This paper presents and assesses a framework for monitoring vulnerabilities related to the residential real estate sector, which can be easily employed for policy purposes. The framework provides intuitive and transparent early warning signals through a composite vulnerability measure, which aggregates indicators in a model-free way across three dimensions of real estate sector vulnerabilities (i.e. valuation, household indebtedness and the bank credit cycle). Our vulnerability measure proves to be a significant predictor of historical real estate crises, with a better forecasting performance than the majority of advantageously in-sample calibrated model-based measures.

We analyze competition and risk management at central counterparties (CCPs) using a granular transaction-level dataset, and find that CCPs decrease collateral in response to lower collateral at their competitors, an effect that becomes stronger as the correlation between positions increases. To interpret our findings, we derive a model in which collateral is driven by risk and CCP competition. Our results are consistent with the model and suggest that a single monopolistic CCP would require more collateral. We also show that amid the substantial increase in collateral during the Covid-19 pandemic, the probability of a margin breach did not significantly change.