CoreLogic climate models help one of the nation’s leading banks quantify their portfolio’s climate risk
As the specter of climate change looms large, the world braces for unprecedented challenges. In the world of real estate, one of those challenges will be the effects of natural catastrophes on property portfolios, homeowners, and communities.
For one of the nation’s leading banks, the consequences of climate change have the potential to rack up billions of dollars in losses from natural catastrophes. The bank approached CoreLogic to use our climate risk models to quantify this risk and the mitigating impacts of hazard insurance on the bank’s loan portfolio.
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While some homeowners will seek safer areas to call home, insurers and lenders are not always so mobile and may be left footing the bill for increasing impacts from natural catastrophes. This stark reality underscores the urgent need to amplify knowledge around climate threats to support proactive measures that can help mitigate the devastating impacts of natural catastrophes. Real estate portfolio management can span solutions from property risk mitigation to climate risk insurance. Still, the question remains: Which are the best solutions for protecting portfolios from these climatic disasters while building resiliency?
This question was posed by leaders at the bank who wondered how their portfolio exposure would fluctuate if these increasingly severe catastrophes caused collateral damage and borrowers began to default.
Turning Climate Risk Analytics Stress Tests Into Future Plans for Portfolio Resilience
To ensure the safety and security of financial institutions, federal agencies are implementing climate-related financial risk policies. The Federal Reserve Board is currently analyzing the initial assessment of how the six largest U.S. banks measure and manage exposure to climate risk, with results due later this year. To this end, the agency is collaborating with the Office of the Comptroller of the Currency and Federal Deposit Insurance Corporation to prepare a high-level framework “to support financial institutions’ efforts to incorporate climate-related financial risks into financial institutions’ risk management frameworks” through “the safe and sound management of exposures to climate-related financial risks.”
With regulatory bodies moving to encourage lenders and banks to incorporate climate change mitigation criteria into their operations, lenders now have a window of opportunity to get ahead of the game and work with CoreLogic to understand the physical and economic threats that future natural hazards will pose to real estate portfolios.
Those who start now will have time to construct climate-resilient real estate portfolios and adjust their business strategies to accommodate future lending environments.
Managing climate-related financial risk to loan portfolios is not a simple undertaking, but not understanding the exposures of climate disasters and preparing accordingly is a risk in itself.
Quantifying Climate Risks: How Climate Risk Modeling For Banks Helps Protect Real Estate Portfolios
Banks face potential risks associated with a changing environment, including significant exposure due to borrower defaults and collateral damage caused by natural disasters. To make informed business decisions, it is crucial to model climate risks to understand and quantify the impact to residential real estate portfolios.
In this case, the bank was keen to understand how insured perils like wildfires and hurricanes, as well as underinsured perils like earthquakes and floods, would affect its portfolio. This lender had a broad portfolio across the United States and needed sophisticated climate risk data modeling that incorporated multiple perils.
The bank was looking for an extensive dataset of property characteristics: valuation and replacement costs, peril models, and climate change models that incorporated a full range of hazard intensities, geographic ranges and probabilities of occurrence considering both the historical record and the best-available scientific insights.
To accomplish this analysis, the bank asked CoreLogic to leverage its best-in-class property data and RQE® (Risk Quantification & Engineering) models to help understand risk and stabilize its real estate portfolio.
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Designing the Study
CoreLogic designed models using its vast property characteristics data and the most recent climate science to capture the full conceivable range of catastrophic events, considering intensity, frequency, and geographic distribution. Utilizing CoreLogic’s Discovery Platform, a spatial data and analytics platform, the bank’s portfolio data was enriched with CoreLogic’s CLIP™ system, which leverages professional-grade, granular data to pinpoint a property’s exact location and attributes.
Data scientists then used these insights in modeling risk for the properties in RQE.
Using these powerful tools allowed the bank to quickly glean insights from the complex modeling and analysis of natural hazard risk on its loan portfolio. After CoreLogic data scientists evaluated the portfolio against all the perils, they created stress tests to reveal the resiliency of the bank’s portfolio when faced with these natural perils.
How Does Climate Risk Modeling Impact Lending Portfolios?
The answer to this is, “it depends.”
Loan characteristics, the features of a property and its environs, the possible mitigating factor of hazard insurance, and the nature of the catastrophic phenomena compose the complex cocktail that drives risk. Furthermore, damage to a mortgaged property — be it from climate change impacts, natural hazards, or negligence — does not automatically imply loss to the lender.
Damage to a property is often absorbed by the property owner or the owner’s hazard insurance, and lenders will only sustain loss if the damage significantly exceeds the owner’s equity in the property. The level of damage, hazard insurance coverage, and the amount of equity the mortgage holder possesses cumulatively determine the potential loss to the lender.
CoreLogic considered each of these inputs when providing the bank with a thorough answer on how climate change will affect the mortgage risk of the lender’s portfolio, which consists of residential mortgage, first lien, second lien, construction, home equity line of credit, and construction loans.
CoreLogic studied the impact of six natural disaster perils: earthquakes; hurricanes, including wind and storm surge; inland flooding; severe convective storms; wildfires; and winter storms. They combined this research with a database of property characteristics, representing the reconstruction cost values and structure types for approximately 172 million U.S. residential properties. CoreLogic then compared these foundational findings to the bank’s residential real estate loan portfolio to estimate the damage these perils can levy on the collateral properties and the impact on potential loan default.
The Insurance Variable
The bank received remarkable insights from this in-depth climate risk analysis.
The study highlighted the fact that the perils presented varying risks to the bank. For example, the analysis showed that the risk resulting from winter storms and severe convective storms were modest – the effect on the portfolio analyzed presented a 1/500 risk to the bank of less than $5 million. On the other hand, in cases where the presence or absence of hazard insurance had the potential to mitigate or intensify losses, the losses were far more pronounced.
The following are examples of the stress test scenarios performed, one where hazard insurance is considered as a mitigating factor — hurricane — and one where it is not — earthquake.
Example 1: Hurricane Stress Test Scenario
This scenario consisted of a single hypothetical Category 4 hurricane that made landfall in a metropolitan area of Florida and caused damage across a large area of the state, resulting in a total of $144.5 million in damage to collateral and $15 million in modeled loss to the bank.
Modeled damage occurred in 643 postal codes with the modeled loan balance totaling $2.8 billion within those postal codes. Modeled damage to collateral was $144.5 million.
As with all perils in this study, hazard insurance had the potential to play a significant role in mitigating risk to borrower and bank alike. There is a possibility of damage to a property from storm surge (coastal flooding), hurricane winds, and inland flooding from the hurricane, and there is a complex interaction between the agents of damage to property, insurance recoveries, and net impacts to the bank.
The hurricane stress test scenario provided a view into the expected behavior of the loan portfolio subject to Florida hurricanes. There were large areas of mild to moderate damage with insurance mitigating the net impact to borrower and bank with small areas of extreme damage and resultant bank losses.
Example 2: Earthquake Stress Test Scenario
This scenario consisted of a simulated year with three earthquakes in California and the Pacific Northwest causing damage to the collateral portfolio, two of which resulted in non-zero modeled bank losses. The primary driver of credit loss to the bank was a magnitude 7.24 earthquake.
The distribution of modeled loan losses is complicated. Earthquakes can result in damage to the built environment across a large geographic area. Damage is impacted by numerous physical features including distance from the rupture, local site conditions, and the depth and magnitude of the earthquake rupture. Financial risk to the bank is a function of the collateral damage and the financial characteristics of the impacted loans. Modeled loss is a function of a property’s location, structure, earthquake severity, and financial/loan characteristics.
The earthquake scenario resulted in modeled damage in 621 postal codes across two states, with a modeled loan balance within those post codes totaling nearly $15.7 billion. Modeled damage to collateral was nearly $3 billion, significantly higher than modeled damage for the hurricane scenario (Table 1).
|
Modeled Damage |
|||
| Metric | Hurricane (Florida) | Earthquake (California) | |
| Total Reconstruction Cost Value ($B) | $4 | $47 | |
| Total Modeled Damage to Collateral ($B) | $0.1 | $3 | |
| Total Modeled Loss to the Bank ($M) | $15 | $60 | |
| Total Modeled Loan Balance in impacted* postal codes ($B) | $2.8 | $15.7 | |
Table 1: Showing the difference in modeled damage between two of the perils modeled for the bank
* Impacted postal codes are postal codes where there was some modeled damage to collateral in the bank’s portfolio.
The earthquake test scenario resulted in significantly higher modeled impacts to the bank than the other perils. This was primarily because hazard insurance was not considered as mitigating risk either to borrower or the bank due to the low penetration of residential earthquake insurance in the western U.S.
One the biggest takeaways for the bank was understanding the complexity of assessing financial risk due to climate disasters.
Large, damaging events don’t necessarily drive large losses to the bank; conversely, some events causing relatively little damage to collateral can be significantly impactful. And each peril presents its own challenges. Loan characteristics, the features of the property at risk and its environs, the possible mitigating factor of hazard insurance, and the nature of the catastrophic phenomenon drive risks in a complex fashion.
What Makes The Most Difference in Mitigating Real Estate Climate Risk Impacts?
Insurance.
The hurricane stress test scenario demonstrated that even if large areas suffer mild to moderate hurricane damage, widespread insurance mitigates the net impact to both borrowers and the bank. Note that there there were still some small areas of extreme damage that resulted in losses for the bank.
Conversely, the low penetration of earthquake insurance in the western U.S. demonstrated that when hazard insurance does not mitigate risk, relatively little damage to collateral can still significantly impact a portfolio’s bottom line. The results results demonstrate the need for adequate insurance coverage in the areas of greatest climate risk to mitigate financial risk, especially considering increasing climate disasters.
Banks can begin to understand their exposure to natural hazards and mitigate risks to their portfolios using CoreLogic’s climate models and Climate Risk Analytics. These solutions offer a comprehensive view of physical risk that combines hyper-local property data with financial information to estimate and help mitigate the impact and cost of future catastrophes. Knowing which threats present the highest risks to portfolios will help banks make informed decisions to protect and preserve them.
About CoreLogic
CoreLogic (NYSE: CLGX) is a leading global property information, analytics, and data-enabled services provider. The company’s combined data from public, contributory, and proprietary sources include over 3.5 billion records spanning more than 40 years, providing detailed coverage of property, mortgages and other encumbrances, consumer credit, tenancy, location, hazard risk and related performance information. The markets CoreLogic serves include real estate and mortgage finance, insurance, capital markets, and the public sector. CoreLogic delivers value to clients through unique data, analytics, workflow technology, advisory and managed services. Clients rely on CoreLogic to help identify and manage growth opportunities, improve performance, and mitigate risk. Headquartered in Irvine, CA, CoreLogic operates in North America, Western Europe, and Asia Pacific. For more information, please visit corelogic.com.
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