Can Build-To-Rent Generate Affordable Housing Outcomes? A Whole-Life Costing Approach to Investment Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it