Optimal Leverage of Commercial Real Estate Investments: A Trade Off Option Theoretic Approach
Bibliographic record
Abstract
The determination of an optimal level of financial leverage for commercial real estate investment transactions is a central concern both for investors and financial institutions. Investors seek the maximization of the economic value of the investment firm while financial institutions need to adequately price the overall risk in accordance with the available regulatory capital budget. Excess leverage and large and/or unexpected shifts in the default risk of commercial real estate portfolios are a relevant potential threat to both objectives and need to be addressed from the perspective of macro prudential guidance. Traditional modelling of (commercial real estate) asset values as geometric Brownian motions may not be sufficient to capture tail events and risks relevant to investors and financial institutions for pricing and risk assessment purposes, since the distribution of returns of such properties does not seem to be normal. We examine the default, prepayment risk and optimal unitranche leverage level of single-borrower non- recourse mortgage loans, based on a trade-off option theoretic structural approach that captures asymmetry and kurtosis of asset values’ returns distributions through the shifted lognormal distribution as in De Luna et al (2025). Our framework includes distress costs expanding the firm value paradigm of Modigliani and Miller (1958, 1963) to model interior optimal leverage solutions. We conduct several numerical experiments to examine how the different parameters of the model but also the returns distribution and load of distress costs affect the pricing of the CRE mortgage loan and the level of optimal and prudent leverage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".