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This Toolkit does not reflect any decisions made in connection with HUD's February 9, 2023 notice of proposed rulemaking and only relates to voluntary fair housing planning conducted pursuant to HUD's June 10, 2021 Interim Final Rule and may be used to support a program participant's certification that they will affirmatively further fair housing.

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Module 5

How to Conduct Fair Housing Planning Data Analysis?

Fair Housing Planning Toolkit

Module 5 Objectives:

  • Icon of check mark Learn HOW to conduct Fair Housing Planning data analysis
  • Icon of check mark Learn WHAT is data analysis
  • Icon of check mark Learn HOW to use data and maps in Fair Housing Planning

Module 5 Content:

On This Page:

Key Players

Photo of group of people standing in business attire
  • Fair Housing Plan Coordinator
  • Data Analyst(s), including a geographer, statistician, or other data professional, if available
    • Program Participants that do not have these professionals on staff may also accomplish their data analysis with HUD-provided data tools, which were created to simplify data analysis for Fair Housing Planning, or by collaborating with data professionals at local universities, regional planning organizations, FHIPs (Fair Housing Initiatives Program)/FHAPs (Fair Housing Assistance Program), or others.

Key Definitions

HUD provides AFFH-related data through the AFFH-T Data and Mapping Tool, available at: https://egis.hud.gov/affht/

HUD’s Community Assessment Reporting Tool (CART) shows the wide variety of HUD investments by city, state, county, metropolitan area, or congressional district, available at: https://egis.hud.gov/cart/

Programs, infrastructure, and facilities that provide opportunity and a desirable environment. Examples of community assets include: high performing schools (as well as quality daycare and childhood educational services), desirable employment opportunities, efficient transportation services, safe and well-maintained parks and recreation facilities, well-resourced libraries and community centers, community-based supportive services for individuals with disabilities, responsive emergency services (including law enforcement), healthcare services, environmentally healthy neighborhoods (including clean air, clean water, access to healthy food), grocery stores, retail establishments, infrastructure and municipal services, banking and financial institutions, and other assets that meet the needs of residents throughout the community.

The term “data” collectively refers to: (1) HUD-provided data and (2) local data. The term “HUD-provided data” refers to metrics, statistics, and other quantified information that may be used when conducting Fair Housing Planning. HUD-provided data will not only be provided to Program Participants but will be posted on HUD’s website for availability to the public. The term “local data” refers to metrics, statistics, and other quantified information relevant to the Program Participant's geographic areas of analysis that can be found through a reasonable amount of searching, are readily available at little or no cost, and may be used to conduct Fair Housing Planning. See also 24 CFR § 5.152

  • Administrative data: data collected and maintained by agencies used to administer (or run) programs and provide services to the public. These data can be either public, private, or available upon request.
  • Local data: data collected by any local authority, subdivision, or organization that is valuable and specific to the community that generates it. These data can be either public, private, or available upon request.
  • Data Analysis: the process of systemically translating raw data into useful information, conclusions, and/or recommendations.
  • Data Collection: the process of gathering and measuring information on topics of interest in a systematic manner.
  • Data Cleaning: the process of improving quality of data by correcting or removing inaccurate, incomplete, non-uniform data entries from a data set.
  • Data Source: data sets and repositories where data is collected and stored.
  • Data Standardization: the process of creating technical specifications and criteria on how data should be formatted, stored, and processed.
  • Data Storytelling: the practice of interpreting visual data with accessible, usable narrative based on data analysis.
  • Data Narrative: a written summary of data that presents findings and conclusions, with a focus on comparative data and data trends and patterns.
  • Data Visualizations: the representation of data through maps, charts, graphs, infographics, plots, and several other types of pictorial or graphic formats.
  • Quantitative data: data expressing a certain quantity, amount, or range. Usually, there are measurement units associated with the data, e.g., meters in the case of the height of a person. It makes sense to set boundary limits to such data, and it is also meaningful to apply arithmetic operations to the data.
  • Qualitative data: data describing the attributes or properties that an object possesses. The properties are categorized into classes that may be assigned numeric values. However, there is no significance to the data values themselves, they simply represent attributes of an object. Often, qualitative data are obtained based on the lived experiences of individuals.

IMS/PIC is responsible for maintaining and gathering data about PIH's inventories of Public Housing Agencies (PHAs), Developments, Buildings, Units, PHA Officials, HUD Offices and Field Staff and IMS/PIC Users. IMS/PIC allows PHAs to electronically submit information to HUD.

A condition within the Program Participant’s geographic areas of analysis in which there is not a high concentration of persons of a particular race, color, national origin, religion, sex (including sexual orientation and gender identity), familial status, or having a disability or a type of disability in a particular geographic area when compared to a broader geographic area. See also 24 CFR § 5.151

See Inventory Management System/PIH Information Center (IMS/PIC).

Protected Characteristics are race, color, national origin, religion, sex (including sexual orientation and gender identity), familial status, and disability. See also 24 CFR § 5.152

A R/ECAP is a geographic area with significant concentrations of poverty and concentrations of people of color (e.g., Black, Hispanic, Asian/Pacific Islander, Native American/Alaska Native individuals, or other designations). To assist communities in identifying racially or ethnically concentrated areas of poverty (R/ECAPs), HUD has developed a census tract-based definition of R/ECAPs. The definition involves a racial/ethnic group concentration threshold and a poverty test. The racial/ethnic group concentration threshold is straightforward: R/ECAPs must have a non-White population of 50 percent or more. Regarding the poverty threshold, neighborhoods of “extreme poverty” are defined as census tracts with 40 percent or more of individuals living at or below the poverty line. Because overall poverty levels are substantially lower in many parts of the country, HUD supplements this with an alternate criterion. Thus, a neighborhood can be a R/ECAP if it has a poverty rate that exceeds 40 percent or is three or more times the average tract poverty rate for the metropolitan/micropolitan area, whichever threshold is lower. Census tracts with this extreme poverty that satisfy the racial/ethnic concentration threshold are deemed R/ECAPs. HUD’s data documentation notes, “While this definition of R/ECAP works well for tracts in CBSAs, places outside of these geographies are unlikely to have racial or ethnic group concentrations as high as 50 percent. In these areas, the racial/ethnic group concentration threshold is set at 20 percent.” See also 24 CFR § 5.151

Segregation is a condition, within the Program Participant’s geographic area of analysis in which there is a high concentration of persons of a particular race, color, national origin, religion, sex (including sexual orientation and gender identity), familial status, or having a disability or a type of disability in a particular geographic area when compared to a broader geographic area. See also 24 CFR § 5.151

Significant disparities in access to opportunity are substantial and measurable differences in access to educational, transportation, economic, healthcare, and other important opportunities in a community based on protected class in housing. See also 24 CFR § 5.151

A fair housing goal is a goal identified through the analysis in the Fair Housing Plan, to overcome fair housing issues. Program Participants are responsible for taking meaningful actions to achieve each fair housing goal identified in their Fair Housing Plan. Meaningful actions are significant actions that are designed and can be reasonably expected to achieve a material positive change that affirmatively furthers fair housing by, for example, increasing fair housing choice or decreasing disparities in access to opportunity.

Timeframes

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We estimate that this Fair Housing Planning task should take approximately 40 business days.

The length of time Fair Housing Planning takes may vary based on the size of the Program Participant, the different types and amounts of resources available to them, or the number of barriers to fair housing choice that must be analyzed. The timeline provides information on how long an estimated planning task might take. The work that goes into Fair Housing Planning is scalable across Program Participants of various sizes, so while it can seem like a complex task, creating a Fair Housing Plan is a manageable task for Program Participants of all sizes and capacities.

  • Fair housing data analysis happens prior to and during the Fair Housing Planning process and includes:
    • collecting data;
    • analyzing data;
    • using data for Community Participation;
    • narrating data in the Fair Housing Plan; and
    • using data to set fair housing goals.
  • Fair housing data analysis affects fair housing goal setting, the Program Participant’s meaningful actions to affirmatively further fair housing, as well as their reporting and record keeping.

Module 5.1: Analyzing Data from a Fair Housing Perspective

The use of data and subsequent data analysis in Fair Housing Planning is of the utmost importance to identify fair housing issues and understand the current fair housing landscape within a Program Participant’s jurisdiction and region.

Data is a collection of facts, figures, and measurements that, when translated or analyzed, serve the purposes of describing, categorizing, qualifying, or quantifying information. Data can be primary, original data collected by Program Participants, or secondary data collected and packaged by another entity/organization.

Good data analysis allows Program Participants to establish baselines, understand fair housing issues, and ultimately set informed goals that lead to meaningful action.

Data should be used in a Fair Housing Plan to assess:

  • Patterns of segregation and/or integration of protected class groups, including racially or ethnically concentrated areas of poverty (R/ECAPs).
  • The relationship of those residential patterns of segregation to access to opportunity for protected class groups.
  • Fair housing issues related to publicly supported housing.
  • Fair housing issues related to fair housing enforcement infrastructure.

This module will build upon the information presented in Module 2 on how to select data sources, data topics, and engage in data collection to prepare for data analysis to inform the Fair Housing Plan. This module will take a deep dive into how to conduct Fair Housing Planning data analysis, including tips and best practices. It will explain how to best use and analyze the data resources presented in this module and in Module 2 for a fair housing analysis. This module will also offer guidance on how to conduct an analysis through the lens of the protected characteristics under the Fair Housing Act, to help identify disparities.

Program Participants should use HUD-provided data, local data, and local knowledge in conducting an analysis for the Fair Housing Plan. Data should be used to assess a Program Participant’s fair housing issues and then to set fair housing goals to overcome those fair housing issues. Data should be assessed across geographic areas—locally and regionally. Fair Housing Planning should include a regional data analysis of fair housing issues since fair housing issues not only cross multiple sectors—including housing, education, transportation, and commercial and economic development—but are also often not constrained by political or geographic boundaries.

Fair Housing Planning should also provide benchmarks to facilitate the measuring of trends and changes over time and should focus on patterns of integration and segregation, R/ECAPs, and disparities in access to opportunities; and these analyses should be conducted through the lens of the protected characteristics under the Fair Housing Act to truly understand the fair housing landscape for all residents.

Module 5.2: Data Visualizations and Data Storytelling

Data visualizations are the representation of data through maps, charts, graphs, infographics, plots, and several other types of pictorial or graphic formats.

When using dot density and thematic maps to complete Fair Housing Planning, Program Participants should keep in mind the following:

  • Study the map to identify geographic patterns or trends of where individuals or communities that are protected under the Fair Housing Act live (e.g., communities of color, LEP communities, LGBTQIA+ communities, individuals with disabilities, etc.) and whether those groups have access to well-resourced areas of opportunity and community assets.
  • Compare different maps to draw connections. For example, look at race/ethnicity dot density maps to identify areas of overlap or isolation among different colored dots representing various racial or ethnic populations. Areas with multiple colors of dots together indicate potential areas of mixing/integration. Areas with dots of one color or one overwhelmingly predominant color may indicate segregation. Clusters of same-colored dots may suggest enclaves. Sharp boundaries between dot color groups may be evidence of segregation, while a “blur” of mixed colors may be a sign of integration. Compare these patterns to trends identified in the thematic maps related to access to opportunity to determine which groups may be lacking access to certain types of opportunities based on their race, national origin, disability, or other protected characteristics.
  • Consider the maps together with the tables. While maps can be helpful for visualizing data, tables can allow for certain detailed analyses. When using tables to complete Fair Housing Planning, Program Participants should consider the following:
    • When reviewing a table, readers should take time to familiarize themselves with the information, paying particular attention to titles, headings and subheadings, the categories displayed, and the units presented. In the initial review of a table, readers also should consider any explanatory notes. In reviewing each table, readers should consider what information the table provides as well as what information it does not provide. For example, a table that lists demographic information for a jurisdiction or region will be helpful in describing the current population. However, if the Program Participant wants to describe demographic change over time, reference to one or more additional tables from various points in time will be required.
    • Tables are arranged with data grouped in rows and columns to make it easy to read and interpret that data. For example, many tables show the protected characteristics of persons or households listed by race/ethnicity groups (White Non-Hispanic, Black Non-Hispanic, Hispanic, Asian or Pacific Islander, and Native American). The tables often show both the total number of persons and the percentage for each group compared to the overall population. This is intended to make it easy for the reader to compare across and between the rows and columns.
    • Program Participants should be watchful for “outliers” – one or more data points that significantly differ from other observations. Outliers can signal the need for additional context that might not be provided by the table (in this instance, Program Participants may find local data, local knowledge, and information learned from Community Participation particularly useful). For example, there may be twice as many people who are elderly residing in HUD’s “Other Multifamily” housing than any other type of publicly supported housing in a region. This outlier could potentially be explained by the fact that “Other Multifamily” units include properties funded through the Supportive Housing for the Elderly program (Section 202). If the housing is not lawfully designated to serve the elderly, it could also signal a possible fair housing issue, such as a policy that illegally excludes families with children.

Data visualizations and data narratives together should tell stories that:

  • Provide context on the people, places, issues, and trends central to the jurisdiction’s story
  • Describe who currently and previously lived in the jurisdiction (and what is believed to have contributed to population changes)
  • Focus on protected class groups
  • Provide data and data analysis that explain historical and contemporary housing stock, trends, and patterns
  • Draw comparisons with neighboring regions and/or regions within the state or county
  • Recommend fair housing goals that are based on the data analysis

Tips for data visualizations:

  • For categorical data (e.g., race, age, educational level), use a bar chart or pie chart
  • Use bar charts or histograms if the data is discrete (whole numbers), or line/area charts if it is continuous (fluctuating numbers, decimals)
  • To show the relationship between values in the dataset such as trends and patterns, use a scatter plot or line chart
  • To compare values, use a pie chart for relative comparison or bar charts for precise comparison

Tools used for data visualizations include: ArcGIS StoryMaps, Flourish, Google Charts, Google Looker Studio, Microsoft Power Bi, and Tableau Public.

Additional guidance and resources include Communicating With Census Data: Storytelling (Census) and Data Visualizations (USWDS).

Module 5.3: Data Collection and Analysis

Data collection is the process of gathering and measuring information on topics of interest in a systemic manner. Program Participants will be collecting and analyzing a combination of administrative, local data, and local knowledge. In Fair Housing Planning, Program Participants analyze their programs, policies, practices, and procedures through a fair housing lens that considers the circumstances of protected class groups.

  • Administrative data are collected and maintained by agencies used to administer (or run) programs and provide services to the public. These data can be either public, private, or available upon request.
  • Local data are collected by any local authority, subdivision, or organization, and valuable and specific to the community that generates it. These data can be either public, private, or available upon request.
  • Local knowledge is information that relates to the Program Participant’s geographic areas of analysis and that is relevant to the Program Participant’s Fair Housing Plan, is known or becomes known to the Program Participant, and is necessary for the completion of the Fair Housing Plan, which may include information from qualitative data sources or research from local or regional university/college.

It is important to note that HUD is not requiring local data to be compiled or obtained if it does not already exist, but encouraging the use of existing, useful data relevant to the Program Participant’s geographic area of analysis that is readily available at little or no cost. Local data and local knowledge can be particularly helpful when it is more up-to-date or more accurate than the HUD-provided data, or when the HUD-provided data do not cover all the protected classes that are required for a fair housing analysis.

Data analysis, Using data, such as indices, maps and tables including local data and knowledge, and any additional data sources deemed necessary. Program Participants’ fair housing data analysis should cover the outlined components below. The following example analysis provides a narrative that is non-exhaustive but demonstrates how data may be used to describe a certain fair housing topic.

Topic of Fair Housing Data Analysis

Example of Data Analysis

Demographic summary, that includes detailed descriptions of historic and current demographic patterns in the jurisdiction and region.

The 2020 Census counted 217,292 persons in the jurisdiction, of whom 47 percent were non-Latinx White, 32 percent were non-Latinx Black, 9 percent were non-Latinx Asian, and 12 percent were multi-racial. Twenty-three percent were Latinx (of any or multiple races). There is diversity based on national origin among Latinx in the jurisdiction. Twenty-seven percent of all Latinx in the jurisdiction have Puerto Rican ancestry, 17 percent have Dominican ancestry, 10 percent Salvadorian ancestry, and 8 percent Colombian ancestry. The Spanish Speaking population (10 percent) and the Vietnamese Speaking population (6 percent) are the predominant limited English proficient populations.

From the 2010 Census to 2020, the Program Participant’s total population grew by 12 percent. As part of this increase, the population of every major racial/ethnic group also increased. The jurisdiction’s Latinx population grew the fastest from 10 percent to 23 percent. Significant growth in the Asian population increased this group’s percentage of the population from 4 to 9 percent. The Black population increased from 14 percent to 22 percent. The White population declined from 67 percent to 57 percent.

Analysis of Community Participation. The Community Participation process is designed to engage the residents of the community or geographic area in which the Program Participant operates; populations affected by housing and fair housing decisions, investments, and challenges; and other interested parties in Fair Housing Planning. Even community members that are not experienced in housing issues and/or fair housing issues can have valuable input.

When people were asked about their concerns, the most frequently mentioned issues were:

  • Rising rents or home prices pushing people out of the neighborhood
  • Lack of affordable housing
  • Racial segregation/discrimination
  • Lack of accessible housing
  • Lack of housing with 3+ bedrooms to accommodate families with children
  • Inability to use housing vouchers due to high rents and discrimination

Participants were also asked, “What do you think the City can do to address racial and ethnic segregation in housing?” The strongest response was for adopting restrictions on rent increases. Other responses included:

  • Promoting land trusts as an anti-gentrification tool
  • Using city-owned parcels to create more affordable housing
  • Building more family-friendly housing in all neighborhoods, but especially well-resourced areas of opportunity
  • More testing and prosecution of landlords and real estate agents who engage in discrimination

Participants were also asked, “What are the major barriers to finding a safe and affordable home in your neighborhood of choice?” The responses were sobering, and not confined to any one part of the city:

  • Affordability, quickly rising rents, shortage of housing for low-income individuals and families
  • Gentrification, building of luxury housing displacing long-term residents
  • Lack of suitable units (disabled and family)
  • Discrimination by landlords and real estate agents (housing voucher, race, national origin, familial status, disability, etc.)
  • Poor credit
  • Lack of good jobs or sufficient income to move (1st month’s rent, last month’s rent, and security deposits)

Participants were also asked, “Are you concerned about high levels of any of the following in your neighborhood?” The major concerns cited include:

  • Displacement and gentrification
  • Discrimination
  • Racial segregation
  • Lack of jobs
  • The burden of increased property values and associated taxes on low-income homeowners

Analysis of Segregation/Integration, that includes patterns of segregation and/or integration in the jurisdiction and region, including changes over time. Identification of areas with high segregation by race/ethnicity, national origin, LEP group, or other groups with characteristics protected under the Fair Housing Act, including trends over time. Location of owner and renter occupied housing in relation to patterns of segregation and other residential patterns related to characteristics protected under the Fair Housing Act.

The jurisdiction still shows strong patterns of racial and ethnic concentration and segregation by residential location. Map 1 shows the population by race and ethnicity. The map shows that the jurisdiction’s racial groups are segregated. For example, White, non-Latinx residents make up 47 percent of the population, and are more than 70 percent of the residents in South City and West County, as well as in portions of Downtown. Black residents make up 32 percent of the population and are concentrated in Northside, North County, and Downtown. The Latinx population today is widely distributed across several neighborhoods, with Puerto Ricans and Dominicans residing more in the Jefferson Park Neighborhood, while Salvadorans, Columbians, Mexicans, and Guatemalans are more concentrated in East City.

An analysis of R/ECAPs, that includes identification of R/ECAPs where groups with characteristics protected under the Fair Housing Act disproportionately reside and the residential patterns; identification of R/ECAP trends over time; and discussion of trends, policies, or practices that could lead to higher levels of segregation or integration.

R/ECAPS are highly concentrated in Shrewsberry, with other R/ECAPs also constituted in the Northside and Chinatown. Residents in R/ECAPs are predominantly Black, Hispanic, and Chinese.

These R/ECAPs can be generally subdivided into three categories (Table 1): 1) tracts where there has been little housing development since 2010 and a very high percentage of income restricted/affordable housing (including public housing), as well as Housing Choice Vouchers (HCVs), 2) tracts that have had significant housing development since 2010, but also have high percentages of income-restricted housing and HCVs, and 3) tracts with little to no development and also little to no income-restricted housing and HCVs.

Analysis of Disparities in Access to Opportunity, by protected characteristic, including but not limited to the locations of poverty; access to healthcare; differences in rates of renter and owner-occupied housing; location of quality schools; location of employment and barriers to employment; transportation connectivity; environmental health hazards or environmental justice issues; and food access.

Related to poverty, there is a clear pattern between areas with the most poverty and a concentration of non-White households. The 19 census tracts that record poverty rates of 40 percent or higher are found in predominantly Black, Latinx, and Asian areas of the jurisdiction, particularly the census tracts with large public housing developments. In some areas, the poverty rate is extremely high largely due to the presence of sizable public housing developments that account for nearly all households in that area. Still, poverty and segregation can go hand-in-hand. Not effectively addressing segregation and racial inequity in housing and economic opportunity makes reducing poverty more difficult.

The City conducted an in-depth analysis of the barriers to employment opportunities in the jurisdiction and region. The neighborhoods with high percentages of unemployment are predominantly Black and Latinx. Black residents (16 to 64 years of age) continue to have the highest unemployment rate in the jurisdiction (11 percent), followed by Latinx (10 percent), Asian (7 percent), and White (5 percent) residents.

There are significant differences in the homeownership rate by race and ethnicity. Figure 37 shows that White households are more likely to be homeowners than any other racial/ethnic group: of all ownership households in Boston, 65 percent are White, 19 percent are Black, 8 percent are Latinx, and 7 percent are Asian. Of all renter households, 44 percent are White and 24 percent are Black.

Analysis of disability-related fair housing issues.

Overall, about 18 percent of the jurisdiction’s population has a disability. Ten percent of the total population has an ambulatory disability (trouble walking or climbing stairs), 5 percent has a cognitive disability, 5 percent has a self-care and/or independent living disability, 3 percent has a hearing impairment, and 2 percent has a vision impairment.

Individuals with disabilities disproportionately reside in R/ECAPs and areas with significant disparities in access to opportunities.

Analysis of Publicly Supported Housing, that includes publicly supported housing demographics, including the location of publicly supported housing, occupancy, and disparities in access to opportunity for those living in publicly supported housing.

This section highlights PHA programs, including information about the residents that the programs serve and where the programs’ services are located. The PHA manages both federal and state public housing developments and administers both federal and state housing vouchers. The PHA has 36 public housing developments: 16 are designated as elderly/disabled developments and 20 are designated as family developments. Nearly all the PHA’s public housing family developments were built before 1955 and are at or near obsolescence. In addition to public housing units, the PHA administers approximately 14,000 federal rental assistance vouchers that allow families to rent in the private market and apply a subsidy to their rent.

When we look at where these HUD publicly supported housing units are located with respect to R/ECAPs, out of a total of 15,722 persons within R/ECAP areas, Black Non-Latinx comprise the largest population in those areas (36 percent) followed by Latinx (29 percent), White (19 percent), and Asian (12 percent). There were 13,651 families living within the R/ECAP boundaries, and more than half (55 percent) of this number were families with children.

LIHTCs are the fastest growing form of publicly subsidized housing in the jurisdiction with 40 LIHTC developments. However, all the LIHTC properties are located in R/ECAPs and areas with significant disparities in access to opportunities. Regionally, USDA provides the most publicly supported housing, with 36 developments scattered through the rural areas. However, the USDA properties are located in rural areas with significant disparities in access to opportunities.

Local Fair Housing Enforcement. The Fair Housing Plan should include data and analysis on the number of housing discrimination complaints filed, what types of complaints are filed, and the breakdown of allegations by protected class. Fair Housing complaint data can be easily obtained by contacting the local HUD field office, or your local non-profit fair housing agencies under HUD’s Fair Housing Initiatives Program (FHIP) and/or Fair Housing Assistance Program (FHAP).

The jurisdiction reported the following state and federal housing discrimination cases in 2022:

  • Disability was most commonly cited as the basis for discrimination in cases reported in 2022 (43 percent). Discrimination based on race (25 percent) was the second most commonly cited, followed by national origin (19 percent).
  • Complaints alleging discrimination based on race and national origin increased from 27 percent to 36 percent of open cases.
  • Consultation with the local FHIP attributes this increase to a national context of increased racism, xenophobia, and anti-immigrant sentiment, with a particular concern for persons who are undocumented, Muslim, or whose national origin is from a primarily Muslim country. The attendant mistrust of government most likely served to lower official complaints, as fair housing advocates, health professionals, and local civil rights organizations reported accounts of immigrant families being unwilling to file discrimination or lead paint complaints because of fears of ICE and deportation, as well as threats of deportation from landlords to force a family to move without an eviction process.
  • Inquiry data provided by the FHIP indicates that highest category for protected class contacts in 2022 was disability (38 percent), followed by race or color (21 percent) and national origin (15 percent). Four percent of contacts included categories for inquiries related to rights for victims of domestic violence.
  • FHIP also reported mediating 23 percent of complaints during the year. The most common basis for the disputes were disability and race.
  • FHAP-filed complaint data show that statewide, the highest percentage of complaints filed in 2022 was on the basis of disability (36 percent), followed by race or color (16 percent), and national origin (8 percent).
  • In 2020, Suffolk Law (a FHIP-funded organization) released the results of matched-pair tests of 50 randomly selected market-rate rental listings in the region. Each test analyzed any differences in treatment based on whether the tester was Black or White. The results of this testing project confirm the significant challenges Black people face in the marketplace, and that differential treatment could be found at each step of the rental process, starting with the initial interaction. While White testers were able to see an apartment in 80 percent of the tests, Black testers were only able to view a unit 48 percent of the time. Earlier tests have shown high levels of discrimination for a range of protected classes. During calendar years 2020 and 2021 the FHIP completed 156 systemic and complaint-based tests (some of these may be follow-up tests related to the same matter). Discrimination was found in the areas of public assistance recipiency, familial status, race and color, and disability. Sexual orientation discrimination, particularly against transgender persons, has also been found to be widespread.
  • Discrimination extends into mortgage lending. A 2005-2006 series of tests conducted by a FHIP-funded organization found differences in treatment that disadvantaged homebuyers of color in 45 percent of the tests (9 of 20 tests).
  • The FHAP had positive outcomes in 62 percent of cases, resulting in $215,000 of monetary relief for 16 households.
  • The FHAP issued charges of discrimination in 9 cases during 2022. These included 4 charges for disability discrimination, 2 charges for sex discrimination, and 3 charges for race discrimination.
  • The FHIP initiated a public service campaign in 2022 targeted for individuals with limited English proficiency, specifically for the local refugee and immigrant population.
  • The FHIP conducted 10 education and outreach events in 2022, with the most requested session being related to reasonable accommodations and modifications and the second most requested session related to race discrimination.
  • The FHIP has collaboration agreements for education and training with five local colleges and universities.

Qualitative data are often obtained based on the lived experiences of individuals. Information is collected using surveys, focus groups, or interviews, and frequently appears in narrative form. For example, qualitative data could be notes taken during a focus group or responses from an open-ended questionnaire. The properties can be categorized into classes that may be assigned numeric values to help analyze the data, which is called coding. Coding can help analyze the data for patterns or meaning. Coding allows the researcher to categorize qualitative data to identify themes that correspond with the research questions and to perform quantitative analysis. Commonly used qualitative data analysis tools include (but are not limited to): ATLAS.ti, Dedoose, nVivo, QCoder, QDA Miner, and Taguette.

Interviews are structured or semi-structured conversations that provide in-depth information and knowledge on the experiences, behaviors, beliefs, and attitudes of the interviewee. Interviews also provide space and opportunity for interviewees to share personal or sensitive information.

See the CDC’s Data Collection Methods Evaluation: Interviews for more tips and information on conducting interviews.

Focus groups are facilitated group interviews with individuals who share similar characteristics or shared interests. Group interactions and responses help generate information and knowledge on shared and divergent experiences, behaviors, beliefs, and attitudes.

Examples of how focus groups may be used in Fair Housing Planning may include, but is not limited to:

  • Sharing data obtained for the Fair Housing Planning process for community reflection/input on the fair housing issues the community experiences;
  • Presenting existing fair housing issues and barriers to housing choice, such as patterns of segregation and disparities in access to opportunity; or
  • Sharing a draft of the fair housing goals for community reflection/input.

See the CDC’s Data Collection Methods Evaluation: Focus Groups Brief for more tips and information on focus groups.

Surveys are questions designed to extract information from a sample of people to learn more about a population’s experiences, behaviors, beliefs, and attitudes. Surveys are conducted in-person, on the telephone, or online (self-administered). Commonly used survey tools include (but are not limited to) Google Forms, SurveyMonkey, Typeform, and Qualtrics.

Examples of how surveys may be used in Fair Housing Planning may include, but are not limited to:

  • Soliciting community input through a survey distributed at community meetings and posted on the Fair Housing Plan webpage;
  • Promoting input from residents who speak a language other than English, when the survey was produced in additional languages;
  • Collecting voluntary demographic data to inform Community Participation, including race/ethnicity, sex, LEP status, familial status, disability status, or others.

See the CDC’s Data Collection Methods Evaluation: Questionnaires and Questions Checklist, or more tips and information on surveys/questionnaires.

Document reviews are the collection and review of existing documents relevant to the research being conducted. Documents can be internal to an organization, institution, or entity, or external (e.g., Consolidated Plan or Public Housing Agency Plan, Comprehensive Plans, public records, public reports, published research, newsletters, funding proposals, meeting meetings, marketing materials). Commonly used tools for document reviews include Google Scholar, Connected Papers, Mendeley, and ResearchGate.

See the CDC’s Data Collection Methods Evaluation: Document Review Brief for more tips and information on document reviews.

To ensure that the data is uniform, accurate, consistent, complete, and valid, Program Participants should both clean and standardize all data collected and analyzed.

Quantitative data express a certain quantity, amount, or range. Usually, there are measurement units associated with the data (e.g., meters, in the case of the height of a person). It makes sense to set boundary limits to such data, and it is also meaningful to apply arithmetic operations to the data.

Helpful fair housing/fair housing-relevant quantitative data sources, which are data sets and repositories where data is collected and stored, are shared in greater detail in Module 2. They include (but are not limited to):

HUD developed its AFFH Data and Mapping Tool (AFFH-T) for Program Participants’ use in Fair Housing Planning. The AFFH-T is available here: HUD AFFH. The AFFH-T provides maps and tables with the following data:

Maps

  • Map 1: Race/Ethnicity
  • Map 2: Race/Ethnicity Trends
  • Map 3: National Origin
  • Map 4: LEP
  • Map 5: Publicly Supported Housing and Race/Ethnicity
  • Map 6: Housing Problems
  • Map 7: Demographics and School Proficiency
  • Map 8: Demographics and Job Proximity
  • Map 9: Demographics and Labor Market Engagement
  • Map 10: Demographics and Transit Trips
  • Map 11: Demographics and Low Transportation Costs
  • Map 12: Demographics and Poverty
  • Map 13: Demographics and Environmental Health
  • Map 14: Disability by Type
  • Map 15: Disability by Age Group
  • Map 16: Housing Tenure
  • Map 17: Location of Affordable Rental Housing
  • Map 18: Racial/Ethnic Dissimilarity Index

Tables

  • Table 1: Demographics
  • Table 2: Demographic Trends
  • Table 3: Racial/Ethnic Dissimilarity Trends
  • Table 4: R/ECAPs Demographics
  • Table 5: Publicly Supported Housing Units by Program Category
  • Table 6: Publicly Supported Housing Residents by Race/Ethnicity
  • Table 7: R/ECAP and Non-R/ECAP Demographics by Publicly Supported Housing Program Category
  • Table 8: Demographics of Publicly Supported Housing Developments, by Program Category
  • Table 9: Demographics of Households with Disproportionate Housing Needs
  • Table 10: Demographics of Households with Severe Housing Cost Burden
  • Table 11: Publicly Supported Housing by Program Category: Units by Number of Bedrooms and Number of Children
  • Table 12: Opportunity Indicators by Race/Ethnicity
  • Table 13: Disability by Type
  • Table 14: Disability by Age Group
  • Table 15: Disability by Publicly Supported Housing Program Category
  • Table 16: Homeownership and Rental Rates by Race/Ethnicity
  • Table 17: Demographics of PHA Assisted Households
  • Table 18: PHA Assisted Residents by Race/Ethnicity

In the AFFH-T, the following Opportunity Indices are provided:

  1. Low Poverty Index: The low poverty index captures poverty in a given neighborhood and is based on the poverty rate.
  2. School Proficiency Index: The school proficiency index uses school-level data on the performance of 4th grade students on state exams to describe which neighborhoods have high-performing elementary schools nearby and which are near lower performing elementary schools.
  3. Jobs Proximity Index: The jobs proximity index quantifies the accessibility of a given residential neighborhood as a function of its distance to all job locations within a core based statistical area (CBSA), with larger employment centers weighted more heavily.
  4. Labor Market Engagement Index: The labor market engagement index provides a summary description of the relative intensity of labor market engagement and human capital in a neighborhood. This is based upon the level of employment, labor force participation, and educational attainment in a census tract.
  5. Low Transportation Cost Index: This index is based on estimates of transportation costs for a family that meets the following description: a 3-person single-parent family with income at 50% of the median income for renters for the region (i.e., CBSA). The estimates come from the Location Affordability Index (LAI). Note: The LAI data do not contain transportation cost information for Puerto Rico.
  6. Transit Trips Index: This index is based on estimates of transit trips taken by a family that meets the following description: a 3-person single-parent family with income at 50% of the median income for renters for the region (i.e., CBSA). The estimates come from the Location Affordability Index (LAI). Note: The LAI does not contain transit trip information for Puerto Rico.
  7. Environmental Health Index: The environmental health index summarizes potential exposure to harmful toxins at a neighborhood level. The index is a linear combination of standardized EPA estimates of air quality carcinogenic (c), respiratory (r) and neurological (n) hazards with i indexing census tracts.

Program Participants may wish to consider other opportunity indices that can inform access to healthcare; location of employment and barriers to employment; transportation connectivity; other environmental health hazards or environmental justice issues; food access; and other equity issues.

Equity Data Resources

General Data Resources

HUD Data Tools and Resources

Health Data Resources

Education Data Resources

Economic Data Resources

Environment Data Resources

Food Access Data Resources

Transportation Data Resources

Additionally, these datasets may be helpful in Fair Housing Planning data analysis:

Source

Data/Tool

HUD

Other Housing Related Datasets

CDC

Census

Community Commons

Data.gov

FHFA

USDA

HUD's Community Assessment Reporting Tool (CART) captures major HUD programs that provide investments within a specific community, which may be relevant to Fair Housing Planning. CART is focused on the following programs:

CART Tab

HUD Programs

CPD Grants

  • Community Development Block Grant (CDBG)
  • HOME
  • Housing Opportunities for Persons with AIDS (HOPWA)
  • Emergency Solutions Grants (ESG)
  • Continuum of Care (COC)
  • Section 108 Loans
  • Disaster Recovery Grants

Rental Assistance

  • Project Based Rental Assistance
  • Section 811
  • Section 202
  • Section 236
  • PHA Funding (Operating Fund & Capital Fund)
  • Public Housing Low-Rent Properties
  • Housing Choice Vouchers

Mortgage Insurance

  • Capital Advance
  • Risk Sharing
  • New Construction
  • Refinancing & Improvements
  • Healthcare and Hospitals
  • Other MF Mortgage Insurance
  • Loans Maturing within Fiscal Year
  • Single Family Housing Insurance in Force
  • Defaults
  • HUD Real Estate Owned (REO) Properties

Fair Housing

  • Fair Housing Assistance Program (FHAP) Grants
  • Fair Housing Initiatives Program (FHIP) Grants

Housing Counseling Agency Grants

  • Housing Counseling Agencies and Grants

Signature Programs

  • Sustainable Communities Grants (Challenge and Regional Planning)
  • Choice Neighborhoods (Implementation and Planning)
  • Rental Assistance Demonstration (RAD) Properties
  • Converted
  • Promise Zone sites
  • Strong Cities Strong Communities (SC2) sites

Demographics

  • US Census American Community Survey (ACS) 5-year data

IMS/PIC is responsible for maintaining and gathering data about all PIH's inventories of PHAs, Developments, Buildings, Units, PHA Officials, HUD Offices and Field Staff and IMS/PIC Users. The first release was successfully implemented on December 15, 1999, and introduced a flexible, scalable, Internet-based approach which enables Housing Authority users and Department personnel to access a common database of Housing Authority information via their web browser from anywhere.

Form 50058 Submission. Form HUD-50058 Submission sub-module collects and validates tenant data uploaded by PHAs that report on families who participate in Public Housing or Section 8 rental subsidy programs. PHAs collect and electronically submit information contained on the Form HUD-50058 to provide HUD with a picture of the people who participate in subsidized rental programs. Users receive error reports covering any problems with data submitted. The tenant data transmissions are encrypted to protect the privacy of tenants.

Below is a table that outlines the IMS/PIC Modules and Sub Modules. User Manuals for all modules and sub-modules are also available.

Topic

Modules

PIH Information

  • SEMAP The Section 8 Management Assessment Program (SEMAP) measures PHA management performance in 14 key areas of the Section 8 tenant-based assistance programs. SEMAP measures the PHA's ability to afford decent rental units at a reasonable subsidy cost as intended by Federal housing legislation and by Congress' appropriation of Federal tax dollars for these programs.
  • The Executive Summary allows users to locate the latest address, contact information, performance scores, funding data, and inventory statistics for a particular PHA. By generating one of these summaries, anyone from a PHA staffer to a senior HUD manager can gain an accurate PHA snapshot 24 hours a day, 7 days a week.
  • The Inventory Removals is where an PHA applies to the Special Application Center (SAC) to remove buildings and units from its inventory. Reasons might include demolition or disposal for economic or structural reasons, homeownership programs, condemnation by local government, or mandatory or voluntary conversion to vouchers.

Form HUD-50058

  • Form HUD-50058 collects and validates tenant data uploaded by PHAs that report on families who participate in Public Housing or Section 8 rental subsidy programs. PHAs collect and electronically submit information contained on the Form HUD-50058 to provide HUD with a picture of the people who participate in subsidized rental programs. Users receive error reports covering any problems with data submitted. The tenant data transmissions are encrypted to protect the privacy of tenants.
  • Form HUD-50058 Viewer provides users the ability to bring up tenant data previously reported for analysis or verification. It also provides a set of reports to monitor portability data and moves between PHAs. The tenant data transmissions are encrypted to protect the privacy of tenants.
  • Form HUD-50058 Reports are a set of detailed, pro-forma monthly summary reports covering 14 different areas of tenant data. These reports permit the PHA to monitor its own reporting to identify favorable and unfavorable trends.
  • Form HUD-50058 Tenant ID Management sub module gives users the ability to identify and correct identity problems. IMS/PIC validates all Social Security Numbers received against the SSA national database monthly to protect against fraud, identity theft, ineligible tenants, and duplications.
  • Form 50058 Ad hoc Report sub module provides access to live tenant data in a flexible format. Users select certain filters and which specific fields from the Form 50058 data for immediate download. These data are used in analysis and special reporting. The download is encrypted to protect tenant privacy.

Moving to Work (MTW)

  • MTW Viewer is analogous to the Form-50058 Viewer but it works on MTW tenant data. The tenant data transmissions are encrypted to protect the privacy of tenants.
  • MTW Reports currently provides one monthly summary report, the MTW Delinquency Report, which is analogous to the Form-50058 Delinquency Report for monitoring an HA’s tenant data reporting rates.
  • Moving To Work (MTW) Ad hoc Report provides access to live tenant data in a flexible format for MTW agencies. Users select certain filters and specific fields from the MTW data for immediate download. These data are used in analysis and special reporting. The download is encrypted to protect tenant privacy.

While it is not necessary for effective Fair Housing Planning, some Program Participants may have access to statistical analysis software that can be used for data analysis.

Statistical analysis software is designed to conduct complex statistical analysis and used to collect, clean, and analyze quantitative data. Statistical analysis theorems and methodologies (e.g., regression analysis, time series analysis, tabulations, measures of central tendencies, measures of variance) are used to investigate, identify, describe, and in some cases predict trends and patterns. Commonly used statistical programs include (but are not limited to): SPSS, SAS, STATA, Microsoft Excel, Microsoft Power BI, Tableau, Google Sheets, ArcGIS, QGIS, Google Looker Studio, and R. HUD’s AFFH-T and Community Assessment Reporting (CART) tools and the Census Bureau’s data exploration platform store essential fair housing data that can be analyzed using statistical analysis software.

See the Office of Evaluation Sciences’ Evaluation Resources webpage for more tips and insight on designing evaluations and conducting quantitative analyses.

In addition to gathering data from relevant federal and local agencies and institutions, Program Participants can also consider conducting interviews, focus groups, surveys, and document reviews for Fair Housing Planning and assessment.

Preparing Data for Analysis

Data cleaning is the process of improving quality of data by correcting or removing inaccurate, incomplete, non-uniform data entries from a data set. Data cleaning techniques include:

  • Fixing errors
  • Handling missing values
  • Remove duplicate entries
  • Removing irrelevant data
  • Standardizing capitalization
  • Standardizing formatting
  • Verifying data

See the State Department’s Data Cleaning for Substantive Analysis resource and the CDC’s Managing Data Workbook for more tips and information on data cleaning.

Data standardization, or data standards, provide specifications and criteria on how data should be formatted, stored, and processed. After data has been cleaned, processes should be established to maintain data standards and quality. When data standards have been established, data standardization issues should address source-to-target mapping (to specify data elements that are used in applications) and reconciliation (to compare different sets of data to confirm that they aligned). This will include (but are not limited to):

  • Data Type
  • Data Identifiers
  • Vocabulary
  • Serialization format (ex. CSV and XML)

See resource.data.gov’s Data Standards webpage and the Federal Committee on Statistical Methodology’s Framework for Data Quality webpage for more tips and information on data standards and standardization.

Data analysis is the process of systemically translating raw data into useful information, conclusions, and/or recommendations. Both qualitative and quantitative data analysis require the organization and thematization of research findings to communicate findings and recommendations to stakeholders.

See the CDC’s Analyzing Quantitative Data for Evaluation and Analyzing Qualitative Data for Evaluation Briefs for more insight and tips on articulating research findings.

Fair housing data analysis is inclusive of both qualitative and quantitative data analysis that provides an overview of historical and contemporary trends based on protected characteristics under the Fair Housing Act. Analysis should also capture trends and patterns of segregation, integration, concentrated poverty, and access to opportunity based on protected characteristics. Analysis of access to opportunities should include the locations of poverty; access to healthcare; location of quality schools; location of employment and barriers to employment; transportation connectivity; environmental health hazards or environmental justice issues; and food access.

How to Visualize Population and Housing Trends Between 2020 and 2010

Module 5.4: Using Data and Mapping in Fair Housing Planning

The use of data and data analysis in a Fair Housing Planning is of the utmost importance to identify fair housing issues and understand the current fair housing landscape within a Program Participant’s jurisdiction and region. The use of data is important for telling a story, which can help a Program Participant better understand their jurisdiction and region. As mentioned in Module 2, the definition of AFFH frames what, at minimum, should be assessed in a Fair Housing Plan, including:

  • Patterns of segregation and/or integration based on the Fair Housing Act’s protected characteristics, including racially or ethnically concentrated areas of poverty
  • The relationship of those residential patterns of segregation to access to opportunity based on protected characteristics
  • Fair housing issues related to publicly supported housing
  • Fair housing enforcement infrastructure

Data based on Protected Characteristics under the Fair Housing Act

In order to properly evaluate patterns of segregation or integration, R/ECAPs, barriers in access to opportunity, and other fair housing issues, a Program Participant should identify which individuals or groups of individuals are impacted and whether disparities exist based on characteristics protected under the Fair Housing Act, which are race, color, national origin, religion, sex (including sexual orientation and gender identity), familial status, and disability. This analysis is critical for understanding the fair housing landscape for all residents in a community. Without this analysis, a Program Participant is unable to set meaningful goals to overcome existing fair housing issues and increase equity and opportunity for all individuals.

Data analyses should also address the intersection and overlap of protected characteristics. For example, it is important to understand disparities in housing and other residential opportunities among individuals with disabilities and individuals without disabilities. However, data can help better understand another layer of differences among, for example, individuals with disabilities of different races, national origins, sexes, or other characteristics. That is why the use of data and this analysis is critical to Fair Housing Planning, to ensure that a Program Participant is making the most informed decisions to ensure equitable opportunities for all its residents.

The following chart offers some demographic information that can assist with an analysis based on protected characteristics. This chart is not exhaustive but offers some basic data sources to help start with this analysis.

Protected Characteristic

Related Data Information

Data Reference

Race. Among other protected characteristics, the Fair Housing Act prohibits housing discrimination based on race.

The Census offers data on both race and ethnicity, including non-Hispanic Whites, considering Hispanics of any race as a separate race/ethnic category that can experience housing discrimination differently than other groups. Similarly, the data provided for the other race groups –Black, Asian and Pacific Islander, Native American, and other – also exclude information for people who identify their ethnicity as Hispanic.

National Origin. Among other protected characteristics, the Fair Housing Act prohibits housing discrimination based on national origin. Additionally, Title VI of the Civil Rights Act of 1964 prohibits discrimination in programs that receive federal financial assistance based on, among other characteristics, national origin which includes limited English proficiency (LEP).

The Census provides data for four indicators of national origin. The first two are the ten most common places of birth of the foreign-born population by jurisdiction and region and the number and percentage of the population that is foreign-born.

The second two indicators are the ten most common languages spoken at home (for the population age 5 years and over) for those who speak English “less than ‘very well,’” and the number and percentage of the population who speak English “less than ‘very well.’”

Limited English Proficiency (LEP). In certain situations, failure to ensure that persons who are LEP can effectively participate in, or benefit from, federally assisted programs may violate Title VI. Recipients of federal funds are required to take reasonable steps to ensure meaningful access to LEP persons. This "reasonableness" standard is intended to be flexible and fact-dependent.

As a starting point to understanding its LEP-related Title VI obligations, a recipient should conduct an individualized data-based assessment that balances the following four factors:

  • The number or proportion of LEP persons served or encountered in the eligible service population ("served or encountered" includes those persons who would be served or encountered by the recipient if the persons received adequate education and outreach and the recipient provided sufficient language services);
  • The frequency with which LEP persons encounter the program;
  • The nature and importance of the program, activity, or service provided by the program; and
  • The resources available and costs to the recipient.

Regarding the first criteria, the number of people in the service area may be significant if:

  • The number is 1,000 or more in the eligible population in the market area or among current beneficiary, or
  • More than 5% of the eligible population or beneficiaries and more than 50 in number.

American Community Survey

National Origin

LEP

Religion. Among other protected characteristics, the Fair Housing Act prohibits housing discrimination based on religion.

The Census Bureau provides information on self-described religious identification.

American Community Survey (ACS), Table 75. Self-Described Religious Identification of Adult Population.

Sex. Among other protected characteristics, the Fair Housing Act prohibits housing discrimination based on sex, including sexual orientation and gender identity.

The Census Bureau provides information on male/female status, as well as information on sexual orientation and gender identity.

American Community Survey (ACS)

Familial Status. Among other protected characteristics, the Fair Housing Act prohibits housing discrimination based on familial status. Familial status includes one or more individuals under the age of 18 being domiciled with a parent, a pregnant person, or other person with legal custody of such individual.

The Census Bureau provides information on families with children. Specifically, familial status is measured as the number and percentage of all families (with two or more related people in the household) that are families with children under the age of 18.

Bedroom Size (families with children often need housing with more than one bedroom). Keating Memo

American Community Survey (ACS)

Disability. Among other protected characteristics, the Fair Housing Act prohibits housing discrimination based on disability.

The Census provides information on disability type and disability status by age group. The disability type and disability status by age group measures are from the ACS. The disability type categories reported by the Census Bureau include hearing difficulty, vision difficulty, cognitive difficulty, ambulatory difficulty, self-care difficulty, and independent living difficulty. The definition of “disability” used by the Census Bureau may not be comparable to the Fair Housing Act.

American Community Survey

Checklist

  • Icon of check mark Have you identified your key players? (Fair Housing Plan Coordinator, Data Analyst(s), Local Universities/Research Organizations, FHIPs/FHAPs?)
  • Icon of check mark Have you identified key data sets and repositories for data collection?
  • Icon of check mark Have you considered and determined the data collection methods best suited for your jurisdiction?
  • Icon of check mark Have you reviewed the data made available on HUD’s AFFH and CART tools?

Resources

AFFH-T

The Inventory Management System/PIH Information Center (IMS/PIC)

CART

Fair Housing Specific Toolkits and Guidance

US Census Bureau Tutorials and Webinars

Urban Institute Tutorials and Webinars

Data Analysis Toolkits

Supplementary Research Publishers/Publications on Housing/Housing-Related Issues and Topics

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