Properties of the poststratification (PS) estimator of variance are evaluated in simulated sampling and 252 settings (3 populations × 4 levels of stratification × 3 sample sizes × 7 methods of stratification) of endogenous poststratification (EPS). Poststratification was done on the basis of predictions from a linear or a nonlinear model fitted to the sample to be stratified. Empirical variances obtained with regression estimators (REG) were generally significantly smaller than EPS variances. The efficiency of a stratification method depends on the population but only marginally on the number of strata or sample size. Optimization of strata boundaries (three methods) leads to a slightly higher empirical variance and a concomitant and mostly significant underestimation of the expected variance. EPS with regression was not more efficient than REG alone. A practical calibration of the biased estimates of variance does not seem possible. For the purpose of map-updating and map-consistent statistics, a nonoptimized EPS holds practical advantages over the regression approach.