Hasan YILDIRIM
(Statistics, Kamil Özdağ Faculty of Science, Karamanoğlu Mehmetbey University, Karaman, Turkey)
Yıl: 2019Cilt: 7Sayı: 2ISSN: 2147-9364 / 2147-9364Sayfa Aralığı: 387 - 404İngilizce

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PROPERTY VALUE ASSESSMENT USING ARTIFICIAL NEURAL NETWORKS, HEDONIC REGRESSION AND NEAREST NEIGHBORS REGRESSION METHODS
In this paper, hedonic regression, nearest neighbors regression and artificial neuralnetworks methods are applied to the real and up to date estate data set belongs to Adana province ofTurkey. Traditionally, hedonic regression methods have been used to predict house prices. Because ofthe nature of the relationships between the factors affecting house prices are generally being nonlinear;some alternative methods have been needed. Nearest neighbors regression (k-nn) and artificial neuralnetworks (ANN) present both flexible and nonlinear fittings. Classical hedonic approach and itsnonlinear alternatives have been employed on a mixed types data set and compared based on someperformance measures including root mean squared error, the coefficient of determination (R squared),the coefficient of determination, and mean absolute error. Cross validation method has been used todetermine 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, k-nn regressionmethod provides reasonable results despite of lower performance than hedonic regression method. Ithas been seen that ANN is a powerful tool for predicting house prices.
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