Analytical Method for Correction Coefficient Determination for Applying Comparative Method for Real Estate Valuation

Gružauskas Valentas (), Kriščiūnas Andrius (), Čalnerytė Dalia () and Navickas Valentinas ()
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Gružauskas Valentas: Kaunas University of Technology, School of Economics and Business, Digitalization Scientific Group
Kriščiūnas Andrius: Kaunas University of Technology, Faculty of Informatics, Department of Applied Informatics
Čalnerytė Dalia: Kaunas University of Technology, Faculty of Informatics, Department of Applied Informatics
Navickas Valentinas: Kaunas University of Technology, School of Economics and Business, Academic Center of Economics, Business and Management

Real Estate Management and Valuation, 2020, vol. 28, issue 2, 52-62

Abstract: Real estate valuation uses 3 main approaches: income, cost and comparative. When applying the comparative method, correction coefficients based on similar real estate transactions are determined. In practice, the coefficients and similar real estate objects are usually determined by using qualitative approach based on the valuators’ experience. The paper provides an analytical method for the determination of correction coefficient, which limits subjectivity when using the comparative method for valuation. The provided analytical approach also integrates macroeconomic indicators in the calculation process. It also addresses issues when available historical real estate transaction data is limited. A machine learning approach was applied to determine the average price of real estate in the region, with the possibility of using this information to obtain correction coefficients where historical data was unavailable. Alternative research usually focuses on final price estimation of the selected real estate object; however, the valuation standard of Tegova released in 2018 does not allow for applying analytically based approaches for individual real estate object evaluation; these approaches can be used only as a supportive tool for valuators.

Keywords: Real estate valuation; Analytical models; Machine learning (search for similar items in EconPapers)
JEL-codes: C55 L85 R32 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:remava:v:28:y:2020:i:2:p:52-62:n:5

DOI: 10.1515/remav-2020-0015

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