At Openframe, we have the ability to analyze sustainability data across many different construction projects – allowing us to identify patterns not necessarily seen in individual projects. By looking at the data on a meta-level, we can both share knowledge and develop features in our platform that make certification more structured and efficient.

One of the ways we do this is by looking at how sustainability targets are set in projects within the DGNB framework. Certification typically takes several years, so goal setting is the earliest place we can gain insight into how sustainability ambitions are taking shape – even before points are awarded.

What we have analyzed
For this proof of concept, we have examined 30 DGNB 2023 projects, focusing on how their score distribution. The projects cover different typologies: residential, office, commercial, hotel and daycare – with residential being by far the largest category (22 projects).

We wanted to investigate

• How consistent the targeting is across typologies
• Whether some types of projects are more aligned than others
• How variation and deviations affect goals

What we found out

• Across all project types, there were 8 indicators where the targets were set exactly the same
• In housing projects separately – the largest typology group – we found 39 goals were set identically –a significantly greater alignment
• At the same time, we saw fewer deviations when focusing on one building type, suggesting that projects naturally converge on the same sustainability priorities

It suggests that structured frameworks like DGNB don’t just help set direction – they make goal setting more predictable and consistent. At Openframe, these insights will form the basis for the development of data-driven templates that support a more effective approach to sustainability from day one.

How we found it

To quantify uniformity and variation in the observed target values, we introduced two metrics:

Data Consistency Index (DCI)
Measures how close the measurements are to each other – by comparing interquartile range (IQR) with the full range.

A A DCI of 1 means that the middle 50% of the values are equal, but it doesn’t necessarily mean that all values are equal – there may still be some deviations. If the range is zero (i.e. all values are exactly the same), the DCI is automatically set to 1.

Normalized Variability Index (NVI)
Shows the total variation by comparing the standard deviation with the highest value.

A Low NVI means fewer outliers and greater consistency. If the highest value is zero, NVI is automatically set to 1. Where DCI measures how similar the center field of data is, NVI shows the variation in the entire dataset. By using both measures together, you get a clearer picture – both of how well projects align and where there are discrepancies.  

Key insights:

• Across all project types, we saw perfect agreement in 8 out of 257 possible measures (DCI = 1, NVI = 0)
• In residential projects separately, it was 39 out of 257 – significantly more uniformity
• The bottom line? When projects share typology, their sustainability goals are much more consistent.

Using these metrics, we found that residential projects are significantly more aligned in their sustainability goals compared to mixed typologies. This supports that typology has a significant impact on how certification strategies are shaped.

What’s the next step?
The above is just the first step in a series of analyses. In the next ones we will take a closer look:

• How goal setting relates to the final certification score
• Which DGNB qualities (environmental, economic, social, etc.) are most – or least – consistent
• How deviations behave in different project types

By continuing to analyze the patterns, we will develop even better tools that make it easier to work structured and targeted with sustainability goals.

Follow us for more insights – and feel free to share your own experiences along the way.