An Evaluation of Alternative Estimation Techniques and Functional Forms in Developing Statistical Appraisal Models

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Abstract
This paper examines the combined effects of multicollinearity, parameter stability, and alternative function forms in hedonic regression models. The results indicate that the significance and stability of the regression coefficients as well as prediction accuracy are sensitive to the choice of functional form and estimation technique. In certain respects nonlinear models proved to be more effective than linear models and ridge regression techniques were generally superior to OLS estimation. Since no single estimation technique or functional form was superior in all respects, the appraiser may have to choose between minimizing the average prediction error or maximizing prediction stability.