The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON SYSTEMS
Transactions ID Number: 88-213
Full Name: Mousa Al-akhras
Position: Assistant Professor
Age: ON
Sex: Male
Address: Amman
Country: JORDAN
Tel: 779328463
Tel prefix: +96277
Fax: +962 6 533 07 04
E-mail address: mousa.akhras@ju.edu.jo
Other E-mails: mousa78@yahoo.com
Title of the Paper: Automatic Valuation of Jordanian Estates Using A Genetically-Optimised Artificial Neural Network Approach
Authors as they appear in the Paper: Mousa Al-akhras, Maha Saadeh
Email addresses of all the authors: mousa.akhras@ju.edu.jo,saadeh.maha@yahoo.com
Number of paper pages: 12
Abstract: Estate appraisal can be defined as valuing a land or property as of a given date using common data utilising standardised methods and statistical testing. Estate appraisal has several applications such as asset valuation for lenders, property tax estimation, insurance estimation, and estate planning, grant new mortgages to new home buyers, and purchase mortgage packages, which can contain thousands of mortgages, as investments. It is also used to guide potential buyers and sellers with making purchasing decisions. The drawbacks of on-site manual property valuation include: it is time-consuming, costy, based on subjective judgments and sometimes it is based on validation using a negotiated price rather than estimating the true market value of a property. In this paper, the authors builds an Artificial Neural Network model for the purpose of automatic appraisal of Jordanian estates to avoid the drawbacks of manual appraisal. The proposed Artificial Neural Network mod!
el is built in two stages: In the first stage, a Genetic Algorithm optimiser is used to determine the best topology for the Artificial Neural Network. In the second stage, the optimised Artificial Neural Network topology is trained for the best values for the weights. In evaluating the property price, several factors are taken into consideration such as area size, location, lot area, establishment year, price for building a square meter, and type of building (commercial or agricultural) and others. Records from Jordanian Department of Lands & Survey are used for training the Artificial Neural Network model and for testing its performance to find the best model that represents the underlying relation between a land/property and its characterising features. Statistical tests were performed to validate the effectiveness of the proposed method.
Keywords: Artificial neural network, Genetic algorithm, Estate appraisal, Automatic valuation model, Computer-assisted mass appraisal, Cost approach, Sales comparison approach, Income approach.
EXTENSION of the file: .pdf
Special (Invited) Session: An Evolutionary-Optimised Artificial Neural Network Approach for Automatic Appraisal of Jordanian Lands and Real Properties
Organizer of the Session: 632-244
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