Large models of complex dynamic systems can be evaluated efficiently using model order reduction methods. Many techniques, for example the iterative rational Krylov algorithm (IRKA), rely on a set of expansion points chosen before the reduction procedure. The number and location of the expansion points has a major impact on the quality of the resulting reduced model and the convergence of the algorithm. Based on the system's geometry and material, the number of modes in a certain frequency range can be computed using wave equations. This mode count allows the choice of both a reasonable size for the reduced model as well as a reasonable distribution of initial expansion points, which improves the convergence of IRKA. Using the mode count in a specific frequency range, a reduced model approximating the full model only in this frequency range can be generated.