Location choice of residential housing supply: An application of the multiple discrete-continuous extreme value (MDCEV) model
Why this work is in the frame
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Bibliographic record
Abstract
The supply location of residential housing is the result of multiple, simultaneous decisions by housing developers. This choice situation can be characterized by the discretionary choice of locations for the housing projects and the amount of housing units to be built at the given locations. Within this context, the modelling of residential housing supply locations, or the allocation of predicted housing supply over space, is a discrete-continuous process. In this paper, we apply a multiple discrete continuous extreme value (MDCEV) model to simultaneously model the location choice and amount of housing supply. The empirical study is conducted in the city of Toronto with a pooled model, and four separated models for each structure type. The prediction results indicate reasonable fits. The developed model can be used to generate housing supply at a given period over space in an urban microsimulation system and serves as a valuable tool for policymakers, urban planners, and researchers in the field of housing supply and urban systems. • Advanced Supply Choice Modelling Framework: The study introduces an advanced multiphases framework for modelling housing supply within the urban microsimulation system context. • Innovative MDCEV Application: The study develops a Multiple Discrete-Continuous Extreme Value (MDCEV) model to simultaneously address location choice and housing supply allocation. • Significance for Urban Planning: The MDCEV model developed is empirically tested using data from the City of Toronto, with separate models developed for different housing structure types, demonstrating strong predictive accuracy and model fitness.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it