Political Calculations
Unexpectedly Intriguing!
October 31, 2014

How would you react if you were enjoying a nice day at the park, when suddenly, you realized that the Grim Reaper was flying behind you, following your every move?


And for those who insist on knowing how the evil magic is done, here is the video again, but explained by its creator....

HT: Core77. Happy Halloween!

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October 30, 2014

Now that we've had to go to some effort to work out how the median household income in the U.S. has evolved over the last several months from payroll tax collection data, we're going to put those estimates to work today to get a better insight into the U.S. real estate market for new homes.

What we found suggests that September 2014 wasn't as positive as some news reports have claimed. Here, reports that the number of new single family homes reached a six year high in September would suggest that things are going swimmingly for the new home market in the U.S.

The problem is that the median sales price for new homes in the U.S. would initially appear to have fallen to $259,000 in September 2014 from a revised level of $286,800 in August 2014, an apparent decline of $27,800. While that initial $259,000 figure will almost certainly be revised higher, as the sales of higher priced homes during a given month often take longer to be incorporated in the official data, the combination of falling price and increased quantity would suggest that the median sale price fell because new home builders were forced to make big discounts because they found that they needed to move a relatively higher level of inventory.

Price and Available Quantity Data
Input Data Values
How has the price of the item changed over a given period of time?
How has the available quantity of the item changed over that same time period?

What's Behind the Change in Price?

We would have to go back to October 2010 to find a similar magnitude month over month decline in the raw data for median new home sale prices.

That decline in the median sale prices for new homes shows up when we account for the seasonality of the U.S. real estate market by calculating their trailing twelve month average and projecting the value against median household income:

U.S. Median New Home Sale Prices vs Median Household Income, December 2000 through September 2014

That sudden decline may account for the sudden momentum by the political cronies of government-supported home lenders to significantly loosen lending standards for mortgages - in a way, it's like fanning the flames of a fading fire in attempting to get it to burn brighter for a little longer. We're going to quote Warren Meyer's full reaction to that change in policy, simply because we can't make the point any better:

97% Mortgages are 100% Insane

I am not sure there was ever any excuse for considering a 97% loan-to-value mortgage as "sensible" or "responsible." After all, even without a drop in the market, the buyer is likely underwater on day one (net of real estate commissions). Perhaps for someone who is very wealthy, whose income is an order of magnitude or two higher than the payments, this might be justifiable, but in fact these loans tend to get targeted at the most marginal of buyers.

But how can this possibly make sense when just 5 years ago the financial markets collapsed in large part due to these risky mortgages? Quasi-public, now fully public guarantors Fannie and Freddie had to be bailed out by taxpayers with hundreds of billions of dollars. There are still a non-trivial number of people trapped deep underwater in such mortgages, still facing foreclosure or trying to engineer a short sale after seeing the small bits of equity they invested swamped by a falling housing market.

But, here they go again: Fannie and Freddie, now fully backed by the taxpayer, are ready to rush out and re-inflate a financial bubble by making what are effectively nothing-down loans:

Federal Housing Finance Agency Director Mel Watt has one heck of a sense of humor. How else to explain his choice of a Las Vegas casino as the venue for his Monday announcement that he’s revving up Fannie Mae and Freddie Mac to enable more risky mortgage loans? History says the joke will be on taxpayers when this federal gamble ends the same way previous ones did.

At his live appearance at Sin City’s Mandalay Bay, Mr. Watt told a crowd of mortgage bankers that “to increase access for creditworthy but lower-wealth borrowers,” his agency is working with Fan and Fred “to develop sensible and responsible guidelines for mortgages with loan-to-value ratios between 95 and 97%.”

The incredible part is that the Obama administration is justifying this based on all the people still underwater from the last time such loans were written. The logic, if one can call it that, is to try to re-inflate the housing market now, and worry about the consequences -- never, I guess. Politicians have an amazing capacity to mindlessly kick the can down the road, where short-term is the next morning's papers and unimaginably far in the distant future is after their next election.

On the bright side, the eventual failure that will be blamed on the market is not being left to chance, nor is the great personal wealth being quietly accumulated by political insiders and their family members.

So at least the full extent of the stupidity involved has been catalogued for future reference.

References

Political Calculations. Divining Median Household Income from Payroll Tax Data. 29 October 2014.

Political Calculations. Supply or Demand: What's Driving the Price?. 21 November 2007.

Sentier Research. Household Income Trends: July 2014. [PDF Document]. Accessed 3 September 2014. [Note: We have converted all the older inflation-adjusted values presented in this source to be in terms of their original, nominal values (a.k.a. "current U.S. dollars") for use in our charts, which means that we have a true apples-to-apples basis for pairing this data with the median new home sale price data reported by the U.S. Census Bureau.]

U.S. Census Bureau. Median and Average Sales Prices of New Homes Sold in the United States. [Excel Spreadsheet]. Accessed 25 October 2014.

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October 29, 2014
A Water Witch Divines where to dig a well - Source: http://blogs.sos.wa.gov/library/index.php/2013/07/water-witches/

Can we use monthly payroll tax collection data to reasonably approximate the median income being earned by U.S. households?

That's the problem we were really seeking to solve when we found that payroll tax collection data is a lagging indicator of recessions for the U.S. economy, but is perhaps a real-time indicator of positive growth conditions.

The reason that's a problem is because our primary source for monthly median household income data, Sentier Research, has temporarily suspended its monthly reports, on account of extensive changes the U.S. Census Bureau has made in its monthly collection of data for its ongoing Current Population Survey (CPS). Sentier indicates that the changes, in which the Census is updating its sampling of the U.S. population to reflect the more up-to-date geographic distribution of the population recorded in the 2010 Census, are generating quite a lot of noise in the Census' monthly CPS data, which will take them time to sort out.

Because that's useful data, we wondered if there might be an alternative way to divine the median household income of U.S. households using other sources of data that are also reported on a monthly basis.

We immediately thought of the U.S.' Federal Insurance Contributions Act (FICA) payroll tax collection data as a good potential alternative source, since it represents the flat-rate taxation of wage and salary income. We then narrowed our focus to just the portion of those taxes represented by Medicare's Hospital Insurance (HI) tax, which in addition to applying to all wage and salary income and which, unlike Social Security's portion of payroll taxes, has been maintained at the same 2.90% of income since the early 1990s.

Before we continue, we'll note that the Affordable Care Act (ACA, more popularly known as "Obamacare") imposed an additional 0.9% Medicare tax on the wages, salaries and investment income of high income earners beginning in 2013. However, since all of those additional tax collections go straight into the general fund of the U.S. Treasury rather than to Medicare, the data that the Social Security Administration reports for its collection of Medicare's HI tax is based only on the 1.45% of income it collects from individual wage and salary earners and the additional 1.45% of income it collects from their employers.

Our next step was to determine if there is any sort of relationship between the federal government's HI tax collections and median household income. Since the HI tax collection data is volatile from month-to-month and seasonal in nature, we calculated the trailing twelve month average of the HI's monthly tax collection data and plotted it against Sentier Research's monthly median household income data, which goes back to January 2000 and extends through July 2014. Our results are graphed below.

Median Household Income vs Trailing Year Medicare Tax Collections, January 2000 - July 2014

All data in the chart are presented in terms of nominal (current) U.S. dollars.

We see that there's a pretty strong correlation between Medicare HI tax collections and Median Household Income, which really shouldn't be all that surprising since wages and salaries make up such a very large portion of household income. That means that we can use the payroll tax data to estimate median household data.

But it's not quite as easy as just running a linear regression on the data to get to what might be considered a precise figure, thanks to things like recessions and their aftermath or sudden growth spurts can cause the relationship to deviate onto a roughly parallel trajectory for an extended period of time.

For projections in the current day, it's coping with the sudden organic growth spurt that occurred in the U.S. economy in the latter half of 2013 for which we need to specifically make adjustments. Here, we find the situation where median household income actually dipped in the months of October and November 2013 while the amount of Medicare HI payroll tax collections surged, setting the overall trajectory of median household income onto a new path.

What happened at that time came in response to the harvest of record bumper crops of nearly every major agricultural crop grown in the U.S. in 2013. Here, we observe that median household income fell, while payroll tax collections increased, as a very large number of previously idle and relatively low-paid agricultural workers became employed. And it would seem that many of them have stayed employed.

To account for that shift in the employment pattern in the U.S., we've drawn a new, parallel trajectory to the overall trend from January 2000 through July 2014, which we'll use to project the median household income for each month since for which we have Medicare HI payroll tax collection data.

Doing that math, we obtain the following estimates of monthly U.S. median household income for each of the indicated months for which we have actual or projected data provided by the Social Security Administration.

Projected Median Household Income Values for Given Level of Medicare Hospital Insurance Tax Collections
Month Trailing Twelve Month Average of Medicare Hospital Insurance Payroll Tax Collections Projected Estimate of Median Household Income
August 2014 $18,931,813,603 $54,105
September 2014 $18,964,894,435 $54,164
October 2014 $19,008,977,769 $54,243

It will be months before we know how close or how far off our projections of median household income are, but at least we now have an independent method of divining this data in the absence of, and until the return of, the actual data!

Data Sources

Sentier Research. Household Income Trends: July 2014. [PDF Document]. Accessed 11 October 2014. [Note: We've converted all data to be in terms of current (nominal) U.S. dollars.]

U.S. Social Security Administration. Social Security and Medicare Tax Data. [Online Database]. Accessed 11 October 2014.

U.S. Social Security Administration. Social Security and Medicare Tax Rates. [Online Document]. Accessed 11 October 2014.


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October 28, 2014

Can monthly tax collection data predict recessions?

That's a question we realized we could answer as we went about solving a different problem. In working on that other problem, we were looking at the U.S. Social Security Administration's data on the taxes it collects each month to fund Part A of the Medicare program (hospital insurance), where we noted that the amount of taxes collected showed a cyclical pattern - one that seemed to correspond to recessions.

That's significant because Medicare's Hospital Insurance tax is levied against all wage and salary income earned by Americans and has been fixed at a steady 2.90% (1.45% of total income paid by the individual wage or salary earner with another 1.45% paid by their employer as part of those FICA payroll taxes) since the early 1990s. And even though a there's a new 0.9% Medicare tax that has been imposed on the wage, salary and investment income of high income earners as part of Obamacare, unlike Medicare's hospital insurance tax, none of that money really goes to the Medicare program. It goes straight into the general fund of the U.S. Treasury instead.

That means that Social Security's data for its Medicare Hospital Insurance tax collections represents a simple, straight percentage of all the wage and salary income earned in the U.S. Which in turn means that we can perhaps use it as a decent indicator of the relative health of the U.S. economy.

Since there have only been two official periods of recession recorded since the early 1990s, which is all as far back as our data source goes, we focused just on the period since January 1999 to see if we can find any correlations between Medicare Hospital Insurance tax collections and the timing of recessions in the U.S. Here's our visualization of the raw data:

Total Monthly Medicare Hospital Insurance Payroll Tax Collections, January 1999 through October 2014

We see there's something there, but also that there's a lot of volatility from month to month, including seasonality in the data, with peaks in the data coinciding with quarterly and annual tax deadlines. To address those issues, we calculated the trailing twelve month average of the monthly Medicare Hospital Insurance payroll tax collections. The chart below shows what we found (the first data point, December 1999, reflects all the data from January through December of that year):

Trailing Twelve Month Average of Total Monthly Medicare Hospital Insurance Payroll Tax Collections, January 1999 through October 2014

After this bit of analysis, we see that the payroll tax data for Medicare's Hospital Insurance tax is really a lagging indicator of the relative health of the U.S. economy when it falls into recession, which we see in the amount of taxes continuing to rise before stagnating and falling as recessions take hold. This observation is consistent with employers being slow to cut people from their payrolls or hours and pay during the early portion of official periods of recession.

But intriguingly, this data seems to also be a real time indicator of improving economic conditions, where employers act quickly to add staff when economic conditions demand it.

We can see that real time indicator at work in the latter half of 2013, when the U.S. economy experienced an organic growth spurt thanks to a record bumper crop of agricultural products, with the greatest growth occurring in the period from September through December, which corresponds to the harvest and export of major commercial crops in the U.S.

Through the projected tax collection data for October 2014, we don't see any similar sign of a surge in payroll tax collections, which suggests that there will not be a similar growth spurt in either incomes or jobs at the present time.

But also no sign of recession, which from what we can tell with the limited data available, wouldn't show up until well after a recession has officially begun.

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October 27, 2014

Last Friday, we had a bit of fun as we made a big deal out of our having correctly anticipated the course of the S&P 500 during the preceding week. The chart below, excerpted from The Upshot (via Barry Ritholtz), shows why that was such a big deal.

Volatility Spiked Last Week, But Has Receded - Source: The Upshot, via Barry Ritholtz)

The ability to anticipate or cope with volatility is one of the major tests of any model of real world phenomena. In the case of our model of how stock prices work, there are two primary causes of volatility with which we're concerned:

  1. Special cause variation, where we observe changes in stock prices that are either consistent with identifiable shifts in the forward-looking attention of investors from one point of time in the future to another in setting their expectations of the future when making current day investment decisions, or that are the result of the reaction of investors to other identifiable factors that do not affect their fundamental expectations of the future, which we often describe as noise events.
  2. Common cause variation, where we observe variation, or typical levels of noise, that is driven by factors or frictions that are always present in the stock market.

Our standard model has done well in addressing both kinds of variation, which we've observed in spades since October 2014 began.

Alternative Futures of S&P 500 Stock Prices, Fourth Quarter 2014, Standard Model, Snapshot Through 2014-10-25

Looking forward through the next two weeks, we would anticipate that in the absence of noise or an announcement from the Fed specifying that they will begin hiking short term interest rates in 2015-Q2, which would prompt a shift in focus, investors will remain primarily focused on 2015-Q3 in setting their expectations as they set stock prices.

Our model would appear to project a very short term dip in stock prices in theis upcoming week, with a short rally in stock prices in teh following week. These are actually artifacts of our use of historic stock price data as the baseline reference points from which we project the future trajectory of stock prices, which are the result of echoes from short term noise events of a year ago - much like the one day rally was on 8 October 2014 and also the one day dip of 22 October 2014. We would anticipate that the actual trajectory of stock prices will more closely connect the "dots" on the opposite sides of both projected "events".

Looking further forward, if the Federal Reserve were to more clearly indicate that 2015-Q2 will be the most likely future quarter in which it will begin hiking interest rates, it will have an advantage in doing so if it communicates that intent in early November 2014.

Here, we note that if investors have reason to remain focused on 2015-Q3, the most likely future trajectory for stock prices will be lower through the end of the 2014. But if the Federal Reserve succeds in shifting the forward looking focus of investors to 2015-Q2, stock prices will shift to a trajectory that is flat to higher than they are today, which is something that the Fed might desire if it wants to maintain the appearance that U.S. markets have confidence in its plans.

That would also be the worst possible thing that the Federal Reserve could do if economic conditions in the U.S. are such that delaying its plan to begin hiking short term interest rates is warranted. That could potentially set up the situation where stock prices would "correct" by at least 5% while costing the Fed a portion of its credibility.

It's going to be fun watching the Fed this month and next!

Analyst Notes

The main reason we've been taking such an apparently self-congratulatory tone lately is because we've nearly reached the successful end of the basic development work for our years-long stock price forecasting project. With the bulk of that work now behind us, we're going to be increasingly moving on to other things.

We plan to somewhat regularly post updates of our forecast model's performance through the end of 2014-Q4, but we would only periodically comment on the S&P 500's stock prices as events might warrant after that point.

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