Acceptance Barriers and Potential of Virtual and Augmented Reality in Real Estate Market: Case Study on User Acceptance

Abstract

This thesis examines the impact of virtual and augmented reality technologies on the real estate industry, particularly in relation to the human factors that drive successful adoption. Five key dimensions influencing the implementation of VR/AR were investigated through a systematic triangulation of literature review, expert interviews, and case studies: demographic factors, adoption barriers, usability, spatial understanding and emotional engagement. The results revealed striking contrasts between theoretical concerns highlighted in academic literature and real-life experiences recorded in practice; obstacles that appear significant in theory frequently reduce in practical applications. Demographics were found to be consistently critical, and it was observed that both technological experience and age significantly shaped acceptance patterns. The study also concluded that spatial understanding and emotional engagement gain greater importance in practical applications as significant experiential advantages that frequently exceed theoretical expectations were observed. Drawing from these insights, a practical implementation framework was created to assist real estate professionals in navigating the journey of VR/AR adoption. Demographically tailored experiences, prioritization of spatial and emotional elements, strategic approaches to adoption barriers, and recognition of the interconnections between these dimensions are emphasized by the framework. Additionally, the results of this study were also contextualized through the TAM framework. This research enhances both academic understanding and industry practice as a result of connecting technological possibilities with human experience in property visualization and marketing. This study offers a roadmap for implementation strategies in the context of the ongoing digital transformation in the real estate sector, emphasizing alignment with actual user needs over theoretical assumptions.