Continuous rescheduling optimization approach for successful real estate projects

Abstract

The economy of large-scale real estate projects (REPs) in Egypt is currently at risk. The increasing demand on residential, office buildings, retail, hotels and recreation as well as public services buildings has been encouraging investors from both private and public sectors to develop new compounds to meet this demand. These projects are huge in size and include several diversified functions and are usually implemented over many years. Real estate developers normally initiate their projects’ master schedule at the early stage of their projects then, refine it every year. The construction of civil infrastructure utilities and networks usually takes place at the beginning of project implementation. This applies to all services such as water, electrical power supply, sewerage, telecom, natural gas and district cooling and heating. The infrastructure investment and construction decision is usually taken based on the ultimate capacity and feasibility studies that are based on the master schedule. Any changes during long-term implementation (which may be expected) might adversely affect infrastructure feasibility. This article aims at developing a model that would consider changes during project implementation and provide decision-makers with recommendations that would minimize the impact on their investment. The model functions through: (1) a central database containing data about real estate components such as function, gross built up area, construction cost and expenditure profile, cash-in profile and type (selling or renting), payment instalments by end users, etc.; (2) a scheduling module, which creates possible implementation scenarios based on certain constraints given to the model such as minimum and maximum allowable construction durations, as well as the construction time frame for the REPs; (3) a financial module, which calculates both cash-in, cash-out and projects’ cash flow for the different scheduling scenarios; and (4) an optimization engine consisting of a genetic algorithm and a simulation module that compares all possible scheduling scenarios and defines which scenario best fits the objective function and maximizes the lifecycle net revenue. The model was implemented on a case study—a major real estate investment in New Cairo, where it proved to be an effective tool in providing decision-makers with different scenarios then recommending the one that minimizes the impact of changes realized on their investment.