more complete measure of this factor. Given that we use tenant socioeconomic status as a control variable here, and are less interested in precise estimates of the separate impacts of individual characteristics versus neighborhood level impacts than in adequately controlling for the confounding effects of both, using area level aggregate data as a proxy for tenant characteristics meets our needs in this study. The literature commonly describes using census tract or zip code socioeconomic data as a proxy for individuals; however, Soobader et al. (2001) have found that block group data systematically reduced the amount of bias introduced by geographic aggregates, particularly with regard to the confounding of race and income; and provide closer estimates than census tracts to actual coefficients for individual socioeconomic characteristics. In this article, we use block group data from the 2012–2016 ACS to proxy for individual tenant socioeconomic status.

To thoroughly account for a neighborhood effect, each parcel in the data set is tagged by census tract, which we use to control for neighborhood characteristics in a second model with census-tract fixed effects.

Site: Fulton County, Georgia

Fulton County, GA is the most populous county in the Atlanta-Sandy Springs Metropolitan Statistical area. Fulton County almost fully encompasses the city of Atlanta as well as several other major municipalities and stretches from southwest metropolitan Atlanta through downtown Atlanta into affluent neighborhoods in the north, providing a wide variety of neighborhood contexts. 

Cityscape 171

Raymond, Duckworth, Miller, Lucas, and Pokharel

By population, the city of Atlanta accounts for half of the county. 2014 census data show Fulton County has 373,0053 households and a population of 967,100. The population is 46 percent White, 44 percent Black, and 10 percent other. Atlanta is one of the most highly segregated cities in the nation, by race and by income (Massey and Denton, 1989, 1993; Massey and Tannen, 2015). 

Slightly under half or 48 percent of all households rent.

Exhibit 3 depicts the distribution of single-family rentals in Fulton County. Single-family rentals are predominantly found in the southwest of the county. Their distribution roughly follow the distribution of past foreclosures and REO properties, which were concentrated in southwest Atlanta and in South Fulton County (Immergluck and Law, 2014b).

Exhibit 3

Single-Family Rentals by Census Tract, Fulton County

Sources: Author calculations, ACS 2014 5-Year Estimates, Fulton County Parcel Tax Assessors Data 4

Data

Our data set is a cross-sectional, parcel-level dataset for all single-family homes in Fulton County in 2015. We define single-family rentals as those with a Land Use Code of 101 or 107, and where owner and property addresses do not tie. We then match eviction records to Fulton County tax assessor’s data and deeds data by address, which provides us with ownership and property characteristics. Our analysis focuses on single-family rental properties so we removed multifamily 3 2014 ACS 5-year estimates 

4 Single-Family Rental units defined as those where Land Use Code of 101 or 107, where owner and property addresses do not match. Calculated as a percentage of all households as defined by 2014 ACS 5-year estimates. 

172 Refereed Papers

From Foreclosure to Eviction: Housing Insecurity in Corporate-Owned Single-Family Rentals and owner-occupied parcels.5 This left 42,674 single-family rentals, 3,152 of which experienced an eviction filing in 2015. We then matched parcels to block group level census data to impute renter characteristics. Summary statistics and sources are displayed in exhibit 4.

Our dependent variable, housing insecurity, comes from eviction records collected from the Fulton County Magistrate Court website. Case data was scraped from the magistrate court’s website in March of 2016 for all 2015 dispossessory filings. Because of the difficulty in ascertaining whether an eviction proceeding resulted in displacement based on the paper trail, and because an eviction notice without displacement is still an adverse outcome for tenants, we use service of an eviction notice as our measure of housing insecurity. The variable is specified as 1/0, 1 indicating the presence of at least one eviction filing at a given parcel in 2015. Around one-fourth of single-family properties in our sample had more than one eviction filed in a year; eviction filings from these properties represented around 44 percent of total eviction filings. These repeated eviction filings may represent a rent collection strategy by landlords, in which evictions are repeatedly filed against the same tenant. The number of evictions per parcel is not normally distributed. After testing the model as ordinary least squares and finding similar results to logistic regression, we opted to use logistic regression to maximize model fit.

Owner information comes from the Fulton County assessor’s parcel database. We used the name in the “owner” field provided by the county for most parcels. However, some companies own significant numbers of properties under different names. For these firms, we collapsed subsidiaries and special purpose vehicles (SPVs) under their corporate umbrella to get an accurate count of the single-family rentals. Information about parent-subsidiary and SPV relationships were obtained from industry news and reports and from EDGAR’s repository of SEC filings.

To identify large corporate landlords, we examined the distribution of number of properties each landlord owned in Fulton County, researched some of the larger property owners, and created a definition of large corporate landlord as one that holds more than 15 single-family rental properties. This definition is consistent with the literature on landlord size in Atlanta during the 2010s. Other research (Herbert et al., 2013; Immergluck and Law, 2014b) has defined large landlords as those acquiring more than 10 REO properties; because we are defining a static variable, and based on the distribution of properties in the data, we chose 15 as the cutoff. This measure identified 79 companies, including Georgia-based firms like Valor Homes 100, LCC 

which owned 110 single-family rentals in 2015; and Summit Realty Services which owned 47. Our regression results were not sensitive to using 15 units rather than 10. We also excluded banks and public entities from the definition of large corporate landlord as these entities were not landlords and are far more likely to be holding properties vacant than renting them out.

By researching large landlord names, and referencing other research on national and global institutional investors in single-family rentals, we also separately identified large institutional investors with a national or global real estate investment holdings (Schwarz and Ferris, 2015). The 5 First, non-residential parcels were removed by dropping parcels where the number of livable units was zero. Single-family parcels were identified by land use codes 101 (“Residential 1 family”) and 107 (“Single Family Residential Townhouse”). Parcels coded with other land uses were dropped. Non-owner-occupied parcels were identified by comparing the property address to the owner’s mailing address. If they matched, the parcel was dropped from sample. 

Cityscape 173

Raymond, Duckworth, Miller, Lucas, and Pokharel

nine national or global firms that were active in Fulton County in 2015 include: Colony American Homes, Starwood Waypoint Residential Trust, American Homes 4 Rent, Silver Bay Realty Trust, Havenbrook Homes, Progress Residential, American Residential Properties Inc, Amherst – Main Street Renewal, and Blackstone – Invitation Homes. Their holdings ranged from 957 homes (Blackstone – Invitation Homes) to 55 homes (Amherst – Main Street Renewal).

To separately capture the impact of foreclosure history on housing insecurity, and to allow us to compare landlord type among post-foreclosure single-family rentals, we use deeds data to tag homes which had a foreclosure during the 2000s real estate crisis.

Although the association between tenant characteristics and evictions is not the primary subject of this paper and has been extensively studied elsewhere, tenant characteristics are an important predictor of evictions. We expect that tenant characteristics will affect the likelihood of eviction and therefore control for them in the model. Research has found that evictions are higher in households under the poverty line, as well as households with children, and those with a female head of household; and among racial minorities (Desmond, 2012; Desmond et al., 2013). We include measures of household income, gross rent, female head of household, families with children, race, poverty, and education. We do not have data at the household level for tenant demographic characteristics. So, all tenant characteristics are imputed from block group data drawn from the American Community Survey, a practice which has been found to provide useful estimates of individual socioeconomic status in the literature (Geronimus and Bound, 1998; Geronimus et al., 1996; Greenwald et al., 1994; Kaufman, 2017; Krieger, 1992; Soobader et al., 2001).

Finally, we expect that neighborhood characteristics will impact eviction rates. In order to control for these effects, and isolate the impact of landlord characteristics, we run the model a second time as a conditional logistic regression with census tract fixed effects.

A review of the literature led us to test the following property characteristics. Research has shown that, excluding homes rented to voucher holders, landlords had higher stability and less turnover at higher price points (Immergluck, 2013). We include three property characteristics to capture price point: age, assessed value per square foot, and assessed value per acre. These measures are indicators of housing quality, housing cost, and location desirability. We expect all three will affect eviction rates, as higher quality properties, more expensive properties, and homes located on desirable and more expensive land, will typically be rented at higher price points.

Exhibit 4

Summary Statistics

Standard 

Variable

Observations

Mean

Minimum

Maximum

Source

Deviation

Eviction

42,674

0.07

0.26

0

1

Author calculated: 

Fulton County 

Magistrates Court

Institutional 

42,674

0.24

1.15

0

10

Author calculated: 

investor

Fulton County Tax 

Assessors data

174 Refereed Papers

From Foreclosure to Eviction: Housing Insecurity in Corporate-Owned Single-Family Rentals Exhibit 4

Summary Statistics

Standard 

Variable

Observations

Mean

Minimum

Maximum

Source

Deviation

Large owner 

42,674

0.09

0.29

0

1

Author calculated: 

(greater than 15 

Fulton County Tax 

properties)

Assessors data

Previous 

42,674

0.44

0.50

0

1

Author calculated: 

foreclosure

deeds data

Year built 

42,674

196.7

2.9

180.0

201.3

Author calculated: 

(decades)

Fulton County Tax 

Assessors data

Home value (U.S. 

42,674

$0.44

$0.46

$0.00

$7.31

Author calculated: 

dollar square foot)

Fulton County Tax 

Assessors data

Land value (U.S. 

42,674

$1.54

$3.36

$0.00

$86.43

Author calculated: 

dollar per acre)

Fulton County Tax 

Assessors data

Percent female 

42,674

0.22

0.13

0.00

0.66

Imputed from  

head of household

Block Group: 

2010–2015 American 

Community Survey

Percent households 

42,674

0.56

0.17

0.00

1.00

Block Group: 

that are families 

2010–2015 American 

with children

Community Survey

Percent Black or 

42,674

0.69

0.34

0.00

1.00

Block Group: 

African-American

2010–2015 American 

Community Survey

Percent with 

42,674

0.22

0.11

0.00

0.51

Block Group: 

Bachelor’s Degree

2010–2015 American 

Community Survey

Block group gross 

42,674

$1.03

$0.31

$0.24

$3.50

Block Group: 

rent ($1,000s)

2010–2015 American 

Community Survey

Block group 

42,674

$4.85

$2.86

$0.53

$21.58

Block Group: 

household income 

2010–2015 American 

($10,000s)

Community Survey

Methods

 After mapping and calculating descriptive statistics of evictions in Fulton County, GA, we segment out single-family rentals for analysis. We use a logistic regression model. Our data contains a cross section of 42,674 single-family rentals with a binary dependent variable which is equal to 1 in the cases that an eviction notice was served on a tenant at a property in 2015; and 0 if it is not.

We use a logistic model with clustered standard errors to the following model:

Logit(p

) = β  + β X + β W + β V + ɛ

eviction

0

1

2

3

X = ownership characteristics

W = property characteristics

V = tenant characteristics

Cityscape 175

Raymond, Duckworth, Miller, Lucas, and Pokharel

This model uses a control strategy to deal with selection issues around the different geographic distribution of large landlords as compared to mom-and-pop investors. As a check, we calculate a census-tract fixed effects conditional logistic regression model to verify that the coefficients obtained in this model do not change substantially when we compare single-family rentals within census tracts.

We expect spatial correlation and account for this by clustering standard errors at a geography large enough to encompass regional factors. So, for the logistic regression model, we clustered errors at the zip-code level with 42 clusters (Angrist and Pischke, 2008).

Results and Discussion

Exhibit 5 depicts eviction filing rates in Fulton county for single and multifamily rentals. The overall rate of eviction filings in Fulton County—combining multi and single-family properties—

is extremely high. In 2015, landlords of all (multifamily and single-family) rental units initiated eviction processes 39,221 times, or 107 times a day. 22.2 percent of all renting households in Fulton County faced eviction proceedings in 2015.

Exhibit 5

Eviction Rates by Single and Multifamily

Multifamily 

 Single Family 

 Total

Eviction Filings

35,775

3,446

39,221

Rental Households

128,572

48,413

176,985

Evictions Filings Rate 

28%

7%

22%

Percent of Total Households 

73%

27%

100%

In Fulton County, evictions are concentrated in multifamily properties. As depicted in exhibit 5, 28 percent of all households in multifamily buildings had an eviction notice filed compared to 7 

percent in single-family rentals.

Rates of eviction filings are also spatially concentrated; in four southwest Atlanta zip codes the rate exceeds 40 percent of all rental households, as depicted in exhibit 6.

Exhibit 6

Spatial Concentration in Evictions: Top Zip Codes by Eviction Rates, all Property Types Geography

(a) 

(b) 

(c) 

(d) 

(a/d)

(b/d)

(c/d) 

Eviction 

Eviction 

Writ of 

Census 

Eviction 

Eviction 

Writ of 

Notice Filed Notice Filed Possession 

2010 # 

Notice   Notice Filed Possession 

and Never  Issued or  Rental HH

Filed

and Never  Issued or 

Dismissed

Tenant 

Dismissed

Tenant 

Vacated/

Vacated/

Ejected

Ejected

1

30,344

3,031

2,180

1,021

6,564

46%

33%

16%

2 

30,291 

1,888 

1,062 

555 

4,260 

44% 

25%

13%

3 

30,337 

1,478 

1,138 

499 

3,339 

44% 

34% 

15%

4 

30,331 

4,088 

2,490 

1,196 

10,063 

41% 

25% 

12%

Notes: Excludes Zip Codes which cross Fulton County boundaries. These rates would be understated for these zip codes, as we do not have eviction counts for surrounding counties.

Sources: Author calculations; Fulton County Magistrate Records

176 Refereed Papers

From Foreclosure to Eviction: Housing Insecurity in Corporate-Owned Single-Family Rentals Our data shed light on how landlords and tenants in Fulton County, GA, navigate the judicial system. On average, eviction cases took 26 days. 15,608 of 39,221 cases were dismissed. Of the completed cases in 2015, 54 percent of the tenants did not answer. Unless these cases were dismissed by the landlord, this led to a default judgment in favor of the landlord. The remaining 46 percent of tenants answered the eviction notice, but over one-half of these were deemed to not have raised a legal defense and were assigned to Dispossessory Court 4 for a default judgment in favor of the landlord. That outcome suggests that there may be a gap between what tenants believe constitutes a defense and that of the justice system (Lempert and Monsma, 1994). Only one-fifth of all cases were assigned to Court 1 or 26 and were therefore heard by a judge or mediator.

Accounting for just 27 percent of all households, single-family rentals represent a relatively small slice of the rental market in Atlanta. Among single-family rentals, post-foreclosure rentals are systematically different than single-family homes that did not go through foreclosure. Overall, 22 

percent of single-family homes in Fulton county are rental properties. By contrast, over half (53 

percent) of all post-foreclosure single-family homes were rentals in 2015. Post-foreclosure homes are not only more likely to be rentals, they also have higher eviction filing rates as shown in exhibit 7. Just 4 percent of single-family homes with no foreclosure history had an eviction in 2015; this rate was 11 percent for post-foreclosure single-family rentals.

Exhibit 7

Housing Insecurity in Single-Family Rentals by Post-Foreclosure Status

Overall eviction filing rates vary by firm size and firm type, as shown in exhibit 8. Small landlords with fewer than 15 properties file evictions on 6 percent of their tenants. Excluding institutional investors, large landlords have a 14 percent eviction filing rate. Institutional investors have a 20 

percent eviction rate, more than three times that of mom-and-pop landlords.

6 These courts are apparently named after their meeting times, which in 2015 were at 1 o’clock, 2 o’clock, and 4 

o’clock. There was no 3 o’clock court and no Court 3.

Cityscape 177

Raymond, Duckworth, Miller, Lucas, and Pokharel

Exhibit 8

Eviction Filing Rates by Landlord Type

The regression results in exhibit 9 uses logistic regression to evaluate the causes of evictions in single-family rentals in Fulton County in 2015. Model 1 evaluates landlord characteristics like institutional investor firm name and size, property level characteristics like post-foreclosure history, ownership, value, and age, and tenant characteristics like family type, race, income, and education. 

Model 2 displays odds ratios and significance when census tract fixed effects are included. In this model, around 53 census tracts containing 2,878 homes were dropped as they had no evictions.

Together, the two models show that overall, post-foreclosure properties are 59 percent more likely to have eviction notices than single-family rentals that have no record of a foreclosure. The housing insecurity that occurred in the form of foreclosures during the late 2000s has been followed by a pattern of housing insecurity in the form of evictions.

Exhibit 9

Eviction in Single-family rentals

Model 1

Model 2

Odds 

Odds 

z

P>z

z

P>z

Ratio

Ratio

Private Equity Firm

Colony American Homes

3.054

6.810

0.000

2.841

6.54

0.000

American Homes 4 Rent

2.809

4.240

0.000

2.694

3.2

0.001

Starwood Waypoint Residential Trust

2.132

5.970

0.000

1.980

2.85

0.004

Progress Residential

1.926

0.600

0.546

1.831

0.56

0.578

Silver Bay Realty Trust

1.923

3.740

0.000

1.837

2.61

0.009

Havenbrook Homes

1.902

3.960

0.000

1.873

4.76

0.000

American Residential Properties

1.577

1.900

0.058

1.522

1.59

0.113

Amherst – Main Street Rental

1.551

1.820

0.069

1.427

0.98

0.325

Blackstone – Invitation Homes

1.113

0.650

0.519

1.058

0.47

0.635

178 Refereed Papers

From Foreclosure to Eviction: Housing Insecurity in Corporate-Owned Single-Family Rentals Exhibit 9

Eviction in Single-family rentals

Model 1

Model 2

Odds 

Odds 

z

P>z

z

P>z

Ratio

Ratio

Large owner ( > 15 properties)

1.634

5.060

0.000

1.676

7.41

0.000

Previous foreclosure

1.585

12.460

0.000

1.569

10.9

0.000

Home value per square foot (U.S. dollars)

0.396

-6.140

0.000

0.606

-3.03

0.002

Land value per acre (U.S. dollars)

0.976

-1.590

0.111

0.997

-0.21

0.833

Year built

1.066

5.760

0.000

1.060

6.08

0.000

Female head of household

1.487

2.190

0.028

1.563

1.69

0.090

Families with children

1.149

1.610

0.107

1.304

1.57

0.117

Black or African-American

2.074

3.440

0.001

1.610

1.83

0.067

High school degree

1.083

0.400

0.689

1.445

1.22

0.223

Gross rent

1.196

1.660

0.098

1.000

0.17

0.863

Household income

0.942

-3.220

0.001

1.000

-1.34

0.181

Census tract fixed effects

no

yes

Number of obs

42,674

39,796

Pseudo R2 

0.0703

0.0281

Ownership characteristics have a strong and significant relationship with housing insecurity. 

Single-family rentals with large corporate owners are 63 percent more likely to have housing insecurity after controlling for housing quality, tenant characteristics, and neighborhood characteristics. Previous research shows that, at least in the early years of the post-crisis decade, large investors were less likely than smaller corporate buyers to purchase in high poverty, high minority neighborhoods, so if there is selection bias that is uncontrolled for in Model 1, we expect that the bias will produce more conservative estimates of the probability of eviction among large corporate landlords. Additionally, we test our results with census tract fixed effects in Model 2 in exhibit 9. This model controls for neighborhood characteristics, estimating a comparison between landlords within the same census tract. We obtain similarly sized coefficients in Model 2; large owners are 68 percent more likely to evict than small owners, controlling for neighborhood factors.

Both models demonstrate that institutional investors are far more likely to pursue eviction than other landlords, even after controlling for property, tenant, and neighborhood characteristics. The dummy variables for institutional investor firm reveal that these firms are between 11 percent and 205 percent more likely to file eviction notices. Colony Capital had the highest propensity to evict, and was 205 percent more likely than other firms to file eviction notices after controlling for property, tenant, and neighborhood characteristics, followed by American Homes 4 Rent, who were 181 percent more likely to file evictions than a small landlord.

Other property-level characteristics have a significant relationship with housing stability. Increase in housing value resulted in a 61 percent decrease in housing insecurity. A one-decade increase in the year a home is built corresponds with a 7 percent increase in housing insecurity.

The second group of characteristics measures tenant characteristics on the eviction rate. These coefficients should be interpreted cautiously as they are simply included as controls, and because Cityscape 179

Raymond, Duckworth, Miller, Lucas, and Pokharel

the use of geographic aggregates as a proxy for individual characteristics has been found to produce slightly biased estimates in the literature. Previous literature on evictions has been more focused on lower and very low-income tenants rather than the moderate-income tenants one might expect in single-family rentals. For example, in this sample, mean household income for single family renters was around $48,532, well above median income for renting households in Fulton County of $37,296 (Census, 2011-2015). The relationships between eviction and characteristics typically associated with poverty may not be significant or as large among this subset of renters.

Of the tenant characteristics included in the model, as the likelihood that a tenant is Black or African-American rises from 0 to 100 percent, that tenant is 107 percent more likely to experience eviction. A household with a 100 percent likelihood of having female head is 49 percent more likely to experience eviction than one with 0 percent likelihood. An additional $10,000 of household income corresponds to 6 percent lower odds of receiving an eviction filing. Measures of gross rent, education, and the presence of children were not significant.

Conclusions

This research describes housing insecurity among single-family renters in Fulton County, GA. 

Overall, we find rates of eviction filings and completed evictions that are far higher than the 2015 

national average of 6.27 percent and 2.37 percent, respectively (Desmond et al., 2018b). We find that overall, evictions are spatially concentrated in predominantly Black census tracts, and that extremely high levels of housing insecurity exist in many areas of southwest Atlanta and Fulton County. The majority of evictions take place in multifamily properties, however, evictions are also common in single-family rentals. Although there is no comparative research or data set to compare single-family rental evictions, the single-family rental rate of eviction filings in Fulton County is more than twice the 2015 national average for all evictions (Desmond et al., 2018b). The data show extremely high levels of residential displacement in Atlanta, levels which in other cities have been linked to high levels of crime, schools beset with constant turnover, lack of community cohesion, and a dilapidated built environment (Desmond and Kimbro, 2015; Desmond and 

Shollenberger, 2015).

We explore the impact of landlord characteristics on evictions in single family because the post-foreclosure, institutional investor-owned single-family homes are the product of emblematic of widespread institutional change in housing and mortgage markets. The impact of the foreclosure crisis on both supply and demand for single family homes, the entry of institutional investors, interconnections with secondary investor markets, and support by the GSEs are all innovations in the single-family rental market. During this period of institutional change in housing markets, it is possible that the United States is tilting away from a homeownership society and toward a rentership society. Large institutional investors have shown a strong appetite for this asset class, and their small share of overall single-family rentals represents opportunity for growth. Understanding housing insecurity in this sector is important because it is a new phenomenon and because it may grow as time goes on.

Large corporate landlords and institutional investors are not guaranteed to engage in practices that will lead to more or less housing insecurity, and in our model, we find a wide range of 180 Refereed Papers

From Foreclosure to Eviction: Housing Insecurity in Corporate-Owned Single-Family Rentals practices, with Blackstone-Invitation Homes being 11 percent more likely to file eviction notices than non-corporate firms; while Colony Capital was extremely aggressive in their filing practices, and were 205 percent more likely to file than non-corporate firms. Depending on their strategies, institutional investors may be more or less likely to maintain properties and attract and retain tenants than smaller investors. Their capital reserves could support economies of scale and a higher capability to provide affordable housing and more likely to absorb short-term losses. Conversely, landlords with national or global scope, with scattered site rentals in any given region may lack of neighborhood embeddedness may make them less flexible in working out rental agreements with low-income tenants outside of the formal justice system. The conversion of owner-occupied housing stock to rentals has some potential benefits. Historically, zoning by housing type has been used to exclude lower income households from desirable neighborhoods. Providing the opportunity to rent in these areas may represent a pathway to opportunity that was not previously available to those who could not, or did not want to buy a single-family home.

We find that even controlling for other factors, Black tenants are particularly more likely than other tenants to face an eviction filing, as well as female heads of household, suggesting that there are distributional justice issues in which communities face eviction in single-family homes.

We find large, significant impacts of firm and landlord type on eviction filing rates, even when compared to demographic data and controlling for neighborhood effects. This finding underscores the importance of understanding how landlords factor into housing insecurity. One possible reason large corporate landlords backed by institutional investors may have higher eviction filing notices is that they may routinely use eviction notices as a rent collection strategy. No peer-reviewed academic research has been conducted into this phenomenon; given the magnitude of the effects found in this paper, even when compared to demographic factors, further investigation into the impact of landlord strategies is warranted. Subsequent research could compare whether large firms are more likely to dismiss eviction notices, or more likely to have a tenant listed as ejected/

vacated in the records. While neither of these is a perfect measure of displacement, it could help differentiate between landlords who use the threat of eviction as a collection strategy and cases of actual displacement.

We find in Fulton County, GA that investor size is correlated with higher levels of housing insecurity among single-family rental properties. These results are noteworthy for Atlanta, but also for other cities where institutional investor backed corporate landlords operate. Looking into the data, there appears to be a company effect, with some firms having significant, and substantially higher, eviction rates than other firms, even after controlling for property quality, location, and foreclosure history. 

High levels of housing insecurity are disruptive to households and neighborhoods, impacting school performance, crime and safety, maintenance of buildings, community cohesion, and other attributes of community well-being. Further research is needed to understand why large corporate landlords increase housing insecurity compared to their smaller peers.

Another implication of the research is the need to work towards providing safe, stable, affordable rental housing for the growing number of households who rent in Atlanta and elsewhere. 

Recognizing the high cost of evictions to cities, neighborhoods, employers and households, some jurisdictions, like the City of New York, have begun providing automatic legal defenses to tenants Cityscape 181

Raymond, Duckworth, Miller, Lucas, and Pokharel

facing eviction. Other research has found that providing tenants with legal defense leads to much better outcomes for tenants with issues around maintenance with landlords (Blasi, 2004). Funding of tenant defense may help reduce the number of evictions, and deter frivolous filings.

Our data show that 12 percent of all rental households received an eviction filing that was not dismissed, a record that makes it more difficult for these tenants to find rental housing in the future, and relegates them to a small number of second-chance apartment complexes. Some states automatically seal eviction records; for example, the state of California automatically seals eviction records unless the landlord prevails, or wins at trial within 60 days of filing. The effect of this law is to prevent damage to credit histories of tenants who had an eviction filing which was not ultimately deemed valid or actionable. Similarly, the state of Wisconsin seals dismissed eviction cases after 2 years and destroys case information (Desmond et al., 2018a). Adopting those policies which restrict the public reporting of evictions, and focus reporting to tenant credit reports to those instances in which there was a judgment or an outstanding payment due would make it easier for the vast majority of tenants whose eviction cases are dismissed or end without a judgment to rent homes in the future. Another policy might be to charge higher eviction filing fees, to encourage landlords to use eviction filings as a method of removing tenants from properties, rather liberally filing as part of a routine rent collection strategy.

The high rates of eviction filings and housing insecurity among the tenants of institutional investors may be a burden to neighborhoods, judicial systems, and employers. Municipalities should attempt to negotiate with institutional investors around issues of code enforcement, maintenance, and evictions. 

Particularly where these institutional investors have a reasonable market share within certain urban submarkets, successful negotiations could affect large numbers of homes within a jurisdiction.

At the federal level, the GSEs have shown their willingness to provide financial support institutional investment in scattered site, single-family rentals. In accordance with their mandate, GSE support should be tied to the provision of affordable rental housing. Specific provisos might limit the number and scale of chargebacks and ancillary fees to tenants; limit the pace of rent increases, and eliminate discrimination based on source of income, so that single-family rentals are available to tenants with section 8 vouchers and other forms of housing subsidy.

Authors

Elora Lee Raymond, Ph.D., is an assistant professor in the School of City and Regional Planning at Georgia Institute of Technology.

Richard Duckworth, MCRP., is a Presidential Management Fellow at the U.S. Department of Agriculture.

Benjamin Miller, Ph.D., is a senior lecturer in the Department of English at Emory University.

Michael Lucas, J.D., is the deputy director of the Atlanta Volunteer Lawyers Foundation.

Shiraj Pokharel is a Ph.D. candidate in the department of computer science at Georgia State University.

182 Refereed Papers

From Foreclosure to Eviction: Housing Insecurity in Corporate-Owned Single-Family Rentals Acknowledgments

The authors thank Karen Leone de Nie, vice president and community affairs officer at the Federal Reserve Bank of Atlanta; Dan Immergluck, professor in the Urban Studies Institute at Georgia State University; Ann Carpenter, community and economic development adviser at the Atlanta Fed; Cole Thaler, project director at Atlanta Volunteer Lawyers Foundation; Rob Call, Policy Fellow for the Philadelphia City Council; Chris Cunningham, Atlanta Fed research economist and assistant policy adviser, and five anonymous peer reviewers for their careful reading and excellent suggestions. Any remaining errors are the authors’ responsibility.

References

Abood, Maya. 2018. “Wall Street Landlords turn American Dream into a Nightmare,” Report prepared for the ACCE Institute, Americans for Financial Reform and Public Advocates. Los Angeles and San Francisco, CA. http://www.publicadvocates.org/wp-content/uploads/

wallstreetlandlordsfinalreport.pdf.

Acolin, Arthur, Laurie Goodman, and Susan Wachter. 2016. “A Renter or Homeowner Nation?” 

Cityscape 18 (1): 145–158.

Angrist, Joshua, and Jörn-Steffen Pischke. 2008.  Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press: NJ.

Blasi, Gary. 2004. “How Much Access? How Much Justice?”  Fordham Law Review 73: 865.

Census, U.S. 2005–2009. B25032 TENURE BY UNITS IN STRUCTURE: Universe: Occupied 

housing units 2005-2009 American Community Survey 5-Year Estimates.

Census, U.S. 2011–2015. B25032 TENURE BY UNITS IN STRUCTURE: Universe: Occupied 

housing units more information 2011-2015 American Community Survey 5-Year Estimates.

Colony Starwood Homes. 2017. Colony Starwood Homes’ (SFR) CEO Fred Tuomi on Q4 2016 

Results - Earnings Call Transcript.

Cox, Robynn, Benjamin Henwood, Eric Rice, and Suzanne Wenzel. 2017. “Roadmap to a unified measure of housing insecurity.” CESR-Schaeffer Working Paper No. 2016-013.

Crane, Maureen, and Anthony Warnes. 2000. “Evictions and Prolonged Homelessness,”  Housing Studies 15(5), 757–773.

Desmond, Matthew. 2012. “Eviction and the Reproduction of Urban Poverty,”  American Journal of Sociology 118: 88–133.

———. 2016.  Evicted: Poverty and Profit in the American City. Broadway Books: New York.

Cityscape 183

Raymond, Duckworth, Miller, Lucas, and Pokharel

———. 2018. “Heavy is the house: rent burden among the American urban poor,”  International Journal of Urban and Regional Research 42 (1): 160–170.

Desmond, Matthew, Weihua An, Richelle Winkler, and Thomas Ferriss. 2013. “Evicting Children,” 

Social Forces 92 (1): 303–327.

Desmond, Matthew, and Carl Gershenson. 2016. “Housing and Employment Insecurity among the Working Poor,”  Social Problems 63 (1): 46–67. doi:10.1093/socpro/spv025

Desmond, Matthew, Carl Gershenson, and Barbara Kiviat. 2015. “Forced Relocation and 

Residential Instability among Urban Renters,”  Social Service Review 89 (2): 227–262.

Desmond, Matthew, Ashley Gromis, Lavar Edmonds, James Hendrickson, Katie Krywokulski, 

Lillian Leung,, and Adam Porton. 2018a. “Eviction Lab Methodology Report v 1.0.” Report prepared for the Princeton Eviction Lab. Princeton Printing Press: NJ. https://evictionlab.org/docs/

Eviction%20Lab%20Methodology%20Report.pdf.

Desmond, Matthew, Ashley Gromis, Lavar Edmonds, James Hendrickson, Katie Krywokulski, 

Lillian Leung, and Adam Porton. 2018b.  Eviction Lab National Database: Version 1.0:  

www.evictionlab.org.

Desmond, Matthew, and Rachel Kimbro. 2015. “Eviction’s fallout: housing, hardship, and health,” 

Social Forces 44 (1): 295–324.

Desmond, Matthew, and Tracey Shollenberger. 2015. “Forced Displacement From Rental Housing: Prevalence and Neighborhood Consequences,”  Demography 52 (5): 1751–1772.

Dwyer, Rachel, and Lora Phillips Lassus. 2015. “The Great Risk Shift and Precarity in the U.S. 

Housing Market,”  The ANNALS of the American Academy of Political and Social Science 660 (1): 199–216. doi:10.1177/0002716215577612.

Eisfeldt, Andrea, and Andrea Demers. 2014.  The Returns to Single Family Rental Strategies. 2014 Meeting Papers 737 Society for Economic Dynamics. https://ideas.repec.org/p/red/sed014/737.html.

Ellen, Ingrid, Josiah Madar, and Mary Weselcouch. 2015. “The Foreclosure Crisis and Community Development: Exploring REO Dynamics in Hard-Hit Neighborhoods,”  Housing Studies 30 (4): 535–559.

Fields, Desiree. 2018. “Constructing a New Asset Class: Property-Led Financial Accumulation after the Crisis,”  Economic Geography 94 (2): 118–140.

Fields, Desiree, Rajkumar Kohli, and Alex Schafran. 2016. “The Emerging Economic Geography of Single-Family Rental Securitization,” Federal Reserve Bank of San Francisco Community Development Investment Center Working Paper 2016-02: 1–12.

Fishbein, A. and Bunce, H. 2001. “Subprime market growth and predatory lending,” In  Housing policy in the new millennium: Conference proceedings, edited by S.M. Wachter and R.L. Penne. 

273–288. Washington, DC: U.S. Department of Housing and Urban Development.

184 Refereed Papers

From Foreclosure to Eviction: Housing Insecurity in Corporate-Owned Single-Family Rentals Fowler, Katherine. A., R. Matthew Gladden, Kevin Vagi, Jamar Barnes, and Leroy Frazier. 2015. 

“Increase in Suicides Associated with Home Eviction and Foreclosure during the US Housing Crisis: Findings from 16 National Violent Death Reporting System States, 2005–2010,”  American Journal of Public Health 105 (2): 311–316.

Geronimus, Arline T., and John Bound. 1998. “Use of Census-Based Aggregate Variables to Proxy for Socioeconomic Group: Evidence from National Samples,”  American Journal of Epidemiology 148 

(5): 475–486.

Geronimus, Arline T., John Bound, and Lisa Neidert. J. 1996. “On the Validity of Using Census Geocode Characteristics to Proxy Individual Socioeconomic Characteristics,”  Journal of the American Statistical Association 91 (434): 529–537.

Goodman, Laurie, Rolf Pendall, and Jun Zhu. 2015. “Headship and Homeownership.” Report prepared for the Urban Institute. Washington, DC: Urban Institute.

Green Street Advisors. 2016.  Single-Family Rental Primer. Report prepared for National Rental Home Council. Washington, DC: National Rental Home Council. Retrieved from https://www.

rentalhomecouncil.org/wp-content/uploads/2017/12/Green-Street-Advisors-Single-Family-

Rental-Primer.pdf.

Greenwald, Howard P., Nayak L. Polissar, Edgar F. Borgatta, and Ruth McCorkle. 1994. “Detecting Survival Effects of Socioeconomic Status: Problems in the Use of Aggregate Measures,”  Journal of Clinical Epidemiology 47 (8): 903–909.

Hacker, Jacob. S. 2008.  The Great Risk Shift: The New Economic Insecurity and the Decline of the American Dream. Oxford: Oxford University press.

Hatch, Megan. E. 2017. “Statutory Protection for Renters: Classification of State Landlord–Tenant Policy Approaches,”  Housing Policy Debate 27 (1): 98–119.

Hays, R. Allen. 2012.  The Federal Government and Urban Housing. New York: SUNY Press.

Herbert, Christopher E., Irene Lew, and Rocio Sanchez-Moyano. 2013. “The role of investors in acquiring foreclosed properties in low-and moderate-income neighborhoods: A review of findings from four case studies.” Report prepared for the Joint Center for Housing Studies. Cambridge, MA: Joint Center for Housing Studies, Harvard University.

Immergluck, Dan. 2011.  Foreclosed: High-Risk Lending, Deregulation, and the Undermining of America’s Mortgage Market. Ithaca, NY: Cornell University Press.

———. 2013. “The Role of Investors in the Single-Family Market in Distressed Neighborhoods: The Case of Atlanta.” Report prepared for the What Works Collaborative. Cambridge, MA: Joint Center for Housing Studies, Harvard University. Retrieved from http://www.jchs.harvard.edu/

sites/default/files/w13-2_immergluck.pdf.

Cityscape 185

Raymond, Duckworth, Miller, Lucas, and Pokharel

———. 2018. “Renting the Dream: The Rise of Single-Family Rentership in the Sunbelt Metropolis,” 

Housing Policy Debate 28 (5): 814-829. https://doi.org/10.1080/10511482.2018.1460385.

Immergluck, Dan, Ann Carpenter, and Abe Lueders. 2017. “Hot City, Cool City: Explaining Neighbourhood-Level Losses in Low-Cost Rental Housing in Southern US Cities,”  International Journal of Housing Policy 3: 454–478.

Immergluck, Dan, and Jonathan Law. 2014a. “Investing in Crisis: The Methods, Strategies, and Expectations of Investors in Single-Family Foreclosed Homes in Distressed Neighborhoods,” 

Housing Policy Debate 24 (3): 568–593.

———. 2014b. “Speculating in Crisis: The Intrametropolitan Geography of Investing in Foreclosed Homes in Atlanta,”  Urban Geography 35 (1): 1–24.

Jackson, Kenneth T. 1987.  Crabgrass Frontier: The Suburbanization of the United States: New York: Oxford University Press.

Joint Center for Housing Studies of Harvard University. 2016.  The State of the Nation’s Housing. 

Report prepared for the Joint Center for Housing Studies of Harvard University. Cambridge, MA: Joint Center for Housing Studies. http://www.jchs.harvard.edu/research-areas/reports/state-nations-housing-2016.

Kalleberg, Arne L. 2012. “Job Quality and Precarious Work: Clarifications, Controversies, and Challenges,”  Work and Occupations 39 (4), 427–448.

Kaufman, Jay S. 2017.  Methods in Social Epidemiology. San Francisco, CA: John Wiley & Sons.

Kim, Jeongseob, and Gi-Hyoug Cho. 2016. “Unending Foreclosure Crisis: Uneven Housing Tenure Trajectories of Post-REO Properties,”  Applied Geography 70: 49–58.

Krieger, Nancy. 1992. “Overcoming the Absence of Socioeconomic Data in Medical Records: Validation and Application of a Census-Based Methodology,”  American journal of Public Health 82 

(5): 703–710.

Lambie-Hanson, Lauren, Christopher E. Herbert, Irene Lew, and Rocio Sanchez-Moyano. 2015. 

“Foreclosed Property Investors in a Strong Housing Market City: A Case Study of Boston,”  Cityscape 17 (2): 239–268.

Lempert, Richard, and Karl Monsma. 1994. “Cultural Differences and Discrimination: Samoans before a Public Housing Eviction Board,”  American Sociological Review 59 (6): 890–910.

Mallach, Alan. 2010. “Meeting the Challenge of Distressed Property Investors in America’s Neighborhoods.” Report prepared for the Local Initiatives Support Corporation. New York, NY: Local Initiatives Support Corporation.

———. 2014. “Lessons from Las Vegas: Housing Markets, Neighborhoods, and Distressed Single-Family Property Investors,”  Housing Policy Debate 24 (4): 769–801.

186 Refereed Papers

From Foreclosure to Eviction: Housing Insecurity in Corporate-Owned Single-Family Rentals Massey, Douglas S., and Nancy A. Denton. 1989. “Hypersegregation in U.S. Metropolitan 

Areas: Black and Hispanic Segregation along Five Dimensions,”  Demography 26 (3): 373–391. 

doi:10.2307/2061599.

———. 1993.  American Apartheid: Segregation and the Making of the Underclass. Cambridge, MA: Harvard University Press.

Massey, Douglas. S., and Jonathan Tannen. 2015. “A Research Note on Trends in Black 

Hypersegregation,”  Demography 52 (3): 1025–1034. doi:10.1007/s13524-015-0381-6.

Molina, Emily. T. 2016. “Foreclosures, Investors, and Uneven Development during the Great Recession in the Los Angeles Metropolitan Area,”  Journal of Urban Affairs 38 (4): 564–580.

Morenoff, Jeffrey D., Robert J. Sampson, and Stephen W. Raudenbush. 2001. “Neighborhood Inequality, Collective Efficacy, And The Spatial Dynamics Of Urban Violence,”  Criminology 39 (3): 517–558.

Newman, Kathe. 2009. “Post-Industrial Widgets: Capital Flows and the Production of the Urban,” 

International Journal of Urban and Regional Research 33 (2): 314–331.

Pattillo, Mary. 2013. “Housing: Commodity Versus Right,”  Annual Review of Sociology 39: 509–531.

Pfeiffer, Deirdre., and Joanna Lucio. 2015. “An Unexpected Geography of Opportunity in the Wake of the Foreclosure Crisis: Low-Income Renters in Investor-Purchased Foreclosures in Phoenix, Arizona,”  Urban Geography 36 (8): 1197–1220.

Raymond, Elora. 2017. “Wholesale Funding and the Increase in Construction Bank-Owned Real Estate in the US Financial Crisis,”  Urban Geography 38 (9): 1374–1392.

Raymond, Elora, Kyungsoon Wang, and Dan Immergluck. 2016. “Race and Uneven Recovery: 

Neighborhood Home Value Trajectories in Atlanta Before and After the Housing Crisis,”  Housing Studies 31 (3): 324–339.

Rothstein, Richard. 2017.  The Color of Law: A Forgotten History of How our Government Segregated America. New York, NY: Liveright Publishing.

Sampson, Robert. J., Stephen. W. Raudenbush, and Felton Earls. 1997. “Neighborhoods and Violent Crime: A Multilevel Study of Collective Efficacy,”  Science 277 (5328): 918–924.

Schwarz, Lorenz, and Laura Ferris. 2015. “Single-Family Rental Securitization: The Evolution of a New and Growing Asset Class,”  Journal of Structured Finance 21 (3): 15.

Soobader, Mah-Jabeen, Felicia B. LeClere, Wilbur Hadden, and Brooke Maury. 2001. “Using Aggregate Geographic Data to Proxy Individual Socioeconomic Status: does Size Matter?”  American Journal of Public Health 91 (4): 632.

Streeck, Wolfgang, and Kathleen Thelen. A. 2009. “Institutional Change in Advanced Political Economies,” in  Debating Varieties of Capitalism: a Reader. Oxford: Oxford University Press.

Cityscape 187

Raymond, Duckworth, Miller, Lucas, and Pokharel

———. 2005.  Beyond Continuity: Institutional Change in Advanced Political Economies. Oxford: Oxford University Press.

Thaler, Cole. 2016. Personal communication (interview). Project director, Atlanta Volunteer Lawyers Foundation, 235 Peachtree Street, Atlanta, GA 30303.

Yalamanchili, Kishore. K. 2016. “Single-Family Rental Securitizations: Residential Wine in a Commercial Bottle,”  Journal of Structured Finance 21 (4): 15–19.

188 Refereed Papers

School Performance of Schools 

Assigned to HUD-Assisted Households

Brent Mast

U.S. Department of Housing and Urban Development

Opinions expressed in this introduction are those of the author and do not necessarily reflect the views and policies of the U.S. Department of Housing and Urban Development or the U.S. government.

by Brent Mast

Abstract

This study examines school performance of schools assigned to households in four U.S. Department of Housing and Urban Development (HUD) rental assistance programs: The Tenant-Based Voucher (TBV); Project-Based Voucher (PBV); Public Housing (PH); and Project-Based Section 8 (PBS8).

School performance is measured by the percentage of fourth grade students proficient in math and reading according to state standardized tests. Past studies have examined performance of schools near, but not assigned to HUD assisted households. Public schools are matched to HUD households by geocoding the household addresses to Maponics school attendance zone data. School zones are then matched to school performance data from GreatSchools.

Results indicate that for households in each program, school performance is well below national averages and below national averages for students eligible for free or reduced-price lunches. Adjusting for differences in the proportion of students that are economically disadvantaged, school performance of schools assigned to assisted households is greater but still below national averages.

Results from statistical models controlling for differences across states in proficiency standards indicate that schools assigned to TBV and PBS8 households are significantly more proficient in reading and math compared to schools assigned to PBV and PH households. Comparisons of schools assigned to TBV and PBS8 households are sensitive to the sample of households analyzed. When all households are analyzed, schools assigned to PBS8 households outperform those assigned to TBV households on average. When the analysis is limited to households with elementary age children, average performance of schools assigned to TBV households is greater than that of schools assigned to PBS8 households.

For each HUD program, average school performance of schools assigned to all assisted households is markedly greater than that of schools assigned to households with elementary age children, which raises questions regarding HUD’s ability to place households with children in opportunity neighborhoods.

Cityscape: A Journal of Policy Development and Research •  Volume 20, Number 3 • 2018

Cityscape 189

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Mast

Introduction

One of HUD’s strategic goals has been to use housing as a platform to improve quality of life (HUD, 2018c). Researchers have measured HUD’s success in achieving this goal by measuring the quality of neighborhoods with HUD-assisted households. Past studies of neighborhoods characteristics for HUD assisted households have focused heavily on poverty and minority concentration (Newman and Schnare, 1997; McClure, 2010).

More recently, researchers examined other neighborhood metrics. For example, Lens et al. (2011) examined crime rates in neighborhoods of Housing Choice Voucher (HCV) households. Numerous studies have also examined school performance of households receiving federal housing assistance (Deng, 2007; Ellen and Horn, 2012; Horn et al., 2014; Ellen et al., 2016, Mast and Hardiman, 2017). HUD’s Affirmatively Furthering Fair Housing (AFFH) rule requires Public Housing Agencies (PHAs) and other program participants to perform a fair housing analysis, which includes analyzing HUD provided data on seven neighborhood opportunity indicators, including a school proficiency index (HUD, 2018b).

This article examines school performance of public schools assigned to HUD assisted households. 

Past studies have examined schools near, but not assigned to, federally assisted households. Such analysis is suboptimal because students may not attend or be assigned to their closest school. In the five cities studied by Blagg et al. (2018), less than half of students attended their closest school. 

In 13 metropolitan areas for which Ellen et al. (2016) had data on elementary school attendance zones, the nearest school for 64 percent of HCV households was their zoned school.

I match public schools to HUD households by geocoding the household addresses to Maponics school attendance zone data. School boundaries are a policy choice of school boards, which are elected local governments. The school of assignment may differ from the nearest school for either practical or political reasons, yet the school of assignment will be the default school for most elementary school students. In 2007, nearly three-fourths of students in the United States attended their assigned public school (National Center for Education Statistics, 2015).

School zones are then matched to school performance data from GreatSchools. I measure school performance based on the percentage of fourth grade students proficient in math and reading according to state standards.

The proportion of students proficient on state exams may vary due to factors beyond the school’s control, particularly student demographics. To control for student socioeconomic status, I compute an adjusted school performance measure that controls for the percentage of economically disadvantaged students in a school. The adjusted performance index attempts to measure schools’ 

“value added” to standardized test scores. The adjusted performance index may be particularly policy relevant in high-poverty areas, where most or all public schools have high percentages of low-income students.

I analyze data for households in HUD’s three largest rental assistance programs: The HCV 

program; PH; and the PBS8 program. Data for HCV households are reported separately for TBV 

and PBV households.

190 Refereed Papers

School Performance of Schools Assigned to HUD-Assisted Households Data are reported for all households in each program and separately for only those households with children ages 6-12 (elementary school age). Results for households with elementary age children are most policy relevant. Results for all households may also be of interest, however, because households without elementary age children may have them in the future. For example, 13.9 

percent of households without elementary age children have children age 5 and under. A school performance measure for all households may also be useful as an indicator of neighborhood quality, as children with greater academic achievement may be less likely to engage in delinquent behavior.

The main hypothesis I test is whether school performance for schools assigned to TBV households is greater on average compared to households receiving project-based assistance. This is because TBV, unlike the other three programs, is a tenant-based rather than unit-based subsidy designed to provide residents with mobility. TBV tenants can choose units in neighborhoods of their choice, provided the unit meets HUD housing quality standards and the family’s portion of rent at move-in is no more than 40 percent of adjusted income. The affordability requirement implies that a unit’s rent must be close to the PHA’s payment standard, which is typically between 90 and 110 percent of fair market rent.

Given the ability of TBV households to choose locations with higher performing schools, they might be expected to be assigned to higher performing schools compared to households in housing projects. Constraints that prevent TBV households from choosing neighborhoods with better performing schools may exist, however. For instance, there might be a lack of affordable rental units in areas with higher performing schools. Ellen et al., (2016) found that families with vouchers were more likely to move toward a better school in metropolitan areas with a relatively high share of affordable rental units located near higher performing schools.

Factors other than affordability may prevent TBV households from locating in attendance zones of high performing schools. As DeLuca and Rosenblatt (2010) note, social networks of low-income households may limit their ability to locate good schools (Horvat et al., 2003; Schneider et al., 2000; Neild, 2005); and minority and low-income households are less likely to consider schools based on academic achievement (Sapporito and Lareau, 1999; Henig, 1995; Teske and Schneider, 2001).

To test the hypothesis, I estimate regression models with state fixed effects to control for differences across states in proficiency standards. Results indicate that—

•  TBV and PBS8 households tend to be residentially assigned to higher performing schools than PH and PBV households.

•  When all households are included in the analysis, school performance of schools assigned to PBS8 households tends to be greater as compared to those assigned to TBV households.

•  When the analysis is limited to households with elementary age children, schools assigned to TBV households tend to outperform those assigned to PBS8 households.

Other findings from the regression analysis include the following:

•  In all HUD assistance programs, average fourth grade performance levels of schools assigned to HUD assisted households are well below national averages.

Cityscape 191

Mast

•  Regression adjusted school performance rates are higher when controlling for differences across schools in the proportion of students who are economically disadvantaged, although they remain below state averages.

•  For households in each HUD program, school performance tends to be lower when the 

analysis is limited to households with elementary age children.

In subsequent sections, I review the relevant literature, describe the data, present national and state summary statistics, and report results from regression models. I discuss regression results, as well as limitations and areas for further research. The final section summarizes the study.

Literature Review

In this section, I review the literature regarding poverty and academic achievement and school performance of schools near federally assisted households. This section draws heavily from Sackett (2016).

Poverty and Academic Achievement

The income-based achievement gap has always been large since it was first measured decades ago, and it’s been growing in recent years (Reardon, 2011). In 2015, fourth graders eligible for free or reduced-price lunches (FRPL) scored on average about two grade levels lower on National Assessment of Academic Progress math tests compared to their higher income peers (Dynarski and Kainz, 2015). Low-income students who attend high-poverty schools face significant academic challenges (Kahlenberg, 2001).

School impacts can conceptually be analyzed as the joint product of peer effects (effects on lower income students attending schools with higher performing students), teacher effects, and schoolwide effects such as curriculum and management. A large literature supports the existence of peer effects (Boozer and Cacciola, 2001; Barrera-Osorio et al., 2008; Brunello et al., 2010; Duflo et al., 2011; De Giorgi et al., 2009; Lugo, 2011; Kiss, 2011; Sojourner, 2011; Luppino, 2012; Antecol et al., 2013; Bursztyn and Jensen, 2014; Feld and ZÖlitz, 2015).

Several studies have examined peer effects and neighborhood effects (effects on lower income students who move to higher income neighborhoods with better chances of attending higher performing schools).

Schwartz (2012) compared academic achievement of children who lived in PH in Montgomery County, Maryland, where PH families are randomly assigned to neighborhoods. She compared outcomes for student assigned to lower poverty schools (0–20 percent of students FRPL eligible) to students assigned to higher poverty schools (20–85 percent of students FRPL eligible). After 2 

years, students in lower poverty schools became more proficient in math and reading compared to students assigned to higher poverty schools. After 7 years, students in lower poverty schools cut the math income achievement gap in half; there was no improvement in the math achievement gap for students assigned to higher poverty schools.

192 Refereed Papers

School Performance of Schools Assigned to HUD-Assisted Households Evidence from the Gautreaux mobility program in Chicago supports Schwartz’s findings. From 1976 through the late 1990s, households in PH or on waiting lists moved from high-poverty, high-minority neighborhoods in the city. About four-fifths moved to higher income, less segregated neighborhoods (Duncan and Zuberi, 2006), including over 115 suburbs (Rosenbaum and DeLuca, 2008). The Gautreaux evidence is less rigorous than Schwartz’s 2012 study because households had some control over where they moved.

Among the group who relocated to the suburbs, 88 percent attended schools with average ACT 

scores of 20 or higher (out of 36 possible), compared to 6 percent for the group that moved to neighborhoods in the city (Rosenbaum, 2005). Eight years later, 54 percent of students that moved to the suburbs attended college, compared to 21 percent of students that moved to the city (Rosenbaum, 2005).

Neighborhood Effects

The most rigorous evidence regarding neighborhood effects comes from the Moving to 

Opportunity (MTO) study. MTO examined the impact of residents receiving TBVs to move out of PH in distressed high-poverty neighborhoods in five cities between 1994 and 1998. The experiment included three groups of residents: A traditional voucher group, a low poverty voucher group, and a control group. The traditional voucher group received a normal voucher with no special counseling to help them locate to lower poverty neighborhoods. The low poverty voucher group received intensive housing search and counseling services to help them relocate to low poverty neighborhoods.

A followup study performed 4–7 years after random assignment found that “MTO had no 

detectable effects on the math and reading achievement of children” (National Bureau of Economic Research, 2017). This finding contradicts many studies that found evidence of peer effects. Possible explanations include the following:

•  Many of the MTO treatment kids went to better neighborhoods but not measurably better schools (Sanbonmatsu et al., 2011).

•  The testing was zero stakes, with no rewards for doing well.

•  Subsequent findings are that despite the lack of measured impact on cognitive skills, MTO 

treatment children who moved before age 13 had higher earnings and college matriculation, more marriages, and less out of wedlock births than controls (Chetty et al., 2016). This could result from either peer or neighborhood effects.

School Performance of Schools Near Assisted Households

Most directly relevant to this study, five studies have examined school quality of schools near, but not assigned to, federally assisted households.

Deng (2007) analyzes schools near HCV households, Low-Income Housing Tax Credit (LIHTC) households, and the broader population of rental households. She finds that both HCV and LIHTC 

Cityscape 193

Mast

households tend to live near lower performing schools compared to the average renting household, with variation in outcomes across metropolitan areas.

Using similar data, Ellen and Horn (2012) and Horn et al. (2014) examine school performance of schools nearest federally assisted households with children. Performance data for the 2008-2009 

school year were matched to data on assisted households with children for 2008.

Ellen and Horn (2012) compare households in the PH, HCV, PBS8, and LIHTC programs. As a proxy for LIHTC units with children, Ellen and Horn (2012) utilize data on LIHTC units with two or more bedrooms.

Ellen and Horn (2012) and Horn et al. (2014) did not have access to sufficient school boundary data to examine schools assigned to federally assisted households. They also examine school performance of schools nearest larger populations based on American Community Survey census tract data; distance was calculated using census tract centroids.

Similar to this study, Ellen and Horn (2012) and Horn et al. (2014) measure school performance based on the share of elementary school students proficient in math and reading according to state exams.

Ellen and Horn’s findings include the following:

•  Schools nearest federally assisted households have much lower performance on average compared to state averages.

•  HCV households live, on average, near lower performing schools compared to PBS8 and LIHTC households.

•  Schools nearest HCV and PBS8 households are higher performing on average compared to those nearest PH households.

Ellen and Horn (2012) expected a priori that, due to the ability of voucher households to choose neighborhoods with better performing schools, performance of schools near HCV households should compare favorably to schools nearest households receiving project-based assistance. They provide the following possible explanations for their findings to the contrary:

•  An insufficient stock of affordable units near high performing schools may exist.

•  A lack of information on affordable units available near good schools may exist.

•  Administrative constraints may prevent HCV households from crossing into higher performing school districts.

•  Most voucher holders are non-White, and patterns of residential segregation and discrimination may preclude minority HCV tenants from living near higher performing schools.

Horn et al. (2014) compare schools nearest HCV households with children to schools nearest other assisted households within the same state and metropolitan area, and to schools matched to other 194 Refereed Papers

School Performance of Schools Assigned to HUD-Assisted Households poor households with children in the same state and metropolitan area. In addition to proficiency rates, they examined school poverty and racial composition.

Horn et al. (2014) report that although HCV families with children live in neighborhoods with higher performing schools than PH households, they live in neighborhoods with lower performing schools than LIHTC households and poor households overall.

Ellen et al. (2016) match HCV household data in 15 states between 2003 and 2012 to school data for school years 2001–2002 through 2010–2011. They match HCV households to their nearest school within the school district and the two schools that are second and third closest.

Ellen et al. (2016) examine whether HCV households living in areas with high performing schools nearby and slack housing markets move towards higher performing schools when their oldest child becomes school eligible. They report that HCV households are more likely to move toward a higher performing school in the year before their oldest child meets the eligibility cut-off for kindergarten. 

The effect is larger in metropolitan areas with a relatively high share of affordable rental units located near high performing schools and in neighborhoods closer to better schools.

Mast and Hardiman (2017) compared school performance for schools near PBV households to a matched sample of TBV households. They measured school performance with a block-group index (on a scale of 1–100) of fourth grade math and reading performance from HUD’s AFFH database for school year 2011–2012 (HUD, 2018a). The AFFH school index is based on proficiency rates in a maximum of three schools within 1.5 miles of the block-group centroid.

Mast and Hardiman (2017) found that school performance was slightly higher for PBV 

households with children than for TBV households with children. The median school 

performance index was 28 (mean was 33.5) for PBV households and 27 (mean was 32.1) for the matched sample of TBV households.

This paper extends the literature by examining proficiency rates in schools assigned to HUD 

assisted households. Compared to analysis of nearest schools, this approach reduces measurement error because the majority of public school students attend their zoned school (National Center for Education Statistics, 2015).

Data Description

I analyze school performance data on fourth grade state math and reading tests from 

GreatSchools for school year 2012–2013 or 2013–2014 (whichever is most recently available for each school). The GreatSchools data includes each state’s main tests, covering all levels of schools (elementary, middle, and high). In the majority of states, the results are broken down by grade and subject. About 88 percent of observations are for school year 2013–2014. 

Kansas and West Virginia are excluded from the analysis because they did not report fourth grade performance data for either school year. I analyze data on all fourth grade students and subgroups of students that are and are not economically disadvantaged where available (discussed subsequently).

Cityscape 195

Mast

PH, PBV, and TBV data are from HUD’s Inventory Management System (IMS)/PIH Information Center (PIC) data system; PBS8 data are from HUD’s Tenant Rental Assistance Certification System (TRACS) data. Household data for December 2012 were matched to GreatSchools data for school year 2012–2013, and household data for December 2013 were matched to GreatSchools data for school year 2013–2014. Data are reported separately for all HUD households in each program and for those with children ages 6-12 (elementary school age).

Public schools were matched to HUD households by geocoding the household addresses to Maponics school attendance zone data for 2016. The Maponics database includes locally sourced school boundaries within school districts to delineate which students within the district will go to what school. Maponics school attendance zone data cover over 94 percent of the U.S. student enrollment.

In some areas (Boston, for example), schools are unzoned within school districts. Magnet schools can also be unzoned. In such cases, the Maponics dataset makes individual elementary school attendance zones equal to district attendance zones. School attendance zones can also overlap (in Fairfax County, Virginia, for example).

I handle the problem of multiple assignment in two ways. First, when such cases occur, households can be matched to multiple schools with districts; this is the case for 18.4 percent of households analyzed. For households matched to multiple schools, all school-household pairs receive equal weight in statistical computations, inversely proportional to the number of matched schools per household. The sum of weights for each household is 1.

Alternatively, to test sensitivity of my statistical estimates to inclusion of households assigned to multiple schools, I also report regression estimates, excluding these households (discussed subsequently).

To combine math and reading proficiency into a single index per school, I construct a school performance index for each school based on the percentage of fourth grade students proficient in math and reading according to state standards. In the formula shown here, i denotes the ith school, r denotes the proportion of fourth grade students proficient in reading, and m denotes the proportion of fourth grade students proficient in math. To adjust for differences across states in proficiency standards and differences across schools years, the index is percentile ranked by school year and state.

Index

1

1

i = [  

2  *  ri +   

2  *  mi]

I also compute an adjusted school performance index that controls for differences across schools in student socioeconomic status. The National Center for Education Statistics (2014) defines high-poverty schools as those with at least 75 percent of students eligible for FRPL.

Following the National Center for Education Statistics, I define the school poverty rate by the percentage of students FRPL eligible. Exhibit 1 reports four categories of school poverty by HUD 

program: 0–24.9 percent, 25–49.9 percent, 50–74.9 percent, and 75 percent and above. Schools assigned to HUD-assisted households are overrepresented in the highest category of school poverty. 

Following the National Center for Education Statistics, I refer to schools with at least 75 percent of students FRPL eligible as high poverty. Of schools assigned to HUD-assisted households, 196 Refereed Papers

School Performance of Schools Assigned to HUD-Assisted Households 54.7 percent are high poverty. Among schools assigned to TBV households, 54.3 percent are high poverty; for schools assigned to PH households, the percentage is 58.8. The corresponding percentages for PBS8 and PBV households are 52.1 percent and 56.2 percent, respectively. School poverty is even higher for schools assigned to HUD households with children ages 6-12. For PH 

households with elementary age children, 65.1 percent of schools are high poverty, while less than 2 percent are in the lowest category of school poverty.

Exhibit 1

Categories of School Poverty by HUD Program

Category of School Poverty

Sample

Program

0%–24.9%

25%–49.9%

50%–74.9%

75% and above

All households

PH

2.6%

16.5%

22.1%

58.8%

PBS8

5.8%

16.8%

25.4%

52.1%

PBV

6.8%

14.8%

22.2%

56.2%

TBV

4.6%

15.7%

25.4%

54.3%

All

4.5%

16.2%

24.6%

54.7%

Households  

PH

1.8%

13.3%

19.7%

65.1%

with children 

ages 6-12

PBS8

2.8%

12.8%

23.7%

60.8%

PBV

5.6%

14.7%

21.1%

58.5%

TBV

3.6%

13.7%

24.9%

57.8%

All

3.1%

13.5%

23.5%

59.9%

PH = Public Housing. PBS8 = Project Based Section 8. PBV = Project Based Voucher. TBV = Tenant Based Voucher.

Note: The school poverty rate is defined as the percentage of students eligible for free or reduced-price lunches.

Sources: Common Core of Data, 2013–2014; IMS/PIC 2012, 2013; TRACS 2012, 2013; GreatSchools 2012–2013, 2013–2014; Maponics 2016.

To account for differences in school demographics, I created an adjusted school performance index to control for the proportion of students that are economically disadvantaged.1 The adjusted index attempts to measure schools’ value added to student proficiency—that is, to separate performance of the school from the initial human capital of the students (as proxied by economic disadvantage). 

Note that the school proficiency index captures peer effects (discussed in the subsequent literature review section), while the adjusted school proficiency index is intended not to.

The adjusted school performance index is an enrollment weighted average of indices for two subgroups of students, percentile ranked separately by school year and state for both groups. In the formula that follows, j=1 denotes economically disadvantaged students, and j=2 denotes students that are not economically disadvantaged. si,j denotes the count of group j fourth grade students in school i, and si denotes total fourth grade enrollment in school i.

Where Indexi,j is a performance index, percentile ranked by school year and state, for group j in school i:

1

1

2

2

1  For Colorado, I used data for students that are and are not eligible for FRPL.

Cityscape 197

Mast

mi,j denotes the fraction of group j fourth grade students proficient in math in school i, and ri,j denotes the fraction of group j fourth grade students proficient in reading in school i.

For example, consider a school where a quarter of students are economically disadvantaged. 

Assume that compared to economically disadvantaged students in other schools across the state, the school’s disadvantaged students rank in the 80th percentile. Also, assume that compared to other non-disadvantaged students in other schools across the state, the schools’ non-disadvantaged students rank in the 60th percentile. The school’s adjusted school performance index is (1/4)*80 + 

(3/4)*60 = 65.

The adjusted school performance index is only available for schools that report test scores for students that are economically disadvantaged to GreatSchools. The adjusted index is not available for any schools in Alabama, Arkansas, Georgia, Hawaii, Illinois, Louisiana, Maryland, Maine, Michigan, Mississippi, Oklahoma, or Utah.2

Summary Statistics

Exhibit 2 reports summary statistics on the school performance index and adjusted school performance index by HUD program. Note that the statistics in this section are not adjusted for differences in state proficiency standards. The mean school performance for all HUD programs is 36.7, which is well below the national average (approximately 50) and below the national average for schools assigned to FRPL-eligible students of 40.3.3

TBV households tend to be assigned to higher performing schools than HUD households receiving project-based assistance. In contrast to findings from Mast and Hardiman (2017), the mean school performance for TBV households (37.2) is well above the mean for PBV households (34.7). The mean index for PH households is 35.7, and the mean for PBS8 households is 36.8.

Exhibit 2

School Performance Index Descriptive Statistics

Variable: school performance index

25th 

75th 

Sample

Program

Households

Median

Mean

Std Dev

percentile

percentile

All 

PH

838,110

9

26

35.724

57

18.356

households

PBS8

1,114,907

12

30

36.764

58

17.266

PBV

64,071

9

27

34.731

57

15.661

TBV

1,694,162

12

31

37.220

58

19.223

All

3,711,250

11

30

36.702

58

18.304

2 An alternative approach for creating an adjusted index would be to regress the unadjusted index on the percentage of economically disadvantaged students and base the adjusted index on the residuals. While this approach would result in the adjusted index being available for more schools, I prefer using actual data on proficiency of economically disadvantaged students.

3 I computed the national mean for FRPL-eligible students as a weighted mean of the school proficiency index for each school, where the weight is each school’s number of FRPL-eligible students.

198 Refereed Papers

School Performance of Schools Assigned to HUD-Assisted Households Exhibit 2

School Performance Index Descriptive Statistics

Variable: school performance index

25th 

75th 

Sample

Program

Households

Median

Mean

Std Dev

percentile

percentile

Households 

PH

188,011

7

22

32.326

50

18.504

with children 

ages 6-12

PBS8

162,872

9

25

32.077

50

16.092

PBV

11,638

9

24

33.384

56

16.255

TBV

516,994

11

28

34.725

54

18.831

All

879,515

10

26

33.704

53

18.159

Variable: adjusted school performance index

25th 

75th 

Sample

Program

Households

Median

Mean

Std Dev

percentile

percentile

All 

PH

527,254

11

35

43.440

75

17.709

households

PBS8

681,358

14

39

42.542

68

15.980

PBV

39,969

12

34

40.070

65

14.194

TBV

1,108,682

15

39

43.261

69

19.078

All

2,357,263

14

38

43.039

70

17.621

Households 

PH

115,283

9

30

39.772

68

18.185

with children 

ages 6-12

PBS8

94,537

11

32

38.206

62

15.054

PBV

7,921

12

29

37.463

62

15.595

TBV

318,309

13

35

40.181

64

18.584

All

536,050

12

33

39.705

64

17.679

PH = Public Housing. PBS8 = Project Based Section 8. PBV = Project Based Voucher. Std Dev = standard deviation. TBV = Tenant Based Voucher.

Sources: IMS/PIC 2012, 2013; TRACS 2012, 2013; GreatSchools 2012–2013, 2013–2014; Maponics 2016

In each program category, average school performance is lower when the analysis is limited to households with elementary age children. For PBS8 households with children ages 6-12, the mean school performance index is 32.1.

Note that although Ellen and Horn (2012) reported that school performance was greater on average for schools nearest PBS8 households with children compared with those nearest HCV households with children, they did not differentiate between HCV households in the TBV and PBV programs.

School performance of HUD-assisted households is notably better controlling for the proportion of students that are economically disadvantaged, although means are still below national averages for each program. The mean adjusted school performance index is 43.0 for all assisted households, 43.4 for PH households, 42.5 for PBS8 households, 40.1 for PBV households, and 43.3 for TBV 

households. For each program category, the mean adjusted index is lower when the sample is limited to households with children ages 6-12.

Exhibit 3 reports quartiles of the school performance index and adjusted school performance index by HUD program. Schools assigned to HUD assisted households are overrepresented in Cityscape 199

Mast

the lower quartiles of school performance. About 45 percent of schools assigned to assisted households are in the lowest quartile of the school performance index, while only 14.1 percent are in the upper quartile.

Exhibit 3

School Performance Index Quartiles

Quartile of School Performance Index

Sample

Program

1st

2nd

3rd

4th

All households

PH

49.5%

21.7%

12.4%

16.4%

PBS8

44.3%

25.4%

16.6%

13.7%

PBV

47.8%

22.9%

15.4%

13.9%

TBV

43.4%

25.7%

17.6%

13.3%

All

45.1%

24.6%

16.1%

14.1%

Households  

PH

54.5%

20.6%

11.2%

13.7%

with children 

ages 6-12

PBS8

51.1%

24.6%

14.3%

10.0%

PBV

51.0%

19.9%

16.5%

12.5%

TBV

47.1%

25.3%

16.3%

11.3%

All

49.5%

24.1%

14.8%

11.6%

Quartile of Adjusted School Performance Index

Sample

Program

1st

2nd

3rd

4th

All households

PH

41.1%

19.8%

14.1%

24.9%

PBS8

37.3%

23.3%

19.6%

19.8%

PBV

42.1%

20.8%

19.6%

17.4%

TBV

36.7%

23.5%

20.0%

19.8%

All

38.0%

22.6%

18.6%

20.9%

Households  

PH

45.7%

20.1%

13.0%

21.3%

with children 

ages 6-12

PBS8

42.8%

23.7%

17.2%

16.2%

PBV

46.9%

19.4%

17.3%

16.4%

TBV

40.6%

23.9%

18.6%

17.0%

All

42.1%

23.0%

17.1%

17.8%

PH = Public Housing. PBS8 = Project Based Section 8. PBV = Project Based Voucher. TBV = Tenant Based Voucher.

Sources: IMS/PIC 2012, 2013; TRACS 2012, 2013; GreatSchools 2012-13, 2013-14; Maponics 2016.

It may be noteworthy that the share of schools assigned to PH households in the upper quartile of the school performance index (16.4 percent) is greater than the corresponding share for TBV 

households (13.3 percent).

School performance tends to be greater when adjusting for the proportion of students that are economically disadvantaged. For PBS8 households, 13.7 percent of schools are in the upper quartile of the school performance index, compared to 19.8 percent for the adjusted school performance index.

For each program category and both school performance indices, the school performance is lower when focusing on households with elementary age children. For all PBV households, 13.9 percent of schools are in the upper quartile of the school performance index; for PBV households with children ages 6-12, the corresponding percentage is 12.5 percent.

200 Refereed Papers

School Performance of Schools Assigned to HUD-Assisted Households Statistical Analysis

Readers should view national statistics with caution because proficiency standards vary by state. In Appendix A, state means are reported in linked micromaps.

To estimate school performance by program controlling for state differences, I employ regression analysis with state fixed effects. I estimate mean effects with least squares regression and performance quartiles with generalized logistic regression.

Linear Regressions

Exhibit 4 reports estimates for four least squares regressions. All regressions contain binary indicators for PH, PBS8, and PBV, with TBV households relegated to the intercept. The models also contain dummies for school year 2012–2013 and states (state coefficients not reported).

Exhibit 4

Linear Regression Estimates

Dependent variable=school performance index

Sample: all households

Variable

Coefficient

Std Error

t-value

P-value

Intercept

45.036

0.258

174.489

<.0001

PH

-3.024

0.024

-125.166

<.0001

PBS8

0.902

0.022

41.395

<.0001

PBV

-2.552

0.071

-35.822

<.0001

School year 2012-13

4.028

0.085

47.171

<.0001

N=9,632,219 R-squared=.078

Dependent variable=school performance index

Sample: households w/children ages 6-12

Variable

Coefficient

Std Error

t-value

P-value

Intercept

43.102

0.581

74.203

<.0001

PH

-4.109

0.048

-86.140

<.0001

PBS8

-1.508

0.050

-30.219

<.0001

PBV

-2.294

0.164

-13.960

<.0001

School year 2012-13

5.461

0.178

30.621

<.0001

N=2,155,276 R-squared=.086

Dependent variable=adjusted school performance index

Sample: all households

Variable

Coefficient

Std Error

t-value

P-value

Intercept

43.945

0.255

172.599

<.0001

PH

-2.557

0.029

-87.003

<.0001

PBS8

0.313

0.027

11.794

<.0001

PBV

-4.202

0.087

-48.373

<.0001

School year 2012-13

1.501

0.106

14.205

<.0001

N=7,674,015 R-squared=.077

Cityscape 201

Mast

Exhibit 4

Linear Regression Estimates

Dependent variable=adjusted school performance index

Sample: households w/children ages 6-12

Variable

Coefficient

Std Error

t-value

P-value

Intercept

41.918

0.576

72.785

<.0001

PH

-3.062

0.060

-51.216

<.0001

PBS8

-1.126

0.064

-17.725

<.0001

PBV

-4.317

0.195

-22.129

<.0001

School year 2012-13

2.069

0.223

9.262

<.0001

N=1,657,608 R-squared=.082

PH = Public Housing. PBS8 = Project Based Section 8. PBV = Project Based Voucher. Std Error=standard error. TBV = Tenant Based Voucher.

Note: The regressions also include state fixed effects.

Sources: IMS/PIC 2012, 2013; TRACS 2012, 2013; GreatSchools 2012–13, 2013–14; Maponics 2016.

The first estimates reported in exhibit 4 model the school performance index for all households. 

The difference between each pair of program coefficients is statistically significant at the .0001 level, which is perhaps not surprising given the large sample size. Mean predictions by program evaluated at the means of the school year and state dummies are reported in exhibit 5. The most noteworthy result is that the mean prediction for PBS8 households is greater than the mean prediction for TBV 

households, which is contrary to national means reported in the previous section in exhibit 2.

Exhibit 5

Mean Regression Predictions by Program

Predicted variable: school performance index

Sample

Program

Mean Prediction

All households

PH

34.134

PBS8

38.060

PBV

34.606

TBV

37.158

All

36.702

Households with children ages 6-12 

PH

30.783

PBS8

33.384

PBV

32.598

TBV

34.892

All

33.704

Predicted variable: adjusted school performance index

Sample

Program

Mean Prediction

All households

PH

41.993

PBS8

44.863

PBV

40.348

TBV

44.550

All

43.994

Households with children ages 6-12

PH

38.766

PBS8

40.702

PBV

37.511

TBV

41.828

All

40.908

PH = Public Housing. PBS8 = Project Based Section 8. PBV = Project Based Voucher. TBV = Tenant Based Voucher.

Sources: IMS/PIC 2012, 2013; TRACS 2012, 2013; GreatSchools 2012–2013, 2013–2014; Maponics 2016.

202 Refereed Papers

School Performance of Schools Assigned to HUD-Assisted Households As shown in exhibit 5—

•  The mean predicted school performance index for PH is 34.1.

•  The mean predicted school performance index for PBS8 is 38.1.

•  The mean predicted school performance index for PBV is 34.6.

•  The mean predicted school performance index for TBV is 37.2.

The second regression estimates reported in exhibit 4 model the school performance index for households with children ages 6-12. The difference between each pair of program coefficients is statistically significant at the .0001 level. Mean predictions by program, reported in exhibit 5, are as follows:

•  The mean predicted school performance index for PH households with children ages 6-12 

is 30.8.

•  The mean predicted school performance index for PBS8 households with children ages 6-12 

is 33.4.

•  The mean predicted school performance index for PBV households with children ages 6-12 

is 32.6.

•  The mean predicted school performance index for TBV households with children ages 6-12 

is 34.9.

For each program, the mean prediction for households with elementary age children is lower than the corresponding prediction for all households in the program. The mean prediction for TBV 

households with children ages 6-12 is greater than the predictions for households with children ages 6-12 receiving project-based assistance. This is contrary to findings from Ellen and Horn (2012) that school performance was greater on average for schools nearest PBS8 households with children compared to those nearest HCV households with children.

The third set of regressions estimates in exhibit 4 model the adjusted school performance index for all households. The difference between each pair of program coefficients is statistically significant at the .0001 level. Mean predictions by program are reported in exhibit 5. For each program, the mean prediction for the adjusted school performance index is greater than the corresponding prediction for the unadjusted school performance index:

•  The mean predicted adjusted school performance index for PH is 42.0.

•  The mean predicted adjusted school performance index for PBS8 is 44.9.

•  The mean predicted adjusted school performance index for PBV is 40.3.

•  The mean predicted adjusted school performance index for TBV is 44.6.

Cityscape 203

Mast

The final set of regressions estimates in exhibit 4 model the adjusted school performance index for households with children ages 6-12. The difference between each pair of program coefficients is statistically significant at the .0001 level. Mean predictions by program are reported in exhibit 5. For each HUD program, the mean predicted adjusted school performance index for households with elementary age children is lower than the corresponding prediction for all households in the program.

As shown in exhibit 5—

•  The mean predicted adjusted school performance index for PH households with children ages 6-12 is 38.8.

•  The mean predicted adjusted school performance index for PBS8 households with children ages 6-12 is 40.7.

•  The mean predicted adjusted school performance index for PBV households with children ages 6-12 is 37.5.

•  The mean predicted adjusted school performance index for TBV households with children ages 6-12 is 41.8.

To test sensitivity of my linear regression estimates to inclusion of households assigned to multiple schools, I report linear regression estimates and mean predictions excluding these households in Appendix B. Estimates indicate that for all programs, predicted school performance is slightly lower, excluding households assigned to multiple schools. The 

relationship between program estimates changed little, however. Predicted mean performance of schools assigned to TBV and PBS8 households is greater than the corresponding predictions for PH and PBV households in all regressions.

Logistic Regressions

To analyze differences in quartiles of school performance by program, I estimate four generalized logistic regressions. The models contain binary indicators for PH, PBS8, and PBV, with TBV 

households relegated to the three intercepts (there are three coefficients for each independent variable, corresponding to the second, third, and fourth quartiles of performance, with the first quartile as the reference category). The models also contain dummies for school year 2012–2013 

and states. Estimated odds ratios, p-values, and 95 percent confidence levels for each pair of programs are reported in exhibit 6.

204 Refereed Papers

School Performance of Schools Assigned to HUD-Assisted Households Exhibit 6

Estimated Odds Ratios

Predicted variable=quartile of school performance index

Sample: all households

Lower 95% 

Upper 95% 

Odds Ratio 

Programs

Quartile

P-value

Confidence 

Confidence 

Estimate

Limit

Limit

PH vs PBS8

2nd

0.797***

<.0001

0.791

0.803

PH vs PBS8

3rd

0.678***

<.0001

0.673

0.685

PH vs PBS8

4th

0.760***

<.0001

0.753

0.767

PH vs PBV

2nd

0.992

0.444

0.972

1.013

PH vs PBV

3rd

0.840***

<.0001

0.820

0.861

PH vs PBV

4th

0.953***

0.0002

0.928

0.977

PH vs TBV

2nd

0.807***

<.0001

0.801

0.812

PH vs TBV

3rd

0.676***

<.0001

0.671

0.682

PH vs TBV

4th

0.885***

<.0001

0.877

0.892

PBS8 vs PBV

2nd

1.245***

<.0001

1.220

1.271

PBS8 vs PBV

3rd

1.238***

<.0001

1.209

1.268

PBS8 vs PBV

4th

1.253***

<.0001

1.222

1.286

PBS8 vs TBV

2nd

1.012***

<.0001

1.006

1.018

PBS8 vs TBV

3rd

0.997

0.380

0.990

1.004

PBS8 vs TBV

4th

1.164***

<.0001

1.155

1.173

PBV vs TBV

2nd

0.813***

<.0001

0.797

0.830

PBV vs TBV

3rd

0.805***

<.0001

0.786

0.824

PBV vs TBV

4th

0.929***

<.0001

0.905

0.952

N=9,632,219, -2 log likelihood=9,071,182.6

Predicted variable=quartile of school performance index

Sample: households w/children ages 6-12

Lower 95% 

Upper 95% 

Odds Ratio 

Programs

Quartile

P-value

Confidence 

Confidence 

Estimate

Limit

Limit

PH vs PBS8

2nd

0.800***

<.0001

0.786

0.813

PH vs PBS8

3rd

0.732***

<.0001

0.717

0.748

PH vs PBS8

4th

0.886***

<.0001

0.866

0.907

PH vs PBV

2nd

1.047*

0.077

0.995

1.101

PH vs PBV

3rd

0.700***

<.0001

0.662

0.739

PH vs PBV

4th

0.896***

0.001

0.841

0.954

PH vs TBV

2nd

0.745***

<.0001

0.735

0.756

PH vs TBV

3rd

0.624***

<.0001

0.613

0.635

PH vs TBV

4th

0.816***

<.0001

0.801

0.830

PBS8 vs PBV

2nd

1.309***

<.0001

1.244

1.376

PBS8 vs PBV

3rd

0.956

0.107

0.905

1.010

PBS8 vs PBV

4th

1.011

0.741

0.949

1.077

PBS8 vs TBV

2nd

0.932***

<.0001

0.919

0.945

PBS8 vs TBV

3rd

0.852***

<.0001

0.838

0.866

PBS8 vs TBV

4th

0.920***

<.0001

0.902

0.939

PBV vs TBV

2nd

0.712***

<.0001

0.678

0.748

PBV vs TBV

3rd

0.891***

<.0001

0.845

0.940

PBV vs TBV

4th

0.910***

0.003

0.856

0.968

N=2,155,276, -2 log likelihood=2,051,739.5

Cityscape 205

Mast

Exhibit 6

Estimated Odds Ratios

Predicted variable=quartile of adjusted school performance index

Sample: all households

Lower 95% 

Upper 95% 

Odds Ratio 

Programs

Quartile

P-value

Confidence 

Confidence 

Estimate

Limit

Limit

PH vs PBS8

2nd

0.826***

<.0001

0.818

0.834

PH vs PBS8

3rd

0.696***

<.0001

0.689

0.704

PH vs PBS8

4th

0.827***

<.0001

0.818

0.835

PH vs PBV

2nd

1.085***

<.0001

1.056

1.116

PH vs PBV

3rd

0.862***

<.0001

0.838

0.887

PH vs PBV

4th

1.256***

<.0001

1.219

1.294

PH vs TBV

2nd

0.827***

<.0001

0.819

0.834

PH vs TBV

3rd

0.688***

<.0001

0.681

0.695

PH vs TBV

4th

0.886***

<.0001

0.878

0.895

PBS8 vs PBV

2nd

1.314***

<.0001

1.278

1.350

PBS8 vs PBV

3rd

1.238***

<.0001

1.204

1.273

PBS8 vs PBV

4th

1.519***

<.0001

1.475

1.565

PBS8 vs TBV

2nd

1.001

0.860

0.993

1.009

PBS8 vs TBV

3rd

0.988***

0.007

0.980

0.997

PBS8 vs TBV

4th

1.072***

<.0001

1.063

1.082

PBV vs TBV

2nd

0.762***

<.0001

0.741

0.783

PBV vs TBV

3rd

0.798***

<.0001

0.776

0.820

PBV vs TBV

4th

0.706***

<.0001

0.685

0.727

N=7,674,015, -2 log likelihood=6,081,156.6

Predicted variable=quartile of adjusted school performance index

Sample: households w/children ages 6-12

Lower 95% 

Upper 95% 

Odds Ratio 

Programs

Quartile

P-value

Confidence 

Confidence 

Estimate

Limit

Limit

PH vs PBS8

2nd

0.833***

<.0001

0.814

0.852

PH vs PBS8

3rd

0.754***

<.0001

0.735

0.774

PH vs PBS8

4th

0.869***

<.0001

0.847

0.892

PH vs PBV

2nd

1.185***

<.0001

1.113

1.261

PH vs PBV

3rd

0.937*

0.055

0.877

1.001

PH vs PBV

4th

1.168***

<.0001

1.091

1.251

PH vs TBV

2nd

0.802***

<.0001

0.788

0.817

PH vs TBV

3rd

0.669***

<.0001

0.655

0.683

PH vs TBV

4th

0.854***

<.0001

0.837

0.871

PBS8 vs PBV

2nd

1.422***

<.0001

1.336

1.515

PBS8 vs PBV

3rd

1.242***

<.0001

1.163

1.327

PBS8 vs PBV

4th

1.344***

<.0001

1.254

1.440

PBS8 vs TBV

2nd

0.963***

0.0001

0.945

0.982

PBS8 vs TBV

3rd

0.887***

<.0001

0.868

0.906

PBS8 vs TBV

4th

0.983

0.121

0.961

1.005

PBV vs TBV

2nd

0.677***

<.0001

0.637

0.720

PBV vs TBV

3rd

0.714***

<.0001

0.670

0.761

PBV vs TBV

4th

0.731***

<.0001

0.684

0.782

N=1,657,608, -2 log likelihood=1,343,212.7

N=1,657,608, -2 log likelihood=1,343,212.7

* statistically significant at the .10 level; ** significant at the .05 level; ***significant at the .01 level PH = Public Housing. PBS8 = Project Based Section 8. PBV = Project Based Voucher. TBV = Tenant Based Voucher.

Sources: IMS/PIC 2012, 2013; TRACS 2012, 2013; GreatSchools 2012–2013, 2013–2014; Maponics 2016.

206 Refereed Papers

School Performance of Schools Assigned to HUD-Assisted Households The first set of odds ratios reported in exhibit 6 is from a logistic regression estimating quartiles of the school performance index for all households. As seen in exhibit 6—

•  The estimated odds of a TBV household being assigned to a school in the second, third, and fourth quartiles of school performance are significantly greater than the corresponding odds for PH and PBV households (all p-values <.0001).

•  Results indicate that the odds of a PBS8 household being assigned to a school in the three upper quartiles of school performance are significantly greater than the corresponding odds for PH and PBV households (all p-values <.0001).

•  The estimated odds of a PBS8 households being assigned to a school in the fourth quartile of school performance is significantly greater than the corresponding odds for a TBV 

household; the odds of being assigned to a school in the second or third quartile are very similar for both programs.

The second set of odds ratio estimates reported in exhibit 6 are from modeling quartiles of the school performance index for households with children ages 6-12. The data show—

•  The estimated odds of a TBV household with elementary school age children being assigned a school in the upper three quartiles of school performance are significantly greater than the corresponding odds for households with elementary school age children in all other programs (all p-values <.0001).

•  For PBS8 household with elementary school age children, the estimated odds of being assigned a school in the upper three quartiles of school performance are significantly greater than the corresponding odds for PH households (all p-values <.0001).

The next set of odds ratios reported in exhibit 6 is from modeling quartiles of the adjusted school performance index for all households, which indicates that—

•  The estimated odds of a TBV household being assigned to a school in the second, third, and fourth quartiles of adjusted school performance are significantly greater than the corresponding odds for PH and PBV households (all p-values <.0001).

•  Estimated odds of a PBS8 household being assigned to a school in the three upper quartiles of adjusted school performance are significantly greater than the corresponding odds for PH and PBV households (all p-values <.0001).

•  The estimated odds of a PBS8 household being assigned to a school in the fourth quartile of adjusted school performance is significantly greater than the corresponding odds for a TBV 

household; the estimated odds of being assigned to a school in the second or third quartile are very similar for the two programs.

The final set of odds ratios reported in exhibit 6 is from modeling quartiles of the adjusted school performance index for households with children ages 6-12, which shows—

Cityscape 207

Mast

•  The estimated odds of a TBV household with elementary age children being assigned to a school in the three upper quartiles of adjusted school performance are significantly greater than the corresponding odds for PH and PBV households (all p-values <.0001).

•  For PBS8 household with elementary age children, the estimated odds of a being assigned to a school in the three upper quartiles of adjusted school performance are significantly greater than the corresponding odds for PH and PBV households (all p-values <.0001).

•  The odds of a TBV household with elementary age children being assigned to a school in the second or third quartile of adjusted school performance is significantly greater than the corresponding odds for a PBS8 household; the estimated odds of being assigned to a school in the fourth quartile of adjusted school performance are very similar for both programs.

Summary

To summarize, key findings of the statistical analysis include the following:

•  Average school performance of schools assigned to HUD-assisted households is well below national averages for all HUD programs.

•  School performance rates are higher controlling for the proportion of students that are economically disadvantaged, although means remain below national averages.

•  School performance rates tend to be lower when the analysis is limited to HUD-assisted households with elementary age children.

•  TBV households tend to be assigned to higher performing schools than PH and PBV households.

•  PBS8 households tend to be assigned to higher performing schools than PH and PBV households.

•  When all households are included in the analysis, school performance of schools assigned to PBS8 households tends to be greater compared to those assigned to TBV households.

•  When the analysis is limited to households with elementary age children, average 

performance of schools assigned to TBV households is greater compared to that of schools assigned to PBS8 households.

Discussion of Regression Estimates

The result that school performance for schools assigned to TBV households is greater on average than for schools assigned to PH households is not surprising given that almost 40 percent of PH 

households reside in census tracts with poverty rates of at least 40 percent, compared to 15.6 

percent of TBV households. Ellen and Horn (2012) also report that average school performance is much greater for schools nearest TBV households with children compared to schools nearest PH 

households with children.

My finding that average school performance is greater for schools assigned to TBV households compared to schools assigned to PBV households contradicts results from Mast and Hardiman 208 Refereed Papers

School Performance of Schools Assigned to HUD-Assisted Households (2017). Mast and Hardiman, however, compared PBV households to a matched sample of TBV 

households; households were matched on household characteristics with propensity score weighting. My regression models did not control for differences in household characteristics.

Ellen and Horn (2012) report that average school performance is greater for schools near PBS8 households with children, compared to schools near HCV households with children. My regression analysis indicates that average school performance is very similar for schools assigned to both TBV households and PBS8 households with elementary age children. Numerous reasons may explain why the results of our studies differ. First, I analyze assigned schools, while Ellen and Horn (2012) analyze nearest schools. Second, we use data for different school years; Ellen and Horn use data for school year 2008–2009, while I use data for school years 2012–2013 

and 2013–2014. Third, Ellen and Horn analyze data for households with any children, while I analyze data for households with elementary age children. Fourth, my regression estimates adjust for differences in state proficiency standards with state fixed effects. Ellen and Horn (2012) did not adjust for state differences (they did report statistics by state and metro area). 

Fifth, Ellen and Horn analyzed data for all HCV households with children, while I report separate estimates for TBV and PBV households.

My finding that, for all HUD programs, average school performance is greater for schools assigned to all households compared with schools assigned to households with elementary age children could be driven in part by less resistance in higher opportunity neighborhoods to assisted households without children. About 77 percent of households without elementary age children have no children; 39 percent are elderly households without children; and 23.2 percent are households with disabilities4 and no children. Landlords in higher opportunity neighborhoods may have preferences for TBV households without children, and there could be a lack of affordable rental units with enough bedrooms for larger families in neighborhoods with better schools. Local governments may also be less resistant to assisted housing developments in higher opportunity areas targeted to the elderly and disabled without children.

Limitations and Areas for Further Research

As did prior studies, I measure school performance based on standardized math and reading exam data. Yet schools can improve students’ lives in many ways other than academic achievement. 

Chetty et al. (2016) found that children moving to lower poverty neighborhoods (with better chances of attending higher performing schools) experienced higher earnings and college matriculation, more marriages, and fewer out of wedlock births. School metrics for such longer-term outcomes are not nationally available, however.

I examine school performance of schools assigned to HUD-assisted households. Some assisted households will not attend their assigned schools due to school choice, magnet or charter school attendance, or private school attendance. In 2007 (the most recent year for which data are available), almost three-fourths of students in the United States attended their assigned public school (National Center for Education Statistics, 2015). The fraction of lower income students 4 I define households with disabilities as households where the household head or spouse/co-head has a disability.

Cityscape 209

Mast

attending their assigned school is likely higher because higher income students are much more likely to attend private school (Kolko, 2014).

I matched school performance data for school years 2012–2012 and 2013–2014 to Maponics attendance zone data for 2016. Some school zones will have changed between the time test score data were collected and the time the attendance zone data were collected. Maponics does not archive its attendance zone data, so I did not have access to attendance zone data for earlier years.

About 18 percent of households in my analysis were assigned to multiple schools, providing them a choice of public schools to attend. In such cases, I gave each household-school match equal weight in statistical computations. Sensitivity analysis indicated that my regression results were not changed substantially by including these households. Future research could analyze such matches with different weighting schemes, such as inverse-distance weighting or only using the closest school.

Ellen and Horn (2012) included LIHTC units in their analysis, using units with two or more bedrooms as a proxy for households with children. I excluded LIHTC from my analysis because it is not possible to identify units with elementary age children. Further research could include LIHTC units in the analysis and explore better identifying units with children.

I analyze performance data for fourth grade students. Evidence from Chetty et al. (2016) suggests that peer and/or neighborhood effects may be most efficacious in students ages 12 and under. 

As such, school performance of elementary and middle schools may be most policy relevant. 

However, future research could also examine school performance of high schools assigned to assisted households.

The regression models included state fixed effects to control for differences in proficiency standards across states. Policy makers may also be interested in how school performance varies by program in the same Public Housing Agencies (PHAs), which could be accomplished with models with PHA fixed or random effects.

My regression models did not control for differences is household characteristics such as those controlled for by Horn et al. (2014) and Mast and Hardiman (2017). Future research could estimate how much variation across programs can be explained by household demographics.

My analysis is cross-sectional. Building on research by Ellen et al. (2016), future research could examine if TBV households that move, particularly those with children, choose neighborhoods with higher performing schools. Researchers could also examine assisted households moving out of project-based assistance with TBVs (due to Rental Assistance Demonstration conversions, for instance).

Conclusion

This study examines school performance of schools assigned to households in four of HUD’s largest rental assistance programs: TBV, PH, PBV, and PBS8. Past studies examined school performance of schools near, but not assigned to, HUD-assisted households. School performance is measured by fourth grade reading and math results on state standardized tests.

210 Refereed Papers

School Performance of Schools Assigned to HUD-Assisted Households The main hypothesis tested is whether, given their choice of neighborhoods, TBV households are assigned to more proficient schools than households in the PBV, PH, and PBS8 programs. Results from regression analysis indicate that schools assigned to TBV households are significantly more proficient in reading and math compared to schools assigned to PBV and PH households.

Comparisons of schools assigned to TBV and PBS8 households depend on the sample of 

households analyzed. When all households are included in the statistical analysis, schools assigned to PBS8 are significantly more proficient compared to schools assigned to TBV households. When the sample is constrained to households with elementary age children, differences in performance between schools assigned to households in the two programs are much smaller.

Results also indicate that for households in all programs, school performance is well below national averages. Adjusting for differences in school demographics and differences in proficiency standards across states, school performance of schools assigned to assisted households is greater but still well below national averages.

For households in each HUD program, average school performance is lower when the analysis is limited to households with elementary age children. This raises questions regarding the success of HUD programs in locating households with children in opportunity neighborhoods.

Appendix A

Exhibit A.1 reports a linked micromap with two program categories: TBV households and 

households receiving project-based assistance (including PH, PBS8, and PBV households). The data are sorting by the mean school performance index for TBV households. Exhibit A.1 shows that—

•  Mean school performance is greater for TBV households compared to households receiving project-based assistance in 25 of 49 states.

•  For TBV households, mean school performance indices range from 22.4 in Connecticut to 61.3 in Louisiana; the median is 34.7 in Iowa.

•  For households receiving project-based assistance, means range from 23.6 in New Jersey to 52.4 in New York, with a median of 36.1 in Indiana.

Cityscape 211

Mast

Exhibit A.1

Linked Micromap of State Mean School Performance Indices

Sources: IMS/PIC 2012, 2013; TRACS 2012, 2013; GreatSchools 2012–2013, 2013–2014; Maponics 2016.

Exhibit A.2 reports a linked micromap with state mean school performance indices for households with children ages 6-12 in two program categories. The data are sorting by the school performance index for TBV households with children ages 6-12. Exhibit A.2 shows that—

•  Mean school performance is greater for TBV households with elementary age children 

compared to households with elementary age children receiving project-based assistance in 37 

of 49 states.

•  For TBV households with elementary age children, mean school performance indices range from 22.2 in Rhode Island to 59.2 in Louisiana, with a median of 33.0 in Kentucky.

•  For households receiving project-based assistance with elementary age children, means range from 15.7 in New Jersey to 49.9 in Louisiana, with a median of 31.6 in Texas.

212 Refereed Papers

School Performance of Schools Assigned to HUD-Assisted Households

Exhibit A.2

Linked Micromap of State Mean School Performance Indices, Households with Children Ages 6-12

Sources: IMS/PIC 2012, 2013; TRACS 2012, 2013; GreatSchools 2012–2013, 2013–2014; Maponics 2016.

Exhibit A.3 reports a linked micromap with state mean adjusted school performance indices in two program categories. The data are sorting by the adjusted school performance index for TBV 

households. Exhibit A.3 shows that—

•  Mean adjusted school performance is greater for households with receiving project-based assistance in 22 of 37 states compared to TBV households.

•  For TBV households, mean adjusted school performance indices range from 30.2 in 

Washington, D.C. to 65.8 in Nebraska, with a median of 40.0 in New Jersey.

•  For households receiving project-based assistance, means range from 20.3 in Virginia to 66.1 

in Ohio, with a median of 40.1 in Massachusetts.

Cityscape 213

Mast

Exhibit A.3

Linked Micromap of State Mean Adjusted School Performance Indices

Sources: IMS/PIC 2012, 2013; TRACS 2012, 2013; GreatSchools 2012–2013, 2013–2014; Maponics 2016.

Exhibit A.4 reports a linked micromap with state mean adjusted school performance indices for households with children ages 6-12 in two program categories. The data are sorting by the adjusted school performance index for TBV households with children ages 6-12. Exhibit A.4 shows that—

•  Mean adjusted school performance is greater for TBV households with elementary age 

children in 22 of 37 states compared to households with elementary age children receiving project-based assistance.

•  For TBV households with elementary age children, mean adjusted school performance indices range from 25.8 in Delaware to 63.3 in Ohio, with a median of 37.7 in Arizona.

•  For households receiving project-based assistance with elementary age children, means range from 11.5 in Virginia to 75.3 in Ohio, with a median of 37.0 in Nevada.

214 Refereed Papers

School Performance of Schools Assigned to HUD-Assisted Households

Exhibit A.4

Linked Micromap of State Mean Adjusted School Performance Indices, Households with Children Ages 6-12

Sources: IMS/PIC 2012, 2013; TRACS 2012, 2013; GreatSchools 2012–2013, 2013–2014; Maponics 2016.

Cityscape 215

Mast

Appendix B: Regression Estimates and Mean Predictions 

Excluding Households Assigned to Multiple Schools

Exhibit B1

Regression Estimates

Dependent variable=school performance index

Sample: all households

Variable

Coefficient

Std Error

t-value

P-value

Intercept

47.177

0.463

101.794

<.0001

PH

-3.035

0.042

-72.996

<.0001

PBS8

1.246

0.036

34.922

<.0001

PBV

-3.221

0.119

-27.147

<.0001

School year 2012-13

4.417

0.143

30.888

<.0001

N=3,027,188 R-squared=.037

Dependent variable=school performance index

Sample: households w/children ages 6-12

Variable

Coefficient

Std Error

t-value

P-value

Intercept

44.234

0.977

45.259

<.0001

PH

-4.526

0.078

-58.032

<.0001

PBS8

-1.351

0.078

-17.230

<.0001

PBV

-3.674

0.265

-13.846

<.0001

School year 2012-13

5.878

0.291

20.232

<.0001

N=728,176 R-squared=.041

Dependent variable=adjusted school performance index

Sample: all households

Variable

Coefficient

Std Error

t-value

P-value

Intercept

44.591

0.522

85.493

<.0001

PH

-3.607

0.060

-59.974

<.0001

PBS8

0.626

0.050

12.447

<.0001

PBV

-4.175

0.167

-25.001

<.0001

School year 2012-13

2.154

0.192

11.194

<.0001

N=3,027,188 R-squared=.029

Dependent variable=adjusted school performance index

Sample: households w/children ages 6-12

Variable

Coefficient

Std Error

t-value

P-value

Intercept

42.062

1.100

38.245

<.0001

PH

-4.531

0.116

-39.168

<.0001

PBS8

-1.071

0.116

-9.212

<.0001

PBV

-4.724

0.357

-13.214

<.0001

School year 2012–13

2.359

0.394

5.986

<.0001

N=728,176 R-squared=.036

PH = Public Housing. PBS8 = Project Based Section 8. PBV = Project Based Voucher. Std Error=standard error. TBV = Tenant Based Voucher.

Sources: IMS/PIC 2012, 2013; TRACS 2012, 2013; GreatSchools 2012–2013, 2013–2014; Maponics 2016.

216 Refereed Papers

School Performance of Schools Assigned to HUD-Assisted Households Exhibit B2

Mean Regression Predictions

Predicted variable: school Performance index

Sample

Program

Mean Prediction

All households

PH

31.566

PBS8

35.847

PBV

31.380

TBV

34.601

All

34.333

Households with children ages 6-12

PH

27.884

PBS8

31.058

PBV

28.735

TBV

32.410

All

31.233

Predicted variable: adjusted school Performance index

Sample

Program

Mean Prediction

All households

PH

38.954

PBS8

43.187

PBV

38.386

TBV

42.561

All

41.968

Households with children ages 6-12

PH

35.461

PBS8

38.922

PBV

35.269

TBV

39.993

All

38.855

PH = Public Housing. PBS8 = Project Based Section 8. PBV = Project Based Voucher. TBV = Tenant Based Voucher.

Sources: IMS/PIC 2012, 2013; TRACS 2012, 2013; GreatSchools 2012–2013, 2013–2014; Maponics 2016.

References

Antecol, Heather, Ozkan Eren, and Serkan Ozbeklik. 2013. Peer Effects in Disadvantaged Primary Schools: Evidence from a Randomized Experiment. IZA Discussion paper 7694. https://ssrn.com/

abstract=2345611.

Barrera-Osorio, Felipe, Marianne Bertrand, Leigh L. Linden, and Francisco Perez Calle. 2008. 

Conditional Cash Transfers in Education Design Features, Peer and Sibling Effects Evidence from a Randomized Experiment in Colombia. Working paper No. w13890. Cambridge, MA: National Bureau of Economic Research. https://ssrn.com/abstract=1112002.

Blagg, Kristin, Matthew Chingos, Sean P. Corcoran, Sarah A. Cordes, Joshua Cowen, Patrick Denice, Betheny Gross, Jane Arnold Lincove, Carolyn Sattin-Bajaj, Amy Ellen Schwartz, Jon Valant. 2018. “The Road to School: How Far Students Travel to School in the Choice-Rich Cities of Denver, Detroit, New Orleans, New York City, and Washington, DC.” Urban Institute Student Transportation Working Group. https://www.urban.org/sites/default/files/publication/97151/

the_road_to_school_6.pdf.

Cityscape 217

Mast

Boozer, Michael, and Stephen E. Cacciola. 2001. Inside the ‘Black Box’ of Project Star: Estimation of Peer Effects Using Experimental Data. Discussion paper 832. Yale Economic Growth Center. 

https://ssrn.com/abstract=277009.

Brunello, Giorgio, Maria De Paola, and Vincenzo Scoppa. 2010. “Peer Effects in Higher Education: Does the Field of Study Matter?”  Economic Inquiry 48 (3), 621–634.

Bursztyn, Leonardo, and Robert Jensen. 2014. How Does Peer Pressure Affect Educational Investments? Working Paper No. w20714. Cambridge, MA: National Bureau of Economic 

Research. https://ssrn.com/abstract=2532295.

Chetty, Raj, Nathaniel Hendren, and Lawrence F. Katz. 2016. “The Effects of Exposure to Better Neighborhoods on Children: New Evidence from the Moving to Opportunity Experiment,” 

American Economic Review 106 (4): 855–902. DOI: 10.1257/aer.20150572.

De Giorgi, Giacomo, Michelle Pellizzari, and Silvia Redaelli. 2009. Be as Careful of the Company You Keep as of the Books You Read: Peer Effects in Education and on the Labor Market. Working Paper No. w14948. Cambridge, MA: National Bureau of Economic Research. https://ssrn.com/

abstract=1401793.

DeLuca, Stephanie, and Peter Rosenblatt. 2010. “Does Moving to Better Neighborhoods Lead to Better Schooling Opportunities? Parental School Choice in an Experimental Housing Voucher Program,”  Teachers College Record 112 (5): 1443–1491.

Deng, Lan. 2007. “Comparing the effects of housing vouchers and low income housing tax credits on neighborhood integration and school quality,”  Journal of Planning Education and Research 27: 20–35.

Duflo, Esther, Pascaline Dupas, and Michael Kremer. 2011. “Peer Effects, Teacher Incentives, and the Impact of Tracking: Evidence from a Randomized Evaluation in Kenya,”  American Economic Review 101 (5):1739–1774. https://ssrn.com/abstract=1915795.

Duncan, Greg J., and Anita Zuberi. 2006. “Mobility Lessons From Gautreaux and Moving to Opportunity,”  Northwestern Journal of Law and Social Policy 1 (1): 110–126.

Dynarski, Mark, and Kirsten Kainz. 2015. “Why Federal Spending on Disadvantaged Students (Title I) Doesn’t Work.”  Evidence Speaks. Washington, DC: The Brookings Institution. http://www.brookings.

edu/research/reports/2015/11/20-title-i-spending-disadvantaged-students-dynarski-kainz.

Ellen, Ingrid Gould, and Keren Horn. 2012.  Do Federally Assisted Households Have Access to High Performing Public Schools?  Poverty & Race Research Action Council. http://prrac.org/pdf/

PRRACHousingLocation&Schools.pdf.

Ellen, Ingrid Gould, Keren Mertens Horn, and Amy Ellen Swartz, 2016. “Why Don’t Housing Choice Voucher Recipients Live Near Better Schools? Insights from Big Data,”  Journal of Policy Management and Analysis 35 (4): 884–905. https://doi.org/10.1002/pam.21929.

218 Refereed Papers

School Performance of Schools Assigned to HUD-Assisted Households Feld, Jan, and Ulf Zölitz. 2015. Understanding Peer Effects: On the Nature, Estimation and Channels of Peer Effects. IZA Discussion paper 9448. https://ssrn.com/abstract=2684278.

Henig, Jeffery. 1995. “Race and choice in Montgomery County, Maryland, magnet schools,” 

Teachers College Record 96: 729–734.

Horn, Keren Mertens, Ingrid Gould Ellen, and Amy Ellen Swartz, 2014. “Do Housing Choice Voucher holders live near good schools?”  Journal of Housing Economics 23: 28–40.

Horvat, Erin McNamara, Elliot B. Weininger, and Annette Lareau. 2003. “From social ties to social capital: Class differences in the relations between schools and parent networks,”  American Educational Research Journal 40: 319–351.

Kahlenberg, Richard D. 2001.  All Together Now: Creating Middle-Class Schools Through Public School Choice. Washington, DC: Brookings Institution Press.

Kiss, David. 2011. The Impact of Peer Ability and Heterogeneity on Student Achievement: Evidence from a Natural Experiment. IWQW Discussion paper. https://ssrn.com/

abstract=1783547 or http://dx.doi.org/10.2139/ssrn.1783547.

Kolko, Jed. 2014. “Where Private School Enrollment Is Highest and Lowest Across the U.S.” 

CityLab. http://www.citylab.com/housing/2014/08/where-private-school-enrollment-is-highest-

and-lowest-across-the-us/375993/.

Lens, Michael, I.G. Ellen, and K. O’Regan. 2011. “Do vouchers help low-income households live in safer neighborhoods? Evidence on the Housing Choice Voucher program,”  Cityscape 13 (3), 135–159.

Lugo, Maria Ana. 2011. Heterogenous Peer Effects, Segregation and Academic Attainment. World Bank Policy Research Working paper 5718. https://ssrn.com/abstract=1876294.

Luppino, Marc. 2012. Peer Turnover and Student Achievement. https://ssrn.com/

abstract=1871269 or http://dx.doi.org/10.2139/ssrn.1871269.

Mast, Brent D., and David Hardiman. 2017. “Project-Based Vouchers,”  Cityscape 19 (2): 301–322. 

https://www.huduser.gov/portal/periodicals/cityscpe/vol19num2/article21.html.

McClure, Kirk. 2010. “The Prospects for Guiding Housing Choice Voucher Households to High-Opportunity Neighborhoods,”  Cityscape 12 (3), 101–122. https://www.huduser.gov/portal/

periodicals/cityscpe/vol12num3/ch6.html.

National Bureau of Economic Research. 2017. “A Summary Overview of Moving to Opportunity: A Random Assignment Housing Mobility Study in Five U.S. Cities. ” http://www.nber.org/mtopublic/

MTO%20Overview%20Summary.pdf.

National Center for Education Statistics. 2014. “Table 216.60: Number and Percentage Distribution of Public School Students, by Percentage of Students in School Who Are Eligible for Free or Reduced Lunch, School Level, Locale, and Student Race/Ethnicity: 2012–2013.”  

https://nces.ed.gov/programs/digest/d14/tables/dt14_216.60.asp.

Cityscape 219

Mast

National Center for Education Statistics. 2015. “Fast Facts: Public School Choice Programs.” 

Washington, DC: U.S. Department of Education, Institute of Education Sciences, National Center 

for Education Statistics. https://nces.ed.gov/fastfacts/display.asp?id=6.

Neild, Ruth Curran. 2005. Parent management of school choice in a large urban district.  Urban Education 40 (3): 270–297.

Newman, Sandra J., and Ann B. Schnare, 1997. “... And a Suitable Living Environment”: The Failure of Housing Programs to Deliver on Neighborhood Quality,”  Housing Policy Debate 8 (4), 703–742.

Reardon, S.F. 2011. “The widening academic achievement gap between the rich and the poor: new evidence and possible explanations.” In Whither Opportunity? Rising Inequality and the Uncertain Life Chances of Low-Income Children, edited by Richard Murnane and Greg Duncan. New York: Russell Sage Foundation.

Rosenbaum, James E. 1995. “Examining the Geography of Opportunity by Expanding Residential Choice: Lessons From the Gautreaux Program.”  Housing Policy Debate 6 (1): 231–269.

Rosenbaum, James, and Stefanie DeLuca. 2008. “What Kinds of Neighborhoods Change Lives? The Chicago Gautreaux Housing Program and Recent Mobility Programs,”  Indiana Law Review 41 (3): 653–662.

Sackett, Chase. 2016. “Breaking Down Barriers: Housing, Neighborhoods, and Schools of 

Opportunity,” U.S. Department of Housing and Urban Development, Office of Policy Development and Research. https://www.huduser.gov/portal/sites/default/files/pdf/insight-4.pdf.

Sanbonmatsu, Lisa, Jens Ludwig, Lawrence F. Katz, Lisa A. Gennetian, Greg J. Duncan, Ronald C. 

Kessler, Emma Adam, Thomas W. McDade, Stacy Tessler Lindau. 2011. “Moving to Opportunity for Fair Housing Demonstration Program Final Impacts Evaluation,” U.S. Department of Housing and Urban Development, Office of Policy Development and Research. https://www.huduser.gov/

portal//publications/pdf/MTOFHD_fullreport_v2.pdf.

Sapporito, Salvatore, and Annette Lareau. 1999. School selection as a process: The multiple dimensions of race in framing educational choice.  Social Problems 46 (3): 418–439.

Schneider, Mark, Paul Teske, and Melissa Marschall. 2000.  Choosing schools: Consumer choice and the quality of American schools. Princeton, NJ: Princeton Univ. Press.

Schwartz, Heather. 2012. “Housing Policy Is School Policy: Economically Integrative Housing Promotes Academic Success in Montgomery County, Maryland.” In  The Future of School Integration: Socioeconomic Diversity as an Education Reform Strategy, edited by Richard D. Kahlenberg. New York: The Century Foundation Press: 27–66.

Sojourner, Aaron J. 2011. Identification of Peer Effects with Missing Peer Data: Evidence from Project Star. IZA Discussion paper 5432. https://ssrn.com/abstract=1745707.

220 Refereed Papers

School Performance of Schools Assigned to HUD-Assisted Households Teske, Paul, and Mark Schneider. 2001. “What researchers can tell us about school choice,”  Journal of Public Policy Analysis and Management 20: 609–632.

U.S. Department of Housing and Urban Development (HUD). 2018a.  AFFH-T Data Documentation. 

https://www.hudexchange.info/resource/4848/affh-data-documentation.

———.2018b.  Affirmatively Furthering Fair Housing (AFFH). https://www.hudexchange.info/

programs/affh.

———. 2018c.  Strategic Plan 2014-2018. https://www.huduser.gov/portal/publications/pdf/

HUD-564.pdf.

Cityscape 221

222 Departments

Departments

In this issue—

•  Affordable Design

•  Data Shop

•  Policy Briefs

•  Graphic Detail

•  Evaluation Tradecraft

Cityscape: A Journal of Policy Development and Research •  Volume 20, Number 3 • 2018

Cityscape 223

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

224 Affordable Design

Affordable Design

The U.S. Department of Housing and Urban Development sponsors or cosponsors three annual competitions for innovation in affordable design: The Innovation in Affordable Housing Student Design and Planning Competition; the American Institute of Architects – HUD Secretary’s Housing Community Design Awards; and the HUD Secretary’s Opportunity & Empowerment Award, co-sponsored with the American Planning Association. This Cityscape department reports on the competitions and their winners. Each competition seeks to identify and develop new, forward-looking planning and design solutions for expanding or preserving affordable housing. Professional jurors determine the outcome of these competitions.

by Regina C. Gray

2018 Innovation in Affordable 

Housing Student Design and 

Planning Competition: Whittier Falls 

in Dover, New Hampshire

Regina C. Gray, compiler

Social Science Analyst, Affordable Housing Research and Technology Division, U.S. Department of Housing and Urban Development

Winning Team: University of Maryland, College Park

Lauren Gilmartin, Daniel Green, Adan Ramos, Nathan Robbins, Sacsheen Scott

Runner-Up Team: University of Colorado Denver

Nora Bland, Adam Buehler, Will Dolenshek, Stacy Ester, Joel Miller

The Jury

M. Scott Ball, Valerie Fletcher, Clayton Mitchell, Kenneth Ogden, John Torti

Introduction

Regina C. Gray

The Innovation in Affordable Housing (IAH) Student Design and Planning Competition invites teams of graduate students from multiple disciplines to submit plans in response to an affordable housing design challenge in an existing home or residential building. Now in its fifth year running, the goals of the competition are to encourage research and innovation in high-quality affordable housing that strengthens the social and physical fabric of low- and moderate-income communities and to foster crosscutting teamwork within the design and community development process. This 

Cityscape: A Journal of Policy Development and Research •  Volume 20, Number 3 • 2018

Cityscape 225

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Gray

brief article includes a recounting of the challenges, solutions, and lessons learned by the first- and second-place student teams and thoughts from the jury on defining innovation in housing design.

The IAH Student Design and Planning Competition is open to graduate students in architecture, planning and policy, finance, and other disciplines. The competition challenges the students to address social, economic, and environmental issues in responding to a specific housing problem identified by a public housing agency.

This year, HUD partnered with the Housing Authority of the City of Dover in New Hampshire, named  Whittier Falls, to develop this year’s challenge: to incorporate innovative design techniques for community engagement strategies for seniors, Veterans, and persons with disabilities for properties managed by the housing authority. The two sites are situated adjacent from one another between Niles and Union Streets. The students submitted site plans involving the construction of 154 units designed specifically for these populations. They were strongly encouraged to go beyond physical improvements and identify ways to improve the provision of community services. 

The students’ ultimate objectives were to explore the sites under construction, ask the housing authority staff probing questions about site-specific issues, and compile useful information to assist with their revised project proposal.

The Niles and Union Court apartments are new construction projects. Niles consists of a total 40 

units: 36 one-bedroom units and 4 two-bedroom units. The Union Court building has 30 one-bedroom units. Students were required to address environmental challenges, such as through the use of materials that are durable and resilient during natural disasters and need little maintenance; design approaches to improve the health, safety, and the well-being of residents; and energy- and water-efficient appliances that preserve natural resources and are cost-effective. The nearby Cricket Brook, a tributary that flows year-round, has implications for storm water management. Students were asked to develop mitigation strategies. Finally, central to Whittier Falls’ mission is enhancing community connectedness through public spaces. Situated near the construction site is the Seymour Osman Community Center where residents can interact, engage in various neighborhood events, beautification projects, and other recreational activities. Students were strongly encouraged Exhibit 1

Whittier Falls, the housing authority in Dover, New Hampshire, is the oldest in the state.

226 Affordable Design

2018 Innovation in Affordable Housing Student Design and 

Planning Competition: Whittier Falls in Dover, New Hampshire

to come up with creative approaches for redesigning this building, making it more adaptable to the needs of the residents.

The competition is designed in two phases. In Phase I, a jury of five practitioners that included a planner, builder, and architects evaluated first-round proposals, which teams from approximately 38 universities submitted electronically. From these submissions, the jury selected four finalist teams. In Phase II, the finalist teams further refined their plans—addressing complex issues, incorporating more detail, improving floor plans, and conducting additional analyses following the site visit to Whittier Falls. The site visit enabled the finalists to expand on their original proposal and submit a revised final project. Several weeks after the site visit, all jurors and finalists traveled to Washington, D.C., for the final competition and awards ceremony event at HUD headquarters on April 18, 2018. At this event, finalist teams presented their revised project solutions in front of the jury and an audience. Following the presentations, the jury selected the team from the University of Maryland, College Park as the winner and the team from the University of Colorado Denver, as the runner-up.

The winning student teams and members of the jury shared their thoughts about the competition. 

The students reflected on the biggest challenges the team faced and how they attempted to address them, opportunities to learn from mistakes, their concept of  innovation, elements observed that provided value to the design of the project, and any tradeoffs that had to be made to get a feasible site plan. Jurors shared the elements of the winning site plans that represented innovative solutions and address whether the proposed solutions could be implemented at Whittier Falls and possibly replicated at similarly situated sites.

The Winning Team: University of Maryland, College Park

Lauren Gilmartin, Daniel Green, Adan Ramos, Nathan Robbins, and Sacsheen Scott

The award-winning site plan from the University of Maryland, College Park, called  Beacon Crossing, is a proposal for new construction totaling $30 million to create livable units for seniors and persons with disabilities. The main features of the plan include an updated functional space for the existing community center and includes a YMCA, a food co-op, and a new community garden with a greenhouse that provides food throughout the year. The three pillars embodied in the project are to (1)  Enhance access to community supportive services, to (2)  Create  community that is connected whereby social interaction and resident engagement are encouraged, and to (3)  Improve the health and well-being of all those living in the community. The team’s proposed design emphasizes social interaction, expanded open space for recreation, and a common area where residents can gather. 

Also noteworthy is the integration of green, sustainable materials throughout the community, such as a new purification system that reuses gray water and stormwater runoff.

The student team’s reflections on the competition experience follow.

On the concept of innovation: The Maryland team struggled with how to identify innovation in their plan, but as one student put it, “working with limited resources to create something that is greater than the sum of its parts” captures how the students approached this task. The innovation Cityscape 227

Gray

Exhibit 2

An aerial view of Maryland’s  Beacon Crossing plan for the Whittier Falls community.

comes when addressing how to deal with the social isolation that many seniors experience, according to the housing authority’s analysis. The challenge, then, would be to create a space that would encourage more interaction—a communal space where residents could gather and engage in a variety of activities that would give the residents a sense of purpose. For example, an abundance Exhibit 3

Maryland’s plan to incorporate sustainable materials into an innovative design.

228 Affordable Design

2018 Innovation in Affordable Housing Student Design and 

Planning Competition: Whittier Falls in Dover, New Hampshire

of research is available on the benefits of green infrastructure and mental health. The students, therefore, designed spaces that would enhance health and wellness, creating walkable bicycle paths and a 12-foot-wide pedestrian bridge over the Cricket Brook waterway. The green walkways offer opportunities for the residents to collaborate on beautification projects. The buildings are situated facing each other and the courtyard so that resident interaction naturally occurs.

On the buildings: The theme of Maryland’s plan includes two apartment buildings and clusters of smaller townhomes, with the clusters creating micro-communities that contain co-living or shared spaces within the units. The team proposed three stages for construction: stage one is the creation of 210 studio, one-, and two-bedroom apartments targeted to mixed-income families; stage two involves the development of 240-unit townhomes and the community center with the YMCA, which is specially designed for persons with disabilities and seniors; and stage three features 302 

additional townhomes that incorporate universal design concepts and use prefabricated modular construction with structural insulated panels (SIPS). All stages of the project include both passive and active design approaches that minimize energy consumption, such as solar heating, buffer spaces and double facades, and high-performance windows.

Exhibit 4

To enhance community living, the Maryland team plan includes a health and wellness center and a food co-op.

On innovation: According to some of the Maryland students, in thinking holistically at the broad conceptual level, the team had to make various tradeoffs, the most notable of which was sacrificing a certain level of detail to ensure the big ideas were incorporated into every aspect of the design. 

To preserve affordability, the project would be developed in phases as described previously—

converting 214 of the public housing units through the Rental Assistance Demonstration (RAD) program and proposing additional units subsidized through the Section 811 program. Additionally, the students flirted with variations in site configuration to accommodate the new affordable Cityscape 229

Gray

housing units while also preserving open space for recreation and resident interaction. The project was financed through the Home Loan program and with funds through the Community Development Block Grant (CDBG) program. The team utilized the 9 percent and 4 percent tax credits as well to promote mixed-income goals; it proposed a commercial ground lease to help finance the co-op and the YMCA. Finally, investments in energy efficiency through a “power purchase” would yield a 30 percent reduction in utility costs per unit.

Exhibit 5

To make the plan more feasible, the Maryland students introduced a phased construction project that blends various types of housing using modular construction and structural insulated panels.

In summary, Maryland’s overall approach to this project was to create an identifiable neighborhood with multiple building types and incorporation of green components designed primarily to enhance mobility, accessibility, and social engagement for older residents and persons with various disabilities.

The Runner-Up Team: University of Colorado, Denver

Nora Bland, Adam Buehler, Will Dolenshek, Stacy Ester, Joel Miller

The University of Colorado Denver (UCD) team was selected as the runner-up this year. The jury lauded the team’s plan as highlighting the central importance of accessible design that addresses health and wellness and connectivity to the larger Dover community. The students presented on the plan’s major themes, which were creating easy access to public amenities through the encouragement of multimodal options and promoting walkable places throughout the development. 

The social aspects of the project were most impressive, according to the judges, incorporating design strategies that improve mental and emotional well-being, and pointing to research confirming that connections to the outdoors and other people lessens boredom, combats loneliness, and contributes to a greater sense of community. For the students, the concept of innovation involved shifting the 230 Affordable Design

2018 Innovation in Affordable Housing Student Design and 

Planning Competition: Whittier Falls in Dover, New Hampshire

paradigm from a central focus on creating a more livable space to a functional space for seniors and persons with disabilities. In other words, the students said that they wanted to build a place not only for people to live, but to create a space for the community to thrive. Thriving communal living means the incorporating the use of innovative technologies that are adaptable to changing lifestyles, whereby residents can live and age in place in a community setting.

Exhibit 6

The UCD plan,  Allied Living, emphasizes the importance of connecting residents to the outdoors.

The University of Colorado Denver (UCD) students also presented a plan for new construction on the site. The plan, called  Allied Living, centers on the concepts of Connectivity, Wellness, Inclusivity, and Experience.  Connectivity means creating public spaces that are flexible and adaptive to community needs and improve access and mobility for a community’s residents. For the UCD 

team, the proposal includes a designated open space for Pedestrian Zones, a dog park, outdoor activity areas, and a new transportation hub with a bus shelter, access to paratransit, and electric vehicle recharging stations.  Wellness emphasizes design for people, the planet, and prosperity—

what students referred to as the Triple Bottom Line approach that stresses the important connection to nature and the outdoors by incorporating garden areas throughout the development, a greenhouse, a commercial kitchen in the shared community dining and event areas, and space for a local farmers’ market. The team also proposed a loop trail around all buildings and an indoor fitness room that is easily accessible for all residents.

To address  Inclusivity, the UCD plan offers a variety in building and units types, incorporating modular construction and phasing, and including convertible ADA-compliant1 units to 

1 The 2010 American Disabilities Act (ADA) revises the historic 1991 ADA’s federal guidance for implementing Titles II and III standards for accessible design which apply only to new construction. To learn more about the 2010 ADA updated guidance, visit: https://www.ada.gov/regs2010/2010ADAStandards/Guidance2010ADAstandards.htm.

Cityscape 231

Gray

Exhibit 7

The greenhouse allows residents to become directly involved in beautification and green projects. 

There is some empirical evidence that these types of activities create a sense of communal belonging.

Exhibit 8

The cornerstone of the UCD plan is the integration of nature and green elements throughout the grounds to enhance the living experiences of residents.

232 Affordable Design

2018 Innovation in Affordable Housing Student Design and 

Planning Competition: Whittier Falls in Dover, New Hampshire

adapt accessible design features to the particular needs of residents with a variety of physical impediments. All buildings integrate gathering spaces strategically throughout the structure. The Experience concept relies on the use of innovative technologies that create a sense of belonging. For example, the students were excited about the prospect of using digital floor projection and colored wayfinding that enhances movement throughout the buildings. Other features, such as the winding trails created from repurposed brick and pathways that incorporate rain gardens and bioswales, were meant to encourage residents to experience improved living through the use of nature, horticulture, and opportunities to engage in physical activities.

Like most of the teams in the competition, the UCD students found the financial modeling required for this competition a most complicated but worthwhile learning experience. The “proforma” financial statement developed by the team included the leveraging from a Special Needs Program Loan, HOME 

Capital subsidy, and a deferred developer fee. To help with the financing, the project utilized the 4- 

and 9-percent Low Income Housing Tax Credits and a private activity bond. To generate revenue and reduce operating costs, the team proposed a Solar Cooperative program that they felt was a creative approach for balancing the goals of energy reduction and preserving affordable housing.

Thoughts from the Jury

M. Scott Ball, Valerie Fletcher, Clayton Mitchell, Kenneth Ogden, and John Torti

The jury for the 2018 IAH Student Design and Planning Competition faced the difficult task of deciding which of the four outstanding student site plans best exemplified an innovative design. 

The members were asked specifically to consider how well the student teams successfully and convincingly addressed the following critical elements—

•  The aspects of the site design that are innovative but that meet the needs of low-income seniors and persons with disabilities.

•  The way in which the proposed design interacts with the existing physical site.

•  The innovative approaches that were employed in developing the design relative to the restrictions or opportunities presented by the site.

•  The innovative energy efficiency, water conservation, and renewable energy strategies that were incorporated into the design.

•  The innovative approaches that were employed to integrate the design that complements the existing cultural and ethnic neighborhood context.

•  The planned services and activities designed to improve the quality of life for the population served.

•  The way in which the project will be financed, including the innovative financing solutions for leveraging and establishing partnerships.

•  The way in which the proposed design integrates innovative practices.

Cityscape 233

Gray

After eliminating two of the four presentations, the jurors emphasized that the deciding factor would be how well the student teams identified and discussed innovation in their site plans. 

Although understanding neighborhood context and the needs of the residents is important, the concept of innovation would be greatly emphasized here. After narrowing the competition down to the University of Maryland and the University of Colorado teams, the jury set about identifying elements of the site plans they thought were particularly innovative while keeping an eye on the critical elements listed previously.

Defining innovative design was not an easy task, but the discussions offered some insight into what the jury considered the essence of innovation: is the design element new or groundbreaking? Is the approach to problem solving something that is untried or unexpected? Is the design concept “out of the box,” defying or challenging generally accepted techniques? Does the approach introduce a new and creative technology that is functional and applicable to human needs? Both teams were lauded for balancing energy-efficient technologies and durable building materials and concerns with preserving affordability. The winning teams paid careful consideration to the perspective of the residents and how their proposals respond specifically to the needs of the population while appreciating the historical and cultural character of the community.

The University of Maryland team’s submission highlighted the community garden with a 

greenhouse as examples of innovative design and as a solution for encouraging resident engagement, as well as preserving water resources in the community. Other standout innovative features that members of the jury noted were the incorporation of Health and Wellness Center and enhanced recreation spaces to promote physical fitness and good nutrition. Similarly, the jurors identified various aspects of the University of Colorado Denver’s  Allied Living plan that they noted as particularly impressive: the incorporation of a transportation hub that provides a variety of mobility options, including a bike and walking path and a traffic island; creatively using public space by reducing parking and increasing density; combining units, where feasible, to acknowledge extended family settings; and making effective use of existing buildings and infrastructure, such as the Passive House and a water infiltration system that addresses issues with the nearby creek. 

The jurors noted that both teams placed a high value on the importance of enhancing social capital through creative use of space that respects the cultural norms of a community. For their thoughtful attention to detail and presentation of new, forward-looking ideas for transforming this small Dover neighborhood into a viable, livable community, the jurors concluded that both the University of Maryland and the University of Colorado students were well deserving of the 2018 IAH student design award.

Acknowledgments

The U.S. Department of Housing and Urban Development (HUD) thanks the award-winning 

student teams from the University of Maryland, College Park and the University of Colorado-Denver, for sharing their thoughts and for all the hard work they put into their submissions for this year’s competition. We also thank the contributions from the remaining two teams that were selected to participate this year: the University of Texas at Austin and the Pratt Institute. HUD 

234 Affordable Design

2018 Innovation in Affordable Housing Student Design and 

Planning Competition: Whittier Falls in Dover, New Hampshire

greatly appreciates the 2018 Innovative Affordable Housing jury members’ dedication and hours devoted to the awards selection process. Finally, HUD thanks Steven Winter Associates for planning and logistics efforts, the work that made this year’s competition a success, and the notes and writings that made a valuable contribution to this piece.

Postscript

The competition is thoroughly documented on the web.

To learn more about the award: https://www.huduser.gov/portal/challenge/about.html.

To read about the 2019 award guidelines: https://www.huduser.gov/portal/sites/default/files/

pdf/2019-IAH-Competition-Guidelines-final.pdf.

To learn more detail about the winning submissions: https://www.huduser.gov/portal/challenge/

past_competitions.html.

Cityscape 235

236 Departments

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 Development 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.

The Housing and Children’s 

Healthy Development Study

Sandra Newman

Johns Hopkins University

Tama Leventhal

Tufts University

by Sandra Newman and Tama Leventhal

Motivations and Objectives

A family’s decision about where to live determines not only the characteristics of their dwelling (for example, size, physical adequacy, and cost) but also other aspects of their residential context including whether the neighborhood is safe and whether children will have access to high-quality resources including schools, suitable neighborhood playmates, and role models. Children’s home, neighborhood, schools, peers, role models, and family define the residential context, both physical and social, in which they grow up. Their social and physical environment strongly influence children’s development.

Because lower income families usually have limited choices about where to live, they face difficult tradeoffs among these different residential features. For example, if parents prize quality schools and low crime rates, they may opt for an expensive apartment that requires them to work additional hours, thereby being less available to their children. This, in turn, may stress the parents to the point of becoming harsh and punitive with their children. It may even prompt another move in search of more affordable housing. All three effects of the parents’ tradeoffs—increased work hours, harsh parenting, and moving—could have deleterious consequences for children’s 

Cityscape: A Journal of Policy Development and Research •  Volume 20, Number 3 • 2018

Cityscape 237

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Newman and Leventhal

development. This example makes clear that if we are to understand the family’s decision, the dynamics it puts into play, and ultimately, the consequences for children, we need to know more than the quality of schools and the crime rate. The Housing and Children’s Healthy Development (HCHD) Study was designed to advance our understanding of the contribution of children’s residential context to their well-being. Insights into low-income parents’ location decisions and tradeoffs; what effects these decisions have on children’s cognitive, social, emotional and health outcomes; and how these effects occur hold promise for developing more effective policies to foster healthy child development.

The HCHD study emanates from a multi-year research effort of the MacArthur Foundation’s 

“How Housing Matters for Families with Children” (“Housing and Children” for short) Research Network. Following the long tradition of MacArthur research networks, the prominent social scientists and policy experts who comprised the Housing and Children Network developed a consensus about the gaps in this topic area and the best research approach to fill them.1 The Network identified the need for a new study that would address the very basic questions of whether and how housing affects children’s healthy development. It recommended the collection of systematic survey data measuring and tracking children’s housing, neighborhoods, families, and schools, along with child and family outcomes. This recommendation addressed the lack of any existing longitudinal data set that measures these domains from a child development perspective. To achieve the goal of estimating causal effects of the child’s residential context, the Network recommended a housing voucher experiment. Distinct from the Moving to Opportunity Demonstration, this experiment is not restricted to households living in public housing (Sanbonmatsu et al., 2011); distinct from the natural experiments in Chicago (Jacob, 2004; Jacob et al., 2015), it is being implemented in more than one location.2

The remainder of this article describes this major new longitudinal study, its sample design, voucher experiment, protocols, and innovative features. We conclude with the study’s current status and plans for the future.

Study Design

The inclusion of a housing voucher experiment required that we conduct the study in 

particular cities or metropolitan areas served by a public housing authority (PHA). Financial constraints drove the decision to focus on only two study sites, the Cleveland and Dallas metropolitan areas.3

1 Network members were T. Cook (chair), D. Acevedo-Garcia, S. DeLuca, G. Duncan, K. Edin, T. Leventhal, J. 

Lubell, J. Ludwig, S, Newman, M. Pattillo, and S. Raudenbush. Project officers E. Poethig and I. Kachoris and Vice-President for Housing M. Stegman played a major role in the initiation and success of the Network.

2 The Welfare to Work Voucher Demonstration (for example, Mills et al., 2006) achieves these goals but did not collect survey data on all domains over time.

3 More precisely, the Cleveland sample area includes all of Cuyahoga County, which covers Cleveland and its suburbs. This area covers 43 zip codes. The Dallas sample area includes 7 counties encompassing 120 zip codes in and around the city of Dallas. These are roughly equivalent to the metro areas so we refer to the two samples as metro areas for simplicity.

238 Data Shop

The Housing and Children’s Healthy Development Study

Criteria for Site Selection

The three criteria for selecting the study sites were variation in geographic location, housing market characteristics, and the racial and ethnic mix of the population in the metropolitan area. 

In addition, the PHA in the site had to use randomization to create their voucher waiting list, be considered a high performer based on HUD’s assessment of PHA management and reputation in the field4, and be committed to participating in the HCHD study.

Samples

The study has a dual-frame sample design consisting of a sample of voucher applicants (the 

“voucher” sample), and a probability sample of modest and low-income households (the 

“population” sample). Both samples share three main eligibility criteria: (1) the household has at least one child between the ages of 3 and 10; (2) the child spends at least 3 nights per week on average in this household; and (3) the interview can be conducted in English or Spanish.

Voucher Sample. The voucher sample consists of applicants for housing vouchers who were randomly assigned to the voucher waiting lists in the Cuyahoga Metropolitan Housing Authority (CMHA) and the Dallas Housing Authority (DHA). The treatment group sample was selected from the randomly sorted applicants on the waiting list who were likely to be offered a voucher within approximately 1 year of the start of data collection. The control group was selected from the randomly sorted applicants who are unlikely to be offered a voucher within this time frame. Both housing authorities included a brief description of the HCHD study on their voucher application form. Applicants who did not want to participate in the study checked an “opt out” box and were not contacted. We are aiming for equal sample sizes in the two sites and equal numbers of treatment and control samples. The targets are 848 households and 1,170 children (that is, 424 households in each site comprised of 212 treatment and 212 control households). Data are being collected from the child’s primary caregiver and up to two randomly chosen children in the household. We describe the voucher experiment in more detail below.

Population Sample.  The population sample design was developed in collaboration with the sampling division of the Survey Research Center (SRC) at the University of Michigan and under the direction of T. Raghunathan, director of SRC.5 It is a stratified, random sample of households in the Cleveland and Dallas metropolitan areas. The population sample was generated through a multistage procedure. At the first stage, all U.S. Census block groups at each site were stratified into three groups (low, medium, and high) based on their median family income according to the 2015 

American Community Survey.6 Then, block groups were sampled with the goal of oversampling low-income block groups, using a ratio of 3:2:1 for low-income, middle-income, and high-income block groups, respectively. Next, within the selected block groups, households were randomly sampled and screened at the doorstep for the same eligibility criteria as the voucher sample. The target sample sizes are the same as those for the voucher sample, also divided evenly across sites (see exhibit 1).

4 Based on interviews with knowledgeable observers. 

5 HCHD Study co-principal investigators G. Duncan and S. Raudenbush were also actively involved in the sample design.

6 Stratification of block groups at the first stage also incorporated the estimated number of eligible households (that is, children ages 3 to 10 and English- or Spanish-speaking) based on multiple data sources.

Cityscape 239

Newman and Leventhal

Exhibit 1

Design of Population Sample

Number of Households

Primary Block Group Strata

Sampling Rate

Cleveland

Dallas

Total

Low-income

0.50

217

217

434

Middle-income

0.33

145

145

289

High-income

0.17

72

72

145

Total

1.0

434

434

868

Source: SRC Sampling Group, March 2017

Protocol Development and Pilot Study

Data collection instruments include a combination of established, tested questions (for example, income, expenditures, cognitive achievement, and PROMIS measures of health7) and newly-developed questions that address the key issues motivating the study (for example, preferences and tradeoffs; child-relevant housing features; biomarker measures of healthy child development). 

We sought input from subject experts either individually or, in the case of housing, through a 

“thinkers’ session.” The draft protocol underwent multiple iterations. As with all surveys, the final instrument represents a balance between including all essential measures and available funding.

In fall 2016, the draft protocol was pilot tested in Dallas with 50 modest-income households having at least 1 child in the 3 to 10 age range. The protocol was revised based on the pilot experience, and we launched the Wave 1-Baseline field work in late May 2017. We expect this first wave of data collection to continue through approximately September 2018.

Main Protocol.  SRC at the University of Michigan is our survey contractor for the HCHD study. 

SRC’s highly trained interviewers are collecting the Wave 1-Baseline data typically in the primary caregiver’s home. We are gathering data using multiple methods. Interviewers are conducting personal interviews with primary caregivers, usually mothers, using Computer-Assisted Personal Interviewing (CAPI). Mothers are also completing a short, self-administered questionnaire. 

Interviewers collect physical measures of mothers and children (for example, height and weight) and, for the voucher sample, blood biomarkers (explained below). In addition, children are administered standardized tests of reading and math achievement and a computerized task evaluating executive functioning, a key component of self-regulation. Interviewers are also collecting systematic observations of the home environment using both established subscales of the Home Observation for Measurement of the Environment, better known as HOME (Caldwell and Bradley, 1984), and other measures; the neighborhood environment defined as the blocks surrounding the households’ housing units; and parent-child interactions. Exhibit 2 lists the topics covered.

7 Patient Reported Outcomes Measurement Information System or PROMIS measures were developed by an NIH 

committee as part of the NIH Roadmap (https://commonfund.nih.gov/promis/index). 

240 Data Shop

The Housing and Children’s Healthy Development Study

Exhibit 2

Topics Covered in HCHD Study Protocols

Additional Assessments  

Adult Interview and Assessments Child Interview and Assessments

and Observations

•  Residential mobility, Crowding,  •  Hearts and flowers  

•  Neighborhood observations

privacy, and space

executive function task

•  Physical environment of home

•  Housing quality

•  Preschool self-regulation 

•  Square footage of living space 

•  Other housing features

assessment

in the dwelling (by laser tape)

•  Housing costs

•  Woodcock-Johnson  

•  PHA applicant questions

(Applied Problems)

•  Preferences and tradeoffs

•  Woodcock-Johnson  

•  Neighborhood

(Letter-Word identification)

•  Neighborhood vignettes

•  Physical measurements  

•  Respondent general information

(height, weight, waist, hips)

•  Employment information

•  Blood spot collection

•  Spouse/partner/other  

•  Thin-slice observation  

parent information

of cognitive sensitivity/ 

•  Household income, assets,  

Lego activity

and debts

•  Mental health

•  Health

•  Physical measures (height, 

weight, blood pressure)

•  Blood spot collection

•  Challenges to parenting

•  Family environment and routines

•  Home Observation for 

Measurement of the 

Environment (HOME)

•  Discipline of child

•  Child demographics

•  Child’s room

•  Child’s residential background

•  Child care and preschool

•  School

•  Child’s behavior

•  Child health

Innovations

The HCHD study includes a number of innovative protocols worth highlighting.

1.  Biomarker Collection from Mothers and Children: Interviewers are collecting blood spots from mothers and children in the voucher sample to test for Interleukin 6, a biomarker for infection and inflammation; C-Reactive Protein, a biomarker for stress; and Glycosylated Hemoglobin, a biomarker for blood sugar levels. No study, to our knowledge, has collected blood from children in a home-based setting. Response rates are high, standing at 93.3 

percent for mothers and 84.4. percent for children as of mid-May 2018.

2.  Child Time Diary: A daily diary was developed for the HCHD study to assess how families’ 

use of space in the home promotes or inhibits children’s healthy development through daily routines, interactions, and parenting. Parents complete the daily diary over two randomly selected days (one weekday and one weekend day). Although the response rate is only about Cityscape 241

Newman and Leventhal

35 percent, the daily diary should provide important exploratory information on families’ use of space pertaining to parenting.

3.  Interviewer Assessments of Parenting: To assess the sensitivity of the primary caregiver’s parenting, interviewers are observing mothers and children participating in a Lego activity and coding the quality of the parent-child interactions using a “thin slice” or impressionistic approach (Prime et al., 2015). This innovative method of measuring parent-child interactions relies on a short observation period of approximately 5 minutes, requires little reliability training, and has minimal coding demands of approximately 7 minutes per interaction. These features make it efficient and cost-effective for a large, complex study of this sort.

4.  Objective Measurement of Interior Square Footage: Interviewers are measuring the square footage of living space in the home using an electronic laser tape measure. This approach provides an objective measure of space in the dwelling and will be helpful when analyzing subjective assessments of crowding, privacy, and clutter. As of this writing, interviewers have collected laser tape data from 88 percent of respondents.

The Voucher Experiment

The voucher sample consists of randomly chosen voucher applicants, some of whom will be offered a voucher and others who will not be offered a voucher. This rigorous research design of random variation in who receives a housing voucher supports the examination of the causal effects of housing on children. Some examples of such effects include how the offer and use of a voucher affects parents’ choices about where to live; the kinds of housing and neighborhood quality tradeoffs low-income families make; how these choices affect their children’s development; the effects of housing on health and other child development outcomes; and how stress, parenting, and stability may transmit the effects of housing and affect children’s healthy development.

The PHAs. Located in the Midwest, the Cuyahoga Metropolitan Housing Authority serves all of Cuyahoga County, which includes the city of Cleveland, Ohio and its inner suburbs. It is a relatively soft housing market, with an estimated 2016 rental vacancy rate in the housing market area of about 9 percent.8 CMHA’s portfolio includes 25,729 assisted housing units. This includes 9,284 public housing units, 15,269 Section 8 vouchers, and 1,176 multifamily units that represent several different HUD project-based assisted housing programs. CMHA’s tenant population includes 33.4 percent who are part of family households with one or more children younger than 18, 27.4 

percent who are households headed by a person 62 years of age or older, and 36.2 percent who are disabled either physically or mentally.9 The large majority of tenants are Black (89.2 percent), 8.4 percent are White, and 2.4 percent are another race. Roughly 7 percent are Hispanic, and 93 

percent are not Hispanic.

Located in the southwest, the Dallas Housing Authority serves the city of Dallas and counties across north Texas. This is a relatively tight rental market, with an estimated 2017 rental vacancy 8 See https://www.huduser.gov/portal/publications/pdf/ClevelandOH-comp-16.pdf. 

9 Categories are not mutually exclusive. 

242 Data Shop

The Housing and Children’s Healthy Development Study

rate in the housing market area of about 6 percent.10 DHA’s portfolio includes nearly 22,000 

assisted housing units—17,000 in which the tenant is using a housing choice voucher, 1,800 

multifamily units, and 3,000 public housing units. The geographic area under DHA’s purview includes seven counties: Collin, Dallas, Denton, Ellis, Kaufman, Rockwall, and Tarrant. Roughly 50 percent of households are families with one or more children younger than 18 years old, 21 

percent are headed by someone 62 years of age or older, and about 24 percent are headed by a non-elderly person who is disabled. The large majority of tenants are Black (85.3 percent), 8.6 percent are White, and the remaining 6.1 percent are other races (including 2 percent who are Asian). In addition, 6.2 percent report being Hispanic while 92.3 percent report being non-Hispanic (1.5 percent declined to report any race).

PHA Liaison. Quadel LLC, a well-known assisted housing consulting firm that has worked with numerous PHAs, has been the liaison between the research team and both CMHA and DHA. 

Quadel maintains regular communications with each PHA, assisted with the development of the Memorandum of Agreement covering the PHAs’ participation in the study and data sharing, oversaw waiting list randomization, helped to develop a protocol to track voucher recipients using administrative data, and continues to assist with general troubleshooting.

Conclusions

At this writing, the Wave 1-Baseline survey data collection of the HCHD Study is nearing completion. Wave 2 is planned to launch 12 months after the Wave 1-Baseline. Our goal is to raise funds for additional waves. Importantly, the HCHD study data will ultimately be released as a public use data set.11

Acknowledgments

Seed funders of the HCHD study are the MacArthur Foundation, the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the U.S. Department of Housing and Urban Development, and the Robert Wood Johnson Foundation.

Authors

Sandra Newman, Ph.D, the HCHD Study Principal Investigator, is a professor of policy studies at Johns Hopkins University.

Tama Leventhal, Ph.D., the HCHD Study Co-Principal Investigator, is a professor in the Eliot- 

Pearson Department of Child Study and Human Development at Tufts University.

10 See https://www.huduser.gov/portal/publications/pdf/DallasTX-comp-17.pdf. 

11 Contact S. Newman at sjn@jhu.edu for updates on the status of the HCHD study public use data. 

Cityscape 243

Newman and Leventhal

References

Caldwell, Bettye M., and Robert H. Bradle. 1984.  Home observation for measurement of the environment. Little Rock: University of Arkansas at Little Rock.

Jacob, Brian A. 2004. “Public housing, housing vouchers, and student achievement: Evidence from public housing demolitions in Chicago,”  American Economic Review 94 (1): 233–258.

Jacob, B., Max Kapustin, and Jens Ludwig. 2015. “The impact of housing assistance on child outcomes evidence from a randomized housing lottery,”  The Quarterly Journal of Economics 130 (1): 465–506.

Mills, Gregory, Daniel Gubits, Larry Orr, David Long, Judie Feins, Bulbul Kaul, Michelle Wood, and Amy Jones. 2006.  Effects of housing vouchers on welfare families. Washington, DC: U.S. 

Department of Housing and Urban Development, Office of Policy Development and Research.

Prime, Heather, Dillon Browne, Emis Akbari, Mark Wade, Sheri Madigan, and Jennifer M. Jenkins. 

2015. “The development of a measure of maternal cognitive sensitivity appropriate for use in primary care health settings,”  Journal of Child Psychology and Psychiatry 56 (4), 488–495.

Sanbonmatsu, Lisa, Lawrence F. Katz, Jens Ludwig, Lisa A. Gennetian, Greg J. Duncan, Ronald C. Kessler, Emma K. Adam, Thomas McDade, and Stacy T. Lindau. 2011.  Moving to Opportunity for Fair Housing Demonstration Program: Final Impacts Evaluation. U.S. Department of Housing and Urban Development.

244 Data Shop

Street Vending in the United States: 

A Unique Dataset from a Survey 

of Street Vendors in America’s 

Largest Cities

Dick M. Carpenter II

Institute for Justice and University of Colorado

by Dick M. Carpenter II

The data described in this article come from an original survey of street vendors in the 50 largest cities in the United States. One of the most persistent, although little understood, features of the urban American environment, street vending is defined as “the retail or wholesale trading of goods and services in streets and other related public axes such as alleyways, avenues and boulevards” 

(Bromley, 2000: 1). Some vending occurs in a fixed location, whereas other vending is mobile and makes use of carts, tricycles, or motor vehicles. Vending may be practiced full time, part time, seasonally, or occasionally by businesses ranging from one-person micro-enterprises through numerous forms of partnerships, family businesses, franchisees, pieceworkers, and wageworkers of brick-and-mortar firms (Bromley, 2000).

Throughout much of its history, street vending was an occupation largely practiced by recent immigrants or others on the first rungs of the economic ladder as a way to make a living (Bluestone, 1991; Newman and Burnett, 2013). Although street vending fell into disrepute during the 20th century (Cross, 2000), the first few decades of the 21st century saw a reversal of the industry’s fortunes. In the 2012 Economic Census, food vendors reported revenues of approximately $660 million (U.S. Census Bureau, 2012). In 2010, New York Times food columnist John T. Edge declared, “Street food is hip” (Allen, 2010). A 2009 Washington Post story on food trucks observed, “Street carts are the year’s hottest food trend. Good, cheap food sates appetites in a recession, and low start-up costs are a magnet for entrepreneurs” (Black, 2009).

Despite the industry’s contemporary growth and popularity, surprisingly little systematic information about it is available (Bromley, 2000). Demographic data about vendors, for example, are rudimentary (Linnekin, Dermer, and Geller, 2011/2012) or geographically limited. Survey studies gathered data from Los Angeles (Loukaitou-Sideris and Gilbert, 2000) and Dhaka, Bangladesh (Etzold, 2015), but those focused on single cities. Moreover, the academic literature about vending and vendors—although large—is overwhelmingly dominated by qualitative, 

Cityscape: A Journal of Policy Development and Research •  Volume 20, Number 3 • 2018

Cityscape 245

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Carpenter

ethnographic, and phenomenological studies (Babb, 2013; Gaber, 1994; Greenberg, Topol, Sherman, and Cooperman, 1980; Jones, 1988; Rosales, 2011; Shepherd, 2009).

This lack of information means elected officials in U.S. cities typically make decisions about vending regulations informed largely by anecdote and plagued by partial understanding. Similarly, academic understanding of vending and vendors lacks systematic data with which to examine and test theories, observations, and effects across numerous contexts. The dataset discussed here begins to help fill this significant void.

The Data

TechnoMetrica, a New Jersey-based polling company, collected survey data by telephone during a three-month period in the fall of 2013. Because of the high representation of immigrants in the vending industry, multilingual speakers administered survey questions that were available in multiple languages.

The survey and data, which appear at http://ij.org/report/upwardly-mobile/street-vending-in-

the-united-states-a-unique-dataset-from-a-survey-of-street-vendors-in-americas-largest-cities, include 233 questions about the vendors, such as personal characteristics, length of time in the industry, specialized training, and past employment. The survey also asks vendors the type of product or service they provide, where they normally vend, how many hours and days they work, and how many people they employ. Most questions are closed-ended using Likert scales or yes/no responses, but 12 are open-ended. The latter typically follow closed-ended questions.

The survey also includes a unique set of questions asked only of vendors in New York City (NYC) that can facilitate an economic contribution analysis. These questions were asked of 209 food and non-food (merchandise and printed material) vendors, or approximately 2 percent of the city’s estimated vendor population (Devlin, 2011). NYC has three categories of licensed vendors—

general merchandise vendors, food vendors, and vendors who are licensed but lack certain permits. The sample was proportionately stratified by these categories and quotas met through random selection. Participants were asked to provide revenue and expenses for one year (2012) on certain business operations. These questions were designed to be used in input-output analysis with the IMPLAN system (Day, n. d.) to measure the broader economic benefits that accrue to a community (Crompton, 2006) from an industry by measuring patterns of spending and re-spending within an economy (Bangsund and Leistritz, 1995).

Sample

For the general survey of all cities, the sample included 763 licensed street vendors across the 50 

largest U.S. cities, by population, as depicted in exhibit 1. The sample was constructed by securing a list from each city of all licensed vendors. The total number of vendors across all city lists was 53,553. For sampling purposes, we treated this as the population of licensed vendors in the 50 

cities, despite the unknown number of people who vend illegally in these cities as part of the informal economy (Mukhija and Loukaitou-Sideris, 2014; Webb, Bruton, Tihanyi, and Ireland, 2013). Categories of vendors who can work without government permission—such as those 

246 Data Shop

Street Vending in the United States: A Unique Dataset  

from a Survey of Street Vendors in America’s Largest Cities

selling written materials in NYC (Devlin, 2011)—result in no lists of such vendors. Because it was impossible to identify such vendors for inclusion in the survey, the results of any analysis using these data can be generalized only to licensed vendors.

The sample was constructed as a stratified random sample. The number of participants in the sample from each city was proportional to each city’s percentage of vendors in the vendor population. After proportional quota frequencies were set for each city, vendors from the respective city lists were called randomly until quotas were filled or lists were exhausted. Lists were declared exhausted only after vendors were contacted multiple times at varying times of the day and week. The data file includes probability and sample weights to reflect the unequal probabilities of participants ending up in the sample and the overrepresentation or underrepresentation of vendors in certain cities due to response biases. Using the population figure of 53,553, a 95 

percent confidence interval, and the sample size of 763, the overall margin error for the survey is approximately 3.5 percent. Margins of error for individual questions were typically between 3 

percent and 4 percent.

Exhibit 1

Sample Cities and Number of Respondents Per City

Number of 

Number of 

City

City

Respondents

Respondents

Albuquerque, NM

9

Louisville, KY

2

Arlington, TX

5

Memphis, TN

2

Atlanta, GA

7

Mesa, AZ

2

Austin, TX

29

Miami, FL

61

Baltimore, MD

3

Milwaukee, WI

7

Boston, MA

8

Minneapolis, MN

3

Charlotte, NC

9

Nashville, TN

2

Chicago, IL

14

New York, NY

209

Cleveland, OH

5

Oakland, CA

25

Colorado Springs, CO

2

Oklahoma City, OK

3

Columbus, OH

20

Omaha, NE

5

Dallas, TX

20

Philadelphia, PA

33

Denver, CO

4

Phoenix, AZ

5

Detroit, MI

1

Portland, OR

11

El Paso, TX

32

Raleigh, NC

6

Fort Worth, TX

24

Sacramento, CA

9

Fresno, CA

4

San Antonio, TX

38

Honolulu, HI

6

San Diego, CA

7

Houston, TX

28

San Francisco, CA

9

Indianapolis, IN

3

San Jose, CA

15

Jacksonville, FL

12

Seattle, WA

5

Kansas City, MO

14

Tucson, AZ

18

Las Vegas, NV

5

Tulsa, OK

5

Long Beach, CA

5

Virginia Beach, VA

3

Los Angeles, CA

5

Washington, D.C.

4

Cityscape 247

Carpenter

As the results of a first-of-its kind survey of vendors, these data are unique, but they are not without limitations. The most significant limitation is that city-specific analyses are essentially impossible. The project for which the data were gathered was designed as a national study. 

Individual city analyses—other than the economic contribution analysis in NYC—were not anticipated. I briefly discuss how other researchers might consider using the survey to gather data for a city study.

What the Data Reveal About Vendors in America’s Largest Cities

With these data, we begin to gain a general understanding of the personal characteristics of vendors, their businesses, and their backgrounds prior to vending. The descriptive statistics present some but not all of the information contained within the survey. Where possible, the data are compared to the general populations in the cities composing the sample (with data drawn from the 2012 American Community Survey) or to businesses.

The face of vending. The people who provide food, merchandise, or services from trucks, carts, and stands in America’s 50 largest cities are a more diverse group than these cities’ general populations. As exhibit 2 illustrates, greater percentages of vendors are minorities compared to the cities’ populations, with the most pronounced difference among Hispanics, at six percent. Moreover, 51 percent of vendors are immigrants to the United States, far outpacing the overall percentage of immigrants in the cities (23 percent). On average, vendors have lived in the United States for 22 years.

Exhibit 2

Vendors More Racially and Ethnically Diverse Than City Populations

248 Data Shop

Street Vending in the United States: A Unique Dataset  

from a Survey of Street Vendors in America’s Largest Cities

More men than women populate vendors’ ranks, with men representing 68 percent of vendors but only 49 percent of the cities’ populations. Vendors also tend to be older than the general populations in their cities. Exhibit 3 illustrates significant disparities in all age categories. A greater percentage of vendors fall into the 25–54 and 55+ age categories than do members of the cities’ 

populations overall, and comparatively very few vendors are younger than 25.

Exhibit 3

Vendors Tend to be Older Than City Populations

Vendors tend to be somewhat less educated than their cities’ general populations. As exhibit 4 

illustrates, a greater percentage of vendors did not complete high school, but the percentages of vendors who completed some college or graduated from college are similar to the cities’ populations.

Exhibit 4

Education of Vendors Compared to City Populations

Cityscape 249

Carpenter

Unlike peddlers of yore who often lacked skills or opportunities, many of today’s vendors enter the vending business from other employment. Indeed, 73 percent held jobs prior to vending. 

As illustrated in exhibit 5, most worked in professions such as sales, accounting, information technology, and the like. Some worked in the service sector, particularly as drivers, house and office cleaners, and food service workers, including cooks, chefs, and restaurant managers. Others worked in areas termed general employment (temporary jobs, retail); manual jobs (construction, auto repair, manufacturing); government (teaching, military, law enforcement, postal service); and social welfare (health care, counseling, nonprofits).

Exhibit 5

Vendors Worked in a Diversity of Occupations Before Vending

Moreover, vendors who work part time (approximately 33 percent) or seasonally (approximately 40 percent) typically work other jobs (see exhibit 6). Of seasonal workers, those not employed during off-seasons typically report disabilities, retirement, or student status as relevant reasons. 

Of those who report working during the off-season, most do so in general industries, such as retail or temporary jobs, or in the service sector providing cleaning, transportation, or food services. Next is information about professional (sales, technology, management, engineering) and manual (construction, manufacturing, auto repair, agriculture) sectors. The remainder works in government or social services.

Exhibit 6

Employment of Part-Time and Seasonal Workers

Seasonal 

Part Time 

(%)

(%)

Not employed

37

28

Employed

   General

20

9

   Service sector

20

31

   Professional

8

11

   Manual

8

10

   Government or social services

7

11

250 Data Shop

Street Vending in the United States: A Unique Dataset  

from a Survey of Street Vendors in America’s Largest Cities

Among part-time vendors, those who report no other employment largely do so for the same reasons as seasonal workers. Of those who work, most do so in the service sector, whereas around the same percentages work in government or social welfare, the professional sector, manual labor, or general industries. If vending was once primarily for those who lacked other opportunities, that is not the case today. Most vendors come to the business from other employment and, in the case of seasonal and part-time vendors, engage in other meaningful employment alongside vending.

Although most vendors come to the business from other jobs, 37 percent complete specialized training to work as vendors. Of those who report completing specialized training, most do so as part of licensure requirements (see exhibit 7). Examples include hygiene classes and testing regimes and take an average of five months to complete. Beyond licensing requirements, the next largest percentage of vendors complete some form of schooling relevant to their business. Schooling may include general businesses courses or specialized training (for example, blacksmithing, leatherworking, photography, or cooking classes). Next are vendors who report receiving on-the-job training from other vendors, parent companies/franchisors, or other relevant businesses (for example, restaurants). The remainder receive training on an ad hoc basis, which includes online resources, personal instruction (such as art or music lessons), or friends.

Exhibit 7

Most Vendors with Specialized Training Received it from Mandatory Licensure Requirements Lack of specialized training does not equate to a lack of success in vending. On average, vendors have worked in the business approximately 8 years and plan to continue for at least another 10. Moreover, more than one-third of vendors who own their businesses plan to expand, and almost half of those who work as employees in a vending business intend to own their own vending business in the future.

The business of vending. If the people who vend are diverse, the types of businesses they operate are less so, at least in broad categories. Almost 78 percent of vendors sell food, followed by 21 

percent who sell merchandise, and less than 1 percent each who provide services or “other.” 

Among food vendors, the greatest percentage sell non-ethnic, nonspecialty foods commonly found at concession stands (such as burgers, hot dogs, fish); slices of pizza; sides; beverages; and desserts (see exhibit 8). A similar percentage offers a mixed menu, somewhat like a small restaurant. They Cityscape 251

Carpenter

sell some specialty items but also offer sides, beverages, and desserts. A slightly smaller percentage sells ethnic foods from around the globe, followed by food vendors who sell sweets; produce; specialty items like lobster rolls, crabs, or pretzels; and beverages.

Exhibit 8

Vendors Sell a Diversity of Food

Of vendors who sell merchandise, 39 percent offer a mixture of items, such as apparel, cosmetics, gifts, novelties, and accessories. Some sell common products (socks, handbags, sunglasses, watches), whereas others offer homemade wares (craft items, finger puppets, woodworking) alongside more standard stock. Other vendors (31 percent) offer specialty items only—often artwork or crafts but also more unusual items like glass light fixtures, Tiffany-style lamps, pistol and rifle shells, and emu oil. The remaining merchandise vendors devote themselves to specific common items (for example, exclusive apparel or cosmetics).

Just as most vendors sell food, the majority (83 percent) operate mobile vending units, such as trucks, carts, or temporary stands. As exhibit 9 indicates, most mobile units are trucks, followed by carts and stands. Permanent stands (kiosks, market booths, designated areas at stadia or arenas) represent the smallest percentage of vending structures. On average, vending structure owners hold title to one unit, but some own 10, 20, or even 50 units. The majority of vendors, however, run a single unit. Those who sell from something other than trucks, carts, temporary stands, or permanent stands most often do so from trailers pulled behind a vehicle but the diversity of operations also includes selling from designated areas within other businesses, suitcases, and personal vehicles—even off a vendor’s person (for example, tickets held in a bag).

252 Data Shop

Street Vending in the United States: A Unique Dataset  

from a Survey of Street Vendors in America’s Largest Cities

Exhibit 9

Most Vendors Operate Mobile Units

Vendors overwhelmingly own the businesses in which they work. Of the 96 percent who own their vending businesses, 90 percent also own the structure from which they vend. The remaining vendors own the vending business but rent the vending unit. Thus, many vendors invest a nontrivial amount of money to establish their businesses through the purchase of a vending unit, which can run into the tens if not hundreds of thousands of dollars.

To make good on this investment, location is critical, just as it is with any other retail business. 

Where vendors locate depends on the type of structure they operate (see exhibit 10). The plurality of mobile vendors operates in business districts, followed by “other” and street fairs and events. 

Smaller percentages serve customers at sporting or event venues, in restaurant and bar districts, or at subway entrances.

Exhibit 10

Locations Vendors Work Most

Mobile Units 

Permanent Stands

Location Type

(%)

(%)

Business district

43

30

Sporting or event venues

8

4

Restaurant and bar districts

2

3

Street fairs and events

22

NA

Subway entrances

1

NA

Other

24

41

Markets

NA

21

Although vendors work 5 days a week on average, the number of days worked differs based on full-time (67 percent of vendors) versus part-time status. Part-time vendors work about 4 days per week, whereas full-time vendors work 5.5 days. Their workdays are long. Each day on average, vendors spend about 7 hours interacting with customers and a little more than 3 hours preparing Cityscape 253

Carpenter

to sell, which can include preparing food or packaging merchandise. In addition, they spend 1–2 

hours per day on organization, such as bookkeeping, purchasing, and the like.

Vendors dedicate this time to their businesses even with full- or part-time help. About 39 percent of vendors who own a business employ full- and part-time workers, despite small budgets. Indeed, vendors pay all of their expenses—supplies, fuel, wages, insurance, taxes, fees, and so forth—from average annual sales totaling about $105,000 per vendor.

From annual sales, these business owners reap an average profit of almost $26,000 per year, but they pay themselves only about $15,000 annually. Food vendors report annual incomes of approximately $16,350. By comparison, this figure is 25 percent of restaurant owners’ $66,115 

salaries (Catinella, 2013) and only about $5,000 more than the federal government’s poverty threshold for a single person (Poverty Guidelines, 2014).

Conclusion

This article presents a new dataset created from a survey of street vendors in the 50 largest cities in the United States, as well as a brief analysis of the data. The analysis provides a snapshot of vendors and their businesses and demonstrates the scope of the data and possibilities for analyses. 

Additional analyses with these data could include—

•  An investigation of the relationship between variables or constructs within the data (such as personal characteristics and business traits).

•  An examination of the relationship between city regulations on vending and characteristics of vendors or vending businesses.

•  An economic contribution analysis as briefly described, using NYC data.

•  A study of how these data reflect broader political and economic government parameters.

A particularly useful follow-up would be a second wave of data collected from the same cities using the same survey. Creating a panel dataset would likely be impossible, but a longitudinal cross-sectional design would still allow for some change-over-time analyses and the stronger analytical designs that go with them. Vendors are a “hard-to-reach” population, however, thereby increasing the survey costs associated with telephone surveys. A lower-cost approach might be to use the survey to gather data in one city. Such data could be paired with ethnographic data in a mixed-methods study to provide a particularly rich description and analysis of the vending industry in a city.

Such studies would provide invaluable insights on a feature of U.S. urban environments that is as old as cities themselves. Given the ubiquity of vending and the challenges cities face in regulating it, the contributions would be more than academic.

254 Data Shop

Street Vending in the United States: A Unique Dataset  

from a Survey of Street Vendors in America’s Largest Cities

Author

Dick M. Carpenter II is a director of strategic research at the Institute for Justice and a professor at the University of Colorado.

References

Allen, Rick. 2010. “‘Street food’ sees uptick in local popularity.”  Ocala Star-Banner. 

http://www.ocala.com/article/20100416/articles/4161007.

Babb, Florence E. 2013. “Street economies in the urban global south: Where are they heading and where are we heading?” In  Street economies in the urban global south, edited by Karen T. Hansen, Walter E. Little, and Lynne Milgram. Santa Fe, NM: SAR Press: 201–212.

Bangsund, Dean A., and F. Larry Leistritz. 1995. Economic contribution of the United States sunflower industry. Fargo, North Dakota: Department of Agricultural Economics, Agricultural Experiment Station, North Dakota State University.

Black, Jane. 2009. Street-food trucks roll into red tape. The Washington Post. http://articles.

washingtonpost.com/2009-12-02/news/36921704_1_food-truck-farmers-markets-fojol-bros.

Bluestone, Daniel M. 1991. “‘The pushcart evil’: Peddlers, merchants, and New York City’s streets, 1890–1940,”  Journal of Urban History, (18) no. 1: 68–92.

Bromley, Ray. 2000. “Street vending and public policy: A global review,”  International Journal of Sociology and Social Policy (20), no. 1/2: 1–29.

Catinella, Dan. 2013. The 2012 StarChefs.com salary survey report. https://www.starchefs.com/

cook/trends/chef-salary-report-2012 (accessed July 31, 2018).

Crompton, John L. 2006. “Economic impact studies: Instruments for political shenanigans?” 

Journal of Travel Research (45): 67–82.

Cross, John C. 2000. “Street vendors, modernity and postmodernity: Conflict and compromise in the global economy,”  International Journal of Sociology and Social Policy (20), no. 1/2:, 29–51.

Day, Frances. (n. d.).  Principles of impact analysis and IMPLAN applications. Huntersville, NC: MIG, Inc.

Devlin, Ryan T. 2011. “‘An area that governs itself’: Informality, uncertainty and the management of street vending in New York City,”  Planning Theory (10), no. 1: 53–65.

Etzold, Benjamin. 2015. “Selling in insecurity—living with violence: Eviction drives against street food vendors in Dhaka, and the informal politics of exploitation.” In  Street vending in the neoliberal city: A global perspective on the practices and policies of a marginalized economy, edited by Kristina Graaff and Noa Ha. New York, NY: Berghahn: 164–190.

Cityscape 255

Carpenter

Gaber, John. 1994. “Manhattan’s 14th street vendors’ market: Informal street peddlers’ complementary relationship with New York City’s economy,”  Urban Anthropology (23), no. 4: 373–408.

Greenberg, Jerome, Martin T. Topol, Elaine Sherman, and Kenneth Cooperman. 1980. “The 

itinerant street vendor: A form of nonstore retailing,”  Journal of Retailing, (56), no. 2: 66–80.

Jones, Yvonne V. 1988. “Street peddlers as entrepreneurs: Economic adaptation to an urban area,” 

Urban Anthropology (17), no. 2/3: 143–170.

Linnekin, Baylen J., Jeffrey Dermer, and Matthew Geller. 2011/2012. “The new food truck advocacy: Social media, mobile food vending associations, truck lots, and litigation in California and beyond,”  Nexus (17): 35–58.

Loukaitou-Sideris, Anastasia, and Liette Gilbert. 2000. “Shades of duality: Perceptions and images of downtown workers in Los Angeles,”  Journal of Architectural and Planning Research, (17), no. 1: 16–33.

Mukhija, Vinit, and Anastasia Loukaitou-Sideris (eds.). 2014.  The informal American city: Beyond taco trucks and day labor. Cambridge, MA: MIT Press.

Newman, Lenore L., and Katherine Burnett. 2013. “Street food and vibrant urban spaces: Lessons from Portland, Oregon,”  Local Environment (18), no. 2: 233–248.

Poverty Guidelines. 2014. U.S. Department of Health and Human Services. Office of the Assistant Secretary for Planning and Evaluation. https://aspe.hhs.gov/2014-poverty-guidelines.

Rosales, Rocio. 2011. “Survival, economic mobility and community among Los Angeles fruit vendors,”  Journal of Ethnic and Migration Studies (39), no. 5: 697–717.

Shepherd, Robert J. 2009. “‘I bought this at Eastern Market’: Vending, value, and social relations in an urban street market.” In  Economic Development, Integration, and Morality in Asia and the Americas, edited by Donald C. Wood. Bingley, UK: Emerald.  Research in Economic Anthropology, no. 29:381–406).

U.S. Census Bureau. 2012. The 2012 Economic Census: United States, NAICS [The North 

American Industry Classification System] 722330, mobile food services. Washington, DC.

Webb, Justin W., Garry D. Bruton, Laszio Tihanyi, and R. Duane Ireland. 2013. “Research on entrepreneurship in the informal economy: Framing a research agenda,”  Journal of Business Venturing (28), no. 5: 598–614.

256 Data Shop

Policy Briefs

The Policy Briefs department summarizes a change or trend in national policy that may have escaped the attention of researchers. The purpose is to stimulate the analysis of policy in the field while the policy is being implemented and thereafter. If you have an idea for future Policy Briefs, please contact david.l.hardiman@hud.gov.

Performance of Small 

Public Housing Authorities: 

Opportunities and Barriers

Andrew J. Greenlee 

Han Bum Lee

Paul E. McNamara

University of Illinois at Urbana-Champaign

by Andrew J. Greenlee, Han Bum Lee, and Paul E. McNamara

Abstract

Most public housing authorities in the United States are relatively small, serving less than 1,000 

combined households. Research on the performance and operations of housing authorities, however, focuses on very large housing authorities. This article examines small housing authorities’ operational performance and discusses management strengths and challenges associated with running small housing authorities. We draw on HUD administrative data and a series of indepth interviews with housing authority administrators in Illinois to examine housing authority performance metrics and accountability structures. We find that program sociodemographic factors and population served influence the likelihood of receiving high performance designation. Small housing authorities, particularly those in rural areas are less likely to be designated as “high-performing,” and regional heterogeneity affects small housing authority performance levels. Semi-structured interviews with nine executive directors and administrators of small housing authorities in Illinois reveal additional management and accountability challenges, suggesting the need for a more holistic means of assessing housing authority performance and service delivery.

Cityscape: A Journal of Policy Development and Research •  Volume 20, Number 3 • 2018

Cityscape 257

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Greenlee, Lee, and McNamara

Introduction

Low-income housing assistance constitutes an integral part of the federal social safety net. HUD-assisted housing alone supports more than five million households annually. In 2015, HUD 

devoted $42.7 billion to housing assistance, which is equivalent to one-half the federal spending on the Supplemental Nutrition Assistance Program (SNAP) and twice what was spent on the Temporary Assistance for Needy Families (TANF) program (Spar and Falk, 2016). Given the visibility of HUD housing assistance as a poverty alleviation strategy, extensive research has been conducted concerning the use of assisted housing in service of the spatial deconcentration of poverty, shaping labor market behaviors, supporting health and well-being, and facilitating the intergenerational transmission of wealth and income stability (for instance, see Finkel and Buron, 2001; Jacob, 2004; Jacob and Ludwig, 2012; Chetty et al., 2016). Despite the visibility of public and subsidized housing, much of the empirical and evaluation research concerned with assessing housing authority service delivery and related household outcomes focuses on a few large housing authorities in the nation’s largest metropolitan areas.

Most public housing authorities (PHAs) in the U.S. are relatively small in size—according to HUD’s 2017 Picture of Subsidized Households (PSH), 83.4 percent of all PHAs in the 48 contiguous states administered 1,000 or fewer assisted housing units, representing 20.7 percent of subsidized units (exhibit 1). In this article, we focus on describing the types of operational and contextual challenges faced by small housing authorities. Specifically, we examine small PHA performance measures and the dynamics of accountability and compliance to local communities and to HUD. We ask these questions assuming potentially important differences in how small housing authorities are run and that size matters when it comes to the needs of local populations served and to accountability structures.

Exhibit 1

Spatial Distribution of the U.S. Small and Larger Public Housing Authorities

Sources: HUD’s Enterprise Geographic Information System (eGIS) and 2017 Picture of Subsidized Households 258 Policy Briefs

Performance of Small Public Housing Authorities: Opportunities and Barriers This assumption implies that successful programs in large urban housing authorities may not translate directly to small housing authorities. Given the renewed focus on small PHAs as reflected within the 2015 authorization of the expansion of the Moving to Work (MTW) program to partially target small housing authorities, the lack of research on small housing authorities’ operational performance and operational strengths and barriers have the potential to limit policymakers’ ability to identify major problems and seek appropriate solutions.

Which PHAs Are More Likely to Be High Performing?

To analyze small housing authority operations and accountability, we take a mixed methods approach. We first analyze HUD administrative data to identify factors affecting housing authority performance ratings. We then conduct a series of semi-structured interviews with small housing authority executive directors and administrators in Illinois to understand their perspectives on (1) their current operating environment and challenges, (2) how small PHAs qualify their impact on assisted tenants, and (3) how small PHAs view their approaches, impacts, and operational challenges in relationship to those faced by very large housing authorities, which are frequently the subject of academic and policy evaluations.

HUD Performance Assessment Measurement

The GAO’s 1982 report  Contributing Causes of Financial and Management Problems in Public Housing Projects reviewed prior GAO, Congressional Budget Office (CBO), Congressional Research Service (CRS), and HUD reports, as well as reports from the Public Housing Authorities Directors Association (PHADA) and the Urban Institute to identify reasons why public housing projects were troubled. The report highlighted density of the public housing stock and the share of management responsibilities related to public housing as contributing factors. Other factors included financial problems such as those related to the gap between federal operating subsidies and rents collected, contextual problems such as the demographic composition of assisted households, and neighborhood problems including isolation and lack of adequate municipal service provision. 

Taken together, however, the report found reasons for problems to be largely idiosyncratic—“it has proven difficult to identify a common link between what makes a good project versus what makes a bad one” (GAO, 1982:2).

HUD developed the Public Housing Assessment System (PHAS) to measure housing authorities’ 

operating performance with respect to the management and delivery of public housing units. 

Section 502(a) of the National Affordable Housing Act of 1990 (NAHA) amended the Housing Act of 1937 to develop 12 indicators of PHA performance, to establish procedures for designating troubled and high-performing housing authorities, and to develop procedures for taking corrective action against troubled housing authorities. Of the 12 indicators identified, 7 are statutorily defined by NAHA and the remaining 5 are defined by HUD. The seven statutory indicators are as follows.

1.  Vacancy Rate: vacancy number and percentage and change in vacancy rate during the past 3 years.

2.  Modernization: utilization of funds for the modernization and rehabilitation of public housing and quantity of unexpended after 3 years.

Cityscape 259

Greenlee, Lee, and McNamara

3.  Rents uncollected: the balance of rents uncollected as a percentage of total rents to be collected.

4.  Energy consumption: energy utilization for PHA offices and facilities and for assisted housing units in which the housing authority supplies utilities.

5.  Unit turnaround: the time taken to repair and turn around vacant housing units.

6.  Outstanding work orders: the proportion of maintenance work outstanding and progress during the past 3 years to reduce the time to complete work orders.

7.  Annual inspection of units: the proportion of units that a PHA fails to inspect on an annual basis.

HUD’s five elective indicators are as follows.

1.  Tenants accounts receivable: percentage of money owed to a PHA by residents for items such as back rent, maintenance charges, damage charges, or excess utilities.

2.  Operating reserves: the percentage of operating reserves maintained by the PHA.

3.  Routine operating expenses: the ratio of operating expenses to operating income and subsidy.

4.  Resident initiatives: evidence of partnership between residents and PHAs to develop a shared agenda and programming to promote self-sufficiency and support resident needs.

5.  Development: PHA capacity to develop new housing units or to rehabilitate existing housing units.

Today, PHAS indicators are organized into four assessments: the Physical Assessment Subsystem, the Management Assessment Subsystem, the Financial Assessment Subsystem, and the Capital Fund Program. The total PHAS score ranges from 0 to 100 points, and housing authorities with scores of 90 points or more are designated as high performers.

The Section Eight Management Assessment Program (SEMAP) was modeled off of the Public 

Housing Management Assessment Program. The 1996 Proposed Rule published in the 

Federal Register stated that SEMAP “… provides an objective system for HUD to measure HA performance in administering the Section 8 tenant-based assistance programs, and to identify HA management capabilities and deficiencies using criteria that are key to effective program administration.” (Section 8 Rental Voucher and Certificate Programs and Management Assessment Program (SEMAP); Proposed Rule, 1996, p. 63930). The rationale for implementing SEMAP 

focused on efficient program monitoring and risk management: “At a time of diminishing HUD 

staffing resources, use of SEMAP will enable the Department to improve its risk assessment and to effectively target monitoring and program assistance to housing agencies that need most improvement and that pose the greatest risk.” (Section 8 Rental Voucher and Certificate Programs and Management Assessment Program (SEMAP); Proposed Rule, 1996, p. 63930). In 1998, a 

Final Rule on SEMAP was published in the Federal Register and an interim rule making technical modifications was published and became effective in 1999. SEMAP currently consists of 14 rating areas on which PHAs are assessed.

260 Policy Briefs

Performance of Small Public Housing Authorities: Opportunities and Barriers 1.  Selection from the waiting list: PHA conformance with its waitlist policies and procedures.

2.  Reasonable rent: the PHA implements a reasonable procedure for determining and negotiating rental payments with landlords.

3.  Determination of adjusted income: at admission or annual reexamination, the housing authority verifies and correctly determines the adjusted income of households participating in the program.

4.  Utility allowance schedule: the PHA maintains an up-to-date utility allowance schedule.

5.  HQS (Housing Quality Standards) quality inspections: the PHA conducts adequate quality control on inspection procedures across a sample of unit types and locations.

6.  HQS enforcement: the PHA follows through on the enforcement of critical housing quality inspection violations and, if necessary, withholds rental payments from landlords of units in violation until issues are fixed.

7.  Expanding housing opportunities: PHAs in metropolitan areas affirmatively work to encourage participation of landlords in racially integrated and low-poverty neighborhoods.

8.  Deconcentration bonus: PHAs offering exception rents or with multiple payment standards demonstrate that these measures are resulting in certain shares of assisted households moving to low-poverty neighborhoods.

9.  Payment standards: the PHA maintains a payment standard schedule that differentiates payment standards by unit size and applicable Fair Market Rent standards.

10.  Annual reexaminations: the PHA completes an annual reexamination for each voucher-assisted household every year.

11.  Correct tenant rent calculations: the PHA correctly calculates the tenant share of the rental payment.

12.  Pre-contract HQS inspections: newly leased units pass HQS inspection prior to the beginning date of the lease and housing assistance payment contract.

13.  Lease-up: the PHA efficiently utilizes the number of HAP contracts which it is authorized to operate.

14.  Family Self-Sufficiency (FSS) enrollment and escrow accounts: for PHAs with mandatory FSS programs, the PHA has enrolled families in the FSS program and actively measures the progress of those households participating in the FSS program.

The current SEMAP scoring process rates these indicators and then combines the results into a score ranging from 0 to 100 points. Housing authorities with SEMAP scores greater than 90 points are designated as high performers.

Cityscape 261

Greenlee, Lee, and McNamara

Data Sources

We make use of the PHAS and SEMAP scoring criteria to examine which housing authorities are more likely to receive a high performing designation. Although our primary question focuses on whether the size of a housing authority matters for the likelihood of receiving the high performing designation, we also examine other contextual factors including geographic location and location demographics.

We use 2 years (2014–2015) of HUD’s PHAS data, reporting PHA performance scores on public housing management. We merge these data with the corresponding 2 years (2014–2015) of HUD’s Picture of Subsidized Households (PSH) data which contain PHA administrative characteristics and demographics for assisted households. We also add regional demographic attributes from the Census Bureau’s 5-year (2011–2015) American Community Survey (ACS) and the rural-urban classification from the USDA Economic Research Service (ERS)’s 2013 Rural-Urban Continuum Codes. Corresponding SEMAP data are not publicly available; however, Fischer and Sard (2016) report the average SEMAP score by the size—the total number of subsidized housing units—of the housing authorities, which helps to understand whether Housing Choice Voucher (HCV) performance varies by agency’s size.

Descriptive Statistics

This study focuses on small housing authorities that administered 1,000 or fewer aggregate subsidized housing units. We consolidate the data at the housing authority level across the 48 

contiguous states. We exclude small housing authorities that operated only HCV program due to the lack of the SEMAP data. We also drop 146 small housing authorities due to missing values in the PHAS and PSH data. Cumulatively, we analyze a final sample of 1,514 small housing authorities with only public housing programs, and 806 small housing authorities with both public housing and HCV programs (exhibit 2).

Exhibit 2

Public Housing Authorities that Administered 1,000 or Fewer Subsidized Housing Units

Public Housing 

Public Housing and 

Program Only

HCV Programs

Small housing authorities in 48 contiguous states

1,610

902

Missing data in PHAS and PSH

96

96

Total sample

1,514

806

Sample reduction (percent)

6.34

10.6

PHAS = Public Housing Assessment System data; PSH = Picture of Subsidized Households data Sources: 2014–2015 PSH

To examine whether PHA size matters for public housing performance ratings, we create a categorical variable that divides housing authorities into quartiles based on their size. When looking at those housing authorities that only administered public housing programs in 2014—

•  63.6 percent of the small housing authorities in the first quartile (administering 34 or fewer public housing units) were designated as high performers (P1).

262 Policy Briefs

Performance of Small Public Housing Authorities: Opportunities and Barriers

•  69.4 percent of the small housing authorities in the second quartile (administering between 35 

and 61 public housing units) were designated as high performers (P2)

•  69 percent of the small housing authorities in the third quartile (administering between 62 

and 121 public housing units) were designated as high performers (P3)

•  59.1 percent of the small housing authorities in the fourth quartile (administering between 122 and 1,000 public housing units) were designated as high performers (P4)

When looking at those housing authorities that administered both public housing and HCV 

programs in 2014—

•  59.3 percent of the small housing authorities in the first quartile (administering 197 or fewer combined subsidized housing units) were designated as high performers (H1).

•  54.7 percent of the small housing authorities in the second quartile (administering between 198 and 357 combined subsidized units) were designated as high performers (H2)

•  61.2 percent of the small housing authorities in the third quartile (administering between 358 

and 580 combined subsidized units) were designated as high performers (H3)

•  57.3 percent of the small housing authorities in the fourth quartile (administering between 581 and 1,000 combined subsidized units) were designated as high performers (H4).

High-performing agencies that operated only public housing programs tended to serve a lower percentage of minority households and a higher percentage of elderly households, compared with non-high-performing agencies.1 In addition, high-performing agencies were less likely to be located in rural counties and serve non-White population. We observe similar patterns for small housing authorities that operated both public housing and HCV programs (exhibit 3).

Exhibit 3

Public Housing Authority’s Characteristics by the High-Performing Designation

Public Housing 

Public Housing and 

Program Only

HCV Programs

High 

Non-High-

High 

Non-High-

Performer

Performer

Performer

Performer

PHA-level characteristics

P1 (reference group) (percent)

24.87

26.76

P2

26.39

21.90

P3

26.49

22.38

P4

22.24

28.95

H1 (reference group) (percent)

25.51 

24.30

H2

23.59

27.11

H3

26.25

23.11

H4

24.65

25.48

1 Minority households indicate households in which the race of head of households is Black, Native American, or Asian or Pacific Islander, or the ethnicity is Hispanic. 

Cityscape 263

Greenlee, Lee, and McNamara

Exhibit 3

Public Housing Authority’s Characteristics by the High-Performing Designation

Public Housing 

Public Housing and 

Program Only

HCV Programs

High 

Non-High-

High 

Non-High-

Performer

Performer

Performer

Performer

PHA-level characteristics (continued)

Percent public housing units

–

–

44.49

43.93

Number of people per unit 

1.78 

1.95 

2.05 

2.15 

(0.51)

(0.53)

(0.40)

(0.41)

Assisted household income

14,596.44 

14,313.07 

13,726.94 

13,266.99 

(3,299.40)

(3,780.86)

(2,667.32)

(2,732.97)

Percent female head

36.52

36.19

38.81

38.00

Percent disability

23.05

20.24

24.38

22.60

Percent elderly

20.67

16.86

15.40

13.33

Percent minority

28.73

42.34

35.48

41.23

County-level characteristics

Percent rural

14.41

17.43

2.24

5.78

More than one housing authority in a county

2.66  

2.86  

3.83  

3.31  

(2.18)

(3.15)

(5.10)

(4.68)

Percent non-White population

16.06

20.48

15.59

17.02

Population-housing ratio

2.17  

2.17  

2.25 

2.24 

(0.32)

(0.33)

(0.34)

(0.32)

Household income

58,066.15 

57,699.87 

66,332.57 

64,837.96 

(12,168.98)

(14628.52)

(18,155.28)

(17,779.22)

Observations

1,978

1,050

937

675

HCV = housing choice voucher. PHA = public housing authority.

Notes: 2014–2015 PSH and PHAS data are used to calculate descriptive statistics of the characteristics. Standard deviations are in parenthesis. 

Factors Affecting PHAS High Performance of Small  

Housing Authorities

To examine which housing authority and regional characteristics influence PHA performance designations, we employ a multilevel probit model. This model specifies each level of the data hierarchy with individual and contextual determinants as well as a random-effects term, which represents unobserved variations between clusters. Specifically, we structure a two-level model in which housing authorities are grouped into counties (clusters). Exhibit 4 presents the results of the multilevel probit models. For small housing authorities operating only a public housing program, those PHAs in the second and third size quartiles (administering between 35 and 122 

public housing units) were more likely to receive a high-performing designation (around 5.6 

percentage points), relative to housing authorities that administered 34 or less public housing units (P1). On the other hand, we find no statistical difference in high-performance ratings between the P1 and P4 groups. Additionally, housing authorities that served a higher percentage of female-headed or elderly households were more likely to receive a high-performing designation, whereas the presence of a higher percentage of minority households and greater number of occupants per housing unit decreased the likelihood of receiving a high performer designation. Assisted 264 Policy Briefs

Performance of Small Public Housing Authorities: Opportunities and Barriers

household income was positively associated with receiving a high-performing designation, whereas housing authorities in rural counties were less likely to be designated high performers.

Exhibit 4

Multilevel Probit Regression Results: Factors Affecting Small Housing Authority’s High Performance on Public Housing

Public Housing 

Public Housing and 

Program Only

HCV Programs

Marginal 

Marginal 

Coefficient

Coefficient

Effect b

Effect b

(1)

(2)

(3)

(4)

P2

0.185 (0.086)**

 5.264

P3

0.205 (0.087)**

 5.828

P4

0.049 (0.093)

 1.389

H2

–0.111 (0.124)

 3.215

H3

0.057 (0.128)

 1.656

H4

–0.105 (0.130)

 3.057

Percent public housing units

0.090 (0.230)

 2.608

Number of people per unit 

–0.241 (0.094)***

–6.864

–0.527 (0.198)***

–0.153

Assisted household income a

0.313 (0.150)**

8.901

1.077 (0.348)***

31.281

Percent female head

2.444 (0.394)***

0.696

2.640 (0.811)***

 0.772

Percent disability

0.449 (0.318)

0.128

0.705 (0.726)

 0.201

Percent elderly

0.864 (0.243)***

0.246

0.854 (0.576)

 0.253

Percent minority

–0.637 (0.158)***

–0.181

–0.043 (0.247)

–0.010

Rural

–0.221 (0.101)**

–6.288

–0.786 (0.260)***

–22.830

More than one housing 

–0.022 (0.016)

–0.612

–0.001 (0.013)

–0.001

authority in a county

Percent non-White population

–0.034 (0.332)

0.010

–0.045 (0.486)

–0.013

Population-housing ratio

0.105 (0.123)

3.001

0.003 (0.173)

0.001

Household income a 

–0.200 (0.206)

–5.691

–0.399 (0.311)

–11.577

Constant 

2.090 (2.081)

–4.611 (2.999)

Year dummy

Yes

Yes

Estimated variance

 County 

  0.563 (0.098)***

 0.697 (0.163)***

Log-likelihood

-1789.261

-1027.389

Observations

3,028

1,612

HCV = housing choice voucher.

Notes: Robust standard errors are in parenthesis. ‘a’ denotes variable measured in natural logarithms. ‘b’ reports the marginal effects—the percentage point changes in housing authorities’ high-performing designation—of a discrete change in a binary variable and a unit change in continuous, logarithm, and percentage variables. * denotes significance at 10-percent level, ** denotes significance at 5-percent level, and *** denotes significance at 1-percent level.

For small housing authorities that operated both public housing and HCV programs, the results show that the agency’s size did not matter in their operational performance of public housing. 

Similar to correlates in column 1, the percentage of female-headed households, number of people per unit, and assisted household income were statistically significant factors affecting PHAs’ high-performer designations. Notably, small housing authorities in rural counties were less likely to receive a high performer designation by 22.8 percentage points. This result can be explained by the Cityscape 265

Greenlee, Lee, and McNamara

limited geography of opportunity in rural areas in terms of insufficient and unstable jobs, limited access to transportation, health care, and affordable housing, and economic dynamics (Albrecht, 1998; Conger and Elder, 1994;). Estimated variances for both models are highly statistically significant, validating the presence of a random-effect at this geographical level. The likelihood-ratio (LR) test statistic is highly significant, which confirms that including a random-effect term improves the goodness of the fit to the data.

Although we cannot access the SEMAP data, according to Fischer and Sard (2016), the average SEMAP score for housing authorities that administered fewer than 250 HCV units was significantly lower—“four times more likely to be designated as “trouble” or “near trouble” under SEMAP and less likely to be designated as high performers”—contrasted to larger housing authorities. Our data show that 50.1 percent of H1 and 47.5 percent of H2 administered fewer than 250 HCV units, whereas 23.6 percent of H3 and only 4 percent of H4 administered fewer than 250 HCV units. 

This indicates the fact that the chance that a housing authority would receive a high-performing designation by PHAS but not by SEMAP increased in the H1 and H2 groups (or PHAs that 

managed 357 or fewer subsidized units).

Why Is It Difficult for Small Housing Authorities to Receive the 

“High Performing” Designation?

We conducted semi-structured interviews with housing authority executive officers in Illinois. 

Illinois was chosen as the geography for case study development due to the range of contexts in which small housing authorities are and based on researcher familiarity with housing authority officials in the state. Although findings from these interviews are not intended to be generalizable to small housing authorities throughout the country, the information provides valuable context for the interpretation of our models of PHA performance.

Interview Process

Using data from the 2016 HUD Picture of Subsidized Households, we identified 87 PHAs in Illinois serving less than 1,000 combined subsidized units. We decided to keep our sample open to include housing authorities that were designated as high performers as well as those that did not receive the designation so that we could compare different perspectives related to performance ratings and accountability. PHA Executive Directors received a letter in the mail inviting them to participate in the research. This was followed up by an additional email and phone call.

We interviewed a total of nine PHA officials at eight small PHAs in Illinois, a recruitment rate of 9 percent. This included five PHAs designated as “high performing.” Each PHA official was asked about the history and background of their PHA, PHA performance and accountability, and perceptions of the MTW program. Interviews were conducted via the  zoom.us video conference software. With the permission of the interviewee, interview audio was simultaneously recorded. 

Following the interview, recorded audio was then transcribed and coded. Interviews lasted between 40 and 70 minutes—a total of 372 minutes of interview audio was captured and transcribed. 

We used an inductive coding approach focused on identifying concepts and patterns and on 266 Policy Briefs

Performance of Small Public Housing Authorities: Opportunities and Barriers highlighting key themes within the data (Creswell and Clark, 2016). Although initial results from our models provide some sense of the relationship between PHA size, location, and performance, we chose to approach our analysis of qualitative data without making use of preconceived notions or categorizations of these data (Corbin and Strauss, 2014). Interview transcripts underwent three rounds of coding—the first round focused on identifying broad trends within the data, the second round focused on identifying specific themes occurring within the data, and the third round focused on thematic refinement.

Challenges in Program Management and Accountability

We began our interviews by asking housing authority administrators about what they perceived as the specific management challenges associated with running small housing authorities. We also asked administrators to describe the challenges associated with reporting and accountability at both the local level and to HUD. Administrators described several tradeoffs associated with being a small housing authority: (1) “bricks and mortar” versus “soft” service, (2) concerns regarding effective demand for housing, and (3) measuring utilization versus outcomes.

A main theme which emerged around management and accountability was a feeling that small housing authorities (and housing authorities more generally) were stuck between a mandate to deliver “bricks and mortar” housing benefits while also needing to provide a series of “soft” 

services for assisted households.

My admissions manager told me this the other day. It’s the best worst job ever. And 

it’s the truth. And I think what’s interesting from a director’s perspective is running a public housing agency, it’s not about bricks and mortar. It is a human service and 

it’s about lives. However, they have us and HUD operates as a bricks and mortar 

regulatory oversight environment. —executive director of a high-performing PHA in 

northern Illinois

This tension permeated the conversation around HUD performance measures and scoring. 

Administrators saw themselves making tradeoffs between looking good for the sake of meeting performance requirements and doing good for assisted households.

It’s like a lot of housing authorities, they’ve had to make decisions between financial stability in some areas or lower PHAS, you make it for your financial stability. So, it’s kind of a ... I think it’s not a good representation to people who are studying if they’re a high performer at housing authorities out there. When they were first looking at 

MTW, they were wrestling with that, and they had the verbiage for high capacity 

housing authorities, which I liked, but somewhere that got lost. —executive director 

of a high-performing PHA in northern Illinois

The housing authority administrators we interviewed were sympathetic towards the challenges which HUD faces in evaluating agency’s performance and making resource allocation decisions impacting the housing authorities. At the same time, many administrators saw the need for ways to account for “soft” forms of progress and service delivery impacts for assisted households.

Cityscape 267

Greenlee, Lee, and McNamara

What is not captured is outcomes of individuals that are utilizing our services. There’s no process that exists to capture that. In the past, HUD has kind of gone back and 

forth on evaluations. We used to have to do evaluations that tenants or program 

participants would do. I don’t think that was the right tool. That didn’t seem to really offer much and that’s probably why HUD ultimately got rid of it. But you know, if I’ve been working with or our program’s been working with a family for five years, I have 

no way to show any form of an outcome or a realization of a goal to any stakeholder. —

executive director of a PHA in central Illinois

Even with comparatively small public housing stocks, administrators described the challenges of balancing physical maintenance concerns and management of public housing units and taking the time to work closely with assisted tenants on a range of other housing and non-housing needs.

Well, the story that’s not getting to them is that a cracked light switch cover fails HQS. 

So it’s not a good indicator of whether you have a strong Section 8 program or not. It’s not a good indicator if you’re working with a quality landlord or not. You know, my 

house won’t pass HQS. That’s a common thing. The White House won’t pass HQS. So, 

it’s just the kind of thing that can be manipulated poorly … other housing authorities with challenges of working in very distressed areas are not going to be able to get that, and that’s not representative of the Housing Authority’s work, or even of the landlords or such that they work with. —executive director of a high-performing PHA in 

northern Illinois

Other administrators pointed to forms of structural disadvantage associated with their housing authority context, which made it challenging to meet HUD targets as well as the broader human service needs of assisted households.

Well, like I said, urban areas have their own particular set of situations. However, they have a lot of strengths that they don’t take as a strength. They have transportation access, they have a support network, all the social services agencies work together. They’re 

serving the same clientele. They have a good clinical and health advocacy situation. You don’t have that in a rural area. —executive director of a PHA in central Illinois

At the same time, some administrators felt that their small size made it more possible to facilitate and sustain relationships with local stakeholders and landlords, thereby increasing available housing opportunities and increasing capacity to administer programs.

Well, I think we have pretty good relationships with everybody in our county, our 

landlords, you know, county officials, other nonprofits and whatnot. You know, I went 

up to our courthouse one day and I had to talk to somebody. He said, “I didn’t know we had a housing authority in this county.” I go, “What?” So that’s been kind of my goal. I’ve been out letting people know what we do and how we do it, trying to get away from this stigma of what we do. You know, I think we’re involved with every landlord we can find in this county. And when landlords have vacancies they call us first. —executive director of a PHA in northern Illinois

268 Policy Briefs

Performance of Small Public Housing Authorities: Opportunities and Barriers A second area where administrators described tradeoffs was around effective demand for housing units. Lack of demand was attributed to factors including unit obsolescence, lack of local employment and economic opportunities, and to a broader spatial mismatch between unit location and employment opportunities.

In our county, we don’t have many people involved in our housing authority. They really don’t care. That’s why I keep bugging them. I think the biggest thing that needs to be measured is are we 100 percent on our utilization of our vouchers and 100 percent 

occupied? Because we have open vouchers, empty units … What are they [people on 

the waitlist] doing? Living on the street. Where? We could be housing them. —executive director of a PHA in northern Illinois

Another administrator described broader patterns of population ageing as impacting service delivery and demand for assisted housing.

What I’m beginning to see is with this rural area my biggest concern is as the elderly begin to age and move out of our units to go into nursing homes, I’m very fearful that there won’t be elderly that’s going to want to move or any elderly’s going to want to move in public housing. Because the people don’t come back to the county after the kids leave high school. They go onto college because there is really no major job opportunities here. 

And so for the long term, to me it is going to be a concern, and even for the families. —

executive director of a PHA in northwest Illinois

HUD’s Moving to Work Demonstration

Previous sections discuss operational performance, and management strengths and challenges associated with running small housing authorities. The following three questions then arise: 1) how can small housing authorities build on their strengths, while overcoming their weaknesses, to enhance housing authorities’ operational performance, 2) what are the institutional and service needs, closely linked to local communities and HUD, that should be addressed, 3) what are the potential federal opportunities where housing authorities can initiate locally designed operating strategies?

HUD’s Moving to Work (MTW) demonstration program may provide the opportunity. Previously focusing predominantly on large housing authorities, the current MTW expansion calls for a substantial expansion in the number of small housing authorities administering 1,000 or fewer aggregate public housing and HCV (Housing Choice Voucher) units that are part of the demonstration. MTW couples operational flexibility and fungibility of operating funds between public housing and HCV programs, which should allow participating agencies to respond more effectively to local conditions and innovate beyond the constraints of federal regulations under the U.S. Housing Act of 1937 (Abravanel et al., 2004).

Prospective Benefits and Costs

Given the prospect of a major expansion of the number of small housing authorities participating in the Moving to Work demonstration, we asked PHA administrators to describe the demonstration Cityscape 269

Greenlee, Lee, and McNamara

in their own words, to describe the prospect of participation in MTW, and to describe what the most attractive and unattractive features of the demonstration were from their perspective. 

Because several of the administrators we interviewed ran PHAs that were ineligible for the demonstration because they were not designated as high performing, we also asked about whether the MTW demonstration served as an incentive to improve performance metrics and other operational standards.

Several insights emerged within this thematic area. First, administrators of small PHAs perceive a high opportunity cost for participating in MTW. Although several administrators thought this opportunity cost was worth the time and effort, several administrators felt that preparing an application would tap into valuable resources that could be spent on service delivery. Second, although the principle of fungibility of funds was very attractive to PHA administrators, many were unsure how other MTW provisions might dovetail with current client needs to deliver new or different benefits. Third, PHA administrators pointed to regional collaboration and collaborative MTW submissions as a potential means of dealing with some of the previously identified barriers to participation in MTW.

We asked administrators to describe their perceptions of the MTW demonstration and expansion and whether their housing authority was planning to apply for the demonstration. Two of the executive directors we talked with were actively planning to apply to the demonstration. 

Several other administrators were aware of the program but had not contemplated applying. 

Administrators saw several potential benefits to expanding the program to target small PHAs. One executive director described the potential for MTW to help rebalance administrative regulations geared towards large urban PHAs.

Now, from the HUD point of view, from looking at the HUD point of view, some of the 

regulations that come down, they are looking at the urban areas, the larger authorities. 

That is extremely onerous to a small rural agency with limited staff. That’s why I’m 

running so hard, because I’ve got to do everything just to keep up. And that’s the reason why I’m applying for MTW, because it allows some flexibility. —executive director of a PHA in central Illinois

Enthusiasm for program benefits was contrasted by a perception that the opportunity cost to applying for the demonstration is high. As one executive director of a high-performing PHA in northern Illinois described it, “… It’s overwhelming to think about walking down the path of a new program or trying to do a new program. That’s the biggest stumbling block. Because like I said, we’re pretty progressive, very open minded and it’s just sheer being overwhelmed...” 

Administrators liked the notion of program flexibility and using experimental frameworks to better highlight how benefits can effectively be delivered to assisted households.

And we are so often just thought of as landlords for the very low income and we’re 

not. I mean, we do so much more than that, but I also think there’s so much more we 

could do with the right resources. And help us design a program that, you know, has 

much more benefit to individuals or families’ lives than just safety sanitary housing. —

executive director of a PHA in central Illinois

270 Policy Briefs

Performance of Small Public Housing Authorities: Opportunities and Barriers The enthusiasm of some administrators was tempered by questions regarding the potential costs of experimentation and intervention in the lives of assisted households.

Whenever we start talking about less oversight or less regulations, yes. We are very 

regulated in this field to the extent that I think there could be some good reform that would be very beneficial and useful by all of us. But I have to always draw the line at how does that impact the population that we intend to serve? —executive director of a PHA in central Illinois

Administrators who were choosing to apply to the demonstration also described significant up-front monetary costs to participation.

I’m relying on a grant writer to help me. And I’m spending about $14,000, I think it is. 

But, you know, also, which we can afford, but I also have a part-time employee that I can’t make full time because of that. You know, some decisions that smaller housing authorities who are probably the ones who are going to fit to cohort that they’re looking at will have to make. —executive director of a high-performing PHA in northern Illinois

Administrators who were not choosing to apply for MTW at the time of our conversation 

brought up perceived monetary costs to applying for the program as a barrier to participation. 

We asked administrators to talk about the types of benefits which they would like to achieve via participation in MTW, or in the case of PHAs who weren’t eligible to apply, via an MTW-like program that granted administrative flexibility and fungibility. With regards to housing, administrators consistently talked about using funds to address capital financing gaps for public housing units. Looking beyond the physical and capital needs of housing units, administrators identified a range of ways in which they might use flexibility of funds to better engage with the needs of assisted households. Increasing employment access and dealing with spatial mismatch between jobs and housing locations was a consistent theme that came up, particularly within rural housing authorities.

And I think in a rural community, a rural county, transportation is a huge thing, because where our public housing units are, the towns have nothing. In fact, one of the towns, their grocery store is shut down. They don’t even have a grocery store anymore… Back 

in the 70s and 80s when they were built there was a need. Now there is not. Things 

change. But I think that’s the biggest thing in our county is transportation. If people had transportation we could keep them working, which is going to help them out. —

executive director of a PHA in northern Illinois.

Leveraging PHA funds to innovate around social service partnerships also came up as a strategy.

Public housing and supportive services, I totally see a need to come together more... 

That is our number one thing we struggle with is our tenants and their daily living 

habits, their mental health needs. So we have been reaching out every which way we can, getting creative, trying to fulfill that need, to make them successfully housed. —executive director of a high-performing PHA in northern Illinois

Cityscape 271

Greenlee, Lee, and McNamara

We asked PHA administrators whether regional collaboration or collaborative MTW 

applications might be an effective strategy for dealing with some of the scale issues which came up in our conversations. Most administrators saw regional collaboration as a potential option to engage with these problems while also creating more effective partnerships to streamline service delivery. The executive director of a PHA in northern Illinois describes the challenge.

They [MTW program designers] need to realize we’re small. So we have seven 

employees – or six employees and one half time employee for our … units. And 

sometimes it is tough to keep up. We do the same stuff as a big housing authority. 

You know, and I realize they have more people. But you know, me as an executive 

director, you know, I get a lot of applications. The other day I was helping an old 

lady get her refrigerator cleaned underneath… in a small housing authority, the 

employees pretty much have to do everything. We can’t just be doing the finance, 

doing the occupancy, doing the background checks. Where bigger housing 

authorities have people for that. They have to realize we’re small and the funding is 

going to be small. So we aren’t able to employ people to do those separate things. 

Which, again, I think working with other housing authorities could solve that. —

executive director of a PHA in northern Illinois

Several administrators suggested that the demonstration should maintain a preference for regional cooperation amongst small PHAs. For example, an executive director from a northern Illinois PHA suggests, “… start it regionally, see where it goes, just take it from there….” At the same time, some administrators pointed to the challenges of collaboration.

It’s really been the personalities. I get along really great with the other executive 

directors. I get along great with the other boards. It’s relationships like working with people they know. And, you know, I’m very open with what they want to do and how 

we can benefit each other. But, you know, those are always as good as the moment. —

executive director of a high-performing PHA in northern Illinois

Blending local politics, figuring out how to share resources, identifying divisions of labor, and allocating accountability all came up as concerns with regards to designing collaborative MTW 

applications and programming.

Discussion and Concluding Remarks

In this article, we examine the performance, assessment, and management strengths and 

challenges associated with small housing authorities. Our results suggest that program sociodemographic factors and the population served influence the likelihood of receiving a high-performance designation. We also find that small housing authorities in rural counties were less likely to receive a high-performing designation, and that regional heterogeneity affects small housing authority’s performance level. Interviews with small PHA administrators revealed challenges in agency management, accountability, and implementation. PHA 

administrators also talked about the need for more holistic and qualitative measures of their impact on assisted tenants and the surrounding community.

272 Policy Briefs

Performance of Small Public Housing Authorities: Opportunities and Barriers HUD uses the Public Housing Assessment System to monitor and rate the overall condition and financial health of local housing authorities, however, PHAS is limited in its ability to detect operational strengths of small housing authorities. They also described multiple languages of accountability—one regarding what needed to be reported to HUD and another that reflected accountability to their board and the community at large. Most interviewed public housing authority administrators argued that the current PHAS and Section Eight Management 

Assessment Program scoring systems do not adequately account for the strengths of small housing authorities—serving assisted households closely and facilitating and sustaining relationships with local landlords and other institutional and governmental stakeholders. There exists little room for measuring non-housing impacts of housing support within the current framework—areas in which appropriate approaches to documenting PHA strengths or allowing extra points to the current high-performing scoring systems will increase the chance of small housing authorities’ 

participation in the demonstration.

Regarding MTW implementation and participation, interviews with housing authority officials in Illinois revealed a substantial variability regarding knowledge of the MTW demonstration program as well as capacity and desire to apply. The small PHA officials we interviewed all had some knowledge about the MTW demonstration and the expansion. We saw different levels of engagement with the prospect of applying, from not considering the program—often due to not being a high-performing agency—to actively pursuing the demonstration. Although our interview sample is small, we see some evidence that the greater administrative capacity of “larger” small PHAs may put them in a better position to apply for the demonstration.

We also note that the 2017 expansion’s experimental design that allocates interventions to PHAs represents a risk for some PHAs that may not find the assigned activities to be the highest priority for meeting needs within their housing market and population served. Although the lack of a more robust and unifying experimental framework is a past criticism of the MTW demonstration (Webb, Frescoln, and Rohe, 2016), which is likely reflected in the decision to assign interventions to participating PHAs, allowing some flexibility or agency regarding the grounds for applying specific interventions could increase the range of housing authorities willing to participate in the demonstration. Furthermore, small housing authorities in rural areas—PHAs that have arguably received less attention from HUD’s demonstration programs—demonstrate wide variations in the level of awareness about the demonstration program, thus housing authority officials were less likely to see the potential value of the demonstration for their housing authorities.

To address the issues of low levels of knowledge, limited capacity to consider and apply for MTW, and the link of a PHA applying and being arranged to the control group, HUD could consider the following steps. HUD might distribute appropriate information about the MTW demonstration and potential value by educating housing authority staff or through supporting other agencies and institutions to spread the information to the housing authorities. In addition, the current expansion call randomly selects treated and control group housing authorities, and this may be a fairly risky tradeoff for PHAs requiring considerable effort to create understanding and persuade participation.

Context matters—many small housing authorities, particularly those in rural areas expressed broader concerns with regards to demand for their current public housing stock, as well as unmet Cityscape 273

Greenlee, Lee, and McNamara

transportation and job access needs, and broader social service and mental health needs. Thinking and communicating about ways in which MTW might be able to help small PHAs meet these needs or incentivize partnerships to meet these needs would help to encourage small PHAs’ participation in the demonstration as well as further address the reasons why individuals are demanding housing support in the first place.

Authors

Andrew J. Greenlee is an Assistant Professor in the Department of Urban and Regional Planning at the University of Illinois at Urbana-Champaign.

Han Bum Lee was a post-doctoral research associate in Agricultural and Consumer Economics at the University of Illinois at Urbana-Champaign at the time of this paper, and is now a visiting assistant professor in the Department of Economics at the University of Texas San Antonio.

Paul E. McNamara is an Associate Professor in Agricultural and Consumer Economics at the University of Illinois at Urbana-Champaign.

References

Abravanel, Martin D., Robin E. Smith, Margery Austin Turner, Elizabeth C. Cove, Laura E. Harris, and Carlos Manjarrez. 2004. Testing Public Housing Deregulation: A Summary Assessment of HUD’s “Moving to Work” Demonstration. Urban Institute.

Albrecht, Don E. 1998. “The industrial transformation of farm communities: Implications for family structure and socioeconomic conditions,”  Rural Sociology 63 (1): 51–64.

Chetty, Raj, Nathaniel Hendren, and Lawrence F. Katz. 2016. The effects of exposure to better neighborhoods on children: New evidence from the Moving to Opportunity experiment.  American Economic Review 106 (4): 855–902.

Conger, Rand D., and Glen H. Elder, Jr. 1994.  Families in Troubled Times: Adapting to Change in Rural America. Social Institutions and Social Change. Hawthorne, NY: Aldine de Gruyter.

Corbin, Juliet, and Anselm Strauss.. 2014.  Basics of qualitative research. Thousand Oaks, CA: Sage Publications, Inc.

Creswell, John W. and Vicki L. Piano Clark. 2016.  Designing and Conducting Mixed Methods Research. 

Los Angeles, CA: Sage Publications, Inc.

Finkel, M., and Buron, L. 2001.  Study on Section 8 voucher success rates, Volume 1: Quantitative study of success rates in metropolitan areas (Final report). Washington, DC: U.S. Department of Housing and Urban Development.

274 Policy Briefs

Performance of Small Public Housing Authorities: Opportunities and Barriers Fischer Will, and Barbara Sard. 2016.  Sweeping Deregulation of Small Public Housing Agencies Would Do More Harm Than Good. Center on Budget and Policy Priorities (CBPP).

Jacob, Brian A., 2004. “Public housing, housing vouchers, and student achievement: Evidence from public housing demolitions in Chicago,”  American Economic Review 94 (1): 233–258.

Jacob, Brian A. and Jens Ludwig. 2012. “The effects of housing assistance on labor supply: Evidence from a voucher lottery,”  American Economic Review 102 (1): 272–304.

Section 8 Rental Voucher and Certificate Programs and Management Assessment Program 

(SEMAP). 16 FR 63930 (December 2, 1996).

Spar, Karen, and Gene Falk. 2016.  Federal Benefits and services for people with low-income: Overview of spending trends, FY2008-FY2015. Congressional Research Service (CRS), United States Congress.

U.S. Department of Housing and Urban Development. 2016. Picture of Subsidized Households 

[Data file]. huduser.gov/portal/datasets/assthsg.html.

U.S. Department of Housing and Urban Development. 2017. Picture of Subsidized Households 

[Data file]. Retrieved from huduser.gov/portal/datasets/assthsg.html.

United States General Accounting Office (GAO). 1982.  Contributing Causes of Financial and Management Problems in Public Housing Projects. (CED-82-31). Washington, D.C.: Government Printing Office

Webb, Michael D., Kirstin P. Frescoln, and William M. Rohe. 2016. “Innovation in US Public Housing: A Critique of the Moving to Work Demonstration,”  International Journal of Housing Policy 16 (1): 111–124. http://dx.doi.org/10.1080/14616718.2015.1085215.

Cityscape 275

276 Departments

Graphic Detail

Geographic Information Systems (GIS) organize and clarify the patterns of human activities 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 community 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 john.c.huggins@hud.gov .

Intersecting Opportunity Zones 

with Vacant Business Addresses

Alexander Din

Maryland Department of Housing and Community Development

by Alexander Din

On December 22, 2017, President Donald Trump signed into law the Tax Cuts and Jobs Act of 2017. One provision of this bill was to create Opportunity Zones, low-income census tracts that encourage economic development by providing tax incentives. The states, territories, and Washington, D.C. were in charge of nominating their own Opportunity Zones and then submitting an application to the U.S. Treasury Department for approval. Each jurisdiction was able to nominate up to 25 percent of its low-income census tracts as Opportunity Zones. Once approved, selected census tracts will remain Opportunity Zones for 10 years. Investors are able to use unrealized capital gains as part of an Internal Revenue Service- (IRS) approved Opportunity Fund for businesses within the Opportunity Zones. Tax incentives for investing in Opportunity Zones include a temporary deferral for capital gains invested into the Opportunity Zone, a step-in basis for up to 15 percent of the original capital gains investment to be excluded from taxation, and a permanent exclusion from taxation on gains made after the investment into the Opportunity Zone and if the investment is held for at least 10 years (EIG, 2018). There has been debate about who will benefit from investments into Opportunity Zones (Looney, 2018).

One hundred forty-nine census tracts were approved as Opportunity Zones for Maryland on May 18, 2018. Opportunity Zones are within every county of the state, including 42 within Baltimore City.1 Baltimore City has long had a problem with residential and commercial vacancy. In the first quarter of 2018, the United States Postal Service (USPS) recorded that 3,740 of 31,458 (11.89 

1 Baltimore City is independent of any county and considered a county-equivalent in Maryland. 

Cityscape: A Journal of Policy Development and Research •  Volume 20, Number 3 • 2018

Cityscape 277

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Din

Exhibit 1

Baltimore City—Percent Vacant Business Addresses Using Natural Breaks Classification Overlaid with Opportunity Zones.

percent) business addresses within Baltimore City were vacant, 4.62 percentage points greater than the statewide rate of 7.27 percent. Baltimore City has 200 census tracts, 160 of which were considered low income and therefore eligible to be designated as Opportunity Zones.2 Low-income census tracts contained 25,036 business addresses, including 3,104 that were vacant, a rate of 12.40 percent. Census tracts designated as Opportunity Zones had 12,312 business addresses, 1,691 of which were vacant for a rate of 13.15 percent. Opportunity Zones in Baltimore City have a business address vacancy rate nearly double the statewide rate. Despite comprising only 21 percent of Baltimore City’s total census tracts, Opportunity Zones cover an area that includes 45.65 percent of the vacant business addresses within the City.

Exhibit 1 shows both the designated Opportunity Zones and the business address vacancy rate in Baltimore City. Designated Opportunity Zones are focused in several portions of the City. These 2 The 160 low income community census tracts are the census tracts immediately eligible to be designated as an Opportunity Zone. Census tracts that are adjacent to designated Opportunity Zones and not considered a low income but are within 125 percent of the Median Household Income of the adjacent Opportunity Zone are eligible to be designated as an Opportunity Zone. A maximum of 5 percent of nominated Opportunity Zones may qualify via this method. There are four Opportunity Zones in Maryland designated under this qualification, none of which are within Baltimore City.

278 Graphic Detail

Intersecting Opportunity Zones with Vacant Business Addresses

areas include industrial regions such as Port Covington near the Inner Harbor district and the ports in the southeast section near Amazon’s recently developed fulfillment center, the downtown core, the inner-west side, and on the east side, and some further-out neighborhoods including Park Heights and around Morgan State University.

One measure to community and economic development is to monitor the business vacancy rate in Opportunity Zones. As Opportunity Funds are created, new businesses should develop within the Opportunity Zones. In addition to new businesses, it is anticipated that other businesses will open that are not part of an Opportunity Fund as revitalization is projected to occur. Another goal is that, as businesses open in Opportunity Zones, workers will be recruited from local communities. 

Improvement to non-car-oriented infrastructure connecting new businesses in Opportunity Zones and low-income community census tracts not designated as Opportunity Zones may help spread the benefits of job creation, as the working poor are less likely to own a car and as cars remain a high cost barrier to economic betterment (NHTS, 2014).

Author

Alexander Din is a housing research and GIS analyst with the Maryland Department of Housing and Community Development.

References

Economic Investment Group (EIG). 2018. “The Tax Benefits of Investing in Opportunity Zones.” 

http://eig.org/wp-content/uploads/2018/01/Tax-Benefits-of-Investing-in-Opportunity-Zones.pdf.

Looney, Adam. 2018. “Will Opportunity Zones help distressed residents or be a tax cut for gentrification?” https://www.brookings.edu/blog/up-front/2018/02/26/will-opportunity-zones-

help-distressed-residents-or-be-a-tax-cut-for-gentrification/.

National Household Travel Survey (NHTS). 2014. “FHWA NHTS Brief Mobility Challenges for Households in Poverty. ” https://nhts.ornl.gov/briefs/PovertyBrief.pdf.

Appendix

Exhibit 2

Business Addresses by Geography for 2018 Q1

Business Addresses

Geography

Census Tracts

Total

Active

Vacant

Vacant %

Maryland

1,396*

240,566

223,079

17,487

7.84%

Baltimore City

200

31,458

27,718

3,740

13.49%

Low Income Communities

160

25,036

21,932

3,104

14.15%

Opportunity Zones

42

12,312

10,621

1,691

15.92%

*Per census tracts retrieved from the Census Bureau’s Cartographic Boundary TIGER geographic database Cityscape 279

280 Departments

Evaluation Tradecraft

Evaluation Tradecraft presents short articles about the art of evaluation in housing and urban research. Through this department of Cityscape, the Office of Policy Development and Research presents developments in the art of evaluation that might not be described in detail in published evaluations. Researchers often describe what they did and what their results were, but they might not give readers a step-by-step guide for implementing their methods. This department pulls back the curtain and shows readers exactly how program evaluation is done. 

If you have an idea for an article of about 3,000 words on a particular evaluation method or an interesting development in the art of evaluation, please send a one-paragraph abstract to 

marina.l.myhre@hud.gov.

Talking to Landlords

Philip M.E. Garboden

University of Hawaiì at Manoa

Eva Rosen

Georgetown University

Disclaimer: The work that provided the basis for this publication was supported by funding under a grant with the U.S. Department of Housing and Urban Development. The substance and findings of the work are dedicated to the public. The author and publisher are solely responsible for the accuracy of the statements and interpretations contained in this publication. Such interpretations do not necessarily reflect the views of the Government.

by Philip M.E. Garboden and Eva Rosen

Abstract

Although evaluations of housing programs have increasingly incorporated a qualitative component to help researchers understand the mechanisms and meanings behind the statistical findings, systematic collection of data from housing suppliers (landlords, property managers, builders, and developers) has been lacking. 

Indeed, no comprehensive set of best practices exist for evaluation teams looking to incorporate the voices of supply-side actors in their work. In response to the lack of information on housing suppliers and a desire to understand what motivates landlord participation in the Housing Choice Voucher Program, HUD 

funded the first ethnographic study of landlords,  Urban Landlords and the Housing Choice Voucher 

Program: A Research Report . This study involved a 5-year data collection effort in Baltimore, MD; Dallas, TX; Cleveland, OH; and Washington, D.C., and conducted 150 interviews with landlords and property managers, most of whom were drawn from a random stratified sample. 

Cityscape: A Journal of Policy Development and Research •  Volume 20, Number 3 • 2018

Cityscape 281

U.S. Department of Housing and Urban Development • Office of Policy Development and Research

Garboden and Rosen

Abstract (continued)

In the article, we explore lessons learned across four key components of the ethnographic study: (1) sampling, including the process of developing a sampling frame, stratification, and pulling a sample; (2) recruitment, focusing on the “under the hood” techniques for getting landlords to participate; (3) the interview itself, exploring how to elicit candid responses that can inform policy development; and (4) ethnographic methods, focusing on how field observation can enrich the interview data and reduce analytic bias. We believe the technical details provided will be of great interest within the housing policy evaluation community and advance the use of qualitative and ethnographic methods in evaluation research going forward.

Introduction

Evaluation researchers have long understood that “for the results to be useful in the policy process, it is imperative that the nature of the program and the characteristics of its participants be carefully documented” (Orr, 1998:16). Indeed, it is now fairly standard for large-scale policy evaluations to include some form of qualitative data collection. When programs fail to have a significant impact—

or unexpectedly succeed—we can often learn why by talking directly to program participants. 

In this way, researchers can glean insight into the mechanisms that drive take-up rates, program attrition, effect heterogeneity, and durability (for an examples of this approach related to the Moving to Opportunity Demonstration see Clampet-Lundquist et al., 2011; DeLuca, Clampet-Lundquist, and Edin, 2016; Edin, DeLuca, and Owens, 2012; Smith et al., 2014).

For housing programs, however, success depends not just on program participants, but also on another set of actors: the rental property owners who decide whether or not to participate by renting to subsidized tenants. On this issue, most evaluations are nearly silent, leaving core questions unanswered. What role do landlords play in explaining why voucher families struggle to find housing in low-poverty neighborhoods? What motivates some landlords to market their properties to subsidized tenants and others to avoid them? For these questions, tenant interviews fall short.

Although the behaviors of rental property owners are highly motivated by profit maximization, the story is much more complex, requiring a more sociological examination of their business strategies, motivations, and ideologies. What little data exist on landlord behavior suggests that for the past 50 years at least one-half of the urban housing stock has been owned by individuals with limited expertise, whose behaviors are hard to predict based on traditional financial metrics (Garboden and Newman, 2012; Mallach, 2006; Sternlieb, 1966). It is necessary, then, to study landlord decision making as one would study any other social group—directly.

Our recent study, conducted in partnership with the U.S. Department of Housing and Urban Development, has confirmed the value of this approach (Garboden et al., 2018; Garboden and Rosen, Forthcoming; Greif, Forthcoming). For example, the seemingly straightforward question 282 Evaluation Tradecraft

Talking to Landlords

of why landlords choose to house subsidized tenants was based not only on their comparative evaluation of voucher and market tenants, but on their personal experiences with voucher program administration and the ideological lens through which they view those experiences. By capturing a systematic random sample of landlords, and engaging them in open-ended interviews and field observations, our goal was to provide empirical data that can be used to guide policy reform.

In this article, we describe the application of indepth interviewing and qualitative field methods (Becker, 1998; Weiss, 1994) to housing policy evaluation. These methods are drawn from a long tradition, especially in the study of urban populations (Anderson, 1999; Duneier, 2001; Edin and Lein, 1997; Liebow, 1967; Pattillo McCoy, 1999; Suttles, 1968; Venkatesh, 2002), but have rarely been applied to supply-side actors. Given the relative paucity of studies that have engaged rental property owners in this way (for exceptions, Greenlee, 2014; Rosen, 2014; Varady, Jaroscak, and Kleinhans, 2017), and the limitations of the sampling designs in the extant literature, the goal of this brief methodological article is to present a set of best practices learned during our data collection process.

Developing A Sampling Frame

Although qualitative studies rarely have sufficient statistical power to generate precise population-level inferences when utilizing a random sample, we do not think it is appropriate to jettison the concept of sampling all together (DeLuca, Clampet-Lundquist, and Edin, 2018; but see Small, 2009). When possible, is vastly preferable to select 100 respondents with stratified random selection than to introduce the bias associated with convenience, venue, or snowball sampling. Like other industries, real estate consists of dozens of niches and hundreds of supply-side networks. 

To sample based on location or referrals, then, is to introduce inaccurate homogeneity into one’s sample and potentially miss significant sections of the market.

Unfortunately, we know of no city that maintains a publicly available list of all rental property owners within its jurisdiction complete with updated contact information. Even if such a list existed, the issue is further complicated by property management companies, who do not appear as the owner of record, but hold key information regarding the management of particular units. 

This complication makes developing a sampling frame, from which a random sample of landlords could be drawn, extremely challenging.

No solution is perfect, but considering the key research questions that the study was designed to address provides some direction. In this study, we were interested in understanding the experience of poor families, with and without vouchers, searching for housing in four cities: Baltimore, Cleveland, Dallas, and Washington, D.C. It was therefore appropriate to construct our sampling frame from active real estate listings in each city, thus capturing the distribution of options available to poor families. Once the data were scraped and geocoded, we were able to construct a sampling frame from which to select our respondents.

Some poor families, of course, do not search for housing online. Instead they use either referrals from friends and family or spatial proximity to identify housing (DeLuca, Rosenblatt, and Wood, forthcoming). For this reason, it is important to assess the limits of the sampling frame by Cityscape 283

Garboden and Rosen

comparing the distribution of respondents to a representative dataset. In our case, we had access to HUD 50058 administrative data, linked to HUD’s Voucher Management System data, to include property owner information and housing type. This analysis enabled us to show that the patterns of ownership conglomeration identified in our qualitative sample were reflective of the overall distribution in each of our three cities: they were concentrated in Dallas, highly fragmented in Cleveland, and Baltimore and Washington D.C. fell somewhere in between. More generally, the administrative data enabled us to determine where each interview fit into a broader distribution, to distinguish between outliers and modal cases.

Getting to Yes: Recruiting Landlords

Once we scraped a sample of rental listings and collected the corresponding contact information, getting landlords to say “yes” to an interview proved less difficult than we had imagined prior to beginning the study. We would call the number listed and ask to speak to the landlord or property manager associated with the property, briefly explain the purpose of the study, and ask if the landlord would be willing to participate in an interview. In most cases—and to our surprise—landlords said yes with little hesitation. For example, when we called Roger, a long-time D.C. landlord, we told him we wanted to learn more about his work as a landlord and he was immediately enthusiastic and eager to share his story. “I’m happy to meet with you and help so that you have the landlord perspective,” he told us. In general, landlords jumped at the opportunity to tell someone about the daily challenges of owning and managing affordable housing. In their role as landlords, most of them spend a lot of time thinking about how to make the system work better—yet they rarely have a listening ear.

However, not all landlords are so easy to recruit. Some simply do not answer their phones. For these folks, persistence is key. For example, after calling a property near the D.C. Navy Yard a few times with no answer, we decided to visit. On arriving we learned that the property was actually made up of three different apartment buildings, each one with its own property manager. The first manager sent us down the street to the next building. The receptionist there was very kind and gave us the card of the property manager, who wasn’t in that day. The fieldworkers called a few times over the next month, and finally the same receptionist declared that the manager wouldn’t have time for at least a month. Finally, after months of back and forth, we made one last visit. The new property manager at the desk who had just been promoted readily agreed to an interview. The people who worked at the front desk were very familiar with us at that point, and they seemed just as relieved as we were to get a “yes.” Timing, in other words, is key. Many landlords are willing to talk but cannot sacrifice work hours to participate. Catching landlords when they happen to have a lighter week and meeting them where and when they are available can be essential.

In other cases, landlords expressed some healthy skepticism about our project and had some questions before agreeing to interview. Many property managers regularly undergo fair housing audits and expressed worry that their corporate office had sent us undercover to interview them about the company and see what they would say. In these cases, having a valid university affiliation was critical. Some asked for scanned copies of our university IDs or requested that the PI email them from an official university address to verify our credentials. We also found it to be beneficial 284 Evaluation Tradecraft

Talking to Landlords

to have a  local university affiliation with which respondents were familiar. After confirming that we were not auditing them, they were happy to participate and were very forthcoming.

Our interview stipend of $50 served an important credibility role in this regard. Although few landlords were meaningfully incentivized by the money (indeed, some declined to accept it), we found that it had significant symbolic value. Not only did it show that we respected their time and were willing to compensate them for it, but it helped remove lingering doubts regarding our legitimacy.

Finding a way to talk to landlords who are less forthcoming has important implications for inference. The busy, suspicious, or reticent respondents may have different skills, knowledge, ideologies, and practices than those who most readily agree to an interview. As with any other population, the landlords most willing to talk are often those with the most intense sentiments, whether positive or negative. These issues have affected previous work where resource constraints often necessitate the use of mass mailings for recruitment (Ellen et al., 2013; Greenlee, 2014). 

While efficient, this method tends to yield low response rates. In our study, we achieved response rates ranging from 35 percent to 75 percent, depending on the site. In all cases, the primary factor in the response rate was the amount of time we had in the field to make contact with hard-to-reach landlords. Notably, our refusal rate—those who actively said no—was below 20 percent in all cities.

Washington, D.C.—our most recent study site—represents an ideal case. Not only was our time in the field essentially unbounded, but the team had the benefit of substantial experience in the field. 

In the district, we successfully interviewed 75 percent of our random sample. Only 3 out of the 36 

landlords we sampled actually refused an interview. We were unable to interview another five, who either never called back, or were too busy to schedule an interview. Persistence, and budgeting the time to be persistent is key.

Nail the Interview: How to Engage Landlords with  

Indepth Interviews

Once an interview is scheduled, it is important to maximize the quantity and quality of the data collected—particularly given that our meetings with landlords were often scheduled to accommodate their hectic schedules. We relied on a number of practices for best understanding how landlords make sense of their world and their actions within it. Interviews are best for learning about respondents’ subjective experiences of the world, narratives, worldviews, beliefs, and meaning-making (Katz, 1999; Lamont and Small, 2008; Lamont and Swidler, 2014; Young, 2004). Although interviews do not always reflect exactly what people  do, they are key for understanding how they  think about (that is, make sense of) what they do (Becker, 1998; DeLuca et al., 2016: appendix; Katz 1999).

Moreover, the tools of ethnographic interviewing include a number of techniques to improve the accuracy and precision of retrospective accounts (Boyd and DeLuca, 2018). For example, Boyd and DeLuca suggest that recalling specific details of one’s life is much more accurate when such details are embedded within stories that pertain to the topics of the research study, by “pegging” events of Cityscape 285

Garboden and Rosen

interest to life milestones (Boyd and DeLuca, 2018). Rapport is key. Many landlords believe that public perception in general, and academia in particular, is normatively biased against supply-side actors. Thus, investing time and energy into building an atmosphere of trust is essential for candid data collection.

Above all else, our goal as interviewers is to say as little as possible while ensuring that the conversation rigorously covers each topic laid out in the interview protocol. Ideally, both the answers, as well as the questions, should come from the respondents as they reflect on their world (Spradley, 1979). Rather than prompting for specific details about, for example, negative experiences with tenants or bureaucratic troubles with the local Public Housing Authority (PHA), we started by asking open-ended questions that enabled our respondents to tell us detailed stories that encompassed details relevant to many of our topics of inquiry at once. When a respondent tells a story, they open a window into an entire experience related to a particular issue and are therefore less likely to massage specific details in response to the wording of a question (problems that psychologists call priming, or social desirability bias).

When we do speak during the interview, we try to elicit stories that are rich in detail. Because many respondents assume that we want top-level summaries, as might be requested in a marketing survey, we begin the interview by “training” them to provide descriptive details and define the terms they use. Rather than focusing on generalities, we ask for specific examples. We say things like: “Tell me more about that,” “Can you give me an example?” or “What would that look like?” 

Rather than asking yes or no questions, we ask for stories about processes (Becker, 1998). We then follow up to ask if an example is an extreme one or a typical one in order to contextualize it.

Like most people, landlords love to complain and vent and rarely have a venue to do so. They are quick to recount the stories of their worst tenants ever. Roger, for example, opened the conversation by stating: “I have lots of crazy stories to share.” Although we are certainly interested in his crazy stories, we are more interested in his day-to-day work and views. But letting landlords vent and air their grievances at the beginning of the interview is important for building rapport and empathy. Thus, the training portion of our interview typically entails between 10 and 40 minutes of open-ended “airing of grievances.” Once these stories are shared, it is much easier to put these frustrations in context and learn how they fit in to a larger set of landlord-tenant relations, at least from the landlord perspective.

We mitigate against hearing only outlier cases using a number of interview techniques. When a landlord tells an extreme story, we ask them to put that story in context, asking questions to learn about how typical or common a particular event might be: “How many other times have you experienced something similar?” “Is that a typical outcome for you?” “Tell me about a time when a tenant responded to the same situation differently.” Our goal of course, is not to produce a perfectly accurate retrospective count of a particular event or outcome, but rather to understand the process and how it varies in range.

Although we always attempt to verify details recounted in an interview with observation or outside sources, this is not always possible, and in some ways, it is not the most important part of the interview. In fact, we are less interested in whether the landlord has a perfect memory, and more 286 Evaluation Tradecraft

Talking to Landlords

in their understanding and interpretation of an event. When Ben, a Baltimore landlord, told us about his tenant who “went ballistic” one day and threw her pots and pans out the window, we were cognizant that this incident may not have unfolded exactly as he recounted it. In Ben’s case, we were most interested in learning about his interpretation of the incident, his views of the tenant, and how they informed his subsequent actions vis à vis his other tenants.

The interview is a speech act from which one can learn a lot about how a respondent thinks and sees the world (Lamont, 1992), as well as a social occasion from which much can be learned in and of itself (Spradley, 1979), but of course, limitations exist, which we address through ethnographic observations.

Talk the Talk, Then Walk the Walk: The Value of  

Participant Observation

A common critique of interview data is that what people say and what they do are not necessarily the same thing (Jerolmack and Khan, 2014). Issues related to this “attitudinal fallacy” can be greatly reduced using the interviewing techniques described previously, as well as being circumspect in what conclusions can be drawn from retrospective accounts (Lamont and Swidler, 2014). But it can nevertheless be hard for researchers to interpret interview data without any immediate observation of the phenomenon. Is a landlord describing, for example, how they think they behave in a particular circumstance, or how they  actually behave? What components of a process are they glossing over, consciously or otherwise?

The most effective way to limit these concerns is to incorporate participant observation into the data collection process. Although it can be prohibitively expensive to implement at scale, moments of focused observation of landlord behaviors can enrich the researcher’s ability to interpret their interview data and expose important areas of inquiry that can be used to improve the interview guide.

When done properly, the interview process can be a moment of trust and rapport building such that most respondents accepted our offer to observe them in their work. Having just described their work to us in great detail during the interview, they were excited to show us, rather than just tell, what it was like. In a few cases, the respondent was engaged in some relevant work immediately following the interview, in which case we would simply tag along. But more often than not, we would agree to meet them later in the week, sometimes spending the whole day with them as they went about their business.

Entire books have been written on the process of collecting ethnographic data (Becker, 1998; Emerson, Fretz, and Shaw, 2011; Spradley, 1979), but here we highlight a few techniques that were particularly relevant to studying landlords.

(1) Managing Your Presence in The Field

During the participant observation, it is important to ensure that one’s presence does not impact what one is observing; ethnographers work to be present yet non-intrusive at all times. 

Cityscape 287

Garboden and Rosen

We were careful to disclose our role as researchers when directly engaging any third parties, but generally such introductions were unnecessary, and we were able to observe events in relative anonymity. Given that real estate is a fairly diverse field in terms of age, gender, and race, obvious demographic differences between our team and our respondents did not greatly impact the observations. But our fieldworkers were careful to blend in when possible, for example in their attire: a fieldworker in a suit at a construction site or a t-shirt at housing court can be a distraction.

More than physical appearance, we trained our fieldworkers to be extremely sensitive to the degree their body language or tone expressed any normative judgements of the respondent him/herself or any other actors they might encounter. When individuals feel they are being judged or evaluated, they may begin to behave unnaturally, damaging rapport and biasing the data. Thus the tendency to critically evaluate an experience—a natural tendency for academic researchers—must be carefully avoided. For example, we toured many investment properties under renovation. During such visits, it was standard to ask questions related to costs of the rehab, project funding, and so forth. It was important in this line of questioning to avoid passing judgement on the quality of the investment. Such a suggestion could put respondents on the defensive, jeopardizing the observation and the relationship.

(2) Ask the Respondent to Interpret What You Are Seeing

Although objective evaluation itself can be extremely valuable, the benefit of extended sessions of participant observation is that we could see something occur and then later hear from the landlord how he/she interpreted the encounter. These post-facto conversations were easy to fit in between observations, particularly in Dallas where we spent hours driving with respondents from one neighborhood to the next.

This technique proved invaluable to data collection related to tenant screening. For example, we spent several days observing a landlord, Clifton in Dallas. He got a phone call from a potential renter, said he was driving and couldn’t talk but that he would text him. He immediately handed the phone to his son, who was working with him that summer. “Send him the photos of [the property],” Clifton said and his son complied. When the applicant texted back that he liked the look of the unit, Clifton told his son to ask what his annual salary was and if he could prove it. 

The applicant texted back a high enough income, but said he was a contractor and so had no pay stubs. “Stop texting him,” Clifton said, and that was the end of it.

When asked to explain what happened, Clifton noted “that guy eats what he kills,” meaning that he worked for cash and thus likely would not have reliable income with which to pay rent. 

Clifton also noted that the text messaging was orchestrated for the purpose of establishing a written record of the interaction with the tenant and his answers to the screening questions (indeed, Clifton had no trouble engaging in extended conversations while driving throughout the day). He said that if the applicant had been able to provide source of income, that a dozen other questions would have followed, and he was prepared to ask him about his previous residential history, downpayment, and so forth.

288 Evaluation Tradecraft

Talking to Landlords

The key here is that just like the interview process, the observation occurs from the standpoint of a learner. Not only is the researcher in the field observing things, but they must recognize that they are ignorant of the correct interpretation of what they are witnessing. Having numerous events occur in real-time and having the respondent interpret them is a core element of data collection.

Conclusion

In this article, we have summarized our techniques for collecting high-quality interview and ethnographic data from a random sample of landlords. For the sake of parsimony, we have not included an exhaustive discussion of what can be learned in the field. However, we aim to provide some key direction for how to engage with landlords using qualitative methods. We make the case that qualitative data contributes to our empirical understanding of housing markets in critical ways. We hope, going forward, that rigorous evaluations of HUD’s programs are accompanied by indepth data collection (survey, interview, and ethnographic) from housing providers.

Acknowledgments

The authors thank Meredith Greif, Stefanie DeLuca, and Kathryn Edin, our partners in this effort.

Jennifer Cossyleon, Jennifer Darrah, Barbara Kiviat, Noah Saganski, Samantha Night, Krista O’Connell, Ann Owens, and Stephen Wong also helped with data collection. Funding for this research was provided by the Department for Housing and Urban Development, the Furman 

Center for Real Estate and Urban Policy, and the MacArthur Foundation.

Authors

Philip Garboden is the HCRC Professor in Affordable Housing Economics, Policy, and Planning at the University of Hawaiì at Manoa. He is available by email at pgarbod@hawaii.edu or by mail at 

University of Hawaiì at Manoa, Department of Urban and Regional Planning, Saunders Hall 107, 2424 Maile Way, Honolulu, HI 96822.

Eva Rosen is an assistant professor at Georgetown University’s McCourt School of Public Policy.

References

Anderson, Elijah. 1999.  Code of the Street: Decency, Violence, and the Moral Life of the Inner City. New York: W. W. Norton & Company.

Becker, Howard S. 1998.  Tricks of the Trade: How to Think about Your Research While You’re Doing It. 

Chicago, IL: University of Chicago Press.

Boyd, Melody, and Stefanie DeLuca. 2018. “Fieldwork with In-Depth Interviews: How to Get Strangers in the City to Tell You Their Stories.” In  Methods in Social Epidemiology, edited by J.S. 

Kaufman. Hoboken, NJ: John Wiley & Sons: 239–307.

Cityscape 289

Garboden and Rosen

Clampet-Lundquist, Susan, Kathryn Edin, Jeffrey R. Kling, and Greg J. Duncan. 2011. “Moving Teenagers Out of High-Risk Neighborhoods: How Girls Fare Better than Boys,”  American Journal of Sociology 116 (4):1154–1189.

DeLuca, Stefanie, Susan Clampet-Lundquist, and Kathryn Edin. 2016.  Coming of Age in the Other America. New York: Russell Sage Foundation.

DeLuca, Stefanie, Peter Rosenblatt, and Holly Wood. Forthcoming. “Why Poor Families Move (And Where They Go): Reactive Mobility and Residential Decisions.”  City & Community.

Duneier, Mitchell. 2001.  Sidewalk. New York: Farrar Straus & Giroux.

Edin, Kathryn, Stefanie DeLuca, and Ann Owens. 2012. “Constrained Compliance: Solving the Puzzle of MTO’s Lease-Up Rates and Why Mobility Matters,”  Cityscape 14 (2):163–178.

Edin, Kathryn, and Laura Lein. 1997.  Making Ends Meet: How Single Mothers Survive Welfare and Low-Wage Work. New York: Russell Sage Foundation.

Ellen, Ingrid Gould, Vicki Been, Andrew Hayashi, Benjamin Gross, Sandra Newman, and Philip Garboden. 2013.  Maintenance and Investments in Small Rental Properties: Findings from New York City and Baltimore. New York: NYU Furman Center.

Emerson, Robert M., Rachel I. Fretz, and Linda L. Shaw. 2011.  Writing Ethnographic Fieldnotes, 2nd Edition. Chicago: University of Chicago Press.

Garboden, Philip, and Sandra Newman. 2012. “Is Preserving Small, Low-End Rental Housing Feasible?”  Housing Policy Debate 22 (4): 507–526.

Garboden, Philip, and Eva Rosen. Forthcoming. “The Threat of Eviction: How Landlords Shape a Contingent Tenure.”  City & Community.

Garboden, Philip, Eva Rosen, Meredith Greif, Stefanie DeLuca, and Kathryn Edin. 2018.  Urban Landlords and the Housing Choice Voucher Program: A Research Report. Washington, DC: U.S. 

Department of Housing and Urban Development, Office of Policy Development and Research.

Greenlee, Andrew J. 2014. “More Than Meets the Market? Landlord Agency in the Illinois Housing Choice Voucher Program,”  Housing Policy Debate 24 (3): 500–524.

Greif, Meredith. Forthcoming. “Regulating Landlords: Unintended Consequences for Poor 

Tenants.”  City & Community.

Jerolmack, Colin, and Shamus Khan. 2014. “Talk Is Cheap: Ethnography and the Attitudinal Fallacy,”  Sociological Methods & Research 43 (2): 178–209.

Katz, Jack. 1999.  How Emotions Work. Chicago: University of Chicago Press.

Lamont, Michèle. 1992.  Money, Morals, and Manners: The Culture of the French and American Upper-Middle Class. Chicago: University of Chicago Press.

290 Evaluation Tradecraft

Talking to Landlords

Lamont, Michèle, and Mario Luis Small. 2008. “How Culture Matters: Enriching Our 

Understanding of Poverty.” In  The Colors of Poverty: Why Racial and Ethnic Disparities Persist, edited by Ann C. Lin and David R. Harris. New York: Russell Sage Foundation: 76–102.

Lamont, Michèle, and Ann Swidler. 2014. “Methodological Pluralism and the Possibilities and Limits of Interviewing,”  Qualitative Sociology 37 (2):153–171.

Liebow, Elliot. 1967.  Tally’s Corner: A Study of Negro Streetcorner Men. New York: Rowman & Littlefield.

Mallach, Alan. 2006.  Bringing Buildings Back: From Abandoned Properties to Community Assets. New Brunswick, NJ: Rutgers University Press.

Orr, Larry L. 1998.  Social Experiments: Evaluating Public Programs With Experimental Methods. 

Thousand Oaks, CA: SAGE Publications, Inc.

Pattillo McCoy, Mary. 1999.  Black Picket Fences: Privilege and Peril among the Black Middle Class. 

Chicago, IL: University of Chicago Press.

Rosen, Eva. 2014. “Rigging the Rules of the Game: How Landlords Geographically Sort Low-Income Renters,”  City & Community 13 (4): 310–340.

Small, Mario Luis. 2009. “‘How Many Cases Do I Need?’: On Science and the Logic of Case Selection in Field-Based Research,”  Ethnography 10 (1): 5–38.

Smith, Robin, Megan Gallagher, Susan Popkin, Amanda Mireles, and Taz George. 2014. “Coercive Sexual Environments: What MTO Tells Us About Neighborhoods and Sexual Safety,”  Cityscape; Washington 16 (1): 85–112.

Spradley, James P. 1979.  The Ethnographic Interview. Independence, KY: Wadsworth Publishing Company.

Sternlieb, George. 1966.  The Tenement Landlord. New Brunswick, NJ: Urban Studies Center, Rutgers, State University.

Suttles, Gerald D. 1968.  The Social Order of the Slum: Ethnicity and Territory in the Inner City. 

Chicago: University of Chicago Press.

Varady, David P., Joseph Jaroscak, and Reinout Kleinhans. 2017. “How to Attract More Landlords to the Housing Choice Voucher Program: A Case Study of Landlord Outreach Efforts,”  Urban Research 

& Practice 10 (2): 143–155.

Venkatesh, Sudhir A. 2002.  American Project: The Rise and Fall of a Modern Ghetto. Cambridge, MA: Harvard University Press.

Weiss, Robert S. 1994.  Learning from Strangers: The Art and Method of Qualitative Interview Studies. 

New York: The Free Press.

Young, Alford. 2004.  The Minds of Marginalized Black Men. Princeton, NJ: Princeton University Press.

Cityscape 291

Symposium

Youth Homelessness ................................................................................................1

Guest Editor: Matthew H. Morton

Guest Editor’s Introduction

Youth Homelessness: Research Insights for Coordinated Community Response ..............3

Homelessness and Housing Experiences among LGBTQ Young Adults in  

Seven U.S. Cities  by Jama Shelton, Jonah DeChants, Kim Bender, Hsun-Ta Hsu,  

Diane Santa Maria, Robin Petering, Kristin Ferguson, Sarah Narendorf, and  

Anamika Barman-Adhikari ............................................................................................9

“Nothing is for free...”: Youth Logics of Engaging Resources While Unstably Housed  

by Gina M. Samuels, Christine Cerven, Susanna R. Curry, and Shantá R. Robinson ......... 35

Linking the TAY-VI-SPDAT Tool to Housing Placements and Outcomes for Youth 

Experiencing Homelessness  by Eric Rice, Monique Holguin, Hsun-Ta Hsu,  

Matthew Morton, Phebe Vayanos, Milind Tambe, and Hau Chan ...................................69

What’s Next? A Grounded Theory of the Relationship Between Ontological Security, 

Mental Health, Social Relationships, and Identity Formation for Young Adults in 

Supportive Housing  by Benjamin F. Henwood, Brian Redline, Sara Semborski, Harmony  

Rhoades, Eric Rice, and Suzanne L. Wenzel ..................................................................87

Predictors of Running Away from Out-of-Home Care: Does County  

Context Matter?  by Amy Dworsky, Fred Wulczyn, and Lilian Huang .............................101

Developing a Coordinated Youth Housing Stability Program for Juvenile Courts  

by Sarah Cusworth Walker, Esteban Valencia, Asia Bishop, Michael Irons, and  

Arina Gertseva ......................................................................................................... 117

Reflections from Canada: Can Research Contribute to Better Responses to  

Youth Homelessness?  by Stephen Gaetz .................................................................... 139

International Commentary: Some Reflections on the Policy History of Youth 

Homelessness in Australia  

by David MacKenzie .................................................................................................147

Refereed Papers ....................................................................................................157

From Foreclosure to Eviction: Housing Insecurity in Corporate-Owned  

Single-Family Rentals  by Elora Lee Raymond, Richard Duckworth, Benjamin  

Miller, Michael Lucas, and Shiraj Pokharel.................................................................. 159

School Performance of Schools Assigned to HUD-Assisted Households  

by Brent Mast ...........................................................................................................189

Departments ............................................................................................................223

Affordable Design

2018 Innovation in Affordable Housing Student Design and Planning  

Competition: Whittier Falls in Dover, New Hampshire  by Regina C. Gray ................... 225

Data Shop

The Housing and Children’s Healthy Development Study  by Sandra Newman  

and Tama Leventhal .................................................................................................. 237

Street Vending in the United States: A Unique Dataset from a Survey of Street  

Vendors in America’s Largest Cities  by Dick M. Carpenter II ....................................... 245

Policy Briefs

Performance of Small Public Housing Authorities: Opportunities and Barriers  

by Andrew J. Greenlee, Han Bum Lee, and Paul E. McNamara ....................................257

Graphic Detail

Intersecting Opportunity Zones with Vacant Business Addresses  by Alexander Din ...... 277

Evaluation Tradecraft

Talking to Landlords  by Philip M.E. Garboden and Eva Rosen .....................................281

Cityscape

Volume 20, number 3 • 2018

Office of Policy Development and Research

Document Outline

Contents

Symposium: Youth Homelessness

Guest Editor’s Introduction: Youth Homelessness: Research Insights for Coordinated Community 

Homelessness and Housing Experiences among LGBTQ Young Adults in Seven U.S. Cities

“Nothing is for free...”: Youth Logics of Engaging Resources While Unstably Housed

Linking the TAY-VI-SPDAT Tool to Housing Placements and Outcomes for Youth Experiencing Homelessness

What’s Next? A Grounded Theory of the Relationship Between Ontological Security, Mental Health, Social Relationships, and Identity Formation for Young Adults in Supportive Housing

Predictors of Running Away from Out-of-Home Care: Does County Context Matter?

Developing a Coordinated Youth Housing Stability Program for Juvenile Courts

Reflections from Canada: Can Research Contribute to Better Responses to Youth Homelessness?

Some Reflections on the Policy History of Youth Homelessness in Australia

Refereed Papers

From Foreclosure to Eviction: Housing Insecurity in Corporate-Owned Single-Family Rentals 

School Performance of Schools Assigned to HUD-Assisted Households

Departments

Affordable Design: 2018 Innovation in Affordable Housing Student Design and Planning Competition: Whittier Falls in Dover, New Hampshire

Data Shop: The Housing and Children’s Healthy Development Study

Data Shop: Street Vending in the United States: A Unique Dataset from a Survey of Street Vendors in America’s Largest Cities

Policy Briefs: Performance of Small Public Housing Authorities: Opportunities and Barriers

Graphic Detail: Intersecting Opportunity Zones with Vacant Business Addresses

Evaluation Tradecraft: Talking to Landlords