Arbeitspapier

Business confidence and forecasting of housing prices and rents in large German cities

In this paper, we evaluate the forecasting ability of 115 indicators to predict the housing prices and rents in 71 German cities. Above all, we are interested in whether the local business confidence indicators can allow substantially improving the forecasts, given the local nature of the real-estate markets. The forecast accuracy of different predictors is tested in a framework of a quasi out-of-sample forecasting. Its results are quite heterogeneous. No single indicator appears to dominate all the others for all cities and market segments. However, there are several predictors that are especially useful, namely the business confidence at the national level, consumer confidence, and price-to-rent ratios. Given the short sample size, the combinations of individual forecast do not improve the forecast accuracy. On average, the forecast improvements attain about 20%, measured by reduction in RMSFE, compared to the naive model. In separate cases, however, the magnitude of improvement is about 50%.

Sprache
Englisch

Erschienen in
Series: DIW Discussion Papers ; No. 1360

Klassifikation
Wirtschaft
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
Forecasting Models; Simulation Methods
Thema
housing prices
housing rents
forecasting
spatial dependence
German cities
confidence indicators
chambers of commerce and industry

Ereignis
Geistige Schöpfung
(wer)
Kholodilin, Konstantin A.
Siliverstovs, Boriss
Ereignis
Veröffentlichung
(wer)
Deutsches Institut für Wirtschaftsforschung (DIW)
(wo)
Berlin
(wann)
2014

Handle
Letzte Aktualisierung
10.03.2025, 11:42 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Kholodilin, Konstantin A.
  • Siliverstovs, Boriss
  • Deutsches Institut für Wirtschaftsforschung (DIW)

Entstanden

  • 2014

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