Can Google Search Data be Used as a Housing Bubble Indicator?

Are Oust and Eidjord Ole Martin

ERES from European Real Estate Society (ERES)

Abstract: The aim of this paper is to test whether Google search volume indices can be used to predict house prices and to identify bubbles in the housing market. We analyse the 06/07 U.S. housing bubble, taking advantage of the hetrogenius house price development in different U.S. states with both bubble and non-bubble states. From 204 housing related keywords, we test both single search terms and indexes with sets of search terms and finds that the several keywords preforms very well as a bubble indicator. Google search for Real Estate Agent displayed the most predictive power for the house prices, of all the keywords and indexes tested, globally in the US. Google searches volume outperforms the well-established Consumer Confidence Index as a leading indicator for the housing market.

Keywords: Google search volume; Google Trends; Housing; Housing Bubble; Price predication (search for similar items in EconPapers)
JEL-codes: R3 (search for similar items in EconPapers)
Date: 2018-01-01
New Economics Papers: this item is included in nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://eres.architexturez.net/doc/oai-eres-id-eres2018-151 (text/html)
https://eres.architexturez.net/system/files/P_20180130152322_640.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arz:wpaper:eres2018_151

Access Statistics for this paper

More papers in ERES from European Real Estate Society (ERES) Contact information at EDIRC.
Bibliographic data for series maintained by Architexturez Imprints ().