Using Large Data Sets to Forecast House Prices: A Case Study of Twenty U.S. States

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.