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The FRED® Blog

Homeowners slide and renters rise

For five hundred twenty-five thousand six hundred minutes each year, people have to live somewhere. And it looks like renting is becoming more popular.

The graph clearly shows the U.S. homeownership rate has steadily declined and that the rental vacancy rate has declined right along with it. So the two trends seem closely related, especially recently. But does a decline in homeownership mean homeowners are moving out of houses and into apartments? Not necessarily. So what is going on? At least two things. 1. The financial crisis: The recent economic downturn left many households wary of investing (or reinvesting) in a home. 2. Kids today: The younger generation seem less interested in living in the suburbs. In quite a few cities, St. Louis included, they seem to prefer to live where they work and spend leisure time. Urban commercial buildings are being converted to apartments to accommodate this increased flow of renters. The rental vacancy rate has still been declining, which means the pace of rental property construction hasn’t been fast enough to keep the rental vacancy rate steady. Be sure to check back with the FRED Blog in a few years to see where all this stabilizes.

How this graph was created: Search for “rental vacancy” and add the quarterly measure to the graph. Then use the “Edit Graph” section: Add a line by searching for “homeownership rate” and move the y-axis to the right for the second graph. Start the sample in 1965-01-01.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: RHORUSQ156N, RRVRUSQ156N

Show me older men: A picture of employment cycles and demographics

“It’s a recession when your neighbor loses his job; it’s a depression when you lose yours.”

Good old Harry Truman gets credit for this colorful adage, along with many others.* But as the top graph shows, the probability two persons remain employed depends on who they are. During recessions, the pool of older workers seems less likely to dwindle: Even during the Great Recession, employment of older workers (55 to 64 years old) declined moderately, while employment of prime-age workers declined more severely.

What gives? Older workers are closer to retirement and in good times may retire early. But a shock to their retirement savings, as in the recent financial crisis, may induce them to stay employed. Older workers also tend to work in more cognitively and less physically intensive jobs, which may be less cyclically sensitive. The younger segments of prime-age workers, especially those under 35, may be less attached to their firms and tend to switch jobs more frequently; they’re also more likely to have young children and higher home-production demands. If their employers are adversely affected by the business cycle, they’re more likely to lose their jobs and potentially have trouble finding new ones.

The bottom graph adds a wrinkle to this perspective: Older men and older women have different employment patterns. During the severe 1981 recession, the employment rate for men fell about 3 percentage points but the rate for women didn’t change. The same story played out in the Great Recession, when men’s employment rate fell by about the same magnitude and women’s again stayed constant. Given that most assets are owned jointly within a household (e.g., houses) and most older workers are married, an asset shock should affect both sexes similarly. Men and women, however, have a different occupational mix at all age groups. Clearly, these differences in employment are complicated. In fact, the data seem to follow another of Truman’s dicta: “If you cannot convince them, confuse them.”

* Truman also allegedly asked for a one-armed economist to avoid the typical “on the one hand…on the other hand” hedging of that profession, but we won’t dwell on that here.

How these graphs were created: For both graphs, search for “employment rate United States”; choose the series you want and click on “Add to Graph.” If you’re overwhelmed by the search results, narrow them by adding the search term “aged” or by playing with the tags in the side bar.

Suggested by David Wiczer.

View on FRED, series used in this post: LREM25TTUSM156S, LREM55FEUSM156S, LREM55MAUSM156N, LREM55TTUSM156S

FRED in North Korea

North Korea is likely the most isolated and secretive place in the world right now. Yet, at the time of this writing, FRED has 52 data series related to this country: Half the series are from the Bureau of the Census and relate to exports to North Korea from 26 states; the other half are from the World Bank.

Some series have zeroes for all observations, such as net migration and Internet users, which seems accurate given the conditions in North Korea. Some series look relatively normal, just as they do for other countries. And some series are just plain peculiar: Above is the youth unemployment rate, which we did not expect to be so high in this mostly command economy. Below is the net interest margin for banks, which is negative by a large margin, indicating a financial sector dominated by non-market forces.

How these graphs were created: Search for North Korea and explore the choices.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: DDEI01KPA156NWDB, SLUEM1524ZSPRK

You can build a house on paper, but you don’t always make it brick

In other words, housing permits don’t always equal housing starts.

So, say you want to build a house… You’ll need to plan for financing and contractor schedules, among other things. But first and foremost, you must apply for and be granted a building permit before you can start to build. FRED has time series for both building permits granted and housing starts. Given that permits and actual construction go hand in hand, you might expect the two series to follow each other closely if not exactly, with possibly a small delay between the two.

As the graph shows, the two series are well connected during booms, when there’s an upswing in construction. But the two series aren’t nearly as well connected when building activity contracts. The lesson here is that a building permit doesn’t guarantee a house will be built. If economic conditions worsen, for example, between the time you apply for a permit and the time you plan to build, you might decide to postpone or even scrap an approved project. It’s during those times when the housing starts series falls faster than the permits series.

How this graph was created: Search for and add the “housing permits” series to the graph. Then open the “Edit Graph” panel to add a line: Search for and add the “housing starts” series. Finally, shorten the sample period to allow for more detail—in this case, starting 2000-01-01.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: HOUST, PERMIT

Engel’s law is still good food for thought

If your income rose by 15%, would your spending also rise by 15%? Maybe. But would all your spending rise by that amount? Ernst Engel surveyed households and published his results in 1857: He found that spending on food did not rise in proportion to a rise in income. Food is clearly a necessity; we all need some. And households that become wealthier will likely increase spending on food to some degree. But the increase in food consumption will be proportionately less than the increase in income.

Engel’s law is remarkably consistent. For the U.S., we can simply take food expenditures in the national account and divide it by GDP. This ratio is pretty much in continuous decline, with the exception of recessionary periods when incomes drop more than usual from unemployment or reduced work time. Engel’s law has held steady for 160 years.

A primer on income elasticity of demand: Food in general is a “normal good,” which means its consumption rises as income rises. It’s a specific type of normal good, though—a “necessity good”—which rises as income rises, but less than one for one. A more formal description is that food has an income elasticity of demand between 0 and 1. Another type of normal good is a “luxury good”—for example, a yacht. Its consumption rises more than one for one as income rises, so its income elasticity of demand is above 1. Consumption of an “inferior good”—for example, bus tickets—actually declines as income rises. Its income elasticity of demand is below 0.

How this graph was created: Search for “food expenditure,” and you’ll see many price indices. To speed up your search, click on the “consumption” tag in the side bar. Once you add the series shown here, open the “Edit Graph” panel and another series to Line 1: GDP. Then apply formula a/b.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: DFXARC1Q027SBEA, GDP

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