While price changes on any particular home are difficult to predict, aggregate home price changes are forecastable. In this context, this paper compares the forecasting performance of three types of univariate time series models: ARIMA, GARCH and regime-switching. The underlying intuition behind regime-switching models is that the series of interest behaves differently depending on the realization of an unobservable regime variable. Regime-switching models are a compelling choice for real estate markets that have historically displayed boom and bust cycles. However, we find that, while regime-switching models can perform better in-sample, simple ARIMA models generally perform better in out-of-sample forecasting. Copyright 2003 by the American Real Estate and Urban Economics Association

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