The impact of lease structures on the optimal holding period for a commercial real estate portfolio
Bibliographic record
Abstract
Purpose – The purpose of this paper is to demonstrate the impact of lease duration and lease break options on the optimal holding period for a real estate asset or portfolio. Design/methodology/approach – The authors use a Monte Carlo simulation framework to simulate a real estate asset’s cash flows in which lease structures (rent, indexation pattern, overall lease duration and break options) are explicitly taken into account. The authors assume that a tenant exercises his/her option to break a lease if the rent paid is higher than the market rental value (MRV) of similar properties. The authors also model vacancy duration stochastically. Finally, capital values and MRVs, assumed to be correlated, are simulated using specific stochastic processes. The authors derive the optimal holding period for the asset as the value that maximizes its discounted value. Findings – The authors demonstrate that, consistent with existing capital markets literature and real estate business practice, break options in leases can dramatically alter optimal holding periods for real estate assets and, by extension, portfolios. The paper shows that, everything else being equal, shorter lease durations, higher MRV volatility, increasing negative rental reversion, higher vacancy duration, more break options, all tend to decrease the optimal holding period of a real estate asset. The converse is also true. Practical implications – Practitioners are offered insights as well as a practical methodology for determining the ex-ante optimal holding period for an asset or a portfolio based on a number of market and asset-specific parameters including the lease structure. Originality/value – The originality of the paper derives from its taking an explicit modelling approach to lease duration and lease breaks as additional sources of asset-specific risk alongside market risk. This is critical in real estate portfolio management because such specific risk is usually difficult to diversify.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".