Financialization and vertical integration enhance renter-perceived property owner effectiveness
Bibliographic record
Abstract
Purpose This work examines the impact of financialization and vertical integration on renter-perceived property owner effectiveness. Design/methodology/approach Based on a sample of 1,186 renters in the US, the UK and Canada, the research addresses gaps in the literature regarding the implications of financialization and vertical integration on renters. Findings In contrast to previous work that shows the negative implications of financialization, the results indicate that financialization is positively correlated with renter-perceived property owner effectiveness, challenging the prevailing narrative that it strictly benefits shareholders and the financial elite. Vertical integration’s effect on renter-perceived property owner effectiveness was also shown to be positive and significant, enhancing the existing vertical integration literature that has not focused on real estate. A post-hoc interaction analysis revealed the benefits of vertical integration on renter sentiment are greater among highly financialized firms. Practical implications The study offers managerial considerations for property owners seeking to enhance renter sentiment and satisfaction as well as contributes to real estate strategy and management literature. Originality/value These results are novel, as previous research has not empirically shown financialization to elicit benefits for broader stakeholder groups.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".