Influence of Transportation Access and Market Dynamics on Property Values
Bibliographic record
Abstract
This paper presents housing price models by using multilevel modeling techniques. The key motivation of using the multilevel modeling technique is that it clearly identifies and differentiates between-cluster heterogeneity (i.e., intrinsic differences across aggregated units) and heterogeneity between units of analysis that are nested within aggregated clusters. Two different specifications are tested: two-level spatial and mixed two-level spatiotemporal random effects models. Whereas the first specification assumes that dwelling units are nested within spatial clusters (i.e., neighborhoods), the second specification hypothesizes that dwelling units are nested within spatiotemporal clusters (neighborhoods in a given time period). The unique contribution of this paper is that it accounts for temporal heterogeneity simultaneously with spatial heterogeneity in the housing price models. The study uses an extensive sample of more than 250,000 housing property transactions in 1987–1995 in the Greater Toronto Area of Canada. The paper examines the functional form of the hedonic price model and chooses a semilogarithmic model for subsequent multilevel housing price modeling. The results suggest that the spatiotemporal model performs better in terms of explanatory power and parameter estimates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".