Valuing future shopping malls is essential for informed decision-making by developers and investors. However, conventional methods often rely on subjective appraiser inputs, leading to inconsistent valuations. This study, conducted at KJPP X, introduces a hybrid model combining Multiple Linear Regression (MLR), Analytical Hierarchy Process (AHP), and barycentric ratios to minimize subjectivity and enhance valuation accuracy. Objective and subjective weights were determined using Likert and AHP scoring. The proposed model achieved high reliability, with an R² value of 0.9498 and minimal prediction errors. This framework facilitates accurate rental rate estimation, demonstrating its practical application in determining rental value with the help of a barycentric ratio for a new shopping mall. Despite the model's effectiveness, the study reveals that objective data alone has limited influence on valuations, highlighting the significance of structured expert inputs. The findings underscore the need for standardized, data-driven valuation methods to improve consistency and stakeholder confidence in the commercial real estate sector.