Purchase Flood Insurance Research Articles

Voluntary uptake of flood insurance in post-fire landscapes: insights from household surveys after two wildfires in northern Arizona

Wildfires can rapidly generate post-fire flood risk for downslope communities, complicating recovery and necessitating accelerated cross-boundary responses on both public and private lands. Flood insurance is one of a suite of efforts to minimize the social and financial impacts associated with post-fire flood events, but little is known about who purchases coverage after wildfires or their experiences with insurance claims. We provide an opportunistic contribution to this research gap by comparing data gathered via three mixed-mode household surveys conducted in areas with modeled flood risk after two wildfires in the greater Flagstaff, Arizona area. Two surveys were administered—one in 2011 and one in 2020—after the 2010 Schultz Fire, which resulted in significant flooding, providing a longitudinal insight into insurance experiences. A third survey was administered in 2022 following flooding adjacent to the 2019 Museum Fire. Together, these surveys resulted in a total of 1,351 usable responses. We find that several groups are significantly less likely to purchase flood insurance, including residents who moved to their property after the studied fire event and renters. The reported cost of flood insurance among households affected by the Schultz Fire doubled over a 10-year period; those who no longer maintained coverage typically reported that they stopped renewing between 2–5 years after the fire. Second homeowners were more likely to report damages that were not covered by insurance. We provide recommendations for engaging residents in uptake, renewal, and use of flood insurance and conclude that there is an urgent need to better understand decision-making surrounding post-fire flood insurance to support more equitable recovery.

Theoretical Boundaries of Annual Flood Risk for Single-Family Homes Within the 100-Year Floodplain

Special flood hazard areas (SFHAs), defined as having an annual probability of occurrence of 1 percent or above, are used by U.S. Federal Emergency Management Agency (FEMA) to demarcate areas within which flood insurance purchase is required to secure a mortgage. However, quantifying flood risk within SFHAs can be challenging due to the lack of modeled flood depth data for all return periods. To address this issue, this research quantifies flood risk indicated by average annual loss (AAL) within the A Zone—the subset of the SFHA where wave heights can potentially range from 0 to 3 feet. The methodology resolves the Gumbel quantile function for four distinct flooding cases (i.e., locations flooded at return periods exceeding 1.58-, 10-, 25-, and 50-year return period events) and generates synthetic flood hazard parameters for these cases within the 100-year floodplain, as well as with additional elevation above the base flood elevation (BFE), known as freeboard, for single-family homes with different attributes. The results indicate that for single-family homes in the A Zone, with the lowest floor elevated to the BFE, the AAL ranges from 0.3 to 1 percent of the building replacement cost value. Adding one foot of freeboard reduces flood risk by over 90% if the annual flood risk is between the minimum and 25th percentiles and the 100-year flood depth is less than two feet. The demonstrated approach helps enhance flood resilience in the A Zone, demonstrating the feasibility of proactive measures to protect communities.

Interpretable Machine Learning Reveals Potential to Overcome Reactive Flood Adaptation in the Continental US

AbstractFloods cause average annual losses of more than US$30 billion in the US and are estimated to significantly increase due to global change. Flood resilience, which currently differs strongly between socio‐economic groups, needs to be substantially improved by proactive adaptive measures, such as timely purchase of flood insurance. Yet, knowledge about the state and uptake of private adaptation and its drivers is so far scarce and fragmented. Based on interpretable machine learning and large insurance and socio‐economic open data sets covering the whole continental US we reveal that flood insurance purchase is characterized by reactive behavior after severe flood events. However, we observe that the Community Rating System helps overcome this behavior by effectively fostering proactive insurance purchase, irrespective of socio‐economic backgrounds in the communities. Thus, we recommend developing additional targeted measures to help overcome existing inequalities, for example, by providing special incentives to the most vulnerable and exposed communities.

A data-driven spatial approach to characterize the flood hazard.

Model output of localized flood grids are useful in characterizing flood hazards for properties located in the Special Flood Hazard Area (SFHA-areas expected to experience a 1% or greater annual chance of flooding). However, due to the unavailability of higher return-period [i.e., recurrence interval, or the reciprocal of the annual exceedance probability (AEP)] flood grids, the flood risk of properties located outside the SFHA cannot be quantified. Here, we present a method to estimate flood hazards that are located both inside and outside the SFHA using existing AEP surfaces. Flood hazards are characterized by the Gumbel extreme value distribution to project extreme flood event elevations for which an entire area is assumed to be submerged. Spatial interpolation techniques impute flood elevation values and are used to estimate flood hazards for areas outside the SFHA. The proposed method has the potential to improve the assessment of flood risk for properties located both inside and outside the SFHA and therefore to improve the decision-making process regarding flood insurance purchases, mitigation strategies, and long-term planning for enhanced resilience to one of the world's most ubiquitous natural hazards.

How do floods affect insurance demand? Evidence from flood insurance take-up in Japan

This study examines the hypothetical impact of floods on people's flood insurance purchase decisions, and how this impact may differ among people with different socioeconomic, geographic, and financial literacy characteristics. We find a significant yet short-lived (1-year) impact of floods on people's demand for flood insurance at an ecological level (prefectures). Different aspects of flood damage have different impacts on flood insurance take-up, with human casualties having the greatest impact on people's perception of flood risk. We also find that groups with different socioeconomic characteristics, such as age, education, and insurance agent density, react differently to floods concerning insurance purchases. We confirm a positive relationship between objective risk exposure and people's perception of the flood experience. Finally, an analysis of the Financial Literacy Survey demonstrates the impact of three cognitive biases (loss aversion, myopic, herding) on flood insurance take-up. These findings provide empirical evidence for various psychological and behavioral theories, and important policy references for countries aiming to develop flood insurance in the future.

Examining “digital” vulnerability to flooding among subsidized housing residents in Florida

Disaster management agencies have increasingly adopted online tools (e.g., social media platforms) for risk messaging, but the disparities in acquiring and comprehending risk information disseminated in online channels may influence the adoption of risk mitigation actions and exacerbate the rooted vulnerability of marginalized populations. However, this “digital vulnerability” has rarely been studied by extant studies. We thereby investigated the relations between digital capabilities, indicated by a series of skills to utilize the Internet and social media channels, risk information acquisition, and mitigation behaviors. We distributed surveys to 200 subsidized housing residents living in flood-prone areas, who are mostly low-income and lack housing options in private markets, and performed various descriptive and statistical analyses. The results showed the contributive role of social media in disseminating risk information and promoting risk mitigation actions. Particularly, we found that risk-aware residents with good digital skills tend to harness social media platforms as additional information channels and implement preventive actions (e.g., purchasing flood insurance and storing emergency supplies) to mitigate flood risks. The research recommends the integration of risk awareness campaigns with digital literacy training. It also encourages a more localized and tailored risk communication campaign focusing on flood-vulnerable communities suffering from the digital divide.

Encouraging resiliency through autoenrollment in supplemental flood insurance coverage

AbstractFlooding is the most common and costly natural disaster, with losses escalating due to climate change. For millions of US homes exposed to flood risk, property‐level investments in flood resilience offer an attractive strategy to manage losses. But financing such investments remains a challenge. One option is to harness insurance. We use field data of residential flood insurance purchases from a private insurer that offered a supplemental policy (FloodReady) to cover the costs of rebuilding with flood‐resilient materials. We exploit a change in the default purchase option to estimate how default enrollment in FloodReady influences take‐up. We estimate that autoenrollment increases take‐up from 12% to 32%. This dramatic increase suggests that behavioral interventions can be used to improve the financial capacity of homeowners to reduce future flood losses. That said, the fact that most policyholders still actively opt‐out even under autoenrollments indicates the limits of default effects in this context. We discuss potential moderators and implications for default effects across settings.

Adaptation and Adverse Selection in Markets for Natural Disaster Insurance

This paper quantifies frictions in uptake, tests for adverse selection, and analyzes welfare effects of proposed reforms in natural disaster insurance markets. I find that willingness to pay is remarkably low. In high-risk flood zones, fewer than 60 percent of homeowners purchase flood insurance even though premiums are only two-thirds of own costs. Estimating flood insurance demand and cost elasticities using house-level variation in premiums from recent US congressional reforms reveals that these homeowners select into insurance based on observable differences in adaptation but not private information about risk. These findings change the sign of predicted welfare effects of proposed policies. (JEL D82, G22, Q54)

Are you prepared for the next storm? Developing social norms messages to motivate community members to perform disaster risk mitigation behaviors.

Preparing for natural disasters and adapting to climate change can save lives. Yet, minimal research has examined how governments can motivate community members to prepare for disasters (e.g., purchasing flood insurance or installing water barriers in homes for floods and hurricanes). Instead, studies have focused on how to communicate actions individuals should take during disasters, rather than before disasters. This study develops messages targeting social norms, which are promising approaches to motivate community members to adopt disaster risk preparedness and mitigation behaviors. Specifically, we developed a variety of messages integrating descriptive norms (i.e., what others do), injunctive norms (i.e., what others believe should be done), and a social norms-based fear appeal, or social disapproval rationale (i.e., a negative social result of [not] taking behaviors). Then, we tested these messages through two between-subject factorial online experiments in flood- and hurricane-prone U.S. states with adult samples (N=2,286). In experiment 1 (i.e., purchasing flood insurance), the injunctive norms message using weather forecasters and the social disapproval rationale message significantly increased social norms perceptions, which in turn influenced behavioral intentions. In experiment 2 (i.e., installing water barriers), the injunctive norms message using weather forecasters, the injunctive norms message using neighbors, and the social disapproval rationale message significantly increased social norms perceptions, which in turn influenced mitigation intentions. However, the descriptive social norms message was not effective in increasing social norms perceptions. We provide some of the first empirical evidence on how organizations' risk communication can empower community members to prepare and mitigate the impact of disasters.

Social interactions and households’ flood insurance decisions

Flooding is the most costly natural disaster faced by US households, yet policymakers are puzzled by the low take-up rates for flood insurance. Leveraging novel transaction-level data, this paper studies the influence of social interactions on households’ insurance decisions. I show that households increase flood insurance purchases by 1–5 percent when their geographically distant friends are exposed to flooding events or to campaigns for flood insurance. These exogenous shocks to far-away friends should not affect local households’ own insurance decisions except through peer effects. I provide evidence suggesting that social interactions facilitate learning through information dissemination and attention triggering.

Experimental Evidence for Coverage Preferences in Flood Insurance

We used a hypothetical choice experiment to estimate the effect of dwelling value and coverage limits on the probability of purchasing flood insurance while holding the probability of flooding and insurance price constant. The results indicate that demand for flood insurance is negatively associated with the amount of insurance coverage. For people assigned higher-valued dwellings, however, the opposite effect is observed. Since more coverage is generally preferred to less, all else being equal, differences in purchase probability dependent on dwelling value indicate an inconsistent approach to home protection. The higher probability of purchasing flood insurance from people in higher-valued dwellings may indicate an investment into the home as a financial asset, a strategy that is not observed to the same extent among people in lower-valued dwellings. This suggests that use of coverage limits may be differentially preferred based on dwelling value, such that low coverage insurance may have lower uptake for those in high-valued dwellings. As Canada evaluates a national flood insurance program, this research suggests that variable coverage maximums could be a way to increase accessibility and uptake of insurance. This research shows an inconsistent demand for flood insurance, dependent on dwelling value and independent of income.

Smallholder farmers’ willingness to pay for flood insurance as climate change adaptation strategy in northern Bangladesh

Bangladesh is one of the most flood-prone countries in the world, resulting in significant losses of agricultural crops and other assets due to the absence of risk mitigation strategies such as flood insurance. In order to ascertain farmers' risk behavior towards flood and their willingness to pay (WTP) for flood insurance as a strategy for climate change adaptation and agricultural sustainability, this study employed survey data collected from 480 flood-affected farm households in northern Bangladesh with experiment-based risk preference and subjective risk perception data. Farmers' flood risk perceptions and attitude were assessed through risk matrix and ELCE technique (equally likely certainty equivalent) and used as independent variables in a probit regression model. Farmers' WTP decisions were found to be influenced by a variety of factors, including land ownership status, off-farm income, flood experience, farmers’ group and access to information and extension services. Additionally, the findings indicated that farmers' subjective risk perceptions and experiment-based risk aversion measures were positively and significantly linked with their decision to purchase flood insurance. Risk-averse farmers were more inclined to obtain insurance and those who perceived greater flood risk factors were more willing to pay for flood insurance than those who perceived lower risks. In designing the insurance scheme, the socio-economic features of flood affected rural farm households as well as their risk perceptions and attitudes should be considered carefully.

A Gradient Boosting Approach to Identify Behavioral and Policy Determinants of Flood Resilience in the Continental US

Annual flood losses in the US currently amount to more than 30 billion US$. A common way for individuals to strengthen household flood resilience is purchasing a flood insurance. However, only less than 5% of all US households currently have a flood insurance. Understanding the determinants of flood insurance purchase is key to foster preparedness and community resilience. Here, we formulate the problem of identifying the determinants of flood insurance purchase as a feature selection problem. We develop a gradient boosting modelling approach to select the most relevant predictors of flood insurance for the continental US. First, we train the gradient boosting model on a large open dataset containing publicly available records on more than 50 million flood insurances purchased between 2009 and 2020, socio-demographic data extracted from more than 25000 census variables, and data on the participation in the Community Rating System, a voluntary incentive program of the US National Flood Insurance Program. Then, we analyze feature importance metrics to identify the most relevant determinants. Numerical results indicate that our model successfully approximates the functional relation between some of the candidate predictors and flood insurance purchase rates in the continental US. Householders purchase flood insurances with a reactive attitude: flood insurances are purchased particularly in areas where householders have already experienced the impacts of flooding. More proactive behaviors can be effectively encouraged by community policies and public incentive schemes.

Game theoretic approach for flood risk management considering a financial model

In process of flood risk management, there is usually a conflict between the governments, insurance companies and people as players in sharing financial responsibilities, because of how calculate insurance premium, compensation and governmental assistances. In order to this conflict resolution, in this paper a new methodology is proposed based on game theoretic approach by using Nash equilibrium method, that for the first time, the payoff functions of insurance companies and government have been associated to each other. In this method, according to maximization of player's payoffs, the best response has been found and for the first time, comprehensive financial model has been developed for sharing players' financial responsibilities in flood risk management plans. In order to increase the accuracy of flood insurance calculations, the parameters of deductible and franchise have been applied. The key findings indicate, success of flood risk management plans depend on purchasing flood insurance with full coverage and government financial assistances simultaneously. The developed flood insurance plan has most sensitivity to parameters of deductible and insurance rate. This model could be utilized as practical and academic model in flood risk management. The applicability and efficiency of the methodology are examined in Northeast of Tehran as capital of Iran.

Agent-based Modeling to Evaluate Human-Environment Interactions in Community Flood Risk Mitigation.

This article deals with household-level flood risk mitigation. We present an agent-based modeling framework to simulate the mechanism of natural hazard and human interactions, to allow evaluation of community flood risk, and to predict various adaptation outcomes. The framework considers each household as an autonomous, yet socially connected, agent. A Beta-Bernoulli Bayesian learning model is first applied to measure changes of agents' risk perceptions in response to stochastic storm surges. Then the risk appraisal behaviors of agents, as a function of willingness-to-pay for flood insurance, are measured. Using Miami-Dade County, Florida as a case study, we simulated four scenarios to evaluate the outcomes of alternative adaptation strategies. Results show that community damage decreases significantly after a few years when agents become cognizant of flood risks. Compared to insurance policies with pre-Flood Insurance Rate Maps subsidies, risk-based insurance policies are more effective in promoting community resilience, but it will decrease motivations to purchase flood insurance, especially for households outside of high-risk areas. We evaluated vital model parameters using a local sensitivity analysis. Simulation results demonstrate the importance of an integrated adaptation strategy in community flood risk management.

Flooded Social Connections

Does salient information on social media influence individuals’ economic decisions and beliefs? Using aggregated data from Facebook and a difference-in-differences strategy, I show that individuals who are socially connected to someone affected by Hurricane Harvey are more likely to purchase flood insurance policies after the event. This effect is stronger in areas at higher risk of flooding. Being socially connected to someone affected by Hurricane Harvey also influences individuals’ perceptions of global warming.

Bankruptcy filings, flooding, real estate prices and Leading Index

PurposeFlood damage to uninsured single-family homes shifts the entire burden of costly repairs onto the homeowner. Homeowners in the United States and in much of Europe can purchase flood insurance. The Netherlands and Asian countries generally do not offer flood insurance protection to homeowners. Uninsured households incur the entire cost of repairing/replacing properties damaged due to flooding. Homeowners’ policies do not cover damage caused by flooding. The paper examines the link between personal bankruptcy and the severity of flooding events, property prices and financial condition levels.Design/methodology/approachA fully modified ordinary least squares (FMOLS) regression model is developed which uses personal bankruptcy filings as its dependent variable during the years 2000 through 2018. This time-series model considers the association between personal bankruptcy court filings and costly, widespread flooding events. Independent variables were selected that potentially act as mitigating factors reducing bankruptcy filings.FindingsThe FMOLS regression results found a significant, positive association between flooding events and the total number of personal bankruptcy filings. Higher flooding costs were associated with higher bankruptcy filings. The Home Price Index is inversely related to the bankruptcy dependent variable. The R-squared results indicate that 0.65% of the movement in the dependent variable personal bankruptcy filings is explained by the severity of a flooding event and other independent variables.Research limitations/implicationsThe severity of the flooding event is measured using dollar losses incurred by the National Flood Insurance program. A macro-case study was undertaken, but the research results would have been enhanced by examining local areas and demographic factors that may have made bankruptcy filing following a flooding event more or less likely.Practical implicationsThe paper considers the impact of the natural disaster flooding on bankruptcy rates filings. The findings may have implications for multi-family properties as well as single-family housing. Purchasing flood insurance generally mitigates the likelihood of severe financial risk to the property owner.Social implicationsNatural flood insurance is underwritten by the federal government and/or by private insurers. The financial health of private property insurers that underwrite flooding and their ability to meet losses incurred needs to be carefully scrutinized by the insured.Originality/valuePrior studies analyzing the linkages existing between housing prices, natural disasters and bankruptcy used descriptive data, mostly percentages, when considering this association. The study herein posits the same questions as these prior studies but used regression analysis to analyze the linkages. The methodology enables additional independent variables to be added to the analysis.

The FLOod Probability Interpolation Tool (FLOPIT): A Simple Tool to Improve Spatial Flood Probability Quantification and Communication

Understanding flood probabilities is essential to making sound decisions about flood-risk management. Many people rely on flood probability maps to inform decisions about purchasing flood insurance, buying or selling real-estate, flood-proofing a house, or managing floodplain development. Current flood probability maps typically use flood zones (for example the 1 in 100 or 1 in 500-year flood zones) to communicate flooding probabilities. However, this choice of communication format can miss important details and lead to biased risk assessments. Here we develop, test, and demonstrate the FLOod Probability Interpolation Tool (FLOPIT). FLOPIT interpolates flood probabilities between water surface elevation to produce continuous flood-probability maps. FLOPIT uses water surface elevation inundation maps for at least two return periods and creates Annual Exceedance Probability (AEP) as well as inundation maps for new return levels. Potential advantages of FLOPIT include being open-source, relatively easy to implement, capable of creating inundation maps from agencies other than FEMA, and applicable to locations where FEMA published flood inundation maps but not flood probability. Using publicly available data from the Federal Emergency Management Agency (FEMA) flood risk databases as well as state and national datasets, we produce continuous flood-probability maps at three example locations in the United States: Houston (TX), Muncy (PA), and Selinsgrove (PA). We find that the discrete flood zones generally communicate substantially lower flood probabilities than the continuous estimates.

Default options and insurance demand

Default options may provide a low-cost way of influencing behaviour without modifying incentives and constraining choices between alternatives. However, an improved understanding is needed on whether they are effective when individuals have experience with making the choice in practice and have preferences for specific alternatives. We study whether defaults can be used to increase insurance coverage against low-probability/high-impact risks, like floods, and whether past flood insurance purchases and flooding experience moderate the effect of defaults. Our study uses a naturally occurring difference in experience, comparing the surveyed flood insurance choices of 1,187 homeowners, half of whom are in the Netherlands, where flood insurance penetration rates are low and recent flooding caused minor losses, and the other half of whom are in the United Kingdom (UK), where the opposite is true. We find defaults are effective amongst homeowners with little to no flood-related experience: in the Netherlands defaults increase the likelihood of insuring by between 17 and 18 percentage points. Although there is no overall effect of defaults in the UK, defaults increase flood insurance coverage for risk averse individuals, and those who have no reported previous flood experience and have not purchased flood insurance. Anticipated regret about not having insurance coverage in the event of a flood, and perceptions about the insurance cost explain between 34 and 37 percent of the relationship between the default and flood insurance demand. We discuss policy implications of our findings.

The FIMA NFIP’s Redacted Policies and Redacted Claims Datasets

The National Flood Insurance Program (NFIP) was created in 1968 and allows homeowners, renters, and businesses to purchase flood insurance from the federal government. During the summer of 2019, without compromising privacy, the Federal Emergency Management Agency (FEMA) released two datasets containing roughly 50 million flood insurance policy observations (beginning in 2009) and 2.5 million flood insurance claims (beginning in 1970). Researchers can now download and evaluate the entire policies and claims datasets in machine-readable format, bypassing the complex request procedures of the past. We explore what is included in this policy and claims data and how they might be used to examine flood insurance related topics. We provide real estate academics and industry professionals with the details of the 44 usable policy data variables and the 37 usable claims data variables, which we group into seven categories: Locational, Structural, Occupancy, Policy Terms, Zone/Elevation/Rating, Premiums, and Claims. In an effort to aid researchers with the initial complexities of working with the data, we provide sample R-code that can be altered to analyze NFIP data. Finally, for illustration, we demonstrate how the NFIP data can be merged with data from both the American Community Survey and Zillow to study the determinants of flood insurance take-up.