["Transactions in GIS, Volume 30, Issue 5, August 2026. ", "\nABSTRACT\nSelecting a suitable property from available options is a critical challenge for prospective buyers and renters, as each individual prioritizes different criteria—such as noise levels, natural lighting, proximity to workplace or schools, and access to recreational facilities and public services. Accurately evaluating and matching these diverse needs with property characteristics requires advanced analytical tools. The integration of Building Information Modeling (BIM), Geographic Information Systems (GIS), and Multi‐Criteria Decision Analysis (MCDA) provides a robust framework for analyzing both spatial and non‐spatial data, enabling a precise and multidimensional evaluation of property features. BIM offers a rich source of physical and geometric information, while its three‐dimensional nature aligns with the reality of properties. GIS contributes spatial analytical tools to measure environmental and locational attributes. MCDA supports the incorporation of individual preferences by gathering and quantifying personal judgments to select the most suitable property alternative. This study proposes a GIS‐ and BIM‐based Spatial Decision Support System (SDSS) for property evaluation and selection according to user preferences. The proposed framework was implemented on five apartment units in Ingolstadt, Germany, and further tested on 100 residential buildings in Karaj, Iran, to assess generalizability. User preferences were collected from 20 participants using the Best–Worst Method (BWM). The consistency ratio analysis confirmed the reliability of the derived weights (mean ξ = 0.0724). A clustering approach categorized users into three distinct preference groups: pollution‐oriented, geometric/indoor‐oriented, and environmental/outdoor‐oriented. Validation results demonstrated high agreement between algorithmic outputs and user judgments (90% for best selections and 85% for worst selections). Sensitivity analysis revealed that the model remains stable under substantial preference changes, with rank reversal occurring only at approximately 135% perturbation. The system's usability was evaluated using the System Usability Scale (SUS), achieving an overall score of 74.45, which exceeds the average benchmark of 68 and indicates good usability.\n"]