Risk Pooling Breaks Down
When the math gets too good, the thing it was built for stops working
In October 2021, the Federal Emergency Management Agency did something it had never done in the fifty-three-year history of the National Flood Insurance Program. It changed how it priced flood risk. The old system — in use since the program’s creation in 1968 — assigned premiums based on flood zone maps, broad geographic designations that sorted properties into categories: in a floodplain or not, in a special hazard area or not. The maps were crude. They were updated slowly, sometimes decades apart. They priced entire neighborhoods identically. A house three feet above the flood line and a house three feet below it paid the same rate if both sat inside the same zone boundary. The system was inaccurate. It was also functional, in the specific sense that it kept the pool together.
Risk Rating 2.0, as FEMA named the replacement, introduced property-level pricing. Each structure is now assessed individually based on distance to a water source, elevation relative to flood levels, the cost to rebuild, the type of flood risk (river, storm surge, coastal erosion, heavy rainfall), and historical loss data. The methodology is, by actuarial standards, a significant improvement. The Government Accountability Office confirmed as much in a 2023 review: the new system is more actuarially sound. Premiums now correspond more closely to the actual risk each property faces. Seventy-seven percent of policyholders saw their premiums increase. Nine percent will eventually require increases exceeding 300 percent. The national average annual premium sits at roughly $926 — but this is a number in transit. The GAO estimates that it will take until 2037 for 95 percent of policies to reach their actuarially correct price, and the cumulative shortfall between what policyholders are currently paying and what the risk actually costs is approximately $27 billion.
The program itself owes $22.5 billion to the U.S. Treasury, borrowed against a $30.4 billion ceiling that Congress periodically raises. It borrowed another $2 billion in February 2025 to cover claims. Eight hyperclustered storm events — all within the last twenty-one years — account for more than half of all claims in the program’s history. The NFIP is not bankrupt in the legal sense, because a federal program cannot be bankrupt. It can only be perpetually insolvent and perpetually re-authorized, which is a different condition with the same arithmetic.
The paradox of accuracy
The instinct is to treat Risk Rating 2.0 as a correction. The old system underpriced risk. The new system prices it accurately. Accurate pricing is better than inaccurate pricing. This seems obvious.
It is not obvious. It depends entirely on what you think insurance is for.
Ian Hacking, in The Taming of Chance, traced how the rise of statistical thinking in the nineteenth century transformed institutions that had previously operated on judgment, custom, and rough approximation into institutions that operated on probability. Insurance was one of the first. The fundamental insight was that individual events are unpredictable but aggregate behavior is not. One house might flood or might not. Ten thousand houses distributed across a geography will flood at a knowable rate. The insurer does not need to know which house. It needs to know the rate. The pool exists to absorb the individual uncertainty that the mathematics have rendered collectively predictable. The price of admission — the premium — is set not at the individual’s actual risk but at the average risk of the pool. This is the mechanism. It works because it is imprecise. The low-risk subsidize the high-risk. The young subsidize the old. The lucky subsidize the unlucky. The cross-subsidy is not a flaw in the system. It is the system.
What happens when you make the pricing precise?
The cross-subsidy disappears. Each person pays according to their own risk profile, and the pool fragments into a collection of individual transactions. The person whose house sits fourteen feet above the flood level pays almost nothing. The person whose house sits two feet above it pays a premium that approaches the expected cost of the flood itself. At the mathematical limit — where pricing is perfectly accurate — the premium equals the expected loss, which means the insurance provides no economic benefit to the policyholder. You are paying a dollar to receive a dollar. The risk transfer has collapsed into an accounting exercise. The pool has not been improved. It has been dissolved.
This is not a theoretical concern. Since FEMA implemented Risk Rating 2.0, new NFIP policy uptake has declined by roughly 11 to 39 percent depending on the magnitude of the premium increase, and renewals of existing policies have dropped by 5 to 13 percent. The declines are largest in lower-income communities. The people leaving the pool are not the people whose risk decreased. They are the people whose risk was most accurately priced — and who discovered they could not afford the accuracy.
The pool is shrinking from the bottom.
What the pool was holding
A flood insurance pool that includes both high-risk and low-risk properties functions. A flood insurance pool that includes only high-risk properties — because the low-risk have been priced into a cheaper tier or have dropped coverage entirely — is not a pool. It is a collection of expected losses, priced at cost, with no surplus to absorb the unexpected. The actuarial term is adverse selection, and it has been understood since at least the 1970s: when pricing becomes granular enough to separate the healthy from the sick, the safe from the dangerous, the cheap from the expensive, the people who remain are disproportionately the people who will file claims. The pool becomes sicker as it becomes more accurately priced. The sicker it becomes, the more expensive it becomes. The more expensive it becomes, the more people leave. The spiral does not reverse.
(I keep circling a formulation here that I cannot land precisely. The NFIP was designed as a public program because the private market would not insure flood risk — the losses were too concentrated, too correlated, too catastrophic. The private market looked at flood risk and said: this cannot be pooled profitably. The government stepped in and said: we will pool it anyway, because the social cost of not pooling it is worse. That was a policy decision, not an actuarial one. Risk Rating 2.0 applies actuarial logic to a program that was created specifically because actuarial logic could not solve the problem. I am not sure whether this means the reform is contradictory or whether it means the original premise was always unstable. Both possibilities lead somewhere I have not yet worked through.)
Consider the specific geography. Along the Gulf Coast, from Galveston to the Florida Panhandle, NFIP premiums for properties in high-risk zones are reaching 20 to 30 percent of the home’s assessed value. A house worth $150,000 carrying $30,000 in annual flood insurance premiums is a house that cannot be sold, because no buyer can finance the carrying cost. It cannot be mortgaged, because lenders require flood insurance in special flood hazard areas and the insurance exceeds what the property can service. It cannot be abandoned without consequences, because the mortgage still exists. The homeowner is locked into a property whose market value the insurance cost has destroyed, unable to sell, unable to refinance, unable to leave, unable to stay without paying a premium that the property itself does not justify. The insurance has not protected the asset. It has priced the asset out of the category of things worth protecting.
There is a particular quality to the FEMA flood map when you look at it on a screen — the color gradations from blue to orange to red, the way a parcel boundary can straddle two zones, the thin black line of your property sitting half in one color and half in another, the precision of it suggesting a certainty that the underlying hydrology does not actually possess. The map is a model. The model is good. The colors are vivid and clean. The experience of locating your own address on it and watching the color tell you what your future costs is not something the model accounts for.
The direction of fragmentation
The NFIP is one program in one country covering one type of risk. The pattern it illustrates is not limited to flood insurance.
Health insurance underwent a version of this fragmentation when the Affordable Care Act’s individual mandate was effectively eliminated in 2019. The mandate existed to keep the pool intact — to ensure that healthy people subsidized sick people, which is the mechanism by which insurance functions. Without it, the pool began to segment. Healthy enrollees left. Premiums rose for those who remained. The coverage became more expensive precisely because it became more concentrated among those who needed it. The same mathematics applied: accurate sorting of who needs insurance most produces a pool composed of the most expensive members, which produces a pool that cannot sustain itself.
Private auto insurance has been granulating for two decades. Telematics devices — small units mounted in vehicles that track speed, braking, cornering, time of day, miles driven — allow insurers to price policies based on individual driving behavior rather than demographic category. Safe drivers pay less. Dangerous drivers pay more. Each driver is priced according to their own data. The cross-subsidy between safe and dangerous drivers shrinks. The dangerous drivers who remain in the pool face premiums that reflect their concentrated risk, and the concentrated risk is expensive enough that some of them stop carrying insurance, which transfers the cost of their accidents to the people they hit, who now bear a risk that the insurance pool was supposed to absorb.
Genetic testing presents the terminal case. If an insurer can assess an applicant’s genome and price the policy according to their heritable disease risk — and in most U.S. states, life insurers and disability insurers face no prohibition against using genetic information — the pool fragments along the most granular possible axis. The person with a BRCA1 mutation pays for the cancer they have not yet developed. The person without it pays almost nothing. The mathematics are impeccable. The pool no longer exists in any meaningful sense, because there is no uncertainty left to distribute. Risk pricing is a form of selection. What it selects, at sufficient resolution, is who deserves to be pooled and who does not — and the category of people who do not deserve pooling grows in exact proportion to the accuracy of the instrument doing the selecting.
What accuracy costs
The pattern is consistent across domains and it moves in one direction. As risk assessment improves — as models become more granular, as data becomes more abundant, as pricing becomes more precise — the cross-subsidy that makes insurance functional erodes. The people identified as high-risk are priced out. The people identified as low-risk see no reason to subsidize anyone. The pool fragments into individual risk profiles, each accurately priced, none of them insured in the sense that the word originally meant.
This creates a problem that better modeling cannot solve, because better modeling is the cause. The accuracy of risk models is their undoing. Perfect information makes insurance impossible — not because the information is wrong but because insurance depends on a specific kind of ignorance: the inability to distinguish, at the point of underwriting, between the person who will need the pool and the person who will not. That inability is what justifies charging both of them the same price. Remove it, and you have not improved insurance. You have replaced it with something else — a system that charges each person the precise cost of their own misfortune, which is not risk transfer. It is prepayment.
Whether a system that converts insurance into prepayment is better or worse than a system that pools risk inaccurately is not a question the actuarial science can answer, because the actuarial science is optimized for accuracy, and accuracy is what produces the fragmentation. The question belongs to a different discipline — one that asks not “what does this person’s risk cost?” but “what happens to a society in which everyone knows exactly what their risk costs and some of them cannot pay it?” The data shows the cost. The data does not show what to do when the cost exceeds what the person can bear. That calculation — the one that weighs the accuracy of the model against the durability of the commons — is not a calculation the model was designed to perform. It may not be a calculation at all. It may be a decision, and decisions require something the model does not have, which is a preference for one outcome over another that cannot be justified by the mathematics alone.
The mathematics are getting better every year. The pool is getting smaller.
-Aimé
Aimé Halden writes Uninsurable, a newsletter about the systems that shape who is protected and who is not. Subscribe for weekly analysis.