The classic banking model is actually quite simple. In this basic model banks collect deposits in the form of savings and checking accounts as well as CDs. They then lend this money out to in a variety of loans, while holding a bit of money in reserve. Banks often make money because they can generally pay a lower average rate on deposits and charge a higher average rate on loans. We can call this difference an interest rate spread . Looking at the whole portfolio of deposits and loans the total difference between interest paid and interest received could be called gross interest income.
This business model does has some potential vulnerabilities. The paramount issue is that some loans will not be paid back. This is called defaulting on a loan. If the costs of defaults exceed the gross interest income then the bank is losing money. Additionally, when a bank admits to a loan gone bad they do a write off or a write down on the load. These write downs are subtracted from the bank’s reserve ratio. If this ratio goes below the required reserve ratio the bank has to fix the problem by borrowing money from other banks, people, companies, or the Federal Reserve (“The Fed”).
Banks employ several techniques to reduce and manage the risks of default. The biggest hammer in their bag of tricks is collateral. Many banks issue a lot of collateralized loans such as mortgages, which stipulate that the borrower will surrender the house to the bank if they default on the mortgage. Thus the bank loses the amount of the unpaid loan, but gains a house. This is OK if the house is worth the same or more than when it was purchase. But it is bad news for the bank if the house is worth less than the outstanding loan balance. The bigger the difference, the bigger the write down.
To try to further reduce default risk, banks seek diversification. One type of diversification is geographical. A bank might issue mortgages on homes in California, Michigan, Washington and Texas. The ideas is that while Michigan, say may have a bad couple of years due to auto industry problems, the other states may fair better. There might be a lots of defaults in Michigan, but the are hopefully offset by fewer defaults in other areas.
A few more words about mortgage defaults and write downs. Defaults happen in good times and bad. When housing prices are stable, they are inconvenient for banks, but generally result in only modest write downs and losses. When housing prices drop significantly, however, banks experience a double whammy. First, each default is more costly — houses are worth much less than the loan. Second, default rates are generally higher due economic problems that tend to go hand-in-hand with falling home prices. Additionally some less credit-worthy individuals simply chose to walk away (default on) from their now “underwater” mortgages where the amount owed is more than the home is worth.
The 2008-2009 “banking crisis” can be explained in large part by the observation that key assumptions were made that turned out to be overly optimist or flat out wrong.
- Assumption#1: Geographic diversification will dramatically reduce overall default risk, especially for large, highly-diversified banks.
- Assumption #2: Housing prices, on average, will continue to rise or at least remain stable.
- Assumption #3: Clever strategies and new financial instruments will help transform, shape, reduce, and even eliminate default and other risks.
The Shiller Housing Index helps illustrate why assumptions #1 and #2 turned out wrong in 2008. I believe that up until this point the basic problem is pretty clear:
Banks make assumptions and lend lots of money. For a while this seems to work. Banks grow more confident and make more optimistic assumptions and rationalize this behavior with risk manager positions, computer risk models, and fancy looking charts, presentations, and Nobelaureate-blessed terminology. When these lofty assumptions suddenly turn false, many banks quickly find themselves spiraling towards collapse.
In a future blog I hope to dive into the more complex problems hinted at but Assumption #3. Understanding the impact of Assumptions #1 and #2 helps explain how fuel and spark were created to start dangerous fires in many banks’ balance sheets. As I delve into Assumption #3 I hope to show how powerful and dangerous financial gasoline was created, stored, and shipped between the banks and other financial institutions. I also hope to delve into how financial building codes and practices have changed– enabling banks and other companies to remove firewalls, sprinkler systems, and other “old fashioned” safety measures because of the implied security of Assumption #3.