The purpose of this paper is to describe prediction efficiencies of various suboptimal predictors relative to the efficient (kriging) minimum mean square error predictor in spatial models containing spatial lags in both the dependent variable and the error term. Suboptimal predictors have been suggested in the literature. One reason is that they are suggested on an intuitive level; another is that they are computationally less tedious. We describe these relative efficiencies theoretically, as well as empirically. Among other things our results suggest that one of the intuitively suggestive suboptimal predictors is especially inefficient.