Perspectives

The rise of alternative real estate (RE) sectors—such as data centers, self-storage, life sciences, and senior housing—has expanded opportunities for institutional investors. While existing literature highlights the return and diversification benefits of these sectors compared to traditional real estate (office, industrial, apartments), practical barriers to entry—such as capital requirements and market concentration—remain underexplored. In addition to navigating these structural challenges, LPs face a fundamental choice: whether to gain exposure to alternative sectors by selecting specialist funds (“betting on the horses”) or by relying on diversified managers to allocate across opportunities (“betting on the jockeys”).

To address these issues, this paper examines the practical barriers that restrict investor access to alternative real estate sectors and compares the investment outcomes of diversified managers (GPs) with results from simulated, investor-led allocation strategies. By modeling how LPs might have performed using their own sector bets, the analysis highlights the risks and trade-offs of independent sector selection versus relying on a diversified GP for real estate allocations and exposure to emerging sectors.

Our Research Approach

To investigate the practical barriers faced by investors in accessing alternative real estate sectors, we use Green Street transaction-level data to quantify obstacles such as viable deal size and buyer concentration. Additionally, we analyze which investor types were the first to reach $100 million in cumulative purchases in each alternative sector to identify early entrants.

To directly assess the implications of the LP allocation decision, we simulate three distinct LP strategies using NCREIF Expanded-NPI leveraged total return data at the subsector level. These strategies include:

• Best/Worst Foresight LPs: Rotating capital into the best (or worst) performing subsectors each quarter, based on perfect three-year foresight, with each investment held for a fixed seven-year period.¹
• Equal Weight LP: Evenly allocating across all subsectors each quarter, representing a simple diversification approach.
• Market Cap Weight LP: Allocating in proportion to subsector market capitalization, mirroring the NCREIF Expanded-NPI index.

For each simulated strategy, we assume a three-year deployment period in which capital is committed quarterly according to the chosen approach. Each investment is held for a fixed seven-year term before complete liquidation. We model cash flows such that capital is invested at the start of each quarter, grows at the realized property subtype-level leveraged total return, and is fully redeemed at the end of the holding period. All returns are reinvested until exit, with no interim distributions. Management fees are deducted quarterly at an annual rate of 2% of NAV, reflecting typical institutional fee structures. To isolate the effects of sector allocation and timing, all simulations exclude the impact of transaction costs and taxes and assume perfect market access and liquidity.²

Finally, we compare the IRRs (internal rates of return) generated by these simulated LP strategies to the actual net IRRs achieved by diversified GPs³. For each GP fund vintage, the simulation is anchored to the same starting year, with a 10-year investment period to mirror the typical fund lifecycle.

Barriers to Entry in Alternative Sectors

Boxplot analysis shows that alternative sectors often present significantly higher capital hurdles than their traditional counterparts. Data Centers and Cold Storage, for instance, have median deal sizes 80–230% greater than traditional industrial properties, while Life Science assets are, on average, 80% more costly than traditional offices. Student Housing and Single-Family Rental (SFR) also stand out, with higher and more volatile medians—often 60–75% above those of traditional apartments. For SFRs, this reflects the influence of institutional investors acquiring large portfolios of homes rather than small-scale individual purchases. Similarly, Data Centers and Life Science properties are experiencing rising deal sizes driven by strong demand for technological and biotech infrastructure; this trend stands in contrast to most other sectors, where transaction sizes are shrinking.

While many alternative sectors demand greater capital commitments, not all niches are out of reach for smaller investors. Manufactured Housing and Senior Housing show lower median deal sizes and tighter spreads despite their niche status. Medical Office transactions also require only half as much capital as traditional offices. Manufactured housing’s lower transaction values are largely due to the cost efficiencies of factory-built construction, while senior housing’s fragmented ownership structure leads to smaller, more frequent deals. Medical office buildings also tend to be smaller and tailored for specialized practices, resulting in lower capital requirements and facilitating more transactions by private investors and healthcare providers.

Traditional Industrial, Data Center, Cold Storage, Self Storage
Sale Price ($) per Transaction
Median, 25th and 75th Percentiles

Traditional Apartment, Student Housing, SFR, Senior Housing, Manufactured Housing
Sale Price ($) per Transaction
Median, 25th and 75th Percentiles

Traditional Office, Medical, Life Science
Sale Price ($) per Transaction
Median, 25th and 75th Percentiles

A heatmap of annual market share among the top 10 buyers further illustrates how buyer concentration compounds capital barriers in alternative sectors. Over the past decade, Data Center and Cold Storage transactions have been dominated by a handful of buyers, with the top 10 accounting for 90–100% of annual transaction value in most years. Manufactured Housing and Single-Family Rentals have also remained highly concentrated. Self-Storage and Senior Housing display moderate but declining concentration, while Student Housing—once highly consolidated—has become less concentrated in recent years. In contrast, traditional segments like Apartments, Industrial, and Office properties show much lower and steadily declining concentration, reflecting broader market access and more competitive dynamics.

Transactions by Top 10 Buyers as a % of Total Transactions

Finally, analysis of early entrants—those first to reach $100 million in cumulative purchases within each alternative sector—shows that REITs and diversified fund managers typically capitalize on new opportunities ahead of specialist managers. Typically, specialist GPs lag initial entry by several years, often waiting for the market to mature and for asset values and operating models to be established by larger, diversified players. However, specialist GPs were among the earliest entrants in Senior Housing and Student Housing. This suggests that in sectors requiring highly specialized operational expertise or where fragmentation allows for niche strategies, specialist managers may be better positioned to act quickly and establish an early foothold.

Timeline of First Entrants by Alt Sector

• 2001: Life Science — Diversified GP
• 2007: Student Housing — Diversified GP & Specialist GP
• 2010: Data Center — REIT
• 2010: Medical Office — Diversified GP
• 2011: Self Storage — REIT
• 2018: Senior Housing — Specialist GP
• 2018: Cold Storage — REIT
• 2019: Manufactured Housing — Diversified GP
• 2022: Single Family Rental (SFR) — Diversified GP

Simulated LP Allocation vs. Diversified GP Outcomes

Actual GP IRR v.s. Simulated LP IRR by Vintaage

• 2007–2013: In the immediate aftermath of the financial crisis (2008–2009), “best” LP and market cap–weighted approaches delivered higher IRRs than diversified GPs—reflecting heightened dispersion and the emergence of strong-performing niches. In other years, GP IRRs were comparable to or better than the “best” LP results and always superior to equal-weighted and market-cap-weighted outcomes. This pattern highlights the resilience and consistency of experienced GPs during periods of market recovery and normalization, while also illustrating that moderately sophisticated sector rotation could occasionally outperform during extreme market dislocation.
• 2014-2018: Simulated “best” LPs outperformed GPs primarily due to outsized returns in industrial sectors, which benefited from the e-commerce boom during COVID-19. The “worst” LP strategy saw much deeper underperformance, underscoring the extreme upside/downside risk of sector picks during this period of heightened dispersion across sector performances. The equal-weighted LP strategy would have outperformed GPs as well, largely because it allocated a greater share to industrials; by contrast, the market cap–weighted index did not allow industrials to dominate, resulting in much lower performance. These results demonstrate that while active sector rotation and equal-weighting could theoretically outperform, such outcomes are highly dependent on picking the right sectors at the right time.

Sophisticated sector rotation strategies may appear to outperform experienced GPs in simulation, but these results rely on perfect foresight and unrestricted access—conditions that are rarely, if ever, achievable for real-world investors. In practice, LPs face significant hurdles, including access barriers, timing risks, and selection constraints, which make such outperformance unattainable.

Altogether, these findings reinforce the importance of entrusting capital to proven, diversified GPs who can actively manage sector exposures and navigate market complexities. This principle is especially critical as LPs consider how best to gain exposure to alternative real estate sectors, where access, timing, and selection risks are heightened. Rather than attempting to construct portfolios through self-directed sector picking, investors are better served by focusing on manager selection and execution—the critical components for effective and resilient real estate allocation.

Jenny Zhang contributed to this piece.