A multi-objective optimization of online real estate property search

The search for the property listings is a time-consuming task. Traditionally, a person who wants to buy or rent a house will search through the tremendous amount of property listings advertised in the local newspapers or brochures. After the preferred property listings have been selected, it is necessary to connect with the property agent for the house viewing and make a price negotiation with the house owner. Once the price negotiation is successful, the contract signing and further legal works for the ownership are processed. The real estate industry had been nurturing such a conventional business model for more than a few decades. Gradually, the technological advancements allow the entrepreneurs to adopt the innovative technologies in the development of the property listing and search services to provide intelligent solutions more efficiently and effectively. Property search on the online web-based platforms is common because it significantly reduces the level of time consumption on the search and increases the search efficiency. Consequently, various kinds of search methods are developed in the online web-based platforms. However, it is discovered that current search methods require the contribution of the customers’ preferences in the search process. It can lead to a situation where some good property listings, which customers might favor, can be filtered out due to the constraint of the preference criteria. Therefore, in this dissertation, a new kind of property search system is proposed as a decision support system, which can be differentiated from existing property search methods. With an adoption of multi-objective optimization techniques, an online web-based property listing and search system is designed to consider multiple criteria in the search with the minimum preference input from the customers and recommend the property listings, which are the ideal possible options for the customers to make an intelligent decision in the property selection. Moreover, in order to achieve the goal of a convenient transition from the selection of a dream home to a successful business contract between the customer and house owner, a price negotiation model is cooperated in the decision support system to perform the appropriate price estimation of the real estate property. The whole dissertation work is mainly organized into three types of data analytics: descriptive analytics, predictive analytics, and prescriptive analytics to go through the lifecycle of design and development of an online web-based property listing and search system. According to the performance assessment, it is discovered that the property listing and search system can perform a good recommendation of the property listings considering three multiple criteria in the search performance: 1) minimizing the price expense, 2) maximizing the facilities offered in the real estate property, and 3) minimizing the distance/duration it takes to go to the specified locations.Master of Engineerin