The Commercial Real Estate Landslide

Disasters are interesting, as evidenced by the success of shows like Destroyed in Seconds (30 minutes of one disaster after another, courtesy of the Discovery channel). A while ago the show aired this video of a landslide in Japan:

The images have stuck with me, and I think there are some strong parallels to what is going on in commercial real estate:

• First and most obviously, a disaster is going on, and if you’re in its path it’s a very bad thing.
• As bad as it is for those to be caught in the path, it’s important to realize the whole mountain is not involved. The landslide affects only a portion of the exposed area of the mountain – most of the mountain remains unchanged.
• The earth in the landslide moves from an unstable position to a stable position.

I was reminded of these facts while visiting with a very experienced real estate investor last weekend. I’m guessing he was in his 70’s, and had some money in a development deal that has a poor prognosis. In this CRE landslide he is going to lose a small portion of his net worth in an unstable deal which was exposed. But, he is confident he will buy other people’s exposed deals at stabilized, lower prices which will recover his losses and more over time.

It’s easy to forget that most CRE is not actively traded, is not fully leveraged, and is owned by people with substantial resources who are looking forward to buying busted deals.

Extremely Improbable Events Happen All the Time

Risk managers assert in their defense that the current economic crisis was an unforeseeable, low probability event. From Andrew Haldane’s paper, Why Banks Failed the Stress Test:

Risk managers are of course known for their pessimistic streak. Back in August 2007, the Chief Financial Officer of Goldman Sachs, David Viniar, commented to the Financial Times:

“We are seeing things that were 25-standard deviation moves, several days in a row”

To provide some context, assuming a normal distribution, a 7.26-sigma daily loss would be expected to occur once every 13.7 billion or so years. That is roughly the estimated age of the universe. A 25-sigma event would be expected to occur once every 6 x 10124 lives of the universe. That is quite a lot of human histories.

How is it possible that extremely low probability events occur? The answer is that, while many events are highly probable over a short period of time, over longer periods events are extremely improbable. It is highly probable that when you go to bed tonight, you will get up in the morning from the same bed. But, think back to where you went to bed twenty years ago, and the events in your life that brought you to where you go to sleep now. How likely was it that you ended up where you are? That you have the job you have? That you have the spouse and kids you do?

From Carl Bialik’s The Numbers Guy blog:

We tend to fixate on those events that are memorable, after they happen. Peter H. Westfall, a statistician at Texas Tech University, notes that any given order of a shuffled 52-card deck has about a one in 10 to the 68th power probability of happening, including the sequence in which all 52 cards appear in order. “Everything we see has about a zero probability,” Westfall said. “Calculating these probabilities after the fact is kind of meaningless.”

The present we’re living has impossibly low odds of occurring.

Bank risk managers acted as though every tomorrow would be similar to the short term past, and didn’t account for less probable but still very possible outcomes (like house prices declining) which could rapidly create a much different environment in just a year or two (like the one we’re living in now).

Whose Error was the Housing Crisis?

Who is responsible for the housing crisis? Some candidates are borrowers, lenders, rating agencies, and securities investors.  Attempts to blame one party or another fail, because the crisis is the result of a combination of errors by different parties which all aligned. Think of a wedge of Swiss cheese; to see through it, all the holes must line up. This approach is explained in James Reason’s Human Error, and illustrated in a diagram from that book:

In the housing crisis, here are some errors which had to align to get to where we are today:

1) Borrowers took out loans they couldn’t afford

2) Lenders made loans to borrowers which the borrowers couldn’t afford

3) Ratings agencies rated securities comprised of these loans as safe

4) Security purchasers relied on the erroneous ratings and bought the securities

Any of these parties could have averted the crisis had they avoided their respective error.

I am not saying that every member of each class made their error; plenty of potential borrowers didn’t borrow, not every lender made bad loans, not every rating was bad, and not every investor bought bad securities. But, enough of each class made these mistakes to trigger the events leading to the current situation.

Also, I am not saying that individual actors didn’t benefit from their actions at the time – there were certainly some winners. And, looking at each individual decision made, it’s not clear that any of them were irrational at the time. These were errors in the sense that, in hindsight, collectively we would have been better off if people had acted differently.

In any complex system, it’s often more likely that a major breakdown is the result of an alignment of errors, rather than the failure of a single component.

The Inevitability of Errors

Errors are inevitable – no matter what the stakes, no matter how much you practice, things are going to go wrong a certain percentage of the time in any complex task or decision. The New York Times has an article with an excellent example: basketball free throws.

There is nothing in sports as straightforward as a free throw; the equipment is always the same, the geometry is constant, and there is no defense interfering. The only variables are the player’s concentration and control over his or her body. And yet, at the highest level of the game, it goes wrong 25% of the time, year after year after year:

In the National Basketball Association, the average has been roughly 75 percent for more than 50 years. Players in college women’s basketball and the W.N.B.A. reached similar plateaus — about equal to the men — and stuck there.

The general expectation in sports is that performance improves over time. Future athletes will surely be faster, throw farther, jump higher. But free-throw shooting represents a stubbornly peculiar athletic endeavor. As a group, players have not gotten better. Nor have they become worse.

“It’s unbelievable,” Larry Wright, an adjunct professor of statistics at Columbia, said as he studied the year-by-year averages. “There’s almost no difference. Fifty years. This is mind-boggling.”

And it’s not like the stakes aren’t high:

Last season, Memphis was 38-2 despite making only 61 percent of its free throws, missing an average of nearly 10 a game. The Tigers lost the national championship game after missing 4 of 5 free throws in the final 72 seconds against Kansas, which had made a late 3-point shot to tie the game and won in overtime…About two-thirds of a winning team’s points in the final minute typically come from the free-throw line…

Obviously, we need to work to eliminate mistakes and design systems to minimize the chance of them occurring. But, a certain percentage of the time errors will happen. Learn what you can from them and move on.

Why Do Lenders Take Excessive Risks? Certainty and Feedback Issues

Out of all the potential deals to be done, some are more risky than others. Ideally, a lender’s underwriting model screens out the risky deals, so the lender only makes loans that do not default. By definition, this means some deals don’t get done.

This creates two problems for lenders. The first relates to certainty. If a deal is screened out, the lender certainly knows it did not collect the income attributable to that deal, but it doesn’t know for sure that the loan would have defaulted. This creates a bias towards screening out fewer deals.

The second issue relates to feedback. If a lender does a deal, there is immediate positive feedback; fee income and interest or servicing income is realized immediately. However, it could be years before a default occurs. The positive feedback occurs immediately, while negative feedback is delayed. Also, except in rare occasions a lender is not aware if a deal it screened out is done by another lender and subsequently defaults, so there is no positive feedback to a successful screening. Again, this creates a bias towards screening out fewer deals.

These issues are outlined in James Reason’s classic Human Error:

All organizations have to allocate resources to two distinct goals: production and safety. In the long term, these are clearly compatible goals. But, given that resources are finite, there are likely to be many occasions on which there are short-term conflicts of interest. Resources allocated to the pursuit of production could diminish those available for safety; the converse is also true. These dilemmas are exacerbated by two factors:

(a) Certainty of outcome. Resources directed at improving productivity have relatively certain outcomes; those aimed at enhancing safety do not, at least in the short term. This is due in large part to the large stochastic elements in accident causation.

(b) Nature of feedback. The feedback generated by the pursuit of production goals is generally unambiguous, rapid, compelling and (when the news is good) highly reinforcing. That associated with safety goals is largely negative, intermittent, often deceptive and perhaps only compelling after a major accident or string of incidents. Production feedback will, except on these rare occasions, always speak louder than safety feedback. This makes the managerial control of safety extremely difficult.