Real-Life Example of Putting It All Together for Households

Many investors, Chris included, thought the housing rebound would continue in 2015. In his mind, it was simple supply and demand— the supply of homes shrank and, given the level of demand, prices got ahead of themselves. Heading into 2015, data from the National Association of Home Builders showed that the available inventory of single-family homes for sale had reached multiyear lows (see month's supply for December 2013, circled, in Figure 4.4). Looking at the data, Chris thought that homebuilders—both publicly traded like D.R. Horton (DHI), Toll Brothers (TOL), and others as well as private ones—would see the market for what it was and build more affordable housing. Keep in mind the severe winter weather in early 2015 exacerbated the housing pain and conventional wisdom led many to think pent-up demand had been created—historically that's how it had typically worked.

Yet by late 2015, as Figure 4.5 shows, home ownership rates were back down to where they were 20 years prior.

So let's look at the condition of potential homebuyers. By the end of the post–financial crisis Great Recession, the unemployment rate had risen to just over 10 percent. By the latter part of 2015, it had fallen back down to 5 percent (see Figure 4.6), which sounds like a good thing for the housing sector, yet we still weren't seeing the expected boom.

The problem with just looking at the unemployment rate is it only reflects those who are actively looking for a job but can't find one. It doesn't take into account those who have simply given up or “fallen out of the labor force,” to use Labor Department terms, nor does it take into account the income level of the previously unemployed person's new job relative to their prior job. For example, someone who loses a $200,000-a-year job who a year later can only find a job that pays $25,000 a year, while technically no longer unemployed, is in a very different financial position than before losing his or her job. This is why earlier we mentioned that the most important metrics when thinking about households are the percentage employed and how much are they earning.

The chart in Figure 4.7 shows that the percent of the population employed rose from 1975 to 1999, but fell dramatically through 2012, only rising slightly by late 2015. The percent of the population employed toward the end of 2015 was back where it was during the early 1980s, and well below the peak at the end of the last millennium. With a lower percentage of the population working, the potential for the economy would necessarily be lower than it was in 1999. That's fairly intuitive. If there are 10 people in a boat, when 8 are rowing the boat can move a lot faster than when 6 are rowing.

There are a number of other employment indicators with one of the better-known ones coming from payroll processing firm ADP. One of our favorite “alternative” measures of job creation and employment is Gallup's Employment to Population Index, which is similar to the one above, but sidesteps all the math games of how many people are in the labor pool and how many are not employed to focus on the number of people with jobs versus the entire U.S. population. We also watch payroll-to-population data published by the Bureau of Labor Statistics each month like hawks and we also keep close watch on other job creation data buried in monthly reports from Markit Economics and the Institute for Supply Management.

As you can see, in 2015 the financial state of the consumer and the availability of mortgages had changed, which is why it is so important to look at more than just a few pieces of data. Even if Joe and Jane Consumer wanted to buy a home, they couldn't afford to, couldn't get a loan, remained too shell-shocked from the previously unprecedented fall in home prices across the country to muster the courage, or knew they were facing (or soon to face) other financial hurdles.

We also saw a change in the usual makeup of those buying homes, with those buying a home for the first time much lower than we'd seen in the past. According to the National Association of Realtors, toward the end of 2015, the first-time buyer had fallen to the lowest level in nearly three decades at just 32 percent of all purchases.¹

We saw that a lot fewer people were working, but there was more to the story. The Census Bureau's September 2015 release of its annual report on “Income and Poverty in the United States” stated that the median household income in 2014 was $53,657, still well below the peak level 15 years prior in 1999 at $56,895. Cardhub reported that average household credit card debt in the United States had hit a post-recession high of roughly $7,500 per household at the end of 2014.² Clearly, Joe and Jane Consumer were having a tough time. The Bureau of Labor Statistics showed median weekly earnings in second quarter of 2014 at $780, down roughly 1 percent from $786 in the third quarter of 2013. The drop in weekly earnings was bad enough on its own, and certainly took some wind out of “all the jobs created” during the first half of 2014 that was touted on Capital Hill, but compared to the 1.7 percent increase in the Consumer Price Index for the 12 months ending August 2014, not only did the consumer have less to spend, but those dollars weren't going as far.

There was also a problem with underemployment. A survey by Accenture released in May 2014 of 1,000 workers who graduated from college in 2013 reported that 46 percent claimed they were in a job that didn't require their degree, a 5 percent increase from the prior year's survey. The Federal Reserve reported in early 2014 that 44 percent of working recent grads were deemed underemployed in 2012. Those who think their job is well below their skill set may not feel confident in their earning abilities, which could make them less likely to take on the risk of buying a home.

Figure 4.8 from the Bureau of Labor Statistics shows how changes in productivity levels rose more than is typical during 2009, but then continued to oscillate below pre-crisis normal levels. The productivity of labor is, to a large degree, a function of capital investment by companies, which again is rather intuitive. With the latest and greatest tools, employees can normally accomplish a lot more than with tools from 10 or 20 years ago.

Productivity isn't exactly stellar looking in that chart. In fact, double-checking with the Commerce Department, we found that by the end of 2014, the average age of fixed assets, such as plants and factories, is about 22 years old, the oldest average going back to 1956! Of course, productivity would be challenging. Think about how much you can accomplish with your smartphone versus a rotary phone!

Putting all these charts together, we can deduce that there is room for improved labor productivity given that it has not been growing all that strongly. But, the contribution of labor into overall economic growth is limited until a higher percentage of the population is working, as those currently with jobs are working at nearly the same level of hours as before the crisis, when the economy was strong. Overall, a lower percentage of the population was working than before, income levels are below historical levels, and an awful lot of workers felt they were underemployed.

That's a very different picture than the one painted by the headlines claiming enormous strides in the employment situation. Sherlock Holmes was able to solve the mystery because he took in so many more data points, picking up on details that Inspector Lestrade never saw. Throughout this book we will not only be showing you what details you ought to look at, but also teach you to have a Holmes-like process.

As the data we just walked through became available, Chris's view on the housing sector softened, which goes to illustrate several points we want to highlight to you. First, the economy is a dynamic and living thing—parts of it can grow or expand while other aspects can shrink or come under pressure. Second, look for a relationship between different parts of the economy. In this case, there was a direct example largely because, for the most part, consumers are the ones who buy single-family homes. If consumers are hurting, then so will the demand for housing. It was not a case of “if you build it, he will come,” and we don't care if Kevin Costner or James Earl Jones is whispering this in your ear.

Third, meaningful data can come from a number of different sources, not just from data that correspond to a particular industry, and it is always good to look around for extra data to verify a hypothesis. As we mentioned earlier, we love data and we always have our eyes and ears open for a piece of information that can be confirming, disproving, or shed some light on some new development. When we get these nuggets, we ask ourselves the following:

• What does it mean? (One of Chris's favorite questions.)
• How does this fit with the other data that we are seeing or have collected?
• Does this tell me anything new or different, or does it simply reinforce what we already know?
• Who does this affect? A demographic cohort, certain industries, both, or more?
• How well known or widely distributed is this piece of information?

Finally, when you think about the household part of the economy, keep in mind the context for where families spend money. You have probably heard that the U.S. economy is driven primarily by personal consumption, which accounts for around 70 percent of gross domestic product. Well, if that's the case, just what are households spending it all on? You'll see from Figure 4.9 that the largest portion of household spending goes to shelter at 20.2 percent, which helps make it clear why the boom and subsequent bust in home prices during the Great Recession was so painful. Conversely, apparel and services account for all of 3.8 percent of average expenditures, so a doubling of cotton prices isn't likely to have a big impact on the economy.

As the John Maynard Keynes saying goes, “When the facts change, I change my mind. What do you do, sir?”

Remember, we told you to focus on vector and velocity. This shows the importance of adding in one more concept when looking at individual metrics or parts of the economy—magnitude. Cotton prices could be moving in all kinds of wild directions, which will have a significant impact on companies in the apparel industry, but given the magnitude of the apparel industry on the average family, cotton prices alone aren't going to significantly impact the average family's spending habits. So now let's take a look at the part of the economy that provides all those goods and services we buy.

Application Example

Earlier in this chapter, we talked about the business cycle and the four states of the cycle: Early, mid, late, and recession. The metrics in Table 4.4 can help you identify just where we are in the business cycle.

Figure 4.10 shows the inventory-to-sales ratio from January 1, 2000, to September 2015. The trough of the first cycle below was November 2001. Notice how from 2001 to 2006 inventories relative to sales declined as the next cycle kicked off. Then in 2006 and 2007 inventories jumped up, which is typical for late stage, then as the recession hit, sales plummeted, so this ratio would naturally jump up significantly as businesses can't sell during a recession what they already have in inventory. During the recession businesses let their inventories fall much lower than during a strong economy, so when sales pick up, this ratio can improve rapidly.

Taking an even further step backward, Figure 4.11 shows an even more interesting trend in inventories that spans across multiple business cycles, with recessionary periods shaded gray.

You'll notice that businesses have been able to reduce their inventories on a fairly consistent basis over the past few decades. That's good news for the business owners, as inventory that just sits on the shelf is a poor use of a company's money. In a perfect world, inventory comes in one door and immediately goes out the other to the customer. Figure 4.11 also gives a great appreciation for the magnitude of the shock caused by the Great Recession. Since then, the trend of ever-falling inventories appears to have been reduced somewhat, with inventory levels rising since 2011.

We also mentioned how capacity utilization levels change throughout the business cycle. Figure 4.12 shows capacity utilization levels from 1990 through September 2015, again with recessions shaded in gray.

Notice how going into a recession, capacity utilization rates fall dramatically and fast. When people stop buying, factories slow down, lay off workers, and sometimes even have to shut down. As the recession ends and the recovery begins, the snows thaw, green shoots appear, and those factories get humming again.

Real-Life Example of Putting It All Together for Businesses

Part of the process of analyzing these data points is to not overextend a trend. Parts of the economy often do not move in tandem; for example, a few pages back, we mentioned the weaker than expected tone of the U.S. housing market reflected in the pressure being felt by the consumer. If that was all you knew, you might think that 2014 was not a good year for the U.S. economy. Although household income restrained overall growth, there were other factors that were spurring the economy along, including strength in auto and truck sales as well as aerospace demand and, generally speaking, overall manufacturing activity was improving.

The Institute of Supply Management (ISM) publishes the Purchasing Managers Index (PMI) on a monthly basis (Figure 4.13), which is widely considered a good indicator of the economic health of the manufacturing sector and is based on five major indicators: new orders, inventory levels, production, supplier deliveries, and employment conditions. A PMI reading of 50 or more represents expansion, under 50 signals a contraction, while an even reading of 50 means no change. We watch this metric out of both Markit and ISM every month.

Remember we mentioned vector and velocity. From 2011 to mid-2013, the ISM PMI was overall falling. During most of 2014, it looked to be improving significantly, but then once again starting falling dramatically at the end of 2014 and throughout 2015. If we look at the PMI on a longer time frame to get more historical context, we can see that in fact, by the latter part of 2014, it was in the region where it normally peaks out, so for those who were paying attention, the slide throughout 2015 would not have been a surprise. Figure 4.14 also gives you a better appreciation for the magnitude of the contraction from the Great Recession (note recessions are shaded gray).

So manufacturing was looking better, but just how impactful that is for the economy is another question. Remember to always keep things in context, so we need to look at just how impactful a strong manufacturing sector is on the rest of the economy. The Bureau of Economic Analysis provides quarterly and annual data on GDP by industry. Table 4.5 shows a summary of that data.

Source: Bureau of Economic Analysis

Here we can see that manufacturing had declined as a percentage of GDP from 16.1 percent in 1997 to 12.1 percent in 2013 and 2014, so while a positive PMI is a good sign for the economy, it doesn't necessarily speak for the entire economy.

We next look for corroborating data on the health of the manufacturing sector. When a company manufactures something—a part, component, subassembly, or even a finished product—it not only has to get the building blocks to create its product, but the product must be transported to the customers, be they other manufacturers, distribution centers, or retailers. That means paying attention to transportation activity data that are widely available to you and me.

Two such pieces of information that we like to track are weekly rail car loadings and truck tonnage. The American Association of Railroads³ not only does a great job of publishing the weekly and year-to-date loadings data, but it also offers several views on the data including intermodal loadings and a deeper dive on 10 carload commodity groupings. It's data like these that offer near real-time insight.

According to the American Association of Railroads, in the second quarter of 2014 U.S. intermodal volume was strong and through the week ending July 12, those car loadings were up 6 percent on a year-to-date basis.⁴ For those who are unfamiliar with the term, intermodal freight transport involves the transportation of freight in an intermodal container or vehicle, using multiple modes of transportation (rail, ship, and truck), without any handling of the freight itself when changing modes. We think it is a great measure of economic activity.

Intermodal was not alone, as U.S. freight carload traffic also rose 4.8 percent for the week ending July 12, 2014.⁵ That was well above the year-to-date average of 3.4 percent as of July 12.⁶ Comparing year-to-date figures at various points during the year lets us know if the traffic and subsequently the economy were picking up speed or not. In this case, it was—the 2014 year-to-date 3.5 percent increase in freight carload traffic for the first 28 weeks of 2014 compared to 2.2 percent for the first 24 weeks of the year.⁷ Some simple math tells us, the economy had indeed picked by mid-July.

The second metric we mentioned was truck tonnage. According to the American Trucking Association, trucks hauled nearly 70 percent of all the freight tonnage (over 9.2 billions tons) moved in the United States.⁸ That makes trucking activity a key barometer of the U.S. economy. Each month the American Trucking Association publishes its truck tonnage index and just like the weekly rail traffic report, it's one we watch for each month.

Around the same time rail data was improving, the data also pointed to a pronounced pickup in the economy. The May 2014 tonnage index reading was up 3.3 percent year-over-year. Even though the June 2014 truck tonnage reading declined 0.8 percent month-over-month, the year-over-year comparison still showed an impressive 2.3 percent improvement. Through the first half of the year, compared to the same period in 2013, the truck tonnage index was up 2.8 percent.⁹

Put the truck tonnage and intermodal commentary together and you can imagine that it would have been a good time for companies such as J.B. Hunt Transport Services (JBHT), which gets more than half of its revenue from intermodal shipments; booming rail traffic means more truckloads to and from the railyard. In 2013 and 2014, it would be difficult to have a discussion of rail traffic without mentioning the proposed Keystone pipeline and all the political strife surrounding it, which brings us to our third and final participant in the economy, the government.

Watching both of these metrics—truck tonnage and weekly rail traffic—in 2015, however, painted a much different picture. For the first 10 months of 2015, total rail traffic volume in the United States was 23.5 million carloads and intermodal units, down 1.4 percent from the same point in the prior year.¹⁰ The truck tonnage index hit a reading of 135.1 for September 2015,¹¹ well off the peak of 135.8 reached in January 2015. Putting these two indicators together tells us the economy cooled dramatically in 2015 compared to 2014, and we see that in both Figures 4.13 and 4.14. As we said before, the economy is a living, breathing dynamic beast, and as investors we need to be aware of its ever-changing vector and velocity.

Application Example: Housing Industry

One way that the U.S. government supports the housing industry is by making a portion of mortgage payments tax deductible. This effectively reduces the cost of a mortgage. For example, you are assessing whether to rent a house for $1,000 a month or buy a home and have a $1,000 monthly mortgage payment. For simplicity's sake, we'll ignore the impact of a down payment. The mortgage deduction makes owning less expensive, given that a portion of that $1,000 can be used to reduce how much money you pay in taxes. On the other hand, you may have to pay property taxes when you own, which raises the cost of owning, but you get the idea. Changes in the deductibility of interest payments on mortgages change the effective cost of owning a home, and thus will impact the desirability of owning versus renting.

Remember in Chapter 3 how frustrated Reilly and Tyler were to see the market value of their home plummet after having purchased it in 2005? How could they have known just how much home prices were going to fall? They knew that they were not exactly buying at the bottom of the market, but everyone they spoke to told them not to worry, that perhaps their home value could drop a bit in the years to come, but not enough to worry about. Ten years later and according to Zillow, the estimated sale price for their home is still less than what they paid for it.

Looking at the data, Reilly and Tyler weren't alone! So very many people were caught up in the home-buying frenzy and we bet that for many, they were simply at a time in their lives when they wanted to settle down in a home for a while, so they went ahead with buying a home, even though they intuited that it wasn't the best time. Looking at the historical trends in home prices before the financial crisis, there never had been a significant and long-lasting downturn in home prices on a national level.

Housing prices in a heated market can go up for a while simply because everyone believes they will continue to rise, but generally there is some fire behind that smoke, something that kicked off the entire process. This was the case with the runup in home prices. In Figure 4.5 we showed how home ownership rates had changed over time. In Figure 4.15, let's look at that data again, but from an even longer-term perspective.

Here we can see that home ownership rates rose dramatically from the latter half of the 1990s to peak in 2004 and have been falling continually since then. Why? Is this good or bad?

Before we answer that, let's remember that in the mid-1990s, homeownership was a major push by both the Democrats and the Republicans over the ensuing decade that led to low to no down payments and pushed for lenders to give mortgage loans to first-time buyers with shaky financing and incomes. Hindsight being 20/20, it's clear the erosion of lending standards pushed prices up by increasing demand, and later led to waves of defaults by people who, for a variety of reasons, never should have bought a home in the first place.

Our interpretation of the data indicates that the peak of home ownership is not something to which we ought to aspire. Not every household should own the place in which they reside, as the costs and risks can easily outweigh the potential benefits. Homeowners can't easily move if they need to change jobs, something we saw coming out of the Great Recession, and we've seen the impact of this in the way the labor market post–financial crisis has been the most inflexible in history with respect to geography. People are unable to rapidly react to changing conditions in the economy that affect their household finances, not to mention the hassle of home ownership and all the costs that are unimaginable beforehand.

Why did home ownership rates increase so much before the financial crisis, to levels that were clearly unsustainable and caused so much pain for so many? Was it just those evil, greedy bankers that somehow tricked people into buying homes? Well, that's partially true. There are some rats in those banks, but that is only part of the story, and a misleading take on all that happened.

You can also thank the federal government. Traditionally, non-FHA mortgages required a minimum of 20 percent down, but in 1994 the Department of Housing and Urban Development (HUD) ordered Fannie Mae and Freddie Mac to supplement and eventually to far surpass the FHA's efforts by directing 30 percent of their mortgages to low-income borrowers, when previously the number had been much lower.¹² This became pretty tough to do, so to meet that goal, Fannie Mae introduced 3 percent down mortgages in 1997.¹³

In 2000, HUD increased the low-income target to be 50 percent of all loans. Now think about that: What bank in their right mind would want to make 50 percent of their loans for the year to low-income families with exceptionally low money down? That means 50 percent of your loans are in the riskiest category! To accomplish this, Fannie launched a 10-year, $2 trillion “American Dream Commitment” program to increase home ownership rates among those who previously had been unable to own homes. So when the government gets itself all focused on getting people who previously couldn't afford a home into one, is it really all that shocking that home prices rose like crazy?

In 2002, Freddie joined the party with the “Catch the Dream” program to accomplish essentially the same thing. Then in 2005, HUD increased the target for low-income loans again to 52 percent! Now here's a bit of irony. The government wanted more people to own homes, so it makes it easier and easier to get a loan. Now we've got more people out in the market to buy homes. Son-of-a-gun, prices go up. Well now, isn't that exciting! Buying a home looks like a really great investment because the prices are just going through the roof! But wait—rising home prices are great for only half the equation. They are great for the owner who looks to sell, but not much fun for the person trying to buy. So in their attempt to increase home ownership by making it easier to buy a home, the government made homes even less affordable.

Oh, but that's OK, as Fannie and Freddie are there to save the day and get you into that home that you really cannot afford with little to no money down and a variable rate mortgage that isn't a ticking time bomb at all! All these subsidies increased the supply of mortgages to low-income aspiring homeowners, but what was the source of the money to fund these loans? Welcome to the mortgage-backed securities (MBS), those weapons of mass destruction. Banks would pool together mortgages that could then be sold as an MBS, and with HUD's desire to get Fannie and Freddie to increase homeownership in the subprime areas, these two agencies were more than happy to back the MBS, which, because they are government-sponsored entities, turned subprime loans with very little money down into AAA-rated bonds!

Serious fairy dust, isn't it?

Now the banks were running around gobbling these things up like there's no tomorrow. Why, you ask? Well, according to the Basel Accords, banks could seriously lower their reserve requirements by holding these GSE (government-sponsored entity) AAA-rated bonds, which improved their profit margins. A bank's reserve requirement is a central bank regulation employed by most of the world's central banks, including the U.S. Treasury, that sets the minimum fraction of customer deposits and notes that each commercial bank must hold as reserves, rather than lend out. Anything that lowers reserve requirements lets a bank lend out more, thus, all things being equal, improving their profit margins.

So What Does All This Mean?

Subsidies distort markets in that they artificially increase demand.

This artificial boost in demand raises prices and pushes the market to allocate more resources (workers, money, equipment, land, etc.) into the subsidized industry than it otherwise would have. When the subsidy ends, as they usually do, the extra resources that were allocated thanks to the subsidized demand have to go elsewhere, but the shift can take considerable time and be very painful.

In the recent housing crisis, the subsidies added a lot of workers to the payrolls of construction firms and mortgage companies and induced investments, such as in extra home-building, that would have otherwise gone elsewhere. When it all came crashing down, those workers had to find jobs in other industries, and the skills they'd developed in construction or mortgage generation became much less valuable, and a lot of the money invested in construction projects was permanently lost.

Regulations alter incentives.

Regulations are typically implemented to keep people safer than is believed they would be without the regulations. This theoretical protection can range from hazardous products to the impacts of foreign competition or financial irresponsibility. Without regulations, companies will behave according to their individual views on the opportunities and risks in the marketplace. Their views and corresponding actions may be contrary to what a regulator or regulation deems appropriate. However, the unfettered behavior in the free markets, which some may argue is more risky, tends to create a diversified set of positions, which helps prevent industrywide failure. All participants don't put all their eggs in the same basket, as companies tend to follow different tactics to gain a competitive edge. Regulations are intended to force companies to all behave in a similar manner with respect to the regulated area, so now everyone has their eggs in the same basket. Thus, the industry as a whole may be more vulnerable to systemic changes that are not anticipated by the regulation.

We'll talk more about how governments and regulations can impact the economy and your investments in the next chapter.

Application Example: Corporate Funding

Government also impacts the way businesses fund themselves and provide investment returns to shareholders. To grow, a business can either borrow money or take equity investors. The U.S. tax code affects the cost of debt funding relative to equity funding because, much like with a mortgage, interest payments on money a business borrows are tax deductible, whereas payments to equity investors to compensate them for the money they have invested are not. This can make debt funding relatively less expensive for the business than equity funding. In addition, the government taxes interest payments (for a loan) to lenders differently than dividends paid to equity investors. Investors must seek out the best returns available for any given level of risk. Thus, taxation that lowers net returns impacts where investors are willing to put their savings and what kind of returns they will demand for their investments. In Chapter 9, we will show you how the different tax treatment for interest payments versus dividends paid to shareholders has affected corporation behavior, particularly in recent years with interest rates at historical lows.

Application Example: Government Debt

You have also probably heard about the large and ever-growing federal debt. As of the end of 2014, the federal public debt was over 100 percent of GDP and it remained that way exiting 2015—just check one of our favorite sites on the Internet, USDebtClock.org. As you look at government debt, keep in mind that the largest holders of government debt throughout most of the world are banks. This is particularly true throughout much of Europe. One of the main holders of sovereign debt is the banking sector. Now here's the thing: As we mentioned in the earlier example and as you'll learn further in the next chapter, banks are highly regulated. When you give them money through deposits like a checking account, they turn around and either give that money to someone else in the form of a loan or invest in other assets like Treasury bonds. They get extra special bonus points, detailed in the next chapter in the section on fractional reserve banking, for holding sovereign debt, similar to the way, as we discussed in the earlier example, they received extra bonus points for holding mortgages guaranteed by Fannie Mae or Freddie Mac. So they can either lend to someone buying a home, starting or growing a business, or to the government. Keep that in mind when you look at the growth of sovereign debt and bank regulations; in a way they are competing against you for loans from banks.

Getting back to vector, velocity, and magnitude, when you look at changes in government spending, deficit, debt, taxes, and their impact on various sectors of the economy on down to companies, it boils down to looking at the direction of the change, the degree of change, and just how impactful it will be. An increase in cigarette taxes, while painful for the smoker, would not impact the overall economy. Removal of the home mortgage deduction would have a significant impact on home prices, which, given the large portion of the household budget allocated to housing, would have a far bigger impact on the overall economy.