Reference-Dependent Residential Location Choice Model within a Relocation Context
Bibliographic record
Abstract
This paper presents a reference-dependent model for residential location choice. The key contribution of the model is its incorporation of reference dependence that explicitly recognizes the role of the status quo and captures asymmetric responses toward gains and losses in making location choice decisions. The study uses a retrospective residential search survey and a dwelling supply data set from the Toronto Real Estate Board in Ontario, Canada, to estimate the model at the elemental level of individual dwelling units. The study applies a mixed logit formulation that captures unobserved heterogeneity and avoids imposing independence of irrelevant alternatives restrictions on the choice probabilities. Several types of variables, including dwelling characteristics, land uses and other zonal attributes, accessibility measures, and household socio-demographics, are tested in the model. Although the current dwelling is assumed to be the reference point in framing evaluation of alternative dwellings, all gains and losses are measured by a comparison of current and prospective dwellings in the modeling framework. The results reveal that households prefer gains in the number of bedrooms, but they are more sensitive to the equal amounts of losses. A similar loss aversion attitude is observed for the percentage of open areas and unemployment rate. It is also found that decision makers are sensitive only to the losses for the level of service attributes. The reference-dependent model performs better than a conventional location choice model in terms of model fit and provides important behavioral insights.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".