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

Yıl 2019, Cilt: 7 Sayı: 2, 387 - 404, 01.06.2019

Hasan Yıldırım

https://doi.org/10.15317/Scitech.2019.207

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 (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. 

Anahtar Kelimeler

Artificial neural networks, Hedonic regression, Housing prices, Nearest neighbors regression, Turkey

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

Öz

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.

Anahtar Kelimeler

En yakın komşuluk regresyon, Ev fiyatları, Hedonic regresyon, Yapay sinir ağları, Türkiye

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