Corporate activities and investment decisions are increasingly of great interest to regulators and investors for environmental and social considerations. The real estate industry has the largest potential to contribute to such activities and achieve environmental and social goals.
Reports from the European Commission state that the real estate industry is responsible for 40% of energy consumption and about 36% of carbon emissions in the European Union (EU). This figure, however, covers the broad spectrum of real estate-related companies as a single group. Where REITs are grouped with construction companies, which is at least debatable when assessing carbon emissions across industries. The environmental performance of owned-assets is of particular importance for the listed property sector, given the direct impact on the market valuation of the entity.
The importance of sustainability at the entity level is now acknowledged globally; however the challenge of measurement and assessment of the various initiatives taken by individual companies remains. Owing to the differences in regulations, social needs and function at the asset level are a very wide range of initiatives. They contribute to the sustainability of the portfolio and hence the business undertaken by the European listed property sector.
For instance, the integration of renewable energy, LED lights, isolation technologies to decrease energy consumption, alternative technologies to decrease water consumption and recycling waste are widely practised in the sector. These initiatives are more under the scope of standardisation as compared to a wider range of initiatives associated with Social and Governance initiatives. The companies actively make social contributions through training programs, subsidies surrounding infrastructure and funds for local community initiatives that are very specific and hence not directly comparable on a like-for-like basis.
The aim of accurately capturing all ESG initiatives by the constituents of the EPRA Developed Europe Index is the first step; a robust model for accurately assessing the collective contribution to society by the sector is the ultimate goal for the research team at Concordia. Our recent EPRA-commissioned research aims to estimate the nominal Capex of contributions to ESG activities conducted by the companies through social contributions and reducing environmental impact. The rationale for approaching it from the cost perspective is motivated by the aim of standardising and demonstrating the contribution coming from the investors’ perspective, as the cost is ultimately for the investor.
Based on only publicly available information, we first evaluate corporate reports of European property companies covered by EPRA index constituents. We develop a text-based algorithm to collect information focusing on environmental and social activities. Then, we categorise environmental and social activities, as shown in Figure 1, and create a dataset on environmental and social activities.
For each category, we develop separate models to estimate contributions in each category. In our models for environmental categories, we follow a similar procedure to the European Commission’s cap-and-trade model. Relative to a global benchmark calculated for each category, we estimate contribution by reducing impact relative to a benchmark (i.e. carbon and water consumption). Then, we multiply the relative contribution by a price factor. We use the price of carbon allowances in the EU and a benchmark water price measure considering water prices across the EU.
An owner of a property and tenants are jointly responsible for carbon emissions and water consumption at the building level. The distribution of environmental responsibility is based on contracts signed between the two entities. Real estate owners have operational control of carbon emissions and water consumption, while tenants are typically responsible for their rental areas.
We apply a machine learning model to estimate building-level intensities for those companies that do not have information on tenants’ usage. In our models we also consider property type and local factors such as demographics and environmental characteristics (i.e. temperature) in locations where property companies own assets. To obtain corporate-level controls, we use location weights that are mainly calculated based on NUTS2 (Nomenclature of Territorial Units for Statistics level 2) of each property owned by a property company. Our machine learning model is dynamic and will improve as more data become available. The performance figures of our model are presented in Figure 2. We mainly train the model with a subset of data (shown in the ‘Model Train’ in the figure) and test the model performance by evaluating out-of-sample data (shown in the ‘Model Test’). Figure 2 reflects that our model performs well with the out-of-sample data.
Overall, we find that an average European property company contributes to ESG activities by reducing carbon emissions at a value of EUR 4.75 million annually, as presented in Figure 3. The value of contributions to ESG activities by reducing water consumption is estimated to be EUR 2.0 million. The contributions to ESG activities through waste recycled is predicted to be EUR 0.66 million. The value of employee training and community donations are annually EUR 0.42 and EUR 0.56 million, respectively. In total, an investor can contribute to ESG activities by investing in a European public property company by EUR 8.40 million. The distribution of total investor contributions is presented in Figure 4. This value also corresponds to a ratio of contributions to Capex on investment property, net operating income (NOI) and total equity, at around 9.50%, 4.60% and 0.25%, respectively.
Our measures on the value of contributions to ESG activities through social contributions and reducing environmental impact help investors – such as asset owners, property managers, pension fund managers and institutional investors – understand how much of every Euro they spend on a property company contributes to ESG activities. Our measures help improve the understanding of how ESG activities affect the financial performance of property companies, particularly on a long-term basis. At this stage, we have refrained from aggregating the EUR 8 million figure for the over a hundred constituents of the Developed Europe Index as we aim to improve the performance of the machine learning model and the outcomes further by enriching the dataset.