Unequal Impact of Zestimate on the Housing Market

Abstract

We study the impact of Zillow’s Zestimate on housing market outcomes and how the impact differs across socio-economic segments. Zestimate is produced by a Machine Learning algorithm using large amounts of data and aims to predict a home’s market value at any time. Zestimate can potentially help market participants in the housing market as identifying the value of a home is a non-trivial task. However, inaccurate Zestimate could also lead to incorrect beliefs about property values and therefore suboptimal decisions, which would hinder the selling process. Meanwhile, Zestimate tends to be systematically more accurate for rich neighborhoods than poor neighborhoods, raising concerns that the benefits of Zestimate may accrue largely to the rich, which could widen socio-economic inequality. Using data on Zestimate and housing sales in the United States, we show that Zestimate overall benefits the housing market, as on average it increases both buyer surplus and seller profit. This is primarily because its uncertainty reduction effect allows sellers to be more patient and set higher reservation prices to wait for buyers who truly value the properties, which improves seller-buyer match quality. Moreover, Zestimate actually reduces socio-economic inequality, as our results reveal that both rich and poor neighborhoods benefit from Zestimate but the poor neighborhoods benefit more. This is because poor neighborhoods face greater prior uncertainty and therefore would benefit more from new signals.

Keywords: Algorithms, Social impact, Economics of machine Learning, Housing markets

JEL Classification: M30, R30, L10, D0