Exploring affordable housing finance for emerging developers in South Africa
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,003 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».