Colocation Financial Models
Executive Summary
Key Takeaways
- ✓ Colocation revenue should be decomposed into space/power revenue, cross-connect revenue, and other ancillary fees, since each has a different pricing basis, margin profile, and churn sensitivity.
- ✓ Occupancy, pricing per unit, and churn are three separable drivers of colocation revenue, and a single blended revenue-per-tenant assumption conceals which driver is responsible for a forecast change.
- ✓ Power, not floor space, is typically the binding capacity constraint in a modern colocation facility, so contracted power (kW) is the more reliable revenue-driving capacity unit than leased square footage alone.
- ✓ Cross-connect and ancillary revenue carries materially higher margin than base space/power revenue and should be modelled and reported as a distinct line, not absorbed into blended average revenue per tenant.
Objective¶
This guide sets out how to build a colocation data centre financial model within Data Centre Financial Modelling, decomposing revenue into its separable underlying drivers.
Revenue Decomposition¶
Colocation revenue should be modelled as the sum of three distinct components rather than a single blended revenue-per-tenant assumption:
Space/power revenue. Occupancy (contracted racks or kW) multiplied by the price per unit of contracted capacity, typically billed per rack, per kW, or a hybrid of the two. See Rack Revenue Models.
Cross-connect revenue. Recurring fees for physical interconnection between tenants within the facility, or between a tenant and a network carrier, typically billed per connection per month. See Cross-Connect Revenue.
Other ancillary revenue. Remote hands support, power metering and billing, and similar service fees that supplement the core space/power lease.
Power as the Binding Capacity Constraint¶
In most modern colocation facilities, available power, not floor space, is the true constraint on how much billable capacity can be leased, particularly as tenant rack density increases with higher-performance computing equipment. A model built solely on available floor space can overstate achievable revenue if power is in fact the binding constraint; contracted power (kW) should be modelled as the primary capacity unit, with floor space tracked as a secondary constraint. See Data Centre Capacity Planning Models.
Occupancy, Pricing, and Churn as Separable Drivers¶
Revenue should be modelled as the product of occupancy, price per unit, and retention (the inverse of churn), rather than a single net revenue growth figure. New bookings and churn should be modelled as gross, separate flows against the existing contracted base, since a net occupancy growth figure conceals whether growth is coming from strong new bookings, low churn, or both, information a reviewer needs to assess the durability of the forecast. See Data Centre Occupancy & Utilisation Models.
Pricing Mechanics¶
Colocation pricing is typically structured per rack (a standard unit, often with a defined power allowance) or per kW of committed power, with premium pricing for higher-density racks. Multi-year contracts frequently include a fixed annual escalator; the model should apply the escalator to the existing contracted base separately from the pricing assumption applied to new bookings, since blending the two can misstate the run-rate revenue base.
Common Construction Pitfalls¶
Blended revenue-per-tenant assumption. Conceals whether occupancy, pricing, or churn is driving a forecast change.
Floor-space-only capacity constraint. Overstates achievable revenue where power is the actual binding constraint.
Cross-connect and ancillary revenue absorbed into base revenue. Understates the facility's true margin composition, since these fees typically carry higher margin than core space/power revenue.
Net occupancy growth without separate gross bookings and churn. Conceals the underlying retention dynamic driving the forecast.
Recommended Practices¶
- Decompose revenue into space/power, cross-connect, and ancillary components, modelled separately.
- Track contracted power (kW) as the primary capacity constraint, with floor space as secondary.
- Model gross new bookings and gross churn separately against the existing contracted base.
- Apply the contract escalator to the existing base separately from new booking pricing assumptions.
Continue Reading¶
Related Pillars¶
Related Technical Guides¶
Related Glossary¶
How OXXON tests thisRun a free structural check with FMAE
Frequently Asked Questions
What is a colocation data centre?
A facility in which an operator leases defined units of physical space and power, typically priced per rack or per kW of committed capacity, to multiple tenants who install and manage their own IT equipment within that leased footprint.
How should colocation revenue be decomposed for modelling purposes?
Into space/power revenue (occupancy multiplied by price per unit of contracted capacity), cross-connect revenue (fees for physical interconnection between tenants or to network providers), and other ancillary fees (remote hands, power metering, etc.), since each has a distinct pricing basis and margin profile.
Why is power the more reliable capacity unit than floor space?
Because in most modern colocation facilities, available power, not floor space, is the binding constraint on how much billable capacity can actually be leased, particularly as tenant rack density increases. A model built on floor space alone can overstate achievable revenue if power is the true constraint.
How should churn be modelled in a colocation revenue forecast?
As a separate assumption from new leasing volume, applied against the existing contracted base, so the model shows gross new bookings and gross churn separately rather than a single net occupancy growth figure that conceals the underlying retention dynamic.
Why does cross-connect revenue deserve its own model line rather than being blended into average revenue per tenant?
Because cross-connect and ancillary fees typically carry materially higher margin than base space/power revenue, and blending the two into a single average revenue per tenant figure obscures the facility's true margin composition and understates the value of network density.
Related Articles
Data Centre Financial Modelling
Data centre financial modelling is the discipline of modelling a data centre operator's revenue, cost, and capital structure from its capacity-denominated drivers, power, space, and cooling capacity, rack density, and tenant contract structure, rather than the generic market-price and headcount-growth drivers used in most corporate models, or the pure occupancy-and-lease-term drivers of conventional commercial real estate. This page is the hub for the Knowledge Centre's data centre financial modelling content: how colocation, hyperscale, and enterprise business models each require a distinct model architecture, how rack revenue and occupancy are decomposed into their separable underlying drivers, and how capacity planning and financial KPIs tie the model together, as this domain expands to cover operations, revenue, investment, and governance practice across the sector.
Data Centre Business Models
Data centre operators run under several structurally different business models, wholesale colocation, retail colocation, hyperscale build-to-suit, enterprise/captive, and managed services, each of which ties revenue, contract tenor, and capital intensity to a different mechanism. This guide sets out how each business model's revenue and cost mechanism differs and, correspondingly, how the financial model architecture appropriate to each differs, since applying a retail colocation-style model to a hyperscale build-to-suit facility, or vice versa, misrepresents the operator's actual revenue and risk exposure.
Rack Revenue Models
Rack revenue is the core billing unit of colocation data centre revenue, priced per rack, per kW of committed power, or a hybrid of the two, with premium pricing for higher-density racks. This guide sets out the mechanics of rack-based pricing, density tiering, and how to model power draw billing and contract escalation without conflating them into a single blended average rate per rack.
Cross-Connect Revenue
Cross-connect revenue arises from recurring fees charged for physical cabling connections between tenants within a colocation facility, or between a tenant and a network carrier present in the facility's meet-me room. Cross-connects typically carry materially higher margin than base space and power revenue, since the incremental cost of provisioning a connection is low relative to its recurring fee, and cross- connect density is often used as a proxy for a facility's network ecosystem value.
Data Centre Occupancy & Utilisation Models
Data centre occupancy modelling requires distinguishing three related but distinct capacity states: billed (contracted) capacity, actually utilised capacity, and total available capacity. This guide sets out how to model each state and the ratios between them, and why conflating billed occupancy with actual utilisation misrepresents both revenue durability and the facility's true remaining capacity headroom.