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Bu çalışmanın temel amacı hane halkının konut sahibi olma kararlarını etkileyen faktörler çerçevesinde, iki farklı ekonometrik metodolojiyi karşılaştırmaktır. Çalışmada kullanılan veri seti, TÜİK tarafından oluşturulan ve yaklaşık 10 bin gözlem değerine sahip “Hane Halkı Bütçe Anketi”nden elde edilmiştir. Ele alınan veri seti çerçevesinde çalışmada ilk olarak hane halkının ev sahibi olma kararını etkileyen faktörlerin etkileme gücü ve yönü belirlenmektedir. Bununla birlikte, geleneksel lojistik regresyon yaklaşımı ile makine öğrenmesi temelli Destek Vektör Makineleri (DVM) yöntemi tahmin gücü açısından karşılaştırılmaktadır. Buna göre, DVM’nin konut sahibi olma ve olmama yönünde ihtimaliyetleri daha iyi tahmin ettği görülmektedir.
The main objective of this study is to compare two different econometric methodologies in the framework of the factors that influence household people’s decision to own housing. The data set used in the study has been obtained from the "House People's Budget Survey" created by TÜIK and with a value of approximately 10,000 observations. In the framework of the data set taken, the study first determines the power and direction of the factors affecting the decision of the household people to be hosted. However, the support vector machines (DVM) method based on machine learning is compared with the traditional logistics regression approach in terms of predictive power. According to this, it seems that DVM is better predicting its possibilities in the direction of being and being housing.
The main purpose of this study is; to compare two different econometric methodologies within the framework of the factors affecting households' decision to become a homeowner. The data set used in the study obtained from the “Household Budget Survey” which is created by TurkStat and has an observation value of about ten thousand. Using this data set, the study primarily investigates the importance of the factors that are likely to affect the decision to host. Additionally, with the traditional Logistic Regression, Support Vector Machines (SVM) algorithm is compared in terms of the accuracy of classification. Accordingly, it is seen that SVM is better predicting the possibility of ownership and non-ownership decisions.
Field : Ziraat, Orman ve Su Ürünleri; Spor Bilimleri
Journal Type : Uluslararası
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