Asset Pillar · Data Center
Data center feasibility study.
Lender-grade feasibility analysis for data center development and acquisition — colocation, hyperscale, edge, AI/GPU clusters, and power-and-site selection. Data center has become the most institutionally-credible asset class in commercial real estate, with AI training cluster demand exceeding supply and multi-billion-dollar projects routinely closing across debt fund, life-co, CMBS SASB, and project-finance capital sources.
Market Positioning
Data center as institutional darling — the AI tailwind.
Three structural shifts since 2023 made data center the most aggressively-financed asset class in commercial real estate. The volume below illustrates the scale.
$200B+
Hyperscaler 2025 Capex
Microsoft, Google, Amazon, Meta, Oracle combined data center capex. Up sharply from prior cycles.
100MW+
AI Training Cluster Minimum
Modern AI cluster deployments. Traditional colocation by contrast runs 10-30MW campus scale.
Under 1.5
PUE Target For Institutional Deals
Power Usage Effectiveness. Hyperscale targets approach 1.1-1.2; older colocation runs 1.5-1.8.
The post-2022 shift toward generative AI restructured data center demand at the foundation. Training-cluster GPU deployments require campus-scale 100MW-plus facilities, often co-located with major utility transmission lines and water resources for liquid cooling. Inference workloads follow conventional colocation density patterns. Traditional cloud and enterprise colocation, the historical core of the asset class, runs at 10-30MW campus scale. The three demand layers compete for the same constrained inputs: power, water, fiber connectivity, and entitled land in tier-1 markets.
Hyperscaler capital expenditure validates the institutional thesis. Microsoft, Google, Amazon Web Services, Meta, and Oracle collectively committed over $200 billion in 2025 data center capex, with similar or larger 2026 commitments announced. Capital flows: debt funds and bridge lenders for ground-up development; life-insurance companies and CMBS SASB for stabilized credit-tenant deals; project-finance structures for hyperscale campus phasing. Healthcare REITs and traditional commercial REITs do not compete in this asset class; specialty data center REITs (Digital Realty, Equinix, Iron Mountain Data Centers) and private vehicles dominate the institutional ownership universe.
The infrastructure constraint is real and binding. Power availability and grid capacity, not real estate per se, determines where new data center capacity can land. Site selection follows utility transmission, not traditional CRE locational logic. The bankable framework's data center scope is built around the infrastructure stack — power, fiber, PUE, water — alongside the conventional CRE analytical lenses.
Lender Matrix
Data center lending by capital source and deal type.
Each capital source serves a different point in the data center lifecycle. Development risk goes to debt funds and project finance; stabilized credit-tenant deals attract life-co and CMBS SASB.
| Deal Type | Debt Fund / Bridge | Life-Co | CMBS SASB | Project Finance | Bank Construction |
|---|---|---|---|---|---|
| Hyperscale ground-up (single tenant) | Strong development | — | — pre-stabilization | Strong phased campus | Good construction |
| Hyperscale stabilized (long-term lease) | Limited refinance only | Strong preferred | Strong SASB-friendly | — | Good refinance |
| AI / GPU cluster build (100MW+) | Strong specialty fund | — specialty risk | — pre-stabilization | Strong institutional | Good construction with takeout |
| Colocation ground-up (multi-tenant) | Strong development | Limited lease-up risk | Limited pre-stabilization | Fair smaller scale | Good construction |
| Colocation stabilized | Fair refinance | Good institutional | Good pool-friendly | — | Good refinance |
| Edge data center (sub-10MW) | Fair smaller mandate | Limited smaller mandate | Limited pool-size | Limited smaller scale | Good regional bank |
| Powered shell (no tenant fit) | Strong dev land | Limited specialty | — unstabilized | Fair phased | Good construction |
| Conversion / repositioning | Strong value-add | — specialty risk | Limited specialty risk | Limited | Fair case-by-case |
Cell ratings reflect typical 2026 underwriting posture. Hyperscaler-tenanted deals carry distinct credit underwriting that compresses pricing across all relevant lender types. Project finance structures typically require commitment from a hyperscaler tenant before debt commitment.
Capital Cost
Capital cost per MW by data center type.
Data center capital cost is benchmarked per megawatt of IT load capacity, not per square foot. Power density and cooling configuration drive most of the variance.
AI / GPU cluster (training)
SHELL-AND-CORE: $12M-$18M per MW
FULLY FITTED: $30M-$45M+ per MW
Liquid cooling infrastructure, high-density power distribution, redundant transmission. Fully-fitted cost includes GPU rack-and-stack at hyperscaler standards. Cost runs 1.5-2x traditional colocation per MW.
Hyperscale (cloud / enterprise)
SHELL-AND-CORE: $9M-$13M per MW
FULLY FITTED: $25M-$35M per MW
Single-tenant or hyperscaler-leased. Standard rack densities, conventional cooling, 100MW+ campus scale typical. Capital cost has trended upward with land and power constraints.
Wholesale colocation
SHELL-AND-CORE: $8M-$12M per MW
FULLY FITTED: $22M-$32M per MW
Multi-tenant wholesale, larger suite configurations (1MW-5MW per tenant). Mix of credit and non-credit tenants. Stabilized lease-up takes 18-36 months typical.
Retail colocation
SHELL-AND-CORE: $9M-$13M per MW
FULLY FITTED: $24M-$34M per MW
Sub-1MW tenant footprints. Equinix and Digital Realty dominate the institutional retail colo market. Cost premium reflects more granular power distribution and operational overhead.
Edge data center (sub-10MW)
SHELL-AND-CORE: $10M-$15M per MW
FULLY FITTED: $25M-$38M per MW
Small-footprint, latency-driven deployments adjacent to population centers. 5G mobile edge, content delivery, IoT processing. Higher per-MW cost reflects smaller scale economics.
Powered shell / pad-ready
SHELL-AND-CORE: $5M-$9M per MW
FULLY FITTED: n/a (sold or leased to fit-out tenant)
Land entitled, power secured, building shell complete. Tenant or buyer completes fit-out. Lower base cost; resale or lease-up timing drives the deal.
Bands reflect 2026 hard cost in tier-1 US markets (Northern Virginia, Dallas, Phoenix, Silicon Valley, Hillsboro). Tier-2 markets (Columbus, Reno, San Antonio, Salt Lake City) trend 5-15 percent below the band. Premium markets with severe land or power constraints can exceed the band's top end by 20 percent or more.
Infrastructure Constraints
Power, fiber, PUE, and water — the four binding constraints.
Data center feasibility analysis routes through four infrastructure constraints. Each constraint can be the deal-killer; bankable scope quantifies all four.
01
Power availability and grid capacity
Power is the foundation. Site selection in 2026 follows utility transmission corridor more than traditional CRE locational logic. Substation capacity, transmission line proximity, and time-to-energization timeline (often 24-48 months in tier-1 markets) determine project viability. Power cost in $/kWh varies 3-5x across markets — Pacific Northwest hydro economics differ fundamentally from Northern Virginia coal-and-gas grid economics.
02
Fiber and connectivity infrastructure
Fiber availability and carrier density drive operational viability. Carrier-neutral cross-connect points within the building and submarket multi-carrier presence affect tenant decision-making. Latency to major peering points (Ashburn, Dallas, Silicon Valley) determines edge versus core positioning. Long-haul fiber to hyperscaler peering hubs is essential infrastructure.
03
PUE benchmarking
Power Usage Effectiveness measures total facility power divided by IT load power. PUE of 1.0 means zero overhead; 1.5 means 50 percent overhead. Modern hyperscale runs 1.1-1.25; older colocation runs 1.5-1.8. Lender expectations cluster around sub-1.5 for institutional deals; AI cluster underwriting expects sub-1.3 with liquid cooling configurations.
04
Water and cooling analysis
Liquid cooling for AI clusters and high-density configurations requires substantial water access. Water rights, withdrawal permits, and ongoing supply economics constrain site selection. Climate suitability — cooler ambient temperature reduces cooling load — drives Pacific Northwest, Nordic, and high-elevation siting. Markets with water stress (Phoenix, parts of California) face increasing regulatory friction.
Market Tiers
Primary markets and emerging tier-2 markets.
Data center capacity concentrates in a handful of markets historically. Tier-2 markets are absorbing rapidly under power and land pressure in tier-1.
| Market | Tier | Inventory (MW) | Power Economics | Dominant Use |
|---|---|---|---|---|
| Northern Virginia (Ashburn) | Tier 1 | 3,000+ MW | Tight; rising rates | Hyperscale + colocation |
| Dallas–Fort Worth | Tier 1 | 800+ MW | Moderate | Hyperscale + AI clusters |
| Phoenix | Tier 1 | 700+ MW | Tight; water concerns | Hyperscale + AI clusters |
| Silicon Valley | Tier 1 | 500+ MW | Very tight; constrained | Enterprise + AI inference |
| Chicago | Tier 1 | 500+ MW | Moderate | Hyperscale + colocation |
| Atlanta | Tier 1 | 400+ MW | Moderate | Hyperscale + colocation |
| Hillsboro / Pacific NW | Tier 1 | 400+ MW | Hydro; cool climate | Hyperscale + AI clusters |
| Columbus, OH | Tier 2 (emerging) | 250+ MW; rising | Moderate | Hyperscale (Intel adjacency) |
| Reno / Northern Nevada | Tier 2 (emerging) | 200+ MW; rising | Cool climate; water concerns | Hyperscale + AI |
| San Antonio | Tier 2 (emerging) | 150+ MW; rising | Moderate; growing | Hyperscale |
| Salt Lake City | Tier 2 (emerging) | 150+ MW; rising | Cool climate; growing | Enterprise + hyperscale |
Inventory figures reflect approximate operational capacity as of 2025. Pipeline development in tier-2 markets is rapidly closing the gap with tier-1 incumbents. Microsoft, Google, Amazon, Meta, and Oracle development announcements drive most of the tier-2 absorption.
AI vs Traditional
AI/GPU cluster versus traditional colocation — underwriting differences.
AI training clusters and traditional colocation operate at fundamentally different scales and demand fundamentally different feasibility methodology. The matrix below covers the practical differences.
| Dimension | AI / GPU Cluster | Traditional Colocation |
|---|---|---|
| Typical campus scale | 100MW-500MW+ | 10MW-30MW |
| Tenant profile | Single hyperscaler training tenant | Multi-tenant colo or single enterprise |
| Cooling configuration | Liquid cooling required | Air cooling typical |
| Power density per cabinet | 50-100kW+ per rack | 5-15kW per rack |
| Fiber latency requirement | Less critical (training is remote-tolerable) | Critical for inference and enterprise |
| Site selection priority | Power + water + climate + transmission | Fiber + tenant catchment + latency |
| Capital cost per MW (fully fitted) | $30M-$45M+ | $22M-$34M |
| Lease term typical | 10-20 years (hyperscaler) | 3-7 years (colo) / 10+ years (single tenant) |
| Capital source typical | Project finance + debt fund / hyperscaler balance sheet | Life-co + CMBS + debt fund |
| Feasibility scope emphasis | Power + transmission + tenant credit | Fiber + tenant mix + lease comp |
Many modern campuses combine both — a 200MW campus with 100MW dedicated to a hyperscaler AI training tenant and 100MW reserved for traditional colocation use. Hybrid campus underwriting addresses both lenses.
Capital By Stage
Capital sources track the deal stage.
Data center capital flows through distinct sources at distinct deal stages. Sponsors aligning capital strategy to deal stage avoid friction at lender introduction; sponsors who approach a stabilized-asset lender with a development deal — or vice versa — burn time and credibility.
Development and construction. Debt funds (Blackstone Real Estate Debt Strategies, Brookfield, KKR Real Estate Finance, Starwood Property Trust, Ares, Mesa West) and bank construction lenders fund ground-up data center development. Project finance structures with hyperscaler tenant commitments enable single-tenant campus phasing at $200M-$1B+ scale. Bridge structures typically run 75-80 percent loan-to-cost, floating rate, 2-3 year terms with extension options. Construction lender takeout assumptions drive much of the underwriting — life-co or CMBS SASB exit at stabilization is the most common path.
Stabilized credit-tenant. Life-insurance companies aggressively pursue stabilized hyperscaler-tenanted data centers. Long-term hyperscaler leases (10-20 years typical) align with life-co's balance-sheet hold preference. PGIM, MetLife, Northwestern Mutual, Pacific Life, and TIAA-Nuveen have all built data center allocations. CMBS SASB transactions securitize trophy data center portfolios at $500M-$2B+ scale, with rating agency methodology drawn from KBRA and S&P published criteria for data center evaluation.
Project finance and hyperscaler balance sheet. Hyperscalers (Microsoft, Google, Amazon, Meta, Oracle) finance much of their own data center capex on balance sheet, in joint venture structures with developers, or through project-finance vehicles that combine debt, tax equity (for renewable-power-paired campuses), and sponsor equity. Project-finance feasibility scope is denser than typical CRE feasibility — power availability, grid energization timing, tenant credit, and offtake economics all factor into the analysis.
Conversion and repositioning. A growing subset of data center deals involves converting industrial, office, or specialty properties into data center use. Powered land with existing utility infrastructure, brownfield power generation sites, and decommissioned manufacturing facilities frequently re-enter the market as data center conversions. The bankable framework's data center scope addresses conversion economics, including stranded asset risk if conversion does not stabilize as planned.
Data Center Sub-Segments
Five data center sub-segments, five deep-dive feasibility approaches.
Each sub-segment carries its own demand drivers, infrastructure profile, and feasibility methodology. Click into the sub-pillar that matches your deal.
Colocation
Multi-tenant wholesale and retail colocation. Tenant mix, lease comp methodology, fiber and carrier-neutral cross-connect, lease-up modeling, stabilized cap rate dynamics.
Read deep-dive →
Hyperscale
Cloud and enterprise hyperscale campuses. Single-tenant credit underwriting, lease term mechanics, hyperscaler balance-sheet analysis, project-finance structures.
Read deep-dive →
Edge data center
Sub-10MW latency-driven deployments. 5G mobile edge, content delivery, IoT processing. Smaller-scale economics, regional bank financing, multi-site portfolio strategies.
Read deep-dive →
AI / GPU cluster
Training cluster deployments at 100MW+ scale. Liquid cooling, transmission infrastructure, water economics, hyperscaler tenant credit, project-finance underwriting.
Read deep-dive →
Power and site selection
Pre-development power availability analysis, substation capacity, transmission corridor, time-to-energization, water rights, and climate suitability methodology.
Read deep-dive →
Methodology Applied
How the bankable framework adapts to data center scope.
Six methodology components specific to data center feasibility analysis. Each adapts the bankable framework's structural approach to the asset class's distinctive analytical demands.
Power availability and cost analysis
Substation capacity, transmission line proximity, time-to-energization, and $/kWh cost modeling. Power constraint analysis is the foundation; deals that fail the power test fail at feasibility regardless of other characteristics.
Fiber and connectivity assessment
Carrier-neutral cross-connect availability, multi-carrier presence, latency to major peering hubs, and long-haul fiber capacity. Fiber methodology adapts to asset use — edge demands tighter latency, hyperscale demands carrier diversity, AI training tolerates higher latency.
PUE benchmarking and cooling design
Power Usage Effectiveness modeling at design and projected operational levels. Cooling configuration analysis (air, evaporative, liquid, immersion). Lender expectations of sub-1.5 PUE for institutional deals; sub-1.3 for AI cluster.
Water and climate analysis
Water rights documentation, withdrawal permits, ongoing supply economics. Climate suitability for ambient cooling efficiency. Regulatory environment for water-intensive cooling configurations.
Tenant demand by market tier
Hyperscaler announcement tracking, colocation absorption, AI cluster development pipeline, edge deployment patterns. Demand modeling varies sharply across tier-1, tier-2, and emerging markets.
Capital cost per MW and exit assumption
Capital cost benchmarked per MW (not per SF) at shell-and-core and fully-fitted basis. Exit cap rate calibrated to tenant credit, lease term, market tier, and 2026 dynamics. Project-finance scenarios model offtake economics through deal life.
FAQ
Data center feasibility frequently asked questions.
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Colocation, hyperscale, edge, AI/GPU, or pre-development power-and-site selection. Single-program or cross-program scope. 30-minute scoping call. Fixed-fee proposal within 24 hours.
Or read the debt fund deep-dive · Life-co deep-dive · CMBS SASB deep-dive
Where we prepare data center feasibility studies
State-specific data center feasibility studies are available in the markets listed below.