Prices paid for farmland in Ontario: Does buyer type matter?
Bibliographic record
Abstract
Abstract In the wake of the substantial increases in farmland values that have occurred in Ontario since 2008, concerns have been expressed regarding the potential influence of nonfarmer buyers, such as investment companies and foreign buyers, on prices paid for farmland. To examine whether these concerns may be warranted, this paper estimates the impact of nonfarmer buyers on sale prices for farmland in Ontario, using a hedonic approach and farmland sales data from 2002 to 2016. Analysis is also conducted to determine whether marginal implicit prices of specific farm attributes differ between farmer buyers and nonfarmer buyers. The results indicate that nonfarmer buyers have paid higher prices for farmland, but only in near‐urban areas. In addition, differences in marginal implicit prices for farmland attributes are found, where farmer buyers value more highly attributes related to the agricultural productivity of the property while nonfarmer buyers value more highly attributes related to nonagricultural use. These results imply that the higher prices paid by nonfarmers may be attributable to the bid‐rent theory, as nonfarmers may be bidding more than farmers for farmland in near‐urban areas due to higher expected returns from future urban use of the land.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".