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

Are Oust () and Ole Martin Eidjord
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Are Oust: Norwegian University of Science and Technology
Ole Martin Eidjord: Norwegian University of Science and Technology

International Real Estate Review, 2020, vol. 23, issue 2, 267-308

Abstract: The aim of this paper is to test whether Google search volume indices can be used to predict house prices and identify bubbles in the housing market. We analyze the data that pertain to the 2006?2007 U.S. housing bubble, taking advantage of the heterogeneous house price development in both bubble and non-bubble states in the U.S. Using 204 housing-related keywords, we test both single search terms and indices that comprise search term sets to see whether they can be used as housing bubble indicators. We find that several keywords perform very well as bubble indicators. Among all of the keywords and indices tested, the Google search volume for ¡§Housing Bubble¡¨ and ¡§Real Estate Agent¡¨, and a constructed index that contains the twelve best-performing search terms score the highest at both detecting bubbles and not erroneously detecting non-bubble states as bubbles. A new housing bubble indicator may help households, investors, and policy makers receive advanced warning about future housing bubbles. Moreover, we show that the Google search outperforms the well-established consumer confidence index in the U.S. as a leading indicator of the housing market.

Keywords: Google Trends; Housing; Housing Bubble Indicator; Housing Bubble; Real Estate Agent (search for similar items in EconPapers)
JEL-codes: L85 (search for similar items in EconPapers)
Date: 2020
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