This paper uses the Dynamic Factor Model (DFM) framework, which accommodates a large cross-section
of macroeconomic time series for forecasting regional house price inflation. As a case study,
we use data on house price inflation for five metropolitan areas of South Africa. The DFM used in this
study contains 282 quarterly series observed over the period 1980Q1-2006Q4. The results, based on the
Mean Absolute Errors of one- to four-quarters-ahead out of sample forecasts over the period of 2001Q1
to 2006Q4, indicate that, in majority of the cases, the DFM outperforms the VARs, both classical and
Bayesian, with the latter incorporating both spatial and non-spatial models. Our results, thus, indicate
the blessing of dimensionality.