Abstract
This study investigates the determinants which drive the evolution of the American real estate prices. Housing prices are modelling by a Standard&Poor index known under the name of Case-Shiller Index Composite 20 which aims to quantify the residential housing market in 20 US metropolitan regions across the United States. Using different regression methods (Ordinary Least Squares and Kalman filter), we examine the time-varying sensibility of the selected risk factors to the Case-Shiller 20 index. Then, an econometric model is proposed to anticipate its monthly time series evolution over the 1991-2009 periods thanks to time-varying betas obtain with OLS and Kalman filter. One of the main difficulty concerns the consideration and the detection of the shift between a positive and a negative price return period (and vis-versa) by the modelling. Hence, the criterion decision about the choice of the best forecasting model is the one which capture the most regime switching. Based on monthly US market data from 1991 to 2009 period, we seek to compute objective and robust forecasts of the American housing prices.
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