This paper quantifies the importance of heterogeneity in regional housing markets for the conduct of monetary policy using a new model called a heterogeneous-agent VAR (HAVAR), which generalizes conventional macro VARs. The HAVAR model integrates a national monetary authority and financial market with regional housing markets, imposing exact aggregation. Monetary policy is transmitted to the national to regional markets via the mortgage rate. Although the HAVAR model is based on linear regional VARs, its aggregate impulse responses exhibit two nonlinearities: (1) time variation, stemming from aggregation over heterogeneous regions; and (2) state dependence on initial economic conditions in regions. Thus, the effects of monetary policy on the economy depend on the extent and nature of regional heterogeneity, which vary over time. Using longitudinal data for a subsample of detailed U.S. regions, we estimate the effects of time variation and state dependence on the dynamic responses of the HAVAR model. The estimated model provides plausible and tangible explanations for "long and variable" lags in monetary policy. To provide a policy-relevant illustration, we show how coastal housing booms influence the efficacy of monetary policy.JEL Codes: E22, E52, R21, R31

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

No abstract

No abstract

This paper quantifies the importance of heterogeneity in regional housing markets for the conduct of monetary policy using a new model called a heterogeneous-agent VAR (HAVAR), which generalizes conventional macro VARs. The HAVAR model integrates a national monetary authority and financial market with regional housing markets, imposing exact aggregation. Monetary policy is transmitted to the national to regional markets via the mortgage rate. Although the HAVAR model is based on linear regional VARs, its aggregate impulse responses exhibit two nonlinearities: (1) time variation, stemming from aggregation over heterogeneous regions; and (2) state dependence on initial economic conditions in regions. Thus, the effects of monetary policy on the economy depend on the extent and nature of regional heterogeneity, which vary over time. Using longitudinal data for a subsample of detailed U.S. regions, we estimate the effects of time variation and state dependence on the dynamic responses of the HAVAR model. The estimated model provides plausible and tangible explanations for "long and variable" lags in monetary policy. To provide a policy-relevant illustration, we show how coastal housing booms influence the efficacy of monetary policy.