Scope and limitations
This document describes a straightforward approach to transition risk scenario analysis for credit risk in mortgage portfolios. The aim of the presentation is to give a set of concrete tools that can be used at an institutional level, to clearly identify the direct transition risks for mortgages.
In addition, we hope to guide the general discussion around climate risks by given a concrete example of how to analyse transition risk for a specific portfolio.
As the focus is on individual banks and institutions, we only consider direct effects from transition-induced policy action. Indirect effects with economy-wide impact, which in turn can affect financial institutions (e.g. labour market composition), are left to transition risk scenarios by supervising entities.
Our research shows that transition risks for mortgage primarily affects collateral value, i.e. loan-to-value (LTV), and are less likely to be the direct root cause of credit losses. Therefore, this guide focusses on assessing impact on risk weights.
Our starting point is a risk scenario of increasing CO2 prices, which can represent a range of transition risks. This can then be transformed into an increase in energy costs, based on the energy composition, which leads to user costs of owning the building based on the energy efficiency. These higher costs will, in turn, affect collateral value, which eventually increases risk weights. The approach can be collapsed into four steps, outlined on the right. As a fifth step, we recommend to consider the robustness of the analysis, as assumptions made along the way will have large impacts on the obtained result.
This document has two tracks. First, we describe the methodology, followed immediately by a hands-on illustration which takes the average EU mortgage portfolio as an example.
Copyright © Energy Efficient Mortgages Initiative
The project DeliverEEM has received funding from the European Union’s LIFE 2023 programme under grant agreement No.101167431. The EeMAP, EeDaPP, EeMMIP projects have received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements No. 746205, No. 784979 and No. 894117 respectively
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