Childless Households Have Become the Norm

by George Masnick
Fellow

In 1960 almost half of all households were families with children under 18.  Since then, the number has fallen to under 30 percent (Figure 1).  By definition, the declining share of family households with children exists because households without children have increased more rapidly (Figure 2).  There are many reasons for this trend: delayed age at marriage and later age at childbearing, smaller family sizes, higher divorce rates, and more couples choosing not to have children (Table 1).  The changes in each of these measures over the last few decades are quite striking. In 1960 the median age at first marriage was 22.8 for men and 20.3 for women, compared to 28.6 and 26.6 in 2012.  The share of households with four or more people in 1960 was over 40 percent, falling to just under 23 percent in 2012.  Women who were 25 in 1960 ended their childbearing years in the mid 1980s with only 8.5 percent of them remaining childless. Women born in 1960 finished childbearing in 2010 with nearly twice as many of them childless (16.3 percent). In 1960, only 13 percent of all households were single persons, but by 2012 that percentage had risen to 28. All of these trends result in households having fewer children and fewer households having any children at all. (Click charts to enlarge.)

Source: Current Population Survey March and annual Social and Economic Supplement, 2012 and earlier. Table FM-1.  Minor children numbers from Census Bureau's population estimates for July 1 of each year.

Source: Current Population Survey March and annual Social and Economic Supplement, 2012and earlier. Table FM-1

Source: Census Bureau Current Population Survey historical tables.

The interesting aspect of this long-term trend is that it continued in spite of the strong upswing in the sheer number of American children, which grew after 1990 (also Figure 1).  That increase is due to the largest baby boomers having their own children (the echo boom) and to childbearing by the flood of immigrants who arrived between 1985 and 2005.  (Note that in 2012, fully 87.5 percent of children under the age of 18 who have an immigrant parent were themselves born in this country.) 

To be sure, baby boomer and immigrant childbearing did increase the actual number of households with children.  For example, the number of households with children under the age of 18 increased from 33.3 million in 1985 to 38.6 million in 2012. This 5.3 million increase was far less than the 11.3 million increase in total number of children in the population over this period because many households with children contained two or more children under the age of 18.  More importantly, however, the increase in households without children surpassed the 5.3 million growth of households with children by a considerable margin.

Two key reasons for the recent increase in childless households have been the aging of the population and increasing longevity. The large baby boom generation (age 45-64 in 2010) is now entering the empty nest stage (at least regarding children under 18). Between 2002 and 2012, households with at least one child, headed by today’s 45-64 year old cohort, declined by 12.3 million. There are still 11.5 million 45-64 year old headed households with children, and most will become households without children over the next decade.  Furthermore, empty nest households headed by those over the age of 65 are surviving longer and longer, making it likely that the trend in the decline of households with children will continue well into the future.

Significantly, the decline in the number of households with children accelerated after 2007.  Much of the decline can be explained by the sharp drop in the number of births. Annual births rose from just over 4 million in 2001 to over 4.3 million in 2007, the highest on historical record, but then fell to just below 4 million in 2011.  The total fertility rate (births per 1000 women age 15-44) fell from 69.5  (a 17 year high) to 64.4, a decline of 7.3 percent over this same period. Both the decline in births and the drop in the fertility rate are linked to the decline in immigration that followed the Great Recession. Because newly arrived immigrants are concentrated in the childbearing ages, and because immigrants have higher fertility than the native born, the loss of immigrants has had a disproportional effect on declining fertility.  The effect of the Great Recession on lowering fertility among the native born is also of importance, but this decline could be temporary.  The echo boom generation began to turn 25 in 2010, and has most of its childbearing years yet ahead of it. A return to higher levels of immigration and/or a rebound in fertility could reverse the decline in number of births and ease the long-term decline in the share of households with children, but will not likely reverse it.

How Much Did LTVs Actually Rise During the Housing Boom?

by Chris Herbert
Research Director

The rise in housing prices that appears to be taking hold in many parts of the country is an important sign of recovery in the market. Among the many ways the upturn in prices is helping the housing market heal is by turning back the tide on the 100-year flood of underwater mortgages.  Still, even as reports from CoreLogic and Zillow document the progress in reducing the inventory of homes with negative equity, these same reports remind us that despite recent improvements there are still millions of housing units saddled with mortgage debt exceeding the value of the home. These homes serve as a warning about the risks of excessive loan to value ratios (LTVs) that are assumed to have become commonplace among homeowners during the housing boom. However, a review of data from the Survey of Consumer Finances (SCF) from the last 20 years finds that there was actually relatively little change in the distribution of LTVs through the boom years.  While outstanding mortgage debt did increase substantially, it essentially kept pace with the rise in home prices. The flood of underwater owners was thus less the result of a greater share of owners having little equity cushion and more the result of the tremendous collapse in housing prices.

Figure 1 shows  trends in the distribution of LTVs among all homeowners between 1989 and 2010, based on a comparison of total outstanding mortgage debt to owners’ estimates of the value of their principal residence. As shown, there was a fairly substantial increase in the average LTV between 1989 and 1995 from 27 percent to 34 percent. This rise reflects a combination of factors, including the greater tendency of households to hold mortgage debt in the wake of the 1986 Tax Act that gave preferential treatment to mortgage interest payments, the sharp fall in house prices in some areas of the country, and an expansion of mortgage lending that occurred as the economic boom of the 1990s began. However, between 1998 and 2007 the average remained largely unchanged at about 37 percent. Average mortgage debt did increase sharply over this period, from $67,000 to $111,000 (in constant 2010 dollars) but the average house value also increased substantially from $185,000 to $317,000. Even at the peak of the boom, the vast majority of owners still had fairly low LTVs.

Source: Joint Center tabulations of Survey of Consumer Finances.

Of course, these averages include a large share of households that continued to hold little or no mortgage debt. The stable average may result from a rise in high LTVs among some owners that is counterbalanced by plunging LTVs for those who did not tap their growing home equity. But there was also little change in the level of LTVs at the upper end of the distribution. As Figure 1 illustrates, at the 75^th percentile of owners LTVs also remained largely unchanged between 1998 and 2007 at roughly 67 percent. Even at the 90^th percentile of the distribution LTVs held steady at 86 percent.  Thus, even at the height of the housing boom the vast majority of homeowners had at least a 15 percent equity cushion, as they had continuously since the mid 1990s.

The data in Figure 1 includes homeowners of all ages, but it might be expected that highly leveraged homeownership was becoming more common among younger households who were more likely to buy homes during the boom years and take advantage of more liberal lending. Figure 2 shows the same distributions of LTVs for homeowners under age 30. While there is more sampling variability for this subgroup, there does appear to be more of a rise in average LTVs among this group than is true of all households. Between 1995 and 2001 the average LTV was generally a little above 60 percent, but rose to more than 65 percent in 2004 and 2007. A similar increase was evident at the 75^th percentile where LTVs reached about 90 percent during the boom years after having been closer to 85 percent during the 1990s. Still, the vast majority of young homeowners had at least 10 percent equity invested in their homes even when lending standards were most relaxed. There was more stability at the 90^th percentile of the distribution, where young homeowners had LTVs of between 95 and 98 percent consistently since 1989. At this upper point of the distribution young homeowners did have little equity in their home, but this was no different during the boom than it was 20 years ago.

Source: Joint Center tabulations of Survey of Consumer Finances.

As both Figures 1 and 2 illustrate, when the bottom fell out of the housing market after 2007 LTVs among homeowners shot up.  According to estimates from the SCF, 9 percent of all homeowners were underwater on their principal residence as of 2010, with the rate more than twice as high among those under age 30. But this sharp rise in LTVs was the result of the unprecedented fall in house prices and not due to an expansion of excessively high LTVs during the boom.

Still, there is no question that homeowners took on much more debt than was prudent during the boom. While outstanding mortgage debt may have been keeping pace with house prices, the level of debt was greatly outracing trends in incomes. This great expansion of credit decoupled from borrowers’ incomes certainly played a role in helping to inflate the housing bubble. The new Qualified Mortgage (QM) standard is designed to avoid this problem in the future by establishing an ability to pay standard for mortgage lending. Notably, the QM standard did not include restrictions on LTVs. However, the still to be announced rules defining the Qualified Residential Mortgage (QRM) may introduce limits on LTVs. While it is true that a greater equity cushion would have helped both homeowners and lenders avoid losses during the housing crash, the housing market has long been characterized by fairly high LTVs at the upper end of the distribution. These higher LTVs were not problematic as long as house prices were not subject to extreme swings. Imposing more stringent LTV requirements are of concern as they will curtail the ability of young households to get a start as homeowners—particularly the growing share of young minority households who have not benefited to the same degree from having parents build wealth through homeownership. Given these concerns, it may be best to have regulatory efforts focus on ensuring that borrowers can afford their mortgages as a means of introducing more stability in the mortgage system, rather than on setting standards for LTVs meant to withstand the next 100-year flood.

Are Renters Less Energy Efficient than Homeowners?

by Elizabeth La Jeunesse
Research Assistant

According to data from the Energy Information Administration, American renters use nearly a third more energy per square foot than homeowners. What accounts for this difference?

In part it’s because rental units are typically smaller, and are therefore more energy intensive. For example, a family in a small apartment needs a refrigerator, stove, and water heater the same way a family in a larger apartment (or a homeowner) does.  These things require a basic amount of energy, regardless of square footage.  Rental units also tend to be older; 75 percent of renters live in units built before 1990 while 68 percent of owners live in older units.

Source: US Department of Energy, Energy Information Administration, 2009 Residential Energy Consumption Survey.

That said, the above chart shows that the amount of energy used by renters can vary depending on whether their utility costs are fixed (built into their rent) or if they pay for utilities themselves.  As the chart illustrates, renters consume considerably more energy when some or all of their utility costs are fixed.  This shows the general tendency of people to consume more of something when there is no added cost for doing so. Such excess energy consumption drives up the amount of energy renters use overall, further accounting for the efficiency gap between owners and renters.  

Even so, renters who pay for utilities separate from their rent still use slightly more energy per square foot than owners.  This suggests a real, structural efficiency gap between rental and owner units. In fact, a recent study found that multifamily rentals in 2009 had 34% fewer energy efficiency features on average than other housing types. Consumer fuels and utility costs have risen over 50 percent over the past decade, outstripping overall inflation, which makes energy efficiency improvements (insulation, energy efficient windows, compact fluorescent lighting, HVAC upgrades, energy efficient appliances) appealing to people wanting to lower their energy bills.  But when tenants pay for their metered energy usage, a property owner’s incentive to perform energy efficiency retrofits is lower, since any cost savings will benefit the tenants, not the owner. Rental property managers also have less control over how their tenants respond to an energy retrofit (e.g. more efficient windows might still be left open in the winter).  These things can keep rental property owners from performing energy efficiency retrofits at the same rate as homeowners which, in turn, keeps energy usage by renters high.

The gap in energy usage between owners and renters suggests that there are real opportunities for savings through some combination of added incentives for property owners to make these investments in retrofits and greater incentives for tenants to conserve energy. Lowering energy use would have the additional benefit of bringing down the cost of rental housing at a time when more renters are paying very high shares of their incomes for housing as a new study by the National Low Income Housing Coalition shows. 

Nonprofits Play Key Role in Repairing U.S. Homes

by Abbe Will
Research Analyst

Private sector spending on improvements and repairs to U.S. homes is approximately $300 billion a year. Yet as a new Joint Center working paper shows, each year nonprofit organizations and public agencies are also investing resources into the rehabilitation and repair of the homes of America’s most vulnerable households—including the elderly, disabled, and those with low-incomes—who might not otherwise be physically or financially able to undertake critical home remodeling and repair projects themselves. Major nonprofits such as Rebuilding Together, Habitat for Humanity, Enterprise Community Partners, the Local Initiatives Support Corporation, and NeighborWorks America, as well as thousands of local community development organizations across the country, are filling a significant and growing need, largely unmet by the private sector, by investing considerable resources—financial, technical, and direct provision of services—to make homes safer, healthier, more energy efficient, and more accessible for disadvantaged households.

The recent foreclosure crisis and sluggish economy undermined years of efforts to stabilize and improve distressed neighborhoods in cities across the country, only adding to the need for nonprofit and public sector involvement. Until this past cycle, housing inadequacy—a measure of the physical condition of housing units—had been on the decline in the United States, largely due to the success of govern­ment housing policies and the growing affluence of the pop­ulation. Since the housing market bust, however, this trend has reversed with the number of moderately or severely inadequate homes increasing by 7% between 2007 and 2011 to 2.4 million units. Certainly the severe housing and economic downturn had a measurable impact on the quality of the nation’s housing.

While a comprehensive data source of home rehabilitation and repair activity by nonprofits and public agencies does not exist, this new Joint Center working paper provides some insight into the topic. Rebuilding Together, one of the nonprofits in the study, provides critical home rehabilitation and modification services to low-income homeowners through its extensive network of local affiliates. A member of the Joint Center’s Remodeling Futures Steering Committee, the organization provided support for an affiliate and homeowner survey that collected data on the various types of projects undertaken by their affiliates, as well as demographic and socioeconomic information about the homeowners served and their experience partnering with Rebuilding Together.

Recent spending on home repairs and replacements, as reported by participating households, suggests that many of the homes worked on by Rebuilding Together have seen significant under-investment over the years. While the average American homeowner spent $3,000 on home improvements and repairs in 2011, according to Joint Center analysis of the American Housing Survey, almost two-thirds of Rebuilding Together program participants reported having spent less than $500 on average in the past year—fully 80% less than the typical homeowner in the U.S. Indeed, according to estimates developed by Rebuilding Together affiliates and the Joint Center’s Remodeling Futures Program, the homes serviced by Rebuilding Together were so in need after years of deferred maintenance, that the average value of the rehabilitation and repair projects undertaken by Rebuilding Together was in excess of $6,000 per home, or twice the annual amount spent by the typical homeowner in the U.S.

Home improvement expenditures under the Rebuilding Together program in 2011 were heavily oriented toward exterior replacements and kitchen and bath improvements—projects that would produce the greatest gains in key program objectives such as health and safety concerns, accessibility, and savings in energy use. Typical projects included additions or replacements of steps, ramps, railings, grab bars, windows and doors, roofing, insulation, energy-saving appliances, as well as painting and plumbing and electrical repairs. In the end, Rebuilding Together participants reported significant improvements in health and safety concerns, improvements in accessibility, and energy use savings as a result of nonprofit involvement.

Source: 2011 Harvard JCHS-Rebuilding Together Household Survey

While a more precise estimate is unavailable, hundreds of millions of dollars are spent each year by nonprofits such as Rebuilding Together, community organizations, and public agencies. Their contributions not only improve conditions for residents, they also help preserve badly-needed affordable housing opportunities, stabilize and revitalize deteriorating neighborhoods—of special importance in recent years—and encourage neighborhood stability by helping long-term residents of the community to remain safely in their homes.

A Surge in Hispanic Household Growth? The Challenge of Interpreting Short-Term Trends in Datasets that are Occasionally Adjusted

by Dan McCue
Research Manager

Interpreting year-to-year changes in annual surveys from the Census Bureau can be a tricky business, especially around decennial censuses.  Because it is the largest and most comprehensive count of the population, after each new decennial census is released, the smaller but more frequently issued surveys available from the Census Bureau, such as the Current Population Survey (CPS), Housing Vacancy Survey (HVS) and American Housing Survey (AHS), are updated, or “re-benchmarked” based on the findings from the new decennial census.  Prior to this, these surveys were controlled to extrapolations based off of the prior decennial census. While it is inevitable that ten years of extrapolation can lead controls to drift off course, failing to recognize when and how datasets are re-benchmarked to correct for this drift can lead to misinterpretations about short-term trends.  The danger is that the re-benchmarking adjustment can be misinterpreted as an actual trend that occurred in a single month or year, rather than what it really is: a discontinuity in the data due to an adjustment made to correct the net sum of ten years of extrapolation errors that had accumulated in the dataset since the last decennial census.

Take for instance, the following data overview in a recent online article:

"The latest U.S. census figures, for June, show year-over-year Hispanic homeownership increased by 7.3 percent, from 6.2 million to 6.7 million. For black-owned households during the same time, the numbers dipped by 1.3 percent, from 6.3 million to 6.2 million. Likewise, whites' homeownership also saw a slight decrease of about 1 percent, from 58.4 million to 57.8 million." - National Journal

On its face, this data leads us to conclude that the number of Hispanic homeowners surged from June 2011 to June 2012, while at the same time the number of homeowners among both blacks and whites dropped significantly, and therefore without growth in Hispanic homeownership the overall number of homeowners in the US would have dropped significantly over the past 12 months instead of growing slightly as was reported.

However, the Census Bureau’s Housing Vacancy Survey (HVS) showed that both Hispanic and non-Hispanic homeownership rates dropped during the June 2011 to June 2012 period, a time wherein Hispanics also suffered higher than average unemployment rates. At first glance, the divergence in the two reports is puzzling. However, on the Census Bureau’s HVS website, there is a short but significant sentence under the “Changes in 2012” section of the Source and Accuracy of Estimates web page:   

“Beginning in the first quarter 2012, the population controls reflect the results of the 2010 decennial census.”  - HVS Source and Accuracy of Estimates

This note is important, because the distribution of occupied households by tenure, race, and ethnicity of households is based on these population controls.  Therefore, any changes in the number of homeowners by race and ethnicity that spans across the first quarter of 2012 is also incorporating change due to the shift in the distribution of households by age, race, and tenure that occurred with the re-benchmarking of the survey..

The adjustment to Hispanic households due to the re-benchmarking appears to be significant. Looking at the Hispanic share of households in HVS before and after Q1 of 2012, we can see that the re-benchmarking in that quarter led to a significant upwards adjustment that forms a discontinuity in this series (Figure 1).  The existence of a discontinuity is corroborated by data from the Current Population Survey, which re-benchmarked to the 2010 Census in 2011. The CPS Table Creator allows us to see the impact of the re-benchmarking directly by comparing the Hispanic share of households in 2011 under both 2000 and 2010 Census weights.  It shows that the 2010 census weights raise the Hispanic share of households a full percentage point, from 11 to 12 percent, compared to the 2000 census weights.  In short, this all suggests that results from the 2010 Census found that the 2000 Census-based population extrapolations had been underestimating Hispanic household growth in the 2000s, and therefore these household counts needed to be shifted upwards in 2012 as a correction.

Figure 1:  The Shift to 2010-Based Population Controls in Q1 of 2012 in the HVS Coincides with an Apparent Discontinuity in the Hispanic Share of Householders

Source: JCHS tabulations of US Census Bureau, Housing Vacancy Survey data.

With the change in population controls in the HVS in Q1 of 2012, the amount to which the shift in the distribution of households towards Hispanic households was underestimated incrementally over the last ten years gets corrected all at once, and gets attributed as change measured between Q4 of 2011 and Q1 of 2012.  And as we see in Figure 2, the quarterly change recorded in Q1 of 2012 has a huge influence over our view of the recent trend in household and homeownership growth by Hispanic ethnicity. 

Figure 2: Concurrent with the Switch to Census 2010-Based Population Controls, The First Quarter of 2012  Has a Large Influence on the Recent Trend in Hispanic and Non-Hispanic Household Growth 

Source: JCHS Tabulations of the 1995-2011 AHS

Without the ability to compare alternative HVS household counts for Q1 of 2012 under both 2000- and 2010-based population controls, it is difficult to determine exactly how much of the change in Hispanic and non-Hispanic households and homeowners in 2011 to 2012 was due to the re-benchmarking and how much was due to actual change measurable in the survey.  We refrain from presenting alternative scenarios here, but because the quarter is such an outlier, most assumptions to smooth or discount that quarter of data would conclude with much lower Hispanic household and homeowner growth and much higher growth among non-Hispanics over the past year.