Modeling Location Choices of Housing Builders in the Greater Toronto, Canada, Area
Bibliographic record
Abstract
An analysis of the spatial choice of housing builders in the greater Toronto area (GTA), Canada, is presented. A spatially disaggregate database of 126,462 new housing units built by 445 builders is used to analyze the determinants of the intrametropolitan location of new housing. Housing starts are classified into four types: single-family detached (SFD), semidetached, condominiums, and row-link housing. An accessibility analysis shows that the GTA remains a monocentric region where accessibility for most activities declines with distance from the central business district. The location choice of homebuilders differs by housing type. For instance, the construction of new condominiums is more likely to be in high-density areas with high accessibility to jobs. Similarly, the likelihood of construction of low-rise (SFD or semidetached) housing is higher in low-density areas with low accessibility to work and other activities. Neighborhood attributes help determine the type of housing likely to be built in the vicinity. Also, the location of low-rise residential units and planned residential construction is influenced by proximity to major transport corridors in the GTA. The location of condominiums is influenced by proximity to the subway system. Builders are attracted to zones with higher dwelling values, where they can obtain higher values for their products. Spatial inertia in housing markets is presented, which implies that the presence of a certain type of housing attracts more housing of that type to the vicinity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".