NY Real Estate Racial Equity Analysis via Applied Machine Learning

TL;DR

This paper employs advanced machine learning models to analyze racial disparities in real estate ownership in New York, revealing significant inequities especially in minority neighborhoods and highlighting the impact of corporate ownership.

Contribution

It introduces a novel LSTM+Geo model with XGBoost filtering for race/ethnicity imputation, validated at 89.2% accuracy, to quantify racial disparities in property ownership.

Findings

White individuals hold a disproportionate share of properties.

Minority communities are underrepresented as property owners.

Ownership disparities are most severe in minority-majority neighborhoods.

Abstract

This study analyzes tract-level real estate ownership patterns in New York State (NYS) and New York City (NYC) to uncover racial disparities. We use an advanced race/ethnicity imputation model (LSTM+Geo with XGBoost filtering, validated at 89.2% accuracy) to compare the predicted racial composition of property owners to the resident population from census data. We examine both a Full Model (statewide) and a Name-Only LSTM Model (NYC) to assess how incorporating geospatial context affects our predictions and disparity estimates. The results reveal significant inequities: White individuals hold a disproportionate share of properties and property value relative to their population, while Black, Hispanic, and Asian communities are underrepresented as property owners. These disparities are most pronounced in minority-majority neighborhoods, where ownership is predominantly White despite a predominantly non-White population. Corporate ownership (LLCs, trusts, etc.) exacerbates these gaps by reducing owner-occupied opportunities in urban minority communities. We provide a breakdown of ownership vs. population by race for majority-White, -Black, -Hispanic, and -Asian tracts, identify those with extreme ownership disparities, and compare patterns in urban, suburban, and rural contexts. The findings underscore persistent racial inequity in property ownership, reflecting broader historical and socio-economic forces, and highlight the importance of data-driven approaches to address these issues.