Our industry is engaged in an important dialogue to improve the efficiency and resilience of real assets through transparency and industry collaboration. This article is a contribution to this larger conversation and does not necessarily reflect GRESB’s position.
As climate risk disclosure becomes a strategic priority of many organizations—driven in part by regulations but also by growing investor expectations—GRESB participants face a familiar roadblock: accessing and making sense of the right data. Whether conducting a physical risk screening, developing a transition plan, or performing scenario analysis, even the most sophisticated asset managers grapple with gaps in data availability, accuracy, interpretability, and usability. It raises a fundamental question: What data should I use, over what time horizon, at what scale, and at what resolution?
In our work with global asset portfolios, we’ve seen that data challenges aren’t about achieving perfection—they’re about achieving clarity of purpose. The objective isn’t to compile the most detailed or comprehensive dataset possible, but rather to build a fit-for-purpose data foundation that enables timely, risk-informed decision-making.
This article explores key lessons from the field to help real asset investors build a climate data foundation that’s strong enough to support reporting—and smart enough to drive resilience.
Lesson 1: Know when you’re screening vs. assessing
One of the most common pitfalls we are seeing is conflating portfolio-level climate screening with asset-level assessments. Yet each serves a distinct purpose and requires a different level of data resolution.
Screening is best for scanning large portfolios to identify geographic or thematic hotspots. It typically uses standardized datasets (e.g., CMIP6 models), potentially at a coarse resolution (25–50 km grids). The goal is breadth, not depth, so you can identify what is most critical to your operations and most vulnerable, enabling deeper evaluation and study.
Asset-level assessments (“assessing”), by contrast, require identification and collection of localized hazard data, more detailed structural characteristics, and often operational context. A more detailed and complex assessment becomes necessary when used to inform site-level adaptation planning, guide investment decision-making, and support more effective engagement with insurers.
Takeaway: Clarifying your purpose helps avoid “data overkill” in screening and “data underwhelm” in asset-level work. One size does not fit all. Think of screening like a routine check-up—broad and designed to flag potential issues across the whole body (or portfolio). Differently, an assessment is like a specialist diagnostic test—targeted, detailed, and aimed at understanding a specific condition so the right treatment (or investment/adaptation) plan can be developed.
Lesson 2: Start with impact potential, not availability
When it comes to real estate investments, not all assets carry the same weight in terms of climate exposure and financial relevance. Institutional investors and pension funds typically apply a structured lens to determine which properties demand deeper analysis and disclosure. For GRESB participants, this means moving beyond uniform data collection and instead building a hierarchy of focus based on key performance indicators (KPIs) that tie directly to financial resilience and business continuity.
Four dimensions for prioritization:
- Insurable value
Assets with the highest insured value naturally represent the greatest potential financial exposure. Insurers are already recalibrating coverage and premiums in response to climate risks; understanding how much capital is at stake gives you a first filter for prioritization. - Asset lifecycle
Assets with long remaining useful lives or significant planned capital expenditure warrant more attention. An older building nearing retirement may not justify expensive climate upgrades, while a newly developed or recently renovated property will need to be future-proofed against decades of evolving climate risk. - Hazard sensitivity
Not all properties face the same perils. Assets in floodplains, wildfire zones, or heat-stressed regions are inherently more sensitive to climate hazards. Prioritization requires overlaying hazard exposure with local adaptation measures and infrastructure resilience. - Mission criticality
Some properties are disproportionately important to a company’s financial performance. Headquarters, key data centers, or logistics hubs may represent a smaller share of portfolio value but are essential to operations. Their disruption carries outsized financial and reputational risk.
GRESB participants are increasingly expected to demonstrate data-driven prioritization of their asset base. By showing that climate risk analysis is weighted by insurable value, lifecycle, hazard sensitivity, and mission criticality, organizations can provide transparency into how climate considerations influence capital allocation and risk management. This approach strengthens resilience reporting and aligns with investor expectations that material climate risks are clearly tied to business performance.
Lesson 3: Understand the tradeoffs in public vs. custom data
Many climate risk assessment platforms rely on publicly available datasets like CMIP6. These can be powerful starting points, but it’s also important to know when to go deeper to produce decision-ready insights.
| Dataset | Pros | Cons |
|---|---|---|
| CMIP6 | Global coverage, consistent across hazards | Only covers temperature, precipitation, and non-cyclone or tornado wind; difficult to use in raw form; coarse resolution (25–50 km); limited to academic models. Not tailored to local or asset-specific conditions |
| WRI Aqueduct | Good for water stress and flood exposure; basin-level data | Not site-specific; may not always account for local adaptation measures or flood defenses |
| Custom Models | Tailored to asset location, structure, and operations | More resource-intensive; requires time and technical expertise to build |
| World Bank Climate Change Knowledge Portal | Easy access to country-level historical and projected climate indicators (temp, rainfall, heat days) | Low resolution: useful for national context only and should not be used for asset-level screening |
| NASA Earth Exchange | Downscaled CMIP5/6 climate data at ~25km grid; open access | Not asset-specific and require technical skills to process |
Takeaway: For disclosure-driven use cases (like GRESB or TCFD), public datasets (like CMIP6) may suffice. But for high-value assets, critical facilities, or resilience investment planning, custom models offer the granularity and confidence needed to inform decision-making.
Lesson 4: Build a data-sharing culture with your partners
One of the most common breakdowns in climate risk analysis happens when critical asset-level data is siloed – often with property managers, JV partners, or operating companies. Establishing alignment early is essential for meaningful, portfolio-wide insights.
Strategies we’ve seen succeed:
- Integrate climate data into ESG questionnaires during due diligence or annual surveys and/or reporting
- Define and assign clear roles for data stewardship within each portfolio company
- Incentivize data sharing by linking contributions to cost-sharing opportunities (e.g., resilience upgrades or insurance negotiations)
Insight: Climate risk data can’t live in isolation – it needs to be a shared asset. Embedding climate data expectations into operating agreements, governance frameworks, or investment terms creates the structure needed for long-term success.
GRESB reporting doesn’t require perfect data—it requires useful data. By clarifying the purpose of your analysis, triaging your data priorities, and working collaboratively across asset teams, you can build a data foundation that not only supports reporting, but actually guides resilience.
Because in climate risk, the foundation is everything.
This article was written by Kaylee Shalett, Global Technical Director, Climate Risk Assessment Strategy and Reporting and Roni Deitz, Global Solution Director for Climate Adaptation, PE at Arcadis. Learn more about Arcadis here.