This paper analyzes the ability of principal component regressions and Bayesian
regression methods under Gaussian and double-exponential prior in forecasting the real
house price of the United States (US), based on a monthly dataset of 112
macroeconomic variables. Using an in-sample period of 1992:01 to 2000:12, Bayesian
regressions are used to forecast real US house prices at the twelve-months-ahead forecast
horizon over the out-of-sample period of 2001:01 to 2004:10. In terms of the Mean
Square Forecast Errors (MSFEs), our results indicate that a principal component
regression with only one factor is best-suited for forecasting the real US house price.
Amongst the Bayesian models, the regression based on the double exponential prior
outperforms the model with Gaussian assumptions.