The Space–Time Autoregressive (STAR) model is one of the most widely used models to represent the dynamics of a certain variable recorded at several locations at the same time, capturing both their temporal and spatial relationships. Its advantages are often discussed in terms of parsimony with respect to space-time VAR structures because it considers a single coefficient for each time and spatial lag for the full time span and the full location set. This hypothesis can be very strong; the presence of groups of locations with similar dynamics makes it more realistic. In this work we add a certain degree of flexibility to the STAR model, providing the possibility for coefficients to vary in groups of locations, proposing a new class of flexible STAR models. Such groups are detected by means of a clustering algorithm. The new class or model is compared to the classical STAR and the space-time VAR by simulation experiments and a practical application.