Simulation techniques allow the author to examine the behavior and accuracy of several repeat sales regression estimators used to construct real estate return indices. He shows that the generalized least squares (GLS) method is the maximum likelihood estimator, and he shows how estimation accuracy can be significantly improved through a Bayesian approach. In addition, he introduces a biased estimation procedure based upon the James and Stein method to address the problems of multicollinearity common to the procedure. Copyright 1992 by Kluwer Academic Publishers
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