Collaborative Model-driven Software Engineering (CoMDSE) involves multiple engineers working together on modifying domain- specific models. This collaborative effort, however, can lead to conflicts during merges due to differing perspectives and goals among engineers. Solutions primarily focus on resolving conflicts during merges, but these approaches can be cumbersome and time-intensive. Thus, researchers also propose secondary strategies such as change awareness to notify engineers of each other’s model changes mitigating conflicts before they happen. Change awareness strategies, however, often overwhelm engineers with a bulk of change notifications, many of which are irrelevant to their immediate tasks. Additionally, they are required to manually analyze these notifications for conflicts, which adds significant overhead, leading to decreased productivity and increased frustration. Thus, in this paper, we propose a novel approach for conflict-based change awareness. It utilizes an incremental growing operation tree data structure to promptly detect potential conflicts after a model change. It specifically targets conflicts detectable through the analysis of changes in property-value based models, such as syntactic conflicts. The obtained results are utilized to filter corresponding change notifications, which are then sent to the engineers. This empowers engineers to proactively identify and address conflicts before merging their work. Moreover, by filtering out irrelevant or non-critical notifications, engineers can focus their attention on changes that are most relevant to their work, reducing cognitive overload and improving efficiency, as demonstrated by related work. With this approach, we aim to enhance collaboration and productivity in CoMDSE environments. We evaluate the completeness of our approach by applying it to a variety of conflict scenarios, as defined by the EMFCompare completeness tests. We also apply our approach in a feasibility CoMDSE scenario using Visio to test the conflict-based notifications. Lastly, we evaluate our approach’s performance evidencing that it can detect a large number of conflicts in a reasonable time as it detected almost 20K conflicts in less than 20 seconds.