Last month we talked about the exciting changes to this year’s GRESB survey. Between April and July, companies around the globe will be collecting data to submit to GRESB, an industry-driven organization that assesses the sustainability performance of real estate portfolios (public, private and direct). Institutional investors use the GRESB benchmark to improve the sustainability performance of their investment portfolios.

Gathering the required information for this annual survey can be an arduous undertaking for a company. In this post, we’ll discuss a framework to help facilitate the process.

Leveraging Big Data

Big data analytics are an integral part of producing a stellar GRESB survey response, particularly for the Performance Indicators (PI) aspect of the survey. In the 2013 survey, the PI aspect represented 25% of the possible points. The PI assesses three metrics: the extent of respondent’s access to performance data for their portfolio, their total resource consumption, and finally, performance.

For real estate fund and real estate investment trust (REIT) entities, or other organizations with more than 20 buildings, responding to the PI aspect can be daunting. A GRESB data set consists of monthly utility performance data for numerous buildings, as well as metadata for each building such as asset type, building area, location, greenhouse gas emission factors, green building certifications, and renewable energy installations.

Most of this data is piecemeal and may come from multiple sources in different formats, exacerbating the reporting challenge.

A Three-step Framework

To manage big data effectively and maximize your PI aspect score, we recommend a three-step framework of GATHER–>TRANSFORM–>SYNTHESIZE, paralleling a common big data industry methodology for managing three disparate database functions that align to a common goal.

While undertaking each step of the process for our clients, we rely heavily on the precept of materiality to ensure that the level of analysis is commensurate with the relevance of the outcome to the overall project goal while maintaining data integrity.

The GRESB report can feel like falling down a rabbit hole. Using materiality and a healthy dose of common sense will allow you to align your time with meaningful outcomes of the reporting process.

Let’s examine of the framework in detail along with our recommendations for managing data and creating a strong survey response.

Step 1: Gather

The data collection phase is the cornerstone of successful reporting. The GRESB PI aspect requires two types of data: metadata and utility data.

Metadata: These are the building blocks that allow you to slice performance data into the categories required for GRESB. Asset name, year of acquisition and disposition, address, zip code, asset type, property type (directly/indirectly managed), utility data source, and floor area are the most essential metadata tags to collect. The asset square foot metadata can be quite challenging. REITs and companies use a number of different asset area types in their documents, including gross area, net area, rentable area, and BOMA area. For GRESB, it does not matter which area type you pick, as long as you stay consistent across all buildings in the portfolio. Parking or exterior site areas should be excluded from the reported area, although the associated utility data should be included in the summary data.


  • Assemble and confirm the complete list of assets in the portfolio, then collect the metadata information for each one.
  • At the same time, collect asset level metadata related to the questions in Aspect 6: Building Certification so you don’t have to duplicate the effort later.
  • Spend time up-front resolving inconsistencies in asset area types, to avoid creating a bottleneck toward the end of the reporting process.

Utility Data: Collecting utility data is usually a lot easier than metadata. Most REITs and property management companies already gather utility data for their managed assets in a central location, either through an energy management software/company or in ENERGY STAR® Portfolio Manager. These websites or tools have a bulk download feature to export the utility data for all assets in their database into an Excel file.

It can be more complicated to obtain utility data for indirectly-managed assets or triple net assets or for any dispositions during the two-year reporting period. In most cases, the data for these assets will have to be manually added to the utility dataset downloaded from your data management tool.


  • Obtain utility data in monthly format, and, most importantly, for a two year period–—the reporting year and the year preceding. That means for the 2014 survey, obtain data for years 2012 and 2013.
  • The “utility source” and “property type” metadata tag will help you identify which assets may not be in the central utility database, such as dispositions made during the reporting period, or triple net properties. Initiate the process of gathering utility information for these assets as early as possible, and identifying the properties for which no data is available.
  • Determine whether it is necessary to assign multiple different property types to a single building. If <10% of an office building represents another asset type (such as retail), it might be reasonable to classify the entire property as office rather than making assumptions about allocation of portions of utilities to different building areas that will not materially change your reported data.

Step 2: Transform                                                         

While gathering data is the cornerstone for successful GRESB reporting, it is equally important to verify the validity of the data. During the transform step, check for data completeness and integrity, and make any required edits and updates to the data set.

Data Completeness: Verify that the full two years’ worth of data is available for all assets. If we identify missing data, we analyze the material impact on the overall performance indicator score; if material – for example, a property that is in the top 25% of the portfolio by floor area – then we engage with regional property management teams to fill in the gaps.


  • If <10% of the data is missing for a particular asset, use scientific extrapolation methods to back-fill the missing data.

Data Integrity: The next test is to verify if the data is in the expected range. We’ve developed a series of queries based on use intensity, building type, climate zone, and occupancy percentages to identify any errors in the data. If you are using software-reporting tools, consider having a building engineer or building data expert review the results for relevance to the portfolio.


  • Engage with regional property managers to understand external factors such as building renovations, high energy use tenants, or physical building characteristics that could push the use intensities outside the expected range.
  • Look for common anomalies, such as data in the wrong units, partial data reported, missing central plant data, or central plant data that includes buildings not part of the portfolio.

Step 3: Synthesize

The final step is to collate the transformed data into the various PI categories required by GRESB. This year, GRESB released an Asset Level Data Template (login required) to help participants gather their data.


Property Type:

  • Complete a separate consumption data table for each property type. For example if a portfolio has three property types, report all assets under each property type in a separate consumption table to simplify transferring information to the GRESB survey form.

Like-for-Like Consumption:

  • Include only those assets that were owned by the entity for the complete reporting year and the year preceding. That means for the 2014 survey, include only assets owned for the whole of 2012 and 2013 in this section.

Data Coverage:

  • GRESB weights the percentage of the portfolio for which performance data is reported. To improve your rating, report as many indirectly managed assets as possible.

Overarching Lessons

In our experience it is most efficient to go through these steps in the linear sequence described above. Proceeding to the transform step before the gather step is complete can result in losing the ability to apply materiality to the process, leading to extra work that does not improve the results.

During the data integrity checks in the transform step, pay close attention to the patterns in utility use. These patterns will provide insights into the biggest resource users in the portfolio and highlight the greatest opportunities for future performance improvements.

Keep the concepts of materiality top of mind when transforming the data. Otherwise, large amounts of time will be spent resolving data issues that have close to no impact on the final results.

In summary: remember your end goal! You are preparing an aggregated data set that represents the portfolio’s performance, but you may not need to perform a detailed by-asset performance analysis. Materiality matters in data analytics, both for your sanity, and for a meaningful outcome.

Energy modeling






Thulasi Narayan is a Senior Consultant, LEED® AP and Lexy Relph, LEED AP® is a Sustainable Business Consultant. They are both in the Seattle office.

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