Reinventing renting? ESG investing and the new landlordism of build-to-rent housing financialization
Bibliographic record
Abstract
This paper traces the social value and risk management strategies of a pension -backed build-to-rent (BtR) housing provider in the United Kingdom. Through these strategies, BtR is positioned as a ‘new’ socially and environmentally responsible model, appealing to institutional investors seeking to do ‘good’ while also filling fiduciary mandates to pay promised pensions. However, this paper reveals how the vision of a redefined rental market, advanced by a major BtR landlord, strengthens aspects of the existing landlord-tenant relationship while also introducing some limited innovations. The case shows that the BtR provider relies on conventional but problematic risk profiling techniques that hierarchically classify households according to ostensibly neutral credit, income, and employment criteria. The result masks the classed and racialised dynamics through which desirable tenants – implicitly cast as uniquely deserving of an elevated rental experience – are separated from those deemed too risky. Through a critical examination of constructions of ‘social value’ and ‘risk’, I show how household risk profiles are transformed into ethical risk for a range of investors. Furthermore, the sector specific investment risks identified by the firm define the limits of how and for whom renting can be reinvented. Therefore, while the risk assessments applied to tenants in BtR developments are not themselves new, their integration into the risk economies of ESG investing brings distinct challenges and opportunities for those interested in housing justice and housing rights.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".