Gap analysis in decision support systems for real-estate in the era of the digital earth

Abstract

Searching for a property is inherently a multicriteria spatial decision. The decision is primarily based on three high-level criteria composed of household needs, building facilities, and location characteristics. Location choice is driven by diverse characteristics; including but not limited to environmental factors, access, services, and the socioeconomic status of a neighbourhood. This article aims to identify the gap between theory and practice in presenting information on location choice by using a gap analysis methodology through the development of a seven-factor classification tool and an assessment of international property websites. Despite the availability of digital earth data, the results suggest that real-estate websites are poor at providing sufficient location information to support efficient spatial decision making. Based on a case study in Dublin, Ireland, we find that although neighbourhood digital earth data may be readily available to support decision making, the gap persists. We hypothesise that the reason is two-fold.Firstly, there is a technical challenge to transform location data into usable information.Secondly, the market may not wish to provide location information which can be perceived as negative. We conclude this article with a discussion of critical issues necessary for designing a spatial decision support system for real-estate decision making.

Sustainable Construction Investment, Real Estate Development, and COVID-19: A Review of Literature in the Field

“…One of the classifications considers the accessibility of urban facilities and services, which include hospitals, schools, educational centers, shopping centers, and recreational centers. This is a primitive factor, but the collection of available urban facilities can make a neighborhood either more or less attractive to a particular purchaser based on their household needs [20].…”

The Assessment of Density Bonus in Building Renovation Interventions. The Case of the City of Florence in Italy

“…In addition to energy needs, building stock renovation can find impulses from changes in individuals' habits and customs. The existing building stock, in some cases, has become outdated and should therefore be adapted to new living needs; as many studies highlight [6][7][8][9], this process has strongly sped up in the last 2 years due to the SARS-COV-2 pandemic.…”

Untitled

“…The results show that most of the respondents are aware of the DDTs and reported AI (22.5% of respondents), big data (12.75%), and VR (12.75%) as the most critical technologies not adopted due to many reasons. (Rabiei-Dastjerdi et al, 2021)…”