Housing is foundational to every aspect of health and well-being. It anchors communities, provides families stability, and structures other key aspects of our lives. It is also a major economic driver, accounting for 16 percent of U.S. gross domestic product (GDP).¹ But housing in the United States is too expensive, in short supply, and deteriorating in quality. Our supply shortage ranges anywhere from 1.5 million to 5.5 million homes depending on who is counting.² The homes that do exist are increasingly unaffordable: Estimates show that 49 percent of all renters spend more than 30 percent of their income on rent and utilities,³ and homeownership is increasingly out of reach, notably for first-time and low- or moderate-income buyers.⁴ All the while, the estimated cost of needed repairs in occupied housing is nearly $200 billion.⁵

The recently enacted 21st Century ROAD to Housing Act is a bright spot, showing bipartisan momentum through a comprehensive piece of federal legislation that incentivizes new construction, cuts burdensome regulations, and eases rules for manufactured housing, among a host of other provisions, to increase supply and lower costs. But the data needed to understand which provisions are successful, in what parts of the country, and for whom, is often out of date, unreliable, or simply non-existent.    

The fact remains that the United States lacks reliable, granular, and coordinated housing data—data needed to build a comprehensive and accurate picture of housing conditions, needs, and outcomes—and track housing as a dynamic system over time. And these housing data gaps have had real consequences, including:  

• Many families facing housing instability and homelessness, including those informally evicted outside the court system, living in cars or extended-stay motels, or doubled up with family or friends, are not visible in collected data, meaning many of the most vulnerable are the least prioritized and often excluded from assistance altogether. 
• Policymakers, without reliable data, often rely on one-size-fits-all housing solutions, such as housing developer tax credits to increase affordable housing, when in many places, allowing manufactured housing or accessory dwelling units would be more efficient. 
• State and local policymakers cannot enforce consequences for negligent property owners, because this ownership is rarely tracked, leaving millions of renters living in substandard conditions with little recourse.
• The public cannot hold decision makers accountable for improving housing conditions or even push them to specify housing-related goals, because the data needed does not exist, is unreliable, or only becomes available years later.  

By contrast, a robust housing data system would enable us to assess the health of housing markets at a given point in time, understand and develop real-time solutions to address the most pressing housing challenges, and hold leaders accountable for improving housing affordability and stability in communities across the United States. 

The Future of Land and Housing program at New America presents a roadmap for building this housing data system, detailing the key components needed to move us from the fragmented and decentralized system we have today to one where the coverage and quality of data needed to ensure that housing is advancing stability and well-being for all.

Housing data in the United States mirrors the housing system writ large, which is highly decentralized and fragmented. Housing decisions are divided across all levels of government and private actors. Local governments shape where and how housing can be built through land-use rules and permitting; states design and administer housing programs and establish regulatory frameworks; and federal agencies influence housing production and preservation through financing, subsidies, and regulation. The result is a collection of geographically distinct housing markets that are highly local, across 3,000 counties and 19,000 municipalities. Within each market, private actors—including developers, financial intermediaries, investors, and property owners—make decisions about housing construction, financing, ownership, and maintenance. This housing ranges from purely market-based to fully subsidized, all within a broader ecosystem of federal, state, and local policies and programs. 

At the federal level alone, housing policy and assistance are overseen by a range of agencies, including the U.S. Department of Housing and Urban Development (HUD),⁶ government-sponsored enterprises (including Fannie Mae and Freddie Mac), the Department of the Treasury, the Department of Veterans Affairs, and the Department of Agriculture.⁷ Outside the federal government, housing data is generated and collected by a constellation of state and local agencies, nonprofit organizations, banks, lenders, courts, and other private actors, all of whom serve different purposes and end users. 

This fragmentation results in housing data that is stored in hundreds of places, subject to different definitions, methodologies, and reporting requirements, and covers different periods and levels of geographic granularity, making data-sharing, integration, and aggregation difficult and, in many cases, impossible.⁸ What’s more, no single entity, public or private, is tasked with overseeing and making sense of the housing data we have. What emerges from this fragmented and decentralized housing data system is a patchwork of partial views of housing, but never a complete picture.

Despite these challenges, much of the housing data produced in the United States is robust and high-quality. In particular, the U.S. Census Bureau is a global leader in conducting high-quality, nationwide surveys, producing a wealth of publicly available housing data at varying levels of geography. This survey data provides a critical backbone for decision-making, supplying trusted, standardized statistics on a wide range of housing issues, from the physical condition of our housing stock in the census and HUD’s American Housing Survey to new construction permits in the Survey of Construction. 

In addition to Census data, the United States collects a wide range of housing data, including surveys conducted by federal agencies, federal administrative data, mortgage and other financial data, state and local administrative data, and private market data. These include:

• Federal surveys conducted by the U.S. Census Bureau, HUD, and other federal agencies primarily focus on household and housing unit characteristics, financing information, building permits and construction, and other data. Census surveys include the American Community Survey, the American Housing Survey, the Building Permits Survey, the Survey of Construction, and the Household Pulse Survey.  
• Federal administrative data from HUD, the U.S. Treasury, and other federal agencies primarily focus on federal housing assistance, including data on homelessness and public subsidies, such as the Housing Choice Voucher Data Dashboard, Homeless Management Information System (HMIS), the Low-Income Housing Tax Credit database, and the point-in-time and housing inventory count, among other sources.   
• Mortgage-related and other financial data collected from banks and other financial institutions by federal agencies as a result of consumer protection legislation or through the Federal Reserve. This includes the Federal Housing Finance Agency (FHFA) House Price Index, Uniform Appraisal Dataset, and National Mortgage Database; Home Mortgage Disclosure Act (HMDA) data; and Consumer Financial Protection Bureau (CFPB) Rental Housing Payment Data, among other sources. 
• State and local administrative records, including deed transfer records, property tax records, eviction and foreclosure court records, building inspection and code violation records, historic preservation designations, environmental hazard records, zoning maps, and state and locally run public subsidies for housing. 
• Private data from vendors that collect consumer, finance, and market data, including ATTOM Data Solutions (covers ownership, mortgage transactions, foreclosure, and climate/hazard risk), BatchData (includes assessor and parcel data), Cotality (collects from mortgage servicers; covers home prices, property taxes, foreclosures, and investor activity), and InfutorData and Data Axle.⁹ Zillow and other real estate companies also use their proprietary listing information to produce estimates of home values and rental prices. 
• Derived datasets created by researchers and nonprofit organizations by integrating multiple data sources. Examples include the National Housing Preservation Database (NHPD), Eviction Lab’s Eviction Tracking System, the National Zoning Atlas, and the Lincoln Institute of Land Policy’s Who Owns America database.

A robust housing data system would encompass a broad range of housing domains. The domains that are critical to track correspond to questions that are most essential for understanding our housing system. These are: 

• Housing stock and its physical condition: What housing exists, where, and in what condition? 
• Housing production, loss, and preservation: How much housing is being added, removed, or preserved? 
• Housing costs and affordability: What does housing cost? And can households afford it? 
• Housing assistance: Who receives housing assistance, and what kind? 
• Demand and household characteristics: Who needs housing currently and in the future, and what kind? 
• Housing finance and capital access: Who can buy, build, or repair housing, and under what conditions? 
• Housing stability: Who, where, when, and why are households facing unstable housing conditions?
• Governance and regulation: What is the policy and regulatory environment in which housing exists?

These housing domains, including specific metrics, are detailed here. It is clear that we do collect relatively robust data in some domains relative to others. For example, data collected on mortgage markets and our housing finance systems are more closely tracked relative to housing stability, or housing preservation. While some domains have large gaps in what is and is not collected, others that do have existing data may not be timely or geographically granular enough to meet state and local housing leaders’ needs or may be proprietary, making access cost-prohibitive and uneven.  

A robust system should also adhere to the principles of data that are essential to a functioning democracy. Table 1 describes these principles and their application to housing data.¹⁸

To move our housing data system from one that is fragmented, decentralized, and incomplete to one that is coordinated, accessible, and robust, we need to invest in two areas: data improvement and data infrastructure.¹⁹ 

Improving housing data involves ensuring that housing data is as comprehensive, timely, geographically granular, standardized, accessible, and privacy-protecting as possible across all domains. Doing so involves creating new tools and pathways to address existing gaps in data quality and coverage, and continuous re-assessment as housing markets and technological innovation evolve over time. 

Building housing data infrastructure involves building the systems that coordinate across fragmented data systems and ensure housing data can be accessed, analyzed, and used by a broad range of housing data actors. 

While data improvement and data infrastructure are interconnected, the types of investments, resources, and capacity needed to support and advance each differ. In the next section, we lay out the overarching infrastructure needed to advance both.

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.

The United States should prioritize filling critical housing data gaps in domains where non-existent or poor-quality data prevent us from addressing the housing crisis—one such area is rental market data. 

While developing the scope and structure of a National Housing Data Authority (NHDA), there should be federal support to begin building the partnerships and shared infrastructure needed to make fragmented and largely proprietary rental market data easier to find, connect, and use. Not only would help to fill a critical gap, but it also could demonstrate the value for the broader institutional and technical architecture that a future NHDA would need, that a federal authority could eventually build upon. 

U.S. rental prices are unaffordable to a growing swath of the population. The latest estimates show that 22.7 million renter households—49 percent of all renters—spend more than 30 percent of their income on rent and utilities.²⁹ For low-income renters, the housing affordability problem is even more acute: Around 74 percent of extremely low-income renters are severely cost-burdened, spending more than half their income on housing.³⁰ 

Meanwhile, address-level data on the rents, ownership, and condition of private-market rental units is lagged and not available in real-time, preventing effective intervention. The real-time private rental market data that does exist is largely proprietary, collected by real estate companies and private data vendors. The software company RealPage, for example, has become the primary source of rental market data nationally despite facing litigation over allegations of price-fixing and market manipulation. When data gaps are filled by data vendors and other private companies that leverage nonpublic information in harmful ways, it raises serious questions about what it means to give opaque systems a monopoly over our understanding of an entire market.

This support could include mapping existing data and identifying gaps, as well as developing common standards, facilitating data-sharing agreements, producing integrated data products and research, and piloting approaches to filling priority gaps. Starting with a data discovery for private rental information, this effort could highlight key questions and identify priorities for improving rental market data. This effort would draw on the expertise and knowledge of housing data actors including government agencies, tenants and tenant organizations, and other rental market actors, and would carefully assess sources such as: 

• The National Housing Preservation Database (NHPD), which combines property-level data on federally assisted rental properties that receive federal grants, low-interest loans, LIHTC subsidies and other tax credits, public housing, and eight other federal programs, and is updated quarterly.³¹
• The Zillow Observed Rent Index (ZORI), which provides the typical market-rate rent at the zip code, city, county, metro, and national levels, and is updated on a monthly basis.
• Rental market characteristics produced from federal government surveys, including the Rental Housing Finance Survey (RHFS), American Community Survey (ACS), and American Housing Survey (AHS), which provide data on rental units, such as number of bedrooms, median gross rent, rental vacancy, and physical property condition, at differing levels of geography and time periods.  

The discovery process would also surface prevalent use cases for this data, including, for example: 

• Assessing which zoning and regulatory changes boost affordable rental supply.³²
• Tracking the impact of housing unaffordability on different forms of housing insecurity, including eviction, foreclosures, and living in low-quality housing or overcrowded conditions.
• Addressing code violations, identifying serial evictors, and preserving rental housing. 

The information from the data discovery, combined with the use cases, should inform an action plan that reflects strategies for improving the granularity, timeliness, and comprehensiveness of rental market data. A rental improvement data action plan, targeted to specific actors, could include, for example:

• Creating a federal pilot mandating or incentivizing state and local governments to develop rental registries, carried out in partnership with HUD through legislation or through a competitive grant program implemented by the State and Local Data Hub. 
• Requiring owners with mortgages guaranteed by Fannie Mae and Freddie Mac to provide property-level rent data, put forth and overseen by the Federal Housing Finance Agency (FHFA) through guidance changes to the GSEs.  
• Expanding the scope of the American Community Survey to collect data on households receiving government subsidies, through a Census Bureau, HUD, and NHDA collaboration to develop and test appropriate questions.
• Providing funding to researchers to create linkages across national and local rental market datasets to track housing affordability and stability over time, through funds made available through the National Housing Data Exchange.
• Creating public-private databases of private-market rent using a combination of local data such that it can be integrated with existing databases such as the NHPD, also potentially through the Exchange or another entity. 

In many cases, these recommendations could inform the priorities and strategic plans of other core functions of the NHDA. Taken together, the components above constitute a multilayered strategy for improving data on rental markets, where the three core functions of the NHDA reinforce each other’s mission.

The United States is in the midst of a housing affordability and supply crisis, prolonged and exacerbated by our fragmented, incomplete, and often inaccessible data landscape that is not well-suited to tracking housing over time. As a result, policymakers, practitioners, and housing leaders are often forced to make consequential decisions about affordability, supply, preservation, homeownership, and housing stability without the information needed to understand local conditions, evaluate interventions, or target resources effectively. 

Addressing this challenge will require more than a one-time investment or small-scale pilots. Building a robust housing data system is a long-term political commitment that requires sustained leadership, governance, and coordination by Congress, federal agencies, states, local governments, researchers, and housing practitioners. This paper puts forward the following plans:  

• Establish a National Housing Data Authority. Over the long term (2-4 years), the United States should create a federal or quasi-federal entity—a National Housing Data Authority (NHDA)—with a clear mandate to build, maintain, and strengthen the data infrastructure necessary to coordinate decision-making across the housing system. The NHDA will provide the connective infrastructure, institutional capacity, and sustained resources to make housing data more interoperable, accessible, consistent, and useful through a National Housing Data Exchange, while also supporting continuous improvements in the quality and coverage through a Housing Data Action Lab, and building capacity for state and local data through a State and Local Housing Data Hub. 
• Conduct a data discovery and action plan for improving rental market data. Over the short term (1 year), there should be a public, private or philanthropically-supported initiative to improve rental-market data, an area where nonexistent, proprietary, or low-quality data prevent us from adequately addressing the housing crisis. It should convene the relevant stakeholders and conduct a rental market data discovery that maps existing data sources, identifies missing or low-quality data, and assesses feasible pathways for improving rental data, and at the same time, create momentum and clarity to the functions of an NHDA.  

For too long, housing actors have been making housing decisions based on what is measured rather than what matters most. But the United States has a long history of creating data infrastructure in policy domains like labor, education, and justice that are of national importance, and where fragmented data systems hinder effective decision-making. At a time when housing costs and quality are straining households across the country, we can no longer afford to treat housing data as anything but critical, and a National Housing Data Authority is a pathway to do that.