Open AccessInternational Real Estate Review
Author(s) -
Are Oust,
Ole Martin Eidjord
Publication year - 2020
Publication title -
journal of the asian real estate society
Language(s) - English
Resource type - Journals
ISSN - 1029-6131
DOI - 10.53383/100302
Subject(s) - bubble , real estate , economic bubble , index (typography) , price index , volume (thermodynamics) , econometrics , economics , computer science , business , financial economics , macroeconomics , finance , physics , quantum mechanics , parallel computing , world wide web
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.