Google Search Queries, Foreclosures, and House Prices

Damian Damianov, Xiangdong Wang () and Cheng Yan ()
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Xiangdong Wang: Durham University Business School
Cheng Yan: University of Essex

The Journal of Real Estate Finance and Economics, 2021, vol. 63, issue 2, No 2, 177-209

Abstract: Abstract 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.

Keywords: Mortgage default risk; Foreclosures; House prices (search for similar items in EconPapers)
JEL-codes: D12 D14 E51 G21 G33 L85 R31 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11146-020-09789-y

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