Identifying the Economic Impacts of Land-Use Policy: A Case Study of Okotoks, Alberta
Bibliographic record
Abstract
Rapid economic and demographic growth is changing the nature of Alberta’s urban and rural landscapes. This has had profound effects on land use, particularly in areas near to Edmonton and Calgary where there is great concern about urban sprawl into surrounding farmlands. In 2012, the Town of Okotoks shifted from a “finite growth” policy to a “continuous growth” policy, thus eliminating a key policy constraint on urban development. This new policy allows for accelerated conversion of open space and makes Okotoks a “natural experiment” of land-use policy change. This thesis aims to examine the economic impacts of the land-use policy which governs development in Okotoks. Relying on data on single-family residential property transactions between 2010 and 2017 in Okotoks and surrounding area, the thesis explores people’s willingness to pay for the pro-development policy, and also for different types of open space that are affected by the policy. A difference-in-difference method is incorporated into a hedonic price model. Spatial lag modeling using a spatial two-stage least squares (S2SLS) technique indicates that individuals value living near livestock pasture land and disvalue the pro-development policy. The average willingness to pay for avoiding the policy is estimated to be $CAD 33,754. A separate analysis is undertaken to assess whether the policy reduces people’s willingness to pay to live near developable open space. An endogenous switching regression allows us to estimate hedonic price models before and after the policy change. The results show that the pro-development policy reduces people’s willingness to pay for developable open space such as forest, pasture and grassland within a 200-meter buffer of their properties. These findings illustrate the ways that municipal land use policies affect residential property values, generating real trade-offs between the values of open space and development.
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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".