Shows how gender influences rental prices in urban China, suggesting algorithmic bias in housing.
Digital platforms are increasingly reshaping how housing is accessed, priced, and experienced in cities. Platform logics, characterized by data-driven processes and algorithmic pricing, can reflect and reinforce existing social inequalities. This study examines how gender intersects with platform dynamics to shape rental outcomes in Nanjing, China, a major metropolis of nearly 10 million residents. Drawing on a year of data from 373,964 bedroom listings on Ziroom, the country’s largest shared rental platform, the study tests whether the gender composition of roommates influences rents. Hedonic regressions controlling for unit, building, and neighborhood characteristics show that all-female-roommate units command a rent premium, suggesting gender homophily is priced as a valued amenity. When interaction terms with room status are included, results show no initial rent difference under “reserve,” but a clear premium for all-female units under two leasing conditions: immediate move-in (“sign”) and subleasing (“sublease”). These findings suggest that the premium does not arise from platform-set base rents but from gendered preferences interacting with algorithmic pricing, whereby units with stronger demand are discounted less. This study contributes to research on platform-mediated housing, PropTech, and housing inequality by quantifying how platform systems reflect and reproduce gendered disadvantage. It also highlights the value of platform data for understanding emerging rental markets and calls for greater attention to gender in the governance of shared and platform-mediated housing.
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W.Y Cao (2026) studied this question.
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