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

Ayşe Yavuz Özalp; Halil Akıncı

Makale detayları

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

Yazarlar:

Ayşe Yavuz Özalp | Halil Akıncı

Yayıncı:

Gumushane University

Ana Başlıklar:

Arazi Yönetimi | Coğrafi Bilgi Sistemleri ve Mekansal Veri Modelleme

Dergi:

Gümüşhane Üniversitesi Fen Bilimleri Dergisi Vol.14 Issue 1, pp.116-130

Belge Tipi:

Makale

Yayın Tarihi:

3/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001313140

pISSN:

2146-538X

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 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.

Makale detayları

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

Yazarlar:

Ayşe Yavuz Özalp | Halil Akıncı

Yayıncı:

Gumushane University

Ana Başlıklar:

Arazi Yönetimi | Coğrafi Bilgi Sistemleri ve Mekansal Veri Modelleme

Dergi:

Gümüşhane Üniversitesi Fen Bilimleri Dergisi Vol.14 Issue 1, pp.116-130

Belge Tipi:

Makale

Yayın Tarihi:

3/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001313140

pISSN:

2146-538X

AdaBoost

GBM

RF

Residential real estate

Valuation

XGBoostAdaBoost

GBM

RF

Mesken nitelikli gayrimenkul

Değerleme

XGBoost

Sayı

Makale detayları

Yazarlar: Ayşe Yavuz Özalp | Halil Akıncı

Yayıncı: Gumushane University

Ana Başlıklar: Arazi Yönetimi | Coğrafi Bilgi Sistemleri ve Mekansal Veri Modelleme

Dergi: Gümüşhane Üniversitesi Fen Bilimleri Dergisi Vol.14 Issue 1, pp.116-130

Belge Tipi: Makale

Yayın Tarihi: 3/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001313140

pISSN: 2146-538X

Sayı

Yazarlar:

Ayşe Yavuz Özalp | Halil Akıncı

Yayıncı:

Gumushane University

Ana Başlıklar:

Arazi Yönetimi | Coğrafi Bilgi Sistemleri ve Mekansal Veri Modelleme

Dergi:

Gümüşhane Üniversitesi Fen Bilimleri Dergisi Vol.14 Issue 1, pp.116-130

Belge Tipi:

Makale

Yayın Tarihi:

3/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001313140

pISSN:

2146-538X