PROPERTY VALUE ASSESSMENT USING ARTIFICIAL NEURAL NETWORKS, HEDONIC REGRESSION AND NEAREST NEIGHBORS REGRESSION METHODS

Öz

In this paper, hedonic regression, nearest neighbors regression and artificial neural networks methods are applied to the real and up to date estate data set belongs to Adana province of Turkey. Traditionally, hedonic regression methods have been used to predict house prices. Because of the nature of the relationships between the factors affecting house prices are generally being nonlinear; some alternative methods have been needed. Nearest neighbors regression (Knn) and artificial neural networks (Ann) present both flexible and nonlinear fittings. Classical hedonic approach and its nonlinear alternatives have been employed on a mixed types data set and compared based on some performance measures including root mean squared error, r squared, the coefficient of determination, and mean absolute error. Cross validation method has been used to determine the appropriate model parameters for nearest neighbors and Ann. According to the results, Ann is found better when compared to other methods in terms of all measures. Besides, Knn regression method provides reasonable results despite of lower performance than hedonic regression method. It has been seen that Ann is a powerful tool for predicting house prices.