Searching for housing in the age of AI: racialized neighborhood recommendations across 10 U.S. cities

Megan Evans and Noli Brazil
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Megan Evans: Max Planck Institute for Demographic Research, Rostock, Germany

No WP-2026-041, MPIDR Working Papers from Max Planck Institute for Demographic Research, Rostock, Germany

Abstract: Urban residents increasingly rely on online tools to searchfor housing, raising new questions as to the role of digital tools in interrupting or reinforcing racialized housing searches. Generative Artificial Intelligence (GenAI), in particular, represents a new intermediary in how neighborhood knowledge is created and distributed in housing platforms and search engines. This study examines whether ChatGPT engages in racial matching by systematically varying user demographics in housing queries across 10 large U.S. cities. Using 14,400 unique queries per city, we asked GPT-4o for neighborhood recommendations and areas to avoid. Merging AI generated recommendations with census data, we investigate racial patterns in how GPT-4o incorporate user characteristics and neighborhood demographics into their housing guidance. Findings suggest that AI recommendations reflect and may amplify existing patterns of residential segregation through systematic differences in how neighborhoods are recommended to different users. Results have important implications for informing both fair housing policy and the responsible development and regulation of AI systems in the housing market.

JEL-codes: J1 Z0 (search for similar items in EconPapers)
Pages: 57 pages
Date: 2026
New Economics Papers: this item is included in nep-ain, nep-hre, nep-mid and nep-uep
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Persistent link: https://EconPapers.repec.org/RePEc:dem:wpaper:wp-2026-041

DOI: 10.4054/MPIDR-WP-2026-041

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