Automatic valuation of Jordanian estates using a genetically-optimised artificial neural network approach

Authors:
Mousa Al-Akhras;Maha Saadeh
Affiliations:
Computer Information Systems Department, King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan;Computer Science Department, King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan
Venue:
WSEAS TRANSACTIONS on SYSTEMS
Year:
2010

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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 build 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 model 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.