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GECO's Weather Forecast for the U.K. Housing Market: To What Extent Can We Rely on Google Econometrics?

Author

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  • Ralf Hohenstatt
  • Manuel Kaesbauer

Abstract

This study follows the stream of research identifying sentiment trends by using online search query data. The potential of the Google data series for the U.K. housing market on a disaggregated level is analyzed in a panel VAR framework. Our findings confirm research based on U.S. samples that Google subcategories, especially “Real Estate Agency,” serve as an indicator of transaction volume. Our main contribution is the detection of contrary dynamics within the Google “Home Financing” subcategory, which to date yields empirically mixed evidence (Hohenstatt, Kaesbauer, and Schaefers, 2011). Sensitivity analysis yields that transaction volume responds twice as sensitively as house prices due to a standard deviation increase of the stress indicator. Most importantly, the derived stress indicator of housing market (un-)soundness works at least as well as in downturns, as opposed to “Real Estate Agency,” which is primarily a suitable indicator during upturns.

Suggested Citation

  • Ralf Hohenstatt & Manuel Kaesbauer, 2014. "GECO's Weather Forecast for the U.K. Housing Market: To What Extent Can We Rely on Google Econometrics?," Journal of Real Estate Research, Taylor & Francis Journals, vol. 36(2), pages 253-282, January.
    • Handle: RePEc:taf:rjerxx:v:36:y:2014:i:2:p:253-282
    DOI: 10.1080/10835547.2014.12091387
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