Sentiment-Based Predictions of Housing Market Turning Points with Google Trends

Purpose - Recent research has found significant relationships between internet search volume and real estate markets. This article examines whether Google search volume data can serve as a leading sentiment indicator and is able to predict turning points in the US housing market. One of the main objectives is to find a model which can be used to produce real-time forecasts in practice.Methodology - Starting from eight individual real-estate-related Google search volume indices, a multivariate probit model is derived by following a selection algorithm. The best model is then tested for its in- and out-of-sample forecasting ability.Findings - The results show that the model predicts the direction of monthly price changes correctly with over 89 per cent in-sample and just above 88 per cent in 1 to 4-month out-of-sample forecasts. The out-of-sample tests show that although the Google model is not always accurate in terms of timing, the signals are always correct when it comes to foreseeing an upcoming turning point. Thus, as signals are generated up to six months early, it functions as a satisfactory and timely indicator of future house price changes.Practical Implications - The results suggest that Google data can serve as an early market indicator and that the application of this data set in binary forecasting models can produce useful predictions of upward and downward movements of US house prices, as measured by the Case-Shiller 20-City House Price Index. This implies that real estate forecasters, economists and policy makers should consider incorporating this free and very current data set into their market forecasts or when performing plausibility checks for future investment decisions. Moreover, the results could potentially be of value for traders investing in Case-Shiller House Price futures and options.Originality - This is the first paper to apply Google search query data as a sentiment indicator in binary forecasting models to predict turning points in the housing market.