Ever wondered how a hurricane might impact the financial sector, or why granular data on property locations is essential? Curious about how these findings might influence future governmental policies and corporate risk management strategies?

The Federal Reserve Board (FRB) was too. This year, the FRB asked six major U.S. banks to scrutinize their resilience to physical climate risks. This pilot study aimed to understand the financial stability of the mortgage loan ecosystem in the face of accelerating climate risk, and the results revealed significant data gaps and reliability issues that banks need to address.

The identification of these gaps underscores the need for detailed, data-driven understanding when measuring the evolving impact of climate risk. 

To discuss the link between understanding climate risk and financial stability, Kent David, Director of Hazard Science and Analytics Consulting, and George Gallagher, Director of Climate Risk and Natural Hazard Solutions join Core Conversations host Maiclaire Bolton Smith.

In this episode, the trio discusses how the banks approached climate risk, the challenges of integrating granular data, and the critical importance of understanding insurance market dynamics in this context.

In This Episode:

2:08 – Why is the Federal Reserve Board (FRB) looking at the intersection between climate risk modeling and enterprise risk management?

5:09 – What exactly did the FRB find in their pilot study?

8:15 – Erika Stanley goes over the numbers in the housing market in The Sip.

9:23 – The FRB study found that there was limited data and limited reliability in model output. What does that mean?

13:40 – How will more granular data help improve models? And what exactly qualifies as quality granular data?

16:45 – Why can’t historical climate patterns be used for forecasting models?

18:27 – What are some of these consequences that the different industries might be facing in the wake of accelerating climate risk?

21:58 – Erika Stanley reviews natural catastrophes and extreme weather events across the world.

22:35 – Is it possible to anticipate what may happen long-term with the climate and how it will affect business operations?

Up Next: SEC Climate Disclosure Guidance Timeline Pause: Why Companies Benefit

Links:

• SEC Climate Disclosure Guidance Timeline Pause: Why Companies Benefit
• SEC Final Climate Disclosure Rule
• Hazard HQ Command Central
• Read CoreLogic Intelligence

Find full episodes with all our guests in our podcast archive here: https://clgx.co/3HFslXD4 Copyright 2024 CoreLogic

George Gallagher:

Consequences by industry. I think that's a great way to phrase the question because in our model, and even in the FRB's request for information from the six banks, there's an explicit focus on financial impact. What is the financial materiality of these events taking place? And that's the wheel. That's where CoreLogi lives, that's where the capital markets, the lending, the insurance live.

Maiclaire Bolton Smith:

Welcome back to Core Conversations: A CoreLogic Podcast where we tour the property market to investigate how economics, climate change, governmental policies, and technology affect everyday life. I am your host Maiclaire Bolton Smith, and I'm just as curious as you are about everything that happens in our industry. How prepared is the US market for the consequences of physical climate risk? The Federal Reserve Board was curious about this so they asked six of the top US banks to examine their portfolio's resilience to climate change. The goal was to better understand the financial stability of the country's mortgage loan ecosystem. What they found was a significant amount of uncertainty surrounding the data.

This pilot study emphasizes that there is still ample room for banks to build their understanding of risk and loss with respect to climate driven perils. It also raises new questions about how to model financial risk associated with climate change since not doing so can have significant consequences. So to talk about these findings and what it means for the future, we have Kent David, director of Hazard Science and Analytics Consulting, and George Gallagher, director of Climate Risk and Natural Hazard Solutions. Kent and George, welcome back to Core Conversations.

GG:

Thank you Maiclaire, both an honor and a privilege.

Kent David:

Thank you Maiclaire. Look forward to the conversation.

Erika Stanley:

Before we get too far into this episode, I wanted to remind our listeners that we want to help you keep pace with the property market. To make it easy, we curate the latest insight and analysis for you on our social media where you can find us using the handle @CoreLogic on Facebook and LinkedIn or @CoreLogic Inc on X, formerly known as Twitter and Instagram. But now let's get back to Maiclaire, George and Kent.

MBS:

Okay. So you've both been here before but never together, so this is going to be fun. Okay. So to get us started, can you just start by explaining why exactly the Federal Reserve Board is looking at the intersection between climate risk modeling and enterprise risk management? Why is this important?

GG:

So let's start with a big overview of what the Federal Reserve Bank does. So just real briefly, the Federal Reserve System, it's the central bank for the United States. So it performs key functions to promote effective operation of the U.S. economy.

But just as a highlight, there's five specific things that they're responsible for. The Federal Reserve conducts the nation's monetary policy, so this is why we're hearing about Jerome Powell in the news regularly. They're the ones who are making movements on interest rates or not making movements on interest rates. Secondly, they promote the stability of the financial system. We're going to really focus on that, but I want to get to the other three as well. Promotes the safety and soundness of the individual financial institutions. They also foster payment and settlement systems for safety and efficiency. We're not really going to focus on that one, but the final one is promotes consumer protection and community development. So when you think about climate change, number two, three and four, promote stability in the financial systems, promotes safety and soundness of individual institutions and promotes consumer protection, you can see why there's an interest in the Federal Reserve actively reaching out to banks to get an understanding of where they are.

MBS:

Sure. Yeah. So I guess though, with all of that in mind, what would be the greatest risk to a bank when we specifically think of climate change exposure,

KD:

We've long known that there's risk to banks resulting from the occurrence of natural catastrophes. We publish reports on traditionally looking at earthquake risk as a largely underinsured peril. So the concept being that when a borrower's house is sufficiently damaged by an event, an earthquake, and with inadequate insurance coverage to protect them, then the bank is also at risk for potential defaults or servicing problems. It's an innate hazard in the industry. Again, traditionally we looked at that mostly for underinsured perils like earthquake. With the changing environment and with kind of a global recognition that climate change could indeed cause similar risk as we discussed for earthquake, but to other perils with increasing peril frequency and severity, that risk is more broad based and should be investigated.

MBS:

Okay. Okay. I guess let's get back to this pilot study that the FRB did. What exactly did they find?

GG:

Sure. So they had two primary objectives and we'll talk about what they actually found from the objectives, but to learn about large banking organizations, climate risk practices and challenges. So are you doing anything and what's keeping you up at night? And then second, to enhance the ability of large banking organizations and supervisors to identify, estimate, monitor, and manage climate related financial risks. Those were the two goals. And from that, they asked participants to do very specific things. I'll let Kent describe that and then we'll go into what many of the outputs were.

MBS:

Okay, yeah, Kent.

KD:

So the goal of the FRB pilot study, I would say there's two goals. One was to get answers to the fundamental questions that they're asking, and we'll get into that in a second. And the second goal I think, goes to the fact that it was a pilot study, and that was to have the respondents provide different approaches to solving the problem. And we'll get into what that means and what some of the conclusions that the FRB published in their report. But being that this is an first attempt in the US to engage with banks in this fashion in an official manner, I believe that one of the intents of the pilot study was to really investigate not just what is the actual risk, but what are different approaches and how well do they work to solve the problem, describe the risk, and provide a useful set of insights to the banks.

So yeah, that's my meta read of the pilot study requirements. To state the goal of the study itself was for the banks to investigate what the impacts on the collateral underlying their loans and what the net impacts of the changing climate risk is on the loans themselves to the financial institution. So for example, if a bank had a loan on a property located in coastal Florida, and this is not part of the study specifically, but necessarily, but what would a rising sea level have on the value of that property? What would a hurricane potentially have on potential default for that homeowner given damage to their property from a catastrophic event? So it was, looking big picture, the intent of the study was to quantify those risks for a changing environment and look at how that changing environment both changes the risk and potentially impacts the financial lending industry institutions as a whole.

ES:

MBS:

I guess too, and Kent, you're probably the right person to answer this one, that I know that one of the things that the study found is among other things that there was limited data and limited reliability in model output. So can you talk a little bit about what does that mean, what the big concerns were of what actually came out of this study and maybe what the recommendation of what should be done?

GG:

There's probably six specific outcomes that I think would be interesting to the listeners. So let me highlight those and then I know Kent has a great answer for that question. From what they learned, what the FRB sought out to do, we've already articulated that, what they learned, there's probably six primary ones. There's more but the ones that I'm focusing on. The participants approaches to the pilot exercise differed significantly. Differences in approach were driven by their business models, their view of risk, access to data, things like that. So a wide-ranging of differences on how they approached it. Secondly, participants generally used existing credit models to estimate the impact of climate related risk, and they found that those credit models needed much more enhancements than just minor tweakings to come up with the results. And that was pretty much universal across the respondents. Third is that participants faced data challenges as they conducted the exercise, and we'll unpack that a little bit.

Most, however, did note that they plan to capture additional data from clients and to source from vendors and to use proxies where necessary. So they developed a plan beyond some of their deficiencies. Quickly, fourth is most participants considered indirect impacts and or chronic risks in the physical risk models, but faced modeling challenges. That actually speaks to CoreLogic's participation in this marketplace. We thought going in that this would be a major lift for these entities. And to their credit, these six banks really did perform very, very well and identified areas where they can improve.

MBS:

Okay.

KD:

So to the question, the statement in the report that the models of the banks used had limited data, lack of back testing capabilities, nonlinear risks, scenario horizons, heavy reliance on judgment, limited reliability of model output and time constraints. To some extent that is a feature, not a bug of the study. And it's revelatory to the fact that the respondents use different approaches to solving these problems, some of which are better suited to solving the problem, some of which are less well suited, I think, ultimately those banks would probably agree. And there's also a problem that crops up commonly in the industry and these conversations as to what a model is and how does it work.

MBS:

Sure.

KD:

The way banks traditionally look at stress test scenarios and stress test modeling uses the language and perhaps a different way than other parts of the industry would. And so I think part of that statement of the challenges is reflective not of a structural problem, but of just a finding a way to find commonality in the language that we use to describe the analytics and the use of models.

MBS:

Okay. So this is really interesting and I want to stay on this data side of things for a little bit here, Kent. And it sounds like more data is needed, more granular data is needed. But can you talk a little bit about what exactly does that mean? What is granular data in this context?

KD:

We know coming at the problem from a catastrophe management or risk management perspective, that catastrophes, the impact of an earthquake or a hurricane or flooding or hail or wildfire can change dramatically. There's high gradient from location to location, even within a relatively small geographic spread. So wildfire, fires stop where firefighters stand, flood stop or flood risk changes dramatically depending on the first floor elevation of a property. Even though we look from satellite images of a hurricane and they're broad, big scale events, their actual impacts are very well differentiated and defined based on the ground truth of each property impacted.

So when we talk about granular data or looking at the problem from a granular standpoint, looking at a phenomena that really does differ from property to property, loan to loan, from an aggregate standpoint, from a zip code level, for example, describing the hazard of apparel at a non-granular feature level, really dulls or destroys the ability to actually understand what the impact is to a bank.

So for example, every loan in a bank's portfolio has financial characteristics that the bank is well aware of and is used to modeling. Each one of those pieces of collateral could have very different physical realities on the ground. That could be, the structures could be built to different building codes. They could be subject to different flooding levels, earthquake levels, fire levels, fire impacts, and that's really important to capture. You need to capture on a atomic level, on a property by property level, what the hazard is there relative to the phenomena you're modeling, what the resilience or the vulnerability of that structure is, the collateral is. At the same time you're looking at the loan characteristics. What's the loan to value ratio? What are the parameters defining the risk from a financial standpoint? So looking at this from a granular level really means looking both at the very unique particulars for every loan that you're analyzing, and at the same time reflecting the fact that these events that could occur are large scale. So you need to do both at the same time.

MBS:

And I guess the other thing I'd throw in there too is we're talking about modeling and I mean this is about climate change, so we can't just use historical climate patterns of what we've seen. We're talking about how the climate is going to change in the future because of this impact of climate change. So yeah. So how does that work with looking at the models moving forward for climate change?

KD:

So I think another one of, when the statement about the nature of the modeling and the uncertainties that was reflected in the report, it reflects the reality. And that is we don't know what the future holds. We know that the climate is changing, we don't know what the pathway is. And dealing with that kind of uncertainty is not a problem. It's a reality, and it's what modeling is intended. Those are questions that catastrophe modeling are intended to answer. So we approach the problem by both inherently modeling uncertainty in our models. So we don't know what a given hurricane damage will cause or what a given earthquake will cause in terms of damage. We also don't know what the future holds in terms of climate change, a changing environment. So we reflect that in our modeling approach by looking at different pathways, different scenarios, different climate scenarios, and independently calculating the impacts or reflecting those different pathways, those different future climate conditions separately in the modeling. So that we're looking at the inherent uncertainties and response as well as the unknowns characterizing the future environment.

MBS:

I'd imagine a lot of unknowns here. And I guess too, when I think of consequences, George, you alluded to this when you talked about different industries, but different industries would have different consequences. So you alluded, we talked about mortgage, we've talked about banking, you mentioned insurance. What might be some of these consequences that the different industries might be facing and how could they be different from each other?

GG:

Sure. Consequences by industry. I think that's a great way to phrase the question because in our model, and even in the FRBs request for information from the six banks, there's an explicit focus on financial impact. What is the financial materiality of these events taking place? And that's the wheel. That's where CoreLogic lives, that's where the capital markets, the lending, the insurance live. So we are a purpose-built model for understanding financial impacts as opposed to a model that's intended to educate the lay community for things like weather exposures and heat exposures and drought exposures.

So the specificity of your model, what you're built for is going to play a large role in how it's going to be adopted and who is going to do that adopting. So to go back to your question, climate impact and natural hazard impact and weather event impact have operational impacts on things like manufacturers, retailers, freight haulers, people who need to interact with other entities and get things done. So if for example, you were a grocery store and a hurricane warning and a hurricane event actually occur that requires you to close your operations or possibly you've sustained damage because of that occurrence, that could have a material impact on your revenues and your outlook for future forecasts. When I mentioned earlier that the SEC, the Securities Exchange Commission, issued its guidance focusing on materiality, those are the things it's asking about.

MBS:

That's helpful.

GG:

In your operations, did a hurricane event or a wildfire event or severe convective storm event or any of a number of natural perils and or climate change issues, did they impact your operations? Or will the progression of climate change require you to materially adapt your facilities or your production, which will then become a cost, which needs to be disclosed?

ES:

Learn more about why the SEC's climate disclosure regulations are paused and what a reinstatement may mean for enterprise risk management. Check out the link in the show notes.

GG:

Not to throw my hat in the ring too much, but I think the laser focus on materiality both from the Fed as well as from the SEC, are the appropriate from focus for business oriented entities. Is it designed to educate the general population? No, unfortunately, that's a limitation of what we're focused on. But we believe, and I don't think it's wrong to say that, identifying and quantifying risk is what's going to shake loose dollars to mitigate that risk. So in the ecosystem of climate change and building resilience and mitigation, we want to be crystal clear on the financial impacts that lead towards efforts to mitigate those potential impacts.

MBS:

Okay. That's really helpful context, I think, to kind of put this all together.

ES:

Before we end this episode, let's take a break and talk about what's happening in the world of natural disasters. CoreLogic's hazard HQ command central reports on natural catastrophes and extreme weather events across the world. A link to their coverage is in the show notes. Summer started hot. In late June there were multiple active wildfires in South Central New Mexico. The three fires that burned simultaneously were the Blue 2, Salt and South Fork Fires. CoreLogic estimated that approximately 1,300 residential properties with a combined reconstruction cost value of nearly $370 million were at risk.

MBS:

I guess just to close here, Kent, you alluded to the uncertainty with a lot of this and the uncertainty with modeling, uncertainty with what's going to happen with climate change. So my question I'd pose to both of you is looking into your crystal ball, is it even possible to anticipate what may happen long term and what do you think it's going to take?

KD:

We're not in the business of predicting.

MBS:

I intentionally said anticipate because I know we can't predict.

KD:

Yeah, no, but just to play off that, predicting climate change or changing climate impacts on the built environment, the economy, that's a challenging problem to solve. What we can do is we can model those potential impacts relative to what that changing climate, the best science says about where the climate may change. So multiple pathways, multiple return periods, multiple views of that risk. We're absolutely in a position where today we can do that modeling. Can't tell you what's going to happen tomorrow. I can't tell you when an earthquake fault is going to rupture. Can't tell you when a hurricane is going to make landfall. But we can say, this is the probability of such an event happening and these are what the consequences are. So we can quantify consequences without having to know what the future will bring, and we can do that across a range of potential outcomes.

GG:

My response to crystal ball is absolutely, it can be men, it can be measured. How precise is that measurement? I believe that science is always evolving and we're narrowing the precision. But as Kent said very eloquently, there is always going to be uncertainty. What are some of the limitations or what helps overcome uncertainty is understanding what were the ingredients in this analysis? Do you understand the first floor height of a building and what the building was construct of? Because that's going to play a very large role in how resilient it will be to water inundation. If you're higher than your neighbor, all things being equal, the lower neighbor is going to have more inundation. So the ability to articulate using very specific, very granular building attributes, locational attributes, and great climate science and catastrophe modeling science enables us to directionally have a defendable process. So from that standpoint, are we on the cusp of directionally having a defendable process? We're absolutely on the cusp of that.

MBS:

Kent, George, this has been great. This has been super helpful, lot of great context, and I look forward to having you both back in the future again, hopefully together at some point. So thank you so much for joining me today on Core Conversations of CoreLogic podcast.

GG:

Absolute pleasure. Thanks Maiclaire.

KD:

Thanks for having us.

MBS:

And thank you for listening. I hope you've enjoyed our latest episode. Please remember to leave us a review and let us know your thoughts and subscribe wherever you get your podcast to be notified when new episodes are released. And thanks to the team for helping bring this podcast to life. Producer, Jessi Devenyns, editor and sound engineer, Romie Aromin, our facts' guru, Erika Stanley, and social media duo, Sarah Buck and Makaila Brooks. Tune in next time for another Core Conversation.

ES:

Our other guest speaker was Kent David. Kent David is the director of Hazard Science and Analytics Consulting. Under his leadership, this team is responsible for quantifying financial impacts resulting from environmental catastrophes. His team specializes in CoreLogic property characteristics, real estate analytics and parcel data to solve problems needing cross-functional and data-driven solutions. This includes helping CoreLogic's clients understand their climate risk in the current environment and prospective climate change scenarios. His team also provides property analytics to the housing, financial services, municipal, utility, and insurance sectors. He has more than 30 years of experience in natural hazard analysis, modeling, and risk assessment for these sectors. Kent holds a bachelor's degree in civil engineering and a master's of science degree in structural engineering and structural mechanics, both from the University of California Berkeley. He is a licensed civil engineer in the state of California.