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Effect of Predictive Algorithms on Home Prices and Racial Biases in the Housing Market

In this paper, I investigate how predictive algorithms change market outcomes in the housing market. By focusing on the effect of the Zestimate home valuation on housing markets, I investigate to what extent the market participants rely on the Zillow’s estimated market value when making the transactions and how it interacts with other information sources. I collect detailed property transaction information for 120,482 properties sold between May 2017 and May 2019 in the Greater Philadelphia Area and the preliminary results suggest that sale prices tend to be higher for those properties that have a higher estimated home value on Zillow.com. Evidence shows that people use this statistic as a summary of the transactional data and rely more on it when it is harder to process the information. Moreover, the Zestimate helps improve racial disparities in the real estate market by providing all subpopulations with less biased information. ∗Shuyi Yu is a doctoral student at MIT Sloan School of Management, MIT, Cambridge, MA 02139. I am indebted to Catherine Tucker for guidance and support.