As the commercial real estate landscape evolves, artificial intelligence (AI), advanced benchmarking, and data science are reshaping portfolio analytics. These advancements are enabling corporate real estate (CRE) teams to move from descriptive insights to predictive intelligence, helping them make smarter, faster, and more effective decisions. This transformation is especially critical in a world redefined by hybrid work, fluctuating demand, and a shift from efficiency-focused metrics to those that emphasize workplace effectiveness.
Why New Portfolio Analytics Matter
The Hybrid Work Revolution
The rise of hybrid work has significantly changed how companies manage office space. With fluctuations in demand and supply, businesses must carefully balance cost pressures with improving the workplace experience. Predicting space requirements is challenging, and operationalizing big data remains complex. AI is proving to be a game-changer by enabling real-time analysis, forecasting, and tailored solutions that address specific building or workplace needs.
Shifting From Efficiency to Effectiveness
Metrics traditionally focused on efficiency—such as square footage per person or seat—are being replaced by effectiveness-oriented measurements. According to CBRE’s 2023 Workplace & Occupancy Report, utilization rate remains the most crucial metric, but now, employee sentiment, attendance rates, and workplace performance metrics are gaining priority.
Understanding workplace effectiveness requires analyzing the connection between the physical and virtual work environments and how they impact organizational goals.
Emerging Trends in Portfolio Analytics
Agile Spaces for Variable Demand
Hybrid work has driven a need for more flexible spaces. Increasingly, organizations are adding agile spaces to their portfolio mix, complementing traditional owned and leased properties.
In CBRE’s 2023 report, 90% of organizations reported hybrid work programs, up from 83% in 2021. AI helps CRE teams optimize these spaces by forecasting demand, improving occupancy, and minimizing risks.
The Rise of Sharing Ratios
As organizations adapt to hybrid work, sharing ratios—the number of employees sharing a single workstation—are becoming critical. Nearly half of surveyed enterprises now report sharing ratios exceeding 1.5:1, supporting space consolidation initiatives and cost savings while accommodating employee flexibility.
Efficiencies depend on specific workstyles and preferences, but optimizing sharing ratios through AI and data modeling can balance organizational goals and employee needs.
Utilization Rates and Underused Spaces
Globally, office space utilization remains low. From Q2 2022 to Q2 2023, average utilization rates were just 35%, a stark drop from pre-pandemic levels of 64%.
Underutilized spaces present significant challenges, but AI-driven utilization data can reveal actionable insights, helping companies align space with real-world demand and enhance employee experience.
Local Market Dynamics and Benchmarking
The effectiveness of office space varies by market. Local dynamics influence perceptions of density, cost, and space utilization. Benchmarking reports tailored to local conditions allow organizations to measure performance, compare costs, and adjust their strategies.
Leveraging AI to Redefine Workplace Effectiveness
High-performing workplaces now measure success using four dimensions:
- Employee Experience
- Organizational Dynamics
- Financial Performance
- Environmental, Social, and Governance (ESG)
AI enhances decision-making by integrating data from occupancy sensors, surveys, and benchmarking tools, creating a Workplace Effectiveness Score. This score informs:
- Capital project prioritization.
- Anticipating space needs for team meetings, events, and other activities.
- Streamlining building services like janitorial tasks or catering through unified systems.
Enhancing Visitor and Employee Experience With AI
AI can also improve day-to-day interactions within workspaces by:
- Optimizing Visitor Journeys: Predicting needs based on past interactions.
- Demand Forecasting: Adjusting resources based on real-time utilization data.
- Timely Interventions: Identifying and resolving issues before they disrupt services.
The Importance of Data Governance and Quality
AI’s transformative potential in CRE depends on robust data governance. Managing Critical Data Elements (CDEs) ensures:
- Accurate decision-making.
- Compliance with regulations.
- Cost savings and innovation.
Standardized and high-quality data enables organizations to operationalize AI effectively and unlock new levels of portfolio performance.
Conclusion
The mandate for corporate real estate has shifted. No longer just a function of efficiency and cost management, CRE now plays a pivotal role in advancing strategic business objectives. The new era of portfolio analytics—powered by AI, benchmarking, and data science—offers unprecedented opportunities for organizations to align their workspaces with evolving needs.
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