This indicator is answered and scored separately for each property sub-type, resulting in multiple scores for the same indicator. Scores are aggregated across property sub-types by taking a weighted mean of the property sub-type scores, weighted by the percentage of GAV reported per property sub-type in R1.1.
The score of this indicator equals the sum of the scores achieved by:
- Data coverage = 8.5 points;
- Like-for-Like data availability = 0.5 points;
- Like-for-Like performance improvement = 2 points;
- Renewable energy = 3 points. The renewable energy score is split as follows:
- On-site renewable energy = 1 point;
- Off-site renewable energy = 0.5 points;
- Performance = 2 points.
Data coverage percentages, based on both area and time for which data is availabe, are scored separately against different benchmarks for landlord and tenant controlled areas for each property sub-type, where "landlord controlled" and "tenant controlled" areas can include:
- Landlord controlled areas: Landlord Controlled Whole Building, Base Building, and Landlord Controlled Tenant Spaces
- Tenant controlled areas: Tenant Controlled Whole Building, and Tenant Controlled Tenant Spaces
Benchmarks are constructed for each separately scored value based on the property sub-type and location of the entity's assets. First, an attempt is made to construct a benchmark by grouping together values from the same property sub-type from other entities operating in the same country. If there are not at least 12 values with that grouping, the specificity of the location classification and then the property type is gradually decreased. If needed, the location classification is dropped and only the property type is used. If it's still not possible to find 12 values for the benchmark, the scoring is done based on static values instead.
Note: Please see the Entity Categorization sub-section in the Scoring Methodology section of the Reference Guide for details on the location based classification.
Note: For the property types please see Appendix 3a of the Reference Guide.
A score is then calculated based on how the value reported by this entity compares to the benchmark values reported by other entities.
The resulting scores are then aggregated to a single score using a weighted mean with weights determined by floor area, except for base building and tenant space for which base building has a static weight of 40% and tenant space has a static weight of 60%. As tenant space can be both landlord and tenant controlled, the 60% weight has to be shared between the two which is done based on relative floor area. If a respondent reports on both base building plus tenant space and whole building, then base building pluss tenant space is given a weight based on their combined floor area which is then split further based on the 40% - 60% weights.
Like-for-Like performance improvement:
Like-for-Like performance is scored based on the percentage change in consumption using a methodology identical to the scoring of data coverage, except for that having a lower value (for example a negative one) always results in a higher or equal score, and that scores are aggregated using Like-for-Like consumption in the previous year as weights instead of area.
Note: data reported for the outdoor area is included in the Like-for-Like scoring and outlier check but excluded from the data coverage scoring.
Like-for-Like data availability:
Points for Like-for-Like data availability are given if any Like-for-Like data is provided and not excluded in the GRESB outlier check.
The scoring of this section is split into two parts. The first part can result in a maximum of 1/3 of the maximum score. This is achieved if any on-site renewable energy was generated in the current year. If this is not the case, but some off-site renewable energy was generated in the current year, then 1/6 of the maximum score is achieved instead.
The remaining 2/3 of the maximum score is given based on the percentage renewable energy in the current year and the improvement compared to the previous year. These two elements are combined using the following formula, where p is the percentage renewable energy and i is the improvement score:
Score = (100 + p) / 200 * p / 100 + (100 - p) / 200 * i
The improvement score is calculated based on the improvement in the percentage renewable energy compared to the previous year. The improvement is scored by comparing it against a benchmark based on the improvements of other respondents. Note that only improvements are included in this benchmarking model, so values <= 0 are ignored. Besides this, the benchmark scoring methodology is identical to the one used for coverage, see details above.
GRESB identifies outliers in performance data reported at the asset level. There are two kinds of outliers flagged by the GRESB Portal: Intensities and Like-for-Like (LFL) change in consumption/emission. Outliers are validated automatically based on fixed thresholds. There are two levels of automatic outlier validation:
- If an outlier is detected above the upper threshold or below the lower threshold, then the data points associated with that outlier will be included in aggregation and scoring. However, they will not be included in the creation of the scoring benchmarks.
- If the outlier is substantially higher than the upper threshold (more than 1000 times greater), the data points associated with that outlier will not be included in aggregation or scoring.
Intensity outliers: The threshold for detecting an intensity outlier varies by data type and property type. Intensity outlier values are normalized by vacancy and by data availability.
Like-for-like outliers: The threshold for detecting a LFL outlier varies between 20 - 30%, based on the previous year’s consumption value. LFL outlier values are normalized by vacancy.
Open text box:
The content of the open text box at the end of the indicator is not used for scoring, but will be included in the Benchmark Report.