When was the U.S. housing downturn predictable? A comparison of univariate forecasting methods

• Predictions of ARIMA, switching, and structural times series models are compared.
• 10-market Case-Shiller housing price index is forecast 6, 12, and 18 months ahead.
• Good in-sample fit does not translate into out-of-sample forecasting performance.
• Structural times series models perform better than other model classes.
• Smooth trend models predict price downturn as early as the middle of 2005.

This paper uses three classes of univariate time series techniques (ARIMA type models, switching regression models, and state-space/structural time series models) to forecast, on an ex post basis, the downturn in U.S. housing prices starting around 2006. The performance of the techniques is compared within each class and across classes by out-of-sample forecasts for a number of different forecast points prior to and during the downturn. Most forecasting models are able to predict a downturn in future home prices by mid 2006. Some state-space models can predict an impending downturn as early as June 2005. State-space/structural time series models tend to produce the most accurate forecasts, although they are not necessarily the models with the best in-sample fit.