Effects of the change of valuer on the assessment of capital value for property portfolio valuations. A qualitative approach in the UK context.
Bibliographic record
Abstract
Property appraisal outcomes are not believed to always accurately reflect the market value of real estate, see Yiu et al. (2006) for a review of the literature. This is of great importance to institutional investors who rely on these periodic appraisals for benchmark purposes, annual accounts, fund in- and outflow as well as the decision to sell assets. Changing valuer on these property portfolio valuations has a significant impact on the assessment of capital value.Evidence of this effect exists in international markets. Research on Dutch property portfolios (Van der Ende & Van der Meulen, 2013; Niemeijer, 2014; Van der Werf and Huibers, 2015) suggests that this is not an incidental but rather structural effect within the Dutch context. Clayton et al (2001) show that the Canadian market perceived large difference as well. However in the UK, the existence of structural capital value changes when changing appraisers valuing investment portfolios, has not been researched so far.The aim of this research is to answer the question whether UK property investors perceive large differences in capital value when changing valuers on their portfolio valuations. To answer this question we will address the following objectives:1.To examine whether the shifts in value are different when UK appraisers value an investment asset for the second (or third or higher) time compared to first time valuations.2.To find, for the UK, whether on average first time appraisers tend to value the asset higher or lower.3.To examine what, according to the UK portfolio appraisers and investors could be the reason for this difference.These objectives will be addressed by conducting semi-structured interviews with two groups of stakeholders. Firstly the clients, the institutional investors, will be interviewed to establish whether they notice differences between first time valuations and their repeated counterparts. Do repeat valuations show different value shifts than the first time valuations and if so, whether they are smaller or larger. Is it perceived as a problem and if so, have investors tried to reduce the impact of this issue. Secondly the agents, the appraisers, will be interviewed to form an opinion of their awareness of this topic. I investigate whether they are aware of differences between first time and repeat valuations. Are they being informed by the institutional investors and whether they pressure them to revise their initial valuations if differences are large.
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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 | 0.004 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".