Does Pet Policy in a Condominium Building Impact Property Values?
Bibliographic record
Abstract
Pets are permitted in some condominium buildings and not in others. Pet owners will therefore be attracted more towards buildings that welcome pets than otherwise. However, the pet-related regulations may altogether restrict all sorts of pets, including small pets, such as cats, while others may include restrictions on the number of pets allowed per unit, certain breeds or set restrictions on the permissible size of a pet. These restrictions may impact the price of condominiums. Using a hedonic price model, this research paper analyses whether and by how much allowance for pets in the building impacts property values in downtown Toronto using condominium sales data from January 2016 to December 2017 and information derived from a pet policy questionnaire. The findings suggest that the price differences are not statistically significant between buildings that allow pets or otherwise. In fact, the real price difference is observed for the degree of pet friendliness. Condominium buildings that allow two or more pets sell for higher prices than those that allow less than two pets. Furthermore, condominium buildings that allow two or more dogs sell for a higher price. Also, condominium buildings that impose weight, size or breed restrictions cost 5.7 percent more than those do not have those restrictions. Keywords: hedonic price model, pet policy, condominiums, GIS, Toronto
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".