Multilevel modeling is an increasingly popular technique for analyzing hierarchical data. We consider the problem of predicting a future observable y*j in the jth group of a hierarchical dataset. Three prediction rules are presented and assessed via a Monte Carlo study that extensively covers both the sample size and parameter space. Specifically, the sample size space concerns the various combinations of level level-1 and level-2 sample sizes, while the parameter space concerns different intraclass correlation values. The three prediction rules employ OLS, Prior, and Multilevel estimators for the level-1 coefficients ?j. The multilevel prediction rule performs the best across all design conditions, and the prior prediction rule degrades as the number of groups J increases.