Is there an additional price premium for single-family houses exposed to urban parks? Insights from causal spatio-temporal matching in Québec city
Bibliographic record
Abstract
Urban parks and green spaces are known for providing positive social and environmental services, which is usually capitalized into real estate prices. While positive externalities extend at the neighbourhood level, negative externalities can be detected close to the infrastructures, making the price premium varying locally for houses exposed. The paper investigates if local price premium for exposition to different types of parks differ between houses connected or adjacent to parks compared to other houses located nearby but not directly exposed. For that purpose, a spatio-temporal propensity score matching identification strategy is proposed and applied on single-family house transactions in Québec City between 2004 and 2020. The estimation results show that, except for two specific situations, direct exposition does not necessarily translate in significant additional house price premiums. However, a complementary quantile analysis suggests that the non-significant mean differential price premium hides an important spatial dimension, pointing to the presence of environmental inequities. • A matching method incorporating spatial and temporal constraints is proposed to examine the trade-off between advantages and disadvantages of exposition to parks. • Except for two situations, exposition does not translate in significant differential price premium for single-family house prices. • However, result points to the presence of local environmental inequities.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".