Companies increasingly rely on Model-Based Systems Engineering to develop Cyber-Physical Systems such as cars, aircraft, or medical devices. The quality of engineering model artifacts is key to efficient collaboration in systems engineering with multi-tier supply chains. Ensuring model artifact quality and comprehensibility for practitioners is challenging. Manual reviews are time- and cost-intensive and subject to bias, whereas existing automated methods based on syntactical rules and model metrics are limited in scope. The paper presents work towards swift quality feedback to system engineers during modeling. The concept allows domain and project-specific context and is applicable to industry-size model artifacts. We implement a data-driven estimation that combines automated model metric extraction with expert quality assessments. We leverage the system model version history from an open-source miniature automotive demonstrator. We assess the model versions' comprehensibility and showcase a semi-automated pipeline to initiate a model quality estimator. We achieve an average accuracy of 0.94 with a random forest approach on our test data.