AN INVESTIGATION INTO CONTEMPORARY METHODS OF REAL ESTATE APPRAISAL

Real estate appraisal plays a vital role in economic and financial decision-making, including property taxation, mortgage lending, insurance, and investment planning. However, conventional and emerging property valuation methods face persistent challenges concerning accuracy, interpretability, data availability, and the selection of appropriate evaluation metrics. This study aims to examine the current state of real estate appraisal methodologies, identify key limitations, and highlight opportunities for future research. A literature review was conducted using empirical studies indexed in the Scopus database and published between 2019 and 2023. The selected studies were analyzed to identify methodological trends, particularly the growing application of artificial intelligence and machine learning in property valuation. The findings reveal an increasing adoption of machine learning techniques, which offer considerable potential to improve valuation accuracy and support automated valuation methods and mass appraisal systems. Nevertheless, challenges remain regarding model transparency, data quality and accessibility, and the consistency of performance evaluation. These limitations are particularly relevant to the development of reliable and interpretable valuation models for practical applications. The study contributes to the understanding of recent advances in real estate appraisal and provides directions for future research aimed at developing more accurate, transparent, and robust valuation systems to support decision-making in the real estate, financial, and economic sectors.