Rise Of Re-Remics

Rise of the Re-Remics

Over the past few several years we have all seen the impact of the economic crisis ostensibly cause by bad loan underwriting standards, rating agency goofs, greed and the rest of it. This has resulted in many, many RMBS bonds formerly rated AAA being downgraded to CCC or worse. What to do with all the downgraded bonds? I know, let’s put them into a new securitization and then tranche the good loans and the bad loans, get the “good tranche” newly rated AAA and so forth. Read on to get a bit more about this process.

You can get a decent basic education on re-remics at this link (it includes an excellent graphic illustrating the basic concepts of why Re-Remics are done).

http://www.theatlantic.com/business/archive/2009/10/re-remics-really/27639/

and a related link to an article from the WSJ here (which does require a subscription to read the entire article):

http://online.wsj.com/article/SB125434502953253695.html

At first glance, these securities look quite a bit like the old “CDO” structure in which other securities were packaged up as assets in a new deal and then tranched by issuing new bonds that were backed by the cash flows on those underlying securities. Of course a CDO had a “collateral manager” whose job it was to monitor the assets and that person also had a “reinvestment period” of several years during which the assets could be traded in and out of to maximize profits etc.

However, Re-Remics are NOT CDO’s – even though they smell like them quite a bit (take that comment any way you want).

These are technically called “ Resecuritization of Real Estate Mortgage Investment Conduits”. In this instance a “REMIC” can be thought of as a plain old RMBS security. And “Resecuritizing” those RMBS securities means the process of packaging them up into a NEW REMIC, ie. A “Re-REMIC”.

From the links earlier you can see that the primary reason for doing a resecuritization is to separate the bad loans from the good loans that are “locked up” inside a formerly AAA RMBS security. In their original form, and because these bonds are backed by a pool of assets which have deteriorated considerably in most cases, the entire RMBS bond is priced very low. However, if we can separate the good from the bad, then we create a new bond backed by the “good” loans and a lower, more risky tranche backed by the “bad loans”. Sort of a mini “good bank / bad bank” sort of situation. The Re-Remic tranche backed by the good loans all of a sudden gets a much higher price; is rated by the rating agencies as AAA (yup, that’s right); and the original investor now does not have to set aside nearly as much regulatory capital as previously. Magic!! What a concept! Of course, the Re-remic tranche backed by the bad loans is priced even lower and is usually not rated by a rating agency. So the amount of regulatory capital set aside for this piece can be quite a bit higher than before. But hey, if we stuff 70% of the new Re-Remic into the AAA and only 30% in to the unrated piece, voila! Net effect is that less money is having to be locked up in set aside regulatory capital and that money becomes freed up to use more productively elsewhere. Financial alchemy, indeed!

A few years, back, much was made of various financial institution’s inability to properly price those complex beasts known as CDO’s. Well the same thing goes for Re-Remics. In any single one of these Re-Remics, we may have up to 50 or more formerly AAA assets, now rated as low as CCC or worse. What is the proper way to value these. If you have a portfolio of these Re-Remic tranches, how do you go about valuing them. The answer is: It’s not simple – just like valuing CDO’s was fraught with complexity – so are these Re-Remics (although we think less so than CDOs).

If you use a predictive modeling company to generate forecast prepayment, default, severity and delinquency vectors (or if you produce these yourself), then you should ideally want to be able to apply these vectors to each and every asset that backs the particular Re-Remic tranche that you’re trying to price. If you are like most firms, you probably produce a wide range of “vector sets” for each economic scenario that you’d like to stress your bonds at. Some firms produce eight economic vectors sets (scenarios), some produce 10, some produce 60 or 100’s (using Monte Carlo Simulation). Once produced, that’s a LOT of vectors and manually loading them into Intex Desktop can be a painful, mistake-prone process and then the process of running the price/yield and cash flow tables can be time consuming, and can also eat up all the processing power on your computer.

You should be able to price these securities easily. Can you do it? Do you have clients that have a large portfolio (or even a fairly small one)? I’d be interested to hear how people are handling the pricing of Re-Remics currently. Are you doing “just enough” to get the job done? What are the stumbling blocks you’re running into? How often do you have to price them? Is that an adequate amount of frequency of pricing and so forth. If you want to take the conversation off-line from this blog, please email me at Jack@thetica.com and I will respond.

In addition to that, we probably all can remember the concept of a CDO “Squared”. This was where a CDO started to invest in the tranches of other CDO deals. Thereby “telescoping” the risk outwards even further. To analyze the underlying bonds required plumbing the depths layers downwards – and this become a particularly difficult task to adequately stress test these bonds.

This past week I have actually heard of a new security called a “Re-Re-Remic”… Similar to a CDO Squared, this is a situation where the Re-Remic tranches have been put into yet another Re-Remic structure and those assets have been “tranched” in order to separate the wheat from the chaff once more. Impossible you state. Nope. It’s for real. Will you be able to prices these securities. I hesitate to personally call these securities “Re-Remic Squared” because of all the horrible connotations associated with the term “CDO Squared”, but that’s essentially what they are.

Is this just another Wall Street concoction guaranteed to give us waves of further financial nastiness down the road? Well, whether they are or not, my question still stands. How are you planning on evaluating and/or pricing these bonds. Depending on your position in the market (trader, risk manager, financial controller, mortgage research analyst, IT professional, analytics provider, and so forth), this may be heading your way or you may already be looking at it square on.

I would love to hear your comments on the overall subject of Re-Remics and also the new-fangled “re-re-remics”.

Best,
Jack S. Broad
Co-Founder
Thetica Systems, LLC
Jack@thetica.com

SEC Investigating

Here is the latest piece of the on-going investigation from ProPublica dealing with CDOs and what happened with the housing bubble:

http://www.propublica.org/article/sec-investigating-deal-between-jpmorgan-and-hedge-fund-magnetar

From the desk of “Quark” – a bit of financial strangeness

Vectors

Predictive Modeling companies attempt to create projections for the future based on various economic scenarios. There are quite a few companies producing these vectors and they have a wide variety of uses for financial institutions.

Inputs to vectors.

Some of the standard inputs for producing vectors are:
*Home Price projections
*Unemployment projections
*Forward Interest Rates
*Property Valuations
*Borrower Credit Profiles
*Loan Characteristics and related historical data

Uncertainty Principles and Quantum Effects

Questions from various market participants arise as to how “granular” should these items be. For example, should we create projections only at the national level or drill down to state or county, even zip code projections? Similarly, with unemployment projections, should we do this at the state or county level or are national level statistics sufficient? Arguments for and against each point of view exist. One view is that doing it at too granular a level gives way too many inputs to take in, and by focusing so intently on the “micro” level, you’re losing sight of the forest. Another opinion is that too much of a macro view gives one not enough insight into what’s happening at the detail level, and you’re losing touch with reality.
Another way to think of this is with a “quantum world” view – where when you attempt to determine the exact position of a particle, you cannot determine its exact speed. If you determine the exact speed, you cannot find its exact position. By drilling down to zip code + 4 unemployment data – you might lose sight of more macro trends that would impact that area. Yet, by focusing on, say, too wide a range of home price indices, to use another example, you tend not to see the actuality of what’s happening with properties relevant to specific RMBS of interest. It’s perhaps a bit of a stretch to apply this to the same quantum effects, but maybe not. Certainly the question arises as to how much granularity is sufficient. When do you need more detail and when do you not?
One vendor I spoke with recently has even gone so far as to produce the property address of homes backing RMBS, but apparently you can only actually SEE the property address for yourself, if you sign a document stating that you will not then go looking at the borrower credit profile (such as from Equifax, Experian, or TransUnion). Because RMBS loans are “anonymous” (aka “de-identified) in the sense that you don’t know who the borrower is, nor do you know the property address, then solving the problem of “where is the property located exactly so that the most granular level of property valuation can be performed” can be highly valuable indeed. Here again, though, we have this almost “quantum” oddity of “being able to determine the exact location of a property, but not then being able to determine the credit profile of the borrower who owns the property.
Of course, if a property is now REO, then the current owner of the loan IS the owner of the property and no credit profile is needed particularly. In the case of RMBS, the Trust itself has become the “owner” of the property. With foreclosures and REO at such high percentages of deals, then the need lessens for credit profiles of the original borrowers of these particular loans, as these original borrowers have been evicted and no longer have any rights to the property itself.
Surely, I’m just imagining this “quantum effect.” It can’t possibly apply to finance… Enough with all this uncertainty!
So what is the way forward here? We maintain that thinking things through in the above manner leaves one without any really defensible viewpoints. How about we go forward and use a “results-driven” approach? In this approach we try a wide variety of approaches – trying each one of them under a wide-variety of levels of granularity. Don’t stick too much on any one approach, but then save these predictions for ALL of these variations. Then each month look at the actualities of what occurred in the real world and see which approaches most closely approximated what was found in the real world. Do this month after month and don’t develop any particular prejudices. In other words, constantly be on the alert as to which approaches produce the most practical real-world results. Perhaps then, a pattern may emerge as to which “solution” fits best.
Hopefully, then, we won’t have a situation where the “solution” itself only “resolves itself” when we observe it closely; but maybe the next time we observe it, it’ll be a different answer – just like, quantumly speaking, when a particle is observed, it’s location cannot be determined.
One thing is for sure, you want your predictive modeling company to be able to show you what their predictions were at various points in time (without the benefit of 20-20 hindsight) and have them show you how did their predictions do. Any predictive modeling company worth their salt for their crystal ball techniques should be able to show you how their predictions performed. We’re not saying they should be 100% perfect in all their predictions under all circumstances, but they should be able to show you exactly how they did – unless of course, they’re embarrassed to show you how badly they did.
In any case, if you want to get information on “the exact property address and home valuation”, check out a vendor which provides a very interesting solution as regards to home property valuations matched against the anonymous loan-level securitized data. See Lewtan’s ABSNet Home Val ™ solution here:
http://www.lewtan.com/products/ABSNEThomeval.html

Have a nice day. See you next observation – maybe.
Quark Out!

Securitization For The Rest Of Us!

Thetica Systems is happy to announce the fall release of our eBook: Securitization For The Rest of Us! Readers will discover the basic building blocks required to turn debts into income, and to utilize the power of numbers to create a flow of billions of dollars with the creation of bonds that profit the adventurous few with the capital to invest. Our eBook also examines the factors that directly resulted in the Wall Street losses at the end of this decade, from which our economy is still recovering. Enjoy this introduction…it starts us out on a journey that will have some hills to climb, but at its end, you will have a much wider viewpoint to understand our economy. This is Securitization For The Rest Of Us!

Introduction – Securitization For The Rest Of Us!

More Pictures from the ABS East 2010 Conference

This is a shot from outside the Fontainbleu Hotel in South Beach, Miami, where the ABS East Conference was held.






Here are a few members of Thetica Systems, including CEO, Ariel Yankilevich(mid) and President, Jack Broad(right), on day three of the ABS East Conference at our exhibitors display booth.



And here is another shot with a couple of friends (notice Jack holding our custom stucky note pad).

Last night, Tuesday, Oct. 5th, we packed up everything and called it a day. And today we head back to base to regroup.

The ABS East Conference 2010

Here are a few pictures of the extravagant Fontainbleu Hotel in South Beach, Miami, where the ABS East Conference is currently taking place and where Thetica Systems has set up shop as an exhibitor.

This is our CEO, Ariel Yankilevich, at our exhibitors display at the ABS East Conference;

And here is Ariel again giving a demo in our prospective client flooded exhibition area.

Data Commoditization in the Securitization Markets (Part 3)

Today we proceed with the third installment and continuing discussion of the RMBS data industry with a section on “Loan Data Vendors”.

2. Loan Data Vendors – these are data vendors who specialize in the collateral (loan) information. This seems to overlap with component #1 item C (in blog entry 2 of this series), and to some degree it DOES overlap, however, Intex has not been known for providing the level of loan detail and in particular, ongoing historical monthly payment information that a proper Loan Data Vendor provides. Intex does provide monthly collateral information but it can be quite difficult to see and/or analyze it from an historical perspective. This thereby creates a market “niche” that various companies have stepped into in order to capture this need. Examples include Loan Performance (this is the largest and most well-known of the loan data vendors) , Black Box, ABSNet (Lewtan), Lender Processing Services, S&P and others. Most importantly this component includes at least the following:

a. Loan Data: this includes many fields of information relating to the actual loan itself including such things as original balance, current balance, purchase price of the property, zip code, state, MSA (Metropolitan Statistical Area), loan purpose (purchase, refinance or cash out), occupancy status (primary residence, investment property); documentation of income or assets, sale price, loan to value ratio, first or second lien, interest rate, loan type (fixed rate or adjustable rate mortgage), if an ARM loan, then what index does the loan reference, Interest Only period (if any), any prepayment penalties and for how long, loan modification details and so forth.

There are over 20 million loans within non-agency securitized deals so you can see that this is a fairly large data set.

b. Historical monthly payment records. These records tell you each month whether the borrow has paid and if so, how much; if the borrower is delinquent and how many days (30, 60, 90+); whether the loan is in foreclosure proceedings and, if it has already been foreclosed, how long it has been in REO. Also, when the property has been liquidated and whether there have been any losses and so forth.

Some loan data vendors provide web-based tools that you can use to query their loan data but many firms also license to routinely receive the data from the loan data vendor onto their own computer systems because they have mortgage research groups who want to be able to analyze the data in depth in order to assist them to predict the future performance of the loans based on what the historical data shows. This provides only a partial picture of the borrower and the property serving as collateral. See the next section for an extremely important additional piece of the puzzle.

**Watch for our next post describing “Enhanced Loan Data Vendors”.