Land as Insurance, Part 1: A Primer
This is a three-part series based on an idea I recently had around the subjects of farming, black swans, and creating a product that can help insure against risk. In this first article I’ll provide a bit of context on how insurance works. The second piece will explore the risks we face today and in the future. The final article will explore a new type of insurance based around perennial regenerative agriculture. Let’s get started!
1. How Insurance Works
I’ll be the first to admit that I am heavily insured. When Michelle and I left the oil and gas industry, we left behind lucrative salaries and benefits to pursue our ecological design careers. Initially, we didn’t think much of insurance because we were young and had no kids, but once we had children we decided that protecting ourselves from low probability but high consequence events would be a prudent thing to do. I have life, disability, and career insurance.
Recently I was thinking about the insurance business model. Basically it works like this:
A large group of people pay premiums to a company that invests in the stock market, real estate, bonds, and other financial instruments and uses that profit to pay out the small number of policies that it sells. All insurance relies on the Gaussian function, which is used to describe distributions. These mathematical distributions can be used to predict a wide range of data, from life expectancy and body mass index to intelligence and grades in a university class. These types of models work when 99.99% of the data points exist within a known range. For example, people almost always live between 0 to 115 years, grades always fall between 0 to 100%, IQ is bracketed within very specific ranges, and human weight has a highly predictable upper and lower limit. Insurance makes sense for companies to make bets and offer products when they have reliable statistical ranges from which to base their models. Money can be made from these known risks.
The basic insurance business model goes something like this. Take life insurance. First, you get approved for a life insurance policy based on specific and testable health metrics. You get offered a package which usually requires you pay a monthly premium for 20-30 years. If you die during your term for reasons the policy insures against, the policy is delivered to a beneficiary and the policy ends. If you don’t die, you get the total sum you paid into the insurance vehicle back at the end of the term.
This sounds great until you realize this is just the dollar amount, not the appreciated amount. The money that was made on your money by the insurance company is how they stay in business and continue to offer their risk products to the general population. Now, people are usually OK with getting this lump sum of money back (even though it’s worth far less than in real terms due to inflation) because they paid for the service of insuring risks that have a huge downside (for example, a spouse dying when the kids are super young and you have a mortgage). Basically, the low probability but high consequence risk is far greater than the aggregate of the monthly payments.
Makes sense so far? Good. Now this system only works under a few specific conditions:
1. There needs to be financial investments that allow the insurance premiums to grow. Again, your real estate, the stock market, bonds, and other financial vehicles.
2. Enough people need to be paying into the system so that the few that will need to cash in their policy will be protected.
3. The risks have to be calculable. In other words, there has to be an empirically known range of outcomes on the population or phenomenon that you are insuring. You cannot have outliers that exist orders of magnitude out of the norm. Systems that contain these outliers bring down insurance companies. The movie “The Big Short” details how banks insured mortgage hedge funds thinking that they were highly secure and used a safety factor of 2-4 when they were in fact out by over 1000. Their model collapsed and companies like AIG evaporated.
Next time, we’ll explore the hidden risks that can cause this system to fail.