Knowing When to Stop

When you think about bad CRE loans, most people picture homes being demolished in Victorville, unsold high rise condos in Miami, or vacant office buildings in Orange County. But how about Minnesota? From a Minneapolis Star Tribune story (hat tip Calculated Risk):

Minnesota ranks fifth nationally, with 50, or 12 percent, of its banks carrying particularly high levels of dead real estate loans, according to an analysis done for the Star Tribune by Foresight Analytics, a financial research firm in Oakland, Calif. Only Florida, Georgia, Illinois and California have more banks at such levels.

A key quote:

Bank consultant Robert Viering, principal of River Point Group Inc. in Monticello, had that lesson drilled into him when he was a regional credit officer at the former Norwest Bank. A credit manual, circa 1990, warned him and his colleagues: "The pivotal issue in CRE lending is knowing when to stop. Restraint must be initiated by bankers because historically borrowers have been unable to recognize the warning signs. Commercial real estate lending should not be viewed as the cornerstone of a loan portfolio."

Stopping, of course, involves saying no before the problem is evident. This is something people are very bad at doing (for more on that, see my post Rising Markets Create Lender Losses).

Zombie Banks’ Distressed Assets

John Reeder’s post Distressed Assets Market and FDIC Closures on Real Property Alpha is a must read for those that want to understand what’s going on with regional banks. An excerpt:

Our business working in the commercial real estate industry (see the Deal Breaker site, or upcoming Sperry Van Ness auction) puts us on the front lines of the current blow-up that is going on in the banking industry.  Capitalization levels in financial institutions have a large impact on whether they are willing or able to dispose of distressed construction loans, commercial REO, or A&D loans.  The general rule of thumb is that the more distressed the bank, the less potential that you are going to be able to make a deal with that Bank on their non-performing assets.  It’s difficult to digest this reality as the potential that a distressed bank offers in the way of inventory can be enticing.    However, the chances are that the bank has not written down the value of the asset to real current market, so selling at today’s prices means that the bank has to take an additional hit to their capital and the really distressed banks can ill afford the additional hit.

Read the whole post, there’s much more. I have two small contributions to John’s points:

• Even if a bank conscientiously marks its bad assets to market, it will still probably incur smaller losses at any given point in time if it holds the asset instead of disposing it. The marks are based on appraisals less a discount for sales costs. This number will almost always be higher than what a bank actually realizes on a sale, because appraisal values tend to lag actual market trends (more on that in the Lansner on Real Estate post “Were Appraiser’s Late to the Price Collapse?”). So, a bank can adopt a hold strategy and still be in regulatory and accounting compliance. The risk, of course, is that by hanging on to the asset, the bank continues to be exposed to further value losses if the market continues to deteriorate, and may ultimately incur an even bigger loss.
• In most cases the management and staff working on the problem assets at the smaller banks are the same people who originated the deals. There are whole sets of cognitive biases which predispose people to overvalue what they own (endowment effect, post-purchase rationalization), continue to do what they've done in the past (status quo bias, sunk cost effects, loss aversion), and expect a positive outcome to their choices (optimism bias, and valence effects). The consequence is the management at these banks may genuinely believe these assets can be salvaged given time, while someone with less involvement would say it’s time to take the loss.

My point is that, while I am sure some banks are consciously manipulating their accounting, I am also sure many banks believe they are doing the right thing.

After the Honeymoon: Trusting Loan Brokers

Should a loan broker who has established a successful relationship with a lender be trusted by that lender? Not according to research by Mark Garmaise, a finance professor at UCLA Anderson (working paper “After the Honeymoon: Relationship Dynamics Between Mortgage Brokers and Banks”). From a July 6, 2009 Financial Times story on the research:

The financial industry’s vaunted belief in trust and long-term relationships is being challenged by research showing that before the crisis US mortgage brokers fed loans of deteriorating quality to the banks they did most business with.

By questioning the prevailing wisdom that dealing with well-known counterparties is more fruitful and less risky than venturing into new relationships, the academic study puts in doubt one of the banking sector’s most enduring beliefs.

The key findings of the study:

• The quality of the loans submitted by the broker deteriorates over the course of the relationship
• The volume of loans submitted grows even as the quality deteriorates
• The effect is stronger for geographically distant brokers
• Even though the bank’s ability to evaluate the quality of the broker’s loans increases over time, the bank is increasingly reluctant to terminate the relationship.

It’s easy to dismiss this as a problem unique to loan brokers, but what if it’s true in other situations where initial monitoring is high and then relaxed over time? For example, the first few times you use a new appraiser you might carefully scrutinize the work. Do you need to do that every time, or can you relax? It’s a big enough topic for a separate post, but I think the answer (for commercial real estate, at least), is to check the key elements every time, no matter who you’re dealing with. Finley Peter Dunne had the right idea: “Trust everybody, but cut the cards.”

Rising Markets Create Lender Losses

People anticipate the future will be like the past. From a DNA article, “Why Economists Can’t See a Recession Coming”:

Robert J Barbera, chief economist, Investment Technology Group, in his book The Cost of Capitalism -- Understanding Market Mayhem and Stabilizing our Economic Future, writes: "Since the economy is not in a recession 80% of the time, the safe strategy is to predict recessions only when they have already arrived! That means you're right 80% of the time. Simply put, forecasting the recent past is the way to go and it is the dominant strategy employed by professional forecasters…Most of the time, tomorrow bears a close resemblance to yesterday. After all, both industry and economic trends tend to last for years, not for days. Once we acknowledge that we confront a world of pervasive uncertainty, it is quite reasonable to decide until circumstances change, we will plan as if present circumstances are likely to persist."

This approach to forecasting guarantees lenders will take losses. If you don’t say no when markets are rising, you are certain to have significant exposure at the top of the market which will create losses when the market softens. This time around, although everyone knew at an intellectual level that home prices could go down, the long term trend of rising house prices made it easy to justify rating models and lending decisions which didn’t adequately weight this possibility.

Waves of Stupid Money, and One Eye Money

 The Psy-Fi Blog has a post on Edward Miller’s research into irrational gambling, which gives some insight into bubble psychology. An excerpt:

Edward M. Miller in Do The Ignorant Accumulate the Money has done some research around the effect on the stockmarket of slot machine investors and reckons that there are periods where waves of stupid money can genuinely cause the rough efficiency of the market to break down. He also shows that these effects can’t last forever – if the stupid money is going into unproductive assets the lower return on these will eventually affect prices, especially as sensible money will be going into cheaper, productive ones.
In fact this isn’t too surprising to anyone with a background in social psychology – you don’t need to really understand economics to recognise that waves of irrational behaviour can sweep through groups linked by social ties. One of the oddest forms of behaviour is that a group’s overall opinion on some subject will tend to be more extreme than the average opinion of the group members. This polarisation effect is to do with the instinct towards group conformity and in the markets can lead people into taking more extreme and committed positions on individual stocks and markets than they would have taken on their own.

“Waves of stupid money” is an apt description of commercial real estate investors and lenders at the peak. Similarly, a general partner I know characterized the money he received from some investors as “one eye money”; cash someone whose primary business was not real estate would give him to invest, and which they would keep only one eye on.

Don’t be part of the wave, and keep both eyes on your money.

Pain and Cheating

These are two entirely separate topics which are both covered in an excellent talk by Dan Ariely on TED (which I found through this post on Geary Behavioral Economics).

Some teaser questions answered in the video:

• Which is better, intense pain for a shorter period, or less intense pain for longer? Short answer: less intense pain over a longer period, ideally with some breaks in between painful intervals. There’s probably a lesson to be learned on investment and loan losses…
• Given an opportunity to cheat, how many people do it, to what extent, and under what conditions? Short answer: many people cheat a little, especially if they perceive peers doing it. I think Ariely’s answers definitely apply to mortgage fraud…

I can read faster than I can listen, so I don’t have much patience for learning by video. However, TED has some terrific material, and this talk is a great example.

Lender Groupthink

Here’s an excerpt from Michael Skapinker’s opinion piece in the Financial Times, “Diversity Fails to End Boardroom Groupthink”:

Disagreeing with the company’s direction is hard enough. Doing so when an entire industry is going in the same direction is harder still. It is not just boards that suffer from groupthink; entire sectors do. The banking industry did.

Any investment banking chief executive who had listened to a director’s warning that complex financial instruments spelt trouble would have been in trouble himself. As Peter Hahn, a fellow at Cass Business School, told the Treasury committee: “If one of those banks in 2005 decided to be more conservative and hold back in their activity, they more than likely would have had their CEO and board replaced in 2006 for failing to take advantage of the opportunities.”

The implication is that we should heed the advice of dissidents, but real life is not so simple. In the 1980’s a lender I worked for had losses in Las Vegas, and as a result of that experience and my general distrust of low constraint markets, I believed Las Vegas was a dangerous place to lend. Today, I’m right – lenders who made loans in Las Vegas after 2005 are going to take losses. But, I was wrong for 20 years. 12 month change in employment growth is a good proxy for the health of CRE in a market, and the chart below shows went went on in Vegas:

CRE loans in general went through an extended period of virtually no losses, and the lenders making speculative land development, condo, and aggressively underwritten loans enjoyed an extended run of success. In at least some cases more conservative lenders decided to join the party at the end, and are now paying the price.

I discuss how difficult it is for credit officers to go against the flow in the post below:

Fox Guarding the Henhouse: Bear Stearns Risk Manager Now at the Federal Reserve

Does Tim Geitner Read My Blog? Are Regulators to Blame for the Housing Crisis?

From a Washington Post interview over the weekend, via Calculated Risk:

Geithner: "For something this big and damaging to happen it takes a lot of mistakes over time. And it is that combination of things. Interest rate here and around the world were kept too low for too long. Investors made - took a bunch of risks without understanding the risks. They were betting on the expectation that house prices would continue to go up - to go up forever. Rating agencies failed to rate these products adequately. Supervisors failed to underwrite loans with sufficiently conservative standards. So those basic checks and balances failed. And people borrowed too much. It took all those things for it to happen."

From my March 21, 2009 post, “Whose Error Was 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.

Calculated Risk takes Geitner to task for not mentioning two other factors:

Although there were many factors in the housing and credit bubble, the two keys were: 1) rapid innovation in the mortgage industry (securitization, automated underwriting, rapidly expanded wholesale lending, etc), and 2) a complete lack of oversight by regulators. As the late William Seidman wrote in his memoir (published in 1993): "Instruct regulators to look for the newest fad in the industry and examine it with great care. The next mistake will be a new way to make a loan that will not be repaid."
Geithner failed to mention the rapid changes in lending and the failure of government oversight as the two critical causes of the bubble. Either Geithner misspoke or he still doesn't understand what happened - and that is deeply troubling.

Although “innovation” and the regulators were factors, they were by no means the key factors. I think the innovations CR refers to should be viewed more as tools than culprits (an NRA bumper sticker for bankers - “Automated Underwriting doesn’t Kill Lenders, Lenders Kill Lenders”). More on this topic at “Why Did WAMU Abandon Underwriting Standards?”

The role of regulators is more complex. Certainly if disclosures were improved or some practices were prohibited, some bad loans might not have been made or bought. However, the errors listed above are so basic and so self-destructive I question the ability of outside intervention to control the behavior.

The Commercial Real Estate Risk Culture at Deutsche Bank

Zero Hedge has published a letter from a former risk manager at Deutsche Bank which speaks to the difficulties of being a risk manager in a lending institution. Some excerpts:

For more than two years, I have been working internally to improve the inadequate governance structures and lax internal controls within Deutsche Bank. I joined the firm in 2006 in one of its foreign subsidiaries, and my due diligence revealed management failures as well as inconsistencies between our internal actions and our external statements.
Beginning in late 2006, my conclusions were disseminated internally on a number of occasions, and while not always eloquently stated, my concerns were honest. Unfortunately, raising concerns internally is like trying to clap with one hand. The firm retaliated, and this raises the question: Is it possible to question management’s performance without being marginalized, even when this marginalization might be a violation of law? Two years later, our mounting losses are gaining attention, and I offer my experiences and my thoughts in the hopes of contributing to the shareholder and public policy debate…

I joined Deutsche Bank in 2006 to build an investment business within its commercial real estate lending operation, and I was generally surprised by the aggressive sales culture within our firm. While many people consider the banking sector’s problems to be caused by residential lending, I witnessed multibillion-dollar loan proposals for commercial property.
With funds provided at more than 90 percent loan-to-value, these loans were “priced to perfection” and assumed that property prices and rental rates would continue to rise. For perspective, a single billion-dollar commercial real estate loan is equivalent to 2,000 residential loans of $500,000.
In general, my colleagues are hard-working, decent people, but the system of incentives encourages people to take risks. I have seen honest, high-integrity people lose themselves in this cowboy culture, because more risk-taking generally means better pay. Bizarrely, this risk comes with virtually no liability, and this system of O.P.M. (Other People’s Money) insures that the firm absorbs any losses from bad trades…

There’s much more at this follow up Zero Hedge post.

Related Post: Fox Guarding the Henhouse?  Bear Stearns Risk Manager Now at the Federal Reserve

Humans Are Wired to See Patterns Where There Are None

From Jonah Lehrer’s post on Frontal Cortex, Patterns and the Stock Market:

Alas, the human mind can't resist the allure of explanations, even if they make no sense. We're so eager to find correlations and causation that, when confronted with an inherently stochastic process - like the DJIA, or a slot machine - we invent factors to fixate on. The end result is a blinkered sort of overconfidence, in which we're convinced we've solved a system that has no solution.

Look, for example, at this elegant little experiment. A rat was put in a T-shaped maze with a few morsels of food placed on either the far right or left side of the enclosure. The placement of the food is randomly determined, but the dice is rigged: over the long run, the food was placed on the left side sixty per cent of the time. How did the rat respond? It quickly realized that the left side was more rewarding. As a result, it always went to the left, which resulted in a sixty percent success rate. The rat didn't strive for perfection. It didn't search for a Unified Theory of the T-shaped maze, or try to decipher the disorder. Instead, it accepted the inherent uncertainty of the reward and learned to settle for the best possible alternative.

The experiment was then repeated with Yale undergraduates. Unlike the rat, their swollen brains stubbornly searched for the elusive pattern that determined the placement of the reward. They made predictions and then tried to learn from their prediction errors. The problem was that there was nothing to predict: the randomness was real. Because the students refused to settle for a 60 percent success rate, they ended up with a 52 percent success rate. Although most of the students were convinced they were making progress towards identifying the underlying algorithm, they were actually being outsmarted by a rat.

Loan underwriters and credit officers are constantly searching for patterns that aren’t there. This is the first in a series of posts that will look at this problem.

I highly recommend Jonah’s book, How We Decide.

Value, Cash Investments, Equity, Cash Out Refinances, Anchoring, and Sunk Costs

When I’m talking to a borrower about a loan workout, there is often a major disconnect between the reality they see and the reality I see. One of the disconnects almost always relates to the equity in the property.

Let’s say Bill Ant buys a property in 2005 for $10,000,000, and I make him a 75% LTV loan. Here are the numbers:

Bill’s equity is the difference between the value and the debt, and is equal to his cash investment.

Now, let’s roll forward to 2007. Values have increased 20%:

The cash investment remains the same, but Bill’s equity has increased 80% (the magic of leverage).

Now it’s 2010, and values have decreased 50% (think that can’t happen? Here’s my post, “Commercial Property Values Down 50%?”):

Here is when the disconnect occurs. When you talk to Bill Ant, he will refer to his $4,500,000 or $2,500,000 of equity in the property. Borrowers tend to anchor on their equity at peak value of the property, or on their cash investment in the property, instead of the equity based on the current value. Bill doesn’t have equity in the property any more – all he has is a sad story.

But, he does have $2,500,000 in sunk cost on the deal. Is that worth anything when it comes to his decision to continue to make the payments in a workout context?

Let’s say Tom Grasshopper did the same deal in 2005, and refinanced in 2007, pulling out all his cash investment with a new loan based on 75% of the higher value, and spent the proceeds on a big house and a boat. Here are the numbers:

Now, it’s 2010. I’ve put Ant’s and Grasshopper’s situations side by side for comparison purposes:

Some people think borrowers who have done cash out refinances are less committed to the property and less likely to support the loan than people who never pulled their cash out. After all, Grasshopper no longer has a sunk cost, and he can walk away and keep his house and boat, while Ant has nothing.

This makes sense in theory, but I can tell you with absolute certainty that in practice both of these borrowers are equally focused on their loss from the peak value, and are equally angry, in denial, willing to bargain, and depressed (depending on what stage of the process they’re at). Grasshopper is more likely to default and is likely to default earlier than Ant, but that’s because he owes more relative to the current value of the property, not because he has less commitment to the property.

To recap, borrowers anchor on what they had to start out with or at the peak of the market, measure their losses from those points, and are not much influenced by any gains they made along the way if they end up underwater.

Economic and Real Estate Post Picks: Week of April 6, 2009

Wholesale Sales Up, Inventories Down: Good news on these indicators

Why This Recession is Different: Unlike most recessions, this one is balance sheet driven

Has the Housing Market Bottomed? Builder stocks and the spread between mortgage and treasury yields are both hopeful signs

Why is Consumer Debt Declining So Sharply? An explanation for the sharp decline in credit card debt

Initial Unemployment Claims and the End of Recessions: Does the recent peak in initial unemployment claims signal we are nearing recovery?

Lenders Blew a Solved Game: When Goals Go Wild

When is the last time you unintentionally lost a game of tic-tac-toe? It probably goes back to when you were around six years old – it’s a solved game. From Alec Wilkinson’s article in the New Yorker, “What Would Jesus Bet?”:

Games for which flawless strategy is known are said to be solved. Tic-Tac-Toe is solved; blackjack is solved; checkers is solved. Chess is not solved, and poker is not, either. Solutions theoretically exist; they are simply too intricate, so far, to be comprehended.

It took 10^14 calculations and 18 years to solve checkers; more on solved games here.

I believe real estate lending was a solved game. Loan to a borrower with good credit and a 20% down payment on a well maintained piece of real estate, and make sure income was sufficient to cover debt service and expenses with at least a 25% cushion. If everyone stuck to those rules, what could go wrong? So, what did go wrong?

I think the short answer is the goal of increased market share caused lenders to go outside the rules of the game. From an article by Drake Bennett on Boston.com (which I found via Wehr in the World):

The argument is not that goal setting doesn't work - it does, just not always in the way we intend. "It can focus attention too much, or on the wrong things; it can lead to crazy behaviors to get people to achieve them," says Adam Galinsky, a professor at Northwestern University's Kellogg School of Management, and coauthor of "Goals Gone Wild," a paper in the current issue of a leading management journal.

Paul Kredosky links to the “Goals Gone Wild” paper, too, and cites this excerpt in his post, “Goals Gone Wild, Ponzis, and the Banks”:

An excessive focus on goals may have prompted the risk-taking behavior that lies at the root of many real-world disasters. The collapse of Continental Illinois Bank provides an example with striking parallels to the collapse of Enron and the financial crisis of 2008. In 1976, Continental’s chairman announced that within five years, the magnitude of the bank’s lending would match that of any other bank. To reach this stretch goal, the bank shifted its strategy from conservative corporate financing toward aggressive pursuit of borrowers. Continental allowed officers to buy loans made by smaller banks that had invested heavily in very risky loans. Continental would have become the seventh-largest U.S. bank if its borrowers had been able to repay their loans; instead, following massive loan defaults, the government had to bail out the bank.

I’ve previously posted on how the quest for market share led Fannie to increase its subprime lending.

Why Doesn’t CRE Loan Underwriting Ever Get Any Better?

Via Zero Hedge, S&P reports lax loan underwriting is responsible for the upcoming losses on CMBS loans:

The rationale for the variation in results among the 2005-2007 vintages is that the underwriting standards, already looser than for past vintages, became progressively worse as the property cash flows and valuations rose quickly during those years. For example, most of the large pro-forma loans were underwritten during 2006 and especially 2007, and 2007 deals also claimed the highest average Standard & Poor's LTVs and lowest average Standard & Poor's DSCs (followed by 2006, then 2005). Thus, when commercial real estate prices and cash flows peaked in 2007, the most recent origination was by far the most vulnerable to cash flow declines…

This is the same explanation used to explain losses during the 1990-1994 real estate recession, the S&L debacle of the 1980’s, and probably every other major real estate downside since lending began. Why don’t we get any better at this?

An obvious explanation is that underwriters knew of the risks, but were ignored by management seeking profits and market share (I’ve posted on the pressures on credit officers here, and on the market share issue here). These are real issues, but I think there is a more fundamental problem; I think most underwriters, and many credit approvers, don’t know how to tell a good loan from a bad loan.

If you take the S&P approach outlined above, you don’t need to get into the details – at the top of the cycle, just tighten your LTV and DSC requirements. Of course, this advice is about as helpful as telling an investor to buy low and sell high. And, anyone who has been in a credit approval position knows how difficult it is to pull back in a strong market. Given that you are going to have to do business during market peaks, how can you select the deals most likely to succeed?

The way people become experts is through thoughtful practice. In his book, How We Decide, Jonah Lehrer profiles Herb Stein, a television director who has shot more than 50,000 scenes and won eight Emmies over a twenty five year career. After shooting an episode, Stein says:

I watch the whole thing, and I just take notes. I’m looking really hard for my mistakes. I pretty much always want to find thirty mistakes, thirty things that I could have done better. If I can’t find thirty, then I’m not looking hard enough.

Bill Robertie, a world class chess master and poker and backgammon player (backgammon World Champion twice), is also profiled:

Robertie didn’t become a world champion just by playing a lot of backgammon. “It’s not the quantity of practice, it’s the quality,” he says. According to Rpbertie, the most effective way to get better is to focus on your mistakes… After Robertie plays a chess match, or a poker hand, or a backgammon game, he painstakingly reviews what happened. Every decision is critiqued and analyzed. Should he have sent out his queen sooner? Tried to bluff with a pair of sevens? What if he had consolidated his backgammon blots? Even when Robertie wins – and he almost always wins – he insists on searching for his errors, dissecting those decisions that could have been a little better. He knows that self-criticism is the key to self-improvement; negative feedback is the best kind.

Underwriters don’t have the opportunity to do this kind of analysis, for three reasons. The first is that lenders do very little meaningful analysis of existing loan performance. Asset management monitors debt service coverage, occupancy, property condition, and that’s about it, which is like trying to diagnose an illness from the patient’s weight, blood pressure, and temperature. These performance metrics are monitored because they’re easy, but they’re just not that revealing. I don’t know of any lenders who systematically analyze underperforming deals to understand why they are underperforming (see my posts, “Why What You Know About Income Property Performance is Probably Wrong”, and “The Slippery Slope to Default” for more on what lenders should be paying attention to).

The second problem is that there is rarely a feedback loop to the underwriters giving them even the minimal performance data collected. The back-end asset management group and front-end underwriting are not connected, and the lessons to be learned at the back from actual performance are rarely conveyed to the front in a useful form.

These two problems are correctable, but the third problem is more difficult. Stein has daily rough cuts to review for mistakes. Robertie has many games each day he can review and analyze. But, CRE underwriting mistakes are generally only revealed when markets are under economic stress, and such events may happen only four or five times in an underwriter’s entire career. The consequences are twofold. First, bad deals are never revealed because they never are stressed by a downturn -almost every CRE loan made from 1995 to 2007 has performed to date, because it was never stressed. Secondly, good deals are overwhelmed by economic events – there were good loans made in 2006-07, but it is very likely many of those loans will default given the severe distress of this market. Stein and Robertie have an opportunity to excel because they have frequent, clear feedback. Even under the best of circumstances, CRE underwriters get infrequent, unclear feedback. I’ve written more on feedback issues in my post “Why Do Lenders Take Excessive Risks? Certainty and Feedback Issues.”

To summarize, underwriting does not get better because the opportunities to learn are infrequent, little effort is made by lenders to learn even when the opportunity presents itself, analysis is confined to superficial symptoms, and the little that is learned is not conveyed to the people who need the information. The only good thing I can say is there is plenty of opportunity for improvement.

Why Do Bankers Take Excessive Risks?

There are plenty of economic explanations being offered  to explain bankers’ risky behavior (e.g., poorly designed incentive plans, inadequate risk modeling). On a more basic level, the old standbys ignorance, hubris, and greed are called on. I’m sure some people were ignorant of the risks, and some people were in it for the money. But how do you explain the participation of very intelligent people who were aware of the risks, had every reason to protect their good reputations, and had no economic reason to jeopardize their already very lucrative positions? And it’s not just bankers – why do successful real estate investors continue to leverage their portfolios to the max and take on high risk deals, when they could secure their positions and be able to ride out any storm?

Maybe it’s their brain chemistry; human brains get high on challenges. From Gregory Berns’ book, Satisfaction: The Science of Finding True Fulfillment:

Any stressor, especially a physical one, results in the release of cortisol. The biochemical interaction of cortisol and dopamine in the striatum suggests that these two chemicals are involved in the achievement of satisfaction, perhaps even transcendence. Alone, neither compound can provide a state resembling satisfaction. Dopamine may be associated with transient euphoria, but you need cortisol to get that satisfying feeling. And because cortisol is released most effectively by stressful situations, the road to satisfying experiences must necessarily pass through the terrain of discomfort.

At the most basic level, people take risks because the biochemical results make them feel good. Understanding this goes a long way towards explaining obviously self-destructive risky behavior.

The Problem With Models

I’ve meant for a long time to post on the problems with using financial models, but there’s just too much to say, and too much that has already been said more clearly than I can say it. Here are some links on this topic:

Data series too short – for example, extreme economic conditions are not captured in the model. See Underestimating the tails, at  Revolutions.

Overreliance on past patterns – assuming the future will be like the past. See Maths and markets at FT.com.

Bad assumptions – for example, housing prices won’t fall. See Don’t Blame the Quants, Felix, at Falkenblog.

Network externalities – for example, failure of your counterparty’s counterparty was not considered in your model. See Andrew Haldane’s “Why Banks Failed the Stress Test” paper starting at page 9.

Failure to adjust models for evolving conditions – see John Kay, “Financial models are no excuse for resting your brain”.

Failure to properly account for low probability events – see Naked Capitalism, More on Global Alpha, Quant Woes, and Joe Nocera, “Risk Mismanagement”.

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).

Your First Loss Is Your Best Loss

Jim Cramer’s reputation as a source of investment wisdom is not at its peak right now, but in the environment today his second commandment of trading is good advice. From a 2005 article on TheStreet.com:

Good trading, no matter what it's based on, technicals, fundamentals, the stars, the news, requires a level of discipline that goes against human nature. We are taught in life to be patient, to let things work out, not to be hasty, yet none of that works when it comes to trading. You have to be willing to cut and run, to use that "flight," not fight, instinct that we supposedly are born with but suppress wholeheartedly when we are grown up.

That's what the second commandment of trading is about, and that's why it is the second commandment of trading:

“Your first loss is your best loss.”

I genuinely believe that most trades need to work almost immediately for them to be right.

John Reeder over at Real Property Alpha has an excellent post making the case that this is true for CRE today, complete with a great example (Lennar’s role in the Newhall ranch development). As John notes, I’ve made the opposite argument –selling in this environment reinforces a downward spiral in values which is hard to stop, with unfortunate consequences for all. It is a classic Prisoner’s Dilemma / Tragedy of the Commons problem, and unfortunately the best individual bank strategy makes the problem worse in the long run. We have met the enemy…and he is us.

Exurbs: How Far Is Too Far?

I’ve previously posted about why the nation’s worst housing markets are in the exurbs. A reader commented:

There has to be a sweet spot for these exurb communities. How far is just right to commute? 30 minutes one way? 45? Surely people think nothing of traveling across a city for work at 50 minutes per trip, so living 30ish miles out of town really isn't as bad. So, how far is too far?

The short answer is, if the commute is more from 30 minutes one way, it’s too much. Tom Vanderbilt, from his book Traffic: Why We Drive the Way We Do:

In the 1970’s, Yacov Zahavi, and Israeli economist working for the World Bank, introduced a theory he called the “travel time budget.” He suggested that people were willing to devote a certain part of each day to moving around. Interestingly, Zahavi found that this time was “practically the same” in all kinds of different locations. The small English city of Kingston-upon-Hull’s physical area was only 4.4% the size of London; nevertheless, Zahavi found, car drivers in both places averaged three-quarters of an hour each day. The only difference was that London drivers made fewer, longer trips, while Kingston-upon-Hull drivers made frequent, shorter trips. In any case, the time spent driving was about the same…

There seems to be some innate human limit for travel – which makes sense, after all, if one sleeps eight hours, spends a few hours eating (and not in the car), and crams in a hobby or a child’s tap dance recital. Not much time is left. Studies have shown that satisfaction with one’s commute begins to drop off at around 30 minutes each way.

However, obviously many people spend more than an hour a day in total commute time. Why is that? Jonah Lehrer suggests it’s a weighting mistake, in his book How We Decide:

As Ap Dijksterhuis, a psychologist at Radboud Univeristy, in the Netherlands, notes, when people are shopping for real estate, they often fall victim to…what he calls a “weighting mistake.” Consider two housing options: a three bedroom apartment located in the middle of the city which will give you a ten minute commute, and a five-bedroom McMansion in the suburbs which will result in a 45 minute commute. “People will think about this trade-off for a long time,” Dijksterhuis says, “and most of them will eventually choose the large house. After all, a third bathroom or an extra bedroom is very important for when Grandma and Grandpa come over for Christmas, whereas driving two hours each day is not really that bad.” What’s interesting is the more time people spend deliberating, the more important that extra space becomes. They’ll imagine all sorts of scenarios (a big birthday party, Thanksgiving dinner, another child) that turns the suburban house into a necessity. The lengthy commute, meanwhile, will seem less and less significant, at least when it’s compared to the lure of an extra bathroom. But, as Dijksterhuis points out, the reasoning is backward: “The additional bathroom is a complete superfluous asset for at least 362 or 363 days each year, whereas a long commute does become a burden after a while.”

I think it’s a big mistake to locate housing more than 30 minutes from major employment centers. Strategies which depend on people making errors in judgment usually don’t work out well in the long run.

Why Fewer Reasons Are Better; Dead Cats and Cul de Sacs

When turning down a workout request or a loan application, you need to explain why. Borrowers expect a fair reason for being turned down, and loan officers and underwriters can learn from each experience and hopefully prevent reoccurrences in the future. You have a choice – you can try to provide a comprehensive understanding of your entire thought process, or you can relate just the factors which are the most important to your decision. In my experience, the latter approach is better, because what people remember won’t be your best reasons.

For example, back in the mid-1980’s I worked for Cambridge Capital originating multifamily loans (the company is long gone and not related to any of the Cambridge Capitals currently doing business). The principals were very hands-on, bright guys who personally inspected every deal we did, and I know I learned a lot about real estate from them. But, my only specific recollection is one deal which was turned down because, when the principal did his inspection of the property, there was a dead cat in the parking lot. I’m sure there were other things he didn’t like about that deal, but I don’t remember them.

I did something similar during a presentation sponsored by a chapter of the Earthquake Engineering Research Institute in Oakland. After the Northridge Earthquake I did consulting work for the Los Angeles Housing Department, and one of the things I did was a drive-by inspection of all the red and yellow tag structures damaged in the earthquake. This was an inductive approach to learning – after you look at a few thousands damaged buildings you start to see patterns. A lot of these patterns were obvious. For example, proximity to the epicenter, hillside or liquefaction zone locations, and brick construction are all know risk factors, and the audience didn’t react when I relayed that information. I did get a reaction, though, when I told them that cul de sac streets were a risk factor. On reflection, this isn’t surprising. Orientation of the structure to the ground motion wave is an important variable, and on a cul de sac one or more structures are guaranteed to be oriented for maximum damage. Also, in Los Angeles a cul de sac is usually related to e geographic risk factor (the cul de sac terminates at a drainage ditch prone to liquefaction or a hillside, for example). But, I didn’t explain this during the presentation, and I know there are people out there who remember me as the idiot who thinks earthquake damage is linked to cul de sac streets.

There is a neurological basis which explains why people lock in on unexpected reasons. From Jonah Lehrer’s “How We Decide”:

The brain is designed to amplify the shock of these mistaken predictions. Whenever it experiences something unexpected – like a radar blip that doesn’t fit the usual pattern, or a drop of juice that doesn’t arrive – the cortex immediately takes notice. Within milliseconds, the activity of the brain cells has been inflated into a powerful emotion. Nothing focuses the mind like surprise.

This is why if you tell a loan officer you’re turning down his loan because the borrower lacks liquidity, the building is poorly maintained, the income is trending down, and there’s a dead cat in the parking lot, you will forever be remembered as the guy who is fixated on dead cats. Unless that’s what you want, you’re better off keeping that reason to yourself.