Could we have predicted the recent downturn in the South African housing market? |
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| Authors: | Sonali Das Rangan Gupta Alain Kabundi |
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| Affiliation: | 1. Logistics and Quantitative Methods, CSIR Built Environment, P.O. Box 395, Pretoria 0001, South Africa;2. Department of Economics, University of Pretoria, Pretoria 0002, South Africa;3. University of Johannesburg, Department of Economics and Econometrics, Johannesburg 2006, South Africa;1. Department of Medicine, New York Downtown Hospital, New York, New York;2. Department of Medicine, Weill Cornell Medical College, New York, New York;1. Department of Econometrics and OR, Tilburg University, CentER for Economic Research, Netspar, PO Box 90153, 5000LE Tilburg, The Netherlands;2. Department of Finance, Tilburg University, CentER for Economic Research, Netspar, PO Box 90153, 5000LE Tilburg, The Netherlands;1. INCEIF, The Global University of Islamic Finance, Lorong University A, 59100 Kuala Lumpur, Malaysia;2. Bank of New York Mellon Asset Management, One Wall Street, New York, NY 10286, USA;1. Tasmanian School of Business and Economics, University of Tasmania, Hobart, TAS 7001, Australia;2. Department of Economics, Macquarie University, Balaclava Road, North Ryde, NSW 2109, Australia;3. Discipline of Finance, The University of Sydney Business School, University of Sydney, NSW 2006, Australia;4. School of Economics, University of New South Wales, NSW 2052, Australia |
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| 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 1980:01–2000:04, 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 2001:01–2006:04. 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 the period of 2003:01–2008:02. |
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