USING ARTIFICIAL NEURAL NETWORK METHOD TO PREDICT HOUSING PRICES

Year 2018, Issue: 20, 285 - 300, 31.01.2018

Özgür Yılmazel , Aslı Afşar , Sibel Yılmazel

https://doi.org/10.18092/ulikidince.341584

Cited By: 15

Abstract

With improvements in technology
artificial neural network has been added among the methods for house price
prediction. This study explores the usage of artificial neural network method
to predict the housing prices in the city of Eskişehir. The physical characteristics
of housing such as the size of the apartment, number of the rooms, if the
apartment is located on the first floor or not, the number of the floors of the
building, whether it has central heating, number of the bathrooms, whether
there is an elevator, and parking lot of the building, if there is built-in
kitchen cabinets, availability of fiber internet connection in the building,
the neighborhood of the apartment and the distance to the nearest tram stop are
used within the neural network model. Different numbers of neurons are tried
out within the generated neural network. The results are compared and in
conclusion it was shown that artificial neural network is an effective method
to predict the housing prices.

Keywords

Housing Sales Price, Artificial Neural Network, ANN, Housing Market in Eskisehir

KONUT FİYAT TAHMİNİNDE YAPAY SİNİR AĞLARI YÖNTEMİNİN KULLANILMASI

Abstract

Teknolojinin
gelişmesiyle konut fiyat tahmini için kullanılan yöntemlere yapay sinir ağları yöntemi
de eklenmiştir. Bu çalışmada Eskişehir ilinde satılık konut fiyatlarının
tahmininde yapay sinir ağlarının kullanılması araştırılmıştır. Konutların
büyüklüğü, oda sayısı, birinci katta bulunup bulunmadığı, konutun bulunduğu
binadaki toplam kat sayısı, merkezi ısıtmalı olup olmadığı, banyo sayısı,
asansörün, otoparkın, ankastre mutfağın ve fiber internet bağlantısının bulunup
bulunmadığı gibi farklı fiziksel özellikleri, bulunduğu mahalle ve tramvaya
uzaklığı parametreleri ile yapay sinir ağları modelleri kurulmuştur.
Geliştirilen yapay sinir ağları modellerinde gizli katman nöron sayıları
farklılaştırılarak 19 adet model elde edilmiş ve bu modellerin
performanslarının karşılaştırması yapılarak en uygun gizli katman nöron sayısı
belirlenmiştir. Sonuç olarak yapay sinir ağlarının konut fiyatının tahmin edilmesinde
etkili bir araç olduğu gösterilmiştir.

Keywords

Konut Satış Fiyatı, Yapay Sinir Ağları, YSA, Eskişehir'de Konut Piyasası

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