Developer and Realtor Perspectives on Factors That Influence Development, Sale, and Perceived Demand for Activity-Friendly Communities
Bibliographic record
Abstract
BACKGROUND: Although public support for physical activity-friendly Traditional Neighborhood Developments (TNDs) appears to be growing, information is lacking on private sector perspectives and how economic factors (eg, fuel prices) might influence the development and sale of TNDs. METHODS: A sample of realtors from the National Association of Realtors (n = 4950) and developers from the National Association of Home Builders (n = 162) were surveyed in early 2009 to assess factors influencing homebuyers' decisions; incentives and barriers to developing TNDs; effects of depressed housing market conditions and financing on sales; trends in buying; and energy considerations (eg, green building). RESULTS: Realtors believed that homebuyers continue to rank affordability, safety and school quality higher than TND amenities. Developers reported numerous barriers to TNDs, including the inability to overcome governmental/political hurdles, lack of cooperation between government agencies, and lack of market demand. Yet, realtors believed clients are increasingly influenced by gas and oil prices, and developers reported that clients are looking for energy efficient homes, reduced commute time, and walkable neighborhoods. Respondents reported consumers are more interested in living in a TND than 5 years ago. CONCLUSIONS: Activity-friendly TNDs appear to be increasing in demand, but developers and realtors reported significant barriers to creating these communities.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".