Spatial versus non-spatial determinants of shopping center rents : modeling location and neighborhood-related factors
Bibliographic record
Abstract
This study is an attempt to model the economic trade-off between spatial and non-spatial determinants of shopping center rents while assessing the role of neighborhood and location attributes in the rent setting process. It is based primarily on physical and financial data obtained for ten super-regional, regional and community shopping centers in Quebec City, Canada; data refer to the 1998-2000 period. In this paper, 999 retail units are used, representing some 4.3 million square feet of gross leasable area. The study also benefits from a 2001 origin-destination (O-D) phone survey which provides an unprecedented and most novel information on some 174 000 daily trips in the Quebec metropolitan region (QMR). The whole database is managed through a regional GIS which also includes the 1996 census information on the QMR population and neighborhood profiles. Three space-related indices are designed, namely the Economic Potential Index (EPI), the Spatial Competition Index (SCI) and the Center Attraction Index (CAI) for successive integration into a unit rent regression model; both net and gross unit rents are modeled. Findings are most conclusive and show that space-related factors act as powerful determinants of both net and gross rents.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".