DSpace
 

Researchspace >
General science, engineering & technology >
General science, engineering & technology >
General science, engineering & technology >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/4481

Title: Predicting downturns in the US housing market: a Bayesian approach
Authors: Gupta, R
Das, S
Keywords: Bayesian vector autoregressive
BVAR
BVAR forecasts
Forecast accuracy
US housing market
Issue Date: Oct-2010
Publisher: Springer
Citation: Gupta, R and Das, S. 2010. Predicting downturns in the US housing market: a Bayesian approach. Journal of Real Estate Finance and Economics, Vol. 41(3), pp 294-319
Series/Report no.: Journal Article
Abstract: This paper estimates Bayesian Vector Autoregressive (BVAR) models, both spatial and non-spatial (univariate and multivariate), for the twenty largest states of the US economy, using quarterly data over the period 1976:Q1–1994:Q4; and then forecasts one-to-four quarters-ahead real house price growth over the out-of- sample horizon of 1995:Q1–2006:Q4. The forecasts are evaluated by comparing them with those from an unrestricted classical Vector Autoregressive (VAR) model and the corresponding univariate variant of the same. Finally, the models that produce the minimum average Root Mean Square Errors (RMSEs), are used to predict the downturns in the real house price growth over the recent period of 2007: Q1–2008:Q1. The results show that the BVARs, in whatever form they might be, are the best performing models in 19 of the 20 states. Moreover, these models do a fair job in predicting the downturn in 18 of the 19 states
Description: Copyright: 2010 Springer. This is the post print version of the work. The definitive version is published in the Journal of Real Estate Finance and Economics, Vol. 41(3), pp 294-319
URI: http://www.springerlink.com/index/w752464096652861.pdf
http://hdl.handle.net/10204/4481
ISSN: 0895-5638
Appears in Collections:Building science and technology
Infrastructure systems and operations
Logistics and quantitative methods
Planning support systems
General science, engineering & technology

Files in This Item:

File Description SizeFormat
Gupta_2010.pdf419.9 kBAdobe PDFView/Open
View Statistics

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

Valid XHTML 1.0! DSpace Software Copyright © 2002-2010  Duraspace - Feedback