Unequal Impact of Zestimate on the Housing Market

Runshan Fu (), Yan Huang (), Nitin Mehta (), Param Vir Singh () and Kannan Srinivasan ()
Additional contact information
Runshan Fu: Stern School of Business, New York University, New York, New York 10012
Yan Huang: Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
Nitin Mehta: Rotman School of Management, University of Toronto, Toronto, Ontario M5S 3E6, Canada
Param Vir Singh: Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
Kannan Srinivasan: Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213

Marketing Science, 2025, vol. 44, issue 6, 1407-1427

Abstract: We study the impact of Zillow’s Zestimate on housing market outcomes and how the impact differs across socioeconomic 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 nontrivial 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 socioeconomic 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 socioeconomic inequality as our results reveal that both rich and poor neighborhoods benefit from Zestimate but that 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 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/mksc.2022.0451 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormksc:v:44:y:2025:i:6:p:1407-1427

Access Statistics for this article

More articles in Marketing Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().