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Post-straticatio n is used to improve the precision of survey estimators when categorical auxiliary information is available from external sources. In natu- ral resource surveys, such information may be obtained from remote sensing data classied into categories and displayed as maps. These maps may be based on clas- sication models tted to the sample data. Such \endogenous post-straticatio n" violates the standard assumptions that observations are classied without error into post-strata, and post-stratum population counts are known. Properties of the endogenous post-straticatio n estimator (EPSE) are derived for the case of sample-tted nonparametric models, with particular emphasis on monotone regres- sion models. Asymptotic properties of the nonparametric EPSE are investigated under a superpopulation model framework. Simulation experiments illustrate the practical eects of rst tting a nonparametric model to survey data before post- stratifying.