Mass Apprasial With A Machine Learning Algorithm: Random Forest Regression

Yıl 2020, Cilt: 13 Sayı: 3, 301 - 311, 31.07.2020

Sibel Canaz Sevgen , Yeşim Aliefendioğlu

https://doi.org/10.17671/gazibtd.555784

Cited By: 8

Öz

Traditional methods in many areas have been replaced by modern methods known as machine learning with the rapidly developing technology and innovations in science. One of these areas is real estate valuation (appraisal) area. Real estate appraisal can be conducted on a single real estate as well as appraisal of more than one real estate together, which is called as mass appraisal, is possible. In this study, a mass appraisal is performed by a Random Forest Regression method, and the results were evaluated. For this purpose, data of 189 flats expected real value and their 13 variables were collected in Yenimahalle, Ankara. 75% of these data were used as training data and 25% as test data. According to the results, a difference of at minimum 600 TL, maximum 60.000 TL and averagely 25.000 TL were observed between the predicted value by the Random Forest regression and the expected real value. According to these results, random forest regression is a successful method in mass appraisal, and it is observed that valuation with different machine learning methods such as random forest regression has a positive effect on time and labor force comparing with valuation of real estate by traditional methods individually.

Anahtar Kelimeler

Machine Learning, Random Forest Regression, Real Estate Valuation, Mass Apprasial

Kitlesel Değerlemede Makine Öğrenme: Rasgele Orman Regresyonu

Öz

Hızla gelişen teknoloji ve bilimde bulunan yenilikler ile birçok alanda geleneksel yöntemlerin yerini makine öğrenme diye anılan modern yöntemleri almıştır. Bu alanlardan biri ise gayrimenkul değerleme alanıdır. Gayrimenkuller tek başına değerlemesi yapılabileceği gibi kitlesel olarak ta birçok gayrimenkulün bir arada değerlemesinin yapılması mümkündür. Bu çalışmada, popüler bir makine öğrenme tekniği olan Random Forest (Rasgele Orman) Regresyonu yöntemi seçilerek gayrimenkullerin kitlesel değerlemesi yapılmış ve sonuçların gerçek değere yakınlığı incelenmiştir. Bu amaçla, Ankara İli Yenimahalle İlçesinde 189 adet apartman dairesine ait değer ve bu gayrimenkullere ait 13 adet değişken verisi toplanmıştır. Bu verinin, %75’i eğitim verisi ve % 25’i ise test verisi olarak kullanılmıştır. Elde edilen sonuçlara göre, tahmin edilen değer ile olması beklenilen değer arasında en az 600 TL, en fazla 60.000 TL ve ortalama 25.000 TL fark gözlemlenmiştir. Bu sonuçlara göre rasgele orman regresyonunun kitlesel değerlemede başarılı olduğu, geleneksel yöntemlerle gayrimenkul değerlemek yerine rasgele orman regresyonu gibi farklı makine öğrenme yöntemleriyle değerleme yapılmasının zaman ve insan gücü tasarrufu açısından pozitif etkilerinin olacağı ortaya konmuştur.

Anahtar Kelimeler

Makine Öğrenme, Rasgele Orman Regresyonu, Gayrimenkul Değerleme, Kitlesel Değerleme

Kaynakça

• S. Özden, A. Öztürk, “Yapay Sinir Ağları ve Zaman Serileri Yöntemi ile Bir Endüstri Alanının (İvedik OSB) Elektrik Enerjisi İhtiyaç Tahmini.”, Bilişim Teknolojileri Dergisi, 11 (3), 255-261, 2018.
• O. Kaynar H. Arslan, Y. Görmez,. “Işık, Makine Öğrenmesi ve Öznitelik Seçim Yöntemleriyle Saldırı Tespiti”, Bilişim Teknolojileri Dergisi , 11(2) , 175-185 , 2018.
• H. Erdal, T. Yapraklı, "Firma Başarısızlığı Tahminlemesi: Makine Öğrenmesine Dayalı Bir Uygulama". Bilişim Teknolojileri Dergisi 9(1), 2016.
• V. Kontrimas, A. Verikas, “The mass appraisal of the real estate by computational intelligence”, Applied Soft Computing, 11, 443-448, 2011.
• R.A. Borst, “Artificial neural networks: the next modelling/calibration technology for the assessment community”, Property Tax Journal, 10(1), 69-94. 1991.
• D. P. Tay., D. K. Ho, 1992. “Artificial intelligence and the mass appraisal of residential apartments.” Journal of Property Valuation and Investment. 1992.
• W. McCluskey, “Predictive accuracy of machine learning models for the mass appraisal of residential property”, New Zealand Valuers Journal, 16(1), 41-47. 1996.
• I.D. Wilson, S.D. Paris, J.A. Ware, D.H. Jenkins, “Residential property price time series forecasting with neural networks”, Knowledge-Based Systems, 15(5), 335-341. 2002.
• X.J. Ge, G. Runeson, “Modeling property prices using neural network model for Hong Kong”, International Real Estate Review, 7(1), 121-138. 2004.
• G. Özkan, Ş. Yalpır, Ş. O. Uygunol, “An investigation on the price estimation of residable real-estates by using artificial neural network and regression methods”, paper presented at the 12th Applied Stochastic Models and Data Analysis International conference (ASMDA), Crete, May 29-June 1. 2007
• K. C. Lam, C. Y. Yu, K. Y. Lam, “An artificial neural network and entropy model for residential property price forecasting in Hong Kong”, Journal of Property Research, 25(4), 321-342. 2008.
• H. Selim, “Determinants of house prices in Turkey: hedonic regression versus artificial neural network”, Expert Systems with Applications, 36(2), 2843-2852. 2009
• A. G. Musa, O. Daramola, E. A. Owoloko, O. O. Olugbara, “A neural-CBR system for real property valuation”. Journal of Emerging Trends in Computing and Information Sciences, 4(8), 611-622. 2013.
• J.N.M. Tabales, C.J.M Ocerin, F.J.R., Carmona, “Artificial neural networks for predicting real estate prices”, Revista de Metodos Cuantitativos para la Economia y la Empresa, 15, 29-44. 2013.
• S. Ahmed, M. Rahman, S. Islam, “House rent estimation in Dhaka city by multilayer perceptions neural network”, International Journal of U-and E-Service, Science and Technology, 7(4), 287-300. 2014.
• P. Morano, F. Tajani, C. M. Torre, “Artificial intelligence in property valuations: an application of artificial neural networks to housing appraisal”, paper presented at the 11th International Conference on Energy, Environment, Ecosystems and Sustainable Development (EEESD ‘15), Canary Islands, 10.12.2015.
• A. Varma, A. Sarma, S. Doshi, R. Nair, “House Price Prediction Using Machine Learning And Neural Networks”. In 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT, 1936-1939). IEEE. 2018.
• A. Worzala, M. Lenk, A. Silva, “An exploration of neural networks and its application to real estate valuation”, Journal of Real Estate Research, 10(2), 185-201. 1995.
• M. M. Lenk, E. M. Worzala, A. Silva, “High-tech valuation: should artificial neural networks bypass the human valuer?”, Journal of Property Valuation and Investment, 15(1), 8-26. 1997.
• W. J. McCluskey, M. McCord, P. Davis, M. Haran, D. McIlhatton, “Prediction accuracy in mass appraisal: a comparison of modern approaches”, Journal of Property Research, 30(4), 239-265. 2013.
• J. Zurada, A. Levitan, J. Guan. “A Comparison of Regression and Artificial Intelligence Methods in a Mass Appraisal Context”, Journal of Real Estate Research, 33(3), 349– 87, 2011.
• E. Saraç, Yapay Sinir Ağlari Metodu İle Gayrimenkul Değerleme, Yüksek Lisans Tezi, İstanbul Kültür Üniversitesi, 2012.
• A. S. Ravikumar, Real Estate Price Prediction Using Machine Learning. National College of Ireland, 2016.
• M. A. Derinpınar, A. Ç. Aydınoğlu, “Bulanık Mantık ile Coğrafi Bilgi Teknolojilerini Kullanarak Taşınmaz Değerlemesi”, 15. Türkiye Harita Bilimsel ve Teknik Kurultayı, Ankara, 25-28 Mart 2015.
• J. Hong, H. Choi, W. Kim, “A House Price Valuation Based On The Random Forest Approach: The Mass Appraisal Of Residential Property In South Korea” International Journal of Strategic Property Management, 24(3), 140-152, 2020.
• R. B., Abidoye, A. P. C. Chan. “Artificial neural network in property valuation: Application framework and research trend”, Property Management, 35(5), 554–571, 2017.
• B. K. A. Afonso, L. C. Melo, W. D. G. Oliveira, S. B. S. Sousa, L. Berton. “Housing Prices Prediction with a Deep Learning and Random Forest Ensemble”, 2019.
• M. Dellstad, Comparing three machine learning algorithms in the task of appraising commercial real estate Degree Project in Computer Science and Engineering. 2018.
• R. Sawant, Y. Jangid, T. Tiwari, S. Jain, A. Gupta, "Comprehensive Analysis of Housing Price Prediction in Pune Using Multi-Featured Random Forest Approach," 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), 1-5, Pune, India, 2018.
• N. Erdem. “Toplu (Küme) Değerleme Uygulama Örnekleri ve Ülkemiz İçin Öneriler”, TMMOB Harita ve Kadastro Mühendisleri Odası, 16. Türkiye Harita Bilimsel ve Teknik Kurultayı, Ankara, 3-6 Mayıs 2017.
• L. Breiman, “Random forests”, Machine learning, 45(1), 5-32, 2001.
• L. Breiman, “Bagging predictors”, Machine Learning, 26(2), 123-140, 1996.
• İnternet: L. Breiman, A. Cutler, “Random Forest”, http:// www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm, 2005.
• İnternet: Hürriyet Emlak. https://www.hurriyetemlak.com. 15.12.2018.
• Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AeroGRID, IGN, and the GIS User Community. 2018