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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.

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File URL: http://www.diw.de/documents/publikationen/73/diw_01.c.398323.de/dp1207.pdf
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Bibliographic Info

Paper provided by DIW Berlin, German Institute for Economic Research in its series Discussion Papers of DIW Berlin with number 1207.

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Length: 30 p.
Date of creation: 2012
Date of revision:
Handle: RePEc:diw:diwwpp:dp1207

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Related research

Keywords: Housing prices; housing rents; forecasting; dynamic panel model; spatial autocorrelation; German cities;

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References

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  1. Arellano, Manuel & Bond, Stephen, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Wiley Blackwell, Wiley Blackwell, vol. 58(2), pages 277-97, April.
  2. Badi Baltagi & Dong Li, 2006. "Prediction in the Panel Data Model with Spatial Correlation: the Case of Liquor," Spatial Economic Analysis, Taylor & Francis Journals, Taylor & Francis Journals, vol. 1(2), pages 175-185.
  3. Girardin , Eric & Kholodilin, Konstantin A., 2010. "How helpful are spatial effects in forecasting the growth of Chinese provinces?," BOFIT Discussion Papers, Bank of Finland, Institute for Economies in Transition 15/2010, Bank of Finland, Institute for Economies in Transition.
  4. repec:sae:ecolab:v:16:y:2006:i:2:p:1-2 is not listed on IDEAS
  5. 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.
  6. Badi H. Baltagi & Georges Bresson & James M. Griffin & Alain Pirotte, 2003. "Homogeneous, heterogeneous or shrinkage estimators? Some empirical evidence from French regional gasoline consumption," Empirical Economics, Springer, Springer, vol. 28(4), pages 795-811, November.
  7. Brücker, Herbert & Siliverstovs, Boriss, 2005. "On the Estimation and Forecasting of International Migration: How Relevant Is Heterogeneity Across Countries?," IZA Discussion Papers 1710, Institute for the Study of Labor (IZA).
  8. Longhi, Simonetta & Nijkamp, Peter, 2006. "Forecasting regional labor market developments under spatial heterogeneity and spatial correlation," Serie Research Memoranda, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics 0015, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
  9. Baltagi, Badi H. & Griffin, James M., 1997. "Pooled estimators vs. their heterogeneous counterparts in the context of dynamic demand for gasoline," Journal of Econometrics, Elsevier, Elsevier, vol. 77(2), pages 303-327, April.
  10. Konstantin A. Kholodilin & Boriss Siliverstovs & Stefan Kooths, 2007. "A Dynamic Panel Data Approach to the Forecasting of the GDP of German Länder," Discussion Papers of DIW Berlin 664, DIW Berlin, German Institute for Economic Research.
  11. Baltagi, Badi H. & Bresson, Georges & Pirotte, Alain, 2002. "Comparison of forecast performance for homogeneous, heterogeneous and shrinkage estimators: Some empirical evidence from US electricity and natural-gas consumption," Economics Letters, Elsevier, Elsevier, vol. 76(3), pages 375-382, August.
  12. Philipp an de Meulen & Martin Micheli & Torsten Schmidt, 2011. "Forecasting House Prices in Germany," Ruhr Economic Papers, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen 0294, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
  13. Badi H. Baltagi & Georges Bresson & Alain Pirotte, 2004. "Tobin q: Forecast performance for hierarchical Bayes, shrinkage, heterogeneous and homogeneous panel data estimators," Empirical Economics, Springer, Springer, vol. 29(1), pages 107-113, January.
  14. Aiolfi, Marco & Timmermann, Allan, 2006. "Persistence in forecasting performance and conditional combination strategies," Journal of Econometrics, Elsevier, Elsevier, vol. 135(1-2), pages 31-53.
  15. Badi H. Baltagi & James M. Griffin & Weiwen Xiong, 2000. "To Pool Or Not To Pool: Homogeneous Versus Hetergeneous Estimations Applied to Cigarette Demand," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 117-126, February.
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Citations

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Cited by:
  1. Rüdiger Budde & Martin Micheli, 2013. "Monitoring regionaler Immobilienpreise," RWI Konjunkturbericht, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, pages 17, December.
  2. 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.

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