Reduction of mass appraisal criteria with principal component analysis and integration to GIS

Yıl 2019, Cilt: 4 Sayı: 3, 94 - 105, 01.10.2019

Fatma Bünyan Ünel Sukran Yalpir

https://doi.org/10.26833/ijeg.458430

Cited By: 6

Öz

In real estate, mass appraisal is a very important subject in the valuation of two and more properties. It can be of benefit in a number of fields including taxation, banking transactions, expropriation, etc. The base problem is which criteria to use for mass appraisal. Because the number of criteria and the criteria themselves vary according to people, regions and countries, they are uncertain. They should be optimum in order to save on time, labor and cost. The aim of this study is to reduce the criteria by determining which ones affect the plot value. A survey which was answered by a total of 2,531 participants was conducted in Turkey. Principal Component Analysis (PCA), one of the criteria analysis methods, was applied to the survey data. The number of criteria was reduced to 14 with separation and to 30 according to the results of PCA. But they decreased in the model verification when criteria data for 558 samples were collected in the Konya study area. An index of the neighborhood and locational features of these criteria was created by using GIS. Three models were established using Multiple Regression Analysis (MRA) and the performance of the models was examined. The prediction values and the market value were integrated into the GIS to compare the spatial distributions of plot values.

Anahtar Kelimeler

Mass Appraisal, Principal Component Analysis (PCA), Geographic Information Systems (GIS), index

Kaynakça

• Altunışık, R., Coşkun, R., Bayraktaroğlu, S., & Yıldırım, E. (2010). Sosyal Bilimlerde Araştırma Yöntemleri SPSS Uygulamalı (6. ed.). Sakarya: Sakarya Yayıncılık.
• Amil, T. A. (2018). Determining Of Different Inundated Land Use in Salyan Plain During 2010 The Kura River Flood Through GIS and Remote Sensing Tools. International Journal of Engineering and Geosciences (IJEG), 3(3), 80-86. doi:10.26833/ijeg.412348
• Bender, A., Din, A., Hoesli, M., & Laakso, J. (1999). Environmental quality perceptions of urban commercial real estate. Journal of Property Investment & Finance, 17(3), 280 - 296.
• Cellmer, R., Senetra, A., & Szczepanska, A. (2012). The effect of environmental factors on real estate value. Paper presented at the FIG Working Week 2012, Rome, Italy.
• Çilli, M. (2007). İnsan hareketinin modellenmesi ve benzeşiminde Temel Bileşenler Analizi yönteminin kullanılması. Hacettepe Üniversitesi, Sağlık Bilimleri Enstitüsü, Doktora Tezi, Ankara.
• Çilli, M., & Arıtan, S. (2010). Temel Bileşenler Analizi yardımı ile elde edilen daha az sayıda değişken kullanılarak farklı hızlarda insan koşusunun fourier tabanlı modelinin oluşturulması. Hacettepe J. of Sport Sciences, 21(1), 1-12.
• El-Gohary, M. (2004). Property valuation model effect of traffic noise on property value. Retrieved from
• Farag, A. A., & Elhabian, S. (2009). A Tutorial on Principal Component Analysis. Retrieved from http://dai.fmph.uniba.sk/courses/ml/sl/PCA.pdf
• Fernandez-Martinez, R., Fernandez-Ceniceros, J., SanzGarcia, A., Lostado-Lorza, R., & Martinez-De-PisonAscacibar, F. J. (2011). Dimensionality reduction in a database related with viticulture crops using wrapper techniques. International Journal of Mathematical Models And Methods In Applied Sciences, 5(5), 866- 873.
• Gaborski, R. S. (2010). Principal Components Analysis. Retrieved from http://www.cse.psu.edu/~rcollins/CSE586Spring2010/le ctures/pcaLectureShort_6pp.pdf
• Henry, E. N. N. (2013). From turtle populations to property values: The effects of lakeshore residential development and The Invasive Zebra Mussel. Michigan State University, PhD, Michigan.
• Kauko, T. J. (2002). Modelling the locational determinants of house prices: neural network and value tree approaches. Utrecht University, PhD, Netherlands.
• Kavas, S. (2014). Konut fiyatlarının çok kriterli bir karar destek modeli ile tahmin edilmesi İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi, İstanbul.
• Kryvobokov, M. (2005). Estimating the weights of location attributes with the Analytic Hierarchy Process in Donetsk, Ukraine. Nordic Journal of Surveying and Real Estate Research, 2(2), 5-29.
• Kryvobokov, M. (2006). Mass valuation of urban land in Ukraine: From normative to a market-based approach. Real Estate and Construction Management School of Architecture and The Built Environment Royal Institute of Technology, PhD, Stockholm.
• Kryvobokov, M., & Wilhelmsson, M. (2007). Analysing location attributes with a hedonic model for apartment prices in Donetsk, Ukraine. International Journal of Strategic Property Management, 11, 157-178.
• Kuşan, H., Aytekin, O., & Özdemir, İ. (2010). The use of fuzzy logic in predicting house selling price. Expert Systems with Applications, 37, 1808–1813. doi:10.1016/j.eswa.2009.07.031
• Li, Q. C. (2010). Neighborhood greenspace’s impact on residential property values: understanding the role of spatial effects. Faculty of the USC Graduate School University of Southern California, PhD, California.
• Lin, C. C. (2010). Critical analysis and effectiveness of key parameters in residential property valuations. State University of New York, The Faculty of The Graduate School of The University at Buffalo, PhD, New York.
• Lughofer, E., Trawinski, B., Trawinski, K., Kempa, O., & Lasota, T. (2011). On employing fuzzy modeling algorithms for the valuation of residential premises. Information Sciences, 181, 5123–5142. doi:10.1016/j.ins.2011.07.012
• MathWorks. (2014). İstatistik, Faktör Analizi. Retrieved from http://www.mathworks.com/products/demos/statistics/fa ctorandemo.html
• McCluskey, W. J., & Borst, R. A. (2007). Specifying the effect of location in multivariate valuation models for residential properties: A critical evaluation from the mass appraisal perspective. Property Management, 25(4), 312- 343. doi:10.1108/02637470710775185
• Minitab. (2014). What are the differences between principal components analysis and factor analysis? Retrieved from http://support.minitab.com/enus/minitab/17/topic-library/modelingstatistics/multivariate/principal-components-and-factoranalysis/differences-between-pca-and-factor-analysis/
• Muehlenbachs, L., Spiller, E., & Timmins, C. (2014). The housing market impacts of shale gas development. Retrieved from Washington, New York:
• Orhan, U. (2013). Makine öğrenmesi. Retrieved from http://bmb.cu.edu.tr/uorhan/DersNotu/Ders11.pdf
• Özdemir, A. T. (2010). Erken ventriküler kasılmalarda YSA tabanlı bir sınıflandırıcının FPGA ile gerçekleştirilmesi. Erciyes Üniversitesi, Fen Bilimleri Enstitüsü, Doktora tezi, Kayseri.
• Polat, K. (2008). Biyomedikal sinyallerde veri ön-işleme tekniklerinin medikal teşhiste sınıflama doğruluğuna etkisinin incelenmesi. Selçuk Üniversitesi, Fen Bilimleri Enstitüsü, Doktora Tezi, Konya.
• Sangün, L. (2007). Temel bileşenler analizi, ayırma analizi, kümeleme analizleri ve ekolojik verilere uygulanması üzerine bir araştırma. Çukurova Üniversitesi, Fen Bilimleri Enstitüsü, Doktora tezi, Adana.
• Saraç, E. (2012). Yapay sinir ağları metodu ile gayrimenkul değerleme. İstanbul Kültür Üniversitesi, Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi, İstanbul.
• Schulz, M. A. R. (2003). Valuation of properties and economic models of real estate markets. HumboldtUniversity, Wirtschaftswissenschaftlichen Fakultat, PhD, Berlin.
• Sdino, L., Rosasco, P., Torrieri, F., & Oppio, A. (2018). A Mass Appraisal Model Based on Multi-Criteria Evaluation: an Application to the Property Portfolio of the Bank of Italy. NMP2018, 128 (7), 1-10.
• Semmlow, J. L. (2004). Biosignal and biomedical image processing, MATLAB-Based applications
• Smigielski, E. (2014). Effect of New York State Forests on residential property values. State University of New York, MSc, Syracuse, New York.
• Son, K. (2012). Regression model predicting appraised unit value of land in San Francisco county from number of and distance to public transit stops using GIS. Texas A&M University, PhD, Texas.
• Timm, N. H. (2002). Applied multivariate analysis Retrieved from http://hbanaszak.mjr.uw.edu.pl/TempTxt/Shorts/_XeL3 THpJaw.pdf.
• Unel, F. B., Yalpir, S., & Gulnar, B. (2017). Preference Changes Depending on Age Groups of Criteria Affecting the Real Estate Value. International Journal of Engineering and Geosciences (IJEG), 2(2), 41-51.
• Ünel, F. B. (2017). Taşınmaz Değerleme Kriterlerine Yönelik Coğrafi Veri Modelinin Geliştirilmesi. Selçuk Üniversitesi, Fen Bilimleri Enstitüsü, Doktora Tezi, Konya.
• Yalpir, S., & Unel, F. B. (2017). Use of Spatial Analysis Methods in Land Appraisal; Konya Example. Paper presented at the 5th International Symposium on Innovative Technologies in Engineering and Science (ISITES2017), Baku, Azerbaijan.
• Yalpır, Ş. (2007). Bulanık mantık metodolojisi ile taşınmaz değerleme modelinin geliştirilmesi ve uygulaması: Konya örneği. Selçuk Üniversitesi, Fen Bilimleri Enstitüsü, Doktora Tezi, Konya.
• Yalpır, Ş., & Ünel, F. B. (2016). Türkiye 'de ve Uluslararası çalışmalarda arsa değerlemede kullanılan kriterlerin irdelenmesi ve Faktör Analizi ile azaltımı. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 16(025502), 303‐322. doi:10.5578/fmbd.28134
• Zavadskas, E. K., Bausys, R., Kaklauskas, A., Ubarte, I., Kuzminske, A., & Gudiene, N. (2017). Sustainable market valuation of buildings by the single-valued neutrosophic MAMVA method. Applied Soft Computing, 57, 74–87.
• Zoppi, C., Argiolas, M., & Lai, S. (2015). Factors influencing the value of houses: Estimates for the city of Cagliari, Italy. Land Use Policy, 42, 367–380.