As a new wildfire season in California is ablaze, answers to
questions about insurers’ pricing, underwriting, and exposure management
functions resulting from the 2017 and 2018 seasons are still taking shape.
According to Milliman estimates, the 2017 wildfire season alone wiped out just
over 10 years of underwriting profits for California homeowners insurers. Moreover,
the combined 2017 and 2018 wildfire seasons wiped out about twice the combined
underwriting profits for the past 26 years, leaving the insurance industry with
an aggregate underwriting loss of over $10 billion for the California homeowners
line of business since 1991.
A historically profitable line of business has recently
become an unprofitable line exposed to a severe peril that is neither easily
measured nor fully understood. As a result, wildfire risk has become a key
focus of Californians, and their property insurers.
Catastrophe simulation models, or “CAT models,” have been
developed for a variety of catastrophic perils, such as hurricanes, floods,
winter storms, earthquakes, and wildfires, to provide insurers with scientific
techniques to quantify and assess their exposure to catastrophic risk. Recognizing
the growing importance of this peril, a number of firms have been working to
apply the latest techniques in catastrophe modeling to wildfires.
In their article “Wildfire catastrophe models could spark the changes California needs,” Milliman’s Eric Xu, Cody Webb, and David D. Evans explain how enhanced quantification and understanding of wildfire risk represents one of the most important challenges for property insurers writing business in the Western United States, and how innovations in the field of catastrophe modeling may assist them with this task.
In 2014, Milliman published a range of articles and videos, covering issues including retirement ideas for Millennials, the pros and cons of catastrophe models, the value of enterprise risk management (ERM) programs, and the impact of the Patient Protection and Affordable Care Act (ACA) on financial statements. We also published on challenges related to healthcare costs and insurance and risk management issues—and about real insurance for fantasy football and insurance for ride sharing. To view this year’s 10 most viewed articles and reports, click here.
Many Cinco de Mayo revelers will be affected by the great lime crisis of 2014. As lime prices climb, restaurants and bars have passed the costs on to customers ordering margaritas, fish tacos, or simply a lime wedge in their cerveza. Others have taken to using lemons as a substitute for limes in recipes, although we haven’t seen the key lemon pie yet. The increased prices are driven by the laws of supply and demand. While demand has increased quite significantly in recent years, the dramatic increase in price this year is due to a shortage in the supply. Poor weather in Mexico’s lime-growing regions and crop disease have severely reduced the supply, and cartel activity has exacerbated the situation (97% of U.S. limes are grown in Mexico). This situation is unprecedented, but the market dynamics driving it are common to the insurance world.
What is the connection between limes and insurers? Extreme weather events, such as Hurricane Katrina, Superstorm Sandy, and this week’s tornados across the southern United States, are a well-known challenge for insurers. As communities recover in the aftermath of a storm, the focus often moves to the rebuilding process—and the related cost. It’s easy to watch the price tag on building supplies, namely lumber and concrete, to see the effects of increased demand. The cost of labor is harder to monitor, and is often a bigger influence on the overall costs of the rebuilding process. Much like restaurants passing on the increased price of limes to their customers, these additional costs are often passed on to the insured.
Insurers incorporate anticipated price spikes into the price of homeowners policies. Catastrophe modeling has become standard across the industry, and these models include loads for the surging price of building materials and labor in high-demand situations. This enables insurers to set appropriate prices over an entire book and helps to keep them solvent. From the point of view of the insured, the increased costs to rebuild after a covered natural disaster may or may not be covered, depending on the policy. Most insureds have policies that cover a set dollar limit, which is usually the replacement cost or an extended replacement cost to cover inflation from the time the policy was written. The payout in these scenarios can often come up short, as out-of-date policies do not cover today’s prices or skyrocketing costs. These issues can be avoided with a guaranteed replacement cost policy, which covers the full cost of rebuilding, but these policies are more expensive and harder to obtain. In the absence of one, rebuilding homeowners may be seeing the same unexpectedly high tab that anyone ordering the ceviche will face this spring.
As the frequency of catastrophic weather events has grown, individual homeowners need to ensure that their coverage is current and that it will optimally cover them in the event of a loss, even if it is not possible to cover surging prices. After all, the last thing the victims of a natural disaster need is the added stress of finding out that their homeowners policy was a lemon.
Catastrophe (CAT) models help organizations understand the risks posed by natural disasters. Conversely, excess dependency on the output from CAT models can have damaging effects an insured loss. A new article authored by Derek Newton entitled “Catastrophe models: Traps and pitfalls” provides an overview of hidden dangers that risk managers should examine.
Here is an excerpt:
Remember that the model is only a fuzzy version of the truth
It is human nature to take the path of least resistance; that is, to rely on model output and assume that the model is getting you pretty close to the right answer. After all, we have the best people and modelers in the business! But, even were that to be true, there can be a kind of vicious circle in which model output is treated with most suspicion by the modeler, with rather less concern by the next layer of management and so on, until summarized output reaches the board and is deemed absolute truth.
We are all very aware that data is never complete, and there can be surprising variations of data completeness across territories. For example, there may not be a defined post or zip code system for identifying locations, or original insured values may not be captured within the data. The building codes assigned to a particular risk may also be quite subjective and there can be a number of ‘heroic’ assumptions made during the modelling process in classifying and preparing the modelling data set. At the very least, these assumptions should be articulated and challenged. There can also be a ‘key person’ risk where data preparation has traditionally resided with one critical data processor, or a small team. If knowledge is not shared then there is clear vulnerability to that person or team leaving. But there is also a risk of undue and unquestioning reliance being placed upon that individual or team, reliance that might be due more to their unique position than to any proven expertise….
Employ additional risk monitoring tools beyond the catastrophe model(s)
Catastrophe models are a great tool, but it is dangerous to rely on them as the only source of risk management information, even when an insurer has access to more than one proprietary modelling package.
Other risk management tools and techniques available include:
• Monitoring total sum insured (TSI) by peril and territory
• Stress and scenario testing
• Simple internal validation models
• Experience analysis
Stress and scenario testing, in particular, can be very instructive because a scenario yields intuitive and understandable insight into how a given portfolio might respond to a specific event (or small group of events). It enjoys, therefore, a natural complementarity with the hundreds of thousands of events underlying a catastrophe model. Furthermore, it is possible to construct scenarios to investigate areas where the catastrophe model may be especially weak, such as consideration of cross-class clash risk.