Repeated neuropsychological assessments are common with older adults, and the determination of clinically significant change across time is an important issue. Regression-based prediction formulas have been utilized with other patient and healthy control samples to predict follow-up test performance based on initial performance and demographic variables. Comparisons between predicted and observed follow-up performances can assist clinicians in making the determination of change in the individual patient. The current study developed regression-based prediction equations for the twelve subtests of the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) in a sample of 223 community dwelling older adults. All algorithms included both initial test performances and demographic variables. These algorithms were then validated on a separate elderly sample (n = 222). Minimal differences were present between Observed and Predicted follow-up scores in the Validation sample, suggesting that the prediction formulas would be useful for practitioners who assess older adults. A case example is presented that utilizes the formulas.