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

Lynn Wu, Erik Brynjolfsson

Chapter in NBER book Economic Analysis of the Digital Economy (2015), Avi Goldfarb, Shane Greenstein, and Catherine Tucker, editors (p. 89 - 118)
Conference held June 6-7, 2013
Published in April 2015 by University of Chicago Press
© 2015 by the National Bureau of Economic Research

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 United States, 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 published 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. This type of "nanoeconomic" data can transform prediction in numerous markets, thereby improving business and consumer decision making.

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This paper was revised on May 27, 2014

Machine-readable bibliographic record - MARC, RIS, BibTeX

Document Object Identifier (DOI): 10.7208/chicago/9780226206981.003.0003

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