Respondent covariates can make hierarchical Bayes choice models look better in sample while predicting entirely new respondents worse. We use a controlled stress test to show how covariates can induce severe overfit, where that overfit appears within the hierarchical model, and how covariate-level regularization can mitigate it. Applied cases show that regularization need not simply flatten meaningful respondent differences. We offer practical guidance for alleviating the downsides of respondent covariates in HB estimation in practice.