We implement several Bayesian and classical models to forecast housing prices in 20 US states.
In addition to standard vector-autoregressive (VAR) and Bayesian vector autoregressive (BVAR)
models, we also include the information content of 308 additional quarterly series in some
models. Several approaches exist for incorporating information from a large number of series.
We consider two approaches – extracting common factors (principle components) in a Factor-
Augmented Vector Autoregressive (FAVAR) or Factor-Augmented Bayesian Vector
Autoregressive (FABVAR) models or Bayesian shrinkage in a large-scale Bayesian Vector
Autoregressive (LBVAR) models. In addition, we also introduce spatial or causality priors to
augment the forecasting models. Using the period of 1976:Q1 to 1994:Q4 as the in-sample
period and 1995:Q1 to 2003:Q4 as the out-of-sample horizon, we compare the forecast
performance of the alternative models. Based on the average root mean squared error (RMSE)
for the one-, two-, three-, and four–quarters-ahead forecasts, we find that one of the factoraugmented
models generally outperform the large-scale models in the 20 US states examined in