Single-Family Housing Value Resilience of Walkable Versus Unwalkable Neighborhoods During a Market Downturn: Causal Evidence and Policy Implications
Bibliographic record
Abstract
OBJECTIVES: This study investigated the resilience of single-family housing values in walkable versus unwalkable neighborhoods during the economic downturn from 2008 to 2012 in Dallas, Texas. METHODS: Using propensity score matching and difference in differences methods, this study established a natural experimental design to compare before-and-after value changes of single-family (SF) homes in walkable neighborhoods with unwalkable neighborhoods during the Great Recession. Two thousand seven hundred ninety-nine SF homes within 18 Tax Increment Financing (TIF) districts were categorized into walkable (Walk Score ≥50) and unwalkable (<50) groups. Six hundred twenty-four dwellings in walkable neighborhoods were matched with the most identical ones in the unwalkable neighborhoods by controlling for the selected structural and residential location variables. Relative average treatment effects were examined for SF values in walkable and unwalkable neighborhoods. RESULTS: On average, the SF homes in walkable neighborhoods held $4566 (2.08%) more value than their how walkable counterparts. CONCLUSIONS: This study aims to help planners and decision-makers by documenting the unmet demand for walkable communities and their sustained economic benefit. Increased awareness of the sustained value of walkable communities can be used by lenders who finance and by policy makers who regulate placemaking. Results from this study can be integrated with research that demonstrates health-care cost savings of walkable environments to create an even more comprehensive set of evidence-based interventions to increase their supply.
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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 | 0.009 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".