Hog Barns and Neighboring House Prices: Anticipation and Post‐Establishment Impacts
Bibliographic record
Abstract
The impact of large‐scale hog barns on residential property values is at the forefront of local concerns about livestock development. In this article, I examine the impact of hog barns on house prices in an intensive production region of Manitoba, Canada. Timing of barn establishment and precise locations of houses and barns are used to gain a better understanding of the dynamic impacts of hog barns on house prices. I find that houses within 2 km of a hog barn sell for 5.7% less than similar houses located a little farther away from a barn. Quasi‐myopic specifications indicate that house prices fall by 6.2% up to three years prior to barn establishment, consistent with market anticipation of the future location of hog barns. Accounting for anticipation increases the post‐establishment discount to 8%, suggesting that ignoring anticipation of new barn establishment biases estimated post‐establishment impacts downwards.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".