Makine Öğrenimi İle Mülk Değerlemesinde Aykırı Değer Tespit Yöntemlerinin Etkisi

Year 2023, Volume: 5 Issue: 1, 9 - 20, 18.07.2023

Cihan Çılgın , Yılmaz Gökşen , Hadi Gökçen

https://doi.org/10.47899/ijss.1270433

Cited By: 2

Abstract

Konut alanlar ve satanlar kadar bir yatırım aracı olarak konut üzerinden yatırımda bulunanalar için de konut fiyatının gerçekçi ve en yüksek doğrulukta tahmin edilmesi gerekmektedir. Tahmin modelinin, piyasanın altında yatan temellerin en uygun temsili olması gerektiği unutulmamalıdır. Aksi takdirde konut değerlemesinde yapılacak hata emlak vergisinin tutarsız ve sağlıksız artırılması veya azaltılması, bazı gruplar lehine aşırı kazanç veya kayıp ve yatırımcılar ile potansiyel konut sahiplerini olumsuz etkilemesi gibi bazı istenmeyen sonuçlara neden olacaktır. Tam bu noktada günümüzde veri odaklı konut değerleme yaklaşımları yüksek doğrulukta ve önyargısız tahminler oluşturmada daha sık tercih edilmektedir. Fakat makine öğrenmesi yaklaşımları ile gerçekleştirilen modellerin tutarlılığı, kesinliği ve doğruluğu veri kalitesi ile doğrudan bağlantılıdır. Bu noktada bu çalışmada elde edilen geniş bir veri seti ile konut değerlemede özellikle aykırı değer tespitinin tahmin performansı üzerine etkileri araştırılmaktadır. Bu amaçla 70.771 konut verisi ve 283 adet değişkene sahip hetorejen bir veri seti ile 4 farklı aykırı değer tespiti yöntemi 3 farklı makine öğrenmesi yaklaşımı ile test edilmiştir. Elde edilen ampirik bul gular farklı aykırı değer tespiti yaklaşımlarının kullanılmasının tahmin performansını farklı aralıklarda artığını ortaya koymaktadır. En iyi aykırı değer tespiti yaklaşımı ile ortalama model performansında % 6,97’lik bir artışla birlikte Rastgele Orman için bu performans artışı % 21,6’lık yüksek bir oranda gerçekleşmiştir.

Keywords

Mülk Değerleme, Makine Öğrenmesi, Aykırı Değer Tespiti, Konut Fiyat Tahmini, Veri Ön işleme

Thanks

Bu makalede bilimsel araştırma ve yayın etiği ilkelerine uyulmuştur. Bu makale Cihan Çılgın tarafından Gazi Üniversitesi Bilişim Enstitüsü Yönetim Bilişim Sistemleri Anabilim Dalı'nda gerçekleştirilen doktora tezinden üretilmiştir.

The Effect of Outlier Detection Methods in Real Estate Valuation with Machine Learning

Abstract

For those who invest in real estate as an investment tool, as well as those who buy and sell real estate, the price of real estate should be predicted realistically and with the highest accuracy. It should be noted that the predict model should be the most appropriate representation of the underlying fundamentals of the market. Otherwise, the mistake to be made in the real estate valuation will cause some undesirable results such as inconsistent and unhealthy increase or decrease of the property tax, excessive gains or losses in favor of some groups, and adverse effects on investors and potential real estate owners. At this point, data-driven real estate valuation approaches are preferred more frequently to create highly accurate and unbiased estimates. However, the consistency, precision and accuracy of the models realized with machine learning approaches are directly related to the data quality. At this point, the effects of outlier detection on prediction performance in real estate valuation are investigated with a large data set obtained in this study. For this purpose, a heterogeneous data set with 70.771 real estate data and 283 variables, 4 different outlier detection methods were tested with 3 different machine learning approaches. The empirical findings reveal that the use of different outlier detection approaches increases the prediction performance in different ranges. With the best outlier detection approach, this performance increase was at a high 21,6% for Random Forest, with a 6,97% increase in average model performance.

Keywords

Real Estate Valuation, Machine Learning, Outlier Detection, House Price Precition, Data Preprocessing

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