Abstract:
Several Bayesian and classical models are used to forecast house prices in 20 states in
the United States. There are two approaches: extracting common factors (principle
components) in a factor-augmented vector autoregressive or factor-augmented Bayesian
vector autoregressive models or Bayesian shrinkage in a large-scale Bayesian vector
autoregressive models. The study compares the forecast performance of the 1976:Q1 to
1994:Q4 in-sample period to the out-of-sample horizon 1995:Q1 to 2009:Q1 period. The
findings provide mixed evidence on the role of macroeconomic fundamentals in
improving the forecasting performance of time-series models. For 13 states, models that
include the information of macroeconomic fundamentals improve the forecasting
performance, while for seven states they do not.
