Decision Support Systems (DSS) Model for the Housing Industry

The housing industry in the developing world and for long time has suffered from underinvestment, the lack of know how and the lack of sufficient strategies and policies. This in turn, led to a total failure in performance, accumulative massive housing demand and underachieving. Consequently, and because of the massive growth in the world's population, especially the Islamic World, people in the poorest countries have been the most affected and forced to live in slums and shanty towns which some worldwide have millions of occupants. This research paper presents a scientific approach to assist governments and decision makers in the Islamic World setting up most sufficient and effective strategies and policies on the mega-level (country level) for the housing industry. The final outcome of this research will produce a decision support system model (DSS) which could be used by decision makers to setting up holistic, realistic and achievable strategies and policies based on the scientific interpretation of the interface of the DSS model. The DSS model operates using five engines and one interface to identify, calculate and compare between financial sources (government, PFI, International Fund and Grants) and total cost of several variables such as, know how (local and foreign), labour (local and foreign), training (local PM and skilled labour), building materials (local and import), land (urban and rural). This in turn gives a clear idea to governments on their financial sources, the total cost of the whole housing project, regulation and legislations necessary and required to facilitate and support the housing industry, etc. The research methodology will consist of two parts; literature review which shed light on DSS model in terms of definition, stages, purposes, mechanism, how it functions, etc. The second will introduce interpretive structural model (ISM), which is used previously in a different stage of research to identify and prioritise housing industry variables and DSS model. Finally, the DSS model will be examined and tested using different scenarios for validation. The findings will be stated in the concluding section.