Michael Lewis (Moneyball and The Blind Side) explains that Americans are making a lot of bad bets In Nature’s Casino:
From Miami to San Francisco, the nation’s priciest real estate now faced beaches and straddled fault lines; its most vibrant cities occupied its most hazardous land. If, after World War II, you had set out to redistribute wealth to maximize the sums that might be lost to nature, you couldn’t have done much better than Americans had done. And virtually no one — not even the weather bookies — fully understood the true odds.
Slowly the finance world has caught on, and hedge funds have sprung up to buy and sell cat bonds:
The buyer of a catastrophe bond is effectively selling catastrophe insurance. He puts down his money and will lose it all if some specified bad thing happens within a predetermined number of years: a big hurricane hitting Miami, say, or some insurance company losing more than $1 billion on any single natural disaster. In exchange, the cat-bond seller — an insurance company looking to insure itself against extreme losses — pays the buyer a high rate of interest.
I naively assumed that insurance companies understood their own line of business:
An insurance company could function only if it was able to control its exposure to loss. Geico sells auto insurance to more than seven million Americans. No individual car accident can be foreseen, obviously, but the total number of accidents over a large population is amazingly predictable. The company knows from past experience what percentage of the drivers it insures will file claims and how much those claims will cost. The logic of catastrophe is very different: either no one is affected or vast numbers of people are. After an earthquake flattens Tokyo, a Japanese earthquake insurer is in deep trouble: millions of customers file claims. If there were a great number of rich cities scattered across the planet that might plausibly be destroyed by an earthquake, the insurer could spread its exposure to the losses by selling earthquake insurance to all of them. The losses it suffered in Tokyo would be offset by the gains it made from the cities not destroyed by an earthquake. But the financial risk from earthquakes — and hurricanes — is highly concentrated in a few places.There were insurance problems that were beyond the insurance industry’s means. Yet insurers continued to cover them, sometimes unenthusiastically, sometimes recklessly. Why didn’t insurance companies see this? Seo wondered, and then found the answer: They hadn’t listened closely enough to Karen Clark.
What Karen Clark did was obvious, yet no one else was doing it:
To better judge the potential cost of catastrophe, Clark gathered very long-term historical data on hurricanes. “There was all this data that wasn’t being used,” she says. “You could take it, and take all the science that also wasn’t being used, and you could package it in a model that could spit out numbers companies could use to make decisions. It just seemed like such an obvious thing to do.” She combined the long-term hurricane record with new data on property exposure — building-replacement costs by ZIP code, engineering reports, local building codes, etc. — and wound up with a crude but powerful tool, both for judging the probability of a catastrophe striking any one area and for predicting the losses it might inflict. Then she wrote her paper about it.The attention Clark’s paper attracted was mostly polite. Two years later, she visited Lloyd’s — pregnant with her first child, hauling a Stone Age laptop — and gave a speech to actual risk-takers. In nature’s casino, they had set themselves up as the house, and yet they didn’t know the odds. They assumed that even the worst catastrophe could generate no more than a few billion dollars in losses, but her model was generating insured losses of more than $30 billion for a single storm — and these losses were far more likely to occur than they had been in the previous few decades. She projected catastrophic storms from the distant past onto the present-day population and storms from the more recent past onto richer and more populated areas than they had actually hit. (If you reran today the hurricane that struck Miami in 1926, for instance, it would take out not the few hundred million dollars of property it destroyed at the time but $60 billion to $100 billion.) “But,” she says, “from their point of view, all of this was just in this computer.”
She spoke for 45 minutes but had no sense that she had been heard. “The room was very quiet,” she says. “No one got up and left. But no one asked questions either. People thought they had already figured it out. They were comfortable with their own subjective judgment.” Of course they were; they had made pots of money the past 20 years insuring against catastrophic storms. But — and this was her real point — there hadn’t been any catastrophic storms! The insurers hadn’t been smart. They had been lucky.
Her big win came with Hurricane Andrew:
Hurricane Andrew made landfall at 5 on a Monday morning. By 9 she knew only the path of the storm and its intensity, but the information enabled her to estimate the losses: $13 billion, give or take. If builders in South Florida had ignored the building codes and built houses to lower standards, the losses might come in even higher. [...] The scuttlebutt from Lloyd’s already had it that losses couldn’t possibly exceed $6 billion, and some thought they were looking at a loss of just a few hundred million. “No one believed it,” she says of her estimate. “No one thought it was right. No one said, ‘Yeah, $13 billion sounds like a reasonable number.’ ” As she ate, she wondered what $13 billion in losses looked like. [...] It took months for the insurers to tote up their losses: $15.5 billion. (Building codes in South Florida had not been strictly enforced.) Fifteen and a half billion dollars exceeded all of the insurance premiums ever collected in Dade County. Eleven insurance companies went bust. And this wasn’t anything like the perfect storm. If it had gone into Miami, it could have bankrupted the whole industry. Clark had been right: the potential financial losses from various catastrophes were too great, and too complicated, to be judged by human intuition. “No one ever called to congratulate me,” Clark says, laughing. “But I had a lot of people call and ask to buy the model.”
One last quote:
Wall Street is a machine for turning information nobody cares about into information people can get rich from.
Go read the whole article.