Abstract:
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.
