Using Large Data Sets to Forecast Housing Prices: A Case Study of Twenty US States

Rangan Gupta, Alain Kabundi () and Stephen Miller

No 916, Working Papers from University of Nevada, Las Vegas , Department of Economics

Abstract: 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 factor-augmented models generally outperform the large-scale models in the 20 US states examined in this paper.

Keywords: Housing Prices; Forecasting; Factor Augmented Models; Large-Scale BVAR models (search for similar items in EconPapers)
JEL-codes: C32 R31 (search for similar items in EconPapers)
Pages: 33 pages
Date: 2009-05
New Economics Papers: this item is included in nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Related works:
Working Paper: Using Large Data Sets to Forecast Housing Prices: A Case Study of Twenty US States (2009)
Working Paper: Using Large Data Sets to Forecast Housing Prices: A Case Study of Twenty US States (2009)
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