For bank's share- and stakeholders a borrower's default can lead to signifcant costs. An appropriate credit risk model is thus needed to ensure that timely enough steps can be taken to avoid failure of rms. A prominent way to model credit risk is survival time analysis which accounts for censored data. This study considers the Austrian hotel sector taking weather, market and macro data into account. As there is a significant difference between rms entering the sample as restructuring cases and rms which are healthy at that time two models are estimated. The final models include macro and weather data and feature an inverted-U shaped estimated hazard rate. This is in-line with the literature and intuition.