What is Office Space Utilization Analysis?

The process of interpreting utilization data to find mismatches between office space supply and demand and decide what to change.

Definition

Office space utilization analysis is the step that turns raw measurements into decisions. It examines utilization data by time, zone, space type and team to identify where space is overused, underused or the wrong type, and it translates those findings into options such as reconfiguring areas, changing policies or adjusting the size of a lease.

Analysis is where many organisations fall short. JLL's Global Occupancy Planning Benchmark Report 2025, as summarised by IFMA's FMJ, analysed 99 organisations managing 745 million square feet and found that organisations with robust analytics substantially outperform those relying on basic measurement approaches.

A good analysis answers concrete questions: how much space do we need at peak, which space types are missing, which areas can be released and what the financial effect would be.

Example

A company with three floors collects eight weeks of booking and sensor data. Analysis by floor shows floor 3 averaging 22% utilization with a peak of 35%, while floors 1 and 2 peak above 80%. Analysis by space type shows that rooms for two to four people are used 85% of available hours, while rooms for twelve people are used 15%.

The analysis concludes that the company can consolidate onto two floors if it converts two large rooms on each remaining floor into six small rooms and moves the teams from floor 3 into adjusted neighbourhoods. The proposal is presented on the floor plan with before-and-after views.

How it relates to an office map

Analysis is easier when the data sits on the floor plan. The Office Map Editor's heatmaps and analytics show utilization by desk and zone, and versions let you sketch the consolidated layout next to the current one before anything moves.

The questions an analysis should answer

Start from decisions rather than data. Every chart in the analysis should help answer one of a short list of questions.

What is our true peak demand, and how often does it occur?
Which zones, floors or buildings are consistently underused?
Which space types are in short supply?
Are rooms the right size for the groups that use them?
How much could we save, or what could we add, by reconfiguring?
What happens to peak capacity if headcount or attendance policy changes?

Slicing the data

Aggregate numbers hide the most useful patterns. Break the data down by day of week and hour to see peaks, by zone and floor to see distribution, by space type to see mismatches and by team to see how different groups use the office.

CBRE's 2025 European survey shows why this matters: the gap between a 46% weekly average and a 71% peak-day average means an office can look half empty overall but still be crowded on specific days.

Peak planning and buffers

Most analyses size space for a high percentile of demand rather than the absolute maximum, because sizing for a once-a-year event is expensive. Decide what level of risk is acceptable, for example running out of seats on two days per quarter, and size accordingly. Document the assumption so leadership understands the trade-off between cost and certainty.

Connecting utilization to cost

IFMA notes that occupancy is commonly an organisation's second-largest operating expense after payroll. Expressing findings in cost terms — cost per occupied desk, cost of an underused floor, savings from consolidation — makes the analysis relevant to finance and leadership.

Be careful with claimed savings figures from vendors or secondary sources. Use your own lease, service and fit-out costs whenever possible.

Presenting the results

Decision makers respond to maps more than to tables. Show heatmaps on the floor plan, then show the proposed layout. Keep supporting charts simple: peak and average by week, utilization by space type and the cost effect. End with a clear recommendation and the measurement that will confirm whether it worked.

Example analysis structure

Section one summarises the method: dates, data sources, capacity definition. Section two shows building and floor utilization. Section three shows space-type findings. Section four presents two or three options with their capacity and cost effects. Section five recommends one option and sets a follow-up measurement date. This structure keeps a complex topic readable for non-specialists.

Common mistakes

Analyses often draw conclusions from too short a period, ignore seasonality or rely on a single data source. Another frequent problem is recommending space cuts without testing whether the remaining space can absorb peak demand. Finally, many reports never close the loop by re-measuring after changes.

Example findings and the actions they lead to

Analyses tend to surface a recurring set of findings, each linked to a typical response. Recognising these patterns speeds up the move from data to decision.

Whatever the finding, express the recommended action on the floor plan and attach a measurable success criterion, such as peak utilization in the Sales zone below 90% after the change. That makes the next round of analysis a simple check rather than a new investigation.

Low average and low peak across a floor: consider consolidation or subletting
Low average but high peak: coordinate team days or add flexible overflow space
Small rooms full, large rooms empty: split large rooms
One zone crowded, neighbour quiet: move the zone boundary
High booking no-shows: introduce check-in and auto-release
Desks used but complaints about noise: add focus space and phone booths

Tools for analysis

Spreadsheets are enough for a single office and a few weeks of data. Larger datasets benefit from business intelligence tools or integrated workplace management systems. Whatever the tool, the floor plan should be the visual centre of the analysis, because it connects abstract percentages to places people recognise.

Related concepts explained

Analysis reports often use specialised vocabulary. These terms describe the most common analytical techniques and outcomes.

Percentile planning: sizing for, say, the 90th-percentile day rather than the absolute maximum
Space type mix: the proportion of desks, rooms, booths and collaboration areas
Consolidation: moving teams to release a floor or building
Right-sizing: matching room sizes to the groups that use them
Re-stack: reallocating teams across floors to balance demand

Putting it into practice

For your first analysis, keep the scope narrow: one floor, one question and four weeks of data. Produce a two-page summary with a heatmap on the floor plan, a chart of peak versus average utilization and one recommended change with its expected effect. Share it with the team leads affected, collect their feedback and refine the recommendation before presenting it to leadership. A small, well-evidenced first analysis builds the credibility you need for larger portfolio decisions later.

Scenario planning

Good analysis looks forward as well as back. Once you understand current peak demand, model scenarios: headcount growth of 10% or 20%, a change from two to three office days a week, or a new team joining the floor. For each scenario, estimate peak desk and room demand and check it against capacity on the floor plan.

Scenario planning prevents a common trap: releasing space based on today's data and then needing it again a year later. It also helps leadership see the consequences of policy changes, such as an attendance mandate, before they are announced.

Involving stakeholders

Utilization analysis affects how people work, so involve team leads early. Share zone-level findings with them, ask whether the numbers match their experience and invite input on the options. Teams are more likely to accept a smaller neighbourhood or a new room layout when they have seen the evidence and helped shape the solution.

Key takeaways

Principles for analysis that leads to good decisions.

Start from decisions, not dashboards
Slice data by time, zone, space type and team
Plan for an agreed level of peak demand
Translate findings into cost and capacity
Present options on the floor plan
Re-measure after every change

Frequently asked questions

How much data do I need for an analysis? Several representative weeks at minimum; for lease decisions, a longer period that captures seasonal variation is safer.

Who should own the analysis? Usually facilities or workplace teams, with input from finance, HR and team leads.

What is the most common finding? A mismatch between space types: too many desks and large rooms, too few small rooms and focus spaces.

Summary

Office space utilization analysis is where data becomes decisions. Start from the question, slice data by time, zone, space type and team, plan for an agreed level of peak demand and express findings in cost and capacity terms. Present options visually on the floor plan, involve team leads and set a measurable success criterion for each change. Re-measure afterwards so that every analysis builds on verified results rather than assumptions, and keep a record of past decisions for future reviews.

Finally, document your assumptions. Write down the time window, data sources, capacity definition and peak-planning rule used in each analysis. When the next analysis is run a year later, this record lets you compare results fairly and understand why earlier decisions were made, even if the people who made them have moved on.