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Could We Have Predicted The Recent Downturn In The South African Housing Market?

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Author Info

  • Sonali Das

    ()
    (CSIR, Pretoria)

  • Rangan Gupta

    ()
    (Department of Economics, University of Pretoria)

  • Alain Kabundi

    ()
    (Department of Economics and Econometrics, University of Johannesburg)

Abstract

This paper develops large-scale Bayesian Vector Autoregressive (BVAR) models, based on 268 quarterly series, for forecasting annualized real house price growth rates for large-, medium- and small-middle-segment housing for the South African economy. Given the in-sample period of 1980:01 to 2000:04, the large-scale BVARs, estimated under alternative hyperparameter values specifying the priors, are used to forecast real house price growth rates over a 24-quarter out-of-sample horizon of 2001:01 to 2006:04. The forecast performance of the large-scale BVARs are then compared with classical and Bayesian versions of univariate and multivariate Vector Autoregressive (VAR) models, merely comprising of the real growth rates of the large-, medium- and small-middle-segment houses, and a large-scale Dynamic Factor Model (DFM), which comprises of the same 268 variables included in the large-scale BVARs. Based on the one- to four-quarters ahead Root Mean Square Errors (RMSEs) over the out-of-sample horizon, we find the large-scale BVARs to not only outperform all the other alternative models, but to also predict the recent downturn in the real house price growth rates for the three categories of the middle-segment-housing over an ex ante period of 2007:01 to 2008:02.

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Bibliographic Info

Paper provided by University of Pretoria, Department of Economics in its series Working Papers with number 200831.

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Length: 15 pages
Date of creation: Oct 2008
Date of revision:
Handle: RePEc:pre:wpaper:200831

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Keywords: Dynamic Factor Model; BVAR; Forecast Accuracy;

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References

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