Hasan YILDIRIM

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

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 (k-nn) 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, the coefficient of determination (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, k-nn 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.

Yapay Sinir Ağları, Hedonik Regresyon Ve En Yakın Komşuluk Regresyon Metotlarını Kullanarak Emlak Fiyatlarının Belirlenmesi

Bu çalışmada hedonik regresyon, en yakın komşu regresyon ve yapay sinir ağları metotları Türkiye’nin Adana iline ait gerçek ve güncel bir veri seti üzerinde uygulanmıştır. Geleneksel olarak, ev fiyatlarının tahmininde hedonic regresyon metotları kullanılmaktadır. Ev fiyatlarını etkileyen faktörler arasındaki ilişkilerin yapısının genel olarak doğrusal olmaması nedeniyle bazı alternatif metotlara ihtiyaç duyulmaktadır. En yakın komşuluk regresyon ve yapay sinir ağları hem esnek hem de doğrusal olmayan uyumlar sunmaktadır. Klasik hedonic regresyon yaklaşımı ve doğrusal olmayan alternatifleri karma yapıda ki bir veri kümesine uygulanmış ve hata kareler ortalaması, belirleyicilik katsayısı (R kare) ve ortalama mutlak hatayı içeren bazı performans ölçütlerine dayanarak karşılaştırılmıştır.En yakın komşu ve yapay sinir ağları için uygun model parametrelerini belirlemek için çapraz geçerlilik metodu kullanılmıştır.Sonuçlara göre, yapay sinir ağları diğer metotlarla karşılaştırıldığında tüm ölçülere göre daha iyi bulunmuştur.Ayrıca en yakın komşu metodu hedonik regresyon metodundan daha düşük performanslı olmasına rağmen makul sonuçlar sağlamaktadır. Yapay sinir ağlarının ev fiyatlarının tahmininde güçlü bir araç olduğu görülmüştür.

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