Search Results for “bruce mitchell” – NCRC https://ncrc.org National Community Reinvestment Coalition Mon, 17 Aug 2026 13:45:59 +0000 en-US hourly 1 https://wordpress.org/?v=7.0.4 https://ncrc.org/wp-content/uploads/2017/10/cropped-512_v1-32x32.png Search Results for “bruce mitchell” – NCRC https://ncrc.org 32 32 New NCRC Report Calls for Community-Led Approach to Rural AI Data Center Development Under New Opportunity Zones https://ncrc.org/new-ncrc-report-calls-for-community-led-approach-to-rural-ai-data-center-development-under-new-opportunity-zones/?utm_source=rss&utm_medium=rss&utm_campaign=new-ncrc-report-calls-for-community-led-approach-to-rural-ai-data-center-development-under-new-opportunity-zones Mon, 17 Aug 2026 13:45:18 +0000 https://ncrc.org/?p=291371 Investment in AI data centers is surging at an unprecedented pace, particularly in rural areas, as demand for the infrastructure powering the latest AI technologies continues to grow. The new Opportunity Zones program could further accelerate data center development in rural communities, making it critical that these projects are community-led and designed to address their growing financial and resource demands, according to a new report from the National Community Reinvestment Coalition (NCRC).

“We’ve seen a lot of recent coverage around communities fighting back against data center development on their lands, and that’s not surprising to those of us who work in community development,” said NCRC’s President and CEO Jesse Van Tol. “People talk. They hear about neighboring communities dealing with a whole swath of problems, including increased utility prices, after a new data center comes online. The tech companies would be wise to take a page from financial institutions and work with communities, not against them.”

The report found that the current AI data centers being developed are larger and more resource-intensive than other traditional data centers, making them more expensive to build and operate while also introducing numerous costs on community life, the environment and natural resources. 

However, rural policymakers are facing years of disinvestment and population declines that have diminished local tax revenue. Without alternative economic development initiatives, the report found that local leaders have accepted AI data centers to maintain financial stability despite their various negative effects and widely-held opposition within the community.

To ensure the Opportunity Zones program doesn’t become a creator of rural AI data center hotspots, the report calls for place-based economic development incentives that ban or condition data center permitting in rural areas on developers’ alignment with community planning goals and the provision of additional development incentives.  

“The placement of a data center should be something that happens with, and not to, the community,” said report coauthor and NCRC’s Principal Research Dr. Bruce Mitchell. “We hope that the report and map enable low- and moderate-income communities to get a better sense of their vulnerability to data center placement under the Opportunity Zone 2.0 program.”

The full report is available here.

]]>
Preventing Hyperscale Harm: Analyzing the Potential Impact of Opportunity Zones 2.0 in Rural Communities Experiencing Accelerating AI Data Center Construction https://ncrc.org/preventing-hyperscale-harm-analyzing-the-potential-impact-of-opportunity-zones-2-0/?utm_source=rss&utm_medium=rss&utm_campaign=preventing-hyperscale-harm-analyzing-the-potential-impact-of-opportunity-zones-2-0 Mon, 17 Aug 2026 13:41:38 +0000 https://ncrc.org/?p=290921

Preventing Hyperscale Harm:

Analyzing the Potential Impact of Opportunity Zones 2.0 in Rural Communities Experiencing Accelerating AI Data Center Construction

August 2026

Simon Wang, Economic Mobility Specialist
Bruce C. Mitchell, PhD, Principal Researcher
Jad Edlebi, GIS Data Engineer

Interactive Map Application

View map icon

The Hyperscale Data Center  interactive map application shows rural low-income communities eligible to become Qualified Opportunity Zones under the new OZ program.

Executive Summary

Data centers are the backbone of the digital economy. Since the rise of the internet, the United States has become the data center capital of the world, housing nearly
40% of the world’s data centers to date. Before recent breakthroughs in large language models (LLMs) shifted the  landscape of artificial intelligence (AI) investment, data centers operated relatively innocuously, often housing institutional data and servicing basic cloud computing needs.

However, the demands of newer AI models have required historic amounts of investment in larger, more resource-intensive data centers. In general, more data centers of all kinds are needed to support the wider integration of AI into the digital economy. While hyperscale data centers are increasingly viewed as critical infrastructure for the digital economy, their rapid expansion places considerable demands on local energy grids, water supplies, land use and public services, generating growing concerns about their effects on host communities.

The Growth and Acceleration of AI Data Center Construction

By the end of this year, an estimated $500 to $700 billion will have been put toward data center development, with a stamp of approval from the federal government to move forward as quickly as possible. In an increasingly stagnant economy suffering
from diminished consumer spending as well as slowing job growth, data centers
and capital expenditure commitments by the so-called “Magnificent 7” Big Tech conglomerates (Apple, Microsoft, Amazon, Google, Meta, Tesla and NVIDIA) have become the engine of the economy. Some estimates suggest spending on data centers contributed as much as 92% of gross domestic product (GDP) growth during the first half of 2025.

While the financial markets continue to show confidence in AI, people on the ground have shown growing opposition to the construction of data centers and apprehension about the broader economic and societal impacts of AI. Poll after poll demonstrates Americans strongly oppose data center development in their communities. The most recent Gallup survey from March 2026 found that seven in 10 Americans oppose constructing AI data centers in their local areas, including 48% who strongly opposed it. Although some question the breadth of data center opposition, the consensus from most polling suggests that Americans across the political spectrum oppose data center development.

The billions of dollars in AI data center investment are placing a physical toll on communities, especially in rural areas. As data center proposals show up across the country, local residents are learning in real time just how powerful OpenAI, Anthropic and the rest of the Big Tech companies really are as well as what the costs of AI are to their land, resources, health and ways of life.

Structural Factors Pushing AI Data Centers to Rural Communities

As AI models have changed, so too has the physical footprint of the data centers
powering them. Recent estimates suggest that the overwhelming majority of new data center projects in the US are planned to be constructed in rural areas, even though most of those currently operating are located in cities. The shift in the geography of data center projects is largely a byproduct of alignment between the needs of the latest AI technology and the advantages of developing newer, larger AI data centers in rural areas of the country.

However, all data centers are not created equal. As frontier AI labs have doubled
down on models that require enormous amounts of data to train and operate, the size and scale of data centers have grown astronomically. These so-called hyperscale data centers and mega-campuses (henceforth referred to as AI data centers) are built specifically for AI and have completely altered the landscape of data center construction across the board, expanding quickly into communities that had largely been left behind in the digital economy until recently.

The move to hyperscale data centers is a marked shift from the wave of data centers built during the rise of the internet and cloud computing. This previous cohort of data centers was built primarily to optimize latency (the time it takes for data to pass from one point on a network to another), which meant building near urban communities with high concentrations of end users or suburban areas with access to high-speed fiber optic cables (e.g., “Data Center Alley” in Northern Virginia).

Figure 1, shown below, shows the dense concentration of data centers found along power lines in Northern Virginia. Access to government users and close proximity to the submarine transatlantic cables that run along the ocean floor made Northern Virginia an ideal location for data center construction.

Figure 1. “Data Center Alley” outside Washington D.C. in Ashburn, VA. Large circles are hyperscale data centers. Orange lines are high voltage power transmission lines.

These data centers were much smaller, housed fewer servers and, by extension, required less resources and introduced fewer adverse effects to the surrounding environment. In fact, most data centers operated relatively innocuously, sometimes housed in downtown buildings nearly indistinguishable from office parks. However, in recent decades, the rise of cloud services like Google Drive and Microsoft SharePoint in addition to growing interest in big data increased interest in larger data centers.

Throughout the 2010s, hyperscale data centers began to emerge around the world to meet this demand for storing and processing data. Although demand for data centers was already rising steadily, the emergence of generative AI has led to a dramatic expansion. A recent McKinsey analysis estimates around 70% of the growing demand is for data centers built specifically to support AI.

To build AI data centers, vast amounts of natural resources (e.g. land, energy, water) are needed in quantities that are magnitudes greater than the data centers primarily used for other purposes. The availability and price of these resources, especially land, in rural areas have made them particularly attractive to companies involved in the construction, management and use of AI data centers. As a result, rural communities are increasingly finding AI data center developers at their doorstep.

There are five key resource and infrastructure requirements that are required to develop an AI data center: land, energy, water, electricity grid and fiber optic network infrastructure. Rural areas vary in the cost and availability of these inputs, but generally they offer more favorable  environments for AI data center construction and operation.

Land
Hyperscale data centers have historically been defined as taking up at least 10,000 square feet for the actual facility. However, more recently, AI data center developers have been buying thousands of acres of land for facilities spanning millions of square feet. Some of the cheapest available raw land is found in large, rural states with low population density and high land supply.

Energy
The equipment housed in a hyperscale data center requires vast amounts of energy to operate. Estimates now suggest that data centers could make up roughly 1% of global power demand and 11% of total domestic electricity consumption by 2030 – a share projected to grow substantially as AI workloads expand.

Water
The heat generated by hyperscale facilities place substantial demands on local water used in cooling systems, with some large facilities consuming millions of gallons yearly. While newer direct liquid cooling, air cooling technologies and closed-loop systems are beginning to reduce this burden, these improvements in water use depend on system design and local climate conditions.

Electricity Grid
AI data centers place growing stress on an already aging nationwide electrical grid, often requiring major improvements to infrastructure in order to gain access to electricity at the scale and frequency needed to support AI development.

Fiber Optic Network Infrastructure
Data centers must be located within a reasonable distance of the fiber optic backbone that spans the country because latency and bandwidth constraints make rural remote siting viable only where it can be accessed or extended economically. Unlike previous eras of data centers focused on latency, distance to internet infrastructure is a secondary factor to the electricity grid when it comes to AI data centers.

Tradeoffs of Rural AI Centers

Demographic Access and Exclusion

As rural communities face growing interest from AI data center developers, disinvestment and demographic shifts away from rural areas have shaped how communities and elected officials have responded. Rural areas of the country have been losing population density for decades, putting local governments under significant financial pressure as their tax bases have declined. Even in communities that recognize the risks and negative  effects of AI data centers, many have been overlooked by other, perhaps more desirable, economic
development projects. As a result, elected officials are left without an alternative.

Based on the lack of interest from other developers, many states and local governments have  embraced AI data centers as the key to restoring tax revenue in rural areas. Some even introduced tax incentives and abatements to compete with other regions and attract data center developers to their communities. In return for subsidizing development, local officials are often promised jobs, tax revenue and other community development initiatives.

However, the costs of an AI data center are not just financial. AI data centers also introduce a multitude of externalities that are not accounted for on the balance sheet, such as straining energy supply and electricity grids, contaminating the local water supply and polluting the air. Ultimately, the net impact of data centers is difficult to forecast, especially without transparency standards, updated zoning codes and environmental impact assessments.

The demographic shifts and resulting financial challenges facing rural communities are yet another contributor to the acceleration of AI data center development. In recent years, grassroots opposition has mounted as people from across the political spectrum are coalescing to reject data center builds.

Stuck between fervent local opposition, pressure from developers to move quickly and the tradeoffs of allowing construction to move forward, rural policymakers have been put in an impossible position. Either circumvent constituents and accept the costs of constructing an AI data center or risk jeopardizing the financial security of their community. All of this has turned AI data centers into one of the most polarizing economic development projects in the country.

Federal Tax Policy May Further Incentivize Rural AI Data Centers

Recent changes to a federal community development initiative called the Opportunity Zones (OZs) program could provide additional economic incentives for AI companies to develop hyperscale data centers in rural areas of the country. The OZ program was first created through the Tax Cuts and Jobs Act of 2017 as a place-based economic development initiative designed to channel private capital into low-income communities.

By offering investors preferential tax treatment on capital gains, policymakers hoped to stimulate business activity, job creation and long-term community development in economically distressed census tracts.

By 2026, it is estimated that investors had directed roughly $100 billion nationwide into economic development projects through the OZ program. However, the effectiveness of that money in uplifting disadvantaged communities has been widely debated. Although some argue the OZ program has attracted capital to communities that had long struggled to secure investments, further analysis reveals the hollow nature of those contributions. Estimates suggest a substantial share of the funds invested through the first OZ program ended up flowing into projects that would have happened even without the tax incentive.

Some analyses have found that many of the projects delivered minimal direct benefits to residents of the low-income communities it was designed to serve. Rather than helping the persistently distressed communities that the OZ program was purportedly created to serve, the investments that did materialize were disproportionately put toward constructing market-rate real estate in the most well-off enclaves of the zone.

By allowing investors to lift and defer taxes on long-term investments, OZs reward economic development projects that are likely to increase in value and generate capital gains over time, such as market-rate housing or AI data centers. As a result, investments made through the OZ program often ended up going to areas with the least need rather than being put toward job-creating businesses in distressed communities.

Despite these concerns, Congress renewed and expanded the program in 2025, making OZs a permanent feature of the federal economic development landscape and creating a new facet of the program called the Qualified Rural Opportunity Fund to focus specifically on driving investment in rural communities. In addition to questions about whether this action will offer tangible benefits to rural communities and not just wealthy investors, the race to develop AI data centers across rural America introduces another set of concerns about whether the projects that rural OZs may attract will translate to meaningful impact.

AI data centers are exactly the kind of capital-intensive, long-duration, asset-heavy investment that OZ-style incentives are structured to reward. Rural areas designated as OZs that offer comparatively cheap land costs, access to affordable energy and proximity to fiber optic infrastructure present an attractive investment profile for AI companies seeking to maximize the tax advantages of long-term asset appreciation.

While this situation might be beneficial for investors, constructing an AI data center imposes many costs on residents without many clear direct benefits beyond some immediate relief to the local tax base and perhaps a handful of long-term jobs. Just as market-rate housing in wealthier metropolitan neighborhoods became the go-to project for OZ investors, AI data centers could repeat this pattern in rural OZs, albeit at a much greater scale of investment and with far greater negative consequences.

Identifying Rural Communities Impacted by Data Center Development in Opportunity Zones

To anticipate which rural communities could become targets for AI data center development, we used ArcGIS to create a map of the United States at the census tract level. Next, we constructed an AI data center siting index using the characteristics identified. The AI data center siting index was constructed using the locations of existing data centers to identify the factors that best predict where future data centers are likely to be located.

These factors include the density of the electrical power grid, their proximity to internet exchange points and the high-speed fiber-optic backbone. Locational data came from the FracTracker Alliance, who publishes a publicly available dataset of more than 1,600 data centers. The map below (Figure 2) shows in darker colors areas with a greater risk of development. Next, the results of the index were overlaid on a map of all low-income census tracts identified in the 2024 Census data. Finally, we plotted the location of the data centers using the data from FracTracker Alliance.

Hyperscale Data Center Interactive Map

Figure 2- Rural low-income communities eligible to become Qualified Opportunity Zones under the new iteration of the OZ program. Darker shaded census tracts are more highly rated under the AI data center siting index for electric grid density, proximity to IXP sites and internet backbone structure. Interstate highways are in yellow.

Overall, 14% of all data centers were located within OZs. However, the concentration was notably higher among approved, permitted and under-construction facilities, with 17.3% being located in OZs. Given the share of all Census tracts designated as OZs, this rate is 46% higher than expected. Operating facilities were also overrepresented, while cancelled projects exhibited the highest concentration, with nearly one in five located in an OZ. 

State and Local Policy Recommendations

OZs should be used to promote real economic development with real benefits for communities rather than jeopardizing community health, safety and autonomy in return for modest economic benefits. Moreover, rural governments should not feel forced to accept projects that their constituents are against. To avoid repeating the mistakes of the first round OZ program, there are two complementary courses of action that state and local governments impacted by rural OZ designations could take to preserve community autonomy and long-term interests.

Incentivize Community-Aligned AI Data Center Alternatives
First and foremost, governors must listen to community groups (vs. industry) and nominate LMI census tracts that have high potential for OZ investment to benefit their communities. Then, local and state governments representing rural communities should pair the OZ designation with additional strategies for attracting community-aligned projects.

That begins with rigorous planning that empowers residents to build a vision for their community’s future. Local governments should ensure they have strong, ambitious plans and goals co-created alongside community members. The resulting plans and objectives offer a roadmap for policymakers and community members that represents their shared priorities. In fact, many communities already engage in this process.

These may include but are not limited to implementing the following:

  • A water usage plan,
  • An energy usage plan,
  • A heat mitigation plan,
  • An economic development plan,
  • Clear environmental standards (such as air pollution and emissions
    reduction guidelines),
  • Concrete environmental justice goals,
  • Public health goals, and
  • A city equity plan.

Using these plans, local officials should first identify the kinds of economic development projects that match residents’ priorities. Then, they should encourage the creation of those projects by stacking additional incentives onto census tracts designated as rural OZs. Doing so will help to direct Qualified Rural Opportunity Fund investments into projects that will more positively impact the community, cause fewer negative effects and align better with residents’ needs.

At the local level, zoning ordinances offer policy levers for both preventing AI data center construction and further incentivizing other projects. For example, municipalities can change the zoning code to restrict data centers from being developed in OZ-designated census tracts. In Atlanta, Georgia, the city council amended their zoning code to prohibit data centers in the Beltline Overlay District, an initiative to link green space, trails, transit and economic development. At the same time, local governments can make it easier to build projects that better align with community priorities. They could, for example, reduce permitting requirements or shorten review periods for affordable housing, local businesses or other preferred developments.

At the state level, lawmakers can pass state‑specific OZ tax credits or subsidies for other kinds of economic development projects that have stronger evidence of delivering long-term community benefits. Stacking tax credits for certain kinds of projects adds another layer of incentives for investing in projects that advance the community’s goals (e.g. state-specific Low-Income Housing Tax Credit programs).

States can also pave the way for residents to participate as investors in their own communities. Offering state tax incentives for state resident investments in local businesses and incentivizing community investment funds allow ordinary investors to participate in OZs and guide OZ investments to locally led projects. Passing state-level Community Reinvestment Acts can also increase the amount of investment from financial institutions that is available to communities in these areas. Overall, this place-based, people-led approach helps to align economic incentives with what the community wants and needs, encouraging more democratic development.

Condition Data Center Permits on Alignment and Commitment to the Community
More broadly, state and local governments should set policies that establish a baseline set of guardrails that ensure data center proposals are introduced transparently and with the support of their communities. Given the power imbalances between Big Tech companies and local governments, AI data centers offer little possibility for parity in shared agreements.

Not every community will have what it takes to organize a successful negotiation with AI companies and developers. Under voluntary frameworks, every shared agreement requires strong organizing campaigns and local leadership to negotiate with a Big Tech company. If the company breaches their agreement, then local governments are also unlikely to have the resources needed to withstand long legal fights.

AI data centers should fit into the existing community planning goals mentioned earlier. Local governments can achieve this by establishing a conditional use permitting process for data centers (e.g. Limerick Township, Pennsylvania). Conditional use permits require developers to apply to the zoning board for permission to use the land for specific purposes. Developers must meet specific conditions set by the local government before they receive approval, with their permits being revoked if conditions are not met throughout the lifespan of the property. Local governments can set rules for what kinds of projects require approval and what is required to be approved for a permit. Approval for AI data centers should be conditioned on adherence to transparency measures as well as alignment with community goals.

The approval process for data center projects should reject applications that fail to comply with these expectations. The burden should be on the data center developer to prove how the project will fit within a community’s priorities, with clear and transparent timelines, plans and accountability measures. Failure to effectively demonstrate this should result in rejection. Moreover, successful applications should be accompanied by a requirement that the developer complete a legally enforceable community benefits agreement (CBA) with residents.

One of the key goals of a CBA is to secure real commitments for community members from data center developers before construction proceeds. They have long been used in a variety of large-scale economic development projects, including professional sports stadiums, clean energy projects and bank investments. In recent years, several localities have signed CBAs with data center developers. In Lancaster, Pennsylvania, a CBA secured a water use cap, emissions controls and a $20 million community fund. In St. Louis, Missouri, the city negotiated binding commitments on noise, sustainability and water use, retaining the right to seek legal recourse if provisions go unmet.

Growing resident concerns about data centers are prompting a range of state and local policy responses, from moratoriums to broader debates over how proposals should be evaluated through permitting and zoning processes. CBAs are increasingly seen as an important piece of that puzzle (e.g., the NAACP CBA framework and the Federation of American Scientists’ CBA framework). By tying the permitting process to developers’ commitment to community priorities by, for example, conditioning approval on completion of a CBA, local governments can ensure that data center projects benefit the community and have buy-in from residents. When paired with broader safeguards, CBAs are one mechanism policymakers and communities may consider for increasing community participation in decisions about whether and how data centers are built.

Conclusion

Advances in AI technology and the expansion of big data in the modern digital economy have vastly increased the demand for large, hyperscale data centers over the past decade. In recent years, AI companies in particular have expanded investment, increasing the pace and scale of their push to construct AI data centers. The scale and intensity of resources needed to build and operate these AI data centers has shifted development to rural areas of the country, which have often been overlooked by other economic development projects.

However, current evidence suggests that the long-term economic impact of AI data centers is limited. Although they can provide much-needed support for the local tax base, AI data centers create very few jobs beyond the construction phase and impose a host of negative consequences on the surrounding environment with significant effects on public health and community life. As grassroots opposition grows, local officials are increasingly squeezed between representing the views of their constituents and protecting the long-term financial security of their communities.

The creation of Rural Opportunity Funds further complicates the picture by providing additional federal tax incentives for large, capital-intensive projects like AI data centers. That said, local and state governments representing rural communities that are designated as OZs can take actions that incentivize Rural Opportunity Fund investments to be used for more beneficial economic development projects than AI data centers.

Moreover, they can introduce formal processes and mechanisms for ensuring that AI data center projects are approved transparently with community input, align with broader community planning goals and remain accountable via community benefits over the entire course of the project. Communities that are successful at aligning economic development policy with planning goals co-created by residents and policymakers can ensure Rural Opportunity Funds are leveraged for meaningful, long-term community impact rather than as an accelerant for the AI data center buildout.

TABLE OF CONTENTS

Subscribe

Get NCRC news and
alerts by email.

]]>
NCRC’s Comment on the FHFA’s Proposed Rulemaking on the Enterprise Duty to Serve (DTS) Underserved Markets https://ncrc.org/ncrcs-comment-on-the-fhfas-proposed-rulemaking-on-the-enterprise-duty-to-serve-dts-underserved-markets/?utm_source=rss&utm_medium=rss&utm_campaign=ncrcs-comment-on-the-fhfas-proposed-rulemaking-on-the-enterprise-duty-to-serve-dts-underserved-markets Fri, 24 Jul 2026 03:51:29 +0000 https://ncrc.org/?p=290685

July 23, 2026

Clinton Jones
General Counsel
Attention: Comments
Enterprise Duty to Serve Underserved Markets
RIN 2590–AB64
Federal Housing Finance Agency
400 Seventh Street SW
Washington, DC 20219

Dear Mr. Jones:

The National Community Reinvestment Coalition (NCRC) appreciates the opportunity to comment on proposed changes to the regulations implementing the Enterprise Duty to Serve (DTS) Underserved Markets, RIN 2590-AB64. The DTS regulations help ensure that the Government Sponsored Enterprises (GSEs), Fannie Mae and Freddie Mac, target their product development, outreach, and loan purchase activities to traditionally underserved markets including rural areas, affordable housing preservation, and manufactured housing. The DTS regulation must measure whether the GSEs are fulfilling their statutory duties to “increase the liquidity of mortgage investments and improve the distribution of investment capital available for mortgage financing for underserved markets.”[1] Accordingly, the FHFA must improve the rigor of its proposed rule so it can accurately measure the GSEs’ activities in underserved markets and encourage an increase in those activities.

NCRC supports aspects of the proposed rule such as requiring the GSEs to formally respond to public comments and eliminating abuse of the plan modification process. However, we urge the FHFA to reconsider major aspects of the proposed rule which could reduce financing for the populations with the most pressing needs by relaxing income targets and instituting a vague ratings regime. The proposed ratings system could make it easier for the GSEs to pass their DTS requirements while not increasing and possibly reducing their financing of lending activity to the DTS underserved markets. Under the proposed ratings system, the GSEs would receive passing or failing numerical ratings that measure their activities (loan product development, outreach, loan purchases, investments and grants) in each of the three DTS markets of rural areas, affordable housing preservation, and manufactured housing. As described in detail below, the proposed ratings system lacks objective measures of performance which could likely result in subjective and inflated ratings.

NCRC is a coalition of more than 750 community-based organizations fighting for a just economy. For nearly 30 years, NCRC has worked to create opportunities for people and communities to build and maintain wealth. NCRC members include community reinvestment organizations, community development corporations, local and state government agencies, faith-based institutions, fair housing and civil rights groups, minority and women-owned business associations, and housing counselors from across the nation. Our community members need the GSEs to help facilitate access to credit and wealth building opportunities for modest income neighborhoods and communities of color.

Our comment will make the following major points:

  • The proposed elimination of a prescribed list of activities for DTS plans and evaluations is likely to result in the GSEs choosing activities that are less impactful in terms of leveraging financing for the DTS markets. The rule must not allow the GSEs to re-label their regular activities as those targeted at DTS markets. This would result in a significant loss of impactful activities that would leverage more investment capital for mortgages serving DTS markets.
  • The relaxation of income targets in the proposed rule would encourage the GSEs to decrease their focus on populations with the most housing needs in favor of higher income populations for which financing housing is not as difficult.
  • NCRC supports the FHFA’s proposed requirements regarding the GSEs’ obligations to help banks comply with Community Reinvestment Act (CRA) obligations and to help create an institutional infrastructure for financing affordable housing that includes nonprofit organizations and state and local governments.
  • While NCRC supports the FHFA requirement that the GSEs explicitly respond to public comments on their draft DTS plans, NCRC requests that FHFA lengthens its proposed public comment period from 45 to 60 days.
  • As part of DTS plan development, NCRC supports the FHFA’s proposal to make it more difficult for the GSEs to modify plan requirements. The current procedure is prone to manipulation in the form of weaking plan goals to avoid GSE failure to achieve plan requirements.
  • NCRC reiterates our long-time position that a two-year plan is more feasible than a three- year DTS plan for the GSEs to achieve. A two-year plan would also reduce the need for modifications and would also provide more opportunities for public input.
  • NCRC generally supports the FHFA’s proposed dissemination of DTS-related documents and annual reports, but we suggest improvements to the proposal.
  • The FHFA proposed rating system for the DTS plans and annual performance is too subjective with an absence of measurable outcomes and benchmarks. The FHFA’s proposed new activities such as program management on DTS evaluations will also likely increase subjectivity and inconsistencies in ratings. The result could be ratings inflation that reward stagnant or declining levels of financing in the DTS markets.

Elimination of Prescribed List of Activities Would Reduce GSE Attention to Impactful Activities

Under the current DTS regulations, the GSEs are required to specify goals for an established set of statutory and regulatory activities for each DTS market (the GSEs have to choose activities within the established set of activities). For example, under the affordable housing preservation market, GSEs are required to develop goals for loan production, outreach, loan purchase, or investments and grants for activities such as financing Section 8 properties, Section 515 properties, or Low-Income Housing Tax Credit (LIHTC) properties for lower income households. The GSEs can also propose additional activities that would receive favorable consideration upon the approval of the FHFA.

The FHFA is proposing to delete these lists of activities and allowing the GSEs to propose their own activities that will promote financing of affordable housing preservation, manufactured housing, and housing in rural areas. The FHFA would then approve or reject these proposed activities.[2] The FHFA believes that the current procedure of established lists stifles innovation and causes the GSEs to chase a list of predetermined activities that might not be the most responsive to need.

The FHFA states that the list of activities “may have incentivized a ‘compliance-centric’ mindset that prioritizes the fulfillment of granular regulatory benchmarks over meaningful market impact.”[3] The FHFA continues that the agency, “is also concerned that prescribing Activities, some of which focus on narrow geographies or specific populations, may have had the unintended consequence of limiting the Enterprises and their ability to support the holistic health of the broader secondary market for very low-, low-, and moderate-income families.”[4]

The FHFA, however, ignores the history of the development of the activities. Over several years, the FHFA had proposed several iterations of the DTS regulation and solicited input from stakeholders via comment periods and during forums. Under previous agency directors, the FHFA carefully developed the list of activities by considering the views of the GSEs, community-based organizations, lenders, and public sector entities regarding the activities’ importance in responding to housing needs. Discarding the current activity list in favor of allowing the GSEs to propose their own activities replaces a set of activities that had achieved stakeholder consensus regarding their importance with a set of activities chosen by the GSEs which might be responsive to needs but could primarily facilitate GSE business activities driven by their profit considerations.

An example of a seemingly arbitrary removal of prescribed activities is the deletion of required water and energy efficiency activities. The FHFA cites an Executive Order that mandates the deletion of incentives for “costly energy efficiency and water use requirements” from regulation.[5] Yet, the FHFA offered no data or analysis concluding that costs of energy efficiency improvements outweighed the benefits. Borrowers can probably better afford their mortgages if home improvement loans for energy efficiency resulted in considerable savings on utility bills. A study conducted by the UNC Center for Community Capital found that homeowners of energy efficient homes are 32 percent less likely to default than those not in energy efficient homes.[6] In addition, the U.S. Department of Energy estimates that its weatherization program saves households an average of $372 annually on utility payments.[7]  Based on this evidence, it is likely that, GSE purchases of loans that included improvements and rehabilitation geared towards energy and water efficiencies would improve the performance of loans in the GSEs’ portfolios or in Mortgage-Backed Securities (MBS) purchased by investors.

The proposed FHFA’s role of reviewing the activities selected by the GSEs might offer some level of protection against activities biased towards GSE business considerations. However, the process would be dominated by interactions between the GSEs and the agency with a lesser role played by community stakeholders in influencing the activities. Community stakeholders would be able to comment on draft GSE DTS plans but the emphasis would now be on GSE-selected activities instead of GSEs responding to a list of activities developed in public processes over several years. The proposed GSEs’ top-down process would bias the selection of activities to the imperatives of the GSEs as opposed to broader public needs.

In addition, the proposal to require the GSEs to develop a list of activities may not be successful as suggested by the track record of CRA strategic plans. Financial institutions prefer a consistent set of rules such as reacting to a prescribed list of activities to a strategic planning process in which they develop their own plans for satisfying community needs for affordable housing. Over the years, banks have opted for regular CRA exams much more frequently than strategic plans under which they establish their own goals. In 2025, for example, the agencies conducted 1,145 exams but evaluated just 20 strategic plans. For all the exam records over the years maintained by the Federal Financial Institutions Examination Council, the agencies conducted 83,727 exams but evaluated just 264 strategic plans.[8] In other words, strategic plans are not a popular choice as revealed by their frequency of less than 1 percent of all CRA exams.

CRA exams assess bank performance on established criteria for lending, investing, and services in low- and moderate-income communities. In contrast, strategic plans require banks to develop their own goals and performance measures for evaluating their success in achieving their goals. The FHFA is pushing the GSEs to adopt an evaluation regime that resembles the CRA strategic plan that benefits neither the GSEs nor communities as much as the current evaluation system.

Finally, the proposal to provide the GSEs with discretion to establish their activities may encourage the GSEs to label their regular activities serving mainstream markets as activities designated for DTS markets. The current prescribed list identified activities that often required institutional partnerships and flexible products so that they could successfully reach DTS markets. Removing this list potentially allows the GSEs to substitute activities that will not serve DTS markets as well because they lack institutional support and flexibility. This would reduce the volume of GSE supported financing for the DTS markets. This adverse outcome could be accelerated if the proposed rating system with subjective measures of performance is finalized as described below.

Proposed Relaxation of Income Targets Would Skew GSE DTS Activities Away from Borrowers and Communities with Most Pressing Needs

The FHFA proposed changes for loan eligibility for GSE purchases in rural areas and for manufactured homes would probably result in a GSE loan portfolio with fewer loans for families with the most needs. For rural areas, the FHFA is proposing that it use the highest of national, county, or state-wide nonmetropolitan income medians for purposes of calculating percentage of median incomes and qualifying loans for borrowers with incomes that are lower than a threshold percentage (such as 80 percent of area median income).

The FHFA believes that the current procedure of not considering national medians artificially depresses GSE rural loan purchases and does not meaningfully target purchasing activity towards those borrowers with lower incomes. The agency cites data analysis suggesting only a modest increase of a few thousand dollars of the median income of borrowers that would be served in the rural market due to its proposal.[9] Firstly, this analysis suggests that the change may not be meaningful because it results in borrowers having just modestly higher incomes.

Alternatively, the proposal may be detrimental. The FHFA did not consider the impact on the incidence of housing problems such as housing affordability or substandard housing by its proposed change. It is possible that a borrower distribution tilted towards a higher income mix could be experiencing a lower level of housing problems. The modestly higher income level of about $4,500 for the typical borrower due to the proposal could make a significant difference in the borrower’s ability to afford monthly housing cost or repairs to convert housing from substandard to habitable.[10] Thus, the proposal could divert affordable finance away from lower income borrowers more in need of the financing.

Housing needs are local, not national. Analysts should not compare housing needs or monthly costs in a rural county to national needs and costs that are often quite different. Likewise, median income levels in a rural community should not be compared to rural national median income levels, which could be quite different. The FHFA’s proposal ignores the interaction between local borrower income levels and rental or mortgage costs, which are influenced by supply and demand on a local level, and not a national level. Considerably lower income in some rural communities could be matched by considerably lower rents or mortgages due to lower costs to build and maintain housing. Thus, making more borrowers eligible for GSE rural financing by using a national income median might be inappropriate and target borrowers with higher incomes that can more easily afford local rents or mortgages. The FHFA does not offer any analysis that considers local housing market dynamics of this sort.

The FHFA offers the example of Puerto Rico and a few other outliers to say that the current procedure not allowing comparisons to national medians dramatically depresses the number of loans that the GSEs can purchase in rural areas.[11]/ However, an untested assumption is that increases in loan purchases are automatically a beneficial impact of the proposed change without analyzing whether increases in loan purchases might crowd out financing for borrowers or families with more pressing needs. If the agency had demonstrated the absence of crowding out in some geographical areas, it could analyze which other market conditions were present and could establish a rule allowing for use of national medians in those markets only.

The FHFA also proposes the same change for determining DTS eligibility for metropolitan areas. The agency will compare the income of the borrower to the highest of the metropolitan area median income, the State median income including metropolitan areas, or national median

income including metropolitan areas.[12] For reasons similar to the case of rural areas, NCRC strenuously opposes the proposed change as resulting in reduced targeting for the lower income and needier households and families.

As with rural areas, the FHFA proposed a change for manufactured housing that lacks complete analysis and could result in reduced access for the households with the most needs. Under current procedures, the agency considers a loan purchase to be DTS eligible for manufactured housing that is in census tracts with incomes at or below median income levels. The agency proposed deleting this location requirement and instead presume that all loan purchases for manufactured housing are eligible under DTS rules. Its data analysis suggests that most purchasers of manufactured housing have low incomes. However, it did not consider that most purchasers are low-income because of the current census tract restriction. That is, most households with limited incomes are unlikely to cast a wide geographical net while house hunting and may reside mostly in lower income census tracts that are more likely to have manufactured housing. The FHFA does not consider this possibility.

Moreover, instead of merely lifting the current census tract restriction, the agency could have considered establishing a mortgage dollar limit based on data analysis that would instead target the GSE manufactured home activity towards lower income households. The median mortgage amount currently for manufactured home communities could be increased with inflation. This would ensure continued targeting of manufactured home communities that serve lower income households and families.

Eliminating targeting mechanisms ensuring that DTS activity in manufactured housing is focused on households with lower income levels and the most pressing needs does not guard against future market developments that may skew away from modest income borrowers. For example, manufactured housing communities may enter other markets including middle-income markets as zoning and other regulatory rules change.

Changes to Rural High Needs Regions and Populations Need to be Implemented Carefully

Just as any changes to income targets need to be judicious, the FHFA should proceed cautiously with changing the definitions of high needs rural populations and deleting reference to Native Americans and agricultural workers. NCRC is concerned that removing explicit references to Native Americans and agricultural workers could weaken the focus on populations that the current DTS framework identifies as having distinct housing needs.

The FHFA suggests that under the rural activity category, the GSEs focus almost entirely on high needs rural areas which are currently defined to include Appalachia, the Lower Mississippi Delta, persistent poverty counties, and Colonias on the Texas Border.[13] By retaining the current high needs rural areas in the rural category as well as making it clear that the GSEs are to serve all rural areas, the FHFA intends for the GSEs to address a broad array of needs in rural areas. Yet, expansion could divert GSEs activities away from high needs rural areas.

A potential compromise is to retain the current high needs areas, make it clear that the GSEs can serve other rural areas, but also map and identify other rural counties exhibiting the highest incidence of cost burdens and substandard housing. The FHFA could then assess the distribution of GSE purchases across rural counties and provide higher ratings to the GSE that purchases higher percentages of loans in the counties with the highest incidence of cost burden and inadequate housing in addition to purchases in the current high needs rural areas. The FHFA should assess the results of any changes carefully and should revise its approach if both GSEs end up purchasing most of their loans in rural counties without the highest needs.

Similarly, the FHFA should carefully evaluate any changes it makes to the definitions of high needs rural populations. NCRC opposes the proposed deletion of agricultural workers and Native American populations for the reasons detailed by the Housing Assistance Council and the National American Indian Housing Council.

If the agency deletes references to Native Americans as a high needs rural population, it should examine whether GSE loan purchases are higher in Native American areas which the agency proposes to add to the high needs rural areas category.[14] NCRC found that tribal areas are lending deserts so we appreciate that FHFA is proposing to add Native American areas as a needs category.[15] However, FHFA should also track lending trends to Native Americans to make sure the GSEs are not neglecting this traditionally underserved category of borrowers. Likewise, it should track purchases of loans financing housing for agricultural workers if it deletes agricultural workers from the high needs population category.[16] As much as possible, data should be used in this assessment as well as interviews with stakeholders providing farm worker housing and social services to agricultural workers.

The FHFA should use the current level of GSE loan purchases and other activities in high needs rural areas and to high needs rural populations as a baseline in its evaluation of changes to the definitions and regulatory requirements. It should establish an expectation that the GSEs should not diminish activities and loan purchases to the current high needs rural areas and populations but should at least maintain the baseline level of activities if they seek to serve other areas with pressing needs.

Lastly, the FHFA should proceed cautiously regarding its proposal to relax the restriction on GSE Low Income Housing Tax Credit (LIHTC) for rural areas only.[17] Large metropolitan areas with several bank headquarters probably do not need GSE LIHTC assistance since CRA requirements are encouraging high levels of bank LIHTC activity in these areas. However, it is possible that smaller metropolitan areas are more in need of GSE LIHTC financing. The FHFA should expect the GSEs to retain and increase their current level of rural LIHTC financing while targeting urban financing to those areas that would receive little LIHTC financing but for the financial assistance of the GSEs. The GSEs should document the “but for” test in the LIHTC part of their DTS plans. In other words, for the urban LIHTC deals, the GSEs should describe how their financing was pivotal for executing the deals.

Plan Requirements Regarding CRA and Description of Mortgage Products Will Assist in Making GSEs More Effective in Serving DTS Markets

In contrast to the loosening of income target requirements, FHFA’s proposed requirements for developing institutional strategies will facilitate GSEs serving DTS markets. The FHFA proposes to require the GSEs to not only discuss “the challenges faced by insured depository institutions in meeting their CRA obligations and general affordability concerns in low-income areas,” but to also develop specific plans for helping banks meet CRA obligations and affordability challenges.[18] In other words, the plans would probably need to discuss the building of institutional relationships with banks and products that GSEs would develop with banks that help them meet CRA requirements and ease affordability challenges for borrowers. Ultimately, CRA works best if primary and secondary institutions collaborate more effectively with community and public sector entities. To the extent that the DTS plans promote more collaboration, lower income populations should receive more access to affordable credit.

Moreover, the FHFA’s proposal emphasizes development of institutional relationships and partnerships with a wide variety of stakeholders. It states:

In brief, proposed § 1283.3(b) would require the Enterprises to: (i) design programs and products that facilitate the use of federal, state, and local governments assistance programs; (ii) develop relationships with nonprofit and for-profit organizations that develop and finance housing with state and local governments; (iii) assist primary lenders to make housing credit available in areas with concentrations of low-income and minority families, and assist insured depository institutions to meet their CRA obligations; and (iv) develop the institutional capacity to help finance low- and moderate-income housing, including for first-time homebuyers.[19]

This proposed language must be finalized. In addition, the FHFA should clarify that under the loan product and outreach criteria, the GSES should describe programs and products developed with public sector entities and nonprofit community-based organizations. The description of the programs and products should also focus on how they will be directed to modest income neighborhoods and communities of color as well as first-time homebuyers.

The proposed list of tables will likewise boost transparency and facilitate stakeholder understanding of the GSE products and activities that would serve DTS markets. The FHFA proposes to require the GSEs to provide:

  • A list of mortgage products that will reach underserved markets,
  • Descriptions of GSE activities under the four statutory criteria of loan products, outreach, loan purchases, and investment and grants, and
  • A separate table of all loan purchase targets over the three years.[20]

This is similar to current requirements but nevertheless is important to maintain and require for future plans.

Plan Development and Public Comment Procedures Are a Good Start but Should be Strengthened

The proposed rule appropriately replaces GSE discretion in responding to public comments with a requirement that the GSEs respond to the substantive points raised in the public comments. Specifically, the FHFA states:

The existing regulation provides that each Enterprise “may, in its discretion, make revisions” based on public input, but does not require the Enterprise to respond to stakeholder concerns. To enhance transparency and ensure a meaningful exchange of information, the proposed rule would replace this discretionary standard with a requirement that the Enterprises provide a reasoned response to substantial points raised during the public comment period. This element is intended to promote accountability and provide the public with a clearer understanding of the technical, operational, or policy constraints informing the Enterprises’ final determinations.[21]

Over the years, NCRC and our member organizations have made several recommendations on draft plans regarding improving data analysis, identifying needs, and more effective targeting of borrowers and communities with pressing needs. These substantiative comments often went unanswered and a succession of draft DTS plans suggested that the GSEs did not seriously consider or incorporate them in their DTS plan development.

When the GSEs and/or the FHFA did not appear to seriously consider comments, either incorporating suggestions or explaining why they did not, the GSEs and/or FHFA undermine the public’s trust in the process. The result was less robust DTS plans that generate fewer and less insightful public comments for improvement. We are pleased the FHFA signals that public comments should receive the respect that they deserve and we urge the FHFA to retain these instructions in the final regulation about the importance of responding to comments in their final rule.

The FHFA would improve upon its insistence on the importance of public comments if it changes its proposed 45-day period to 60 days for receiving public comments on draft DTS plans.[22] NCRC itself is a coalition of 750 member community-based organizations. Engaging our members in outreach and conference calls regarding complicated DTS draft plans takes a significant amount of time that a 45-day comment period cannot accommodate. First, NCRC and our member organizations wishing to comment must develop their responses to provisions in lengthy draft DTS plans. Then NCRC and our members hold conference calls to further flesh out and revise our opinions. A minimum of a month to a month and a half is needed for this process. This is followed by drafting comment letters and reviewing them, which takes at least another month. In order for FHFA to receive thoughtful input from coalitions like NCRC, a comment period must be a minimum of 60 days.

NCRC is just one example of the thousands of stakeholders that would want to review DTS plans, both organizations that are involved in coalitions as well as interested members of the public that are not formally in coalitions. The FHFA seeks to solicit input from a wide variety of organizations ranging from community-based organizations, public sector agencies, to primary lending institutions and their trade associations. Moreover, several organizations located in rural and other underserved areas have less access to the internet and the policy milieu based in Washington DC. To more fully solicit the views of communities that are the intended targets of DTS plans, the FHFA must provide a 60-day comment period. The FHFA itself must undertake strenuous efforts in outreach and information sessions that are better accommodated by a 60-day comment period.

The FHFA is proposing a six-month period for the development and revisions of draft DTS plans. Increasing the public comment period from 45 to 60 days would not interfere with achieving a six-month development period and would benefit the plans by generating more insightful suggestions for improving their effectiveness in reaching underserved markets.

After plans are finalized, the FHFA must ensure that the GSEs implement them faithfully by instituting effective monitoring processes. The first step is to widely disseminate GSE annual reports in which the GSEs report on their progress in achieving the DTS plan goals. NCRC supports the FHFA proposal to require the GSEs to place their annual reports on their websites.[23] However, we urge the FHFA to widely publicize when the GSEs do this, and to encourage the public to review the plans. The FHFA should hold information sessions on the annual reports and receive public comments regarding GSE performance.

The FHFA proposes deleting publication of quarterly data by the GSEs as redundant.[24] NCRC does not agree with the assertion that such reporting is onerous since the GSEs must routinely collect data. A potential compromise is to require reporting halfway through the year in addition to the annual report. Thus, the GSEs would report data twice a year instead of four times. The FHFA should make the six-month data publicly available. This would increase the chances of GSE goal attainment by increasing the public accountability of the GSEs.

Increasing public accountability would have an additional benefit of making the GSEs increase their efforts at achieving plan goals and lessen their habit of seeking plan modifications during the year when they realize they will not meet their annual or three-year plan goals. The FHFA proposes to reduce the tendency of GSEs to request frequent plan modifications (about 15 modification requests annually for each GSE) by establishing a higher bar for modification requests. The FHFA proposes that it would grant modification requests only in response to special circumstances such as “significant market disruptions” or a “systematic financial crisis.”[25]

As well as supporting the FHFA tightening of plan modifications to avoid DTS plan manipulation, NCRC reiterates our suggestion that the plan term be two years instead of three years. A three-year plan presents difficulties for goal attainment because economic forecasting used for GSE goal establishment is more uncertain for three-years than two-years in the future. In addition, two-year plan cycles create more opportunities for public comment, engagement, and partnership building with the GSEs.

The FHFA itself understands challenges of a three-year planning cycle. It states, “However, FHFA recognizes the difficulty in projecting loan purchase targets up to three years in the future.”[26] Instead of contemplating “narrow” GSE loan purchase modification requests in the fourth quarter of the prior year for the next year, the FHFA should instead institute a two-year DTS plan requirement.[27]

New Evaluation System and Ratings Likely to Inflate Performance and Result in Reduced DTS Activities

The FHFA proposes to discard the current ratings for evaluating compliance with GSE DTS plans and replace them with an under-developed ratings system that in its proposed form would probably lead to ratings inflation. If not bolstered, the proposed ratings system would result in reduced GSE financing of rural housing, manufactured housing, and preservation of the low- and moderate-income housing stock.

The FHFA is correct in assessing that the current rating system is confusing. The difference between impact and concept scores under the current rating system is baffling for stakeholders. However, the point system corresponding to the current ratings allows for more gradations in ratings and more transparency in depicting differences in performance. NCRC therefore urges the FHFA to retain a point system for its proposed ratings.

The Proposed Five Ratings are Needed for a Rigorous Scoring System

The FHFA proposes establishing a ratings system from 1 to 5 with 1 indicating the best performance and 5 indicating the worst performance. The FHFA asserts the proposed numerical ratings system is consistent with CAMELSO, the rating system used by the FHFA for other compliance exams and consistent with compliance exams for banks.[28] NCRC approves of the five ratings because they are better able to reveal gradations in performance than the four ratings on CRA exams (in contrast, the component tests on CRA exams have five ratings which are more revealing of distinctions in performance). However, NCRC also urges the FHFA to assign words to each of the numerical ratings such as the CRA component test ratings of Outstanding, High Satisfactory, Low Satisfactory, Needs-to-Improve, and Substantial Noncompliance. The public has a better understanding of words than numbers for ratings.

NCRC supports the FHFA proposal that the two worst numerical ratings, not just the worst rating, indicate failure of the exam. As the FHFA states:

FHFA believes that the Enterprises have now had ten years of experience with developing and implementing Plans, such that performance previously justifying a Minimally Passing should not, in the future, be deemed as complying with the Duty to Serve. FHFA does not intend the Enterprises to target “minimal” performance; it intends the Enterprises to provide meaningful support to the underserved markets.[29]

These assertions are correct since the GSEs have had a decade of experience with DTS plans. Moreover, banks are subjected to failure on CRA exams if they receive one of the two worst ratings, not just if they receive the worst rating. This proposal would help level the playing field among banks and the GSEs by expecting the GSEs to achieve ratings that more accurately reflect compliance with DTS requirements.

Proposed Activities for Evaluating GSEs are Flawed and Subjective

The FHFA proposes to assign a rating for each of the three DTS underserved markets (rural, manufactured housing, and affordable housing preservation), based on how the GSEs perform on each of the required activities. The activity categories include current activities, which are loan purchases, loan product development, investments and grants, and outreach.[30] The current activities are clear and can be readily evaluated and rated.

However, the FHFA proposes adding two more activities, program requirements and program management, that are not well defined and would probably lead to ratings inflation. Program requirements, according to the proposed rule, regards the “effort and execution” of the GSE to meet the DTS requirements.[31] The proposal does not provide any further explanation of how effort and execution would be measured, which suggests that judging this activity could be subjective and arbitrary. Assessing effort is also redundant since the outcomes under the four existing activities such as loan purchases is the best way to measure effort. If a GSE does not meet loan purchase targets, for example, it merits a low rating. There is no reason to assess a GSE’s effort in addition to achievements under the existing activities.

Likewise, the FHFA intends to assess program management by evaluating the extent to which a GSE’s board and senior staff are involved in the DTS programs.[32] Evaluating program management is redundant because the performance under the existing activities suggests the extent to which the board and management are involved. It is unlikely that a GSE could meet its loan purchase goals if the board and senior staff are not involved in the DTS program. The results on the loan purchase activity would speak for itself, suggesting the program management activity is unnecessary. It is possible that if a GSE is performing poorly under the existing activities, the FHFA could use subjective judgements under program requirements and management to artificially boost a rating to one of the passing ratings. Alternatively, if the FHFA is not favorably disposed towards the GSEs for some reason, good performance on the existing activities could be dragged down by unfairly harsh judgements on these proposed subjective activities. The possibilities of exam manipulation should be eliminated by not adopting the program requirement and management activities.

The last proposed activity is any additional activity that the FHFA judges worthy of evaluating. The FHFA would add any new activity in the year prior to the first year of a DTS plan.[33] The proposal is unclear about when the agency would add an activity. The GSEs need ample lead time regarding the activities under which they will be evaluated so they can adequately develop their DTS plans. A lack of clarity regarding when the agency would adopt additional activities makes this proposal flawed. Furthermore, the additional activity option could introduce exams that evaluate a different range of activities in different time periods, making it difficult to judge whether the GSEs are improving over time in their DTS activities.  In the history of either DTS, Affordable Housing Goals, or CRA exam for banks, NCRC is not aware of criteria being added to exams at any time outside of a rulemaking process involving public comment periods. The FHFA must not implement this potentially arbitrary feature to DTS evaluations.

NCRC not only urges the FHFA to refrain from adding new activities but also should assign weights to the existing activities. Loan purchases are the most important activity since purchasing loans from primary market lenders and providing more resources for lenders to make more loans is the reason why the GSEs exist. Accordingly, loan purchases should be weighted at 50 percent.

Loan product development is also important because without continual innovations in underwriting and assessing borrower creditworthiness, the GSEs will not adapt to changes in the primary market and cannot be as effective in purchasing loans. Accordingly, a weight of 20 percent is appropriate for this activity. Twenty percent would also be appropriate for investments and grants because this activity builds an infrastructure for making loans. Grants support housing counseling agencies that produce creditworthy borrowers. Similarly, housing-related investments build and maintain the housing stock which ultimately generates more lending opportunities.

Finally, outreach is important but would be weighted less at 10 percent. If a GSE is not performing outreach to find banks, non-bank lenders, nonprofit community-based organizations, and public sector entities with which to do business, it will not be successful under the loan purchase activity. Therefore, outreach would be a natural byproduct of needing to excel on loan purchase activity. GSEs wishing to do well on their DTS evaluations would automatically engage in rigorous outreach, which does not therefore need as high a weight for motivating the GSEs to engage in this activity.

A Lack of Standards for Judging GSE Activity Performance Likely to Inflate Ratings

The proposal to create two ratings indicating failure is seriously undercut by a lack of standards for assessing how GSE performance corresponds to the proposed ratings. The proposed standards for awarding the ratings to various activities are subjective and unclear. For example, the FHFA states that “A rating of “1” could be achieved if the Enterprise demonstrated exceptional leadership in the market in developing loan products and flexible underwriting guidelines to facilitate a secondary market for mortgages for very low-, low-, and moderate-income families in the underserved market.”[34] The key words, “exceptional leadership,” are not defined. Would the leadership be exceptional if the loan product development was judged to be more innovative, flexible, and responsive compared to the other GSE or to the other GSE and primary lenders, for instance. The proposal lacks benchmarks for creating objective judgments and ratings.

The lack of benchmarks is a pressing issue for the loan purchase category. There is no reason why benchmarks could not have been proposed similar to benchmarks used on CRA exams. For rural housing, for example, a performance measure such as the percentage of purchases that qualified as rural purchases could have been developed and this percentage could have been compared to the other GSE and the primary market. Another comparison also could have been a demographic benchmark such as the percentage of households or homeowners that reside in rural areas. Alternatively, a benchmark measuring loans per household in rural areas could have been developed and compared against the other GSE and the primary market. A rating of “1” could have been assigned if a particular GSE’s performance based on an objective benchmark exceeded the other GSE and the primary market. A rating of “2” could have been awarded if the particular GSE was better than the other one and was at least comparable to the primary market on the benchmark. Further guidelines for the other ratings could also have been developed by comparing the GSEs to industry peer and demographic benchmarks.

Other benchmarks for judging performance could include objective comparisons with GSE past performance. Under the loan purchase category, for example, is the GSE DTS plan committing the GSEs to increasing their loan purchases compared to past performance and did the GSE achieve these increased loan purchases?

Another problematic aspect of the proposed rule is that there is no standard for determining how many component ratings would correspond to the overall rating. For instance, the proposed rule says that to achieve a “1,” a GSE would need to have a “1” rating for most components of a DTS market.[35] The proposal could have eliminated the ambiguity surrounding the word “most” by proposing a certain percentage of the component ratings would need to be a “1.” NCRC submits that to receive the highest rating, the GSE must have the highest rating on at least 60 percent of the component tests and preferably 70 percent. A similar grading system should have been developed for the other overall ratings.

The FHFA must not finalize this proposed rule without developing benchmarks and standards for evaluating GSE performance. The FHFA should either re-propose this rule or propose guidance subject to public comment that contains objective standards for assigning ratings. If the FHFA does not take these steps, the public does not have an objective means for assessing whether this proposed rule would enhance or worsen GSE compliance with their DTS obligations.

Annual Dissemination of Ratings

NCRC approves FHFA’s proposal to codify the publishing of DTS market ratings in its annual report. It is also positive that the FHFA is proposing to produce narrative describing GSEs’ performance.[36] The narrative should guide readers to assessing strengths and weaknesses in the GSEs’ performance. NCRC suggests that the narrative would be more interactive if it also published substantive public comments received by the GSEs or FHFA during the year and the GSEs’ response to those comments.

Conclusion

While the FHFA proposes reforms that would bolster the importance of public comments and accountability, other proposed changes would overwhelm the positive proposals and result in more subjective DTS exams. The totality of the changes would probably reduce the amount of GSE financing of housing in rural areas, manufactured housing, and preservation of the affordable housing stock. The FHFA proposes that the two worst ratings instead of just the worst rating would cause a GSE to fail its exam. This would provide a significant incentive to improve performance but is undercut by a lack of objective performance measures and benchmarks, and too much discretion for the GSEs to choose the activities against which they would be evaluated.

Likewise, it is commendable that the FHFA proposes to require the GSEs to respond thoughtfully to substantive public comments on draft plans and reduce exam manipulation by decreasing permissible modifications to DTS plan goals. Again, however, these positive proposals are undermined by relaxed requirements to target and verify that regions and borrowers with the lowest incomes and most pressing needs are the focus of DTS plans.

On balance, if the FHFA does not make significant changes to its proposed rule, future GSE financing of DTS market needs will decrease. These harmful outcomes would be driven by the proposals that would decrease the focus on the communities with pressing needs, increase the subjectivity of exams, and provide too much discretion to the GSEs for choosing activities for the DTS evaluations in place of a public process for choosing activities for evaluation. The FHFA must not finalize a rule that would make it too easy for the GSEs to label their activities for mainstream markets as those for DTS markets. This would result in the GSEs not adhering to the “but for” maxim in that if it were not for the GSE financing, the DTS markets would continue to be underserved.

Thank you for the opportunity to comment on this important matter. If you have any questions, you can email me on jvantol@ncrc.org or Josh Silver, Senior Fellow and author of this comment, on endredline77@gmail.com.

Sincerely,

Jesse Van Tol
President and CEO
National Community Reinvestment Coalition

 

[1]12 U.S. Code § 4565 – Duty to serve underserved markets and other requirements, Cornell Law School, Legal Information Institute, https://www.law.cornell.edu/uscode/text/12/4565
[2] Federal Housing Finance Agency (FHFA), Notice of Proposed Rulemaking (NPR), Enterprise Duty to Serve Underserved Markets, RIN 2590–AB64, Federal Register, Vol. 91, No. 120, Wednesday, June 24, 2026, p.  37850
[3] FHFA, NPR, 37852
[4] FHFA, NPR, 37852
[5] FHFA, NPR, 37853
[6] UNC Center for Community Capital, Home Energy Efficiency and Mortgage Risks, March 2013, https://imt.org/wp-content/uploads/2018/02/IMT_UNC_HomeEEMortgageRisksfinal.pdf, p. 1
[7] U.S. Department of Energy, Weatherization Assistance Program, https://www.energy.gov/cmei/scep/wap/weatherization-assistance-program
[8] Review of CRA ratings and types of exams accessed on July 3, 2026 on website of the Federal Financial Institutions Examination Council, https://www.ffiec.gov/data/cra/ratings
[9] FHFA, NPR, 37865
[10] FHFA, NPR, 37865
[11] FHFA, NPR, 37865
[12] FHFA, NPR, 37872.
[13] FHFA, NPR, 37855
[14] FHFA, NPR, 37855
[15] Dave Castillo, Bruce C. Mitchell, Jason Richardson, Jad Edlebi, Redlining The Reservation: The Brutal Cost Of Financial Services Inaccessibility in Native Communities, NCRC, December 23023,  https://ncrc.org/wp-content/uploads/2023/11/Tribal-Lands-Report-v14.pdf
[16] FHFA, NPR, 37853
[17] FHFA, NPR, 37854
[18] FHFA, NPR, 37858
[19] FHFA, NPR, 37856
[20] FHFA, NPR, 37858
[21] FHFA, NPR, 37858
[22] FHFA, NPR, 37859
[23] FHFA, NPR, 37861
[24] FHFA, NPR, 37861
[25] FHFA, NPR, 37859
[26] FHFA, NPR, 37860
[27] FHFA, NPR, 37860
[28] FHFA, NPR, 37862
[29] FHFA, NPR, 37862
[30] FHFA, NPR, 37862
[31] FHFA, NPR, 37862
[32] FHFA, NPR, 37862
[33] FHFA, NPR, 37862
[34] FHFA, NPR, 37863
[35] FHFA, NPR, 37863
[36] FHFA, NPR, 37863

]]>
New Data for Clearer Answers: Tracking Gentrification and Neighborhood Change Across US Cities (1970s – 2010s) https://ncrc.org/new-data-for-clearer-answers-tracking-gentrification-and-neighborhood-change-across-us-cities-1970s-2010s/?utm_source=rss&utm_medium=rss&utm_campaign=new-data-for-clearer-answers-tracking-gentrification-and-neighborhood-change-across-us-cities-1970s-2010s Tue, 07 Jul 2026 17:55:12 +0000 https://ncrc.org/?p=289877 NCRC is pleased to announce the release of a new longitudinal dataset designed to help communities, advocates and policymakers better understand how neighborhoods have changed over time and where gentrification pressures are most evident.

This dataset draws on US Census data spanning multiple decades (from the 1970s through 2010s) and covering thousands of neighborhoods tracking demographic, economic and housing trends at the neighborhood (census tract) level across metropolitan areas nationwide.

Why this matters

Gentrification is one of the most debated topics in the housing and community development fields. Depending on how it’s measured, the same neighborhood can look like a success story or a displacement crisis. This dataset was built to cut through that confusion. It applies five research-tested methods for identifying gentrification so users can see how conclusions shift depending on the approach — and which methods are best supported by real-world evidence.

NCRC’s own research, published in the Journal of Urban Affairs, found that the methods included in this dataset were among the most accurate at tracking actual patterns of class change, racial turnover and housing market pressure. Using these methods, we estimate that roughly 60,000 Black residents left gentrifying neighborhoods in Washington, DC between 1990 and 2020 alone.

This new dataset has provided researchers and advocates with a more transparent and evidence-based way to understand where and how gentrification is occurring.

What’s in the dataset

The dataset includes neighborhood-level indicators over time, such as:

  • Population and demographic change
  • Income and poverty levels
  • Housing values and market activity
  • Employment and occupational shifts
  • Gentrification indicators by decade

These indicators allow researchers to make consistent, decade-by-decade comparisons across thousands of neighborhoods in urban areas nationwide.

What we found

Using this dataset and our evaluation approach, we found that in Washington, DC – one of the most studied cases of gentrification – neighborhoods identified as gentrifying were also the primary sites of significant Black population loss between 1990 and 2020.

However, this does not mean every move was forced. But it does show that population changes were not random as they were concentrated in areas experiencing rapid economic and social transformation.

How this helps communities

This dataset is designed to support:

  • Better public understanding of neighborhood change;
  • More informed policy decisions, including tenant protections and anti-displacement strategies;
  • Targeted investment by focusing resources where pressures are greatest; and allows for more
  • Local analysis, allowing cities to apply consistent evaluation methods to their own communities.

A tool for action

By grounding the measurement of gentrification in real-world indicators, this dataset helps move the conversation beyond competing definitions and toward practical solutions.

When we can clearly identify where change is happening — and who is affected — we are better equipped to protect communities and promote equitable development.

To access the dataset and learn more about the evaluation methods, please see the following resources: 

Funding Sources 

National Institutes of Health’s National Institute on Aging (R01AG080401)

 

 

Bruce C. Mitchell with the Principal Researcher with NCRC’s Research team.

Photo credit: Erik McLean via Pexels.

]]>
Decades of Disinvestment: Redlining, Segregation & Current Lending Patterns https://ncrc.org/event/decades-of-disinvestment-redlining-segregation-current-lending-patterns/?utm_source=rss&utm_medium=rss&utm_campaign=decades-of-disinvestment-redlining-segregation-current-lending-patterns Wed, 05 Aug 2026 13:00:00 +0000 https://ncrc.org/?post_type=tribe_events&p=289859

August 5 , 9:00 am EDT – 12:00 pm EDT

Join Dr. Bruce Mitchell, NCRC Principal Researcher, at the Obama Presidential Center as he explores how the color-coded maps used by federal officials from the 1930s through the 1960s continue to shape mortgage lending and where economic opportunity flows today.

Over the past four decades, neighborhoods labeled “hazardous” more than 80 years ago have received approximately 3,000 fewer mortgages than neighborhoods graded “best,” with lending declining steadily across each lower grade, decade after decade. Even after accounting for differences in housing stock, vacancy rates, and owner occupancy, these disparities persist alongside today’s racial wealth gap, where Black households hold roughly $15 in wealth for every $100 held by White households.

]]>
Historic redlining continues to color mortgage odds, wealth opportunities today https://ncrc.org/historic-redlining-continues-to-color-mortgage-odds-wealth-opportunities-today-2/?utm_source=rss&utm_medium=rss&utm_campaign=historic-redlining-continues-to-color-mortgage-odds-wealth-opportunities-today-2 Tue, 02 Jun 2026 17:16:07 +0000 https://ncrc.org/?p=288862 April 30, 2026, WJLA, Historic Redlining Continues To Color Mortgage Odds, Wealth Opportunities Today

The Fair Housing Act of 1968 made redlining illegal, but the old red lines still influence who can and can’t get a mortgage today. 

The National Community Reinvestment Coalition (NCRC), a nonprofit organization focused on underserved communities, analyzed Home Mortgage Disclosure Act (HMDA) data from 1991 and 2021. Neighborhoods once marked “hazardous” on HOLC maps received 2.5 times fewer loans than areas once graded as “best.” The disparity remains even after accounting for vacancy, owner-occupancy rates and housing age.

“Formerly redlined communities had reduced mortgage investment for over a century,” says Bruce Mitchell, a principal researcher at the NCRC. “The impact was especially deep in redlined Black neighborhoods, where mortgage availability was further suppressed until the most recent decade.”

Even today, homeownership rates in formerly “hazardous” areas are still roughly half those in greenlined neighborhoods, according to the most recent data from the Federal Reserve Bank of St. Louis. 

“Mortgage markets are backward-looking, relying on historical sales to determine present values,” says Jason Richardson, senior director of research at the NCRC.

]]>
Historic redlining continues to color mortgage odds, wealth opportunities today  https://ncrc.org/historic-redlining-continues-to-color-mortgage-odds-wealth-opportunities-today/?utm_source=rss&utm_medium=rss&utm_campaign=historic-redlining-continues-to-color-mortgage-odds-wealth-opportunities-today Tue, 02 Jun 2026 15:32:23 +0000 https://ncrc.org/?p=288840 April 30, 2026, Bankrate, Historic Redlining Continues To Color Mortgage Odds, Wealth Opportunities Today 

The Fair Housing Act of 1968 made redlining illegal, but the old red lines still influence who can and can’t get a mortgage today. 

The National Community Reinvestment Coalition (NCRC), a nonprofit organization focused on underserved communities, analyzed Home Mortgage Disclosure Act (HMDA) data from 1991 and 2021. Neighborhoods once marked “hazardous” on HOLC maps received 2.5 times fewer loans than areas once graded as “best.” The disparity remains even after accounting for vacancy, owner-occupancy rates and housing age.

“Formerly redlined communities had reduced mortgage investment for over a century,” says Bruce Mitchell, a principal researcher at the NCRC. “The impact was especially deep in redlined Black neighborhoods, where mortgage availability was further suppressed until the most recent decade.”

Even today, homeownership rates in formerly “hazardous” areas are still roughly half those in greenlined neighborhoods, according to the most recent data from the Federal Reserve Bank of St. Louis. 

“Mortgage markets are backward-looking, relying on historical sales to determine present values,” says Jason Richardson, senior director of research at the NCRC.

The lack of lending in one year depresses values, which makes lending more difficult the next. That is how lending discrimination became self-reinforcing.— Jason Richardson, Senior Director of Research, NCRC

]]>
Video: Opportunity Zones 2.0: What You Need to Know and How to Act Now https://ncrc.org/video-opportunity-zones-2-0-what-you-need-to-know-and-how-to-act-now/?utm_source=rss&utm_medium=rss&utm_campaign=video-opportunity-zones-2-0-what-you-need-to-know-and-how-to-act-now https://ncrc.org/video-opportunity-zones-2-0-what-you-need-to-know-and-how-to-act-now/#comments Thu, 28 May 2026 20:03:11 +0000 https://ncrc.org/?p=287141 Online Event Archive Recorded: May 20, 2026

Opportunity Zones 2.0 (OZ 2.0) is an updated federal investment incentive program created under the 2025 One Big Beautiful Bill Act (OBBBA). It provides tax benefits to encourage private investment in low-income census tracts.

OZ 2.0 will shape the community investment landscape for the next decade. The process is competitive and time-sensitive. Members who act early, build strong cases and engage state decision-makers will be best positioned to direct investment to the communities that need it most.

For more information, visit: https://ncrc.org/opportunity-zones-2-0-what-you-need-to-know-and-how-to-act-now/

 

Speakers

Bruce C. Mitchell, PhD Principal Researcher, NCRC
Manan Shah, Policy Advisor, NCRC

Transcript:

NCRC video transcripts are produced by a third-party transcription service and may contain errors. They are lightly edited for style and clarity.

Shah 0:01
All right, welcome everybody. It’s good to see many people on the call. Feel free to put in the chat, you know, which organization you’re with, where you’re tuning in from. Really glad to have you guys on the call today. My name is Manan Shah. I’m the policy advisor here with NCRC, and I’m joined by my colleague today, Dr. Bruce Mitchell, our principal researcher, to talk about the newest iteration of the Opportunity Zone program. As many of you have known or heard, state governors are currently nominating new census tracts to designate as opportunity zones for the next 10 years. We want to share with community advocates like yourself what the process looks like to nominate your census track and really ensure that capital is flowing to communities that need it the most. So today, we are going to share some of our own research on OZs, including a new mapping tool our research team has developed to help you identify and nominate your census tract for each state. The deadline to nominate census tracts is coming up pretty soon this summer, and so it’s really vital that community organizations act early, and so let’s go ahead and dive right in. There’s some housekeeping. NCRC’s code of conduct applies to all of our gatherings. We don’t allow any AI chatbots to join the call, but we will have a recording available after this presentation. If you have any questions throughout the presentation, please use the Q and A button at the bottom of your Zoom screen. If you have any, if you’re having any trouble with that, you can also use the chat button, but for now, just use the Q and A button, and we will get to as many of those as we can at the end of the presentation. But feel free to follow up with us. And for those of you who are new to NCRC, we are the National Community Reinvestment Coalition. We are a member organization, membership network of 700 grassroots organizations advancing to build a just economy for all. We work with banking institutions specifically to hold them accountable to the Community Reinvestment Act to get capital flowing in low- to moderate-income communities, really to expand housing, small businesses, and workforce opportunities. And so I’m going to pass it over to my colleague Bruce, who can start us off on some of his research findings.

Dr. Mitchell 2:41
Well, thank you very much, Manon. Appreciate that. As you might know, we’ve been interested in the opportunity zones for quite some time now. We’ve done a number of reports on the OC 1.0 which I’ll.. when I say that, I’m referring to the former program established under the 2017 Tax Cuts and Jobs Act. And that program, OZ 1.0, that’s due to sunset at the end of this year to be replaced by a new program, which we’ll call OZ 2.0. But if we look at the performance of the first version of the Opportunity Zone program, there’s some interesting factors about it, you know. First off, it’s the largest federal community development program, you know, in history. Basically, $100 billion has flowed into this program since 2017, about $20 billion a year, mid 2019 and 2022. This has flowed into 6,000 of what are called qualified opportunity funds, and these funds are the primary mechanism by which financing flows into opportunity zones. Across the United States, in the old program, about 8,700 census tracts across the country were designated as opportunity zones, most of these in urban areas, some in rural areas also. But what we found is, and others have found who have researched this, is that the capital in this program has been deeply concentrated. That the top 1% of the opportunity zones, they receive 42% of all investments, and the top 5% received three quarters of all investments. So it’s been highly concentrated in that old program, and there have been some revisions to new program, which we’ll go over and cover those, but we’re interested in hearing what you have to say about the opportunity zones in your community, so we have a short poll, if you would mind just checking that off on seeing whether you agree or disagree that the opportunity zones have had an impact on community contributing to community development in your community. We’ll leave that up for a couple of a seconds there, so that you can have a chance to answer that and see what the results are. So that’s really interesting. There seems to be a split between people who agree that opportunity zones have had some benefit in their communities, and others who disagree and say they haven’t had much of a benefit. So, that’s a really interesting finding, and we’re interested in hearing more from our members on this particular issue, the types of mechanisms and the types of investments that are flowing into their communities as a result of the qualified opportunity funds. That would be very interesting to us, because we’d like to really, you know, help our members work with them, so that they can, you know, better identify ways of attracting capital within their communities through this program.

Next slide, please, because what we’ve seen from the opportunity zone 1.0 is what do the residents actually get, well, in terms of gains, you know, research has found that there were modest positive impacts on employment earnings and in rates of poverty for zone residents. They also found that most of the investment, 75% of the investment in the qualified opportunity funds went into real estate, and that in fact most of that real estate investment went into market rate real estate, so you know rather than building affordable houses necessarily, it’s been building market rate housing of some sort in those areas, and any sort of building of housing these days is probably a positive impact, considering the whole state of the housing industry in this country, and the need for more housing to be built, but it’s been highly concentrated in real estate, and we’ll talk about why that is, as we go on. Also the investments that went in, they tended to favor already improving areas. That census tracts that already had higher incomes had rising home values and strong pre-existing growth. Most of the funding tended to fund those rather than to highly distressed communities. Probably the most, one of the most troubling aspects of this, though, was that it’s been very difficult to assess and see how the opportunity zones have performed, because there was minimal accountability and reporting included in that Tax Cuts and Jobs Act legislation. There was no agency that was designated to collect data, so really all the data that we have came out of the IRS reporting or from states like Ohio, which set up a separate opportunity zone program, which had more rigorous reporting requirements in that. So Urban Institute based a lot of their studies off of that Ohio-based data to see what sort of benefits were derived there.

Next slide, so as a result of this, we can see here that OZ 1.0, OZ 2.0, what have been the changes? Well, first off is the program duration, right. OZ 1.0 is due to sunset at the end of this year, so will no longer be in effect, and those census tracts will no longer be designated opportunity zones at the end of this year, which means that we’re going to have an entirely new reset of new census tracts, potentially new census tracts in this next nomination time period. The program’s also been made permanent now. It’s a permanent program in which the opportunity zones are designated every 10 years. So this nomination period, after it’s over at the end of this year, those will be set for the next 10 years as qualified opportunity zones, so it’s very important that if you have a project that you’re interested in attracting funding from a qualified opportunity fund to, that you get your tract nominated and designated as an opportunity zone, because of the long timeframe this will be in effect. Additionally, OC 1.0, the eligibility under that was 80% of median family income for the area, or for rural areas for the state. That’s become stricter in OC 2.0. It’s now at 70% of median family income, there’s also a 20% poverty inclusion, so if a tract has a 20% or greater poverty rate, it can be considered as a low-income community that could be nominated as a qualified opportunity zone. They’ve also eliminated the contiguous tract rule. What this was was that a higher income tract that was next to a low income tract or a qualified opportunity zone under OZ 1.0, that higher income tract could also become an opportunity zone. That’s been eliminated in this go-around, so you’re no longer going to have higher-income tracts being potential nominees. The capital gains deferral, and that’s one of the main benefits of this, is the ability of investors to defer their capital gains on investment. In the past, that was fixed at a December 31, 2026, deadline. In the new version, it’s going to be a rolling five year from investment date, so if you made an investment, say in 2030, then the deferral would then be fully realized as of 2035. So it’s a rolling date at this point of five years. Also, the basis step-up has changed. The basis step-up formally, it was 10% would be deferred after five years, 15% after seven years of holding that investment. It’s been simplified, so now it’s a uniform 10% after five years in the new version. There are additional rural incentives, and these are for qualified rural opportunity funds. This is a new mechanism for OZ 2.0 and in these, there’s an even higher step up. There’s a 30% deferral step up on the qualified row opportunity funds. Also, there’s a 50% improvement threshold in rural areas. What’s 100% in urban areas? And I’ll go over that in a little bit more detail in the next slide, too. And then finally, there’s enhanced reporting. They’ve improved the reporting on this, so that now the number of units purchased or constructed, the number of employees hired if a business is being invested in the assets, and the track locations they are now required and reporting, so we’ll be able to get better metrics on how OZ 2.0 is performing. So, these are all improvements in the program.

Next slide, please. So, let’s just go into a little bit more detail on how does the investment mechanism work for a qualified opportunity fund? Because the qualified opportunity fund, or QAF, that’s the main mechanism by which capital flows into urban opportunity zones. So, let’s say an investor, they sell an asset and they realize capital gains on the sale of that asset. Well, instead of paying the taxes on those capital gains, what they do is they take that capital gains amount and they invest it in a qualified opportunity fund, right? The qualified opportunity fund then takes those funds and puts them into a qualified opportunity zone, invests in there. Then after that investment is held for five years, 10% of the original deferred capital gains are excluded from taxation, so they get an exclusion from paying taxes on that 10% after five years, but here’s where the real enhancement is, that if they hold that investment for 10 full years, all of the gains that they made on that investment then become tax free to the investor. So they’re deferring their taxes, exempt a portion of their taxes, and then the investor, in addition, is able to realize any gains off of that investment at the end as being tax free after 10 years, so this is a substantial gain, potentially from an investor on this. It also tends to favor the highest rate of return, right, because you want to maximize that investment and the amount of money made off that investment. Talked a little bit about the property improvement requirement in urban areas where qualified opportunity funds function in those urban areas. If you buy pre-existing property, say for $100,000 then this property improvement requirement is 100% so you’d have to invest a further $100,000 in improving that property in order for it to qualify in urban areas, that changes for the qualified rural opportunity funds, which we’ll see in the next slide. So for qualified rural opportunity funds, the same, you know, first thing here, realize that capital gains on a sale, instead of going into a QAF, it goes into qualified rural opportunity fund, and these are unique in that 90% of the funds that are invested in those, need to then flow into a rural census tract outside of the CBSA, a metro area, right. The enhancement on the qualified rural opportunity fund is after five years, they realize actual a 30% deferment of capital gains on that original amount that was invested, so 30% exclude from taxation, and then again after 10 years, all of the gains earned in that are tax free for the investor. The property improvement requirement for the rural areas, it’s relaxed, so you no longer have to make 100% of the invested amount, you may need to only invest 50% in improvement of that property, so there’s some substantial, you know, things here that would, you know, make a rural investment be more attractive. One of our worries about this, talk to some people about this. Is that we’re kind of concerned that the type of investment that might be flowing into their into these could benefit things like AI data centers and construction of AI data centers in rural areas, but we’ll go into a little bit more about what sort of investments are favored.

Next slide, please. Because what this does, it drives investment. Because first off, all gains on investment held for 10 years are tax-free. So this tends to favor long hold strategies. That’s the whole point of it, is you wanted the investment to be a long-lasting investment within that community, but it also makes risky investments something to be avoided, right. So, you might be less likely to invest in a small business in these areas, because the investment risk there is higher than, say, investing in market-rate real estate and a persisting structure. So, investment in durable structures has been favored, particularly market-rate housing, and that’s what we’ve seen, where 75% of the investment has flown to market-rate housing. So those are some of our concerns about it, and probably some of the mechanisms that will continue to drive OZ 2.0 as they drove OZ 1.0 into the future. Next slide, please.

But before those investments can be made, we have this whole issue of the nomination process, and I’m going to share with you here a mapping application which NCRC has developed, and what this is, this is showing a map of low-income communities that can be potentially designated as opportunity zones in the next round of nominations. So, this is something that you can use. You can look at tracks and distracts where you’re interested in investing and having a project occur, and get some information about that tract, and I’m going to first off start with Salt Lake City here, and take a look at Salt Lake City, and let me just explain a little bit of what we’re seeing here, right. You see these areas with the red boundaries? Those are the old opportunity zones back from the OZ 1.0 and there’s a little widget up here you can switch back and forth, so you can show areas that were gentrified or areas that were OZ 1.0. So, I’m going to leave it on areas that were gentrified for right now. And what we see here in downtown Salt Lake City, we see these gentrified areas in the downtown area all outlined in the red. We also see these different color designations for the census tracts. We see this resident designation. This is taken from Urban Institute. The data here is taken from Urban Institute, and what they’ve done is an analysis to look at where has capital been most recently flowing in terms of small business investment, in terms of mortgage investment, in terms of overall capital investments. They’ve done analysis, and in their determination, these red areas, these are likely to attract capital even without an opportunity zone designation. The areas here in the gold color, these are more likely to attract OZ investment with a larger impact in their estimation. And then the blue areas are less likely to attract OZ investment, so the blue areas are areas that are low-income communities that haven’t seen much investment going to them currently. So if we zoom in, if you know Salt Lake City, this tract is probably pretty familiar to because the state capital is right here, it’s the area directly below the state capital, and we see this area was not gentrified. But if we look at OZ 1.0 in the former, these tracts that were below it were qualified opportunity zones. Now this tract is a low-income community and could potentially be invested in if it’s nominated as a profit opportunity. So, if you click on it, some data comes up, and this data is from the 2024 census American Community Survey data, latest census data. And this is designed to help you get some immediate type of information about the tract, so that you can, you know, potentially construct a case for your census tract to be nominated, so we have median household income in that tract, and what these bubbles are. This shows the spectrum of that whole metro area, where that tract lies within the spectrum of that metro, so it’s reasonably high in terms of median household income, in terms of median home value. 26% of the population are minority within that census tract, has high rates of college education. Begin to wonder, why is it a low-income tract cost burden 18% vacancy is fairly high, 12% unemployment 5.9% and here poverty you see is 20.1% So this is probably why this is qualifying as a low-income tract. It has very high rates of poverty within that, within that census tract, and would probably be the qualifying mechanism for it. Number of home owners, owner occupied 24% and then finally the median rent for that tract. So you can take all this data, use it to make the case for your tract. I’ll just go over to one more city here, and let’s take a look at Denver. And if we zoom in on Denver, I’m going to go to the gentrified widget there and show it. This is a city that seems substantial gentrification. It’s downtown core. Denver has been one of the most gentrified cities over the past 10 years in the country, so you see many of these low-income tracks, they’re likely to attract capital even without an opportunity zone designation, but here we’ve got one of those golden tracks that Urban Institute thought was more likely to attract OZ investment with larger impact, also indicating gentrification, but that might be one to look at, and then you have these other tracks here that were gentrified and are not eligible as low-income communities. And if you click on any of these, of course, the same data comes up here. It also shows here that this overlaps with an area that was an OZ 1.0 in the past. That one was not so. There’s some interesting data that you can get from this, the same sort of data down here to make the case for your track.

And I’ll go ahead and stop sharing on that. And if we could just bring up the slide deck again, please, because I’m going to talk a little bit about the nomination process. We’ve seen some of the things that you can, that you can utilize to make the case for your track, but this is the nomination timeline that we’re talking about, and why this becomes so critical, because as of July 1, 2026 the federal window for nominating tracks as qualified opportunity zones, it opens up, and it only lasts for 90 days until september 28 when state governors have to have all of their tracks nominated and have that filed with the federal government. Now, there’s a potential to get a 30-day extension after that, but for right now, September 28, that is the deadline. So it’s really important to get involved this nomination process if you’re interested in having a project and having your track nominated as a qualified opportunity fund. Some of the states, as we can see below the timeline here, Kansas has a had a June 1 deadline, Ohio, April 26 – they opened up a 30-day portal. Texas is accepting submissions now, and what we’re hearing is that qualified opportunity funds of people who are in development, that they are actively lobbying with their states, their state housing agencies, and their state governor’s offices to try and get their tracks nominated, so if you’re interested in having a track nominated, you want to rapidly act on this. Call your state, confirm when they’re accepting nominations, and start your advocacy work. And Manan is going to talk a little bit more about that process and the nomination process of how that works, Manan.

Shah 25:04
Yes, thanks, Bruce. As we see here, this is a process in which governors are selecting the low-income eligible census tracts. Governors may nominate up to 25% of their state’s low-income community census tracts, or a minimum of 25 state tracks for smaller states. There is no rural mandate. It was actually removed in the final version of the One Big Beautiful Bill, but as Bruce mentioned earlier, there is some rural incentives that can really push investors to invest in these rural opportunity zones, but that’s usually at the governor’s discretion. And Treasury reviews for these opportunity zones, but full discretion, the full decision making process rests with the governors on which tracks to select, and I just want to emphasize it’s important to build that bipartisan coalition, regardless of party. When it comes to nominating these census tracts, it’s important that we emphasize the benefits to low-income census checks, but also using language and evidence that shows this will benefit local economic activity, result in increases in affordability, job creation, and overall economic development. And so, like Bruce mentioned earlier, there’s no automatic renewal of the OZ 1.0 census tracts. This this new OZ 2.0 selection of census tracts will be existent for the next 10 years up until 2036. And here are some of the steps, sort of like an action plan for community advocates to nominate your track. First, we encourage you to confirm the eligibility of your census tracts. You can use our NCRC mapping tool that Bruce just presented, or Urban Institute’s eligibility map, to see if your census tracks meet its income and poverty thresholds. It’s using the American Census data from 2020 to 2024 so it’s very recent, updated. Next, please contact your economic development or housing agency, and really figure out what the state internal deadline might be. You want to make sure you’re submitting these nominations against the state deadline and not wait too long and submit before the September federal deadline. Thirdly, you want to make sure in building investment case to why investors should invest in these low-income eligible census tracts, making sure you document developer interests, what available land is there, transit access, any public investments to make sure this census track would be attractive to OZ capital to flow towards and then fourthly, you want to make sure you’re demonstrating community need. Why should investors invest? Because documenting poverty rates, unemployment, housing cost burdens, and showing that these investments can really reduce income and wealth disparities in these census tracks, while also addressing any risks for gentrification, and I think the overlay that Bruce presented with our map could provide some good evidence regarding that. And then lastly, we encourage you to build coalitions within your state, make sure aligning with local government, other CDFIs, other community development organizations and developers, NCRC can actually help you in that process and connect you with other organizations in your state that are working towards the same goal. We definitely believe that stronger coalition could have more chances and getting those census tracts submitted rather than solo efforts, so feel free to reach out to us if you need help coordinating that coalition process. And just to provide these action steps, again, you know, check your census tract availability, contact your development or housing agency, try to build that bipartisan coalition with local elected officials and prepare a one-page nomination summary. An NCRC policy and research team can really help you provide the data, you know, that the census tracks that you need to build your investment case and show that it’s going to benefit your communities, and please feel to reach out with these emails that you’ve listed below. We can really help you prepare that one-pager. And then again submit before your state’s internal deadline, not the September 28 federal cutoff, like Bruce mentioned. There’s a lot of competition from the industry in regard of these, and so we want to make sure community voices are elevated and fully represented. So, with that, that’s the end of our presentation, and we can take questions.

Dr. Mitchell 30:15
Just one thing I want to clarify, and that is that the federal deadline is September 28 there was one slide on there that I failed to correct since September 29. It should be September 28 is the federal deadline for filing.

Shah 30:33
All right, we’ve got some questions here, Bruce. The first one is, What government unit authorizes or approves qualified opportunity funds. Is it the Department of Treasury?

Dr. Mitchell 30:47
So, qualified opportunity funds. My understanding is that yes, the filing for the funds will have to be made through Treasury, like they were in OZ 1.0 Qualified Opportunity Fund, I mean, it can be a bank could form one, primarily they’re limited liability corporations, but a man could form even a CDFI could form a qualified opportunity fund, and some of these I’d say probably a majority of them that we’ve heard of have been, you know, equity investors, where they’re trying to, you know, maximize the rate of return, but in some cases they’re impact investors that are involved in these also, and would be more interested in projects that would say enhance affordable housing within an area. One mechanism we’ve heard is to try and structure these so that the low-income housing tax credit, new market tax credit and other types of aid can be structured in a package that might benefit a project within a qualified opportunity zone. We’re very interested in that. I’m not a finance person, but we’re very interested in that, and hearing your ideas about how those things might be structured. My guess is, though, that a lot of these are going to be depending on local considerations and how they can be configured locally, and what local need is.

Shah 32:13
Bruce, we had another question in the chat around the QoS. Are you able to monetize the future value of the QOF with a private equity firm?

Dr. Mitchell 32:24
I’m sorry, I don’t know the answer to that. The financing portion of this, I’m not, as I guess, read up on that aspect of, and how QOFs operate. I’m not a QOF person, so I’ll have to defer that question some other time.

Shah 32:47
Yeah, that’s go ahead, Bruce.

Dr. Mitchell 32:49
I do see one question is, how do I find what area in my community is an opportunity zone, and that’s what this whole process is. These low-income census tracts across the United States, they’re about, they’re more than 20,000 of them, right, but only 25% of those are going to be nominated as qualified opportunity zones, so probably only around 6,000 to 7,000 out of that 20 or 1,000 more tracks will actually make the cut of being a qualified opportunity zone in the nomination process, that’s why your action is important on this issue.

Shah 33:33
Next question is, can we get a link to this map? Yes, we should have sent that in the chat. You can also visit our website. We have a landing page dedicated to this OZ 2.0 with a video, basically outlining this presentation. So, feel free to check that out. And we have another question: Are there examples of opportunity zones being used to fund affordable housing or other projects that increase community investment without driving gentrification?

Dr. Mitchell 34:09
This is information I’ve heard. I mean, part of the problem here is that the reporting on the OZ 1.0 has been so poor, so it’s been very hard to assess many of these things as a result of those OZ 1.0. I have heard that there have been some cases of that, particularly in Chicago, that there are cases in which actual impact investors have funded affordable housing in some places in Chicago on a limited basis, and that has resulted in some construction affordable housing, despite the tendency for market rate housing to be built within the overall program.

Shah 34:50
Next question is around the nomination process. Do current OZ tracks need to be resubmitted for consideration, or will they be likely redesignated?

Dr. Mitchell 35:00
Yes, yes, they have to be resubmitted for designation. The map is going to be wiped clean after December of this year, so it’s a whole new process. And just because an opportunity zone is designated in the last round, it won’t necessarily be in the next round. So, yes, it’s important. If you already have investment opportunity zone from the pre-existing map, that you nominate that tract again if you have a project plan there.

Shah 35:34
Next question, if a census tract is not eligible for designation due to a low poverty rate, but really should be eligible, because the area is underdeveloped, but has few residents, mostly commercial. How could we appeal or revisit that a track should be eligible?

Dr. Mitchell 35:54
Oh boy, I don’t know of any mechanism for doing that. The legislation is really quite clear about what the requirements are, and the requirements are that it be at 70% or below of the area median income. If it’s in a metro area, that would be for that metro. If it’s in a state rural area, that would be for the state overall, or it has to meet that 20% poverty threshold, and that’s using the 2024 census ACS data. So that mapping tool, if it’s not a low-income community designated on there, it’s unlikely that it could be nominated as an opportunity zone. You may, however, you know, want to pull together and see if you can make the case with your state governor’s office that they could potentially include it, but yeah, it’s pretty clear cut in the legislation.

Shah 36:51
Next question on the 25% track maximum, is it a 25% maximum track nominated, and 25 track minimum, is that correct for governors?

Dr. Mitchell 37:07
Right, so, if let’s say only 25% of the tracks are low-income communities, and you could potentially nominate all, so it’s all the tracks in your state, so it’s 25% of tracks within the state that are low-income tracts that can be nominated.

Shah 37:30
Are there different application guidelines for tribes? Also, if a tribe has more than one census tract, would we need to submit more than one application?

Dr. Mitchell 37:42
Answer the second part of that is yes, I would submit applications for each of those tracks. All right, make different cases for each of the tracks for nomination. As far as I know, there’s nothing specific to tribal considerations within the legislation. As far as I know.

Shah 37:59
Next question, it seems to me that if we do have a project, we need to develop relationships with a qualified fund. Is there a source or directory we can go to to get a list of those funds?

Dr. Mitchell 38:16
That is an absolutely great question, and I don’t know of a unified source to look for qualified opportunity funds. At this point, I don’t know if such a thing exists. I have searched for that, and I’ve not been able to find it. If someone does find it, I’d be very interested in seeing that and hearing about such a list, but yes, and that’s I think part of the difficulty of this is the next step of this, get your track nominated, then how do you attract funding into that area, and that’s something that I’d be really interested in hearing from people who’ve had more experience in this, who have managed to attract a qualified opportunity fund, to see what sort of strategies they’ve utilized, so that we could make those available to our members, and they could perhaps pursue those types of sorts of strategies. I see a comment here: Novogradac has a lot of information about qualified opportunity zone funds. Yes, they, they do. Novogradac is one source where you can get quite a bit of information about this, and that would be one potential resource for you.

Shah 39:32
We have a double question here. The small business investment aspect of OZ 1.0 has not panned out as anticipated. Do you see this improving in 2.0?

Dr. Mitchell 39:46
We’ve certainly made some improvements in 2.0 right, but the same, I think, baked mechanisms are, you know, in place, and those mechanisms would tend to favor persistent structures, holding investments over long term and favor less risky investments, so instead of investing in a small business, potentially you might choose to invest in real estate simply because the risk is less for you and your return might be greater long term, so I think the same mechanisms are still in place in OZ 2.0 as they were in OZ 1.0 just with better reporting and some stricter guidelines on which areas qualify.

Shah 40:33
And regarding the reporting, will there be transaction-level reporting requirements like the dollar amount of the OZ investment in a given project?

Dr. Mitchell 40:42
My understanding is yes, that that level of granularity should be available in the reporting now. When that reporting is going to first start coming out, I’m not sure how long it’s going to take before we start being able to see, you know, some of the reporting from these qualified opportunity funds.

Shah 41:02
Some more questions about the QOF. With the QOF, what all will they be able to help with? For example, acquiring land or construction?

Dr. Mitchell 41:15
My understanding is yes, they would be able to write the full development process. QOF would be able to invest in in a project in a qualified opportunity zone. Sure,

Shah 41:28
And do we see investors investing in one project or in multiple projects within one QOf?

Dr. Mitchell 41:36
It depends on the QOf, and I think that many of these QOfs, they have multiple projects, which they will invest in across a range of different projects. QOfs, they vary in size, and some of these larger QOfs, yes, they’ll have a number of projects that they’ll be investing in.

Shah 41:59
And one question around sort of building relationships for philanthropic for philanthropic organizations focused on economic development and capital absorption. What would be some good ways to support city agencies through this process?

Dr. Mitchell 42:18
to city agencies?

Shah 42:21
Yeah, like local governments, sort of building coalitions.

Dr. Mitchell 42:27
I’m not quite sure on that question. I mean, certainly, you know, cities oftentimes work with developers in areas in planning development and redevelopment projects, so there would certainly be city input in that, and city involvement in that, and you know, if you have a qualified, if you have a low-income community that you’re interested in attracting investment to, I would get with that with that city development department and work with them to get that track nominated.

Shah 43:02
Since it’s is good…. Does or hold on, can you provide more context on how CDFIs can create their own OZ fund and engage with investors in OZ geographies?

Dr. Mitchell 43:21
Wow, um CDFIs can create qualified opportunity funds. The mechanism, and how they would go about that, I don’t have specifics on, and, like I said, that’s kind of a next step that I think that we need to go to in our next presentation on this, with more details about how the funding mechanisms would work for this, because I think I’m detecting a lot of interest from our audience in that aspect of it, and we’ve been really focused on this nomination process, so I don’t have much information about how to form a fund or the financial end of this.

Shah 43:57
And can you explain the reasons a track might be less likely to attract OZ investment?

Dr. Mitchell 44:05
Well, from what we’ve seen, and what the data has shown is that the investments have tended to favor tracks that already are in an upward trajectory in terms of increasing home values, increasing metrics on rents, median rent increase also tracts that are adjacent to a gentrifying area or within a gentrifying area, so if there’s already a positive economic development trajectory going on within that tract, then it’s they have tended to be the ones that benefit most from this program, right? The deeply distressed tracks did not see that much investment, at least that’s what the research has told us about OZ 1.0 and that capital was highly concentrated in these areas that we’re already on kind of a positive trajectory to begin with. That being said, under OZ 1.0, what we found when we did our research on this was that most of the tracks that were at the bottom in terms of income, at the bottom in terms of poverty levels, that they did qualify as opportunity zones, so in the track selection process itself it seemed to be fairly equitable in selecting tracks that were in dire need of economic development. There was that provision for contiguous tracks, though, where a higher income track could qualify, and a lot of investment went to those higher income tracks that were adjacent to a low income area.

Shah 45:48
And Bruce, you mentioned LITC earlier. Can you speak to how OZ 1.0 was paired with LITC, and how that might look different than 2.0>

Dr. Mitchell 45:59
Yeah, on that question again, I’m gonna have to defer that I don’t have much information on LITC, and how those two things would have worked together. I know that we’ve talked with some of our members, and the idea of structuring LITC with opportunity zones in the OZ 2.0 and stacking these investments could be a possible mechanism for making them more attractive to investors.

Shah 46:27
And have you heard of communities advocating to ensure their census tracts are not nominated for OZ 2.0, for example, out of a desire to protect their area from development with potential negative effects?

Dr. Mitchell 46:40
Yeah, that’s a really interesting question. So, we have talked to some of our members, and they’ve had very different kind of perspectives on this, and in some areas they felt that the opportunity zone designation has actually made that tract much more difficult to compete in for affordable housing than for the construction market rate housing, and has placed that track outside of their ability to invest in affordable housing, and has basically created a situation of gentrification on steroids in those tracks. So that’s been one concern about this program, is that an opportunity zone may put it out of the range of having affordable housing built. By the same token, we’ve heard from other members who have said that what we need to work with the system as best we can and take advantage of it the best we can in order to maximize affordable housing and development within our area. So they’re two very different perspectives, and your perspective may depend on your local situation, and what the local conditions of economic development are within your city, and within the area that you’re operating. Also, you know, initially we talked about, mentioned a little bit earlier, or the rural opportunity zones and concerns about development in those areas, and we’re a bit concerned about the AI data center angle of this, and that something like an AI data center would be probably a very favorable investment for a qualified opportunity fund. So, there’s a bit of concern about that, and how it might fuel investment in those areas. With AI data centers, I just like to say that with that, as with gentrification, we want to work with members to make sure these are things that happen with the community and not to the community. That this sort of investment be with community involvement, so communities are able to maximize the type of advantages and development that they get rather than have it be an opaque process in which they basically have no role or saying what’s happening with their areas.

Shah 48:53
And then what is the significance of the MFI ratio of the eligibility criteria, is a higher or lower MFI ratio, a better signal of community benefit?

Dr. Mitchell 49:05
So, the MFI, that’s the level of median family income compared to that metro area. So, before in OZ 1.0 it was 80% so it was higher, so that tracks with a bit higher income level would qualify. By lowering that down to the 70% of MFI level, they’ve restricted these low-income communities somewhat, so that is an enhancement and improvement, and it directs more capital or more of the tracks that hopefully will be nominated as being lower income communities than an OZ 1.0 so that restriction was an important one to put in place, and we’re happy to see that restriction in place.

Shah 49:51
Alright, Bruce, that looks like the last of all the questions. Let’s give a virtual round of applause for Bruce, for his expertise and just quickly, if you’re interested in becoming NCRC member, if you liked what you heard and saw today, you want to partner with us for more research or policy advocacy opportunities, you can check out our website. We also have consistent training and events from NCRC. You can visit our events website, including our national conference in the spring and regional summit in the fall. And so again, thank you all for joining. If you have any questions, feel free to reach out to Bruce and I. We’re happy to help build some coalitions in your state or help put together those one-pagers with data and other elements of OZ research, so thanks again, and we’ll see you soon.

Dr. Mitchell 50:46
Thank you, everyone.

Transcribed by https://otter.ai

]]>
https://ncrc.org/video-opportunity-zones-2-0-what-you-need-to-know-and-how-to-act-now/feed/ 2
Redlined Then, Burdened Now: New Research on the Connections Between Land Use and Mental Health https://ncrc.org/redlined-then-burdened-now-new-research-on-the-connections-between-land-use-and-mental-health/?utm_source=rss&utm_medium=rss&utm_campaign=redlined-then-burdened-now-new-research-on-the-connections-between-land-use-and-mental-health Wed, 06 May 2026 14:00:51 +0000 https://ncrc.org/?p=286156 “LULUs” sounds like a funny word, but the health burdens tied to LULUs are no laughing matter. Locally unwanted land uses (LULUs) – industrial sites, highways, power plants and waste facilities – are essential to how cities function. However, living close to them can also mean greater exposure to pollution, excessive noise and other environmental stressors.

A large body of research links these kinds of exposures to a wide range of health concerns. Air pollution is associated with millions of premature deaths globally each year alone and is tied to heart and lung disease, especially in communities located near industrial activity and heavy traffic. Researchers are also increasingly documenting links between environmental conditions and mental health. 

Long-term exposure to air pollution has been associated with depression and anxiety, while environmental noise from traffic and industrial infrastructure have been linked to stress and anxiety symptoms. More broadly, environmental pollution is now understood to affect a person’s physical and mental well-being, with evidence linking it to cognitive decline, psychological distress and worsening mental health over time.

The health burdens of LULUs are not distributed evenly. Decades of environmental justice research have shown that those patterns reflect long-standing inequalities in political power, land use decision-making and access to resources.

One of the clearest historical frameworks for understanding these patterns is residential segregation and its relationship to redlining. From the 1930s until the 1960s, the federal government graded neighborhoods based in part on race and ethnicity, with many Black and immigrant communities being redlined – or labeled as high-risk for investment. Redlining did not create segregation on its own, but it formalized and reinforced patterns of disinvestment that shaped neighborhoods.

A growing body of research finds that neighborhoods with a history of redlining also tend to have higher concentrations of environmental burdens, including industrial land uses, heavily trafficked transportation arteries and other LULUs. These same neighborhoods often continue to face socioeconomic disadvantages and elevated exposure to environmental stressors. A recent study conducted by researchers at the University of Michigan, the Ohio State University and the National Community Reinvestment Coalition (NCRC) reflects these broader patterns in Chicago. 

Chicago is the largest hub in the railroad industry. Six of the seven Class I railroads meet in Chicago, with the exception being the Kansas City Southern Railway. As of 2002, severe freight train congestion caused trains to take as long to get through the Chicago region as it took to get there from the West Coast of the country (about 2 days). About one-third of the country’s freight trains pass through the city, making it a major national bottleneck. (Photo courtesy of Wikipedia Commons.)

“We found that neighborhoods with higher levels of historical redlining also tended to have greater concentrations of certain LULUs, especially industrial land uses, vacant land or construction sites,” said University of Michigan lead researcher Dr. Liang Chen.

These neighborhoods also tended to report higher levels of self-rated poor mental health. In our analysis, industrial land, construction sites and vacant land were especially important in understanding how redlining, neighborhood conditions and mental health are patterned together across the city.

Chicago Housing Authority’s 1938 copy of the Federal Housing Administration’s Neighborhood Ratings of Chicago, IL. (Image via University of Chicago Map Collections; https://www.federalreservehistory.org/essays/redlining.)

These findings suggest that the geography of LULUs today overlaps closely with the geography of historical housing discrimination defined by redlining. In Chicago, as in many US cities, neighborhoods that suffered decades of disinvestment continue to bear heavier environmental burdens and worse health outcomes. This illustrates how the legacies of segregation and land use continue to shape mental health outcomes today.

Addressing these patterns requires intentional policy action. Tools such as the Community Reinvestment Act (CRA) can help by encouraging investment in historically underserved neighborhoods, supporting affordable housing, community facilities and redevelopment efforts that improve neighborhood conditions and expand access to resources that promote community health.

 

Bruce C. Mitchell, PhD. is the Principal Researcher with NCRC’s Research team.

Photo credit: Chait Goli via Pexels.

]]>
Opportunity Zones 2.0: What You Need to Know and How to Act Now https://ncrc.org/event/opportunity-zones-2-0-what-you-need-to-know-and-how-to-act-now/?utm_source=rss&utm_medium=rss&utm_campaign=opportunity-zones-2-0-what-you-need-to-know-and-how-to-act-now Wed, 20 May 2026 17:00:00 +0000 https://ncrc.org/?post_type=tribe_events&p=286522

May 20 , 1:00 pm EDT – 2:00 pm EDT

Opportunity Zones 2.0 (OZ 2.0) is an updated federal investment incentive program created under the 2025 One Big Beautiful Bill Act (OBBBA). It provides tax benefits to encourage private investment in low-income census tracts.

OZ 2.0 will shape the community investment landscape for the next decade. The process is competitive and time-sensitive. Members who act early, build strong cases and engage state decision-makers will be best positioned to direct investment to the communities that need it most.

For more information, visit: https://ncrc.org/opportunity-zones-2-0-what-you-need-to-know-and-how-to-act-now/

Speakers:

  • Bruce C. Mitchell, PhD Principal Researcher
  • NCRC Manan Shah, Policy Advisor, NCRC

]]>