iBuyers and “Race for Profit”

iBuyers use automated valuation algorithms and streamlined home-buying processes, including exemption of repairs before selling and cash offers, to purchase homes. Previous literature has examined the roles and limitations of iBuyers in the housing market, but there is a lack of empirical research on the racial implications of these algorithmic home-buying processes. Using spatial lag model, this study identifies spatial clustering of iBuyer profit margins and a statistically significant and positive correlation between profit margins and the rise in the proportion of Black or Latinx residents within a given neighborhood tract. Specifically, Opendoor’s transactions demonstrates a statistically significant and positive correlation between profit margins and an increase in the percentage of Black residents in a tract, while Zillow’s exhibits a similar trend with respect to profit margins and the growing percentage of Latinx residents.The implications of these findings suggest that the more adeptly iBuyers can forecast housing values to maximize profits, the greater the potential for them to capitalize on homeowners in marginalized communities of color. Consequently, this research contributes to the comprehension of how technological mechanisms operate within a purportedly race-neutral framework and advocates for the development and deployment of algorithmic products guided by the principles of antisubordination, rather than relying solely on notions of “fairness” and anticlassification.

This work will be submitted to Journal of Urban Affairs.