Spatial Intelligence for CRE: Optimize Occupancy with Thermal Sensing

Core use cases for CRE

• Space utilization and right-sizing
  • Measure actual occupancy across floors, zones, and rooms to identify underused areas.
  • Support decisions to consolidate space, implement flexible leasing, or repurpose areas for higher-value uses.
• Energy and operational savings
  • Adjust HVAC and lighting schedules based on occupancy patterns and predictive forecasts.
  • Reduce energy waste during off-peak hours and quickly react to peak loads.
• Health, safety, and emergency response
  • Use real-time counts and density maps to support evacuation planning and crowd management.
  • Monitor unusual occupancy spikes and trigger alerts for security or health protocols.
• Tenant analytics and amenity optimization
  • Assess amenity usage (conference rooms, lounges, cafeterias) to justify investments and adjust services.
  • Provide landlords with anonymized occupancy reports to enhance tenant retention and renewals.
• Leasing and valuation support
  • Demonstrate verified utilization metrics to prospective tenants.
  • Quantify revenue opportunities from reconfiguration or amenity monetization.

Important metrics to track

• Occupancy rate: percentage of space in use versus available capacity.
• Peak density and peak times: highest concurrent occupant counts and when they occur.
• Average dwell time: how long occupants remain in a space.
• Turnover rate: frequency of new occupants entering a space across a period.
• Utilization by zone: percent time each room or area is actively used.
• HVAC runtime vs. occupancy: mismatch indicating waste.

Tracking these metrics over time enables trend analysis, benchmarking, and predictive planning.

Integrating thermal sensing into building systems

• Building Management Systems (BMS): feed occupancy data to optimize HVAC schedules and setpoints.
• Lighting and controls: enable demand-based lighting and daylighting strategies.
• Space management platforms (CAFM/IWMS): update real-time room availability and utilization reports.
• Tenant dashboards and BI tools: provide stakeholders with tailored insights and reports.

Integration often uses standard protocols and APIs; choose solutions that support secure, well-documented interfaces and flexible data export options.

Privacy, compliance, and trust

• No visual imagery: heat-based sensors do not capture faces or detailed personal identifiers.
• Anonymized data: counts and movement vectors are processed without linking to individual identities.
• Data governance: retention policies, role-based access, and clear use-case boundaries help maintain trust.

Document privacy practices clearly for tenants and staff. Provide transparency about what is measured and how data is used to avoid resistance.

Implementation checklist for CRE teams

1. Define objectives

• Identify primary goals (reduce energy, increase utilization, inform leasing).
• Set target KPIs and timeframes.

2. Map spaces and prioritize zones

• Start with high-value or high-variance areas: lobbies, conference clusters, shared amenities.
• Pilot one or two floors before rolling out portfolio-wide.

3. Choose the right sensing hardware and architecture

• Decide between wireless vs. wired sensors based on building constraints.
• Confirm sensor coverage, range, and mounting considerations.

4. Plan integrations

• Engage BMS, facilities, and IT teams early to map data flows and security requirements.
• Establish APIs, data formats, and alerting pathways.

5. Deploy, calibrate, and validate

• Calibrate sensors for local conditions and validate data against manual counts or access systems.
• Iterate placement to close coverage gaps.

6. Operationalize insights

• Create dashboards for facilities, leasing, and operations teams.
• Translate insights into policies: HVAC schedules, booking rules, or reconfiguration plans.

7. Measure impact and scale

• Compare pre- and post-deployment metrics for energy, utilization, and tenant satisfaction.
• Use results to expand deployment and refine strategy.

Common challenges and how to overcome them; ROI considerations; Getting started with a pilot

Common challenges

• Incomplete coverage: Start with prioritized zones and use hybrid sensing (door counters, badge data) to fill gaps.
• Stakeholder buy-in: Share pilot results that demonstrate energy savings, tenant benefits, or leasing advantages.
• Integration complexity: Use vendors with proven BMS and CAFM connectors, and allocate IT resources planning time.
• Data overload: Focus on a few high-value KPIs and automate alerts for anomalies rather than raw data streams.

ROI considerations

• Energy savings from reduced HVAC and lighting runtime.
• Lower cleaning and maintenance costs via demand-based services.
• Increased lease renewals and higher rents through evidence-based tenant engagement.
• Faster decision-making for space reconfiguration that increases revenue per square foot.

Quantify savings by benchmarking utility costs and operational labor before deployment, then measure improvement against those baselines.

Getting started with a pilot

• Duration: 8–12 weeks provides enough cycles to capture variability.
• Scale: 1–2 floors or a cluster of common spaces.
• Outputs: weekly dashboards, a final impact report, and recommended next steps.

Vendors like Butlr provide thermal sensors and analytics specifically aimed at CRE needs, enabling rapid pilots that preserve privacy and deliver actionable occupancy insights. The Butlr CRE 4December Campaign highlights CRE-focused use cases and deployment paths for organizations ready to accelerate spatial intelligence initiatives.

Conclusion

Thermal sensing is a pragmatic, privacy-forward way to bring spatial intelligence into CRE operations. By measuring how spaces are actually used — not how planners expect them to be used — landlords and operators can drive energy savings, enhance tenant experience, and unlock new revenue opportunities. Start with a focused pilot, measure the right KPIs, and integrate occupancy data into existing building systems to realize fast, scalable impact.