Flood Insurance and Claims: The Impact of the Community Rating System

Frimpong, Eugene; Petrolia, Daniel R.; Harri, Ardian; Cartwright, John

doi:10.1093/aepp/ppz013

Cited by 16 publications

(11 citation statements)

References 28 publications

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“…Multiple studies have shown a significant positive correlation between slope and flood losses [ 13 , 35 , 36 ]. The argument is that higher slopes result in greater rainfall concentration and subsequently result in faster and higher stream peaks and mean annual flows [ 37 ].…”

Section: Methodsmentioning

confidence: 99%

Predicting flood damage using the flood peak ratio and Giovanni Flooded Fraction

et al. 2022

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A spatially-resolved understanding of the intensity of a flood hazard is required for accurate predictions of infrastructure reliability and losses in the aftermath. Currently, researchers who wish to predict flood losses or infrastructure reliability following a flood usually rely on computationally intensive hydrodynamic modeling or on flood hazard maps (e.g., the 100-year floodplain) to build a spatially-resolved understanding of the flood’s intensity. However, both have specific limitations. The former requires both subject matter expertise to create the models and significant computation time, while the latter is a static metric that provides no variation among specific events. The objective of this work is to develop an integrated data-driven approach to rapidly predict flood damages using two emerging flood intensity heuristics, namely the Flood Peak Ratio (FPR) and NASA’s Giovanni Flooded Fraction (GFF). This study uses data on flood claims from the National Flood Insurance Program (NFIP) to proxy flood damage, along with other well-established flood exposure variables, such as regional slope and population. The approach uses statistical learning methods to generate predictive models at two spatial levels: nationwide and statewide for the entire contiguous United States. A variable importance analysis demonstrates the significance of FPR and GFF data in predicting flood damage. In addition, the model performance at the state-level was higher than the nationwide level analysis, indicating the effectiveness of both FPR and GFF models at the regional level. A data-driven approach to predict flood damage using the FPR and GFF data offer promise considering their relative simplicity, their reliance on publicly accessible data, and their comparatively fast computational speed.

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Section: Methodsmentioning

confidence: 99%

Predicting flood damage using the flood peak ratio and Giovanni Flooded Fraction

et al. 2022

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“…We are not able to comment on the CRS program's effectiveness of reaching its other two goals: (1) encouraging a comprehensive approach to floodplain management and (2) reducing flood damage to insurable property. See, for example, Frimpong et al (2019), Petrolia, Landry & Coble (2013 and Burton (2015) for the positive impact CRS participation has on flood loss reduction and disaster recovery outcomes.…”

Section: Robustness Checksmentioning

confidence: 99%

“…Heeding the panel's message, this paper contributes to a group of studies evaluating the effectiveness of the CRS program in reaching its goals. Generally speaking, participation in the CRS program is associated with greater insurance penetration rates, fewer flood damages and better disaster recovery outcomes (Burton 2015, Highfield & Brody 2017, Frimpong, Petrolia, Harri & Cartwright 2019. A 2006 RAND report provided evidence that insurance penetration rates are higher amongst CRS communities only because of lower insurance prices and not because of the CRS activities themselves (Dixon, Clancy, Seabury & Overton 2006).…”

Section: Introductionmentioning

confidence: 99%

Public Flood Risk Mitigation and the Homeowner’s Insurance Demand Response

Borsky

Hennighausen

2022

Land Economics

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This paper investigates the influence of public risk mitigating activities on individuals' decisions to privately mitigate their disaster risks. We exploit heterogeneity in measures under the Community Rating System in the U.S. to empirically demonstrate that government investment in flood risk communication activities crowd-in private flood insurance demand while activities that lower the flood hazard residents face crowd-out private flood insurance demand. We also give evidence that flood insurance abides by the law of demand: as communities receive price discounts on their insurance policies, demand increases. Our results imply that governments can amplify the price effect by investing in additional risk communication activities, or dilute it by investing in hazard mitigation. This paper contributes to the discussion of the efficacy of disaster risk mitigation strategies and who ultimately bears the costs of natural disasters.

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“…Flood insurance premium rates are determined mainly by Flood Insurance Rate Maps (FIRMs) which are updated periodically. Other factors that affect premiums include whether the structure is elevated above historic flood levels and the extent to which the community participates in the community rating system (Frimpong et al, 2020). As such, the assessed flood risk level for a structure can change when new FIRMs take effect.…”

Section: National Flood Insurance Programmentioning

confidence: 99%

Homeowner Preference for Household-level Flood Mitigation in US: Analysis of a Discrete Choice Experiment

Frimpong

Howard

Kruse

2022

J. Agric. Appl. Econ.

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The Federal Emergency Management Agency (FEMA) offers a portfolio of flood risk mitigation options for high-risk homeowners, hoping to reduce flood damages. Buyout (home acquisition) and home retrofit (e.g., home elevation) are candidates available to homeowners. FEMA has recently amended and increased its buyout efforts. This study examines homeowners’ stated preference for buyout and home elevation contracts using survey data. Results indicate multiple factors influence the decision to participate in home acquisition and elevation programs. Importantly, we find that preferences vary with the timing (whether the contract is offered before or after a damage event) of the contract offered.

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Flood Insurance and Claims: The Impact of the Community Rating System

Cited by 16 publications

References 28 publications

Predicting flood damage using the flood peak ratio and Giovanni Flooded Fraction

Predicting flood damage using the flood peak ratio and Giovanni Flooded Fraction

Public Flood Risk Mitigation and the Homeowner’s Insurance Demand Response

Homeowner Preference for Household-level Flood Mitigation in US: Analysis of a Discrete Choice Experiment

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