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Forecasting House Prices in Germany

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  • an de Meulen, Philipp
  • Micheli, Martin
  • Schmidt, Torsten

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.

Suggested Citation

  • an de Meulen, Philipp & Micheli, Martin & Schmidt, Torsten, 2011. "Forecasting House Prices in Germany," Ruhr Economic Papers 294, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    • Handle: RePEc:zbw:rwirep:294
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    References listed on IDEAS

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    7. Thomas Bauer & Sven Feuerschütte & Michael Kiefer & Philipp an de Meulen & Martin Micheli & Torsten Schmidt & Lars-Holger Wilke, 2013. "Ein hedonischer Immobilienpreisindex auf Basis von Internetdaten: 2007–2011," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 7(1), pages 5-30, August.
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    9. 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, H. O. Stekler Research Program on Forecasting, revised Feb 2011.
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    Cited by:

    1. 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.
    2. Rüdiger Budde & Martin Micheli, 2013. "Monitoring regionaler Immobilienpreise," RWI Konjunkturbericht, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, pages 17, December.
    3. Laurynas Narusevicius & Tomas Ramanauskas & Laura Gudauskaitė & Tomas Reichenbachas, 2019. "Lithuanian house price index: modelling and forecasting," Bank of Lithuania Occasional Paper Series 28, Bank of Lithuania.
    4. Konstantin A. Kholodilin & Boriss Siliverstovs, 2017. "Think national, forecast local: a case study of 71 German urban housing markets," Applied Economics, Taylor & Francis Journals, vol. 49(42), pages 4271-4297, September.
    5. 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.
    6. Budde, Rüdiger & Micheli, Martin, 2013. "Monitoring regionaler Immobilienpreise," RWI Konjunkturberichte, RWI - Leibniz-Institut für Wirtschaftsforschung, vol. 64(4), pages 31-47.
    7. Sara Ferreira Filipe, 2018. "Housing prices and mortgage credit in Luxembourg," BCL working papers 117, Central Bank of Luxembourg.
    8. 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.

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    More about this item

    Keywords

    House price forecasts; forecast combination; hedonic price index; House price forecasts; forecast combination; hedonic price index;
    All these keywords.

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • 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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