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Real Problem versus Many Perspectives 
The perspectives reported on the Mortgage Mess varies very widely. From the political right is the Wall Street editorial “We’re not aware of a single case so far of a substantive error” to the political left Congressman Alan Grayson’s “the easiest way to make a buck is to steal it”. This one really shocked me, financial institutions and their mortgage servicing departments hired hair stylists, Walmart floor workers and people who had worked on assembly lines and installed them in “foreclosure expert” jobs with no formal training.

In my opinion these are details that subtract from the big picture. Without understanding the big picture we would not have a context in which to address this Mortgage Mess.

What is the big picture? It is, is our fiduciary responsibility to our shareholders or is our fiduciary responsibility to our customers? Correct picture but wrong perspective.

Our fiduciary responsibility is to our shareholders and that this fiduciary responsibility is derived from our fiduciary responsibility to our customers. We’ll discuss each perspective below.

Residential Mortgage Industry is Shrinking
First some context to this discussion. From the Wall Street reported data I estimate that 15.4% or about 1 in every 6 US homes are in foreclosure, and this does not include recent past foreclosures. That is a huge amount and bank troubles pale in comparison to this number. What does this number tell us?

It says that the mortgage market size will shrink by 15% as foreclosures are executed because foreclosed home owners will be barred for at least 7 years from participating in this market. The lowest estimate I was able to find was 10%. Therefore to remain profitable banks will have to lay off at least 15% of their residential mortgage staff.

Further, as reported in the New York Times, if title companies shy away from insuring foreclosed properties because they think those properties are vulnerable to claims, this will further depress the market, as investors too will shy away. 

Looking at the CMBS industry for clues, even though the reported commercial property appraisals are hovering in the 55% range, the anecdotal prices I have heard suggest purchase prices averaging between 10 to 20 cents on the dollar, and there are no reported foreclosure problems. Therefore, we can infer that the residential mortgage industry will experience similar appraisal versus selling price discrepancies and is therefore not out of the woods, yet.

Good News for Banks?
The Florida attorney general’s office says it doesn’t have the power to investigate banks but it has started an investigation into the law practices. To complicate matters, one needs to be aware that there is a difference between industry practice and actual mortgage regulation. There are also enormous variations from state to state with respect to foreclosure procedures. I found that in one state banks can start foreclosures on day 2 (1 day delinquent). This means that even though the 50 State Attorney General’s offices have launched investigations into the mortgage industry, in my opinion the banks don’t have to worry about them as these offices have no teeth. But some how this does not sound like good news, right?

First Fiduciary Responsibility
This Bloomberg article very nicely summarizes the current Mortgage Mess. That there are two fronts, “against U.S. homeowners challenging the right to foreclose and mortgage-bond investors demanding refunds that could approach $200 billion”. Of course this is an evolving situation and it is very likely many more fronts will open up.

The first fiduciary responsibility is to your customer. This is very clear in the many securities regulations since 1933. Of course banks are governed by banking regulations and in many instances are exempt from securities regulations as these exemptions are covered in the banking regulations.  Therefore I wonder if the banking regulations are any where nearly as concerned about protecting bank customers as the securities regulations require of investment advisors and broker-dealers?

Though the individual contracts appear to be in favor of the banks, investors are using every means possible to force banks to buy back bad mortgages. The final outcome, however, may play out in terms of power of buyers versus power of sellers, and not through legal means. That is investors in the future will seek assets from players who are amenable to buy backs than from those who are not.

This may be a good thing for the economy as insufficient principal protection may cause investors to seek alternative investments such as manufacturing, R&D driven technology licensing, and new materials, to name a few. Why? Because residential mortgages are no more safer than R&D.

Therefore, why did the residential mortgage market develop to the size it did? I can only conjecture that the existence of GSEs led to the mistaken belief that residential mortgages were one of the safest forms of investments.

Second Fiduciary Responsibility

The second fiduciary responsibility is to shareholders as this is a derivative of the fiduciary responsibility to customers. It is in this context that one would ask the question, how did this Mortgage Mess come to be?

In this context, given the Wall St. crash of 2008 and in the light of the Goldman Sachs hearings, the Wall Street editorial opinion that “We’re not aware of a single case so far of a substantive error” is difficult to justify as this would raise other questions.

As a general rule organizational seniority and salaries increase with fiduciary responsibility to shareholders. Therefore the questions, why did we did not have in place the systems and procedures to detect “substantive error”? Why were we paying managers so much if they did not know what was happening? What were these managers thinking?

Quite obviously operational risk and credit risk methodologies were insufficient. And may be they were ignored? A rethink of these methodologies and how risk committees are staffed and to whom they report to is in order.

Some Likely Future Outcomes

In the context of the First Fiduciary Requirement we can infer some future outcomes. 

Firstly, if there genuinely were mistakes in the foreclosure process, the second lien holder should now have a claim to the funds recovered from the sale of the property as the first lien holder did not conduct his fiduciary responsibility correctly. (Check this with your attorney as he may disagree.)

Second, title insurance fees may increase. Title insurers have two options either do not insure the title or substantially increase the fact checking required. The latter will increase title insurance fees. Assuming that Congressman Alan Grayson’s findings are correct, I believe that title insurance firms will choose not to insure as it would be more expedient to not insure than to dig up bad documents. Therefore, don’t expect title insurance for foreclosed homes unless the bank owns the title insurance firm, but this should raise questions of bias. My guess is that the title insurance problem is only going to get more complicated.

Third, future bank purchases will be structured more like an asset sale than an acquisition or merger. Why? First you don’t absorb the bad management team. Second by insisting only on asset purchases you put into place a screening process that substantially ensures that you are not purchasing a barrel of bad apples. Sure its a lot of work but that comes back to the banks’ fiduciary responsibility to its shareholders. An asset sale would allow the purchaser to include a clause that any future claims due to fraud, misstatement or omissions are the liability of the seller’s management team. Therefore one can infer that Bank of America’s purchase of Countrywide as a single company was not a good strategy, and that any subsequent M&A activity in the banking sector would require a rethink.

Fourth, further changes to securities regulation. Dodd-Frank indeed may turnout to be insufficient or in the worst case irrelevant as private action against mortgage industry participants further tighten regulation. For example if we assume that the alleged wrong doings were conducted by a handful of employees and not an issue of management culture, checks and controls, then we can expect changes to portions of the securities and banking regulations that would provide investors more time to seek recourse. For example Securities Act of 1934, Section 10(b), investors have two years from discovery of the fraud or five years from when it occurred to file their claim, while Sections 11 and 12(a)(2) claims against misstatements or omissions must be brought within one year of discovery and three years of the securities filing. These timing could be changed to 10 years or simply no statue of limitations for any fraud, misstatements or omissions.

Fifth, we would expect bond investors require,
1) A buy back clause in any future securitization, and that buy back clause is automatically transferrable to any and all future bond holders. At the very least in the event of the failure of the insurance provider.

2) That any third party providing a fee based opinion about a or soon to be securitized deal state that they have fully examined the collateral backing the deal.

Summary
This downside risk assessment of the residential mortgage industry suggest that an end to the industry turmoil is not in sight. Further, we can expect substantial private sector initiated changes to investment contracts that will provide both investors and home owners with better uniform protection.

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Disclosure: I’m a capitalist too, and my musings & opinions on this blog are for informational / educational purposes and part of my efforts to learn from the mistakes of other people. Hope you do, too. These musings are not to be taken as financial advise, and are based on data that is assumed to be correct. Therefore, my opinions are subject to change without notice. This blog is not intended to either negate or advocate any persons, entity, product, services or political position. Nor is this blog post to be construed as investment advice. 

Contact: Ben Solomon, Managing Principal, QuantumRisk
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It is critical for investors & real estate professionals to know which cities to invest in and which to stay away from for the time being.

We are very pleased to announce that CoStar’s Watch List featured some of our May 2010 Analytics in their article, “Impact of CRE Distress Varies Widely Market to Market” receiving more than 10,000 reads within 24 hours. A sample report for All Properties is available at http://www.QuantumRisk.com/.  

Our July 2010 CMBS Property Risk Analytics** (CPRA) shows that CMBS defaults & losses vary across the US by city from 0.0% to 80.0% defaults & 0.0% to 78.0% loss severities. Defaults rates continue to increase but loss severities continue to decline. How?

July 2010 CMBS Default Rates

The July CMBS Property Risk Analytics shows that the CMBS default rates continue to increase, and is at 5.79%. Note the graph is a snap shot of the CMBS pipeline as of the end of July 2010.

July 2010 CMBS Severity of Loss 

The July CMBS Property Risk Analytics shows that the CMBS severity of loss (before recovery) continues to decline and is now at 5.51%. Note, the severity of loss numbers do not include loss due to appraised value reductions. Note the graph is a snap shot of the CMBS pipeline as of the end of July 2010.

FDIC’s Mixed Report on Banks
FDIC’s list of “problem banks” reached 829 in 2Q 2010, NY Times August 31 2010. Even so, bank earnings continue to rebound posting $21.6 billion industry profits. “Across nearly every category, troubled loans started falling for the first time in more than four years. The sole exception was commercial real estate loans, which continued to show increased weakness. Still, the nation’s 7,830 banks remain under pressure.”

 New York Times / Jonathan Ernst / Reuters

“Without question, the industry still faces challenges,” Sheila Bair said in a news statement. “But the banking sector is gaining strength. Earnings have grown, and most asset quality indicators are moving in the right direction.” The agency expects a “recovery, sluggish and slow”.

The FDIC is cautioning that even though the outlook is becoming positive it may not be positive enough for a strong recoveery. On the other hand Russell Abrams of Titan Capital Group LLC, is betting the market is underestimating the likelihood of a crash (Bloomberg August 30, 2010)

So whose outcome is more likely, the FDIC’s small positive or Abrams’ second market crash leading to a double dip recession?

Will This Recession Be A Double Dip?
Our CMBS Property Risk Analytics shows that defaults are increasing but loss severities are declining. Apparently contradictory behaviors when you take into account that defaults and loss severities are usually positively correlated.

What is happening in the economy is that up to about a year ago CMBS defaults were dominated by newer loans that were backed by over priced (compared to today’s) valuations. Therefore, the large severity of losses late in the pipeline. The more recent defaults are from much older loans. Therefore smaller severity of losses early in the pipeline.

This tells us two things. First, industry losses that were primarily driven by over priced valuations have been fully absorbed by the industry – good news. Second, the industry losses has transitioned to a second stage, insufficient revenue. That is the more established older loans are defaulting due to insufficient business revenue.

It is this second stage that worries me. Our CMBS Property Risk Analytics shows that at the national level City DSCRs – a proxy for business revenue – are at 1.366 (April), 1.367 (May), 1.376 (June) and 1.397 (July). About constant between April, May, June and a 2.3% increase in July.

Could the July 2.3% increase be a one off ‘bump’ in the reported data?

Looking at the national level City Occupancies, our CMBS Property Risk Analytics show that City Occupancies were at 88.22%, 88.51%, 90.16% & 89.33% respectively. That is in the last 4 months there has been a general upward trend in CMBS City Occupancies of 0.5% increase per month – also good news – and if sustainable will reflect a general economic environment that will avert a second market crash & double dip.

Therefore, in my opinion a double dip recession is unlikely and I disagree with Russell Abrams opinion that a second market crash is likely to occur. I concur with Sheila Bair that even though a recovery is in place, at this point in time, a recovery is not likely to be as fast as we would like it to be.

Disclaimer: There is a certain amount of opacity in any business. For example the collapse of Lehman Brothers took us all by surprise. Therefore, if for example a major bank were to collapse that would alter this expected outcome.

CMBS Property Risk Analytics Pricing & Promotion
For Single Users, the CMBS Property Risk Analytics monthly reports are priced as follows:

 Item Title Monthly Price
QR CPRA Retail $135.00
QR CPRA Office $135.00
QR CPRA MultiFamily $135.00
QR CPRA Hotels/Lodgings $135.00
QR CPRA All Properties $370.00

 

The prices shown do not include discounted annual price, sale tax for Colorado residents/companies or Multi User pricing. For more information on pricing visit our website http://www.QuantumRisk.com/.

The corresponding April, May, June & July reports will be provided free for all 12-month or annual subscriptions paid by September 10, 2010. For PayPal payment instructions, please contact Ben Solomon. Note, an email address is required for receipt of ftp user id, ftp password and decryption password for each monthly report.

A sample report is available at http://www.QuantumRisk.com/Subscriptions/QRCPRA/SampleReports/(00)CMBSPropertyRiskAnalytics(2010-04)01-AL-SampleReport.zip

How the CPRA Report is Generated?
Every month we analyze reported data on more than 85,000 properties backing more than 52,000 loans to identify default probability, loss severity before recovery, loan to value ratio (LTV), debt service coverage ratio (DSCR), occupancy rates & change in property appraisal value for more than 400 U.S. markets, by property type, by city, by SMSA/MSA by state across the US. Five property type reports are generated: All Properties, Lodgings/Hotels, MultiFamily, Office & Retail.


** Property Risk Analytics is the registered trademakr of QuantumRIsk LLC.

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Disclosure: I’m a capitalist too, and my musings & opinions on this blog are for informational/educational purposes and part of my efforts to learn from the mistakes of other people. Hope you do, too. These musings are not to be taken as financial advise, and are based on data that is assumed to be correct. Therefore, my opinions are subject to change without notice. This blog is not intended to either negate or advocate any persons, entity, product, services or political position. Nor is this blog post to be construed as investment advice. 

Contact: Ben Solomon, Managing Principal, QuantumRisk
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Our latest CMBS product, QuantumRisk CMBS Property Risk Analytics (*1) (12 Mb Excel 2007 worksheet) will soon be available as an annual or monthly subscription or one-off purchase on the 15th of each month. 

QuantumRisk LLC invested more than $250,000 in research to develop the algorithms required to produce this product, CMBS Property Risk Analytics, on a monthly basis. For those of you who are familiar with the raw CMBS data know that this is no small feat.  

Robust,  valid and reliable market data is indispensable for actionable CMBS investment decisions and we are extremely proud to be the only one if not one of the very few companies providing this level of detail for CMBS defaults and losses. Thus providing more insightful commercial real estate business intelligence to our clients. 

On a monthly basis we analyze more than 85,000 properties backing more than 52,000 loans to report default probability, loss severity before recovery, loan to value ratio (LTV), debt service coverage ratio (DSCR), occupancy rates & change in property appraisal value for more than 400 U.S. markets, by property type, by city, by SMSA/MSA by state. Every month! 

The purpose is for sophisticated investors, investment bankers, underwriters and fund managers to know what is happening where it is happening when its happening. Even the muni bond professionals and local & state governments can use this report to figure out what is happening in their local market, as the commercial real estate market is reflective of the local business environment and therefore reflective of the local economy. 

We are also very pleased to announce that CoStar’s Watch List featured some of our May 2010 analytics in their newsletter article, Impact of CRE Distress Varies Widely Market to Market. This article received more than 10,000 reads within the first 24 hours. 

  

How Will Investors Benefit?
Because we analyze more than 85,000 CMBS properties & 52,000 loans on a monthly basis we have had to develop proprietary data algorithms to process this very large amount of data generated by the manual data entry origination-securitization-servicing business process. These CMBS Property Risk Analytics are organized into more than 420 tables for easy instant access of the data directly from your own Excel 2007 models. 

We report CMBS Property Risk Analytics by property type for cities, SMSA/MSA & states where there are 5 or more good property information in that property-geographic-statistic bucket, thereby further reducing the noise in the data. We don’t guarantee the end result is error free because we have no control over the origination-securitization-servicing business process but we do assure you that we have done our very best to give you the very best. 

Because the latest data is available on a monthly basis you get the most up to date information about what is happening across the United States in the commercial real estate world. 

Included in each monthly Excel 2007 report are 8 tutorials on how to use these analytics so that you the subscriber is benefiting from these reports within minutes of receiving them. 

  

What Questions Can Investors Answer?
1.Too much or too little capital?
Are you putting down too much capital with an LTV of 0.6, and want to know what Current (*2) LTVs are in Columbus, OH? 

Answer: You are most likely putting down too much capital as Current LTVs in Columbus OH are averaging 0.71. You could probably reduce your capital requirements by 11% by seeking other lenders. 

2. Realistic income generation?
Will the local or regional economy facilitate an income stream reflective of a DSCR of 1.2 in White Plains NY? 

Answer: Not likely as Current (*2) DSCRs in White Plains NY are averaging 1.03. A DSCR of 1.2 may be acceptable in a few years when the economy improves, but not today. If you were a muni bond professional or in local or state government the DSCRs would provide a quick & dirty indicator of whether you would need to raise taxes or not for general obligation bonds. 

3. Expected loan loss before recovery?
What is my expected loan loss in North Las Vegas, NV?   

Answer: The expected loan loss (before recovery) of North Las Vegas, NV, is 7.45% with a probability of default of 27.59% and severity of loss at 27.01%. As of May 2010 North Las Vegas is a high risk lending environment. Even though Las Vegas is high risk (2.40%, 15.79% & 15.23% respectively) it is less risky than North Las Vegas.  

4. City not found?
OK there is no data about Lewiston, ME, can I substitute with the SMSA Lewiston-Auburn, ME or the state level data? 

Answer: Yes. If a property count for a statistic is less than 5 (*3) we do not report this city, SMSA or state level statistic. 

5. Realistic occupancy?
In the past, CMBS cash flow models have generally assumed occupancies of about 99%. Is this a valid assumption especially since this Great Recession? So what would be a reasonable occupancy rate for Fort Worth TX? 

Answer: As of May 2010 the occupancy rate for Fort Worth TX is 85.97%. This rate will definitely increase as the local Fort Worth / Texas economy improves but at this time any occupancy rate much greater that 85.9% would be considered optimistic. The occupancy numbers presented in our CMBS Property Risk Analytics does not include completely vacant properties. 

6. An estimate of recent appraisal discounts?
What is the average reported property appraisal change (*4) in the state of Texas, over the last 15 months? 

Answer: As of May 2010, in the state of Texas the reported property appraisals are at 61.37% of appraisals done at origination. 

7. Comparative local economics?
Which city poses less commercial property risk? Pasadena CA or Beverly Hills CA? 

Answer:

State:City # Of Reported Properties in City Probability of Default Severity of Loss Expected Loss
CA:Beverly Hills 45 2.22% 2.22% 0.05%
CA:Pasadena 47 0.00% 0.00% 0.00%

With our CMBS Property Risk Analytics we can answer this question conclusively. It is Pasadena CA. 

Notes: 

(*1) “Property Risk Analytics” is the trademark of QuantumRisk LLC. 

(*2)  Current LTV and Current DSCR are calculated using the most recent appraisal values, outstanding balances, NCF DSCRs & NOI DSCRs. 

(*3) The number of properties used to determine a statistic (after processing) varies from 5 to several thousands depending on the size of the city/SMSA/state, property type and the type of statistic being reported.
(*4) Appraisal changes are not as well reported as LTVs or DSCRs. For example, there may 400 SMSA reported for defaults but only 35 for appraisal changes.

  

The Big Surprise: Multi-Property/Cross-Collateralized Loans  

Since we had all this processed data I thought I would check to see if single property loans were at a higher risk than multi-property loans, because from a loss perspective why would we put multiple properties into a single loan or even cross-collateralize a loan?  

I set up 2 pools of loans. The first pool consisted of 42,488 single property loans of all property types and the second of 2,177 multi-property & cross collateralized loans. The surprisingly results below show that multi-property & cross collateralized loans are at a higher risk of default than single property loans. So much for the assumed portfolio diversification effects. With respect to losses, for a better understanding of how portfolio diversification does or does not work see my blog post Loss Containment: Portfolios

 

Mortgage Pool  Mortgage Count  Total Mortgage Original Principal Balance ($1E6)  Mortgage Default Rate  Average Mortgage Severity of Loss without Recovery 
Multi-Property  2,177  18,356  7.49%  7.19% 
Single-Property  42,488  454,809  5.94%  5.65% 

 

Why Do We Recommend a Monthly Subscription?  

With a monthly subscription you can look at trends in the data and your decision making process is enhanced by the knowledge of the local trends. The table below the DSCRs of 3 SMSA/MSA present in the data, Denver-Boulder, CO, Atlanta,GA, and Dallas-Fort Worth, TX.   

      Denver-Boulder, CO     Atlanta,GA     Dallas-Fort Worth, TX 
   Reported Properties  Reported Current DSCRs  Reported Properties  Reported Current DSCRs  Reported Properties  Reported Current DSCRs 
2010/05  112  1.32  237  1.20  212  1.28 
2010/04  239  1.36  534  1.21  520  1.24 
% Change     -2.64%     -0.94%     3.53% 

  

In this example DSCRs, the ability to generate income to cover debt payments, is used as a proxy for business revenue and therefore local economic activity. Comparatively speaking Atlanta, GA has the worst reported DSCRs of the 3 SMSA/MSAs. We can see the different lags in the local economy even though the national economy is experienceing positive GDP growth. The Atlanta, GA, local economy is still contracting (-0.94%) but not as severely as the Denver-Boulder CO local economy (-2.64%). While the Dallas-Forth Worth, TX, local economy is expanding at 3.53%.  

These contractions and expansions will change from month to month, and a general trend will show where to or not to invest in the near term.

  

Summary 

These questions & answers presented above show that with QuantumRisk CMBS Property Risk Analytics there are many news ways to infer what is happenning in the local and state economies that can mitigate risk and reduce expenses. 

Further, we have shown how muni bond professionals, local & state governments can use this data to determine a quick & dirty assessment (and not a substitute for a thorough evaluation) of whether general obligation bonds can be issued without raising taxes and which part of a state needs further attention in terms of recession assistance or business policy matters. 

For a limited time we are making these QuantumRisk CMBS Property Risk Analytics available at a discounted price. Please contact me, Ben Solomon, for further information or to place orders.

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Disclosure: I’m a capitalist too, and my musings & opinions on this blog are for informational/educational purposes and part of my efforts to learn from the mistakes of other people. Hope you do, too. These musings are not to be taken as financial advise, and are based on data that is assumed to be correct. Therefore, my opinions are subject to change without notice. This blog is not intended to either negate or advocate any persons, entity, product, services or political position. Nor is this blog post to be construed as investment advice. 

Contact: Ben Solomon, Managing Principal, QuantumRisk
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QuantumRisk looks at downside risk & lessons we can learn from industry failures. We don’t provide rosy scenarios of the future, that is better left to others. We neither negate nor advocate any persons, entities, products, services or political positions. Neither are we attorneys. Nor do we profess legal opinions on any subject.
 
However, as much as is reasonably possible we present opinions, information and facts that can be tested and verified by & for the benefit of our readers. In so doing, we explore downside risk, their consequences and how we could avoid these future scenarios. Sometimes, the conclusions arrived at may not be desirable but many times if we don’t look an undesirable outcome straight in the eye we usually cannot find a more amenable solution. 
 
An ideal outcome would be the rethinking of private & public policies that lead to a more secure future for all of us.

Goldman Sachs Senate Hearings
I had originally planned to write Part 2 of Loss Containment but realized that the Goldman Sachs Senate Hearings was more pertinent in this time frame. I will get back to Loss Containment next month, if something more pertinent does not surface.

On April 27 2010 the Senate Subcommittee on Investigations held a hearing in which current & former Goldman Sachs employees (including the CEO & CFO) testified. In all fainess we need recognize that many parties by ommission or commission share the blame for this financial crisis. I watched about 7 hours of the hearings, and have several observations:

1. More Main Street
Senator Carl LevinThe goals of these hearings (Goldman Sachs hearing was one of several) are threefold:
1. To construct a public record of the facts to deepen public understanding of what happened and to try to hold some of the perpetrators accountable.
2. To inform the current legislative debate about the need for financial reform; and
3. To provide a foundation for building better defenses to protect Main Street from the excesses of Wall Street.

Sen. Levin had repeatedly brought up the need to protect Main St. and therefore I infer that future regulation will be supportive of Main St. – Wall St. engagements and discourage Wall St. – Wall St. engagements. In this light, we recognize why Glass-Steagall worked at the operational level. It ensured that a substantial portion of the financial services industry was focused on the needs of Main St, and by use of barriers protected these segments of the industry from the influences of its more glamorous kin.

2. Discourage Non-Collateral Based Instruments
Senator Claire McCaskill“It’s gambling, pure and simple raw gambling” said Sen. Claire McCaskill.  This sentiment was echoed by Sen. Carl Levin and was repeated many times by the senators, suggesting that future financial regulation would some day discourage if not curb the development and use of financial instruments that are not directly identifiable with a real physical asset. That futures-like products are acceptable but synthetic-like products are not. This is going to be tricky to do but I infer that this will be the direction of future legislation.

With respect to Mortgage Backed Securities there are two possible requirements to ensure compliance with a possible collateral-based security requirement. First, that an analyst is able to follow the cash flow from the assset to the security, and show how a specific asset default, defeasance, or prepayment would affect the cash flow to a specific security. Second that the master or even the special servicer is able to do the same.

The first requirement is usually a given for straight forward/non-exotic deals, but the second is a problem even for some ‘basic’ deals as some of the data is missing. The MLCFC 2007 9 WF or GSMS 2007 EOP WF deals are good examples where the servicer has difficulty in providing some of the data. The slicing & dicing of some of the properties in these deals has no effect on the risk characteristics of the deals but has resulted in a breakdown in the servicers’ ability to provide current information right down to the property level.

3. Strengthening of Fiduciary Responsibilities
Sen Levin and his colleagues had repeatedly asked questions relating to clients’ interests versus company’s interests. Their focus suggest that future legislation will prevent a bluring of roles with respect to client versus company interests. Therefore we should expect a compartmentalization of financial services to protect the integrity of fiduciary responsibilities

Goldman Sachs Employees Testify (Fox News/AP Photo)

4. Individual Responsibility
Seven current and former Goldman Sachs employees including Chief Executive Officer Lloyd Blankfein testified at the Senate Hearings. First a disclaimer. My comments are based on my inferences as a management consultant working with teams and not as an attorney.

The fact that the Subcommittee required both current and former employees to testify before it suggests that individual employees cannot hide behind company policy or lack of, to defend themsleves against allegations of wrong doing. Further, everybody is doing it will no longer suffice as an acceptable defense or immunity for one’s actions.

Conclusion
History shows that financial regulation (Securities Act of 1933, Securities Exchange Act of 1934, Trust Indenture Act of 1939, Investment Company Act of 1940, Investment Advisers Act of 1940) spawned after the crash of 1929 took 11 years to formulate and implement. Similarly we can expect to see several more new or strenghtened regulations being formulated and implemented over the next 10 years. 
 
Future regulation will further compartmentalize both financial services (e.g. retail banking, investment banking, trading and public-type versus private-type fund management) and financial products (e.g. basic, middle and exotic products).   
 
There will be stronger, clearer guidelines as to how products can be structured. For example (and this is only a suggestion) basic products can be used to structure deals where AAA accounts for up to 90% of the deal. Exoctic products cannot have more than 25% AAA, and middle products cannot have more that 60% AAA

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Disclosure: I’m a capitalist too, and my musings & opinions on this blog are for informational/educational purposes and part of my efforts to learn from the mistakes of other people. Hope you do, too. These musings are not to be taken as financial advise, and are based on data that is assumed to be correct. Therefore, my opinions are subject to change without notice. This blog is not intended to either negate or advocate any persons, entity, product, services or political position. Nor is this blog post to be construed as investment advice. 

Contact: Ben Solomon, Managing Principal, QuantumRisk
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Some Thoughts on Default Methods
Summary: Asset defaults (ratio of events) are statistically different from dollar defaults (function of ratio of magnitudes).  

Multiple Distributions: I had originally thought I’d just discuss long tails, but found that some matters needed to be clarified before discussing long tails. Individual asset losses have fat and long tails; the result of default and loss severities that obey binomial, lognormal or gamma distributions.  

Default Methods: There are only 2 broad methods of determining default probabilities in the mortgage industry. The first default method is asset default Pa defined as the number of assets defaulted divided by the total number of assets in the portfolio. This is a statistic of proportion or ratio of events.  

The second is what I term dollar defaults Pd (a.k.a. structural models). A dollar default is said to have occurred when the ratio of the default boundary value to original value decreases below a specific value. I use term them dollar default because they are primarily driven by the ratio of magnitudes to estimate credit risk; severity of loss and 1 – severity of loss are examples. These are statistics of proportion or ratio of magnitude and we can term these ratios severity of loss type statistic

Industry usage: The 2 ways this is used in CMBS are: 

(1) CDR (Constant Default Rate): CDR is the ratio of outstanding balance at default (default boundary value) divided by original principal balance (original value). In CMBS deal structuring CDRs are presented as a time series of ratios a.k.a. loss vectors; severity of loss in its most basic form. There is no need to model defaults as they are assumed to have occurred (very neat!) and severity of loss is predetermined by the CDR statistic. 

(2) DSCR loss models: A default occurs when DSCR gets below 1.0. Property cash flows are reduced by 2% (or some suitable value) per annum until this default event occurs. The ratio of magnitude, the ratio of the outstanding principal balance (default boundary value) to the original principal balance (original value) is determined when DSCR drops below 1.0. To determine severity of loss, the default event is specified by a rate of deterioration of cash flows, which is itself a ratio of magnitudes

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Empirical Data Confirms Biases
Summary: Empirical data confirms dollar default biases 

Empirical Confirmation: Empirical research (by others) confirm that dollar default methods (a.k.a. structural models) underestimate default probabilities. My own research on DSCR loss models concur with these results, that DSCR loss methods underestimate losses early in the life of a loan. My concern is not so much with these models’ expected values as with the shape of their tails. 

Test 1

Illustration:In a non-rigorous way we can illustrate why. Dollar defaults Pd, as a function of proportion of magnitude, have a different statistical behavior from asset defaults Pa, a proportion of events. We can see this by writing assets & dollar defaults, respectively, as some function of economic & industry factors f(x)

Asset defaults as a function of economic and industry factors:
Pa = f(x) = number of default events / total number of assets 

Dollar defaults as a function of economic, industry and asset size, s: 
Pd = g( f(x), s) = some function of ($ outstanding balance / $ original balance) 

   

Test 2

Using 2 portfolios to illustrate. Portfolio A consists of 2 assets of $100,000 each, and portfolio B consists of 3 assets of $100,000 each. Should one asset in each portfolio experience a loss (a good assumption if defaults are small) of $70,000, Portfolio A’s loss is 35% (70,000/200,000) & B’s is 23% (70,000/300,000). 

 

Different Statistics: However, Portfolio A’s asset default rate is 50% (1/2) and Portfolio B’s is 33% (1/3) but their respective severities are 35% & 23%, and being less could result in an underestimation of asset default probabilities. But wait. Should the loss have been $20,000 then Portfolio A’s & B’s losses are 1% and 7% respectively, but the asset default would still be 50% & 33% respectively. That is, for each asset default there are multiple severity of losses, and therefore, dollar and asset defaults have different underlying statistical behaviors.

The 2 figures (click on figures to enlarge) above, Test 1 & Test 2, show very different statistical distribitions that are dependent on the underlying nature of the risk drivers. It is clear from the graphs that the probability distributions of these severity of loss type statistics used to generate defaults, do not exhibit Binomial behaviors; Test 2 is not Lognormal, and Test 2’s tail is much fatter and longer than Test 1’s. 

Alternative Explanation: Researches currently believe that this consistent underestimation of default probabilities is due to missing factors such as liquidity and recovery. But including recovery will only reduce the severity of loss statistic and would further depress the dollar default estimations. My analysis, however, suggests an alternative explanation for the underestimation, that of different statistical properties. 

Undesirable Statistic: The dollar defaults statistical properties may even be undesirable. Using the form sum of (probability of default x outstanding balance at default) to estimate expected portfolio loss, we see that dollar defaults introduce asset size twice and asset defaults only once. Therefore, dollar default methodologies may not be desirable for determining default probability. 

Beta Distribution: An additional caution for those of you who model default & severity of loss. In my opinion, using the beta distribution is an assurance that your results are incorrect. Why? In my 30+ years working with large data sets, the beta distribution is the single most unstable distribution I have come across. This distribution will change shape when you are not looking! It is so unstable that small changes in its parameters can lead to significant changes in its shape. 

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Reducing Impact of Loss Tails
Summary: Portfolios alter the shape of the tail for the better. 

Therefore, we drop the use of dollar default methods. Most of us use portfolio diversification to reduce risk as measured by standard deviation of returns. But portfolios have little known properties, they can reduce the effect & change the shape of long tails. 

Severity Reduction: A portfolio consists of many assets, and each asset will have default probabilities and loss severities associated with it. All other factors being equal, the impact of a portfolio’s tail loss can be reduce by increasing the number of assets in the portfolio. Using the 2 portfolios above to illustrate this; the severity of loss of Portfolio A is 35% but that of Portfolio B is 23%. The severity of loss to a portfolio is reduced by the size of the portfolio (given all other factors being equal). 

Shape Change: Taking this a step further CMBS loss severities tend to be Gamma distributions while portfolio losses ought to approach Normal distributions (but not quite). Therefore for the same mean & standard deviation, the Gamma’s tail can be 25 to 35 times longer than the Normal’s tail. Why not quite?. In lay man’s terms, the Central Limit Theorem justifies the approximation of large-sample statistics with the normal distribution, and therefore large portfolio statistics should look Normal. However, default probabilities tend to be small, in the 1 to 2% range. Therefore, there aren’t enough observations to substantially shrink the loss tail, and therefore, appear lognormal or at least skewed to the right. 

Multiple Properties: Likewise, having multiple properties (and beware of cross collaterized loans, they are usually synonymous with multi-property loans) under a single mortgage can lead to catastrophic failure if there are a few properties. The loan defaults if a single property’s loss of income causes the loan’s DSCR to drop below 1.0. In this case a multiple property mortgage magnifies the effect of a single default. To reduce this impact one needs to either reduce the number  of these assets (loans) in the deal or increase the number of properties in the mortgage. However, the latter is not a good solution as it defeats the purpose of deal structuring. 

Spatial Correlations: Another problem with multiple property mortgages is that these properties tend to be in the same MSA (Metropolitan Statistical Area), and therefore are at risk to spatial correlations (see for example Prof. Tom Thibodeau, CU Boulder) that properties in close proximity tend to rise and fall together. 

Multiple Liens: More obviously, the reverse is also true, multiple mortgages on a single property causes all loans to be in default should the property’s income fall. In this case the mortgages should be assigned to different portfolios, thereby reducing the severity of loss to a specific portfolio. 

Wrong Signals: Note that RBS has tried an approach to reduce underwriter’s risk by not closing the loans as they are pooled. Interesting. While it does not reduce investors’ risk, don’t you think this sends the wrong market signals?  

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Some Lessons
Summary: Some lessons from a loss perspective. 

1. Avoid single-mortgage-multiple-property (& cross collateralized) assets (loans). 

2. Avoid CMBS deals with multiple cross collateralized assets as portfolio diversification may not be what it appears to be.  

3. Multiple-mortgages-single-property assets reduce risk for the same total principal.  

4. My experience with CMBS data suggests that CMBS deals should be in the 150+ asset range. The RBS $309.7 million, 81 property deal is small, and it should be interesting to see how a small deal at the bottom of the market fares in the future. 

5. Check your methodology. 

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Disclosure: I’m a capitalist too, and my musings & opinions on this blog are for informational/educational purposes and part of my efforts to learn from the mistakes of other people. Hope you do, too. These musings are not to be taken as financial advise, and are based on data that is assumed to be correct. Therefore, my opinions are subject to change without notice. This blog is not intended to either negate or advocate any persons, entity, product, services or political position. Nor is this blog post to be construed as investment advice. 

Contact: Ben Solomon, Managing Principal, QuantumRisk
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In the Feb 25 2010 Bloomberg article, Laurie Goodman, senior managing director at Amherst Holdings LLC made the most incisive statement I’ve heard in 30 months, “You can’t throw 12 million people out of their homes“. Even for someone who specializes in downside risk, this statement got my attention.

I would add one tiny correction – 12 million families.

Using the reported January median home price of $164,700 and the assumption of an average low equity of 20% in these mortgages, $12 million foreclosures translates to $395 billion or $0.4 trillion loss of individual homeowners’ wealth.

To give some idea of the size of this figure, it is about 2x that of the CMBS industry at its peak in 2007, and about 20% of the MBS industry. The question we need to ask ourselves is, do we really want to destroy $0.4 trillion of other people’s wealth? This $0.4 trillion figure would suggest that optimistic near term economic forecasts are too rosy. 

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Disclosure: I’m a capitalist too, and my musings & opinions on this blog are for informational/educational purposes and part of my efforts to learn from the mistakes of other people. Hope you do, too. These musings are not to be taken as financial advise, and are based on data that is assumed to be correct. Therefore, my opinions are subject to change without notice. This blog is not intended to either negate or advocate any persons, entity, product, services or political position. Nor is this blog post to be construed as investment advice.

Contact: Ben Solomon, Managing Principal, QuantumRisk
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Lets face it. The CMBS industry is only expected to be $10-15 billion this year, compared to about $250 billion at its peak in 2007. A lot of good people have left their firms or even the industry, for example in my outreach I found that the senior CMBS staff at Credit Suisse are no longer there, and many other firms don’t expect to get back into this industry for quite a while.

So how do we make profits? We have to look at other sources. One new emerging source is the radical technologies of the future. Well, they will only be future technologies if we work on them today.

One is probably correct to say that these are very high risk ventures, but wait a minute, didn’t we lose 50% of our principal in RMBS and CMBS? Thankfully that puts a new perspective on the risk of ‘radical’ technologies, doesn’t it?

In this post we explore some of the radical physics and technologies that I have been personally involved in. Yes, some of you (especially those of you who have worked with me at Capmark & at Goff Capital) know that I have been working on gravity modification technologies and later photon technologies these past 11 years.

I have published or presented 12 papers on this subject and given 4 radio / Internet interviews. See iSETI LLC; and am particularly proud of 2 papers that are now available as American Institute of Physics (AIP) Conference Proceedings. These 2 papers have be archived at The Smithsonian / NASA Astrophysics Data System:

1. An Approach to Gravity Modification as a Propulsion Technology
(http://adsabs.harvard.edu/abs/2009AIPC.1103..317S)
Paper Link: http://www.iseti.us/WhitePapers/SPESIF2009/SolomonGravityModificationSPESIF(2008-11-14)REVISED.pdf

2. Non-Gaussian Photon Probability Distribution (http://adsabs.harvard.edu/abs/2010AIPC.1208..261S)
Paper Link: http://www.iseti.us/WhitePapers/SPESIF2010/012_Solomon_ED(2009-10-18)01.pdf

In April 2009 I was honored to accept the position of Session Co-Chair of the A.03.1 Theories, Models and Concepts, Frontiers in Propulsion Science track at SPESIF, an AIP conference. The Session Chair is Dr. Martin Tajmar, Head of Space Propulsion & Advanced Concepts, Austrian Research Centers GmbH – ARC.

It is heartening to know that many, many scientist, engineers & technologist are working to improve our world and to reach for the stars. Well managed privately funded research will accelerate this and provide good returns for new investors.

Gravitational Acceleration Without Mass!
The formula on the right, g=tau.c^2, was published in my paper An Approach to Gravity Modification as a Propulsion Technology and derived using extensive numerical models (the same methods used in finance) and a new approach to gravity, that particles deform in a gravitational field. Briefly, gravitational acceleration is an indirect effect of mass, and therefore gravitational mass is not required to calculate the force. What is required to be known is the distortion present in spacetime at the location where the particle is, or tau or dt/dr the change in time dilation divided by the change in distance. Elegantly simple!

This formula has been numerically proven to be correct for gravitational forces, electromagnetic forces and mechanical forces i.e. unification of gravitational with electromagnetic forces that our current physical theories have not been able to reach. Lets think about this. This means that gravity modification as a propulsion mechanism is theoretically feasible!

Michio Kaku, the famous string theorist said (April 25 2008 The Space Show radio interview), that gravity modification is hundreds of years away. That is great news for small business research labs. Why? Because we cannot compete with the multi-billion dollar labs, and therefore, not being fully accepted (just yet) by the main stream physics community gives us breathing space to pursue our research to commercial success.

Therefore, we can expect new propulsion technologies, storage technologies – by slowing down time, new force field technologies, new solid-state gravitational accelerometers and much, much more.

Non-Gaussian Photons? What is That?
Quantum Theory is based on the premise that a particle’s probability distribution is a Gaussian distribution. The familiar Bell Curve or Normal distribution is the most famous member of this family of distributions.

In my paper Non-Gaussian Photon Probability Distribution, I showed that the photon’s probability distribution is not a Gaussian function but a modified Gamma distribution. As a consequence I was able to present shielding, cloaking and invisibility as different manifestations of this one and the same modified Gamma distribution. This also suggests new ways of looking at quantum entanglement. All this is done without taking into consideration the photon’s electromagnetic nature. This is a radical shift in photon modeling.

What does this mean? We can look forward to new materials technologies, such as cloth-like radiation shielding materials for space exploration, etc.

The Book

My book, An Introduction to Gravity Modification: A guide to using Laithwaite’s and Podkletnov’s experiments and the physics of forces for empirical results, (Universal Publishers, Boca Raton) published in 2008, presents what the physics of forces would look like given gravity modification.

The book details the work of selected experiments & researchers, Laithwaite (British experiments in the 1970s), Hayasaka & Takeuchi (Japanese experiments in the 1980s), Podkletnov (Russian experiments in the 1990s), and Luo, Nie,  Zhang, & Zhou (Chinese experiments in the early 2000s).

 In the 1970s Prof. Laithwaite, Heavy Electrical Engineering, Imperial College, UK,  showed that a rotating-spinning wheel would lose weight. His work was largely ignored because no one could mathematically explain why. I solved this and the equation is presented in the book. This could only be solved by assuming that Non-Inertia, Ni, fields exists.

It is important to note that in the 1990s Podkletnov was the first to bring to our attention the possibility that a spinning superconducting disc could have gravity modifying effects. However, his published papers are scant on technology details. The book provides a reverse engineering of why Podkletnov observed gravity shielding effects, using my findings that gravitational acceleration  is an indirect effect of mass.

If you chose to purchase this book, please bear in mind that there are some typo errors and it needs some revisions to include Non-Gaussian photon probability distributions. I expect to revise the book by December 2010, if I do get the time, else it will be next year.

The book is available directly from Universal Publishers or on Amazon (read the reviews).

Funding Summary
The Wall St. crash of 2008, the mortgage crisis, and severe asset deflation, tells us that the perception of risk rather than actual risks usually determines our investment decisions.

From my own personal experience with the grants process at the National Science Foundation I infer that it is difficult to get funding for any project that is not directly related to Relativity, Quantum Theory or String Theory. This is not surprising given that it is tax payer supported and there is a need to prioritize and be accountable for the disbursements. Therefore radical technologies will require private investors.

To get an idea of the size of the investment opportunities in the US, the 2010 NSF budget is $6 trillion (12 zeroes), and the size of the 2008 DoD SBIRs was $1.2 billion, other SBIRs/STTRs are estimated to around $2 billion. Therefore, innovation funding in the US is approximately $6.5 trillion not including internal corporate funding. Compare this to 2007 CMBS of $250 billion and gross U.S. issuance of agency & non-agency MBS of about $2 trillion. This reflects a huge opportunity in innovation research funding if we know how to tap it.

But wait, this now famous photograph shows 11 of Microsoft’s early pioneers. Who would have guessed…

If one is willing to invest patient money into radical high-risk technologies of the future, there are a lot of opportunities. In my opinion radical technologies can have higher sustainable rewards. 

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 About QuantumRisk
QuantumRisk provides the following services for investment companies, dealers & underwriters, fund managers, major municipal issuer(s) and corporate clients (no retail clients),

1. Structured Finance: 

1.1 CMBS Loss Vectors & Black Swans (more)

1.2 CMBS Defeasance Structuring with Prepayment Charges and Yield Maintenance Analysis.

1.3 Municipal Tax-Exempt & Taxable Bonds Refunding Analysis including Escrow Analysis & Structuring. 

2. Management Consulting:

2.1 Financial Analysis & Modeling
2.2 Business Process Reengineering
2.3 Business Strategy

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Disclosure: I’m a capitalist too, and my musings & opinions on this blog are for informational/educational purposes and part of my efforts to learn from the mistakes of other people. Hope you do, too. These musings are not to be taken as financial advise, and are based on data that is assumed to be correct. Therefore, my opinions are subject to change without notice. This blog is not intended to either negate or advocate any persons, entity, product, services or political position. Nor is this blog post to be construed as investment advice.

Contact: Ben Solomon, Managing Principal, QuantumRisk

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Here are two interesting articles from the Wall Street Journal describing events in both the RMBS and CMBS industries, that give us an idea of what is happenning at to opposite ends of the investment spectrum. Mortgage Funds Brace for Major Shift and Small Investors Lost It All in Memphis.

In the first article there are 2 points worth noting:
1.1) Mortgages that are underwater. 
The Markit index of subprime securities issued in the second half of 2006 recovered from below 60 (March 2009) to around 80. This points to the need for staying power to recover losses if one is already in the market or the discounts to look for if one is getting into the market.

1.2) Prepayment & loss assumptions.
My belief is that many mortgage deals (CMO, CMBS etc) were based on ‘optimistic’ (or ‘pessimistic’ depending on your point of view) prepayment assumptions which no longer hold. Low rates would suggest an increase in prepayments, but this will not the case because banks are essentially not lending. That is, bond structures based on 100 to 350 PSAs are probably seeing prepayment rates at 0 PSA. That means one has to rethink the support & subordinate bonds.

The second factor, losses, is a very severe factor and in my opinion, not adequately built into deal portfolios. An early estimate is that CMBS asset defaults are now around 6.4% – 6.8%. A huge increase from a previous 1.8%. However, given the two recent big defaults Crescent Real Estate Equities and Stuyvesant Town and Peter Cooper Village, one should expect the dollar defaults to be greater because of the dollar skew further to the right. The only way to handle losses is to factor transactions at deep discount. This articles shows that some fund managers are doing that.

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In the second article, we note
2.1) CMBS delinquencies.
CMBS delinquencies climbed to 6.5%. This is in line with our first pass default estimates of 6.4% to 6.8%.

2.2) Banks are having difficulties providing credit.
40 banks turned down the opportunity to refinance the Cherry Road property. Without a healthy functioning securitization industry, banks are unable to provide credit, and therefore hampering this economic recovery.

2.3) Duration Matching.
My guess given the wisdom of hindsight, properties like Cherry Road should have be financed with a longer term debt, 10 years instead of 5.

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When juxtaposed these two stories tell us:
3.1) Shift Focus.
The need to shift the focus of cash flow securitization analytics from prepayment modeling to loss modeling.

3.2) Preserve Capital.
Investors’ primary concern should be the preservation of their capital and therefore a realistic strategy for the preservation of capital is the high LTV capital structure.

3.3) Liquid Assets.
TIC are really illiquid and all things being equal, CMBS AAA bonds might have been a better investment, in that one would probably not have lost $7 million. Google results on TICs.

3.4) Optimism Can Be Misplaced.
With hindsight, the use of TICs with low LTVs would suggest investors with an optimistic perspective on the economy and that there was probably insufficient downside risk assessment. But then we never really know until things are too late, right?

3.5) Sophisticated Investors.
The concept that investors are ‘sophisticated’ based on their net worth needs some rethinking.

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Disclosure: I’m a capitalist too, and my musings & opinions on this blog are for informational/educational purposes and part of my efforts to learn from the mistakes of other people. Hope you do, too. These musings are not to be taken as financial advise, and are based on data that is assumed to be correct. Therefore, my opinions are subject to change without notice. This blog is not intended to either negate or advocate any persons, entity, product, services or political position. Nor is this blog post to be construed as investment advice.

Contact: Ben Solomon, Managing Principal, QuantumRisk

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QuantumRisk is starting a new CMBS service – providing econometric loss forecast for CMBS deals.

Why econometric CMBS loss forecasting? Our studies show that

1. The Triangular  Matrix Method is incorrrect (Excel 2007 example).

2. DSCR are not good predictors of defaults (see brochure).

3. Stress testing usually not done correctly (see post).

4. Black Swans can be used to differentiate between two deals with similar cashflows (see brochure).

Contact  Ben Solomon (benjamin.t.solomon@QuantumRisk.com) for more details. Company brochure here.

Several news items (WSJ’s Tishman Venture Gives Up Stuyvesant Project, Control of Stuyvesant Takes Center Stage and the NYT’s Fallout Is Wide in Failed Deal for Stuyvesant Town, As Doubts Grow, U.S. Will Judge Banks’ Stability) got me thinking about stress testing.

Stress testing is an approach to evaluating how a deal or a balance sheet will behave when selected factors are changed to induce a loss. It is done to answer the question what is the downside financial risk should a bad situation occur?

Around February 2009, the Feds and the Treasury impleted stress testing of the largest banks. This illustrates that stress testing is used in both the private and public sectors. And for those who are not familiar with this methodology, it is not a Republican or Democrat issue, as we can see that Ben Bernake is a Republican and Timothy Geithner is an independent having been a Republican.

Figure 1 illustrates a single factor that is used to stress test the deal or the balance sheet. I purchased some structured finance models a few weeks ago and was reviewing them. My observations about 3 important failings of the stress tests are based partly on these models:

Figure 1: Factors Skewed Right

1. Right/Wrong Distribution: I realized that the inherent assumption behind one of the stress parameters, the loss statistic, is that it is Normally distributed, because it is based on average values. In this example the mean is the 5.0% dashed green vertical line.  Therefore, this factor should behave like the red-dashed Normal curve in Figure 1. However, losses are skewed right and fat tailed per the blue curve. The mean is still 5.0%, however, the mode is 4.6% (solid purple vertical) with a long tail extending out beyond 17.9%.

2.  Insufficient: Stress testing multiplies the loss number by 2x to 10.0% or 3x to 15.0%. In this example 15.0% is insufficient to cover 17.9% tail loss.

Figure 2: Factors Skewed Left

3. Irrelevant: In Figure 2, we see that the skew is to the left. As an example some returns distributions can be skewed to the left. Therefore, the stress test has no bearings with reality. The stress test stresses the loss statistic in a manner that can never happen because it is outside both Normal and Left Skewed distributions, and therefore, the results are irrelevant to the real world.

So take care with your stress testing.

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Disclosure: I’m a capitalist too, and my musings & opinions on this blog are for informational/educational purposes and part of my efforts to learn from the mistakes of other people. Hope you do, too. These musings are not to be taken as financial advise, and are based on data that is assumed to be correct. Therefore, my opinions are subject to change without notice. This blog is not intended to either negate or advocate any persons, entity, product, services or political position neither is it to be construed as investment advice.

Contact: Ben Solomon, Managing Principal, QuantumRisk

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