The Impact of Student Loan Debt on the Housing Decisions of Young Renters

by Irene Lew
Research Analyst

In the last several presidential debates, both Democratic and Republican candidates have referenced the mounting costs associated with a college education, which have contributed to the dramatic growth in student loan debt over the past decade. Two weeks ago, the nonprofit Institute for College Access and Success released their tenth annual report which showed that students' average debt at graduation rose 56 percent, from $18,550 in 2004 to $28,950 in 2014. Aggregate outstanding student loan balances more than tripled in real value over the same timeframe, rising from an average of $380 billion in 2004 to $1.1 trillion in 2014, according to data from the Federal Reserve Bank of New York’s Consumer Credit Panel. In fact, student loan debt was the only type of consumer debt to rise steadily during the Great Recession, even as households shed other types of non-housing-related debt such as credit card debt.

Many in the housing industry are concerned that unmanageable student debt is holding back Millenials from becoming first-time homebuyers. Households aged 25 to 34 typically account for just over half of all first-time buyers, but homeownership rates among this group have dropped by more than 9 percentage points since 2004. A 2014 survey conducted by the National Association of Realtors found that only a third of 2014 homebuyers were first-time purchasers—the lowest share since 1987—and that among the 23 percent of first-time homebuyers who reported difficulties with saving for a down payment, over half (57 percent) cited student loans as a factor. In a new research brief I analyze the extent to which young renter households in their 20s and 30s are burdened by their student loan payments and explore the potential implications of these payment burdens on future decisions to pursue homeownership. I also build on the findings described in an earlier blog post to further describe the growth and prevalence of student loan debt among various demographic groups, especially among minority households and those without a four-year college degree.

My analysis draws on cross-sectional data from the Federal Reserve Board’s triennial Survey of Consumer Finances (SCF), which describes changes in debt, wealth, and assets at the household level. My brief utilizes the thresholds for student loan debt burdens outlined by the Consumer Financial Protection Bureau, which define burden according to percentage of monthly income made up by each monthly payment: for low, medium and high burdens, respectively, this percentage is less than 8, between 8 and 14, and more than 14. Reflecting both increases in student loan payment amounts and income declines among young renters, I find that the prevalence of young renters with medium or high student debt burdens accelerated following the Great Recession. Between 2007 and 2013, the share of young renters with high student loan burdens nearly quadrupled, from 5 percent to 19 percent (Figure 1).

Young renters at the lower end of the income distribution are more likely to bear the brunt of student debt payment burdens. When factoring in other non-housing debt payments on top of student loan payments, the mean payment-to-income ratios increase to 22 percent for young renters in the bottom quartile and to 8 percent for those in the top quartile (Figure 2). Yet although the lowest-income renters are faced with the highest payment burdens, even the lower payment burdens among renters in the top quartile are large enough to be factored into the ability to purchase a home.

While a causal relationship among student loan debt, housing consumption, and the tenure decisions of young renters cannot be drawn without additional analysis that disentangles other economic factors such as local employment and housing market conditions, student loan payment burdens are likely contributing to downward pressure on the homeownership rates of young households. Indeed, homeownership rates have been consistently lower among households with medium and high payment burdens relative to those with low burdens (Figure 3).

My analysis of student debt burdens excludes households that have not begun making payments on this debt due to deferral or forbearance, suggesting that the number of young renter households with student debt payment burdens is likely to increase in coming years as this group enters the repayment cycle. Indeed, as of 2013, nearly half of the $711 billion in student debt observed in the SCF data was held by households that have at least one student loan in deferral—and 45 percent of renter households aged 20-39 with student loan debt have not yet made any payments toward their outstanding student loan balances (Figure 4).

Another concern is rising student loan default rates, which reflect a growing share of borrowers struggling to pay down their debt. According to the U.S. Department of Education’s Federal Student Aid Data Center, 3.2 million borrowers are in default as of the third quarter of 2015, up by more than half (52 percent) from the same quarter two years ago. Federal student loan borrowers faced with unexpectedly low earnings can take advantage of several income-driven repayment plans that reduce monthly payments and can help minimize payment burdens, but most do not, instead opting for standard repayment plans, not based on current income, where monthly payments are amortized over a 10-year period. Unlike income-driven repayment plans, standard repayment plans do not account for reductions in a borrower’s income and do not establish timelines for forgiveness of any remaining loan balances.

Rising student debt levels and payment burdens among young renters are likely to impact this group’s long-term finances and their decisions to transition to homeownership. Delinquency and default can harm the ability of young renters to access low-cost credit and qualify for a home-purchase mortgage. Furthermore, student loan payments reduce young renters’ discretionary income and can delay the accumulation of savings toward a down payment on a home. Indeed, according to the SCF, college-educated renters in their 20s and 30s with student loan debt had just $3,500 in cash savings and negative net wealth of -$9,640 at the median, compared to $27,000 in net wealth and more than double the amount of cash savings ($7,500) among those without student debt. With lower incomes, wealth, and savings, young renters with student debt may face challenges qualifying for a mortgage to purchase their first home or setting aside a sufficient financial cushion for a down payment on a home.

Can Demolitions and Property Rehabilitations Alter Nearby Crime Patterns?

by Jon Spader

Senior Research
Associate

The potential for vacant and abandoned structures to attract crime has long concerned local policymakers. Newly vacant structures may attract crime, to the extent that they contain appliances, copper pipes, or other targets for burglary and theft. Additionally, abandoned structures offer suitable locations for criminal activity away from the public eye, such as public order offenses like vandalism or drug use. The presence of such crimes can reduce public safety for other neighborhood residents and require public dollars to police.

The foreclosure crisis sparked increased anxiety about these issues in many communities where concentrated foreclosures left properties vacant. Indeed, a large and growing body of research indicates that foreclosures caused increased crime during this period, largely as a result of the vacancies that occurred during the foreclosure process. In addition to the possibilities mentioned above, researchers hypothesized that foreclosures might increase crime through several additional channels: falling property values might reduce the local resources available for crime prevention; turnover of neighborhood residents might reduce the extent of monitoring in public spaces; and, reduced maintenance might alter offenders’ perceptions about whether the unit is occupied.

These potential vectors for increased crime raise important questions about what options are available to policymakers who seek to prevent vacant properties from becoming sources of blight to their surrounding communities. A recent Joint Center for Housing Studies working paper examines one possible strategy, measuring the extent to which demolitions and property rehabilitations effectively reduced the incidence of crime on or near foreclosed and vacant properties. Specifically, this paper measures the impacts of demolitions and property rehabilitations funded by the Neighborhood Stabilization Program between 2009 and 2013 in Cleveland, Chicago, and Denver. For more information about the Neighborhood Stabilization Program, additional analyses are available here, here, and here.

These three maps below show the location of demolitions and property rehabilitations in Cleveland, Chicago, and Denver. The number, type, and location of NSP activities are displayed, overlaying this information with shading that illustrates the underlying rates of crime in the neighborhoods surrounding the property demolitions and rehabilitations. The working paper measures the average impact of demolitions and property rehabilitations on the incidence of crime that occurs on the property or within 250 feet in any direction. This distance increment reflects the impact of these investments on the property itself or in the areas immediately adjacent to the property (relative to the trend observed in other areas of the neighborhood).

In addition, the map for Denver shows a small number of financing activities which provided down payment assistance to low-income homebuyers to purchase homes that had recently experienced foreclosure. Unfortunately, the number of such properties is too small to allow similar analysis of whether the reoccupancy of these properties affected the incidence of crime or near the financing properties.

 

The results suggest that the demolitions conducted by the NSP grantee in Cleveland reduced the incidence of burglary and theft within 250 feet of the demolished properties. The measured impacts of demolitions were present during the period of the demolition and persisted for one year following the demolition, before dissipating. In total, these estimates imply a reduction of just over 1 reported property crime for every 2 demolitions completed.

The findings do not show similar impacts for the observed set of property rehabilitations in Cleveland, Chicago, and Denver, nor for the observed set of demolitions in Chicago. Because the sample sizes are much smaller for these activities, we are unable to determine whether these activities had no impact on crime, or whether this outcome is due to limitations of the study’s data and methods. For example, the set of property rehabilitations conducted in each city, as well as the set of demolitions in Chicago, each included a heterogeneous set of property  and neighborhood types, which may limit the estimates of the ‘average effect’ of these investments.

Nonetheless, the findings carry useful implications about the potential for demolitions to alter nearby crime patterns. The direct implication is that a strategy of concentrated demolitions may be effective in altering neighborhood crime patterns under certain conditions. The map of Cleveland above illustrates the extent to which Cleveland clustered its demolition activity, targeting its demolitions primarily to neighborhoods with high levels of vacancy and abandonment. The reductions in crime surrounding these demolitions add to the potential benefits that policymakers should consider in weighing the use of demolition to remove vacant and abandoned properties. The caveat is that the size of the impacts is relatively modest. Policymakers will need to weigh the overall benefits of demolishing vacant and abandoned structures against the costs—which averaged $13,970 per demolition for the NSP program.

Some caution is also needed in applying the findings to other neighborhoods and cities. As the above discussion suggests, the estimates reflect Cleveland’s use of concentrated demolitions in neighborhoods with high levels of vacancy and abandonment and moderate levels of crime. Their demolition strategy might therefore be readily applied to nearby Midwestern cities facing similar challenges, but could be less exportable to neighborhoods with different levels of crime, different built environments, or that are located in different regions of the United States.

Variable Population Growth is Driving an Uneven Housing Recovery in the Nation’s Large Metropolitan Areas

by George Masnick
Senior Research Fellow

We easily accept the proposition that the housing recovery will be greatest in parts of the country where population growth and job growth are occurring more rapidly. But we often forget that the longer-term trends in population growth that drive housing demand are not only highly variable across metropolitan areas, but also tend to be persistent over time within metros. Places leading the housing recovery are the same places where the engines of population growth have had the greatest long-term sustained horsepower. These are places where the young adult population is growing most rapidly. Such places have younger age structures because they are destinations for both international and domestic migration, and their younger age structures sustain population growth from higher natural increase as well. Slow growth metros, where the demand for new housing is lower, are generally places with older age structures and a lack of net in-migration – places where these variables are not likely to change in a fundamental way in the foreseeable future. That being said, there have been changes in population growth among the nation’s large metros since the end of the Great Recession that are worth noting. Metros that have grown more slowly since before 2010 are still trying to put the effects of the economic downturn behind them. Metros that have higher population growth 2010-2014 are generally those where rising housing prices and rents have squeezed household budgets most severely.

The latest release of Census Bureau 2014 population estimates for metropolitan areas underscores the existence of large differences in population growth among the nation’s large metros. The nation’s 100 largest metropolitan areas in 2014 are home to about two thirds of Americans. The largest of these is the New York-Northern New Jersey-Long Island metro at just over 20 million, and the smallest is Durham-Chapel Hill at about 550,000. If we compare population growth that took place during the first decade of this century with what has occurred more recently, we can see both the longer-term growth differences among metros and identify places where population growth has accelerated or declined between 2010 and 2014.

Figure 1 plots annual population growth in the 97 of the 100 largest metro areas in 2010 that also made this list in 2014. Most of the 97 metros cluster together at under 25,000 annual population growth for both periods, and are growing moderately, slowly, or not at all. Only a couple of dozen metros exhibit population growth that sets them apart. For these, the higher the population growth in 2000-2010, the higher the growth in 2010-2014. For example, the Houston-Sugarland-Baytown metro area added an average of 123,000 people per year in the decade 2000-2010 and 134,000 per year from 2010 to 2014. Dallas-Fort Worth-Arlington increased 126,000 annually during the 2000s and 124,000 annually so far this decade. New York-Northern New Jersey-Long Island grew at an annual rate of 124,000 during each period.

The diagonal red line separates the scatterplot into metros that grew at a faster numerical annual rate during 2010-2014 compared to 2000-2010 (above the line) from those that grew more slowly (below the line). Among other moderately-higher growth metros, Atlanta-Sandy Springs-Marietta, Phoenix-Mesa-Scottsdale, Riverside-San Bernardino-Ontario, and Las Vegas-Paradise have grown more slowly since 2010, while Los Angeles-Long Beach-Santa Ana, the DC-VA-MD-WV metro, and Miami-Fort Lauderdale-West Palm Beach have grown more rapidly. Some metros with moderate growth during the previous decade have begun to grow more rapidly and add 30,000 or more people per year during the first half of the current decade. In addition to San Francisco-Oakland-Hayward and Boston-Cambridge-Quincy, Seattle-Tacoma-Belleview, Denver-Aurora-Lakewood, San Jose-Sunnyvale-Santa Clara and San Diego-Carlsbad are on this list of metros with significantly increased population growth. These are also places with the greatest increases in housing prices.

The Charlotte-Gastonia-Concord metropolitan area is an outlier in how much slower its population has grown in the recent period compared to 2000-2010. Such a slowdown should be surprising since Charlotte was not hit by the Great Recession and the bursting of the housing bubble as much as other metros falling well below the red line in Figure 1. In fact, its faster growth during 2000-2010 stems primarily from a large (+26%) adjustment to its baseline 2010 census count. It is the only metro in the top 100 with such a large percentage adjustment, which the Census Bureau states could be "due to legal boundary updates, other geographic program changes, and Count Question Resolution action."

Decomposing population growth into its two broad components, net migration and natural increase (excess of births over deaths), allows us to better understand these recent metropolitan population growth trends. Figure 2 shows that the greater the net migration the greater the natural increase. Since migrants are generally young adults, metros that are migrant destinations have a greater excess of births over deaths. This is especially true of metros like Houston-Sugarland-Baytown, Dallas-Fort Worth-Arlington, DC-VA-MD-WV and Atlanta-Sandy Springs-Marietta, where both domestic and international migration are strongly positive (Table 1). Places that are retirement destinations like Miami-Fort Lauderdale-West Palm Beach, Orlando-Kissimmee-Sanford, and especially Tampa-St. Petersburg-Clearwater, have much lower rates of natural increase (fewer births and more deaths) because of their older age structures.

New York-Northern New Jersey-Long Island is an outlier, with its high level of international immigration largely being offset by domestic outmigration (annually 141,000 and -124,000 respectively during 2010-2014). But the New York metro’s high share of minority population (50.2 percent according to the 2010 census) produces a large growth from natural increase because of younger age structure and above-replacement fertility for minorities. Los Angeles-Long Beach-Santa Ana’s profile is similar to New York’s in that levels of recent annual international in-migration are offset by high levels of domestic migration losses (62,000 and -49,000 respectively), and its high natural increase is fueled by its minority population (67.6 percent in 2010). The Chicago-Naperville-Elgin metro area has recently experienced more than twice the level of domestic out-migration than immigration according to Census Bureau estimates. Still, Chicago’s minority population (46 percent in 2010) produces a significant level of natural increase, which has kept the Chicago metro’s overall population growth positive.

Population age structure and percent minority are demographic characteristics that change little from year to year, and these are the characteristics that largely determine growth from natural increase. Places with high natural increase should continue to produce and excess of births over deaths. Looking forward, unless patterns of domestic or international migration change dramatically in the short run, high-growth metros should remain high and low-growth metros remain low.

Remodeling Spending Expected to Accelerate into 2016

by Abbe Will
Research Analyst

After several quarters of slackening growth, home improvement spending is projected to pick-up pace moving into next year, according to the Leading Indicator of Remodeling Activity (LIRA) released today by the Remodeling Futures Program at the Joint Center for Housing Studies of Harvard University. The LIRA projects annual spending growth for home improvements will accelerate from 2.4% last quarter to 6.8% in the second quarter of 2016.

Home improvement spending continues to benefit from the last years’ upswing in housing market conditions, including new construction, price gains, and sales. Strengthening housing market conditions are encouraging owners to invest in more discretionary home improvements, such as kitchen and bath remodeling and room additions, in addition to the necessary replacements of worn components such as roofing and siding.

Although we expect remodeling activity to strengthen through the first half of 2016, further gains could be tempered. Current slowdowns in shipments of building materials and remodeling contractor employment trends, as well as restrictive consumer lending environments, are lowering remodeler sentiment and could keep spending gains in the mid-single digit range moving forward.

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The Leading Indicator of Remodeling Activity (LIRA) is designed to estimate national homeowner spending on improvements for the current quarter and subsequent three quarters. For more information about the LIRA, including how it is calculated, please visit the LIRA page on the Joint Center’s website. The LIRA is released by the Remodeling Futures Program at the Joint Center for Housing Studies in the third week after each quarter’s closing.

Single-Family Rentals Have Risen to Nearly a Third of Rental Housing

by Rachel Bogardus Drew

Post-Doctoral Fellow

According to the Census Bureau, the national homeownership rate dropped again in the second quarter of this year, to 63.4 percent. This level represents a nearly 50 year low, and continues the trend of declining homeownership that has been in effect since the end of the mid-2000s housing boom (see my previous blog post on this topic). The flip side of lower homeownership rates, however, is higher shares of households renting their homes. Indeed, rental housing is now more in demand than it has been for decades. While new construction of rental units has picked up in response to this demand, the majority of it has been served by conversions of existing units from owner- to renter-occupied, mostly from the single-family housing stock. As a result, since 2006 the number of single-family detached homes occupied by renters has increased by a third, from 9 million to over 12 million (Figure 1), and now accounts for 29 percent of all rental housing.

Note: Other single-family housing includes attached units and manufactured housing.
Source: Tabulations of the 2000-2013 American Community Survey.

My newest paper takes a new look at single-family detached rental housing, exploring the ways in which the stock and residents of these units differ from other rental housing, and how they have changed over the last decade. It finds that single-family rentals offer an important alternative to single-family owned and multifamily rental housing. Specifically, single-family rentals allow their residents to reap many of the advantages of single-family living, such as larger units than typically found in multifamily housing, while retaining the affordability and flexibility that makes renting an attractive option to households that do not or cannot own. Because most single-family rentals were formerly owner-occupied, however, they tend to be smaller, older, and have fewer amenities than currently owned single-family units (Figure 2).

Note: Other rentals include rented single-family attached and manufactured units.

Source: Tabulations of the 2013 American Community Survey.

While the characteristics of single-family rentals align closely with those of single-family owned units, residents of these homes more closely resemble other renter households. For example, the share of minority households among single-family detached renters (39 percent) is closer to the share among multifamily renters (48 percent) than among single-family detached owner-occupants (21 percent). The same is true of the age distribution of households; 30 percent of single-family renters are under age 35, compared to 38 percent of multifamily renters in this age group but only 10 percent of single-family owner-occupants. The pattern breaks down by family type, however, as single-family detached rental units stand out as having the highest share of families with children (Figure 3).

Note: Multifamily rentals include rented single-family attached and manufactured units.
Source: Tabulations of the 2013 American Community Survey.

While single-family rentals have characteristics that are different from single-family owned and multifamily rental units, also of interest is how these units have been changing as they have grown to become a larger segment of the rental stock. Looking at these changes over time may provide some insights into whether the recent surge in single-family detached rentals is a harbinger of housing demand going forward, or a temporary reaction to the downturn in the housing and home buying markets. Most changes observed over time in the structures themselves, for instance, reflect the evolution of single-family housing in general, which continually replaces smaller, older units leaving the stock with larger and newer units in desirable locations. Some changes in the characteristics of single-family renter households, however, do not follow the same trends as in all households. One notable example of this is the share of middle-aged households (i.e., headed by someone age 35-54), which has been declining in recent years among all housing types except single-family rentals (Figure 4). The same is true of families with children, who generally prefer the features associated with single-family housing, even if they do not or cannot own their homes.

Note: Multifamily rentals include rented single-family attached and manufactured units.

Source: Tabulations of the 2000-2013 American Community Survey.

It is unlikely, however, that these shifts represent a permanent change in the rental market. The middle-aged and family households that account for large increases in single-family rentals are traditionally those most active in the trade-up and first-time home buying market. If economic conditions change in the near future such that home purchases become affordable and attainable to more households, these new classes of single-family renters will probably be among the first to seize their chance to own their own home. In such an event, detached single-family units will decline as a share of all rentals, though likely only back to their former level of around a quarter of the stock, as these units will continue to provide alternative to multifamily rentals and single-family homeownership, and a necessary component of the national housing stock.