We examine the time-series relationship between house prices in eight Southern California metropolitan statistical areas (MSAs). First, we perform cointegration tests of the house price indexes for the MSAs, finding seven cointegrating vectors. Thus, the evidence suggests that one common trend...

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...

The 2006 sudden and immense downturn in U.S. House Prices sparked the 2007 global financial crisis and revived the interest about forecasting such imminent threats for economic stability. In this paper we propose a novel hybrid forecasting methodology that combines the Ensemble Empirical Mode...

We employ a 10-variable dynamic structural general equilibrium model to forecast the US real house price index as well as its turning point in 2006:Q2. We also examine various Bayesian and classical time-series models in our forecasting exercise to compare to the dynamic stochastic general...

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...