From data to decisions: Leveraging data for climate risk portfolio assessment

Climate change is creating both significant challenges and new opportunities for the real estate sector. Total losses from physical climate events hit USD 162 billion in the first half of 2025 alone ^[1], with the U.S. accounting for a record USD 126 billion for this period. The need for immediate action to protect assets and people from increasingly frequent and severe weather events is clear. However, the challenge to mitigate risk also creates a significant opportunity. For forward-thinking portfolio managers, it’s a chance to secure long-term value and generate resilient investment returns. As the climate becomes more unpredictable, the increasing frequency and complexity of these events demands an approach that prioritizes data and technology to inform investment, portfolio planning, and strategic climate adaptation decisions.

To build a climate-resilient real estate portfolio, firms must first assess physical climate risk. This involves using climate models to forecast a portfolio’s exposure to threats such as extreme heat and flooding. However, the lack of standardization in the rapidly evolving climate analytics market creates significant challenges when selecting a vendor. Key challenges include the lack of transparency in vendor methodologies and the prohibitive costs of large-scale portfolio assessments. To manage limited budgets, firms should initially focus on assets with a known history of climate-related risks or those in high-risk zones, such as Southern Europe and the Western and Southeastern regions of North America. Ultimately, choosing the right vendor requires aligning their offerings with companies’ specific needs, including the desired granularity and geographic scope. In particular, the selected vendor must demonstrate robust Model Risk Management (MRM) to ensure the outputs are reliable enough for critical financial decisions.

Integrating climate data into real estate risk management is key, yet the greatest challenge lies in consistently collecting high-quality, reliable data for large and diverse portfolios. While vendors can conduct initial assessments using basic locational data, generating actionable insights requires more detailed asset information, including building characteristics, asset values, reinstatement costs, and rental yields.

This asset data is often fragmented across different sources and lacks a standardized format, making aggregation difficult and potentially producing inaccurate risk assessments. To overcome this, portfolio managers should proactively collaborate with facilities and asset managers to establish standardized data collection frameworks for climate data. They can also leverage existing data from sources like GRESB. Looking ahead, the rise of artificial intelligence and smart building systems offers powerful new opportunities for automated data collection, improving the reliability of data to aid decision-making and increase confidence in climate adaptation strategies.

Assessing a portfolio’s physical climate risk exposure goes beyond identifying threats; it empowers strategic action. To bridge the gap between high-level portfolio analysis and asset-level adaptation implementation, more granular, localized assessments are essential. By pinpointing the most vulnerable assets and regions, managers can effectively direct capital toward measures that build resilience or adjust the portfolio to lower its overall climate risk while still pursuing strong returns. Investors lacking climate mitigation or adaptation strategies will increasingly experience ongoing liquidity constraints and lower sales prices.

JLL research estimates that 15-20% of assets in Europe have immediate actionable risk, positioning them to unlock strategic opportunities in new CapEx. To identify these assets, on-site technical assessments can take historic data and projected climate risk into account. A comprehensive technical assessment considers the building from both a macro (whole building systems such as the Building Management System) and micro (sub-systems such as the drainage system) perspective to better understand the building’s integrated vulnerabilities. Both play a role in protecting critical systems, such as the roof, where damage could significantly impact building operations, occupant safety, and/or business continuity. This approach requires strong collaboration between landlord and tenant to ensure comprehensive assessments and seamless data sharing—with even greater cooperation needed for implementation. Data availability can be an issue due to limited control, data privacy concerns, or communication barriers. However, it also presents the opportunity to establish a secure data exchange partnership and align on minimum standards or best practice guidelines, which can also benefit other endeavors such as decarbonization and adaptation.

After consolidating findings from the climate risk modelling and asset-level deep dives, risk hotspots can be identified. These hotspots combine high-risk hazards, high-vulnerability asset typologies or characteristics, and critical adaptation measures necessary to build resilience. Identified measures must address both the most imminent, current-day threats and long-term chronic risks, particularly as risks overlap and evolve under different climate scenarios. To avoid overspending on capital expenditures, further categorization and prioritization are necessary to identify efficiency opportunities across the portfolio, such as alignment with decarbonization strategies.

Depending on the investment or operational strategy, various scales of analysis can assess portfolio-level risk, from whole geographies to specific asset classes. For example, a Pan-European investor might identify its Southern European assets as most at risk of heat stress. As a result, a targeted systematic approach to asset planning and maintenance can be adopted across these assets. Perhaps the data center typologies are most at risk within this region, and even more specifically in locations at high risk of drought. This shift from consideration of the broader context to deeper analysis offers stakeholders a comprehensive, detail-oriented view of risk across the portfolio. In turn, this demonstrates maturity to investors, offering benefits such as lower insurance risk. Research suggests climate concerns are increasingly reflected in local markets, with 21% of deals in the UK impacted by climate risk, and 94% of respondents to JLL’s UK Investor Survey (July 2024) stating they are actively implementing or considering climate risk mitigation and adaptation within their portfolios^[2].

Future adaptation strategies should address the most imminent risk hot spots while also building long-term resilience against chronic risks to form an integrated resilience plan. Prioritization typically considers implementation timeframes, capital expenditure requirements, and embodied carbon impacts to optimize investments and achieve efficiencies across the portfolio. Successful adaptation planning requires a flexible and adaptable prioritization framework. This allows for regular updates due to constantly evolving scenario analysis and consideration of interdependencies such as geopolitical and human behavior developments. Therefore, a system that monitors and measures the performance of adaptation planning is crucial to best align with this changing risk profile. Concurrently, strong collaboration and engagement between different parties—tenants, landlords, investors, and local communities—remains essential to ensure adaptation planning is constructive. This can be realized through robust data management systems and data sharing agreements that prioritize the quality of data.

The importance of data quality in assessing climate risk cannot be overstated. Although climate data will always contain some level of uncertainty, this is not a reason for portfolio managers to put off assessing risk. It is best to start with the data you have, no matter how limited, and plan to improve its quality and availability over time. This approach ensures continuous refinement of risk assessments and prioritizes actions effectively. Companies that already have strong reporting processes and participate in frameworks like GRESB have a clear advantage. Their established asset, financial, and portfolio data provide a solid foundation for starting an initial climate risk analysis. Ultimately, no matter the size of your dataset, the core principles of data quality—consistency, transparency, and completeness—are what matter most. For forward-thinking portfolio managers, climate risk presents a genuine opportunity to build long-term value, demonstrate maturity to investors, and ensure resilience to an evolving climate.

1. Aon. (2025). 1H 2025 Global Catastrophe Recap. Accessed [September 02, 2025], from https://www.aon.com/getmedia/01e165ae-1788-4997-a51b-9225bce850dd/1H-2025-Global-Catastrophe-Recap.pdf

2. JLL. (n.d.). From Climate Risk to Climate Resilience. Retrieved [September 02, 2025], from https://www.jll.com/en-uk/insights/from-climate-risk-to-climate-resilience

This article was written by Florence Rose and William Hirons, Sustainability Consultants at JLL. Learn more about JLL here.