The Fed’s Climate Scenario Analysis: A Brief Outlook on US Real Estate Market

Introduction

In early-May 2024, The Federal Reserve Board released findings of the 2023 pilot climate scenario analysis exercise, a first of its kind, highlighting concerning data on and limited assessment capacity of transition and (especially) physical climate risk exposures of financial portfolios including corporate loans, commercial and residential real estate. As the US real estate market as well as its investors are increasingly at risk of interconnected climate disasters, it is crucial to examine what these results signal in terms of understanding of actual climate-related financial risks associated with real estate portfolios at banking level, and a potential shift in appetite for improving climate risk assessment and transition of real estate portfolios from an investors’ perspective. Without further due, let’s dive into this.

Key Facts about the Fed’s Climate Scenario Analysis Results and Limits

Source: Federal Reserve Board

Results from the 2023 pilot climate scenario analysis (CSA) exercise – involving large US 6 banks – revealed that:

• Participants utilised climate scenario analysis to evaluate the robustness of their business models against various climate scenarios, and to identify potential weaknesses over short and long-term timeframes.

• Participants employed diverse strategies to build detailed physical and transition risk scenarios for the pilot CSA exercise, and to convert these scenarios into estimates of climate-adjusted credit risk parameters. The variations in approach were largely influenced by participants’ business models, risk perceptions, data accessibility, and previous involvement in climate scenario analysis exercises in foreign jurisdictions.

• Majority participants depended on existing credit risk models to assess the effects of physical and transition risks on their portfolios, assuming that historical correlations between model inputs and outputs would persist as the climate and economic structure evolve.

• Participants encountered significant data and modelling obstacles in calculating climate-related financial risks – mainly regarding availability, quality, viability. For instance, they pointed out a lack of comprehensive and consistent data on building features, insurance coverage, and counterparties’ strategies to manage climate-related risks. In many instances, participants turned to external vendors to address data and modelling deficiencies.

• Participants indicated that a better comprehension and monitoring of indirect impacts (such as disruptions to local economies) and chronic risks (e.g. sea level rise) are crucial for managing climate-related financial risks.

• Participants underscored the significant role of insurance in reducing the risks of climate change for consumers, businesses, and banks. They emphasized the need to track changes within the insurance industry, including alterations in insurance costs over time, and the effects of these changes on consumers and businesses in specific markets and segments. However, it is equally important to note that results from the first-ever conducted stress test by US insurance companies show that the industry could reportedly lose billions of dollars from transition risks, limiting their role in terms of risk transfer – for instance, the anticipated losses for the examined bonds (associated with coal, oil and gas, power, and automotive industries) within the portfolios of insurers are substantial under all scenarios. Furthermore, these losses escalate significantly if the transition is delayed.

• Participants pinpointed key design decisions that significantly influenced the insights derived from the exercise. These included decisions related to the scope of the shocks, scenario severity, the exercise’s starting point, insurance assumptions, and balance sheet assumptions.

• Participants suggested that climate-related risks are highly uncertain and difficult to quantify. The uncertainty surrounding the timing and magnitude of climate-related risks made it challenging for participants to determine the best way to integrate these risks into their risk management frameworks on a routine basis.

• Although not the main focus of the pilot CSA exercise, participants’ estimates of climate-adjusted credit risk parameters, such as the probability of default (PD), demonstrated significant variability in impact across sectors, regions, and counterparties.

Key Takeaway

The results of the climate scenario analysis have several implications:

1. Robustness Evaluation: Using climate scenario analysis can help banks understand how their real estate assets might perform under different climate scenarios, to identify potential vulnerabilities and inform strategic decision-making. However, there are challenges associated with the complexity and uncertainty involved in predicting future climate scenarios and their impact on business models. It requires more sophisticated models and a deeper understanding of both climate science and business operations.

2. Risk Assessment: The construction of detailed physical and transition risk scenarios provides insights into how climate change might impact property values and rental income – for instance, properties in areas prone to flooding or extreme heat might see a decrease in value or tenant demand. In this context however, the challenge lies in the diversity of approaches to constructing risk scenarios – which leads to inconsistencies in the results, making it difficult to compare and aggregate risks across different businesses or sectors.

3. Data and Modelling Challenges: The lack of comprehensive and consistent data related to building characteristics and insurance coverage can make it difficult to accurately assess climate-related risks. This could lead to underestimation of these risks and potential financial losses. On the one hand, the lack of such data on various factors leads to inaccuracies in risk assessment. On the other hand, the models used are based on historical data and may not accurately capture future risks as the climate and economic structure evolve.

4. Indirect Impacts and Chronic Risks: Understanding and monitoring indirect impacts and chronic risks is crucial. These factors can have a significant impact on the real estate market, affecting property values and rental income. But the challenge here is the difficulty in predicting and monitoring these risks due to their complex and long-term nature.

5. Role of Insurance: Insurance plays a significant role in mitigating the risks of climate change. Changes in insurance costs and coverage can have a direct impact on the profitability of real estate investments. In this case however, the industry itself is facing substantial losses from transition risks. This is likely to limit its capacity to provide adequate coverage, not to mention that the yearly insurance policy adjustment (especially for residential real estate) could cause even greater uncertainty around its future contribution to climate mitigation.

6. Design Choices in Risk Assessment: The choices made in the risk assessment process, such as the scope of the shocks and the severity of the scenarios, can significantly influence the insights gained. These choices need to be carefully considered to ensure a realistic assessment of climate-related risks. In fact, the challenge is that the insights derived from the exercise are significantly influenced by various design decisions. These decisions are prone to introduce biases and uncertainties into the results.

7. Uncertainty of Climate-Related Risks: The high level of uncertainty associated with climate-related risks makes it challenging to incorporate these risks into regular risk management frameworks. However, ignoring these risks could lead to significant financial losses in the future.

8. Variability in Impact: The impact of climate change on real estate assets can vary significantly across sectors, regions, and counterparties. Understanding this variability can help banks develop more effective risk management strategies. But it is difficult to develop effective risk management strategies in this case, as the understanding of this variability is still at an early stage.

Conclusion

The climate scenario analysis reveals key insights and challenges for banks and investors in the US real estate market. It underscores the importance of robustness evaluation, risk assessment, data and modelling, understanding of indirect impacts and chronic risks, the role of insurance, design choices in risk assessment, uncertainty of climate-related risks, and variability in impact. However, each area presents its own set of challenges, from predicting future climate scenarios and constructing risk scenarios, to data availability, predicting and monitoring indirect impacts and chronic risks, potential losses faced by the insurance industry, biases introduced by design decisions, uncertainty of climate-related risks, and difficulty in managing variability in impact. Despite these challenges, the analysis provides valuable insights that can help improve climate risk assessment and transition strategies, potentially signalling a shift in appetite for improving climate risk assessment and transition of real estate portfolios from an investors’ perspective.