Tag Archives: real estate

Weekend Must-Reads: Unions, Unemployment, Real Estate, Asset Allocation and Auditors

10 Apr

Some of the following links have been open for over a week, but have gotten buried under the onslaught of reading material I’ve accumulated since.  Regardless, the following articles are all very interesting and well-worth reading, unless you think ignorance is, in-fact, bliss.

The Economist, Enemies of progress: The biggest barrier to public-sector reform are the unions

“John Donahue at Harvard’s Kennedy School points out that the egalitarian culture in Western civil services suits those who want to stay put but is bad for high achievers. Heads of departments often get only two or three times the average pay. As Mr Donahue observes, the only American public-sector workers who earn well above $250,000 a year are university sports coaches and the president of the United States. Hank Paulson took a 99.5% pay cut when he left Goldman Sachs to become America’s treasury secretary. Bankers’ fat pay packets have attracted much criticism, but a public-sector system that does not reward high achievers may be a much bigger problem for America.”

Federal Reserve Bank of Atlanta: President Lockhart Describes “Multifaceted” Employment Challenges

Guess what? Between just structural issues and productivity increases, “normal” unemployment could be anywhere from ~5.5%-8%, far higher than the ~5% pre-crisis.  Here’s a thought: productivity has increased for the past ~10-20 years, but it took a shock like the crisis for firms to realize it them.  Anyone who’s worked in a large corporation and isn’t blind to what’s going on around them can attest, there is (was?) LOTS of fat to cut in the employment rolls.  The question is whether firms have cut-down to appropriately lean size, and if so, whether such realization of productivity gains are sustainable.

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Wells Fargo Pick-A-Pay Portfolio: Presented without Comment*

4 Mar

Some of you have may have noticed the ongoing debate regarding the Pick-a-Pay portfolio of Warren Buffett’s beloved Wells Fargo (NYSE: WFC). In order to put this horse to pasture, I present the following with no pejorative words and only a few minor comments.

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“Data” From Trade & Industry Groups Is NOT To Be Trusted, Part 74,301

18 Feb

I don’t care whether the data if from the National Retail Federation, the Mortgage Bankers Association, the National Association of Home Builders or, my personal favorite, the National Association of Realtors or any other industry group.  It’s ALL garbage!  DO.NOT.TRUST.THEM!  They exist for the singular and sole purpose of drumming-up business for their members; retailers, mortgage brokers, home builders, and realtors, respectively.  Their “data” and “analysis” will, with almost 100% certainty, grossly overstate any realistic projections and even worse, produce misleading historical “data” to boot!

Compare Recent data from the National Association of Realtors (pdf) with the data from analytics company Corelogic (CoreLogic_December_2010_HPI, I have the Excel file but can’t upload to WordPress).  NAR says the price on existing home sales increased 0.5% in 2010, while the price on new homes increased 2.2%.  Corelogic’s national single-family combined home price index shows a -5.46% change.

Who do you believe?  The industry group that represents people who make money when houses are bought/sold for the most amount of money, or an independent analytical shop that uses a pretty robust methodology (reproduced below)?

The CoreLogic HPI incorporates more than 30 years worth of repeat sales transactions, representing more than 55 million observations sourced from CoreLogic industry-leading property information and its securities and servicing databases. The CoreLogic HPI provides a multi-tier market evaluation based on price, time between sales, property type, loan type (conforming vs. nonconforming), and distressed sales. The CoreLogic HPI is a repeat-sales index that tracks increases and decreases in sales prices for the same homes over time, which provides a more accurate “constant-quality” view of pricing trends than basing analysis on all home sales. The CoreLogic HPI provides the most comprehensive set of monthly home
price indices and median sales prices available covering 6,208 ZIP codes (58 percent of total U.S. population), 572 Core Based Statistical Areas (85 percent of total U.S. population) and 1,027 counties (82 percent of total U.S. population) located in all 50 states and the District of Columbia.

Not only does the National Association of Realtors (and really, ALL trade groups) issue over-optimistic future predictions (to get people to buy/sell houses), but they misrepresent historical data!  That should be a crime (if it isn’t already)!

I’m not saying Corelogic’s data is 100% accurate, but I will take their #’s over a trade group’s every.single.time.

UPDATE: Jonathan Miller of Miller/Samuel has a good post about this as well, and includes the NAR’s explanation of their bogus methodology.  Highly recommended read!

Open Source Rent v. Buy Model version 1.2!

15 Jun

Available for download @ 1-2 Knockout here.  Made a few updates, added/fixed some calculations, detailed in the “release notes” tab.

Working on modeling the floating rate/Option-ARM payments/interest expense.  Anyone who wants to jump-in (that’s you, mortgage finance guys) go wild (please)!

Introducing Our Open Source Rent v. Buy Model

11 Jun

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!