Clustering Neighborhood Economic Trajectories: Gentrification Stages, Executed Evictions, and Other Housing Characteristics in New York City

Abstract

In recent years, gentrification has frequently been linked to housing inequality and mass evictions and portrayed in a negative light. This study defines gentrification in terms of neighborhood median household income, income diversity, and their level and trajectories over 14 years (2010–2023), and classifies neighborhood gentrification stages using hierarchical clustering. It then adopts a quantitative, data-driven approach to examine the pairwise statistical and geospatial relationships among gentrification stages, executed evictions (2023–2024), and other housing characteristics (rent, socioeconomic status, and demographics) across New York City neighborhoods. The results demonstrate that pre-gentrifying neighborhoods were 39.08 times more likely to have eviction rates 1.5 standard deviations above the mean, 14.19 times more likely to be geospatial eviction hotspots, and three times more likely to experience evictions per neighborhood than fully gentrified neighborhoods. Income-adjusted regression models indicate that gentrification-stage membership explains variation in eviction severity beyond median household income alone, increasing explained variance from 27.9% to 42.6%; late-stage gentrifying and stable low-change neighborhoods showed significantly lower eviction severity than fully gentrified neighborhoods at comparable income levels. Spatially, the Bronx, the most economically disadvantaged borough, contained 11 of 17 eviction-outlier NTAs and 37 of 48 geospatial eviction hotspots, making it the most eviction-concentrated area in the city. This clustering-based classification framework relies only on widely available Census and administrative data and can be applied to other metropolitan areas or adapted to related urban domains such as land-use and zoning analysis. Targeted, actionable recommendations are offered to direct eviction-prevention resources toward the most vulnerable communities.