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

Author

Listed:
  • Bełej Mirosław

    (Departament of Spatial Analysis and Real Estate Market, University of Warmia and Mazury in Olsztyn, ul. Oczapowskiego 2, 10-719 Olsztyn, Poland)

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.

Suggested Citation

  • Bełej Mirosław, 2023. "Predicting Housing Price Trends in Poland: Online Social Engagement - Google Trends," Real Estate Management and Valuation, Sciendo, vol. 31(4), pages 73-87, December.
    • Handle: RePEc:vrs:remava:v:31:y:2023:i:4:p:73-87:n:8
    DOI: 10.2478/remav-2023-0032
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    References listed on IDEAS

    as
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    More about this item

    Keywords

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    JEL classification:

    • R20 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - General
    • R30 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - General
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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