Can Google Search Data be Used as a Housing Bubble Indicator?
Are Oust and
Eidjord Ole Martin
ERES from European Real Estate Society (ERES)
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)
New Economics Papers: this item is included in nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
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)
Bibliographic data for series maintained by Architexturez Imprints ().