What are the Impacts of Fertility Rates on Housing Markets?

by George Masnick
Senior Research Fellow

Since families with children are primary drivers of household formation and housing consumption, changes in fertility rates can have significant impacts on housing markets. But tracking and understanding those changes can be challenging, as illustrated by two seemingly contradictory high-profile accounts of changing fertility patterns that appeared earlier this year.

First came the Pew Research Center, which in January 2018 issued a report titled "They're Waiting Longer, but US Women today are More Likely to Have Children Than a Decade Ago." However, less than a month later, The New York Times published the seemingly contradictory headline: "American Women are Having Fewer Children than They'd Like."

Is it possible that both headlines were accurate? Is it possible that more women are having children while the overall fertility rate also is trending downward?

Answering these questions requires paying attention to both the measures being used to describe fertility trends and the data source used to measure the trend. Such an approach shows that it is quite possible for more women to become mothers and for all women to have fewer children overall.

Explaining the Trends

The General Fertility Rate (GFR)—the number of births per 1000 women age 15-49—has been trending downward over the past decade, according to the National Center for Health Statistics (NCHS). The drop is due to sharp declines in the number of children born to mothers younger than 30, somewhat but not completely offset by increases in the birthrate for mothers older than 30. Consequently, the total fertility rate has declined (Figure 1).

Figure 1. The General Fertility Rate Has Been Declining Due to Steep Declines Among Young Mothers

Note: Since births to women ate 45-49 are so few, they were excluded from this figure, which makes the GFR line an approximation of the General Fertility Rate.

Source: JCHS tabulations of National Center for Health Statistics, National Vital Statistics System, Data Brief 287, Births in the United States, 2016.

It is noteworthy that, in 2016, for the first time ever, the fertility rate for 30-34 year olds exceeded the rate for 25-29 year olds. In contrast, the birth rate for women in their early 30s was about twice the birth rate for women in their late 30s, a trend that has no changed significantly in the past decade. Fertility rates for women in their early 40s did inch upward over the past decade, but remain at exceedingly low levels (rising from just under 1 percent to just over 1 percent).

What about the increase in motherhood highlighted in the Pew report? Motherhood is measured in that report by the share of women in each cohort having ever had a live birth by age 40-44. While that report indicates that the share of mothers is rising, there are important questions about the magnitude of its reported increase in motherhood. Specifically, the Pew report is based on an analysis of the Current Population Survey's biannual June Supplement, which asks women detailed questions concerning all children they have had over a lifetime. And these CPS data appear to show a significant recent increase in motherhood.

However, comparing the CPS data on the share of older women who have become mothers with vital statistics data from the NCHS suggests that the CPS may exaggerate the recent trend toward greater motherhood (Figure 2). Although the NCHS estimates are only available until 2010, the trends from the two data sources roughly parallel one another and show a sharp downward trend followed by a trend upward. But most importantly, the upward trend in the NCHS data began in 2000 and is more modest (a 2.1 percent increase over a 10-year period), while the CPS upward trend began in 2006 and is more dramatic (a 6.0 percent increase in 10 years). Significantly, most of the upward trend in the CPS since 2006 is accounted for by the change between 2010 and 2012, a period which accounted for more than half of the 6-point gain between 2006 and 2016.

Figure 2. Motherhood Is on the Rise, but Perhaps Not as Much as the Current Population Survey Indicates

Source: JCHS tabulations of US Census Bureau, Current Population Surveys, National Center for Health Statistics, National Vital Statistics System, Data Brief 287, Births in the United States, 2016.

The NCHS percentages of women who have become mothers are higher, partly due to the fact that the NCHS women are slightly older (all age 44), while some of the CPS women (age 40-44) were still having children in their early 40s. The NCHS trend line is also much smoother because it is based on much larger number of people in the vital statistics database.

However, some of the differences are due to measurement errors that produced the lower motherhood shares in the CPS prior to 2012. As a 2015 Census Bureau working paper on this topic noted: "The June 2012 Current Population Survey (CPS) Fertility Supplement data showed a significant decrease from 2010 in the percent of women aged 35-44 who are childless... However, due to numerous changes in data and data processing, it is reasonable to think that some of the apparent changes shown in the data may be artifacts of changes in measurement, not an indication of an actual demographic shift."

We should not be surprised that the more reliable NCHS data show that the percentage of all women age 44 who are mothers has been trending only modestly upward since about 2000. One key factor in this shift is that the percentage of women in this age group who are Hispanic also increased, rising form just over 10 percent in 2000 to just under 20 percent in 2016 (Figure 3). The growth in the share of women in their early 40s who are Hispanic is due to two trends. First, the number of Hispanic women age 40-44 has been increasing, as the younger migrants from previous decades approach middle age. Second, the total number of women age 40-44 has been declining because many members of the smaller Generation-X cohort are now entering their early 40s.

Figure 3. The Increase in Motherhood is Due in Large Part to the Growing Share of Hispanic Women

Source: JCHS tabulations of US Census Bureau, Intercensal Population Estimates and 2016 Historical Series.

This is significant because Hispanic women at the end of their reproductive ages are more likely to have become mothers than non-Hispanics of the same age. Unfortunately, the NCHS fertility data are only available for all whites (including Hispanics) and all blacks (including Hispanics). Consequently, we cannot use the data to calculate motherhood by both ethnicity and race. However, the CPS, which does ask about both race and ethnicity, shows significant racial and ethnic differences in the share of women in their early 40s who have had at least one child. The Pew report, for example, which averaged CPS data for 2012, 2014, and 2016, calculated that 90 percent of Hispanic women in their early 40s had at least one child, compared to only 83 percent of non-Hispanic white women; 85 percent of non-Hispanic black women, and 86 percent of non-Hispanic Asian women.

Potential Impacts

The NYT article emphasized that the fertility decline in the US is consistent with declines in other developed countries; that American women are bearing far fewer children that they would like to; that declines in marriage (and sexual activity among unmarried women), along with increasing use of reliable contraception, are at the root of the fertility shortfall; and that the fertility decline has been widespread throughout the country. Regardless of the reasons, this delay in childbearing could have a variety of impacts not only on individuals, families, and society, but on housing markets as well.

Fewer births to teens and women in their early 20s, for example, should mean that more women are likely to complete high school, pursue higher education, and secure higher-paying jobs. The Pew report describes how the largest increases in motherhood have been among college-educated older women, the group with historically the lowest levels of completed fertility and the highest percent childless. Discounting the fact that these motherhood gains might not be as large as the CPS data indicate, and are partly driven by increases in college-educated Hispanics, such a trend could have important implications for housing demand. College-educated mothers are likely to have higher incomes which means they are more likely to have the financial resources to become homeowners, should the choose, or to rent larger units in locations better suited for growing families.

However, fewer overall births and smaller family sizes could impact housing consumption by making renting more likely or by reducing the demand for larger housing units. Moreover, fewer births will produce a smaller future labor force that may find it hard to support the very large generation of millennials when they reach retirement. If doing so requires higher taxes on young workers, then households may have less disposable income that might otherwise be used to pay for housing.

Regardless of the impact on housing, it is clear that some subtle but significant changes are likely to continue to affect both the overall fertility rate, and the total number of children in the US. The fertility decline would be further exacerbated if, as some policy makers are proposing, the country reduces the number of immigrants allowed to enter the United States, or prioritizes immigrants likely to have fewer children.

Wait... What? Ten Surprising Findings from the 2017 State of the Nation’s Housing Report

by Daniel McCue
Senior Research Associate

Every year, when we release our State of the Nation’s Housing report, we’re asked some variation of the question: “What surprised you in this year’s report?” Given all the time and effort that goes analyzing the data and writing the report, we are so close to it that little surprises us by the time of publication. Nevertheless, here are 10 findings in this year’s report that were new and maybe even a bit surprising:

1. For-sale inventories dropped even lower over the past year.   

For the fourth year in a row, the inventory of homes for sale across the US not only failed to recover, but dropped yet again. At the end of 2016 there were an historically low 1.65 million homes for sale nationwide, which at the current sales rate was just 3.6 months of supply - almost half of the 6.0 months level that is considered a balanced market.

2. Fewer homes were built over the last 10 years than any 10-year period in recent history.

Even with the recent recovery in both single-family and multifamily construction, markets nationwide are still feeling the effects of the deep and extended decline in housing construction. Over the past 10 years, just 9 million new housing units were completed and added to the housing stock. This was the lowest 10-year period on records dating back to the 1970s, and far below the 14 and 15 million units averaged over the 1980s and 1990s.

3. Single-family construction grew at a faster pace than multifamily construction.

The slow recovery in single-family construction picked up its pace in 2016. For the first time since the Great Recession, the rate of growth in single-family construction outpaced multifamily construction.

4. Smaller homes may be coming back.
Behind the growth in single-family construction, and as a new development in 2016, construction of smaller homes is back on the rise. The median square footage of newly completed single-family homes declined slightly, due to increase in construction of smaller-sized homes (less than 1,800 sqft).
 
5. Rental markets are still strong.  

Although there are signs of moderation, the slowdown in multifamily rental markets appears to be limited, so far, to a small number of markets. Indeed, last year, multifamily construction levels were still on the rise in most of the country, rents declined in just 10 of the 100 markets, multifamily loan originations and lending volumes both hit new record highs, and rental vacancy rates were at a 30-year low.

6. Long-term, metro-area home price trends show surprisingly wide variations.

Home prices have rebounded widely across the nation. In 2016, prices were up in 97 of 100 metros, and 41 metros had regained their nominal peak price levels from the mid-2000s. Over the longer period of time, however, the combined impact of the boom and bust has resulted in significant differences in home price appreciation across the country. In some metros (particularly on the coasts) real home prices have grown by 50 percent or more since 2000, while prices in 16 of the top 100 metros (mainly in the Midwest and South) were below 2000 levels, after adjusting for inflation.

7. The 12-year decline in the US homeownership rate may be nearing an end.

Homeownership rates flattened last year and the number of homeowners increased for the first time since 2006, suggesting trends in homeownership may be strengthening. In addition, first-time homebuyers accounted for a higher share of sales in 2016 than the year before. Still, lending remained skewed to highest credit score borrowers.

8. The homeownership gap between whites and African-Americans widened to its largest disparity since WWII.

The post-2004 decline in homeownership has been especially severe for African-Americans and has pushed black homeownership rates to fully 29.7 percentage points lower than that for whites. Comparing census data going back to WWII, the white-black difference in homeownership rates has never been wider.

9. More than half of all poor now live in high-poverty neighborhoods.

Poverty is growing, concentrating, and suburbanizing all at the same time. Overall, the total number of people living in poverty in the US increased by nearly 14 million in 2000-2015. Moreover, 54 percent of the nation’s poor live in high-poverty neighborhoods (those with poverty rates over 20 percent).

10. Poverty is growing across metros and in rural areas.

Poverty has been on the rise throughout cities, suburbs, and rural areas. Indeed, while the number of poor living in high-poverty tracts in dense, urban areas grew by 46 percent between 2000 and 2015, the number of poor living in high-poverty tracts in moderate- and lower density suburban areas more than doubled.

Read the full State of the Nation’s Housing report on our website.

Growing Demand and Tight Supply are Lifting Home Prices and Rents, Fueling Concerns about Housing Affordability

A decade after the onset of the Great Recession, the national housing market has, by many measures, returned to normal, according to the 2017 State of the Nation’s Housing report, being released today by live webcast from the National League of Cities. Housing demand, home prices, and construction volumes are all on the rise, and the number of distressed homeowners has fallen sharply. However, along with strengthening demand, extremely tight supplies of both for-sale and for-rent homes are pushing up housing costs and adding to ongoing concerns about affordability (map + data tables). At last count in 2015, the report notes, nearly 19 million US households paid more than half of their incomes for housing (map + data tables).

National home prices hit an important milestone in 2016, finally surpassing the pre-recession peak. Drawing on newly available metro-level data, the Harvard researchers found that nominal prices in real prices were up last year in 97 of the nation’s 100 largest metropolitan areas. At the same time, though, the longer-term gains varied widely across the country, with some markets experiencing home price appreciation of more than 50 percent since 2000, while others posted only modest gains or even declines. These differences have added to the already substantial gap between home prices in the nation’s most and least expensive housing markets (map).

“While the recovery in home prices reflects a welcome pickup in demand, it is also being driven by very tight supply,” says Chris Herbert, the Center’s managing director. Even after seven straight years of  construction growth, the US added less new housing over the last decade than in any other ten-year period going back to at least the 1970s. The rebound in single-family construction has been particularly weak. According to Herbert, “Any excess housing that may have been built during the boom years has been absorbed, and a stronger supply response is going to be needed to keep pace with demand—particularly for moderately priced homes.”

Meanwhile, the national homeownership rate appears to be leveling off. Last year’s growth in homeowners was the largest increase since 2006, and early indications are that homebuying activity continued to gain traction in 2017. “Although the homeownership rate did edge down again in 2016, the decline was the smallest in years. We may be finding the bottom,” says Daniel McCue, a senior research associate at the Center.

Affordability is, of course, key. The report finds that, on average, 45 percent of renters in the nation’s metro areas could afford the monthly payments on a median-priced home in their market area. But in several high-cost metros of the Pacific Coast, Florida, and the Northeast, that share is under 25 percent. Among other factors, the future of US homeownership depends on broadening the access to mortgage financing, which remains restricted primarily to those with pristine credit.

Despite a strong rebound in multifamily construction in recent years, the rental vacancy rate hit a 30-year low in 2016. As a result, rent increases continued to outpace inflation in most markets last year. Although rent growth did slow in a few large metros—notably San Francisco and New York—there is little evidence that additions to rental supply are outstripping demand. In contrast, with most new construction at the high end and ongoing losses at the low end (interactive chart), there is a growing mismatch between the rental stock and growing demand from low- and moderate-income households.

Income growth did, however, pick up last year, reducing the number of US households paying more than 30 percent of income for housing—the standard measure of affordability—for the fifth straight year. But coming on the heels of substantial increases during the housing boom and bust, the number of households with housing cost burdens remains much higher today than at the start of last decade. Moreover, almost all of the improvement has been on the owner side. “The problem is most acute for renters. More than 11 million renter households paid more than half their incomes for housing in 2015, leaving little room to pay for life’s other necessities,” says Herbert.

Looking at the decade ahead, the report notes that as the members of the millennial generation move into their late 20s and early 30s, the demand for both rental housing and entry-level homeownership is set to soar. The most racially and ethnically diverse generation in the nation’s history, these young households will propel demand for a broad range of housing in cities, suburbs, and beyond. The baby-boom generation will also continue to play a strong role in housing markets, driving up investment in both existing and new homes to meet their changing needs as they age. “Meeting this growing and diverse demand will require concerted efforts by the public, private, and nonprofit sectors to expand the range of housing options available,” says McCue.

---

Live Webcast Today @ Noon ET

Tune into today's live webcast from the National League of Cities in Washington, DC, featuring:

Kriston Capps, Staff Writer, CityLab (panel moderator)
Chris Herbert, Managing Director, Joint Center for Housing Studies
Robert C. Kettler, Chairman & CEO, Kettler
Terri Ludwig, President & CEO, Enterprise Community Partners
Mayor Catherine E. Pugh, City of Baltimore, Maryland

Tweet questions & join the conversation on Twitter with #harvardhousingreport

What Impact Do Changing Interest Rates Have on Mortgage Demand?

by Stephanie Lo
JCHS Meyer Fellow

Could the post-Great Recession drop in housing demand have been driven in part by an increase in mortgage credit spreads across borrowers? In a new Joint Center working paper that uses proprietary data on the spread of mortgage rates across borrowers with different credit, I find that mortgage demand does react to mortgage interest rates in economically and statistically significant ways. 

This finding is significant because little is known about the extent to which changes in interest rates affect the demand for mortgages. Measuring this effect is difficult because both interest rates and the demand for mortgages are driven by macroeconomic factors. For example, after the financial crisis in the late 2000s, interest rates fell as the Federal Reserve attempted to stimulate the economy, but the demand for mortgages also fell because individuals faced adverse macroeconomic conditions. A naive estimate would suggest that over this period, lower interest rates drove lower housing demand, which is clearly not correct.

My study uses Loan Level Price Adjustments (LLPAs) to address this issue.  Instituted by FHFA in November 2007, LLPAs are additional fees paid upfront by the lender to Fannie Mae or Freddie Mac. The fees are higher for loans with higher loan-to-value ratios and borrowers with lower credit scores, and feature discrete cutoffs at certain credit scores, as measured at mortgage origination. Put simply, a borrower with a 700 credit score will face the same LLPA as a borrower with a 701 credit score, but will benefit from a discretely lower LLPA than a borrower with a 699 credit score.

Using administrative mortgage rate data, I find that LLPAs are completely passed through to borrowers, so while lenders receive the same mortgage rate across credit scores, borrowers just below a credit-score cutoff pay a higher mortgage rate than those just above that cutoff point (Figure 1). I further show that borrowers across these credit scores are virtually identical, and for high credit scores, lenders do not differentially screen across these cutoffs. This allows me to apply a regression-discontinuity design to examine how mortgage demand changes for borrowers just above and below several credit score cutoff points—660, 680, 700, and 720—where the interest rates offered to borrowers change. 

Notes: Rates are for conforming 30-year FRM. The numbers shown reflect the mean across the entire baseline sample for the exact FICO score shown, on the weekly level, from October 2008 to December 2014. Higher FICO scores tend to benefit from lower mortgage rates due to lower upfront payments induced by LLPAs. Source: Optimal Blue and Fannie Mae; Author’s calculations.

The results show that borrowers respond to changes in interest rates in economically and statistically significant ways (Figure 2). I estimate that a 25 basis point cut in interest rates results in a 50 percent increase in the likelihood of a potential borrower to demand a loan. In a given month, this increases the number of mortgage originations from about 100 per 100,000 individuals to 140 per 100,000 individuals. I also find that a 25 percent basis point cut in interest rates results in an increase in loan size of approximately $15,000, or about 10 percent of the average origination volume.

Notes: The mortgage rate series comes from the Freddie Mac Primary Mortgage Rates survey. Mortgage originations data is calculated as the total recorded origination amount for purchase mortgages by year, using the proprietary McDash LLC data.

These estimates help to explain the post-crisis drop in mortgage demand from low-income and low-credit borrowers. A back-of-the-envelope calculation using my estimates suggests that, had 680-719 FICO borrowers been subject to the same LLPA as 720 FICO borrowers, this group would have generated $15 billion more in mortgage demand over six years, which would have been a 33 percent increase in mortgage lending to this group alone. More generally, my estimates suggest that borrowers were very sensitive to mortgage rates after the crisis, implying that the Federal Reserve’s efforts to lower interest rates, which in turn lowered mortgage rates, may have been very effective in bolstering the housing market.

Defining the Generations Redux

by George Masnick
Senior Research Fellow

How should we define the baby boom, Generation X, and the millennial generation?

In a Joint Center blog published in 2012, I argued that using 20-year age spans for each generation would make it easier to compare them. Since many researchers still use generational definitions that span different and inconsistent age ranges, particularly for millennials, it is perhaps timely to reframe and restate my case.

In keeping with my recommendations, the Joint Center has long identified the cohort born between 1945 and 1964 as baby boomers.Those born between 1965 and 1984 are Generation X, and the cohort born between 1985 and 2004 are millennials (Figure 1). 

However, other analysts use several different earlier dates to usher in the millennial generation, apparently because they want to ensure that the oldest member of this cohort were considered adults at the dawn of the new millennium (i.e. they had turned 18 or 20 in the year 2000). This definition meant that by 2015, the oldest millennials were in their mid-30s, old enough to prompt compelling stories about how many 30-somethings were still living with parents, living in cities, forsaking marriage and childbearing, and delaying homeownership. In contrast, under my recommended cut-off dates, the oldest millennials turned age 20 in 2005 and didn’t start entering their 30s until 2015.

Besides making it easier to compare generations, there are several reasons why the millennial generation should start with those born in 1985 and turning 20 in 2005. As I noted in my 2012 blog, 1985 was the year that U.S. births once again exceeded 3.7 million, the approximate number that demarcated the beginning and the end of the baby boom, as well as the beginning and the end of the “baby bust” that defines Generation X.

Three other big changes occurred shortly after 2005 that significantly altered the way young adults live. First, social media participation skyrocketed. Facebook became available to everyone age 13 and older with a valid e-mail address in September 2006. Twitter became public in 2006. The first iPhone was released in June of 2007. As a result of these and other changes, the share of adults using social media rose from five percent in 2005 to 69 percent in 2016, according to a recent Pew Research publication.

Second, student loan debt outstanding more than tripled between 2005 and 2016, rising from $400 billion to over $1.3 trillion. This high level of debt is thought to affect everything from leaving the parental home, to getting married and starting a family, and purchasing a first home. 

Third, and perhaps most importantly, the economic changes that led to the Great Recession hit hardest among young adults who were in their 20s shortly after 2005. The unemployment rate of adults older than 25 without a high school degree rose from below six percent in late 2006 to 15 percent in mid-2009. (Those with a high school degree or more followed this trend within a year.) Unemployment rates of those with a high school degree or more have slowly improved, but still remain above pre-recession levels. Unemployment rates for those with less than a high school degree have returned to their pre-recession elevated levels, but people in this group generally are making less money and receiving fewer benefits than they did before the recession. Meanwhile, housing costs have returned to, or now exceed, their pre-recession levels.

Using equally broad 20-year age spans produces several important findings about the different generations. To start with, the millennial generation has been larger than the baby boom generation, now or at any other previous time since the boomers were age 10-29 in 1975 (Figure 2). Millennials now number almost 87 million compared to less than 79 million for baby boomers at the same age. This is in contrast to findings of a 2016 Pew Research study that compared generations using millennials with a smaller age range and found roughly equal numbers between these two generations in 2015 (75 million).

Source: Census Bureau Population Estimates 1975-2015

Using consistent age spans also shows the changing ways that immigration has affected the number of people in each generation. In 1995, when Generation X was age 10-29, it was smaller than the baby boom generation was in 1955, when it was the same age. However, because of immigration, by 2005, when Generation X was age 20-39, it already exceeded the number of baby boomers at the same age. 

Immigrants also make up a small but growing share of millennials. In 2015, 9.6 percent of millennials were foreign born compared to 21.4 percent of Generation X, and 15.3 percent of baby boomers (Figure 3). However, according to the latest Census Bureau population projections, the share of millennials who are foreign born is expected to rise to 20.9 percent in 2035 when they are age 30-49, which will boost the number of millennials to 97.3 million (Figure 4).  

Source: Census Bureau Population Projections for 2015

* Data do not allow 85-89 year olds from 85+ age group

Source: Census Bureau Population Projections for 2035

Finally, the constant-age-span approach allows us to identify significant generational differences in race and ethnicity. Overall, in 2015, 45.4 percent of millennials, 41 percent of Generation X, and 28.6 percent of baby boomers were minorities (i.e. non-Hispanic Blacks, non-Hispanic Asian/Others, or Hispanics of any race). Moreover, because of continued immigration, the share of millennials who are minorities is projected to rise to almost 50 percent in 2035 and the share of Generation X is projected to rise slightly to 42.4 percent. In contrast, the share of baby boomers who are minorities is projected to hold constant at 28.6 percent. 

These differences reflect changes for both foreign-born and native-born members of each generation. In 2015, fully 85 percent of both foreign-born millennials and foreign-born members of Generation X were minorities.  In contrast, only 78.5 percent of foreign-born baby boomers were minorities. Moreover, while 41.2 percent of native-born millennials were minorities, only 29 percent of native-born members of Generation X and 19.6 percent of native-born baby boomers were minorities (Figure 5).

* Data do not allow 85-89 year olds from 85+ age group

Source: Census Bureau Population Projections

Looking forward to 2035, the size of the baby boom cohort will drop to about 60 million people because a growing number of baby boomers will pass away. Many millennials and members of Generation X will want to live in the housing units formerly occupied by those baby boomers. Their ability to do so will not only be shaped by the fundamental economic and social changes discussed above but also by whether the large numbers of racial and ethnic minorities in these two generations will have full access to those housing markets, and with it, the ability to achieve the American dream. 

When Do Renters Behave Like Homeowners? High Rent, Price Anxiety, and NIMBYism

by Michael Hankinson
Meyer Fellow

In theory, renters and homeowners disagree about proposals to build new housing in their communities, particularly if that housing is close to where they live. However, in practice, this is not always the case. 

Rather, in a new Joint Center working paper that is based on new national-level experimental data and city-specific behavioral data, I find that in high-housing cost cities, renters and homeowners both oppose new residential developments proposed for their neighborhoods. However, in high-cost markets renters are still more likely than homeowners to support citywide increases in the supply of housing. Since changes in city governments over the past several decades have generally strengthened the power of neighborhood-level opponents to proposed projects, my findings help explain why it is so hard to build new housing in expensive cities even when there is citywide support for that housing.

NIMBYism and the Rising Cost of Housing

Since 1970, housing prices in the nation’s most expensive metropolitan areas have dramatically increased. Real prices have doubled in New York City and Los Angeles and nearly tripled in San Francisco. Driving this appreciation is an inability of new housing supply to keep up with demand. Even accounting for the cost of materials and natural geographic constraints on supply, the dominant factor behind this decoupling of supply and demand is political regulation, such as limits on the density of new housing developments and caps on the number of permits issued by a localities’ government.

                                               

These limits are a classic example of the NIMBY (Not in My BackYard) phenomenon. Even if residents support a citywide increase in the supply of housing, they may still oppose specific projects in their neighborhood. This seeming disconnect between views on citywide and local development policies creates a classic collective action problem for those policymakers who must find ways to reconcile the conflicting views.  

Photo by Michael Hogan/Flickr

Despite its popularity as a scapegoat, there is no individual-level, empirical data on how NIMBYism operates and among whom. Students of urban politics generally assume that homeowners have strong NIMBY tendencies not only because they benefit from rising house prices but also because they worry that nearby new housing units, particularly nearby subsidized housing units, might decrease the value of their home.

There is less consensus on (or studies of) how renters view new development. New supply may help ease prices for renters but their pro-development views may not be reflected in local policies because renters are less likely to become politically involved than highly motivated homeowners.  Alternatively, renters might not favor new projects if they believe the units will increase demand in their neighborhood, which, in turn, will lead to increased housing prices. To date, however, there has been very little research on how renters view development projects and whether their views differ from those of homeowners.

                                               

Measuring NIMBYism

To measure NIMBYism and general support for new housing, I collected two unique datasets. I conducted the first experimental tests of NIMBYism through an online survey of 3,019 respondents across 655 cities in 47 states. Respondents were asked about their support for development policies, including whether they would support a 10 percent increase in their city’s housing supply, with the question customized to each respondent’s city, stating how many homes and apartments currently exist and how many more would be built. Respondents also participated in an experiment where they were presented with two housing developments and asked which of the two proposals they preferred for their city. Each proposed development was described using several attributes, such as height and affordability level. To measure NIMBYism, respondents were also told how far each the of developments would be from their home, from two miles away to ⅛ mile away. By randomly varying this distance along with the other attributes, I was able to measure respondents’ sensitivity to proximity (NIMBYism), holding all other attributes equal.

To supplement this national survey, I also conducted a 1,660-person exit poll during the 2015 San Francisco election. Voters at 26 polling locations were asked their opinions on several housing-related ballot propositions similar to those presented in the national survey.

When Renters Behave Like Homeowners

As noted, renters and homeowners are expected to disagree on support for new housing, with NIMBY homeowners opposing citywide and neighborhood development and renters likely supporting the new supply. In line with existing theory, homeowners in my national survey largely opposed the proposed 10 percent increase in their city’s housing supply (28 percent approval), while a majority of renters supported the new supply (59 percent approval). Likewise, when asked in the experiment which of two randomly generated buildings they would prefer for their city, homeowners exhibited consistent NIMBYism, preferring buildings that were farther away from their home. In contrast, renters on average did not pick buildings based on distance from their home. If anything, renters preferred affordable housing that was closer to their home, displaying a YIMBY or ‘Yes in My BackYard’ attitude. In short, homeowners and renters tend to have very different attitudes towards both NIMBYism and the citywide housing supply.

However, in high-rent cities, renters look far more like homeowners. Instead of paying little attention to the location of proposed new housing, renters in expensive cities are just as NIMBY towards market-rate housing as homeowners. Moreover, this renter opposition to nearby development does not mean they support less new development overall. In fact, renters in expensive cities show just as much support for a 10 percent increase in their city’s housing supply as renters in more affordable cities. The main difference between these groups of renters is their NIMBYism.

Results from the San Francisco exit poll show a similar combination of supporting supply citywide, but opposing it locally. When asked about a 10 percent increase in the San Francisco housing supply, both renters and homeowners expressed high levels of support, at 84 percent and 73 percent approval, respectively. But, somewhat surprisingly, when asked if they would support a ban on market-rate development in their neighborhood, renters showed far more NIMBYism than homeowners, with 62 percent of renters supporting the NIMBY ban compared to 40 percent of homeowners.

NIMBYism and How We Permit Housing

Renters in high-rent cities generally both want new housing citywide but behave like homeowners when it comes to their own neighborhood. These scale-dependent preferences present a policy challenge for keeping cities affordable. Over the past 40 years, city governments have increasingly empowered neighborhoods to weigh-in on housing proposals through formal planning institutions. In doing so, these decisions have amplified NIMBYism and the ability to reject new housing, without maintaining a counterweight for the broader interest for new supply citywide. In other words, while most residents may support new housing for the city as a whole, both homeowners and renters are willing and increasingly able to block that supply in their own neighborhood, effectively constraining the housing supply citywide. This is housing’s collective action problem.

In separate research, I am empirically testing the effect of these strengthened neighborhood institutions on the rate of housing permitting since 1980. Likewise, I am conducting further experimental research on what types of citywide housing proposals are able to win the greatest support among both homeowners and renters. Hopefully, by measuring the tradeoffs between the ‘city’ and ‘neighborhood’ in the politics of housing, we can better address the deepening affordability crisis facing many American cities.

New Census Data on Incomes Suggests Growing Demand for Housing

Dan McCue
Senior Research Associate

New data released by the Census Bureau on Tuesday suggests that the demand for housing – particularly among young adults – may be growing.

As many news outlets have reported, the CPS 2016 Annual Social and Economic Supplement with household income data for 2015  showed that real median household income rose 5.2 percent from 2014 to 2015, to $56,516. It was the first annual increase in median household income since 2007. Median household incomes were up for each region of the country, and for non-Hispanic white, black, and Hispanic households, and across all age groups (Figure 1).

Source: JCHS tabulations of US Census Bureau, Current Population Survey Annual Social and Economic Supplements

The data release also showed significant increase in real median earnings at the person level in 2015, which grew 5.0 percent for all adults over age 15. Incomes were up most sharply among younger adult age groups under the age of 40 (Figure 2).

Source: JCHS tabulations of US Census Bureau, Current Population Survey Annual Social and Economic Supplements

For several reasons, this growth is likely to have significant implications for housing markets. First, at the individual level, higher incomes increase demand for housing. For young adults, housing independence is closely linked to income. Those with higher incomes are more likely to be able to rent their own apartment. Analyses in the Joint Center’s 2016 State of the Nation’s Housing report describe this, adding that nearly half of the decline in household formation among young adults aged 25-34 across the Great Recession was due to declines in income suffered by people in this age group. Income growth can therefore work in the reverse, helping enable young adults to move out of their parents’ basement and into their own home.

At the household level, income growth also increases housing demand, particularly for homeownership. As higher income households are more likely to own homes, increases in incomes among households will work against the continued decline in the US homeownership rate. In terms of affordability, the strong association between household income and housing cost burdens also means income growth may help alleviate some people who are stressed with housing costs, but on the affordability front there is still a long way to go.

Finally, it is worth noting that the Current Population Survey Annual Supplement is a relatively small survey with a high degree of annual volatility year to year, so the exact movements of household income and personal earnings measured year to year should be viewed with the wide margin of error they require. That said, the income growth reported in the latest survey is still a good sign that improvement in jobs and the economy is now translating into increased earnings that is likely to lead to growth in households and greater demand for housing.

Emerging Consumer Interest in Home Automation

 by Abbe Will
Research Analyst

Home automation is poised for significant growth with the rising prevalence of smartphone use, advancements in wireless technologies, and entrance of the millennial generation—the largest and arguably most tech-savvy generation to date—to the housing and home improvement markets. To better understand this emerging market segment, our Remodeling Futures program is undertaking research to measure the current and future size of this market, investigate the most promising technologies and services for homeowners, identify key players operating in the market, and explain homeowners' perceptions of the benefits and drawbacks to automating their homes.

A first look at homeowner attitudes and behaviors around home automation trends comes from a 2015 consumer survey by The Demand Institute. According to Joint Center tabulations of this survey data, of homeowners who said they were likely to do a home improvement project in the next three years, nearly half expressed excitement to incorporate more “smart home” technology into their homes, and nearly 30 percent reported that they are somewhat or very likely to install home automation products or features. About 29 percent of homeowners likely to remodel placed high importance on their homes having the latest technology, like built-in speakers, remote-controlled thermostats, electronic window coverings, etc. Another 44 percent said having the latest home technologies was somewhat important. Yet only 16 percent said that their current home could be described as already having the latest home automation technologies, which suggests a large gap in current home automation use and interest (Figure 1).

Note: Provided examples of latest home automation technologies included built-in speakers, remote-controlled thermostats and electronic window coverings. Source: JCHS tabulations of The Demand Institute 2015 American Communities Survey: Consumer Interview data

Compared to homeowners who place little or no importance on their home having the latest automation technologies, those who place a lot of importance on home automation are younger, have higher incomes and home values, and live in more urban areas (Figure 2). These homeowners are also much more likely to be planning a home improvement project of any kind in the next three years—68 percent compared to 56 percent of those placing some importance on having the latest home technology and 44 percent for those placing little or no importance. Over half of homeowners who place a high level of importance on having high-tech homes and are likely to remodel in the coming years reported that they are somewhat or very likely to install home automation products or features (52 percent) compared to 28 percent of homeowners expressing some importance and only 10 percent of owners expressing no importance for having an automated home.

Notes: Tabulations are of responses to the following question: How important is it to you that your home has the latest technologies, like built-in speakers, remote-controlled thermostats and electronic window coverings, etc., where 1=not at all important and 10=extremely important? Very important includes rankings of 8-10, not important includes rankings of 1-3.Source: JCHS tabulations of The Demand Institute 2015 American Communities Survey: Consumer Interview data.

There is a dramatic difference in attitudes toward home automation products and services by age of owner. Only 28 percent of homeowners age 65 and over who are likely to remodel in coming years expressed excitement to incorporate “smart home” technologies, compared to over two-thirds of owners under age 35 who either somewhat or strongly agreed with this sentiment. And where only 13 percent of homeowners age 65 and over reported being somewhat or very likely to install home automation products or features in the coming three years, almost 43 percent of owners under age 35 reported the same intent. A slightly higher share of owners age 35-44 expressed likelihood to install home automation improvements at 45 percent, but this share fell sharply for owners age 45-54 (30 percent) and age 55-64 (23 percent).

Although many homeowners are motivated to automate their homes, it is unclear how thoroughly they will act on their enthusiasm. According to a 2015 poll reported by The Demand Institute, homeowners may be hesitant to fully engage in home automation products and features because of high product costs, security flaws and glitches, and concerns for whether smart products will function as well as traditional home products. More research on the emerging home automation market will be shared in the forthcoming 2017 Improving America’s Housing report.

What Explains the Uneven Recovery in House Prices?

by George Masnick
Senior Research Fellow

Our latest State of the Nation’s Housing report identifies the upswing in house prices since the Great Recession as one of the bright spots in the overall housing recovery, but emphasizes that the increase has been uneven for different parts of the country. This is clear at the county level in a series of annual maps produced by the New York Fed, available back to 2009. In Figure 1, which uses CoreLogic data, states in the far west, Colorado, and Florida are seeing significant increases in home prices, while the picture is more mixed in the Northeast and Midwest.

Maps changes in home prices each month compared with prices one year earlier, by county, based on CoreLogic overall house price indexes. Source: https://www.newyorkfed.org/home-price-index/index.html

Greater depth on trends in house price indicators for 24 large cities using a different data source is available from The Economist in five interactive graphs. A longer time frame (going back to 1980), and additional information make The Economist graphs especially useful. In these (see Figure 2), the user is able to select various charts plotting 1980-2015 trends. Using the Zillow house price index, the charts show prices in real terms, the price-to- income ratio, the price-to-rent ratio, and the percentage change in prices. What is particularly useful is that trends in selected cities can be compared; the charts can be reconfigured by adding or removing individual cities and the user can change the start and stop dates by dragging quarterly date locators along the x-axis. This allows the user to focus separately on variability in price change during the pre-bust upswing, the Great Recession downswing, and the recovery. 

Source: http://www.economist.com/blogs/graphicdetail/2015/11/daily-chart-0

While differences in incomes and rents account for some of the variability in trends in the house price recovery among these 24 cities, we are unable to look at variability in trends in the supply and demand for housing in these cities. 

The Census Bureau recently updated their estimates of  county population growth and changes in the housing stock, so we can add this information to the price data. For the principal counties of the 24 cities included in The Economist charts, both population and housing stock change during the recovery have been quite variable. Table 1 presents the percentage change between 2011 and 2015 in population size, the size of the total housing stock, and the gap between these changes. The cities are sorted from high to low on the gap. Denver has the highest population growth (6.6 percent higher than housing stock growth during that period), and Detroit has the lowest (population growth 2.4 percent lower than housing growth). Also included in Table 1 is the percent increase in house prices between 2011q2 and 2015q2 drawn from The Economist interactive chart. I have ranked the house price increase in order to demonstrate that the tightest housing markets (those with the largest gap between population and housing stock growth) also tend to be those with the highest price increases. Similarly, those with the smallest (or negative) gap show the lowest price increases. There are several cities that are counter to this generalization, and I have highlighted three that deserve further comment.

Washington, DC has the second largest gap between population and housing growth, but ranks 17^th in price increase during the recovery. During the boom in housing prices between 1998 and 2006, Washington, DC increased by 120 percent, from a median price of $229.4K to $505.5K. This compares to a 42 percent increase for the total US. Subsequently, the downturn between 2006 and 2011 saw DC’s median price decline by 36 percent, to $322.8K. But this is still over 40 percent higher than the 1998 value. The percentage rise in median price during the recovery is just above the US average at 11.5 percent. Still, the median home price of $360K in DC is now twice the national average – about the same as Boston and Seattle. Young adults who move to Washington to take government jobs and internships have heavily fueled the area’s recent population growth, but they are less likely to become homeowners and drive prices even higher.

Detroit, at the other extreme, has had negative population growth during the recovery, but the 6^th highest percent increase in housing prices. However, Detroit’s price trajectory is somewhat unique. During the national run up in prices between 1998 and 2006, median home prices in Detroit increased only 4.1 percent – a tenth of the national average – from $172.6K to $179.6K. The Great Recession saw Detroit’s median price fall by more than half, to $83.3K in 2011. The recovery rise to $116.6K in 2015 still places the median price at just 59 percent of the 2006 value. The only reason the percentage rise since 2011 is a relatively high 39.9 percent is that the starting point is so low.

San Francisco is the third city that deserves further comment. While in the middle of the pack on the gap between population growth and housing growth, it ranks second in price increase during the recovery.  San Francisco has, by far, the highest median house price among those cities listed in Table 1. At $753.5K, it is $275K higher than San Diego, the next highest city, and over four times the national average. We would need to examine factors other than population growth or median income growth to account for the city’s unique position. Low vacancy rates, increases in income levels that fall well above the median, the high ownership rates of Asians (who make up a large share of San Francisco’s population), and perhaps even foreign ownership increases (similar to trends in New York City), would likely need to be considered.

With these caveats in mind, Table 1 makes a strong case for the gap between recent changes in supply and demand exerting a strong upward pressure on house prices. Except for DC, six of the top eight cities with the biggest gap rank in the top ten for percentage price increases. And except for Detroit, six of the bottom eight cities with the smallest gap are ranked in the bottom for price increases during the recovery. Houston and Dallas are both in the middle of the pack on price increases, despite being near the top of the list on population growth. The key here is that they also lead the 24 cities in growth in their housing stock. None of this should be surprising, of course, but it doesn’t hurt to remind ourselves of the overriding importance of the imbalance between population growth and housing stock growth in explaining trends in prices.  

Are Renter Worst Case Housing Needs Easing?

by Ellen Marya
Research Associate

Every two years, the Department of Housing and Urban Development (HUD) issues its Worst Case Housing Needs Report to Congress (WCN). This report highlights the challenges faced by low-income renter households in finding affordable, good-quality housing. In addition to data on characteristics of renter households and units, HUD’s report provides a count of renters facing worst case needs—defined as households who earn less than 50 percent of the area median income (AMI) who do not receive housing assistance from the government, who also are severely cost burdened (spending more than 50 percent on income on housing costs), and/or live in severely inadequate units. 

In its most recent WCN report released in May 2015, HUD noted a full 9 percent decline in the number of households with worst case needs, falling from 8.5 million in 2011 to 7.7 million in 2013. It was the first decline in that measure since a slight (1 percent) decrease in 2005-2007 and followed two periods of increases of about 20 percent. The change was surprising given that it coincided with a time of broadly stagnant incomes, rising rents, and a rapid increase in the number of renters. Do HUD’s numbers reflect genuine improvements in conditions for renters or are other factors at work?

A potential explanation for the decrease in worst case needs explored by HUD is a change in the income limits the agency uses to identify households earning less than 50 percent of AMI (very low-income households). Between 2011 and 2013, HUD reduced the maximum income for very low-income households by $516, decreasing the number of households in this group eligible to be counted among those with worst case needs by about 1 percent. When HUD compared the tallies of renters with worst case needs using the new and old cutoffs, however, it found that only 20,000 of the 750,000 total reduction 2011–2013 could be attributed to the new lower income limit.

Much of the decrease in worst case needs is due to a drop in households with severe cost burdens, which account for the vast majority of worse case needs. HUD reported that the total number of renter households with severe cost burdens fell from 10.4 million in 2011 to 9.7 million in 2013, a decline of over 6 percent. Counter to these findings, however, calculations from the Joint Center for Housing Studies (JCHS) using a different data source, the American Community Survey, found a negligible decline (just over 1 percent) in severely cost burdened renters, from 11.3 million in 2011 to 11.2 million in 2013.

Notes: Severe burdens are defined as housing costs of more than 50% of household income. In HUD tabulations, households with zero or negative income are excluded unless they pay Fair Market Rent or more for housing. For households paying no cash rent, utility costs are used to represent housing costs. In JCHS tabulations, households with zero or negative income are assumed to have severe burdens, while renters paying no cash rent are assumed to be without burdens.

Sources: HUD Worst Case Housing Needs: 2015 Report to Congress and JCHS tabulations of US Census Bureau, American Community Surveys.

Several unique methodological differences help contextualize why HUD and JCHS estimates vary (Figure 1). The first key distinction between the measures reported by HUD and JCHS is the source data. HUD estimates of cost burdens rely on the American Housing Survey (AHS), a biennial survey jointly administered by HUD and the Census Bureau assessing characteristics of the housing stock and its occupants. JCHS calculates cost burdens using the American Community Survey (ACS), an annual Census Bureau survey of households designed to supplement the short form decennial census. The surveys vary in size and scope. The AHS reaches 50,000-70,000 housing units in its national longitudinal sample, gathering detailed information on housing quality and cost, assisted status, and location. The ACS reaches 3.0-3.5 million households in the years since its full implementation and collects information on many demographic, economic, and employment characteristics, along with selected housing cost and unit information.

In addition to their variations in design, the AHS and ACS use distinct methods for defining occupied units that result in different counts for the most basic variables of total households (equivalent to total occupied housing units) and households by tenure. While several reports have examined these differences in more depth, essentially the ACS uses a broader definition of occupancy and makes more attempts to contact sampled households. These features of the survey tend to increase the number of occupied units reported and can count households in a seasonal residence (often rented) rather than their usual residence (possibly owned), increasing the number of renter households over the AHS (Figure 2). While not unique to the 2011-2013 period to explain the divergent trends, this difference in methodology consistently results in about 2 million more renter households in the ACS over the AHS, likely contributing in part to a higher ACS count of burdened renters

Sources: HUD Worst Case Housing Needs: 2015 Report to Congress and JCHS tabulations of US Census Bureau, American Community Surveys.

There are also important distinctions in how cost burdens are measured and what adjustments are made to the data. According to its WCN report, HUD excludes households reporting zero or negative income when calculating cost burdens, unless these households report paying the local Fair Market Rent (FMR) or more for housing. In this case, HUD assumes the negative income reported to represent a temporary situation and imputes a higher income for the household. If these households pay more than FMR and live in adequate, uncrowded housing, an income slightly higher than the local area median is assigned, again assuming a temporary period of income loss. HUD also makes adjustments for households that report paying no cash rent. For these households, HUD relies on reported utility costs to represent housing costs and identify housing cost burdens.

In contrast, JCHS assumes all households reporting zero or negative income to be severely cost burdened and all those paying no cash rent to be unburdened (in the case of a household reporting both zero or negative income and no cash rent, the household is assumed to be unburdened). The difference in adjustments may have had an especially large impact in 2011-2013 as JCHS tabulations of the AHS find the number of renter households reporting zero or negative income rose by nearly 13 percent, about four times the rate of growth of renters reporting positive income. ACS numbers do not mirror this rise, as renters reporting zero or negative income increased by 3 percent 2011-2013. Excluding zero or negative income households may better isolate households with perennially low incomes from those potentially higher-wealth households reporting temporary annual business losses. However, excluding these households from ACS analysis finds that severe cost burdens still do not drop nearly as much in 2011-2013 as HUD methods shows. Subtracting all households with zero or negative incomes from the ACS burden count shifts the totals to 10.4 million severely burdened renters in 2011 and 10.3 million in 2013, a decline of just 1.4 percent—much smaller than that reported by HUD for the period. Conversely, if all zero or negative income households in the AHS were considered burdened regardless of rent level, the decline in renters with severe cost burdens 2011–2013 would be about 4.6 percent.

In addition to varying counts of zero and negative income households, a disparity in median renter income patterns between 2011 and 2013 may also explain part of the divergent cost burden trends in that period. HUD cites an increase in median renter income of 7.2 percent in 2011-2013 in real terms as a factor driving down the number of severely burdened renters. While JCHS estimates of ACS data also find an increase in real median renter income in that time—the first increase since 2006-2007—the gain is a distinctly smaller 5.2 percent. HUD notes in its WCN report that some of the observed increase in median income may be due to newly formed higher income renter households, but does not further explore this possibility. Analysis of ACS data indeed shows that an influx of higher income renters occurred over this time. Of the net 1.7 million increase in renter households measured in the ACS 2011-2013, fully 1 million or 60 percent had incomes of $75,000 or more, over twice the median renter income (Figure 3). With this group pulling up the median figure, aggregate income gains may not have impacted lower income households sufficiently to meaningfully decrease the number of severely burdened renters.

Source: JCHS tabulations of US Census Bureau, American Community Surveys.

Indeed, analysis of the most recent 2014 ACS reveals the number of severely burdened renters is once again on the rise, climbing to 11.4 million—the highest number on record. Whether new AHS data expected in the upcoming months and the next WCN report due the following spring confirm this trend or show a further drop in severely burdened renters, the results of both surveys again underscore the acute unaffordability of housing for millions of renter households. Understanding whether affordability pressures are worsening or easing is therefore crucial to making informed decisions concerning rental assistance and other housing policy actions. Given additional data showing persistent rent growth and  tightness in the rental market, the larger sample size of the ACS, the benefit of an added year of ACS data showing rising burdens, and the unusually large recent shifts in renter incomes in the AHS, it seems likely that the enduringly high severe cost burden levels reported by the ACS are more accurate and affordability pressures for renter households continue to build.