Varying Effects of Urban Tree Canopies on Residential Property Values across Neighborhoods
Bibliographic record
Abstract
As more land area than ever is covered with impermeable surfaces causing problems in the environment, urban trees are effective not only in mitigating environmental problems in the built environment and reducing buildings’ energy use, but also in increasing social and economic benefits. However, the benefits urban trees provide are not evenly distributed but rather disproportionately distributed in high-income neighborhoods. The purpose of this study is to estimate the varying effects of urban trees based on a variety of factors that have influence on tree canopy coverage, including land constraints, new developments, financial capacity to maintain trees, and neighborhood characteristics. Using a unique dataset that includes 24,203 single-family residential sales from 2007 to 2015 merged with Urban Tree Canopy (UTC), this study utilizes spatial models to empirically evaluate the impact of UTC on residential property values in the housing market. Multi-Level Mixed (MLM) models are used to capture the varying effects of tree cover, based on land constraints, new development, financial capacity, and neighborhood characteristics. The findings show the effect of trees is positive and varies across neighborhoods, and implication of the results to best achieve specific desired outcomes in tree-related policies and urban development are apparent.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".