Average annual loss (AAL) is traditionally used as the basis of assessing flood risk and evaluating risk mitigation measures. This research presents an improved implementation to estimate building-specific AAL, with the flood hazard of a building represented by the Gumbel extreme value distribution. AAL is then calculated by integrating the area under the overall loss-exceedance probability curve using trapezoidal Riemann sums. This implementation is compared with existing AAL estimations from flood risk assessment. A sensitivity analysis is conducted to examine the variability in AAL results based on depth-damage function (DDF) choice. To demonstrate the methodology, a one-story single-family residence is selected to assess the financial benefits of freeboard (i.e., increasing lowest floor elevations). Results show that 1 ft. of freeboard results in annual flood risk reduction of over $1,000, while 4 ft of freeboard results in annual flood risk reduction of nearly $2,000. The sensitivity result suggests that the DDF selection is critical, as a large proportion of flood loss is counted below the top of the first floor. The findings of this paper will enhance DDF selection, improve flood loss estimates, encourage homeowners and communities to invest in flood mitigation, and provide government decision-makers with improved information when considering building code changes.

Evaluating average annual loss (AAL) is an essential component of assessing and minimizing future flood risk. A robust method for quantifying flood AAL is needed to provide valuable information for stakeholder decision-making. Several recent studies suggest that the numerical integration method can provide meaningful AAL estimates since this technique includes the full loss‐exceedance probability of flood. While past research focuses on applying the numerical integration method on a single, one-family residential house, calculations across space are necessary for assessing community vulnerability. This research develops a computational framework in MATLAB for integrating across the full loss-exceedance probability curve through space to evaluate flood AAL for multiple single-family homes, including loss to the structure, content, and time spent in refurbishing it (i.e., use), over a case-study census block in Jefferson Parish, Louisiana, USA. To further inform flood mitigation planning, the AAL is also calculated for one, two, three, and four feet of freeboard and separately for each owner-occupant type of residence (i.e., homeowner, landlord, and tenant). Although previous studies provided essential information related to the structure and content loss for one type for ownership-occupant type (homeowner), the wider scope of this study allows for consideration of the use loss and the remaining ownership-occupant types. Results show that individual building AAL varies within a community because of its building attributes. Besides, results highlight the difference of AALs by owner-occupant type, with homeowners generally bearing the highest total AAL and tenants incurring the lowest total AALs. A simple elevation of only one foot can decrease the AAL by as much as 90 percent. A sensitivity analysis underscores the importance of using the exact values of the base flood elevation (BFE) compared to rounding to the nearest integer and excluding damage lower than first flood height (FFH) in the depth-damage functions (DDFs). Expanding the application of the numerical integration method to a broad-scale study area may enhance validity and accuracy as compensating errors are likely to make bulk estimates more reasonable, which might augment its utility at the community level. In general, such techniques improve resilience to flood, the costliest natural hazard, by assisting in better understanding of risk with and without mitigation efforts.  

Average annual loss (AAL) is used as the basis for the evaluation of risk mitigation measures.  However, the current AAL implementations in flood risk assessment have several shortcomings. For instance, results generated using Riemann trapezoids for the available return periods of a site are typically gross approximations, especially when damage changes rapidly with depth. Monte Carlo simulations offer improvements in precision but at the expense of being computationally intensive. The log-linear method that extrapolates losses to higher return periods and performs piece-wise Riemann sum with these limited return periods can fail to capture the non-linear flood behavior. This paper presents an improved implementation that quantifies AAL at the micro-scale level including the full range of loss‐exceedance probabilities. To demonstrate the methodology, the financial benefit of increasing the lowest floor elevation for a one-story single-family residence is assessed. Several depth-damage functions (DDFs) are selected and compared to examine the variability in AAL results related to the DDF choice. Results demonstrate the need for an AAL estimate that includes the full loss‐exceedance probabilities. Results also highlight the need to assess flood risk at the micro-scale level for a more localized and accurate assessment, whereupon the estimate can be expanded to broader-scale risk estimations with a higher degree of accuracy. The more realistic AAL estimates results could encourage homeowners and communities to take action and support government decision-makers by investing in flood mitigation and considering building code changes.

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., 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.

Accurate loss assessment plays a vital role in understanding the economic risk of natural hazards, for planning, mitigation, and actuarial purposes. Because of its juggernaut status as the most widespread and costly hazard, both nationally and around the world, loss assessment due to flood is particularly important. One of the shortcomings in existing flood loss models is to partition the structure (or building) economic value of loss into that borne by the homeowner and that covered by flood insurance. The goal of this research is to model the loss incurred by the homeowner and that incurred by the National Flood Insurance Program, considering flood damage, building replacement value, flood insurance coverage amount, deductible, and flood characteristics (slope and y-intercept of the loss vs. return period curve). A Monte Carlo approach is used to calculate the annual average loss due to flood at the individual homeowner scale. Multiple linear regression (MLR) and Classification and Regression Tree (CART) models are trained to provide the output of the owner’s share of the loss. The CART model outperformed the MLR model with lower RMSE and MSE values and a higher R² value (0.95) on the test data set. Because out-of-pocket expenses due to flood can be devastating to financial security, the results of this study support and inform the proactive decision-making process that homeowners can use to self-assess their degree of preparation and vulnerability to the flood hazard.