Bibliographic record
Abstract
Determining the impact of detrimental conditions and environmental features such as electricity distribution equipment remains one of the more difficult aspects of property valuation. Since the 1950ís, research aimed at establishing the impact of HVOTLs on the value of residential property has been conducted in the USA, Canada and to a lesser degree New Zealand, where transaction data is available for analysis. Studies have either investigated the impacts on value by analysing transaction data, or investigated the opinions, attitudes and perceptions of market participants. In the UK, by comparison, research has focused almost exclusively on public and professional opinions towards distribution equipment and found that attitudes were generally negative towards the presence of HVOTLs near residential property. However, no apparent attempt was made to establish whether or not negative perceptions translated into lower values or longer marketing periods, arguable due to the lack of available transaction data for analysis. Transaction data that is in the public domain is either difficult to obtain and prohibitively expensive from the Land Registry or not property specific; either referring to the mean value of similar property within a specific location or to property tax bands that are too wide to allow for small variations in value to be apparent. Valuers are left with the difficult task of placing a value on the impact of HVOTLs and other distribution equipment without a benchmark to provide some guidance. This paper compares the results of three UK case studies using a hedonic approach and regression analysis to determine the actual impact of a HVOTL on residential house prices. This paper presents the conclusions from a body of work conducted in partial fulfilment of a PhD Thesis.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".