Gupta, RanganDas, Sonali2011-01-112011-01-112010Gupta, R & Das, S 2010, 'Predicting downturns in US housing market: a Bayesian approach', Journal of Real Estate Finance and Economics, vol. 41, no. 3, pp. 294-319. [http://www.springerlink.com/content/102945/]0895-563810.1007/s11146-008-9163-xhttp://hdl.handle.net/2263/15618This 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.enSpringer. The original publication is available at www.springerlink.comBayesian vector autoregressive (BVAR) modelBVAR modelBVAR forecastsForecast accuracySpatial Bayesian Vector Autoregressive (SBVAR) modelSBVAR modelSBVAR forecastsVector autoregressive (VAR) modelVAR modelVAR forecastsHousing marketHousing -- Prices -- United StatesHousing forecasting -- United StatesPostprint Article