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 equilibrium model, estimated using Bayesian methods. In addition to standard
vector-autoregressive and Bayesian vector autoregressive models, we also include the
information content of either 10 or 120 quarterly series in some models to capture the influence
of fundamentals. We consider two approaches for including information from large data sets –
extracting common factors (principle components) in a Factor-Augmented Vector Autoregressive or Factor-Augmented Bayesian Vector Autoregressive models or Bayesian shrinkage in a large-scale Bayesian Vector Autoregressive models. We compare the out-of-sample forecast performance of the alternative models, using the average root mean squared error for the forecasts. We find that the small-scale Bayesian-shrinkage model (10 variables) outperforms the other models, including the large-scale Bayesian-shrinkage model (120 variables). Finally, we use each model to forecast the turning point in 2006:Q2, using the estimated model through 2005:Q2. Only the dynamic stochastic general equilibrium model
actually forecasts a turning point with any accuracy, suggesting that attention to developing
forward-looking microfounded dynamic stochastic general equilibrium models of the housing market, over and above fundamentals, proves crucial in forecasting turning points.