Pod Insights Catalyst with Shayle Kann How climate disasters are shaping insurance markets About this episode 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.
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Transcript Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:01 Speaker 00 Latitude Media, podcast at the frontier of climate technology.
0:07 Shayle Kann? I'm Shail Khan. This is Catalyst.
0:11 Speaker 02 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.
0:23 Shayle Kann? Coming up, how homeowners insurance is being affected by climate change.
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2:09 Shayle Kann? I'm Shayle 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
2:52 Shayle Kann? 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 Jud. Jud, welcome.
3:33 Speaker 02 Thank you. Great to be here.
3:34 Shayle Kann? All right. Insurance and climate change is a 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 this applies 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?
4:09 Speaker 02 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 homeowners insurance, right? We have these Uh, increasing trends in disaster losses um from all kinds of events, from floods, from hurricanes, from wildfires, from severe windstorms and hail. And uh, we have seen really big increases in aggregate losses from those things, uh, particularly in the last ten or so years. And when you add all that up, it's, you know, it's coming through uh, the prices that that homeowners are paying for insurance.
4:50 Speaker 02 So if you think about individual events that have, that have really been important, you know, we've had um, terrible wildfire seasons in California in 2017 and 2018, which are something that I've thought a lot about in my own research. Um, we've had lots of uh, bad hurricane events in Florida. We had Hurricane Ian. We've um, 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, uh, things that used to be complete outliers are happening with greater and greater frequency and those are adding up um, to really stress insurance markets.
5:33 Shayle Kann? Okay, so I mostly want to focus on where the industry is today or what's coming. Obviously, you know, this, we're recording this, what, like, I don't know, is it two months since the LA wildfires, something like that?
5:45 Speaker 02 Yep.
5:45 Shayle Kann? Um, but since then, there've also been a raft of other major weather incidents in other parts of the country. Um, I guess the first question is when, like, where is the state of the insurance industry right now? When stuff like this happens, i- is it economically um, 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?
6:12 Speaker 02 Well, I mean, they're hurting in the sense that their, you know, their profits are, are not what they would like them to be. Um, 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 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 it, you know, for reasons that we'll talk about, I think. That's less likely in the short run. What is likely in the in the short run is that prices are going to need to go up. Returns to the equity investors in these companies that that have equity investors are not going to be good.
6:53 Speaker 02 You know, the 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 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, you know, we expect major failures of property insurers.
7:31 Shayle Kann? When you talk about the pulling back coverage thing, this is one thing I've been wondering about. How how sophisticated is that analysis, right? Like, it's it seems like it's a like, 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 or just react quickly and say, OK, I'm pulling out of this market or out of this region or whatever it might be. Is that a rational choice? Is it a reflection of their poor analysis a priori? Like, how should we think about that?
8:10 Speaker 02 It's a good question. When we think about how these firms are making these decisions, the 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
8:53 Speaker 02 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, which are tools that insurers and consultants to insurers use to, 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, they have some sense of what their disaster
9:37 Speaker 02 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, where the bad thing is happening. And that, I agree that there's this, 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 this, it's a really difficult information environment. And so there's other reasons we can, we can talk about it, but that's, that's my high level take.
10:14 Shayle Kann? 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 in weather. And so maybe that breaks the cat models. I don't know. But two, 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, what, 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?
10:59 Speaker 02 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, right? 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 CAP models is we try to use the model to basically fill in, fill in those gaps in the observed data record, right?
11:42 Speaker 02 So if I'm trying to offer coverage in a given city, I have some sense that there's some small probability that that city's going to flood, or that city's 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, is 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
12:27 Speaker 02 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 CAP model is to help you project out 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.
13:11 Speaker 02 It's basically going to redraw from that event distribution randomly a bunch of different times to do this simulation-based, you know, rerunning of the world, imagine rerunning the world thousands and thousands of different times, and that kind of...
13:30 Speaker 02 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. And then the, so the, the last thing I would, last thing I would say, Shayle, to your question is, the spirit of your question is sort of, what, what are we not getting right in cat models? And that's a, that's a fair question. And I, I do believe that there are opportunities on the engineering side to improve these things, you know? Maybe AI gives us an ability to, to find trends in the data that we hadn't been finding before.
14:12 Speaker 02 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, 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 ball.
14:35 Shayle Kann? 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, they are good enough to, I mean, I guess they are the best we have, but what do we know about their accuracy?
14:58 Speaker 02 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, 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 X post evaluation is useful. It's, 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,
15:42 Speaker 02 you know, 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 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
16:24 Speaker 02 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 in the cases where it was off. And these, you know, these models are competing with each other as well, right? So they're they there's market pressures that make it a little hard to interpret what we hear about the validity of the models.
16:45 Shayle Kann? 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 has there been major advances or are there planned major advances?
17:01 Speaker 02 Yes, the models are getting better. There are a few major firms that have that have been doing this for a long time. They are making interesting investments to make the models better. I I think the other thing that's exciting is you're seeing entry. So that, you know, 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. What is there something that I can figure out how to apply here that that will give me a model that's better, give me a big advantage, and I can, you know, license that to an insurance
17:46 Speaker 02 company or or otherwise make a ton of money?
17:48 Shayle Kann? I'm curious how the cat models relate to pricing. And so 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 is 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?
18:21 Speaker 02 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 of the expected losses that that house or shopping mall or whatever it is is going to suffer? And it's even 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
19:06 Speaker 02 to talk about reinsurance and total worst case losses. So the model is really is delivering a lot. We've asked these models to calculate a lot of parameters that are quite granular. And that, so where does that come from, right? Where does that dollar, dollar-denominated loss prediction come from? The cat 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
19:48 Speaker 02 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
20:02 Speaker 02 Price investments that people make in making their property less vulnerable. So if you live in the flood plain, 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.
20:40 Shayle Kann? 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?
21:00 Speaker 02 Yeah, absolutely. And this is very much at the core of the challenges that we're seeing in these markets. Let me, 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.
21:42 Speaker 02 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.
22:27 Speaker 02 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 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, 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
23:09 Speaker 02 within the firm, but that's an expensive thing to do to, you know, 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, 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
23:54 Speaker 02 in a, in a given year.
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26:09 Shayle Kann? 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.
26:34 Speaker 02 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
27:19 Speaker 02 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 an 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 share in Florida of insurers that have questionable
28:02 Speaker 02 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 were going to be made whole when the disaster happens. And then... That 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?
28:43 Speaker 02 But fundamentally, we want to avoid that that whole problem by making sure that we have solvency regulation in place and and good risk rating that's making sure these firms are able to cover the losses they're promising to cover.
28:58 Shayle Kann? I guess final question for you, I sort of mentioned it, but I'm curious your perspective on, you know, this the rise of stuff like parametric insurance, which, as I understand, it basically says like, okay, if X happens, we pay you out Y 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?
29:24 Speaker 02 Yeah, I think parametrics are really interesting. You know, parametrics solve a lot of the contracting problems that 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, 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, you know, the disaster or whether it was caused by something else, some lack of maintenance prior to the event, all kinds of stuff.
30:06 Speaker 02 And those those fights lead to situations where homeowners feel like their insurer is paying them less than the, less than the true value of their claim, or insurers feel like the homeowner is 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 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.
30:50 Speaker 02 And so if a hurricane, you know, with wind speeds above X strikes between this place and this place on the coast in 2025, we will pay you $20,000. That, 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 of, with wind speed, you know, X minus 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, the challenge, the needle you have to thread with a parametric policy is how
31:34 Speaker 02 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?
31:45 Shayle Kann? Why is it that reinsurance is so hard to get and so expensive? Like, it seems like, I don't know, the financial markets would have solved for that somehow.
31:53 Speaker 02 That is the million dollar research question right now and the million dollar policy question. So it, you know, 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. You know, it's pretty uncorrelated. So why aren't there more entities that are wading in and selling reinsurance and pushing the price of this product down? You know, we really don't understand the answer to that question and it's something that we absolutely have to figure out. So we've, if you look at people that study reinsurance, they're, the persistent fact is there's not a lot of capital entering these markets and
32:37 Speaker 02 You know, 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, you know, creating friction in these reinsurance markets is, in my view, like, 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.
33:14 Shayle Kann? All right, Jed, this is super interesting. More to talk about in insurance world, I'm sure. So we'll have another opportunity, but thanks so much for the time.
33:21 Speaker 02 Thanks, Joe. This is great.
33:23 Shayle Kann? 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 betterment of people and planet. Learn more at preludeventures.com. This episode was produced by Daniel Waldorf. Mixing and theme song by Sean Marquand. Stephen Lacey is our executive editor. I'm Shayle Khan, and this is Catalyst.
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