Skip to main content

Research Repository

Advanced Search

Google search queries, foreclosures, and house prices

Damianov, Damian S.; Wang, Xiangdong; Yan, Cheng

Google search queries, foreclosures, and house prices Thumbnail


Xiangdong Wang
PGR Student Doctor of Philosophy

Cheng Yan


We study whether Google search behavior for “mortgage assistance” and “foreclosure help” aggregated in the mortgage default risk indicator (MDRI) of Chauvet et al. (2016) helps predict future house prices and foreclosures in local residential markets. Using a long-run equilibrium model, we disaggregate house prices into their fundamental and bubble components, and we find that MDRI dampens both components of house prices. This negative relationship is robust to various model specifications and time horizons. A higher intensity of search online, however, is associated with lower future foreclosure rates. We also find that foreclosure rates increase after a decline in the fundamental component of home values, but are not sensitive to their transitory (bubble) component. Foreclosure rates are higher in metropolitan areas located in non-recourse states. We interpret these findings as evidence for strategic household behavior. Our paper sheds new light on the predictive power of household sentiment derived from Google searches on prices and foreclosure rates in local housing markets.


Damianov, D. S., Wang, X., & Yan, C. (2021). Google search queries, foreclosures, and house prices. Journal of Real Estate Finance and Economics, 63(2), 177-209.

Journal Article Type Article
Acceptance Date May 13, 2020
Online Publication Date Aug 29, 2020
Publication Date 2021-08
Deposit Date Jul 17, 2020
Publicly Available Date Sep 9, 2020
Journal Journal of Real Estate Finance and Economics
Print ISSN 0895-5638
Electronic ISSN 1573-045X
Publisher Springer
Peer Reviewed Peer Reviewed
Volume 63
Issue 2
Pages 177-209
Public URL


Published Journal Article (Advance online version) (949 Kb)

Publisher Licence URL

Copyright Statement
Advance online version This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit

You might also like

Downloadable Citations