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

33 Pages Posted: 22 May 2009 Last revised: 8 Apr 2012

See all articles by Rangan Gupta

Rangan Gupta

University of Pretoria - Department of Economics

Alain Kabundi

University of Johannesburg - Department of Economics

Stephen M. Miller

University of Nevada, Las Vegas - Department of Economics; University of Connecticut - Department of Economics

Date Written: May 18, 2009

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

JEL Classification: C32, R31

Suggested Citation

Gupta, Rangan and Kabundi, Alain and Miller, Stephen M., Using Large Data Sets to Forecast Housing Prices: A Case Study of Twenty US States (May 18, 2009). Journal of Housing Research, 20(2), 2011, Available at SSRN: https://ssrn.com/abstract=1406698

Rangan Gupta

University of Pretoria - Department of Economics ( email )

Lynnwood Road
Hillcrest
Pretoria, 0002
South Africa

Alain Kabundi

University of Johannesburg - Department of Economics ( email )

P.O. Box 524
Auckland Park 2006, Johannesburg
South Africa

Stephen M. Miller (Contact Author)

University of Nevada, Las Vegas - Department of Economics ( email )

4505 S. Maryland Parkway
Box 456005
Las Vegas, NV 89154
United States
702-895-3776 (Phone)
702-895-1354 (Fax)

HOME PAGE: http://faculty.unlv.edu/smiller/

University of Connecticut - Department of Economics

365 Fairfield Way, U-1063
Storrs, CT 06269-1063
United States

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
102
Abstract Views
1,716
rank
282,886
PlumX Metrics