Examination of the Factors Affecting Household Rental Housing Demand Through Data Mining

Abstract

Houses are an irreplaceable tool for the need for shelter However, as a result of the global economic, cultural and technological changes encountered in recent years they have become a source of assurance for the future and one of the most significant indicators of life-style and wealth and social prestige as well as meeting the basic need for shelter.  This causes house to become a heterogeneous good and to involve many characteristics.  The effectiveness level of different characteristics of the house on the market value of the house is predicted using hedonic pricing model based on micro-economic theory. The aim of the study is to analyze the factors affecting rental housing demand of households through data mining methods. 2341 household data has been referenced and 49 data title was selected as the basis ofthe study in which Household Budget Survey data of 2015 has been used. As a result of the study, it has been determined that Decision Tree algorithm which is one of the Data Mining methods yielded the best result.

Keywords

• Housing Market,Hedonic Pricing,Data Mining

Hanehalkının Konut Kira Talebine Etki Eden Faktörlerin Veri Madenciliği ile İncelenmesi

Abstract

Konut, barınma açısından vazgeçilmez bir araç olmasına rağmen son yıllarda dünyada yaşanan ekonomik, kültürel ve teknolojik değişimlerin bir sonucu olarak konut, barınma temel ihtiyacını karşılama amacı dışında, gelecek için güvence kaynağı, yaşam tarzı ve zenginliğin en önemli göstergelerinden biri olmakla kalmamış toplum içindeki saygınlığın da ifadesi olmuştur. Bu durum konutun heterojen bir mal olmasına ve birçok özelliği içinde barındırmasına neden olmaktadır. Konutun sahip olduğu farklı özellikleri konutun piyasa değeri üzerinde ne derece etkin olduğu mikro iktisat teorisine dayalı hedonik fiyat modeli ile tahmin edilmektedir. Çalışmanın amacı, hanehalkının kiralık konut talebini etkileyen faktörleri Veri Madenciliği süreç ile analiz etmektir. 2015 yılı Hanehalkı Bütçe Anket verilerinin kullanıldığı çalışmada, 2341 hane verisi çalışmaya kaynaklık etmiş ve 49 farklı veri başlığı temel alınmıştır. Çalışma sonucunda, Veri Madenciliği yöntemlerinden Karar Ağacı Algoritmasının en iyi sonuç verdiği tespit edilmiştir.

Keywords

• Konut Piyasası,Hedonik Fiyatlama,Veri Madenciliği,Karar Ağaçları Yöntemi.

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