A Systematic Review of Smart Real Estate Technology: Drivers of, and Barriers to, the Use of Digital Disruptive Technologies and Online Platforms

Abstract

Real estate needs to improve its adoption of disruptive technologies to move from traditional to smart real estate (SRE). This study reviews the adoption of disruptive technologies in real estate. It covers the applications of nine such technologies, hereby referred to as the Big9. These are: drones, the internet of things (IoT), clouds, software as a service (SaaS), big data, 3D scanning, wearable technologies, virtual and augmented realities (VR and AR), and artificial intelligence (AI) and robotics. The Big9 are examined in terms of their application to real estate and how they can furnish consumers with the kind of information that can avert regrets. The review is based on 213 published articles. The compiled results show the state of each technology’s practice and usage in real estate. This review also surveys dissemination mechanisms, including smartphone technology, websites and social media-based online platforms, as well as the core components of SRE: sustainability, innovative technology and user centredness. It identifies four key real estate stakeholders—consumers, agents and associations, government and regulatory authorities, and complementary industries—and their needs, such as buying or selling property, profits, taxes, business and/or other factors. Interactions between these stakeholders are highlighted, and the specific needs that various technologies address are tabulated in the form of a what, who and how analysis to highlight the impact that the technologies have on key stakeholders. Finally, stakeholder needs as identified in the previous steps are matched theoretically with six extensions of the traditionally accepted technology adoption model (TAM), paving the way for a smoother transition to technology-based benefits for consumers. The findings pertinent to the Big9 technologies in the form of opportunities, potential losses and exploitation levels (OPLEL) analyses highlight the potential utilisation of each technology for addressing consumers’ needs and minimizing their regrets. Additionally, the tabulated findings in the form of what, how and who links the Big9 technologies to core consumers’ needs and provides a list of resources needed to ensure proper information dissemination to the stakeholders. Such high-quality information can bridge the gap between real estate consumers and other stakeholders and raise the state of the industry to a level where its consumers have fewer or no regrets. The study, being the first to explore real estate technologies, is limited by the number of research publications on the SRE technologies that has been compensated through incorporation of online reports.

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

“…Figure 2 for S2 and highlights that, in the case of the focus on smart real estate and property management, recent literature revolves around keywords such as housing, decision-making, urban area, forecasting, data mining, behavioral studies, humancomputer interactions, artificial intelligence, energy utilizations, economics, learning system, data mining, and others. This shows a central focus on data utilizations for improving human decisions, which is in line with recent articles such as Ullah et al [18], Felli et al [36], and Ullah et al [20], where it was highlighted that smart real estate consumers and tenants have regrets related to their buy or rent decisions due to the poor quality or lack of information provided to them. Figure 2 for S2 and highlights that, in the case of the focus on smart real estate and property management, recent literature revolves around keywords such as housing, decision-making, urban area, forecasting, data mining, behavioral studies, human-computer interactions, artificial intelligence, energy utilizations, economics, learning system, data mining, and others.…”

“…Firstly, the articles were divided into five types: original research and big data technologies, review, conference, case study, and others, as shown in Figure 7. Expectedly, the shortlisted articles mainly focused on big data technologies (59), followed by others (29), review (23), conference (18), and case study (10). Similar analyses were conducted by Martinez-Mosquera et al [37]; however, none of the previously published articles explored big data applications in the context of smart real estate or disaster and risk management, which is the novelty of the current study.…”

“…For such immersive visualization in smart real estate, Felli et al [36] recommended 360 cameras and mobile laser measurements to generate big data, thereby visualizing resources to help boost property sales. Similarly, Ullah et al [18] highlighted the use of virtual and augmented realties, four-dimensional (4D) advertisements, and immersive visualizations to help transform the real estate sector into smart real estate. For disaster management, Ready et al [56] introduced a virtual reality visualization of pre-recorded data from 18,000 weather sensors placed across Japan that utilized HTC Vive and the Unity engine to develop a novel visualization tool that allows users to explore data from these sensors in both a global and local context.…”

“…Big data has applications in various filed including smart real estate [18] and disaster management [19], which were explored and highlighted in current study. These areas were selected Note: S1: "Big Data" OR "Technology for big data filtering" OR "Refining big data", S1 The aim of this paper is to shed light on big data analysis and methods, as well as point toward the new directions that can possibly be achieved with the rise in technological means available to us for analyzing data.…”

The Performance Gap in Energy-Efficient Office Buildings: How the Occupants Can Help?

“…These have been selected for a thorough investigation in the current study.Despite the development of energy-efficient buildings locally or anywhere else, their reported energy savings are far from design expectations and there is a gap between the predicted and the actual energy use [16][17][18]. This gap is partly due to the lack of feedback from the building administration to designers, faults in the design or its assumptions, improper modeling tools, build quality, over usage of installed equipment, changes in latent conditions, poor facility management, or behavior of the occupants that is hindering the process of smart building or real estate management [17,[19][20][21][22][23][24]. These causal factors affect energy performance [18] and must be addressed to achieve the desired efficiency.…”