Modelling Housing Market Dynamics and Residential Location Choice for a Next Generation Integrated Land Use and Transportation Model
Bibliographic record
Abstract
Understanding the decision-making processes of both housing buyers and sellers is critical in advancing housing market modelling within land-use and transportation interaction (LUTI) frameworks. This dissertation addresses key research gaps in existing housing market models, which often focus exclusively on buyer decisions and overlook the role of seller behaviour and emerging technology shifts, such as telecommuting. Drawing on data from the Greater Toronto Area (GTA), the dissertation presents empirical investigations on five research topics aiming to improve the representation of agent decision-making in the residential housing market.On the buyer side, the research investigates: 1) the mismatch between households’ preferred and actual residences through a joint revealed preference and stated preference model; 2) preference heterogeneity using a latent class discrete choice model that captures attitudinal and behavioural variation; 3) the influence of telecommuting availability on relocation decisions, distinguishing between household segments with different responsiveness; and 4) the latent goals that households wish to pursue during relocation and their goal adaptation behaviours using the multiple goal-based choice model. These empirical investigations provide insights into the motivations, trade-offs, and constraints shaping residential location choices, while uncovering how telecommuting availability influences relocation decisions. On the seller side, the dissertation develops joint modelling frameworks to estimate the listing duration and outcomes of residential properties in the GTA. A copula-based joint model and a discrete-continuous model are estimated using 2021 property listing data. Both models include a competing risk hazard component to estimate listing durations and a discrete choice component to model the probabilities of sale and various termination outcomes. Separate models are developed for first- and second-round listings to capture changes in seller behaviour across listing attempts. These model results shed light on determinants of listing durations and exit decisions, supporting improved representation of seller behaviour in LUTI housing market models. By integrating these buyer- and seller-side investigations, this dissertation introduces advanced approaches to improve the modelling of the housing market and market-clearing processes within LUTI frameworks. The findings support more behaviourally realistic urban simulation, policy evaluation, and housing scenario testing in the context of changing work patterns and urban dynamics.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".