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Generative AI and Price Discrimination in the Housing Market

Jitsama Tanlamai¹; Warut Khern-Am-Nuai²; Maxime C. Cohen³

¹ McGill University Desautels Faculty of Management, Information Systems, Montreal, Canada · ² McGill University, Desautels Faculty of Management, Montreal, H3A 1G5, Canada · ³ McGill University, Management, Montreal, H3A 0G4, Canada

Housing discrimination has been recognized as an important societal issue for decades. While 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 AI models, even those specifically designed to address discrimination. In this paper, we first compare AI-generated versus 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 exploration provides empirical evidence regarding underlying mechanisms that drive such a counter-intuitive result. Practical and policy implications are also discussed.

DOI

10.1287/isre.2024.1234

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en

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