Abstract:
This article provides out-of-sample forecasts of linear and nonlinear
models of US and four Census subregions’ housing prices. The forecasts
include the traditional point forecasts, but also include interval and density
forecasts, of the housing price distributions. The nonlinear smooth-transition
autoregressive model outperforms the linear autoregressive model in
point forecasts at longer horizons, but the linear autoregressive and nonlinear
smooth-transition autoregressive models perform equally at short
horizons. In addition, we generally do not find major differences in
performance for the interval and density forecasts between the linear and
nonlinear models. Finally, in a dynamic 25-step ex-ante and interval
forecasting design, we, once again, do not find major differences between
the linear and nonlinear models. In sum, we conclude that when forecasting
regional housing prices in the United States, generally the additional
costs associated with nonlinear forecasts outweigh the benefits for forecasts
only a few months into the future.
