Vol. 1 No. 1 (2021)
Articles

Housing Valuation Model in Samsun, Atakum District with Artificial Neural Networks and Multiple Regression Analysis

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Published 2021-09-30

Keywords

  • Real estate appraisal,
  • Multiple regression analysis,
  • Artificial neural network,
  • Artificial intelligence,
  • Matlab

How to Cite

TABAR, M. E. ., BAŞARA, A. C. ., & ŞİŞMAN, Y. . (2021). Housing Valuation Model in Samsun, Atakum District with Artificial Neural Networks and Multiple Regression Analysis. Advanced Geomatics, 1(1), 27–32. Retrieved from https://publish.mersin.edu.tr/index.php/geomatics/article/view/44

Copyright (c) 2021 Advanced Geomatics

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Abstract

Valuation, in its simplest form, is the determination of the amount that a property will be processed at a certain date. Valuation can be done for many purposes. These; can be listed as buying and selling, transfer, tax assessment, expropriation, inheritance distribution, investment, financing and credit. There are various methods of valuation. These methods are examined under 3 main groups as traditional, statistical and modern valuation methods. The aim of the article is to provide an overview of regression analysis, one of the statistical valuation methods, and artificial neural networks, one of the modern valuation methods, and to compare the accuracy values. Matlab software was used for artificial neural network modeling and Minitab software was used for regression analysis. The accuracies of the obtained values were determined by the average absolute percent error (MAPE) formula. 

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References

  1. Açlar A & Çağdaş V (2002). Taşınmaz (gayrimenkul) değerlemesi. TMMOB Harita ve Kadastro Mühendisleri Odası, Ankara.
  2. Atik M, Köse Y, Yilmaz B & Erbaş M (2015). Şehirlerin İlerleme Yönlerinin Gayrimenkul Değerleri Üzerindeki Etkisinin Ölçülmesi. Çankırı Karatekin Üniversitesi, 5, 443-458.
  3. Brown R K (1965). Real estate economics: an introduction to urban land use: Houghton Mifflin.
  4. Canan S (2006). Yapay sinir ağları ile GPS destekli navigasyon sistemi. Selçuk Üniversitesi Fen Bilimleri Enstitüsü.
  5. Chatterjee S & Hadi A S (2015). Regression analysis by example: John Wiley & Sons.
  6. Durmuş B (2016). Konut Fiyatlarını Etkileyen Parametrelerin Çoklu Regresyon Analizi Yöntemiyle İrdelenmesi Ve Kentsel Dönüşüme Katkıları. Fen Bilimleri Enstitüsü.
  7. Efe E, Bek Y & Şahin M (2000). SPSS’te Çözümleri ile İstatistik Yöntemler II, Kahramanmaraş Sütçü İmam Üniversitesi Rektörlüğü Yayın No: 73, Ders Kitapları Yayın No: 9, KS Ü. Basımevi, Kahramanmaraş, 214.
  8. Fernández-Cabán P L, Masters F J & Phillips B M (2018). Predicting roof pressures on a low-rise structure from freestream turbulence using artificial neural networks. Frontiers in Built Environment, 4, 68.
  9. Graupe D (2013). Principles of artificial neural networks (Vol. 7): World Scientific.
  10. Güngör A & Sevindir H C (2013). Isparta İlindeki Atmosferde Bulunan Kükürt dioksit (SO2) ve Partikül Madde (PM) Konsantrasyonunun Çoklu Doğrusal Regresyon Yöntemi İle Modellenmesi. Journal of Natural and Applied Science, 17(1), 95-108.
  11. Karacabey A & Gökgöz F (2012). Çoklu Regresyon Modeli. Anova Tablosu, Matrislerle Regresyon Çözümlemesi, Regresyon Katsayılarının Yorumu.
  12. Livingstone D J (2008). Artificial neural networks: methods and applications: Springer.
  13. Özdamar N (2004). 4822 Sayılı Yasa İle Değişik 4077 Sayılı Yasa’da Tanımlanan Konut Nedir. Türkiye Barolar Birliği Dergisi, Yıl, 17, 317-331.
  14. Öztürk K & Şahin M E (2018). Yapay Sinir Ağları ve Yapay Zekâ’ya Genel Bir Bakış. Takvim-i Vekayî, 6(2), 25-36.
  15. Pagourtzi E, Assimakopoulos V, Hatzichristos T & French, N (2003). Real estate appraisal: a review of valuation methods. Journal of Property Investment & Finance.
  16. Ring A A & Dasso J J (1977). Real estate principles and practices: Prentice Hall.
  17. Sisman Y (2014). The optimization of GPS positioning using response surface methodology. Arabian Journal of Geosciences, 7(3), 1223-1231.
  18. Sisman Y, Elevli S & Sisman A (2014). A statistical analysis of GPS positioning using experimental design. Acta Geodaetica et Geophysica, 49(3), 343-355.
  19. Tabar M E & Şişman Y (2020). Bulanık Mantık ile Arsa Değerleme Modelinin Oluşturulması. Türkiye Arazi Yönetimi Dergisi, 2(1), 18-24.
  20. Yegnanarayana B (2009). Artificial neural networks: PHI Learning Pvt. Ltd.