Unequal Impact of Zestimate on the Housing Market

54 Pages Posted: 28 Jun 2023

See all articles by Runshan Fu

Runshan Fu

New York University (NYU) - Leonard N. Stern School of Business

Yan Huang

Carnegie Mellon University - David A. Tepper School of Business

Nitin Mehta

University of Toronto - Rotman School of Management

Param Vir Singh

Carnegie Mellon University - David A. Tepper School of Business

Kannan Srinivasan

Carnegie Mellon University - David A. Tepper School of Business

Date Written: June 15, 2023

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

Suggested Citation

Fu, Runshan and Huang, Yan and Mehta, Nitin and Singh, Param Vir and Srinivasan, Kannan, Unequal Impact of Zestimate on the Housing Market (June 15, 2023). Available at SSRN: https://ssrn.com/abstract=4480469 or http://dx.doi.org/10.2139/ssrn.4480469

Runshan Fu (Contact Author)

New York University (NYU) - Leonard N. Stern School of Business ( email )

44 West 4th Street
Suite 9-160
New York, NY NY 10012
United States

Yan Huang

Carnegie Mellon University - David A. Tepper School of Business ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States

Nitin Mehta

University of Toronto - Rotman School of Management ( email )

105 St. George Street
Toronto, Ontario M5S 3E6 M5S1S4
Canada

Param Vir Singh

Carnegie Mellon University - David A. Tepper School of Business ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States
412-268-3585 (Phone)

Kannan Srinivasan

Carnegie Mellon University - David A. Tepper School of Business ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States

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