Kitlesel Değerlemede Makine Öğrenme: Rasgele Orman Regresyonu

Hızla gelişen teknoloji ve bilimde bulunan yenilikler ile birçok alanda geleneksel yöntemlerin yerini makine öğrenme diye anılan modern yöntemleri almıştır. Bu alanlardan biri ise gayrimenkul değerleme alanıdır. Gayrimenkuller tek başına değerlemesi yapılabileceği gibi kitlesel olarak ta birçok gayrimenkulün bir arada değerlemesinin yapılması mümkündür. Bu çalışmada, popüler bir makine öğrenme tekniği olan Random Forest (Rasgele Orman) Regresyonu yöntemi seçilerek gayrimenkullerin kitlesel değerlemesi yapılmış ve sonuçların gerçek değere yakınlığı incelenmiştir. Bu amaçla, Ankara İli Yenimahalle İlçesinde 189 adet apartman dairesine ait değer ve bu gayrimenkullere ait 13 adet değişken verisi toplanmıştır. Bu verinin, %75’i eğitim verisi ve % 25’i ise test verisi olarak kullanılmıştır. Elde edilen sonuçlara göre, tahmin edilen değer ile olması beklenilen değer arasında en az 600 TL, en fazla 60.000 TL ve ortalama 25.000 TL fark gözlemlenmiştir. Bu sonuçlara göre rasgele orman regresyonunun kitlesel değerlemede başarılı olduğu, geleneksel yöntemlerle gayrimenkul değerlemek yerine rasgele orman regresyonu gibi farklı makine öğrenme yöntemleriyle değerleme yapılmasının zaman ve insan gücü tasarrufu açısından pozitif etkilerinin olacağı ortaya konmuştur.

Mass Apprasial With A Machine Learning Algorithm: Random Forest Regression

Traditional methods in many areas have been replaced by modern methods known as machine learning with the rapidly developing technology and innovations in science. One of these areas is real estate valuation (appraisal) area. Real estate appraisal can be conducted on a single real estate as well as appraisal of more than one real estate together, which is called as mass appraisal, is possible. In this study, a mass appraisal is performed by a Random Forest Regression method, and the results were evaluated. For this purpose, data of 189 flats expected real value and their 13 variables were collected in Yenimahalle, Ankara. 75% of these data were used as training data and 25% as test data. According to the results, a difference of at minimum 600 TL, maximum 60.000 TL and averagely 25.000 TL were observed between the predicted value by the Random Forest regression and the expected real value. According to these results, random forest regression is a successful method in mass appraisal, and it is observed that valuation with different machine learning methods such as random forest regression has a positive effect on time and labor force comparing with valuation of real estate by traditional methods individually.

Keywords:

Machine Learning, Random Forest Regression Real Estate Valuation, Mass Apprasial,

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