The goal of this paper is to explore how different modeling approaches for constructing function structure models and different levels of model completion affect the ability to make inferences (reason) on the resulting information within the respective models. Specifically, the function structure models are used to predict market prices of products, predictions that are then compared based on their accuracy and precision. This work is based on previous studies on understanding how function modeling and the use of topological information from design graphs can be used to predict information with historical training. It was found that forward chaining was the least favorable chaining type irrespective of the level of completion, whereas the backward-chaining models performed relatively better across all completion levels. Given the poor performance of the nucleation models at the highest level of completion, future research must be directed toward understanding and employing the methods yielding the most accuracy. Moreover, the results from this simulation-based study can be used to develop modeling guidelines for designers or students, when constructing function models.