UK recurring portfolio appraisals: development of a conceptual model
Bibliographic record
Abstract
Clayton et al. (2001) and Van der Werf and Huibers (2015) showed that first-time appraisals differ from their successive repeat valuations in Canada and The Netherlands. First-time appraisals seem to follow the normative valuation process more closely and use more contemporary comparable data. Repeat valuations tend to be sluggish and tend to anchor on the prior valuation. In bearish, contracting markets this leads to assessed values that, on average, are higher than the unobservable market value. In the UK context there has not been similar research into first time appraisals thus far, although evidence of anchoring to either selling price (Gallimore and Wolverton, 2008) or previous estimated (Havard, 1999; Havard, 2001) in the UK context exists. The concept of recurring valuations and the effect of possible anchoring on the assessment of market value has been researched as well (Gallimore and Wolverton, 2008; Diaz and Hansz, 1997; Hansz, 2004; McAllister et al., 2003; Crosby, Lizieri and McAllister, 2010), though, as far as we know, no study has examined the rotation of appraisers specifically to understand what processes are occurring when a new appraising firm is being appointed or assets rotate between a number of appraising firms. Based on interviews, combined with literature review a conceptual model has been developed to describe the processes that take place when appraisers are undertaking either first-time or repeat valuations. Potential weak spots have been revealed that are vulnerable for biases. Further analysis of these weak spots could potentially lead to less valuation bias.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".