Forecasting Residential Real Estate Price Changes from Online Search Activity
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. 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 cityâ€™s future abnormal housing price change. 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.
Volume (Year): 35 (2013)
Issue (Month): 3 ()
|Contact details of provider:|| Postal: |
Web page: http://www.aresnet.org/Email:
|Order Information:|| Postal: Diane Quarles American Real Estate Society Manager of Member Services Clemson University Box 341323 Clemson, SC 29634-1323|
Web: http://pages.jh.edu/jrer/about/get.htm Email:
When requesting a correction, please mention this item's handle: RePEc:jre:issued:v:35:n:3:2013:p:283-312. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (JRER Graduate Assistant/Webmaster)
If references are entirely missing, you can add them using this form.