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Market Predictors of Homelessness: How Housing and Community Factors Shape Homelessness Rates Within Continuums of Care

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Market Predictors of Homelessness: How Housing and Community Factors Shape Homelessness Rates Within Continuums of Care Market Predictors of Homelessness: How Housing and Community Factors Shape Homelessness Rates Within Continuums of Care Multidisciplinary Research Team March 26, 2019 Prepared by: Hiren Nisar Mallory Vachon Charles Horseman Jim Murdoch 2M Research ACKNOWLEDGMENTS The study team would like to thank Dallas Elgin for assistance with the literature review and Madison Davis for assistance with data collection and cleaning. Gail Clark, MacKenzie Regier, Joshua Townley, and Cindy Romero provided invaluable editing and formatting support throughout the study. The study team would also like to acknowledge our partners at the U.S. Department of Housing and Urban Development’s (HUD) Office of Policy Development and Research, with specific assistance from Galen Savidge-Wilkins, Nicole Watson, Ransford Osafo-Danso, Lydia Taghavi, and Benjamin Houck. We also thank William Snow, Norman Suchar, and Harper Sutherland from HUD’s Office of Special Needs Assistance Programs. Disclaimer The contents of this report are the views of the authors and do not necessarily reflect the views or policies of HUD or the U.S. Government. | i FOREWORD Reducing homelessness is a key objective within HUD’s FY 2018–2022 Strategic Framework. Recent changes in how homelessness manifests within communities, most visibly in west coast communities where unsheltered homelessness has increased in recent years, is shaping HUD’s approach to meeting this objective. Though total homelessness has generally been declining nationally since 2011, unsheltered homelessness rose by nearly 25 percent in major cities and largely urban Continuums of Care (CoCs) between 2015 and 2017. While the causes of homelessness are complex and interconnected, community-level factors can be strong predictors of homelessness rates. The national data set developed for this study helps to fill this gap by disentangling which factors matter most in different types of communities and by offering some clues about places where more targeted analysis is needed. By assessing the relative effects of housing, economic, safety net, demographic, and climate factors on homelessness rates, the study’s findings support the idea that improving the availability and affordability of rental housing is often a community’s best line of defense against homelessness. Across the country, housing market factors more consistently predicted rates of total homelessness than other economic factors. This finding is consistent with what many communities have experienced—increases in homelessness where rents are high. Finding opportunities to relax restrictions on producing a greater supply of housing that is affordable in these places may provide relief. High median rents, overcrowding, and evictions were particularly strong predictors of total homelessness rates in urban areas and tight, high-cost housing markets. Holding these factors constant, the study finds that increased housing density is protective against homelessness. HUD’s work to reduce regulatory barriers to affordable housing production, including an increase in the supply of unsubsidized middle-market housing for workers, and to encourage private landlord participation in subsidized housing programs is, therefore, especially timely and directly relevant to reducing the incidence of homelessness nationally. As Chair of the White House Council on Eliminating Regulatory Barriers to Affordable Housing, Secretary Ben Carson is leading efforts to boost the supply of housing that is affordable by identifying policies, regulations, and administrative obstacles to cost-effective development. This initiative serves related goals—to open avenues for increasing both the quantity and density of affordable homes where regulatory barriers are currently prohibitive. HUD’s task force to increase private landlord participation in subsidized housing programs is similarly intended to increase the efficiency of rental subsidies in the private market by making existing stock more accessible to vulnerable populations. Demographics also play a role, particularly in ways that reflect strain on a community’s housing supply. The study finds higher levels of net in-migration and more one-person households correspond with higher rates of homelessness across several market types, suggesting that housing production goals should account for both population growth and adequate unit types to reduce homelessness risk. Consistent with this study, HUD’s biennial Worst Case Housing Needs Report to Congress shows that people living alone are often vulnerable to severe housing problems as rents rise. More efficiently tapping existing housing stock by increasing opportunities to share housing—both on the private market and within subsidized housing programs—is an important companion to constructing more affordable homes as we work to end homelessness nationally. Creating opportunities for vulnerable households to | ii better withstand rent increases may also provide relief in challenging markets. For example, HUD has proposed changes to its rental assistance programs to encourage income gains by relaxing rent penalties and by eliminating barriers that can discourage household formation such as the current annual rent certification structure. HUD has long pushed for communities to bring their own public, private, and philanthropic resources to the table to address homelessness. Indeed, there are communities around the country making major progress on ending homelessness by bringing these often disconnected actors together. This shows up in HUD’s data in different ways. For example, a local church group offering space for emergency shelter could mean fewer people get counted as unsheltered but are still counted as homeless. Decisions around resources are often just as complex and interconnected as the factors in this study, so while it’s helpful to have breakouts of rates of total homelessness, readers should be cautious about interpreting differences between factors related to unsheltered and sheltered homelessness. Other studies of community-level factors tend to focus on a specific slice of the larger picture of what causes homelessness. This study is intended to be a wider, exploratory look at as many predictive influences on homelessness as possible to see which rise to the top, which certain housing market factors did. It is important to view these findings as a valuable foundation upon which further research can build, which may provide conclusions about the causal relationship between these factors and homelessness. Seth D. Appleton Assistant Secretary for Policy Development and Research U.S. Department of Housing and Urban Development | iii TABLE OF CONTENTS Acknowledgments ............................................................................................... i Foreword ............................................................................................................ ii Executive Summary ............................................................................................ 1 Introduction ........................................................................................................ 6 Understanding the Problem ....................................................................................................................... 6 Literature Review on Homelessness ........................................................................................................... 8 Data .................................................................................................................. 15 Dependent Variables: HUD Point-in-Time Counts ...................................................................................... 15 Independent Variables: Predictors of Homelessness ................................................................................. 17 Empirical Strategy ............................................................................................. 23 Descriptive Analysis .................................................................................................................................. 23 Variable Selection ..................................................................................................................................... 30 Statistical Model and Analysis ................................................................................................................... 30 National Model of Homelessness ...................................................................... 32 Subgroup Analysis............................................................................................. 38 Urbanicity ................................................................................................................................................. 38 Tight, High-Cost Rental Markets ................................................................................................................ 49 Unsheltered Homelessness on the West Coast .......................................................................................... 57 Conclusion ........................................................................................................ 61 Appendix A: Research Questions ...................................................................... 67 Appendix B: Other Data Sources Considered ..................................................... 68 Appendix C: Creation of Continuum of Care-to-County Crosswalk .................... 71 Appendix D: Additional Tables .........................................................................
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