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Forecasting the Prices and Rents for Flats in Large German Cities

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  • Konstantin A. Kholodilin
  • Andreas Mense

Abstract

In this paper, we make multi-step forecasts of the monthly growth rates of the prices and rents for flats in 26 largest German cities. Given the small time dimension, the forecasts are done in a panel-data format. In addition, we use panel models that account for spatial dependence between the growth rates of housing prices and rents. Using a quasi out-of-sample forecasting exercise, we find that both pooling and accounting for spatial effects helps to substantially improve the forecast performance compared to the benchmark models estimated for each of the cities separately. In addition, a true out-of-sample forecasting of the growth rates of flats' prices and rents for the next six months is done. It shows that in most cities both prices and rents for flats are going to increase. In some cities, the average monthly growth rate even exceeds 1%, which is a very strong increase compared to the overall price level increase of about 2% per year.

Suggested Citation

  • 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.
    • Handle: RePEc:diw:diwwpp:dp1207
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    References listed on IDEAS

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    4. Konstantin A. Kholodilin & Andreas Mense, 2012. "Internet-Based Hedonic Indices of Rents and Prices for Flats: Example of Berlin," Discussion Papers of DIW Berlin 1191, DIW Berlin, German Institute for Economic Research.
    5. repec:zbw:rwirep:0294 is not listed on IDEAS
    6. Konstantin Arkadievich Kholodilin & Boriss Siliverstovs & Stefan Kooths, 2008. "A Dynamic Panel Data Approach to the Forecasting of the GDP of German Länder," Spatial Economic Analysis, Taylor & Francis Journals, vol. 3(2), pages 195-207.
    7. 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.
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    Cited by:

    1. 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.
    2. Matías Mayor & Roberto Patuelli, 2015. "Spatial panel data forecasting over different horizons, cross-sectional and temporal dimensions," Revue d'économie régionale et urbaine, Armand Colin, vol. 0(1), pages 149-180.
    3. Rüdiger Budde & Martin Micheli, 2013. "Monitoring regionaler Immobilienpreise," RWI Konjunkturbericht, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, pages 17, December.
    4. 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.
    5. Budde, Rüdiger & Micheli, Martin, 2013. "Monitoring regionaler Immobilienpreise," RWI Konjunkturberichte, RWI - Leibniz-Institut für Wirtschaftsforschung, vol. 64(4), pages 31-47.
    6. 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.
    7. Xueting Zhao & J. Burnett, 2014. "Forecasting province-level $${\text {CO}}_{2}$$ CO 2 emissions in China," Letters in Spatial and Resource Sciences, Springer, vol. 7(3), pages 171-183, October.

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    More about this item

    Keywords

    Housing prices; housing rents; forecasting; dynamic panel model; spatial autocorrelation; German cities;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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