Econometric Support of a Mass Valuation Process

Doszyń Mariusz ()
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Doszyń Mariusz: University of Szczecin, Institute of Economics and Finance, Mickiewicza 64, 71-101Szczecin, Poland

Folia Oeconomica Stetinensia, 2020, vol. 20, issue 1, 81-94

Abstract: Research background: The issues undertaken in the paper include the specification of an econometric model in real estate mass appraisal. Advantages and disadvantages of using econometric models in real estate mass appraisal are discussed.Purpose: The issue of aiding the valuation process with an econometric model based on the Szczecin algorithm of real estate mass appraisal is discussed in the paper. Such problems like multicollinearity, lack of coincidence and nonmonotonic influence of attributes are pointed out. Also, potential solutions to these problems are mentioned. Moreover, the paper features a discussion of cases in which econometric appraisal is not sufficient.Research methodology: The base for constructing an econometric model is the so-called Szczecin algorithm of real estate mass appraisal. Based on the algorithm, the econometric model was created to enable determining the impact of real estate attributes and location on their value.Results: problems related with specification, estimation and verification of the real estate mass appraisal econometric model are discussed in an empirical example.Novelty: A non-linear model is proposed, which features explanatory variables introduced into the model, and by taking into consideration the scale of their measurement. The proposed model, by introducing dummy variables, also account for the impact of a location, which significantly improves the fit to empirical values.

Keywords: econometric modelling; real estate mass appraisal algorithm; multicollinearity; qualitative variables (search for similar items in EconPapers)
JEL-codes: C10 C13 R33 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:foeste:v:20:y:2020:i:1:p:81-94:n:5

DOI: 10.2478/foli-2020-0005

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