Thursday, November 20, 2014

Housing Cost Burdens Continue to Strain Renters

by Ellen Marya
Research Assistant
The (somewhat) good news: according to the newly-released 2013 American Community Survey (ACS), housing cost burdens declined for the third straight year in 2013.  Last year, 39.6 million households spent more than 30 percent of their income on housing, down from 40.9 million in 2012 and a peak of 42.7 million in 2010.  Still, just over a third of U.S. households (34 percent) were cost burdened in 2013, including about a quarter of all homeowners (26 percent) and half of all renters (49 percent) (Figure 1).




Notes: Moderate (severe) burdens are defined as housing costs of 30-50% (more than 50%) of household income. Households with zero or negative income are assumed to have severe burdens, while renters paying no cash rent are assumed to be without burdens.
Source: JCHS tabulations of US Census Bureau, American Community Surveys.

Last year’s decline in the number of cost-burdened households, however, occurred almost exclusively among homeowners.  Nearly 19 million owners were cost burdened in 2013, down from 20.3 million in 2012.  The number of owners with severe cost burdens – paying more than 50 percent of income for housing – also slid, from 8.5 million in 2012 to 8.1 million in 2013.  The easing of owner cost burdens is due in part to a dramatic decline in median homeowner housing costs.  After surging during the housing bubble, inflation-adjusted owner costs have dropped to about 2.5 percent below their 2001 level (Figure 2).  Owner burdens are also down due to a significant reduction in the overall number of homeowners –  fully 294,000 fewer households in 2013 than 2012.  This decline in the number of homeowners for the third straight year (and the fifth time since 2007) suggests that many burdened owners dropped out of ownership, moving into the costly rental market.


Notes: Median costs and incomes are real  values adjusted using the CPI-U for All Items. Owner housing costs are first and second mortgage payments, property taxes, insurance, homeowner association fees, and utilities. Renter housing costs are cash rent and utilities.
Source: JCHS tabulations of US Census Bureau, American Community Surveys.

With many exiting ownership and new households forming, the number of renter households was up by 615,000 in 2013.  Indeed, a major reason why renter cost burdens remain persistently high is that the overall number of renters continues to grow.  Despite a slight decline in cost-burdened share, the sharp growth in renter households pushed the number with cost burdens up for the twelfth consecutive year, reaching 20.8 million in 2013.  Of these, about 11.2 million were severely burdened in both years.  Cost pressures also continue to drive burdens higher as over the past decade, renter costs have largely gone up, while renter incomes have declined.  As Figure 2 shows, real median renter costs in 2013 were about five percent higher than in 2001 while, even with modest income gains in 2013, median incomes were nearly 11 percent lower.  If past patterns hold and income growth remains stagnant, rental costs continue to climb, and affordable ownership stays out of reach, rental cost burdens will only continue to grow.

Tuesday, November 4, 2014

Why Does Mortgage Debt Continue to Rise Among Older Homeowners?

by George Masnick
Senior Research Fellow
According to the Federal Reserve Bank of New York, aggregate mortgage debt stood at $8.6 trillion in Q2 2014, down from its peak of $10.0 trillion in Q3 2008. Many have interpreted this decline as a sign that consumers have become chastened by the Great Recession’s bursting of the housing bubble and are voluntarily paying down their mortgage debt to more sustainable levels. For those thinking in such terms, I recommend a paper further analyzing the same Consumer Credit Panel data that produces the aggregate debt estimates just citedIn a masterful exercise, Fed economist Neil Bhutta concludes that the recent drop in mortgage debt has more to do with shrinking inflows than with expanding outflows, including mortgage defaults:

"While few borrowers, compared to prior years, have been increasing their mortgage debt, they also do not appear to be aggressively paying down their mortgages… It is therefore possible that many borrowers might actually be credit constrained (they would like to increase their debt, but cannot find a willing lender …).” (p. 3)

A critical limitation of the Fed’s Consumer Credit Panel data is that it includes very limited demographic information (only the age of the borrower). But Bhutta’s findings are supported by a recently released Census Bureau report on the growing wealth inequality in the U.S. that reports on trends in mortgage debt broken down by a wide variety of household demographic characteristics. These data, collected by the Survey of Income and Program Participation (SIPP), clearly show a post-Great Recession decline in the share of young households with home debt (Figure 1) – consistent with a dramatic slowing of movement into first-time homeownership. At the same time, the report also shows that the percentage of older households with home debt has continued to increase. Since 2000, the share of homeowners aged 65-69 with home debt increased by almost 33 percent, and the share of those aged 70-74 increased by almost 65 percent. This trend is consistent with today’s older owners failing to pay down their mortgages as diligently as did earlier generations. Both equity extractions to garner cash to pay for other expenditures, and simple refinancing and extending the payment period to lower monthly payment costs will slow the pace at which homeowners pay off their mortgages.



Source: Census Bureau tabulations of Survey of Income and Program Participation (SIPP) data

Moreover, among those households with home debt, overall median debt outstanding has continued to increase post-Great Recession, albeit at a diminished pace (Figure 2). The increase in median home debt is especially true among the elderly. Median outstanding home debt for homeowners aged 65-69 with a mortgage increased by 46 percent between 2000 and 2005, and another 8 percent between 2005 and 2011. The corresponding figures for 70-74 year old owners with home debt are 18 and 33 percent. This doesn’t necessarily indicate a recent rise in refinancing activity among these older households. Rather it likely is attributable to the aging of 60-64 and 65-69 year olds (with higher mortgage debt from the previous periods) into the 65-69 and 70-74 age groups.



Source: Census Bureau tabulations of Survey of Income and Program Participation (SIPP) data

Growing mortgage debt among the elderly is troubling. Declining income later in life is inevitable for most households. With mortgage payments a continuing part of the monthly household budget, in addition to real estate taxes and the expense of home repairs, many elderly with high housing cost burdens will need to postpone retirement or spend less on other needs like food or health care. Fewer will be able to draw on wealth accumulated through growth in home equity to help pay the bills late in life. Some will let their homes fall into disrepair or will be forced to sell their homes when they would prefer to age in place. This is a trend worth our continuing attention and concern. 

Tuesday, October 28, 2014

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. 


Tuesday, October 21, 2014

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 5th 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 18th 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 4th 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. 

Thursday, October 16, 2014

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.