Abstract
Conjoint Analysis (CA) is probably the most known and widely used tool in marketing research and demand forecasting. The premise of CA is that every product, or service, has multiple attributes, each with a different utility value for the consumer and that individual utilities can be quantified and assessed in a market simulation perspective. In spite of its popularity and wide diffusion, there has been little systematic research relating to the specific market – like the real estate market. Our work starts from this lack and it is organised in three sections: the first introduces a short history of CA and compares the performances of different interview designs: Adaptive Conjoint Analysis (ACA) and Choice Based Conjoint (CBC). The second section presents the case-study – the housing market of Turin – and shows the results of a research experiment. We compare ACA and CBC designs and discuss about the best when the goal is valuation – in other hands, the price is present between the attributes. The third section concludes and focuses on the problem of real estate market simulation and demand forecasting.