![]() ![]() Cross validation method has been used to determine the appropriate model parameters for nearest neighbors and Ann. 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. Nearest neighbors regression (Knn) and artificial neural networks (Ann) present both flexible and nonlinear fittings. Because of the nature of the relationships between the factors affecting house prices are generally being nonlinear some alternative methods have been needed. Traditionally, hedonic regression methods have been used to predict house prices. 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. ![]()
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