Abstract
AbstractThe article examines whether expert knowledge improves the estimation results of real estate mass appraisal models. Six econometric models were compared: OLS, mixed, the Bayesian model, the Inequality Restricted Least Squares (IRLS) model, ridge and LASSO regression (with regularization). In three of the models (mixed, Bayesian, and IRLS) prior knowledge was applied. In mixed and Bayesian models priors took the form of intervals for model parameters. In IRLS, restrictions in the form of inequalities were applied. In the empirical example mass appraisal models were applied in the valuation of undeveloped land for residential purposes. Models with prior knowledge turned out to be the best with regard to the consistency of estimates with theory. Also, prediction accuracy was better for models with prior knowledge. In the case of low quality data expert knowledge might significantly improve estimation results of real estate mass appraisal econometric models.
Highlights
Information about real estate value is a very important factor that influences the efficiency of every real estate market
Knowledge about real estate value is necessary for almost all kinds of decisions made by customers, commercial and public institutions, governments
Mass appraisals may be needed for tax purposes, mortgage banks, estimating the economic effects of changes to spatial development plans or updating real estate fees
Summary
Information about real estate value is a very important factor that influences the efficiency of every real estate market. Knowledge about real estate value is necessary for almost all kinds of decisions made by customers, commercial and public institutions, governments. Because the number of properties is high, mass appraisals might be necessary. Mass appraisals may be needed for tax purposes, mortgage banks, estimating the economic effects of changes to spatial development plans or updating real estate fees. Many kinds of statistical and econometric models are used in real estate mass appraisal. Real estate information is often incomplete and uncertain. Real estate features such as transport availability or neighborhood are qualitative variables; they are prone to subjectivity. The variability of real estate features is often very low (e.g., neighborhoods of the same type)
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