A hybrid recommendation framework for real estate based on geographic information and big data

• Grant number: 15/08191-5
• Support Opportunities: Research Grants - Innovative Research in Small Business - PIPE
• Start date: February 01, 2016
• End date: October 31, 2016
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Paulo Scarpelini Neto
• Grantee: Paulo Scarpelini Neto
• Company: Enterup Tecnologia em Sistemas Ltda - ME
• City: São José do Rio Preto
• Pesquisadores principais: Carlos Henrique El Hetti Laurenti
• Associated researchers: Carlos Roberto Valêncio
• Associated research grant(s): 16/14823-7 - A recommendation system as a service for real estate market based on geographic information, AP.PIPE
• Associated scholarship(s): 16/00209-5 - A hybrid recommendation framework for real estate based on geographic information and big data, BP.PIPE; 16/00202-0 - A hybrid recommendation framework for real estate based on geographic information and big data, BP.PIPE

Abstract

The real estate market is taken as one of the pillars of the modern economy. In Brazil the recent decades has provided a significant growth in this sector, mainly driven by the appreciation of real estate in big cities and in the less populated regions, furthermore, real estate projects and the demand for new properties remained in growth, mainly in the program "Minha Casa Minha Vida" from Federal Government.Front of this reality the real estate supply has grown considerably and the customers have faced a considerable amount of options, especially on the Internet where real estate websites gather in a single platform a large volumes of properties. In this context, the search for a property that meets the customer's needs has become a slow task, hindering the negotiations and decreasing the billing market.Thus, the development of a Recommendation System with a real estate focus consists of an interesting opportunity, but the existing techniques of suggestion are not meeting the specifics of this market satisfactorily - this is because the pricing, supply and demand of properties have specific characteristics that are directly affected by the location of the real estate projects. In addition, recent research has shown that the large volume of data generated by this type of system coupled to high levels access to the real estate market websites generates massive datasets, also referred by the Big Data term, which requires the development of an algorithm and computational techniques to distributed and scalable processing.The project consists in analysis of the technical feasibility of a hybrid framework of recommendation for real estate industry, which include the following technological and scientific challenges: 1) developing an innovative strategy of recommendation system that considers the geographic location of the property and meets with the specific needs of the real estate market. 2) Build a framework combining existing strategies with the spatial strategy, which will be developed. 3) Develop a distributed and scalable application for the treatment of large volumes of data. The project includes the creation of a prototype that will allow the validation of the strategy through the precision for recommendations measurements and runtime.The framework will be different of proposed approaches, since it takes an innovative approach for the suggestion of items based on spatial data. It is expected that the system greatly reduces the search time and provide high precision for the suggestion of real estate. Thus, the system will operate in the enhancement of sales and consequent increase in revenue. Once validated, the system becomes a commercially innovative tool that can be integrated with two products that are in development by EnterUp Technology, placing the company and products in a prominent position facing the existing solutions in the market. (AU)