Generative AI and Price Discrimination in the Housing Market

Housing discrimination has been recognized as an important societal issue for decades. Although this issue can manifest in multiple ways, one of the most observed avenues is price discrimination, where houses in white-dominant neighborhoods are worth more than houses in minority-dominant neighborhoods that are otherwise similar. Prior studies have empirically documented such pricing discrimination and attributed it to human biases. In addition, recent studies have shown that issues of this kind are unlikely to be addressed by traditional artificial intelligence (AI) models, even those specifically designed to address discrimination. In this paper, we first compare AI-generated with human-generated housing prices using a sample of 284,749 U.S. properties. We then study the impact of generative AI in the context of price discrimination in the housing market and find that it can help alleviate this issue. Our mechanism explorations provide evidence regarding underlying mechanisms that drive such a counterintuitive result. Practical and policy implications are also discussed.

History: Ravi Bapna, Senior Editor; Kevin Hong, Associate Editor.

Funding: This research was supported by the Social Sciences and Humanities Research Council of Canada [Insight Grant 435-2025-0672], the Natural Sciences and Engineering Research Council of Canada [Discovery Grant RGPIN-2021-02668], the Fonds de Recherche du Québec-Société et Culture [Grant 380210], and the research chair programs from Scale AI and from Fonds de Recherche du Québec and Institut de Valorisation des Données.

Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2024.1234.