Impact of ownership and architectural design on property disputes in multi-owned buildings. (June 2021)

• Record Type: Journal Article
• Authors: Shin, Jihye; Rajabifard, Abbas; Kalantari, Mohsen; Atazadeh, Behnam
• Is Part Of: Habitat international. Volume 112(2021)
• Publication Date: 2021-06
• Subjects: Dispute -- Strata title -- Condominium -- Property ownership -- Architectural design -- Machine learning technique; Human settlements -- Periodicals; 307
• DOI: 10.1016/j.habitatint.2021.102371
• Languages: English
• ISSNs: 0197-3975

Abstract

Abstract: With the trend towards increased property disputes in multi-owned buildings (MOBs), a greater focus is needed on dispute management to prevent huge litigation costs, high-stress levels, and disharmony in urban communities. Existing studies have recognized that pinpointing sources of disputes allows proactively minimizing dispute occurrence and influence. Ownership structure and architectural design of MOBs are viewed as the critical sources, but the effects of their detailed aspects on disputes remain unclear. This study aims to identify ownership and architectural factors of MOBs impacting on the incidence and intensity of disputes within communities in the property. It first defines crucial MOB factors causing disputes and a measurement model of the dispute intensity in the context of Victoria, Australia. It then investigates relationships between the factors and the intensity by employing Boosted Classification Tree, a machine learning technique, for 101 tribunal cases. This research confirms six factors highly associated with the dispute intensity: design of common areas, ownership space boundaries, ownership space arrangement, allocation of co-ownership share, design of individual units, and accessory spaces for each unit. The results shed light on MOB factors to be controlled at planning stages for mitigating disputes and their influence. Highlights: The impact of ownership and architectural factors of multi-owned buildings (MOB) on disputes has been empirically explored. A method for measuring the intensity of disputes on MOB owners has been developed. Machine learning on tribunal case records has been performed. Six factors of MOBs with significant impacts on the dispute intensity have been identified.