An over time comparison of linear and spatial regression model parameters to predict average housing values: Evidence from two Canadian cities
Bibliographic record
Abstract
Abstract Residential real estate models are a key component of planning support systems, which aid in shaping future urban land use and transportation. These models typically rely on parameters estimated from historical data corresponding to a specific base year, assuming that such parameters remain stable over time. This paper examines the temporal stability of parameters linked to variables influencing average housing values at the dissemination‐area level. It does so by comparing parameter estimates across multiple time periods in two Canadian cities: Ottawa, Ontario and Calgary, Alberta. Using Canadian census data, pooled regression analyses were conducted for the years 2006, 2011, and 2016 to model average housing values. The findings indicate general consistency in the factors affecting housing values across both cities. Interestingly, the parameters of the Ottawa pooled models suggest strong stability over time compared to Calgary. Additionally, the Spatial Autoregressive model outperformed both linear and spatial error models in terms of accuracy and predictive performance .
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".