Market Efficiency Analysis in Determining Property Prices in Urban Areas

Abstract

This study examines market efficiency in determining property prices in urban areas using a qualitative approach combined with modern analysis techniques. The research background is based on the transformation of urbanization and digitalization, which increases the need for transparency and accuracy of property market data. The methods used include primary data collection through in-depth interviews and observations, as well as secondary data analysis from previous studies and research documents. Thematic analysis and machine learning integration are used to uncover price fluctuation patterns and develop adaptive prediction models. The results show that the developed model improves the accuracy of predictions and is closer to the reality of the market, while also identifying the key factors that affect market efficiency. The practical implications of this research provide a basis for investors, developers, and regulators to improve information systems and strategic decision-making in the property sector. The proposed methodological innovations also open up opportunities for further research in property market analysis in the digital era.