Predicting Housing Price Trends in Poland: Online Social Engagement - Google Trends

Bełej, Mirosław

doi:10.2478/remav-2023-0032

Abstract

Various research methods can be used to collect housing market data and predict housing prices. The online search activity of Internet users is a novel and highly interesting measure of social behavior. In the present study, dwelling prices in Poland were analyzed based on aggregate data from seven Polish cities relative to the number of online searches for the keyword dwelling tracked by Google Trends, as well as several classical macroeconomic indicators. The analysis involved a vector autoregressive (VAR) model and the Granger causality test. The results of the study suggest that the volume of online searches returned by Google Trends is an effective predictor of housing price dynamics, and that unemployment and economic growth are important additional variables.

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Predicting Housing Price Trends in Poland: Online Social Engagement - Google Trends

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