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Volume 35, Number 3, 2013

Forecasting Residential Real Estate Price Changes from Online Search Activity
 

Eli Beracha
College of Business
University of Wyoming
1000 E University Ave
Laramie, WY 82071
Email: eberacha@uwyo.edu

M. Babajide Wintoki
The School of Business
University of Kansas
1300 Sunnyside Ave
Lawrence, KS, 66045-7585
Email: jwintoki@ku.edu



 

 

Abstract:

The intention of buying a home is revealed by many potential home buyers when they turn to the internet to search for their future residence. Therefore, the aggregated amount of today's real estate related searches is likely to provide information about the future demand for housing and possibly predict future housing price trends. This paper examines the extent to which future cross sectional differences in home price changes are predicted by online search intensity in prior periods. Our findings are economically meaningful and suggest that abnormal search intensity for real estate in a particular city can help predict the city's future abnormal housing price change. These findings hold even after we control for momentum in house prices. On average, cities associated with abnormally high real estate search intensity consistently outperform cities with abnormally low real estate search volume by as much as 8.5% over a two-year period. This outperformance appears to exhibit eventual reversal and is particularly noticeable for cities with lower supply land elasticity. Moreover, the results show that home prices are more sensitive to an "uptick" rather than a "downtick" in search intensity - consistent with the upward "stickiness" characteristic of home prices.

 
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