Validation: Quantitative

Automatic data validation and Quality Control Process (QCP)

Automatic Data Validation

The GRESB Portal has a built-in automatic validation system to flag inconsistencies as participants complete their assessment response.

Scope: What is automatically validated?

Automatic validation consists of errors and warnings displayed in the GRESB Assessment for entity-level responses to ensure that all data input is complete and accurate.

The scope of these controls is categorized as follows:

Reporting characteristics: Checks on the core performance values reported by the entity. These play a key role in calculating metrics such as intensities and, as such, must be closely monitored.

• Gross Asset Value

• Revenue

• Ownership values

• Portfolio completeness

• Fund-asset links (Infrastructure Fund Assessment checks are used to make sure a fund has created a link in the GRESB Portal to all of its submitted asset assessments in order to get the correct score in the results season. This can be a simple human error, but it can have unintended consequences on scores of the Fund Performance component.)

Performance data: Operational metrics reported by the entities at the asset-level across the various tables, with particular attention paid to metrics that drive scoring. These include Data Coverage of time and floor area, Like-for-Like, and Intensities. The Performance Component metrics for which tests are run are the following:

• Energy

• GHG (Scopes 1 & 2), including specific tests on the validity of Market and Location-based GHG data

• Water

• Waste

• Biodiversity & Habitat

• Health & Safety

Criteria: What checks are conducted?

General Requirements

• Information completeness: The portal ensures mandatory evidence uploads are present, that all required open text boxes are completed, and that answers are provided for all indicators.

• Data types: Certain fields must adhere to specific data types, such as numbers, percentages, text, etc.

• Information accuracy: percentage values must range between 0 and 100, and metrics are restricted to absolute values (i.e., only non-negative numbers).

Outcomes: What are errors and warnings, and how are they handled?

Errors

Errors appear in red and are generated per asset as a result of logical checks amongst the different data fields in the assessment response.

Participants cannot submit their assessment until all validation errors are resolved.

Warnings

Warnings, displayed in grey, are generated when values appear to be abnormal based on the context of previous responses (for instance, if they remained identical to the previous year). GRESB encourages participants to review warnings to ensure data accuracy, but does not require them to be resolved for assessment submission.

Quality Control Process (QCP)

The GRESB team conducts QCP outreach to flag inconsistencies during and after participants submit their assessments.

Scope: What is captured by the Quality Control Process?

GRESB's QCP is an additional data quality validation stream aiming to enforce participants' compliance with the GRESB reporting requirements in full, and that data is reported in line with the guidance provided in the official GRESB guidance.

GRESB uses statistical modeling tools to flag additional outliers (beyond those captured automatically in the Portal). We carry these checks out on the following Reporting Characteristics metrics, which play a key role in calculating metrics such as intensities and must be closely monitored.

• Gross Asset Value

• Revenue

• Asset floor size

• Ownership values

• Portfolio completeness (verification that portfolios report all assets and are not cherry-picking)

Criteria: What checks are conducted?

Check Type	Description	Example
Descriptive Statistics	Includes measures of central tendency (mean, median, mode) and dispersion (min, max, range, variance, standard deviation, standard error). Used to detect anomalies.	An asset reporting high or low energy use while others report typical usage ranges flagged based on min/max/mean/median or standard deviation thresholds.
Interquartile Range (IQR)	Flags intensity data points that fall outside 1.5× the interquartile range of peer group. IQR does not assume a normal distribution—helps identify extreme values robustly.	A Scope 1/m2 or energy consumption/GAV emissions value 3× higher than all other assets in the same country and sector is flagged as an outlier beyond the IQR bounds.
Log-Transformed Regression	For right-skewed distributions (e.g., Gross Asset Value), log transformation is used before regression to normalize data and reduce influence of outliers.	A log-linear model uses prior year GAV and floor area to predict current GAV. Large residuals (prediction errors) are flagged for investigation.
Content & Accounting Checks	Focuses on consistency in reporting logic and alignment with protocols like the GHG Protocol—e.g., correct use of market- vs. location-based Scope 2 emissions reporting.	An entity reports Scope 2 emissions using location-based values but deducts green energy certificates—a practice only valid under the market-based method.
Energy Efficiency Intensity	Year-on-year performance trends and floor area changes are monitored to detect unexpected jumps or drops in intensity.	An asset’s energy intensity improves dramatically with no corresponding floor area change—triggering a review of the data or methodology used.

Outcomes: How is an inconsistency handled?

When GRESB detects an outlier, we contact the participant and ask them to provide additional clarification and/or use the Assessment Correction to amend their reported data.

In case of non-compliance, GRESB reserves the right to reject the corresponding component submission.