Spatio-temporal modelling of housing starts in the greater Toronto area
Bibliographic record
Abstract
This dissertation offers a spatio-temporal analysis of housing starts in the GTA. Informative and revealing results were obtained when housing units were disaggregated by structural type (i.e., detached, semi-detached, apartments, and others [link/row] housing). This dissertation also found that high development charges in the suburbs encouraged low-rise, low-density development. In addition, it was observed that recently built residential units and planned residential construction were located along the major transportation corridors in the GTA. This dissertation discovered that the post-1991 housing market is significantly more restrained, and has adopted a self-correcting regime in which prices and construction activity remained very close to the aggregate trend. Furthermore, the GTA remains a mono-centric region, where accessibility for most activities declines with distance from the CBD. The temporal autocorrelation structures differ by housing type. The OLS-based distributed lag models offer reliable out-of-sample forecasts. This dissertation also found that both short and long time series returned very comparable results. Time series models revealed that statistically significant variables explaining the variance in the number of housing starts differed by housing types. Similarly, model fits differed by housing type. While detached starts returned the best fit, apartment starts offered the poorest fit. Spatial choice models show that the spatial choices of real estate developers differ by housing type. The location patterns of apartment housing are different from the other three housing types. Consider, for example, the fact that variables serving as a proxy for built urban form returned negative coefficients for detached, semi-detached, and others housing, while the same variables returned positive coefficients for apartments. The choice of housing type is conditional upon the location of new housing. It appears that the location and type of housing decisions feed off of each other. Attributes of the neighbourhood or zone can help determine what type of housing is more likely to be built there. This dissertation also presented the concept of spatial inertia in housing markets, which implies that the existing stock of a particular 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".