U.K. House Prices: Bubbles or Market Efficiency? Evidence from Regional Analysis
Bibliographic record
Abstract
This paper studies U.K. regional house prices across nine regions from January 2005 to December 2017 to identify regional versus national effects on house prices and potential house price bubbles. It uses a version of the Gordon dividend discount model, modelling house prices as the present value of imputed rents as a measure of fundamentals. It differentiates between long-term and short-term effect using pooled mean group (PMG) and mean group estimation (MG) to determine variations in regional house prices during different periods relating to the most recent financial crisis. The results confirm that the crisis had differentiating effects in the short term, but there is reversion back to long-run fundamentals. Regional trend analysis shows that the house price growth in the regions has been affected differently in the short run and each region has varying long-run fundamentals. Residential property values in London have shown strongest short-run momentum.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".