Real estate appraisal: a review of valuation methods

Оценка недвижимости: обзор методов оценки
Elli Pagourtzi, Vassilis Assimakopoulos, Thomas Hatzichristos, Nick French
2003-08-01

ARIMA modelsartificial neural networkshedonic pricingreal estate valuationvaluation methods
The valuation of real estate is a central tenet for all businesses. Land and property are factors of production and, as with any other asset, the value of the land flows from the use to which it is put, and that in turn is dependent upon the demand (and supply) for the product that is produced. Valuation, in its simplest form, is the determination of the amount for which the property will transact on a particular date. However, there is a wide range of purposes for which valuations are required. These range from valuations for purchase and sale, transfer, tax assessment, expropriation, inheritance or estate settlement, investment and financing. The objective of the paper is to provide a brief overview of the methods used in real estate valuation. Valuation methods can be grouped as traditional and advanced. The traditional methods are regression models, comparable, cost, income, profit and contractor’s method. The advanced methods are ANNs, hedonic pricing method, spatial analysis methods, fuzzy logic and ARIMA models.
1
Advanced valuation methods include artificial neural networks, hedonic pricing, spatial analysis, fuzzy logic, and ARIMA models.
2
Property valuation determines the amount for which real estate would transact on a specified date, serving purposes including sales, taxation, inheritance, investment, and financing.
3
Real estate value depends on the property’s use and the supply and demand for the products generated through that use.
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The paper provides a concise review organizing real estate valuation techniques into traditional and advanced methodological groups.
5
Traditional real estate valuation methods include regression, comparable, cost, income, profit, and contractor’s approaches.

real estate

real estate valuation methods and their classification into traditional and advanced approaches

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Publication Date
2003-08-01
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Authors
Elli Pagourtzi
Vassilis Assimakopoulos
Thomas Hatzichristos
Nick French
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