This paper evaluates the ability of Bayesian shrinkage-based
dynamic predictive regression models estimated with hierarchical priors (Adaptive
Jefferys, Adaptive Student-t, Lasso, Fussed Lasso and Elastic Net priors) and nonhierarchical
priors (Gaussian, Lasso-Lars, Lasso-Landweber) in forecasting the
U.S. real house price growth. We also compare results with forecasts from
bivariate OLS regressions and principal component regression. We use annual
dataset on 10 macroeconomic predictors spanning the period 1890 to 2012. Using
an out-of-sample period of 1917 to 2012, our results based on MSE and
McCracken (2007) MSE-F statistic, indicate that in general, the non-hierarchical
Bayesian shrinkage estimators perform better than their hierarchical counterparts
as well as the least square estimators. The Bayesian shrinkage estimated with
Lasso-Landweber is the best-suited model for forecasting the U.S. real house price.
Among the least square models, the individual regression with house price
regressed on the fiscal policy variable outperforms the rest. Also results from
Lasso-Landweber portray the fiscal policy variable as the best predictor of the U.S.
house prices especially in the recent times while the short-term interest rate and
real construction cost also did well at the beginning and middle of the sample.