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Forecasting House Prices in Germany

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  • Philipp an de Meulen
  • Martin Micheli

    ()

  • Torsten Schmidt

Abstract

In the academic debate there is a broad consensus that house price fluctuations have a substantial impact on financial stability and real economic activity. Therefore, it is important to have timely information on actual and expected house price developments. The aim of this paper is to measure the latest price movements in different real estate markets in Germany and forecast near-term price developments. Therefore we construct hedonic house price indices based on real estate advertisements on the internet platform ImmobilienScout24. Then, starting with a naive AR(p) model as a benchmark, we investigate whether VAR and ARDL models using additional macroeconomic information can improve the forecasting performance as measured by the mean squared forecast error (MSFE). While these models reduce the forecast error only slightly, forecast combination approaches enhance the predictive power considerably..

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File URL: http://repec.rwi-essen.de/files/REP_11_294.pdf
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Bibliographic Info

Paper provided by Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen in its series Ruhr Economic Papers with number 0294.

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Length: 23 pages
Date of creation: Dec 2011
Date of revision:
Handle: RePEc:rwi:repape:0294

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Related research

Keywords: House price forecasts; forecast combination; hedonic price index;

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References

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  1. William D. Larson, 2010. "Evaluating Alternative Methods of Forecasting House Prices: A Post-Crisis Reassessment," Working Papers 2010-004, The George Washington University, Department of Economics, Research Program on Forecasting, revised Feb 2011.
  2. Teekens, R & Koerts, J, 1972. "Some Statistical Implications of the Log Transformation of Multiplicative Models," Econometrica, Econometric Society, vol. 40(5), pages 793-819, September.
  3. Gupta, Rangan & Kabundi, Alain & Miller, Stephen M., 2011. "Forecasting the US real house price index: Structural and non-structural models with and without fundamentals," Economic Modelling, Elsevier, vol. 28(4), pages 2013-2021, July.
  4. Aiolfi, Marco & Timmermann, Allan, 2006. "Persistence in forecasting performance and conditional combination strategies," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 31-53.
  5. Kelvin J. Lancaster, 1966. "A New Approach to Consumer Theory," Journal of Political Economy, University of Chicago Press, vol. 74, pages 132.
  6. Brachinger, Hans Wolfgang, 2002. "Statistical Theory of Hedonic Price Indices," DQE Working Papers 1, Department of Quantitative Economics, University of Freiburg/Fribourg Switzerland, revised Aug 2003.
  7. Gattini, Luca & Hiebert, Paul, 2010. "Forecasting and assessing Euro area house prices through the lens of key fundamentals," Working Paper Series 1249, European Central Bank.
  8. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
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Citations

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Cited by:
  1. Konstantin A. Kholodilin & Andreas Mense, 2012. "Forecasting the Prices and Rents for Flats in Large German Cities," Discussion Papers of DIW Berlin 1207, DIW Berlin, German Institute for Economic Research.
  2. Christian Pierdzioch, 2012. "Macroeconomic Factors and the German Real Estate Market: A Stock-Market-Based Forecasting Experiment," Review of Economics & Finance, Better Advances Press, Canada, vol. 2, pages 87-96, May.
  3. Konstantin A. Kholodilin & Boriss Siliverstovs, 2014. "Business Confidence and Forecasting of Housing Prices and Rents in Large German Cities," Discussion Papers of DIW Berlin 1360, DIW Berlin, German Institute for Economic Research.
  4. Rüdiger Budde & Martin Micheli, 2013. "Monitoring regionaler Immobilienpreise," RWI Konjunkturbericht, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, pages 17, December.

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