Covariance Risk and the Ripple Effect in the UK Regional Housing Market
Bibliographic record
Abstract
This study combines two increasingly popular areas of the housing literature, by incorporating a measure of the ripple effect into a model of house price volatility. Using UK data for the English regions and Wales from 1995 to 2016, a multivariate GARCH model is initially used to produce time-varying covariances between house prices in London and other regions. These covariances are then incorporated into an EGARCH model of house price volatility showing that this covariance term is highly significant and positively signed in all regions, such that an increase in the covariance with London has a positive effect on a region¡¯s house prices. However the GARCH term in the mean equation produces a negative risk/return relationship across regional housing markets, although it is not robust to different specifications. This suggests that in the UK, when treating regional housing market risk, policies need to include the relationship of the region¡¯s house prices with those of London. Similar considerations could also apply in other countries exhibiting ripple effects in their housing markets.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".