Gupta, RanganKabundi, AlainMiller, Stephen M.2012-05-242012-05-242011Gupta, R, Kabundi, A & Miller, SM 2011, 'Using large data sets to forecast house prices : a case study of twenty U.S. states', Journal of Housing Research, vol. 20, no. 2, pp. 161-191.1052-7001 (print)http://hdl.handle.net/2263/18872Several 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.enAmerican Real Estate SocietyBayesian vector autoregressive (BVAR) modelVector autoregressive (VAR) modelFactor-augmented VAR (FAVAR) modelSpatial Bayesian VAR (SBVAR) modelSpatial Bayesian FAVAR (SFABVAR) modelSpatial large-scale BVAR (SLBVAR) modelHousing -- Prices -- United States -- ForecastingArticle