Establishing a National Housing Data Authority

To improve housing data and build the necessary infrastructure, the United States needs a centralized coordinating institution—a National Housing Data Authority (NHDA). The NHDA must have a clear mandate to build, maintain, and strengthen the data infrastructure necessary to coordinate decision-making across the housing system. The NHDA would not replace existing data collection efforts within federal agencies, state and local governments, or other institutions, nor would it attempt to consolidate all housing data in a single repository. Instead, it would provide the connective infrastructure, institutional capacity, and sustained resources needed to make existing data more interoperable, accessible, consistent, and useful, while supporting continuous improvements in the quality and coverage of housing data. 

Crucially, the NHDA must be conceived not as a one-time investment or short-term pilot, but as a durable public institution with the authority, expertise, resources, and political commitment to evolve alongside the housing system. As in other areas of national importance in the United States where fragmented data systems have constrained effective decision-making, addressing housing data challenges requires sustained institutional capacity rather than a singular technological innovation or temporary initiative.

The establishment of an NHDA would follow the well-trodden practice of establishing dedicated agencies to coordinate data collection within sectors deemed crucial to the U.S. economy. Between 1862 and 1992, the United States established 13 principal statistical agencies, all housed within federal agencies, including the Bureau of Labor Statistics (BLS) within the Department of Labor, the National Center for Education Statistics (NCES) within the Department of Education, and the Bureau of Justice Statistics (BJS) within the Department of Justice.²⁰ Each of the 13 agencies reflect a national policy priority, and recognition that sustained investment in high-quality data collection and analysis is critical to making informed decisions that shape people’s lives and communities.

Over time, these agencies have developed some of the most rigorous and trusted data collection efforts in the country, including the Census Bureau’s American Community Survey, which serves as the premier source for detailed social, economic, housing, and demographic information nationwide, and NCES’s Integrated Postsecondary Education Data System (IPEDS), which covers data from every college, university, and technical and vocational institution participating in federal student financial aid programs. And yet, no similar agency exists for housing.

While the Department of Housing and Urban Development (HUD) makes available administrative data on housing choice vouchers, the Low-Income Housing Tax Credit (LIHTC), and other HUD-administered programs (and collects survey data on housing market characteristics such as vacancy and physical housing stock²¹) it does not have the same statutory mandate as the principal statistical agencies. NCES, for example, is tasked with producing “accurate, timely, and nonpartisan insights on the condition of education—from early childhood to adult education—with the goal of improving student outcomes.” Its parent agency, the Institute of Education Sciences, has a mission to “provide data that describes how well the United States is educating its students.”

The NHDA could be established as a fully federal entity, akin to the principal statistical agencies, or as a quasi-federal entity with a federated structure. Regardless of the exact structure, the common denominator is federal support and investment, without which the Agency will not be set up to produce the kinds of insights that will meaningfully impact housing outcomes.   

In a federated structure, a central federal agency could coordinate the core functions of the NHDA (described in more detail below), but some of those functions could be carried out by external entities. One example is a Federal Reserve System-style model, where a central entity—in this case, the central bank of the United States—coordinates a decentralized, largely private system of banks, credit unions, and financial regulators to maintain the safety, soundness, and stability of the country’s financial system. Another model is that of the Legal Services Corporation (LSC), a congressionally established and federally funded nonprofit organization to provide civil legal assistance to low-income Americans. 

The Fed and LSC are examples of a hybrid public-private structure in areas of national importance—monetary policy and civil legal aid—while maintaining independent governance and funding structures. Each have a data and research component as well: The Fed’s regulatory authority over banks, credit unions, and financial regulators allows it to collect and aggregate housing finance data from several public and private sources, and LSC’s Civil Court Data Initiative provides real-time access to civil data, including local eviction data for 1,250 counties and municipalities.

University partnerships may make sense for carrying out some of the core activities of an NHDA, such as advancing more complex housing research.²²

The National Housing Data Authority should oversee three core functions: 

• Increasing access to and analysis of existing national housing data through a National Housing Data Exchange (the Exchange).
• Generating high-quality, robust housing data through an innovative Housing Data Action Lab (the Lab).
• Building state and local capacity to improve and standardize data through a State and Local Housing Data Hub (the Hub). 

Table 2 gives an overview of each of the NHDA’s core functions, including its core activities, and the human resources needed to establish them effectively. 

In its first year, the NHDA would require an annual budget of $75 million to $100 million. This level of funding is sufficient to bring together relevant expertise across technical, legal, governance, data, and housing areas and establish the necessary infrastructure, and aligns with investment in other federal statistical agencies.²³ This budget should also include funding for meaningful grants and incentives to states, localities, and researchers for data improvement efforts.

While the federal government need not be the sole source of funding, federal resources and engagement will be critical to enabling the NHDA’s cross-sector coordination and driving meaningful improvements in housing data. Key enabling conditions include a clear federal mandate that establishes institutional authority and clarifies governance structures and decision-making processes; sustainable funding, ideally through annual federal appropriations complemented by other public and private sources; a flexible implementation and incentive funding budget to support adoption and innovation; and broad buy-in and trust among federal, state, and local governments, academic institutions, nonprofits, private-sector actors, and the general public. 

Below we discuss each core function in more detail, including the key challenge it addresses, the proposed solution (or core function), and its core activities. 

Challenge: The United States has robust housing data on many of the domains discussed in the previous section, but access to this data is varied and often requires specialized knowledge, technical skills, time, and resources—for example, negotiating data-sharing agreements or purchasing proprietary data from private vendors. This not only creates unequal access, but prevents the housing data that is collected from being effectively used for better policymaking, advocacy, and research. 

Solution: A National Housing Data Exchange would develop pipelines to public and private national housing datasets that already exist. This entity would not centralize data in a single repository but rather provide a centralized point of access to disparate housing datasets, creating linkages, shared frameworks, and common benchmarks and indicators that speak to the condition of housing in the United States. Staff would help housing actors access and navigate the data. 

Core Activities of the Exchange:

• Create pipelines to existing public and private national housing and real estate datasets to improve access and use, including by facilitating access to proprietary or cost-prohibitive housing datasets through negotiated agreements with commercial data owners and proprietary government databases.
• Develop data linkages across sources and household- or address-level data, enabling integration with other data sources and researchers to tie housing outcomes to data on criminal justice, education, employment, and other areas. 
• Establish shared governance frameworks and protocols for data use and accountability, including standardized metadata, and processes for requesting access to proprietary data sources. 
• Develop housing data benchmarks, research, and analysis, using existing datasets at the national, regional, state, and local levels (where possible). This includes, for example, standardized production and preservation targets broken out by place, specifying the number of market-rate and subsidized units that must be created or preserved at varying levels of affordability, and developing new metrics to assess renter instability.  
• Provide research funding to advance housing knowledge, especially for complex, controlled, multi-method research that integrates public and private data and paints a more comprehensive picture of housing outcomes over time.

Challenge: Our current data infrastructure is not equipped to capture housing as a dynamic system that evolves over time. Critical data are often incomplete, inconsistent, outdated, or unevenly available across geographies and markets, and in some cases, essential information is not collected at all. A robust housing data system must therefore do more than organize existing information: It requires a sustained process for identifying data gaps, improving data quality and coverage, and developing new sources of information as housing markets, policies, and needs evolve. 

Solution: A Housing Data Action Lab would assess critical gaps in our housing data system through a “data discovery” process, prioritizing critical and urgent gaps in our housing data. The Lab would also develop viable pathways for addressing these gaps through action plans, and work with relevant public and private entities to advance those action plans.    

Core Activities of the Lab: 

• Carry out housing data discovery processes. Conducting a comprehensive “data discovery” involves mapping existing data sources, identifying missing or low-quality data, and assessing feasible pathways for improving collection and access. To do so, data discoveries would convene practitioners, researchers, statisticians, and other groups impacted to weigh in on key questions, including: 
  • What data currently exists to address key questions within a housing domain, and what can this data actually tell us? 
  • What are the characteristics of the sources of these data, and how do they align with the criteria for robust data (timely, comprehensive, privacy-protecting, etc.)?
  • Which entities are responsible for collecting, maintaining, and governing this data? 
  • Where are shared definitions, standards, and taxonomies needed to support consistency across jurisdictions and datasets?

• Develop and carry out improvement action plans. Data improvement action plans include the different pathways for improving the collection and access to each specified data type along with the trade-offs involved. An action plan should identify concrete ways to improve data through pilots, legislation or executive action, or expanding the scope of existing collection efforts. The section below illustrates a range of strategies for improving and generating high-quality housing data.

Action plans would specify the relevant actors, the funding or technical assistance needed, and the pathway for implementation. Implementation of these action plans can take several forms depending on the relevant actor, including providing funding for technical assistance and incentives, developing a pilot program that can be scaled, or through recommendations to federal agencies, including HUD, the Federal Housing Finance Agency (FHFA), and the Census Bureau. In some cases, components of the action plan can be carried out by the NHDA (e.g., if grant funding or incentives were involved to pilot the use of a new data collection method or tool). 

Crucially, data discoveries should seek to fill gaps in ways that are responsive to local use cases and grounded in the practical realities of data collection by practitioners. Otherwise, data discoveries and action plans will, at best, create efficiencies around insights that are not useful and, at worst, make inaccurate or incomplete information accessible at scale. 

• Enforce action plans. The NHDA could take a graduated approach to enforcing action plan implementation, from developing guidance and common protocols to conditioning federal funding on data compliance, in partnership with HUD or other federal agencies. Depending on the institutional authority of the NHDA and relevant actors (e.g., federal agencies, financial institutions, public housing authorities, etc.), enforcement could take several different forms, including: 
  • Required agency response plans and progress reporting. 
  • Public scorecards and designations of compliance or noncompliance.
  • Conditions on federal funding or program participation. 
  • Binding data and reporting standards.
  • Referral to relevant oversight or regulatory bodies in cases of persistent noncompliance.

The NHDA could also use incentives—including technical assistance, competitive grant programs, and additional funding—to encourage implementation. This graduated approach would allow the NHDA to tailor its enforcement mechanisms to the authority it has over a particular actor while ensuring that action plans translate to measurable progress as opposed to simply recommendations or guidance.

Challenge: Most housing programs and policies are carried out at the state and local level by an ecosystem of service providers, nonprofit organizations, and housing data intermediaries. This means that much of the most useful housing data is generated at the state and local level, often via administrative processes.²⁵ State and local housing data commonly lacks standardization and requires piecing together information across multiple sources. But because there are scant resources and capacity to do this work, the data most necessary to obtain a complete picture of housing stability, affordability, and supply—local data that is geographically granular, detailed, and useful for informing policy, program, and intervention strategies—varies across and even within geographies. 

Solution: A State and Local Housing Data Hub would have the mandate, expertise, capacity, and funding to invest specifically in improving state and local data. This Hub would provide funding and technical assistance to city, county, and state entities that maintain and improve housing data, and would develop data playbooks, dictionaries, taxonomies, and frameworks for stewarding state and local housing data.

Core Activities of the Hub:

• Provide funding, technical assistance, and capacity-building support for state and local housing data needs. This could include funding and technical assistance for modernizing legacy and paper-based data systems, helping jurisdictions design data collection processes around specific policy use cases and interventions, and providing technical assistance for standing up innovative methods (e.g., creating rental registries or documenting state-level land record documentation as in Iowa).
• Develop shared taxonomies, standards, and data documentation (or metadata). Establish or support the development of frameworks for cross-jurisdictional cataloging of data and data use that individual data-generating entities may not be able to facilitate. 
• Fund state and local housing data improvement and implementation. Provide grants and funding opportunities for housing data pilots, projects, and local innovation, including supporting data standardization efforts across multiple geographies. 
• Elevate state and local data best practices and tools for replication. This includes creating implementation playbooks and templates to help other jurisdictions replicate successful approaches to housing data collection and analysis,²⁶ including guides for the types of questions that can be asked and answered with specific kinds of data.²⁷ 
• Create incentives for better housing data, including, for example, grants tied to adoption of developed data standards or for jurisdictions that meet data quality benchmarks; funding for places that participate in multi-jurisdiction or state data initiatives; and matching grants that reward local investment in data infrastructure.