An evolutionary-optimised artificial neural network approach for automatic appraisal of Jordanian lands and real properties

Authors:
Mousa AL-Akhras
Affiliations:
Computer Information Systems Department, King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan
Venue:
NN'10/EC'10/FS'10 Proceedings of the 11th WSEAS international conference on nural networks and 11th WSEAS international conference on evolutionary computing and 11th WSEAS international conference on Fuzzy systems
Year:
2010

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Abstract

Automatic property appraisal has several applications such as asset valuation for lenders, property tax estimation, insurance estimation, and estate planning, grant new mortgages to new homebuyers, and purchase mortgage packages, which can contain hundreds or even thousands of mortgages, on the secondary market as investments. Automatic property appraisal is also used to guide potential buyers and sellers with making purchasing decisions. In this work the author builds an Artificial Neural Network (ANN) model for the purpose of automatic property appraisal. Using Pythia- Neural Network Designer tool, the ANN model is built in two stages: In the first stage a Genetic Algorithm optimiser is used to determine the best structure for the ANN (course-tuning). In the second stage the optimised ANN structure is trained (fine-tuned) for the best values for the weights. In evaluating the property price several factors are taken into consideration such as area size, location, lot size, type of building, type of organisation (commercial or) and others. In the future more factors can be added such as the recency of property sale. More than a thousand record from Jordanian Department of Lands & Survey are used for training the ANN model and for testing its performance to find.