Study links RealPage algorithmic rent pricing to racial disparities

A new economics paper by Advay Ranade provides the first tract-level evidence that corporate landlord concentration—especially when enabled by algorithmic pricing tools—is linked to disproportionately higher rent growth in communities of color. The research comes amid the 2024 DOJ antitrust complaint against RealPage, which named five major REITs for coordinating algorithmic rent pricing across hundreds of thousands of units.

Ranade constructed a novel dataset by geocoding SEC EDGAR 10-K property filings to census tracts across 10 major U.S. metropolitan areas, covering 665 tracts. Rent outcomes were measured using the Zillow Observed Rent Index (ZORI). To control for selection bias, the study introduced an Algorithmic Housing Burden Index (AHBI) combining pre-existing rent burden and market tightness. The results show that doubling REIT concentration is associated with 2.8 percentage points higher rent growth overall. However, in majority-minority tracts within the same metro, the association jumps to 5.9 percentage points higher than comparable white tracts. An XGBoost model predicted 44% of out-of-sample rent variance, with SHAP analysis confirming that the contribution of corporate landlord concentration is positive in minority tracts and negative in white tracts. These findings raise critical questions about the role of algorithmic pricing in exacerbating housing inequality.