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Trend-Spotting in the Housing Market

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  • Askitas, Nikos

    () (IZA)

Abstract

I create a time series of weekly ratios of Google searches, in the US, on buying and selling in the Real Estate Category of Google Trends. I call this ratio the Google US Housing Market BUSE Index or simply the BUSE index. It expresses the number of "buy"-searches for each "sell"-search which, by means of certain regularity assumptions on the distribution of Internet users, I think is a good proxy of the number of prospective home buyers for each prospective home seller in the pool of prospective housing market participants. I show this ratio to have several unique, desirable properties which make it useful for understanding and nowcasting the US housing market. Firstly it has a significant correlation with the US national S&P/Case-Shiller Home Price Index. Since the latter is monthly and published as a three-month moving average with a two month lag and the Google Trends data is weekly we can have a short term nowcasting of housing prices in the US. In the seasonal variations of this ratio the BUSE index recaptures traces of prospect theory whose applicability in the housing market has been well documented. I show how these Google data can be used to create a consistent narrative of the post bubble burst dynamics in the US housing market and propose the BUSE index as an instrument for monitoring housing market conditions.

Suggested Citation

  • Askitas, Nikos, 2015. "Trend-Spotting in the Housing Market," IZA Discussion Papers 9427, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp9427
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    References listed on IDEAS

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    1. Karl E. Case & Robert J. Shiller & Anne K. Thompson, 2012. "What Have They Been Thinking? Homebuyer Behavior in Hot and Cold Markets," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 43(2 (Fall)), pages 265-315.
    2. Askitas, Nikos & Zimmermann, Klaus F., 2011. "Detecting Mortgage Delinquencies," IZA Discussion Papers 5895, Institute of Labor Economics (IZA).
    3. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    4. Askitas, Nikos, 2015. "Calling the Greek Referendum on the Nose with Google Trends," IZA Discussion Papers 9569, Institute of Labor Economics (IZA).
    5. Kahneman, Daniel & Tversky, Amos, 1979. "Prospect Theory: An Analysis of Decision under Risk," Econometrica, Econometric Society, vol. 47(2), pages 263-291, March.
    6. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
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    Cited by:

    1. Fabo, B., 2017. "Towards an understanding of job matching using web data," Other publications TiSEM b8b877f2-ae6a-495f-b6cc-9, Tilburg University, School of Economics and Management.
    2. Coble, David & Pincheira, Pablo, 2017. "Nowcasting Building Permits with Google Trends," MPRA Paper 76514, University Library of Munich, Germany.

    More about this item

    Keywords

    behaviour; complexity; S&P/Case-Shiller Home Price; complex systems; Google Search; Google Trends; housing market; computational social science; data science; nowcasting;

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • E65 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Studies of Particular Policy Episodes
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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