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

Marco Oestmann and Lars Bennöhr

VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy from Verein für Socialpolitik / German Economic Association

Abstract: There is a broad literature on determinants of house prices, 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 price dynamics. 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) from 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 and substantially improves the accuracy of forecasts.

JEL-codes: C23 R21 R31 (search for similar items in EconPapers)
Date: 2015
New Economics Papers: this item is included in nep-ure
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https://www.econstor.eu/bitstream/10419/113198/1/VfS_2015_pid_849.pdf (application/pdf)

Related works:
Journal Article: Determinants of house price dynamics. What can we learn from search engine data? (2015) Downloads
Working Paper: Determinants of house price dynamics. What can we learn from search engine data? (2014) Downloads
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