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Tag Archives: CMBS Deal Risk

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? 


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. 


(*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.



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.


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

The Opportunity:
What would you pay to know the Black Swan of your CMBS deal risk?

Reverse that question!

What would a deal manager pay you to know the Black Swan of his CMBS deal?

The Need:
Inspite the bad publicity surrounding CDOs and structured finance the tranche strucutre is one of the most efficient methods of creating differentiated classes of assets, as The Committee on the Global Financial System explains:

A key goal of the tranching process is to create at least one class of securities whose rating is higher than the average rating of the underlying collateral pool or to create rated securities from a pool of unrated assets. This is accomplished through the use of credit support (enhancement), such as prioritisation of payments to the different tranches.

However, what really caused the market to substatially undervalue these assets was that the default rate and the severity of the loss was much greater than even the complex rating processes had estimated them to be.  Have you noticed that some (many?) of the AAA tranches had losses?

The Solution:
The answer is an econometric assessment of the default and loss distributions of CMBS assets. Note, not a single point value but the whole distribution. This distribution will provide the CMBS Deal’s 95% or 98% VaR and CVaR loss estimates.

From this distribution we can then recalculate the loss estimates for each tranche, and be pretty certain what the loss characteristics of these tranches are independently of the ratings assigned to the tranches. A backup second opinion, if you would.

Why would this provide better answers?
1. We know the assigned ratings did not cut it.
2. We know the vintage or triangular matrix method provided incorrect default and loss curves. I was the first & only person to correctly identify that a major tool, vinatges/triangular matrix method, used in the mortgage idustry was providing incorrect results. The link provides access to an Excel worksheet that allows you to confirm this for yourself. To understand the magnitude of this finding one only need to look at the vast array of mortgage analyses, from the Esaki-Snyder reports to the Wachovia 2008 CMBS Loss Study, that use this tool.
3. The DSCR loss model used in the CMBS industry does not match the historical data. I discovered this weakness in the method through extensive testing against the historical data. Do you know of anyone who has personally tested these tool against the historical data?

What would the solution look like?

The graph below is a simulated long term VaR & CVaR loss outcome for a simulated CMBS deal.


This graph is based on a set of CMBS loss & default distributions, and provides a second method to evaluating bond pricing. After all at the end of the day, isn’t cashflow analysis and DSCR’s about bond pricing? Notice how VaR (green dash) consistently underestimates CVaR (purple dash). We can add in Black Swans into this report. Note that my initial assessement was that CMBS Black Swans are on the order of 20% to 80%.

I am seeking partnerships with banks/investment funds/ratings companies to fund the development of this econometric CMBS business, which I expect to be transferrable to the RMBS sub-industry.

Call me 303-618-2800 or email me at benjamin . t . solomon AT QuantumRisk . com if you are interested in this business.

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.