Application of Artificial Neural Networks to Predict the Selling Price in the Real Estate Valuation Process

Abstract

An artificial neural networks (ANN) approach was applied to develop a mathematic model which predicts the sales price of residential properties. The study is based on evaluation of sales of homes in Casablanca, Morocco Kingdom. North of Africa. A feed forward network with one hidden layer was trained using original set of residential property valuation database. The ANN was obtained by 148 sets of input-output patterns applying back propagation algorithm. For the networks, the Levenberg-Marquardt learning algorithms, the hyperbolic tangent sigmoid transfer function and the linear transfer function were used. The best fitting training data set was obtained from an ANN architecture composed by five neurons in the hidden layer, which made possible to predict the sales price of homes. The model gave good predictions with high correlation coefficient (R2=0.952). Also, the validation of the data set simulations was in good agreement with the original data. It is suggested that the new ANN model could be used as a tool for the reliable prediction of selling price values.