A Two-Stage Panel Model of Residential Search and Location Choice Decisions
Bibliographic record
Abstract
This study develops a two-stage modeling framework for investigating residential location choice decisions. In the first stage, the study employs a fuzzy logic method to investigate the residential location search process using longitudinal information. The model accounts for the influence of prior locations on generating the potential choice set for the subsequent location. The second stage involves modeling the decision of residential location choice using the potential location choice set generated in the earlier stage of this study. The study develops a panel-based random-parameters logit (RPL) model which recognizes the temporal dynamics by accounting for the effects of repeated location choices of households during their housing career. The RPL model uses parcel-level data as the choice unit to represent the decision process at a finer-grained scale. The residential search and location choice models are developed utilizing retrospective survey data from the Household Mobility and Travel Survey (HMTS) conducted in Halifax, Canada. Model results suggest that life-cycle events, parcel attributes, accessibility measures, and neighborhood characteristics are the most significant determinants in location choice decisions. For instance, households prefer a larger lot size when an increase in the household size occurs due to birth of a child or addition of a member. In contrast, households prefer smaller lot size in the case of decrease in the household size due to the death or moving out of a member. The model confirms a two year lagged effect of the above mentioned life-cycle events. Moreover, locations closer to schools, transit stations and health services are preferable. Households are relatively more sensitive in the case of the accessibility to school. Finally, this model is expected to be implemented within the micro simulation-based integrated Transportation, Land Use, and Energy Modeling System (iTLE) for Halifax.
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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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".