At the core of Sust Global’s offering is our composable geospatial intelligence platform, a breakthrough technology stack that fuses multi-modal geospatial datasets with observed, modeled and inferential data derived from proprietary deep learning approaches. This platform seamlessly integrates:

• High-resolution climate hazard data: Powered by satellite-derived observations and frontier climate models, Sust Global provides granular, asset-level risk assessments for hazards such as flooding, wildfires, and cyclones.

• Financial datasets: Sust Global’s platform synthesizes diverse data types—spanning CMBS loan-level data (outstanding balances, geographic distribution, collateral characteristics), and climate-adjusted prepayment models and pricing dynamics (Conditional Prepayment Rates, Option-Adjusted Spreads and Weighted Average Maturity). Through geospatial data fusion techniques, Sust Global is able to use AI-powered inference models to derive forward-looking risk insights.

• API-driven flexibility: Sust Global’s APIs enable Yield Book to integrate climate intelligence into their processes and workflows, enhancing the precision of CMBS prepayment and valuation models.

Reducing investor exposure to emergent risks

The financial sector is increasingly vulnerable to the economic fallout from more frequent and severe climate events, with the impacts affecting market dynamics and investment strategies. However, financial securities today do not properly account for these risks. A recent global survey of institutional asset owners managing a combined $34.5 trillion worth of investments found that the vast majority of them do not think financial securities properly capture climate risks.

Analysts at financial institutions are therefore seeking ways to effectively measure and manage these emergent risks, with a consistent, data-driven approach to risk assessment.

Sust Global’s composable geospatial intelligence platform provides transformative capabilities for investors – such as Yield Book’s customers – to solve these challenges. Designed to enable climate-informed decision making, the geospatial platform fuses advanced remote sensing, geospatial artificial intelligence, and a variety of climate and financial models into a seamless, API-driven framework. This approach ensures that institutional investors and financial institutions can adopt consistent, data-rich methodologies for climate risk assessment.

Our solution encompasses:

• Forward-looking Scenario Analysis: Leveraging data derived from the latest climate science and geospatial datasets, Sust Global provides predictive insights into emergent risks, including physical climate risk.

• Multi-model Risk Assessment: The platform enables comprehensive fusion of spatial and financial datasets, via unique data fusion approaches. This architecture enables investors to address direct exposures (e.g., asset-level hazards) and indirect financial impacts (e.g., operational disruptions) from climate across a range of asset classes and geographies.

• Dynamic Data Integration: Through API-led workflows, Sust Global supports flexible data aggregation across granular (asset-level) and macro (sector-wide) scales, ensuring adaptability for diverse use cases.

Our proposition combines the latest in remote sensing, data modeling, and geospatial artificial intelligence. This enables robust validation and is tailored for large-scale, global risk analysis, offering insights that financial entities and actors can seamlessly integrate into valuation models, prepayment analysis, and asset management processes.

Composable climate analytics at Yield Book

Sust Global’s APIs serve geospatial data, financial analytics and industry standard climate metrics, enabling new and existing investor processes across financial services and investor communities. Each such workflow is a composition of the foundational building blocks of analysis powered by our geospatial intelligence platform representing a new generation of financial processes and workflows that incorporate alternative datasets, such as climate analytics, and other types of non-standard geospatial data. 

Sust Global’s partnership with Yield Book is a great example of the fusion of advances in geospatial AI inference, composable data tooling and alternative data as investor insights. Together, Sust Global and Yield Book recently launched tailored physical climate risk analytics for US commercial and residential mortgage-backed securities (MBS).

The joint solution combines Yield Book’s leading financial analytics and asset-level datasets with our geospatial processing platform to provide a view into the pricing impact of various climate risks on US MBS portfolios. Risk to MBS assets is then aggregated at the loan and pool level.

• Scoreseverity = latest severity of risk event

• Scoretrend = Change in severity for most recent periods

• Scoreavg = historical average severity measure

YBCC Metric_risk = βseverity * Scoreseverity + βtrend * Scoretrend + βavg * Scoreavg where risk = cyclone, flood and wildfire, β is the corresponding weight.

The collaboration introduces two main API integrations:

Climate analytics for properties and loans

For on-demand climate analytics, Sust Global processes individual loans and issuer level assets through batch processing and search APIs, serving climate risk analytics on hazards like wildfire, floods, cyclones, sea level rise, heat stress and water stress based on frontier climate models and latest satellite-derived hazard event observations.

Climate metrics for listed CMBS

Yield Book is able to analyze mortgage pools based on their underlying securities. This process covers securities including Agency RMBS (Residential Mortgage-Backed Securities) at state level, Non-Agency RMBS at zip code level, and Listed CMBS (Commercial Mortgage-Backed Securities) loans. As the loan level information in MBS pools is relatively static (a "fixed universe"), it can be processed and updated all at once. Yield Book integrates with a bulk query API from Sust Global to replicate data to their data warehouse, enabling partner applications like Yield Book plugin and Yield Book Classic with climate metrics.

These integrations enable a detailed, climate-aware evaluation of these investment instruments. The end product applies to a range of industry use cases, including automating reports to monitor climate risk metrics on portfolios and update key stakeholders, deal structuring, back-testing, and deep research and risk mitigation strategies using historical data.

Outcomes for climate-informed structured finance

The Yield Book analytics team researched the impact of physical climate risk on Agency CMBS, powered by a combination of Sust Global data and their own industry-standard MBS models. They analyzed 54K active loans backing CMBS securities with a total $1,122B exposure, including $392B in Agency CMBS and $730B in non-Agency CMBS. Here are the key outcomes from their research:

1. Climate-informed prepayment analysis

Left: Geospatial distribution of Yield Book Metric: Metric designed and computed based on satellite derived actuals of flood risk exposure at the zip code level using Sust Global’s global climate risk API. Right: Actuals analysis of prepayment speeds (Low Flood Risk vs High Flood Risks): This analysis was run on GNPL actuals for lifetime average CPR. Analysis based on satellite derived actual flood risk exposure over loan periods from Sust Global’s global.

Conditional Prepayment Rate (CPR) is a vital metric in the MBS market, influencing cash flows, yields, duration, and overall valuation. CPR provides a standardized measure of the annualized rate of prepayment of principal on mortgage loans. Since MBS are backed by these mortgages, prepayment rates directly affect the securities' value and performance.
Prepayments can occur when homeowners refinance, sell their homes, or make larger than required payments on their mortgage. The valuation of MBS is heavily dependent on the projected prepayment rates. CPR is a key input in financial models used to value these securities, affecting decisions on buying, selling, or holding these assets.

Modeling CPR helps in modeling prepayment risk – the risk that prepayments will occur at an unfavorable time, affecting the profitability of the investment. This is particularly important for portfolio managers and investors in structuring and choosing their MBS investments.

From their recently published study, the Yield Book research team found a connection between increased physical climate risk and lower prepayment rates in loans which in turn enables more accurate valuation models for MBS.

2. Climate-informed pricing analysis

Left: Geospatial distribution of Sample for Weighted Average Maturity (WAM) analysis: 100FN DUS securities (pools) backed by properties located in high flood risk areas. Right: Valuation impact by Weighted Average Maturity (WAM) (Climate scenarios: SSP2-RCP4.5 and SSP5-RCP8.5): Most pronounced valuation disparities in securities with longer WAM. The more severe climate scenario (SSP5-RCP8.5) manifests a bigger price impact. Analysis based on climate projections on flood risk exposure from Sust Global’s global climate risk API.

Weighted Average Maturity (WAM) calculates the average time until the mortgages in an MBS portfolio are paid off. It's weighted based on the principal balance of each loan, giving a more accurate representation of the portfolio's maturity profile compared to a simple average.
WAM is a key factor in assessing the interest rate risk of an MBS. Longer WAM indicates a longer duration, meaning the security is more sensitive to changes in interest rates. This is crucial for investors who need to manage the risk associated with rate fluctuations. Investors use WAM to tailor their portfolios to match their investment horizons or risk preferences. For instance, a portfolio with a shorter WAM might be preferred by investors seeking less interest rate risk and a quicker return of principal.

The Yield Book research team concluded that repricing risks with climate considerations, in this case accounting for flood risk projections, leads to prices that deviate from market norms.

3. Customized, instrument-level risk metrics

Yield Book has developed a risk metric encapsulating the predominant climate risks, integrating present-day hazard severity, contemporary trends, and historical averages. This metric enables the representation of risk for such analysis applied to active loans. The metric will vary across loans based on their geographic distribution.

While this metric is indicative of representative climate risk quantification for CMBS, it is representative of climate informed metrics designed to address specific instruments in structured finance in the future.

Global data coverage

Sust Global offers global data coverage. We have processed data for over 150,000 client assets worldwide using our platform and via our APIs across varied dynamic customer workflows .

Sust Global's partnership with Yield Book is a major landmark in the journey towards climate-aware financial markets. Such partnerships help investors quantify and visualize not only what climate risks and opportunities look like for them today, but also what they might look like in 2030, 2040, and 2050.

Commenting on our collaboration Cornelia Andersson, Group Head of Sustainable Finance and Investing, London Stock Exchange Group, said:

“This strongly supports LSEG’s role in mobilizing capital for a sustainable global economy. This product is a great addition to our existing portfolio and efforts to support clients across the globe on sustainability challenges, through quantitative integration of climate risks and considerations in existing workflows and tooling.”

We are actively partnering to expand the use of AI-driven climate data and geospatial analytics in financial decision-making as we strive to ensure that every decision is climate-informed.

Reach out to us below and let’s talk about how we can work together climate-informed decision making across structured finance products.

Further reading:

The software of climate adaptation, Gopal Erinjippurath, December 2021

Do asset prices fully reflect climate risks and opportunities, KPMG, February 2022

The role of AI in fireproofing forest carbon, Gopal Erinjippurath, May 2023

Assessing physical climate risks on Agency CMBS, Yield Book, September 2023