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Think national, forecast local: a case study of 71 German urban housing markets

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  • Konstantin A. Kholodilin
  • Boriss Siliverstovs

Abstract

In this article, we examine whether the local indicators are able to predict the city-level housing prices and rents better than national indicators. For this purpose, we assess the forecasting ability of 126 indicators and 21 types of forecast combinations using a sample of 71 large German cities. There are several predictors that are especially useful, namely price-to-rent ratios, national-level business confidence, and consumer surveys. We also find that combinations of individual forecasts are among the top forecasting models. On average, the forecast improvements attain about 20%, measured by a reduction in root mean square error, compared to the naive models.

Suggested Citation

  • 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.
  • Handle: RePEc:taf:applec:v:49:y:2017:i:42:p:4271-4297
    DOI: 10.1080/00036846.2017.1279272
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    1. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 254-259, April.
    2. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
    3. repec:zbw:rwirep:0294 is not listed on IDEAS
    4. Capistrán, Carlos & Timmermann, Allan, 2009. "Forecast Combination With Entry and Exit of Experts," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 428-440.
    5. 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.
    6. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    7. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    8. Timmermann, Allan, 2006. "Forecast Combinations," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 4, pages 135-196, Elsevier.
    9. 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.
    10. Wenzel, Lars, 2013. "Forecasting regional growth in Germany: A panel approach using business survey data," HWWI Research Papers 133, Hamburg Institute of International Economics (HWWI).
    11. G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
    12. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
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