Editor's Choice
Statement: A research paper on forecasting is most often published sometime after the realization of the forecast. The author is, therefore, clearly not influenced by events that have been realized in the macroeconomic environment, which may have influenced the behavior of the phenomenon under study and emerged after the research had been carried out.
  • social activity on the internet can anticipate market behaviour
  • google trend can support housing price forecasting
  • vector autoregressive model with Granger causality analysis helps to predict housing prices
  • unemployment and economic growth are also important factors affecting housing prices
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.
The author expresses his sincere gratitude to the Journal Editor and the anonymous Reviewers who spent their valuable time providing constructive comments and assistance to improve the quality of this paper.
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