Signaling effects of recurrent list‐price reductions on the likelihood of house sales
Bibliographic record
Abstract
Abstract Recurrent list‐price reductions for a house may signal the impatience of sellers to conclude a sell transaction more quickly, leading to more visits and a higher likelihood of being sold (positive signal). Recurrent list‐price reductions may also provide a market signal that the listing is problematic and thus harder to sell without a list‐price reduction, leading to a lower likelihood of being sold (negative signal). Unlike standard survival analysis, we investigate which signal prevails using a joint frailty model that accounts for the interdependence among recurrent list‐price reductions and the association between the recurrent reductions and the sold event. Our novel data set contains the time‐dated recurrent list‐price reductions for each house listed on the market. The results from the joint frailty model show time‐varying negative impacts of list‐price reductions on the likelihood of a house sale, supporting the dominance of the negative signaling effects of recurrent list‐price reductions. Although listings with frequent list‐price reductions are less likely to be sold, sold houses sell at a higher ratio of sold price to last list price, which incorporates current market conditions and fairer pricing, holding constant the initial list price and the aggregate list‐price reduction from the initial list price.
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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 | 0.004 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".