Sentiment-Based Commercial Real Estate Forecasting with Google Search Volume Data

Purpose – This article examines internet search query data provided by ‘Google Trends’, with respect to its ability to serve as a sentiment indicator and improve commercial real estate forecasting models for transactions and price indices. Methodology – The study uses data from CoStar the largest data providers of US commercial real estate repeat sales indices. We design three groups of models: baseline models including fundamental macro data only, those including Google data only and models combining both sets of data. One-month-ahead forecasts based on VAR models are conducted to compare the forecast accuracy of the models. Findings – The empirical results show that all models augmented with Google data, combining both macro and search data, significantly outperform baseline models which abandon internet search data. Models based on Google data alone, outperform the baseline models in all cases. The models achieve a reduction over the baseline models of the mean squared forecasting error (MSE) for transactions and prices of up to 35% and 54% respectively. Practical Implications – The results suggest that Google data can serve as an early market indicator. The findings of this study suggest that the inclusion of Google search data in forecasting models can improve forecast accuracy significantly. This implies that commercial real estate forecasters should consider incorporating this free and timely data set into their market forecasts or when performing plausibility checks for future investment decisions. Originality – This is the first paper applying Google search query data to the commercial real estate sector.