Reliable physical risk models require access to climate expertise and granular property data
This is the first installment of a three-part series on physical, climate-related financial risk assessments. In parts two and three, we will dive deeper into the intersection of peril risk models and climate models, as well as the ability to translate climate risk analyses into actionable financial information.
Recent scientific studies indicate that human impacts have likely created a new geologic chapter in the earth’s history. If that statement is surprising, just look at current weather events. Only six months into 2023, the U.S. had already experienced 12 disasters exceeding $1 billion each and spanning a range of natural hazards, including:
- July 2023 was the hottest month ever recorded.
- Massive wildfires have more than doubled Canada’s yearly carbon emissions.
- Florida sea water hit hot tub-level temperatures, which has fueled hurricane development.
Climate change is a real and present danger, and the consequences of climate-related financial risks have already begun to manifest via conventional pathways, such as credit risk, market risk and liquidity risk.
A significant portion of climate-related financial risk stems from natural hazards, including hurricanes, earthquakes, wildfires, floods and tornados. Climate change will affect these perils in years to come, so it is imperative to have a baseline assessment of today’s peril risk to develop a complete understanding of tomorrow’s climate-related financial risk.
And that requires a reliable peril risk model.
Good Enterprise Risk Management Needs Good Property Data
When modeling anything — from financial forecasts to fantasy football predictions — the quality of both data input and structure limits or expands the reliability of a model’s outputs accordingly. Those outputs will then have an outsized impact on any potential insights.
Discover All 8 Best Practices for Physical Risk Modeling
Peril risk modeling is no exception. These models require enormous amounts of data for construction and validation, making data quality and availability a paramount concern. Additionally, reliable peril risk models must be created with an understanding of the physical mechanisms that control the frequency and severity of natural hazards.
Currently, there are no federally established standards for assessing physical peril risk. As a result, the financial and real estate markets are flooded with third-party service providers that claim to offer accurate physical peril risk assessments. However, many of these service providers have neither the breadth nor the quality of data necessary for reliable and actionable analyses.
From our unique position at the intersection of property-specific housing data and climate science, CoreLogic has the requisite data archives and industry expertise to guide regulators and financial institutions to prepare for the future.
8 Opportunities to Future-Proof Enterprise Risk Management Strategies
Regulators and financial institutions seeking to address climate-related financial risks face three common roadblocks. The first of these obstacles is addressed in the initial installment of our white paper series.
Through the research and explanation of eight best practices, CoreLogic seeks to address the current confusion surrounding the proper level of data granularity required to conduct peril risk analyses, the types of property and structure information needed to produce reliable results and additional model characteristics that have a substantial effect on the quality of a model’s outputs.
- All inputs – and outputs – must be granular to the individual property level.
While ZIP code-level and census tract-level information can be useful for some broader purposes, such as those that are regional, portfolio-level analyses for national financial institutions require a deeper level of granularity when developing an actionable understanding of peril risk and its effects on both day-to-day operations and forward-looking assessments.
- Detailed valuation data is necessary to develop a true understanding of risk.
Peril risk is often understood via conventional financial risk terminology and pathways. However, this risk can be conveyed in financial terms only with a reliable valuation of each affected property, since damage is a direct function of building characteristics.
- All model inputs must be updated as often as possible.
Depending on the specific data set, the location from where the data is retrieved and the ability to efficiently transmit this information, update intervals can vary from minutes to years. Needless to say, having a peril risk modeler that understands these nuances is critical.
While there are many attributes to consider when selecting a peril risk model, only a few have the potential to influence the future financial health of your institution. In the first part of this CoreLogic® white paper, we identify the eight best practices to follow when selecting a physical risk model to ensure reliable and actionable results.
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