Portfolio Construction Strategy for Global Non-Listed Office Real Estate Investment in Interest Rate Cycles
Bibliographic record
Abstract
Office is one of the core sectors within the buildings sector, attracting tens of billions of dollars in global real estate investment flows. Most of these are achieved through non-listed investments, where office real estate represents one of the major sectoral investment exposures for many global institutional real estate investors and investment managers. The rising interest rates in recent years have been a significant concern, impacting the global real estate markets significantly. Based on these premises and by using quarterly total returns of non-listed office real estate across the US, UK, Germany, Canada, and Australia from June 2008 to June 2024, this research assesses the risk-adjusted performance and portfolio diversification benefits of non-listed office real estate across the five markets over both interest rate cut and interest rate hike cycles. The results empirically validate the added-value role of non-listed office real estate in institutional multi-asset portfolios across the UK, Germany, Canada, and Australia during the interest rate hike cycle preceding the COVID recession. In the 10% capped real estate allocation, the average allocation was 0.7% in the UK, 0.4% in Germany, 0.7% in Canada, and 9.1% in Australia. Over the interest rate hike cycle after the COVID recession, Australian non-listed office real estate offered enhanced benefits as part of the multi-asset portfolio, constituting an average of 0.8% in the capped real estate allocation. In the global non-listed office real estate portfolio, the US dominated the portfolio across varying interest rate cycles, with an average allocation of approximately 65%. The average allocation to Australia was 24.2% over the interest rate hike cycles, while the average allocation to Germany was 32.0% over the interest rate cut cycles. These findings offer institutional real estate investors and investment managers critical and practical insights into how the investment performance and portfolio construction strategy of office assets—an essential component of the buildings sector and a major non-listed real estate investment exposure for global institutional real estate investors—respond to macro-financial and interest rate cycles. The investment implications of the findings are also discussed.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".