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Record W2198735736 · doi:10.1177/0042098015610482

A new perspective on the ripple effect in the UK housing market: Comovement, cyclical subsamples and alternative indices

2015· article· en· W2198735736 on OpenAlexaboutno aff
Steve Cook, Duncan Watson

Bibliographic record

VenueUrban Studies · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsRippleEconometricsIndex (typography)House priceConvergence (economics)Price indexPerspective (graphical)Contrast (vision)MacroeconomicsMathematicsPower (physics)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.434
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.085
GPT teacher head0.281
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations77
Published2015
Admission routes1
Has abstractyes

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