The availability of affordable housing for households with low or moderate incomes is an important precondition for families to achieve housing stability and to be able to move out of poverty. In contrast, housing instability and homelessness undercut families’ prospects for upward mobility.

Metric: Ratio of affordable and available housing units to households with low and very low income levels

This metric reports the number of available housing units affordable for households with low incomes (below 80 percent of area median income, or AMI), very low incomes (below 50 percent of AMI), and extremely low incomes (below 30 percent of AMI) relative to every 100 households with these income levels. Housing units are defined as affordable if the monthly costs do not exceed 30 percent of a household's income. Values above 100 suggest there are more affordable housing units than households with those income levels. Values below 100 indicate a shortage of affordable housing for households with those income levels. For this metric, the stock of available housing units includes both vacant and occupied units and both rental and homeowner units. A unit is considered available for households at a given level of income if its monthly cost is affordable at that income level (regardless of the income of the current occupant).

Validity: Affordable housing ratios of this type are widely applied in studies of local housing market conditions and trends. Both the income categories and the affordability standard are well established and accepted in both research and policy.

Availability: These ratios can be constructed using data from the Census Bureau’s American Community Survey and income categories defined by the US Department of Housing and Urban Development, both of which are publicly available nationwide.

Frequency: These ratios can be updated annually.

Geography: Affordable housing ratios can be computed by city or county. For less populated areas, several years of data may need to be pooled to obtain reliable estimates.

Consistency: Affordable housing ratios can be computed consistently for all counties and cities over time. Because the income categories are calculated relative to AMI, the affordability metric appropriately reflects local economic conditions.

Subgroups: Because these ratios focus on the characteristics of the housing stock, stratifying by demographic subgroups is not relevant. However, housing units in each affordability category can be stratified by size (number of bedrooms) and tenure (owned or rented).

Limitations: These shares do not reflect the quality of the available and affordable housing units. Units counted as available and affordable for households with low or very low incomes may be poor quality or too small to meet household needs. This metric is somewhat sensitive to patterns of residential mobility. For example, if the number of households with very low incomes were to decline (because of outmigration), this metric would show improvement even if no additional affordable units were produced.

Housing instability and homelessness undercut families’ prospects for upward mobility. Homelessness is an extreme manifestation of powerlessness and loss of belonging. It both reflects and contributes to unemployment and financial insecurity, disrupts children’s education, and undermines both physical and emotional health.

Metric: Number and share of public school children who are ever homeless during the school year

This metric identifies the number of children (age 3 through 12th grade) who are enrolled in public schools and whose primary nighttime residence at any time during a school year was a shelter, transitional housing, or awaiting foster care placement; unsheltered (e.g., a car, park, campground, temporary trailer, or abandoned building); a hotel or motel because of the lack of alternative adequate accommodations; or the housing of other people because of loss of housing, economic hardship, or a similar reason.

Validity: Data are reported by school administrators and generally verified by local liaisons and state coordinators. This is a direct and well-established measure of homelessness for children that results from and reflects housing instability among families and unaccompanied children. The definition of homelessness used for this metric extends beyond literal homelessness to effectively include the full range of circumstances in which a family does not have a stable home of their own.

Availability: The US Department of Education requires every local education agency to collect and report these data.

Frequency: New data for the metric are available annually.

Geography: The boundaries of local education agencies can be mapped onto to the city and county levels.

Consistency: This metric is consistently defined, collected, and reported for all local education agencies nationwide.

Subgroups: This metric can be disaggregated based on students’ disability status and whether they are enrolled in English as a Second Language courses.

Limitations: This metric does not include homeless adults who are childless, and it does not capture homelessness among children who do not enroll in public school. Further, it could show improvement if the families of homeless children move to a neighboring jurisdiction or if policies “push” them out. This metric is quite sensitive to patterns of residential mobility if large numbers of families with very low incomes flow into or out of a local education agency’s boundary.

Limited levels of both economic inclusion and racial diversity curtail families’ choices about where to live, block access to neighborhoods with better opportunities, and create areas of concentrated poverty and distress. Segregation also perpetuates exclusion and prevents people of different classes, races, and ethnicities from building the social ties that foster mutual respect, dignity, and belonging. Economically segregated areas of concentrated poverty are associated with an increase in teenage pregnancy, male joblessness, single motherhood, and high school dropout. An increase in economic segregation exacerbates differences in educational attainment between high- and low-income children. Racial and economic segregation can negatively affect people at all stages of the life course and impact the belonging and economic dimensions of mobility from poverty.

Economic Inclusion Metric: Share of residents experiencing poverty living in high-poverty neighborhoods.

This metric measures the share of a jurisdiction’s residents experiencing poverty who live in high-poverty neighborhoods (measured by census tract). A high-poverty neighborhood is one in which over 40 percent of the residents are experiencing poverty.

Validity: Measures of poverty concentration have been widely used to measure the extent and severity of economic exclusion and isolation. The more concentrated and separate people in poverty are from better-resourced neighbors, the more isolated they are from the larger community and the social and economic resources and opportunities it can provide.

Availability: Data required to compute poverty concentration are available from the Census Bureau’s American Community Survey (ACS).

Frequency: New data for the metric are available annually.

Geography: This metric can be computed for all cities and counties nationwide. For less populated areas, several years of data may need to be pooled. Because this metric reflects the structural conditions facing a city or county’s residents, changes in the metric possibly caused by people moving into or out of a jurisdiction do represent changes to those structural conditions.

Consistency: Poverty concentration can be consistently defined and calculated across all cities and counties over time.

Subgroups: The metric can be disaggregated by race or ethnicity, gender, and other demographic factors. For less populated areas and for certain demographic groups, several years of data may need to be pooled to obtain reliable estimates.

Limitations: This metric can be sensitive to the overall poverty rate of a city or county. Therefore, changes in poverty concentrations need to be assessed with reference to the city or county’s overall poverty rate.

Racial Diversity Metric: Neighborhood exposure index, or the share of a person’s neighbors who are people of other races and ethnicities.

This metric is constructed separately for each racial or ethnic group and reports the average share of that group’s neighbors who are members of other racial or ethnic groups. For example, the exposure index would report the share of people who are Black and Latinx in the census tract of the average white person, the share of people who are white and Latinx in the census tract of the average Black person, and the share of people who are Black and white in the census tract of the average Latinx person. Higher values of the index indicate more neighborhood diversity and more day-to-day exposure of people to neighbors of different races and ethnicities.

Validity: The exposure index is one of several widely used measures of residential segregation or inclusion. It effectively captures the multiracial or multiethnic diversity of American communities, it reflects the experience of individuals of all races and ethnicities, and it provides a comprehensive picture of neighborhood racial and ethnic composition.

Availability: Data required to compute neighborhood exposure indexes are available from the ACS.

Frequency: New data for the metric are available annually.

Geography: The data are available for cities and counties, but also at the neighborhood level. Because this metric reflects the structural conditions facing a jurisdiction’s residents, changes in the metric that may be caused by people moving into or out of a jurisdiction represent changes to those structural conditions.

Consistency: Exposure indexes can be consistently defined and calculated for all jurisdictions over time.

Subgroups: This metric is by definition disaggregated by race or ethnicity.

Limitations: This measure can be sensitive to the overall racial or ethnic composition of a city or county. Therefore, changes in exposure indexes need to be assessed with reference to the city or county’s overall racial or ethnic composition. Further, although this index can be constructed annually, appreciable changes may take many years to observe.

Belongingness reflects a person’s sense that they are valued within a group. Research shows that a sense of belonging is associated with improved physical and mental health outcomes and reduced emotional distress, suicide, mental illness, and depression. A sense of belonging in school contributes to positive academic outcomes; low absenteeism; higher completion rates; positive attitudes toward learning; higher academic self-efficacy; and reductions in disruptive behavior, emotional distress, and other risky behaviors.

Metric: Inclusion of Other in the Self scale

This metric is a single-item question that measures how connected the respondent feels with another person or group (e.g., family, neighborhood, school, or community organization). Respondents see seven pairs of circles that range from just touching to almost completely overlapping, with one circle in each pair identified as “self” and the second circle identified as “other.” Respondents must answer the question, “Which picture best describes your relationship with [this person/group]?” The researcher identifies what the person or group for the “other” is being represented. The question takes less than one minute to administer. See the report for additional details.

Validity: Researchers have used the scale to measure belonging with various populations, including 5-year-olds, teens, adults, people with low incomes, and formerly incarcerated people. In the example of formerly incarcerated people, those who felt more belonging in their communities experienced greater residential stability and community readjustment and lower rates of recidivism than less connected formerly incarcerated people. This survey is easily understood by respondents.

Availability: This information is not available widely enough in existing data sources to provide coverage at the local level across many geographies.

Frequency: The frequency of how often data for the metric would be collected would depend upon local data collection efforts, but we recommend regular follow-up data collection at least every two years.

Geography: The level of geography that the metric would represent (e.g., county, city, or zip code) would depend on the sampling frame, stratification, and the number of people ultimately surveyed to obtain sufficient power for the survey.

Consistency: The degree of consistency in this metric across different places will vary with the extensiveness of the survey design and number of people surveyed in each place. Ideally, the metric could be consistent across some base level of geography (such as the city or county), but some places would likely have more extensive coverage of residents who have taken the survey. The selection of the “other” should be used consistently at the local level within age groups.

Subgroups: Like geography, the range of subgroups represented and the ability to compare subgroups (e.g., people of color and white people; married and single people; people with children and those without) would depend on the sampling frame, stratification, and the number of people surveyed.

Limitations: The primary limitation is that these data will need to be collected directly by communities. Communities will need to identify through which vehicles data can be gathered. Original data collection may also make benchmarking against other places challenging, depending on the scale and representativeness of data collection in other places. Further, this metric can be sensitive to residential mobility if the same people cannot be followed over time.

Without adequate transportation access, families can struggle to accomplish daily activities and be forced to trade expensive commutes for other needs and goods. Limited transportation access can also restrict opportunities for work and education. Evidence suggests that living in proximity to a bus or subway stop or having frequent transit services is associated with lower levels of unemployment, limited access to public transit is associated with higher levels of unemployment, and greater access to public transit reduces the likelihood of a household being on public assistance. Evidence indicates that living in neighborhoods far from jobs and without affordable transportation options undermines employment and economic success.

Metric: Transit trips index

This metric reflects the number of public transit trips taken annually at the census tract by a three-person single-parent family with income at 50 percent of the area median income for renters. This number is percentile ranked nationally into an index with values ranging from 0 to 100 for each census tract. Higher scores reflect better access to public transportation.

Validity: This metric was designed in partnership with the US Department of Transportation and has been used by the US Department of Housing and Urban Development in community efforts to affirmatively further fair housing. Several scholars have also used this metric and data in peer-reviewed journals. Although other arrangements of family composition, income, and housing status are possible in constructing this index and are available in the data, these characteristics were intended to more closely characterize a lower-income household in the community and are the most validated of other household combinations.

Availability: The estimates come from the location affordability index, which is publicly available.

Frequency: The location affordability index data are updated every three years.

Geography: This metric can be measured at the census tract or neighborhood level. Values can be averaged at higher levels of geography. For example, one can calculate a population-weighted average value among all census tracts in a county to determine a county-level value.

Consistency: This metric can be calculated the same way over time.

Subgroups: This metric is based on a lower-income population, notably single-parent families earning half the local area median income among renters. This metric can also be disaggregated by subarea when used in combination with the ACS to identify the racial or ethnic composition of neighborhoods (census tracts) with different levels of access. We distinguish census tracts that are majority nonwhite, that have no majority race or ethnicity, and that are majority non-Hispanic white. We define a majority as at least 60 percent of residents. 

Limitations: This metric cannot alone capture the concept of transportation access. This must be used in partnership with the low transportation cost index to cover geographies that may not have an extensive public transportation system, such as rural areas.

Metric: Low transportation cost index

This index reflects local transportation costs as a share of renters’ incomes. It accounts for both transit and cars. This index is based on estimates of transportation costs for a three-person, single-parent family with income at 50 percent of the median income for renters for the region (i.e., a core-based statistical area). Although other arrangements of family composition, income, and housing status are possible in constructing this index, these characteristics were intended to more closely characterize a lower-income household in the community. Values are inverted and percentile ranked nationally, with values ranging from 0 to 100. The higher the value, the lower the cost of transportation in that neighborhood.

Validity: This metric was designed in partnership with the US Department of Transportation and has been used by the US Department of Housing and Urban Development in community efforts to affirmatively further fair housing. Several scholars have also used this metric and data in peer-reviewed journals.

Availability: The estimates come from the location affordability index, which are publicly available.

Frequency: The location affordability index data are updated every three years.

Geography: This metric can be measured at the census tract or neighborhood level. Values can be averaged at higher levels of geography. For example, one can calculate a population-weighted average value among all census tracts in a county to determine a county-level value.

Consistency: This metric can be calculated the same way over time.

Subgroups: This metric is based on a lower-income population, notably single-parent families earning half the local area median income among renters. This metric can also be disaggregated by subarea when used in combination with the ACS to identify the racial or ethnic composition of neighborhoods (census tracts) with different levels of access. We distinguish census tracts that are majority nonwhite, that have no majority race or ethnicity, and that are majority non-Hispanic white. We define a majority as at least 60 percent of residents. 

Limitations: Transportation costs may be low for a variety of reasons, including greater access to public transportation and the density of homes, services, and jobs in the neighborhood and surrounding community. It is important consider this metric not by itself but rather in combination with the transit trips index to more fully measure the concept of transportation access.

Social capital measures the resources provided by social networks, including day-to-day supports provided by close relations as well as access to information and broader resources provided by extended relationships. Research demonstrates a positive relationship between social capital and education, child well-being, lower crime rates, health, tolerance, happiness, and economic and civic equality.

Metric: Selected questions from the Social Capital Community Benchmark Survey

These questions measure the resources provided by a person’s social networks, including both close relations and extended relations, and our metric uses a few items from this survey. The questions offer information on the relative strengths and areas for improvement in communities' civic behavior. The metric for social capital is a selection of seven questions from the Social Capital Benchmark Survey covering participation in community organizations, religious attendance, number and racial diversity of friends, engagement with neighborhoods, and the ability to find information on new jobs. These questions provide indicators of generalized social capital, bonding social capital, bridging social capital, and racial diversity of friends at the individual level. See the report for additional details. No widely available data on social capital exist at the local level, so this metric would require new data collection.

Validity: Measures of social capital have repeatedly been shown to be associated with individual and community well-being and upward mobility. However, no standard measure or set of measures exist to capture the relationship between social capital and mobility. Though the survey and larger blocks of questions from the survey have been used in peer-reviewed studies before, the subset of questions for this metric has not yet been validated together. We anticipate using the initial round of data collection to validate this metric for widespread use and to refine and revise as necessary.

Availability: This information is not available widely enough in existing data sources to provide coverage at the local level across many geographies. To ease data collection, we have attempted to minimize the number of questions needed to measure each of the indicators of social capital (e.g., generalized social capital and bridging and bonding social capital).

Frequency: The frequency of how often data for the metric would be collected would depend upon local data collection efforts, but we recommend regular follow-up data collection at least every two years.

Geography: The level of geography that the metric would represent (e.g., county, city, or zip code) would depend on the sampling frame, stratification, and the number of people ultimately surveyed to obtain sufficient power for the survey.

Consistency: The degree of consistency in this metric across different places will vary with the extensiveness of the survey design and number of people surveyed in each place. Ideally, the metric would be consistent across some base level of geography (such as the city or county), but some places would likely have more extensive coverage of residents who have taken the survey.

Subgroups: Like geography, the range of subgroups represented and the ability to compare subgroups (people of color and white people; married and single people; people with children and those without) would depend on the sampling frame, stratification, and the number of people surveyed.

Limitations: The primary limitation is that these data will need to be collected by communities. Additional limitations include the need to further validate this particular set of questions. Original data collection may also make benchmarking against other places challenging depending on the scale and representativeness of data collection in other places.