There is mounting pressure on (re)insurers to quantify the impacts of climate change, notably on the frequency and severity of claims due to weather events such as flooding. This is however a very challenging task for (re)insurers as it requires modeling at the scale of a portfolio and at a high enough spatial resolution to incorporate local climate change effects. In this paper, we introduce a data science approach to climate change risk assessment of pluvial flooding for insurance portfolios over Canada and the United States (US). The underlying flood occurrence model quantifies the financial impacts of short-term (12–48 h) precipitation dynamics over the present (2010–2030) and future climate (2040–2060) by leveraging statistical/machine learning and regional climate models. The flood occurrence model is designed for applications that do not require street-level precision as is often the case for scenario and trend analyses. It is applied at the full scale of Canada and the US over 10–25 km grids. Our analyses show that climate change and urbanization will typically increase losses over Canada and the US, while impacts are strongly heterogeneous from one state or province to another, or even within a territory. Portfolio applications highlight the importance for a (re)insurer to differentiate between future changes in hazard and exposure, as the latter may magnify or attenuate the impacts of climate change on losses.

Community Rating System (CRS) incentivizes investments in risk reduction above NFIP standards using discounts on insurance premiums. These discounts are cross-subsidized by increasing premiums in non-CRS communities. We examine the distribution of these subsidies and find that redistribution does occur, but the gains and losses are not economically large with 95% of households gaining or losing no more than 0.3% of household income. We also examine their relationship with other community characteristics and find that the strongest predictor of premium reductions is the underlying flood risk level within the community. Thus, CRS appears to reduce the cost of living in the riskier communities.

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.

Ambiguity – the capacity to have multiple meanings – is endemic to politics. Ambiguity creates political opportunities, structures debates and provides leeway for political entrepreneurs to advance their interests. I use the 2012 passage and 2014 rollback of reforms to the National Flood Insurance Program to show how ambiguity enables political entrepreneurship. In this puzzling case, Congress enacted and rolled back changes that threatened to impose politically unpalatable costs. Using semi-structured interviews and congressional testimony, I show how political entrepreneurs engaged with ambiguity in the buildup to the reforms’ passage. They used information strategically to interpret problems, solutions, rules, and goals; shape legislators’ perceptions of the reforms’ political implications; and adapt their arguments to the policy windows that opened. The case shows that ambiguity facilitates policy reform, but the direction of change depends on the priorities that are salient when a policy window opens and on the interests of political entrepreneurs.