Bigger Houses, Smaller Lots

The housing crisis of the last decade has not slowed the steady trend towards bigger houses with more bathrooms and multicar garages but these more spacious new homes are now on smaller lots.

The U.S. Census Bureau collects data on characteristics of new housing for the Department of Housing and Urban Development using the Survey of Construction. Annual data from the survey show that the proportion of single-family homes completed in 2015 with four or more bedrooms and three or more bathrooms has been on the rise since 1987. The share of new homes that are 3,000 square feet or more has been increasing since 1999. The same upward pattern applies to homes that are even larger — 4,000 square feet or more.

The survey shows that the median size of the 648,000 single-family homes completed in 2015 was 2,467 square feet. Of those new homes, 47 percent had four or more bedrooms compared to 35 percent five years earlier and 38 percent had three or more bathrooms compared to 25 percent.

Despite the trend towards living large, homes are going up on smaller lots. The share of homes completed on lots under 7,000 square feet has been growing since 1999. The vast majority of homes are built inside metropolitan areas.

Check out this interactive graphic on new single-family homes in 2015.

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America’s Age Profile Told through Population Pyramids

Author: Luke T. Rogers, Statistician/Demographer, Population Estimates Branch

Today, the U.S. Census Bureau released population estimates by age, sex, race and Hispanic origin for the nation, states and counties. These data enable us to learn about the U.S. population, including its age structure. Age structure is often displayed using a population pyramid. You can learn about the makeup of the U.S. population as a whole by looking at its population pyramid, below.

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An examination of this population pyramid reveals peaks and valleys. Why do the age groups have different size populations? Let’s examine the baby boom generation (50- to 69-year-old population). During the baby boom, the U.S. population rapidly grew because of high fertility rates following World War II. This population surge is reflected in the U.S. population pyramid as an outward bump in those baby boomer age groups. As the baby boomers grew up, many had kids of their own. The children of these baby boomers, frequently called the echo boomers or millennials, can be seen as a similar bulge in the 15- to 34-year-old population. Even with lower fertility among baby boomers compared to their parents’ generation, the birth of the millennials still represented a mini population boom, simply because there were so many potential boomer parents.

Looking at the U.S. population pyramid, we also see how noticeably larger the older female population (age 80 and over) is when compared to the male population at the same ages. This size differential stems from the fact that, generally, women live longer than men do. As a result, older women tend to outnumber older men. Women’s higher share of the older population is one of the more consistent features in almost all population pyramids, regardless of region or level of geography.

We can see more detail when we study population pyramids for smaller geographies, such as states, metropolitan areas or counties. Let’s take a tour of a few interesting examples. We start with West Virginia, which experienced natural decrease (more deaths than births) between 2014 and 2015.

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West Virginia’s aging population is visible in the shape of the population pyramid above. Since some of the age groups near the top are wider than those at the bottom, we can tell that there are more older people than younger people. Similar to what was seen in the U.S. population pyramid, the baby boom generation is visible and distinct from the rest of the population. Meanwhile, the number of people of childbearing age (those roughly between the ages of 15 and 49) is comparatively smaller. This shape often leads to natural decrease because of deaths to the larger older population and lack of births from a smaller young population. Many rural areas have long-standing trends of natural decrease and a loss of people through migration, making age structures like this one common. In West Virginia, there have been seven consecutive years of natural decrease and three consecutive years of net migration loss.

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On the other end of the population pyramid spectrum is the Salt Lake City metropolitan area in Utah. Here we see growth from both positive net migration and natural increase (where more people are being born than are dying). Looking at Salt Lake City’s population pyramid, you can see that there are relatively more people that are of childbearing age, especially when compared to the older population. Additionally, a long history of natural increase is evident in Salt Lake City’s age structure, since it gradually gets wider toward the bottom. As high fertility levels and net migration gains persisted, the base of Salt Lake City’s population pyramid widened over the years, resulting in the shape it has now.

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Births and deaths aren’t the only components that influence the shape of a population’s age structure. Net migration (both domestic and/or international) can play a major role as well. Ouray County, Colo., for example, is defined by the Economic Research Service at the U.S. Department of Agriculture as a recreation county. Roughly speaking, being a recreation county means that the number of people who make their livings through recreational activities and the number of seasonal housing units are both relatively high. Recreation counties generally have similar age structures to one another. In Ouray County’s case, you can see the proportionately large share in the 50- to 74-year-old age groups. Like many recreation counties, in Ouray County, the primary driver of population growth is net domestic migration. Much of that growth was in the 65-and-older population, which implies that a substantial number of people are retiring in Ouray County.

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We can contrast the “older” age structure in Ouray County, Colo., with Centre County, Pa., which contains a large university. As can be seen in many college counties, Centre County has a noticeable spike in the 15- to 24-year-old population. Centre County gained residents between 2014 and 2015 from both natural increase and net migration, but — like Ouray County — net migration was the primary driver of its population increase. In the last year, Centre County experienced a decrease in the population under age 25 and an increase in the 25-and-older population. Despite these changes, the college-age population is a distinctive feature in Centre County’s population pyramid.

chart 6a

Areas with large military installations can also have unusual age structures. Take, Christian County, Ky., for example. Christian County is the location of part of a large military installation. We see the impact on the population pyramid as a disproportionately large male 20- to 29-year-old population relative to all other age and sex groups. In Christian County’s case, the resident population decreased slightly in the last year due primarily to a net loss of domestic migrants. Specifically, while the 18-to 24-year old and 65-and-older age groups increased slightly in the last year, this increase was offset by the loss of people ages 25 to 64 and children under the age of 18. As a side note, a similar kind of age structure to Christian County’s is seen often, but in a very different kind of place. Aside from the bulge of young children (less than 10), the age structure seen in Christian County would resemble the shape of counties with proportionally large incarcerated population.

As you can see in Christian County, and the rest of the U.S., age structure tells us a lot about an area’s population and how it changes over time.

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Majority of Workers Take Health Insurance Offered by Their Employers

Written by: Joelle Abramowitz, Economist, Social, Economic and Housing Statistics Division

New statistics from the U.S. Census Bureau show that in 2015, 78.8 percent of employees worked for an employer who offered insurance to any of its employees, 71.0 percent of workers were eligible to take offered coverage, and 54.3 percent took the coverage offered by their employers.

Employer-Sponsored Insurance

The most common reason cited for not taking employer-sponsored coverage was being covered by another insurance plan (11.5 percent of workers), followed by not working enough hours per week or weeks per year to be eligible for coverage (5.3 percent) and the coverage being too expensive (5.0 percent). The percent of respondents who reported not working enough hours per week or weeks per year to be eligible for coverage and percent of respondents reporting the coverage being too expensive as the reasons for not taking employer-sponsored coverage are not statistically significantly different from each other.

The new data on the offer and take-up of employer-sponsored insurance were collected as part of the 2014 and 2015 Current Population Survey Annual Social and Economic Supplement (CPS ASEC). The 2014 and 2015 research data files are available here.

The new questions are asked of respondents who were employed but did not have employer-sponsored coverage. The questions ask: 1) whether their employer offered coverage to any of its employees, 2) whether they were eligible for that coverage, if offered, 3) why they were ineligible, if offered and ineligible, and 4) why they chose not to take the coverage, if eligible. The questions refer to current coverage at the time of interview, covering February through April of the survey year.

Comparing estimates over early 2014 and 2015 shows an increase in the proportion of workers offered coverage by their employers (0.5 percentage points), as well as in the proportion of workers who were eligible to take offered coverage (0.9 percentage points).The increase in the proportion of workers offered coverage by their employers is not statistically significantly different from the increase in the proportion of workers who were eligible to take offered coverage. However, the data also show a decrease in the proportion of eligible workers who took offered coverage (1.5 percentage points), and, as a result, the proportion of all workers taking coverage remained stable over the period. For workers who reported that they did not take employer-sponsored coverage because they had coverage through another plan, the proportions with Medicaid, direct purchase, and a combination of private coverage types increased, while the proportions with military coverage and with dependent employer-sponsored coverage decreased.

The new questions on the offer and take-up of employer-sponsored insurance were added to the CPS ASEC as part of the 2014 redesign of its questions on health insurance. These new questions represent a significant data development, as they provide a large sample of employee-level data, complementing firm-level data available from other outlets. Similar offer and take-up questions were originally asked by the Bureau of Labor Statistics in the CPS Contingent Worker Supplement, but have not been asked since 2005.

For more information on these estimates and changes in the offer and take-up of employer-sponsored insurance, see New Estimates of Offer and Take-up of Employer-Sponsored Insurance. For more information on health insurance coverage in general, see the Census Bureau’s Health Insurance website.


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After Hurricane Katrina: Where Are They Now?

Population and housing estimates from last decade show how Hurricane Katrina affected Gulf Region

Written by: Sarah Gibb, statistician/demographer, Population Division

As you might know, we released the population estimates for cities and towns last week. However, following Hurricane Katrina in August 2005, the U.S. Census Bureau did not release these estimates for four Mississippi Gulf Coast communities— Bay St. Louis, Long Beach, Pass Christian and Waveland in 2006. The cities sustained severe damage from Katrina, and the impact to their populations and housing stock could not be reliably measured.

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In the aftermath of the storm, the Gulf Coast would face many years of rebuilding, and learning how populations were rebounding would be critical for community leaders. For the Census Bureau, producing population estimates for places where many homes had been destroyed and people displaced presented a unique but vital challenge.

Between 2006 and 2009, the Census Bureau used a variety of methods and data sources, including data from the U.S. Postal Service, to estimate the impact to the population and housing stock in the counties and parishes hit hardest by the hurricane.

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In 2008, we used the number of active utility connections to produce a complete time series of housing and population estimates for these cities, going back to 2006. By 2009, the Census Bureau had resumed the pre-2006 methods for estimating housing units and populations for almost all cities and towns across the country. We estimated 2006 county and parish housing units in Orleans and St. Bernard parishes by first calculating the ratio of the 2006 household population to the 2005 household population. We applied the ratio to the 2005 county housing unit estimate to produce the 2006 estimated housing.

The population and housing unit estimates produced last decade, along with the 2010 Census counts and the 2015 estimates released today, provide a basis for understanding how Hurricane Katrina affected the Gulf Coast, and in particular the four Mississippi cities discussed in this blog.


Bay St. Louis — On July 1, 2005, the population stood at 11,287. Just one year later, it had declined by more than 2,000 people, or about 18 percent. Its population remained flat through 2010 but recovered over the next five years, increasing by about 2,800 (30 percent) to 12,030, or about 700 more people than in July 2005, before Hurricane Katrina. Of the four cities we looked at, it was the only one to surpass its pre-Katrina population.

Long Beach — Prior to Hurricane Katrina, Long Beach numbered 16,855, making it the largest of the four cities in terms of population. It also had the largest numeric loss after the storm. By July 1, 2006, its population had dropped by a little more than 2,200 (13 percent). By 2010, the city’s population had recovered to 14,790, or approximately 88 percent of its population before the hurricane. Like Bay St. Louis, the city of Long Beach saw its population increase from 2010 to 2015. Ten years after the hurricane, the city remained about 1,300 people shy of its pre-Katrina population with a population of 15,555.

Pass Christian — About a year after the hurricane, Pass Christian’s population had dropped 15 percent from its pre-Katrina estimate of 5,845, putting it just under 5,000 people. Its population continued to decline until 2010, when it reached a low of 4,613. From that point forward, however, the trend reversed and on July 1, 2015, the population estimate reached 94 percent of its pre-Katrina level.

Waveland — Of these four Gulf Coast communities, Waveland had the largest percent decrease in population in the year after Hurricane Katrina. On July 1, 2005, Waveland’s population was 7,849. A year later its population had declined by 18 percent and would remain mostly unchanged over the next four years. Between 2010 and 2015, the city’s population declined by another 40 people. Its population on July 1, 2015, was at about 82 percent of its population 10 years earlier.

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Housing Units

Every year, the Census Bureau releases population estimates for cities and towns across the country, and housing unit estimates for the nation, states, and counties. Of the four cities discussed previously, Bay St. Louis and Waveland are in Hancock County. Long Beach and Pass Christian are in Harrison County.

Hancock County — On July 1, 2005, Hancock County had 24,179 housing units. About one year after Katrina, it declined by about 7,000 housing units, or 30 percent of its housing stock. By 2010, the housing stock had returned to approximately 90 percent of its pre-Katrina level. As of July 1, 2015, Hancock County was back up to 24,083 housing units, a mere 96 shy of where it stood 10 years earlier.

Harrison County — On July 1, 2005, Harrison County had 88,281 housing units, nearly four times as many as Hancock County. Almost a year after Hurricane Katrina, Harrison County’s housing stock decreased by more than 14,000 housing units, or about 16 percent of its housing estimate before the hurricane. Between July 1, 2006, and April 1, 2010, the reference day for the 2010 Census, almost 11,000 housing units were added, an increase of about 15 percent. By July 1, 2015, it had 90,749 housing units, about 2,500 more than it had 10 years earlier.

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These estimates reflect several years of special processing to produce a time series that accurately reflects the impact Hurricane Katrina had on these communities.

If you would like to continue to explore these estimates, or examine other population trends in the United States, please go to<>. You may also wish to contact the State Data Center of Mississippi for their perspectives on the population and housing trends highlighted here. To learn more about the characteristics in these areas, check out data from the American Community Survey and economic data.

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A Look at the Nearly 1 Million Who Ride Their Bikes to Work in the U.S.

The proportion of workers who commute by bicycle has remained small, but relatively steady over the last few decades. The number of  bike commuters, which has grown to nearly 1 million, has increased at roughly the same rate as the labor force, which has not been the case for some modes of commuting such as transit and walking.

Number-Bicycle-Commuters v2

Brian McKenzie, a sociologist in the U.S. Census Bureau’s Journey to Work and Migration Statistics Branch, shares some additional insights into bike commuters:

Are men or women more likely to bike to work?

The number of men who bicycle to work still exceeds that of women, but the gender gap is narrowing. Women workers made up 28 percent of bike commuters in 2014, up from about 23 percent in 2006. Men made up about 77 percent of bicycle commuters in 2006, compared with 72 percent in 2014.


Do bike commuters tend to be younger?

Yes, the bicycle commuting rate generally declines as age increases. Younger workers not only had the highest rate of bicycle commuting but have also had  comparatively large gains in bike commuting since the mid-2000s. Between 2006 and 2013, the rate of bike commuting for ages 16 to 24 increased from 0.8 percent to 1.1 percent. The rate also went up for those age 25 to 29. The highest rates tend to be in small college towns. For example, 9.7 percent of workers in Berkeley, Calif., and 23.2 percent in Davis, Calif. — both  home to University of California campuses — biked to work in 2014.

Where do you see bike commuting on the rise the most?

Much of it is in metropolitan areas, specifically in cities, where bicycle commuting has increased over the last decade, both in number and as a proportion of all workers. The proportion of bicycle commuters in principal cities nearly doubled from 0.7 percent in 2006 to 1.2 percent in 2014. Many cities have invested in infrastructure to accommodate bicycle commuting. Starting in about 2010, bike-sharing systems started showing up in cities, large and small. Many cities have also invested in dedicated bicycle lanes and other elements of the built environment that make streets more bicycle friendly. Portland, Ore., for example, increased its bicycle commuting rate from 4.2 percent in 2006 to 7.2 percent in 2014. In Minneapolis, the rate went from 2.5 percent to 4.6 percent during that period.


Cycling Commuters graphic


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