Estimation of Flood Risk on a residential mortgages portfolio

Abstract

In the context of the rapid changes that have occurred in recent years, characterized by veritable 'black swans' such as the COVID-19 pandemic and extreme weather events that are occurring with increasing frequency, the issue of climate change has come into the focus of banking regulators and supervisors. Therefore banking institutions, if they are subject to the Single Supervisory Mechanism, have been called upon to develop (and, subsequently, to integrate into their business practices) methodologies for the identification, quantification and management of such risks, mainly under the profiles of: - Transition Risk, associated with policies undertaken by governments to foster climate change mitigation and adaptation; - Physical Risk, associated with the occurrence of extreme climatic events and its impact on the bank's assets. This paper analyzes one of the most significant hazards within the Physical Risk domain, which is Flood Risk. The measurement is focused on the prospective evolution of the flood events on a portfolio of mortgages secured by residential properties. The impact of this risk driver is subsequently reflected through the movement of appropriate transmission mechanisms on the LGD and PD parameters relating to the exposures in the scope. Finally, the effect on loan adjustments is provided, by recalculating the expected losses that result from the stressed projections. The flood risk projection is executed on a long-term timeframe, developing over 3 climate scenarios up to 2050. The choice of this hazard is due to its relevance in terms of frequency of events and harmfulness, a relevance that is confirmed by its inclusion in both the top-down climate stress testing exercises carried out by the ECB and in the bottom-up climate stress testing exercise promoted by the ECB itself in 2022 and carried out by the SSM Banks. A comprehensive simulation framework, structured as follows, is then presented: - a macro-climate scenario simulation engine; - the downscaling of these scenarios to obtain localized climate effects on individual properties; - the transmission of these effects into a depreciation formula for the individual property; - the LGD stress associated with the devaluation of the collateral property, and the PD stress that goes along with it, obtained by correlation.