Might expert knowledge improve econometric real estate mass appraisal?

Mariusz Doszyń ()
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Mariusz Doszyń: University of Szczecin

The Journal of Real Estate Finance and Economics, 2024, vol. 69, issue 4, No 5, 719-740

Abstract: Abstract The 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.

Keywords: Econometric models of real estate mass appraisal; Expert knowledge; Inequality Restricted Least Squares; Mixed estimation; Bayesian regression; Ridge regression; LASSO (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11146-022-09891-3

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