Analyzing housing supply location choice: a comparative study of the modelling frameworks
Bibliographic record
Abstract
The purpose of this study is to predict the location of new housing supply and compare two different modelling frameworks. Housing supply significantly influences land use simulations in urban microsimulation systems, closely linked with demographic, transportation, and environmental modules. The supply of new dwellings in urban simulation models have evolved from static, exogenous inputs to dynamic, agent-based determinations. This study follows this trend to examine two approaches to modelling the spatial distribution of new housing supply: the first approach models the development choice of each location; the second approach models the location choice of each residential project. Multinomial logit and nested logit models are applied to a Toronto empirical dataset. The results show that although the first approach achieves higher goodness-of-fit and prediction accuracy, the second approach performs better in explaining the locational preference of individual projects. Project characteristics such as structure type and construction cost, as well as location characteristics such as housing price, number of sales, and population density affect the spatial distribution of new housing supply. Both approaches are evaluated regarding estimation, prediction, and microsimulation system integration. The findings enhance housing modelling literature and inform urban microsimulation's housing supply model configuration.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".