Can Build-To-Rent Generate Affordable Housing Outcomes? A Whole-Life Costing Approach to Investment Analysis
Bibliographic record
Abstract
Doubts remain among stakeholders in academia and the housing industry about the potential success of build-to-rent to generate positive outcomes for institutional investors and affordable dwellings for low- and moderate-income households. However, a systematic study on the viability of build-to-rent to deliver affordable housing in Australia is largely rare and non-existent in the literature. We fill this gap in the literature by investigating the financial viability of build-to-rent and its potential to generate affordable rental housing outcomes in Brisbane, Australia. Using rental prices from CoreLogic (Formerly RP data) and construction-related costing data from WT Partners Australia for 2019, we apply the whole-life costing approach to investment analysis and confirm that build-to-rent can be feasible in Australia under equity financing. Also, we find that under the current regulatory regimes and market structure, build-to-rent will fail to deliver affordable housing outcomes. Moreover, providing free land alone cannot help to make build-to-rent affordable. Thus, significant public subsidy and tax concessions, particularly on Goods and Services Tax (GST) on construction-related costs, may be required if build-to-rent developments are to generate affordable housing outcomes in Australia.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".