Financialising affordable housing? For-profit landlords and the marketisation of socially responsible investment in rental housing
Bibliographic record
Abstract
Real estate investment trusts, institutional investors and other global asset managers have gained a controversial reputation for becoming for-profit landlords in the public and private rental housing sector. However, in recent years of increased public attention they have sought to improve their image by presenting themselves as patient investors that are willing to focus on long-term investment rendering steady but stable cash flows with beneficial social outcomes. In this paper, we criticise this discursive reframing of the ‘patient’ or ‘responsible’ corporate landlord. Rather than contributing to affordable and sustainable housing solutions, we argue that financial profit-making remains the prime interest of actors like Ampere Gestion (France), Bartra Capital Property (Ireland) and Vonovia (Germany). In doing so, we make two contributions to ongoing debates on housing financialisation and social impact financing. First, by deploying the narrative of corporate responsibility we scrutinise how for-profit landlords seek to create public goodwill and deflect social criticism for their otherwise profit-driven housing operations. Second, by demonstrating that states actively facilitate this emerging sub-market of ‘socially responsible’ housing investment, we show that traditional boundaries between public, affordable and private rental housing are increasingly blurring.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".