A knowledge discovery mechanism to user requirement identification in building design

Abstract

The purpose of this paper is to investigate how the knowledge of real estate market can be used to support user requirement identification. A construction project well adjusted to the user requirements increase value and causes minors changes during its life cycle. As a consequence, renewal, refurbishments, and demolition are less present, reducing waste generation, reworking and material consumption. It is especially important in housing customization markets. However, one of the challenges faced by designers is frequently concerned about how properly to identify user requirements, wishes and needs, which are on the essence of the briefing phase. In this context, real estate data can be useful to designers, since it reflects the users’ evaluation of the building attributes. The research strategy uses a knowledge discovery mechanism, composed by five steps: (1) formulation of a general database; (2) specific data selection using Case-Based Reasoning; (3) enrichment of data-sample; (4) development of hedonic price models using regression analysis; and (5) simulation of the value of design alternatives. Based on an application of an hedonic price model, using data from the medium-class housing market of Porto Alegre, Brazil, the main results indicate that adjusted price models have sufficient detailing and statistical precision to support decisions in the initial stage of design.