Relevant and even prescient commentary on news, politics and the economy.

Catch-Up Links

I have been a Bad Blogger this week. (As opposed to my usual practice, which seems to be described as Blogging Badly.)

While I intend to continue the New Tradition (think of me as Waylon, without the speed), following are Snow Day Links:

D-Squared was on fire on Wednesday: both Bank Lending Channel and The Foundations of Mathematics and the Roots of Finance are essential.

For all those of you—looking straight at you, o six-footed one—who believe TARP was the right idea to save the economy, here’s another data point: “Overall bank lending in the US economy shrank 7.4% in 2009 — the sharpest drop since 1942.”

James Hamilton looks at Those Other Programs that support the banks without providing any funds to the rest of the economy (though I don’t think he put it that way).

With all the talk of Liquidity needs and Greek bonds, jck at Alea posts an essential chart.

Say It Ain’t So

NBER paper of the day:

We analyze asset-backed commercial paper conduits which played a central role in the early phase of the financial crisis of 2007-09. We document that commercial banks set up conduits to securitize assets while insuring the newly securitized assets using credit guarantees. The credit guarantees were structured to reduce bank capital requirements, while providing recourse to bank balance sheets for outside investors. Consistent with such recourse, we find that banks with more exposure to conduits had lower stock returns at the start of the financial crisis; that during the first year of the crisis, asset-backed commercial paper spreads increased and issuance fell, especially for conduits with weaker credit guarantees and riskier banks; and that losses from conduits mostly remained with banks rather than outside investors. These results suggest that banks used this form of securitization to concentrate, rather than disperse, financial risks in the banking sector while reducing their capital requirements.

UPDATE: If anyone knows of Tom points us to an “ungated” version from three weeks ago, please mention it in comments or e-mail.

FICO Scores and Mortgage Payment Performance

I had an informal discussion with a manager in an MBS IT area last month. Just a general conversation about the field and the data people check.  He mentioned FICO scores and I noted that I’m not fond of using them to evaluate a mortgage, especially for first-time homebuyers.

Part of this is simple: it’s relatively easier—even in the densely-populated metropolitan areas (e.g., NYC, SF), and certainly in sub- and exurban areas—to maintain a good credit rating if you don’t own a residence.  No property taxes, no major repairs, no appliance replacement, no general maintenance, no landscaping, no snow shoveling.  And it’s very easy, especially the first time, to underestimate just how much those expenses will be.  Looking at just the cost of commuting, renting, storage, parking, etc. makes homeownership appear to be a better economic decision than it is.*

Well, the Federal Reserve Bank of New York recently released some data on mortgage payments by type. It’s not directly comparable—the subprime and Alt-A loans have a more granular level of data, most especially with respect to late and current payments—but there are some interesting relationships.

I looked at the data for States where the subprime loans are current for either (1) more than 55% of the borrowers or  (2) less than 45% of the borrowers, which includes 24 states and the District of Columbia.  The overall breakdown was 16 states in the first group and eight states and the District of Columbia in the second.

Of the six states that have more than 100,000 subprime loans outstanding, three—Illinois, Florida, and California—are in the More Delinquent category, while only one (Texas) is in the “so far, so good” realm.**

So I ran a regression on those states and the District, using as factors the percent of the subprime loans that were not Owner-Occupied, the Average FICO score for the state, the percent of subprime loans issued to borrowers with a FICO below 600, and the percent of subprime loans issued to borrowers with a FICO score above 660.  The result was

PctwithCurrPymt = –1.18*(FICO>660) + .292*(FICO<600) + .266*(Average FICO Score) –0.9*(Pct Not Owner-Occupied) –93.66

R-squared = 0.4213  (Adjusted R-squared= .3056) F = 3.64  (Prob > F = 0.0220)

However, none of the coefficients passes the t-test.

If we assume that there is a solid distinction between a FICO score below 600 and one above 660, then we must note that the signs of this regression are precisely the opposite of what we should expect.  The more loans with an initial FICO score above 660, the fewer the number of households that are expected to be current in their payment. Conversely, the more households with a FICO score below 600, the better the Current Payment Performance should be expected to be.

This would seem to be a Very Bad Regression—both methodologically, since it takes two separate sets of data and treats them as if they are part of the same set and intuitively, since it produces results that are not compatible with rational assumptions—but that may not be so.

California, for instance, has the third-highest percentage of Owner-Occupied Properties, the highest Average FICO Score, the lowest percentage of subprime loans to borrowers with FICO scores below 600 and the highest percentage of subprime loans to borrowers with a FICO score above 660.  But it falls into the group where fewer than 45.0% of the borrowers are current.***

Which means that, were you to use FICO scores as an input to your model for buying Whole Loans to securitize, you would likely have bought more currently-dicey CA paper than not.

But, as noted, we may believe this to be a Very Bad Regression. The greatest likelihood is that there is/are (an) excluded variable(s) in the equation.  If we consider the entire set of data, this becomes clearer.  The regression equation for all of the states and the District of Columbia is:

PctwithCurrPymt = –1.019*(FICO>660) + .6118*(FICO<600) + .7685*(Average FICO Score) –0.38*(Pct Not Owner-Occupied) –422.80

R-squared = 0.1471  (Adjusted R-squared= .0730) F = 1.98  (Prob > F = 0.1128)

The signs remain consistent—and counterintuitive—but there is a much lower explanatory power and it is much more likely that the regression fails the F-test.  And again, none of the coefficients passes a t-test.

Adding variables whose signs are more likely to produce indeterminate results—the Average Age and the Average Interest Rate of the Loans—corrects the two original signage issues, but produced a third (and possibly a fourth):

PctwithCurrPymt = 1.375546*(FICO>660) –1.639*(FICO<600) – 1.3423*(Average FICO Score) –0.223*(Pct Not Owner-Occupied) + 16.5340 AvgInterestRate + 0.2632 AvgLoanAge + 775.9700

R-squared = 0.4661  (Adjusted R-squared= .3991) F = 6.40  (Prob > F = 0.0001)

The additional variables have significantly raised the explanatory power of the model, and we now see that the FICO scores point in the intuitive directions. But the Average FICO score has ceased to be a positive contributor to the model, and the Average Interest Rate—the only variable that passes a t-test for significance—indicates that the higher the rate, the higher the likelihood of payment.

So we are left suspecting that the initial FICO score does not significantly affect the ability of the borrower to keep their loan payment(s) current.  This also seems intuitive, since a FICO score is a stock variable, while mortgage payments are flow variables.

But, as with credit ratings, good FICO scores can only go downward.  And it is very rare—especially in an environment in which there is downward pressure on wages—for a good FICO score to go upward.  Indeed, dropping the positive FICO score and the Average FICO score as a variables makes for a better regression:

PctwithCurrPymt = –0.663*(FICO<600)  –0.238*(Pct Not Owner-Occupied) + 15.4976 AvgInterestRate + 0.1469 AvgLoanAge – 47.325

R-squared = 0.4466  (Adjusted R-squared= .3985) F = 9.28  (Prob > F = 0.0000)

While the Average Interest Rate still has a counterintuitive sign, we should note that the Averages range from 6.69 to 8.66%—even the high end is neither an overwhelming burden for subprime borrowers nor a level from which it is likely to have been worth refinancing. Additionally, while AvgInterestRate remains the only coefficient that completely passes a t-test, both FICO<600 (-3.17) and the constant (-2.54) are negative for all values within a 95% confidence interval. Dropping Non-Owner-Occupied from the equation sharpens matters even more:

PctwithCurrPymt = –0.6747*(FICO<600)  + 15.7738 AvgInterestRate + 0.1400 AvgLoanAge – 50.7117

R-squared = 0.4407  (Adjusted R-squared= .4050) F = 12.34  (Prob > F = 0.0000)

With the t-values for both FICO<600 (-3.25) and the constant (-2.83) now both more than 99% probable and, again, the values being negative for the entirety of a 95% confidence interval. In summary, the use of FICO scores as a predictor of mortgage repayments appears to be questionable at best, for the same reason that “junk” bonds tended to outperform high-grade securities on a risk-adjusted basis: it is much easier for a rating to decline than it is for it to improve.  The value of a FICO score as a predictor of loan performance appears to be much more for lower scores than it is for higher ones.  Whether there is greater value on a risk-adjusted basis, as there legendarily has been for corporate bonds, is left for further, more detailed research. *None of which is to suggest that the non-economic reasons aren’t valid.  But credit scores deal with how you manage credit, and how you manage credit has to do with the options you have as much as the choices you make.  Homeowners have fewer options on the allocation of funds to lodging than renters do. **New York State and Ohio are in the middle range.with 46.8% and 52.0% current, respectively. ***Only Hawaii had tighter FICO standards than California—and they have the second-highest (worst) level of non Owner-Occupied Subprime loans (and the worst of any area with more than 10,000 subprime loans outstanding), while California is fifth-best (lowest) in that metric.

In Honor of the Super Bowl

Favorite papers from the 2008 AEA in New Orleans (all PDF, ungated):

Emily Haisley on lottery tickets and perception. I heard about this paper before reading it. Such a simple idea, such a direct experiment.

Michele Tertilt: Women’s Liberation: What’s in it for Men (with M. Doepke). The next step is to figure out why so many rulers started having a significant number of female children. But that’s for sociologists, whose work is harder than that of economists.

Dean Yang and Sharon Maccini: Under the Weather: Health, Schooling, and Socioeconomic Consequences of Early-Life Rainfall. The paper that convinced me that Economics really is a good field in which to work.

Marcellus Andrews, “Risk, Inequality, and the economics of disaster.” This was much better live, where he prefaced it by taking about coming the hotel as an insurance inspector and pointed to the “sh*t line.” After the presentation, people were coming out, talking about how if they had wanted a sermon, they would have gone to church. Only person I went out of my way to thank for his talk, interrupting him conversation with Jamie Galbraith in the process.

Acemoglu and Finkelstein, Input and Technology Choices in Regulated Industries: Evidence from the Health Care Sector. Two future Nobelists collaborate. What’s not to like?

Dani Rodrik, Second Best Institutions. The best of a set of presentations.

The Meaning of "Monty Python and the Meaning of Life"

Robert Waldmann

Barry Ritholtz argues that the problem with mortgages was underwriting standards and not securitization. He appeals to the very great authority of Monty Python. Click the link.

Ritholtz seems not to be familiar with this new idea in economic theory called “Nash equilibrium”. Over -rated yes. Totally irrelevant not so much. One can not assume that underwriting standards are exogenous. If there had been no MBS, no firm would have underwritten those mortgages. It was exactly because it was possible to blend them, and then sell them to people who didn’t spin the mortgage tapes before buying, that the mortgages existed in the first place.

Let me work with his analogy. First, while I have great respect for the Monty Python team, few people have been killed by canned Salmon. Even blended into mousse, it kills fairly quickly and can be tracked back to the canner. The way bacteria work is that if you mix some contaminated stuff with other stuff you have trouble for sure. It doesn’t work that things seem fine until people notice.

At a way lower cultural level than Ritholtz I appeal to road runner cartoons. Wile E. Coyote runs along in mid air until he notices. Then he falls. As noted by everyone, this is the way financial markets really work. The non Monty Python quality humor is based on the fact that gravity doesn’t really work that way. Neither do bacteria. Analogies between rotten mortgages and rotten Salmon fail for this reason.

Notably, the ingredients in the Salmon mousse are few enough that the dead diners immediately know what went wrong when death points at the mousse. That’s not the way MBS work let alone CDOs of MBSs or CDOS of tranches of CDOS.

A better analogy would be making hamburger. Bits from hundreds of steers end up in the same package at the supermarket. If one bit has E. coli on it, you can get sick. If they tried to sell you that bit, you wouldn’t buy it because it would stink. However, mixed in with hundreds of uncontaminated bits of beef, it doesn’t stink.

Is there a hamburger problem? Yes there is. One is much more likely to get food poisoning from hamburger than from unprocessed meat. Is the solution special regulation of hamburger? It sure is.

Heavy Flow (not an iPad post)

Was 2009 a great year to be a bank? The headlines all say so. (The 140 U.S. banks that were closed by the FDIC last year may disagree some.) But, as Isabelle Kaminska of Alphaville notes, very little of the gains posted for last year came from anything related to talent:

Deutsche Bank reported net income of €5bn for the year 2009 on Thursday, compared to a €3.9bn loss in 2008.

This, we would say, is a pretty impressive turnaround in anyone’s business….

Deutsche attributes much of that growth to the successful re-orientation of its business towards customer business and liquid, ‘flow’ products. While it’s not broken out within the results, we’re willing to bet that a large slice of that re-orientation was therefore focused on managing flow emanating from the group’s ever growing synthetic exchange-traded-product and foreign exchange businesses — both of which happen to do very well when spreads are wide, and volatility is high.

When I first started working in the investment side of the banking industry, 20-some years ago, the traders and marketers were especially careful to distinguish themselves from the “retail” side of banking. Indeed, the retail bankers were described as “9-6-3” people: lend at 9%, take deposits at 6%, and be on the golf course by 3:00.

Now that that same type of effort is producing all those record profits, is it time to decide that the legendary “management skills” of Jimmy Cayne, Vikram Pandit, and Neutron Jack (who turned GE from a products company into a finance company) might not have been all that different from that of a polyester-suited small-town bank manager?

Students who Whine Like This are not Long for Class

The Battle of Late January has ended, as Amazon yields, gracelessly:

We have expressed our strong disagreement and the seriousness of our disagreement by temporarily ceasing the sale of all Macmillan titles. We want you to know that ultimately, however, we will have to capitulate and accept Macmillan’s terms because Macmillan has a monopoly over their own titles, and we will want to offer them to you even at prices we believe are needlessly high for e-books. Amazon customers will at that point decide for themselves whether they believe it’s reasonable to pay $14.99 for a bestselling e-book. We don’t believe that all of the major publishers will take the same route as Macmillan. And we know for sure that many independent presses and self-published authors will see this as an opportunity to provide attractively priced e-books as an alternative. [emphases mine]

Whenever a company talks about how it is looking out for your interested, Jim Henley’s consumer-surplus version of reality notwithstanding, check for your wallet; it’s probably missing.

The first italic is obvious: any firm that calls complete stopping of sales “expressed our strong disagreement” is either really stupid or exercising monopoly power—and no one thinks Jeff Bezos is stupid.

The second is even sillier: Amazon accuses Macmillan of exercising “monopoly power” and declares that they “will have to capitulate.” Someone ask the people at Hachette about how Amazon has to yield in a clash between it and publishers.

We’ve all seen the claim that starts this article: “On Christmas Day, for the first time in its history, Amazon.com (AMZN) sold more digital books than the old fashioned kind.” Not for the Xmas season; just on the day. And even there, it’s an Amazon declaration—not verifiable from the publishers, since e-book sales are confidential information. But Tobias Bucknell lays out the details from his royalty statements:

Well, I have my eBook sales figures of Crystal Rain, a book that has sold in the five figures in print, meaning people who have purchased in print, print online and in bookstores. That’s a nice run, it’s my bestselling book of the 3 Xenowealth books (Crystal Rain, Ragamuffin, Sly Mongoose), but leaves me still a midlist writer….

In 2008, for a brief while, Crystal Rain was available for free via download. Number of Kindle users who downloaded it: low thousands. Number who’ve purchased it for sale after that: low hundreds.

So five figures in volume compared to three figures. That’s an order of magnitude difference.

This magnitude difference holds steady. I sell hundreds of copies of eBooks, and thousands of paper copies.

The difference between Hachette and Macmillan isn’t one of size. It’s that the Amazon monopoly—the proprietary e-reader format of the Kindle—now has another viable rival: the poorly-named iPad, which uses the ePub format that is the standard among non-Kindle readers.

Apple is confident: the iPad will do more things than read books, so it can sell books that can also be read on other devices. Amazon, for all that it offers other products, lacks that ability, and is trying to protect itself through proprietary formatting.

It appears—given the speed with which they ended their ostracizing of Macmillan—that they may need a new business strategy soon.

Those Low Rates

Via (what else?) Alea’s Twitter feed, John Taylor defends himself against Ben Bernanke:

“The evidence is overwhelming that those low interest rates were not only unusually low but they logically were a factor in the housing boom and therefore ultimately the bust,” Taylor, a Stanford University economist, said in an interview today in Atlanta.

It’s not actually that they’re not saying the same thing. Bernanke argued (and I agreed) that low rates did not cause the housing bubble. We have had low rates without producing housing bubbles before. (Other asset bubbles are another question.) Indeed, the last lasting housing bubble peaked just as the Federal Funds rate did:

More accurately (and also via ATF), Caroline Baum takes Bernanke to task for sleight-of-hand:

For example, Bernanke takes great pains to rebut criticism that the funds rate was well below where the Taylor Rule…suggested it should be following the 2001 recession. The Taylor Rule uses actual inflation versus target inflation and actual gross domestic product versus potential GDP to determine the appropriate level of the funds rate.

Substitute forecast inflation for actual inflation, and the personal consumption expenditures price index for the consumer price index, and — voila! — monetary policy looks far less accommodating, Bernanke said.

It’s always easier to start with a desired conclusion and retrofit a model or equation to prove it.

Ouch. Is it a great day when the journalist is making more sense about the economist’s work than another economist is?

But more to the point, the argument that rates were kept unnaturally low from ca. 2002 through ca. 2005 depends very much on the idea that the Fed does not have two jobs. (Once again, h/t to Dean Baker.)

The other half below the break

As Baker notes at the link above, “the dual mandate [of the Fed] is full employment (defined as 4.0 percent unemployment) and price stability.”

Let’s be generous. I’ve plotted the Civilian Employment/Population Ratio and the Official Unemployment Rate below. The blue line at 4.5 applies only to the Unemployment Rate (red line). (I didn’t plot it at 4.0 because that would be cruel.)

So what we have is a situation where (1) the Employment/Population Ratio by the end of 2006 is barely back near the level it was at the end of the recession of 2001 and (2) it is only near the end of 2006 that the Official Unemployment Rates approaches the official target rate (which it hadn’t seen since before the 2001 recession).

It seems apparent that Taylor’s “Rule” (which considers inflation and GDP, but not employment per se) is not compatible with official Fed mandates. In such a context, Caroline Baum’s “gotcha” is more a case of her using inappropriate variables—and Bernanke substituting a more appropriate model, given the Fed’s mandates—than it is a case of Bernanke “retrofitting.”

No wonder John Taylor says we should worry about inflation; in his world, we never have to worry about unemployment, so long as there are enough bubbles to inflate GDP.

Bernanke: We Didn’t Do a Good job Regulating, so Let Us Regulate More

UPDATE: CR appears to agree with me, even as he raises another point:

Bernanke used data from other countries to suggest monetary policy was not a huge contributor to the bubble … however, Bernanke didn’t discuss if non-traditional mortgage products contributed to housing bubbles in other countries. This would seem like a key missing part of the speech.

I’m willing to believe that my interpretation of this speech is inaccurate, but here’s the evidence:

Some observers have assigned monetary policy a central role in the crisis. Specifically, they claim that excessively easy monetary policy by the Federal Reserve in the first half of the decade helped cause a bubble in house prices in the United States, a bubble whose inevitable collapse proved a major source of the financial and economic stresses of the past two years. Proponents of this view typically argue for a substantially greater role for monetary policy in preventing and controlling bubbles in the prices of housing and other assets. In contrast, others have taken the position that policy was appropriate for the macroeconomic conditions that prevailed, and that it was neither a principal cause of the housing bubble nor the right tool for controlling the increase in house prices. Obviously, in light of the economic damage inflicted by the collapses of two asset price bubbles over the past decade, a great deal more than historical accuracy rides on the resolution of this debate.

If I have to pick, I’ll take the latter group. Easy money alone doesn’t cause a crisis. So when he says:

Can accommodative monetary policies during this period reasonably account for the magnitude of the increase in house prices that we observed? If not, what does account for it?

The first answer is clearly “No.” And the second answer is important. Eventually, he answers it:

I noted earlier that the most important source of lower initial monthly payments, which allowed more people to enter the housing market and bid for properties, was not the general level of short-term interest rates, but the increasing use of more exotic types of mortgages and the associated decline of underwriting standards. That conclusion suggests that the best response to the housing bubble would have been regulatory, not monetary. Stronger regulation and supervision aimed at problems with underwriting practices and lenders’ risk management would have been a more effective and surgical approach to constraining the housing bubble than a general increase in interest rates. Moreover, regulators, supervisors, and the private sector could have more effectively addressed building risk concentrations and inadequate risk-management practices without necessarily having had to make a judgment about the sustainability of house price increases.

The Federal Reserve and other agencies did make efforts to address poor mortgage underwriting practices. In 2005, we worked with other banking regulators to develop guidance for banks on nontraditional mortgages, notably interest-only and option-ARM products. In March 2007, we issued interagency guidance on subprime lending, which was finalized in June. After a series of hearings that began in June 2006, we used authority granted us under the Truth in Lending Act to issue rules that apply to all high-cost mortgage lenders, not just banks. However, these efforts came too late or were insufficient to stop the decline in underwriting standards and effectively constrain the housing bubble. [emphases mine]

As Albert Brooks once noted, he “buried the lede.” Bernanke notes that the “nontraditional” products constituted around 1/3 of the market by 2003. (As many others have noted, those mortgages were not passed through/to FHA/Fannie/Freddie, either.) Two years later, guidelines were being developed.

An institution that did not attempt to regulate claiming that it should be given more regulatory power is an invitation to disaster. Or am I missing something?