Analyzing the Popular Proposals for Mortgage Principal Writedowns, Part III

Two weeks ago we began a series of posts written by The Managing Partners of Peterson Bliss Advisors, analyzing the popular proposals for mortgage principal writedowns and their the effects thereof. In that first article, we examined:

• The numbers quoted in the proposal as stated in the media
• The issues surrounding the stated numbers

In the second:

• The costs to the Borrowers
• The costs to the Taxpayers
• The costs to the Banks (and eventually the Taxpayers) and Timing

In today’s third installment, we start to round-up the debate and discuss:

• The “fairness” of the proposals
• The most recent proposal from banks to a “finite number of current borrowers”
• The ultimate solution to the decline in home values

Enjoy reading and please leave your thoughts in the comments section, as we know this is a rather contentious issue. –JT

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Analyzing the Popular Proposals for Mortgage Principal Writedowns, Part II

Yesterday, we posted the first part of a series of articles written by The Managing Partners of Peterson Bliss Advisors, analyzing the popular proposals for mortgage principal writedowns and their the effects thereof. In that first article, we examined:

• The numbers quoted in the proposal as stated in the media
• The issues surrounding the stated numbers

Today, we’re going to delve further into the debate and look at:

• The costs to the Borrowers
• The costs to the Taxpayers
• The costs to the Banks (and eventually the Taxpayers) and Timing

There will be (at least) one more post in this series so keep that in mind. Enjoy reading and please leave your thoughts in the comments section, as we know this is a rather contentious issue. –JT

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Analyzing the Popular Proposals for Mortgage Principal Writedowns, Part I

This article is from The Managing Partners of Peterson Bliss Advisors

This is the first in a series of articles intended to review the matter of principal reductions for “underwater” residential mortgages and has been created for, and in collaboration with, Stone Street Advisors. In a few installments, we will discuss:

• The numbers quoted in the proposal as stated in the media
• The issues surrounding the stated numbers
• The costs to the Borrowers
• The costs to the Taxpayers
• The costs to the Banks (and eventually the Taxpayers) and Timing
• The “fairness” of the proposal
• The most recent proposal from banks to a “finite number of current borrowers”
• The ultimate solution to the decline in home values

Today, we’re going to focus on the first two points. Enjoy reading and please leave your thoughts in the comments section, as we know this is a rather contentious debate. –JT
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Deutsche Bank's 2011 MBS and Securitization Conference

A journey of 1,000 miles begins with a single step.

It’s fitting my inaugural post is a review of Deutsche Bank’s inaugural MBS and Securitization Conference.  The conference attended by some 60 investors from roughly 40 companies highlights DB’s poor standing in the world of structured finance (SF). Deutsche Bank has never been a major player in the securitized finance market though it has spent billions trying to be. In 1996, DB bought Morgan Grenfell to create Deutsche Morgan Grenfell and hired senior bankers from Goldman Sachs and Merrill Lynch.  When the Russian Currency crisis hit, DMG fired all but 3 of its SF staff after paying almost a billion dollars of guaranteed bonuses. They tried again after the Banker’s Trust purchase and this is their third attempt. Third time is a charm right? Nope.  The conference demonstrated the lack of gravitas so evident at Goldman, Morgan Stanley and even, may they rest in peace, Bear Stearns.

Deutsche bank event planners decided to hold this poorly attended conference not in a fancy hotel (like Citi or Wachovia) but in an auditorium two stories below street level in their “marque building” 60 Wall Street. Fortunately, the poor catering did not make me sick.  Good job of impressing investors.

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Open Source Mortgage Model, With ARM Functionality

Check it out.  Updates in the “release notes” tab.  Right now the model allows for both fixed-rate and ARM mortgages.  ARM reset/recast equations are set (pretty sure about it at least!), home price appreciations fluctuate according to a random normal distribution bounded by mean/stdev from Case-Shiller Composite-10 data by default, although if a user has less optimistic assumptions (particularly about the mean monthly appreciation) it changes the outcome significantly.

Eventually we’re going to put this into an integrated tool with monte-carlo simulation showing the distribution/probabilities of outcomes to help users select the best option given their inputs/assumptions.  Eventually being the key word.

**This is available for use with attribution, please email us if you have any questions**

Here it is.

Introducing Our Open Source Rent v. Buy Model

Ladies & Gentlemen, today Stone Street Advisors is officially unveiling the start of our commitment to help the Public make informed financial decisions, namely when it comes to finding a place to call home.  In the immortal words of Taste_arbitrage:

One of the things that puts a huge smile on my face is listening to people talk about this particular trade they are getting into, because its one they really get excited about.

Imagine if you will, you are an analyst trying to pitch this to your PM. ”So I have this trade.  We have got to put this on, I love it!  The underlying is largely illiquid, even at the highest volume ever recorded, comparable securities only trade once ever 17.6 years.  We are going to put it on at 400% leverage, and just lever down over the course of trade.  I have not looked at the carrying cost of the position but I just assume they are negligible, because that makes it sound better to me.  I have done zero top down analysis on the macro themes that could affect the trade.  I have done zero bottom up analysis on what the security is worth based on cash flows or liquidation value.  I have however looked at comparable securities in the sector and based entirely on that I think it’s cheap.  Oh and here is the best part!  The value of the underlying is roughly 2.3 times our firm wide revenue so if it goes against us or we are cash strapped we will have no choice but to file for bankruptcy.”

If this sounds like one of the worst ideas ever put together that’s because frequently it is.  Welcome to the fund creating real systemic risk: American Dream Capital.

Sadly, that’s exactly how most people approach deciding whether to rent or buy, and how much they can afford to spend on each.  With the internet, literally more information than 99% of people can comprehend is at your fingertips, and there’s absolutely NO REASON for anyone to be able to claim they bit off more (mortgage) than they could chew because they didn’t know the rate was going to jump or whatever other bullsh*t excuse they may try to use.  If you know how to read, and you have access to a computer (i.e. anyone who’s buying a home or signing a rental lease), there’s several calculators online to figure out what you’re getting into.  I didn’t love any of the tools I found online, for example this overly-simplistic one – in my opinion – from 2 months back in the NY Times, so I decided that we’d build our own.

Thus, I present to you version 1.1 of our Open Source Rent v. Buy Model (note: for some reason WordPress won’t allow us to upload the Excel file here so that’s why its hosted @1-2 Knockout on typepad).  This model takes (or will take, in certain situations) into account every expense we could think one might encounter when renting or buying a home.  Certain figures are hard-coded based on our research.  Others can be modified by the user (potential buyer/renter) based on their estimates (which themselves SHOULD be based-upon solid research) to help them decide to rent or buy, and how much they can afford to spend on each.  Just to make this as clear as possible before anyone jumps down my neck: I did this model quick & dirty, that is, don’t be surprised if there’s a few screwed-up formulas/typos/etc.  Part of the motivation for making this Open Source is so you CAN find any mistakes and fix and/or improve them!  Basically what I’m saying is that it’s a team effort, and that was our intention right from the start.

So, while this is a work-in-progress, i.e. we know the Model isn’t complete, however, because the basic structure and functionality is in-place, we’ve decided to publish it and make it publicly available to view and modify in/from its current form.  Anyone is welcome to download the Model and play around with it or use it for their own purposes, however, we request that it not be used for any sales/marketing purposes, and assume zero liability for any decisions made as a result of using the Model, nor any responsibility for the accuracy of any of the figures or calculations contained therein.

Beyond that, we hope to be able to work from here and develop a robust decision tool to help people make informed financial choices.  Any/all constructive commentary is welcomed, nay, encouraged!  Let’s all put our skills and effort together to do something that’d help everyone, financial professional or not, avoid being the dumb money.

UPDATE:

Thanks to @zerobeta, I’ve uploaded the document to Google Docs.  Email me to get permission to view, or just download the .xls, edit and email me the doc with your updates and I’ll share it with the world!