Preference based Reinforcement Learning (PbRL) removes the need to hand specify a reward function by learning one from preference feedback over policy behaviors. Current approaches to PbRL do not address the credit assignment problem inherent in determining which parts of a behavior most contributed to a preference resulting in data intensive approaches and subpar reward models. We address such limitations by introducing a credit assignment strategy (PRIOR) that uses a forward dynamics world model to approximate state importance within a trajectory and then guides rewards to be proportional to state importance through an auxiliary predicted return redistribution objective. Incorporating state importance into reward learning improves the speed of policy learning, overall policy performance, and reward recovery on both locomotion and manipulation tasks. For example, PRIOR achieves 80% success rate with half the amount of data compared to baselines. The performance gains and our ablations demonstrate the benefits even a simple credit assignment strategy can have on reward learning and that state importance in forward dynamics prediction is a strong proxy for a state's contribution to a preference decision.