Sustainable or same-old? An Investigation into the Communication of Green Features within Online Real Estate Listings across Five Southern Ontario Municipalities
Bibliographic record
Abstract
Green features in residential houses are widely attributed to a reduction in both residential energy usage and regional greenhouse gas emissions, with prospective homebuyers well-positioned to be enablers of a low-carbon future. However, despite their potential for energy cost savings, improved dwelling comfort, and ample environmental benefits, considerations for green features in residential real estate transactions are limited. Presently, the disclosure of a home’s energy consumption information remains voluntary at the time of sale. To provide insights into the information communicated to homebuyers in pursuit of a home with green features, this study will take an exploratory approach to understand the consumer experience during the first stage in the home buying experience - the online home listing review. To explore the experience of a prospective homebuyer, this research will feature e-mystery shopping of online real estate listings in the five southern Ontario municipalities of Hamilton, Milton, Burlington, Oakville, and Brant. The resulting data will identify the information on green features that is currently communicated to prospective homebuyers in the initial fact-finding stage of the home buying process. In accordance with the Customer Based Social Marketing and Marketing Mix frameworks, the study findings will discuss how online real estate listing, as a form of communication from the real estate professional, can be used as a tool to encourage the consideration and sale of energy efficient homes. Understanding the information being communicated to prospective homebuyers who are interested in green real estate is integral to the promotion and sale of homes that are energy efficient. The benefits of this research are two-fold; both driving the demand for homes with green features and the potential knock-on effect of retrofitting within the existing housing stock to meet a growing market demand.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: yes | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Qualitative | low |
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".