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.
Energy data in real estate is messy. You’ve probably felt it. Some buildings have smart meters logging every kilowatt hour; others rely on spreadsheets dug out from inboxes once a year. And somehow, all of this gets lumped together in sustainability reporting. But not all data is equal. Not when it comes to quality, trust, and how you use it to make decisions.
Let’s talk about measured data.
What counts as “measured”?
It sounds obvious; measured data is what’s captured directly from utility meters. But you’d be surprised how often that definition gets bent. Someone manually typing in a bill into your system? That’s not measured. A PDF from a tenant emailed once a quarter? Still not measured.
True measured data comes straight from the source: the meter. Better still if it’s pulled automatically from the utility provider. The reason behind this is traceability. You should be able to follow a data point all the way back to the hardware that recorded it.
But, why does this matter?
Trusting all data equally is a risk
If your system can’t tell the difference between data pulled from a meter and data input by someone in a hurry on a Friday afternoon, you’ve got a problem.
Let’s say your portfolio is 80% covered by metered data. Great. But if your system treats estimated or manually entered values the same way, you’ve just flattened that 80% into noise. A reading taken straight from a building’s main electricity meter? According to your system, it’s just as trustworthy as Greg from accounting’s Excel upload.
When reporting to GRESB or any other framework, this could put your credibility at risk. Worse, it clouds your internal decisions. Are those spikes in usage real? Or just a data entry issue?
Metered ≠ perfect
Here’s where it gets tricky. Just because a value comes from a meter doesn’t mean it’s flawless. Meters can be wrong, readings can be delayed, and sometimes the data doesn’t line up with what you expect. But the difference is this: you can trace measured data. You can ask questions, investigate anomalies, and fix errors with confidence. You can’t do that with an estimate.
Think of it like buying a used car. Would you rather have the actual mileage pulled from the odometer, or someone’s best guess?
Virtual meters: a word of warning
Let’s talk virtual meters. These get set up when there’s no metering of a specific tenant or sub-unit and when consumption from one actual meter is apportioned to multiple tenants or sub-units.
They can be helpful for filling gaps, but don’t mistake them for the real thing, as they’re not connected to the physical world in the same way. By calling them meters, people may believe the data they see from them. So, if your reporting system treats them as equal to actual meters—again, you’re risking muddying the water.
It starts with structure
If you want to build a high-quality dataset, you need a solid digital structure: your assets, your buildings, your tenant spaces—all mapped clearly. Then connect your meters to the right place in that structure. It takes time to set up, but once you do, you’ve got a living, breathing picture of how your assets consume energy.
Start from the bottom (meter level) and build your way up. Don’t feed in data at the asset level and hope it all adds up. It won’t.
The value of transparency
Your systems should let you label data: what’s measured, what’s estimated, and where it came from. That way, when you hand over a report—or make a decision based on that data—at least you know what you’re working with. If GRESB asks, “Where did this data come from?” Your answer shouldn’t be “I think from the tenant.” It should be “From the electricity meter in building 4B, pulled directly from the utility provider via API.”
It’s also worth noting that the more human steps you have in the process, the harder it is to give that kind of confident answer. Manual inputs create room for error and create a reliance on individuals to gather the data.
Don’t treat all data the same
So where do you start if you want cleaner, more reliable data? What you can do now is:
- Audit your data sources—know which values are measured, estimated, or virtual
- Label and structure data properly—this helps with both internal decisions and external reporting
- Push for automation—the closer you get to real-time, source-level data, the better.
Measured data brings certainty, accountability, and trust. But only if you keep it separate from the rest. When your systems and processes blur those lines, you lose the ability to see what’s real—and risk making decisions based on guesswork.
And in this market, guesswork’s expensive.
This article was written by Maja Christenson, Marketing Manager at EVORA. Learn more about EVORA here.