Kling on Freddie and Fannie

My interest in Peak Rent is decades old, but this blog is the offspring of my more recent love affair with the blog Cafe Hayek and the podcast EconTalk. I enthusiastically recommend both. A central thesis of this blog is bolded below.

This post replies to now obsessive talk of the credit crunch following the deflation of the housing bubble and the mortgage backed securities that propelled it, but this blog places these events in a broader context.

This episode of EconTalk features Arnold Kling, a former economist at the Federal Home Loan Mortgage Corporation, Freddie Mac, one of the main characters in the Mortgage Backed Securities story.

Kling says, "The mortgages can default, but the bonds cannot, so we (Freddie Mac) are in the middle taking the default risk."

He doesn't continue, "... until we can't bear the risk anymore, whereupon we transfer the risk to taxpayers."

Freddie Mac sells rents to rent seekers. Seeking rents was always its business, even before the subprime debacle. If it doesn't sell rents to rent seekers, it needn't sell bonds at all.

With Congressional authority, GSEs could simply create money to lend and then remove this money from circulation as it's repaid, or (equivalently) Congress could endow FNMA with funds, and FNMA could lend these funds and then relend them as loans are repaid, charging enough interest to keep its funds stable or growing rapidly enough to meet a growing demand for credit. It's only function then would be to police the credit. It would never pass yields to anyone except the growing pool of home sellers.

If the buyers of bonds don't stand to lose when credit is poorly policed, then they play no role in the policing of credit, so they're pure rent seekers; therefore, selling rents to rent seekers is the primary motive of Freddie Mac's business definitively. That's not a theory. It's an observation.

Referring to mortgage backed securities, Russ says, "The law of large numbers is such that it's going to be more predictable ..."

The law of large numbers need not apply in this context at all and presumably does not apply. This point is crucial.

First, "Law of Large Numbers" technically doesn't refer to a statistical average (like the yield of a package of many mortgages); however, this usage apparently is common. Here is Wikipedia's page on the subject.

Techinically, Wikipedia doesn't describe the Law of Large Numbers at all. The page describes a result involving a statistical average when a distribution has a well defined mean. This result is a consequence of the Law of Large Numbers, but it's not the Law of Large Numbers. At least, it's not what I learned to call "the Law of Large Numbers" in graduate school. The Law of Large Numbers does not involve a statistical average or assume a distribution with a well defined mean.

The Law of Large Numbers says that the proportion of outcomes, in random samples, with a particular characteristic approaches the probability (or frequency) of this characteristic, so for example, the proportion of heads approaches one half in a sequence of fair coin tosses. You can verify this usage of "Law of Large Numbers" by googling. Here's a page at Berkeley for example.

This Law of Large Numbers (not the result described at Wikipedia) is always true for any probability distribution, because it's essentially tautological. It's true because of what we mean by "probability" and "random sample". As such, it's more of like a "law", i.e. it's true without exception.

The result described at Wikipedia is what Russ means by "Law of Large Numbers", but this result involves an assumption about the distribution of values, so it's not true in general. The Wikipedia article states this assumption when it says, "Given a random variable with a finite expected value ..."

"Expected value" is a property of a statistical distribution, but some distributions don't have a well defined (finite) expected value. A "fat tailed distribution" doesn't have a finite expected value, by definition.

In a fat tailed distribution, rare values contribute significantly to statistical samples, so that larger and larger sample averages don't converge to a well defined mean value. The standard example is Cauchy's distribution.

We typically study thin tailed distributions in elementary statistics, the standard example being the Gaussian (or Normal) distribution, but fat tailed distributions may be the rule rather than the exception in Economics. See the EconTalk with Nicholas Taleb.

The yield of a mortgage backed security essentially is an average of the yields of mortgages backing the security. If the distribution of the underlying yields is fat tailed, the yield of the security need not be more predictable than the yield of a single mortgage.

So why might the underlying distribution be fat tailed? Can we simply blame politicians encouraging subprime mortgages? I don't think so. The problems we're seeing with mortgage backed securities are not limited to these political influences or to these securities.

Similar problems exist with all CDOs and also with mutual funds and even with corporations, since the yield of a corporation essentially is an average of yields of its factors of production.

The underlying distribution of yields is fat tailed, because Black Swans are everywhere. A particular political influence is only one example. We can't point to one Black Swan and say that we've explained the problem. The next Black Swan could be red.

Kling said, "The creativity of mortgage bankers in delivering fraudulent loans is tremendous ..."

Basically, Kling indicts the "unregulated market" here, but it's a mistake simply to blame criminal "fraud" for the fat tail. This fraud is just another Black Swan. We can't kill all the Black Swans by passing a law. Killing them all is not possible, even in principle, just as a centrally planned economy without market prices can't be more productive than a free market economy in principle.

Kling said, "I'm not sure what made investors willing to buy these securities ..."

Kling asks the right question. It's not about the sellers. It's about the buyers. We have too many buyers. When so many people demand rents, riskier credit is an inevitable outcome, because all the rent seekers exhaust the less risky rents. No political influence is required.

Fundamentally, we're exploring a fat tailed distribution of yields including many negative yields. State regulators can't make the distribution thin tailed without simply commanding unproductive rents. If they could, we'd all be socialists by now, even more than we are.

So what if politicians persuade their cronies in a GSE to package many risky mortgages into still risky "securities"? If less risky securities exist with acceptable yields, who buys the GSE's paper?

We (our pension funds and other institutions) bought the GSE's paper, because we couldn't buy enough rent elsewhere. Just look at the yield on ten year Treasuries. Can you retire on three and a half percent, with inflation at five percent?

Kling said, "Why not subsidize the downpayment?"

Kling hasn't heard of the $7500 tax credit for "first time home buyers" enacted earlier this year. "First time" means that you haven't owned a house in three years.

http://www.federalhousingtaxcredit.com/

Of course, this credit is a pass-thru benefit for rent seekers and has little to do with helping home buyers.