Landscaping and house values : an empirical investigation
Bibliographic record
Abstract
The current paper investigates the effect of landscaping on house values, based on a detailed field survey of 760 single-family homes transacted between 1993 and 2000 on the territory of the Quebec Urban Community (CUQ), using for that purpose the hedonic approach. Conducted during the summer of 2000, this survey focuses on landscaping characteristics of homes and their immediate environment. Environmental information was captured from the front and side of houses and includes 31 attributes dealing with tree as well as ground cover - with trees being classified by size class and type of species-, flower arrangements and rock plants, hedges, landscaped curbs, density of visible vegetation as well as roof, patio and balcony arrangements. Once the basic model (Model 1) is calibrated using the physical, census and access characteristics of properties, landscaping features are added to the hedonic equation, with both individual attributes and interactive variables being used. By and large, a positive tree cover differential - or a more-than-unity ratio - between the property and its immediate neighborhood translates into a higher house value, although a negative adjustment is required where early boomers - aged 45-64 - dominate (Model 3). While the relative importance of tree cover in the visible surroundings also exerts a positive impact on property prices, the effect is all the more enhanced in areas with a high proportion of retired persons (Model 2). If trees seem to be valued by most homeowners, a high percentage of ground cover (lawn, flower arrangements, rock plants, etc.) also commands a market premium in the case of bungalows and cottages (Model 2); moreover, the price of cottages benefits from an above-average ground cover whereas a below-average one is detrimental (Model3). Quite interestingly, the density of the vegetation visible from the property impacts negatively on prices (Model 3), in line with Payne's (1973) conclusions regarding excessive tree cover. Finally, a hedge, a landscaped patio as well as landscaped curbs all command a substantial market premium : while it amounts to between 3.6% (Model 3) and 3.9% (Model 2) of property value for a hedge, it reaches 12.4% in the case of a patio and 4.4% for landscaped curbs (Model 3).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".