Agglomeration economies and retail concentration as determinants of shopping center rents
Bibliographic record
Abstract
This study investigates whether, and to what extent, agglomeration economies and retail concentration within shopping centers affect rent levels. It is based primarily on physical and financial data obtained for eight super-regional, regional and community shopping centers in Quebec City, Canada; data refer to the 1998-2000 period. In this paper, a 1007 retail units data base (between 935 and 941 cases after filtering), representing some 4.4 million square feet of gross leasable area (GLA), is used to model unit base rent. In order to investigate the agglomeration economies and retail concentration issues, two indices are designed - namely the Agglomeration Index (AGGLINDX) and the Concentration Index (CONCINDX) - and integrated into a regression model of unit rents. Findings suggest that, by and large, agglomeration economies impact positively on base rents while retail concentration has the opposite effect, due to the higher bargaining power of dominant tenants. They also suggest that both phenomena impact differently on rents depending on the retail category or subcategory considered, which tends to corroborate Hardin et al.’s (2002) findings about the existence of distinct retail submarkets.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".