Enhancing Real Estate Valuation in Kazakhstan: Integrating Machine Learning and Adaptive Neuro-Fuzzy Inference System for Improved Precision
Barlybayev A. Ongalov N. Sharipbay A. Matkarimov B.
October 2024Multidisciplinary Digital Publishing Institute (MDPI)
Applied Sciences (Switzerland)
2024#14Issue 20
The concept of fair value, defined by the valuation of assets and liabilities at their current market worth, remains central to the International Financial Reporting Standards (IFRS) and has persisted despite critiques intensified by the 2008 financial crisis. This valuation method continues to be prevalent under both IFRS and the US Generally Accepted Accounting Principles (GAAP). The adoption of IFRS has notably enhanced the role of accounting in information analysis, vital for owners who prioritize both secure accounting practices and reliable data for strategic management decisions. Real estate, a significant business asset, has long been a focal point in accounting discussions, prompting extensive research into the applicability and effectiveness of various accounting standards. These investigations assess the adaptability of standards based on property type, utility, and valuation techniques. However, the challenge of accurately determining the fair value of real estate remains unresolved, signifying its importance not only in the corporate manufacturing realm but also among development companies striving to manage property values efficiently. This study addresses the challenge of accurately determining the fair market value of real estate in Kazakhstan, leveraging a multi-methodological approach that encompasses statistical models, regression analysis, data visualization, neural networks, and particularly, an Adaptive Neuro-Fuzzy Inference System (ANFIS). The integration of these diverse methodologies not only enhances the robustness of real estate valuation but also introduces new insights into effective asset management. The findings suggest that ANFIS provides superior precision in real estate pricing, demonstrating its potential as a valuable tool for strategic management and investment decision-making.
ANFIS , neural network , real estate , real estate valuation criteria , real estate value forecasting , regression learner
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Faculty of Information Technologies, L.N. Gumilyov Eurasian National University, Astana, 010008, Kazakhstan
Higher School of Information Technology and Engineering, Astana International University, Astana, 010008, Kazakhstan
Faculty of Information Technologies
Higher School of Information Technology and Engineering
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