What drives you to relocate your home? Investigating preferences and residential mismatching of recent (prior to COVID and during-after COVID) movers in the Greater Toronto Area
Bibliographic record
Abstract
This paper presents a survey of homeowners and renters who moved within or into the Greater Toronto Area (GTA), Canada, since January 2016. This timeline covers pre- and during-COVID movers. The survey investigates households' residential preferences and the potential dissonance between their preferred and actual residential location choices. The respondents are asked to answer questions about their previous and current residences, their housing search, and hypothetical residential location choices given the option of telecommuting. Empirical analysis of the actual and hypothetical residential location choices is conducted through a joint revealed preference and stated preference (RP-SP) error component mixed logit model. The model estimations identify discrepancies between RP and SP choices, indicating the presence of residential dissonance. The positive effect of transit accessibility variables shows that people are more likely to choose residential locations with better accessibility. However, dissonance in transit accessibility is found in both models, suggesting homeowners and renters may settle for lower accessibility in real life. Moreover, the model results show that homeowners and renters are more likely to relocate when working remotely, indicating the significant influence of telecommuting on residential dissonance.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".