Frequently Asked Questions

Climate Risk Analytics (CRA) on Araya is an advanced financial intelligence tool that translates complex climate science into actionable, property-level risk assessments.

By blending Cotality’s high-definition catastrophe peril models with real-world building characteristics and financial valuations, CRA allows you to pinpoint the physical and economic impacts of climate change on individual properties or entire portfolios.

ESG is an acronym for Environmental, Social, and Governance. It is a framework that helps stakeholders understand how an organization manages risks and opportunities related to these three criteria, operating on the holistic view that sustainability extends far beyond just environmental issues.

• Environmental factors refer to an organization's environmental impacts and risk management practices.

• These include direct and indirect greenhouse gas emissions, management's stewardship over natural resources, and the firm's overall resiliency against physical climate risks (e.g., climate change, flooding, fires).

Climate change refers to long-term shifts in temperatures and weather patterns. These shifts may be natural, such as through variations in the solar cycle. However, since the 1800s, human activities have been the main driver of climate change, primarily due to burning fossil fuels like coal, oil, and gas. (Source: UN.org)

Extreme weather and natural disasters caused a total of $92.9 billion in damages in the United States in 2023. The U.S. experienced 28 separate weather or climate disasters that each resulted in at least $1 billion in damages.

The greatest financial risk is concentrated in the U.S. real estate industry—the biggest single asset class on the planet, valued at over $43 trillion. (Source: climate.gov)

There is a clear, present, and increasing danger to our planet:

• The New Normal: 2023 was the fourth consecutive year (2020–2023) in which 18 or more separate billion-dollar disaster events impacted the U.S.

• Rising Frequency & Cost: The 1980–2023 annual average is 8.5 events with $18 billion in losses (CPI-adjusted); however, the annual average for the most recent 5 years (2019–2023) jumped to 20.4 events with $18 billion in losses (CPI-adjusted). (Source: climate.gov)

As a result, government agencies and corporations are rapidly increasing requirements for climate risk disclosures and regulations.

Cotality uses detailed property characteristics, valuations, replacement costs, peril models, and climate change models to help organizations understand the physical and economic risks of climate change and the financial impact on their portfolios. This allows them to confidently measure, mitigate, and model risk today and in the future.

To deliver relevant, asset-level insights, Climate Risk Analytics dynamically pairs four core data components:

• Property-Level Physical Insights: Building age, construction material, First Floor Height (FFH), and other features foundational to ~190 million structures.

• Property-Level Financials: Specific local replacement cost values and asset valuations.

• Specific Peril Impacts: Nine perils and sub-perils, including the frequency and severity of damage exacted on properties.

• Climate Scenarios: Pathways along which specific environmental conditions change at the macro level. When dynamically and statistically downscaled, scenarios are locally applied using third-generation large peril-event sets.

Within the banking industry, executives can leverage familiar processes like CCAR (Comprehensive Capital Analysis and Review) and other forms of stress testing. Banks will find that leaning on these existing workflows is the most efficient way to incorporate Climate Risk Analytics into their risk reporting.

• Public Sector/ Government: Setting policy and risk boundaries.

• Financial Services/ Non-Mortgage: Physical risk assessment.

• Capital Markets: Physical risk assessment and regulatory compliance.

• Adjacent Markets (Retail, Utilities, Telecom, Corporations): Physical risk assessment and long-term business planning.

• Data Granularity: Obtaining data at the exact level of granularity needed for asset-level risk evaluation.

• Data Breadth: Finding data with the breadth to cover all perils across all climate scenarios, time horizons, and RCPs/SSPs.

• Portfolio Assessment: Understanding portfolio risk at the property level alongside its associated economic impact while preparing for changing regulations.

Cotality High-Definition Catastrophe (CAT) Models These represent a new generation of models incorporating high-definition stochastic event sets and a 300,000-year stochastic simulation consistent with the physical processes of natural events. Combined with comprehensive vulnerability and fragility functions developed via deep structural engineering knowledge, claims data, and exposure data, Cotality's CAT models produce a unique view of global catastrophe risk. Our models have been rigorously examined and benchmarked against recent climate events and real-world claims data.

Climate-Coupled-Catastrophe Models™ Quantifying future climate risk requires a complete understanding of future severity and frequency of climate conditions across time and space. The Cotality AR6 generation of Coupled Climate and Catastrophe models is vital for an accurate understanding of climate risk.

Older generations of climate risk modeling depended on conditioning Catastrophe models with future climate using frequency factors applied to current climate conditions; these models are significantly less accurate because they fail to capture a consistent representation of changing climate frequency and severity.

Global Climate Models (GCMs) simulate the biological, physical, and chemical processes of the Earth's climate system. Because of intense computational requirements, GCMs typically model the climate system at a coarse 100-km horizontal resolution. To quantify future climate risk at the property level, this resolution must be enhanced significantly. This enhancement process is called downscaling.

There are two primary classes of downscaling:

• Dynamical Downscaling: Uses high-resolution Regional Climate Models (RCM) guided by scientific laws (physical and dynamical principles) expected to hold under climate change. They preserve physical consistency, dependency, and correlation between climate variables. Cotality believes dynamical downscaling is essential to produce accurate climate change risk measures at the property level.

• Statistical Downscaling: Relies on establishing statistical relationships between GCM output and high-resolution observation datasets in the same historical period. Assuming the relationship will continue to hold in the future, it can then be applied to downscale future GCM data.

Climate Risk Analytics (CRA AR5) provides a detailed financial assessment of climate impacts based on the IPCC's 5th Assessment Report. It contains 20+ detailed risk measures, including Average Annual Loss (AAL) and multiple Probable Maximum Loss (PML) calculations across various climate scenarios. These are normalized to a 0–100 Composite Risk Score.

Climate Risk Analytics (CRA AR6) reflects the natural progression of climate science. Our AR6 Climate-Coupled-Catastrophe Models™ are downscaled from the latest CMIP6 models for incredible granularity. AR6 includes socioeconomic factors (SSPs) designed to function in combination with improved versions of RCPs. In this way, different climate policy futures (e.g., switching to renewable energy) can be superimposed on socioeconomic trends.

This is a first-of-its-kind project to use state-of-the-art dynamically downscaled Climate-Coupled-Catastrophe Models™ to quantify property-level climate change risk.

SSPs (outlined in CMIP6) build upon and look beyond the greenhouse gas concentration-based RCPs from CMIP5 by adding social and economic context.