Who Doesn't Want to Own a Home?

by Rachel Bogardus Drew
Post-Doctoral Fellow

In a previous post, I described recent research about drivers of decisions to own homes, with emphasis on the role of behavioral factors. That research confirmed that there is a widespread and deep-seeded preference for homeownership in the U.S., founded largely on beliefs in the benefits of owning, such as wealth development and better outcomes for children. Yet for all homeownership’s assumed advantages, 35 percent of households still rent, and of them, 20 percent report no intentions to buy in the future. This begs the question: who doesn’t want to own a home? Some follow-up research on this topic seeks to answer that question.

We know from my prior research that some demographic groups are less likely to expect to own in the future, including whites, older renters, those with lower incomes, and those without families (Figure 1). Even after controlling for personal characteristics, though, race, age, and income remain important predictors of future tenure intentions; renters over 55 years old, for example, are 28 percent more likely to always rent relative to those under 35 years old. Yet regression analyses based on demographic variables alone can account for only about 10 percent of the variation in renters’ future tenure plans. Thus we must consider some attitudinal factors when seeking to understand what drives intentions to rent for the long term.

Note: Sample includes renters ages 25-64 who plan to move in the future. Bars are the % shares of each socio-demographic subset within the sample that expect to always rent. All characteristics were significant in regression analyses of intentions to rent (results not shown).

Source: Fannie Mae National Housing Survey, June 2010-December 2012.

There are many reasons why someone might not plan to buy a home in the future: perhaps they prefer the flexibility and convenience of renting, which not only allows them to change residences easily but also frees up money that would otherwise be used for a down payment to invest or spend on other needs and desires. Or they may doubt their ability to qualify for or afford a mortgage, and thus do not consider owning to be an option. Or maybe they are pessimistic about the likelihood of receiving many of the assumed financial and personal benefits from owning, particularly given recent events in housing markets.

The same survey data that yielded only weak results with respect to demographic differences in renters who do not intend to buy homes in the future also includes some questions about their preferences and reasons for renting. When asked the primary reason why they currently rent, for example, a third of renters that plan to always rent said they enjoyed the reduced hassle and stress of renting versus owning. Yet when asked why they do no plan to own in the future, financial constraints were a more common response than lifestyle benefits (Figure 2). Specifically, more than half of renters said a major reason they do not intend to buy is because they think they cannot afford it or their credit is not good enough. A similar share, when asked in 2010-2012, said they did not think it was a good time to buy. Reduced maintenance, flexibility to move, and other opportunities for investment, meanwhile, were indicated as a major reason by less than 40 percent of respondents who plan to stay renters in the future.

Note: Bars are the % shares of the sample expecting to always rent that report a major reason they do not own. 

Source: Fannie Mae National Housing Survey June 2010-December 2012.

These results suggest that about a third of renters, or 10 percent of all households, rent because of lifestyle and personal preferences. That their reasons appear to be largely idiosyncratic, rather than systematically related to their personal characteristics, further indicates that those who rent by choice do so in spite of strong social biases towards ownership that encourage the remaining 90 percent of households to view owning favorably. More than half of lifetime renters, however, see their tenure options as constrained, either by their own financial circumstances or by macroeconomic conditions. With mortgage lending remaining tight, home prices rising in many markets, and income growth still sluggish (especially for low-income households), these renters are unlikely to change their tenure plans anytime soon. 

How Does Geographic Diversity in Age Structures Impact Housing Market Dynamics?

by George Masnick
Senior Research Fellow

As the youngest of the baby boom generation has now turned 50, there is much talk about the overall aging of the U.S. population. But recently released Census Bureau population estimates for states and counties tell a more nuanced story about the diversity in age structures in the U.S.  The census release notes that the oldest county (Sumter County-FL) has a median age of 65.5, while the youngest (Madison-ID) has a median age of 23.1.  Quite a difference!  Other counties among the oldest include Charlotte-FL (57.5), Alcona-MI (56.9), Llano-TX (56.9), and Jefferson-WA (55.9).  The five youngest counties also include Radford City-VA (23.3), Chattahoochee-GA (23.9), and Harrisonburg City-VA (24.2), and Utah County-UT (24.2).  The U.S. median age is 37.6. 

We should perhaps not be surprised that the county with the oldest population is in Florida, or that Idaho and Utah, with their Mormon influences, should have the counties with the youngest populations. But what is going on in Michigan, Texas, and Washington counties to rank among the oldest, and in Georgia and Virginia to produce places with the youngest populations?

There are three main demographic factors that influence the age structure of a population: 

1. Domestic migration patterns of both young adults and the elderly; 
2. Settlement patterns of international immigrants; 
3. Levels of fertility of both the immigrant and native born populations.  

Differences in life expectancy could also influence age structures if those differences are large.  For states and counties in the U.S., however, mortality differences are not sufficient to affect differences in median age. 

Places with net domestic out-migration of young adults, and/or in-migration of elderly will be older (younger if these migration patterns are reversed).  Florida is a destination state for retirement migration, as are North Carolina, Arizona, and other warm weather and low-tax states in the south and west.  Maine, West Virginia and many rust belt and Great Plains states lose young adults on net, so places in these states will also have an older age structure.

Immigrants tend to be young and have higher fertility compared to the native-born, so places that are immigrant destinations will be younger.  While states on the coasts and along our southern border still attract the majority of immigrants, states in the interior have increasingly become immigrant destinations as immigrant networks have spread beyond gateway states. 

Finally, fertility levels are the primary determinant of a population’s age structure.  When fertility is above replacement (more children born than reproductive-age adults in a family) the population pyramid is broader at the base, and median age is lower. The pyramid becomes more mushroom-shaped when fertility is below replacement, and median age is higher. 

When the population unit is relatively small, as with most of the counties listed above, these demographic factors can reinforce one another and create extreme values.  For larger units of population, such as large counties, metropolitan areas and states, differences should be less extreme, but they can still be significant. 

The population estimates from which median ages were calculated contain detail by race/Hispanic origin and sex, allowing us to examine the percent minority as a surrogate for the influence of immigration and the boost to overall fertility levels that immigrants and native-born minorities provide.  We can also look at a measure of recent total fertility by calculating the ratio of children age 0-4 to women in the primary reproductive ages of 20-44.  We cannot get a direct estimate of net domestic migration by age group from the published population estimates, however.  

The table at the bottom of this post, constructed from the 2013 population estimates, ranks states on median age, percent minority, and fertility.  While Florida has the county with the highest median age, the state as a whole is only the 5^th oldest, surpassed by Maine, Vermont, New Hampshire and West Virginia.  The lower the percentage minority in a state, the higher the median age (Figure 1). The oldest states are those where young immigrants and native-born minorities with higher fertility have not settled.  Maine, Vermont, West Virginia and New Hampshire rank the lowest on percent minority. In addition, the lower the total fertility rate, the higher the median age (Figure 2). This second relationship is the stronger of the two that are graphed, and the relationship holds fairly well across the entire range of fertility (discounting DC as an outlier).  The New England states collectively are also near the bottom of the ranking on total fertility.

Source: U.S. Census Bureau Population Estimates

Older states may be destination states for retirement migration, but can also have lost young adults from out-migration to states with bigger cities and more job opportunities.  For example, according to the 2012 American Community Survey, Maine gained 27,500 residents from other states during the previous year, but lost 38,500.  If most of the out-migration from Maine were young adults, the effect would be to increase the median age.

The youngest states, however, are more of a mixed bag.  Utah’s very high fertility level – the highest in the nation – is sufficient to secure its ranking as the state with the youngest median age. Utah is not completely lacking in diversity - its percent minority (20.3%) is just the 18^th lowest, but the total fertility rate in Utah is primarily driven by its non-Hispanic white population’s high rate of childbearing.  Alaska, the second youngest state, has a large minority population (mostly native Alaskans), as well as levels of fertility that are well above the U.S. average.  Its young ranking, however, is likely also determined by in-migration of young adults to work in energy and nature oriented jobs, and out-migration of the elderly to warmer climates.  The District of Columbia has achieved its ranking as the third youngest in all likelihood because of in-migration of young adults to work in Washington for a spell.  These adults are largely single, as suggested by DC’s extremely low fertility. But also contributing to DC’s young age structure is the fact that the percent minority is the highest on the mainland (64.2%).  Texas is the 4^th youngest state, both due to its high percent minority (56%) and high fertility.  Texas has received consistent growth from both immigrants and young domestic migrants in recent years.  The final state among the top five youngest is North Dakota, which has been the beneficiary of considerable in-migration of young adults to work in the booming energy sector in the western part of the state.  North Dakota’s fertility rate is also among the highest, attesting to the impact of a favorable economy on family formation.   

Geographic diversity in age structures has direct implications for housing market dynamics.  Places with younger age structures will require new construction to house young adults, both now and in the future.  If the young age structure is created by higher fertility, homes will need to be larger to accommodate larger families.  If the younger age is created by in-migration of singles, a different housing mix is required, at least in the short run. 

Places with older populations are expected to show a greater balance between supply and demand for existing housing.  An older age structure brought about by low fertility and out-migration of young adults will have less need for new construction.  This is especially true if the existing housing is located in places where young adults want to and can afford to live.  However, if future demand for existing housing by young adults or older in-migrants is not there, older adults may be less able to sell their homes, and we can expect higher rates of aging in place. In these places there would be a greater need for modification and upgrading of existing housing to help the elderly safely stay in their homes.  On the other hand, if the older age structure is primarily the result of in-migration of retirees, and if that in-migration is sustained, there will be more opportunities for new construction and for the elderly to sell their homes in order to adjust their housing needs.  

Source: 2013 Census Bureau population estimates for states and counties.

*Fertility Rate is the number of children age 0-4 per 1000 women age 20-44. 

Home Improvement Spending Continues Toward More Moderate Growth

by Abbe Will
Research Analyst

Reflecting the slow pace of recovery in the overall housing market, the home remodeling industry is expected to continue its path of moderating growth, according to the Joint Center's most recent Leading Indicator of Remodeling Activity (LIRA), released today.  The LIRA projects annual growth in home improvement spending to ease to 3.1% through the second quarter of 2015.

Stronger gains in remodeling activity are unlikely given the recent slowdowns we’ve seen in housing starts, sales, and house price gains. While the continued recovery in employment should ultimately keep the market on an upward trajectory,  remodeling is likely to see slower growth rates moving into 2015.  Growth in home remodeling activity continues to hover around its longer-term average of mid-single digit gains. Even though the housing market overall has been lackluster, many areas of the country remain economically healthy and remodeling contractor sentiment remains high.

NOTE ON LIRA MODEL:  An important change was made to the LIRA estimation model beginning with the first quarter 2014 release. With the upheaval in financial markets in recent years, the traditional relationship between interest rates and home improvement spending has significantly deteriorated. As a result, long-term interest rates have been removed from the LIRA estimation model.  For more information on the implications of this change, please read our blog post from April.

For more information about the LIRA, including how it is calculated, visit the Joint Center website.

LBJ’s Biggest Housing Program that No One Remembers

by Alex von Hoffman
Senior Research Fellow

Now that we have reached the half-century mark since President Lyndon Johnson began passing legislation to achieve his vision of a Great Society, it is worth remembering one momentous law that has been largely forgotten: the Housing and Urban Development Act of 1968. When he signed the act, LBJ declared it to be “the most farsighted, the most comprehensive, the most massive housing program in all American history.” Truly, its goal was breathtaking: to replace within ten years every slum dwelling in the country by building six million homes for low- and moderate-income families.

The 1968 act forever changed the politics and policy of low-income housing in this country, and for that reason it has long fascinated me. A few years ago I published my early research on the origins of the 1968 housing law as a working paper, and more recently have written about the causes and effects of the act for the journal Studies in American Political Development in the article “Calling upon the Genius of Private Enterprise: The Housing and Urban Development Act of 1968 and the Liberal Turn to Public-Private Partnerships.”

President Lyndon Johnson signing the 1968 Housing and Urban Development Act
(LBJ Library photo by Donald Stoderl)

The great accomplishment of the 1968 act was to shift housing policy toward programs that used the private sector, not government, to create and run low-income housing. Until the law was passed, public housing was the nation’s principal social-welfare program.  The public housing program dated from the 1930s and, as a creation of the New Deal, used government agencies to develop, own, and manage apartments that were rented to low-income people.  In the 1960s, few federal programs used private developers to provide social housing, and those that did had produced only small numbers of dwelling units.

And then came the long hot summers.  Violent riots rocked the African-American ghettos of American cities, leaving hundreds dead, thousands injured, and tens of millions of dollars of damage from burning and looting.  The situation called for action, especially in housing.  Most observers – including the famous Kerner Commission (officially named the National Advisory Commission on Civil Disorders) – were convinced that a major reason that African Americans were rioting in the streets was that they were condemned to live in ghetto slums.

12th Street and Clairmount, Detroit, July 23, 1967 (Detroit Free Press)

Seeking a way to right this wrong, President Johnson established a blue-ribbon committee to rebuild the slums of America. But the president made clear that he wanted recommendations that would fall outside the traditional confines of the government-run programs that he had long championed.  “We should call upon the genius of private industry,” LBJ asserted, “to help rebuild our great cities.”  

At the time, executives from hundreds of businesses were volunteering to help solve the nation’s pressing urban and social problems.  The president welcomed these “public-private partnerships,” in part because the soaring costs of the Vietnam War prevented him from asking Congress to foot the entire bill for his ambitious social programs. Thus, LBJ named the president of the corporate conglomerate Kaiser Industries, Edgar Kaiser, to head his housing committee, stacked the committee with business executives, and asked them to propose ways that businesses – not government – could rebuild the slums. 

Taking Johnson’s cue, Democratic Congressional leaders, Robert Weaver – by then secretary of the new Department of Housing and Urban Development (HUD) – and the Kaiser committee worked together to write the ambitious social housing legislation to be carried out by the private sector.  In the summer of 1968, Congress passed the bill by overwhelming margins.

Remarkably, the once bitterly divided housing field came together to support the bill.  For decades, liberal interest groups had fought ardently to promote public housing, while industry trade associations fiercely opposed public housing at every turn.  Now liberals recognized that public housing was politically unable to generate six million new low-income housing units and agreed to try a new approach.  The industry trade groups naturally approved of the 1968 act’s private-sector programs.  After all, the officers of the National Association of Home Builders had worked with the Kaiser committee, the Democratic congressional leaders, and HUD to shape the bill’s provisions.  This political truce proved to be a historic turning point: since the 1968 liberal reformers and housing industry leaders have worked together to lobby the government for low-income housing. 

The 1968 act contained three major housing programs.  Perhaps the most successful was the rental housing scheme, called Section 236.  It provided private developers of low-income housing with financial incentives, including subsidizing the interest rate they paid on their mortgages.  By end of the 1970s, this rental program had encouraged the development of about 540,000 apartments, which exceeded the output of the public housing program. The financial incentives were poorly conceived, however, and in 1974 the federal government replaced the program with the better known and even more productive Section 8 program, which relied on the simpler mechanism of rental assistance to promote new construction. In 1986, the government enacted low-income housing tax credits, which has become the most productive of all the private sector type of housing programs.  As a result of the changes started by the 1968 act, today private organizations, about three-quarters of which are for-profit companies, develop most subsidized low-income housing in the United States.

Another of the 1968 act’s programs helped low-income families purchase their homes.  When a popular Republican senator from Illinois, Charles Percy, proposed a homeownership plan that utilized nonprofit agencies to counsel and first-time low-income home buyers, Senate Democrats and HUD officials pushed aside his plan and passed a large-scale building program that subsidized private lenders and promoted private home builders. In the 1970s this program produced 419,000 new homes for low-income families.  It was temporarily halted in the mid-1970s due to a scandal and terminated in 1987, but low-income homeownership returned in the 1990s to become a permanent fixture of American housing policy.

Madison Park Village in the Roxbury neighborhood of Boston;
an example of subsidized housing built after 1968 (Photo by Glenna Lang)

The Kaiser committee contributed the third component of the 1968 act’s social provisions for private housing finance.  It established the National Corporation for Housing Partnerships (NCHP), which raised money from corporate members and investors to provide working capital and, if needed, technical assistance, to developers of local housing projects.  It turned out that Section 236 tax incentives, especially the use of accelerated depreciation schedules, attracted the bulk of investors so that in ten years the NCHP helped develop a relatively meager 40,000 dwellings.  It pioneered the syndication of low-income housing, which resurfaced later and particularly after 1986 when syndicators found corporate investors to purchase low-income housing tax credits in return for equity investment in housing projects.

The Housing and Community Development Act of 1968 began a transformation of American housing policy.  At a time of national urban crisis, it brought warring housing interest groups together in a political alliance that has persisted ever since.  Moreover, the law turned away from government-centered public housing and firmly committed the federal government to using private-sector agents – especially for-profit businesses – to develop and run social housing.  Reflecting the thoroughness of this transformation, private developers now routinely redevelop public housing projects. As we commemorate the many landmark Great Society laws, don’t you think we should recall the 1968 housing act? 

The Changing Profile of Student Loan Debt: Who Has It, How Big is Their Burden, and How Much Education Did They Receive?

by Irene Lew
Research Assistant

In recent years, the Federal Reserve Bank of New York’s Consumer Credit Panel (FRBNY CCP), which provides quarterly data on outstanding loans using individual consumer credit report data acquired from Equifax, has been utilized extensively to highlight the dramatic growth in student loan debt. Indeed, according to the FRBNY CCP, the increase in student loan debt over the past decade is alarming: aggregate balances averaged $1 trillion in 2013, $775 billion higher than the annual average in 2003, and accounted for over a third (36 percent) of non-housing debt held in aggregate in 2013, up from just 12 percent in 2003.

Since data is released quarterly, the FRBNY CCP is useful for providing up-to-date numbers on current federal and private student loan debt levels. However, the CCP data does have several drawbacks: historical data on student loan debt is not available before 2003, the CCP’s sample is limited to households in which at least one adult has a credit report, only aggregate numbers on outstanding student loan balances are released quarterly to the public, and public information on the demographic characteristics of student loan debtors is limited.

In contrast, the newly released triennial Survey of Consumer Finances (SCF) from the Federal Reserve Board dates back to the 1980s, includes households without credit reports, and is publicly available as a micro-dataset with detailed information on student loan debt balances, as well as a variety of demographic and financial characteristics, including age, income, tenure, education level, race, assets, and other types of outstanding debt.

Both the CCP and the SCF indicate continued growth in aggregate student loan debt, but the SCF shows much slower growth in recent years than data from the FRBNY CCP. In 2013, the SCF’s estimate of $710 billion of aggregate student loan debt was 44 percent lower than the $1 trillion estimate of student loan debt cited by the FRBNY Consumer Credit Panel. The gap between the aggregate estimates of student loan debt from these two sources may be attributed to several factors. As this FRBNY paper points out, the SCF’s sample excludes those in institutions, which may lead to underreporting of debt held by students living in dorms and other institutional housing. Secondly, the SCF’s use of a single survey respondent as a proxy for household finances could result in underreporting of student debt held by adult children or other household members of which the respondent is unaware. However, given that only a decade’s worth of data on student loan debt is available through the CCP, the SCF is still a better dataset for analyzing education-related debt trends because researchers can track changes in student loan debt over a longer period of time under various economic conditions. As many borrowers in the SCF sample are interviewed ten years or more after taking on student loans, researchers are able to analyze the long-term impact of carrying student loan debt. Furthermore, given the greater availability of variables on demographic characteristics and other financial information, one can create a more robust profile of households with student loan debt.

Despite its overall lower estimate of student debt, the SCF still shows that over the past decade, it has become increasingly common for households across all age groups to carry student loan debt (Figure 1). Among households aged 20-29, 43 percent are carrying outstanding student debt, a slight uptick from the 41 percent share in 2010 and 14 percentage points higher than the share in 2001. At the other end of the age spectrum, 23 percent of households aged 40 to 49 and nine percent of those aged 50 and over, have student loan debt, more than double the shares of same-aged households in 2001.

Source: JCHS tabulations of Federal Reserve Board, Survey of Consumer Finances.

On the whole, hefty student loan balances are not common, but the share shouldering a substantial amount of debt has climbed steadily over the past two decades: in 2013, 17 percent of households with student loans had a balance of $50,000 or more, more than double the share in 2001 (7 percent) and nearly five times higher than the share in 1989 (Figure 2). And both younger and older households are saddled with higher student loan debt balances. In 2013, 15 percent of households aged 20 to 29 carried a balance of $50,000 or more, up from just 2 percent in 2001, and 14 percent of households aged 50 and over had a similar debt burden, double the share of same-aged households in 2001.

Note: Excludes households without student loan debt. Shares are based on values that have been adjusted to 2013 dollars using the CPI-U for All Items. 
Source: JCHS tabulations of Federal Reserve Board, Survey of Consumer Finances.

While recent reports, including this one from New America, have attributed the growth in student debt levels to those who are obtaining graduate and professional degrees, the SCF shows that households headed by a recipient of a graduate degree or higher made up 35 percent of those with $50,000 or more in outstanding student loans in 2013, down from 72 percent in 2001 (Figure 3). In fact, the most indebted households are now more likely to be headed by an adult who earned a bachelor’s degree or less. Among the most indebted households headed by an adult without a bachelor’s degree, those who started but did not complete college represented more than a third (37 percent) of this group. Furthermore, those under the age of 40 accounted for 57 percent of the most indebted households headed by an adult without a bachelor’s degree in 2013, though nearly a quarter of this group is aged 50 or over.

Note: Excludes households without student loan debt. Shares are based on values that have been adjusted to 2013 dollars using the CPI-U for All Items. Education level is for highest degree earned by head of household. Less than a bachelor’s degree includes households with a head who started but did not complete college, who earned an associate degree, or those whose highest educational attainment was a high school diploma, GED or less. Graduate degree or higher refers to households headed by a recipient of a graduate degree, doctorate or other professional degree. 
Source: JCHS tabulations of Federal Reserve Board, Survey of Consumer Finances.

Much of the discussion around student loan debt is around the idea that it is a major stumbling block in the housing recovery, inhibiting young households’ access to homeownership. However, other groups with rising student loan burdens—older households and those without a bachelor’s degree—have not garnered as much attention. The growing share of less-educated households with a significant amount of student loan debt is especially worrisome, given that this group is much less likely to earn sufficient income to meet their monthly debt obligations. We’ll be doing a more thorough analysis to address these issues over the coming months.