Improving Understanding of Evictions and Gentrification with Better Data
(Housing and Community Development)
*Names in bold indicate Presenter
Evictions and gentrification are both closely connected to the neighborhoods individuals live in; are potentially shaped by many of the same trends in neighborhood change; and potentially have many similar effects on individual location and well-being. Both have received increasing attention from researchers and policymakers, but data limitations have left many questions unanswered.
This panel consists of four papers from public policy, planning, and economics. Each assembles novel data sets to overcome previous limitations and provide new insights into evictions and gentrification.
The first paper, by Gromis, Lens, and Nelson, combines evictions data from two unique sources: a tenant-screening and public records data warehousing company and unsealed eviction court records from the Los Angeles Superior Court website obtained using web scraping and manual data collection. The authors use these detailed, case-level data to describe the spatial concentration of evictions; the characteristics of tenants facing eviction; and how neighborhood change is connected to eviction.
The second paper, by Collinson and Reed, focuses on the effects of eviction on low-income households. They construct a novel administrative data set by linking the universe of housing court cases in New York City from 2006 to 2016 to outcomes from city and state agencies, including neighborhood location, public benefits receipt, homelessness, employment and earnings, hospitalizations, and credit access and spending. They estimate causal effects of eviction on these outcomes by leveraging the random assignment of housing court cases to courtrooms.
The third paper, by Lei Ding, examines the effects of gentrification on lower income homeowners, in contrast to the usual focus on renters. He does this using property-level data on tax assessments and tax payments from the City of Philadelphia and an exogenous policy change that substantially increased the degree to which gentrification could increase the assessed values of homes. He tests whether this shock to affordability increased the displacement of lower income homeowners, further accelerating the pace of gentrification.
Finally, Brummet and Reed use pilot Census Bureau data to match individuals responding to the Decennial 2000 and any year of the American Community Survey 2005-2014. The result is a national panel data set of millions of individuals and their locations, characteristics, and outcomes at two points in time. They use these data to provide new insight into the gentrification process and to estimate causal effects of gentrification on the location and well-being of original residents using plausibly exogenous variation in neighborhood housing demand.
Together, these four papers provide answers to important questions surrounding evictions and gentrification. They do so by leveraging new administrative data and new data partnerships at the city, state, and national level. These better data allow for better research and, hopefully, will contribute to better policy.