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

Konferenzbeitrag

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Beiträge zur Jahrestagung des Vereins für Socialpolitik 2015: Ökonomische Entwicklung - Theorie und Politik - Session: Urban Economics I ; No. A15-V3

Classification and Topics

• Classification: Wirtschaft ; Urban, Rural, Regional, Real Estate, and Transportation Economics: Housing Demand ; Housing Supply and Markets ; Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Oestmann, Marco ; Bennöhr, Lars
• Event: Veröffentlichung
• (when): 2015

Further information

• Handle: http://hdl.handle.net/10419/113198
• Last update: 10.03.2025, 11:45 AM CET