Knowledge-based FIS and ANFIS models development and comparison for residential real estate valuation
DOI: https://doi.org/10.3846/ijspm.2018.442Abstract
There has been an increasing concern on the development of alternative approaches to overcome the problems and deficiencies that occur during the application of real-estate valuation methods. This study was established to investigate the usability of the expert knowledge based fuzzy logic methodology in determining real-estates values. In addition, valuation with the Adaptive Neuro-Fuzzy Inference System (ANFIS) method provided model comparison. Samples were administered a questionnaire for the parameters planned for these models regarding the parameters that affect real estate values. To make value estimations for the Fuzzy Inference System (FIS) model by using the parameters obtained from the questionnaire analyses, the criteria that produced the best results were acquired from the various criteria alternatives. An algorithm was created and the valuation process for real estate was performed using the FIS in Konya/Turkey. As a result of poll studies the area, age, floor conditions, physical properties and location of the real-estate property were considered as the input variables and the market value as the output variable. The memberships were established with poll analysis and were rule based on expert knowledge. The model structure was formed by using the Mamdani structure in the MATLAB fuzzy toolbox. Model prediction performance was evaluated statistically with the Mean Absolute Percentage Error (MAPE) and a high accuracy of the model results to the market values indicated the reliability of the established model for residential real-estate valuation.
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residential real-estate, valuation, market value, Mamdani, model establishment, fuzzyHow to Cite
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Copyright (c) 2018 The Author(s). Published by Vilnius Gediminas Technical University.
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References
Brondino, N. C. M., & da Silva, A. N. R. (1999). Combining artificial neural networks and GIS for land valuation process. Paper presented at the 6th International Conference on Computers in Urban Planning and Management, 8–11 September 1999. Venezia, Italy.
Din, A., Hoesli, M., & Bender, A. (2001). Environmental variables and real estate prices. Urban Studies, 38(11), 1989-2000. <a href= "https://doi.org/10.1080/004.209.80120080899" target="_blank" rel="noopener"> https://doi.org/10.1080/004.209.80120080899</a>
Fischer, D. (2008). Evolution with teleology: the genetic programming heuristic approach to modeling. Journal of Real Estate Literature, 16(3), 345-362.
Fischer, D., & Lai Pi-Ying, P. (2008). Land price modeling with genetic algorithms and artificial neural network procedures. Proceedings of the 14th Annual Conference of the Pacific Rim Real Estate Society (pp. 1–9), 20-23 January 2008. Kuala Lumpur, Malaysia.
Gonzalez, M. A. S. (2008). Developing mass appraisal models with fuzzy systems. İn T. Kauko & M. d’Amato (Eds.), Mass appraisal methods: an international perspective for property valuers (pp.181-202). Oxford: Wiley-Blackwell. <a href= "https://doi.org/10.1002/978.144.4301021.ch9" target="_blank" rel="noopener"> https://doi.org/10.1002/978.144.4301021.ch9</a>
Heine, K. (2001). Potential application of fuzzy methods in geodetic fields. Proceedings of the First International Symposium on Robust Statistics and Fuzzy Techniques in Geodesy and GIS (pp. 87-93), 12–16 March 2001. Zürich, Switzerland.
Ho, D. W. C., Zhang, P. A., & Xu, J. (2001). Fuzzy wavelet networks for function learning. IEEE Transactions on Fuzzy Systems, 9(1), 200-211. <a href= "https://doi.org/10.1109/91.917126" target="_blank" rel="noopener"> https://doi.org/10.1109/91.917126</a>
IVS. (2005). International Valuation Standards (IVS) (7th ed.). International Valuation Standards Council.
Kontrimas, V., & Verikas, A. (2010). The mass appraisal of the real estate by computational intelligence. Applied Soft Computing, 11(1), 443-448. <a href= "https://doi.org/10.1016/j.asoc.2009.12.003" target="_blank" rel="noopener"> https://doi.org/10.1016/j.asoc.2009.12.003</a>
Kusan, H., Aytekin, O., & Özdemir, I. (2010). The use of fuzzy logic in predicting house selling price. Expert Systems with Applications, 37, 1808-1813. <a href= "https://doi.org/10.1016/j.eswa.2009.07.031" target="_blank" rel="noopener"> https://doi.org/10.1016/j.eswa.2009.07.031</a>
Lewis, O. M., Ware, J. A., & Jenkins, D. H. (2001). Identification of residential property sub-markets using evolutionary and neural computing techniques. Neural Computing & Applications, 10, 108-119. <a href= "https://doi.org/10.1007/s005.210.170003" target="_blank" rel="noopener"> https://doi.org/10.1007/s005.210.170003</a>
Lokshina, I. V., Hammerslag, M. D., & Insinga, R. C. (2003). Applications of artificial intelligence methods for real estate valuation and decision support. Hawaii International Conference on Business, 18–21 June 2003. Honolulu, Hawaii, USA.
Mak, S., Choy, L., & Ho, W. (2010). Quantile regression estimates of Hong Kong real estate prices. Urban Studies, 47(11), 2461-2472. <a href= "https://doi.org/10.1177/004.209.8009359032" target="_blank" rel="noopener"> https://doi.org/10.1177/004.209.8009359032</a>
Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1-13. <a href= "https://doi.org/10.1016/S0020–7373(75)80002–2" target="_blank" rel="noopener"> https://doi.org/10.1016/S0020–7373(75)80002–2</a>
Mert, Z. G., & Yilmaz, S. (2009). Fuzzy modeling approach based on property location quality for grading neighborhood level of family housing units. Expert Systems with Applications, 36, 3603-3613. <a href= "https://doi.org/10.1016/j.eswa.2008.02.023" target="_blank" rel="noopener"> https://doi.org/10.1016/j.eswa.2008.02.023</a>
Pagourtzi, E., Assimakopoulos, V., Hatzichristos, T., & French, N. (2003). Practice briefing real estate appraisal: a review of valuation methods. Journal of Property Investment & Finance, 21(4), 383-401. <a href= "https://doi.org/10.1108/146.357.80310483656" target="_blank" rel="noopener"> https://doi.org/10.1108/146.357.80310483656</a>
Sarip, A. G., & Hafez, M. B. (2015). Fuzzy logic application for house price prediction. International Journal of Property Sciences, 5(1), 24-30.
Sarip, A. G., Hafez, M. B., & Daud, Md. N. (2016). Application of fuzzy regression model for real estate price prediction. Malaysian Journal of Computer Science, 29(1), 15-27. <a href= "https://doi.org/10.22452/mjcs.vol29no1.2" target="_blank" rel="noopener"> https://doi.org/10.22452/mjcs.vol29no1.2</a>
Sen, Z. (2001). Fuzzy logic and modeling principles. Bilge Art Production Pub. Rec. Pap. Tourism Industry Trading Limited Company. Istanbul.
Sygnowski, M., Trawinski, B., & Zgrzywa, A. (2008). An attempt to use a type-2 fuzzy logic system to assist with real estate appraisals. Proceedings of the 1st International Conference on Information Technology (pp. 19-21), 18–21 May 2008. Gdansk, Poland. <a href= "https://doi.org/10.1109/INFTECH.2008.462.1620" target="_blank" rel="noopener"> https://doi.org/10.1109/INFTECH.2008.462.1620</a>
Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, 15(1), 116-132. <a href= "https://doi.org/10.1109/TSMC.1985.631.3399" target="_blank" rel="noopener"> https://doi.org/10.1109/TSMC.1985.631.3399</a>
Yalpir, S. (2007). Bulanık mantık metodolojisi ile taşınmaz değerlenme modelinin geliştirilmesi ve uygulaması. Konya Örneği, PhD Dissertation (in Turkish). Selcuk University, Konya.
Yalpir, S., & Ozkan, G. (2008). The usage of artificial ıntelligence in determining the residential real-estate prices in urban areas and the comparison of valuation methods. Integrating Generations FIG Working Week 2008, 14–19 June 2008. Stockholm, Sweden.
Yalpir, S., & Ozkan, G. (2011). Fuzzy logic methodology and multiple regression for residential real-estates valuation in urban areas. Scientific Research and Essays, 6(12), 2431-2436.
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Copyright (c) 2018 The Author(s). Published by Vilnius Gediminas Technical University.
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