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Record W7125763642

Exploring affordable housing finance for emerging developers in South Africa

2025· other· en· W7125763642 on OpenAlexaboutno aff
Gloria Boitumelo Selowa

Bibliographic record

VenueOpen University of Cape Town (University of Cape Town) · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsAffordable housingEmerging marketsGovernment (linguistics)Human settlementQuarter (Canadian coin)Real estateSocioeconomic statusInformal settlements
DOInot available

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0040.004
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.067
GPT teacher head0.226
Teacher spread0.160 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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