Abstract:
This paper tests whether housing prices in the five segments of the South African housing market, namely
large–middle, medium–middle, small–middle, luxury and affordable, exhibit non-linearity based on smooth
transition autoregressive (STAR) models estimated using quarterly data from 1970:Q2 to 2009:Q3. Findings
point to an overwhelming evidence of non-linearity in these five segments based on in-sample evaluation of
the linear and non-linear models. We next provide further support for non-linearity by comparing one- to
four-quarters-ahead out-of-sample forecasts of the non-linear time series model with those of the classical
and Bayesian versions of the linear autoregressive (AR) models for each of these segments, for the out-ofsample
horizon 2001:Q1 to 2009:Q3, using the in-sample period 1970:Q2 to 2000:Q4. Our results indicate
that barring the one-, two and four-step(s)-ahead forecasts of the small segment, the non-linear model
always outperforms the linear models. In addition, given the existence of strong causal relationship amongst
the house prices of the five segments, the multivariate versions of the linear (classical and Bayesian) and STAR
(MSTAR) models were also estimated. The MSTAR always outperformed the best performing univariate and
multivariate linear models. Thus, our results highlight the importance of accounting for non-linearity, as well
as the possible interrelationship amongst the variables under consideration, especially for forecasting.
