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

• Autorzy: Valentas Gružauskas , Andrius Kriščiūnas , Dalia Čalnerytė , Valentinas Navickas
• Języki publikacji: EN

Abstrakty

EN

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. (original abstract)

Słowa kluczowe

EN

Real estate valuation; Economic models; Machine learning

PL

Wycena nieruchomości; Modele ekonomiczne; Uczenie maszynowe

• Czasopismo: Real Estate Management and Valuation
• Rocznik: 2020
• Numer: vol. 28, iss. 2
• Strony: 52--62

Twórcy

autor

Valentas Gružauskas

• Kaunas University of Technology, Lithuania

autor

Andrius Kriščiūnas

• Kaunas University of Technology, Lithuania

autor

Dalia Čalnerytė

• Kaunas University of Technology, Lithuania

autor

Valentinas Navickas

• Kaunas University of Technology, Lithuania

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Identyfikatory

DOI

doi.org/10.1515/remav-2020-0015