An integrated BIM-GIS framework for building quality assessment based on price estimation: Evaluating design and location factors

The quality of buildings is shaped largely by design and planning strategies established in the early stages of a project. Despite growing interest in data-informed decision-making, there still is a lack of integrated frameworks that enable quantitative assessment of building design and location quality before construction. To address this gap, this study proposes a framework that combines building information modeling (BIM), GIS, and machine learning to evaluate quality using building price as a practical, market-aligned proxy. A tool was developed based on this framework to support designers during the early design phase. By incorporating explainable artificial intelligence (XAI) techniques, the tool provides clear, visual feedback on how specific design and location features influence estimated price, helping users explore and refine design alternatives in real time within a BIM environment. This approach introduces a replicable, data-driven method for linking early-stage design choices to measurable outcomes. A case study in Tehran demonstrated the tool's potential to guide decision-making and improve projected market value. Although promising, the current implementation is limited by the scope of the data set and the use of price as a single quality indicator. Future research should expand its application across diverse contexts and incorporate additional quality metrics.