Hurricane Andrew’s landfall in Florida in 1992 changed the face of property catastrophe insurance and kick-started many new initiatives, including the development of hurricane risk modeling. However, with significant exposure growth, the impact of social inflation, and climate change complications, the insurance market could struggle to respond to a repeat of Andrew. The wide-ranging impact of Hurricane Andrew on the Florida insurance market is a familiar story within the risk management world. However, 30 years on from August 24, 1992, when Andrew made landfall in Dade County, Florida, memories appear to be getting shorter, as the insurance industry once more seems to be in danger of underestimating its exposure to a Category 5 storm hitting the state. Image from the GOES-7 satellite shows Hurricane Andrew at its peak intensity on August 23, 1992, before making landfall near Homestead, Florida. Image source: NOAAWhen Hurricane Andrew came ashore as the first named tropical storm of the 1992 North Atlantic hurricane season, it followed a seven-year hiatus in major hurricane activity in Florida. Industry predictions at the time were that it would cost insurers around US$4 billion to US$5 billion, but Andrew ended up costing the insurance industry US$15 billion (in 1992 values) for Florida claims, and it caused the deaths of 44 people in the state. Following Hurricane Andrew, more than 650,000 claims were filed, leaving eight insurers becoming insolvent and a further three driven into insolvency the following year. Fast forward to today, and RMS® predictions for a repeat of Andrew would see the insured loss for wind and surge in the range of US$80 billion (GR) and US$90 billion (GU), in which other non-modeled losses and social inflation could lead to a US$100 billion event. Aftermath of Andrew The losses from Hurricane Andrew vindicated the need for catastrophe modeling solutions including the use of multiple simulated storms beyond those previously experienced in history. Catastrophe models enabled the new Bermuda reinsurance market: eight new reinsurers were established without the need for their own historical experience. In time, catastrophe models would enable the creation of insurance-linked securities such as catastrophe bonds, to tap into capital markets for alternatives to reinsurance. Without Hurricane Andrew, it might have taken much longer for this revolution to happen. The crisis caused by Andrew certainly precipitated some rapid and innovative changes to help manage a much larger hurricane risk cost than previously recognized, allowing the market to prepare for the hyperactive Florida hurricane seasons of 2004 and 2005. However, the following years were unusually quiet for intense storms landfalling in Florida, encouraging actions that further raised insurers’ hurricane risk costs. Among these was the 25 percent roof replacement rule in 2007, which mandated that if 25 percent or more of a roof is ‘repaired, replaced or recovered’ in any 12-month period, then the entire roofing system or roof section must be brought up to the latest building code. “Until the hurricanes returned with a vengeance in 2017,” says Peter Datin, senior director of modeling at RMS, “the significant additional cost imposed on insurers due to this code update was not clear.” Development of Hurricane Modeling Before Hurricane Andrew, exposure mapping by the insurance industry involved tracking premiums at a fairly coarse ‘Cresta Zone’ resolution. Post-Andrew, as modelers provided insurers with the ability to model exposures at a finer scale, insurers recognized how higher resolution data could provide a more accurate assessment of risk. RMS released its first hurricane model in 1993. Since then, there have been many updates and innovations, from basin-wide stochastic tracks, coupled ocean-atmosphere storm surge modeling, and significant enhancements in damage assessment modeling. After Hurricane Katrina in 2005, Robert Muir-Wood, chief research officer at RMS, coined the term ‘post-event loss amplification’ (PLA) to cover all processes that can raise losses after a major catastrophe, such as demand surge and claims inflation. Auguste Boissonnade, vice president of model development at RMS, who designed the first RMS hurricane model, worked on how to quantify these different factors in generating the overall insurance loss after cat events. Hurricane Katrina floodingFor the most extreme catastrophes, when damage requires the long-term evacuation of large parts of a city, the definition of a “super catastrophe” (or “super-cat”) event applies, where secondary consequences can be a significant component of the original damage. The flooding of New Orleans after Hurricane Katrina was such a super-cat. “With the hurricane catastrophes of 2004 and 2005 came the realization that cat loss models needed to allow for undervaluation of insured exposures as well as the secondary impact of economic, social, and political factors that could amplify the losses,” Boissonnade says. After major hurricanes, RMS vulnerability modelers review lessons that can be learned from the events and the resulting claims data. “Through claims analyses, it has been possible to quantify the degree to which changes in wind design codes have reduced damage and losses to buildings and incorporate those learnings into cat models,” added Datin. Current Market Dynamics The average cost of an annual homeowner’s policy in Florida is expected to soar to US$4,231 this year, almost three times the U.S. annual average, according to the Insurance Information Institute. Five Florida market insurers have already gone insolvent so far in 2022, faced with rising claims costs and increased costs for reinsurance. Meanwhile, the number of policies written by Citizens, a post-Andrew creation, has risen to over a million, as insurers have either gone insolvent, withdrawn capacity from the market, or had their ratings downgraded, making it harder for insureds to secure coverage that will meet their mortgage lenders’ approval. In July 2022, rating agency Demotech wrote to 17 insurers warning them they could be downgraded from A (exceptional) to S (substantial) or M (moderate), potentially impacting millions of policyholders whose mortgage providers demand home insurance from the strongest-rated carriers. Florida legislators then looked to circumvent the use of Demotech ratings with a new stopgap measure, where Citizens take on a reinsurance role to pay claims for insolvent insurers. At the same time, insurers are struggling to secure reinsurance capacity, and Citizens only managed to get a third of its desired reinsurance cover, making it harder for carriers to deploy sufficient capacity to meet the demand for hurricane coverage. There has also been a huge increase in the volume of catastrophe claims in recent years, driven by social inflation and undervaluation of exposures. Likely Impact of Andrew Now “Our prediction that a repeat of Andrew today could cause as much as US$100 billion in insured losses is based in large part on changes in exposure and population since 1992, coupled with updated predictions of the impact of wind and storm surge, with significant anticipated post-event loss amplification. Together these components reveal a more complete picture of potential economic and insured losses,” says Mohsen Rahnama, chief risk modeling officer at RMS. Combined wind and surge losses for a repeat of Hurricane Andrew are estimated at US$87 billion. Post-event loss amplification, whether it is from a slow recovery, supply chain issues from COVID-19, or current inflationary trends, could take the ultimate loss closer to US$100 billion. The impact of storm surge, particularly with the climate change-related rise in sea levels, is also more pronounced now compared to estimates at the time of Andrew. South Florida property developmentAdded to this is the significant demographic shift in Florida. As of this year, the population of Florida is estimated at over 22 million – a 61 percent increase from the number of people in 1992. Building counts in Andrew’s wind and surge footprints have increased by 40 percent to 1.9 million and by 32 percent to 55,000 respectively. Economic exposure has also increased by 77 percent in the wind footprint and 67 percent in the surge footprint. And in Miami-Dade County, the number of high-rise buildings that are over 15 stories has tripled since 1992, many of which are now potentially in Andrew’s surge footprint. “While the wind was the main driver of loss in 1992, the number of new, high-valued buildings near the coast suggests that storm surge losses may play an increasing role in a repeat of this event,” says Rahnama. In constant-dollar terms, economic exposure has grown substantially within both Andrew’s wind and surge footprints, based on an analysis of the total built floor area (see Figure 1). On top of this, cost inflation since 1992 has been substantial, with replacement costs in Florida estimated to have increased between two times and 2.5 times since 1992, based on historical construction cost indices. Figure 1: Exposure growth in Hurricane Andrew’s footprint (in constant dollars). Source: RMSOne key uncertainty in estimating the losses from a repeat of Hurricane Andrew concerns the impact of claims litigation. “Irma in 2017 was the first significant hurricane to make landfall since the 25 percent roof replacement rule was expanded in 2017 to all buildings across Florida, and it contributed to a significant increase in claims frequency and severity, as roof damage sustained during the storm attracted many roofing contractors, who handed over their exaggerated claims to be pursued by attorneys,” recalls Datin. An estimated US$15 billion has been paid to claimants by insurers in Florida since 2013, driven by assignment of benefits (AOB) cases, where litigation has capitalized on the 25 percent roof replacement rule, with a significant portion of the cost being driven by attorney’s fees on both sides. However, a new law passed by the Florida legislature in May 2022 changed the 25 percent roof replacement rule to exempt roofs “built, repaired, or replaced in compliance with the 2007 Florida Building Code, or any subsequent editions of the Florida Building Code.” “This means that only the damaged portion of the roof on newly built or upgraded roofs needs to be repaired after a damaging wind event instead of the entire roof or roofing system. Most importantly for insurers, the right of the contractor or assignee to obtain compensation for attorney fees – that drives up the cost of claims even further – has been removed,” adds Datin. Muir-Wood adds: “There is further hope for insurers following a recent appeal court ruling in Florida which could provide the blueprint for insurers to successfully argue against contractors in such lawsuits. Here we have at least one factor that is now being brought under control, which has significantly raised the insurance costs of hurricane losses. However, insurers will be watching closely to see if there is any reduction in social inflation because of recent legislative measures.” Can the US$100 Billion Repeat of Andrew be Prevented? Should another Category 5 hurricane make landfall in southeast Florida today, not only will the insured loss be more considerable, but the insurance industry will face major challenges that could severely impact its ability to withstand the event. What can the risk management industry do to mitigate losses? Risk modeling has advanced dramatically. “Insurers need to collect detailed data on their exposures and values and then employ high-resolution modeling alongside all those factors that can affect the ultimate loss, whether from post-event loss amplification or from more resilient construction standards,” says Muir-Wood. The spirit of the industry working together with regulators, similar to post-Andrew, needs to be resurrected. “To help insurance carriers to remain competitive, regulators and legislators have been working with the industry to prevent claims litigation from getting out of control and potentially threatening the viability of hurricane insurance in Florida,” adds Boissonnade. “And legislators also need to keep a close eye on how claims respond to the changes to the 25 percent roof replacement rule, and in measures that reduce the need for litigation, so as to reduce vexatious claims,” he adds. Datin acknowledges the role that risk modelers can play, “The catastrophe modeling community has already helped drive positive change in Florida by demonstrating the impacts of building codes and the effects of AOB-driven claims inflation on modeled risk.” In addition, says Rahnama: “It’s crucial that modeling for hurricane risk takes greater account of the effects of climate change on global warming and sea level rise, and the impact those will ultimately on wind and storm surge in the event of another hurricane like Andrew. Let’s not sleepwalk into another Andrew-type scenario. The insights are there, and the warning signs have flashed – we just need to learn from history.”
The value of data as a driver of business decisions has grown exponentially as the importance of generating sustainable underwriting profit becomes the primary focus for companies in response to recent diminished investment yields. Increased risk selection scrutiny is more important than ever to maintain underwriting margins. High-caliber, insightful risk data is critical for the data analytics that support each risk decision The insurance industry is in a transformational phase where profit margins continue to be stretched in a highly competitive marketplace. Changing customer dynamics and new technologies are driving demand for more personalized solutions delivered in real time, while companies are working to boost performance, increase operational efficiency and drive greater automation. In some instances, this involves projects to overhaul legacy systems that are central to daily operation. In such a state of market flux, access to quality data has become a primary differentiator. But there’s the rub. Companies now have access to vast amounts of data from an expanding array of sources — but how can organizations effectively distinguish good data from poor data? What differentiates the data that delivers stellar underwriting performance from that which sends a combined operating performance above 100 percent? A Complete Picture “Companies are often data rich, but insight poor,” believes Jordan Byk, senior director, product management at RMS. “The amount of data available to the (re)insurance industry is staggering, but creating the appropriate insights that will give them a competitive advantage is the real challenge. To do that, data consumers need to be able to separate ‘good’ from ‘bad’ and identify what constitutes ‘great’ data.” For Byk, a characteristic of “great data” is the speed with which it drives confident decision-making that, in turn, guides the business in the desired direction. “What I mean by speed here is not just performance, but that the data is reliable and insightful enough that decisions can be made immediately, and all are confident that the decisions fit within the risk parameters set by the company for profitable growth. “While resolution is clearly a core component of our modeling capabilities at RMS, the ultimate goal is to provide a complete data picture and ensure quality and reliability of underlying data” Oliver Smith RMS “We’ve solved the speed and reliability aspect by generating pre-compiled, model-derived data at resolutions intelligent for each peril,” he adds. There has been much focus on increasing data-resolution levels, but does higher resolution automatically elevate the value of data in risk decision-making? The drive to deliver data at 10-, five- or even one-meter resolution may not necessarily be the main ingredient in what makes truly great data. “Often higher resolution is perceived as better,” explains Oliver Smith, senior product manager at RMS, “but that is not always the case. While resolution is clearly a core component of our modeling capabilities at RMS, the ultimate goal is to provide a complete data picture and ensure quality and reliability of underlying data. “Resolution of the model-derived data is certainly an important factor in assessing a particular exposure,” adds Smith, “but just as important is understanding the nature of the underlying hazard and vulnerability components that drive resolution. Otherwise, you are at risk of the ‘garbage-in-garbage-out’ scenario that can foster a false sense of reliability based solely around the ‘level’ of resolution.” The Data Core The ability to assess the impact of known exposure data is particularly relevant to the extensive practice of risk scoring. Such scoring provides a means of expressing a particular risk as a score from 1 to 10, 1 to 20 or another means that indicates “low risk to high risk” based on an underlying definition for each value. This enables underwriters to make quick submission assessments and supports critical decisions relating to quoting, referrals and pricing. “Such capabilities are increasingly common and offer a fantastic mechanism for establishing underwriting guidelines, and enabling prioritization and triage of locations based on a consistent view of perceived riskiness,” says Chris Sams, senior product manager at RMS. “What is less common, however, is ‘reliable’ and superior quality risk scoring, as many risk scores do not factor in readily available vulnerability data.” “Such capabilities are increasingly common and offer a fantastic mechanism for establishing underwriting guidelines, and enabling prioritization and triage of locations based on a consistent view of perceived riskiness” Chris Sams RMS Exposure insight is created by adjusting multiple data lenses until the risk image comes into focus. If particular lenses are missing or there is an overreliance on one particular lens, the image can be distorted. For instance, an overreliance on hazard-related information can significantly alter the perceived exposure levels for a specific asset or location. “Take two locations adjacent to one another that are exposed to the same wind or flood hazard,” Byk says. “One is a high-rise hotel built in 2020 and subject to the latest design standards, while another is a wood-frame, small commercial property built in the 1980s; or one location is built at ground level with a basement, while another is elevated on piers and does not have a basement. “These vulnerability factors will result in a completely different loss experience in the occurrence of a wind- or flood-related event. If you were to run the locations through our models, the annual average loss figures will vary considerably. But if the underwriting decision is based on hazard-only scores, they will look the same until they hit the portfolio assessment — and that’s when the underwriter could face some difficult questions.” To assist clients to understand the differences in vulnerability factors, RMS provides ExposureSource, a U.S. property database comprised of property characteristics for 82 million residential buildings and 21 million commercial buildings. By providing this high-quality exposure data set, clients can make the most of the RMS risk scoring products for the U.S. Seeing Through the Results Another common shortfall with risk scores is the lack of transparency around the definitions attributed to each value. Looking at a scale of 1 to 10, for example, companies don’t have insight into the exposure characteristics being used to categorize a particular asset or location as, say, a 4 rather than a 5 or 6. To combat data-scoring deficiencies, RMS RiskScore values are generated by catastrophe models incorporating the trusted science and quality you expect from an RMS model, calibrated on billions of dollars of real-world claims. With consistent and reliable risk scores covering 30 countries and up to seven perils, the apparent simplicity of the RMS RiskScore hides the complexity of the big data catastrophe simulations that create them. The scores combine hazard and vulnerability to understand not only the hazard experienced at a site, but also the susceptibility of a particular building stock when exposed to a given level of hazard. The RMS RiskScore allows for user definition of exposure characteristics such as occupancy, construction material, building height and year built. Users can also define secondary modifiers such as basement presence and first-floor height, which are critical for the assessment of flood risk, and roof shape or roof cover, which is critical for wind risk. “It also provides clearly structured definitions for each value on the scale,” explains Smith, “providing instant insight on a risk’s damage potential at key return periods, offering a level of transparency not seen in other scoring mechanisms. For example, a score of 6 out of 10 for a 100-year earthquake event equates to an expected damage level of 15 to 20 percent. This information can then be used to support a more informed decision on whether to decline, quote or refer the submission. Equally important is that the transparency allows companies to easily translate the RMS RiskScore into custom scales, per peril, to support their business needs and risk tolerances.” Model Insights at Point of Underwriting While RMS model-derived data should not be considered a replacement for the sophistication offered by catastrophe modeling, it can enable underwriters to access relevant information instantaneously at the point of underwriting. “Model usage is common practice across multiple points in the (re)insurance chain for assessing risk to individual locations, accounts, portfolios, quantifying available capacity, reinsurance placement and fulfilling regulatory requirements — to name but a few,” highlights Sams. “However, running the model takes time, and, often, underwriting decisions — particularly those being made by smaller organizations — are being made ahead of any model runs. By the time the exposure results are generated, the exposure may already be at risk.” “Through this interface, companies gain access to the immense datasets that we maintain in the cloud and can simply call down risk decision information whenever they need it” Jordan Byk RMS In providing a range of data products into the process, RMS is helping clients select, triage and price risks before such critical decisions are made. The expanding suite of data assets is generated by its probabilistic models and represents the same science and expertise that underpins the model offering. “And by using APIs as the delivery vehicle,” adds Byk, “we not only provide that modeled insight instantaneously, but also integrate that data directly and seamlessly into the client’s on-premise systems at critical points in their workflow. Through this interface, companies gain access to the immense datasets that we maintain in the cloud and can simply call down risk decision information whenever they need it. While these are not designed to compete with a full model output, until a time that we have risk models that provide instant analysis, such model-derived datasets offer the speed of response that many risk decisions demand.” A Consistent and Broad Perspective on Risk A further factor that can instigate problems is data and analytics inconsistency across the (re)insurance workflow. Currently, with data extracted from multiple sources and, in many cases, filtered through different lenses at various stages in the workflow, having consistency from the point of underwriting to portfolio management has been the norm. “There is no doubt that the disparate nature of available data creates a disconnect between the way risks are assumed into the portfolio and how they are priced,” Smith points out. “This disconnect can cause ‘surprises’ when modeling the full portfolio, generating a different risk profile than expected or indicating inadequate pricing. By applying data generated via the same analytics and data science that is used for portfolio management, consistency can be achieved for underwriting risk selection and pricing, minimizing the potential for surprise.” Equally important, given the scope of modeled data required by (re)insurance companies, is the need to focus on providing users with the means to access the breadth of data from a central repository. “If you access such data at speed, including your own data coupled with external information, and apply sophisticated analytics — that is how you derive truly powerful insights,” he concludes. “Only with that scope of reliable, insightful information instantly accessible at any point in the chain can you ensure that you’re always making fully informed decisions — that’s what great data is really about. It’s as simple as that.” For further information on RMS’s market-leading data solutions, click here.
Severe convective storms can strike with little warning across vast areas of the planet, yet some insurers still rely solely on historical records that do not capture the full spectrum of risk at given locations. EXPOSURE explores the limitations of this approach and how they can be overcome with cat modeling Attritional and high-severity claims from severe convective storms (SCS) — tornadoes, hail, straight-line winds and lightning — are on the rise. In fact, in the U.S., average annual insured losses (AAL) from SCS now rival even those from hurricanes, at around US$17 billion, according to the latest RMS U.S. SCS Industry Loss Curve from 2018. In Canada, SCS cost insurers more than any other natural peril on average each year. Despite the scale of the threat, it is often overlooked as a low volatility, attritional peril Christopher Allen RMS “Despite the scale of the threat, it is often overlooked as a low volatility, attritional peril,” says Christopher Allen, product manager for the North American SCS and winterstorm models at RMS. But losses can be very volatile, particularly when considering individual geographic regions or portfolios (see Figure 1). Moreover, they can be very high. “The U.S. experiences higher insured losses from SCS than any other country. According to the National Weather Service Storm Prediction Center, there over 1,000 tornadoes every year on average. But while a powerful tornado does not cause the same total damage as a major earthquake or hurricane, these events are still capable of causing catastrophic losses that run into the billions.” Figure 1: Insured losses from U.S. SCS in the Northeast (New York, Connecticut, Rhode Island, Massachusetts, New Hampshire, Vermont, Maine), Great Plains (North Dakota, South Dakota, Nebraska, Kansas, Oklahoma) and Southeast (Alabama, Mississippi, Louisiana, Georgia). Losses are trended to 2020 and then scaled separately for each region so the mean loss in each region becomes 100. Source: Industry Loss Data Two of the costliest SCS outbreaks to date hit the U.S. in spring 2011. In late April, large hail, straight-line winds and over 350 tornadoes spawned across wide areas of the South and Midwest, including over the cities of Tuscaloosa and Birmingham, Alabama, which were hit by a tornado rating EF-4 on the Enhanced Fujita (EF) scale. In late May, an outbreak of several hundred more tornadoes occurred over a similarly wide area, including an EF-5 tornado in Joplin, Missouri, that killed over 150 people. If the two outbreaks occurred again today, according to an RMS estimate based on trending industry loss data, each would easily cause over US$10 billion of insured loss. However, extreme losses from SCS do not just occur in the U.S. In April 1999, a hailstorm in Sydney dropped hailstones of up to 3.5 inches (9 centimeters) in diameter over the city, causing insured losses of AU$5.6 billion according to the Insurance Council of Australia (ICA), currently the most costly insurance event in Australia’s history . “It is entirely possible we will soon see claims in excess of US$10 billion from a single SCS event,” Allen says, warning that relying on historical data alone to quantify SCS (re)insurance risk leaves carriers underprepared and overexposed. Historical Records are Short and Biased According to Allen, the rarity of SCS at a local level means historical weather and loss data fall short of fully characterizing SCS hazard. In the U.S., the Storm Prediction Center’s national record of hail and straight-line wind reports goes back to 1955, and tornado reports date back to 1950. In Canada, routine tornado reports go back to 1980. “These may seem like adequate records, but they only scratch the surface of the many SCS scenarios nature can throw at us,” Allen says. “To capture full SCS variability at a given location, records should be simulated over thousands, not tens, of years,” he explains. “This is only possible using a cat model that simulates a very wide range of possible storms to give a fuller representation of the risk at that location. Observed over tens of thousands of years, most locations would have been hit by SCS just as frequently as their neighbors, but this will never be reflected in the historical records. Just because a town or city has not been hit by a tornado in recent years doesn’t mean it can’t be.” To capture full SCS variability at a given location, records should be simulated over thousands, not tens, of years Shorter historical records could also misrepresent the severity of SCS possible at a given location. Total insured catastrophe losses in Phoenix, Arizona, for example, were typically negligible between 1990 and 2009, but on October 5, 2010, Phoenix was hit by its largest-ever tornado and hail outbreak, causing economic losses of US$4.5 billion. (Source: NOAA National Centers for Environmental Information) Just like the national observations, insurers’ own claims histories, or industry data such as presented in Figure 1, are also too short to capture the full extent of SCS volatility, Allen warns. “Some primary insurers write very large volumes of natural catastrophe business and have comprehensive claims records dating back 20 or so years, which are sometimes seen as good enough datasets on which to evaluate the risk at their insured locations. However, underwriting based solely on this length of experience could lead to more surprises and greater earnings instability.” If a Tree Falls and No One Hears… Historical SCS records in most countries rely primarily on human observation reports. If a tornado is not seen, it is not reported, which means that unlike a hurricane or large earthquake it is possible to miss SCS in the recent historical record. “While this happens less often in Europe, which has a high population density, missed sightings can distort historical data in Canada, Australia and remote parts of the U.S.,” Allen explains. Another key issue is that the EF scale rates tornado strength based on how much damage is caused, but this does not always reflect the power of the storm. If a strong tornado occurs in a rural area with few buildings, for example, it won’t register high on the EF scale, even though it could have caused major damage to an urban area. “This again makes the historical record very challenging to interpret,” he says. “Catastrophe modelers invest a great deal of time and effort in understanding the strengths and weaknesses of historical data. By using robust aspects of observations in conjunction with other methods, for example numerical weather simulations, they are able to build upon and advance beyond what experience tells us, allowing for more credible evaluation of SCS risk than using experience alone.” Then there is the issue of rising exposures. Urban expansion and rising property prices, in combination with factors such as rising labor costs and aging roofs that are increasingly susceptible to damage, are pushing exposure values upward. “This means that an identical SCS in the same location would most likely result in a higher loss today than 20 years ago, or in some cases may result in an insured loss where previously there would have been none,” Allen explains. Calgary, Alberta, for example, is the hailstorm capital of Canada. On September 7, 1991, a major hailstorm over the city resulted in the country’s largest insured loss to date from a single storm: CA$343 million was paid out at the time. The city has of course expanded significantly since then (see Figure 2), and the value of the exposure in preexisting urban areas has also increased. An identical hailstorm occurring over the city today would therefore cause far larger insured losses, even without considering inflation. Figure 2: Urban expansion in Calgary, Alberta, Canada. European Space Agency. Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017). Available at: maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf “Probabilistic SCS cat modeling addresses these issues,” Allen says. “Rather than being constrained by historical data, the framework builds upon and beyond it using meteorological, engineering and insurance knowledge to evaluate what is physically possible today. This means claims do not have to be ‘on-leveled’ to account for changing exposures, which may require the user to make some possibly tenuous adjustments and extrapolations; users simply input the exposures they have today and the model outputs today’s risk.” The Catastrophe Modeling Approach In addition to their ability to simulate “synthetic” loss events over thousands of years, Allen argues, cat models make it easier to conduct sensitivity testing by location, varying policy terms or construction classes; to drill into loss-driving properties within portfolios; and to optimize attachment points for reinsurance programs. SCS cat models are commonly used in the reinsurance market, partly because they make it easy to assess tail risk (again, difficult to do using a short historical record alone), but they are currently used less frequently for underwriting primary risks. There are instances of carriers that use catastrophe models for reinsurance business but still rely on historical claims data for direct insurance business. So why do some primary insurers not take advantage of the cat modeling approach? “Though not marketwide, there can be a perception that experience alone represents the full spectrum of SCS risk — and this overlooks the historical record’s limitations, potentially adding unaccounted-for risk to their portfolios,” Allen says. What is more, detailed studies of historical records and claims “on-leveling” to account for changes over time are challenging and very time-consuming. By contrast, insurers who are already familiar with the cat modeling framework (for example, for hurricane) should find that switching to a probabilistic SCS model is relatively simple and requires little additional learning from the user, as the model employs the same framework as for other peril models, he explains. A US$10 billion SCS loss is around the corner, and carriers need to be prepared and have at their disposal the ability to calculate the probability of that occurring for any given location Furthermore, catastrophe model data formats, such as the RMS Exposure and Results Data Modules (EDM and RDM), are already widely exchanged, and now the Risk Data Open Standard™ (RDOS) will have increasing value within the (re)insurance industry. Reinsurance brokers make heavy use of cat modeling submissions when placing reinsurance, for example, while rating agencies increasingly request catastrophe modeling results when determining company credit ratings. Allen argues that with property cat portfolios under pressure and the insurance market now hardening, it is all the more important that insurers select and price risks as accurately as possible to ensure they increase profits and reduce their combined ratios. “A US$10 billion SCS loss is around the corner, and carriers need to be prepared and have at their disposal the ability to calculate the probability of that occurring for any given location,” he says. “To truly understand their exposure, risk must be determined based on all possible tomorrows, in addition to what has happened in the past.”  Losses normalized to 2017 Australian dollars and exposure by the ICA. Source: https://www.icadataglobe.com/access-catastrophe-data. To obtain a holistic view of severe weather risk contact the RMS team here
Severe convective storms (SCS) have driven U.S. insured catastrophe losses in recent years with both attritional and major single-event claims now rivaling an average hurricane season. EXPOSURE looks at why SCS losses are rising and asks how (re)insurers should be responding At the time of writing, 2019 was already shaping up to be another active season for U.S. severe convective storms (SCS), with at least eight tornadoes daily over a period of 12 consecutive days in May. It was the most May tornadoes since 2015, with no fewer than seven outbreaks of SCS across central and eastern parts of the U.S. According to data from the National Oceanic and Atmospheric Administration (NOAA), there were 555 preliminary tornado reports, more than double the average of 276 for the month in the period of 1991-2010. According to the current numbers, May 2019 produced the second-highest number of reported tornadoes for any month on record after April 2011, which broke multiple records in relation to SCS and tornado touchdowns. It continues a trend set over the past two decades, which has seen SCS losses increasing significantly and steadily. In 2018, losses amounted to US$18.8 billion, of which US$14.1 billion was insured. This compares to insurance losses of US$15.6 billion for hurricane losses in the same period. While losses from SCS are often the buildup of losses from multiple events, there are examples of single events costing insurers and reinsurers over US$3 billion in claims. This includes the costliest SCS to date, which hit Tuscaloosa, Alabama, in April 2011, involving several tornado touchdowns and causing US$7.9 billion in insured damage. The second-most-costly SCS occurred in May of the same year, striking Joplin, Missouri, and other locations, resulting in insured losses of nearly US$7.6 billion. “The trend in the scientific discussion is that there might be fewer but more-severe events” Juergen Grieser RMS According to RMS models, average losses from SCS now exceed US$15 billion annually and are in the same range as hurricane average annual loss (AAL), which is also backed up by independently published scientific research. “The losses in 2011 and 2012 were real eye-openers,” says Rajkiran Vojjala, vice president of modeling at RMS. “SCS is no longer a peril with events that cost a few hundred million dollars. You could have cat losses of US$10 billion in today’s money if there were events similar to those in April 2011.” Nearly a third of all average annual reported tornadoes occur in the states of Texas, Oklahoma, Kansas and Nebraska, all states that are within the “Tornado Alley.” This is where cold, dry polar air meets warm, moist air moving up from the Gulf of Mexico, causing strong convective activity. “A typical SCS swath affects many states. So the extent is large, unlike, say, wildfire, which is truly localized to a small particular region,” says Vojjala. Research suggests the annual number of Enhanced Fujita (EF) scale EF2 and stronger tornadoes hitting the U.S. has trended upward over the past 20 years; however, there is some doubt over whether this is a real meteorological trend. One explanation could be that increased observational practices simply mean that such weather phenomena are more likely to be recorded, particularly in less populated regions. According to Juergen Grieser, senior director of modeling at RMS, there is a debate whether part of the increase in claims relating to SCS could be attributed to climate change. “A warmer climate means a weaker jet stream, which should lead to less organized convection while the energy of convection might increase,” he says. “The trend in the scientific discussion is that there might be fewer but more-severe events.” Claims severity rather than claims frequency is a more significant driver of losses relating to hail events, he adds. “We have an increase in hail losses of about 11 percent per year over the last 15 years, which is quite a lot. But 7.5 percent of that is from an increase in the cost of individual claims,” explains Grieser. “So, while the claims frequency has also increased in this period, the individual claim is more expensive now than it was ever before.” Claims go ‘Through the Roof’ Another big driver of loss is likely to be aging roofs and the increasing exposure at risk of SCS. The contribution of roof age was explored in a blog last year by Stephen Cusack, director of model development at RMS. He noted that one of the biggest changes in residential exposure to SCS over the past two decades has been the rise in the median age of housing from 30 years in 2001 to 37 years in 2013. A changing insurance industry climate is also a driver for increased losses, thinks Vojjala. “There has been a change in public perception on claiming whereby even cosmetic damage to roofs is now being claimed and contractors are chasing hailstorms to see what damage might have been caused,” he says. “So, there is more awareness and that has led to higher losses. “The insurance products for hail and tornado have grown and so those perils are being insured more, and there are different types of coverage,” he notes. “Most insurers now offer not replacement cost but only the actual value of the roofs to alleviate some of the rising cost of claims. On the flip side, if they do continue offering full replacement coverage and a hurricane hits in some of those areas, you now have better roofs.” How insurance companies approach the peril is changing as a result of rising claims. “Historically, insurance and reinsurance clients have viewed SCS as an attritional loss, but in the last five to 10 years the changing trends have altered that perception,” says Vojjala. “That’s where there is this need for high-resolution modeling, which increasingly our clients have been asking for to improve their exposure management practices. “With SCS also having catastrophic losses, it has stoked interest from the ILS community as well, who are also experimenting with parametric triggers for SCS,” he adds. “We usually see this on the earthquake or hurricane side, but increasingly we are seeing it with SCS as well.”
Marleen Nyst and Nilesh Shome of RMS explore some of the lessons and implications from the recent sequence of earthquakes in California On the morning of July 4, 2019, the small town of Ridgecrest in California’s Mojave Desert unexpectedly found itself at the center of a major news story after a magnitude 6.4 earthquake occurred close by. This earthquake later transpired to be a foreshock for a magnitude 7.1 earthquake the following day, the strongest earthquake to hit the state for 20 years. These events, part of a series of earthquakes and aftershocks that were felt by millions of people across the state, briefly reignited awareness of the threat posed by earthquakes in California. Fortunately, damage from the Ridgecrest earthquake sequence was relatively limited. With the event not causing a widespread social or economic impact, its passage through the news agenda was relatively swift. But there are several reasons why an event such as the Ridgecrest earthquake sequence should be a focus of attention both for the insurance industry and the residents and local authorities in California. “If Ridgecrest had happened in a more densely populated area, this state would be facing a far different economic future than it is today” Glenn Pomeroy California Earthquake Authority “We don’t want to minimize the experiences of those whose homes or property were damaged or who were injured when these two powerful earthquakes struck, because for them these earthquakes will have a lasting impact, and they face some difficult days ahead,” explains Glenn Pomeroy, chief executive of the California Earthquake Authority. “However, if this series of earthquakes had happened in a more densely populated area or an area with thousands of very old, vulnerable homes, such as Los Angeles or the San Francisco Bay Area, this state would be facing a far different economic future than it is today — potentially a massive financial crisis,” Pomeroy says. Although one of the most populous U.S. states, California’s population is mostly concentrated in metropolitan areas. A major earthquake in one of these areas could have repercussions for both the domestic and international economy. Low Probability, High Impact Earthquake is a low probability, high impact peril. In California, earthquake risk awareness is low, both within the general public and many (re)insurers. The peril has not caused a major insured loss for 25 years, the last being the magnitude 6.7 Northridge earthquake in 1994. California earthquake has the potential to cause large-scale insured and economic damage. A repeat of the Northridge event would likely cost the insurance industry today around US$30 billion, according to the latest version of the RMS® North America Earthquake Models, and Northridge is far from a worst-case scenario. From an insurance perspective, one of the most significant earthquake events on record would be the magnitude 9.0 Tōhoku Earthquake and Tsunami in 2011. For California, the 1906 magnitude 7.8 San Francisco earthquake, when Lloyd’s underwriter Cuthbert Heath famously instructed his San Franciscan agent to “pay all of our policyholders in full, irrespective of the terms of their policies”, remains historically significant. Heath’s actions led to a Lloyd’s payout of around US$50 million at the time and helped cement Lloyd’s reputation in the U.S. market. RMS models suggest a repeat of this event today could cost the insurance industry around US$50 billion. But the economic cost of such an event could be around six times the insurance bill — as much as US$300 billion — even before considering damage to infrastructure and government buildings, due to the surprisingly low penetration of earthquake insurance in the state. Events such as the 1906 earthquake and even Northridge are too far in the past to remain in public consciousness. And the lack of awareness of the peril’s damage potential is demonstrated by the low take-up of earthquake insurance in the state. “Because large, damaging earthquakes don’t happen very frequently, and we never know when they will happen, for many people it’s out of sight, out of mind. They simply think it won’t happen to them,” Pomeroy says. Across California, an average of just 12 percent to 14 percent of homeowners have earthquake insurance. Take-up varies across the state, with some high-risk regions, such as the San Francisco Bay Area, experiencing take-up below the state average. Take-up tends to be slightly higher in Southern California and is around 20 percent in Los Angeles and Orange counties. Take-up will typically increase in the aftermath of an event as public awareness rises but will rapidly fall as the risk fades from memory. As with any low probability, high impact event, there is a danger the public will not be well prepared when a major event strikes. The insurance industry can take steps to address this challenge, particularly through working to increase awareness of earthquake risk and actively promoting the importance of having insurance coverage for faster recovery. RMS and its insurance partners have also been working to improve society’s resilience against risks such as earthquake, through initiatives such as the 100 Resilient Cities program. Understanding the Risk While the tools to model and understand earthquake risk are improving all the time, there remain several unknowns which underwriters should be aware of. One of the reasons the Ridgecrest Earthquake came as such a surprise was that the fault on which it occurred was not one that seismologists knew existed. Several other recent earthquakes — such as the 2014 Napa event, the Landers and Big Bear Earthquakes in 1992, and the Loma Prieta Earthquake in 1989 — took place on previously unknown or thought to be inactive faults or fault strands. As well as not having a full picture of where the faults may lie, scientific understanding of how multifaults can link together to form a larger event is also changing. Events such as the Kaikoura Earthquake in New Zealand in 2016 and the Baja California Earthquake in Mexico in 2010 have helped inform new scientific thinking that faults can link together causing more damaging, larger magnitude earthquakes. The RMS North America Earthquake Models have also evolved to factor in this thinking and have captured multifault ruptures in the model based on the latest research results. In addition, studying the interaction between the faults that ruptured in the Ridgecrest events will allow RMS to improve the fault connectivity in the models. A further learning from New Zealand came via the 2011 Christchurch Earthquake, which demonstrated how liquefaction of soil can be a significant loss driver due to soil condition in certain areas. The San Francisco Bay Area, an important national and international economic hub, could suffer a similar impact in the event of a major earthquake. Across the area, there has been significant residential and commercial development on artificial landfill areas over the last 100 years, which are prone to have significant liquefaction damage, similar to what was observed in Christchurch. Location, Location, Location Clearly, the location of the earthquake is critical to the scale of damage and insured and economic impact from an event. Ridgecrest is situated roughly 200 kilometers north of Los Angeles. Had the recent earthquake sequence occurred beneath Los Angeles instead, then it is plausible that the insured cost could have been in excess of US$100 billion. The Puente Hills Fault, which sits underneath downtown LA, wasn’t discovered until around the turn of the century. A magnitude 6.8 Puente Hills event could cause an insured loss of US$78.6 billion, and a Newport-Inglewood magnitude 7.3 would cost an estimated US$77.1 billion according to RMS modeling. These are just a couple of the examples within its stochastic event set with a similar magnitude to the Ridgecrest events and which could have a significant social, economic and insured loss impact if they took place elsewhere in the state. The RMS model estimates that magnitude 7 earthquakes in California could cause insurance industry losses ranging from US$20,000 to a US$20 billion, but the maximum loss could be over US$100 billion if occurring in high population centers such as Los Angeles. The losses from the Ridgecrest event were on the low side of the range of loss as the event occurred in a less populated area. For the California Earthquake Authority’s portfolio in Los Angeles County, a large loss event of US$10 billion or greater can be expected approximately every 30 years. As with any major catastrophe, several factors can drive up the insured loss bill, including post-event loss amplification and contingent business interruption, given the potential scale of disruption. In Sacramento, there is also a risk of failure of the levee system. Fire following earthquake was a significant cause of damage following the 1906 San Francisco Earthquake and was estimated to account for around 40 percent of the overall loss from that event. It is, however, expected that fire would make a much smaller contribution to future events, given modern construction materials and methods and fire suppressant systems. Political pressure to settle claims could also drive up the loss total from the event. Lawmakers could put pressure on the CEA and other insurers to settle claims quickly, as has been the case in the aftermath of other catastrophes, such as Hurricane Sandy. The California Earthquake Authority has recommended homes built prior to 1980 be seismically retrofitted to make them less vulnerable to earthquake damage. “We all need to learn the lesson of Ridgecrest: California needs to be better prepared for the next big earthquake because it’s sure to come,” Pomeroy says. “We recommend people consider earthquake insurance to protect themselves financially,” he continues. “The government’s not going to come in and rebuild everybody’s home, and a regular residential insurance policy does not cover earthquake damage. The only way to be covered for earthquake damage is to have an additional earthquake insurance policy in place. “Close to 90 percent of the state does not have an earthquake insurance policy in place. Let this be the wake-up call that we all need to get prepared.”
Are (re)insurers sufficiently capitalized to withstand a major earthquake in a metropolitan area during peak hours? The U.S. workers’ compensation insurance market continues to generate underwriting profit. According to Fitch Ratings, 2019 is on track to mark the fifth consecutive year of profits and deliver a statutory combined ratio of 86 percent in 2018. Since 2015, it has achieved an annual average combined ratio of 93 percent. The market’s size has increased considerably since the 2008 financial crisis sparked a flurry of activity in the workers’ compensation arena. Over the last 10 years, written premiums have risen 50 percent from approximately US$40 billion to almost US$60 billion, aided by low unemployment and growth in rate and wages. Yet market conditions are changing. The pricing environment is deteriorating, prior-year reserve releases are slowing and severity is ticking upwards. And while loss reserves currently top US$150 billion, questions remain over whether these are sufficient to bear the brunt of a major earthquake in a highly populated area. The Big One California represents over 20 percent of the U.S. workers’ compensation market. The Workers’ Compensation Insurance Rating Bureau of California (WCIRB) forecasts a written premium pot of US$15.7 billion for 2019, a slight decline on 2018’s US$17 billion figure. “So, the workers’ compensation sector’s largest premium is concentrated in the area of the U.S. most exposed to earthquake risk,” explains Nilesh Shome, vice president at RMS. “This problem is unique to the U.S., since in most other countries occupational injury is covered by government insurance schemes instead of the private market. Further, workers’ compensation policies have no limits, so they can be severely impacted by a large earthquake.” Workers’ compensation insurers enjoy relatively healthy balance sheets, with adequate profitability and conservative premium-to-surplus ratios. But, when you assess the industry’s exposure to large earthquakes in more detail, the surplus base starts to look a little smaller. “We are also talking about a marketplace untested in modern times,” he continues. “The 1994 Northridge Earthquake in Los Angeles, for example, while causing major loss, occurred at 4:30 a.m. when most people were still in bed, so had limited impact from a workers’ compensation perspective.” Analyzing the Numbers Working with the WCIRB, RMS modeled earthquake scenarios using Version 17 of the RMS® North America Earthquake Casualty Model, which incorporates the latest science in earthquake hazard and vulnerability research. The portfolio provided by the WCIRB contained exposure information for 11 million full-time-equivalent employees, including occupation details for each. The analysis showed that the average annual estimated insured loss is US$29 million, which corresponds to 0.5 cents per $100 payroll and $2.50 per employee. The 1-in-100-year insurance loss is expected to exceed US$300 million, around 5,000 casualties including 300 fatalities; while at peak work-time hours, the loss could rise to US$1.5 billion. For a 1-in-250-year loss, the figure could top US$1.4 billion and more than 1,000 fatalities, rising to US$5 billion at peak work-time hours. But looking at the magnitude 7.8 San Francisco Earthquake in 1906 at 5:12 a.m., the figure would be 7,300 injuries, 1,900 fatalities and around US$1 billion in loss. At peak work hours, this would rise to 22,000 casualties, 5,800 fatalities and a US$3 billion loss. To help reduce the impact of major earthquakes, RMS is working with the Berkeley Research Lab and the United States Geological Survey (USGS) to research the benefits of an earthquake early warning system (EEWS) and safety measures such as drop-cover-hold and evacuating buildings after an EEWS alarm. Initial studies indicate that an EEWS alert for the large, faraway earthquakes such as the 1857 magnitude 7.9 Fort Tejon Earthquake near Los Angeles can reduce injuries by 20 percent-50 percent. Shome concludes: “It is well known in the industry that workers’ compensation loss distribution has a long tail, and at conferences RMS has demonstrated how our modeling best captures this tail. The model considers many low probability, high consequence events by accurately modeling the latest USGS findings.”