AI-ENABLED TURNOVER RENT ANALYTICS FOR RETAIL REAL ESTATE IN SAUDI ARABIA: IMPROVING REVENUE ASSURANCE AND TENANT PERFORMANCE UNDER VISION 2030

The rapid transformation of Saudi Arabias retail real estate sector under Vision 2030 has increased the need for intelligent, data-driven approaches to improve revenue management and operational efficiency. Turnover rent management, which depends on accurate tenant sales reporting and performance evaluation, remains a complex process due to fragmented data sources, delayed reporting, and challenges in identifying revenue inconsistencies. This research proposes an Artificial Intelligence (AI)-enabled turnover rent analytics framework designed to enhance revenue assurance and tenant performance evaluation in large-scale retail real estate environments. The proposed framework integrates tenant sales data, lease contract information, historical revenue patterns, and machine learning techniques to enable predictive analysis, anomaly detection, and strategic decision-making. The study explores how AI-driven analytics can support shopping mall operators in optimizing lease management, reducing revenue leakage, improving tenant relationships, and strengthening financial transparency. The research contributes to the emerging field of PropTech by presenting an intelligent model suitable for Saudi Arabias retail ecosystem and aligned with Vision 2030 objectives for digital transformation, innovation, and sustainable economic development.