Delineating the spatio-temporal pattern of local authority house prices variation in England between 2009 and 2016
Bibliographic record
Abstract
Most spatio-temporal studies of house price in the UK are carried out at national or regional scale, but house prices differences could be better understood at finer spatial scales. Since England’s house prices, standardised by the size of the property (£/m2), have been shown to be somewhat clustered at local authority level and highly clustered at Middle Layer Super Output (MSOA) level, in the period 2009 to 2016, this research aims to further explore the nature of spatial and temporal variation in house prices at local authority level in England. Growth curve modelling offers a model-based description of the spatio-temporal patterns of local authority house price variation. This research explores local authority effects and three different time effects (quarter, half-year and year) on house price spatio-temporal variation. Results show that these three time effects are essentially identical and are extremely small, in comparison with local authority effects. Since annual effects provide the best fit, local authority annual house price trajectories between 2009 and 2016 are further explored. Local authorities with higher house prices in 2009 are found to have faster growing prices over the eight-year period than local authorities with lower house prices. Moreover, two clear geographic hubs of house price change over the period are observed, one centred on London, the other on Bristo
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".