A new perspective on the ripple effect in the UK housing market: Comovement, cyclical subsamples and alternative indices
Bibliographic record
Abstract
An alternative perspective is provided on the existence of a ripple effect in the UK housing market. In contrast to previous studies, the analysis involves consideration of information on the changes in house prices to which the hypothesis of house price diffusion posited by the ripple effect relates, rather than their levels. In an examination of changes in house prices in London relative to other regions of the UK, directional forecasting methods are employed to establish the extent of the relationship between geographical proximity and comovement across the three month window provided by quarterly data. Consequently, the analysis provides a direct examination of the ripple effect which refers to changes in prices rather than the convergence of levels which has become a feature of the empirical literature. The literature is extended further by both the application of dating techniques to perform the analysis across cycles and phases of cycles (recovery and recessionary periods) in the UK housing market, and the use of data from two alternative house price index providers. Striking results in support of the presence of a ripple effect are noted, particularly for the less commonly considered Halifax price index where the most significant results for comovement with London are exhibited by its contiguous regions. In addition, the cyclical subsamples considered indicate comovement to be greater during upturns, rather than downturns in the market. This is consistent with previous research showing London to correct – that is, exhibit differing behaviour to other regions – during downturns.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".