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Assessing the Forecasting Performance of Regime‐Switching, ARIMA and GARCH Models of House Prices
Author(s) -
Crawford Gordon W.,
Fratantoni Michael C.
Publication year - 2003
Publication title -
real estate economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.064
H-Index - 61
eISSN - 1540-6229
pISSN - 1080-8620
DOI - 10.1111/1540-6229.00064
Subject(s) - econometrics , economics , unobservable , autoregressive conditional heteroskedasticity , univariate , autoregressive integrated moving average , house price , bust , context (archaeology) , real estate , volatility (finance) , time series , boom , financial economics , statistics , mathematics , multivariate statistics , finance , engineering , environmental engineering , paleontology , biology
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.