Home Insights Real estate Powering the digital age: Data center trends and opportunities
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Executive Summary

Data centers remain underrepresented in many institutional real estate portfolios despite strong historical performance in both the public and private markets. They offer stable, long-term cash flows, relative insulation from economic cycles, and strong growth potential driven by cloud adoption, AI, and the explosion of digital data.

Growth in the sector is fueled by rising digital activity and AI demand. For instance, the U.S. colocation market doubled between 2020 and 2024 according to JLL Mid-Year 2024 U.S. Data Center Report, and AI-driven applications could multiply data center needs up to 100-fold over the next decade according Dell’s Founder and CEO, Michael Dell at the South by Southwest (SXSW) conference in March 2024. Rising demand is pushing vacancies in major markets to historic lows according to DataHawk. But you can’t build a data center anywhere: key site factors include access to low-cost, reliable power, robust network connectivity, disaster resilience, and renewable energy. AI and generative AI workloads are increasingly shifting new builds to non-traditional, power-rich markets.

Data centers are evolving. Traditional hyperscale and colocation definitions have become more nuanced based on the property serving cloud, AI inference, and generative (i.e, training) AI workloads, each with distinct power and density requirements. We see the most attractive risk-adjusted returns in cloud and AI inference data centers in both the U.S. and Europe, as they address established, durable demand. An investment in a data center located in an Availability Zone (AZ) may help mitigate exposure to fluctuations in cloud demand, while offering potential benefits from continued AI advancement. By contrast, generative AI data centers are more speculative, driven largely by forecasts of future AI adoption, and are a key source of today’s near-term oversupply. We may pursue land acquisitions and site preparation for potential generative AI development but would avoid building these facilities ourselves. We also see emerging opportunities in the shift towards edge computing.

Power, cooling, and sustainability. Power remains a major cost, though efficiency has improved—average PUE fell from ~2.5 in 2007 to ~1.5 in 2024, with some facilities performing better. Liquid cooling is becoming essential for high-density AI workloads while sustainability is now a core priority, driven by regulation, tenant demand, and renewable integration that lowers both costs and environmental impact.

Looking ahead, future-proofing is essential. Many older facilities are being redeveloped to support higher-density workloads and meet new regulatory requirements, while new data centers are designed for flexibility to adapt to evolving AI and cloud demands. With land and power increasingly constrained in major markets, these assets remain key drivers of long-term value.

Key theme #1: Location matters

Critical factors include: low-cost, reliable power; low natural disaster risk; strong network connectivity; favorable tax regimes; renewable energy access; and sites outside FEMA 500-year floodplains. Simply put, data centers are complex and can’t be built just anywhere. Leading U.S. hubs include Northern Virginia, Dallas-Fort Worth, Phoenix, Atlanta, and Chicago. In Europe, FLAP-D markets (Frankfurt, London, Amsterdam, Paris, Dublin) dominate, with emerging hubs in Madrid, Warsaw, Milan, Zurich, and Stockholm.

Power costs to market size comparison (U.S. only)

Chart displaying U.S. cities and the size of the markets in MWs by the cost of electricity in said market

Source: Green Street. Includes labor and direct personnel, repairs, and maintenance.

Generative AI is shifting the landscape further. Unlike traditional cloud and enterprise data centers that cluster in major metro areas, AI data centers require enormous power and land, pushing them into smaller, non-traditional markets. U.S. examples include Mount Pleasant, WI; Kuna, ID; Council Bluffs, IA; Papillon, NE; and Umatilla, OR. In Europe, AI facilities are moving to power-rich regions like Northern England, Hamina (Finland), and Murcia (Spain).

Key theme #2: Evolving data center types

Traditionally, data centers were categorized based on ownership and tenancy:

  • Enterprise: Single-tenant facilities with limited scalability requirements, typically used to handle organizations’ internal data and IT workloads. For instance, a bank that runs its own data center to manage customer transactions. Many enterprises have or are shifting away from building / owning facilities and instead lease from colocation provides or have moved workloads to public cloud. According to Synergy Research Group, they accounted for 56% of data center capacity in 2018 compared to 34% today and it’s expected to drop to 22% by 2030.
  • Hyperscale: Single-tenant facilities, typically leased for 15 years with options to extend. Usually leased by large global technology companies. They account for nearly 44% of the worldwide capacity of all data centers according to Synergy Research Group. Over half of that hyperscale capacity is built and owned by large cloud providers (known as Cloud Service Providers or CSPs) with the balance being leased according to Synergy Research Group. Looking ahead to 2030, hyperscale will account for 61% of all capacity.
  • Colocation: Multi-tenant facilities where companies lease space (usually 5–7 years) to house their IT equipment. This accounts for 22% of data center capacity in 2024 and, while colocation share of total capacity will slowly decrease by 2030 according to Synergy Research group, colocation capacity will continue to increase each year at nearly double-digit rates.

We focus our attention on leased hyperscale properties and colocation since they are income producing real estate. Green Street estimates that 50% of data centers in the U.S. are owned by third-party landlords (based on % of live mega-watt) compared to 65% in Europe and 80% in Asia-Pacific. As a rule of thumb, the more complex the business environment, the more likely a data center is to be third-party leased.

Data center capacity trends

Chart showing data center capacity trends from 2018, 2024, and forecasted 2030

Source: Synergy Research Group

Key theme #3: Cooling and energy efficiency

Cooling has become increasingly important, as rising electrical loads generate more heat. This trend is accelerating with AI workloads, which operate at far higher densities. For example, Nvidia’s Hopper GPU architecture (2024) was designed for ~41 kW per rack, while its Blackwell generation (2025) is built for 120 kW. A roadmap announced in 2025 projects racks reaching 600 kW by 2027.

Historically, two main cooling methods have been used:

  • Closed-loop air-cooled chillers: Reject heat to ambient air using refrigerant loops and fans. They consume less water but require more energy.
  • Evaporative systems (cooling towers, adiabatic, direct/indirect evaporative cooling): More water-intensive but generally more energy-efficient.

In regions with water constraints, or abundant renewable power, developers are increasingly adopting air-cooled chillers, which circulate water in a closed loop to remove heat from data halls and release it outdoors.

However, rising rack densities are pushing these systems beyond their limits. Liquid cooling, which delivers coolant directly to server racks, is becoming essential for handling the extreme heat of AI and other advanced workloads. Unlike air systems, liquid cooling supports significantly higher power densities.

AI calls for massively denser rack deployments

Chart showing data center density slowly increasing from 2011 to 2020 and then rapidly increasing from 2021 to forecasted 2027

Source: 2011, 2017, and 2020 data points reflect average density per rack as reported by Uptime Institute in December 2020. 2024, 2025, and 2027 data points reflect Nvidia GPU architecture specifications.

Conclusion

Data centers remain a relatively new asset class for many institutional investors, with most portfolios still underweight. They have delivered strong performance in both public and private markets, driven by stable, long-term cash flows and robust growth from cloud adoption, AI, and broader digital trends. The long-term growth prospect remains strong given the emergence of a secular mega trend, but the sector also faces new considerations as AI innovation may alter data growth patterns and there’s been rapid capital inflows. This puts a focus on several key considerations including location, type, power, and sustainability, with successful sites offering reliable, low-cost power, strong connectivity, and access to talent.

Traditionally data centers were defined as either hyperscale or colocation based on tenant size, but properties are now increasingly separated into cloud, AI inference, and generative AI centers based on their workloads. Each has distinct risk-return profiles. We are focused on cloud and AI inference data centers that have durable demand given the rapid and sustained expansion of AI applications but are cautious on generative AI data centers that we view as more speculative given uncertainty around the long-term scale of training demand. By contrast, the rapid and sustained expansion of AI applications is driving significant growth in both inference and storage requirements, positioning AI inference data centers as the more durable and attractive segment of the market. We also see emerging opportunities to identify and retrofit existing assets to meet the requirements of edge computing, presenting a unique investment opportunity in a rapidly changing landscape.

Energy efficiency and cooling are critical, with innovations like liquid cooling enabling higher-density AI workloads while sustainability initiatives, including renewable integration and energy-efficient design, offer operational and competitive benefits. Looking forward, future development emphasizes flexibility and future-proofing, with new and retrofitted facilities designed to adapt over a 20+ year lifespan, supporting both high-density AI training today and lower-density inference in the future, thereby preserving long-term asset value and operational resilience.

For more on the under allocation of the data center sector and how to navigate the next phase of data centers, access the full report here .

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Disclosure

Risk Considerations
Investing involves risk, including possible loss of principal. Past Performance does not guarantee future return. All financial investments involve an element of risk. Therefore, the value of the investment and the income from it will vary and the initial investment amount cannot be guaranteed. Potential investors should be aware of the risks inherent to owning and investing in real estate, including value fluctuations, capital market pricing volatility, liquidity risks, leverage, credit risk, occupancy risk and legal risk. All these risks can lead to a decline in the value of the real estate, a decline in the income produced by the real estate and declines in the value or total loss in value of securities derived from investments in real estate.

Data center properties and will only be attractive to a unique type of tenant. A limited tenant base increases the risk of vacancy. Additionally, a property designed to be a data center property, may be difficult to relet to another type of tenant or convert to another use and will be more likely to become functionally obsolete when compared to other properties. For example, if converted to industrial use, the expected rents would be lower than that projected for data centers. Thus, if operating a data center were to become unprofitable, the liquidation value of properties may be substantially less than would be the case if the properties were readily adaptable to other uses.

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MM14718 | 10/2025 | 4853377-102026

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