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A Multi-Attribute Sales Comparison Method for Real Estate Valuation

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Tartalom: https://pp.bme.hu/so/article/view/13897
Archívum: PP Social and Management Sciences
Gyűjtemény: Articles
Cím:
A Multi-Attribute Sales Comparison Method for Real Estate Valuation
Létrehozó:
Farkas, András
Porumb, Bogdan
Kiadó:
Budapest University of Technology and Economics
Dátum:
2019-12-12
Téma:
real-estate valuation
sales comparison method
multi-attribute utility model
Tartalmi leírás:
The theory and practice of real-estate valuation has attracted immense interest over the past decades. This paper is concerned with the sales comparison approach. First, a brief survey of some procedures used worldwide, the sales comparison by adjustments, the hedonic regression and the hedonic price index method, is presented. To improve the versatility of property appraisals a new valuation method, as a combination of multi-objective optimization (MOO) and multi-criteria decision analysis (MCDA) is developed. A unique feature of this model is that it enables the appraiser to evaluate the characteristics of a property on those scales of measurement to which they belong de facto. To comply with this objective, distinct metric distance functions on each scale of measurement, including the two qualitative scales (nominal and ordinal), are employed. After that, the physical worth of a property is derived as a weighted sum of the composite scores. This can be measured on an interval scale. To predict the monetary worth of a property, a simple linear regression model is developed. The benefits of the use of this multi-attribute valuation method is also discussed. A comprehensive real-world study showing the application of the procedure is included.
Nyelv:
angol
Típus:
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Formátum:
application/pdf
Azonosító:
10.3311/PPso.13897
Forrás:
Periodica Polytechnica Social and Management Sciences; Vol. 28 No. 1 (2020); 1-11
1587-3803
1416-3837
Kapcsolat:
Létrehozó:
Copyright (c) 2019 Periodica Polytechnica Social and Management Sciences