Summary. Auxiliary variables that are associated with both key survey variables and response propensity are important for post-survey non-response adjustments, but rare. Interviewer observations on sample units and linked auxiliary variables from commercially available household databases are promising candidates, but these variables are prone to error. The assumption of missingness at random that underlies standard weighting or imputation adjustments is thus violated when missingness depends on the true values of these variables, leading to biased survey estimates. The paper applies pattern–mixture model estimators to this problem, analysing data from a survey in Germany that links commercial data to a national sample.