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

Rangan Gupta (), Alain Kabundi () and Stephen Miller ()

No 2009-13, Working papers from University of Connecticut, 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)
New Economics Papers: this item is included in nep-ure
Date: 2009
Note: We acknowledge the assistance of D. Liu and D. W. Jansen, who provided the data on the 308 macroeconomic indicators, as well as for clarifying all the data related issues.
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Published in Journal of Housing Research, 20(2) 2011

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