Exploring the Role of Peri-urban Areas in Driving Self-help Housing: A Case Study of Tea Estate, Inanda

Rapid urbanisation, population growth, and widening economic inequality continue to exacerbate South Africa’s urban housing crisis, particularly for households within the gap market that struggle to access formal housing programmes such as the Social Housing Programme (SHP) and the Finance Linked Individual Subsidy Programme (FLISP). As a result, many households increasingly turn to peri-urban areas as alternative spaces for accessing housing through self-help development. This study explores the role of peri-urban areas in driving self-help housing using Tea Estate in Inanda, within the eThekwini Municipality, as a case study. Anchored in neoliberalism and the enabling approach theoretical frameworks, the study adopted a qualitative methodology. Data was collected through semi-structured interviews with self-help housing homeowners, municipal officials, and traditional leadership, supported by observations and document analysis. The findings indicate that peri-urban areas function as important knowledge-rich spaces for housing delivery, shaped by live experience, social relations, and traditional land governance systems. Accessible land, flexibility in housing construction, and the ability to maintain cultural and spiritual practices were key drivers motivating self-help housing. However, these advantages are accompanied by persistent challenges, particularly limited access to basic municipal services such as water, sanitation, electricity, and waste removal. Land access in Tea Estate is mediated through traditional authorities, who play a central role in land allocation and spatial organisation. In the context of an increasingly digital and data-driven built environment, the study highlights the continued importance of grounded, place-based and socially embedded knowledge in understanding peri-urban housing dynamics. Recognising peri-urban areas as strategic knowledge spaces can support more inclusive, resilient, and sustainable approaches to housing and land-use planning. Predicting Residents Thermal Comfort in Rural Mud Houses Using Machine Learning Classifiers Al Rafayet Rahman1, Md. Tafhimul Hasan1*, Md. Sojib Sheikh1, Md. Estiak Ahmed1,