Retail concentration and shopping center rents : a comparison of two cities
Bibliographic record
Abstract
This study aims primarily at testing whether, and to what extent, retail concentration within regional and super-regional shopping centers affects rent levels as well as the differential impact it may exert in different urban contexts. In this paper, 1,499 leases negotiated over the 2000-2003 period are being considered, representing over 5.3 million square feet of gross leasable area (GLA) distributed among eleven regional and super-regional shopping centers in Montreal (6 centers, 2.3 M. sq. feet, 653 retail units) and Quebec City (5 centers, 3.0 M. sq. feet, 846 units), Canada. Unit base rents (base rent per sq. ft.) are regressed on a series of descriptors that include retail unit size (GLA, in square feet), lease duration (in years) since the first landlord-tenant negotiation took place as well as shopping center age, weighted to account for expansions and modifications to the building. A time variable (time elapsed since January 1971, in years) is also designed so as to capture rent inflation over time. In addition, 31 retail categories or category groupings based on the North American Industry Classification System (NAICS) are defined while the Herfindahl index is used as a measure of retail concentration. Regression models are calibrated using a log-linear functional form; a logarithmic transformation is also applied to the store size (GLA) variable. Following the literature, a series of eight research hypotheses are tested, all of which addressing the tenant mix and retail concentration issues. While most are confirmed by the study, the hypothesis to the effect that retail concentration drives rents up cannot be supported. Rather, findings lead to the opposite conclusion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".