The Future of Prediction: How Google Searches Foreshadow Housing Prices and Sales

43 Pages Posted: 14 Mar 2012 Last revised: 2 Dec 2015

See all articles by Lynn Wu

Lynn Wu

University of Pennsylvania - The Wharton School

Erik Brynjolfsson

National Bureau of Economic Research (NBER); Stanford

Date Written: August 30, 2013

Abstract

We demonstrate how data from search engines such as Google provide an accurate but simple way to predict future business activities. Applying our methodology to predict housing market trends, we find that a housing search index is strongly predictive of future housing market sales and prices. For state-level predictions in the US, the use of search data produces out-of-sample predictions with a smaller mean absolute error than the baseline model that uses conventional data but lacks search data. Furthermore, we find that our simple model of using search frequencies beat the predictions made by experts from the National Association of Realtors by 23.6% for future US home sales. We also demonstrate how these data can be used in other markets, such as home appliance sales. In the near future, this type of “nanoeconomic” data can transform prediction in numerous markets, and thus business and consumer decision-making.

Keywords: Online Search, Prediction, Housing Prices, Real Estate, Google Trends

Suggested Citation

Wu, Lynn and Brynjolfsson, Erik, The Future of Prediction: How Google Searches Foreshadow Housing Prices and Sales (August 30, 2013). Available at SSRN: https://ssrn.com/abstract=2022293 or http://dx.doi.org/10.2139/ssrn.2022293

Lynn Wu (Contact Author)

University of Pennsylvania - The Wharton School ( email )

3733 Spruce Street
Philadelphia, PA 19104-6374
United States

Erik Brynjolfsson

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Stanford ( email )

366 Galvez St
Stanford, CA 94305
United States

HOME PAGE: http://brynjolfsson.com

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