Background Overview of the National Flood Insurance Program In 1968, Congress created NFIP, with the passage of the National Flood Insurance Act, to help reduce escalating costs of providing federal flood assistance to repair damaged homes and businesses. According to FEMA, NFIP was designed to address the policy objectives of identifying flood hazards, offering affordable insurance premiums to encourage program participation, and promoting community-based floodplain management. To meet these policy objectives, NFIP has four key elements: identifying and mapping flood hazards, floodplain management, flood insurance, and incentivizing flood-risk reduction through grants and premium discounts. NFIP enables property owners in participating communities to purchase flood insurance and, in exchange, the community agrees to adopt and enforce NFIP minimum floodplain management regulations and applicable building construction standards to help reduce future flood losses. A participating community’s floodplain management regulations must meet or exceed NFIP’s minimum regulatory requirements. Insurance offered through NFIP includes different coverage levels and premium rates, which are determined by factors that include property characteristics, location, and statutory provisions. NFIP coverage limits vary by program (Regular or Emergency) and building occupancy (for example, residential or nonresidential). In NFIP’s Regular Program, the maximum coverage limit for one-to-four family residential policies is $250,000 for buildings and $100,000 for contents. For nonresidential or multifamily policies, the maximum coverage limit is $500,000 per building and $500,000 for the building owner’s contents. Separate coverage is available for contents owned by tenants. NFIP also offers Increased Cost of Compliance coverage for most policies, which provides up to $30,000 to help cover the cost of mitigation measures following a flood loss when a property is declared to be substantially or repetitively damaged. Flood Hazard Mapping Through NFIP, FEMA maps flood hazard zones on a Flood Insurance Rate Map, which participating NFIP communities must adopt. According to FEMA, floodplain management standards are designed to prevent new development from increasing the flood threat and to protect new and existing buildings from anticipated flooding. FEMA has a division responsible for flood mapping activities and policy and guidance, but stakeholders from various levels of government and the private sector participate in the mapping process, as appropriate. A community’s Flood Insurance Rate Map serves several purposes. They provide the basis for setting insurance premium rates and identifying properties whose owners are required to purchase flood insurance. Since the 1970s, homeowners with federally backed mortgages or mortgages held by federally regulated lenders on property in a special flood hazard area have been required to purchase flood insurance. Others may purchase flood insurance voluntarily if they live in a participating community. The maps also provide the basis for establishing minimum floodplain management standards that communities must adopt and enforce as part of their NFIP participation. As of May 2020, 22,487 communities across the United States and its territories voluntarily participated in NFIP by adopting and agreeing to enforce flood-related building codes and floodplain management regulations. Community-Level Flood Hazard Mitigation FEMA supports a variety of community-level flood mitigation activities that are designed to reduce flood risk (and thus NFIP’s financial exposure). These activities, which are implemented at the state and local levels, include hazard mitigation planning; adoption and enforcement of floodplain management regulations and building codes; and use of hazard control structures such as levees, dams, and floodwalls or natural protective features such as wetlands and dunes. FEMA provides community-level mitigation funding through its HMA grant programs. In addition, FEMA’s Community Rating System is a voluntary incentive program that recognizes and encourages community floodplain management activities that exceed the minimum NFIP requirements. Flood insurance premium rates are discounted to reflect the reduced flood risk resulting from community actions that meet the three goals of reducing flood damage to insurable property, strengthening and supporting the insurance aspects of NFIP, and encouraging a comprehensive approach to floodplain management. Property-Level Flood Hazard Mitigation At the individual property level, mitigation options include property acquisition—or “buyouts”—to either demolish a building for green space or relocate a building to a low flood risk area, elevation, or floodproofing. Acquisition and demolition (acquisition) is one of the primary methods by which states or localities use FEMA funding to mitigate flood risk. Through this process, a local or state government purchases land and structures that flooded or are at risk from future floods from willing sellers and demolishes the structures. The community restricts future development on the land, which is maintained as open space in perpetuity to restore and conserve the natural floodplain functions. According to FEMA officials, an advantage of property acquisition is that it offers a permanent solution to flood risks, whereas other mitigation methods make properties safer from floods but not immune. Property acquisition and demolition is a voluntary process, and property owners are paid fair market value for their land and structures. Acquisition is typically done on a community-wide scale, purchasing several or all properties in an at-risk neighborhood. Acquisition projects typically require building consensus from property owners and sustained communication and collaboration between residents and the government executing the project. Acquisition and relocation (relocation) refers to purchasing a structure and moving it to another location instead of demolishing it. Through this process, state or local governments use FEMA funding to help purchase land from willing sellers and assist the property owners with relocating the structure. The structure must be sound and feasible to move outside of flood-prone areas. Relocation is a voluntary process and property owners are paid fair market value for their land. Elevation involves raising a structure so that the lowest occupied floor is at or above the area’s base flood elevation. Structure elevation may be achieved through a variety of methods, including elevating on continuous foundation walls; elevating on open foundations, such as piles, piers, or columns; and elevating on fill. Structures proposed for elevation must be structurally sound and capable of being elevated safely. Further, elevation projects must be designed and adequately anchored to prevent flotation, collapse, and lateral movement of the structure from flooding, waves, and wind. Floodproofing falls into two categories: dry floodproofing and wet floodproofing. Dry floodproofing involves sealing a structure to prevent floodwater from entering. Examples of dry floodproofing measures include using waterproof coatings or coverings to make walls impermeable to water, installing waterproof shields, and installing devices that prevent sewer and drain backup. Dry floodproofing is appropriate only where floodwaters do not exceed three feet, the speed of flood waters is low, and the duration of flooding is relatively short because walls and floors may collapse from the pressure of higher water levels. Wet floodproofing involves changing a structure to allow floodwaters to enter and exit with minimal damage. Wet floodproofing is used in parts of a structure that are not used as living space, such as a crawlspace, basement, or garage. Examples of wet floodproofing measures include installing flood openings in the foundation and enclosure walls below the base flood elevation, using flood-resistant building materials and furnishings located below the base flood elevation, and either elevating or floodproofing all utility systems and associated equipment to protect them from damage. FEMA Mitigation Grant Programs FEMA administers three HMA grant programs that can be used to fund flood mitigation projects: the Hazard Mitigation Grant Program (HMGP), Pre-Disaster Mitigation (PDM), and Flood Mitigation Assistance (FMA). Eligible HMA applicants include states, territories, and federally recognized tribal governments. Local communities cannot apply directly to FEMA for HMA funding but instead must collaborate as sub-applicants with their state, territory, or tribal government and then receive funding through that entity. Certain nonprofit organizations can act as sub- applicants but only under HMGP. Generally, individuals may not apply for HMA funding, but they may benefit from a community application. Applicants to all three programs must have FEMA-approved hazard mitigation plans. FEMA evaluates HMA applications based on technical feasibility and cost-effectiveness, among other factors. In fiscal year 2019, HMA awarded $859 million in funding. Eligible activities differ for the three programs but must be consistent with FEMA’s National Mitigation Framework. The Hazard Mitigation Grant Program helps communities implement hazard mitigation measures following a presidential major disaster declaration to improve community resilience to future disasters. HMGP provides funding to protect public or private property through various mitigation measures based on state or tribal priorities. Mitigation project examples include acquisition, relocation, retrofitting structures to minimize damages from various natural hazards, and elevating flood prone structures. HMGP recipients (states, territories, and federally recognized tribal governments) are primarily responsible for prioritizing, selecting, and administering state and local hazard mitigation projects. According to FEMA guidance, although individuals may not apply directly to the state for assistance, local governments engage interested property owners during the application process. A formula based on the size of the presidential disaster declaration determines the amount of money available to HMGP. Pre-Disaster Mitigation seeks to reduce overall risk to the population and structures from future natural hazard events, while also reducing reliance on federal funding in future disasters. PDM grants fund mitigation plans and eligible projects that reduce or eliminate long-term risk to people and property from natural disasters, such as property acquisition, property elevation, earthquake hardening, and construction of tornado and high-wind safe rooms. Generally, local governments (i.e., sub-applicants) submit mitigation planning and project applications to their state, territory, or federally recognized tribal government (i.e., applicants) for review and prioritization. The state, territory, or federally recognized tribal government then submits one PDM grant application to FEMA for consideration. Annual Congressional appropriations fund these grants, and FEMA awards them on a nationally competitive basis. In fiscal year 2019, Congress appropriated $250 million to PDM, which was the program’s final year of funding. In 2018, Congress passed the Disaster Recovery Reform Act, which included amendments to PDM, which FEMA calls the Building Resilient Infrastructure and Communities program. According to FEMA officials, this program is replacing PDM in fiscal year 2020 and will be funded through the Disaster Relief Fund as a 6 percent set-aside from the estimated total amount of grants for each major disaster declaration. FEMA has solicited public input on the program and said it expects to release a notice of funding opportunity in summer 2020. Flood Mitigation Assistance is designed to reduce or eliminate flood insurance claims by funding cost-effective flood mitigation projects that reduce or eliminate long-term risk of flood damage to structures insured under NFIP. Typical projects may include acquisition of RL properties, elevation of buildings, and neighborhood-scale flood defense investment. Generally, local communities will sponsor applications on behalf of homeowners and then submit the applications to their state. A state or federally recognized tribal government must submit the grant applications to FEMA. Annual Congressional appropriations fund FMA grants, and FEMA awards them on a nationally competitive basis. FMA appropriations have remained relatively stable at about $175 million for fiscal years 2016 through 2019. Repetitive Loss Properties RL properties present a financial challenge for NFIP. FEMA has three definitions for such properties that vary slightly to meet the specific needs of different programs: NFIP Repetitive Loss refers to an NFIP-insured structure that has incurred flood-related damage on two occasions during a 10-year period, each resulting in at least a $1,000 claim payment. FEMA uses the NFIP RL definition for insurance purposes related to the Community Rating System, for local hazard mitigation plans, and for eligibility determinations for preferred risk policies and individual assistance. FMA Repetitive Loss refers to an NFIP-insured structure that (a) has incurred flood-related damage on two occasions in which the cost of repair, on average, equaled or exceeded 25 percent of the value of the structure at the time of each such flood event; and (b) at the time of the second incidence of flood-related damage, the flood insurance policy contained Increased Cost of Compliance coverage. FEMA uses this definition for FMA purposes, as these properties are eligible for the largest federal cost share for mitigation, up to 90 percent. This is also the same definition NFIP uses to approve an Increased Cost of Compliance payment. Severe Repetitive Loss refers to an NFIP-insured structure that has incurred flood-related damage for which (a) four or more separate claims have been paid that exceeded $5,000 each and cumulatively exceeded $20,000; or (b) at least two separate claim payments have been made under such coverage, with the cumulative amount of such claims exceeding the fair market value of the insured structure. FEMA has two severe RL definitions for mitigation and insurance, which are similar except that the insurance definition includes only residential structures, while the mitigation definition includes all structures. FEMA uses the severe RL definition for grant eligibility and cost share, the Community Rating System, and insurance rate setting. FEMA Grant Programs Are Key Funding Sources for Property Acquisition FEMA Funds Acquisitions through Three Grant Programs That Have Varying Characteristics and Funding Levels HMGP is the largest of FEMA’s three HMA programs and, unlike the others, it is based on the amount of disaster assistance a state or territory receives following a presidential disaster declaration (see table 1). PDM and FMA are smaller grant programs that receive annual appropriations and are not directly tied to an immediately preceding disaster. Because these programs do not require an immediate disaster declaration, FEMA considers them pre-disaster programs, as their intent is to mitigate potential damage before disasters occur. HMGP and PDM can be used for projects that mitigate the risk of many hazards, including flood, wind, fire, earthquake, and drought, but FMA can only be used to mitigate the risk of flood (see table 1). Furthermore, FMA funds can only be used to mitigate properties that are insured by NFIP, but HMGP and PDM funds can be used to mitigate properties without NFIP coverage. Properties mitigated in a special flood hazard area, where the structure remains on the parcel, must maintain a flood insurance policy after project completion. HMA grants fund a variety of methods to mitigate the flood risk of properties, including acquisition, elevation, relocation, and floodproofing. In most cases, HMA grants cover up to 75 percent of the project cost, and the grantee generally must contribute the remainder using nonfederal funds (although there are some exceptions, discussed below). However, PDM will cover up to 90 percent of project costs for communities that meet FEMA’s definition of small and impoverished. Moreover, FMA will cover up to 90 percent for projects that mitigate RL properties and up to 100 percent for severe RL properties. Funding levels for the three programs have varied over time because they have depended on disaster declarations and annual appropriations (see fig. 1). HMGP is the largest of the three programs—adjusted for inflation, annual HMGP grants have reached $2.9 billion, while PDM and FMA have never exceeded $300 million. According to FEMA officials, the estimated annual funding for the Building Resilient Infrastructure and Communities program, the successor to PDM, will average $300 million to $500 million, as it will be funded by a 6 percent set aside of annual estimated disaster grant expenditures. HMA funding also varies by state. Louisiana has obligated the most funding. After adjusting for inflation, it has obligated more than $3.1 billion from all three programs since HMGP was created in 1989, followed by California ($2.0 billion), Texas ($1.8 billion), New York ($1.6 billion), and Florida ($1.5 billion), while the bottom 18 states and territories each obligated less than $50 million (see fig. 2). Because HMGP is the largest program and is tied to presidential declarations, these totals reflect, in part, the extent to which states and territories have experienced natural disasters in this time period. States and Localities Can Use Other Federal Programs to Fund Cost Share Requirements for Acquisitions Typically, recipients of federal mitigation grants must use nonfederal funds to meet cost share requirements because federal law prohibits the use of more than one source of federal disaster recovery funding for the same purpose. However, according to FEMA, some federal programs are exempt from these requirements due to authorizing statutes and therefore may be used in concert with HMA funds. Department of Housing and Urban Development’s Community Development Block Grant (CDBG) program. The Department of Housing and Urban Development awards CDBG funds to state and local governments to support a variety of community and economic development needs. According to FEMA’s HMA Cost Sharing Guide, HMA applicants may use several categories of CDBG funds as a source of project cost share, as long as the project meets Department of Housing and Urban Development rules. CDBG Disaster Recovery funds are the most frequently used form of HMGP cost share from a federal agency, according to FEMA. FEMA Increased Cost of Compliance coverage. NFIP offers Increased Cost of Compliance coverage, which provides up to $30,000 for policyholders to fund mitigation efforts on their property if they experience substantial damage or if their structure is an RL property. Between 1997 and 2014, the vast majority (99 percent) of Increased Cost of Compliance claims met the substantially damaged property definition, according to a 2017 report from the University of Pennsylvania. Unlike CDBG, which is awarded to states and local governments, Increased Cost of Compliance is awarded directly to individuals. According to FEMA, it is eligible as an HMA nonfederal cost share because it is considered a direct contract between the insurer and policyholder. FEMA allows recipients to assign their funds to the community as part of a collective mitigation project, and the community is then obligated to provide HMA funding to any property owner who contributed Increased Cost of Compliance dollars toward the nonfederal cost share. As of September 2019, FEMA had closed more than 38,000 Increased Cost of Compliance claims with dates of loss since 1997, totaling more than $877 million. Small Business Administration disaster loans. Small Business Administration disaster loans provide up to $200,000 for repairing or replacing a primary residence and $40,000 for repairing or replacing personal items that have been affected by a disaster. The interest rate cannot exceed 4 percent for applicants unable to access credit elsewhere, and cannot exceed 8 percent for all others. Secondary or vacation homes are not eligible, but qualified rental properties may be eligible under the Small Business Administration’s business disaster loan program, which offers loans of up to $2 million. According to FEMA guidance, these loans can serve as a source of cost share if HMA grants are disbursed early enough; however, the differing award timelines often make these funding sources incompatible. Further, disaster loans may not be eligible in conjunction with HMA funds due to duplication of benefits, but general-purpose Small Business Administration loans are not subject to this restriction, according to FEMA. Other Federal and Nonfederal Programs Fund Acquisitions In addition to FEMA’s three HMA programs, other federal, state, and local programs have helped acquire properties. Community Development Block Grants. In addition to its use as a cost- share complement to HMA grants, states and communities can use CDBG Disaster Recovery funding as a stand-alone source of property acquisition funds, according to the Department of Housing and Urban Development. Availability of CDBG Disaster Recovery funds is subject to supplemental appropriations following a presidential disaster declaration and must be used in response to that specific disaster. CDBG Disaster Recovery funds are disbursed to state and local governments and not to individuals directly. However, the governmental recipient can award CDBG Disaster Recovery funds to private citizens, nonprofits, economic development organizations, businesses, and other state agencies. The Bipartisan Budget Act of 2018 appropriated funding for CDBG, of which the Department of Housing and Urban Development allocated almost $6.9 billion for CDBG mitigation funds for the first time, as a result of the 2015 to 2017 disasters. Unlike CDBG Disaster Recovery funds, which the recipient must use in response to a specific disaster, recipients may use CDBG Mitigation funds to mitigate risks from future disasters. U.S. Army Corps of Engineers’ National Nonstructural Committee. The Army Corps of Engineers (Corps) conducts a range of mitigation measures through the National Nonstructural Committee, including acquisitions, elevations, relocations, and floodplain mapping. Nonstructural refers to measures that attempt to mitigate the consequences of floods, as opposed to structural measures intended to prevent floods from occurring. According to the Corps, except for limited research funding, it does not offer grants for flood risk management projects, and large projects generally require specific authorization from Congress. However, the Corps’ Continuing Authority Program allows it to execute smaller projects at its discretion. For example, for one of the programs, the federal government funds 65 percent of a project’s cost, and the project sponsor must provide all land, easement, rights-of-way, relocations, and disposal areas required for the project. The sponsor’s cost share includes credit for provision of the requirements above and pre-approved work-in-kind, but at least five percent must be provided in cash. Department of Agriculture’s Natural Resources Conservation Service Emergency Watershed Protection Program. The Federal Agriculture Improvement and Reform Act of 1996 enables the Emergency Watershed Protection Program to purchase floodplain easements on residential and agricultural land for flood mitigation purposes and to return the land to its natural state. For agricultural and residential land, this program pays up to the entire easement value and also funds property demolition or relocation, according to the Department of Agriculture. Land generally must have flooded in the past year or twice within the previous 10 years to be considered eligible. State and local acquisition programs. While state and local governments are active participants in federal acquisition projects, some have also developed their own acquisition programs. These programs vary on the extent to which they rely on federal funds, if at all. For example: The Harris County Flood Control District, a special purpose district, in Texas acquired about 3,100 properties between 1985 and 2017, according to a 2018 report from Rice University, using a combination of FEMA grants, Corps funds, and local dollars. Charlotte-Mecklenburg Storm Water Services, a joint city-county utility in North Carolina, has acquired more than 400 homes since 1999. Initially, it primarily used federal funds, but now it uses almost solely stormwater fees and other local revenue to fund acquisitions. The utility’s Quick Buys program allows it to acquire properties soon after a flood, before homeowners invest in repairs, whereas federal acquisitions often occur after property owners have begun rebuilding, according to FEMA officials. New Jersey, through its Blue Acres program, plans to acquire up to 1,300 properties damaged by Superstorm Sandy. The program has used state funds, including $36 million in bonds, as well as more than $300 million in federal funding received from multiple agencies. FEMA Has Funded the Mitigation of Many Properties, but the Number of Repetitive Loss Properties Continues to Rise Most Flood Mitigation Spending Is Used for Property Acquisitions after Flooding Occurs Since 1989, the primary means by which FEMA has mitigated flood risk at the property level has been by funding property acquisitions. Acquisitions accounted for about 75 percent of FEMA’s $5.4 billion in flood mitigation spending, adjusted for inflation, from 1989 to 2018 (see fig. 3). Most of the remaining spending was used to elevate properties, with smaller amounts used to floodproof and relocate properties. The average federal cost-per-property was $136,000 for acquisitions and $107,000 for elevations, according to 2008-2014 FEMA data. As seen in figure 4, FEMA-funded property acquisitions have fluctuated over time but have generally increased since FEMA’s HMA programs began. For example, from 1989 through 1992—the first four years of HMGP funding and prior to the creation of PDM and FMA—less than $8 million, adjusted for inflation, was obligated for property acquisitions each year, resulting in fewer than 200 acquisitions each year (see fig. 4). The highest acquisition funding generally was associated with years that had significant flood events, such as Superstorm Sandy (2012) and Hurricanes Harvey, Irma, and Maria (2017). From fiscal years 1989-2018, approximately $3.3 billion of property acquisition funding, adjusted for inflation, occurred through HMGP, resulting in the acquisition of 41,458 properties (see fig. 5). HMGP represented about 90 percent of all property acquisitions and 82 percent of all acquisition funding, with PDM and FMA representing the remainder. As a result, most FEMA-funded acquisitions occurred following flood events. Most of the funding, adjusted for inflation, for HMGP’s and PDM’s flood mitigation projects has been for property acquisition (83 percent and 89 percent of total funds, respectively), while most FMA funding has been for elevation (49 percent). Despite Acquisition and Other Mitigation, Nonmitigated Repetitive Loss Properties Have Increased in Number Although FEMA mitigated more than 57,000 properties for flood risk from 1989 to 2018, including more than 46,000 through acquisition, the number of nonmitigated RL properties increased from 2009 to 2018. Figure 6 shows that this growth in the number of RL properties has outpaced efforts to mitigate their flood risk. From 2009 through 2018, FEMA’s inventory of new RL properties grew by 64,101. During this period, FEMA mitigated 4,436 RL properties through its three HMA programs, and an additional 15,047 were mitigated through other federal or state programs. As a result, the number of nonmitigated RL properties increased by 44,618—more than double the number of RL properties that were mitigated in that time period. Some States Have Mitigated More Properties than Others Relative to Their Population of Repetitive Loss Properties States varied in the extent to which they mitigated high-risk properties, including RL properties, between 1989 and 2018. While FEMA does not require a property to be an RL property to receive flood mitigation funding, the number of properties mitigated by a state relative to its population of RL properties provides context to its flood mitigation progress. For example, some states with large numbers of RL properties, such as Texas, Louisiana, Florida, and New York, mitigated few properties relative to their numbers of RL properties (see table 2). Other states, such as Missouri and North Carolina, have far fewer RL properties but have mitigated more properties relative to their numbers of RL properties. States also varied in their methods for flood mitigation (see table 2). For example, while property acquisition accounted for 81 percent of mitigated properties nationwide, it represented closer to half of mitigated properties in Virginia, New Jersey, and Florida and only 19 percent in Louisiana. According to some FEMA and local officials, high property values in some regions can make acquisitions cost prohibitive and other mitigation methods such as elevation more attractive because they do not incur the cost of purchasing the land. Many other factors could affect mitigation, including homeowners’ preferences. Further, the voluntary nature of FEMA’s HMA programs may limit states’ ability to acquire properties with known flood risk. According to FEMA, acquisition permanently addresses flood risk because, unlike elevation or floodproofing, it moves individuals and structures away from flood risk rather than mitigating a structure in place. In a subsequent report, we plan to explore in more detail the factors, including homeowner demand for acquisition, that have affected the extent to which states have used acquisition to mitigate flood risk. While Property Acquisitions Help Reduce Flood Risk for Properties, Insufficient Premium Revenue Perpetuates Fiscal Exposure NFIP represents a fiscal exposure to the federal government because its premium rates have not kept pace with the flood risk of the properties it insures. Addressing this imbalance would mean reducing the flood risk of the insured properties, increasing premium revenue, or some combination of both. Despite FEMA’s efforts to mitigate its insured properties’ flood risk, premium rates for many properties do not reflect the full estimated risk of loss. As we have reported previously, mitigation alone will not be sufficient to resolve NFIP’s financial challenges; structural reforms to the program’s premium rates will also be necessary. Recent Catastrophic Flood Events and Projections Indicate Potential Increases in Flood Risk NFIP’s total annual flood claim payments have grown in recent years, potentially indicating an increase in flood risk. For example, the eight years of the highest annual NFIP claims have all occurred since 2004, with particularly catastrophic flood events accounting for much of these claims: In 2005, claims reached $17.8 billion ($23.3 billion, adjusted for inflation), largely due to Hurricanes Katrina, Rita, and Wilma. In 2012, claims reached $9.6 billion ($10.7 billion, adjusted for inflation), largely due to Superstorm Sandy. In 2017, claims reached $10.5 billion ($11.0 billion, adjusted for inflation), largely due to Hurricanes Harvey, Irma, and Maria. These severe weather events appear to be contributing to the long-term increases in claims paid by NFIP, as would be expected with infrequent but severe events. As seen in figure 7, the amount of claims paid per policy, adjusted for inflation, does not show a steady increase in claims but rather substantial spikes in certain years associated with catastrophic flooding events. RL properties have contributed heavily to NFIP’s claims and, as noted earlier, the number of RL properties continues to rise despite FEMA’s mitigation efforts. Of the $69.7 billion in claims NFIP paid out from 1978 to 2019, $22.2 billion was for flood damage sustained by RL properties (32 percent). The frequency and intensity of extreme weather events, such as floods, are expected to increase in coming years due to climate change, according to the U.S. Global Change Research Program and the National Academies of Sciences. Further, numerous studies have concluded that climate change poses risks to many environmental and economic systems and a significant financial risk to the federal government. For example, according to the November 2018 National Climate Assessment report, the continued increase in the frequency and extent of high-tide flooding due to sea level rise threatens America’s trillion-dollar coastal property market. According to the National Oceanic and Atmospheric Administration, minor flood events (sometimes referred to as nuisance flooding) also are projected to become more frequent and widespread due to climate change. Several Categories of Premium Rates Do Not Fully Reflect Flood Risk While it is uncertain the exact extent to which flood risk has changed and will continue to change, NFIP’s fiscal exposure will persist as long as premium rates do not keep pace with flood risk. As we have been reporting since 1983, NFIP’s premium rates do not reflect the full risk of loss because of various legislative requirements and FEMA practices. To set premium rates, FEMA considers several factors, including location in flood zones, elevation of the property relative to the community’s base flood elevation, and characteristics of the property, such as building type, number of floors, presence of a basement, and year built relative to the year of the community’s original flood map. Most NFIP policies have premium rates that are deemed by FEMA to be full-risk rates, which FEMA defines as sufficient to pay anticipated losses and expenses. However, FEMA’s overall rate structure may not reflect the full long-term estimated risk of flooding, as discussed below. Subsidized rates. NFIP offers some policyholders subsidized rates—that is, rates that intentionally do not reflect the full risk of flooding. These premium rates are intended to encourage the widespread purchase of flood insurance by property owners and encourage floodplain management by communities. Subsidized rates generally are offered to properties in high-risk locations (special flood hazard areas) that were built before flood maps were created. FEMA staff said they have begun increasing rates for certain subsidized properties as prescribed under the Biggert-Waters Flood Insurance Reform Act of 2012 and the Homeowner Flood Insurance Affordability Act of 2014. In addition, the percentage of subsidized policies is decreasing. According to FEMA data, the percentage of NFIP policies receiving subsidized rates dropped from about 22 percent in July 2013 to about 17 percent in June 2019. In 2013, we recommended that FEMA obtain elevation information to determine full-risk rates for subsidized properties. As of January 2020, FEMA had not fully implemented this recommendation but was in the process of doing so. For example, FEMA had requested proposals from third-party vendors for obtaining the elevation information and was reviewing these proposals. This information remains necessary for FEMA to determine the adequacy of its premium rates and the costs of any subsidization. It will also allow Congress and the public to understand the amount of unfunded subsidization within the program and the federal fiscal exposure it creates. Grandfathered rates. FEMA allows some property owners whose properties are remapped into higher-risk flood zones to continue to pay the premium rate from the lower-risk zone. FEMA data show that about 9 percent of NFIP policies were receiving a grandfathered rate as of June 2019. In 2008, we recommended that FEMA collect data to analyze the effect of grandfathered policies on NFIP’s fiscal exposure. As of February 2020, FEMA officials said they had not fully implemented this recommendation but were in the process of doing so. The officials told us they had finished collecting data on grandfathered policies and that they planned to analyze it as they completed efforts to update their premium rate setting approach. Collection and analysis of data on grandfathered policies will help FEMA understand and communicate the extent to which these policies are contributing to NFIP’s fiscal exposure. Rates designated full-risk. As we reported in 2008 and 2016, it is unclear whether premiums FEMA considers to be full-risk actually reflect the full long-term estimated risk of loss. For example, NFIP full-risk premium rates do not fully reflect the risk of catastrophic losses or the expenses associated with managing them. Private insurers typically manage catastrophic risk using capital, reinsurance, and other instruments, such as catastrophe bonds, and include the associated expenses in premium rates. By contrast, FEMA has traditionally managed catastrophic risk by relying on its authority to borrow from Treasury. In January 2017, FEMA began purchasing reinsurance to transfer some of its flood risk exposure to the private reinsurance market. However, FEMA has not accounted for these expenses in setting its NFIP premium rates. Reinsurance could be beneficial because it would allow FEMA to recognize some of its flood risk and the associated costs up front through the premiums it must pay to the reinsurers rather than after the fact in borrowing from Treasury. However, because reinsurers must charge FEMA premiums to compensate for the risk they assume, reinsurance’s primary benefit would be to manage risk rather than to reduce NFIP’s expected long-term fiscal exposure. Insufficient Premium Revenue Contributes to NFIP’s Fiscal Exposure Congress has directed FEMA to provide discounted premium rates to promote affordability for policyholders but did not provide FEMA with dedicated funds to pay for these subsidies. As a result, premium revenue has been insufficient to pay claims in some years, requiring borrowing from Treasury to make up for the shortfall. While Congress passed reforms to NFIP in 1994 and 2004, neither set of actions sufficiently addressed program revenue. In 2005, Hurricanes Katrina, Rita, and Wilma hit the Gulf Coast and resulted in NFIP borrowing nearly $17 billion from Treasury to pay claims (see fig. 8). In July 2012, Congress passed the Biggert-Waters Flood Insurance Reform Act, which contained significant reforms to NFIP’s premium rates. But a few months later, Superstorm Sandy occurred, pushing NFIP’s debt to $24 billion. Following policyholders’ concerns about the rate increases authorized by the 2012 act, Congress slowed the pace of many of these rate increases in 2014 with the Homeowner Flood Insurance Affordability Act. In the fall of 2017, Hurricanes Harvey, Irma, and Maria occurred, prompting additional borrowing from Treasury and causing NFIP to reach its borrowing limit. In response, Congress canceled $16 billion of NFIP’s debt in October 2017, which allowed NFIP to pay claims from these storms. Since September 2017, NFIP has been operating under a series of short-term authorizations, the most recent of which expires in September 2020. As of March 2020, NFIP’s debt remained at $20.5 billion. To improve NFIP’s solvency and enhance the nation’s resilience to flood risk, we suggested in 2017 that Congress could make comprehensive reforms that include actions in six areas. We reported that it was unlikely that FEMA would be able to repay its debt and that addressing it would require Congress to either appropriate funds or eliminate the requirement that FEMA repay the accumulated debt. However, eliminating the debt without addressing the underlying cause of the debt—insufficient premium rates—would leave the federal taxpayer exposed to a program requiring repeated borrowing. To address NFIP’s fiscal exposure, there are two general approaches: decrease costs or increase revenue. Decreasing costs to the program in the form of claims involves mitigating insured properties’ flood risks. Mitigation can be very costly, but there will be some properties for which the cost to mitigate will be outweighed by the benefit of reduced flood risk and, ultimately, fiscal exposure. Mitigation may be a cost-effective option for those properties for which full-risk rates would be cost-prohibitive. Increasing revenue would require reforms to NFIP’s premium rates. FEMA has begun increasing rates on subsidized properties. But, as we suggested in 2017, Congress could remove existing legislative barriers to FEMA’s premium rate revisions. Members of Congress and others have raised concerns about such reforms because raising premium rates may make coverage unaffordable for some policyholders. To address these concerns, we suggested that all policies include full-risk premium rates, with targeted, means-based, appropriated subsidies for some policies. This would improve the program’s solvency while also addressing affordability concerns. Assigning full-risk premium rates to all policies would remove subsidies from those who do not need them, helping improve solvency. It would also more accurately signal the true flood risk to property owners and enhance resilience by incentivizing mitigation measures, such as acquisition. Means-based subsidies would ensure that property owners who needed help would get it, and an explicit appropriation for the subsidies would make their true cost transparent to taxpayers. We maintain that a comprehensive approach that includes mitigation and rate reform is needed to address NFIP’s fiscal exposure. Concluding Observations Because several categories of NFIP premium rates do not reflect the full risk of flood loss, FEMA has had to borrow $36.5 billion from Treasury to pay claims from several catastrophic flood events since 2005. To address this, some have suggested additional funding to mitigate RL properties. While we acknowledge that mitigation is part of the solution, we maintain that a more comprehensive approach is necessary to address the program’s fiscal exposure. We have made two recommendations to FEMA that, if implemented, could help inform Congress’ efforts to reform NFIP. In 2008, we recommended that FEMA collect information on grandfathered properties and analyze their financial effect on NFIP, and in 2013, we recommended that FEMA obtain elevation information on subsidized properties. By implementing these recommendations, FEMA would better understand NFIP’s fiscal exposure and be able to communicate this information to Congress. Further, we suggested in 2017 that Congress take a comprehensive approach to reforming NFIP. One important first step would be to implement full-risk premium rates for all policies, with appropriated means-based subsidies for some policies. Full-risk premium rates would remove subsidies from those who do not need them, helping improve solvency, and also more accurately signal the true flood risk to property owners and incentivize efforts to mitigate flood risk. Further, means- based subsidies would ensure that property owners who need help will get it, and having Congress explicitly appropriate for the subsidies would make the true cost of the subsidy transparent to taxpayers. While this would be an important step to putting NFIP on a sustainable path, comprehensive reform of the program should also address the other issues we have identified, including mitigating the flood risk of insured properties. Agency Comments We provided a draft of this report to the Department of Homeland Security for its review and comment. The agency provided technical comments, which we incorporated as appropriate. We are sending copies of this report to the appropriate congressional committees, the Acting Secretary of Homeland Security, and other interested parties. In addition, the report is available at no charge on the GAO website at https://www.gao.gov. If you or your staff have any questions about this report, please contact me at (202) 512-8678 or cackleya@gao.gov. Contact points for our Offices of Congressional Relations and Public Affairs may be found on the last page of this report. GAO staff who made key contributions to this report are listed in appendix III. Appendix I: Objectives, Scope, and Methodology This report addresses the Federal Emergency Management Agency’s (FEMA) National Flood Insurance Program (NFIP). Our objectives were to examine (1) funding programs available for property acquisitions, (2) FEMA’s flood mitigation efforts, and (3) factors contributing to NFIP’s fiscal exposure. To describe funding programs available for property acquisitions, we reviewed authorizing legislation, the Code of Federal Regulations, and FEMA guidance and manuals, including the Hazard Mitigation Assistance Guidance and Cost Share Guide, to identify program characteristics, eligibility requirements, and application guidelines. To identify funding for these programs, we analyzed FEMA’s project-level Hazard Mitigation Assistance (HMA) data from its Enterprise Applications Development Integration and Sustainment system, which FEMA uses to track mitigation projects funded through its HMA grant programs. To summarize Increased Cost of Compliance coverage, which NFIP policyholders can use to fund mitigation efforts, we analyzed FEMA’s NFIP claims database to identify the number and amount of such claims. We also interviewed the FEMA officials responsible for administering these grant programs. Further, we identified other federal agency programs that can fund property acquisitions or meet cost share requirements and reviewed their authorizing legislation and their relevant federal regulations. Finally, to identify examples of state and local programs that have been used to fund property acquisitions, we reviewed academic reports, including from the University of North Carolina and Rice University. To review FEMA’s flood mitigation efforts, we analyzed FEMA’s project- level HMA data from the “Mitigation Universe” of its Enterprise Applications Development Integration and Sustainment system. We analyzed several variables in this dataset, including number of properties, federal share obligated, mitigation type category, grant program area, grant program fiscal year, and state. For the analyses by mitigation type category, we excluded projects (79 percent of the total records) that did not include a flood mitigation activity (those with values of “Other” or “Pure Retrofit”). Of the remaining records, 98 percent were “Pure,” meaning all properties within each project were of a single mitigation method type (acquisition, elevation, floodproof, or relocation). The remaining 2 percent were “Mixed,” indicating a project contained at least one acquisition and at least one elevation but could also contain other mitigation methods. For analyses by grant program area, we treated projects funded through the Severe Repetitive Loss and Repetitive Flood Claims grant programs as being part of the Flood Mitigation Assistance program and projects funded through the Legislative Pre-Disaster Mitigation program as being part of the Pre- Disaster Mitigation program. For data on the number of flood mitigated properties, we used the final number of properties mitigated by a project. For data on funding, we used the federal share of the project’s obligated funding. To analyze mitigated and nonmitigated repetitive loss (RL) properties, we summarized FEMA’s RL property mitigation report, which tracked the cumulative number of RL properties by year from June 2009 through June 2018. To describe the number of RL properties by state, we analyzed FEMA’s list of RL properties as of August 31, 2019, which included every property that at any point FEMA had designated as an RL property under any of its three definitions. The list included properties that had since been mitigated, as well as those that are no longer insured by NFIP. To examine factors contributing to NFIP’s fiscal exposure, we analyzed FEMA’s claims dataset as of September 30, 2019. This dataset includes the more than 2 million claims paid to NFIP policyholders since the beginning of the program. We excluded records whose status was “open” or “closed without payment.” Further, we excluded records whose year of loss was before 1978 because FEMA officials told us that that was the first year they considered their claims data to be reliable and complete. To identify factors that contribute to NFIP’s fiscal exposure and illustrate how this fiscal exposure has materialized and changed over time, we reviewed several of our previous reports and the Department of the Treasury’s statements of public debt. Finally, to summarize how flood risk could change in the future, we reviewed our previous reports on climate change. In general, we adjusted for inflation any dollar figures that we compared or aggregated across multiple years and indicated this accordingly. To do this, we used the Bureau of Labor Statistics’ Consumer Price Index for All Urban Consumers. To assess the reliability of all of the datasets we analyzed for this report, we requested and reviewed preliminary versions of the data and accompanying data dictionaries. We used the data dictionary to identify potential variables for use in our analyses and output statistics on these variables (e.g., frequencies of values, number of blanks or zero values, minimum, maximum, and mean) to identify any potential reliability concerns such as outliers or missing values. We met with relevant FEMA officials to discuss each of the data sets to understand how FEMA collected, used, and maintained the data; the reliability and completeness of key variables; reasons for any potential discrepancies we identified; and whether our understanding of the data and approach to analyzing them were accurate and reasonable. After these meetings, we requested updated versions of the data and updated our analyses accordingly. We determined that all data elements we assessed were sufficiently appropriate and reliable for this report’s objectives. We conducted this performance audit from January 2019 to June 2020 in accordance with generally accepted government auditing standards. Those standards require that we plan and perform the audit to obtain sufficient, appropriate evidence to provide a reasonable basis for our findings and conclusions based on our audit objectives. We believe that the evidence obtained provides a reasonable basis for our findings and conclusions based on our audit objectives. Appendix II: Significant Events and GAO Reports Related to the National Flood Insurance Program’s Fiscal Exposure January 1983: We recommended that FEMA improve its rate-setting process to ensure adequate income for NFIP and suggested that Congress either limit FEMA’s borrowing for extraordinary losses or establish an emergency fund for such losses, and pay for NFIP subsidies with appropriations. March 1994: We found that NFIP’s premium income was insufficient to meet expected future losses because of subsidized rates and suggested that Congress consider how any changes in premium rates would affect policyholder participation. September 1994: National Flood Insurance Reform Act. Developed a mitigation assistance program and expanded the mandatory purchase requirement. June 2004: Flood Insurance Reform Act. Authorized grant programs to mitigate properties that experienced repetitive flooding losses. August-October 2005: Hurricanes Katrina, Rita, Wilma. Caused $17.1 billion in NFIP claims. FEMA debt to Treasury increased to $16.9 billion in fiscal year 2006. March 2006: We added NFIP to our high-risk list. October 2008: We recommended that FEMA collect data to analyze the effect of grandfathered policies on NFIP’s fiscal exposure. November 2008: We identified three options for addressing the financial impact of subsidies: increasing mitigation efforts; eliminating or reducing subsidies; and targeting subsidies based on need. June 2011: We suggested that Congress allow NFIP to charge full- risk premium rates to all property owners and provide assistance to some categories of owners to pay those premiums. July 2012: Biggert-Waters Flood Insurance Reform Act. Required FEMA to increase rates for certain subsidized properties and grandfathered properties; create a NFIP reserve fund; and improve flood risk mapping. October 2012: Superstorm Sandy. Caused $8.8 billion in NFIP claims. FEMA debt to Treasury increased to $24 billion in fiscal year 2013. February 2013: We added limiting the federal government’s fiscal exposure by better managing climate change risks to our high-risk list. July 2013: We recommended that FEMA obtain elevation information to determine full-risk rates for subsidized policyholders. March 2014: Homeowner Flood Insurance Affordability Act. Reinstated certain rate subsidies removed by the Biggert-Waters Flood Insurance Reform Act of 2012; established a new subsidy for properties that are newly mapped into higher-risk zones; restored grandfathered rates; and created a premium surcharge that would be deposited into the NFIP reserve fund. October 2014: We recommended that FEMA amend NFIP minimum standards for floodplain management to encourage forward-looking construction and rebuilding efforts that reduce long-term risk and federal exposure to losses. July 2015: We recommended that the Mitigation Framework Leadership Group establish an investment strategy to identify, prioritize, and guide federal investments in disaster resilience and hazard mitigation-related activities. August-October 2016: Hurricane Matthew and Louisiana floods. Caused $3.1 billion in NFIP claims. FEMA debt to Treasury debt increased to $24.6 billion in early fiscal year 2017. April 2017: We suggested that Congress make comprehensive reforms to NFIP that include actions in six areas: (1) addressing the debt; (2) removing legislative barriers to full-risk premium rates; (3) addressing affordability; (4) increasing consumer participation; (5) removing barriers to private-sector involvement; and (6) protecting NFIP flood resilience efforts. August-September 2017: Hurricanes Harvey, Irma, and Maria. Caused $10 billion in NFIP claims. FEMA reached the limit of its Treasury borrowing authority of $30.4 billion. September 2017: NFIP’s last long-term authorization ended, resulting in a string of short-term reauthorizations. October 2017: Congress canceled $16 billion of NFIP’s debt to enable FEMA to continue paying flood claims. This reduced FEMA’s debt to Treasury to $20.5 billion. March 2020: FEMA’s debt to Treasury remained at $20.5 billion. September 2020: NFIP’s current short-term authorization ends. Appendix III: GAO Contact and Staff Acknowledgments GAO Contact Staff Acknowledgments In addition to the contact named above, Patrick Ward (Assistant Director), Christopher Forys (Analyst in Charge), Emily Bond, Christina Cantor, William Chatlos, Eli Dile, Lijia Guo, Holly Halifax, Laura Ann Holland, Yann Panassie, Stephen Ruszczyk, Jessica Sandler, Joseph Silvestri, Jena Sinkfield, and Kelsey Wilson made key contributions to this report.


Background Payment for Hospital Services under Medicare Under traditional Medicare, hospitals are paid for the inpatient and outpatient services they provide under two distinct payment systems. Inpatient stays, including services incurred after being admitted to the hospital, are paid under the IPPS. Under this system, Medicare pays hospitals a flat fee per beneficiary stay, set in advance, with different amounts generally based on the beneficiary’s condition. Payment rates are also influenced by hospital-specific factors such as the relative hourly wage in the area where the hospital is located, and whether the hospital qualifies for other case- or hospital-specific additional payments. Outpatient services, including services obtained through the emergency department or other services incurred without being admitted to the hospital, are paid under the outpatient prospective payment system. Under this system, Medicare pays hospitals a flat fee per service, set in advance, with different amounts for each type of service. As with the IPPS, payment rates are adjusted for geographic factors. Congress has established payment adjustments for certain hospitals under the IPPS by changing the qualifying criteria for IPPS payment categories, creating and extending exceptions to IPPS rules, or exempting certain types of hospitals from the IPPS. These adjustments may help ensure beneficiary access to care or to help hospitals recruit and retain physicians and other medical professionals. MDH Designation Eligibility Criteria Created through the Omnibus Budget Reconciliation Act of 1989, the MDH designation is an example of how Congress can enhance payments to certain hospitals. To qualify as an MDH, a hospital must demonstrate that it is: Medicare-dependent, defined as having at least 60 percent of their inpatient days or discharges attributable to Medicare beneficiaries; small, defined as having 100 or fewer beds; and rural, defined as being located in a rural area, though hospitals can also be eligible if they are located in a state without any rural areas. CMS regulations provide that hospitals can meet the requirement of demonstrating a 60 percent Medicare share of days or discharges using two of the three most recently settled cost reports, or using cost reports from 1987 or 1988. We refer to hospitals that meet this criterion using 1987 or 1988 cost report data as “legacy MDHs.” MDH Designation Payment Criteria and Payment Methodology Some, but not all, MDHs are eligible to receive additional payment each year if they meet the payment criterion. Specifically, MDHs are assigned a payment rate—known as the hospital-specific rate (HSR)—based on their historic reported inpatient operating costs, trended forward to adjust for inflation and other factors, from one of three years (1982, 1987, or 2002). If the payment based on the HSR is higher than what the MDH would have otherwise received under IPPS, the MDH receives an additional payment. In this case, the MDH additional payment is calculated as 75 percent of the difference between the HSR and the IPPS amount. If the IPPS amount were higher than the HSR, the MDH would receive no additional payment. (See fig. 1.) Hospitals with an MDH designation are also eligible to receive other benefits. For example, MDHs are eligible for a separate additional payment if the hospital experiences at least a 5 percent decline in inpatient volume due to circumstances beyond its control. The MDH program does not provide for additional payments for outpatient services. The MDH Program Differs from Other Medicare Rural Hospital Payment Designations in Terms of Eligibility Criteria, Financial Benefit, Legislative Permanence, and Relative Size In addition to the MDH designation, four other rural hospital designations exist: (1) critical access hospitals (CAH), (2) sole community hospitals (SCH), (3) low-volume adjustment hospitals (LVA), and (4) rural referral centers (RRC). Our review of CMS documentation shows that the MDH payment designation differs from the other rural payment designations in terms of eligibility criteria, financial benefit, extent of legislative permanence, and size—that is, the number of hospitals receiving the designation. (For detailed information on the five rural payment designations, see app. II.) Eligibility Criteria. The MDH designation differs from the other designations in terms of eligibility criteria. As noted earlier, MDHs must have at least 60 percent of their inpatient days or discharges attributed to Medicare patients, must be small and, with few exceptions, rural. In contrast, both the SCH and CAH designations require hospitals to be remote rural hospitals (i.e., located a specified distance from the nearest hospital). Similarly, LVAs are generally required to be more than 15 miles from the nearest hospital. Rural hospital designations also differ in terms of eligibility criteria related to bed size. CAH-designated hospitals are required to have 25 inpatient beds or fewer, while MDHs must have 100 beds or fewer. RRCs must have at least 275 beds or meet other criteria, such as serving a high proportion of remote patients, among other things. Financial Benefit. The MDH designation has a relatively small financial benefit compared to most of the other rural hospital designations, and the benefit only applies to costs associated with inpatient services. MDHs generally can only receive 75 percent of the difference between payment based on their HSR and the payment they would have otherwise received based on the IPPS rate as an additional payment added to their IPPS rate payment. In contrast, the SCH and CAH designations have both inpatient and outpatient payment benefits. Hospitals with an SCH designation can receive an additional payment added to their IPPS rate payment equal to 100 percent of the difference between payment based on the HSR and what the hospital would otherwise receive as payment based on the IPPS rate, as well as a 7.1 percent addition to their outpatient payments. The CAH designation results in the highest financial benefit by generally providing 101 percent of the hospital’s reported costs in the current year for both inpatient and outpatient Medicare services. LVAs generally can receive up to 25 percent in additional payments, and while RRCs receive no direct financial benefit, they are exempt from certain requirements related to geographic reclassification (as are SCHs). Legislative Permanence. Unlike all but one other rural payment designation, the MDH program is a temporary program and must be extended periodically by Congress in order to continue. Historically, the extension by Congress has sometimes occurred after the program has expired and as a result there were temporary lapses in payments to MDH designated hospitals. The Bipartisan Budget Act of 2018 included a provision to extend the MDH program through fiscal year 2022. The only other designation that must be extended is the LVA designation. In 2010, the Patient Protection and Affordable Care Act temporarily expanded the LVA designation eligibility criteria to include hospitals with a higher volume of discharges and located closer to other hospitals than in previous years. These expanded eligibility criteria have been amended and extended through fiscal year 2022. If Congress does not extend the expanded eligibility criteria beyond fiscal year 2022, the LVA designation will return to the narrower eligibility criteria that were in place prior to the Patient Protection and Affordable Care Act. Relative size and overlap. Of the 2,204 rural hospitals in fiscal year 2017, a relatively small share of these hospitals were MDHs. (See fig. 2.) In total, 138 hospitals, or 6.3 percent of those rural hospitals with at least one designation, were MDHs. In contrast, CAHs comprised the largest proportion of rural hospitals with a designation. In fiscal year 2017, 1,246 rural hospitals—or 56.5 percent of those rural hospitals with at least one designation—were CAHs. Of the five designations, three—CAHs, MDHs, and SCHs—are exclusive to each other, meaning a hospital can only have one of the three designations at any time. Hospitals designated as MDHs and SCHs may also be designated as LVAs, RRCs, or both. Approximately 75 percent of MDHs and 81 percent of SCHs had at least one concurrent designation in fiscal year 2017; in contrast, none of the CAHs received a secondary designation because CAHs are not eligible to receive other designations. Those MDHs with a concurrent designation consisted of 88 that had an LVA designation, 14 that had an RRC designation, and 2 that had both an LVA and RRC designation. (For detailed information on the 5 rural payment designations including LVA and RRC eligibility and financial benefit, see app. II.) The Number of MDHs Declined over Time, As Did the Inpatient Share of Medicare Revenue and Profit Margins From fiscal years 2011 through 2017, the number of MDHs declined, as well as the number of MDHs that received an additional payment under the program. In addition, during this period MDHs varied on other operational and financial metrics, including the share of Medicare revenue coming from inpatient care, various measures of Medicare dependence, and profit margins. From Fiscal Years 2011 through 2017, the Number of MDHs Declined by 28 Percent, and the Number of MDHs Receiving Additional Payments Decreased by 15 Percent Our analysis of CMS data shows that the number of MDHs declined from 193 to 138—a 28 percent decrease over the 7-year period from fiscal year 2011 through fiscal year 2017. (See fig. 3.) This decline can be due to a number of factors, including hospital closures, mergers, or changes in designation. For example, we previously reported that 16 MDHs closed between 2013 and 2017. Moreover, our review of Medicare Administrative Contractor documentation found that some MDHs became ineligible for the program due to no longer meeting eligibility criteria. In addition, the number of MDHs that received an additional annual payment also declined, from 92 MDHs in fiscal year 2011 to 78 MDHs in fiscal year 2017—a 15 percent decrease. Among MDHs that received an additional payment, the amount received and the share of the hospital’s total revenue this payment represented varied widely across the years, though the average amount generally increased over time. (See table 1.) For example, in fiscal year 2017, one hospital received around $1,000 in additional payment while another received almost $10.5 million. While the trend was not uniform among all MDHs, the median additional payment increased from about $695,000 in fiscal year 2011 to about $812,000 in fiscal year 2017. Our analysis of CMS data also shows that the average additional payment MDHs received ranged from less than 0.1 percent up to 8.7 percent of total facility revenue, with a fairly consistent average of 1.2 to 1.6 percent. (See table 2.) This underscores that the additional payment under the MDH program can be small relative to the overall revenue that the hospital receives. MDHs Varied over Time on Select Operational and Financial Metrics Our analysis of CMS data also shows that from fiscal years 2011 through 2017, MDHs varied on selected operational and financial metrics: the mix of Medicare revenue that came from inpatient versus outpatient care, various measures of Medicare dependence, and profit margins. Inpatient/Outpatient Mix On average, MDHs experienced a decline in the share of Medicare revenue that came from inpatient services. (See fig. 4.) In fiscal year 2011, around 66 percent of MDH Medicare revenue came from inpatient services compared to 58 percent in fiscal year 2017—a 13 percent decrease. This trend was slightly greater than that for all rural hospitals (an 11 percent decrease) and all hospitals (a 10 percent decrease). Measures of Medicare Dependence The trends across three measures of Medicare dependence varied for MDHs over time. Looking at the Medicare share of total revenue for MDHs, we found this share decreased when comparing fiscal years 2011 and 2017, from 25 to 22 percent. (See fig. 5.) In contrast, in terms of the number of inpatient days and discharges attributable to Medicare beneficiaries, we found these measures both increased slightly over time. Specifically, the median share of MDH inpatient days attributable to Medicare beneficiaries increased, although by less than a percentage point, and the median Medicare share of inpatient discharges increased by about 2 percentage points, when comparing fiscal years 2011 and 2017. (See figures 6 and 7.) To obtain additional context on the relationship between MDH eligibility criteria and the various measures of Medicare dependence, we ran regression models to identify the extent to which hospitals’ bed size and rural status were associated with the Medicare share of days, discharges, and total care revenue for all hospitals from fiscal years 2011 through 2017. We found that rural hospitals with fewer beds were associated with higher Medicare shares of inpatient days and discharges, holding all other factors constant. This indicates that by targeting smaller, rural hospitals in its eligibility criteria, the MDH program is targeting hospitals that are Medicare-dependent defined in terms of inpatient volume. At the same time, rural hospitals with fewer beds generally received a smaller share of their total care revenue from Medicare compared with other hospitals. This suggests that hospitals associated with high Medicare inpatient volume may not have relatively high shares of total care revenue coming from Medicare. For more technical detail on our regression analyses and findings, see appendix III. Profit Margins Our analysis of self-reported data from hospitals shows that Medicare profit margins and total facility profit margins declined for MDHs from fiscal year 2011 through 2017. (See table 3.) The degree to which Medicare margins declined for MDHs during this time period (6 percentage points) was greater than the degree to which they declined for rural hospitals (4 percentage points) and all hospitals (3 percentage points). The self-reported data show that unlike rural and all hospitals, MDHs were not profitable in 2017—meaning that the revenue they received from Medicare and other payers was less than their reported costs for providing services. Specifically, the total facility profit margin turned from positive to negative and dropped almost two percentage points between fiscal years 2011 and 2017. We also ran regression models to examine the relationship between all hospitals’ total profit margins and the various measures of Medicare dependence. We found that hospitals with a higher Medicare share of total-care revenue had lower total facility margins on average, holding all other factors constant; in contrast, there was no significant relationship between total facility margins and the inpatient volume-based measures of Medicare dependence. This indicates that a higher volume of inpatient services was not associated with lower profitability. Agency Comments We provided a draft of this report to the Department of Health and Human Services for comment. The Department of Health and Human Services provided technical comments, which we incorporated as appropriate. We are sending copies of this report to the Secretary of the Department of Health and Human Services. In addition, this report is available at no charge on the GAO website at http://www.gao.gov. If you or your staffs have any questions about this report, please contact me at (202) 512-7114 or farbj@gao.gov. Contact points for our Offices of Congressional Relations and Public Affairs may be found on the last page of this report. GAO staff who made key contributions to this report are listed in appendix IV. Appendix I: Scope and Methodology This appendix explains the quantitative scope and methodology used to examine how the Medicare-dependent hospital (MDH) designation differs from the other Medicare rural hospital designations. This appendix also explains the scope and methodology used to describe changes in the number and selected metrics of MDHs and other hospital types, including those used for a regression analysis to provide information on the relationship between MDH program criteria and Medicare dependence. Differences between MDH and Other Designations To describe how the MDH designation differs from other rural hospital designations, we used CMS data—specifically, the Provider Specific File (PSF)—to identify the number of MDHs, critical access hospitals (CAH), sole community hospitals (SCH), rural low-volume adjustment hospitals (LVA), and rural referral centers (RRC) in fiscal year 2017. We then identified all rural hospitals without a designation in 2017 using the 2018 CMS Inpatient Prospective Payment System (IPPS) Impact File because those data are prepared in the middle of the year preceding the fiscal year. We define rural hospitals using the CMS MDH programmatic definition; that is, those hospitals that are not located in metropolitan statistical areas, as well as those hospitals that reclassified as rural for CMS payment purposes. We next identified the number of hospitals with each designation and the value of additional payments received under the rural designations that each hospital had in that year using data provided by each hospital through their Medicare Cost Report (MCR). The MCR is submitted to CMS by hospitals each fiscal year and contains information such as facility characteristics, utilization data, and costs to provide services to Medicare beneficiaries and all patients. Because CAHs are paid based on cost under a different payment system than the other hospitals, we did not have complete data to estimate what those hospitals would have been paid under the inpatient prospective payment system and thus could not identify the additional payments received by CAHs. In addition, RRCs only receive indirect payment benefits, and thus we could not calculate a comparable additional payment for that group of hospitals. For all analyses, we excluded hospitals within the Indian Health Service, as well as hospitals in Maryland and those outside of the remaining 49 states and the District of Columbia. We also excluded hospitals with reporting periods greater than 14 months or less than 10 months and those that reported zero or negative Medicare revenue. Number of MDHs and Selected Metrics To describe changes in the number and select metrics of MDHs and other hospital types, we examined MCR data for fiscal years 2011 through 2017. To first identify the universe of MDHs, rural hospitals, and all acute care inpatient prospective payment system (IPPS) hospitals, we used the PSF and MCR for fiscal years 2011 through 2017, as well as CMS Impact Files for fiscal years 2012 through 2018. Then, we used the MCR to calculate the number of MDHs that received the MDH payment adjustment and the distribution of additional payments among MDHs in each year. Using those same data sources, we then calculated several metrics and examined trends for MDHs as compared to all rural hospitals and all hospitals overall. The first metric is the median proportion of total Medicare payments—referred to as revenue—each hospital group received from providing inpatient and outpatient care to Medicare beneficiaries. The second metric is hospitals’ median profit margins—a profitability measure calculated as the amount of revenue the hospital received minus reported costs, divided by the amount of revenue received. We calculated profit margins specific to Medicare revenue and costs (Medicare profit margins) but also for revenue and costs beyond Medicare (total facility profit margins), including payments for treating non-Medicare (including privately insured) patients. We calculated Medicare and total facility profit margins at the hospital level using hospital-reported costs and revenues from the MCRs, and reported the median margins for each hospital group. The Medicare margin reflects only payments and costs received for inpatient and outpatient services (about 90 percent of total Medicare revenue, according to CMS officials) and excludes payments and costs for other hospital-based services, such as those for skilled nursing and home health care. Third, we calculated hospitals’ degree of Medicare dependence using three separate definitions, or measures, of dependence: (1) the amount of revenue the hospital received from Medicare as a share of all the revenue the hospital received for inpatient and outpatient services (total care revenue), (2) the share of inpatient days of care the hospital provides that are attributed to Medicare beneficiaries, and (3) the share of inpatient discharges that are attributed to Medicare beneficiaries. We also calculated these metrics separately for those MDHs that were eligible for the program based on data from the 1980s—legacy MDHs. To do so, we used data provided by Medicare Administrative Contractors—third-party entities that administer Medicare program payments and determine MDH eligibility. Regression Analysis To provide additional context on the relationship between MDH eligibility criteria and the various definitions of Medicare dependence, we developed an econometric model to analyze the association between bed size, rural status, and the three measures of Medicare dependence. We conducted the regression analysis using data from the CMS IPPS Impact Files and MCRs from fiscal years 2011 through 2017. We used the following measures as dependent variables: (1) the amount of revenue the hospital received from Medicare as a share of all the revenue the hospital received for inpatient and outpatient services (total care revenue), (2) the share of inpatient days of care the hospital provides that are attributed to Medicare beneficiaries, and (3) the share of inpatient discharges that are attributed to Medicare beneficiaries. Dependent Variables 𝑌𝑌𝑖𝑖𝑖𝑖=log (𝑅𝑅𝑖𝑖𝑖𝑖) . Where 𝑅𝑅𝑖𝑖𝑖𝑖 represents the Medicare share of revenue, inpatient days or discharges, and the i and t subscripts represent the hospital and year, respectively. This formulation has the advantage of restricting the models’ predicted values to be positive and also allows for a relatively straightforward interpretation of the parameter estimates. Explanatory Variables We included hospital capacity or size as measured by the number of hospital beds. The number of beds is itself one of the criteria for MDH eligibility, and we were interested in whether hospitals of smaller sizes have more or less Medicare dependency. We included an indicator variable flagging whether the hospital is in a rural location. Rural location is one of the criteria for MDH program eligibility, and so this was a key variable in our model. We included the ownership category of the hospital, such as whether a hospital is for-profit or not for-profit, or whether it is a public or private institution. This organizational category may determine institutional characteristics, which affects the likelihood that the hospital serves either more or fewer Medicare beneficiaries. We included the degree of proximity to other hospitals of substantive size; specifically, the distance from the closest hospital with at least 100 beds. In addition to our rural indicator variable, this controlled for whether more remote hospitals are more likely to be more dependent on Medicare. We included whether the state in which the hospital is located has expanded Medicaid to provide coverage to low-income, non-elderly adults, because it is possible that an increased number of Medicaid- eligible patients may affect the number of Medicare patients using hospital services. This variable may be associated with less Medicare dependence if Medicaid becomes a relatively larger payer source, or it may be associated with more Medicare dependence if Medicaid eligibility brings Medicare-eligible people into the health care system. We included the percent of population in the hospital’s county over age 65, because areas with larger numbers of people over age 65 may be more likely to have a higher proportion of Medicare beneficiaries using health care services. We included the percent growth in county population, which allowed us to control for areas with declining populations that may be more likely to contain Medicare-dependent hospitals. Our model included time fixed effects (a dummy variable for each year in the analysis). The time fixed effects controlled for factors affecting hospitals nationally in as given year—in particular, those factors for which data were unavailable. We included a set of state fixed effects (a dummy variable for each of the states in the analysis) to control for effects that are common to a specific area, but for which data may have been unavailable. We estimated specifications that included interactions between our bed size categories and rural location. This allowed us to determine whether bed size had the same impact on Medicare dependence for hospitals in rural locations compared with those in urban locations. Model Specification ln (𝑅𝑅𝑖𝑖𝑖𝑖)=�𝑓𝑓𝑖𝑖𝐹𝐹𝑖𝑖 +𝑋𝑋𝑖𝑖𝑖𝑖𝛽𝛽+𝐶𝐶𝑠𝑠𝑖𝑖𝛾𝛾+𝜀𝜀𝑖𝑖𝑖𝑖,𝑡𝑡=1,…,𝑇𝑇; 𝑖𝑖=1,…,𝐻𝐻. The dependent variable is the logarithm of our measure of Medicare 𝑋𝑋𝑖𝑖𝑖𝑖 is a 1 x k vector of hospital characteristics and possible 𝛽𝛽 is a k x 1 vector of parameters associated with the hospital interactions of these characteristics, where i denotes the ith hospital ∑ ∑ 𝐶𝐶𝑠𝑠𝑖𝑖 is a 1 x m vector of time-varying county-level variables hospital and their associated (lower case) parameters. 𝛾𝛾 is an m x 1 vector of parameters associated with the state-level characteristics such as the percent of the population over 65 and the dependence, 𝑅𝑅𝑖𝑖𝑖𝑖. and t denotes the year. 𝑋𝑋𝑖𝑖𝑖𝑖 contains key explanatory variables such as characteristics, 𝑋𝑋𝑖𝑖𝑖𝑖. ownership type, the number of beds, rural or urban location, whether 𝑇𝑇𝑖𝑖=2a hospital receives MDH program monies and other characteristics. represents the set of time (year) dummy variables (upper 𝑆𝑆𝑠𝑠=2 represents the set of state dummy variables (upper case) case) and their associated (lower case) parameters. characteristics such as the percent of the population over 65 and the county population growth rate. characteristics, 𝐶𝐶𝑐𝑐𝑖𝑖. Our model includes an interaction effect between the rural dummy variable and each of the characteristics except the geographic fixed effects. 𝜀𝜀𝑖𝑖𝑖𝑖 is a well-behaved Gaussian random error term that may have a heteroskedastic and/or clustered structure. We used Stata® to estimate the regression model, using fixed effects at the state-level to account for unobserved heterogeneity and clustering at the county-level. Specification of the Bed Size Categories and Geographic Fixed Effects Our focus was on the main criteria for MDH eligibility—namely hospital size as measured by number of beds and rural versus no-rural hospital location. We divided the hospitals into five bed number categories: 50 beds or fewer Over 50 beds to 100 beds Over 100 beds to 300 beds Over 300 beds to 400 beds This categorization strikes a balance between having too many categories, which would reduce the statistical power of our analysis, and having too few categories, which would fail to identify any non-linear pattern in the statistical relationship. These categories also contain the 100 bed criterion as one of the cut-off points. Our analysis controls for location and possible heterogeneity by using geographic fixed effects but we also want to identify the impact of rural location. Selecting too detailed a level of geographic fixed effect such as county or zip code would limit our ability to identify the rural effect so we used states. We recognized that state fixed effects may not identify more localized effects; this is a limitation of our model. Total Facility Profit Margins and Measures of Medicare Dependence We also modeled the effects of hospital characteristics on total facility profit margins; that is, the difference between revenue and costs as a percent of revenue. For MDHs in our analysis, we excluded any MDH additional payment from the margin calculation in order to isolate and remove the program impact on financial status. We used the same explanatory factors in our econometric model of hospital margins as in our models of Medicare dependence but we supplement these factors with our three measures of Medicare dependence—a separate model for each measure. This allowed us to assess how our different measures of Medicare dependence are associated with financial well-being. We assessed the reliability of the relevant fields in each of the data sets we used for these analyses by interviewing CMS officials, reviewing related documentation, and performing data checks. On the basis of these steps, we concluded that the data were sufficiently reliable for the purposes of our reporting objective. Appendix II: Medicare Rural Hospital Payment Designation Eligibility and Payment We identified five Medicare rural hospital payment designations and categorized them into two categories: (1) primary payment designations and (2) secondary payment designations. Primary designations include critical access hospitals (CAH), sole community hospitals (SCH), and Medicare-dependent Hospitals (MDH). Each designation has distinct eligibility requirements and payment methodologies. Appendix III: Full Regression Results This appendix describes the full results for our modeling of Medicare dollars as a percentage of total revenue, the percent of inpatient days, the percent of inpatient discharges, and total hospital profit margins. Results for Modeling Medicare Revenue as a Share of Total Revenue We tested for the hypothesis that key groups of parameters were significantly different between urban and rural locations. We performed a k-parameter post-estimation Wald linear restriction where 𝛽𝛽𝑘𝑘𝑢𝑢 and 𝛽𝛽𝑘𝑘𝑟𝑟 are matrices of the estimated urban and rural parameters, respectively, for each of the k categories (bed-size, ownership type, etc.). We rejected the null hypothesis of parameter equality for bed-size, ownership types, Medicaid expansion, and year dummies at the 5 percent level. The miles distance parameters rejected the hypothesis at marginally above the 5 percent level. Rural hospitals generally were associated with larger Medicare shares of revenue than urban hospitals. In every bed-size category, the parameters for rural hospitals were significantly greater than for urban hospitals. In addition, controlling for urban-rural location, with the exception of the largest hospital category (over 400 beds) hospitals with fewer beds had a smaller Medicare share of revenue, as shown in figure 8. Hospitals in counties with higher percentages of people over age 65 were significantly associated with greater Medicare dependence. Results for Modeling Medicare as a Share of Total Inpatient Days Our Wald tests rejected the null that parameters rural and urban were equal in the bed-number categories and in the ownership categories. As with the Medicare share of total revenue, our model for Medicare share of inpatient days showed, controlling for bed numbers, that rural hospitals generally had significantly greater Medicare dependence than urban hospitals. In most bed-size categories, the parameters for rural hospitals were greater than for urban hospitals. The pattern for bed size was different for Medicare dependence measured in revenue in that for rural hospitals, dependence fell as bed numbers rose, but, for urban hospitals, we observed a hump- shape distribution with the middle bed-number categories having higher dependence than the smallest and largest categories, as shown in figure 9. Hospitals located in counties with higher percentages of people over age 65 had higher dependence. Results for Modeling Medicare as a Share of Total Inpatient Discharges Our model for the Medicare share of inpatient discharges showed that, controlling for bed numbers, rural hospitals generally had greater Medicare dependence than urban hospitals. In most bed-size categories, the parameters for rural hospitals were significantly greater than for urban hospitals. Our Wald tests rejected the null hypothesis that parameters for rural and urban were equal in the bed-size categories, Medicaid expansion variables, and in the ownership categories. The pattern for bed numbers was also different to Medicare dependence measured in revenue. The urban hospitals had a hump- shape distribution with the middle bed-number categories having higher dependence than the smallest and largest categories, whereas the rural showed largest effects at the smallest and the larger intermediate categories, as shown in figure 10. Hospitals located in counties with higher percentages of people over age 65 had higher dependence. Results for Modeling Hospital Profit Margins The Medicare share of total revenue was significantly associated with smaller total facility profit margins and was the only statistically significant measure of Medicare dependence in the margin models. In general, hospitals with small numbers of beds—fewer than 100—were associated with smaller hospital margins relative to our reference category of large urban hospitals. However, there was no significant difference in any of the bed-number categories between urban and rural hospitals. Appendix IV: GAO Contact and Staff Acknowledgments GAO Contact Staff Acknowledgments Jessica Farb, (202) 512-7114 or farbj@gao.gov In addition to the contact named above, Gregory Giusto (Assistant Director), Kate Nast Jones (Analyst-in-Charge), Britt Carlson, Rachel Gringlas, Michael Kendix, Vikki Porter, Caitlin Scoville, Jennifer Rudisill, and Jeffrey Tamburello made key contributions to this report.