HUD Income Limits are collected and published to determine the maximum income a household may earn to participate in certain housing subsidy programs. Home income limits from the year 2019 were used. Median income is developed for each metropolitan area (and applies to all counties in the metro area), and each non-metropolitan area (and is a county level measure). Data was obtained for communities in all 50 states, Puerto Rico and U.S. Virgin Islands. The calculations stem from median family income data provided by the Census and adjusted for certain local conditions.

This dataset and map service provides information on the U.S. Housing and Urban Development's (HUD) low to moderate income areas. The term Low to Moderate Income, often referred to as low-mod, has a specific programmatic context within the Community Development Block Grant (CDBG) program. Over a 1, 2, or 3-year period, as selected by the grantee, not less than 70 percent of CDBG funds must be used for activities that benefit low- and moderate-income persons. HUD uses special tabulations of Census data to determine areas where at least 51% of households have incomes at or below 80% of the area median income (AMI). This dataset and map service contains the following layer.

The Community Development Block Grant (CDBG) program requires that each CDBG funded activity must either principally benefit low- and moderate-income (LMI) persons, aid in the prevention or elimination of slums or blight, or meet a community development need having a particular urgency. Most activities funded by the CDBG program are designed to benefit low- and moderate-income (LMI) persons. That benefit may take the form of housing, jobs, and services. Additionally, activities may qualify for CDBG assistance if the activity will benefit all the residents of a primarily residential area where at least 51 percent of the residents are low- and moderate-income persons, i.e. area-benefit (LMA). [Certain exception grantees may qualify activities as area-benefit with fewer LMI persons than 51 percent.]The Office of Community Planning and Development (CPD) provides estimates of the number of persons that can be considered Low-, Low- to Moderate-, and Low-, Moderate-, and Medium-income persons based on special tabulations of data from the 2016-2020 ACS 5-Year Estimates and the 2020 Island Areas Census. The Low- and Moderate-Income Summary Data may be used by CDBG grantees to determine whether or not a CDBG-funded activity qualifies as an LMA activity. The LMI percentages are calculated at various principal geographies provided by the U.S. Census Bureau. CPD provides the following datasets:Geographic Summary Level "150": Census Tract-Block Group.The block groups are associated with the HUD Unit-of-Government-Identification-Code for the CDBG grantee jurisdiction by fiscal year that is associated with each block group.Local government jurisdictions include; Summary Level 160: Incorporated Cities and Census-Designated Places, i.e. "Places", Summary Level 170: Consolidated Cities, Summary Level 050: County, and Summary Level 060: County Subdivision geographies.In the data files, these geographies are identified by their Federal Information Processing Standards (FIPS) codes and names for the place, consolidated city, or block group, county subdivision, county, and state.The statistical information used in the calculation of estimates identified in the data sets comes from the 2016-2020 ACS, 2020 Island Areas Census, and the Income Limits for Metropolitan Areas and for Non Metropolitan Counties. The data necessary to determine an LMI percentage for an area is not published in the publicly-available ACS data tables. Therefore, the Bureau of Census matches family size, income, and the income limits in a special tabulation to produce the estimates.Estimates are provided at three income levels: Low Income (up to 50 percent of the Area Median Income (AMI)); Moderate Income (greater than 50 percent AMI and up to 80 percent AMI), and Medium Income (greater than 80 percent AMI and up to 120 AMI). HUD is publishing the margin of error (MOE) data for all block groups and all places in the 2020 ACS LMISD. These data are provided within the LMISD tables.The MOE does not provide an expanded range for compliance. For example, a service area of 50 percent LMI with a 2 percent MOE would still be just 50 percent LMI for compliance purposes. However, the 2 percent MOE would inform the grantee about the accuracy of the ACS data before undergoing the effort and cost of conducting a local income survey, which is the alternative to using the HUD-provided data.CPD Notice 24-04 announced the publication of LMISD based on the 2020 ACS, and updated CPD Notice 19-02 as well as explains policy about the accuracy of surveys conducted pursuant to CPD Notice 14-013.Questions about the calculation of the estimates may be directed to Formula Help Desk.Questions about the use of the data should be directed to the staff of the CPD Field Office.

Graph and download economic data for Estimate of Median Household Income for Marion County, OH (MHIOH39101A052NCEN) from 1989 to 2023 about Marion County, OH; OH; households; median; income; and USA.

The Community Development Block Grant (CDBG) program requires that each CDBG funded activity must either principally benefit low- and moderate-income (LMI) persons, aid in the prevention or elimination of slums or blight, or meet a community development need having a particular urgency. Most activities funded by the CDBG program are designed to benefit low- and moderate-income (LMI) persons. That benefit may take the form of housing, jobs, and services. Additionally, activities may qualify for CDBG assistance if the activity will benefit all the residents of a primarily residential area where at least 51 percent of the residents are low- and moderate-income persons, i.e. area-benefit (LMA). [Certain exception grantees may qualify activities as area-benefit with fewer LMI persons than 51 percent.]The Office of Community Planning and Development (CPD) provides estimates of the number of persons that can be considered Low-, Low- to Moderate-, and Low-, Moderate-, and Medium-income persons based on special tabulations of data from the 2016-2020 ACS 5-Year Estimates and the 2020 Island Areas Census. The Low- and Moderate-Income Summary Data may be used by CDBG grantees to determine whether or not a CDBG-funded activity qualifies as an LMA activity. The LMI percentages are calculated at various principal geographies provided by the U.S. Census Bureau. CPD provides the following datasets:Geographic Summary Level "150": Census Tract-Block Group.The block groups are associated with the HUD Unit-of-Government-Identification-Code for the CDBG grantee jurisdiction by fiscal year that is associated with each block group.Local government jurisdictions include; Summary Level 160: Incorporated Cities and Census-Designated Places, i.e. "Places", Summary Level 170: Consolidated Cities, Summary Level 050: County, and Summary Level 060: County Subdivision geographies.In the data files, these geographies are identified by their Federal Information Processing Standards (FIPS) codes and names for the place, consolidated city, or block group, county subdivision, county, and state.The statistical information used in the calculation of estimates identified in the data sets comes from the 2016-2020 ACS, 2020 Island Areas Census, and the Income Limits for Metropolitan Areas and for Non Metropolitan Counties. The data necessary to determine an LMI percentage for an area is not published in the publicly-available ACS data tables. Therefore, the Bureau of Census matches family size, income, and the income limits in a special tabulation to produce the estimates.Estimates are provided at three income levels: Low Income (up to 50 percent of the Area Median Income (AMI)); Moderate Income (greater than 50 percent AMI and up to 80 percent AMI), and Medium Income (greater than 80 percent AMI and up to 120 AMI). HUD is publishing the margin of error (MOE) data for all block groups and all places in the 2020 ACS LMISD. These data are provided within the LMISD tables.The MOE does not provide an expanded range for compliance. For example, a service area of 50 percent LMI with a 2 percent MOE would still be just 50 percent LMI for compliance purposes. However, the 2 percent MOE would inform the grantee about the accuracy of the ACS data before undergoing the effort and cost of conducting a local income survey, which is the alternative to using the HUD-provided data.CPD Notice 24-04 announced the publication of LMISD based on the 2020 ACS, and updated CPD Notice 19-02 as well as explains policy about the accuracy of surveys conducted pursuant to CPD Notice 14-013.Questions about the calculation of the estimates may be directed to Formula Help Desk.Questions about the use of the data should be directed to the staff of the CPD Field Office.

The U.S. Department of Housing and Urban Development (HUD) requires local municipalities that receive Community Development Block Grant (CDBG or CD) formula Entitlement funds to use the 5-year 2016-2020 American Community Survey (ACS) Low and Moderate Income Summary Data (LMISD) data file to determine where CDBG funds may be used for activities that are available to all the residents in a particular area ("CD area benefit" or "CD-eligible area"). A CD-eligible census tract refers to 2020 census tracts where the area is primarily residential in nature and at least 51.00% of the residents are low- and moderate-income persons as per the LMISD data file. For New York City, a primarily residential area is defined as one where at least 50.00% of the total built floor area is residential. Low- and moderate-income persons are defined as persons living in households with incomes below 80 percent of the area median household income (AMI). In addition, floor area percentages have been updated with the most recent floor area data (PLUTO 24v4). Persons who are interested in determining their individual household eligibility for CD-funded programs should refer to HUD's household low- and moderate-income limits for the given year. For more information about how geographic datasets are used for compliance purposes, please refer to the following HUD Office of Community Planning and Development (CPD) Notice CPD-24-04.

Graph and download economic data for Estimate of Median Household Income for Pasco County, FL (MHIFL12101A052NCEN) from 1989 to 2023 about Pasco County, FL; Tampa; FL; households; median; income; and USA.

Graph and download economic data for Estimate of Median Household Income for Williamson County, TX (MHITX48491A052NCEN) from 1989 to 2023 about Williamson County, TX; Austin; households; TX; median; income; and USA.

From landing page:FHFA establishes annual single-family and multifamily housing goals for mortgages purchased by Fannie Mae and Freddie Mac. The Enterprise Housing Goals include separate categories for single-family mortgages on housing that is affordable to low-income and very low-income families, as well as refinanced mortgages for low-income borrowers. FHFA also establishes separate annual goals for multifamily housing. Loans that are eligible for housing goals credit are mortgages on owner-occupied housing with one to four units. The mortgages must be conventional, conforming mortgages, defined as mortgages that are not insured or guaranteed by the Federal Housing Administration or another government agency and with principal balances that do not exceed the conforming loan limits for Enterprise mortgages. This page provides data on Enterprise performance and activity related to the single-family housi​​ng goals. A full glossary of terms is provided below. Single-Family Enterprise Mortgage Acquisitions: Race and Ethnicity Data The new housing goals data tables provide insight on the racial and ethnic composition of loans acquired by the Enterprises that are eligible for housing goals credit. FHFA has provided the racial and ethnic distribution of the Enterprises' acquisitions across each of the current single-family housing goals categories. ​ Single-Family Housing Goal Loan Segments: State-Level Data FHFA is publishing state-level data for each single-family goal loan purchase and refinance segment. It is important to note that FHFA does not set state-level targets but only at the national level. These tables provide the Enterprises' share in each state along with the market share, as calculated by FHFA using the 'static' HMDA data for each year to determine Enterprise housing goals performance each year. It is important to note that HMDA state-level data are impacted by the number of HMDA-exempt reporters in each state. For more information on HMDA reporting requirements, visit the CFPB HMDA Reporting Requirements page.Low-Income Census Tracts, Minority Census Tracts and Designated Disaster Areas Data The Federal Housing Enterprises Financial Safety and Soundness Act of 1992 (Safety and Soundness Act) provides for the establishment of single-family and multifamily goals each year, including a single-family purchase money mortgage goal for families residing in low-income areas. The Safety and Soundness Act defines "low-income area" for the single-family low-income areas home purchase goal as: Census tracts or block numbering areas in which the median income does not exceed 80 percent of area median income (AMI). In addition, for the purposes of this goal, "families residing in low-income areas" also include: Families with income not greater than 100 percent of AMI who reside in minority census tracts. Families with income not greater than 100 percent of AMI who reside in designated disaster areas. ​A "minority census tract" is a census tract that has a minority population of at least 30 percent and a median income of less than 100 percent of the AMI. A "low-income census tract" is census tract in which the median income does not exceed 80 percent of the AMI. Designated disaster areas are identified by FHFA based on the three most recent years' declarations by the Federal Emergency Management Agency (FEMA), where individual assistance payments were authorized by FEMA. A map of census tracts identified as minority census tracts in 2024 can be ​found here. A map of census tracts identified as low-income census tracts in 2024 can be found here. ​Learn more about low-income census tracts, minority census tracts, and designated disaster areas.

Graph and download economic data for Estimate of Median Household Income for Benton County, WA (MHIWA53005A052NCEN) from 1989 to 2023 about Benton County, WA; Kennewick; WA; households; median; income; and USA.

There is more to housing affordability than the rent or mortgage you pay. Transportation costs are the second-biggest budget item for most families, but it can be difficult for people to fully factor transportation costs into decisions about where to live and work. The Location Affordability Index (LAI) is a user-friendly source of standardized data at the neighborhood (census tract) level on combined housing and transportation costs to help consumers, policymakers, and developers make more informed decisions about where to live, work, and invest. Compare eight household profiles (see table below) —which vary by household income, size, and number of commuters—and see the impact of the built environment on affordability in a given location while holding household demographics constant.*$11,880 for a single person household in 2016 according to US Dept. of Health and Human Services: https://aspe.hhs.gov/computations-2016-poverty-guidelinesThis layer is symbolized by the percentage of housing and transportation costs as a percentage of income for the Median-Income Family profile, but the costs as a percentage of income for all household profiles are listed in the pop-up:Also available is a gallery of 8 web maps (one for each household profile) all symbolized the same way for easy comparison: Median-Income Family, Very Low-Income Individual, Working Individual, Single Professional, Retired Couple, Single-Parent Family, Moderate-Income Family, and Dual-Professional Family.An accompanying story map provides side-by-side comparisons and additional context.--Variables used in HUD's calculations include 24 measures such as people per household, average number of rooms per housing unit, monthly housing costs (mortgage/rent as well as utility and maintenance expenses), average number of cars per household, median commute distance, vehicle miles traveled per year, percent of trips taken on transit, street connectivity and walkability (measured by block density), and many more.To learn more about the Location Affordability Index (v.3) visit: https://www.hudexchange.info/programs/location-affordability-index/. There you will find some background and an FAQ page, which includes the question:"Manhattan, San Francisco, and downtown Boston are some of the most expensive places to live in the country, yet the LAI shows them as affordable for the typical regional household. Why?" These areas have some of the lowest transportation costs in the country, which helps offset the high cost of housing. The area median income (AMI) in these regions is also high, so when costs are shown as a percent of income for the typical regional household these neighborhoods appear affordable; however, they are generally unaffordable to households earning less than the AMI.Date of Coverage: 2012-2016 Date Released: March 2019Date Downloaded from HUD Open Data: 4/18/19Further Documentation:LAI Version 3 Data and MethodologyLAI Version 3 Technical Documentation_**The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updates**

Title: Location Affordability Index - NMCDC Copy

Summary: This layer contains the Location Affordability Index from U.S. Dept. of Housing and Urban Development (HUD) - standardized household, housing, and transportation cost estimates by census tract for 8 household profiles.

Notes: This map is copied from source map: https://nmcdc.maps.arcgis.com/home/item.html?id=de341c1338c5447da400c4e8c51ae1f6, created by dianaclavery_uo, and identified in Living Atlas.

Prepared by: dianaclavery_uo, copied by EMcRae_NMCDC

Source: This map is copied from source map: https://nmcdc.maps.arcgis.com/home/item.html?id=de341c1338c5447da400c4e8c51ae1f6, created by dianaclavery_uo, and identified in Living Atlas. Check the source documentation or other details above for more information about data sources.

Feature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=447a461f048845979f30a2478b9e65bb

UID: 73

Data Requested: Family income spent on basic need

Method of Acquisition: Search for Location Affordability Index in the Living Atlas. Make a copy of most recent map available. To update this map, copy the most recent map available. In a new tab, open the AGOL Assistant Portal tool and use the functions in the portal to copy the new maps JSON, and paste it over the old map (this map with item id

Date Acquired: Map copied on May 10, 2022

Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 6

Tags: PENDING

Urban Displacement Project’s (UDP) Estimated Displacement Risk (EDR) model for California identifies varying levels of displacement risk for low-income renter households in all census tracts in the state from 2015 to 2019(1). The model uses machine learning to determine which variables are most strongly related to displacement at the household level and to predict tract-level displacement risk statewide while controlling for region. UDP defines displacement risk as a census tract with characteristics which, according to the model, are strongly correlated with more low-income population loss than gain. In other words, the model estimates that more low-income households are leaving these neighborhoods than moving in.This map is a conservative estimate of low-income loss and should be considered a tool to help identify housing vulnerability. Displacement may occur because of either investment, disinvestment, or disaster-driven forces. Because this risk assessment does not identify the causes of displacement, UDP does not recommend that the tool be used to assess vulnerability to investment such as new housing construction or infrastructure improvements. HCD recommends combining this map with on-the-ground accounts of displacement, as well as other related data such as overcrowding, cost burden, and income diversity to achieve a full understanding of displacement risk.If you see a tract or area that does not seem right, please fill out this form to help UDP ground-truth the method and improve their model.How should I read the displacement map layers?The AFFH Data Viewer includes three separate displacement layers that were generated by the EDR model. The “50-80% AMI” layer shows the level of displacement risk for low-income (LI) households specifically. Since UDP has reason to believe that the data may not accurately capture extremely low-income (ELI) households due to the difficulty in counting this population, UDP combined ELI and very low-income (VLI) household predictions into one group—the “0-50% AMI” layer—by opting for the more “extreme” displacement scenario (e.g., if a tract was categorized as “Elevated” for VLI households but “Extreme” for ELI households, UDP assigned the tract to the “Extreme” category for the 0-50% layer). For these two layers, tracts are assigned to one of the following categories, with darker red colors representing higher displacement risk and lighter orange colors representing less risk:• Low Data Quality: the tract has less than 500 total households and/or the census margins of error were greater than 15% of the estimate (shaded gray).• Lower Displacement Risk: the model estimates that the loss of low-income households is less than the gain in low-income households. However, some of these areas may have small pockets of displacement within their boundaries. • At Risk of Displacement: the model estimates there is potential displacement or risk of displacement of the given population in these tracts.• Elevated Displacement: the model estimates there is a small amount of displacement (e.g., 10%) of the given population.• High Displacement: the model estimates there is a relatively high amount of displacement (e.g., 20%) of the given population.• Extreme Displacement: the model estimates there is an extreme level of displacement (e.g., greater than 20%) of the given population. The “Overall Displacement” layer shows the number of income groups experiencing any displacement risk. For example, in the dark red tracts (“2 income groups”), the model estimates displacement (Elevated, High, or Extreme) for both of the two income groups. In the light orange tracts categorized as “At Risk of Displacement”, one or all three income groups had to have been categorized as “At Risk of Displacement”. Light yellow tracts in the “Overall Displacement” layer are not experiencing UDP’s definition of displacement according to the model. Some of these yellow tracts may be majority low-income experiencing small to significant growth in this population while in other cases they may be high-income and exclusive (and therefore have few low-income residents to begin with). One major limitation to the model is that the migration data UDP uses likely does not capture some vulnerable populations, such as undocumented households. This means that some yellow tracts may be experiencing high rates of displacement among these types of households. MethodologyThe EDR is a first-of-its-kind model that uses machine learning and household level data to predict displacement. To create the EDR, UDP first joined household-level data from Data Axle (formerly Infogroup) with tract-level data from the 2014 and 2019 5-year American Community Survey; Affirmatively Furthering Fair Housing (AFFH) data from various sources compiled by California Housing and Community Development; Longitudinal Employer-Household Dynamics (LEHD) Origin-Destination Employment Statistics (LODES) data; and the Environmental Protection Agency’s Smart Location Database.UDP then used a machine learning model to determine which variables are most strongly related to displacement at the household level and to predict tract-level displacement risk statewide while controlling for region. UDP modeled displacement risk as the net migration rate of three separate renter households income categories: extremely low-income (ELI), very low-income (VLI), and low-income (LI). These households have incomes between 0-30% of the Area Median Income (AMI), 30-50% AMI, and 50-80% AMI, respectively. Tracts that have a predicted net loss within these groups are considered to experience displacement in three degrees: elevated, high, and extreme. UDP also includes a “At Risk of Displacement” category in tracts that might be experiencing displacement.What are the main limitations of this map?1. Because the map uses 2019 data, it does not reflect more recent trends. The pandemic, which started in 2020, has exacerbated income inequality and increased housing costs, meaning that UDP’s map likely underestimates current displacement risk throughout the state.2. The model examines displacement risk for renters only, and does not account for the fact that many homeowners are also facing housing and gentrification pressures. As a result, the map generally only highlights areas with relatively high renter populations, and neighborhoods with higher homeownership rates that are known to be experiencing gentrification and displacement are not as prominent as one might expect.3. The model does not incorporate data on new housing construction or infrastructure projects. The map therefore does not capture the potential impacts of these developments on displacement risk; it only accounts for other characteristics such as demographics and some features of the built environment. Two of UDP’s other studies—on new housing construction and green infrastructure—explore the relationships between these factors and displacement.Variable ImportanceFigures 1, 2, and 3 show the most important variables for each of the three models—ELI, VLI, and LI. The horizontal bars show the importance of each variable in predicting displacement for the respective group. All three models share a similar order of variable importance with median rent, percent non-white, rent gap (i.e., rental market pressure calculated using the difference between nearby and local rents), percent renters, percent high-income households, and percent of low-income households driving much of the displacement estimation. Other important variables include building types as well as economic and socio-demographic characteristics. For a full list of the variables included in the final models, ranked by descending order of importance, and their definitions see all three tabs of this spreadsheet. “Importance” is defined in two ways: 1. % Inclusion: The average proportion of times this variable was included in the model’s decision tree as the most important or driving factor.2. MeanRank: The average rank of importance for each variable across the numerous model runs where higher numbers mean higher ranking. Figures 1 through 3 below show each of the model variable rankings ordered by importance. The red lines represent Jenks Breaks, which are designed to sort values into their most “natural” clusters. Variable importance for each model shows a substantial drop-off after about 10 variables, meaning a relatively small number of variables account for a large amount of the predictive power in UDP’s displacement model.Figure 1. Variable Importance for Low Income HouseholdsFor a description of each variable and its source, see this spreadsheet.Figure 2. Variable Importance for Very Low Income HouseholdsFor a description of each variable and its source, see this spreadsheet. Figure 3. Variable Importance for Extremely Low Income HouseholdsFor a description of each variable and its source, see this spreadsheet.Source: Chapple, K., & Thomas, T., and Zuk, M. (2022). Urban Displacement Project website. Berkeley, CA: Urban Displacement Project.(1) UDP used this time-frame because (a) the 2020 census had a large non-response rate and it implemented a new statistical modification that obscures and misrepresents racial and economic characteristics at the census tract level and (b) pandemic mobility trends are still in flux and UDP believes 2019 is more representative of “normal” or non-pandemic displacement trends.

Racially Concentrated Areas of Affluence (RCAA's)

The concept of Racially Concentrated Areas of Affluence (RCAAs) was originally developed by scholars at the University of Minnesota to illustrate the flip side of the Racially and Ethnically Concentrated Areas of Poverty (R/ECAPs) metric used by the California Department of Housing and Community Development (HCD) in the 2015 Affirmatively Furthering Fair Housing (AFFH) rule to more fully tell the story of segregation in the United States.

As stated in HCD’s AFFH Guidance Memo, when analyzing patterns and trends of segregation and proposing policy approaches in the Housing Element, localities should not only focus on communities of color. Segregation is a continuum, with polarity between race, poverty, and affluence, which can be a direct product of the same policies and practices. To better evaluate these conditions, both sides of the continuum should be considered and compare patterns within the community and across the region. This more holistic approach will better unveil deeply rooted policies and practices and improve identification and prioritization of contributing factors to inform more meaningful actions.

HCD has created a new version of the RCAA metric to better reflect California’s relative diversity and regional conditions, and to aid local jurisdictions in their analysis of racially concentrated areas of poverty and affluence pursuant to AB 686 and AB 1304. HCD’s RCAA metric is provided as a resource to be paired with local data and knowledge – jurisdictions are encouraged but not required to use the RCAA layer provided by HCD in their housing element analyses.

To develop the RCAA layer, staff first calculated a Location Quotient (LQ) for each California census tract using data from the 2015-2019 American Community Survey data. This LQ represents the percentage of total white population (White Alone, Not Hispanic or Latino) for each census tract compared to the average percentage of total white population for all census tracts in a given Council of Governments' (COG) region. For example, a census tract with a LQ of 1.5 has a percentage of total white population that is 1.5 times higher than the average percentage of total white population in the given COG region.

To determine the RCAAs, census tracts with a LQ of more than 1.25 and a median income 1.5 times higher than the COG Area Median Income (AMI) (or 1.5x the State AMI, whichever is lower) were assigned a numeric score of 1 (Is a RCAA). Census tracts that did not meet this criterion were assigned a score of 0 (Not a RCAA).

COG AMI was determined by averaging the 2019 ACS established AMI's for each county within the given COG region. 2019 ACS AMI limits can be found here: https://www.census.gov/quickfacts/fact/table/US/PST045219 [census.gov]. State AMI was based on the ACS 2019 California state AMI ($75,235), which can be found here: https://www.census.gov/quickfacts/fact/table/CA/INC110219 [census.gov].

Census tracts with a total population of less than 75 people, in which the census tract was also largely contained within a non-urbanized area such as a park, open space, or airport, were not identified as RCAAs.

Data Source: American Community Survey (ACS), 2015-2019

References: Wilson, William J. (1980). The Declining Significance of Race: Blacks and Changing American Institutions. Chicago: University of Chicago Press.

Damiano, T., Hicks, J., & Goetz, E. (2017). Racially Concentrated Areas of Affluence: A Preliminary Investigation.

To learn more about R/ECAPs visit: https://www.huduser.gov/portal/periodicals/cityscpe/vol21num1/ch4.pdf [huduser.gov]

Original data created by HCD, PlaceWorks 2021

Graph and download economic data for Estimate of Median Household Income for Pacific County, WA (MHIWA53049A052NCEN) from 1989 to 2023 about Pacific County, WA; WA; households; median; income; and USA.

Graph and download economic data for Estimate of Median Household Income for Aiken County, SC (MHISC45003A052NCEN) from 1989 to 2023 about Aiken County, SC; Augusta; SC; households; median; income; and USA.

Graph and download economic data for Estimate of Median Household Income for Denton County, TX (MHITX48121A052NCEN) from 1989 to 2023 about Denton County, TX; Dallas; households; TX; median; income; and USA.

Graph and download economic data for Estimate of Median Household Income for Fort Bend County, TX (MHITX48157A052NCEN) from 1989 to 2023 about Fort Bend County, TX; Houston; households; TX; median; income; and USA.

Graph and download economic data for Estimate of Median Household Income for Winnebago County, WI (MHIWI55139A052NCEN) from 1989 to 2023 about Winnebago County, WI; Oshkosh; WI; households; median; income; and USA.

Graph and download economic data for Estimate of Median Household Income for Moore County, NC (MHINC37125A052NCEN) from 1989 to 2023 about Moore County, NC; NC; households; median; income; and USA.

Graph and download economic data for Estimate of Median Household Income for Cherokee County, GA (MHIGA13057A052NCEN) from 1989 to 2023 about Cherokee County, GA; Atlanta; GA; households; median; income; and USA.