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An Early Warning System to Predict the House Price Bubbles

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  • Christian Dreger
  • Konstantin A. Kholodilin

Abstract

In this paper, we construct the country-specific chronologies of the house price bubbles for 12 OECD countries over the period 1969:Q1- 2010:Q2. These chronologies are obtained using a combination of a fundamental and a filter approaches. The resulting speculative bubble chronology is the one that provides the highest concordance between these two techniques. In addition, we suggest an early warning system based on three alternative approaches: signalling approach, logit and probit models. It is shown that the latter two models allow much more accurate predictions of the house price bubbles than the signalling approach. The prediction accuracy of the logit and probit models is high enough to make them useful in forecasting the future speculative bubbles in housing market. Thus, our method can be used by the policymakers in their attempts to timely detect the house price bubbles and attenuate their devastating effects on the domestic and world economy.

Suggested Citation

  • Christian Dreger & Konstantin A. Kholodilin, 2011. "An Early Warning System to Predict the House Price Bubbles," Discussion Papers of DIW Berlin 1142, DIW Berlin, German Institute for Economic Research.
  • Handle: RePEc:diw:diwwpp:dp1142
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    Cited by:

    1. Lenarčič, Črt & Zorko, Robert & Herman, Uroš & Savšek, Simon, 2016. "A Primer on Slovene House Prices Forecast," MPRA Paper 103552, University Library of Munich, Germany.
    2. Caporale, Guglielmo Maria & Costantini, Mauro & Paradiso, Antonio, 2013. "Re-examining the decline in the US saving rate: The impact of mortgage equity withdrawal," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 26(C), pages 215-225.
    3. Helmut Herwartz & Konstantin A. Kholodilin, 2014. "In‐Sample and Out‐of‐Sample Prediction of stock Market Bubbles: Cross‐Sectional Evidence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(1), pages 15-31, January.
    4. Ansgar Belke & Jonas Keil, 2018. "Fundamental Determinants of Real Estate Prices: A Panel Study of German Regions," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 24(1), pages 25-45, February.
    5. Brian Micallef, 2016. "Property price misalignment with fundamentals in Malta," CBM Working Papers WP/03/2016, Central Bank of Malta.
    6. Michael Berlemann & Julia Freese & Sven Knoth, 2012. "Eyes Wide Shut? The U.S. House Market Bubble through the Lense of Statistical Process Control," CESifo Working Paper Series 3962, CESifo.
    7. Muellbauer, John & Geiger, Felix & Rupprecht, Manuel, 2016. "The housing market, household portfolios and the German consumer," Working Paper Series 1904, European Central Bank.
    8. Czerniak, Adam & Borowski, Jakub & Boratyński, Jakub & Rosati, Dariusz, 2020. "Asset price bubbles in a monetary union: Mind the convergence gap," International Review of Economics & Finance, Elsevier, vol. 67(C), pages 288-302.
    9. Christophe André, 2016. "Household debt in OECD countries: stylised facts and policy issues," Chapters from NBP Conference Publications, in: Hanna Augustyniak & Jacek Łaszek & Krzysztof Olszewski & Joanna Waszczuk (ed.), Papers presented during the Narodowy Bank Polski Workshop: Recent trends in the real estate market and its analysis - 2015 edition, chapter 2, pages v1, 33-85, Narodowy Bank Polski.
    10. Michael Berlemann & Julia Freese & Sven Knoth, 2020. "Dating the start of the US house price bubble: an application of statistical process control," Empirical Economics, Springer, vol. 58(5), pages 2287-2307, May.
    11. Alona Shmygel, 2022. "House Price Bubble Detection in Ukraine," IHEID Working Papers 22-2022, Economics Section, The Graduate Institute of International Studies.

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    More about this item

    Keywords

    House prices; early warning system; OECD countries;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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