Modeling home property listings’ time-on-market duration and listing outcome using copula-based competing risk method
Bibliographic record
Abstract
Modeling housing market dynamics is an important component of land use and transport interaction (LUTI) models, particularly for microsimulation models and how they handle the market clearance mechanism. However, most of these models include key assumptions not derived or validated through empirical testing, such as when and what action a seller would take if a property could not be sold within an expected time. However, these are key decision elements of the housing market clearance process. To fill this research gap, this study uses real estate sale listing data to investigate the factors influencing a property listing’s time-on-market (TOM) duration, listing outcome, and correlation. A copula-based structure is developed to jointly estimate the TOM and listing outcome through a competing hazard duration model and a nested logit model. The results show statistically significant and positive correlations between the TOM of terminated listings and termination choices (i.e., whether the terminated listing will be withdrawn from the market, converted to a lease, or re-listed as a sale). This implies that the unobserved factors that may increase a seller’s probability of terminating a listing would decrease its TOM duration until the termination. It is also found that an increase in the asking price of a property listing can significantly increase its TOM duration and probability of being terminated. The copula-based joint model can be integrated into a LUTI microsimulation framework to parameterize the maximum TOM duration of each simulated property for sale in the housing market, improving its market-clearing process to represent real-world behavior better.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".