Diaspora Cooperatives and Sustainable Real Estate Finance: A Discrete Choice Modelling Approach
Bibliographic record
Abstract
This study examines the trade-offs Nigerian diaspora investors will consider funding energy-efficient affordable housing in high-sovereign-risk markets and maps the residual viability gap. An integrated theoretical lens informed a Bayesian D-optimal partial-profile discrete-choice experiment. Employing a sample of 350 diaspora investors in the UK, USA, Canada and Germany, 12 investment profiles varying in ticket size, expected IRR, certification and risk shielding were evaluated. Data were analysed using a multinomial logit model implemented in Python's xlogit library to interpret the willingness-to-accept (WTA) estimates. Results show that investors waive up to 1.06% for sustainable real estate investment cost premium, which falls below the standard 2–7% incremental cost. Conversely, escrow, audit and FX-plus-sovereign cover elicit 1.32–2.27% discounts, confirming loss-aversion’s (Prospect Theory) dominance over altruistic impact (Risk-Return Impact Frontier). Wealth, experience and risk tolerance significantly moderated preferences (McFadden pseudo-R²=0.287). Practical implications of the study entail that developers should securitise sustainable investment projects with transparent escrow and FX cover. Policymakers could absorb the remaining spread through green guarantees and diaspora-indexed bonds. This study quantifies diaspora investors' willingness- to-accept yield reductions for sustainable housing (up to 1.06% for LEED-Gold certification), and demonstrates that risk mitigation mechanisms command higher premiums than sustainability features, informing blended finance design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".