Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
In this paper, we show that policymakers can distinguish between good and bad credit booms with high accuracy and they can do so in real time. Evidence from 17 countries over nearly 150 years of modern financial history shows that credit booms that are accompanied by house price booms and a rising loan‐to‐deposit ratio are much more likely to end in a systemic banking crisis than other credit booms. We evaluate the predictive accuracy for different classification models and show that characteristics observed in real time contain valuable information for sorting the data into good and bad booms.