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Determinants of house price dynamics. What can we learn from search engine data?

Author

Listed:
  • Oestmann Marco

    (Helmut Schmidt University Hamburg, Department of Economics, Holstenhofweg 85, D-22043 Hamburg)

  • Bennöhr Lars

    (Helmut Schmidt University Hamburg, Department of Economics, Holstenhofweg 85, D-22043 Hamburg)

Abstract

There is a broad literature on determinants of house price dynamics, which received increasing attention in the aftermath of the subprime crisis. Additional to macroeconomic standard variables, there might be other hard to measure or even unobservable factors influencing real estate prices. Using quarterly data, we try to increase the informational input of conventional models and capture such effects by including Google search engine query information into a set of standard fundamental variables explaining house prices. We use the house price index (HPI) published by Eurostat to perform fixed-effects regressions for a panel of 14 EU-countries comprising the years 2005-2013. We find that Google data as a single aggregate measure plays a prominent role in explaining house price developments.

Suggested Citation

  • Oestmann Marco & Bennöhr Lars, 2015. "Determinants of house price dynamics. What can we learn from search engine data?," Review of Economics, De Gruyter, vol. 66(1), pages 99-127, April.
    • Handle: RePEc:lus:reveco:v:66:y:2015:i:1:p:99-128
    DOI: 10.1515/roe-2015-0106
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    Cited by:

    1. Camilo Acosta & Luis Baldomero-Quintana, 2024. "Quality of communications infrastructure, local structural transformation, and inequality," Journal of Economic Geography, Oxford University Press, vol. 24(1), pages 117-144.
    2. Jorge Onrubia & Fernando Pinto & María del Carmen Rodado Ruíz, 2025. "From digital search to deed: Forecasting UK housing purchases in Spain using Google Trends across the Brexit disruption," Working Papers 2025-08, FEDEA.
    3. Coble, David & Pincheira, Pablo, 2017. "Nowcasting Building Permits with Google Trends," MPRA Paper 76514, University Library of Munich, Germany.
    4. David Coble & Pablo Pincheira, 2021. "Forecasting building permits with Google Trends," Empirical Economics, Springer, vol. 61(6), pages 3315-3345, December.

    More about this item

    Keywords

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    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand
    • 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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