Learning from Online Appraisal Information and Housing Prices

53 Pages Posted: 12 Dec 2019

See all articles by Guangli Lu

Guangli Lu

University of British Columbia, Sauder School of Business

Date Written: March 21, 2018

Abstract

This paper studies how housing prices react to an information shock due to the update in the most popular home valuation algorithm. I find that housing prices react gradually to the information shock: while the reaction in the first month is small, a 1% increase in the online appraisal leads to a 0.65% increase in sale prices 12 months after the shock. The magnitude of the reaction in a ZIP Code increases with its average trading volume over the sample period. In ZIP Codes with top 5% average trading volume, I find that a 1% increase in the online valuation leads to a 1.6% increase in sale prices 12 months after the shock, suggesting overreaction to the information shock. Findings in this paper indicate that machine-learning-based online valuations causally affect housing market transaction prices.

Keywords: Algorithm update, online information shock, Housing price reaction

JEL Classification: G00, R30

Suggested Citation

Lu, Guangli, Learning from Online Appraisal Information and Housing Prices (March 21, 2018). Available at SSRN: https://ssrn.com/abstract=3489522 or http://dx.doi.org/10.2139/ssrn.3489522

Guangli Lu (Contact Author)

University of British Columbia, Sauder School of Business ( email )

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