The Effect of Agricultural Zoning on Rural Residential Property Values: An Application to Ontario's Greenbelt
Bibliographic record
Abstract
Although previous studies on property value effects of land use policies have focused primarily on agricultural properties and on residential properties in close proximity to preserved areas, this paper examines for the effect on rural residential property values within the preserved area. This effect is examined in the context of Ontario's Greenbelt legislation, which prohibits urban development of rural land within a large area around the Greater Toronto Area (GTA). This preserved area includes not only agricultural properties but also a substantial amount of rural residential properties due to proximity to the GTA. With the amenity value that rural residential properties derive from the surrounding rural landscape, the imposed development restrictions that permanently preserve this open space are anticipated to increase the values of these properties. This expectation is confirmed by the results of a hedonic approach, which indicate a positive effect on the values of rural residential properties within the Greenbelt's boundary. This effect is found to be greater for properties with more surrounding open space and those that are relatively closer to the GTA. These results also provide an estimate of the property value impact of converting all surrounding developable open space to permanently preserved open space.
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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 | 0.005 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".