Using data quality to improve real asset values 

Our industry is engaged in an important dialogue to improve sustainability through ESG transparency and industry collaboration. This article is a contribution to this larger conversation and does not necessarily reflect GRESB’s position.

In the realm of large commercial real estate (CRE), the importance of high-quality, granular data is increasingly being recognized. The CRE market has seen a proliferation of new data sources and technology tools, leading to better projections, cost metrics, purchasing decisions, and business deals. However, it has generally lagged behind other financial sectors in capitalizing on modern data analytics capabilities. This is due in part to historical adoption but mainly the sheer scale of the challenge, not only the number of property assets but the complexity of legacy building systems operating the assets and a lack of a standardized way to integrate systems to optimise the asset‘s operational efficiency.   

Data quality issues can have a direct impact on an organization’s financial performance. According to a study by Gartner, poor data quality costs organizations an average of USD 12.9 million per year. And according to an IBM report, this could amount to an annual USD 3.1 trillion loss attributable to poor-quality data, in the US alone. These costs stem from revenue loss, increased operational risks, and the complexity added to data ecosystems. In the long term, poor data quality affects decision-making processes, hindering the organization’s ability to leverage data effectively. 

In today’s rapidly advancing world, energy efficiency has become a top priority for commercial building owners and operators. The need to reduce carbon emissions, optimize building performance, and enhance occupant experience has led to a growing focus on leveraging highly granular commercial building systems data to drive improvements. By harnessing the power of data, businesses can unlock valuable insights, make informed decisions, and ultimately enhance the value of their real assets. 

Energy efficiency is a critical aspect of sustainable building operations. By optimizing energy consumption, businesses can significantly reduce their carbon footprint, contribute to global carbon reduction efforts, and align with Environmental, Social, and Governance (ESG) reporting standards. Moreover, energy-efficient buildings often enjoy lower void rates and utility costs, improved net operating income, and enhanced occupant experience, making them highly desirable in the market. 

To achieve energy efficiency and its associated benefits, businesses must adopt a data-driven approach to building operations. This involves collecting highly granular data from various building systems, such as HVAC, lighting, occupancy sensors, and more. By integrating these systems into a cohesive master systems integration platform, businesses can effectively monitor and control building performance in real-time. 

Data quality plays a pivotal role in leveraging building systems data to improve real asset values. Accurate, reliable, and timely data ensures that businesses can make informed decisions and take proactive measures to optimize energy consumption, avoid stranded assets, and enhance overall building operations. High-quality data enables predictive maintenance, identifies areas of improvement, and supports the development of effective energy efficiency strategies.

Data analytics is a powerful tool for driving energy efficiency and improving real asset values. By leveraging advanced analytics techniques, businesses can gain deep insights into energy consumption patterns, identify inefficiencies, and develop targeted strategies for improvement. Through the adoption of a single unified view of integrated systems and analysis of highly granular data, businesses can optimize space utilization, identify underutilized areas, and make data-driven decisions to enhance building occupancy and operational efficiency. 

Predictive maintenance is another key aspect of leveraging data to improve building operations and energy efficiency. By monitoring and analyzing data from various building systems, businesses can detect anomalies and potential equipment failures before they occur. This proactive approach allows for timely maintenance, reduces downtime, and ensures optimal performance of building systems, leading to improved energy efficiency and cost savings. 

Achieving net-zero energy consumption is a long-term goal for many businesses. By leveraging highly granular commercial building systems data, businesses can develop and implement effective strategies to move closer to this goal. The analysis of energy consumption patterns, identification of energy-saving opportunities, and integration of renewable energy sources enable businesses to reduce reliance on traditional energy sources and make significant progress towards a net-zero energy strategy. In addition by utilising harmonised data from multiple integrated systems, operations teams can continue to identify new optimization opportunities going forward. 

The adoption of highly granular commercial building systems data quality has a direct impact on real asset values. Energy-efficient buildings are in high demand, attracting environmentally conscious tenants, and commanding higher rental rates. In the US, data from the representative body for REITs (real estate investment trusts), Nareit, shows that green–certified buildings can translate into a 31% increase in sales values, 23% higher occupancy rates, and an 8% increase in rental incomes. Global property agent, Knight Frank, has gone as far as creating its own price model to calculate the contribution of green ratings to sales values for prime offices in London, Sydney, and Melbourne. It found an 8% to 18% price premium for green-rated offices compared to those without any sustainability certification. In central London it showed a 13% premium on rents and 10.5% on sales prices on BREEAM outstanding- and excellent-rated buildings. Additionally, businesses that can demonstrate their commitment to sustainability and energy efficiency through accurate ESG reporting enjoy enhanced reputations and increased investor interest. 

In conclusion, leveraging highly granular commercial building systems data quality is key to improving real asset values. Post-covid the challenges posed by energy costs, interest rates, hybrid working, and increasing carbon legislation are significant, however, by harnessing the power of data analytics, businesses can drive energy efficiency, reduce carbon emissions, and optimize building operations. Through the adoption of data–driven predictive maintenance, net-zero strategies, and a focus on enhanced occupant experience, businesses can stay ahead in the competitive real estate market while making a positive impact on the environment. Those that do embrace data-driven approaches and maintain a high standard of data quality to differentiate their assets, will undoubtedly pave the way for a more sustainable and profitable future. 

This article was written by Paul Reid, Commercial Lead for Digital Buildings at Onnec iQ.