Exploring affordable housing finance for emerging developers in South Africa
Bibliographic record
Abstract
The United Nations regards South Africa as the most diversified and financially integrated economy in Africa, ranked 13th among developing countries. Affordable housing contributes significantly to the socioeconomic role in developing countries, because housing is considered the highest expenditure, estimated at a quarter of the total household budget. Recently, significant progress has been recorded in the Housing Sector, with the National Department of Human Settlements spearheading all government housing and human settlement initiatives and programmes. Affordable housing delivery is at its peak requirement, specifically in the favourable low-to-medium-end market for affordable housing in well-located areas; however, the participation of emerging developers is lacking. This current study explored the barriers to entry for emerging property developers in the affordable housing property sector, focusing on the Gauteng and Western Cape provinces of South Africa. Additionally, this study is focused on identifying the shortcomings in the financing of emerging property developers, specifically relating to ways of enabling market entry and access to finance. This was achieved by focusing on answering the research questions regarding the challenges encountered by emerging property developers in accessing affordable housing finance, as well as the challenges encountered by lenders (Development Finance Institutions [DFIs]) when financing emerging developers. The study employed a qualitative approach using semi-structured interviews with participants. The interview responses were analysed using thematic analysis. The findings revealed that finance is one of the barriers to entry for emerging developers, albeit not the only factor, as other factors were identified from the themes of the interviews. Access to well-structured finance with specific and effective funding arrangements and conditions, with the correct balance of debt, bonds, and equity cofounded by both private equity and public financial arrangements, was one of the themes. Moreover, enhancements of developer capacity through initiatives such as mentorship programs and training focused on technical skills within property development. Easy access to land and resources for emerging developers to enable them to enter the property market. Collaboration and partnerships among all stakeholders within the Department of Human Settlements, i.e. Housing authorities, local governments, and financial institutions, were also unanimously highlighted to assist in effective communication, sharing of information and expertise, which will result in efficient implementation of policies, programs and developments. The major themes from the challenges encountered by DFIs in granting affordable housing finance to emerging property developers included the effective implementation of public- private partnerships. Solutions to the bottlenecks and regulatory barriers in Land-use planning and legislation systems to promote sustainable development practices, and the drawbacks of the land-use planning system and legislation. Government policies that are targeted towards emerging developers such as financial assistance through land grants or low-interest development finance loans. Lastly, the need for robust finance schemes that are targeted at emerging developers, inclusive of both financial and non-financial support. Addressing these concerns requires a collaborative effort that focuses on capacitating emerging developers, while focusing on factors that include access to land, funding, capacity, stakeholder management, compliance, and legislation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".