In all the years that Political Calculations has been around, we've never featured a cat video. Not one. Ever. They just don't impress us.
Until today. Enjoy!
HT: Core77, who says its all about the videography and editing. Oh, and every jump over 183 centimeters is more than six feet above the floor.
It's easy to forget that it takes three components to make a fire: fuel, oxidizer and a spark. Typically, you only see two of the fire-making components coming together: fuel and the spark. But that's only because the oxidizer is already there, in the form of the invisible oxygen molecules that are in the air. Without it, you're not going to succeed in making a fire. But with it, not only can you make a fire, you can make a huge fire.
In this third installment of our series, we'll explore how oxidizer, in the form of the "environmental" factors of financial and governmental policies, contributed to the fire that was the first U.S. housing bubble.
After consistently throwing fuel on the fire that was the first U.S. housing bubble during the first years of its existence, the Federal Reserve became increasingly concerned that the U.S. economy was becoming too overheated by June 2004, a year after it had lowered its benchmark Federal Funds Rate to what was then an all-time record low level.
The Fed's Open Market Committee then used every opportunity provided by its regular meeting schedule to jack up interest rates by quarter-point intervals over the next two years, with mortgage interest rates following suit.
But the rise in home prices that defined the inflation phase of the first U.S. housing bubble continued on unabated. Prices rose at the same rate they did before the June 2004 interest rate hike all the way through September 2005 before shifting to a slower upward trajectory.
But the forces behind the bubble's upward inflation continued, until the bubble finally peaked in March 2007, as it plateaued near that level for the next several months before finally entering its deflation phase after November 2007, as the U.S. economy entered into recession.
But why was the Fed's policy of increasing interest rates to halt the inflation of the bubble economy so impotent? With homebuyers worldwide traditionally very sensitive to changes in mortgage interest rates, how could the bubble in U.S. home prices have continued for so long after the Fed began acting to correct the situation?
The answer to these questions may be found in the reaction of financial institutions and government policy makers to the prospect of slowing economic growth, who responded to the Fed's efforts by fanning its flames in opposition to the Fed's actions - feeding oxygen to what might otherwise have been a fading fire and making it burn more brightly instead.
Recalling that the first U.S. housing bubble was initially sparked by investors looking for something to do with the money they took out from the stocks they sold during the deflation phase of the Dot Com Stock Market Bubble, we should note that a lot of that money entered the housing markets of the U.S. in the form of large cash down payments.
Not coincidentally, the kinds of mortgages that were taken out to fund home purchases in the initial phase of the housing bubble were conventional mortgages, which really benefited from the Fed's low interest rate policy.
But as time progressed and interest rates began to rise again in 2004, those mortgages became more expensive. Along with the increase in home values that were taking place, the combination of rising rates and rising prices should have cooled the market.
But financial institutions, both private (PLS) and government-supported enterprises (GSE), enjoying the outsize profits they were making on their real estate loans fought back against the Fed's actions by funneling home buyers into adjustable rate mortgages (ARM) instead of fixed rate mortgages (FRM), which offered lower introductory interest rates, and which lowered the initial costs of buying a home.
Soon, even that change wasn't enough to continue the flow of profits, and more aggressive financial institutions began degrading their mortgage underwriting standards to fan the flames of the bubble. Subprime loans were increasingly pushed to bring individuals who would not otherwise qualify for a mortgage into homes they couldn't otherwise afford. Ultimately, outright fraud became a common practice in the form of "liar loans" and the "robosigning" of mortgage documents.
But how did that come to happen? And where was the government in all this?
As it turns out, many of these actions were actually enabled years earlier by seemingly well-intentioned government policies, and pushed by seemingly well-meaning politicians and regulators.
What happens when powerful politicians use the power of their offices to sway regulators to push policies that promote home ownership, no matter the cost?
It's a tenet of economics to observe that people respond to incentives. And if those incentives come in the form of political and regulatory pressure to comply with a well-meaning politician's goal, or the goals of their major campaign contributors, then something is going to happen, especially if it puts money in certain people's pockets.
Unfortunately, we often only find out that particular dynamic after charges have been filed. Often years after the fact:
Washington, D.C., Dec. 16, 2011 — The Securities and Exchange Commission today charged six former top executives of the Federal National Mortgage Association (Fannie Mae) and the Federal Home Loan Mortgage Corporation (Freddie Mac) with securities fraud, alleging they knew and approved of misleading statements claiming the companies had minimal holdings of higher-risk mortgage loans, including subprime loans.
Russ Roberts offers a perspective of the government's role in the years leading up to and through the inflation phase of the first U.S. housing bubble:
The SEC suit against former execs of Fannie and Freddie appears to vindicate the Pinto/Wallison view that government housing policy pushed Fannie and Freddie into unsafe loans and caused the financial crisis.
I think Pinto and Wallison are half right. Fannie and Freddie did help cause the financial crisis. But not in the way Pinto and Wallison claim and not without a lot of help from the investment banks. Fannie and Freddie helped push up the demand for housing between 1995 and 2003. During that time, they expanded their activities, particularly among low-income borrowers. They started making loans with low down payments. This was not a secret by the way. Fannie Mae’s CEO, Franklin Raines bragged about it in 2000. Josh Rosner wrote about it in 2001. All of that activity drove up housing prices. That in turn, made it imaginable to lend to people who normally would not qualify for a mortgage and to lend them money without very much or any down payment. That in turn made the financial alchemy of AAA-rated mortgage-backed securities (MBS) possible. So yes, Fannie and Freddie had something to do with the crisis.
This aspect of the government's involvement in enabling the housing bubble helps explain why so many minority and low-income earning individuals found themselves badly burned when the housing bubble finally entered into its deflation phase after November 2007 - they were the intended beneficiaries of the policies the government had established in the 1990s to help them become home owners. As things played out, they became the hardest hit victims of the degradation of underwriting standards championed for them by U.S. politicians, as many were financially incapable of sustaining the payments on the houses they bought when the economy turned downward.
But that's not the full scope of the damage. Roberts goes on to identify the "too-big-to-fail" moral hazard aspect of the government's involvement in backing the risks being taken by the U.S. investment banks and Government Supported Enterprises (GSEs) like Fannie Mae and Freddie Mac and how that directly led to the financial crisis in 2008:
To really explain the housing boom and bust followed by the financial crisis, you need an explanation of why Fannie and Freddie AND the investment banks were so reckless. The Pinto/Wallison explanation is that Fannie and Freddie were reckless because the government made them do it. The left’s explanation is that the investment banks were reckless because the govnernment let them do it. Both left and right ignore the role of the other part of the market. But more importantly, both the left and the right leave unexplained how the reckless risktakers–the GSE’s and the investment banks–were able to do it–how they all were able to borrow money at relatively low rates despite ridiculous levels of leverage. How were they able to borrow all that money at so low rates when leverage meant high risk for the lenders?
My answer is that they were all GSE’s, all government sponsored enterprises–Fannie and Freddie and Bear and Citi and Goldman and Lehman and on and on. They all had an implicit guarantee from the government that allowed them to borrow at low rates (often from each other), rates that were well below market because of the implicit guarantee. And they were able to borrow at low rates even though they were highly leveraged which made them vulnerable to defaulting on their debt. Despite that vulnerability, they were still able to borrow at low rates. When things fell apart, almost all the creditors, lenders, and bondholders got all their money back, 100 cents on the dollar. The only exception was Lehman. The rest were all taken care of despite funding really bad bets.
For institutions like these, it must be nice to have Uncle Sam either backing or directing your every ill-advised move.
We find then that the steady degradation of underwriting standards, often prompted by legislation or by political pressure placed upon financial institutions during the late 1990s and early 2000s, that enabled the first U.S. housing bubble to become as large as it did by supplying the oxygen that fanned the flames of the bubble when it might otherwise have died out. We can see this in the growing number of sub-prime loans, as well as outright "liar loans" made increasingly throughout the bubble's inflation phase. That Fannie, Freddie, Bear, Citi, Goldmann, Lehman, etc. were all behaving as if they could reliably expect a bailout from the taxpayers no matter what only contributed to that environment.
The fuel for the U.S. housing bubble was the interest rate policies of the Federal Reserve, which held interest rates well below where the market would have otherwise set them, and especially so in 2003 and 2004, following the terrorist attacks of September 11, 2001 and subsequent recession. The inflation phase of the housing bubble didn't begin to decelerate until the Fed began pushing up U.S. interest rates to where they should have been all along in 2005 and 2006, as defined by the Taylor Rule.
But the spark that ignited the fire was the bursting of the Dot-Com stock market bubble, which began after August 2000, as the outflow of funds from that event provided a good portion of the money that helped push up real estate prices at a time the U.S. was entering into recession, breaking the established relationship between housing prices and household incomes. That factor, combined with the development and expansion of low-and-no money down mortgage products, as well as relaxed underwriting standards, was sufficient to counteract what typically happens in a recession, when housing prices fall in lockstep with falling household incomes and to cause it to grow beyond anyone's imagination.
And thus the bonfire that became the first U.S. housing bubble was formed. All it took was fuel, oxidizer and a spark.
Sharp-eyed readers will note that we've modified our chart detailing the relationship between median U.S. new home sale prices and median household incomes since we debuted this series two days ago!
Incorporating just released data for February 2013 into it, we can make a pretty good, but only preliminary argument that the recent run-up in home prices since July 2012 only inflated at rates consistent with the inflation of the first U.S. housing bubble through December 2012. We'll be able to confirm if that's indeed the case with just two to three more months of data. (We don't cite Keynes often, but we find this alleged quote applies.)
If that is the case, what we saw from July 2012 through December 2012 is more consistent with the shift in home prices that occurred following the enactment of the Tax Reform Act of 1986. Here though, we suspect we'll find the fingerprints of the reaction to the risk of higher taxes related to the fiscal cliff crisis at the end of December 2012, which we'll take on in upcoming posts.
Now that we've addressed the combination of factors that sparked off the first U.S. housing bubble back in November 2001, we're going to turn our attention toward answering one of the most difficult questions regarding it:
To understand why that's a difficult question to answer, consider that the two of the three contributors to creating the bubble were national in scope: money leaving the U.S. stock market in the deflation phase of the Dot-Com Bubble, which provided the spark for igniting the housing bubble, and the Federal Reserve's slashing of interest rates to record low levels, which was the fuel for the fire that followed.
So why didn't the first U.S. housing bubble inflate equally everywhere?
As you can see in the chart above for the years spanning from 2000 through 2006, the years covering the inflation phase of the first U.S. housing bubble, the U.S. housing bubble would appear to have been most concentrated in just six states. Or actually in just four states, since the growth in house prices in Maryland and Virginia was largely driven by the rapid expansion of the U.S. federal government following the 11 September 2001 terrorist attacks.
So that leaves California, Nevada, Arizona and Florida as the four states where the U.S. housing bubble had its greatest, most disproportionate impact on the value of housing, while most other states saw either moderate, and in some cases, almost no impact on housing prices.
The key to unraveling all this is the role of what we've described as oxidizer - the "environmental" factors behind the first housing bubble. And the first environmental factor to consider is where the money that provided the spark went first after leaving the U.S. stock market.
To put it simply, it went home. It first went to wherever those who had invested in stocks during the Dot-Com Bubble lived. As it happens, a very large portion of those investors lived in California, home to 1 out of every 8 Americans, but more importantly, whose Silicon Valley was the epicenter of the technology companies who benefited from the gains in the shares of stocks they owned and sold during the Dot-Com Bubble.
That combination meant that a lot more than 1 out of every 8 dollars leaving the stock market ended up in California. And subsequently, in California's real estate as many of these investors were young people who were also employed at the technology firms that directly benefited from the Dot Com Bubble. Many of these individuals took advantage of their windfalls to move up into nicer homes in the areas near where they worked, where housing prices were already high.
As for those stock market investors who lived in other parts of the country, many did the same thing with the money from their former investments. But with so much money in hand, and housing prices so much lower than California in most of the other places where investors pocketing Dot-Com Bubble dollars lived, a lot of that surplus money went toward second homes. Particularly in places where people would rather live, at least during certain times of the year. Such as during the winter months for the large number of Americans who live in the densely populated, but snowy northeast, who pursued the chance to own second homes in much warmer places known for their vacation resorts and other recreational amenities.
And that's why Florida, Nevada, and Arizona saw such a sharp rise in home prices compared to nearly all other states. Investors snapped up the available properties for sale in these states at a very rapid clip in the early stages of the first U.S. housing bubble, sending house prices skyrocketing in the places they coveted.
That activity, in turn, created opportunities for people who found it took very little effort to buy a house, wait a few months, then resell for a large profit as the inflated demand for housing stimulated further increases in home values, which then stimulated demand for building new houses.
And it wasn't just these home flippers and home builders who benefited from the rise in home values. Long term home owners took advantage of the rise in valuations to refinance their mortgages at the very low interest rates that took hold, taking out the gains in their home's equity and spending it, further stimulating the bubble economy that developed.
The Federal Reserve, for its part, aided and abetted the ignition of the first U.S. housing bubble by throwing more fuel on the fire it helped start, cutting interest rates in November 2002 and again in June 2003 to record lows, then holding them well below where the Taylor Rule indicated they should have been for a sustained period of time, as pointed out by Bud Conrad in the following chart from Casey Research.
Side Note: This is probably a good to time to ask the question: "Why wasn't there a housing bubble in the United States from 1973 through 1979?" After all, the Fed appears to have set interest rates far too low during that period with respect to the Taylor Rule according to the chart above.
The answer of course is that there is a missing element for making an economic bubble: the spark needed to ignite it. During that time, there was no relative windfall of cash suddenly coming into the hands of Americans looking to buy houses the way there was following the bursting of the Dot-Com Bubble two and half decades later. As a result, the Fed's policy of holding interest rates too low during much of the 1970s contributed to the high levels of inflation that characterized the economy during those years, instead of having the effects concentrated in the housing sector of the economy.
The party however couldn't go on forever, as the Fed became increasingly concerned about the economy becoming overheated. In June 2004, the Fed began cranking up interest rates a quarter point at a time in each of its Open Market Committee meetings for the next two years.
But it wasn't enough to stop the bubble, which didn't even begin to start slowing down its rapid expansion phase until September 2005, before changing trajectory to a much slower pace of increase after May 2006. Even then, home prices didn't peak until March 2007, after which they went mostly flat before the first U.S. housing bubble finally entered its deflation phase in November 2007, six full years after it originally ignited.
So how did the first U.S. housing bubble resist the Fed's efforts to extinguish it for so long?
In the third installment of our series, we'll point to the role of oxidizer once again in fanning the flames of the first U.S. housing bubble.
As all fans of the Discovery Channel's Mythbusters know, it takes three things to make a fire: fuel, oxidizer and a spark. If you combine these three things in the right proportions, you too can make a fire.
Economic bubbles work much the same way.
Let's start by considering the circumstances that launched the first U.S. housing bubble back in 2001. Here, the leading role of "fuel" will be played by the Federal Reserve's policy regarding interest rates. The role of "oxidizer" will be represented by a number of factors that are always present in the economic environment, much like oxygen is always present throughout the Earth's atmosphere. These factors include things like mortgage loan underwriting policies, state and local zoning policies, etc.
But the key role of the spark belongs to another bubble, whose own origins we've already addressed: the Dot-Com Stock Market Bubble.
Let's set the stage. The Dot-Com Bubble, which began in April 1997, reached its peak in 2000. Here, stock prices had inflated dramatically until the S&P 500 peaked at a value of 1,527.46 on 24 March 2000, after which the stock market bubble entered into its deflation phase as stock prices began moving sideways for the next five months.
That plateau lasted through August 2000, which coincidentally marks the peak of the S&P 500 when measured as the average of the index' daily closing values during a calendar month, with an average value of 1,485.46. By comparison, the average value of the S&P 500 back in March 2000 when it marked its peak daily closing value was 1,442.21.
Stock prices then began to fall steadily in September 2000. By the end of 2000, the S&P 500 had lost 10% of its value from its August 2000 average peak. Stock prices then rebounded a bit in January 2001 before falling back in February 2001. And then the deflation phase of the Dot Com Bubble really took hold as the U.S. economy entered into recession.
Stock market investors got whipsawed over the next several months, as stock prices became extremely volatile. Overall, stock prices fell sharply, as many investors sold off their holdings and exited the stock market, taking whatever profits they still had and capping off their losses.
The sheer amount of funds that left the market at that time is astounding. From September 2000 through September 2001, the market capitalization of the S&P shrank by 25%, as investors pulled 3.2 trillion dollars out of the U.S. stock market. They then began looking for something else to do with all that money.
And that's where the fuel represented by the Federal Reserve's policy on interest rates comes onto the scene. Recognizing that the U.S. economy was falling into recession, the Fed began implementing a series of interest rate reductions throughout the year, reaching historic lows. In response, mortgage interest rates soon reached levels not seen in years.
So what did the investors who had pulled their money out of the stock market react to that situation? Well, they started buying real estate. Lots of real estate, as the following story that appeared in the Chicago Tribune in July 2002 indicates....
Some figures suggest that buyers are funding the bigger down payments by cutting their losses in the market. It's estimated that investors withdrew more than $20 billion from mutual funds during the last month.
Bill Stofko, a native of Fort Lauderdale, Fla., who has long invested in historic properties in Key West, Fla., moved some of his capital back to his hometown in the last year.
He purchased a unit in a high-rise condo under development. It has gone up 10 percent in value even though construction hasn't started.
And he purchased a corner-lot triplex for $350,000. The lot is zoned for townhouses, which could sell for about $400,000 each.
"I have money in the stock market, but in the last two years it's gone down," Stofko said. "I'm putting it in real estate."
In just a matter of months following the monthly peak of the S&P 500 in August 2000, the available inventory of housing for sale in a number of regions across the United States was depleted, creating relative shortages of homes for sale in the local markets seeing the greatest infusion of Dot-Com Bubble dollars. Consequently, with demand now greatly elevated in these regions, home prices began to rise sharply in November 2001, which marked the beginning of the inflation phase of the first U.S. housing bubble at the national level.
That month also marks the official end of the 2001 recession.
In Part 2 of our story, we'll discuss how the "oxidizer" element of the U.S. housing bubble, which was present all this time, affected not just where the housing bubble really caught fire, but also who would ultimately come to be burned by it.
Aside from relatively minor noise in the market, the S&P 500 is continuing to behave as expected.
To underscore that point, let's revisit the "what if" game we started playing three weeks ago, following the Italian election fiasco noise event, when the S&P 500 had closed the previous Friday, 1 March 2013 with a value of 1518.20.
Playing "what if", we asked the question: "What if investors continue to focus on 2013-Q2 in setting today's stock prices and the change in the expected growth rate of dividends for that quarter stays constant with where it is today. How much would the value of the S&P 500 have to change on average per trading day to converge with that level by 21 March 2013 - the last day we have shown on our chart?"
We came up with an average increase of 2.75 points per trading day.
Spanning 14 trading days from 4 March 2013 through 21 March 2013, the S&P 500 would have to have risen to a level of 1556.70 before the end of that period for our "what if" game to hold true, a 38.5 point gain from the Friday, 1 March 2013 closing value of 1518.20 over those three weeks of calendar time.
The S&P 500 did just that on Wednesday, 20 March 2013, when the index closed at a value of 1558.71.
On Thursday, 21 March 2013, the market responded to a new noise event, with the S&P 500 pacing the reaction of global markets to uncertainty related to the EU's bailout of Cyprus' banks that day. But, that noise event was short-lived and the market recovered to close at 1556.89 on Friday, 22 March 2013.
So, aside from relatively minor noise in the market, the S&P 500 is continuing to behave as expected. Here is where the market stands at present:
This will be the last updated version of this chart that we'll be featuring for some time.
In the meantime though, here are three thoughts to consider for this week. First, not all noise events have a negative effect on stock prices. Second, following up a seemingly random comment of ours last week, you don't have to wait for a 150 point decline in the value of the S&P 500 for a clear sell signal. There are other, less clear ones that you might also consider!
And third, yes, what we've just demonstrated over the last three weeks is nearly impossible. We just have a knack for being able to execute that sort of thing.
We'll explain more of that second thought later this week....
Most inventions are meant to make life easier in some way. Even the Rube Goldberg-inspired contraption for removing the creme filling in Oreo cookie that we featured last week does that, in that it automates what might otherwise be a manual effort. It's a complicated way to do the intended task, but other than to load Oreos in and to unload the creme-filling free chocolate wafers out at the end is the only physical exertion required of the invention's user.
So what are we to make then of the following invention, which has been specifically developed to make a common physical task much more difficult to accomplish (HT: Core77):
Here, the Labyrinth Security Door Chain makes the act of opening a restroom door much more complicated. But why?!
Believe it or not, the Labyrinth Security Door Chain solves a problem, and does in fact make life easier. Just not for the people who will be the most likely ones who will be directly challenged by the invention.
In this case, the people for whom this invention makes life easier are the owners and employees of establishments that serve lots of alcohol-based beverages. It helps them identify those customers who have had so much to drink that their ability to quickly solve the maze and enter the restroom has become significantly impaired, as has likely their ability to accomplish other tasks that might expose the establishment to liability, such as if they attempt to drive while under the influence of alcohol and get into an accident.
That information, in turn, allows the owners and staffs of alcohol-serving establishments to decide how to deal with the impaired customer, where options may range from changing the services being offered to them, say exchanging food service for drink items, to declining to continue serving them altogether and making arrangements for them to be driven home.
You have to admit - it's a simple and relatively inexpensive way to sort out which customers have a greater potential to expose the establishment to the risk of costly liability-related actions.
What is the risk that your baby will become overweight or obese at some point during their childhood?
Today, we're going to help you find out using new research developed by a number of international researchers to determine the likelihood that an infant will be overweight or obese as they grow older, based only upon a handful of risk factors that are evident when they are infants.
Just enter the indicated data in our tool below, and we'll do the researchers' math for you!
In the tool above, perhaps the most difficult input value to consider is your child's race or ethnicity, where you can only select one option. In case those individual options don't apply for your child's race or ethnicity, you should select the option that would correspond to a greater risk of obesity for your child, as determined by statistical health studies of each group in the U.S. population, which have found the black or African American population to be the most at risk, followed by Hispanics, then the white population and finally the Asian population.
Morandi A, Meyre D, Lobbens S, Kleinman K, Kaakinen M, et al. (2012) Estimation of Newborn Risk for Child or Adolescent Obesity: Lessons from Longitudinal Birth Cohorts. PLoS ONE 7(11): e49919. doi:10.1371/journal.pone.0049919.
Not long ago, we featured a pretty cool looking chart illustrating the many minimum wages that have applied at the federal and for various states in the U.S. since 1994. Today, we're streamlining things a bit to determine the national average minimum wage for the United States!
To do that, we've calculated the percentage share of each state's population with respect to the combined population of all 50 states and the District of Columbia, and multiplied each state's share of the U.S. population by the greater of either the federal minimum wage or the state's minimum wage. We then summed up the results for each year from 1994 through 2012 to find the population-weighted national average minimum wage for the United States.
Those basic results are presented below:
And here are the results for each year again, this time adjusted for inflation to be in terms of 2012 U.S. dollars!
![Inflation-Adjusted Federal vs National Average Minimum Wage, 1994-2012 [Constant 2012 U.S. dollars]](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh-DB5_XUbVfBu_IZkVUFgy68TL_7Ddr0oUTOPAavbdGFHbpJXl1sirqXmIhoPCxMGH3y_yA42XXN1WSGXaFxgOB4sxcIChK6LXaL24lZKIgcGKlcjnMa6Wlg5KdQpN3SipCxVKH_LyfcQ/s1600/zb-real-federal-vs-national-average-minimum-wage-1994-2012.png)
In these charts, the biggest deviations from the federal minimum wage in any given year can be mainly attributed to large population states that have set their minimum wages well above the level set by the federal government. The largest deviation occurred at the beginning of 2007, when states like California ($7.50), Florida ($6.67), Illinois ($6.50), Massachusetts ($7.50), New York ($7.15) and Washington ($7.93) had set their minimum wages significantly above the U.S. minimum wage of $5.15 per hour.
Together, these six states accounted for almost one-third of the U.S. population in 2007, which was enough, when combined with the higher-than-federal minimum wages of smaller population states to boost the population-weighted national average minimum wage to $6.35 per hour, 23% higher than the federal minimum wage on 1 January 2007.
The timing of when these large population states increased their minimum wages over the years also explains an apparent anomaly for those analyzing U.S. national employment data. Namely, why increases in the federal minimum wage would not appear to generate the large reductions in the number of the employed that might be expected in economic theory.
Here, by increasing their minimum wages in advance of when increases in the federal minimum wage have taken place, many states would bear the brunt of reduced employment earlier as a result of this action. By the time the federal minimum wage was increased with respect to the earlier actions of these states, a good portion of the job loss that might reasonably be expected if it were the only minimum wage in the U.S. would have already taken place.
We think that factor goes a long way to explaining why the Age 15-24 population of the U.S. with incomes saw a net decline during the years from 2004 through 2006, which were otherwise characterized by solid economic growth in the U.S.
It would seem that all it took to make that decline happen during these years was for the large population states of Florida, Illinois, New Jersey, New York and Wisconsin to rashly boost their minimum wages above the federal minimum wage level of $5.15 per hour, as those five states together account for over one-fifth of the U.S. population.
Meanwhile, virtually all of the net decline in the number of Americans between the ages of 15 and 24 with incomes during these years occurred at the levels of annual income that would be most directly affected by the minimum wage increases that occurred in each of these states.
(Note: The data for the Age 15-24 segment of the U.S. population is the most likely to show the real effects of minimum wage increases, because American teens and young adults make up approximately half of all individuals earning wages at or near the federal minimum wage level.)
By the time the federal minimum wage was increased to $5.85 per hour nearly two-thirds of the way through 2007, the impact that might otherwise have occurred was muted, which we see in the number of American 15-24 year olds with incomes declining much less than might otherwise have been expected from the 13.5% increase in the federal minimum wage that took effect on 24 July 2007.
And that's what separates our minimum wage impact analysis from other efforts that only look at the federal minimum wage - we've accounted for the different minimum wages that most affect the population of the United States!
Has the U.S. housing bubble begun to reinflate?
Update 27 March 2013: See our update comment below this post!
In the past several months, there has been a lot of speculation to that effect, but so far, no one other than David Stockman has really come out and committed to an affirmative answer. And even Stockman didn't specify when such a new bubble in the U.S. housing market might actually have begun.
But what really sparked our interest in this topic today is the unexpected strength in the number of initial unemployment insurance claims being filed during the last several weeks, which along with the strength of the construction industry cited in the latest employment situation report, suggests that the U.S. housing industry is finally growing signs of robust growth, at least as measured by rising sale prices for homes.
Unfortunately, the apparently robust growth of housing prices in the last several months is suggestive of something other that fundamental factors at work. Fortunately, we developed an early detection method that might be used to confirm if a bubble is present in the housing market and if so, to identify specifically when it began. So, we're going to revisit the data once more to see just what might be brewing under the surface of the U.S. housing sector.
In doing that, we're going to push the envelope with our methods, as we'll be tapping new sources of data for median new home sale prices and median household incomes, in which these data items are reported monthly.
Let's get to work. Our first chart reveals the trailing twelve month average of the median sale prices of new homes sold each month in the United States from January 1963 through January 2013, as reported by the U.S. Census Bureau. The first data point spans the 12 months from January 1963 through December 1963, the second data point spans the 12 months from February 1963 through January 1964, et cetera.
alt="Trailing Twelve Month Average of Median U.S. New Home Sale Prices, January 1963 - January 2013" style="display: block; width: 600px; margin: 10px auto;" />In preparing this chart, we calculated the trailing twelve month average for median new home sale prices to account for the well-known effect of seasonality in housing sale data.
In looking at the chart, certain things stand out with respect to the apparently steady long term trends that are otherwise evident in the chart. Going from left-to-right, the first unusual thing we see the small upward bump that begins around December 1986 and ends about four years later, as a new steady upward trend takes hold. Continuing to the right, we get to the 800-lb gorilla that represents the inflation and deflation phases of the U.S. housing bubble in the form of the large lump that appears to begin around December 2003 and appears to end around December 2008. We then see a steady trend resume in the two years that follow, which is followed by what appears to be a new spike upward. Could that be a new bubble forming as so many people are speculating just based on house prices alone?
The truth is that you can't really tell from this chart. It may be, or it may not be. For example, what about that four year long small lump from 1987 through 1990? Isn't that a bubble, if only a small one, too? How come we haven't heard about any of that in the economic history books?
The reason for that analytical vagueness is that housing prices are not really a function of time, although they are often treated as if they are.
In reality, housing prices are a very strong function of income. Although other factors can and do affect them, their prices are primarily determined by the household income of those who live in them. What's more, housing prices are very linear functions of income - if you look at housing expenditures by income level, you'll find that it follows a very straight trajectory.
That linear characteristic also applies over time. Here, for example, we would expect to see house prices follow a steady upward trend as household incomes steadily rise over time. If we see deviations from that basic pattern, that tells us that something other than income is affecting house prices, which is what makes our analytical methods so effective.
Today, we'll be doing that with Sentier Research's monthly median household income data, for which we thank Doug Short for converting into nominal (non-inflation adjusted) form, which saves us the hassle of having to match the different inflation-adjustment scales used by the U.S. Census Bureau and Sentier Research.
The downside to using Sentier Research's data is that it only goes back to January 2000. To get around that limitation, we'll also be presenting the U.S. Census Bureau's annually-reported median household income data, which goes back to 1967, and which we'll use as the backdrop for establishing the long-term trends evident in the U.S. housing market.
As we did with the monthly median new home price data, we'll be calculating the trailing twelve month average for these figures as well, so they have had the same adjustment, providing as much as an apples-to-apples basis for drawing conclusions from what we find. Our initial result is presented below:
In this chart, we're able to determine that there have been two major long-term steady trends. The first ran from 1970 through 1986, as median new home sale prices were consistently about four times (4.07X) the value of the median household income.
This trend ended when the Tax Reform Act of 1986 made it more desirable to have a large mortgage when the tax deductibility of other kinds of consumer debt was eliminated. Enacted into law on 22 October 1986, median new home prices began increasing significantly after November 1986, rising rapidly in 1987 before settling onto a new steady, long-term trajectory with respect to median household income, in which median new home sale prices averaged about 3.6X the amount of median household income. It turns out that the dip at the end of the "small lump bubble" is really the result of the recession that accompanied the Persian Gulf War following Iraq's invasion of Kuwait in 1990, which depressed housing prices along with incomes at the time.
That new trend continued through 2000, until the onset of the U.S. Housing Bubble in December 2001.
Here, after the Dot-Com Stock Market Bubble peaked as a monthly average in August 2000, large amounts of money began flowing out of the U.S. stock market. It was slow at first, as the market declined by less than 10% through March 2001, but that quickly changed as the deflation phase of the Dot-Com Bubble became much more volatile as the U.S. economy went through a period of recession.
With stock prices swinging by 10%-20% of its peak value in any given month through October 2001, many stock market investors either took their losses or pocketed their gains from the Dot-Com Bubble and exited the market. That money didn't sit around idly, as much of it went into the U.S. housing market instead during that time, which enjoyed growth despite the recession throughout 2001 as a result. The recession ended in November 2001, just as interest rate cuts by the Federal Reserve helped pull mortgage rates to their lowest level in more than a generation. November 2001 marks the true launching point for the U.S. Housing Bubble.
Afterward, housing prices began skyrocketing month after month as the U.S. Federal Reserve compensated for both the recession and the 11 September 2001 terrorist attacks by holding interest rates at levels far lower than economic conditions would warrant for a sustained period of time. Our next chart focuses more closely on the U.S. housing bubble years:
U.S. housing prices continued their rapid ascent through September 2005, before beginning to decelerate on their upward trajectory as the U.S. housing bubble neared its peak, as the Fed's series of quarter point interest rate increases finally boosted them to levels that actual economic conditions warranted. The peak came on March 2007, after which median new home sale prices held level through October 2007. The deflation phase of the U.S. housing bubble then began in the following months, as the U.S. entered into deep recession.
The trailing twelve month average of median new home sale prices then bottomed in December 2009 before beginning to recover and rise in 2010. However, median household income continued to fall for another year, and it was not until December 2010 that a new steady, upward trend began to form in the U.S. housing market as median household incomes began to rise once again.
The new period of order in the U.S. housing market saw median new home sale prices stabilize at roughly 3.34X the value of median household income, which is fairly consistent with the other long-term periods of relative order in the U.S. housing market.
That period of order came to an end after July 2012. Beginning in August 2012, something else other than household income has begun affecting the median sale prices of new homes in the United States. Through January 2013, median new home sale prices are growing at a rate that is consistent with what we observed during the initial inflation phase of the U.S. housing bubble following the end of the U.S. recession in November 2001.
We therefore conclude that the U.S. housing bubble has effectively reignited, with a new inflation phase having taken hold since July 2012.
The question that remains to be answered is "why?" We'll take that question on in upcoming posts.
Update 27 March 2013: We've digested the just-published income and new home price data for February. It will take another two-three months of data to confirm this, but the early indication is that the "bubble" we identified above ended in December 2012, with a new trend taking effect beginning in that month. Our preliminary thinking is that the rapid runup in home prices from July 2012 through December 2012 may be related to the fiscal cliff crisis at the end of 2012. We'll have more on what we're seeing in April....
Sentier Research. Table 1. Household Income Trends: January 2000 to January 2013 (in January 2013 $$). [Excel Spreadsheet with Nominal Median Household Incomes courtesy of Doug Short]. Accessed 13 March 2013.
U.S. Census Bureau. Median and Average Sales Prices of New Homes Sold in the United States. [Excel Spreadsheet]. Accessed 13 March 2013.
U.S. Census Bureau. Income, Poverty, and Health Insurance in the United States: 2011. Current Population Survey. Annual Social and Economic Supplement (ASEC). Table H-5. Race and Hispanic Origin of Householder -- Households by Median and Mean Income. [Excel Spreadsheet]. 12 September 2012. Accessed 13 March 2013.
Before we get to the end of this post, we're going to leave you in limbo. In more ways than one. Let's get to it, shall we?
The U.S. stock market, as represented by the S&P 500, is continuing to behave almost exactly as expected. Through last Friday, 15 March 2013, investors remained focused on the future as defined by the second quarter of 2013, and the rally in stock prices began to slow. Here's an excerpt of the story we foretold weeks ago, as just told by Reuters:
Stocks have soared in 2013, with the Dow .DJI climbing almost 11 percent to hit a series of new all-time highs while the S&P 500 .SPX has jumped 9.4 percent, falling just short of its all-time closing high after rising for 10 of the past 11 weeks. And yet, analysts for the most part see equities as fairly cheap.
The rally has slowed, however. In the last eight trading sessions, the S&P 500 has managed a daily gain of more than 0.5 percent just once.
Let's see how that played out on our chart showing the change in the growth rates of stock prices and the expected level of dividends in future quarters, which addresses some of the why's of what has happened in the U.S. stock market:
Right now however, the market is in a state of limbo. Not so much the boundary at the edge of hell, so much as we are now in a period where one future has ended, but a new one has not yet begun.
Specifically, we're referring to the conclusion of the dividend futures contract for the first quarter of 2013, for which the third Friday of March, 15 March 2013, marked the final date and the new dividend futures contract that will apply for the first quarter of 2014, which has not yet taken shape.
At present, we expect investors to remain focused where they are at present, on the future as defined by the dividend futures contract for the second quarter of 2013. At least, until they are compelled to look to a more distant future in setting their expectations while setting stock prices. When that happens, we expect many investors might wish for a return to the current state of limbo....
In the meantime, we thought it would be a good idea to update our detailed chart showing the current state of order in the stock market:
Going by this chart, a 150-point decline in the value of the S&P 500 would be a very clear sell signal - just in case anyone is considering setting up a pretty generous stop-loss order so they don't have to hover over their stock tickers. Please note that we're not saying that such a change is imminent and we note that there are some wild card factors at play in the market today that reduce the odds of such a thing happening to less than a 100% probability. Our policy however is to point things like this out when it matters most: when you still have time to figure out what actions you might need to take in response to such an event, just in case you might suddenly have such a need.
However, if you're the type of investor who believes in "reversions to the mean", we've just shown you enough to make you very nervous. We're afraid that you cannot be helped....
And because it will be extremely relevant to news that we are going to break on 19 March 2013, here is an updated version of our chart showing the various periods of order and chaos that have prevailed in the U.S. stock market since December 1991:
As for the news that we are going to break, it might very well affect your investment outlook, because it will represent a new wild card factor for you to consider. For what it's worth, that point at the very top of the dark red section of the chart above identified as "Chaos: 04-1997 to 06-2003 (Dot Com Bubble)" is the one that is especially relevant, but not because there is any sort of bubble at work in today's stock market (really - we don't believe there is much of any type of bubble at work in today's stock market at all.) We'll explain more later this week....
See? We weren't kidding. Welcome to limbo!
Update 8:50 AM EDT: Sharp-eyed readers will note that we didn't mention Cyprus in our commentary above. In our view, it's a noise event, but one that might appear to have an outsize impact considering where things stand in the markets today.
How much does a guy have to dislike the creme filling in delicious Oreo cookies that he is inspired to devise an invention as intricate as what you're about to see just to accomplish the desired task of removing the creme without coming into direct contact with any of it?
Now that's genius! (HT: Core77)
How many single person households were there in 1909? Or 1945? Or 2011?
Those aren't necessarily easy questions to answer, but today, we're going to first by visualizing the number of single person households in the United States since 1900, and then by presenting a tool to extract the data from our visualization. To do that, we'll use all the data we've been able to obtain from the U.S. Census Bureau, which up until 1960, isn't very much. That limitation is what makes those questions not so easy to answer!
To get around that limitation, we've created a model of the percentage share of single person households for each year since 1900 for the data that we do have available, which we can then use to estimate the number of single person households over time in conjunction with our previously introduced model describing the total number of U.S. households since 1900!
All that work comes together in our chart below:
You can use the following tool to extract the estimated number of households or single person households for any given year shown in the chart above, along with the percentage of single person households among all U.S. households.
Political Calculations. Modeling U.S. Households Since 1900. 8 February 2013.
U.S. Census Bureau. Statistical Abstract of the United States: 2003. Table No. HS-12. Households by Type and Size: 1900 to 2002. [PDF Document].
U.S. Census Bureau. Demographic Trends in the 20th Century. Table 13. Households by Size for the United States: 1900 to 2000. [PDF Document].
U.S. Census Bureau. Table HH-4. Households by Size: 1960 to Present. Excel Spreadsheet].
Social Indicators 1976. Selected Data on Social Conditions and Trens in the United States. Table 2/17. Average Household Size, Single-Person Households as a Percent of All Households, and Number of Divorces per 1,000 Population, Selected Countries and Years: 1955-1975. [Online Book]. December 1977.
On Monday, 11 March 2013, President Obama[1] secretly asked us to explain the dynamics of how cuts in R&D spending might affect the nation's economic growth.
Here's the proof, as documented by StatCounter:
alt="StatCounter Detail 11 March 2013 - Executive Office of the President visit to Political Calculations' November 2009 post - The Link Between Intangible Investments and GDP" style="display: block; width: 600px; margin: 10px auto;" />Here are the key points of what the President learned about how R&D cuts in the private sector can affect the economy, which we've tweaked from the material we originally presented for greater clarity:
In practice, when a company anticipates that it will not be able to afford its current level of new product development into the future, its management will begin a process of winnowing the list of research and development projects that it is willing to develop. That process occurs well in advance of the layoffs of the people who actually carry out the work involved with them.
Development projects are often reviewed and ranked according to their prospective return on investment (ROI) in that process. Here, projects that fail to meet a certain ROI threshold are discontinued, with the people engaged in those projects initially reallocated to other projects. In addition, the company's management may also kill a high ROI project, should they judge it to involve too much risk to continue developing in the fading economic climate it foresees.
Following this phase, the company's management evaluates its staffing needs to support the "winning" projects it will continue and this may lead to the resulting layoffs of its creative staff. Then the process repeats until whatever cost reduction target the company's leadership has set for its intangible investments has been met.
Before those layoffs occur however, it is the termination of the low ROI and high risk projects that may negatively impact GDP in the current timeframe, since the cancellation of the projects also terminates the relationships established between the company and its suppliers, vendors and customers to support them, to the extent that their businesses were relying upon the cancelled R&D projects for their revenue.
That change then ripples into the larger economy and also into the future, as the parties involved in these projects alter their own efforts to compensate for the loss. This contraction of economic activity then only subsides once sufficient new work makes it possible to reverse the process or when their costs have been brought in line to support their new level of revenue.
And from there, it's a question of whether those who might have been laid off have the skills needed for the new economy that forms. If there's a massive mismatch between the skills of the creative people who were let go and the opportunities available to them, the downturn for them can drag out for a very long time, especially for those employed in the industries that went from being the most high-flying to the most distressed.
Now, here is what the President has not yet learned, courtesy of MyGovCost, where the relative impact of government spending cuts and tax increases on the economy was recently discussed:
To understand why spending cuts like those of the sequester are considered to be so much less harmful to the economy than increasing taxes, let’s consider the real nature of government spending and taxes.
Here, when government raises taxes to support its discretionary spending, what it is doing is hurting a lot of people a little to benefit just a handful of politically-connected people, who just coincidentally happen to benefit a lot from government contracts (wink-wink). Because the harm is so widespread and the benefit limited to so few, the general economy suffers quite a bit as a result. Those effects are worse when the threat of additional tax increases remain after tax rate hikes are implemented.
But when a government cuts its spending, those dynamics work in reverse. Instead of lots of people being harmed a little, only a handful of people are. And since those people are significantly less likely to be engaged in sustainable economic activity in the first place, the economy at large is barely affected when their access to taxpayer money to fund their business income is reduced.
And that, in a nutshell, is why spending cuts are better for the economy than tax hikes for balancing a government’s budget.
The bottom line: Cuts in the U.S. government's R&D expenditures will have much less of an impact upon the U.S. economy than cuts in corporate R&D investments, thanks to the government's bizarre strategy of "investing" in wasteful, politically-driven, high-risk, low-return R&D efforts, such as those associated with "green" energy programs.
[1] Or more likely, one of the President's lowly minions trying to drum up arguments to oppose the spending cuts affecting government-funded R&D programs that are being negatively affected by the spending cuts mandated by President Obama's proposed budget sequester agreement from 2011.
