The Future of Prediction: How Google Searches Foreshadow Housing Prices and Sales
University of Pennsylvania - The Wharton School; Massachusetts Institute of Technology (MIT)
Massachusetts Institute of Technology (MIT) - Sloan School of Management; National Bureau of Economic Research (NBER)
August 30, 2013
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.
Number of Pages in PDF File: 43
Keywords: Online Search, Prediction, Housing Prices, Real Estate, Google Trendsworking papers series
Date posted: March 14, 2012 ; Last revised: September 10, 2013
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