Are Renters Less Energy Efficient than Homeowners?

by Elizabeth La Jeunesse
Research Assistant

According to data from the Energy Information Administration, American renters use nearly a third more energy per square foot than homeowners. What accounts for this difference?

In part it’s because rental units are typically smaller, and are therefore more energy intensive. For example, a family in a small apartment needs a refrigerator, stove, and water heater the same way a family in a larger apartment (or a homeowner) does.  These things require a basic amount of energy, regardless of square footage.  Rental units also tend to be older; 75 percent of renters live in units built before 1990 while 68 percent of owners live in older units.

Source: US Department of Energy, Energy Information Administration, 2009 Residential Energy Consumption Survey.

That said, the above chart shows that the amount of energy used by renters can vary depending on whether their utility costs are fixed (built into their rent) or if they pay for utilities themselves.  As the chart illustrates, renters consume considerably more energy when some or all of their utility costs are fixed.  This shows the general tendency of people to consume more of something when there is no added cost for doing so. Such excess energy consumption drives up the amount of energy renters use overall, further accounting for the efficiency gap between owners and renters.  

Even so, renters who pay for utilities separate from their rent still use slightly more energy per square foot than owners.  This suggests a real, structural efficiency gap between rental and owner units. In fact, a recent study found that multifamily rentals in 2009 had 34% fewer energy efficiency features on average than other housing types. Consumer fuels and utility costs have risen over 50 percent over the past decade, outstripping overall inflation, which makes energy efficiency improvements (insulation, energy efficient windows, compact fluorescent lighting, HVAC upgrades, energy efficient appliances) appealing to people wanting to lower their energy bills.  But when tenants pay for their metered energy usage, a property owner’s incentive to perform energy efficiency retrofits is lower, since any cost savings will benefit the tenants, not the owner. Rental property managers also have less control over how their tenants respond to an energy retrofit (e.g. more efficient windows might still be left open in the winter).  These things can keep rental property owners from performing energy efficiency retrofits at the same rate as homeowners which, in turn, keeps energy usage by renters high.

The gap in energy usage between owners and renters suggests that there are real opportunities for savings through some combination of added incentives for property owners to make these investments in retrofits and greater incentives for tenants to conserve energy. Lowering energy use would have the additional benefit of bringing down the cost of rental housing at a time when more renters are paying very high shares of their incomes for housing as a new study by the National Low Income Housing Coalition shows. 

Nonprofits Play Key Role in Repairing U.S. Homes

by Abbe Will
Research Analyst

Private sector spending on improvements and repairs to U.S. homes is approximately $300 billion a year. Yet as a new Joint Center working paper shows, each year nonprofit organizations and public agencies are also investing resources into the rehabilitation and repair of the homes of America’s most vulnerable households—including the elderly, disabled, and those with low-incomes—who might not otherwise be physically or financially able to undertake critical home remodeling and repair projects themselves. Major nonprofits such as Rebuilding Together, Habitat for Humanity, Enterprise Community Partners, the Local Initiatives Support Corporation, and NeighborWorks America, as well as thousands of local community development organizations across the country, are filling a significant and growing need, largely unmet by the private sector, by investing considerable resources—financial, technical, and direct provision of services—to make homes safer, healthier, more energy efficient, and more accessible for disadvantaged households.

The recent foreclosure crisis and sluggish economy undermined years of efforts to stabilize and improve distressed neighborhoods in cities across the country, only adding to the need for nonprofit and public sector involvement. Until this past cycle, housing inadequacy—a measure of the physical condition of housing units—had been on the decline in the United States, largely due to the success of govern­ment housing policies and the growing affluence of the pop­ulation. Since the housing market bust, however, this trend has reversed with the number of moderately or severely inadequate homes increasing by 7% between 2007 and 2011 to 2.4 million units. Certainly the severe housing and economic downturn had a measurable impact on the quality of the nation’s housing.

While a comprehensive data source of home rehabilitation and repair activity by nonprofits and public agencies does not exist, this new Joint Center working paper provides some insight into the topic. Rebuilding Together, one of the nonprofits in the study, provides critical home rehabilitation and modification services to low-income homeowners through its extensive network of local affiliates. A member of the Joint Center’s Remodeling Futures Steering Committee, the organization provided support for an affiliate and homeowner survey that collected data on the various types of projects undertaken by their affiliates, as well as demographic and socioeconomic information about the homeowners served and their experience partnering with Rebuilding Together.

Recent spending on home repairs and replacements, as reported by participating households, suggests that many of the homes worked on by Rebuilding Together have seen significant under-investment over the years. While the average American homeowner spent $3,000 on home improvements and repairs in 2011, according to Joint Center analysis of the American Housing Survey, almost two-thirds of Rebuilding Together program participants reported having spent less than $500 on average in the past year—fully 80% less than the typical homeowner in the U.S. Indeed, according to estimates developed by Rebuilding Together affiliates and the Joint Center’s Remodeling Futures Program, the homes serviced by Rebuilding Together were so in need after years of deferred maintenance, that the average value of the rehabilitation and repair projects undertaken by Rebuilding Together was in excess of $6,000 per home, or twice the annual amount spent by the typical homeowner in the U.S.

Home improvement expenditures under the Rebuilding Together program in 2011 were heavily oriented toward exterior replacements and kitchen and bath improvements—projects that would produce the greatest gains in key program objectives such as health and safety concerns, accessibility, and savings in energy use. Typical projects included additions or replacements of steps, ramps, railings, grab bars, windows and doors, roofing, insulation, energy-saving appliances, as well as painting and plumbing and electrical repairs. In the end, Rebuilding Together participants reported significant improvements in health and safety concerns, improvements in accessibility, and energy use savings as a result of nonprofit involvement.

Source: 2011 Harvard JCHS-Rebuilding Together Household Survey

While a more precise estimate is unavailable, hundreds of millions of dollars are spent each year by nonprofits such as Rebuilding Together, community organizations, and public agencies. Their contributions not only improve conditions for residents, they also help preserve badly-needed affordable housing opportunities, stabilize and revitalize deteriorating neighborhoods—of special importance in recent years—and encourage neighborhood stability by helping long-term residents of the community to remain safely in their homes.

A Surge in Hispanic Household Growth? The Challenge of Interpreting Short-Term Trends in Datasets that are Occasionally Adjusted

by Dan McCue
Research Manager

Interpreting year-to-year changes in annual surveys from the Census Bureau can be a tricky business, especially around decennial censuses.  Because it is the largest and most comprehensive count of the population, after each new decennial census is released, the smaller but more frequently issued surveys available from the Census Bureau, such as the Current Population Survey (CPS), Housing Vacancy Survey (HVS) and American Housing Survey (AHS), are updated, or “re-benchmarked” based on the findings from the new decennial census.  Prior to this, these surveys were controlled to extrapolations based off of the prior decennial census. While it is inevitable that ten years of extrapolation can lead controls to drift off course, failing to recognize when and how datasets are re-benchmarked to correct for this drift can lead to misinterpretations about short-term trends.  The danger is that the re-benchmarking adjustment can be misinterpreted as an actual trend that occurred in a single month or year, rather than what it really is: a discontinuity in the data due to an adjustment made to correct the net sum of ten years of extrapolation errors that had accumulated in the dataset since the last decennial census.

Take for instance, the following data overview in a recent online article:

"The latest U.S. census figures, for June, show year-over-year Hispanic homeownership increased by 7.3 percent, from 6.2 million to 6.7 million. For black-owned households during the same time, the numbers dipped by 1.3 percent, from 6.3 million to 6.2 million. Likewise, whites' homeownership also saw a slight decrease of about 1 percent, from 58.4 million to 57.8 million." - National Journal

On its face, this data leads us to conclude that the number of Hispanic homeowners surged from June 2011 to June 2012, while at the same time the number of homeowners among both blacks and whites dropped significantly, and therefore without growth in Hispanic homeownership the overall number of homeowners in the US would have dropped significantly over the past 12 months instead of growing slightly as was reported.

However, the Census Bureau’s Housing Vacancy Survey (HVS) showed that both Hispanic and non-Hispanic homeownership rates dropped during the June 2011 to June 2012 period, a time wherein Hispanics also suffered higher than average unemployment rates. At first glance, the divergence in the two reports is puzzling. However, on the Census Bureau’s HVS website, there is a short but significant sentence under the “Changes in 2012” section of the Source and Accuracy of Estimates web page:   

“Beginning in the first quarter 2012, the population controls reflect the results of the 2010 decennial census.”  - HVS Source and Accuracy of Estimates

This note is important, because the distribution of occupied households by tenure, race, and ethnicity of households is based on these population controls.  Therefore, any changes in the number of homeowners by race and ethnicity that spans across the first quarter of 2012 is also incorporating change due to the shift in the distribution of households by age, race, and tenure that occurred with the re-benchmarking of the survey..

The adjustment to Hispanic households due to the re-benchmarking appears to be significant. Looking at the Hispanic share of households in HVS before and after Q1 of 2012, we can see that the re-benchmarking in that quarter led to a significant upwards adjustment that forms a discontinuity in this series (Figure 1).  The existence of a discontinuity is corroborated by data from the Current Population Survey, which re-benchmarked to the 2010 Census in 2011. The CPS Table Creator allows us to see the impact of the re-benchmarking directly by comparing the Hispanic share of households in 2011 under both 2000 and 2010 Census weights.  It shows that the 2010 census weights raise the Hispanic share of households a full percentage point, from 11 to 12 percent, compared to the 2000 census weights.  In short, this all suggests that results from the 2010 Census found that the 2000 Census-based population extrapolations had been underestimating Hispanic household growth in the 2000s, and therefore these household counts needed to be shifted upwards in 2012 as a correction.

Figure 1:  The Shift to 2010-Based Population Controls in Q1 of 2012 in the HVS Coincides with an Apparent Discontinuity in the Hispanic Share of Householders

Source: JCHS tabulations of US Census Bureau, Housing Vacancy Survey data.

With the change in population controls in the HVS in Q1 of 2012, the amount to which the shift in the distribution of households towards Hispanic households was underestimated incrementally over the last ten years gets corrected all at once, and gets attributed as change measured between Q4 of 2011 and Q1 of 2012.  And as we see in Figure 2, the quarterly change recorded in Q1 of 2012 has a huge influence over our view of the recent trend in household and homeownership growth by Hispanic ethnicity. 

Figure 2: Concurrent with the Switch to Census 2010-Based Population Controls, The First Quarter of 2012  Has a Large Influence on the Recent Trend in Hispanic and Non-Hispanic Household Growth 

Source: JCHS Tabulations of the 1995-2011 AHS

Without the ability to compare alternative HVS household counts for Q1 of 2012 under both 2000- and 2010-based population controls, it is difficult to determine exactly how much of the change in Hispanic and non-Hispanic households and homeowners in 2011 to 2012 was due to the re-benchmarking and how much was due to actual change measurable in the survey.  We refrain from presenting alternative scenarios here, but because the quarter is such an outlier, most assumptions to smooth or discount that quarter of data would conclude with much lower Hispanic household and homeowner growth and much higher growth among non-Hispanics over the past year.  

The Return of Substandard Housing

by Kermit Baker
Director, Remodeling
Futures Program

The magnitude of the housing bust that began in the middle of the past decade is well documented, with a 75 percent plunge in housing starts, 45 percent decline in existing home sales, and 30–35 percent slide in house prices. Less well known is how the housing bust and the ensuing cutbacks in residential investment have eroded the condition of existing homes.

Reasons for concern over the potential for underinvestment in the housing stock are numerous, from the aging of the rental stock to the rising share of homeowners with underwater mortgages to the surge in foreclosures and short sales. In fact, there has been a significant decline in spending on homes during the housing bust. Average annual improvement spending by owners declined 28 percent between 2007 and 2011 after adjusting for inflation, totally erasing the run-up in spending during the boom years. Rental units never saw a run-up last decade, so per unit spending was down 23 percent between 2001 and 2011.

Figure 1

Sources: JCHS tabulations  of 2001-07 C-50; 2001-11 AHS; and Estimating National Levels of Home Improvement and Repair Spending by Rental Property Owners by Abbe Will, JCHS Research Note N10-2, October 2010.

There has been surprisingly little concern in policy circles that this significant reduction in housing investment might be producing deterioration in our housing stock. Thanks largely to the success of government housing programs and the increasing affluence of our population, the condition of the housing stock has largely dropped from policy makers’ radar screens in recent decades.

The first Census of housing in 1940 labeled 45.4 percent of owner-occupied units as substandard, which was defined as housing which lacked complete plumbing facilities or was dilapidated. This share dropped sharply over the next several decades, falling all the way to 6.1 percent in 1970 according to Clemmer and Simonson’s analysis in a 1983 article in the AREUEA Journal. Because measures of housing quality were dropped after the 1970 Census due to unreliability of data and the subjective measurement of structural quality, more recent statistics on the number of substandard units are not available from the Census.

However, beginning in the early 1970s, information from the American Housing Survey (AHS) points to the structural condition of the housing stock continuing to show slow but continuous improvement.  As of the 2007 AHS, just 2.27 million owner-occupied homes, or 3.0 percent of the total, were characterized as moderately (with fairly minor structural problems) or severely inadequate (with more major structural problems), down from 3.24 million or 5.1 percent of the total in 1995.

Figure 2

Notes: A housing unit is defined as inadequate through a combination of gross unit attributes such as lacking complete kitchen or bathroom facilities or running water, as well as signs of disrepair such as leaks, holes, cracks, peeling paint, and broken systems. For a complete definition, see the US Department of Housing and Urban Development’s Codebookfor the American Housing Survey, Public Use File: 1997 and Later.  Source: JCHS tabulations of the 1995-2011 AHS.

Since the housing market bust, however, this trend has reversed. By 2011, more than 2.4 million owner-occupied homes were classified as inadequate, an increase of 160,000 from the 2007 AHS. While this increase seems fairly minor in the big picture, the importance of it is not. Given available data, this appears to be the first significant increase in the share of homes with structural problems since the government was able to track them beginning with the 1940 census.

Now that the housing market is recovering and residential investment is increasing, this dip in the quality of the housing stock may well reverse as these homes are improved. However, recent Joint Center analysis concludes that once a downward cycle in housing quality is underway, for many homes it doesn’t get reversed. This analysis focused on owner-occupied homes that were characterized as inadequate in 1997, looking at their experience over the following decade but before the dramatic rise in distressed properties.

According to the 1997 AHS, 4.4 percent of owner-occupied homes were considered inadequate. By 2007, these units accounted for almost 8 percent of homes in this 1997 cohort that were no longer owner-occupied (vacant, or converted to rental or nonresidential uses), suggesting that they were less in demand. Even more telling is that these inadequate units accounted for almost 17 percent of all 1997 owner-occupied homes that were demolished within the decade.

The longer-term fate of the current slightly larger number of inadequate homes is unknown. Many of these homes likely will be renovated to provide affordable housing opportunities. However, many may not recover without extra help. Given the extraordinary circumstances that many homes have gone through in recent years, particularly foreclosed homes that often were vacant and undermaintained for extended periods of time as they worked their way through the foreclosure process, they may be more at risk than their inadequate predecessors. It’s probably time to put the structural condition of the housing stock back on the housing policy agenda.

Different Data Sources Tell Different Stories About Declining Geographic Mobility

by George Masnick
Fellow

The Census Bureau recently released its usual extensive Current Population Survey (CPS)-based package of tables on geographic mobility for 2011-12.  A new feature of this release is a series of historical charts, one of which is reproduced below. Examining just the last decade, mobility rates took a sharp turn downward in 2007-08, with most of the decline occurring in moves between states (Figure 1).  This sharp decline has been the impetus for many stories about the decline in the geographic mobility rate and its implications for housing (see this example).  Most have assumed that the mobility decline was caused by the Great Recession: with reduced job opportunities across the country, there was less inducement to change residence in search of employment, particularly among young adults who were unable to leave the parental nest. Some have asserted that the loss in housing values and tight mortgage lending have “locked in” owners who otherwise would like to move, especially those owners who are now under water on their mortgages.

Figure 1

Source: U.S. Census Bureau, Current Population Survey, 1948-2012, select years.

Yet some recent efforts to scrutinize mobility rate trends and associations have raised doubts about the basic facts. Research at the Minneapolis Fed suggests Interstate Migration Has Fallen Less Than You Think.  Another series of papers have debated the strength of the association between negative equity and reduced mobility. Findings published in 2010 that owners with negative equity are one-third less mobile were challenged as largely a result of the authors dropping some negative-equity homeowners' moves from the data.   The challenge received a rebuttal that was lukewarm at best.

But a more fundamental question is whether there was indeed as sharp a decline in mobility in the late 2000s as the CPS data in Figure 1 suggests. Since 2006 the Census Bureau’s American Community Survey (ACS) has also provided annual estimates of mobility rates that are consistently higher than those of the CPS and suggest a different trend (Figure 2). One factor contributing to higher ACS rates could be that, starting in 2006, the ACS included in its sample the more mobile institutional population. In contrast, the CPS sample excludes most people that live in group settings such as correctional facilities, military barracks, and college dormitories.  The Census Bureau has recalculated the ACS mobility rate based only on the population living in households for 2006 through 2009.  These modified ACS rates plotted in Figure 2 are significantly lower than those with the group quarters population included, are in line with the long-term more gradual decline in the pre-2000 CPS trend, and definitely do not show as sharp a decline around 2007.

Figure 2

Source: Current Population Survey (CPS) and American Community Survey (ACS) published tables.  The Census Bureau has recalculated the ACS mobility rate based on population living in households for 2006 through 2009 (www.census.gov/prod/2011pubs/p20-565.pdf).  The ACS did not cover the entire U.S. until 2005.

The differences between the ACS and CPS mobility rates in Figure 2 are supported by additional analyses of inter-county and inter-state migration trends from these two sources. This research also shows that the ACS levels and trends are mirrored almost exactly by migration rates from IRS data, further adding credence to the ACS. Mobility rates of household heads calculated from the American Housing Survey (AHS) also closely follow the levels from both the ACS and the IRS data.  The persistently lower rates of mobility in the CPS since 2000 are not well understood, but might be explained by the CPS data being collected primarily by a telephone survey that might not fully reflect the recent growth of cell phone-only households - assuming that those households contain persons that are among the most mobile.  (The ACS is primarily a mail survey, IRS data are from filed tax returns, and the AHS follows the occupants of particular housing units over time.)

If there is a story in the ACS trend, aside from one of gradual decline over the long-term, it is that the period immediately leading up to the Great Recession was one of above-trend mobility.  More people were moving than might have been expected during the peak of the housing boom. IRS migration trends in the analysis cited above support this story as well.  The bursting of the housing bubble has mostly just returned geographic mobility rates to their long-term trend.  The long-term decline in mobility is likely due to a host of broad social, economic, and demographic trends: the aging of the population; delays in the transition to adulthood; the increase in dual-career households; the changing race/Hispanic origin of the population; more working from home; more homogenized employment opportunities across different locations; the increase in long-distance commuting patterns; etc.  Absent another housing boom, we should expect near-term mobility rates to continue to gradually decline.