Kasey Zapatka is a postdoctoral scholar at the Urban Displacement Project and in the Department of Sociology at the University of California, Berkeley. His research seeks to better understand the dynamics of urban inequality through the lens of housing, neighborhoods, and household socioeconomic stratification. He has published studies on the sequence of gentrification, the benefits of New York City’s rent regulation for stabilized tenants, and neighborhood diversification in immigrant cities.

At Berkeley, he is currently co-teaching a year-long graduate course in Computational Social Science that focuses on machine learning, causal inference, and natural language process in R and Python. He is also working on a project with Tim Thomas at Berkeley’s Institute of Governmental Studies to develop a longitudinal eviction database to evaluate household outcomes before and during the pandemic. The project aims to both paint a picture of what happens to households who have been evicted and to better understand the social and structural mechanisms that underpin eviction.

In his own research, Kasey focuses on the ways in which space shapes processes. In a working paper from his dissertation, he looks at how gentrification shapes neighborhood affordability using spatial econometric techniques to identify gentrification’s spillover effects. In a paper for Urban Studies, Kasey and his co-author adapt a Granger Causality method to identify the sequence of gentrification in New York, showing that gentrifier demand precedes developers producing housing stock. In a City & Community article, he and his co-author leveraged hedonic regression and Augmented Inverse Probability Weighting to show how much non-stabilized tenants would have saved had they been stabilized and argue for expanded rent stabilization. He has also shown how ethnoracial neighborhood integration patterns in suburban areas are beginning to converge with those in urban neighborhoods in the New York metropolitan area.

Kasey has also been actively involved in various public-facing web-based projects, including one to help New York City tenants better understand rent regulation laws and another to visualize the changing diversity in metropolitan New York. He primarily uses quantitative methods in his research, particularly causal inference, spatial econometrics, and machine learning. Kasey holds a PhD in Sociology from The City University of New York, The Graduate Center.

Contact Kasey Zapatka via email at kaseyzapatka@berkeley.edu.