Catalyst with Shayle Kann - How climate disasters are shaping insurance markets

Episode Date: April 10, 2025

Premiums are rising. Insurers are leaving markets. But people keep building in risk-prone areas, and the climate disasters just keep coming. Can insurance markets adapt? In this episode, Shayle talks to Dr. Judd Boomhower, an assistant professor of economics at the University of California-San Diego and a faculty research fellow at the National Bureau of Economic Research. He studies how insurance markets are reacting to climate change. Shayle and Judd cover topics like: Why insurers are limiting coverage in California, Florida, and other high-risk markets How disaster insurance, unlike auto or health insurance, faces a flood of claims all at the same time How catastrophe models (or “cat models” for short) work and why AI and other improvements struggle the solve the fundamental problem: a lack of historical data needed to predict future events The challenges of private “black-box” catastrophe models that can’t be reviewed by third parties Reinsurance markets and why they’re not attracting more capital to shore up insurers The pros and cons of parametric insurance, an emerging category of insurance products Undercapitalized “fly-by-night” insurers that risk insolvency and failing to pay out claim Recommended resources NBER: How Are Insurance Markets Adapting to Climate Change? Risk Classification and Pricing in the Market for Homeowners Insurance Brookings: “How is climate change impacting home insurance markets?” Credits: Hosted by Shayle Kann. Produced and edited by Daniel Woldorff. Original music and engineering by Sean Marquand. Stephen Lacey is executive editor. Catalyst is brought to you by Anza, a platform enabling solar and storage developers and buyers to save time, reduce risk, & increase profits in their equipment selection process. Anza gives clients access to pricing, technical, and risk data and tools that they’ve never had access to before. Learn more at go.anzarenewables.com/latitude. Catalyst is brought to you by EnergyHub. EnergyHub helps utilities build next-generation virtual power plants that unlock reliable flexibility at every level of the grid. See how EnergyHub helps unlock the power of flexibility at scale, and deliver more value through cross-DER dispatch with their leading Edge DERMS platform, by visiting energyhub.com.

Latitude Media, podcast at the Frontier of Climate Technology. I'm Shail Khan, and this is Catalyst. Whatever statistical methods we want to throw at this thing, the basic limitation is that we're trying to fill in gaps in the historical record, and so fundamentally these things are always going to be a little bit of a crystal ball. Coming up, how homeowners insurance is being affected by climate change. When utilities need flexible capacity they can count on, they turn to Energy Hub.

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Predictive, verifiable, and designed to perform when it counts. Learn more at energyhub.com. Trillions of dollars are flowing into clean and critical infrastructure, but those investments aren't driven by technology alone. They're shaped by markets, by policy, by capital, and by the institutions that connect them. I'm Alfred Johnson, CEO of Crux, and host of a brand new podcast, Critical Capital. Each episode, I talk with people deploying capital, shaping policy, and building the clean economy. Tune in as we unpack how progress is actually made. Listen to critical capital on Spotify, Apple, or wherever you get your podcasts.

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com. I'm Shale Khan. I lead the frontier strategy at energy impact partners. Welcome. So as most of you know, I live in California. And if you live in California,

as I'm sure is true, if you live in Florida or parts of the southeast or maybe Texas or really a lot of places, then you know that homeowners insurance seems to be sort of unstable in a good chunk of the country. In the case of California, everyone that I know is maximum one degree removed

from someone whose homeowners insurance was dropped, or at least had the price increased by some multiple, because insurance companies recently reevaluated wildfire risk, or maybe just made a decision to leave the state or something. Actually, that's sort of the point. It seems to be fairly intuitive that climate change and the resulting increase in frequency and severity of weather events would make the insurance business tougher. But I haven't actually been clear on the mechanism. Like, what's actually happening here? How are these companies adapting? Is it the models that are failing them? And if so, why are they just making decisions with broad brushes, et cetera.

So I wanted to talk through it because it's an area that I admittedly don't know a whole lot about. So I found a great guest. My guest here is Dr. Judson Boomhauer. He's an assistant professor of economics at UC San Diego and a faculty research fellow at the National Bureau of Economic Research. And he's been writing for a long time about the impacts of climate change on insurance markets. So here's Judd. Judd, welcome. Thank you.

Great to be here. All right, insurance and climate change is the topic that I've been wanting to learn more about for a while, to be honest, but just haven't gotten around to it. So you're my foil. I think we're going to focus mostly here on homeowners insurance in particular, though I'm sure some of the supplies more broadly. Let's start with some history here. Are there, like in recent history, I guess, are there major events, climate events, weather events, I guess, that have caused significant changes in how the insurance industry. models or prices or covers homes? Yeah, absolutely.

I mean, I think that, you know, when you think about how climate change is affecting, especially people that live in the United States, one of the salient ways that we're feeling it is through homeowner insurance, right? We have these increasing trends in disaster losses from all kinds of events, from floods, from hurricanes, from wildfires, from severe windstorms and hail. and we have seen really big increases in aggregate losses from those things, particularly in the last 10 or so years. And when you add all that up, it's coming through the prices that homeowners are paying for insurance.

So if you think about individual events that have really been important, you know, we've had terrible wildfire seasons in California in 2017 and 2018 and 2018. which are something that I've thought a lot about in my own research. We've had lots of bad hurricane events in Florida. We had Hurricane Ian. I think, and it's also not so much any individual exceptional event. It's the rate at which the exceptional events are coming, right? We're having a lot of them, things that used to be complete outliers are happening with greater and greater frequency, and those are adding up to really

stress insurance markets. Okay, so I mostly want to focus on where the industry is today or what's coming. Obviously, you know, we're recording this, what, like, I don't know, is it two months since the L.A. wildfires, something like that. Yep. But since then, there have also been a raft of other major weather incidents in other parts of the country. I guess the first question is when, like, where is the state of the insurance industry right

now? When stuff like this happens, is it economically? really problematic for these companies? Are they prepared for it? I mean, we're hearing all these stories of companies, you know, dropping coverage in locations, things like that. But like, are they really, are they hurting because of it? Well, I mean, they're hurting in the sense that their, you know, their profits are not what they would like them to be.

They're certainly, you know, they're having a very bad time in terms of how their business is doing. I think kind of a subtext of what you're asking it is how likely is it that we're actually going to see insolvencies or failures of major insurers as a result of these events. And for reasons that we'll talk about, I think that's less likely in the short run. What is likely in the short run

is that prices are going to need to go up, returns to the equity investors and these companies that have equity investors are not going to be good. The insurers are not doing well in terms of profits. And they are certainly choosing to pull back on the places that they're willing to do business. So to the extent that

they see areas where they have a big concentration of climate risk exposure and particularly where they think that they can't charge prices that reflect that exposure, they're definitely choosing to pull back on offering coverage. We're not at the point yet where we expect major failures of property insurers. When you talk about the pulling back coverage thing, this is one thing I've been wondering about how sophisticated is that analysis, right? It seems like it's a, I don't know, it feels fairly rudimentary in the sense that sometimes what you'll see is one major event happens in one location.

Presumably that major event was predictable on some likelihood. And the result of that major event is that a bunch of insurers either use it as an excuse or just react quickly and say, okay, I'm pulling out of this market or out of this region or whatever it might be. Is that a rational choice? Is it reflection of their poor analysis a priori? Like, how should we think about that? It's a good question. When we think about how these firms are making these decisions, the basic thing to understand is that these risks are really hard to price and they put firms in a difficult position. So if you think about something like heart attack risk or auto accident risk.

There's a lot of historical actuarial data on those events that makes it pretty easy for insurers to know how much risk they're taking on when they write a given policy. Natural disaster events are just fundamentally different because we, you know, thankfully have not had a thick enough historical record of disaster events that we can price these things using the statistical methods that are standard in other lines of insurance. And that means that we're reliant on a set of tools that are much more kind of simulation and engineering based. And so chief among those is what we call catastrophe models, which are tools that insurers and consultants to insurers use to develop a best guess of the range of

the range of disaster outcomes that might affect a given customer in a given year. And so when you say why are insurers pulling out of the places that they're pulling out of, they have some sense of what their disaster exposure is that comes from these cat models. It's not perfect. There is also absolutely a sense of kind of reactiveness here that something bad happens, and let's get out of the place where the bad thing is happening. And that I agree that there's this ad hoc, a little bit of an ad hoc sense to it.

But fundamentally, I think it comes from the fact that it's a really difficult information environment. And so there's other reasons we can talk about it. But that's my high-level take. Let's talk more about the cat models a little bit. Can you actually just go into a little bit more detail about exactly what are the cat models? how have they been built historically? And then this is one of those areas that you could imagine, two things. You could imagine, one, that whatever technique we use to build a cat models historically

is insufficient for today's environment because the climate change is causing more volatility and weather. And so maybe that breaks the cat models, I don't know. But, too, you could also imagine that, like, we've developed a suite of modern tools, AI and so on that would make cat models better. And there's a lot of AI for weather forecasting and things like that. So I guess start with just like how do the cat models actually work today? And then is that a source of the problem or is it going to be the solution?

Yeah, well, I mean, the fundamental challenge that you're trying to solve with a cat model is that if you look at a given house or a given city, you don't have enough historical data to understand the likelihood of a disaster. If you have a disaster that occurs with a one quarter of one percent probability in a given year, in order to really nail down the expected annual losses from that kind of event in a given place, you need hundreds and hundreds of years of data. And we just don't have that. And so the problem that we try to solve with cat models is we try to use the model to basically fill in those gaps in the observed data record. So if I'm trying to offer coverage in a given city, I have some sense that there's a, there's some

small probability that that city is going to flood or that city is going to have a fire, we don't have the hundreds of years of observations for that city to know exactly what that probability is, but there's another city in another state that kind of looks the same, right? It has kind of the same vegetation, it's kind of the same elevation, and maybe there's a hundred more of those cities around the country, and if you start to put together the experiences of those different cities, then you can start to build a statistical model about what's going to happen on average. But in order to do that, you've got to make assumptions about which cities are good proxies for other cities, which types of homes are good proxies for the dollar damages that

other types of homes are going to experience when an event comes through. So basically the role of the cat model is to help you project our... of sample to understand what the damages are going to be in a place where we've never actually seen this event, but we think there's some possibility this event could happen. So in terms of how does the model actually do that, it's going to take what's called an event set, which is a real list of actual historical events that have happened, you know, major hurricanes, major earthquakes, major floods, depending on what the thing is that you're trying to model. it's basically going to

redraw from that event distribution randomly a bunch of different times to do this simulation-based you know, re-running of the world. Imagine rerunning the world thousands and thousands of different times and that kind of

resampling from the historical event distribution is going to give you a shape of possible future scenarios and then you, you know, you sort all of those. possible future worlds. That gives you a probability distribution of losses for a given property or a given city or a given country. So the last thing I would say, Shail to your question is the spirit of your question is sort of what are we not getting right in cat models? And that's a, fair question. And I do believe that there are opportunities on the engineering side to improve

these things, you know, maybe AI gives us an ability to find trends in the data that we hadn't been finding before. But it's also important to recognize that we have kind of a fundamentally unsolvable problem here that whatever statistical methods we want to throw at this thing, the basic limitation is that we're trying to fill in gaps in the gaps in the historical record. And so fundamentally, these things are always going to be a little bit of a crystal Right. And I guess do we know, have the cat models, maybe we don't have enough data to answer this question, but have they been sufficiently accurate recently? Like, is there any question as to whether they are good enough to, I mean, I guess they are the best we have, but what do we know about their accuracy? They are the best we have. They are the best we have. And I, you know, I am beating up on cat models a little bit.

I don't want to come across as saying that they're not useful or that we shouldn't rely on them. They are the best we have. And so what we can do is when we have major events, you know, major new events, we can compare the losses in that event to what the cat model said the losses were going to be. And I think that kind of explosive evaluation is useful. It's limited because, you know, we're observing one event and these are models. so there's always going to be some prediction error for any given event. And it's really hard to know if you see the cat model was off a little bit,

is that what do we take from that one observation about the validity or accuracy of the overall model? It's still a useful exercise to do. The other challenge we have here, though, is that almost all of the models that are applied by insurers today are developed by for-profit private companies. It makes them all a little bit of a black box. It makes them all basically impossible for objective third parties to evaluate. And so we're sort of dependent on, you know, these companies to tell us after the new hurricane or after the new wildfire how accurate their model was.

And if you're cynical, you can imagine that there's an incentive to, at minimum, you know, be really public about how accurate the model was in the cases that it worked and maybe be a little less public about how it was in the cases where it was off. And these models are competing with each other as well, right? So there's market pressures that make it a little hard to interpret what we hear about the validity of the models. Right. And is there a big effort to improve the cat models? Obviously, like I said, there's like a ton of activity around AI for weather forecasting, which is a different thing from cat modeling. But have there been major advances?

Or are there planned major advances? Yes. the models are getting better. There are a few major firms that have been doing this for a long time. They are making interesting investments to make the models better. I think the other thing that's exciting is you're seeing entry. So as the importance of this problem gets bigger, innovators are innovating. And you're seeing startups trying to come in and exactly motivated by what you're saying.

like, hey, seems like this is a really important risk. It seems like these models could be better. Is there something that I can figure out how to apply here that will give me a model that's better, give me a big advantage, and I can license that to an insurance company or otherwise make a ton of money? I'm curious how the cat models relate to pricing. We've talked about insurers pulling out of markets.

The other thing that's happening is just the prices rising in some markets where all of a sudden there's higher perceived risk. Do the cat models inform pricing in the sense that, like, I guess what I don't have a clear view of is the cat model telling you the likelihood and severity of a given event, a hurricane, or whatever it might be, or is it also saying for this property at this address, here is the likely damage and thus what the insurance risk would be? The model is delivering the latter.

So what the model promises to deliver is for a given property, in a typical year, what is the dollar amount of the expected losses that that house or shopping mall or whatever it is is going to suffer? And it's even more ambitious than that. It's also promising to deliver a probability distribution. So a variance of losses in a typical year and the covariance of losses for that property with other properties that you might be insuring in your portfolio. That covariance, that relationship between the probability of losses on one property and the probability of losses on another property is really important when we start

to talk about reinsurance and total worst-case losses. So the model is really delivering a lot. We've asked these models to calculate a lot of parameters that are quite granular. So where does that come from? Where does that dollar denominator loss prediction come from? The cap model is basically coming up with a probability that the house is going to face an event in a given year,

which we sometimes call the hazard. And then the damages in dollars that the insurance company is going to have to pay if the house is affected by that event, which you could call vulnerability. And so one of the places that the models are really working on getting better in an interesting way is understanding how to price investments that people make and making their property less vulnerable. So if you live in the floodplain but you elevate your home, that's going to greatly reduce the damages that you're going to experience when a small flood comes through. if you live in wildfire country and you put a class A roof on your house or you manage vegetation around your home in a way that's responsible,

those things are going to reduce the likelihood that your house is going to burn down when a wildfire comes through. And so an ideal cat model would capture the effects of both of those things. There's another actor in this whole equation that I know plays a big role. and I honestly don't fully understand how, which is the reinsurance market. Can you describe, I mean, what is reinsurance and then like what is the role that it plays in pricing and coverage for homeowners insurance and, you know, major weather event-driven areas? Yeah, absolutely. And this is very much at the core of the challenges that we're seeing in these markets. Let me start by saying one of the fundamental differences between disaster insurance and insurance.

for other risks is that these are risks that are correlated, right? And so imagine being an insurer who's writing insurance policies for everyone who lives in a given city. If you're covering health insurance, let's say every person in that city has a 1% chance of having a heart attack in a given year, those heart attack risks are pretty close to independent. And that means the law of large numbers, is going to be your friend, it's extremely likely that in any given year you'll pay claims for

very close to 1% of the population. And it's basically inconceivable that you would have a year where everyone in the city has a heart attack at the same time. Disaster insurance is fundamentally different. If you think that you're covering everyone in this city against a flood that has a 1% chance of happening in a given year but is going to flood the entire city, then 99% of the time you have no claims, which is great, but in one out of 100 years, the entire customer population is filing a claim. And so that huge variance of losses, that huge tail risk that you're going to have to pay out a lot of claims at the same time, is like a fundamental difference in disaster insurance relative to other kinds of insurances. And that's, that's, that's a,

That's the problem that drives all of this discussion about reinsurance. Insurers need to make sure that they can access enough capital to pay these enormous potential claims with a high probability. We think about insurers trying to protect their solvency. And one way that they can do that is that they can hold a bunch of surplus capital within the firm. But that's an expensive thing to do. to basically put capital in low-risk investments to be sure that they're going to have that money when they need it. The other thing that they can do is that they can appeal to reinsurance markets, which are basically large, globally diversified pools of capital that are able to hedge that risk of flood in that given city

against tons and tons of other things that they're invested in, tons and tons of other potential losses to smooth away that right tail of the loss distribution and try and get back to the state where the law of large numbers is telling us that we're not going to have huge right-tail losses for the entity in a given year. Virtual power plants are becoming a reliable way for utilities to manage capacity, but enrolling devices is just the start. What really matters is confidence, knowing those resources will perform when dispatched, and being able to prove it from the control room to the living room.

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delivering 3.4 gigawatts of flexible capacity. See what that looks like. at energy hub.com. We're living through a profound economic shift, and energy sits at the center of all of it. Trillions of dollars are flowing into power plants, transmission lines, battery factories, data centers, but the future of energy isn't shaped by technology alone. It's shaped by markets, by policy, by capital, and by the institutions that connect them. I'm Alfred Johnson, CEO of Crux, the capital platform for the clean economy.

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or translate complex ideas and technologies into tangible, compelling stories that resonate with the media, fish tank can help. Check out fish tankpr.com. That's F-I-S-C-H-F-TankPR.com. One thing that I've seen happen a little bit is in these markets where some of the larger insurers do pull out or they raise prices a lot. There's like an emergence of a new class of insurers, either those that are offering a different product, like parametric insurance or something like that, or just new upstart, smaller ones. Is that a healthy dynamic? I mean, you want more coverage in the market, I suppose.

Yeah, so, you know, we were just talking about reinsurance. And one question that you might ask if you're an insurance company is, well, reinsurance is expensive and difficult to buy. Why should I care if I, as a company, can't pay my claims in a given year, then I'm going to go bankrupt. And that's terrible for my policyholders. But, you know, my losses are basically limited by the value of my company. that sort of cold-hearted economic logic is absolutely something that's been a problem in the insurance company for as long as insurance has existed.

And so we do want to make sure that when companies are writing policies, that they actually have the ability to pay those claims in almost every possible realization of the loss distribution. So one of the things that we've seen in places like Florida is that there has been, increase in the number of really small insurers who are taking on property risk. And some of these insurers have capital balances that are potentially concerning. And so there's a whole weedy discussion of which credit rating agencies are reliable and not reliable. But the punchline of this story is that we've seen an increase in the market chair in Florida of insurers that have questionable or potentially questionable ability to pay claims. And we have also seen as hurricane events have become worse, we've seen multiple insolvencies

of these insurers. So we've actually seen these insurers go under, not be able to pay all the claims associated with a given loss event. And that, of course, is breaking the promise of what insurance is supposed to do. These household paid claims, they expected that they're going to be made whole when the disaster happens. And then that throws you into a whole different question of, does the state step in and bail out those homeowners and do they take a haircut? But fundamentally, we want to avoid that whole problem by making sure that we have solvency regulation in place and good risk rating that's making sure these firms are able to cover the losses they're promising to cover. I guess final question for you. I sort of mentioned it, but I'm curious your perspective on the rise of stuff like parametric insurance,

which, as I understand it basically says, like, okay, if X happens, we pay you out why, and you don't go through this complicated claims process. You can get paid out much quicker. There seems to be a rise in that, particularly as a result of climate-driven events. How do you see that playing a role? Yeah, I think parametrics are really interesting. You know, parametric solve a lot of the contracting problems that exist in any insurance market, be it climate or not climate. So whenever you have what's called an indemnity insurance contract, that's a traditional insurance contract that pays you whatever you lose,

you have to, there's a fundamental potential difference of opinion between the insurer and the customer in how much the actual value of the loss was, whether the loss was caused by the disaster or whether it was caused by something else, some lack of maintenance prior to the event, all kinds of stuff. And those fights lead to situations where homeowners feel like their insurer is paying them less than the true value of their claim or insurers feel like the homeowner is trying to exaggerate the value of the claim. And those those problems make people less excited to participate in these markets. And so the interesting thing about parametrics is that these are insurance policies that take away

that whole question of trying to, how much do we need to give you to make you whole after a disaster? And it's just a contract that says, if some readily observable trigger happens, we will give you a pre-specified amount of money. And so if a hurricane with wind speeds above X strikes between this place and this place on the coast in 2025, we will pay you $20,000. That really simplifies a lot of the contracting side of the market. The challenge is it introduces what's called basis risk. So what if a hurricane with wind speed, you know, X minus a risk?

Epsilon strikes, that's probably still not great for me as a homeowner, but the parametric policy is going to give me no coverage because we didn't actually hit the trigger. So the challenge, the needle you have to thread with the parametric policy is how do you make the basis risk small enough that it still provides enough insurance value to the customer without basically tiptoeing back into the world of something that looks like indemnity insurance? Why is it that reinsurance is so hard to get and so expensive? It seems like, I don't know, the financial markets would have solved for that somehow. That is the million-dollar research question right now and the million-dollar policy question.

So in your cartoon model of finance or economics, this is a problem that's not that hard to solve because there are a lot of risks in the world and the beta on climate risk is pretty nice relative to those other things. It's pretty uncorrelated. So why aren't there more entities that are waiting in and selling reinsurance? pushing the price of this product down, we really don't understand the answer to that question, and it's something that we absolutely have to figure out. So if you look at people that study reinsurance,

the persistent fact is there's not a lot of capital entering these markets, and is that because there's not enough people that feel confident that they understand the risks? Is that because there's something that looks like a cartel between a small number of players who are benefiting from market power and keeping prices high, is that some other reason, whatever it is that's creating friction in these reinsurance markets is, in my view, one of the absolutely most important things that we can figure out to make property insurance work, because that inability to lay off that tail risk to the global capital

markets is really, really at the core of the problem that we're seeing in property insurance. All right, Jed, this is super interesting more to talk about an insurance world, I'm sure. So we'll have another opportunity, but thanks so much for the time. Thanks, Joe. This is great. Dr. Jed Boomhauer is an assistant professor of economics at UC San Diego and a faculty research fellow at the National Bureau of Economic Research. This show is a production of Latitude Media. Head over to Latitudemedia.com for links to today's topics. Latitude is supported by Prelude Ventures. Prelude backs visionaries, accelerating climate innovation that will reshape the global economy for the better

of people and planet. Learn more at preludeventures.com. This episode is produced by Daniel Waldorf, mixing and theme song by Sean Marquan. Stephen Lacey is our executive editor. I'm Shale Khan, and this is Catalyst.