Three Decades of Global Institutional Investment in Commercial Real Estate
Bibliographic record
Abstract
Alternative assets represent an increasing share of pension fund assets, and real estate is a cornerstone of that allocation. This article investigates the trends in pension fund real estate investments over the past 3 decades, both in private and in public real estate, focusing on the performance of the asset class for the ultimate asset owners. The development of pension fund allocations to real estate differs across regions, with allocations increasing in Canada, stationary in the US, and shrinking in Europe. Slightly more than 10% of the real estate exposure is through publicly listed vehicles. Within the real estate portfolio, the authors observe a continuing increase in the use of external fund managers. Investment costs are stationary, with pension funds in the US structurally paying more to their external private real estate managers than their peers in Canada and Europe. Costs relating to public real estate are more equal across regions. In terms of performance, the authors observe rather stable total returns for both private and listed real estate over the past 3 decades, contrasting volatile performance of private equity and infrastructure. Intermediated investment management for private real estate is costly, leading to disproportionately lower net returns. Key Findings ▪ Current pension fund allocation to real estate is 8.3%, on average, with a 90/10 split between private real estate and public real estate. ▪ Pension funds deploy a wide range of real estate allocation strategies, with intermediation growing in popularity over the past decades. ▪ Real estate has provided stable returns over the past decades, with gross returns similar to stocks, and net returns in between bonds and stocks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".