This paper analyzes whether a wealth of information contained in 126 monthly series
used by large-scale Bayesian Vector Autoregressive (LBVAR) models, as well as
Factor Augmented Vector Autoregressive (FAVAR) models, either Bayesian or
classical, can prove to be more useful in forecasting real house price growth rate of
the nine census divisions of the US, compared to the small-scale VAR models, that
merely use the house prices. Using the period of 1991:02 to 2000:12 as the in-sample
period and 2001:01 to 2005:06 as the out-of-sample horizon, we compare the forecast
performance of the alternative models for one- to twelve–months ahead forecasts.
Based on the average Root Mean Squared Error (RMSEs) for one- to twelve–months
ahead forecasts, we find that the alternative FAVAR models outperform the other
models in eight of the nine census divisions.