Household-level dynamics in residential location choice modelling with a latent auction method
Bibliographic record
Abstract
A longstanding deficiency in the modelling of residential location choices is the common assumption of a single household decision-maker. This paper contributes to a growing literature on methods to capture intra-household dynamics in this decision process. A latent auction approach is employed to estimate a residential location choice model for the Greater Toronto Area. The present work extends the consideration of individual utility factors beyond simple commute time, to include frequency of automobile use, transit use, and the cost of parking at the destination. Results suggest a weakening differential between male and female roles in the residential location choice, as multi-worker households are increasing in response to increasing costs of living. The strength of the latent auction model is confirmed as a means of linking bid-rent with observed market prices. Several conclusions are drawn out of the model results, including a pattern of larger households preferring the larger and cheaper houses characteristic of suburban areas of the Greater Toronto Area.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".