The fastest growing states are also the most at risk from severe weather.
The eight storms we examined that occurred between 2017 and 2021 resulted in more than $130 billion in damages and claims, some of which remain unpaid. Why the delay?
Because the traditional insurance industry relies on a manual claim event to trigger the payment process. One day, when the red tape has been hacked through, you hopefully get some kind of payout.
That reactive approach is not a formula that works in the wake of a natural disaster. What’s needed is insurance coverage for severe weather triggered when the storm starts, not months or years after.
Enter parametric insurance -> quick, automatic payouts based on fixed thresholds, like the size of hailstones, or the wind speed of a hurricane. When the threshold is met, payment is triggered. The worse the event, the bigger the payout.
It’s a fascinating technical and business innovation at the right moment in time.
Could adding mobility data to the equation help grow the market for parametric insurance?
Unacast studied population flow, severe weather events and poverty rankings between 2017 and 2021 across the 48 contiguous United States.
In addition to using our own Migration Patterns dataset, we referenced the United States Climate Change Index (CCI), the 2022 Poverty Rate by state, and generally available information on damages and claims associated with the storms we studied.
To explore the correlation of population growth to incidents of severe weather, we focused on eight of the fastest growing states as of Q3 2022: Alabama, South Carolina, Oklahoma, Arkansas, Mississippi, Florida, North Carolina and Texas.
We selected eight severe weather events that had occurred across these eight states between 2017 and 2021: Hurricane Irma, Hurricane Harvey, Hurricane Florence, Hurricane Dorian, Hurricane Laura, Hurricane Ida, the Kentucky tornado and the Texas winter storms.
Use cases for parametric insurance in severe weather events were gathered from web resources and client-partner conversations. Our extrapolation to parametric use cases for migration patterns and foot traffic data is theoretical.
Table of Findings
Crowds are swarming where clouds are forming
Each of the eight fast-growing states we studied experienced at least one severe weather event between 2017 and 2021. Texas (4 events) and Florida (3 events) were the hardest-hit. Five of the states are in the southeast (FL, MS, AL, NC, SC), the remainder are all in the south.
Every one of the eight states studied ranks in the bottom-third in the nation for its 2022 poverty rate, the national average sitting at about 11.2%. In many cases, there is a corollary between a state’s poverty ranking and its climate change ranking. For example, Mississippi (T-5th in population flow) is ranked dead last in poverty rate (19.07%) and second to last on the Climate Change Index (23.1).
We see a similar pattern in AR, AL, OK, SC and NC, where poverty and climate risk rankings are well-aligned. TX and FL are the exception to the rule due to environmental protections enacted over the last several years. That variance aside, viewed from above, the aggregate data table tells a clear tale:
More people are choosing to move to the most poverty-stricken, disaster-prone places in America than anywhere else, placing themselves directly at risk.
Perhaps the migration is because of warmer weather, attractive income tax rates, or lower costs of living. In any event, as a result of the growth there’s a good chance that the next ‘once in a century’ storm will threaten more lives, damage more property, and interrupt more businesses, at even greater cost than the last.
How can the insurance industry both underwrite the totality of that looming risk and build the technology to simplify payments and the recovery process? By evolving its perspective, and adapting its products.
Weather events 2017 - 2021
Parametric Insurance and Natural Disasters
Though not well understood outside the industry, parametric insurance is an established global product for coverage of severe weather events and other natural disasters. To date, parametric products have most frequently been implemented in developing economies, though there are proposals in the U.S. around flood insurance. Three examples of existing use cases for parametric insurance follow:
Protecting coral reefs and business in Mexico
Not far off the coast of Cancun lies the Mesoamerican Reef. Though slowly degrading, the reef still prevents an estimated $63 million in damages to business annually. To protect the reef and those businesses, the government took out a parametric policy with payouts triggered when windspeed meets certain thresholds – at 100 knots it’s 40%, at 130 knots it’s 80%, etc. The policy, which is capped at a maximum of $3.8 million annually, was triggered during Hurricane Delta in 2020.
Pooling resources to manage risk in the Caribbean
The Caribbean Catastrophe Risk Insurance Facility is an insurance company that allows Caribbean nations to get parametric insurance coverage against weather catastrophes, with each country paying much less than if they had gone through the private market.
Hail coverage in Australia’s Northern Territory
The actual value of damage from a weather event is impossible to calculate before it happens and insurance doesn’t like uncertainty. So, by setting a threshold value for a claim, like the size of the hailstones that are falling, Mainstay Underwriting obviates that uncertainty for both policy holders and their own business.
Mobility data use cases for parametric insurance
Each of the use cases noted above is unique. In the case of Australia, a physical measurement (size of hailstones) is used to validate the threshold trigger. In Mexico, it’s wind speed, another measurable data point. In the Caribbean, it’s a pooled-market innovation that’s created less risk and more certainty on both the buy and sell sides.
We think mobility data can be added to the parametric insurance data stream in order to introduce new products and expand the addressable market.
Here are three use case concepts for mobility data in parametric insurance that invite your discussion.
Migration patterns for assessing growing risk due to population density
The first use case for mobility data in parametric insurance is simply introducing it to the marketplace. Like in the Caribbean, where the buyer’s market was the business innovation that introduced the technical innovation, mobility data needs to be contextualized as an empirically demonstrable indicator of future damages and claims. If that sounds a step far, please understand that the process is already underway.
Population flow - where we began this discussion - is now being harnessed as an indicator of future risk due to overpopulation, or increased population density, in areas often hit by severe weather events. The logic is simple: where there are more people, so is there greater risk to life, property, and the normal process of business. Ergo, the opportunity and need for the insurance industry to contemplate population flow and migration patterns as a part of the parametric equation.
Foot traffic for measuring business interruption
Here we move into the hypothetical but it feels like a comfortable step. We begin with a question:
What if foot traffic patterns were used as the threshold trigger for business interruption events due to natural disasters?
Foot traffic data is easy to ingest, location specific, and measurable then, now and in the future. So, the insurer can look at actual foot traffic patterns to a physical location before, during and after an event and clearly see how the business has been impacted. If foot traffic is down 50%, maybe that’s a threshold payout trigger. When it’s down 75%, that’s another trigger.
The nice thing about using foot traffic this way is that, in addition to seeing the immediate effect of the trigger event, you can also see how the business is recovering in concrete terms, and versus its historical performance. This would allow the insurer to craft more targeted products, while allowing the insured to buy in a way that truly makes sense for their business.
Is the Covid-19 pandemic over? Is there another one coming? When? We don’t really know the answer to these questions, nor can we. But we do know that if another pandemic does come along we collectively need to be better prepared for it. The business interruption use case jumps out again here.
How many businesses big and small closed for good due to a lack of foot traffic during the pandemic? How many others are still struggling to recover 30 months down the line? If they could prove and quantify - against validated ground truth - that their business was harmed because of the pandemic, could they use that data to get insured against the next one?
For example, if average foot traffic levels in downtown Smithville dropped 70% overnight due to new pandemic restrictions, and the Smithville BIA had collectively purchased a parametric insurance policy for pandemic coverage on their businesses, every BIA member would get a triggered payout within days of the foot traffic drop off to help stay afloat. If the interruption lasted for a long time, or recovery was slow, they would continue to receive triggered payments until such time as traffic levels returned above the threshold.
The end result would be more clients for insurers and more businesses surviving the next pandemic.
It’s not if the next big storm is coming to Florida, or Mississippi, or Texas, or any of the other states we featured, it’s when.
Migration patterns tell us that, when it happens, there may well be more people, property and businesses at risk than in the last storm.
Certainly, damages to property and business interruption claims will follow, perhaps in greater volumes than the insurance industry has ever before faced. Are we really ready for another Harvey?
Defining that moment when the winds howl and hail plummets will be critical to triggering parametric coverage.
Measuring foot traffic before and after that trigger moment could be critical to crafting insurance products to fill the market’s long term needs.
Got some thoughts on the use of mobility data in parametric insurance? Book a meeting now and let’s talk.