Your GRESB score is capped by data you estimated.

Data coverage and data quality, more than the headline performance numbers. An asset with modest performance and complete, evidenced data frequently scores better than a strong performer whose figures are largely estimated.

Because estimated data is scored lower than measured data for the same consumption. Knowing the ratio per asset and per indicator before the submission window opens is what lets you fix the assets that are dragging the score, while there is still time to obtain the readings.

Well before the submission window, because the fixes that move a score are data collection tasks with lead times: getting a landlord meter read, obtaining a tenant’s consumption, chasing a managing agent for waste tonnages. None of those can be done in the final fortnight.

Yes, and it should. They ask different questions of the same underlying consumption and emissions data. Maintaining two datasets is how the two submissions end up disagreeing, which is a question neither framework asks but an auditor will.

It is recorded against the asset with its source and its landlord-versus-tenant split stated, rather than being merged into a single building figure. GRESB distinguishes between them and a dataset that does not cannot answer the indicator accurately.

Enough to show where each number came from: the reading or invoice, the period it covers, the source it arrived from, and who accepted it. Sustainify AI records that automatically as data is imported, rather than requiring it to be assembled afterwards.