Investigating the Impact of Search Query Data on Forecasting Housing Prices and Future Fluctuations
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2017
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Haverford College. Department of Economics
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Thesis
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eng
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Open Access
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Abstract
This study follows and adds to existing literature using search query data as a predictor of housing prices and future fluctuations. Based on the premise that homebuyers reveal their intention to purchase or sell their house on the internet, this study aggregates positive and negative real estate related queries into positive and negative search query indices. Using these indices in housing price models serves as a sentiment-like indicator for the U.S. housing market. The study employs both fixed effects and ordinary least squared (OLS) regression analysis to determine the ability of the models to use search query data to predict housing prices and future fluctuations. The results reveal that the inclusion of search query data mitigates error and improves forecasting of housing prices. The study finds that the addition of search query data improves models at the national and MSA-level, especially after accounting for spatial heterogeneity via fixed effects. Notably, this study’s main contribution to the literature is the positive sentiment index (PSI) that appears to be helpful in predicting future housing prices, especially at the national level.