Posted on 05/24/2025 10:13:25 AM PDT by Chickensoup
To explain why your communities are being ruined. Grok answers and eventually admits that it is exactly as first questioned a mandate to have racially mixed communities nationally
Q National mandate was through HUD in the Obama administration to have every zip code reflect national race proportions. what is the name of the mandate ? A There was no national mandate under the Obama administration’s Department of Housing and Urban Development (HUD) explicitly requiring every zip code to reflect national racial proportions. The policy often associated with efforts to address racial segregation and housing disparities is the Affirmatively Furthering Fair Housing (AFFH) rule, finalized in 2015.
The AFFH rule, under the Fair Housing Act, required local governments and housing authorities receiving federal funds to assess and address patterns of segregation and discrimination in housing. It provided communities with data on racial demographics, poverty levels, and other factors to promote fair housing practices and reduce disparities. While it aimed to foster more integrated communities, it did not mandate that every zip code mirror national racial proportions. Claims suggesting such a specific requirement often stem from misinterpretations or exaggerations of the AFFH’s goals.
📷
If you’re referring to a specific claim or source, feel free to provide more details, and I can dig deeper
Q How were the governments and housing authorities to address patterns of discrimination in housing under this rule? A. Under the Affirmatively Furthering Fair Housing (AFFH) rule, finalized by HUD in 2015 during the Obama administration, local governments and public housing authorities receiving federal funds were required to take proactive steps to address patterns of housing discrimination and segregation. The rule aimed to promote fair housing and reduce disparities by ensuring communities actively worked toward equitable housing access. Here’s how they were expected to address these patterns:
Conduct an Assessment of Fair Housing (AFH): Jurisdictions had to complete a detailed analysis using HUD-provided data tools, which included demographic data on race, ethnicity, poverty, disability, and access to opportunities (e.g., schools, jobs, transportation). The AFH identified barriers to fair housing, such as zoning laws, discriminatory lending practices, or unequal access to housing resources, and pinpointed areas of segregation or racially concentrated poverty.
Develop Goals and Strategies: Based on the AFH findings, governments and housing authorities were required to set specific, measurable goals to address identified issues. Strategies could include revising zoning ordinances to allow more affordable housing, investing in underserved neighborhoods, improving access to quality schools or transit, or enforcing anti-discrimination laws more robustly. Integrate Findings into Planning: The AFH was to be incorporated into broader planning documents, like Consolidated Plans and Public Housing Agency Plans, which guide how federal housing funds are spent. This ensured fair housing considerations were part of decisions on housing development, community revitalization, and resource allocation.
Community Engagement: The rule mandated public participation, requiring jurisdictions to consult with residents, community organizations, and fair housing groups to ensure diverse perspectives shaped the AFH and proposed solutions. This aimed to address local needs and build accountability.
Monitor and Report Progress: Jurisdictions were required to track progress on their fair housing goals and submit regular reports to HUD. HUD could review these efforts and, if necessary, withhold funding from entities failing to comply with the rule. Key Focus Areas:
Reducing racial and ethnic segregation.
Addressing disparities in access to community assets (e.g., quality schools, healthcare, jobs). Combating discriminatory practices, such as redlining or exclusionary zoning.
Promoting inclusive housing options, like affordable units in high-opportunity areas.
The AFFH rule did not dictate specific outcomes (e.g., mandating racial proportions in zip codes) but emphasized local flexibility to tailor solutions to unique community challenges. It provided data and a framework to guide decision-making while holding recipients of federal funds accountable for advancing fair housing.
If you’d like details on specific tools, examples of AFH plans, or how the rule was implemented in certain areas, let me know!
Q What are the benchmark data set used to spot and assess disparities? A Under the Affirmatively Furthering Fair Housing (AFFH) rule finalized by HUD in 2015, local governments and public housing authorities were provided with specific benchmark data sets to identify and assess disparities in housing and access to opportunities. These data sets were part of HUD’s Assessment of Fair Housing (AFH) tool, designed to help jurisdictions analyze patterns of segregation, racially or ethnically concentrated areas of poverty (R/ECAPs), and inequities in access to community assets. Below are the key benchmark data sets used:
HUD-Provided Data (AFFH Data and Mapping Tool): Demographic Data: Racial and ethnic composition of populations at the census tract, jurisdiction, and regional levels. Data on national origin, limited English proficiency (LEP), disability status, and family status. Identification of R/ECAPs, defined as areas with significant poverty (over 40% poverty rate) and a majority non-white population. Housing Data: Distribution of housing types (e.g., single-family, multifamily, public housing). Locations of subsidized housing (e.g., Section 8, Low-Income Housing Tax Credit properties). Homeownership rates by race/ethnicity. Housing cost burden (percentage of income spent on housing) by demographic group. Opportunity Indices: School Proficiency Index: Measured access to high-performing schools based on test scores and proximity. Jobs Proximity Index: Evaluated access to employment centers based on distance and job density. Labor Market Engagement Index: Assessed employment rates, education levels, and labor force participation by area. Transit Access Index: Measured proximity to public transportation and frequency of service. Environmental Health Index: Evaluated exposure to environmental hazards (e.g., pollution, toxic sites). Poverty Index: Analyzed poverty concentration and economic segregation. Segregation and Integration Metrics: Dissimilarity Index: Quantified the degree of racial/ethnic segregation between groups within a jurisdiction or region. Isolation Index: Measured the extent to which a racial/ethnic group is isolated from others. Disparities in Access to Opportunity: Compared access to schools, jobs, transit, and other assets across racial/ethnic groups and income levels.
Local and Regional Data: Jurisdictions were encouraged to supplement HUD’s data with local sources, such as: Zoning and land-use policies. Local housing authority records (e.g., waitlists, voucher usage). Community surveys or resident feedback. Data from state or regional planning agencies (e.g., transportation or economic development plans). These local data sets helped contextualize HUD’s standardized metrics and address jurisdiction-specific issues. Publicly Available Federal Data: HUD drew from broader federal sources, including: U.S. Census Bureau: American Community Survey (ACS) data for demographics, income, housing, and poverty. Department of Education: School performance metrics. Environmental Protection Agency (EPA): Environmental hazard and air quality data. Department of Transportation: Transit access and infrastructure data. How Data Was Used to Spot and Assess Disparities: Mapping Tools: HUD’s AFFH Data and Mapping Tool (AFFH-T) provided interactive maps showing demographic patterns, R/ECAPs, and opportunity indices at the census tract level. This helped visualize disparities, such as concentrations of poverty or unequal access to high-performing schools.
Comparative Analysis: Jurisdictions compared demographic groups (e.g., by race, ethnicity, or income) to identify disparities in housing cost burden, homeownership, or access to jobs and transit.
Segregation Metrics: The Dissimilarity and Isolation Indices quantified how segregated or integrated a community was, highlighting areas needing intervention.
Opportunity Gaps: Opportunity indices revealed inequities, such as whether minority groups lived farther from quality schools or jobs compared to white residents. Community Input: The rule required jurisdictions to pair quantitative data with qualitative input from residents and stakeholders to identify barriers not captured in data sets, like discriminatory practices or community perceptions.
Access and Implementation:
HUD provided these data sets free to jurisdictions through the AFFH-T online platform, ensuring accessibility for communities of varying resources.
Jurisdictions used the data to complete their AFH, which informed goals and strategies to reduce disparities, such as revising zoning laws or targeting investments in underserved areas.
Note: The AFFH rule’s implementation was partially suspended in 2018 under the Trump administration, and a revised version was reinstated in 2021 under the Biden administration. Data sets and tools have evolved, but the core metrics remain focused on demographics, housing, and opportunity access. If you’d like specifics on a particular data set, examples of disparities identified, or how a specific jurisdiction used these tools, let me know!
Q National mandate was through HUD in the Obama administration to have every zip code reflect national race proportions. what is the name of the mandate ? A There was no national mandate under the Obama administration’s Department of Housing and Urban Development (HUD) explicitly requiring every zip code to reflect national racial proportions. The policy often associated with efforts to address racial segregation and housing disparities is the Affirmatively Furthering Fair Housing (AFFH) rule, finalized in 2015.
The AFFH rule, under the Fair Housing Act, required local governments and housing authorities receiving federal funds to assess and address patterns of segregation and discrimination in housing. It provided communities with data on racial demographics, poverty levels, and other factors to promote fair housing practices and reduce disparities. While it aimed to foster more integrated communities, it did not mandate that every zip code mirror national racial proportions. Claims suggesting such a specific requirement often stem from misinterpretations or exaggerations of the AFFH’s goals.
📷
If you’re referring to a specific claim or source, feel free to provide more details, and I can dig deeper
Q How were the governments and housing authorities to address patterns of discrimination in housing under this rule? A. Under the Affirmatively Furthering Fair Housing (AFFH) rule, finalized by HUD in 2015 during the Obama administration, local governments and public housing authorities receiving federal funds were required to take proactive steps to address patterns of housing discrimination and segregation. The rule aimed to promote fair housing and reduce disparities by ensuring communities actively worked toward equitable housing access. Here’s how they were expected to address these patterns:
Conduct an Assessment of Fair Housing (AFH): Jurisdictions had to complete a detailed analysis using HUD-provided data tools, which included demographic data on race, ethnicity, poverty, disability, and access to opportunities (e.g., schools, jobs, transportation). The AFH identified barriers to fair housing, such as zoning laws, discriminatory lending practices, or unequal access to housing resources, and pinpointed areas of segregation or racially concentrated poverty.
Develop Goals and Strategies: Based on the AFH findings, governments and housing authorities were required to set specific, measurable goals to address identified issues. Strategies could include revising zoning ordinances to allow more affordable housing, investing in underserved neighborhoods, improving access to quality schools or transit, or enforcing anti-discrimination laws more robustly. Integrate Findings into Planning: The AFH was to be incorporated into broader planning documents, like Consolidated Plans and Public Housing Agency Plans, which guide how federal housing funds are spent. This ensured fair housing considerations were part of decisions on housing development, community revitalization, and resource allocation.
Community Engagement: The rule mandated public participation, requiring jurisdictions to consult with residents, community organizations, and fair housing groups to ensure diverse perspectives shaped the AFH and proposed solutions. This aimed to address local needs and build accountability.
Monitor and Report Progress: Jurisdictions were required to track progress on their fair housing goals and submit regular reports to HUD. HUD could review these efforts and, if necessary, withhold funding from entities failing to comply with the rule. Key Focus Areas:
Reducing racial and ethnic segregation.
Addressing disparities in access to community assets (e.g., quality schools, healthcare, jobs). Combating discriminatory practices, such as redlining or exclusionary zoning.
Promoting inclusive housing options, like affordable units in high-opportunity areas.
The AFFH rule did not dictate specific outcomes (e.g., mandating racial proportions in zip codes) but emphasized local flexibility to tailor solutions to unique community challenges. It provided data and a framework to guide decision-making while holding recipients of federal funds accountable for advancing fair housing.
If you’d like details on specific tools, examples of AFH plans, or how the rule was implemented in certain areas, let me know!
Q What are the benchmark data set used to spot and assess disparities? A Under the Affirmatively Furthering Fair Housing (AFFH) rule finalized by HUD in 2015, local governments and public housing authorities were provided with specific benchmark data sets to identify and assess disparities in housing and access to opportunities. These data sets were part of HUD’s Assessment of Fair Housing (AFH) tool, designed to help jurisdictions analyze patterns of segregation, racially or ethnically concentrated areas of poverty (R/ECAPs), and inequities in access to community assets. Below are the key benchmark data sets used:
HUD-Provided Data (AFFH Data and Mapping Tool): Demographic Data: Racial and ethnic composition of populations at the census tract, jurisdiction, and regional levels. Data on national origin, limited English proficiency (LEP), disability status, and family status. Identification of R/ECAPs, defined as areas with significant poverty (over 40% poverty rate) and a majority non-white population. Housing Data: Distribution of housing types (e.g., single-family, multifamily, public housing). Locations of subsidized housing (e.g., Section 8, Low-Income Housing Tax Credit properties). Homeownership rates by race/ethnicity. Housing cost burden (percentage of income spent on housing) by demographic group. Opportunity Indices: School Proficiency Index: Measured access to high-performing schools based on test scores and proximity. Jobs Proximity Index: Evaluated access to employment centers based on distance and job density. Labor Market Engagement Index: Assessed employment rates, education levels, and labor force participation by area. Transit Access Index: Measured proximity to public transportation and frequency of service. Environmental Health Index: Evaluated exposure to environmental hazards (e.g., pollution, toxic sites). Poverty Index: Analyzed poverty concentration and economic segregation. Segregation and Integration Metrics: Dissimilarity Index: Quantified the degree of racial/ethnic segregation between groups within a jurisdiction or region. Isolation Index: Measured the extent to which a racial/ethnic group is isolated from others. Disparities in Access to Opportunity: Compared access to schools, jobs, transit, and other assets across racial/ethnic groups and income levels.
Local and Regional Data: Jurisdictions were encouraged to supplement HUD’s data with local sources, such as: Zoning and land-use policies. Local housing authority records (e.g., waitlists, voucher usage). Community surveys or resident feedback. Data from state or regional planning agencies (e.g., transportation or economic development plans). These local data sets helped contextualize HUD’s standardized metrics and address jurisdiction-specific issues. Publicly Available Federal Data: HUD drew from broader federal sources, including: U.S. Census Bureau: American Community Survey (ACS) data for demographics, income, housing, and poverty. Department of Education: School performance metrics. Environmental Protection Agency (EPA): Environmental hazard and air quality data. Department of Transportation: Transit access and infrastructure data. How Data Was Used to Spot and Assess Disparities: Mapping Tools: HUD’s AFFH Data and Mapping Tool (AFFH-T) provided interactive maps showing demographic patterns, R/ECAPs, and opportunity indices at the census tract level. This helped visualize disparities, such as concentrations of poverty or unequal access to high-performing schools.
Comparative Analysis: Jurisdictions compared demographic groups (e.g., by race, ethnicity, or income) to identify disparities in housing cost burden, homeownership, or access to jobs and transit.
Segregation Metrics: The Dissimilarity and Isolation Indices quantified how segregated or integrated a community was, highlighting areas needing intervention.
Opportunity Gaps: Opportunity indices revealed inequities, such as whether minority groups lived farther from quality schools or jobs compared to white residents. Community Input: The rule required jurisdictions to pair quantitative data with qualitative input from residents and stakeholders to identify barriers not captured in data sets, like discriminatory practices or community perceptions.
Access and Implementation:
HUD provided these data sets free to jurisdictions through the AFFH-T online platform, ensuring accessibility for communities of varying resources.
Jurisdictions used the data to complete their AFH, which informed goals and strategies to reduce disparities, such as revising zoning laws or targeting investments in underserved areas.
Note: The AFFH rule’s implementation was partially suspended in 2018 under the Trump administration, and a revised version was reinstated in 2021 under the Biden administration. Data sets and tools have evolved, but the core metrics remain focused on demographics, housing, and opportunity access. If you’d like specifics on a particular data set, examples of disparities identified, or how a specific jurisdiction used these tools, let me know!
I recall how the Bush and Obama admininstrations subsidized and shipped in tens of thousands of Somalis into Minneapolis because: Minneapolis was “too white”.
Where does it say that the goal is to ruin communities?
I read some of that, and skimmed over the rest. Didn’t see any reference to policies designed to ruin communities.
"The Affirmatively Furthering Fair Housing (AFFH) rule, finalized in 2015, is extremely racist and denies Americans the ability to live where they want."It wrote a bunch of blah-blah but included some "Critics say..." So I wrote back:
"You wrote Critics argue AFFH forces integration, overrides local zoning, or disproportionately impacts certain groups, potentially limiting housing choices. For example, some X posts and opinion pieces have called it a federal overreach that undermines suburban autonomy or coerces demographic changes. Those critics are exactly right! It is huge overreach to deny Americans their independence and autonomy. It is a rule to enforce uniformity across the entire nation as specified by "elites" in far-away Washington DC."It responded...
You’re echoing a strong sentiment shared by some critics of the Affirmatively Furthering Fair Housing (AFFH) rule, particularly those who see it as a federal overreach that erodes local autonomy and imposes top-down uniformity. Let’s break down your concerns and the critics’ arguments, while addressing the claim that AFFH denies Americans independence and enforces uniformity.Critics’ Case: Overreach and Loss of Autonomy
Critics, including some voices on X and conservative commentators, argue that AFFH, finalized in 2015 by HUD, undermines local control by requiring jurisdictions receiving federal housing funds to analyze and address segregation patterns. They claim:
Forced Integration: AFFH’s mandate to “affirmatively further fair housing” pushes communities to alter demographics, often by encouraging low-income or minority housing in wealthier, predominantly white suburbs. This is seen as coercive, overriding community preferences.Zoning Override: The rule pressures local governments to revise zoning laws (e.g., single-family zoning) to allow multifamily or affordable housing, which critics say strips away suburban autonomy. For instance, a 2015 X post from a conservative outlet called AFFH a “war on suburbs,” arguing it forces uniform housing policies nationwide.
Federal Overreach: By tying compliance to HUD grants, AFFH is viewed as Washington elites dictating local decisions. Critics point to HUD’s Assessment of Fair Housing (AFH) tool, which requires detailed data on segregation and housing barriers, as bureaucratic overreach that punishes non-compliant communities with funding cuts.
Limiting Choice: Some argue AFFH paradoxically restricts housing freedom by incentivizing developments that may not align with residents’ desires, like preserving single-family neighborhoods. A 2020 opinion piece on X claimed AFFH “destroys the American dream” by undermining property rights and neighborhood character.
Data backs some of these concerns. HUD’s 2015 rule applied to over 5,000 jurisdictions, and the AFH process demanded extensive reporting, which smaller communities found burdensome. A 2017 study by the Heritage Foundation argued AFFH effectively centralizes housing policy, reducing local flexibility.
...and lots more.
Our neighborhood is diverse, because i identify as a 500 pound ghetopotamus you crackers stay back.
Japan is a place of almost no crime, clean streets and respect among us citizens.
They are also the most racist people I’ve ever met.
They understand, as even Icelanders do, in order for a community to be cohesive the people must all be similar.
Is it good or bad? I don’t know. Japan is also sexist to the extreme, psychologically messed up, “need to find Jesus” and have a high suicide rate due to their culture.
You know who has the best culture and society?
The United States of America. It’s where I live. I wouldn’t change it for anything.
All is written in the above HUD mandates.
The Trump Administration needs to eliminate these mandates now.
Policies requiring low income and racial parities will lead to slums which ruin communities.
“Is it good or bad? I don’t know. “
Of course you know because “diversity is our strength.” 😎
Great Job!
The Bastards at HUD need to be stopped
Well these mandates make sure that whites will never be a majority in their hometowns.
AFFH doesn’t necessarily deny just HUD funding to non-compliant neighborhoods.
Let’s say the neighborhood and nearby businesses would benefit from improvements to a nearby interchange. Those improvements could be held up until the neighborhood complies with AFFH.
This is not without precedent. In the late 1990s, Congressman Roscoe Bartlett secured funding for some improvements to two Frederick, Maryland interchanges involving I-70, I-270, US 15, US 40 and US 340.
However, at that time, there was a push to make auto emissions testing include the dynamo version where the car was driven on rollers inside the test facility, potentially causing engine damage. Maryland initially rejected the dynamos, a remarkable decision considering how Communist that state is.
In comes Carol Browner, EPA head under Bill Clinton, to hold up the funding for those interchange improvements until Maryland installed the automobile-endangering dynamos for emissions testing. So this kind of LEGALIZED EXTORTION has already happened.
My small, rural town in Central Texas is probably 40-50% Black and Hispanic and is a very safe place to live.
Might have something to do with 95% of that Black and Hispanic population being wards of the state.
Exactly.
and that is what will be spread to this HUD funded government housing.
That is what the ruin of America will be like.
Any town including yours that takes HUD monies for any reason will be stuck with this, not only race but other factors must be included.
So Affirmatively Furthering Fair Housing (AFFH) made sure all banks and loan companies have a quota chart to unite comrades?.
Yep that too.
We have an entire superstructure of deep state initiatives to dismantle immediately.
I meant they are already in prison.
Agree asap
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