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Title: Forecasting house prices using online search activity Authors:  Erik Christian Montes Schutte - Aarhus University (Denmark) [presenting]
Abstract: It is shown that Google search activity is a strong out of sample predictor of future growth in U.S. house prices and that it strongly outperforms standard predictive models based on macroeconomic variables as well as autoregressive models. We extract the most important information from a large set of search terms related to different phases of the home search process into a single Google based factor and then use it to predict movements in future house prices. At the one-month forecast horizon, the Google factor delivers an out of sample R2 statistic of about 50 percent for the aggregate U.S. market over the period 2009-2018. We show that the strong predictive power of Google search activity holds for longer forecast horizons, for various house price indices, for seasonally unadjusted and adjusted data, and across individual U.S. states.