Calculating Fair Market Value in Legal Valuations: Do Adjustments in Value for Non-Systematic Risk Violate the Fair Market Value Standard?
Bibliographic record
Abstract
Generally-accepted appraisal practice assumes the Hypothetical Buyer is not well-diversified because the typical real-world buyer does not possess sufficient wealth to own a well-diversified portfolio with assets each in similar value to the subject closely-held interest under appraisal (e.g., see Estate of Hendrickson v. Commissioner, T.C. Memo 1999-278, 78 T.C.M. (CCH) 322). This reflects a failure to appreciate the distinction, in all its implications, between the purely fictional Hypothetical Buyer and the typical real-world buyer. “The world of fair market value is not the real world”; it is not “populated by real people” (Mercer and Brown 1999, p.16). “The particular characteristics of these hypothetical persons are not necessarily the same as those of any specific individual or entity” (Estate of Noble v. Commissioner (T.C. Memo. 2005-2), p.12), such as the typical real-world investor. Fair Market Value is determined under hypothetical market “conditions other than those that actually exist” in real-world markets (Bonbright 1937, p.27). “[‘]The effort is to find out not what a real buyer and a real seller, under conditions actually surrounding them, do, but what a purely imaginary buyer will pay a make-believe seller, under conditions which do not exist[’]” (Bonbright 1937, p.61, citing McGill v. Commercial Credit Co. (243 Fed. 637, 647 (D. Md. 1917))). Being simultaneously financially able, well-informed, and rational, all Hypothetical Buyers are defined to possess the following concurrent characteristics: All (a) command the financial resources needed to purchase the subject closely-held interest, (b) know the benefits of diversification, and (c) behave rationally by investing in a well-diversified portfolio of assets—each in a similar dollar amount to the subject interest—prudently aimed at maximizing portfolio return. Always rational and well-diversified, no Hypothetical Buyer requires any form of discount in value for non-systematic risk.
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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 | 0.035 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".