Bibliographic record
Abstract
Purpose The purpose of this paper is to show an indication that the asymmetric volatility between house price movement may account for the defensiveness of the housing market. Design/methodology/approach First the UK nation‐wide house price data from the last quarter (Q4) of 1955 to the last quarter of 2005 are used and then the most suitable mean and variance equations to estimate the conditional heteroscedasticity volatilities of the returns of house prices are selected. Second, a variable that examines the leverage effect of volatility is incorporated into the model. The GJR‐GARCH model is used. Findings The results of the empirical test show that while the lagged innovations are negatively correlated with housing return, that is when there is bad news, the current volatility of housing return might decline. Research limitations/implications The results indicate that the volatilities between house prices moving up and moving down are asymmetric. Practical implications The results show that there is a defensive effect in the UK housing market during the data periods used. Originality/value Although several articles have documented that there is heteroscedasticity and autocorrelation in the volatilities of real estate prices, few of those papers have noted one of the most important advantages of the housing market, its defensiveness, from the viewpoint of volatile behavior.
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 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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".