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

TLDR

Nano‑economic data from search engines can transform prediction across many markets, reshaping business and consumer decision‑making. The study aims to show that Google search data provide an accurate, simple way to forecast future business activities. The authors build a predictive model that uses search‑frequency indices from Google to estimate future market activity, and demonstrate its applicability to other sectors such as home appliances. Using this approach, they find that a housing‑search index strongly predicts future housing sales and prices, yielding lower mean absolute error than conventional baselines and outperforming National Association of Realtors experts by 23.6 %.

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.