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Tag Archives: CMBS

Here is the March 16th 20011 CREPIG podcast interview with JW Najarian & Robert Schecter. It has been described as ‘educational’.

This interview covers many topics, the economy, residential mortgages, commercial properties, distressed property industry, and especially methodology errors.

This interview is also available at the QuantumRisk website, http://www.QuantumRisk.com/.

I hope you find this interview informative and an enabler to executing better investment decisions.

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On a monthly basis QuantumRisk analyses more than 102,000 commercial properties with a total original appraised value of $1.5 trillion, backing more than 64,000 loans, with an outstanding debt of $680 billion, to report default rates, loss severity before recovery, loan to value ratio (LTV), debt service coverage ratio (DSCR), occupancy rates, cap rates & change in property appraisal value for more than 400 U.S. markets, by property type, by city, by MSA by state.

There are 5 types of CMBS Property Risk Analytics* reports:

1. CMBS Deals
2. CMBS Warehouse/Portfolios
3. CMPB Property Risk by City by State
4. CMBS Property Risk for a specific City
5. CMBS Property Risk for a specific MSA

There are 24 sample example reports of rigorous evaluations of the downside risk a CMBS Deal, Warehouse or Portfolio may be subject to given the current, this past month’s, economic conditions. These reports may be used to assess the potential upside risk. To keep your costs to a minimum QuantumRisk provides one-off reports for a specific set of deal / warehouse / portfolio requirements.

The reports are ideal for deal/bond restructuring, associating default risk to the bond stack, negotiating portfolio pricing based on today’s loss characteristics, and sub-optimal investment avoidance i.e. the portfolio may look great on paper but without an evaluation of the loss characteristics one may not be fully informed of the downside risks.

To purchase any of these reports, please conatct Ben Solomon.

*Property Risk Analytics is the registered trademark of QuantumRisk LLC.

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Disclosure

Guan Jianzhong, Chairman of Dagong Global Credit Rating Co. Ltd

Bloomberg reports that China’s Dagong Global Credit Rating Co. reduced its credit rating for the U.S. to A+ from AA citing a deteriorating intent and ability to repay debt obligations.

Having worked on both sides of the world, I have come to associate such statements with shortsightedness. My past experience as a senior research analyst for a brokerage firm in Asia-Pacific would suggest that we are going to see problems in China by 2012.

In this blog post we take a look at how consumers could cope with the Mortgage Mess. Yes, we need strong banks otherwise the Dagong rating will be fulfilled, but this economy is 70% consumer driven. We therefore need stronger consumers, as strong consumers underpin the health of the banks, and not the other way around. Using China as an example, as China’s income per capita rises its banks and financial services companies become more confident of themselves, therefore the Dagong ratings comment above. 

I want to inform our readers that QuantumRisk’s CMBS Property Risk Analytics promotion ended September 10, 2010 per an earlier newsletter. The new pricing, valid until March 2011, is available here.

Home Prices Sag in August 2010

The State of the Housing Market
In my August 2009 blog post Have we hit the Housing Bottom? I had suggested that the house prices will bottom in 1Q 2010. Given the graph, I must say that this was a pretty good estimate of timing. 

The second question I had attempted to answer then, was home price recovery sustainable? My answer at that time was that it was more likely not.

The economic analysis presented by HiddenLevers (see picture) suggests that house prices are struggling to maintain an upward momentum. 

Why? There are two reasons. First the glut in foreclosed homes (1 in 4 for sale are foreclosed) will keep supply substantially greater than demand. Second, 1 in 6 homes in foreclosure translates into 1 in 6 homeowners who will not be able to participate in homeownership for at least 7 years or a 15% reduction in demand.

The graph shows that house prices are now hovering in the 68% to 71% range of their 2006 peak values.  From an economic cycle perspective, the housing industry collapsed before the commercial property industry. Given a 15% reduction in residential ownership capacity, it is very likely that commercial property sector will recover before the residential sector does. That is, the trough in the residential sector will be longer than that of commercials.

What is not reported in the news is the residential vacancy rates. A friend of mine who works for a utility company told me a few weeks ago, this utility is seeing 1 in 7 homes vacant. This is 50% more than reported by US Census Bureau for 2Q. That means rental incomes will not increase in the medium term. It also means that utility revenues will fall by 14%.

Some other bank just increased the difficulty of Chief Executive Officer Brian T. Moynihan 'hand to hand combat' over mortgage disputes.

The Next Big Wave: Legal Risk
Most of us are focused on market, credit & operational risks, but the next big wave will be legal risk.

I recently found out that banks are selling the second mortgage on foreclosed homes to debt collectors. Sure this maybe legally possible but lets weigh the pros & cons. The pros. Maybe banks think they can get back they principal in the second mortgage by selling the second mortgage to a debt collector. Sounds great, high fives to the managers who thought up this one. And at worst you don’t even have to write it off your balance sheet just yet. Kudos.

But wait. Does anyone really think they can get their money back from homeowners who could not even pay their first mortgage? Especially if they are unemployed? It also raises another question, what was the function and scope of collateralization?

Now the cons. What this action has done is to clarify that in the event of a foreclosure / repossession, the bank recognizes that collateralized debt survives ownership and can be put back to owner / originators. (Check with legal counsel for an informed opinion.) In the United States one cannot have one set of laws for one group of people and another set for another group of people.

Therefore, investors who bought RMBS bonds can now recognize that their securitized bonds survive any asset ownership issues, and banks are now liable for securitized bonds because they survive ownership.

I found out about a bank’s access to your personal funds some years ago. When I contacted the FDIC about it they said they could not do anything about it. Some mortgage contracts include a single sheet document that states that the bank has the right to move your funds around to keep your mortgage current. This I believe is antithetical to the securities law because securities law does not allow financial services companies to move funds around for a client for the benefit of the company.

The problem here is given such a ‘contract’ will or does the bank have the right to reach out to your 401(k) or similar funds?

Prime fixed rate foreclosures jump

How Consumers Can Protect Themselves
There are several ways consumers can protect themselves from future messy mortgage problems:

 1. House Pricing: The graph above (picture in State of the Housing Market, above) suggests that with today’s market conditions a home buyer should consider as an upper limit a purchase price of about 70% of the 2006 appraisal. If the housing situation deteriorates, this 70% number will drop. Looking at CMBS for guidance, this number can get to be as low as 54%.

 2. Appraiser Selection: Before purchasing, get an appraisal of the property by an appraiser who does not have links to banks as this minimizes banker bias.

 3. Mortgage Origination: If you are purchasing a foreclosed property, it is not recommended that you get your mortgage from the same bank that foreclosed the property. Why? At least in theory, in the event that there are ownership issues, you have a different bank behind you. 

 4. Safeguards: Given the state of the housing market, it would be prudent for the home buyer not rush into a purchase as the housing market is not likely to recover anytime soon. If you do so, you would need to have staying power. Therefore, before making a purchase, here are some points you should consider:

 4.1 Title Insurance: Don’t sign an S&P if you cannot get title insurance.

4.2 Deposit: Make your deposit conditional upon getting a clean title.

4.3 Indemnification: Require that the seller and/or the mortgage provider accepts liability for any future ownership claims in the event of the failure of the title insurance company. The lesson of 2008 was that many securitized bond credit enhancements (credit insurance) turned out to be worthless when the economy as a whole turned south.

4.4 Survival: Require that in the event of a foreclosure/repossession that all collateralized claims (1st, 2nd & 3rd liens) cannot survive the ownership.

4.5 Delinquency: Require that the mortgage provider cannot start foreclosure proceedings until the mortgage is at least 90 days past due. In the state of Colorado there are no laws to prevent a lender from foreclosing on day 2. Yes, even I was surprised by this, and know of at least one recent case where the foreclosure proceeding was started on day 50. 

4.6 Miscellaneous Contracts: Do not allow the mortgage lender have access to your other funds. Remove all such ‘subcontracts’ from your S&P agreement.

 5. Walk Away: If there are any doubts about the price, property or claims on the property, walk away. This market in not going to recover any time soon, and there will be plenty of second chances.

 There are many really good managers in banks, but as a general rule banks rotate their managers. So the great manager you see today could be replaced by a rogue manager tomorrow. Therefore do not feel ‘uncomfortable’ including these conditions in your S&P. You may even have to hire your own legal counsel to protect yourself. Remember it is wiser to walk away then to be burdened by a debt for a property you no longer own.

Summary
The real sad story is that we will eventually see 1 in 6 families homeless. To put things into perspective, James Fry, founder of Mean Street Ministry, reports that when he started this ministry about 10 years ago, there were 2 suicides per year, today there are 2 a week. We as a family have known James Fry, his family & his ministry for many years. Let us in Thanksgiving help someone in return.

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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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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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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.

Many unintended consequences in business & finance occur because upside (profitability) and downside (loss) risk have very different characteristics.

Background
In 2007 I sifted through 330,000 RMBS (Residential Mortgage Backed Securities) assets or loans with the purpose of developing a default & loss econometric model that would enable my then employer differentiate between good and bad RMBS bonds.

I had to do a lot of data structuring and clean up to standardize incoming data from about 20 different servicers. I should say data structuring and data scrubbing. And after that sift through this asset data set to filter out the bad data. So you can imagine the amount of work that went into this.

My strategy to identify the loss quality of a bond was to use the origination and close data to forecast future losses. Why? Because once you have purchased the bond you are stuck with whatever losses that unfold.  If this was possible we could very quickly eliminate the poor quality bonds, and use cash flow methodologies (more on this in a later blog) to evaluate the better quality bonds. We did not want to expend effort valuing poor quality bonds.

Dichotomy of Metrics
Well, at least that was the theory. What went wrong? In residential mortgages FICO scores are a major component, if not the primary driver for assessing future default behavior. I found that for residential mortgage FICO scores at origination were poor predictors of future defaults. How? Build two probability distributions of FICO scores. The first is a distribution of FICO scores of the borrowers at origination for all assets. The second is the distribution of the FICO scores at origination for defaulted assets only. You could not tell the difference between the two distributions!

Bewildering. Was there an analogy in other areas of finance? Yes. Company pro forma profitability analysis are good predictors of future profitability, but they are poor predictors of severe losses or bankruptcy risk. To get a handle on future company bankruptcy risk one requires a tool like Altman’s Z-Scores.

Here we observe a dichotomy in forecasting characteristics. On the one hand pro forma informs us of future profitability and on the other hand Altman’s Z-Scores informs on bankruptcy risk. We concluded that FICO scores behave like pro forma profitability, that they are good predictors of success but poor predictors of defaults.

I must add that unlike investment bankers and fund managers who are focused on upside risk or profitability, my career in financial services has been focused on downside risk. Therefore I did not test if FICO scores are good predictors of success.

When we recognized this dichotomy we abandoned RMBS bonds as a viable investment vehicle. But this story does not end here. I have an extensive business reengineering background and investigated how FICO scores are generated.

How Credit Cards Drive FICO Scores
From a business process perspective recent FICO scores are generated by your credit card issuer. Really. You the card holder use your card to transact purchases. In so doing you create debt. How much of this debt you pay down, how quickly, etc., is reported to the rating agencies. This reporting provides an insight into your debt payment characteristics which the rating agencies convert into FICO scores. FICO scores appear to predict defaults because they are adjusted downwards as your credit card payments deteriorate. That is, in my opinion, FICO scores have a limited window of about 90-days within which they work, but by then it is too late for the card issuer. Or no credit card visibility no FICO adjustments.

Here is the weak link in this whole business process. Without your credit card spending and payments reported by your card issuer, the rating agencies would not have a clue what your recent credit rating should be.

Take 2 people John & Jay. John has house and car payments and uses his credit card for all his purchasing transactions. Jay also has house and car payments, but he only uses cash for his purchasing transactions.

Now both have suffered a partial loss of income. They both continue to make house and car payments. Therefore the ratings agencies cannot tell from these payment that there has been a partial loss of income. Both reduce their spending to manage within their new realities. But John has now slowed his credit card payments. The rating agencies see this and are able to adjust John’s FICO scores accordingly but not Jay’s.

The use or not of credit cards provides asymmetric information to rating agencies. Now guess what happens as credit card companies ramp up interest rates, consumers pay down their card debt and stop using their cards.

Unintended Consequences
All manner of unintended consequences occur.

1. The banking industry loses it recent FICO information for a much larger proportion of the population.

2. Consumers do get penalized with lower FICO scores for not having recent payment histories.

3. First time home buyers will face higher rates because they don’t have sufficient recent history.

4. FICO scores are no longer as “effective” as they were “supposed” to be.

5. Because of this additional “FICO risk” for the same “risk” bonds with credit card debt should trade at a discount to bonds with residential mortgages which in turn should trade at a discount to CMBS bonds.

6. As I had stated in my Feb/02/09 post Risk-Reward is Non-Linear, increased rates will increase defaults. The unintended consequence of this is that banks that have rushed to increase rates to 30% before the new laws come into effect, will have locked themselves into a population demographics that experiences a higher default rate. Therefore we can expect to see an increase in credit card losses.

Summary
I’m sure there are a lot of very smart people in the credit card and rating industries, but… This situation is not dissimilar to what I observed as a management consultant reengineering manufacturing companies – one may have the best, the brightest and the most experienced managers but surprisingly when they come together at their management meetings they do strange things.

There needs to be a better way to address bank losses on credit cards and a better way to treat customers. Remember your customers are not your enemies.

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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.
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Giacomo John Roma pointed me to an interesting article by Taleb & Spitznagel as part of the LinkedIn discussion and here are my thoughts on this and the related discussion.

I infer that Taleb’s primary concern is leverage. Debt provides leverage, and unconstrained debt provides unconstrained leverage. That is the lesson of 2008. Taleb wants us to reduce the extent of the leverage in our economy, because debt has a property that affects our perception of risk, i.e. debt hides volatility.

Regarding VaR:

(1) If you examine the historical data, outside of social sciences and quality control, there are very few thing that are Normally distributed. Taleb’s point here is that much of the mathematics that is used in finance and economics is based strongly on the assumptions of normality, and therefore, are not good forecasters of their metrics. They only appear to work when the law of large numbers can be applied. Therefore Taleb is recommending that a lot of these mathematics and their resulting analytical tools are erroneous in the long run.

(2) There are serious problems with VaR even without the normality assumption. In my experiece (I never assume normality) with years of building econometric CMBS loss and default models I found that VaR would consistently underestimate losses in the first 3 to 5 years of the loan.

Black Swans are a real problem in mortgages. The historical data shows that they are real. Just look at 2008. My own estimates are that Black Swans in CMBS bonds are on the order of 20% to 80%.

I also found that the only realistic way to handle undisciplined leverage was to implement non-linear economic or risk capital controls. This comes back to Taleb’s point on non-linearity.

Non-linearity is not new. Credit card companies use non-linearity a lot. They impose 2x and even 3x or 30% rates on delinquent card holders. Try imposing that on institutions, and all hell breaks loose. This is the power of buyers and sellers at play, not mathematics and not finance.

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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.
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This series of blogs is derived from my discussions on the LinkedIn Quant Finance: What is the best approach to handling CMBS &/or RMBS Credit Risk analysis? discussion forum.

Here is the problem with vintage per the triangular matrix method. (I gave it that term because nobody else I talked to knew what it was named.) If you look at the Esaki-Snyder report or the Wachovia CMBS 2008 Loss Study, there is this averaging method, that looks like a triangular matrix, to determine the average over vintages by loan age.

It gives one an incorrect shape of loss or defaults over the life of an asset. There is a simple test to prove this. Assume that all defaults (or losses) are constant over the life of the loan say 2% every year. The age-default profile should be a horizontal line. Now say that as the economy improves the defaults decrease by 0.2% per vintage, ie yr 2000=2%, yr 2001 = 1.8%, yr 2002 =1.6%, … but remains constant per vintage through the life of the asset.

You can do the same with increasing rates.

The method is biased because the averaged defaults (or loss) is no longer a horizontal line (the original correct input), and tends towards the value of the oldest vintage.

To make it easier for the reader I have provided the link to the Triangular Matrix Method Test Excel 2003 Spreadsheet. (Note: you may have some problems with IE8, try saving to disc.)

 

 Discalimer: This blog is purely for informational/educational purposes and is not intended to either negate or advocate any product, service, political position or persons.
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