Understanding high‐involvement product purchase through an innovative machine learning approach: A case of housing type choice

Abstract

AbstractAdopting the conservation of resources theory, this research explores the joint influence of multiple factors affecting the choice of housing, an instance of a high involvement product. We develop an innovative Machine Learning approach to identify the most significant set of factors affecting consumers' housing choices. It was found that housing choices were primarily bound by energy resources and secondarily by other personal, conditional, and object resources. The study identified 25 key factors, many of which previous studies have not explored. The new factors include government payments, vehicle possession, time living with children, marriage duration, current location duration, and health conditions. The results suggest that joint effects of factors are more prominent than individual effects in influencing the choice of high‐involvement products. Further, the study suggests that ML methods are more robust than traditional methods and can be applied to analyse other types of high involvement products. The findings can assist real estate investors, policymakers and other stakeholders to understand sophisticated market behaviours to develop better and tailored housing strategies.