Thursday, July 30, 2015

New Multifamily Construction is Out of Reach for Most Renters

by Elizabeth La Jeunesse
Research Analyst
A major theme of the Joint Center’s 2015 State of the Nation’s Housing Report is the record growth in demand for rental units in recent years.  From 2010-14, the pace of renter household growth accelerated to 900,000 per year on average.  This puts the 2010s on track to be the strongest decade for renter growth in history (Figure 1).

Relative to this surge in demand for rental housing, both the quantity and the pricing of new rental construction has been inadequate.  Annual rental unit completions have ramped up over the past four years, but as of 2014 totaled only 280,000 new units—falling far short of annual growth in renters.  In addition, the rising costs of development pushed the median asking rent for newly constructed multifamily units up to approximately $1,290 per month as of 2013, an increase of $180 in real terms compared to 2012 according to data from the US Census Bureau’s Survey of Market Absorption of New Multifamily Units.

Source: JCHS tabulations of US Census Bureau, Decennial Censuses and Housing Vacancy Surveys.

Meanwhile, typical renter incomes increased by less than half as much, or $60 a month, from $32,000 in 2012 to $32,700 in 2013 according to data from the American Community Survey.  According to the standard definition of housing affordability, where rent should be equal to no more than 30 percent of income, the median or typical renter household could afford a maximum rent of just $820 per month in 2013.  In other words, newly constructed units are truly out of reach for the typical renter household, with the cost of a typical new multifamily unit eating up 47 percent, or almost half, of its total income.

To afford a typical new multifamily unit, a household would need to earn at least $51,440, but less than a third of renters earn this much.  In 2013, the gap between the price of a typical new multifamily unit and what a typical renter could afford was large across all regions of the US, ranging from a difference of around $390 in in the Midwest and South to as much as $475 in the Northeast (Figure 2).  The Northeast and West saw the highest typical asking rents, of $1,350 or more per month.

Notes: *Reported median asking rent was top-coded at $1,350, so the actual median asking rent in the Northeast and West was even higher.  Asking rent data are for privately financed, nonsubsidized units.  Affordable housing is defined as costing no more than 30 percent of gross household income. Source: JCHS tabulations of US Census Bureau, Survey of Market Absorption of New Multifamily Units, and 2013 American Community Survey.

Unfortunately, reported rent data from the Survey of Market of Absorption is top-coded at $1,350, meaning that the actual median asking rent for units completed in these regions was even higher.  For example, results from the American Community Survey suggest that among all units built in 2012-13 that rented at $1,350 or more, well over a third rented for at least $2,000 per month.  This rent would require an annual salary of at least $80,000, placing such units even further out of reach of the typical renter.  Fully 84 percent of new multifamily units in the Northeast and 67 percent of those in the West were priced at a monthly rate of $1,350 or above in 2013.  In the South and Midwest, by comparison, new units in the $1,350+ rent range made up only about a third of growth, suggesting a more even regional supply of new units by price. 

If renters were simply upgrading from lower- to higher-cost housing, the concentration of growth in multifamily construction at the high end would not be a problem.  But available evidence suggests that this is not the case.  According to Joint Center analysis of Housing Vacancy Survey data, more than 90 percent of the decline in rental vacancies over 2013-14 was driven by the 12 percent decrease in the number of vacant, low-rent (i.e., less than $800 per month) units.  And among professionally managed properties, higher occupancy rates were typically found among older units with lower rent levels.  Data from MPF Research also suggest that as of Q4 2014, rents were increasing fastest among older, lower-rent units, further signaling rising demand for a shrinking pool of affordable units.

Other analysis supplied by MPF Research indicates lease renewal rates have been rising over the past five years, as renters increasingly delayed the move to homeownership, leaving even fewer units to filter down to incoming renter households.  Renters’ declining mobility is likely due to a mixture of several factors, including increased difficulty of qualifying for a mortgage, uncertainty about wage growth, debt burdens, and possibly even the difficulty of affording search costs (realtor fees, moving costs, etc.) for an increasingly small number of low-rent units.  As of 2013, nearly half of renters were paying more than 30 percent of their income on housing, indicating that a significant share of renters have already hit a budget ceiling and are likely strapped in their efforts to find other affordable housing options.

The result is that while new multifamily construction is easing some of the demand for new units, it is currently not sufficient to ease the broader affordability problems facing renters. Closing the gap between what it costs to produce this housing, and what economically disadvantaged households can afford to pay, requires the persistent efforts of both the public and private sectors.  For more information on residential construction trends and the affordability challenges renters face, see our full report.

Wednesday, July 22, 2015

For Housing Demographers It’s All About the Data – But Sometimes the Data Come Up Woefully Short

by George Masnick
Senior Research Fellow
Housing demographers are often frustrated by data that range from inconsistent to totally unavailable when attempting to research demographic and housing trends. The inconsistencies between various data sources on estimates of household numbers and household growthvacancy rates, and homeownership rates are well documented and continue to be dissected and discussed, but there are other metrics that have been even more elusive to pin down that would help enormously to better understand today’s demographic/economic trends and their housing implications.

Two broad areas of housing consumption are particularly difficult to measure.  The first concerns the doubling up of generations living in a single residence.  The second is the opposite – when a single household lives in more than one housing unit on a regular basis. 

We would like to be able to answer many questions about the increasing trend of young adults who live with their parents. We have also identified a growing trend of grandparents who live with their grandchildren (and in many cases the grandchildren’s parent or parents as well) but we cannot identify grandparents who might not live with their grandchildren but live close by and provide support in childrearing.  We would like to know more about how delayed marriage/partnering, and divorce/remarriage, affect housing consumption of multiple units.

For the most part, existing data cannot tell us what actually takes place in the housing history of specific households over time as individuals age and change their household configurations, marital/partnership status, and employment/income profiles.  Most difficult to measure are the linkages between generations in structuring patterns of geographic mobility (affecting both those who move and those who do not move in order to be close to family), young adult household formation, and housing consumption across the age spectrum.

Data sets that do have information about life-course household transitions and some information about relationships between generations rarely have any data on housing.  Data sets that have housing information lack information about life-course changes preceding and during current occupancy.  Information about extended family members that do not reside in the household being interviewed is generally totally lacking.

We would like to know not only how many adult children presently live with their parents, but how many have boomeranged and the type of housing boomerang children moved out of when they moved back home.  How often does moving back home occur for specific households, and how long does it last?  Short spells of returning home presumably have much different consequences than long ones.  Chronic returns might have very different causes and consequences than one-off situations.  Returns to large houses with higher-income parents have different consequences than returns to small homes having low household income. 

We would like to know more about the background details of children when they leave a parental household – reasons for leaving, characteristics of housing (on both ends of the move), and, household size and composition (again on both ends of the move).  Are boomerang kids and their household/housing characteristics different from those who leave and do not return?  This information would be immensely helpful in better understanding the present and future housing consumption of those Millennials who have been slow to form independent households and become homeowners.  

Ideally, answers to the kinds of questions just raised require panel data that track individuals and their housing over time.  The few nationally representative panel surveys with public use micro data, such as the Panel Study of Income Dynamics (PSID) or the National Longitudinal Survey of Youth (NLSY), have limited housing data.  And even these surveys have historically been deficient on the collection of data on individuals and their extended families: the PSID has collected data at regular intervals since 1968, but only in 2013 added a Family Roster and Transfer Module in which respondents and their spouses are asked to enumerate all living parents and children over 18 and to report about recent and long-term transfers of time and money to these individuals.  The new PSID module is the first to fully enumerate all biological, adopted, and step-relationships of parents, parents-in-law, and adult children, and it is the first major data collection effort on transfers of time and money in the PSID since 1988.

Other efforts to assemble panel data to directly study co-residence patterns between adult children and parents, such as in a recent Federal Reserve Bank of New York Report (utilizing its own Consumer Credit Panel (CCP)) are neither a nationally representative sample of all households nor available to other researchers, raising concerns about the reliability of the data. For example, the CCP data set reports a much higher rate of co-residence than other data sources such as the Current Population Survey.  Still, because of the scarcity of panel data to answer some of our questions, data sets the FRBNY CCP cannot be entirely dismissed.

Another reason for growing inter-generational co-residence is the need to support and take care of grandchildren.  Increasing grandparent-grandchild co-residence certainly has important consequences for housing choices, further postponing independent household formation among some Millennials, and perhaps delaying downsizing among the Baby Boomer grandparents.  We would like to know if older Americans who live close to their grandchildren are different from grandparents who live with their grandchildren.  Do they also play financial and childcare roles with respect to their grandchildren?  Are retirees more likely to move to be close to their children if their grandchildren are young?  Does the existence of young grandchildren make retirement moves that put greater distance between them and their grandchildren, or that are to age restricted communities, less probable?  Do older empty nesters with young grandchildren actually downsize less than those with older grandchildren? 

Available data on the rise of co-resident grandparents and their grandchildren are mostly from cross-sectional surveys, like the Current Population Survey, and are biased toward intergenerational families where those in the first wave of Millennials had their children relatively quickly, often while teenagers or still in school, or when not yet absorbed into the labor force.  Such early births are more likely to be to parents without a college education and to be non-marital.  The large and growing share of Millennials who pursue higher education are more likely to postpone childbearing (thus postponing grandparenthood for many Baby Boomers) and their births are more likely to be marital.  Will first grandchildren who come along later in life, when their parents are older and more economically secure and their grandparents are more likely to be retired, be more or less likely live with grandparents?  Live close to their grandparents?   

Unfortunately, nationally representative data that allow us to identify who is even a non-coresident grandparent are practically non-existent.  The sole exception is from the Survey of Income and Program Participation (SIPP), a longitudinal survey following panels of households for 2½-to-4 years, which for three of its panels has asked if a person has any biological children and if those children, in turn, have any biological or adopted children.  The SIPP data overlook persons who are not biological grandparents but are grandparents through marriage, either as stepparents themselves or who became a grandparent when their children partnered with someone who already has children, but has not adopted them.

Analyses of SIPP data on grandparents have been published for the 2001 panel, the 2004 panel, and the 2008 panel.  A 2014 panel is now in the process of data collection.  These data estimate that there were 64 million grandparents in 2009 (second wave questionnaire of the 2008 panel), of which one-in-ten lived with their grandchildren. According to these data, only 22 percent of co-resident grandparents were over the age of 70 compared to 34 percent of grandparents who did not live with grandchildren. We have little insight into proximity of these non-coresident grandparents to their children and the housing choices they have made if they have recently moved.

One additional panel survey that collects data to answer questions about grandparents is the University ofMichigan’s Health and Retirement Study (HRS).  It does allow identification of all grandparents, co-resident grandparents, reasons for moving, and does have some housing data, but it is somewhat limited by a sample design that selects particular birth cohorts.  Still, more analysis of these data, collected annually from 1992 to 1996 and on alternate years since, can help us better answer some of our questions.  

Shifting gears, how people utilize multiple housing units (their own and/or other’s) at different times during the week, month, or year is almost a complete mystery.  We would like to know more about middle-aged and older people who sometimes dwell in two or more housing units while maintaining control of each.  There is a catch-all category of households in some data sets identifying people who have a primary or usual residence elsewhere, and this category has been growing in recent years.  However, households interviewed at their primary residence are not asked if they sometimes live in another home, and data are not collected about the characteristics of that home and the reasons for living in it.  Are grandparents spending some time living with their grandchildren on a regular basis, or buying or renting a residence that they occupy occasionally to be close to their young grandchildren?  Are retirees who once lived close by their young grandchildren retaining a previous residence for a longer period of time to facilitate occasional visiting after retirement migration?  Are more adult children still living in retirees’ previous homes after they retire and move elsewhere?

We would also like to know how long individuals maintain an active consumption of multiple housing units when they change jobs or form new relationships.  Is “living together” increasingly less a status and more a process that could involve two or more housing units over an extended period of time?  Is the rise of long-distance telecommuting predicated on being able to spend some time in two or more locations, and is it the case that housing units in multiple places are owned or rented to facilitate this?  If people are now better able to rent out or share housing with others on a part-time basis (for example, through VRBO and Airbnb), are they more likely to maintain multiple units for their own occasional use? 
Until panel surveys from nationally representative samples collect data on life-course transitions, on intergenerational relationships, and on housing consumption more broadly defined, analysts will continue to try to research trends with data that are usually inadequate to the task, and to have questions that simply cannot be answered.

Thursday, July 16, 2015

Pick-Up Projected in Home Improvement Activity Moving into 2016

by Abbe Will
Research Analyst
The extended easing of gains in residential improvement spending is expected to change course by early 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. The LIRA projects annual spending growth for home improvements will accelerate to 4.0% by the first quarter of 2016 (Figure 1). 

Notes: (e) – estimated; (p) – projected.  Historical data from the second quarter 2014 onward is estimated using the LIRA. Source: Joint Center for Housing Studies of Harvard University.  

One strong signal of a pick-up in home improvement activity is the recent rise in home sales activity, since recent homebuyers typically spend about a third more on home improvements than non-movers, even after controlling for any age or income differences. In addition, rising home prices across the country mean rising equity, which should encourage improvement spending by homeowners.

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.

Monday, July 13, 2015

Reconciling Different Household Counts from Census Bureau Surveys

by Dan McCue
Senior Research Associate
One of the major challenges faced by housing analysts and demographers is the lack of consistency among various Census Bureau surveys.  Particularly troublesome is the persistently wide range of difference reported by surveys in the number of households in the US, a key measure of housing demand.  In 2013, for instance, household counts reported by various Census Bureau surveys ranged from a low of 114.6 million in the Housing Vacancy Survey (HVS), to 116.3 million in the American Community Survey (ACS), to a high of 122.5 million in the Current Population Survey’s March ASEC (CPS/ASEC) - a span of fully 7.8 million households (Figure 1).  Annual surveys also differ widely in their measures of growth in the number of households, confounding efforts to gauge recent trends.  Indeed, household growth measures for 2013 ranged from 0.3 to 1.4 million depending on the survey, leaving data observers unsure whether growth in that year was historically weak or incredibly strong.

Source: JCHS tabulations of US Census Bureau data.

Given their importance to much of our work, the Joint Center has recently released a research note to highlight some of our current thinking on Census Bureau survey counts including: the differences in household counts among annual Census Bureau surveys and their causes; which surveys we believe to have more accurate counts than others and why; how we use different household count data for different analyses at the Joint Center; and how much of a difference using alternative headship rates based on different household counts would have on the current JCHS household projections.

The research note finds that the major source of difference among survey counts of households appears to be whether or not the surveys are person-based or stock-based.  Person-based estimates, which count the number of people who report being heads of households, consistently result in higher numbers of households than stock-based estimates, which count the number of housing units that are occupied.  We don’t exactly know why there is such variance in person-based and stock-based survey results, but the magnitude of difference between these two approaches is big and can be roughly approximated by the difference in household counts of the HVS and CPS/ASEC, because they are essentially stock-based and person-based versions of the same underlying survey sample. 

Among the annual surveys, the person-weighted CPS/ASEC has an advantage in that its household counts have come in closest to decennial census counts, which we take to be the benchmark. It is also a relatively timely survey and has the longest track record of matching census growth over the decades.  However, the CPS/ASEC does have a number of other shortcomings. One of the its major shortcomings, year-to-year volatility, can be reduced by smoothing over the data using rolling averages, but that approach also reduces the timeliness of the survey for measuring short-term trends in household growth. 

The second major annual household survey from which to get household counts, the stock-based HVS, is the most timely measure, providing quarterly results in addition to annual counts, and it also offers a series of annual household counts that use a consistent weighting vintage, which provides a more stable framework for measuring annual household growth trends than surveys that adjust their underlying survey controls year to year.  However, those vintage weighting controls do not eliminate a high amount of annual volatility.  Recent results underscore this fact, for it is highly unlikely that household counts in the HVS jumped fully 1.3 million between the third and fourth quarters of 2014 as reported in the HVS.

Finally, lack of timeliness is also a major disadvantage of using the third major annual census survey, the ACS, for analyzing annual counts of households: while other annual survey results have been out for months, the 2014 ACS is not due out until late 2015.  The ACS also has much lower household counts and appears to be essentially a stock-based survey with slightly higher household counts than HVS that is most likely a result of its more inclusive rules for determining occupancy of a unit.  Still, even with its lower base of counts, as a large and detailed survey the ACS may prove to provide a reliable measure of the growth trend in households, but so far this survey has had only a few years under a consistent weighting methodology in which to judge its reliability.

Overall, there is no satisfying conclusion to which annual survey is best for measuring annual household growth trends and none is perfect. CPS/ASEC is volatile but can be smoothed over at the expense of timeliness; HVS is timely and attractive in that its counts are pinned to stable consistent weighting vintages across years but it is still volatile, and possibly the vintage controls bias growth too low; and ACS is not timely enough to be helpful in measuring recent trends and is a survey without much history in which to judge its accuracy, though it has promise as a large and detailed survey that receives a relatively high amount of resources from the Census Bureau.  In terms of the number of households, however, there is reason to believe that higher counts of the CPS/ASEC, obtained from person-based weighting approaches, do appear to be preferable to lower counts of the HVS and ACS in offering counts closest to that of the official decennial censuses.  It is largely for these reasons we have used household counts from CPS/ASEC as a key input in Joint Center household projections, which apply current headship rates (the ratio of households to people) to population projections to produce household projections that form a baseline for estimating future housing demand.

Wednesday, July 8, 2015

Aging Society and Inaccessible Housing Stock Suggest Growing Need for Remodeling

by Abbe Will
Research Analyst
Over the coming decades, the number and share of U.S. households age 65 and over will rise dramatically as the oldest members of the baby-boom generation reach retirement age. Inevitably, with increasing age comes the growing presence of disability and problems using components of the home without assistance. Surely, some aging households will look to move into homes that are better suited to their changing needs, but the majority of older households continue to plan to “age in place.” Since much of the housing stock is currently ill-equipped with even basic accessibility features, older homeowners aging in place will need to invest in retrofitting their homes in order to age comfortably and safely. New research released by the Joint Center sheds light on the implications of an aging society for the home improvement market by analyzing the remodeling activity by older owners and estimating the projected demand for and supply of homes with basic accessibility features in the near future.

Older homeowners have already been exerting significant influence on the home remodeling market due to changing trends in longevity, mobility, wealth, homeownership rates, and labor force participation. Since 2007, the share of total market spending for home improvements by owners age 55 and over has increased considerably, from less than a third to nearly a half by 2013. Reaching $90 billion in 2013, spending by older owners was just 6 percent less than during the last market peak in 2007 and for the first time surpassed the share and level of spending by middle-age homeowners. Combining historical spending data from the American Housing Survey with recent consumer housing survey data of expected spending from the Demand Institute suggests that total improvement expenditure by older homeowners could surge by an additional $17 billion annually over the next three years.

The Joint Center estimates that of the over 25 million households age 65 and over today, 44 percent have some need for home accessibility features due to disability or difficulty using components of the home, such as kitchen or bathroom facilities, without assistance (Figure 1). And yet the current housing stock is not especially equipped to meet the accessibility needs of an aging nation, as not even a third of homes have what could be considered basic accessibility features, such as a no-step entry and bedroom and full bathroom on the entry level (Figure 2). Although 45 percent of older homeowners plan to undertake improvement projects in the next several years with the intent of making their homes easier to live in as they age, surprisingly few owners are focused on home accessibility as part of aging in place comfortably and safely. Given the attitudes of today’s older homeowners, the remodeling industry will need to bridge a substantial mismatch between owners’ wanting to age in place and their actually being able to do so safely with appropriate accessibility features.

Note: Households with accessibility need are defined as those with a disabled member or members with serious difficulties using components of the home without assistance. For more detail, see Appendix A in Abbe Will, Aging in Place: Implications for Remodeling, JCHS Working Paper, July 2015. Source: JCHS tabulations of HUD, American Housing Survey.

Note: Basic accessibility features are defined as a no-step entry and bedroom and full bathroom on the entry level of the home. Source: JCHS tabulations of HUD, American Housing Survey.

As the number and share of older households rise sharply over the coming decade, construction of new housing with basic accessibility features is projected to fall considerably short of increased demand in the Northeast and Midwest regions of the country. Fully 40 percent of the net gain in households age 65 and older with accessibility needs in these regions is projected to have unmet demand, suggesting the need for significant retrofit spending on existing homes to narrow this supply-demand gap (Figure 3). Older households in the South and West regions of the country are already better accommodated for aging in place, with relatively more homes in these regions having basic accessibility features, and this trend is not expected to change over the coming decade. Ultimately, the dramatically rising number of older households aging in place, strong and growing home improvement spending by older owners, and the unsuitability of the current housing stock for safely and comfortably aging in place all support the expectation for substantial growth in demand for homes with accessibility features moving forward.

Note: Basic accessibility features include a no-step entry and bedroom and full bathroom on the entry level of the home. Source: Abbe Will, Aging in Place: Implications for Remodeling, JCHS Working Paper, July 2015.

Wednesday, July 1, 2015

Homebuying Affordability Strong in Many Metros but First-Time Buyers Face Financial Hurdles

by Rocio Sanchez-Moyano
Research Analyst
Stagnant incomes and tight credit since the recession have worked in tandem to keep many renters from becoming homeowners in recent years, even as prices plummeted. Now, as the housing market continues to recover, questions about homebuying affordability are beginning to creep back into the public discussion. But nationally, the affordability outlook remains favorable. The ratio of the mortgage payment on a median priced home to median incomes remains near all-time lows, buoying the National Association of Realtors® affordability index. Median home prices have yet to fully rebound from their mid-2000s peaks in two-thirds of the metros for which we have data, and interest rates have kept borrower costs historically low (Figure 1).

Note: Prices adjusted to constant 2014 dollars using CPI-U for All Items.
Source: JCHS tabulations of NAR Affordability Index and Single-Family Median House Price, annualized by Moody’s Analytics; US Census Bureau, Current Population Surveys; Freddie Mac Primary Mortgage Market Survey.

But house prices have been rising faster than incomes. Home prices increased in 83 percent of metros for which NAR data was available from 2013-14. Approximately 50 percent of metros experienced annual appreciation of between 0 and 5 percent, and another 33 percent of metros had appreciation above 5 percent. Incomes, on the other hand, generally grew more slowly, with only 6 percent of metros experiencing nominal income growth of more than 5 percent. In fact, in 73 percent of metros analyzed, median incomes grew more slowly than the monthly mortgage payment on the median home.

The growing disparity between incomes and home prices can make it difficult for renters to transition into homeownership. So, how many renters in metro areas across the country can afford the monthly costs of owning a home? To estimate this, the Joint Center calculated the monthly mortgage payment on a 30-year, fixed-rate loan with a 5 percent downpayment for a median-priced home in each metro area, adding monthly costs for property taxes and insurance. We then determined affordability by comparing monthly costs to the incomes reported by renters in the 2013 American Community Survey, applying the Consumer Financial Protection Bureau’s qualified mortgage rule standard that all debt payments cannot exceed 43 percent of household income. We assumed that 8 percent of a household’s income services non-housing debt, like credit cards, car loans, and student loans, based on the median non-housing debt load among households in the Survey of Consumer Finance, which leaves 35 percent of household income available for monthly housing payments. To the extent that renters transitioning to homeownership buy “starter homes” priced below the median, our estimate of the number of renters able to afford monthly owner costs will be conservative.

Using this standard in the 168 metros for which data was available, 36 percent of all renters can afford the monthly costs of owning a median-priced home in their area. (See interactive map below.) Expensive and renter-heavy cities like New York, San Francisco, Miami, and Boston make up the list of nineteen metros which are affordable to 30 percent or less of the renters living in them. But despite accounting for only 11 percent of the metros for which data was available, these expensive metros house nearly a third of all the renters included in our analysis. On the other hand, in 46 metros, monthly costs for the median-priced home are affordable to at least half of renters. Many of these areas are smaller Midwestern metros like Topeka, Kansas, and Appleton, Wisconsin.

Homebuying Affordability Can Vary Greatly Metro to Metro 
(Click map to launch)

Renters aged 25-34, who are in the prime age to transition to homeownership for the first time, generally have higher incomes than the typical renter, and are actually better positioned to afford the monthly costs of owning as a result. Across all of our metros, 42 percent of renters in this age group could afford to own the median-priced home. And more than half of the metros we analyzed were affordable to the majority of 25-34 year old renters living in them. Southern and Midwestern metros account for many of the most affordable metro areas. In all, 31 percent of renters aged 25-34 lived in these highly affordable metros, while only 23 percent lived in metros where no more than 30 percent of renters of this age could afford the costs of ownership.

Favorable affordability conditions are currently present in some of the metros hardest hit by the housing crisis. On average, more than 45 percent of renters in the 30 metros with the largest declines in homeownership rates from 2006-13 can afford to own a home. Las Vegas, which saw the largest decline in homeownership, is affordable to more than half of its renters. Phoenix and many hard-hit metros in Florida are affordable to at least 40 percent of their renters. And Atlanta, which experienced a severe foreclosure crisis, is affordable to 53 percent of its renters (Figure 2).

Source: JCHS tabulations of US Census Bureau, 2013 American Community Survey; Freddie Mac, Primary Mortgage Market Survey; NAR, Median Existing Single-Family Home Sales Price.

But despite favorable cost-to-income ratios in many metro areas, aspiring homeowners may face other hurdles on their path to homeownership. Tight underwriting standards can make it difficult to obtain a mortgage, and even for renters with high credit scores, saving enough for a downpayment is often challenging. A downpayment of just 5 percent of the value of the median-priced home can range from $3,800 in Youngstown, OH to $42,800 in San Jose, CA. Meanwhile, the median net wealth of renters in 2013 was $5,000, an amount sufficient for a 5 percent downpayment in only 5 of the 168 metros for which data is available. Financial concerns like student loan debt and unaffordable rents can compound the difficulties of saving for a downpayment. Twenty percent of US households had student loan debt in 2013, and among renters aged 25-34, the rate is more than double the US total, at 41 percent. And about half of all renters are cost burdened, with housing costs that exceed 30 percent of their incomes; the share is nearly as high among renters of prime first-time buyer age. In all, rising interest rates and other financial constraints may be headwinds to a robust entry of renters into homeownership, but price appreciation is expected to slow in many parts of the country this year, potentially allowing incomes to catch up and keep many metros affordable.