Hotspots of Eviction

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Abstract

Recent studies have shown that a small number of buildings account for a significant portion of evictions in major U.S. cities, suggesting targeted policy interventions for these hotspots. However, focusing solely on eviction volumes can mislead policymakers by implying that property owners are the primary drivers of high eviction rates. This study investigates the spatial structure of eviction filings at the Census Block Group (CBG) level to determine if high eviction rates are due to neighborhood characteristics or other factors like landlords’ practices. We addressed three research questions: 1) the relationship between eviction filings due to nonpayment of rent and neighborhood characteristics, 2) the differences between eviction filings due to nonpayment and those for other reasons, and 3) the extent to which high rates of eviction filings in certain CBGs can be attributed to neighborhood characteristics versus unexplained spatial effects. We used Restricted Spatial Generalized Linear Mixed Models (RSGLMMs) with Hamiltonian Monte Carlo (HMC) sampling to estimate neighborhood fixed effects and spatial random effects, using data from Dallas County. Our findings confirm that important neighborhood factors identified in previous studies are consistently significant. Our spatial analysis revealed a noticeable difference between raw eviction filing counts and those adjusted for neighborhood characteristics, identifying CBGs with excessive eviction filings even after accounting for the neighborhood context. Based on these results, we propose a dual-track policy intervention: for hotspot buildings in CBGs with moderate spatial effects, we recommend tenant support measures like rental assistance and legal aid; for those with high spatial effects, we suggest prioritizing in-depth investigations of these buildings and landlordfocused interventions such as education on fair housing laws and landlord-tenant mediation services. All relevant code and data from this project are available in my GitHub repository.

The complete paper is accessible here.