National Flood Insurance Program Research Articles

Pareto‐efficient risk sharing in centralized insurance markets with application to flood risk

AbstractCentralized insurance can be found in both the private and public sectors. This paper provides a microeconomic study of the risk‐sharing mechanisms in these markets, where multiple policyholders interact with a centralized monopolistic insurer. With minimal assumptions on the risk preferences of the market participants, we characterize Pareto optimality in terms of the agents' risk positions and their assessment of the likelihoods associated with their loss tail events. We relate Pareto efficiency in this market to a naturally associated cooperative game. Based on our theoretical results, we then consider a model of flood insurance coverage via an illustrative example. The lessons drawn from our theoretical results and this example lead to important policy implications for the existing National Flood Insurance Program in the United States.

Reading in the dark: Shifting governmentalities and the spatial dimensions of legible U.S. flood risk

Following the Great Mississippi Flood of 1927, U.S. private insurers abandoned flood coverage after deeming it incalculable, precipitating the National Flood Insurance Program (NFIP) in 1968. The NFIP continued to underwrite illegible risk in the public interest. For decades, hydraulic models were limited to simplified “one-dimensional” simulations ill-equipped to characterize uncertainty to the standard of market carriers. However, recent advancements in multidimensional characterization have galvanized the flood sector. The NFIP has licensed some of the most expansive 2-D models to date from eager tech firms; it has pledged to modernize its risk portfolio, invest in financing, and attract private carriers in order to “lift all boats” within the flood sector. This article intervenes by examining the “multidimensional turn” as a fix for “crises of calculation.” The article rejects teleological narratives crediting models for “changing how we think” about flood, and illustrates how underwriters enframe illegible floodplains as unruly problems. The incalculability of flood risk is an “insurantial logic” naturalized as a physical truth. Economic geographers have interrogated the materiality of such truths as integral to the production of nature under capitalism. The article examines how invested state and non-state actors operationalize impediments to legible risk to realize their financial interests. It further argues that FEMA’s costly efforts to realize an allusive market are undermined by intractable conflicts between pure-market and affordable coverage. The NFIP will likely continue to do the heavy lifting with respect to underwriting, and selective geographies of private coverage will mirror the drive for surplus value.

Impact of post-disaster federal assistance on the national flood insurance program

This research tested the hypothesis that post-disaster federal relief program crowds out the demand for flood insurance in the U.S. To do that, this research has collected historical data between 2016 and 2020, and conducted propensity score-based analysis to understand the causality between post-disaster federal assistance and flood insurance enrollment in the flood affected counties in the U.S. In the first part, the treatment variable has been considered as binary to compare the effect of the availability of federal assistance to that of non-availability of federal assistance in a flood affected county by using propensity score matching method. It was found that the availability of the federal payout in a county in a year increased the number of flood insurance policies by 5.2 % and the total insured value of the policies by 4.6 % in the following year. Next, the treatment variable was considered continuous and was used in a generalized propensity score method to develop a dose response function, i.e., a function that depicts the changes in the outcome variables based on different levels of treatment. It was found that for each 1000 households in a county that received federal assistance, the percentage increase in the number of flood insurance policies was 3.41 %. On the other hand, for each million-dollar of federal assistance in a county, the total insured value of the flood insurance policies increased by 1.96 %. Therefore, this research has concluded that, contrary to the hypothesis, the availability of post-disaster federal assistance increased the flood insurance enrollment in the U.S.

Freeboard life-cycle benefit-cost analysis of a rental single-family residence for landlord, tenant, and insurer

Flood risk to single-family rental housing remains poorly understood, leaving a large and increasing population underinformed to protect themselves, including regarding insurance. This research introduces a life-cycle benefit-cost analysis for the landlord, tenant, and insurer [i.e., (U.S.) National Flood Insurance Program (NFIP)] to optimize freeboard [i.e., additional first-floor height above the base flood elevation (BFE)] selection for a rental single-family home. Flood insurance premium; apportioned flood risk among the landlord, tenant, and NFIP by insurance coverage and deductible; rental loss; moving and displacement costs; freeboard construction cost; and rent increase upon freeboard implementation are considered in estimating net benefit (NB) by freeboard. For a 2,500 square-foot case study home in Metairie, Louisiana, a two-foot freeboard optimizes the combined savings for landlord and tenant, with joint life-cycle NB of $23,658 and $14,978, for a 3% and 7% real discount rate, respectively. Any freeboard up to 2.5 feet benefits the tenant and NFIP, while the landlord benefits for freeboards up to 4.0 feet. Collectively, results suggest that at the time of construction, even minimal freeboard provides substantial savings for the landlord, tenant, and NFIP. The research provides actionable information, supporting the decision-making process for landlords, tenants, and others, thereby enhancing investment and occupation decisions.

Damage Curves Derived from Hurricane Ike in the West of Galveston Bay Based on Insurance Claims and Hydrodynamic Simulations

Hurricane Ike, which struck the United States in September 2008, was the ninth most expensive hurricane in terms of damages. It caused nearly USD 30 billion in damage after making landfall on the Bolivar Peninsula, Texas. We used the Delft3d-FM/SWAN hydrodynamic and spectral wave model to simulate the storm surge inundation around Galveston Bay during Hurricane Ike. Damage curves were established through the relationship between eight hydrodynamic parameters (water depth, flow velocity, unit discharge, flow momentum flux, significant wave height, wave energy flux, total water depth (flow depth plus wave height), and total (flow plus wave) force) simulated by the model and National Flood Insurance Program (NFIP) insurance damage data. The NFIP insurance database contains a large amount of building damage data, building stories, and elevation, as well as other information from the Ike event. We found that the damage curves are sensitive to the model grid resolution, building elevation, and the number of stories. We also found that the resulting damage functions are steeper than those developed for residential structures in many other locations.

Advancing flood damage modeling for coastal Alabama residential properties: A multivariable machine learning approach

Flooding is a global threat and predicting flood risk accurately is vital for effective mitigation and increasing society's awareness of the negative impacts of floods. Over the years, researchers have worked on physical and data-driven models to predict flood damage, striving to improve accuracy and understanding. However, the challenge lies in the scarcity and limitedness of comprehensive datasets needed to develop these models. This study aims to enhance the National Flood Insurance Program (NFIP) claims dataset from Hurricane Katrina in coastal Alabama to make it adequate for multi-variable flood damage assessment. The NFIP claims dataset was combined with the Alabama property dataset, simulated flood hazard information, and property location characteristics. Oversampling techniques are employed to address data imbalance in the datasets. Subsequently, several ensemble machine learning approaches, including random forest, extra tree, extreme gradient boosting, and categorical boosting, are utilized to develop multi-variable flood damage models. The validation of these models demonstrates that extreme gradient boosting performs best, achieving satisfactory results in identifying damaged properties with precision (0.89), recall (0.90), and F1-score (0.90), as well as determining relative damage with R-squared (0.59), root mean squared error (0.21), and Spearman correlation (0.70). Utilizing data oversampling techniques improves the model performance of imbalanced flood damage datasets. Despite the dataset's limitations and data augmentation techniques employed, the model's output explanation based on SHapley Additive exPlanations (SHAP) is constructive as it aligns with the study's expectations regarding the interaction of different features to produce the final results.

Learning inter-annual flood loss risk models from historical flood insurance claims

Flooding is a natural hazard that causes substantial loss of lives and livelihoods worldwide. Developing predictive models for flood-induced financial losses is crucial for applications such as insurance underwriting. This research uses the National Flood Insurance Program (NFIP) dataset between 2000 and 2020 to evaluate the predictive skill of past data in predicting near-future flood loss risk. Our approach applies neural networks (Conditional Generative Adversarial Networks), decision trees (Extreme Gradient Boosting), and kernel-based regressors (Gaussian Processes) to estimate pointwise losses. It aggregates them over intervals using a bias-corrected Burr–Pareto distribution to predict risk. The regression models help identify the most informative predictors and highlight crucial factors influencing flood-related financial losses. Applying our approach to quantify the county-level coastal flood loss risk in eight US Southern states results in an R2=0.807, substantially outperforming related work using stage-damage curves. More detailed testing on 11 counties with significant claims in the NFIP dataset reveals that Extreme Gradient Boosting yields the most favorable results, and bias correction significantly improves the similarity between the predicted and reference claim amount distributions. Our experiments also show that, despite the already experienced climate change, the difference in future short-term risk predictions of flood-loss amounts between historical shifting or expanding training data windows is insignificant.

Exploring the relationship between flood insurance claims, crowdsourced rainfall, and tide levels for coastal urban communities: Case study for the mid-Atlantic United States

The Federal Emergency Management Agency (FEMA) recently released a large dataset (∼2.5 million records) of flood insurance claim transactions from the National Flood Insurance Program (NFIP) that are geolocated by census tract. In this study, we present a methodology for using these NFIP flood claims along with agency rainfall and tide level data, combined with high-resolution rainfall data collected from growing networks of crowdsourced Personal Weather Stations (PWSs), to investigate the characteristics of storm events that result in flood insurance claims. Using 25 cities/counties in five metropolitan areas in the Mid-Atlantic region as case studies for testing the methodology, we find that the majority of flood claim events in this region were associated with rainfall below a 1-year return period and a tide level below the NOAA minor flood risk tide level. When storm events exceeded these rainfall and tide thresholds, the probability of a flood claim occurring could reach 100% for rainfall only events and 33% for tidal only events, depending on the region. While compound events of both high rainfall and high tide were rare, when seen in Virginia Beach, the probability of insurance claims being made during these events far exceeded the probability that would be expected if rainfall and tide were independent, showing how compound events in this region exacerbate flood impacts. Additionally, analysis of 110 heavy rainfall events showed that crowdsourced PWSs, can better capture rainfall extremes associated with insurance claims due to their higher spatial density compared to federal agency rainfall networks. This suggests that the PWS networks, once quality controlled, can offer greater insights into which rainfall events are likely to contribute to insurance claims. These region-specific findings and the general methodology presented in this study can benefit multiple stakeholders including researchers, risk management officials, insurers as well as the general public in understanding the impacts of rainfall, tidal, and compound flooding, along with the value of crowdsourced data in assessing flood risk.

Are Underserved Populations Left Out of National Flood Mitigation Efforts and Facing Greater Impact? A Method to Assess Racial Inequality at the Census Tract Level

Abstract Development policies have systematically relegated certain populations to undesirable locations including areas at risk of flooding. Over time many properties will no longer be inhabitable and others will see damage from significant flooding. Current U.S. federal policy funds flood risk mitigation measures, such as property acquisition, relocation, and retrofitting; however, depending on various factors at the subcounty level, these actions can in some contexts provide disproportionate benefit to higher income, mostly White areas, failing to appropriately benefit underserved and exposed populations. Here, we present an exploratory method, building off existing research on programmatic-wide and event-specific analysis to explore patterns allowing state- and county-level decision-makers to identify inequalities in federal funding, potentially supporting reprioritization. This work evaluates the National Flood Insurance Program (NFIP) claims data from 1975 to 2019 and Federal Emergency Management Agency (FEMA) mitigation efforts from 1989 to 2018 to explore the distribution of mitigation measures related to tract to county racial disproportionalities (for example, majority non-White census tracts in majority White counties). We find that for majority non-White census tracts in majority White counties, there are disproportionately fewer NFIP claims. This supports previous work, which shows lower levels of assistance in flood recovery, resilience, and preparedness in these areas.

A reproducible and streamlined approach to geospatial modelling for the Community Rating System

Building flood resilience has become a priority in the United States as flood risks continue to rise. The National Flood Insurance Program's Community Rating System (CRS) serves as an excellent framework for local-level resilience planning by incentivizing a wide range of flood management practices. Despite the short-term and long-term benefits, resource barriers and limited technical capacity constrain communities' ability to participate in the program. In this study, we develop a GIS-based decision support tool to facilitate communities' participation in CRS. Specifically, we focus on Open Space Preservation (OSP) in the floodplain, a high credit earning CRS activity that is also promising in terms of flood protection. Most communities already preserve lands in the floodplain, indicating a missed opportunity for policyholders across the United States to receive financial benefit. Furthermore, OSP aligns with a growing national interest in the use of natural infrastructure for flood protection. Implementing OSP, however, requires extensive GIS analysis. Many communities lack the technical capacity needed to fulfill the program requirements. To address this challenge, the tool identifies areas that are already preserved and calculates credit estimates, providing communities with an indication of the financial benefit they are eligible to receive. In addition, the tool implements a novel methodology for mapping unprotected open space areas in the floodplain that could be eligible for CRS credit if preserved. These maps, along with estimates of future crediting scenarios, help communities pursue additional OSP credits through flood resilient land-use planning. The tool was applied to communities in the Commonwealth of Virginia as a case study. Statewide, over 39,000 unclaimed OSP credits were identified, suggesting an opportunity for significant expansion of the CRS in Virginia. Across the country, communities can use the GIS tool to perform the necessary GIS work more quickly and easily, engage with stakeholders, and make a strong financial argument for proactive flood management practices.

Housing market dynamics of the post-Sandy Hudson estuary, Long Island Sound, and New Jersey coastline are explained by NFIP participation

How flooding affects home values can determine the path of economic recovery for communities and have lasting impacts on national and global financial systems. Yet, our understanding of how flood insurance, community risk perception, and past flooding events shape future housing prices (HPs) remains limited. To explore this, we used a socio-environmental (SE) model and studied the temporal impacts of flooding on mean housing values across 496 coastal census tracts of New York, Connecticut, and New Jersey, US, from 1970 to 2021. The modeling exercise demonstrated that the initial economic impact of Hurricane Sandy was largely absorbed by the National Flood Insurance Program (NFIP); however, the region then exhibited a long-term decline in home values, which was well described by an interrupted time series model. We found significant correlations between SE model parameters describing HP change and those describing tract-scale behaviors and perceptions, suggesting that the salience of past flooding events and NFIP participation may be important regional drivers of HPs. Tracts with greater post-flood change in active insurance policies exhibited larger decreases in mean home values than those with more stable NFIP participation. An improved understanding of relationships between HPs, flood insurance, and community perceptions could support more equitable distributions of resources and improved policy interventions to reduce flooding risk.

Flood Insurance, Building Codes, and Public Adaptation: Implications for Airport Investment and Financial Constraints

This paper investigates the impact of flood management policies on airport investment and the resulting financial constraints. Specifically, it examines the effects of flood insurance, building codes, and public adaptation investment on the investment decisions of 100 United States airports located in flood-prone areas. The paper estimated the financial loss from extreme precipitations and flooding using novel data from the United States Federal Emergency Management Agency, and a differences-in-differences framework leveraging the introduction of the 2012 Biggert–Waters reform of the National Flood Insurance Program. The findings reveal that while flood insurance costs negatively influence overall airport investment, they do not significantly affect investment–cash sensitivity. On the other hand, the introduction of stricter building codes and public adaptation investment leads to increased cash usage for investment purposes, particularly among airports exposed to extreme precipitation and flood risks. Furthermore, the analysis suggests that the observed increase in financial constraints resulting from stricter building codes and public adaptation investment is likely driven by the asymmetry of information rather than the materiality of flood risk. In other words, public investment in flood risk reduction appears to signal to investors that the airport is exposed to flood risk, potentially leading to increased financial constraints. This finding highlights the importance of considering information asymmetry when assessing the impact of flood management policies on financial constraints. Understanding the underlying drivers of these effects is crucial for supporting resilient infrastructure development and informing effective decision-making in flood-prone areas.

Risk as a process: a history informed hazard planning approach applied to the 2018 post-fire debris flows, Montecito, California

Historical information about floods is not commonly used in the US to inform land use planning decisions. Rather, the current approach to managing floods is based on static maps derived from computer simulations of the area inundated by floods of specified return intervals. These maps provide some information about flood hazard, but they do not reflect the underlying processes involved in creating a flood disaster, which typically include increased exposure due to building on flood-prone land, nor do they account for the greater hazard resulting from wildfire. We developed and applied an approach to analyze how exposure has evolved in flood hazard zones in Montecito, California, an area devastated by post-fire debris flows in January 2018. By combining historical flood records of the past 200 years, human development records of the past 100 years, and geomorphological understanding of debris flow generation processes, this approach allows us to look at risk as a dynamic process influenced by physical and human factors, instead of a static map. Results show that floods after fires, in particular debris flows and debris laden floods, are very common in Montecito (15 events in the last 200 years), and that despite policies discouraging developments in hazard areas, developments in hazard zones have increased substantially since Montecito joined the National Flood Insurance Program in 1979. We also highlight the limitation of using conventional Flood Insurance Rate Maps (FIRMs) to manage land use in alluvial fan areas such as Montecito. The knowledge produced in this project can help Montecito residents better understand how they came to be vulnerable to floods and identify action they are taking now that might increase or reduce their vulnerability to the next big flood. This science-history-centric approach to understand hazard and exposure evolution using geographic information systems (GIS) and historical records, is generalizable to other communities seeking to better understand the nature of the hazard they are exposed to and some of the root causes of their vulnerabilities, in other words, both the natural and social processes producing disasters.

Potential Benefits in Remapping the Special Flood Hazard Area: Evidence from the U.S. Housing Market

A typical U.S. homebuyer's understanding of whether a property faces flood risk is based on whether the property is located inside the National Flood Insurance Program's (NFIP) Special Flood Hazard Area (SFHA). The SFHA boundary, however, may bias homebuyers’ perceptions of flood risk relative to unobserved true risk because the SFHA is an incomplete, and sometimes inaccurate, representation of flood hazards. Using a national dataset of property transactions, flood hazard data from the First Street Foundation, state-level measures of flood disclosure laws, and a spatially restricted triple-difference design, we distinguish capitalization effects of policy-driven (SFHA) versus quasi-objective (First Street) indicators of flood hazard. We identify these effects by assessing thousands of local spatial interactions between property SFHA designations, measures of quasi-objective flood hazard, and being in a state that mandates flood history disclosures. Being inside the SFHA generates a risk discount, but the signal is muted relative to underlying hazard exposure. Further, the SFHA signal can result in inefficient discounts for properties erroneously mapped in the SFHA. Disclosure requirements about flood history accentuate price effects for hazardous properties and result in more adequate risk internalization. In the absence of disclosures, however, we find either no or a weak risk signal for houses outside the SFHA that face flood hazard. Our results highlight potential benefits of updating SFHA boundaries to include all houses that may experience flooding, and argue in favor of requiring flood-related disclosures from sellers to improve the market's ability to internalize flood risk.

Characterizing the 2019 Midwest Flood: A Hydrologic and Socioeconomic Perspective

Abstract During the spring of 2019, severe flooding across the U.S. Midwest caused widespread damage to communities in the Missouri and Mississippi River basins. While it is known that flood magnitude and economic damage are often related, little work exists to examine these factors simultaneously. In this study, we analyze both the hydrologic and socioeconomic characteristics of the 2019 Midwest flood to gain a comprehensive understanding of impacts to individuals, households, and communities. We examine flood magnitude, duration, and probability of occurrence in tandem with claim and grant applications from federal disaster recovery programs, such as the National Flood Insurance Program (NFIP) and the Individual and Households Program (IHP). Overall, we find that many areas, particularly in Nebraska and Iowa, experienced moderate or major flooding due to historic discharge magnitudes. In these states, NFIP claims totaled more than $31 million and IHP applications exceeded $42 million in reported damages. In most cases, counties that reported a high density of insurance claims or grant applications overlapped with regions with significant flooding. We also identify the economic advantages to NFIP policyholders for flood recovery in terms of aid eligibility and financial aid amounts.

Flood insurance reforms, housing market dynamics, and adaptation to climate risks

This paper examines the impact of two nationwide reforms to the National Flood Insurance Program on both flood insurance and property markets. The 2012 and 2014 reforms aimed to phase out subsidies on flood insurance premiums. Using a difference-in-differences framework comparing treated and similar but untreated properties, we find that the reforms led to a 14.3% relative increase in the price of flood insurance, an 8.2% decrease in insurance demand, a 4.2% decrease in property prices and a 2.3% decrease in property transaction volumes. As flood risk continues to accelerate across the United States, properly pricing insurance premiums can effectively discourage households from living in risky areas, but may involve potential trade-offs such as the unintended outcome of a large drop-off in insurance coverage.

Does the National Flood Insurance Program Drive Migration to Higher Risk Areas?

Previous articleNext article No AccessDoes the National Flood Insurance Program Drive Migration to Higher Risk Areas?Abigail Peralta and Jonathan B. ScottAbigail Peralta Search for more articles by this author and Jonathan B. Scott Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by Journal of the Association of Environmental and Resource Economists Just Accepted Published for the Association of Environmental and Resource Economists Article DOIhttps://doi.org/10.1086/726155 HistoryAccepted May 18, 2023 © 2023 Association of Environmental and Resource Economists. All Rights reserved.PDF download Crossref reports no articles citing this article.

Exploring flood mitigation governance by estimating first-floor elevation via deep learning and google street view in coastal Texas

Flood mitigation governance is critical for coastal regions where flooding has caused considerable damage. Raising the First-Floor Elevation (FFE) above the base flood elevation (BFE) is an effective mitigation measure for buildings with a high risk of flooding. In the U.S., measuring FFE is necessary to obtain an Elevation Certificate (E.C.) for the National Flood Insurance Program (NFIP) and has traditionally required labor-consuming field surveys. However, the advances in computer vision technology have facilitated the handling of large image datasets, leading to new FFE measurement approaches. Taking Galveston Island (including the cities of Galveston and Jamaica Beach) in Coastal Texas as a case study, we explore how these new approaches may inform flood risk management and governance, including how FFE estimates may be combined with BFE estimates from flood inundation probability mapping to model the predicted cost of raising buildings’ FFE above their BFE. After establishing the FFE model’s accuracy by comparing its results with previously validated FFE estimates in three districts of Galveston, we generalize the workflow to building footprints across Galveston Island. By combining the FFE data derived from our workflow with multidimensional building information, we further analyze the future flood control and post-disaster maintenance strategies. Our findings present valuable data collection paradigms and methodological concepts that inform flood governance for Galveston Island. The proposed workflow can be extended to flood management and research for other vulnerable coastal communities.

Risk Rating without Information Provision

We describe the initial impact of risk-based flood insurance pricing in the United States. More than two-thirds of National Flood Insurance Program policyholders are paying higher premiums under “Risk Rating 2.0” (RR2.0), and increases will continue in the coming years. Premium increases are largest for policyholders classified as low risk on the Federal Emergency Management Agency's (FEMA's) flood maps, and the average premium gap between those in and out of the FEMA-mapped floodplain is narrowing. FEMA's flood maps, which fail to account for the new risk information, are still relied on by homeowners and regulators, blunting the adaptation benefits of RR2.0.

Federal incentives for community-level climate adaptation: an evaluation of FEMA’s Community Rating System

In response to growing threats of climate change, the US federal government is increasingly supporting community-level investments in resilience to natural hazards. As such federal programs become more widespread, evaluating their efficiency and equity is essential. The Community Rating System (CRS), which is part of the National Flood Insurance Program (NFIP), is a promising example of a federal policy designed to reduce flood losses by providing financial incentives for local climate adaptation. In exchange for community engagement in a range of risk communication and risk reduction activities, CRS provides discounts on NFIP premiums ranging from 5% to 45%. Using national-scale NFIP claims, policies, and CRS data between 1998 and 2020, we assess the program, asking whether it has been effective in reducing flood losses, how it can be improved, and what lessons it holds for similar programs. We find that participation in CRS is associated with reduced flood damage, with the percent reduction in claims roughly proportional to NFIP premium discounts. Among CRS activities, those related to ‘Flood Damage Reduction’ are most effective in reducing flood losses and are associated with a 20%–30% decrease in NFIP claims. Between 1998 and 2020, cumulative damage reductions attributable to CRS were $11.4 billion; over the same period, cumulative NFIP premium discounts were $12.1 billion. This close match is an endorsement of CRS historically and supports its future continuation. To improve the efficiency and equity of CRS, we recommend that Federal Emergency Management Agency: (a) reexamine the surcharge levied on NFIP premiums that cross-subsidizes premium discounts, and (b) allocate greater resources towards supporting participation among smaller, under-resourced communities. In general, CRS serves as an effective model for other federal market-based programs seeking to stimulate community-level investment in climate resilience.