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Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/2652

Title: Predicting downturns in the US housing market: a Bayesian approach
Authors: Gupta, R
Das, S
Keywords: Housing market
Bayesian Vector Autoregressive (BVAR) model
US economy
Issue Date: Mar-2008
Publisher: CSIR
Citation: Gupta, R and Das, S. 2008. Predicting downturns in the US housing market: a Bayesian approach. Working paper, pp 21
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 to 1994:Q4; and then forecasts one-to-four quarters ahead real house price growth over the out-of-sample horizon of 1995:Q1 to 2006:Q4. The forecasts are then evaluated by comparing them with the ones generated from an unrestricted classical Vector Autoregressive (VAR) model and the corresponding univariate variant 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 to 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, however, they always under-predict the size of the decline in the real house price growth rate
URI: http://hdl.handle.net/10204/2652
Appears in Collections:Building science and technology
Sustainability science
Construction
Logistics and quantitative methods
Planning support systems
General science, engineering & technology

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