Asylum is a legal protection granted by a state to individuals who demonstrate a well-founded fear of persecution or who face real risk of being subjected to torture in their country. However, asylum adjudication often depends on the decision maker’s subjective assessment of the applicant’s credibility. To investigate potential sources of bias in asylum adjudication practices researchers have used statistics and machine learning models, finding significant sources of variation with respect to a number of extra-legal variables. In this paper, we analyse an original dataset of Danish asylum decisions from the Refugee Appeals Board to understand the variables that explain Danish Adjudication. We train a number of classifiers and, while all classifiers agree that candidate credibility is the single most important variable, we find that performance and variable importance change significantly depending on whether data imbalance and temporality are taken into account. We discuss the implications of our findings with respect to the theory and practice of predicting and explaining asylum adjudication.