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

Ayşe Yavuz Özalp; Halil Akıncı

doi:10.17714/gumusfenbil.1363531

EN TR

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

Öz

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.

Anahtar Kelimeler

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

Mesken nitelikli gayrimenkul fiyat tahmininde ağaç tabanlı makine öğrenmesi algoritmalarının karşılaştırılması

Öz

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.

Anahtar Kelimeler

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

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Arazi Yönetimi, Coğrafi Bilgi Sistemleri ve Mekansal Veri Modelleme

Bölüm

Araştırma Makalesi

Yazarlar

Ayşe Yavuz Özalp ^*
0000-0002-8297-9034
Türkiye

Halil Akıncı
0000-0002-9957-1692
Türkiye

Yayımlanma Tarihi

15 Mart 2024

Gönderilme Tarihi

20 Eylül 2023

Kabul Tarihi

31 Ekim 2023

Yayımlandığı Sayı

Yıl 2024 Cilt: 14 Sayı: 1

DOI

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

IZ

https://izlik.org/JA97DL87WA

APA

Yavuz Özalp, A., & Akıncı, H. (2024). Comparison of tree-based machine learning algorithms in price prediction of residential real estate. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 14(1), 116-130. https://doi.org/10.17714/gumusfenbil.1363531

AMA

1.Yavuz Özalp A, Akıncı H. Comparison of tree-based machine learning algorithms in price prediction of residential real estate. Gümüşhane Üniversitesi Fen Bilimleri Dergisi. 2024;14(1):116-130. doi:10.17714/gumusfenbil.1363531

Chicago

Yavuz Özalp, Ayşe, ve Halil Akıncı. 2024. “Comparison of tree-based machine learning algorithms in price prediction of residential real estate”. Gümüşhane Üniversitesi Fen Bilimleri Dergisi 14 (1): 116-30. https://doi.org/10.17714/gumusfenbil.1363531.

EndNote

Yavuz Özalp A, Akıncı H (01 Mart 2024) Comparison of tree-based machine learning algorithms in price prediction of residential real estate. Gümüşhane Üniversitesi Fen Bilimleri Dergisi 14 1 116–130.

IEEE

[1]A. Yavuz Özalp ve H. Akıncı, “Comparison of tree-based machine learning algorithms in price prediction of residential real estate”, Gümüşhane Üniversitesi Fen Bilimleri Dergisi, c. 14, sy 1, ss. 116–130, Mar. 2024, doi: 10.17714/gumusfenbil.1363531.

ISNAD

Yavuz Özalp, Ayşe - Akıncı, Halil. “Comparison of tree-based machine learning algorithms in price prediction of residential real estate”. Gümüşhane Üniversitesi Fen Bilimleri Dergisi 14/1 (01 Mart 2024): 116-130. https://doi.org/10.17714/gumusfenbil.1363531.

JAMA

1.Yavuz Özalp A, Akıncı H. Comparison of tree-based machine learning algorithms in price prediction of residential real estate. Gümüşhane Üniversitesi Fen Bilimleri Dergisi. 2024;14:116–130.

MLA

Yavuz Özalp, Ayşe, ve Halil Akıncı. “Comparison of tree-based machine learning algorithms in price prediction of residential real estate”. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, c. 14, sy 1, Mart 2024, ss. 116-30, doi:10.17714/gumusfenbil.1363531.

Vancouver

1.Ayşe Yavuz Özalp, Halil Akıncı. Comparison of tree-based machine learning algorithms in price prediction of residential real estate. Gümüşhane Üniversitesi Fen Bilimleri Dergisi. 01 Mart 2024;14(1):116-30. doi:10.17714/gumusfenbil.1363531

e-ISSN: 2146-538X Yayın Modeli: Sürekli Yayın Atıf Dizinleri: TR Dizin , SCOPUS

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

Öz

Anahtar Kelimeler

Mesken nitelikli gayrimenkul fiyat tahmininde ağaç tabanlı makine öğrenmesi algoritmalarının karşılaştırılması

Öz

Anahtar Kelimeler

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

Kaynak Göster