Local Influences on Zonal Rents in Alberta: Implications for Integrated Modeling
Bibliographic record
Abstract
A hedonic price model was estimated for residential developments in the province of Alberta in Canada. The model is designed to be used with the Production, Exchange and Consumption Allocation System (PECAS) land use modeling framework, which forecasts future development patterns on each parcel based on developer return-on-investment functions, allocates households and jobs, and calculates economic benefit measures. In PECAS, base year rents by land use zone (LUZ) are a calibration target, while future rents by LUZ are calculated in a bid-rent allocation, so the estimation separates out local effects from neighbourhood (LUZ) effects. Local effects include proximity to busy collectors, light rail transit (LRT) stations, schools, and amenities such as parks, as well as local road and off-road access to the nearest network link. A non-linear treatment was required to separate out the value of land from the value of space, especially on large parcels at low intensities of development. A Bayesian approach was used to 1) incorporate prior knowledge from previous PECAS work in the U.S., and 2) to provide a model that can forecast future prices on any parcel in Alberta, even in areas where certain types of development are not currently sufficient in quantity to confidentally estimate parameters. The estimation quantifies the current price landscape in Alberta by LUZ, showing higher prices (and a tendency for future development, where allowed) in major cities, major oil industry locations, and national parks. It also quantifies the positive impacts of local road access, LRT station proximity, schools, rivers, and natural areas; the negative (nuisance) impact of being too near to busy roads and schools; and the depreciation of older buildings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".