Sticky Homelessness (Working Paper)

Abstract

Homelessness in American cities is becoming an ever more prominent issue, but its causes remain contested, ranging from mental health and substance abuse to housing affordability and local labor markets. To shed light on this issue, I construct a novel MSA-level national panel of homelessness counts using data from the U.S. Department of Housing and Urban Development. Using a long-differencing regression specification with the changes in rent entered in piecewise linear form, I find that rent increases predict large increases in homelessness rates, but decreases have little to no effect. The same conclusions are reached when I use a quasi-differencing moment condition, assuming a multiplicative mean specification. Then, I propose a theoretical model of the low-end housing market that explains the asymmetry I find in the data. Finally, I outline an IV strategy that instruments rent changes with a Bartik instrument of predicted employment growth interacted with local housing-supply elasticities. My findings suggest that homelessness is a housing problem; however, because the response is sticky downward, effective policy must complement housing-market interventions with measures that address barriers faced by people experiencing homelessness.