This paper uses the Dynamic Factor Model framework, which accommodates a large
cross-section of macroeconomic time series, for forecasting regional house price
inflation. In this study, we forecast house price inflation for five metropolitan areas of
South Africa using principal components obtained from 282 quarterly macroeconomic
time series in the period 1980:1 to 2006:4. The results, based on the root mean square
errors of one- to four-quarters-ahead out of sample forecasts over the period of 2001:1
to 2006:4 indicate that, in the majority of the cases, the Dynamic Factor Model
statistically outperforms the Vector Autoregressive models, using both the classical
and the Bayesian treatments. We also consider spatial and non-spatial specifications.
Our results indicate that macroeconomic fundamentals in forecasting house price
inflation are important.