COMPARING THE PRACTICAL DIFFERENCES BETWEEN DECISION TREE AND RANDOM FOREST ALGORITHMS IN ESTIMATING HOUSING PRICES

Abstract

The process of estimating the price of houses is becoming increasingly important in light of the changing economy worldwide, as houses are considered a basic need and a source of investment. This process is aimed at preventing losses, market monitoring, minimizing problems, and arriving at accurate conclusions in the face of complex structures and issues. To achieve this, modern technology introduces the concepts of artificial intelligence and machine learning, which are integrated into all areas of life, to make progress in the process. Although machine learning and the algorithms used in this field have become widespread in recent years, there are still not enough studies on housing pricing. At the same time, people remain unaware of the field of machine learning and its applicability in every sector. Machine learning in general; It expands the data pool and enables new prediction results to be created by making future predictions based on data. Decision Tree Algorithm; In addition to facilitating understanding and interpretation in every field, it can handle multi-output problems and minimize preparation with its easy integration structure. Random Forest Algorithm can prevent the overfitting problem in classification problems and can be applied in both regression and classification problems. The aim of the study is to popularize the use of machine learning algorithms in the real estate sector. This will allow for effective housing price predictions during times of uncertainty and help in selecting the appropriate method by comparing the algorithms. Additionally, this study aims to reduce existing problems using these algorithms. A data set called "California Housing Prices," containing 20.640 samples and eight features, was used in this study. The results of the Decision Tree and Random Forest Algorithms were examined on this data set. Performance evaluation and comparison were made using MSE, RMSE, R2, and MAE metrics. It was observed that the Random Forest Algorithm produced better results and was superior to the Decision Tree Method when predicting house prices.

Keywords

• Machine Learning
• Decision Tree Algorithm
• Random Forest Algorithm

References

1. [1] Louati, A., Lahyani, R., Aldaej, A., Aldumaykhi, A., Otai, S., Price forecasting for real estate using machine learning: a case study on Riyadh city. Concurrency and Computation: Practice and Experience, 34(6), e6748, (2022).
2. [2] Iban, M. C., An explainable model for the mass appraisal of residences: The application of tree-based Machine Learning algorithms and interpretation of value determinants. Habitat International, 128, 102660, (2022).
3. [3] Yı̇ğı̇ter, Ş. Y., et al., Kira sertifikası fiyat değerlerinin makine öğrenmesi metodu ile tahmini. International Journal of Islamic Economics and Finance Studies, 4.3: 74-82 (2018).
4. [4] Acı, M.; Dogansoy, G.A, Demand forecasting for e-retail sector using machine learning and deep learning methods. Journal of the Faculty of Engineering and Architecture of Gazi University, 37.3: 1325-1339 (2022).
5. [5] Barut, Z.; Bilgin, T. T., Konut Fiyatlarının Tahmini için Polinomsal Regresyon ve Yapay Sinir Ağları Yöntemlerinin Uygulamalı Karşılaştırılması. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 27.1: 152-159 (2023).
6. [6] Burhan, H. A., Konut Fiyatları Tahmininde Makine Öğrenmesi Sınıflandırma Algoritmalarının Kullanılması: Kütahya Kent Merkezi Örneği. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi 76: 221-237 (2023).
7. [7] Ozdemir, M.; Yıldız, K.; Büyüktanır, B., Housing price estimation with deep learning: a case study of Sakarya Turkey. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, 9.1: 138-151 (2022).
8. [8] İslamoğlu, E.; Bulut, H., Nevşehir ili konut fiyatlarını etkileyen faktörlerin hedonik fiyat modeli ile incelenmesi, 1st International Symposium of Silk Road Academic Studies, (2018).

9. [9] Keleş, Ş.; Atabeyli, O.C., Konut ve hedonik fiyat bir yapısal eşitlik modeli uygulaması. The International New Issues in SocialSciences, 6.2: 111-128 (2018).
10. [10] Aydemir, E.; Aktürk, C,; Yalçınkaya, M.A., Yapay zekâ ile konut fiyatlarının tahmin edilmesi. Turkish Studies, 15.2: 183-194 (2020).
11. [11] Akay, E.Ç., et al., Türkiye konut fiyat endeksi öngörüsü: arıma, rassal orman ve arıma-rassal orman. Press Academia Procedia, 10.1: 7-11 (2019).
12. [12] Oral, M.; Okatan, E.; Kırbaş, İ., Makine öğrenme yöntemleri kullanarak konut fiyat tahmini üzerine bir çalışma: Madrid örneği. 3 rd International Young Researchers Student Congress, p. 263-272 (2021).
13. [13] Yılmazel, Ö.; Afşar, A.; Yılmazel, S., Konut fiyat tahmininde yapay sinir ağları yönteminin kullanılması. Uluslararası İktisadi ve İdari İncelemeler Dergisi, (20), 285-300 (2018).
14. [14] Gülağız, F.K.; Ekinci, E., Farklı Regresyon Analizi Yöntemleri Kullanılarak Ev Fiyatlarının Tahmini. Conference: International Symposium on Industry, p. 203-207 (2017).
15. [15] Özmaden, M.Ş; Erdal, M., Konut yapılarının maliyet tahmininde kullanılan yöntemlerin performans analizi. Konya Journal of EngineeringSciences, 8.4: 970-985 (2020).
16. [16] Gökalp, Ö. M. Makine öğrenmesi. Gazi Üniversitesi, Gazi BilişimEnstitüsü, Adli Bilişim Bölümü (2022).
17. [17] Pal, Nikhil R., et al., A possibilistic fuzzy c-means clusterin galgorithm. IEEE transactions on fuzzy systems, 13.4: 517-530 (2005).
18. [18] Fan, C.; Cui, Z.; Zhong, X., House prices prediction with machine learning algorithms. Proceedings of the 2018 10th International Conference on Machine Learning and Computing. p. 6-10(2018).
19. [19] Madhuri, CH Raga; Anuradha, G.; Pujitha, M. Vani., House price prediction using regression techniques: A comparative study. In: 2019 International conference on smart structures and systems (ICSSS). IEEE, p. 1-5(2019).
20. [20] Özşahin, M; Yılmaz, K.B; Akcan, S., Mühendislikte Araştırma ve Değerlendirmeler II: Makine öğrenmesi algoritmalari ile ev kirasi tahmini, Gece Kitaplığı Yayınevi, 2022