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Determinants of house price dynamics. What can we learn from search engine data?

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  • Oestmann Marco

    ()

  • Bennöhr Lars

    (Helmut Schmidt University Hamburg, Department of Economics, Holstenhofweg 85, D-22043 Hamburg)

Abstract

There is a broad literature on determinants of house price dynamics, which received increasing attention in the aftermath of the subprime crisis. Additional to macroeconomic standard variables, there might be other hard to measure or even unobservable factors influencing real estate prices. Using quarterly data, we try to increase the informational input of conventional models and capture such effects by including Google search engine query information into a set of standard fundamental variables explaining house prices. We use the house price index (HPI) published by Eurostat to perform fixed-effects regressions for a panel of 14 EU-countries comprising the years 2005-2013. We find that Google data as a single aggregate measure plays a prominent role in explaining house price developments.

Suggested Citation

  • Oestmann Marco & Bennöhr Lars, 2015. "Determinants of house price dynamics. What can we learn from search engine data?," Review of Economics, De Gruyter, vol. 66(1), pages 99-127, April.
  • Handle: RePEc:lus:reveco:v:66:y:2015:i:1:p:99-128
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    References listed on IDEAS

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    1. Charles Himmelberg & Christopher Mayer & Todd Sinai, 2005. "Assessing High House Prices: Bubbles, Fundamentals and Misperceptions," Journal of Economic Perspectives, American Economic Association, vol. 19(4), pages 67-92, Fall.
    2. Nikolaos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 55(2), pages 107-120.
    3. Bouwman, Kees E. & Jacobs, Jan P.A.M., 2011. "Forecasting with real-time macroeconomic data: The ragged-edge problem and revisions," Journal of Macroeconomics, Elsevier, vol. 33(4), pages 784-792.
    4. Marian Alexander Dietzel & Nicole Braun & Wolfgang Schäfers, 2014. "Sentiment-Based Commercial Real Estate Forecasting with Google Search Volume Data," ERES eres2014_17, European Real Estate Society (ERES).
    5. Hideaki Hirata & M. Ayhan Kose & Christopher Otrok & Marco E Terrones, 2013. "Global House Price Fluctuations: Synchronization and Determinants," NBER International Seminar on Macroeconomics, University of Chicago Press, vol. 9(1), pages 119-166.
    6. Kearl, J R, 1979. "Inflation, Mortgages, and Housing," Journal of Political Economy, University of Chicago Press, vol. 87(5), pages 1115-1138, October.
    7. Charles H. Wurtzebach & Glenn R. Mueller & Donna Machi, 1991. "The Impact of Inflation and Vacancy on Real Estate Returns," Journal of Real Estate Research, American Real Estate Society, vol. 6(2), pages 153-168.
    8. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 38(2), pages 112-134.
    9. James M. Poterba, 1984. "Tax Subsidies to Owner-Occupied Housing: An Asset-Market Approach," The Quarterly Journal of Economics, Oxford University Press, vol. 99(4), pages 729-752.
    10. 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.
    11. Luca GATTINI & Paul HIEBERT, "undated". "Forecasting and Assessing Euro Area House Prices Through the Lens of Key Fundamentals," EcoMod2010 259600061, EcoMod.
    12. Konstantin A. Kholodilin & Maximilian Podstawski & Boriss Siliverstovs & Constantin Bürgi, 2009. "Google Searches as a Means of Improving the Nowcasts of Key Macroeconomic Variables," Discussion Papers of DIW Berlin 946, DIW Berlin, German Institute for Economic Research.
    13. Nicholas Taylor, 2014. "Economic forecast quality: information timeliness and data vintage effects," Empirical Economics, Springer, vol. 46(1), pages 145-174, February.
    14. Charles Goodhart & Boris Hofmann, 2008. "House prices, money, credit, and the macroeconomy," Oxford Review of Economic Policy, Oxford University Press, vol. 24(1), pages 180-205, spring.
    15. Simeon Vosen & Torsten Schmidt, 2011. "Forecasting private consumption: survey‐based indicators vs. Google trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(6), pages 565-578, September.
    16. Michael Berlemann & Julia Freese, 2013. "Monetary policy and real estate prices: a disaggregated analysis for Switzerland," International Economics and Economic Policy, Springer, vol. 10(4), pages 469-490, December.
    17. Smith, Lawrence B, 1969. "A Model of the Canadian Housing and Mortgage Markets," Journal of Political Economy, University of Chicago Press, vol. 77(5), pages 795-816, Sept./Oct.
    18. Maddala, G S & Wu, Shaowen, 1999. " A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(0), pages 631-652, Special I.
    19. Paul Louis Ceriel Hilbers & Angana Banerji & Haiyan Shi & Willy W. Hoffmaister, 2008. "House Price Developments in Europe; A Comparison," IMF Working Papers 08/211, International Monetary Fund.
    20. Aoki, Kosuke & Proudman, James & Vlieghe, Gertjan, 2004. "House prices, consumption, and monetary policy: a financial accelerator approach," Journal of Financial Intermediation, Elsevier, vol. 13(4), pages 414-435, October.
    21. Buckley, Robert & Ermisch, John, 1982. "Government Policy and House Prices in the United Kingdom: An Econometric Analysis," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 44(4), pages 273-304, November.
    22. Haucap, Justus & Kehder, Christiane, 2013. "Suchmaschinen zwischen Wettbewerb und Monopol: Der Fall Google," DICE Ordnungspolitische Perspektiven 44, University of Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    23. Christian Hott, 2007. "Explaining house price fluctuations," Proceedings 1055, Federal Reserve Bank of Chicago.
    24. Eli Beracha & M. Babajide Wintoki, 2013. "Forecasting Residential Real Estate Price Changes from Online Search Activity," Journal of Real Estate Research, American Real Estate Society, vol. 35(3), pages 283-312.
    25. Plamen K Iossifov & Martin Cihak & Amar Shanghavi, 2008. "Interest Rate Elasticity of Residential Housing Prices," IMF Working Papers 08/247, International Monetary Fund.
    26. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    27. Dougherty, Ann & Van Order, Robert, 1982. "Inflation, Housing Costs, and the Consumer Price Index," American Economic Review, American Economic Association, vol. 72(1), pages 154-164, March.
    28. McLaren, Nick & Shanbhogue, Rachana, 2011. "Using internet search data as economic indicators," Bank of England Quarterly Bulletin, Bank of England, vol. 51(2), pages 134-140.
    29. David M. Drukker, 2003. "Testing for serial correlation in linear panel-data models," Stata Journal, StataCorp LP, vol. 3(2), pages 168-177, June.
    30. Kajuth, Florian & Knetsch, Thomas A. & Pinkwart, Nicolas, 2013. "Assessing house prices in Germany: Evidence from an estimated stock-flow model using regional data," Discussion Papers 46/2013, Deutsche Bundesbank.
    31. repec:arz:wpaper:eres2014-17 is not listed on IDEAS
    32. Peter Abelson & Roselyne Joyeux & George Milunovich & Demi Chung, 2005. "Explaining House Prices in Australia: 1970-2003," The Economic Record, The Economic Society of Australia, vol. 81(s1), pages 96-103, August.
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    Cited by:

    1. Coble, David & Pincheira, Pablo, 2017. "Nowcasting Building Permits with Google Trends," MPRA Paper 76514, University Library of Munich, Germany.

    More about this item

    Keywords

    Google Trends; House Price Index; Real Estate;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand
    • 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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