Comparing the Performance of Neural Networks and Multivariate Regression in the Estimation of Housing Prices (Case Study: Ahvaz City)

Housing is a basic need in the community always. Housing market has been one of the most fluctuation sectors of the economy of Iran country over the past years. Since the housing sector changes have a great impact on other sectors of the economy, so one of the significant needs of housing is predicting the price of this good. In this context, in this study by using of multi-layer perceptron neural network, presented a model to predict housing price in the city of Ahvaz and the results compared with the multivariate regression model. This study is a practical–developmental and its method is analytical- descriptive. To do this, 233 samples of statistical unit in 1392 were analyzed on the basis of 16 relevant variables. The results show that multi-layer neural network with 91 percent accuracy have been more accurate compared with the multivariate regression in the predicting housing prices. In addition to evaluating the performance of models coefficients R^2 and RMSE were used. Coefficient of determination (R^2) by using multivariate regression is .789 and its value for neural network is .918. The result of the regression model indicates weaker performance of this model compared to artificial neural network approach.