Capturing Residential Preference Changes through Perception Detections of Rationally Inattentive Decision Makers
Bibliographic record
Abstract
This dissertation focuses on the changes in aggregate residential trends and disaggregate perturbations in residential location choice behaviour. The empirical investigation uses the contexts of the COVID-19 pandemic in the Greater Toronto Area (GTA). Two conceptual models are presented in the study, each providing a unique perspective on analyzing residential preferences and forming the foundation of the research. The study delves into both the short- and long-term effects of the pandemic on residential location choice behaviour and aims to understand how preferences differ across various demographic groups. The research is based on Stated Preference (SP) surveys collected in July 2020 and July 2021 and housing price data for different dwelling attributes from January 2019 to August 2021. The dissertation proposes a new Efficient Adaptive Stated Preference (EASP) survey design that detects respondents’ tastes while doing the survey and integrates present information in choice experiment designs to improve the quality of SP data collection. The effectiveness of the EASP design is evaluated using a data-driven Neural Network model. The thesis also introduces an empirical model of rational inattention discrete location choice based on latent preferences and attention span. This model contributes to explaining the heteroskedasticity of different demographics in decision-making. The proposed methodology is validated by comparing its performance with comparable classical discrete choice models. Overall, the dissertation highlights the importance of high-quality data collection in the context of residential location choice and how the proposed survey design and empirical models can help improve data collection accuracy and inform policy decisions. The research has theoretical and practical implications for the planning of residential neighbourhoods, especially in the context of pandemics and other unexpected demand shocks.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".