HOUSING AFFORDABILITY

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.

To learn more about how this predictor is linked to upward mobility, read its assessment in the Evidence Resource Library.

Metric: Ratio of affordable and available housing units to households with low, very low, and extremely 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.

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 STABILITY

Stable housing and a lack of homelessness improve 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.

To learn more about how this predictor is linked to upward mobility, read its assessment in the Evidence Resource Library.

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.

ECONOMIC INCLUSION

Limited levels of economic inclusion curtail families’ choices about where to live, block access to neighborhoods with better opportunities, and create areas of concentrated poverty and distress. 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.

To learn more about how this predictor is linked to upward mobility, read its assessment in the Evidence Resource Library.

Metric: Share of people experiencing poverty who live 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

Racial segregation perpetuates exclusion, blocks access to neighborhoods with better opportunities, concentrates poverty, and prevents people of different classes, races, and ethnicities from building the social ties that foster mutual respect, dignity, and belonging. Racial diversity and inclusion positively affects people at all stages of the life course and impact the belonging and economic dimensions of mobility from poverty.

To learn more about how this predictor is linked to upward mobility, read its assessment in the Evidence Resource Library.

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.

SOCIAL CAPITAL

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.

To learn more about how this predictor is linked to upward mobility, read its assessment in the Evidence Resource Library.

Metric: Number of Membership Associations per 10,000 People.

Based on the US Census Bureau’s County Business Patterns (CBP) data, which measure the total number and type of establishments for all counties in the US, this metric is a ratio of the number of membership associations (e.g., civic organizations, bowling centers, golf clubs, fitness centers, sports organizations, religious organizations, political organizations, labor organizations, business organizations, and professional organizations) that exist per 10,000 people in the jurisdiction.

Validity: The CBP data from the US Census Bureau are well established and widely used by academics and other researchers across the country. Research supports its use as a measure for social trust because social trust is enhanced when people belong to voluntary groups and organizations (Rupasingha, Goetz, and Freshwater 2006). People who belong to such groups tend to trust others who belong to the same group. The more such groups per person, the more likely that individuals in those communities belong to one or more groups.

Availability: The data required to compute this metric are available from the Census Bureau’s CBP data. CBP datasets are available for download from the US Census Bureau website.ⁱⁱⁱ

Frequency: The data are collected annually in the US Census Bureau’s Business Register.

Geography: This measure can be computed for all counties and zip codes.

Consistency: This metric is clearly defined and consistently measured across time since 2012 and for the entire population of the US. CBP data are derived from the Business Register, maintained and updated by the Census Bureau to track all known single- and multi-establishment employer companies in the US.

Structural equity* and subgroups: Because this metric focuses on the number of membership associations, disaggregating by demographic subgroups is limited. In a jurisdiction comprising more than one zip code, it is possible to compare organizations per 10,000 residents in zip codes whose residents are disproportionately representative of any particular subgroup (e.g., comparing the metric for zip codes in which 60 percent or more of the residents are Black people to zip codes that are more mixed).

Structural relevance*: This metric reflects the availability of opportunities for engagement and relationship-building and important structural support for social capital.

Limitations: This metric captures only a certain aspect of social capital. It is trying to measure social associations within a community and is likely the best measure within the context of business and professional organizations. Nevertheless, it cannot capture (a) social associations at the granular, individual level, or (b) smaller, more informal organizations of groups of people that would not be in a position to self-report to the Business Register.

* These selection criteria on structural equity and structural relevance were explicitly added in review of new metrics.

ⁱⁱⁱ The Social Associations metric is also available through County Health Rankings & Roadmaps, a program of the University of Wisconsin Population Health Institute. The metric is available nationally in standardized form for all counties, and can be constructed from its constituent data components (CBP and population data from the Census Bureau).

Metric: Ratio of Facebook Friends with Higher Socioeconomic Status to Facebook Friends with Lower Socioeconomic Status.

“Economic connectedness” measures the extent to which low- and high-socioeconomic status individuals are friends with each other. Specifically, the economic connectedness metric is twice the average share of high-socioeconomic-status friends (e.g., individuals from households ranked in the top half of all income-earning households) among low-socioeconomic-status individuals (e.g., individuals from households ranked in the lower half of all US households based on income) in a given community. An economic connectedness measure of 1 represents a community that is perfectly integrated across socioeconomic status, with half of all low-socioeconomic status individuals’ friends being of high socioeconomic status. The metric is meant to be a measure of the interconnectivity, by location, between people from different economic backgrounds (e.g., with different levels of social capital and exposure).

Validity: This metric measures an important aspect of social capital: the extent to which members of a community associate with people with varying social statuses. The connections made through this type of social capital help facilitate and develop an individual’s power, autonomy, and sense of belonging in their community. These data come from research that has been peer reviewed and recently published.^iv

Availability: The metric is made available through Opportunity Insights’ Social Capital Atlas.

Frequency: These data are expected to be released in summer 2022. These data were released in summer 2022. Because this was an inaugural data release contingent on the publication of new research, it is unclear if it will be updated or with what frequency.

Geography: The metric is available nationally in a standardized format for all counties. The smallest geography for which this metric is available is the ZIP code (ZCTA) level.

Consistency: This metric is clearly defined and will be consistently measured across populations and geographies. We cannot know if it will be consistently measured across time, because it is new and has yet to be updated.

Structural equity* and subgroups: This metric will help identify structural equity in the community by identifying the relative abundance or lack of social cohesion between community members with different levels of privilege and access to resources. These data do not contain the race and ethnicity of individuals, but we are able to characterize zip codes by the racial and ethnic composition of residents using American Community Survey data. We distinguish ZIP codes 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.

Structural relevance*: This metric is a systemic condition of economic mobility because it speaks to what level of socioeconomic intermingling is supported in the social environment of a given area. This metric, however, is also an outcome of greater economic mobility, because were one’s mobility from poverty to improve, so would their economic connectedness.

Limitations: The biggest limitation of this metric is its novelty. Although it was developed by reputable scholars and peer reviewed, it lacks the established track record of other metrics. Moreover, it focuses on the financial aspects of social capital and does not capture other important elements, like popularity and community ties. This metric may be sensitive to residential mobility into and out of a city or county, but not to an extent likely to affect its aggregate values. This metric also relies on the continued popularity and use of Facebook as a social media platform, the user base of which has been skewing older as time goes on. Without continued engagement from the same user groups and the introduction of younger populations as they age, comparability and consistency over time may be compromised. The metric will be calculated only for zip codes containing at least 100 people.

* These selection criteria on structural equity and structural relevance were explicitly added in review of new metrics.

^iv This metric is constructed using proprietary social media source data from Meta (previously known as Facebook). Social media profile information for individuals that use the Facebook platform is collected, anonymized, and analyzed to determine individual socioeconomic status as well as friendship circles and interconnectivity.

TRANSPORTATION ACCESS

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.

To learn more about how this predictor is linked to upward mobility, read its assessment in the Evidence Resource Library.

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: 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.