Information Quality Assessment (IQA) is an important, but often overlooked aspect, of the Building Information Modeling (BIM) process. Models with information quality issues, such as incomplete and incorrect information, may cause rework during the design process if detected early. Otherwise errors may propagate downstream, leading to significant cost consequences to stakeholders in the Architecture, Engineering and Construction (AEC) industries. Current approaches of IQA show significant efforts on addressing information completeness issues but are limited when addressing information correctness. Greater understanding of the features of these quality issues is necessary to begin to detect these issues. This paper addresses this problem by proposing an IQA framework that incorporates three identified features: IQ Dimensions, Arity and Data Characteristics. From this framework, 3 classes of algorithms are further defined to detect these features. A validation test was conducted against current modeling guidelines used for BIM quality assurance in both architectural and structural disciplines. The results indicated more than 80% of the rules were able to be categorized using the framework. Guidelines that were not categorized included those that were overly ambiguous, or did not directly involve BIM. The outcome of the paper will enable BIM managers to ensure a fit-for-purpose, quality assured model that can reduce rework, and engender greater trust in the model creation process.