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.
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LARS BENNÖHR MARCO OESTMANN
Zusammenfassung/ AbstractThere is a broad literature about fundamental determinants of house prices, which received increasing attention in the aftermath of the subprime crisis. However, there might be several other partly unobservable socio-demographic, psychological or individual factors influencing real estate price dynamics. Using quarterly data, we try to capture such effects by including relevant Google search engine query information into a set of standard fundamental variables. We perform fixed-effects regressions for a panel of 14 EU-countries comprising the years 2005-2013. As dependent variable the house price index (HPI) from Eurostat is employed. We find that Google data as a single aggregate measure of unobserved variables plays a substantial role in explaining house price developments.
DanksagungenFinancial support from the Fritz Thyssen Foundation is gratefully acknowledged. We would like to thank Christian Pierdzioch, Michael Berlemann, Max Steinhardt and Claudia Buch for helpful comments. Egle Wahl and Karol Sewielski provided excellent research assistance.
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