Locally Weighted KNN-Based Fuzzy Regression for Property Valuation under Market Uncertainty

Authors

  • Hazmira Yozza Department of Mathematics, Universitas Andalas, Indonesia
  • Riswan Department of Mathematics, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris
  • Dr. Azah Department of Mathematics, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris
  • Izzati Department Mathematics and Science Data, Andalas University
  • Ridho Department Mathematics and Science Data, Andalas University

DOI:

https://doi.org/10.25077/jmua.15.2.163-178.2026

Keywords:

house price, the modified Cheng and Lee k-nearest neighbor fuzzy regression, possibilistic fuzzy regression, predictive performance, triangular fuzzy number

Abstract

Accurate evaluation of house valuation is crucial. Misestimation of house prices creates serious consequences for a variety of stakeholders. House prices can be modeled as a function of their constituent attributes. House prices are fuzzy due to negotiation and unpredictable market conditions.  This study aims to develop rules to predict triangular fuzzy numbers of price for a given new house by implementing locally weighted KNN-based fuzzy regression, and to compare its performance with possibilistic fuzzy regression.  The dataset used is the house valuation dataset. Data is examined using the modified Cheng and Lee k-nearest neighbor fuzzy regression and Tanaka’s possibilistic fuzzy regression. It is found that the modified Cheng and Lee k-nearest neighbor fuzzy regression outperforms possibilistic fuzzy regression in predicting the triangular fuzzy number of the house prices. The best performance is achieved when data is trained using the modified Cheng and Lee k-nearest neighbor fuzzy regression using:  = 29 nearest neighbors, Minkowski distance with exponent parameter  = 1.6 and an unequal weighting scheme with r = 1.

Author Biographies

Hazmira Yozza, Department of Mathematics, Universitas Andalas, Indonesia

Scopus ID = 57208426438

Riswan, Department of Mathematics, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris

 

Sinta ID : 6039764. Scopus ID : 35995174900

Izzati, Department Mathematics and Science Data, Andalas University

Sinta ID 6176711

References

[1] Chen, C., Ma, X., Zhang, X., 2024, Empirical Study on Real Estate Mass Appraisal Based on Dynamic Neural Networks, Buildings, 14(7), 2199.

https://doi.org/10.3390/buildings14072199

[2] Erciyes, A. H., 2025,. Learning The Value Of Place: Machine Learning Models for Mass Valuation, Buildings, 15(15), 2773, https://doi.org/10.3390/buildings15152773

[3] Jafary, P., Shojaei, D., Rajabifard, A., Ngo, T. D., 2024, Automating Property Valuation at The Macro Scale of Suburban Level: A Multi-Step Method Based on Spatial Imputation Techniques, Machine Learning and Deep Learning, Habitat International, 148, 103075, https://doi.org/10.1016/j.habitatint.2024.103075

[4] Karanikolas, N., 2025, Artificial Intelligence and Real Estate Valuation, Information, 16(12), 1049, https://doi.org/10.3390/info16121049

[5] Ota, A., 2024, Variation in Property Valuations Conducted by Artificial Intelligence Services, Equilibrium Economics, 1(3),13, https://doi.org/10.3390/2813-8090/1/3/13

[6] Jiang, E.X., Zhang, A.L., 2025, Collateral Value Uncertainty and Mortgage Credit Provision, Journal of Financial Economics, Vol. 169 (104054). https://doi.org/10.1016/j.jfineco.2025.104054.

[7] El Jaouhari, A., Samadhiya, A., Kumar, A., Šešplaukis, A., Raslanas, S., 2024, Mapping The Landscape: A Systematic Literature Review on Automated Valuation Models and Strategic Applications in Real Estate. International Journal of Strategic Property Management, 28(5): 286–301, https://doi.org/10.3846/ijspm.2024.22251

[8] Jaroszewicz, J., Horynek, H., 2024, Aggregated Housing Price Predictions with No Information About Structural Attributes—Hedonic Models: Linear Regression and a Machine Learning Approach. Land, 13(11), 1881. https://doi.org/10.3390/land13111881

[9] Nor, M. I., Hussein, S. N., 2025, Modeling Residential Property Prices in Emerging Climate-Responsive Urban Markets: A Hybrid Modeling Framework for Baidoa City-Somalia. Frontiers in Built Environment, 11, 1615229. https://doi.org/10.3389/fbuil.2025.1615229

[10] Rey-Blanco, D., Zofío, J. L., González-Arias, J., 2024, Improving Hedonic Housing Price Models by Integrating Optimal Accessibility Indices Into Regression and Random Forest Analyses. Expert Systems with Applications, 235, 121059,

https://doi.org/10.1016/j.eswa.2023.121059

[11] Wan, H., Roy Chowdhury, P. K., Yoon, J., Bhaduri, P., Srikrishnan, V., Judi, D., Daniel, B., 2025, Explaining Drivers of Housing Prices with Nonlinear Hedonic Regressions. Machine Learning with Applications, 21, 100707. https://doi.org /10.1016/j.mlwa.2025.100707

[12] Wang, Z., Wang, Y., Xia, X., Chen, S., Jiang, W., 2025, How Does Built Environment Influence Housing Prices in Large-Scale Areas? An Interpretable Machine Learning Method by Considering Multi-Dimensional Accessibility. ISPRS International Journal of Geo-Information, 14(11), 436. https://doi.org/10.3390/ijgi14110436

[13] Dubois, D., Prade, H., 1980. Fuzzy Sets and Systems: Theory and Applications: Mathematics in Science & Engineering. Academic Press, New York.

[14] Zadeh, L.A., 1965. Fuzzy Sets. Information and Control, 8: 338–353.

[15] Chaira, T., 2019, Fuzzy set and its extension. the intuitionistic fuzzy set. Wiley

[16] Zimmermann, H.-J., 2001, Fuzzy Set Theory and its Application 4th edition. Springer Sciences+Business Media, New York.

[17] Bector, C.R., Chandra, S., 2005, Fuzzy Mathematical Programming and Fuzzy Matrix Games. Studies in Fuzziness and Soft Computing. Springer.

[18] Tanaka, H., Uejima, S., Asai, K., 1982, Linear Regression Analysis with Fuzzy Model. IEEE Trans. Syst. Man Cyb, 12(6): 903–907

[19] Tanaka, H., 1987, Fuzzy Data Analysis by Possibilistic Linear Models. Fuzzy Sets and Systems, 24:363–375.

[20] Tanaka, H., Watada, J., 1988, Possibilistic Linear Systems and Their Application to the Linear Regression Model, Fuzzy Sets and Systems, 27:275–289.

[21] Sakawa, M., Yano, H., 1992, Multiobjective Fuzzy Linear Regression Analysis for Fuzzy Input–Output Data. Fuzzy Sets and Systems 47:173–181.

[22] Hojati, M., Bector, C.R., Smimou, K., 2005, A Simple Method for Computation of Fuzzy Linear Regression. European Journal of Operational Research, 166:172-184.

https://doi.org/10.1016/j.ejor.2004.02.013

[23] Kohli, S., Godwin, G.T., Urolagin, S., 2021, Sales Prediction Using Linear and KNN Regression. In Patnaik, S., Yang, X., and Sethi, I.K. Advances in Machine Learning and Computational Intelligence. Springer, Singapore. https://doi.org/10.1007/978-981-15-5243-4_29

[24] Song, Y., Liang, J., Lu, J., Zhao, X., 2017, An Efficient Instance Selection Algorithm for k Nearest Neighbor Regression. Neurocomputing, 251: 26–34. https://doi.org/10.1016/j.neucom.2017.04.018

[25] Kumbure, M.M., Luukka, P., Collan, M., 2020. A New Fuzzy k-Nearest Neighbor Classifier Based on the Bonferroni Mean. Pattern Recognition Letters, 140:172-178. https://doi.org/10.1016/j.patrec.2020.10.005.

[26] Fathabadi, A., Seyed Morteza Seyedian, S.M., Malekian, A., 2022. Comparison Bayesian, k-Nearest Neighbor, and Gaussian Process Regression Methods for Quantifying Uncertainty of Suspended Sediment Concentration Prediction. Science of The Total Environment, 818, 151760, https://doi.org/10.1016/j.scitotenv.2021.151760.

[27] Cheng, C., Lee, E. 1999. Nonparametric Fuzzy Regression—K-NN and Kernel Smoothing Techniques. Computers & Mathematics with Applications, 38(3-4):239-251. https://doi.org/10.1016/S0898-1221(99)00198-4

[28] Yozza, H., 2026. The Integrated KNN Regression in Improvement of Fuzzy Regression Performance (Unpublished doctoral dissertation). Universiti Pendidikan Sultan Idris.

[29] Kim, B., & Bishu, R. R. (1998). Evaluation of fuzzy linear regression models by comparing membership functions. Fuzzy Sets and Systems, 100(1-3):343-352. https://doi.org/10.1016/S0165-0114(97)00100-0

[30] Yeh, I. (2018). Real Estate Valuation [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5J30W.

[31] Rahman, H.M., Arbaiy, N., Wen C. C., Efendi, R., 2019, Autoregressive Modeling with Error Percentage Spread based Triangular Fuzzy Number, International Journal of Recent Technology and Engineering (IJRTE) 8(252). https://doi.org/10.35940/ijrte.B1007.0782S219

Downloads

Published

30-04-2026

Issue

Vol. 15 No. 2 (2026)

Section

Articles

License

Copyright (c) 2026 Departemen Matematika dan Sains Data FMIPA UNAND

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All articles published in Jurnal Matematika UNAND (JMUA) are open access and licensed under the Creative Commons Attribution-ShareAlike (CC BY-SA) license. This ensures that the content is freely available to all users and can be shared and adapted, provided appropriate credit is given and any adaptations are distributed under the same license.

Copyright Holder

The copyright of all articles published in Jurnal Matematika UNAND is held by the Departemen Matematika dan Sains Data, Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA), Universitas Andalas (UNAND). This applies to all published versions, including the HTML and PDF formats of the articles.

Author Rights

While the Departemen Matematika dan Sains Data FMIPA UNAND holds the copyright for all published content, authors retain important rights under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA). This license grants authors and users the following rights:

  • Reuse: Authors can reuse and distribute their work for any lawful purpose, including sharing on personal websites, institutional repositories, or in subsequent publications.
  • Attribution and Adaptation: Authors and others may remix, adapt, and build upon the published work for any purpose, even commercially, as long as proper credit is given to the original authors, and any derivative works are distributed under the same CC BY-SA license.

Creative Commons License (CC BY-SA)

Under the terms of the CC BY-SA license, users are free to:

  • Share: Copy and redistribute the material in any medium or format.
  • Adapt: Remix, transform, and build upon the material for any purpose, even commercially.

However, the following conditions apply:

  • Attribution: Users must give appropriate credit to the original author(s) and Departemen Matematika dan Sains Data FMIPA UNAND, provide a link to the license, and indicate if changes were made. Attribution must not imply endorsement by the author or the journal.
  • ShareAlike: If users remix, transform, or build upon the material, they must distribute their contributions under the same license as the original.

For more information about the CC BY-SA license, please visit the Creative Commons website.

Third-Party Content

If authors include third-party material (such as figures, tables, or images) that is not covered by a Creative Commons license, they must obtain the necessary permissions for reuse and provide proper attribution. Authors are required to ensure that any third-party content complies with open-access licensing requirements or includes permissions for redistribution under similar terms.

Copyright and Licensing Information Display

The copyright and licensing terms will be clearly displayed on each article's landing page, as well as within the full-text versions (HTML and PDF) of all published articles.

No "All Rights Reserved"

As an open-access journal, JMUA does not use "All Rights Reserved" policies. Instead, the CC BY-SA license ensures that the works remain accessible and reusable for a wide audience while still protecting both the authors' and the copyright holder's rights.

Â