Spatial Data for CRE: Boost Occupancy and Energy Efficiency

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Commercial real estate (CRE) is changing. Owners and operators face pressure to reduce operating costs, improve tenant satisfaction, and adapt spaces for hybrid work. Spatial data — information about how people move through and occupy physical spaces — provides a practical, data-driven way to meet these goals. This article explains what spatial data is, how it benefits CRE, and concrete steps to use it to raise occupancy and energy efficiency.

What is spatial data in CRE?

Spatial data describes the presence, location, and movement of people and objects within a built environment. In CRE, it usually comes from sensors, cameras, badge systems, or building systems and helps answer questions like:

Which areas of a floor plan are most used?
When do meeting rooms sit empty?
How do people flow through lobbies at peak times?

Key terms

CRE: Commercial Real Estate — buildings used for business purposes, including offices, retail, and industrial space.
Occupancy analytics: Analysis of where, when, and how spaces are used.
Heat-based sensing: A sensor approach that detects body heat signatures rather than visual images, enabling anonymized occupancy counts.
Energy Use Intensity (EUI): A metric expressing energy consumption per square foot per year.

Defining these terms early helps make subsequent recommendations actionable for property teams and portfolio managers.

Why spatial data matters now

Several market forces are driving adoption:

Hybrid work models reduce predictable desk occupancy, making manual scheduling and assumptions unreliable.
Tenants demand smarter, healthier, and more efficient buildings.
Energy costs and sustainability targets put pressure on operators to optimize HVAC, lighting, and cleaning schedules.
Investors and ESG reporting increasingly call for measurable performance metrics.

Spatial data shifts decisions from guesswork to evidence. It reveals underutilized space, shows actual peak demand, and identifies energy waste tied to occupancy patterns.

How spatial data boosts occupancy

Understanding actual space use creates opportunities to increase occupancy rates and tenant satisfaction.

Benefits for occupancy

Right-size space offerings: Identify underused areas to reconfigure for common spaces, collaboration zones, or flexible leases that attract tenants.
Improve tenant experience: Use real occupancy patterns to place amenities where they matter — more meeting rooms in high-demand zones, better wayfinding, and optimized cleaning schedules.
Increase leasing velocity: Showcase data-driven utilization reports to prospective tenants, proving value and transparency.
Support hybrid work policies: Allocate touchdown spaces and bookable desks where analysis shows consistent demand.

Typical insights from spatial data

Peak and average occupancy by hour, day, and week.
Real-time availability of desks and rooms.
Heatmaps showing foot-traffic corridors and congregation points.

These insights help facilities teams and leasing agents make faster, higher-confidence decisions that attract and retain tenants.

How spatial data improves energy efficiency

Buildings consume most energy through HVAC and lighting. When systems run on fixed schedules or conservative assumptions, they waste energy during low-occupancy periods. Spatial data enables demand-driven energy control.

Ways spatial data reduces energy use

Demand-based HVAC control: Modulate heating, cooling, and ventilation based on real-time occupancy rather than set schedules.
Zoned lighting: Dim or switch off lights in unused areas; maintain comfort where occupants are present.
Optimize ventilation: Improve indoor air quality while avoiding unnecessary energy use by reducing ventilation in unoccupied zones.
Targeted maintenance: Identify persistently underutilized zones that can be consolidated to reduce conditioned area.

Measured impacts typically include lower runtime for HVAC equipment, reduced peak demand, and improved EUI. Over time, these gains compound into significant operating savings and carbon reductions.

Sensor approaches and privacy considerations

Spatial data can be collected via many sensor types. Two common approaches in CRE are camera-based and heat-based sensing.

Heat-based sensing (defined)

Uses thermal detection to sense body heat signatures and movement. It provides anonymized occupancy counts without capturing identifiable imagery. Advantages include privacy preservation, lower bandwidth needs, and straightforward integration with analytics platforms.

Privacy and compliance best practices

Prefer anonymized sensors (heat-based, aggregated counts) over identifiable imaging where possible.
Publish clear tenant-facing privacy policies that explain what is collected and why.
Limit data retention and use strong access controls.
Ensure systems comply with local privacy regulations and tenant contracts.

Adopting privacy-first technology reduces tenant resistance and regulatory risk while still providing high-quality occupancy data.

Analytics and AI: Turning data into decisions

Raw occupancy counts are just the start. Analytics and AI are required to translate spatial data into operational actions.

What good analytics deliver

Anomaly detection: Spot unusual patterns like after-hours gatherings or errant HVAC behavior.
Predictive schedules: Forecast occupancy to pre-condition spaces just in time.
Space optimization recommendations: Suggest leasing or reconfiguration opportunities based on trends.
Automated controls: Integrate with building management systems (BMS) to enact energy-saving measures automatically.

AI models learn typical usage patterns and improve over time, refining HVAC setpoints, lighting schedules, and staff allocation for cleaning or security.

Implementation roadmap: From pilot to portfolio

A practical rollout follows clear stages. Below is a concise roadmap for CRE teams.

Define goals - Set measurable objectives (e.g., reduce HVAC energy by 20%, increase usable desk utilization to 65%).
Audit current systems - Map existing sensors, BMS capabilities, and data sources.
Start with a pilot - Deploy anonymized sensors in a high-potential area (lobby, a floor, or common areas).
Integrate analytics - Feed data into an analytics platform that can visualize heatmaps, occupancy trends, and KPIs.
Connect controls - Link analytics to HVAC and lighting controls gradually, starting with non-critical zones.
Measure and iterate - Track outcomes, refine models, and expand to other floors or buildings based on results.
Scale with governance - Standardize data practices, privacy policies, and maintenance processes across the portfolio.

This phased approach limits disruption and delivers measurable wins early to build stakeholder buy-in.

Use cases and measurable KPIs

Operators should focus on a few high-impact KPIs to demonstrate value quickly.

Key use cases

Right-sizing leases and repurposing space.
Automating HVAC adjustments to occupant presence.
Improving tenant experience with real-time space availability.
Reducing cleaning frequency by targeting occupied zones.

Suggested KPIs

Occupancy rate and peak occupancy
Desk and room utilization percentages
HVAC runtime reduction and energy saved (kWh)
Change in EUI over baseline period
Tenant satisfaction scores related to comfort and amenities

Tracking these metrics shows both operational and financial returns from spatial data investments.

Challenges and best practices

Adopting spatial data has challenges, but they are manageable with proper planning.

Common challenges

Data silos and integration hurdles with legacy BMS.
Tenant concerns about privacy.
Upfront costs and proving ROI to stakeholders.
Ensuring sensor coverage and maintenance.

Best practices

Use privacy-preserving sensors and transparent policies.
Start small and scale based on success metrics.
Prioritize integration with existing building systems.
Set cross-functional teams (facilities, IT, leasing, sustainability) to align objectives.

Proven vendors and solutions

Leading smart building platforms combine anonymized sensing hardware with cloud analytics and controls. For example, Butlr offers an AI-driven platform using anonymous, heat-based sensing technology through wireless and wired thermal sensors. Such providers often support easy deployment, privacy-first data collection, and integrations with BMS and workplace apps, making them practical partners for CRE teams seeking immediate impact.

Conclusion: Start with data, act on insight

Spatial data turns subjective impressions into objective strategies. By understanding how spaces are used, CRE operators can increase occupancy, improve tenant experiences, and cut energy waste — all while maintaining privacy and compliance. Begin with clear goals, pilot in high-value areas, and use analytics to automate energy and space decisions. With the right technology and process, spatial data becomes a continuous source of efficiency and competitive advantage.

If you’re running a CRE portfolio and want to prioritize occupancy and efficiency this quarter, consider piloting anonymized heat-based sensing and AI analytics to generate fast, measurable results aligned with the Butlr CRE 4December Campaign focus on CRE data-driven outcomes.