Mesken nitelikli gayrimenkul fiyat tahmininde ağaç tabanlı makine öğrenmesi algoritmalarının karşılaştırılması

Year 2024, Volume: 14 Issue: 1, 116 - 130, 15.03.2024

Ayşe Yavuz Özalp , Halil Akıncı

https://doi.org/10.17714/gumusfenbil.1363531

Abstract

Mesken nitelikli gayrimenkuller, güvenli ve karlı bir yatırım aracı olarak kabul edilirken aynı zamanda temel insan hakkı olan barınma ihtiyacını da karşılamaktadır. Konutun hem değerini etkileyen hem de yere, kişiye ve zamana göre değişen çok sayıda parametrenin varlığı değerleme sürecini zorlaştırmaktadır. Bu bakımdan, doğru ve gerçekçi fiyat tahmini başta alıcılar olmak üzere sektörün tüm paydaşları için büyük önem taşımaktadır. Klasik matematiksel modelleme yöntemlerine alternatif olan makine öğrenme algoritmaları, fiyat tahmin modellerinin etkenliğini ve başarısını artırma bağlamında önemli olanaklar sunmaktadır. Dolayısıyla bu çalışmanın amacı, Artvin Kent Merkezinde ağaç temelli makine öğrenme algoritmalarından Rastgele Orman (RF), Gradyan Artırma Makinaları (GBM), AdaBoost ve Aşırı Gradyan Artırma (XGBoost) yöntemlerinin konut değerlemede uygulanabilirlikleri ve tahmin performanslarının araştırılmasıdır. Çalışmanın sonucunda, XGBoost ve RF algoritmaları, konut değerini tahmin etmede Korelasyon Katsayısı (Correlation Coefficients- R2), Ortalama Mutlak Hata (Mean Absolute Error - MAE) ve Karesel Ortalama hata (Root Mean Squared Error- RMSE) ölçütlerinin tümünde (sırasıyla 0.705 ve 0.701) en iyi performansı göstermiştir. Böylece, başta XGBoost ve RF olmak üzere makine öğrenme algoritmalarının konut değerlemede az veri durumunda bile yeterli performans gösterdiği, veri setinin genişletilmesiyle birlikte başarı performansının daha da artacağı söylenebilir.

Keywords

AdaBoost, GBM, RF, Mesken nitelikli gayrimenkul, Değerleme, XGBoost

Comparison of tree-based machine learning algorithms in price prediction of residential real estate

Abstract

Residential real estate is regarded as a safe and profitable investment tool while also meeting the basic human right to housing. The fact that there exists a large number of parameters both affecting the value of a house and varying based on place, person, and time makes the valuation process difficult. In this regard, accurate and realistic price prediction is critical for all stakeholders, particularly purchasers. Machine learning algorithms as an alternative to classical mathematical modeling methods offer great prospects for boosting the efficacy and success rate of price estimating models. Therefore, the purpose of this study is to investigate the applicability and prediction performance of the tree-based ML algorithms -Random Forest (RF), Gradient Boosting Machine (GBM), AdaBoost, and Extreme Gradient Boosting (XGBoost)- in house valuation for Artvin City Center. As a result of the study, the XGBoost and RF algorithms performed the best in estimating house value (0.705 and 0.701, respectively) as determined by the Correlation Coefficients (R2), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) metrics. Thus, it can be said that ML algorithms, particularly XGBoost and RF, perform satisfactorily in residential real estate appraisal even with modest amounts of data and that the success rate grows as the amount of data increases.

Keywords

AdaBoost, GBM, RF, Residential real estate, Valuation, XGBoost

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