Rent Continues to Rise in America

This article was written by Mary Schwartz. It was originally published on December 7, 2017 in the Census blog, Random Samplings.

The nation experienced an overall average increase of $21 in median gross rent according to statistics released from the 2012-2016 American Community Survey (ACS) five-year estimates, compared to 2007-2011 ACS five-year estimates results, which have been adjusted for inflation.

Gross rent is the contract rent plus the estimated average monthly cost of utilities (electricity, gas, and water and sewer) and fuels (oil, coal, kerosene, wood, etc.) if these are paid by the renter (or paid for the renter by someone else).

Changes in Metropolitan Gross Rents

Of the 382 metropolitan areas in the United States, the median gross rent in 156 areas did not change between 2007 to 2011 and 2012 to 2016, and seven would not allow for comparison due to boundary changes. Of the 219 that did change, increases outnumbered decreases four to one with 175 increases and 44 decreases.

  • San Jose-Sunnyvale-Santa Clara, Calif., was among the metro areas with the greatest increase in gross rent, with an increase of $246 from 2007 to 2011 to 2012 to 2016 (from $1,555 to $1,801).
  • Carson City, Nev., experienced one of the largest decreases in gross rent, with a decrease of $158 (from $985 to $827).

Click the map to zoom to your area.

The 50 most populous metropolitan areas mirrored all metros in that increases outnumbered decreases four to one with 32 increases, eight decreases, and nine had no changes.

Changes in Micropolitan Gross Rents

Of the 551 U.S. micropolitan areas, the median gross rent in 360 areas did not change between 2007 to 2011 and 2012 to 2016 and 45 would not allow for comparisons due to boundary changes. Of the 146 areas that did change, increases outnumbered decreases more than two to one with 107 increases and 39 decreases.

  • Among the micropolitan areas with the highest increases in gross rent was Andrews, Texas, which had a $352 increase (from $608 to $960).
  • Clewiston, Fla., had one of the largest decreases in gross rent, with a decrease of $152 (from $844 to $692).

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County-Level Gross Rent Changes

In the 264 counties with 250,000 or more people, the median gross rent in 79 did not change between 2007 to 2011 and 2012 to 2016. Of the 185 that did change, increases outnumbered decreases by more than four to one with 151 increases and 34 decreases.

  • At $252, Santa Clara County, Calif., had one of the greatest increases in gross rent (from $1,561 to $1,813).
  • Among this group, Clark County, Nev., had one of the largest decreases in gross rent with a decrease of $108 (from $1,121 to $1,013).

In counties with 35,000 and 49,999 people, 241 of 322 counties had gross rents that didn’t change between 2007 to 2011 and 2012 to 2016. Of the counties with changes, increases outnumbered decreases 2 to 1 with 56 increases and 25 decreases.

  • Among the counties with the largest increase was Isle of Wight County, Va., which had an increase in gross rent of $208 (from $811 to $1,019).
  • Among the counties with the largest decrease was Hendry County, Fla., which had a decrease of $152 (from $844 to $692).
  • Note that the 2007 to 2011 estimates for Isle of Wight County ($811) and Hendry County ($844) are not statistically different from each other.

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These results were compiled to provide communities with important statistics to measure housing affordability. These data help determine whether adequate housing is affordable for residents and provide and fund housing assistance programs. They also help enforce laws, regulations and policies designed to eliminate discrimination in private-market housing, government programs and in society.

In addition, the Department of Housing and Urban Development uses these data to establish Fair Market Rents used to determine the level of tenant subsidies in housing assistance programs.

Incorporating Health into Physical Needs Assessments

This article was written by Elizabeth Zeldin and Emily Blank. It was originally published on the Building Healthy Places blog on June 27, 2017.

In May, the New York City Department of Housing Preservation and Development (HPD) and New York State Homes and Community Renewal (HCR) launched a new Integrated Physical Needs Assessment tool to provide affordable housing owners with a comprehensive protocol to assess the range of options available to upgrade their buildings. The tool will allow owners to take advantage of incentives and opportunities to make their properties as sustainable and safe as possible. A key component of the Integrated Physical Needs Assessment is a new health overlay, providing guidance on health-focused upgrades as well as operations and maintenance protocol.

As readers of this blog are aware, the quality of our housing determines the quality of our health . But while most affordable housing slated for rehabilitation is reviewed by an engineer prior to the creation of a scope of work to determine needs and upgrade options, this process does not typically focus on factors related to tenant health. Recognizing that building rehabilitation provides an important opportunity to improve health conditions, Enterprise Community Partners (Enterprise), and the Local Initiatives Support Corporation (LISC), in partnership with New York City’s health and housing agencies and Tohn Environmental Strategies, created a health overlay for the standard physical needs assessment (PNA) that is required for most affordable housing preservation in New York State.

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The potential public health impact of healthier affordable housing rehabilitations is immense, particularly for vulnerable and low-income households. There is a clear link between the quality of housing and epidemics like childhood asthma and other respiratory ailments. People in low-income communities are more likely to live in housing that is inadequate, and low income families are more likely to live in substandard housing that has elevated risks of lead poisoning and asthma. A health-focused assessment tool has the potential to reduce some of the housing-related health issues that plague low-income communities.  Housing upgrades with a health lens have repeatedly been shown to improve resident health outcomes.

In 2015, HPD began to require all building owners receiving low-interest financing to perform a Green PNA to enhance energy efficiency.  The process of revisiting the PNA to incorporate green components sparked an interest in refining the tool even further to add a health focus. To make this happen, Enterprise and LISC spearheaded a unique collaboration among professionals in the health sector, building scientists, government agencies, and nonprofit affordable housing organizations.  The collaboration was critical in ensuring the effectiveness of this tool.

While many building owners would like to incorporate health-focused interventions, the affordable housing industry has only recently begun to create tools that help prioritize their many options. With the health overlay to the PNA, owners can better understand the balance between costs and health outcomes of various measures and structure maintenance practices for long term sustainability. As the evidence base for the connection between health and housing grows, this tool will be invaluable in ensuring that properties incorporate essential healthy housing practices.

Map of overcrowded housing units

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One of the biggest challenges in creating the health overlay was to keep the assessment protocol cost-effective and clear to a building scientist not necessarily well-versed in public health, while still laying out a comprehensive set of solutions. For example, air quality testing was considered but ultimately excluded due to the difficulties in assessing the results and subsequent interventions. Similarly, in-depth interviews with existing residents were also discussed but ultimately not included in the process.

Owners will also need to carefully weigh the costs and benefits to health-focused interventions. Although there are many low-cost health upgrades that owners should consider, certain upgrades can come at a high cost. For example, ventilation upgrades, which can greatly improve indoor air quality and minimize mold and moisture issues, are quite expensive and might not be feasible in projects with limited ability to take on debt.

In order for housing to serve as a platform for success, it must be both affordable and healthy. As the evidence base connecting health and housing grows, the health overlay to the PNA will be invaluable in ensuring that a building offers the healthiest living conditions for those who call it home.

Homes on the Range: Homeownership Rates Are Higher in Rural America

This article was written by Christopher Mazur. It was originally published to the Census blog, Random Samplings, on December 8, 2016

For decades, homeownership rates have been an important indicator of the health of housing markets all across the United States. Communities use these data to recognize the changing landscape of their housing markets by analyzing patterns in the percentage of owner-occupied housing units compared  to all occupied units. Homebuilders, financial institutions and realtors all depend on homeownership data to determine what type of housing to build, finance and sell to prospective homebuyers. While last decade’s housing crash and Great Recession altered the economy and conditions of housing markets throughout the nation, studies have shown that most Americans continue to believe homeownership is both desirable and attainable.

Data from the American Community Survey provide the opportunity to learn more about homeownership in America and how it differs between rural and urban areas. With the American Community Survey 5-year data for geographies with populations less than 65,000, homeownership can be analyzed beyond just the nation and regions but also by rurality of a county. For more information about how the U.S. Census Bureau defines urban and rural geographies, see Defining Rural at the U.S. Census Bureau.

According to 2011-2015 American Community Survey data, there were 116.9 million occupied housing units in the nation. Of these occupied housing units, 22.6 million (19.3 percent) were located in rural areas and 94.4 million (80.7 percent) in urban areas.

In general, rural areas in the United States have higher homeownership rates than urban areas.  Compared with urban areas, where the homeownership rate was 59.8 percent, rural areas had a homeownership rate of 81.1 percent. In all four regions, the homeownership rate was higher in rural areas than in urban areas. In the Northeast, rural areas had a homeownership rate of 83.8 percent, whereas urban areas had a rate of 58.2 percent. Homeownership rates in rural areas accounted for 83.6 percent in the Midwest and 79.8 percent in the South, while in urban areas it accounted for 63.3 percent and 60.7 percent in those regions, respectively. The West had a homeownership rate of 77.3 percent in rural areas and a rate of 56.7 percent in urban areas.

Click the map to zoom to your area

County level analysis was conducted by categorizing counties into three levels of rurality based on the percentage of the decennial census population living in the rural areas of the county in 2010. The counties were delineated as completely rural (100.0 percent rural), mostly rural (50.0 to 99.9 percent rural) and mostly urban (less than 50.0 percent rural). (See all U.S. counties and their level of rurality here.)

Based on the three levels of rurality for counties, homeownership rates increase as the proportion of the population living in rural areas increases. Both completely rural (76.2 percent) and mostly rural (74.4 percent) counties had higher median homeownership rates than the 68.2 percent in mostly urban counties (there was no statistical difference between the completely rural and mostly rural rates).  Similar to national findings, the homeownership rates in completely rural and mostly rural counties in the Midwest, South and West regions were higher than those counties that were mostly urban (again, there was no statistical difference between the completely rural and mostly rural rates). Completely rural counties in the Northeast had homeownership rates higher than both mostly rural and mostly urban counties in that region.

To understand why homeownership rates are higher in rural areas than urban areas, some of the characteristics that have traditionally been indicators of homeownership can be taken into account. The likelihood of owning a house increases as age increases. With a median age for the adult population of 51 in rural areas compared to 45 in urban areas, the adult population in rural areas tend to be older and naturally in stages of life in which owning a house is more likely. Furthermore, householders age 65 or older, an age group that regularly has some of the highest homeownership rates, accounted for 27.7 percent of all households in rural areas and 22.4 percent in urban areas.

Click the map to zoom to your area.

Households consisting of married couples are a group that generally has higher homeownership rates compared to non-married households. Nationally, the homeownership rate for married-couple households was 79.8 percent, higher than a homeownership rate of 49.0 percent for non-married households. Married-couple households comprised 58.6 percent of all households in rural areas, higher than the 45.8 percent of households in urban areas. Furthermore, there were a higher proportion of owner-occupied houses owned by married-couple households in rural areas (89.0 percent) than in urban areas (77.0 percent).

Being a homeowner carries a huge financial commitment. Paying a monthly mortgage, utilities, real estate taxes, property insurance and any applicable fees for living in a condominium or mobile home can quickly add up. However, these housing costs were not as high in rural areas as they were in urban areas. In rural areas, the median monthly housing cost for households paying a mortgage was $1,271, lower than the median monthly housing cost of $1,561 in urban areas.

Click the map to zoom to your area.

Another interesting fact about owner-occupied households in rural and urban areas is that  a higher percentage of housing units were owned “free and clear” in rural areas (44.0 percent) than urban areas (32.3 percent). That is, there was no mortgage or loan on the house. As a result, by not having a mortgage these households have lower monthly housing costs which frees up more financial resources for discretionary use.

Understanding our changing population and housing stock is important to the well-being of all American communities.  The American Community Survey is the only data source that gives all areas, whether they are big or small, rural or urban, the ability to better plan and address local housing trends and needs.

Young Adults Seeking Public Housing….Good Luck.

This post was originally written by and was featured on Youth Radio on March 21, 2016.

It’s normal for millennials to still live at home these days. But what if you’re a millennial who doesn’t have a home to go back to?

“I didn’t mind sleeping on the floor. I didn’t mind sleeping on the couch,” said 23-year-old Alkeisha Porter.

Growing up, she says she didn’t like her mom’s husband and her dad had a drug problem. So at 16, she moved out and became homeless.

“I was basically just house-hopping from friends to family members. Hey, it was comfortable to me. It wasn’t cold. I wasn’t sleeping outside,” she said.

Young people – including eighteen to 24-year-olds — make up one of the fastest growing homeless populations in the country. In many big cities like New York, Los Angeles, and San Francisco where housing is at a premium, finding affordable housing is especially hard.

In San Francisco, one-bedroom apartments rents average $3490 (more than 3400) a month. There are about 1600 homeless young adults in the city on any given night, and public housing is out of reach for many of them.

Housing Cost Burden Map

Click map to zoom to your community.

Porter found an apartment for herself and her baby in a building located on Ocean Avenue in San Francisco, run by a non-profit called Mercy. It provides subsidized affordable housing for low-income residents, including 25 apartments reserved for 18 to 24-year-olds.

“This is my first dream to be on my own, get my own apartment, paying rent, paying bills, like a normal adult does,” said Cinthia Mendoza, one of Porters neighbors.

Domestic violence forced Mendoza out of her home and into foster care when she was 17. Now, at 21, she’s showing me around her new apartment. She listed off the appliances at her new residence.

“Behind the door there’s a refrigerator, a huge one, which I’ve never had in my life. And then a microwave, and then I have a big stove, and it’s brand new. Everything was brand new when I got here,” she said.

Mendoza is one of the lucky ones. There are far more low-income youth who need housing than there are subsidized apartments available. Technically, Mendoza and Porter could qualify for federal public housing instead of living at Mercy.

Side by Side Map Housing and Poverty

Map Legend Click map to zoom to your community.

“It’s very simple,” said Ron Ashford, a director at the federal housing authority. “[Young adults looking for public housing] just need to find their local housing authority, go to the housing authority and apply.”

But for young adults, the problem isn’t applying; it’s getting in. Their demographic is considered a lower priority.

Ashford explains, “Remember that you are competing against families who do not have a home. When I was in the New York City Housing Authority some 20 years ago, the waiting list was more than 10 years.”

In San Francisco, the waiting list for public housing is so long that it’s closed for the time being. There’s another obstacle specific to young adults: being enrolled in college classes presents extra rules that limit access to Section 8 federal housing.

“We need to be rethinking some of these rules around housing for students,” said Eric Rice,
a professor of social work at the University of Southern California.

Rice’s work includes a focus on homeless youth. He says he’s frustrated that taking a few courses can jeopardize a young person’s ability to get housing.

“Because if we have students who are homeless who are low-income, they need higher education as a long-term solution for alleviating their situation with respect to poverty. And we want to make that easier, not harder,” Rice said.

Before 2005, students didn’t have to meet such strict rules when it came to public housing. But then a scandal broke, in which well-off college athletes in schools across the country were caught living in Section 8 public housing. This controversy prompted the rules to change, affecting most full- and part-time students.

This is just one reason local solutions like Mercy Housing have popped up, to provide alternatives for 18 to 24-year-olds. The nonprofit welcomes students, but has to limit student residents to part-timers to qualify for federal tax credits. These tax credits make it possible to build this sort of public housing alternative in the first place.

The Mercy apartments on Ocean Avenue are situated right across the street from San Francisco City College. This was an intentional decision on the part of Mercy, to make it easier for the young residents to enroll if they choose to.

These student-residents don’t have to worry about being kicked out of their homes just for taking a few classes.

See more at Youth Radio.

Mapping Factors that Influence Social Mobility

Upward social and economic mobility from one generation to the next is harder in the U.S. compared to other developed countries. While many Americans have access to resources and opportunities that bolster their chances of achieving the  “American Dream” for their families, many more lack the opportunity to simply break from the cycle of poverty. The difference between the two is often separated by just one street.

In a study looking at social and economic mobility in the US, Harvard and UC-Berkeley economics professors aptly stated, “The U.S. is better described as a collection of societies, some of which are `lands of opportunity’ with high rates of mobility across generations, and others in which few children escape poverty.”

The researchers describe high mobility areas as generally having:

  • Less residential segregation
  • Less income inequality
  • Better primary schools
  • Greater social capital
  • Greater family stability

The income gap between wealthy and non-wealthy Americans shows no signs of slowing down or reversing. Of course, this is not a new or unique trend, especially in some of America’s major cities. However, the starkest disparity is seen in the nation’s capitol. Poorer Americans not only have lower incomes, but also have more debt and less savings than higher income earners. In Washington, D.C., for example, the top 20 percent earn $151,132 per year, while the bottom 20 percent earn Less than $20,151 per year. In an area with one of America’s highest costs of living, the disparities echoe across neighborhoods, schools, and family households.

Let’s look at how the researcher’s indicators of social mobility play out in Washington, D.C.

Segregation in Residential Areas


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Income Inequality and Poverty

Average Family IncomeDC

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Click the map to zoom to your community.

Quality of Schools


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Family Stability


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Outlook for social mobility in the U.S.

So, where are the most upwardly mobile cities?  Salt Lake City, Pittsburgh, and San Jose top the list. That means, children born into low income families in these cities generally fare better at moving up the income ladder as they age compared to other U.S. cities.

Why do these areas see higher intra- and intergenerational mobility? It depends.

San Jose has less concentrated poverty, proximity to innovative areas like Silicon Valley, and fewer single-family households, yet it’s schools under-perform and it has a higher level of racial and economic segregation. In Salt Lake City, they have a strong public school system, solid middle-class, and less income inequality.

However, despite these high rankings, the all too familiar trends of increasing rent and fewer middle class jobs, which many parts of the country are already experiencing, is beginning to impact even these cities. A similar problem is beginning to grow in San Jose as housing becomes unaffordable and middle class jobs dwindle. And as Salt Lake City becomes more diverse, increasing segregation and intergenerational poverty is making community workers take notice.

Issues like the non-ceasing, coast to coast trend of unaffordable housing and a weakening middle class have gained national attention. But it’s at the local level  where we’ll see real change- school funding, neighborhoods, access to jobs, affordable transportation, more housing options, and inclusive communities – are all factors researchers have underscored time and time again as having the greatest impact on upward mobility. When segregation of affluence, race, and resources exists, all members of the community see less upward social mobility. Most factors that impact social mobility are conditions children are born into- and where they’re born into it. For the U.S., creating one “land of opportunity” means building from the neighborhood on up.

To locate areas of opportunity in your community, check out Community Commons’ Location Opportunity Footprint Tool (LOFT).

Does Growing Up In Assisted Housing Affect Earning Potential?

Originally published on the U.S. Census Bureau Research Matters Blog on April 1, 2016 by Mark J. Kutzbach, Center for Economic Studies

In 2000, nearly 3 million children under age 18 lived in voucher-supported or public housing sponsored by the U.S. Department of Housing and Urban Development (HUD). Although assisted housing programs have been in place for some time, research on the long-term effects on resident children is scarce and hampered by methodological limitations. To shed light on this topic, my colleagues and I combined Census Bureau data with administrative data to track children through assisted housing and into the labor force as adults.

The Census Bureau often combines survey and administrative datasets to produce new statistics, but these data can also help answer complex research questions. For this project, we identify families with multiple teenage children counted in the 2000 Census and link them to HUD administrative records to observe how they move into and out of assisted housing between 1997 and 2005. We then match the children to their adult earnings from 2011 to 2013 using data from the Longitudinal Employer-Household Dynamics program.

To identify the impact of assisted housing on earnings, we compare adult earnings between siblings who experienced different amounts of assisted housing as teenagers. As siblings share many of the background characteristics that affect adult earnings — for example, household poverty and parental motivation — we are able to distinguish the effect of assisted housing participation from the effect of any other shared childhood experiences.

Assisted Housing and Poverty

We also look at whether public and voucher housing might have different effects on boys and girls. In public housing, a household lives in a project run by the local housing authorities, whereas in voucher-based housing, the housing authority pays a large portion of a household’s rent and utilities in private housing chosen by the household.

We first observe that children growing up in assisted housing tend to have lower adult earnings compared with other children, even those from similarly low-earning households. However, observing a difference in adult earnings between children who participated in assisted housing and those who did not is not enough to conclude that the assisted housing participation caused the difference. For example, participating households are required to earn below specified thresholds in order to be eligible. Beneficiary children are therefore likely to come from impoverished backgrounds and — even in the absence of assisted housing participation — earn less as adults.Voucher Housing and African AmericansHousing Voucher Legend

When we use only between-sibling differences, we find that assisted housing participation is associated with increases in adult earnings for girls and only modest, often statistically insignificant, decreases in earnings for boys. In other words, holding constant family characteristics, the negative effect of assisted housing disappears for most children.

The overall result that assisted housing raises earnings for girls more than boys might depend on the community under study. To shed additional light on this difference, we look at results separately for white, black and Hispanic households. The between-siblings effects are consistently positive only for black non-Hispanics, who represent roughly half of all HUD residents (and are mostly not distinguishable from zero effect for whites and Hispanics). The figure below shows both the between-siblings estimates and the naïve estimates, those that do not compare siblings, for black non-Hispanic households.

In the between-siblings model, girls in black non-Hispanic households earn 6.5 percent more for each year spent in voucher housing and 4.3 percent more for each year spent in public housing while a teenager. Boys in black non-Hispanic households earn 2.6 percent more for each year they spent in voucher housing and 3.8 percent more for each year spent in public housing. The difference in the effects of voucher housing for girls in black non-Hispanic households relative to boys is statistically significant.

How might housing assistance affect children? While housing assistance relieves families of a major expenditure, other studies have shown that it may also concentrate low-earning households together in large public housing buildings or low-income neighborhoods, exposing children to high-poverty settings. Thus, the net effects are not certain without an empirical analysis. When distinguishing between the effects of assisted housing programs and family characteristics, we found that more time spent in assisted housing participation for siblings led to increases in adult earnings, especially for black non-Hispanic girls.

We are doing more research to try to unravel what aspects of housing assistance might have the greatest effects and to explore other adult outcomes that may be influenced by housing. We are also trying to understand why we observe girls benefiting more than boys for any type of housing assistance.

For more information, see Childhood Housing and Adult Earnings: A Between-Siblings Analysis of Housing Vouchers and Public Housing, a joint paper written by Fredrik Andersson, Office of the Comptroller of the Currency; John C. Haltiwanger, University of Maryland and U.S. Census Bureau; Mark J. Kutzbach, U.S. Census Bureau; Giordano Palloni, International Food Policy Research Institute; Henry O. Pollakowski, Harvard University; and Daniel H. Weinberg, Virginia Tech.

Mapping the Impact of Rising Rents

There is a growing shortage of affordable housing in the U.S. With more middle class families turning towards renting instead of home ownership, low-income families are being squeezed out of rentals that were once affordable. But for both middle class and low-income families, renting continues to take up a growing percentage of household income.

The Shift Towards Renting

Demand for rental housing is at its highest since the 1960’s- and home ownership at a 48-year low. While home ownership is still more affordable than renting in many U.S. markets, it is not an option for individuals or families who do not qualify for loans, have incomes that are too low, or are saddled by debt like student loans. In 2008, a quarter of rental applicants were paying off student debt, by fall 2015 that number had risen to half.

With high-income earners forgoing home ownership, increasing urban populations, baby boomers downsizing, and the housing crash fresh in the minds of would-be homeowners, vacancy rates for rentals are at an all-time low. And with that comes ever increasing rent costs.

Rent Taking Up More of Americans’ Paychecks

Affordable rent is considered less than 30 percent of a household’s income, though today, most rents surpass that. According to the National Low Income Housing Coalition’s 2016 Out of Reach Report, U.S. renters need to earn an average of $20.30 per hour to afford a modest two- bedroom apartment- the average wage of a U.S. renter is $15.42.

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What about those making the federal minimum wage of $7.25? First, there is no state where a full-time worker making minimum wage could afford a fair market value, one-bedroom apartment – anywhere. Second, in order to afford a one-bedroom apartment, that worker would need to put in 90 hours per week at work or 112 hours per week for a two-bedroom apartment. For single moms or dads not only holding down full-time employment, but also part-time evening work, that is often a reality- and the only option.

 Housing Wages (By State) Needed to Afford a Modest Two-Bedroom Apartment

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But let’s take a closer look at it this way, too:

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Unaffordable rent is a critical issue millions of Americans face. These severely cost-burdened households are a critical issue for the U.S. as well. With more and more of workers income going towards rent, less and less is being spent in the market. The Out of Reach report also found that families who spend more than half their income on housing spend 50 percent less on clothing, one-third less on food, and 80 percent less on medical care compared to those with affordable rent.

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It’s interesting to note that when we look at labor force participation, it seems to be worse in many areas that have more cost-burdened households and higher rents.

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More “Low-Income” Housing is Not Always the Answer

As long as higher-income earners are willing to pay high prices, there will continue to be a shortage of affordable apartments. With federal housing funds cut in half over the last 10 years, and fewer families qualifying for rental subsidies, the shortage will remain. Though building more “low-income” housing (via tax credits to developers) often seems to be the go-to solution, that in fact has shown to be not only ineffective overall, but actually more expensive than simply increasing the purchasing power of renters.

A New Tool to Find Areas of Opportunity

Last week Community Commons revealed a new tool developed as part of a six-week software development sprint organized by the White House’s Opportunity Project. The goal of the sprint was to develop on-line tools demonstrating “the art of the possible” using open data from federal and local sources.

When we saw the new data, we were inspired to create a new tool, the Location Opportunity Footprint, or LOFT.

LOFT is designed for:

  • neighborhood leaders seeking a grant or preparing for a community planning meeting;
  • those in economic development working to improve opportunities in community, and;
  • advocates making a case.

LOFT enables users to view the intersection of school proficiency, housing and transportation costs, and nearby jobs to find areas of opportunity. Like our Vulnerable Populations Footprint, the indicator thresholds can be modified to fit with the local context and priorities.

To create a Location Opportunity Footprint visit the Maps and Data page and select “Location Opportunity Footprint.”



LOFT is available for use anywhere in the nation. Simply enter in an address or a community name and watch the map zoom in!


The map will load with the following thresholds for opportunity already defined:

  • a school proficiency index of 50 or higher;
  • over 100 jobs available nearby per worker; and
  • the monthly costs for housing and transportation for a family at 50% adjusted monthly income of $2,000.

The areas shaded in dark red in the map below are the opportunity footprint meaning all three thresholds are met. The areas in the lighter red or orange indicate that just two of the three thresholds are met. The areas shaded in blue, purple, or yellow are where just one of the three thresholds (school proficiency, jobs, or monthly costs respectively) are met.


Like our vulnerable populations footprint, the thresholds can be customized using the sliders on the right to meet your own priorities and local context. In the image below, the sliders have been changed to show schools with higher school proficiency index, more jobs available per workers and a lower monthly cost for housing and transportation.


There’s lots more to do after you’ve defined your opportunity footprints. You can:


  • save your footprint to use later;
  • create a short demographic report;
  • create a comprehensive indicator report; or
  • map your footprint from other data in Community Commons.

For example, here is a short demographic report:


And here is a page from a comprehensive indicator report. The data are summarized for your footprint area and compared with surrounding counties, the state, and the nation.


Finally, you can map your footprint alongside the thousands of other data in Community Commons. Find the complete list of data you can map with your footprint hereloft_mapwithotherdataWe’d love to know your thoughts on this tool and how you are using it. If you’re a developer and have ideas about how this new opportunity data could be used join the conversation at opportunity.census.gov.

2010-2014 ACS Data Now Available in the Map Room

The team at Community Commons has been working hard in recent months to roll out all the latest American Community Survey data. We’re pleased to announce that at this time, all ACS data in the Community Commons Map Room is updated.

Next, our team will continue to incorporate the ACS data in our publicly available reports, like the Community Health Needs Assessment (CHNA), over the next month, so stay tuned!

In the meantime, check out a few of our ACS favorites below.

Public Transport Commute 2014

See how public transit use has changed since the last release. Click the map to explore changes in the areas around Terre Haute and Bloomington, IN, for example.

Why does the Census collect this information?

  • For mass transportation and metropolitan planning
  • For employment planning, define banking and housing markets, and planning emergency response
  • To plan programs and services for the disabled population, bicycle commuters, carpool and ride shares, and many other groups.
  • To estimate and study the effects of long commutes on health (obesity, hypertension, etc.), and on the environment (emissions, contaminants, etc.).

Learn more from the American Community Survey.

Uninsured Population

Uninsured populations have decreased across Kentucky. Click the map to toggle between 2010-2014 and 2009-2013 data to see the changes.

What’s so interesting about health insurance data?

  • Helps identify vulnerable populations that may be at disproportionate risk of experiencing limitations in health care access, poor health quality, and sub-optimal health outcomes
  • To project the demand for VA extended health care services
  • To determine where health insurance is lacking as part of research into infectious disease and contaminants
  • To review and analyze the unmet needs of people with disabilities and to identify the characteristics of the target service population

Learn more from the American Community Survey.

Average Rent

Average rent has increased in several counties in New Mexico. Click the map to see rent prices in your area.

How can you use housing data in your work?

  • To identify rental distribution of housing units used to determine Fair Market Rents (FMRs)
  • To describe the balance of owners and renters
  • If you are a grantee receiving block grant funds from the Community Development Block Grants, HOME Investment Partnership Program, Emergency Solutions Grant and Housing Opportunities for Persons with AIDS programs, you can use these maps to describe housing needs
  • To advocate for the allocation of low-income housing assistance in a fair and equitable manner
  • Businesses and mortgage lenders use these statistics to guide future operations.

Learn more from the American Community Survey.

Have questions about what is available now or what will be available later? Check out our “What’s New” page or contact us.