The Blessing of Dimensionality in Forecasting Real House Price Growth in the Nine Census Divisions of the U.S.

Abstract

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 the real house price growth rate of the nine census divisions of the United States, 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, this study compares 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, the findings reveal that the alternative FAVAR models outperform the other models in eight of the nine census divisions.