Professor Eric Jondeau highlights the challenges of data collection when measuring ESG performance in the Swiss real estate sector.
The contribution of the real estate sector to global greenhouse gas emissions is near 40%, of which 70% comes from buildings operations, according to Forbes (5 April, 2022). And, at a time when the world faces a growing energy crisis alongside on-going climate-change related drought conditions, it’s more important than ever for real estate companies to provide solid data to ensure ESG transparency and compliance.
Several certifications and labels are already commonly used to evaluate the environmental and sustainability performance of buildings. At a portfolio level, however, only a few certifications and labels have been developed, which allow real-estate investment vehicles to show the ESG quality (or at least the environmental quality) of their portfolios.
These labels are usually expensive because it can be a long and detailed exercise to undertake if several hundreds of buildings are involved. Therefore, only a large fund with a high-quality portfolio will typically embark on the evaluation process – and only if it expects a good score which it can then capitalise on for communication purposes and engagement with key stakeholders. These same reasons may prevent smaller funds from taking part in ESG evaluations, diminishing the diversity and scope of available information.
Collecting, harmonising and aggregating real estate ESG data brings with it a number of challenges. One challenge with quantitative metrics (usually measuring environmental issues) is that a variety of technologies are deployed by property owners to track data. The range of different calculation methodologies, metrics and indicators at building level may result in a large heterogeneity across funds in quantitative metrics.
To mitigate the impact of this heterogeneity, one usually transforms the actual numbers (say, the energy consumption) into the ranking of the funds. But then, by using the rank of the funds to create scores, you eliminate the potentially significant scale of the difference between funds. For instance, a number one ranked fund with a ‘very good’ score for carbon emissions, can perform substantially better than a fund with a number two ranking. Rankings can be perceived as reasonably similar when there are large differences, making it not so easy to differentiate a real estate investment vehicle that’s doing well from one that’s performing poorly.
The picture is complicated further by the largely qualitative nature of social and governance metrics as opposed to mainly quantitative environmental metrics, making it more complex to derive unified ESG key indicators. One difficulty with qualitative data is when many funds give the same answer to a question.
“For instance, imagine that a large group of real estate investment funds all say that they are doing well, which would inflate scores. A way to mitigate this issue is to construct the score by counting how many funds have a lower value than a particular fund plus half of the number of funds with the same value as this particular fund and divide by the total number of funds. ”
Quantitative and qualitative indicators
The issue with using a mix of quantitative and qualitative data is in the creation of a comparable three pillar score when you are combining data from quantitative indicators for the E score with data from qualitative indicators for the S and G scores.
Additionally, when it comes to the accurate representation of qualitative indicators, a key challenge is how to deal with this information over an extended period. Most S and G indicators are hard to verify, which brings with it a risk that scores could be biased or have a tendency to increase over time due to inflated positive answers.
To address these limitations and potential biases, in 2021 the Center for Risk Management at Lausanne (CRML), part of the University of Lausanne’s business school (HEC Lausanne), conducted an inaugural survey of sustainability practices amongst Swiss real estate investment vehicles (real estate companies, funds, and foundations).
The survey was performed directly at the portfolio level, and it provides a ranking of investment vehicles based on the ESG scores (available on the CRML website). The aim of the free-of-charge survey is to help institutional investors to select investment vehicles which rate highly on ESG quality.
Based on the collection of more than 500 data points, we constructed 24 indicators that we used to build the three E, S and G scores. For the E score, 5 out of 7 indicators in our survey were quantitative ones, including greenhouse gas emissions, energy intensity, waste generation or water use.
Here, we found that gathering such indicators is challenging for many entities. For example, our findings show that only 42% of respondents were able to measure heating and electricity consumption separately. Additionally, most respondents were not able to provide measures of waste generation and water use.
The key issue when it comes to this type of data is that the owners of the buildings do not have access to the individual energy consumption data from tenants. This is currently a major limitation as the only areas where they have full data are shared common areas such as lobbies, corridors, lifts and staircases.
One potential solution is to go to the electricity provider, but this would require extensive co-ordination as well as permission from tenants for this data to be collected, so this is not currently feasible.
In general, the gathering of data in the property sector is complicated by the fact that the average age of a building in Swiss real estate portfolios is 44 years, which means that they were built at a time when climate change and energy saving weren’t on the radar. As more new buildings are built, new technologies for capturing data, new regulations, increased compliance and greater public awareness and buy-in will all help to improve data collection.
For S and G scores, all our indicators are qualitative ones. For instance, the community impact indicator (S) is based on the sum of seven specific socioeconomic impacts on the community when a development project is planned.
Here again we found that there are limitations such as lack of space around pre-existing buildings when it comes to investment in improved social amenities. As a result, there’s less available data for social scores. Into the future this will change and the design of new buildings is already starting to incorporate improved facilities such as childcare amenities and bicycle parking, all positive data points for social rankings.
Similarly, for the governance score, the qualitative leadership indicator is based on the commitments of the leadership and management to a list of 16 ESG guidelines/objective frameworks.
Wide range of metrics
For our second survey, which is currently underway, we’ve added new quantitative questions related to the S and G pillars to try to address the current lack of quantitative metrics.
For the S pillar, our questions now include gender diversity, investment in training and development and employee turnover. When it comes to the G score, we have added boardroom composition, the experience and expertise of board members on ESG and board tenure to help provide a more in-depth profile.
In terms of the speed of progress, we’re seeing a positive momentum now that gathering data for estate investment vehicles is improving with most employing at least one person to help with gathering data and answering an increasing number of questions from investors.
In conclusion, whilst there’s still a long way to go, good progress is being made. The fact is that investors are giving greater weight to ESG factors as well as the ever-increasing geopolitical importance of environmental issues will help galvanise further improvement in data collection and reporting.