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sustainability
Review
A Systematic Review of Smart Real Estate Technology:
Drivers of, and Barriers to, the Use of Digital
Disruptive Technologies and Online Platforms
Fahim Ullah * ID , Samad M. E. Sepasgozar and Changxin Wang
Faculty of the Built Environment, University of New South Wales, Kensington, 2052 Sydney, Australia;
samad.sepasgozar@gmail.com (S.M.E.S.); cynthia.wang@unsw.edu.au (C.W.)
*Correspondence: f.ullah@unsw.edu.au; Tel.: +61-451-728-281
Received: 15 August 2018; Accepted: 30 August 2018; Published: 3 September 2018
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.
Keywords:
smart real estate (SRE); smart real estate management (SREM); real estate technologies;
Big9 disruptive technologies; online technology dissemination platforms; technology adoption;
decision regrets
Sustainability 2018,10, 3142; doi:10.3390/su10093142 www.mdpi.com/journal/sustainability
Sustainability 2018,10, 3142 2 of 44
1. Introduction
Real estate, globally, has attracted huge financial investment in recent years. Since 2012, real estate
technology based companies have raised almost $6.4 billion in funding across 817 deals in the United
States alone [
1
]. Real estate technology is defined as the hardware gadgets, online platforms and
software tools used by different participants in the real estate industry, including real estate-focused
lenders, brokers, property owners, investors, and managers, as well as the consumers to collect and
distribute data related to the real estate industry [
1
]. However, as the report of CB-Insights [
2
] and
Warburton [
3
] points out, the global industry is lagging the technology curve by five years, a point
also made by Ferren Bran at the RealCOMM conference [
4
]. Contrary to its industrial counterparts,
almost a third of the global real estate industry, worth $11 trillion, is managed on spreadsheets;
innovative information technology (IT) tools are missing in action. Yet innovative and disruptive
technologies are an integral part of the modern world [
5
]. Disruptive technologies, a term coined by
Professor Clayton Christensen and colleagues, are defined as a set of technologies that displaces the
existing methods or technologies and shakes up the industry to open new avenues for innovation and
business development [
6
]. On the one hand, they are revolutionising the modern world; on the other,
they present a challenge for traditional industries such as construction and real estate. While such
digital technologies are vital for an industry’s growth, their adoption and usage are always questioned,
perhaps due to their disruptive nature [
7
]. As a result, disruptive technologies have been reviewed
and assessed in various industries according to such criteria as computer education and students’
perspectives [
8
], radical technology development in value-added supply chains [
9
], enhancing learners’
experience in massive open online courses [
10
], risk management using disruptive technologies [
11
],
social relations in higher education [
12
], clinical trials [
13
] and others. The stated studies provide
useful insights into how disruptive technologies may be applied, but until now their application to
real estate has not been reviewed [
14
]. The current study reviews the potential application of various
disruptive technologies to real estate to highlight their importance, current uses and applications,
and how they can be used to address the needs of real estate stakeholders.
A growing concern for the industry is the increase in post-purchase or post-rental regrets that
consumers have been reporting [
15
]. According to Trulia [
15
], 44 per cent of real estate consumers
(almost one in two) regret their purchase or rent decision. The main causes of these regrets are a lack
of information about properties and the complexity of the purchase process, whereby key information
including fees remains hidden. When consumers later discover such details or fees, their regrets are
compounded. Chen et al. [
16
] argue that 30 per cent of Chinese residential consumers and 46 per cent
of homebuyers regret their decisions; their detailed investigation highlighted a key cause of regret
as being a lack of information. Similarly, Marte [
17
] says that 22 per cent of respondents to a survey
quoted a lack of information as a key cause of regret. Such regrets can be eliminated or reduced by
drawing on disruptive technologies to furnish consumers with sufficient, detailed information before
they make real estate decisions. This could tackle the problem of post-purchase regret. This study,
therefore, aims to explore the adoption of various disruptive technologies and their potential usefulness
in providing detailed information to consumers, so minimising regrets.
This paper investigates the potential of different disruptive technologies in the real estate
sector and utilizes the Big9 technologies to address the key regrets of real estate stakeholders.
A special focus is on the regrets related to lack of information that may be addressed through
proper utilization of the Big9 technologies. Therefore, the paper introduces the term SRE and
reviews key factors that convert traditional real estate into SRE, with the focus on technology and
online platforms. Therefore, this paper: (1) reviews the technological aspects of SRE—its user
centredness, sustainability and technological innovation; (2) explores online dissemination platforms
such as websites, smartphone technologies and social media; (3) identifies real estate stakeholders
and investigates their needs and interactions; (4) identifies the Big9 technologies and performs
an opportunities, potential losses and exploitation levels (OPLEL) analysis for each technology;
(5) integrates the Big9 technologies, dissemination platforms and stakeholder needs through a holistic
Sustainability 2018,10, 3142 3 of 44
technology adoption model (TAM) framework that incorporates them into online real estate platforms
and results in greater consumer satisfaction as well as reduced regrets. This paper offers insights
into: the key components of smart real estate management (SREM) (distinguishing it from traditional,
rigid real estate management); disruptive Big9 technologies and their use for addressing a lack of
information provided to consumers; and stakeholder needs and the dissemination of information to
them through a conceptual TAM framework.
1.1. Technological Disruption and Innovation in the Real Estate Industry
Although the global real estate industry has fallen behind the curve of information technology
(IT)-based innovation, instead relying on traditional transaction methods, investment in real estate
technologies is on the rise. This shows the business appeal of the industry to investors and potential
clients. For evidence, look at the $1.5 billion global investment in 2015 and the $1.6 billion invested
in the first half of 2016. In this context, the US is seen as the centre of commercial real estate and
its technological advance [
18
]. A report published by Forum [
19
] compared countries on seven
criteria related to technological advance and adoption capability. These included: network readiness,
which measures the capacity of countries to leverage internet and communication technologies (ICT)
for increased competitiveness and wellbeing; the availability of the latest technologies such as mobile
4G, 5G and Big Data incorporation; individuals using the internet; firm-level technology absorption;
the capacity for innovation; business-to-consumer internet use, and government success in ICT
promotion. Table 1shows how the US, Britain and Australia rank among 139 countries according to
these seven criteria. These three countries were selected because data was available for each criterion;
other countries lacked some data. Table 1shows the clear dominance of the US in terms of technological
advancement and readiness for innovation. But Britain leads when it comes to individual use of the
internet, business-to-customer internet use and government success in ICT promotion. The rankings
show the overall position of the countries among the 139 investigated. The value is out of seven and
shows the extent to which a criterion is being adopted in the studied country. Overall, the top three
countries for technology adoption readiness as the report states are Singapore, Finland and Sweden
respectively. All the values were obtained through a global questionnaire; the assessment was based
on how the respondents scored to attain a holistic value. The values show that the US, Britain and
Australia are leveraged to adopt and implement the latest technologies. The findings also highlight
the likelihood of obtaining benefits from an investment in these countries, specifically in relation
to online real estate. These benefits are more likely because internet-based innovations have been
implemented successfully and there exists a readiness to adopt them. Positive government strategies
further incentivise investors, explaining why nations such as the US are attracting huge investments
(as evident from the $6.4 billion in funding that has flowed its way since 2012). Among the countries
studied, the US ranks the highest on most criteria followed by Britain and Australia respectively.
Such big investments and the readiness of some countries to embrace technological disruption
encourage the movement towards smart real estate (SRE) that is more sustainable and augmented by
technology. Various real estate start-ups have emerged as technological tycoons, including Trulia [
20
],
Zillow [
14
] and Redfin and Trulia [
20
,
21
]. Ms Pitman, working with the smart city-focused CityConnect
program in Australia, quotes start-ups Zillow, WeWork and Airbnb as key enablers of SRE and smart
cities. She says technological solutions to real estate problems are at the core of the smart-city revolution.
City densification and the jobs they accumulate make real estate technology and built-environment
solutions more critical to good city functioning. By improving market transparency, adapting to
evolving customer desires, facilitating speedier transactions and enhancing asset utilisation, as well as
regulatory requirements [22], the industry can reduce friction in the market.
Sustainability 2018,10, 3142 4 of 44
Table 1. Global technology adoption assessment for the US, Britain and Australia.
Pillar US Britain Australia
Rank Value Rank Value Rank Value
Network readiness 5 5.8 8 5.7 18 5.5
Availability of latest technologies 2 6.5 5 6.5 24 5.9
Individuals using the internet 13 87.4% 8 91.6% 19 84.6%
Firm-level technology absorption 3 6.1 14 5.7 22 5.6
Capacity for innovation based on adoption
capabilities 2 5.9 10 5.4 25 4.8
Business-to-consumer successful transfer over
the internet use 2 6.3 1 6.4 25 5.5
Government success in internet and
communication technologies (ICT) promotion 25 4.8 15 4.9 55 4.2
Note: The ranking is out of 139 studied countries and the value is out of 7.
1.2. Smart Real Estate (SRE): Definition, Core Components, Technologies and Stakeholders
Innovative technologies and their use are common to smart cities and the real estate industry.
Technology has always been an indicator of smartness [
23
]. A key determinant of the “smart city”
globally is whether it adopts the latest disruptive technologies [
24
]. A smart city uses various collections
of electronic data as well as technologically advanced sensors to manage its assets and resources
efficiently [
25
]. While technologies change over time, becoming more complex, it is the adoption
capability and its open, innovative nature that makes a city, or real estate, smart [
5
,
26
,
27
]. Thus, smart
cities need SRE and smarter management to succeed. So, it is imperative to develop assessment criteria
for SRE in order to turn the dream of the smart city into reality.
SRE, like smart cities, can be assessed according to various parameters. In the absence of a globally
accepted definition of SRE, the current review looks at relevant studies to develop a definition. Allameh
et al. [
28
] say user-centredness is a key component, and a virtual reality-based experiment elicits user
preference for buying or using a smart home. Miller et al. [
29
] stress that sustainability is required if
real estate is to become smarter and attract greater global investment. Miller [
30
] stresses innovation
and says the industry needs to demonstrate its smarts by enlisting virtual showcases, crowdfunding,
AI, smart homes, online marketplaces and smartphone apps. Shulman [
31
], in speaking of the
technological needs of Smart Real Estate, presents the example of Airbnb, highlighting that it raised
$10 billion in early 2014 and therefore gained greater market capital than such well-known hotels
as the Hyatt. Summarising these findings and studies, this review defines SRE as: “A property or
land that uses various electronic sensors to collect and supply data to consumers, agents and real
estate managers that can be used to manage assets and resources efficiently. The key features are
user-centredness, sustainability and the use of innovative and disruptive technologies in such a way
as to attain holistic benefits that are otherwise not attainable”.
A boom in the demand for “sustainability” and “smartness” has led to these terms often being
used in conjunction. Cities contribute more than 70 per cent of world greenhouse gases whileoccupying
only 2 per cent of global land area. Changes in climatic conditions, such as rising sea levels and global
urbanisation, call for more ecofriendly buildings. It has been estimated that by 2020 all buildings in
countries with advanced economies will have a sustainability rating [
32
]. This need for sustainability
requires due attention. Real estate, valued at $215 billion in 2015 and rising, attracts investors because
of its return potential. If it is to continue attracting investors, it needs to be sustainable [
29
]. By 2020,
investable real estate will have risen by more than 55 per cent since 2012 [
32
]. While “going green”,
“clean energy”, and “building for the future” are critical concepts, sustainability holds a key position
in SRE.
Smart real estate management refers to the cumulative management of SRE that keeps the core
principles of innovative technology adoption, sustainability and user-centredness at its core. This is
Sustainability 2018,10, 3142 5 of 44
not only restricted to infrastructure but includes asset and resource management. In a similar vein to
smart cities and disruptive innovations, SREM’s key technologies include drones, the internet of things
(IoT), clouds, software, big data, 3D scanning, wearable technologies, virtual and augmented realities
(VR and AR), and artificial intelligence (AI) and robotics [
30
]. These technologies, referred to as the
Big9, are critical to SRE and are the focus of the reviews undertaken in this study. Other technologies
may exist in addition to these Big9 technologies in other fields such as cryptocurrencies, 4G and 5G
networks, Water harvesting from air, high speed travels, self-driving cars, renewable energies, medical
innovations and others as provided in the 100 latest disruptive technologies report by Richard of the
What’s Next: Top Trend website [
33
]. The current study considers only the Big9 technologies mainly
due to their established or published potential in the real estate sector. The remaining are not included
in the study since these are either irrelevant to the SRE sector or, are too nascent to be analysed for
their applications. Some key concepts, definitions and explanations pertinent to SRE, its technologies
and stakeholders are given in Table 2.
Table 2. Key terms used in this paper including their definitions and explanations.
Key-Term Explanation
Smart real estate (SRE)
This is an amalgam of user-centred, sustainable and innovative technologies for
managing real estate resources efficiently in an urban area, whereby the key
information is made available to consumers, managers and agents.
The technologies and systems must be sustainable, user-centred and innovative,
thereby disrupting traditional practices [28,34].
Smart real estate management (SREM)
Just like its industrial doppelganger, the smart city, SREM is the management of
the SRE process, including data collection and its processing and dissemination
through computers and networked technologies to promote the overall life
quality of consumers using real estate services. It has specific measures about
privacy and data security [23,28,35].
Big9 technologies
These nine disruptive technologies are the focus of this study. They include big
data, virtual and augmented realities (VR and AR), the internet of things (IoT),
clouds, software as a service (SaaS), drones, 3D scanning, AI and wearable techs.
Technology adoption model (TAM)
This is an information systems theory used for modelling the use and acceptance
of technologies by end users. It starts with the perceived ease of use and
usefulness to a user of technology that might effect behavioural change as they
start to use the technology, thus providing a holistic mechanism [36,37].
Consumers
This refers to buyers, renters, end users or sellers of real estate. These are the
primary beneficiaries of the transactions because at the end of the process they
are the ones with the resources to keep the process alive. They are therefore at the
centre of the system [38,39].
Agents and associations (AA)
These stakeholders provide services to the consumer in exchange for revenue.
This category includes the real estate managers, developers, private investors and
other services providing bodies. Associations exist to guide agents and ensure
their compliance with codes of ethics and local, state and federal laws [40,41].
Government and regulatory authorities (GRA)
Governments aim to protect citizens in exchange for tax revenues. Regulatory
authorities exist at local, state and/or federal levels to ensure compliance and to
formulate laws for the real estate industry [42,43].
Complementary industries (CI)
These industries aim to facilitate consumers, agents and associations in the
buying or selling of property. They receive revenues in exchange for their services.
They include banks, law firms, inspectors, contractors, lenders and others [
44
,
45
]
1.3. Online Rent or Buy Process
The stakeholders described in Table 2have various needs and requirements that can be
addressed by Big9 technologies. For example, big data analytics can address the consumer’s need
for neighbourhood information. Such technologies should have a role in the search process when
a potential consumer looks for properties to shortlist. Figure 1shows a typical property search from
the moment that a seller/owner uploads property detail. The website administrator may then accept
the search terms or seek their modification, and properties are eventually presented to the consumer
who wants to rent or purchase one.
Although the scope of SRE is vast and covers many domains, the current study focuses on
buyer-seller interactions and the disruptive technologies that can improve them. Based on a review
Sustainability 2018,10, 3142 6 of 44
of literature about online real estate searches, such as the work of Grant and Cherif [
46
], Rae and
Sener [
47
], and Rae [
48
], consumers’ online behaviour can be summarised by Figure 1. It illustrates
the mechanism for posting a property online and how potential consumers search, view and select
properties. It also shows a number of requirements for a property to be posted online.
In a high-tech, SRE environment, sophisticated platforms are required to disseminate property
information to consumers, who comprise the payers in the real estate industry. Disruptive technologies
such as neighbourhood analytics derived from big data and VR-based virtual tours are usually
disseminated online for the benefit of consumers. Online platforms that disseminate SRE technology
include websites, smartphone technologies such as apps, and social media such as Facebook, Instagram,
WhatsApp and YouTube. Zillow uses websites to provide prices for existing properties and predict
new prices in any given neighbourhood through its “Zestimate” function. Other information that can
be delivered online includes crime rates, walking scores and transit scores that tell consumers the
safety, walkability and ease of travel in a particular neighbourhood [49].
Sustainability 2018, 10, x FOR PEER REVIEW 6 of 44
mechanism for posting a property online and how potential consumers search, view and select
properties. It also shows a number of requirements for a property to be posted online.
In a high-tech, SRE environment, sophisticated platforms are required to disseminate property
information to consumers, who comprise the payers in the real estate industry. Disruptive
technologies such as neighbourhood analytics derived from big data and VR-based virtual tours are
usually disseminated online for the benefit of consumers. Online platforms that disseminate SRE
technology include websites, smartphone technologies such as apps, and social media such as
Facebook, Instagram, WhatsApp and YouTube. Zillow uses websites to provide prices for existing
properties and predict new prices in any given neighbourhood through its “Zestimate” function.
Other information that can be delivered online includes crime rates, walking scores and transit scores
that tell consumers the safety, walkability and ease of travel in a particular neighbourhood [49].
Figure 1. Typical online buy or rent process in SRE.
Real estate agents use online platforms to present their properties to potential buyers or renters.
The information they provide can prevent consumer regrets that arise from ill-informed decisions
and so reduce the post-purchase friction between agents and consumers.
Once a website’s requirements are completed, an administrator can approve or reject some or
all the features. In the case of rejection, the owner must modify the property or its details, or upload
missing components. In the case of approval, the property becomes visible to potential buyers or
renters. Consumers use online filters to find properties that meet their requirements. The website
displays properties that match these requirements and generates a shortlist or selection. Consumers
can also provide feedback or demand further information of the property owner. In the latter case,
Figure 1. Typical online buy or rent process in SRE.
Real estate agents use online platforms to present their properties to potential buyers or renters.
The information they provide can prevent consumer regrets that arise from ill-informed decisions and
so reduce the post-purchase friction between agents and consumers.
Once a website’s requirements are completed, an administrator can approve or reject some or
all the features. In the case of rejection, the owner must modify the property or its details, or upload
missing components. In the case of approval, the property becomes visible to potential buyers or
renters. Consumers use online filters to find properties that meet their requirements. The website
Sustainability 2018,10, 3142 7 of 44
displays properties that match these requirements and generates a shortlist or selection. Consumers
can also provide feedback or demand further information of the property owner. In the latter case,
the request is sent first to the administrator, after whose approval it is forwarded to the property
owner and vice versa before a deal is finalised. Thus, while technology is important, the dissemination
mechanism occupies a critical position.
Dissemination mechanisms and technological adoption go hand-in-hand, since proper
dissemination encourages adoption and vice versa [
49
,
50
]. Adoption is the incorporation of technology
by accepting and subsequently using it for various functions. Technology is adopted properly and
made available to end users using a TAM. TAM has been employed to integrate technologies and
IT-based dissemination platforms. It links the technologies to be disseminated through proper adoption
while keeping user satisfaction at its core [
51
,
52
]. In the current study, a TAM framework is proposed
to meet stakeholder needs through various disruptive technologies.
2. Materials and Methods
To begin with, this paper systematically reviews the published literature on real estate and its
technologies, which the authors gathered through online search engines such as Google Scholar,
ASCE Library, Scopus, Web of Science and others. Both keyword and semantic searches were
used to search the literature. The selected literature was then categorised into core components
of SRE, dissemination mechanisms and Big9 technologies. OPLEL were tabulated for each of the
Big9 technologies. The basic and secondary needs of four key real estate stakeholders—consumers,
agents and associations, government and regulatory authorities and complementary industries—were
highlighted through an SRE stakeholder analysis. Next, an analysis involving how (process, need and
mechanism), what (technology), and who (stakeholder) highlights the use of technology in addressing
stakeholder needs with special focus on reducing consumers’ regrets. Additionally, the paper discusses
data collection and the dissemination framework. Finally, it hypothetically links identified technologies
and stakeholder needs with traditional TAM drivers to hypothesise a conceptual framework for the
adoption of technology in real estate.
The method used in this study comprises keyword searches, screenings and categorisation.
Keyword searches used the terms “Real Estate”, “Real Estate Technologies”, “Smart Real Estate”, “Real
Estate Technology Adoption”, “Technology Dissemination in Real Estate”, “Information Dissemination
in Real Estate” and “Smart Real Estate Management”. Search tools included popular and powerful
search engines such as Google Scholar, the American Society of Civil Engineers (ASCE) library,
Taylor & Francis Online, Emerald Insight, Science Direct, Web of Science and Scopus databases.
The search process involved keyword and Symantec searches where key phrases such as “role of
technology in real estate” were applied. Identical keywords and associations between them were used
to search each database. For example, “Real Estate Tech OR Real Estate Technology OR Disruptive
Technologies in Real Estate OR Smart Real Estate OR Real Estate Technology Acceptance OR Real Estate
Technology Adoption” AND “Information dissemination OR Web based dissemination OR Apps for
dissemination” AND “NOT information retrieval”. The search strings were carefully designed to
exhaust each database, as shown in Table 3.
All the selected articles were written in English and articles in the form of editorials, notes, errata,
letters or comments were excluded. A total of 139 articles matched the search parameters: 47 from Web
of Science, 40 from Scopus and 52 from Google Scholar and others. Overall, Google Scholar yielded
the highest number of results, with 6542 articles, followed by Web of Science with 5896 and Scopus
with 5761. After applying the search filters, Google Scholar yielded 1300 results, and after adjusting for
duplicates and restrictions this fell to 52. Similarly, for Web of Science and Scopus, the corresponding
results were 1142 and 407, which were restricted to 47 and 40 respectively. The leading reasons for
rejecting or excluding the articles from the review were the outdated focus (pre-2010), non-focus,
and non-presence of the keywords in the title or abstract, non-English language and duplications.
Sustainability 2018,10, 3142 8 of 44
Table 3. Search strategy and results.
Search Engine Strings and Filters Articles Retrieved Duplicates
Google Scholar, ASCE Library,
Taylor & Francis, Emerald
Insight, Science Direct.
TOPIC: Real Estate Tech OR Real Estate Technology
OR Disruptive Technologies in Real Estate OR Smart
Real Estate OR Real Estate Technology Acceptance
OR Real Estate Technology Adoption
Information dissemination OR Web based
dissemination OR Apps for dissemination
Information retrieval
1 and 2 not 3
English Language Only Limit
2010 and onwards
Editorial or erratum or letter or note or comment
Limit
6 not 7
Remove Duplicates
6542
8760
1780
13522
11800
1840
560
1300
52
462
786
Web of Science
TOPIC: Real Estate Tech OR Real Estate Technology
OR Disruptive Technologies in Real Estate OR Smart
Real Estate OR Real Estate Technology Acceptance
OR Real Estate Technology Adoption
TOPIC: Information dissemination OR Web based
dissemination OR Apps for dissemination
LANGUAGE: (English)
DOCUMENT TYPES: Article OR Abstract OR Book
OR Book Chapter OR Meeting Abstract OR
Proceedings Paper
Indexes = SCI-EXPANDED Timespan = 2010–2018
TS = “information retrieval”
NOT 4 and 6
NOT Duplicates
5896
7523
11853
1256
1634
2864
1120
47
378
695
Scopus
TITLE-ABS-KEY (Real Estate Tech OR Real Estate
Technology OR Disruptive Technologies in Real
Estate OR Smart Real Estate OR Real Estate
Technology Acceptance OR Real Estate Technology
Adoption
TITLE-ABS-KEY (Information dissemination OR
Web based dissemination OR Apps for
dissemination)
TITLE-ABS-KEY (Information retrieval)
PUBYEAR AFT 2010 AND LANGUAGE (English)
4 not 3
DOCTYPE Limit
5 and not 6
Not Duplicates
5761
6894
1923
2514
591
184
407
40
171
196
Grand Total 139
Note: ABS: Abstract; KEY: Keywords; DOCTYPE: Document type.
Figure 2details the search mechanisms used in this study. The screens refer to the results obtained
from the search engines mentioned above; new additions relate to either a keyword used in conjunction
with a previously used keyword or a new search engine/database. Such screening has been used:
to extract literature pertinent to clinical information [
53
], in social science literature reviews [
54
], and for
PRISMA analysis that incorporates systematic reviews and meta-analysis to extract preference-based
information [
55
]. The purpose of the screen here is to omit irrelevant information in accordance with
the study objectives and to keep a tight focus on disruptive technologies in SRE. Four screens were
used, in which Screen 1 limits the literature to real estate, Screen 2 in general limits the literature to SRE,
Screen 3 adds the technology aspect to the literature and Screen 4 adds aspects of online dissemination.
Screen 1, for example, yielded 15,302 results for real estate and real estate management that were
too generic; only a few of those papers focused on technology in real estate. As a result, the keyword
“smart” was used in Screen 2 to yield 872 papers that focused on SRE and SREM. A review of
relevant studies highlighted three indicators of SRE, including user centredness, sustainability and
innovative technologies. These three are represented by indicators 1 to 3 in Figure 2. They provided
key terms for searching and subsequent screening in Screen 2. From there, the focus was refined
and reduced to disruptive technologies, taking a leap from the “technology” indicator. Nine key
disruptive technologies (the Big9) provided the search focus using the defined databases in Screen
Sustainability 2018,10, 3142 9 of 44
3 and yielded 412 results. These Big9 technologies clustered around three domains—data mining,
networking, and hardware. They include: drones, IoT, big data, 3D scanning, VR and AR, software as
a service (SaaS), clouds, AI and robotics, and wearable tech. Thus Screen 3 used these Big9 technologies
as keywords and so the results were refined further. Turning the focus to online technologies, three
types of dissemination platforms were investigated to highlight the use of the online technologies
mentioned and to observe the dissemination mechanisms. The three platforms in focus were websites,
smartphone applications, and social media. These three platforms were used as keywords in Screen 4,
yielding 213 relevant results.
The screening process revealed that very few papers exist on the subjects in focus. Therefore,
to augment the number of relevant publications, the search was enhanced to include online
reports, webpages and magazines. As a result, 213 relevant publications were retrieved in total.
This enhancement was deemed necessary because the final selection of 139 papers (as shown in Table 3)
had not covered some aspects of SRE and its technologies. This was due mainly to a dearth of literature
on specific technologies and the nascency of SRE. The scope of the study had to be widened, and this
was achieved through the inclusion of reports and webpages. To tackle the process of reinventing
the wheel, papers published before 1 January 2010 were discarded. This kept the focus on recent
disruptive technologies. The exception to this rule was the inclusion of several earlier articles that
contained basic definitions and so prevented extravagant claims or the reinvention of key terms.
Sustainability 2018, 10, x FOR PEER REVIEW 9 of 44
Screen 2 in general limits the literature to SRE, Screen 3 adds the technology aspect to the literature
and Screen 4 adds aspects of online dissemination.
Screen 1, for example, yielded 15,302 results for real estate and real estate management that were
too generic; only a few of those papers focused on technology in real estate. As a result, the keyword
“smart” was used in Screen 2 to yield 872 papers that focused on SRE and SREM. A review of relevant
studies highlighted three indicators of SRE, including user centredness, sustainability and innovative
technologies. These three are represented by indicators 1 to 3 in Figure 2. They provided key terms
for searching and subsequent screening in Screen 2. From there, the focus was refined and reduced
to disruptive technologies, taking a leap from the “technology” indicator. Nine key disruptive
technologies (the Big9) provided the search focus using the defined databases in Screen 3 and yielded
412 results. These Big9 technologies clustered around three domains—data mining, networking, and
hardware. They include: drones, IoT, big data, 3D scanning, VR and AR, software as a service (SaaS),
clouds, AI and robotics, and wearable tech. Thus Screen 3 used these Big9 technologies as keywords
and so the results were refined further. Turning the focus to online technologies, three types of
dissemination platforms were investigated to highlight the use of the online technologies mentioned
and to observe the dissemination mechanisms. The three platforms in focus were websites,
smartphone applications, and social media. These three platforms were used as keywords in Screen
4, yielding 213 relevant results.
The screening process revealed that very few papers exist on the subjects in focus. Therefore, to
augment the number of relevant publications, the search was enhanced to include online reports,
webpages and magazines. As a result, 213 relevant publications were retrieved in total. This
enhancement was deemed necessary because the final selection of 139 papers (as shown in Table 3)
had not covered some aspects of SRE and its technologies. This was due mainly to a dearth of
literature on specific technologies and the nascency of SRE. The scope of the study had to be widened,
and this was achieved through the inclusion of reports and webpages. To tackle the process of
reinventing the wheel, papers published before 1 January 2010 were discarded. This kept the focus
on recent disruptive technologies. The exception to this rule was the inclusion of several earlier
articles that contained basic definitions and so prevented extravagant claims or the reinvention of
key terms.
Figure 2. Research method for SRE core components, technologies, online platforms and the
incorporation of TAM-based stakeholders’ needs. Note: SRE: smart real estate; SREM: smart real
estate management; Tech 1, 2, 3: Technologies 1, 2, 3. The numbers in brackets show the number of
retrieved publications.
Once the 213 relevant publications from all sources had been retrieved, two types of
categorisations were performed. Categorisation 1 focused on three key criteria: SRE core components,
marketing and business, and information dissemination systems. Technology is a core component of
SRE. It has been studied in three distinct domains—networking tools, data mining technologies, and
Figure 2.
Research method for SRE core components, technologies, online platforms and the
incorporation of TAM-based stakeholders’ needs. Note: SRE: smart real estate; SREM: smart real
estate management; Tech 1, 2, 3: Technologies 1, 2, 3. The numbers in brackets show the number of
retrieved publications.
Once the 213 relevant publications from all sources had been retrieved, two types of categorisations
were performed. Categorisation 1 focused on three key criteria: SRE core components, marketing and
business, and information dissemination systems. Technology is a core component of SRE. It has been
studied in three distinct domains—networking tools, data mining technologies, and data collection
technologies—each with Big9 technologies as sub domains. Thus, a total of nine technologies were
in focus, and OPLEL tabulations were performed for each. Publications were reviewed accordingly,
and the results are compiled in the results section of this study. These publications were reviewed by
all the authors and combined to discern the holistic essence of the retrieved publications. Additionally,
to tackle the authors bias, multiple rounds of reviews were carried out. In each round, the articles
shortlisted by one author were reviewed by the others and its relevance verified for inclusion in the
shortlist. Thus, three rounds of review were conducted where all the retrieved publications were
reviewed by the authors and its inclusion in the study justified. This process not only focused the
Sustainability 2018,10, 3142 10 of 44
inclusion of publications in the review process but was extended to the findings as well. Thus, all the
shortlisted publications and their findings were cross-verified by all authors.
Categorisation 2 linked key SRE technologies with the tasks or processes performed through
them using stakeholder analysis for enhancing SRE and SREM. Thus, key stakeholders affected
by these technologies are the focus of categorisation 2, which deals with the what (technology),
who (stakeholder) and how (needs and processes) of each technology as well as pertinent examples
and uses in the field.
3. Search Results and Selected Publications
Synthesising the 213 retrieved publications began by categorising them and counting them
according to categorisation 1 (Big9 technologies). Three types of publication were distinguished:
journal/conference papers, online source publications, and others (for example, books and theses).
Journal/conference publications were categorised as technology, case study or review-based papers.
Online sources were distributed into reports and webpages. The “others” category comprised of thesis
and book chapters.
The synthesis based on these divisions is shown in Table 4, which allots the publications to
each of the mentioned categories. The “general” column refers to publications on real estate and
“technologies” in general; these cannot be categorised into a specific technology. The MultiTech column
lists publications that discuss two or more technologies. Starred cells represent papers counted as
MultiTech papers. For example, two papers discuss multiple technologies: one each for big data and
IoT. Thus 2* means there are two such papers, and the big data and IoT columns are starred accordingly
to highlight where these papers have been counted. It must be noted that MultiTech papers are counted
as single publications to avoid doubling, as represented by the starred cells.
Because of the breakdown in Table 4, the total portion of journals/conferences, online sources
and others is 65.3 per cent, 29.1 per cent, and 5.6 per cent respectively. Thus, in line with
academic publications, the retrieved publications are dominated by journal/conference-based research
publications. Technology-based papers represent the highest share of papers, which aligns with the
aims of this study.
It is also noted that very few review papers exist on individual disruptive technologies, and
no other study to date has covered these. The proportion of review papers focusing on individual
technologies among the studied literature here is 5.6 per cent, which indicates a lack of review
focus in this area. Furthermore, technologies such as IoT, VR and AR, wearable tech and SaaS have
not been covered by comprehensive reviews, as shown by the results of the current study’s search
method. Therefore, the Big9 technologies as they apply to the real estate domain have fallen through a
review gap.
Sustainability 2018,10, 3142 11 of 44
Table 4. A breakdown of the retrieved publications as found in three databases through four screens and online sources from 2010 to 2018.
Type Sub Type Data Mining Networking Tools Data Collection Dissemination General Multi
Tech Total Share (%) Portion (%)
Big
Data
AI and
Robotics Cloud SaaS IoT Drones 3D
Scanning
Wearable
Tech
VR and
AR
Journal/Conference
papers
Technology-based 6 * 5 5 2 3 * 5 7 3 5 13 36 2 * 90 42
65
Case studies 2 2 3 * 1 2 1 2 1 1 11 11 1 * 37 17
Review papers 2 1 1 1 * 1 2 4 1 * 12 5
Online Sources Reports 1 1 3 12 17 7 29
Webpages 2 2 5 4 4 2 2 8 5 2 9 45 21
Others Theses 1 1 1 * 3 1 * 6 2 5
Book chapters 1 1 2 1 1 6 2
Total 13 10 17 7 11 9 13 12 13 32 76 5 * 213
Note: * on a number gives the number of multitech publications. Share shows the percentage of a sub-type of publication whereas portion shows the percentage of the type of publications.
Sustainability 2018,10, 3142 12 of 44
4. The SRE Conceptual Model and Definitions of its Key Components
Figure 3focuses on categorisation 1 for the current study, where three critical aspects of SRE—core
real estate components, information dissemination systems, and marketing and business—are
considered. Analysis yielded 48 publications for marketing and business, 56 for information
dissemination systems, and 42 for core SRE components. Twenty-eight publications were identified
as merging SRE core components and marketing and business within the domains of innovation
and sustainability. Thirty-two publications were classified as merging SRE core components and
information dissemination systems within the domain of user centredness. Only seven publications,
comprising one journal paper, one conference paper and five online webpage articles, were identified
as sitting within the domain of innovative technologies.
It must be remembered that “innovative technologies” in Figure 3incorporates dissemination
mechanisms and marketing, along with the Big9 technologies. Thus, the number of publications on
SRE technologies is very limited. Once again, this points to a large research gap in the real estate
literature. The zone with a star indicates shared components of SRE, information dissemination
systems, and marketing and business, in accordance with categorisation 1. Sustainability falls into the
domains of SRE core components and marketing and business; user centredness is common to SRE
core components and information dissemination systems, while innovative technologies are at the core
of all domains. Thus, SRE in the current study covers three key components: innovative technologies,
sustainability and user-centredness. Because there is little relevant literature and what there is fails to
address buyer-seller interactions, the current study focuses on SRE technologies. An OPLEL analysis
was carried out on each of the Big9 technology and tabulated to fill lacunae in the literature. This
OPLEL analysis highlights statistics for each technology. In order to reach the acceptable level of
intersubjectivity, multiple steps were carried out. Firstly, the OPLEL analyses tables were scrutinised
by all the authors and interdepartmental discussion was conducted among the research cohort working
on real estate domain. Secondly, the keywords were pre-agreed by all the authors for categorizing the
statements as an opportunity, potential loss or exploitation level. Lastly, in case of any ambiguity, the
authors of the shortlisted publications were contacted for clarification and upon reception of reliable
and clear explanation, the statements were placed in the suitable domains. This was aimed at ensuring
the inter-researcher reliability and validity.
Sustainability 2018, 10, x FOR PEER REVIEW 12 of 44
4. The SRE Conceptual Model and Definitions of its Key Components
Figure 3 focuses on categorisation 1 for the current study, where three critical aspects of SRE—
core real estate components, information dissemination systems, and marketing and business—are
considered. Analysis yielded 48 publications for marketing and business, 56 for information
dissemination systems, and 42 for core SRE components. Twenty-eight publications were identified
as merging SRE core components and marketing and business within the domains of innovation and
sustainability. Thirty-two publications were classified as merging SRE core components and
information dissemination systems within the domain of user centredness. Only seven publications,
comprising one journal paper, one conference paper and five online webpage articles, were identified
as sitting within the domain of innovative technologies.
It must be remembered that “innovative technologies” in Figure 3 incorporates dissemination
mechanisms and marketing, along with the Big9 technologies. Thus, the number of publications on
SRE technologies is very limited. Once again, this points to a large research gap in the real estate
literature. The zone with a star indicates shared components of SRE, information dissemination
systems, and marketing and business, in accordance with categorisation 1. Sustainability falls into
the domains of SRE core components and marketing and business; user centredness is common to
SRE core components and information dissemination systems, while innovative technologies are at
the core of all domains. Thus, SRE in the current study covers three key components: innovative
technologies, sustainability and user-centredness. Because there is little relevant literature and what
there is fails to address buyer-seller interactions, the current study focuses on SRE technologies. An
OPLEL analysis was carried out on each of the Big9 technology and tabulated to fill lacunae in the
literature. This OPLEL analysis highlights statistics for each technology. In order to reach the
acceptable level of intersubjectivity, multiple steps were carried out. Firstly, the OPLEL analyses
tables were scrutinised by all the authors and interdepartmental discussion was conducted among
the research cohort working on real estate domain. Secondly, the keywords were pre-agreed by all
the authors for categorizing the statements as an opportunity, potential loss or exploitation level.
Lastly, in case of any ambiguity, the authors of the shortlisted publications were contacted for
clarification and upon reception of reliable and clear explanation, the statements were placed in the
suitable domains. This was aimed at ensuring the inter-researcher reliability and validity.
Figure 3. SRE, marketing and dissemination, * Note: Technology (zone with a star) refers to the
technologies covering all three domains (marketing and business, information dissemination systems,
Figure 3.
SRE, marketing and dissemination, * Note: Technology (zone with a star) refers to the
technologies covering all three domains (marketing and business, information dissemination systems,
and SRE core components) at the same time. The numbers in brackets indicate the number of
retrieved papers.
Sustainability 2018,10, 3142 13 of 44
Figure 4is an extended diagram with a core bubble of the Big9 technologies and the three online
dissemination mechanisms in focus here. These components all have dependent key variables and
aspects that are discussed subsequently while keeping SRE as the core focus.
Sustainability 2018, 10, x FOR PEER REVIEW 13 of 44
and SRE core components) at the same time. The numbers in brackets indicate the number of retrieved
papers.
Figure 4 is an extended diagram with a core bubble of the Big9 technologies and the three online
dissemination mechanisms in focus here. These components all have dependent key variables and
aspects that are discussed subsequently while keeping SRE as the core focus.
Figure 4. Technologies and dissemination mechanisms in SRE. * Note: AI: artificial intelligence; VR
and AR: virtual and augmented realities; IoT: internet of things.
4.1. User-Centredness
One of the key criterion for assessing “smartness” is user-centredness [51,52]. Terms such as
consumer satisfaction, customer value, customisation and other concepts are sometimes used in lieu
of user-centredness [56]. Understanding this empowers organisations to cater to consumer needs by
adapting to or introducing new and disruptive technologies that can attract new consumers, retain
existing ones, and lead to more and improved business.
For the benefit of consumers, both smart cities and smart homes propound such key domains as
privacy, multi-functionality, flexibility, the facility to work from home, tele-activity and time-saving
measures [28]. Allameh [23] says the key domains of user-centredness in SRE involve changes in
technology, lifestyle and space. Technological changes include smart kitchen tables with touchscreen
surfaces, hot zones, temperature controls, appliances with multimedia networking, online recipe
guides, dynamic cooking panels and sensor technologies that recognise user activity. There are smart
walls that enable changeable scenery and entertainment, touch-sensitive interactive electronic
devices, internet-based tele-activities such as tele-education and tele-caretaking, environment control
systems such as HVAC, and lighting systems. There are also smart furnishings, with embedded
computers that are interconnected and thus allow easy and flexible moving. These may be sensitive
to user preference, responsive and programmable; they are interactive due to touchscreens and
multifunctional, enabling virtual activities, entertainment and environment control [34]. Similarly,
there are smart living spaces and smart garages.
The main aim of smart technologies—be they kitchens, TVs, furnishings or living spaces—is
consumer satisfaction [51,52]. Other aims may include wellbeing, ease of use, enhanced productivity,
perceived enjoyment, immersion, playfulness and personalisation [26,27]. Consumer satisfaction has
three levels: physical, functional and psychological. Physical comfort involves temperature, lighting,
sound, air and safety. Functional comfort revolves around consumers’ needs and their interaction
with the environment through the intelligent design of space and technology. Psychological
satisfaction refers to human lifestyles and needs; it is achieved through the smart integration of
technology and space with a person’s everyday life.
Figure 4.
Technologies and dissemination mechanisms in SRE. * Note: AI: artificial intelligence; VR
and AR: virtual and augmented realities; IoT: internet of things.
4.1. User-Centredness
One of the key criterion for assessing “smartness” is user-centredness [
51
,
52
]. Terms such as
consumer satisfaction, customer value, customisation and other concepts are sometimes used in lieu
of user-centredness [
56
]. Understanding this empowers organisations to cater to consumer needs by
adapting to or introducing new and disruptive technologies that can attract new consumers, retain
existing ones, and lead to more and improved business.
For the benefit of consumers, both smart cities and smart homes propound such key domains as
privacy, multi-functionality, flexibility, the facility to work from home, tele-activity and time-saving
measures [
28
]. Allameh [
23
] says the key domains of user-centredness in SRE involve changes in
technology, lifestyle and space. Technological changes include smart kitchen tables with touchscreen
surfaces, hot zones, temperature controls, appliances with multimedia networking, online recipe
guides, dynamic cooking panels and sensor technologies that recognise user activity. There are smart
walls that enable changeable scenery and entertainment, touch-sensitive interactive electronic devices,
internet-based tele-activities such as tele-education and tele-caretaking, environment control systems
such as HVAC, and lighting systems. There are also smart furnishings, with embedded computers that
are interconnected and thus allow easy and flexible moving. These may be sensitive to user preference,
responsive and programmable; they are interactive due to touchscreens and multifunctional, enabling
virtual activities, entertainment and environment control [
34
]. Similarly, there are smart living spaces
and smart garages.
The main aim of smart technologies—be they kitchens, TVs, furnishings or living spaces—is
consumer satisfaction [
51
,
52
]. Other aims may include wellbeing, ease of use, enhanced productivity,
perceived enjoyment, immersion, playfulness and personalisation [26,27]. Consumer satisfaction has
three levels: physical, functional and psychological. Physical comfort involves temperature, lighting,
sound, air and safety. Functional comfort revolves around consumers’ needs and their interaction with
the environment through the intelligent design of space and technology. Psychological satisfaction
refers to human lifestyles and needs; it is achieved through the smart integration of technology and
space with a person’s everyday life.
Sustainability 2018,10, 3142 14 of 44
4.2. Sustainability
In line with the triple-bottom line, sustainability in cities must cover social, environmental and
financial aspects [
57
]. According to Addae-Dapaah [
58
], the sustainability facets of SRE include
price, reliability, technology, effectiveness and environmental effects. Miller [
29
] highlights SRE
sustainability concerns as governance and policy issues, valuation, investment, management, finance
issues, adaptation and redevelopment. Wise [
57
] points to social aspects of sustainable cities and real
estate and argues that opportunities such as volunteering, education and training can help attain
smartness because they increase social bonds and community pride. Robinson and McAllister [
59
]
say eco-certified real estate assets and buildings are usually overpriced and often not sustainable;
superficially, the value offered by such assets may not be offset by their high costs. Crosby [
60
]
says the absence of a real estate market valuation and lending model has increased sustainability
concerns. The author says such a system could balance real estate booms with uplift in financial
downfalls and counteract severe and unsustainable rises. According to Deloitte [
61
], sustainability in
SRE can be achieved by assessing the energy and environmental efficiency of buildings, performing
sustainability-related risk assessments, and using locale-specific tools for effective implementation.
Critical to achieving sustainability in SRE are plans to track environmental, social and governance key
performance indicators (KPIs) as part of performance measurement and decision support, obtaining
environmental certifications and compliance, and conducting external assurance.
4.3. Innovative Technologies
Another key indicator of smartness is technological innovation and its adoption [
62
]. SRE,
as in smart cities, is innovative and adopts disruptive technologies. The four forces of smart cities
highlighted by Angelidou [
62
] include innovation and knowledge, a technology push, urban futures,
and application pulls. Tukiainen [
63
] says smart cities and SRE improve everyday life, conduct
consumer experiments, implement the latest technologies, and innovate. Innovation translates ideas
or inventions into services or goods, creating value or meeting consumer demands in the process.
In doing so, it yields financial benefits [
64
]. Innovation aims to take advantage of potential solutions
and associated case-based facilitation to add value for business benefits.
According to Tangkar and Arditi [
65
], the key innovation models in construction and real estate
are incremental, radical, autonomous, and systemic. The process from innovation to adoption moves
through six cyclic steps: need, creation, invention, innovation, diffusion and adoption. Thompson [
66
]
discusses five key aspects of innovation, namely: research, development, production, marketing
and feedback. Furthermore, the four contexts of innovation are political, social, economic and
technological. According to Blayse and Manley [
67
], the key influences on real estate innovation are
clients and manufacturers, production structure, consumer–industry relations and industry–external
relations, procurement systems, regulations and associated standards, and the quality and nature of
organisational resources. Key aspects of SRE include digital technologies and their associated adoption,
virtual showcasing, crowdfunding, AI, smart homes, online marketplaces and smartphone apps [
30
].
Virtual real estate science and technology parks are also significant to real estate innovation as they
explore new ways of adopting disruption. For example, a science and technology park were established
in Portugal as part of an online innovation project under the umbrella of a virtual European network
of science and technology parks. The key thematic areas for this endeavour are technology assessment
and watch, networking, audits, marketing innovation and financing innovation [
68
]. Innovation in
real estate is, therefore, multi-dimensional.
SREM revolves around high-tech, innovative technologies. It is the adaptive nature of SRE that
distinguishes it from traditional real estate. Innovative and disruptive technologies make SREM
challenging yet rewarding. The management of these technologies creates a make or break situation,
where properly managed SRE can yield greater financial and business benefits while mismanaged
technology, where understanding is lacking, can result in huge financial loses. Globally, investment
in eeal estate technologies has surged. In 2016 alone, $2.6 billion was raised by private real estate
Sustainability 2018,10, 3142 15 of 44
tech start-ups across 235 deals, setting a record in both deals and dollar value. Funding for real estate
technology companies rose 40 per cent in the same year [
1
]. Recently, the online real estate company
OpenDoor Labs
®
introduced a unique model for buying and selling homes. It has raised as much
as $350 million in three years [
69
]. Its business model includes purchasing a home directly from the
homeowner, improving it by installing the latest technologies and gadgets for virtual tours, and listing
it on the market as fast as possible. It might be called tech-enabled house flipping. The company gains
from the appreciation in the property price and protects itself from losses by taking a fee from the
homeowner related to the estimated value of the property and associated market risks. For the home
seller, potential benefits include faster and simpler sales and lower real estate transaction costs because
the model avoids commissions. OpenDoor Labs also provides more information to consumers because
they can extract data from installed gadgets and tools. When they get their hands on more information,
consumers tend to be more satisfied and more inclined towards buying or using a home. Faster
sales result and the company makes good money from its transactions. The process also eliminates
consumer regrets by supplying more information. Airbnb, an online hoteling service that offers private
accommodation in people’s homes, is another example. It features cheap and convenient rooms that
appeal to clients with limited finances [
31
]. In early 2014, Airbnb received venture financing of $10
billion, which is greater than the market capitalisation of the Hyatt hotel group.
Real estate technology is the combination of online platforms and software tools that are used by
industry stakeholders, including investors, brokers, real estate-focused lenders and property owners,
mortgage providers and managers, as well as consumers [
2
]. The category includes online real estate
rental and buying guides. These tech companies reportedly rely on the use of clouds, software, big data,
IoT, drones, 3D scanning, wearable tech and gadgets, VR and AR and AI and robotics. If made available
to consumers, information from these Big9 technologies could avert regrets by informing consumers
and so empowering them to make better decisions. In general, these technologies can be divided into
three domains: data-mining technologies, data-collection technologies and networking tools.
5. A Review of State-of-the-Art Technology
This section, as mentioned in the methodology, reviews Big9 technologies for their applications
in real estate. The opportunities presented, current exploitation level and potential losses due to
non-usage are tabulated for each technology. This section also discusses the role of each technology in
answering, reducing, or eliminating consumer regrets.
5.1. Data-Mining Technologies
Data mining is the process by which large data sets are sorted to identify patterns and establish
relationships between the data sets and so solve problems through data analysis [
70
]. It allows
organisations to predict trends, learn about consumers and decrease business costs. In real estate it
has been used to develop early warnings and forecasts [
71
], providing multimedia-based real estate
services over the internet [
72
], and other applications. It comes in different forms but the current study
looks at big data [73] and AI and robotics [74,75].
5.1.1. Big Data
Big data, as suggested by the name, refers to a huge volume of data that cannot be easily processed
by traditional software [
76
]. Winson-Geideman and Krause [
20
] define it as a collective term for larger
and interrelated databases as well as the associated processes for extracting useful knowledge from
the digital data stream. Although many definitions exist for big data, almost all of them have three
common data-mining characteristics: massive data volume, processing speed and data coverage [
77
].
In terms of real estate management, and more specifically SREM, speed and analysis are key
components. Big data offers these attributes through what is known as “big data analytics” [
78
]. Real
estate contains a wide set of data, and in the absence of big data analytics based on data-mining
techniques, complicated analyses consume a lot of time. Big data thus frees real estate organisations,
Sustainability 2018,10, 3142 16 of 44
agents and professionals to focus on their core roles and leave the analysis to technology. This is
achieved through “data-centricity”, which places better, reliable, more accessible and relevant data at
the core of decision making to boost productivity [
3
]. Big data may enable real estate organisations to
integrate financial, marketing, sales, e-commerce and consumer surveys to obtain a holistic view of the
business performance and achieve overall organisational goals.
Distinctive values that big data offers the real estate domain include saving time, speedy sales,
consumer insights, consumer accessibility to aid better decision making, and empowering real estate
owners to understand trends and patterns that in itself can help to overcome inefficiencies and reach
target buyers or renters [
78
,
79
]. Some practical examples of online real estate websites that use big data
are Realestate.com.au, Zillow.com and Domain.com.au. These websites not only provide core residential
insights for consumers but also neighbourhood insights, crime rates, market accessibility, sale patterns,
average property prices and travel rates [
49
]. A key regret for real estate consumers, as highlighted by
Phillimore [
80
], is their lack of foresight about a neighbourhood. People not only regret leaving their
old neighbourhoods but are also uncomfortable about moving into neighbourhoods they know little
about. This is where big data analytics come in. Big data can provide neighbourhood insights such as
the average time people spend in the neighbourhood, common professions, the median age and the
livability rating. This information is produced by algorithms that mine huge volumes of data obtained
through various ongoing surveys, the results of which can be disseminated online to consumers and
empower better decision making while eliminating regrets. The OPLEL analysis for big data is shown
in Table 5.
Table 5. Opportunities, potential losses and exploitation levels (OPLEL) analysis for big data.
Opportunities Potential Losses Exploitation Level Domain Ref
By 2020, 50% of software queries
will be over search features,
natural language processing, or
voice recognition.
Three out of 5 leaders fear that
inability to adapt to big data
will lead to obsolescence.
Six million job opportunities.
Only 37% success so far. Wal-Mart
customers’ transactions provide
them with about 2.5 petabytes of
data a day.
Business
intelligence [81]
The digital universe of data to
44 trillion gigabytes (2020). Fifty
billion smart devices were
connected globally in 5 years A
10% increase in data accessibility
results in more than $65 million
additional net income.
At present, less than 0.5% of
all data is ever analysed.
In 2017, nearly 80% of photos
were taken on smartphones. 73%
of organisations had already
invested in big data in 2016.
Big data
revolution [82]
Examples and uses of big data in real estate include databases of building performance by
Prudential Real Estate Investors and USAA Real Estate Company [
83
] and BEM Prototype for online
collaborative searching [
84
]. Other uses include real estate development and marketing [
78
], property
value analysis by Zillow.com, crime rate indices, and value forecasting.
5.1.2. Artificial Intelligence (AI) and Robotics
AI refers to the performance of complex and intelligent functions such as those done by the
human brain but with computers and intelligent programs and minimal human intervention [
85
].
Robotics involves AI-equipped robots conducting complicated tasks with precision [
86
]. Initially used
in the medical field to enhance the capacity of people with physical disabilities to carry out complex
operations with high levels of precision, robotics are increasingly welcomed in construction and real
estate [
75
]. AI is growing fast and has been adopted by various industries. It is expected that global AI
revenue will be about $36.8 billion by 2025. By the end of 2018, it is predicted that AI will be used by
75 per cent of developer teams in one or more real estate business applications or services [87].
According to Bock [
88
], the construction field shows that automated technology, microsystems
technology and robot systems are continually being merged with the built environment and thus
becoming inherent to building components and elements as well as furniture. In real estate, AI and
robotics offer driverless cars to transport clients and customers to property sites, 3D renderings
Sustainability 2018,10, 3142 17 of 44
of interior spaces, and the collection of waste materials and recycling. They can also help with
routine inspections and maintenance and the cleaning of hard to reach places [
3
]. Furthermore,
AI helps real estate agents to screen potential customers by gathering information from data-mining
search algorithms. It also helps them to sharpen marketing strategies and to reach potential clients
through social media and emails, thereby streamlining their work flows. Such immersive systems and
mechanisms aim to inform consumers from the very start of their property hunt. AI-based systems
can link consumers to their dream homes through filters that allow them to nominate the items they
deem necessary. Such intelligent matching can avert regrets that arise from human error. AI bots can
also assist consumers to refine their search and find relevant properties based on the big data sets
used by AI through predictive analysis. AI-based voice recognition is another application that can
provide useful information to consumers, in turn reducing information-related regrets among end
users. The OPLEL analysis for AI and robotics is shown in Table 6.
Table 6. OPLEL analysis for artificial intelligence (AI) and robotics.
Opportunities Potential Losses Exploitation Level Domain Ref
Buyer-seller customisations.
Predictive analytics.
68% automation for agents and
auctioneers.
Link 83% people to properties.
PurpleBricks technology
bringing commissions
down.
Manage multiple properties:
200,000 in USA.
Rex bot: answer queries and
charges 2% commission only.
‘Rita’: AI digital assistant.
Future of Real
Estate [89]
Agenda for next year: 31% of enterprises.
72% business advantage.
61% innovation.
Can manage 85% of customer interactions.
Can manage 40% of mobile interactions.
Can decrease labour productivity by 40%.
AI could jeopardise between
40–75 million jobs
worldwide by 2025.
AI is being used by 15% of
enterprises at present.
77% of consumers use an
AI-powered service globally.
Only half of the largest companies
with at least 100,000 employees
have an AI strategy.
AI as emerging
technology [90]
Note: AI: Artificial Intelligence.
Examples of AI in real estate include blockchain taxation, smart property ownership,
and automated renting [
91
]. There is the detection of concrete swap-based tax fraud schemes [
92
]
and unit selling price prediction in Bari Italy [
93
], and many other applications, including real estate
business forecasting [
85
], sales renaissance by LG to cut off customer visits to service centres [
94
] and
machine learning-based sale and build decisions [95].
5.2. Networking Tools
Networking is the use of digital telecommunications to enable devices over a network to share
resources with each other. These computing devices exchange data through wired or wireless
connections using data links [
96
]. Networking has been used in various domains in real estate,
such as co-ordinating multi-site construction projects through federated clouds [
97
], door detection
in the indoor environment [
98
], reconstruction and modelling of as-built 3D pipelines and more [
99
].
There are various networking applications in real estate, but the current study has restricted its focus
to clouds, IoT and SaaS.
5.2.1. Clouds
Traditionally, a company’s IT infrastructure was situated locally, with all servers networked and
data centres onsite. This not only consumes a lot of space but also gives rise to security concerns
and other hazards. Cloud computing resolves these issues by allowing an organisation to access
data and software applications over the internet rather than on a hard drive. In the internet age, this
access is particularly favoured by the younger generation. About 60 per cent of respondents in a US
survey indicated a willingness to adopt and invest in cloud computing over the next year [
100
]. Cloud
computing in real estate increases scalability, flexibility, device integration and data security while
reducing IT costs through networking [101].
Sustainability 2018,10, 3142 18 of 44
According to Mladenow [
102
], cloud computing in real estate has three-fold benefits that relate
to high-valued objects, longer time periods and different actors in the industry. Data archiving and
storage for valuable assets would cost a lot in the absence of clouds. The benefits of cloud computing
attract larger investments, as evident in Warburton [
3
], who quotes the global cloud computing
market as reaching a value of $270 billion with an expected growth of 30 per cent from 2015 to
2020. In real estate, the use of clouds can reduce communication requirements, as highlighted by
Carter [
103
]. A lack of communication can promulgate regrets, as consumers may consider it to be
the intentional withholding of information. Cloud-based software such as PropertyMe grants access
to agents, consumers and owners and shares key requirements such as maintenance and renovation
information among stakeholders. It also makes the financial details of the properties accessible to
stakeholders, thereby reducing regrets by providing more information. The OPLEL analysis for clouds
is shown in Table 7.
Table 7. OPLEL analysis for clouds.
Opportunities Potential Losses Exploitation Level Domain Ref
73% of companies plan to install software
data centres in two years.
Private cloud use shows 77% growth,
hybrid 71% and enterprise as 31%.
In future, about 28% of an organisation’s
budget will be for clouds.
49% of companies are
delaying it due to a lack of
skills.
Growth from 19% to 57% in the
past three years.
46% of organisations are
integrating cloud APIs for
databases, messenger systems and
storage systems.
Cloud adoption
and security. [104,105]
25% annual adoption increase 10–30%
company growth potential.
41% of businesses plan to invest in
clouds.
32% of companies accept
they lack skills for it.
52% of companies lack
adoption strategies.
30% of Microsoft revenue
expected from clouds in 2018.
Amazon uses 31% clouds at
present.
Clouds and
information
technology (IT).
[106]
Software-based service to grow by 20% to
$46.3bn.
60–70% of all software will be cloud
based by 2020.
79% losses in competition by
2011.
22% growth rate in 2017.
Spending increase from 4.5 times
in 2009 to 6 times through 2020.
Cloud forecasts
for business
applications.
[107–109]
Clouds are used in real estate for: optimising the total cost of ownership and elastic resource
utilisation [
110
], Platform as a Service (PaaS) as used in Google Engine, Microsoft Azure, Force platform
and Salesforce.com, and Infrastructure as a Service (IaaS) as used in Amazon EC2 and Flexiscale [
111
].
EPIQR
®
, developed by CalCon, provides a building lifecycle management option. FlowFact AG
®
is a German tool that is also used in Switzerland and Austria to match customers and agents with
properties. MaklerManager®is used in Germany for real estate portfolio management [102].
5.2.2. Software as a Service (SaaS)
Generally, software in tech industries operates under the umbrella of Software as a Service (SaaS).
It provides remote access and the functionalities of software through web-based networked services
rather than existing on a user’s PC [
112
]. Access is provided over the internet and it offers a cost
advantage for users as well as agents.
Its major advantages for investors and property managers include the integration of huge,
multifamily organisations across portfolios through the networking of different software. It also
makes the integration of multiple solutions easier by accommodating different property management
services under one umbrella, along with the potential to scale a changing portfolio up or down.
Not surprisingly, the adoption of SaaS from 2015 to 2016 increased 21 per cent [113,114].
Most real estate agents find they need to spend time outside the office to meet customers and
present properties. SaaS enables them to increase business by remote access. It has been used in such
areas of real estate management as construction, client management, marketing, billing, maintenance,
retailing, customer relations management, E-marketing and
lease administration [3,7,115,116]
.
SaaS-based software packages such as RealSpace and PropertyBase enable the sharing of information
about maintenance, security, lease and tenancy, contracts and work orders among key stakeholders,
including consumers. Such features can deliver the type of information that consumers need to reduce
post-purchase regrets. The OPLEL analysis for SaaS is shown in Table 8.
Sustainability 2018,10, 3142 19 of 44
Table 8. OPLEL analysis for SaaS.
Opportunities Potential Losses Exploitation Level Domain Ref
Offer 5 times higher returns.
3.2% loss of revenue for
fast-growing SaaS-equipped
companies with $255 MRR.
Median SaaS firms lose
about 10% at rate of 0.83%
per month.
48% median revenue growth in
2016.
3.9% growth ratio for global SaaS
companies.
SaaS
oerformance. [117]
Generate 33% per cent more home views
per user session. -
Smarter Agent Mobile registered
more than 4,000,000 unique app
downloads in 2014. More than 1
billion properties viewed.
Mobile real
estate. [118]
Faster follow-up and management
abilities of more than 5000 contacts
simultaneously.
-87% of agents with income over
$100,000 use SaaS more. Marketing. [119]
Note: SaaS: software as a service.
Examples of SaaS in real estate include Immoware24, a German company that supports
property managers with order processing, accounting, bookkeeping, customer management and
diverse administrative tasks. Onventis provides account support and e-auction in real estate [
102
].
Management Reports International uses SaaS model for vendor support. And Honest Buildings, eBid
eXchange and Vendor INSIGHT use SaaS for supplier management [3].
5.2.3. Internet of Things (IoT)
IoT, a term coined and first used by Kevin Ashton in 1999, refers to internet-based, networked
physical devices that can sense the physical aspects of the world such as temperature, humidity,
illumination and other attributes [
120
]. Yang [
121
] defines it as a novel internet-based paradigm that
connects a variety of things or objects around us through wireless or wired technologies for attaining
desired goals. A stand-out characteristic is the “intelligence” of these devices, which enables them to
self-configure using sensors, rather like the human nervous system.
By 2020, it is estimated that the IoT will have more than 21 billion connected devices and thus
be worth more than $7 trillion. From 4.9 million things connected in 2015 to 3.9 billion connected in
2016, the increase has been huge, and the trend is continuing upwards [
122
]. This trend shows the
development of smarter cities, secured routers and enhanced artificial intelligence. In real estate and
property management, IoT devices are used to monitoring buildings and their surroundings constantly
to control temperature, relative humidity, indoor air quality, and lighting levels. Building management
systems can thus be interconnected with tenants’ own systems, allowing a novel level of control and
effective monitoring. For agents, tenants and property owners, air-conditioning, security, power and
fire systems monitoring and control in real-time is a
distinct advantage [123–125]
. Key applications
include understanding and reacting to an occupant’s behaviour, proactive repair and maintenance,
linking security systems to smartphones and wearables, digitised logistic management, push
notifications that enhance security and sensor-based dust bins to inform local authorities of the need for
a clean-up [
125
,
126
]. Other applications include the Entrance Guard System, the Intelligent Residential
District, Smart Homes, and Intelligent Communities [
123
]. Such comprehensive connectivity informs
consumers more fully and thus can prevent information-related regrets. It keeps users more immersed
and connected to the environment, inducing a sense of being at home and of ownership. These elevated
feelings promote a positive attitude in stakeholders and so reduce regrets. The OPLEL analysis for IoT
is shown in Table 9.
Examples of IoT in real estate include the real-time production logistics synchronisation
system [
127
] and Fog computing for wind farms, smart grids and smart cities [
128
]. Domotics home
automation device units and Nest temperature controllers are other examples.
Sustainability 2018,10, 3142 20 of 44
Table 9. OPLEL analysis for IoT.
Opportunities Potential Losses Exploitation Level Domain Ref
The number of IoT devices will
increase by 31% to 82.5 million by
2020.
By 2019, 1.9 billion smart home
devices are expected to be shipped.
24.75 billion smart clothes are
expected to be purchased by 2021.
87% of consumers are
unaware of the “IoT”.
28.3 million units of IoT devices
used in 2016.
Samsung bought SmartThings®
to launch itself into smart homes
968,000 smart clothes sold in
2015.
IoT potential [129]
32.4% growth is predicted between
2016 and 2022.
$1.3 trillion to be invested in 2019
with a compound annual growth rate
of 17%.
$591.7 billion invested in 2014.
20 billion connected devices
counted in 2013.
IoT market
forecasts [130]
Note: IoT: internet of things.
5.3. Data Collection Technologies
Data collection technologies concern hardware and gadgets. These are physical and tangible
components of any system, which combine in a systematic way to achieve value for a product or
service [
131
]. Gadgets used in real estate include those for digital story telling that rely on augmented
information [
132
], collaborative decision making through VR and BIM integration [
133
], real-time
tracking for near-miss accidents [
134
] and as-built modelling using Lidar and 3D laser scanning [
135
],
among others. Various data collection tools and gadgets have been developed over the years to
augment real estate practices. The current study looks at drones, 3D scanning, wearable tech and VR
and AR.
5.3.1. Drones
Drones are highly accurate unmanned aerial vehicles that are controlled by remote control or
a ground control station [
136
]. They were initially used in warfare but have recently been introduced
into property and real estate management to obtain 3D views and photographic functions for data
collection [75].
The US has encouraged the use of drones to enhance the buying and selling of real estate. Aerial
videos and images provide more information and visual insights to consumers. Drones allow agents
and property managers to obtain views and present them to potential consumers that would otherwise
be unavailable. Consumers can see the broad expanse of a property’s views, and check the distances to
neighbouring houses and facilities, without being there in person. Results, include reduced inspection
times and enhanced sales [
137
,
138
]. On average, 49 per cent of drones are used in real estate; the US is
at 72 per cent, France at 52, Britain at 48, and Germany at 24. In total, 72 per cent of agents report using
drones for aerial photography and 48 per cent for surveying [136].
Drones armed with high-resolution cameras that have aerial as well as wall-climbing capabilities
are changing perspectives of properties as well as the marketing of real estate [
139
]. They are saving the
expense of planes and helicopters and offering buyers a new perspective for viewing entire properties
and neighbourhoods. Indoor wall-climbing drones can cling to ceilings and capture the entire area for
potential clients [
140
,
141
]. Pictures taken by drones provide wider and more comprehensive angles
for consumers, meaning they can see more of a property and so make better informed decisions [
139
].
Drone images can show sun paths, nearby greenery, locales and distances to parks, schools and
amenities. Inside the house, high quality, zoomable images can show finer details at angles that are
otherwise inaccessible to consumers. The result is more information for consumers that can promote
positive decision-making and prevent post-purchase regrets. Although such aerial views raise privacy
and security concerns, associations such as the Federal Aviation Authority (FAA) of the US have begun
to hand out licences for drones used in real estate, making them attractive to investors and realtors.
The OPLEL analysis for drones is shown in Table 10.
Sustainability 2018,10, 3142 21 of 44
Examples of the use of drones in real estate include aerial photography and property marketing
by KnightPrank
®
UK [
142
], automated mapping such as Drone2 mapping for ArcGis, and Phathom3
by SkyMedia NorthWest that makes volumetric calculations [
143
]. Skylark drones are used for 3D
pictures and the development of land surface data [144].
Table 10. OPLEL analysis for drones and 3D scanning.
Opportunities Potential Losses Exploitation Level Domain Ref
83% of home sellers prefer working with
an agent who uses drones.
Global real estate drone demands are
expected to reach $20.5 billion over
2017–2025.
-
3.5x greater customer attraction
among agents using drones than
their counterparts.
Drones future. [145]
Homes with aerial images sell 68% faster
than homes with standard images. -
Only 9% of agents create listing
videos using drones.
403% increase in traffic was noted
for an Australian real estate firm
with video-based listings.
Real estate
marketing. [146]
Structured light scanners are forecast to
grow at a CAGR of over 10.4%.
Architectural and engineering usage of
3D scanners will increase by 22% by 2025.
-
The 3D laser scanners valued at
$3.32 billion generated revenue of
about $US2.26 billion in 2015.
Short-range 3D scanners had a
market share of over 67.9% in 2015.
Scanners’ market
size and trends. [147]
5.3.2. 3D Scanning
3D scanning is another game changer. It is a disruptive data collection technology that was
introduced only recently. Hand-held scanners such as Lidar are used increasingly in real estate
and construction to produce as-built drawings that provide digital data that can be used to revise
drawings or track maintenance and repairs [
148
]. Mobile scanners based on laser scanning technologies
are increasingly welcome in this domain too. Not only are these scanners 50 per cent less costly,
but they also assist construction managers to update data efficiently and revise drawings through
updated multi-dimensional models [
149
]. A recent study that involved a survey of online real estate
platforms concluded that by using these advanced visualisation tools, property managers could
communicate more easily with consumers, and consumers could examine properties more easily
from the point of view of their requirements without a physical inspection. 3D modelling, views and
pictures provide customers with more credible and reliable property information that is mutually
beneficial to key stakeholders: property owners, consumers and real estate agents [
135
]. Another
application of 3D scanning relates to heritage conservation and recording [
150
]. Here, scanned
models can make restoration, renovation and preservation works easier for government officials and
archaeological departments.
Real estate consumers can obtain a more realistic image and feel for properties from collections of
scanned drawings and images in the absence of personal inspections. This information can reduce
buyer remorse. Such drawings and scans can also provide a realistic view of a property’s layout
and design, allowing consumers to plan any changes, renovations or additions they might want to
make. Mahdjoubi et al. [
151
] argue that 3D-scanned real estate can be merged with building models
to provide information to consumers and thereby promote sales. Such matched, spatial information
can eliminate regrets, especially those arising from a misapprehension of the layout of a property.
The OPLEL analysis for 3D scanning is shown in Table 10.
Examples and uses of scanning include Leica Nova MultiStation MS60 and Trimble SX10 laser
response for 3D modelling and structural monitoring [
152
], as well as Mobile LIDAR for rapid as-built
BIM [
153
]. The Riegl LMS-Z210 3D laser scanner and PolyWorks software for 3D modelling that use
point data are other examples. LMS-Z210 3D is used for powerful 3D image-based data collection [
154
].
Sustainability 2018,10, 3142 22 of 44
5.3.3. Wearable Technology
Wearable technology and gadgets comprise electronic devices that have been incorporated into
clothing or wearable accessories—such as helmets and walkie-talkies—for data collection [
155
]. Most of
these gadgets use the internet, sensors and scanners with one-way or two-way communication between
the gadget and a receiver, allowing real-time communication between the device and consumers [
156
].
Devices include glasses, walkie-talkies, hats, body kits, cuffs, jewellery, commuter trucker jackets, smart
eyewear, fitness jackets, rings, watches and bracelets [
157
,
158
]. By wearing such devices, consumers
can remain connected to a building and obtain real-time updates about maintenance, fire hazards,
gas leaks and other issues. This immersive connectivity forges a stronger association between the
user and the building, allowing them to take greater involvement in daily decision-making and even
develop a greater affection for the property. Such connection (and affection) reduces regrets that
might otherwise arise from a lack of information, particularly after purchase. Useful data can also be
recorded, without further intervention, to inform subsequent tenants or owners about the property.
In the context of real estate, wearable technologies have great potential. They can keep track of
maintenance and equipment, provide visual alerts for building components and display public data
to potential buyers [
3
]. Moreover, integrating gadgets with building management systems supplies
such benefits as the extraction of as-built drawings and fault detection. These give consumers greater
knowledge and so empower them to make more informed decisions [
159
]. The OPLEL analysis for
wearable tech is provided in Table 11.
Examples of wearable tech in real estate include devices that monitor worker health and
safety [
160
], real-time labour and equipment [
156
]. Jewellery such as Pesciolino Robot, Funny Penguin
Twinkling Light and Cavallino Filoguidat, hailing from Rome, Italy, are experimenting with body
mediation applications in real estate [
161
]. Smart watches for health services, location, tracking and
monitoring, such as the Apple smart watch, are another application [
162
]. And smart bracelets have
been developed by Agile UX for real estate [163].
5.3.4. Virtual Reality (VR) and Augmented Reality (AR)
Although significantly different, the terms VR and AR are often used in conjunction. But let us
differentiate the two. Simply put, VR is concerned with creating virtual worlds without referencing
the real world; AR is about augmenting or adding to the real world, thus creating a blend of real life
and digital reality [
164
]. Globally, it has been predicted that there will be 216 million AR/VR gamers
by 2025, $22.8 million AR/VR glasses shipped globally by 2022, and a $215 million AR/VR market
size by 2020 [165].
Table 11. OPLEL analysis for wearable tech and VR and AR.
Opportunities Potential Losses Exploitation Level Domain Ref
Production set to exceed 250 million
smart wearables, or 14x more than in
2013.
14 times more sales in 2018 expected as
compared with 2013.
-
129% increase from 2013.
40% of all wearables are used in
North America.
Smart watches
and bands. [167]
Expected growth of 35% by 2020.
70% of wearable shipments will be smart
watches.
-
Apple Watch accounts for 40%
count and 48% share of the smart
watch market in 2017.
Wearables future.
[168]
Real estate listings with virtual home
tours garner 87% more views.
Virtual tours keep people looking at a
website 5 to 10 times longer.
54% of buyers will
not look at a
property unless it
has a virtual tour.
6 million people take virtual tours
every day. Virtual tours. [169]
VR penetration will reach 25.5% of
households by 2021.
VR/AR software revenues to be $2.6
billion by 2025.
-In 2016, 150,000 shipments of AR
glasses were made.
VR/AR future
and market. [166]
Note: VR: virtual reality; AR: augmented reality.
Sustainability 2018,10, 3142 23 of 44
VR and AR tech has already attracted huge investment, raising about $13 billion in 2017 alone.
This makes its way to real estate consumers in the forms of home touring and sales efficiency,
buying sight-unseen, fix and flips for rehabilitation, quality advertisements and improved tenant
communication. But all key industry stakeholders can benefit. Architectural firms do so from
pre-visualisation, walk-throughs and consumer requirements gleaned from virtual interactions.
Developers can create virtual showrooms and display properties rather than investing in replicas.
Technologists, by integrating VR and AR with BIM, can easily collaborate with construction
professionals throughout the construction process. The consumer can get a feel for a property and
envision their plans for it through walk-throughs and 3D experiences [135,166].
VR can also be a rescue tool given that 87 per cent of managers want to improve their
communication with tenants and 80 to 90 per cent of properties are vacant within 10 months of
a lease due to misleading advertisements [
103
]. By 2025, the VR and AR market in real estate is
expected to reach at least $80 billion. The key benefits of these technologies are time savings, global
buying and selling, tackling intangibility, and narrowing the interest pool for buyers [
170
]. Further
opportunities include virtual property showcases, guided visits, interactive visits, virtual staging,
architectural visualisation and virtual commerce [
171
]. Using visualised tours and visits, consumers
can get a feel for a property before inspecting it in person. They can change layouts and designs and
interact with the building to obtain a realistic feel for it. Such immersed visualisations can improve
consumer feelings about a property and increase their purchase confidence because of the information
they have gathered. Such elevated confidence and abundant information can tackle post-purchase
regrets. The OPLEL analysis for VR and AR appears in Table 11.
Examples and uses of VR and AR in real estate are diverse. ArX Solutions creates pre-construction
virtual designs of apartment buildings using 360-degree cameras and VR headsets. Sotheby’s
International Realty in Los Angeles uses VR to tour potential buyers through their multimillion-dollar
properties using the company-owned Samsung Gear VR headset. Common Floor Retina, a VR real
estate company, provides its own cardboard headsets coupled with a smartphone application to
provide remote tours for potential buyers [
172
]. Vuforia, an augmented reality app for smartphone
devices, uses computer vision technology to recognise and track planar images and simple 3D objects
such as boxes and furniture in real-time [
173
]. Aurasma, an East London company, uses smartphone AR
video to generate 4D advertisements. Smartphone AR is used in Mexico city for outdoor commercial
spaces [174].
6. Disseminating Information to Consumers in Smart Real Estate
Dissemination platforms provide a mechanism for distributing technologies and making them
available to consumers. Like the technology itself, the dissemination mechanism is of great importance;
a poor dissemination mechanism can restrict the reach of the technology and, therefore, result in both
financial losses and a loss of consumer appeal. Several mechanisms exist to disseminate information
and they can be divided into two key groups: manual clusters and online clusters, as shown in Figure 4.
This study reviews online dissemination mechanisms. However, it restricts its attention to platforms
such as smartphone apps, websites and social media sites, including YouTube and Facebook.
6.1. Information Dissemination on Real Estate Websites
Websites are one of the most useful and powerful platforms for disseminating information online.
However, at present almost none of them use disruptive technologies to disseminate information
to consumers; such technologies are limited to agents and their organisations. If made accessible to
consumers, this information could enhance satisfaction levels and attract more interest. In the modern
era, almost all of the market leaders in real estate have a well-known and easily accessible website.
According to REALTORS [
175
], 95 per cent of online home searches involve websites. This huge
proportion indicates a reliance on websites for online searching, making it one of the hot areas of
research in real estate. Websites come with various features and add-on tools for better presentation
Sustainability 2018,10, 3142 24 of 44
and ease of consumer use. These include photos, videos, 3D images, virtual tours, interactive maps
and neighbourhood information [
49
,
50
]. The survey of REALTORS [
175
], found that 89 per cent of
respondents reported photos as very useful, 85 per cent backed information about properties, 50 per
cent said virtual tours were very useful, 44 per cent cited neighbourhood information as very useful,
and 41 per cent found interactive maps very useful.
In terms of their uptake of disruptive technologies, however, real estate websites lag by some
margin. A recent study by Ullah [
49
], based on a SWOT analysis, found that half of the top 10 studied
websites in Australia and the US presented more than 50 per cent improvement opportunities. On the
one hand, this points to a good investment opportunity; on the other hand, it shows a lack of disruptive
innovation and growth in real estate technology. According to Zillow.com, an average buyer spends
12 weeks and investigates 12 homes before deciding whether to rent or buy. From the studied total,
51 per cent of those surveyed said finding the right house using website information was the most
difficult part of the process [
176
]. Homes.com reported consumers taking 60 days to make that decision.
Nine per cent of Australian home seekers spend more than two years looking online for a home [
177
].
Various websites exist for the purpose of renting or buying real estate. These include Zillow.com,
Rent.com, Realestate.com.au, Domain.com.au, Flatmate.com, Trulia.com, Realtor.com and Homes.com.
These offer distinct advantages while competing to be market leaders. Figure 5, developed from
Batten [
178
] and Stewart [
179
], shows the number of consumers, website reach, average visits per
consumer and average time spent on a website by consumers for the top five Australian real estate
websites. It clearly shows that websites with greater reach and consumer interactivity attract more
consumers, and vice versa. These statistics give rise to two key questions: why do some websites get
fewer visitors than others? How can online information be improved to facilitate consumers and attract
more of them? Ullah [
49
] recommends using 3D visualisation, VR technology, 360-degree images and
cameras and interactive gaming to enhance website visits and attract more consumers.
Since most online information is disseminated through real estate websites, it is imperative
that the information available on these websites be enhanced to prevent consumer regrets.
This improvement can take the form of accurate, detailed and reliable information being made available
to consumers. With improved information, consumers would be better able to make decisions that
avert post-purchase regrets.
Sustainability 2018, 10, x FOR PEER REVIEW 25 of 44
average time spent on a website by consumers for the top five Australian real estate websites. It
clearly shows that websites with greater reach and consumer interactivity attract more consumers,
an d vice ve rsa. T hese s tatis tics g ive ri se to two key qu estio ns: wh y do some websi tes ge t fewe r visi tors
than others? How can online information be improved to facilitate consumers and attract more of
them? Ullah [49] recommends using 3D visualisation, VR technology, 360-degree images and
cameras and interactive gaming to enhance website visits and attract more consumers.
Since most online information is disseminated through real estate websites, it is imperative that
the information available on these websites be enhanced to prevent consumer regrets. This
improvement can take the form of accurate, detailed and reliable information being made available
to consumers. With improved information, consumers would be better able to make decisions that
avert post-purchase regrets.
Figure 5. Top 5 Australian real estate website statistics.
6.2. Smartphone Applications for Disseminating Information to Real Estate Consumers
Smartphone technologies and apps have disrupted global industries. You might call this the era
of the smart and the compact; the real estate industry is also observing online applications-based
disruption from powerful, smartphone applications and gadgets. A survey by REALTORS [175]
found that among information sources used to search for homes, 72 per cent of searches take place
on smartphone apps; 52 per cent of millennials use such apps for this purpose, 48 per cent of
generation X and 33 per cent of young boomers. By contrast, 94 per cent of realtors reported using
mobile phones daily, fuelling the attraction of these technologies to inverstors.
Various smartphone apps have been developed over time to promote real estate. “Juwai” is an
app for a Chinese audience. It has been integrated with a Chinese social channel and has online
Chinese social media features. Not surprisingly, it attracts a big audience among both Chinese and
English speakers [180]. What makes it stand out is its multi-language feature, which has been
clustered with firewall architecture to allow access from outside China, unlike local websites.
However, there are clear improvements that could be made to the service to attract a larger audience,
given for starters the absence of virtual tours and big data analytics.
“HAYBOL” is an android-based apartment locator app that aims at being used by a particular
community, specifically students, as they search for boarding houses or apartments [181]. It presents
convenient and affordable housing based on location-specific parameters. StereoCam3D is an
android smartphone app that enables the capture of real-time 3D pictures and videos using VR and
image stitching [182].
Tablet PCs are also offering distinct advantages. For instance, a realtor from John L. Scott Real
Estate reported using a tablet PC to bring his office closer to its clients. He held meetings with clients
in coffee shops near their locations. Using the wireless network provided at the coffee shop, the
realtor could pull all the relevant information needed to answer consumer questions and promote
the business [183]. So far, hundreds of apps have been developed for both real estate agents and
consumers. Some of the key apps for agents or owners include Premier Agent®, OpenHome Pro®,
Figure 5. Top 5 Australian real estate website statistics.
6.2. Smartphone Applications for Disseminating Information to Real Estate Consumers
Smartphone technologies and apps have disrupted global industries. You might call this the
era of the smart and the compact; the real estate industry is also observing online applications-based
disruption from powerful, smartphone applications and gadgets. A survey by REALTORS [
175
]
found that among information sources used to search for homes, 72 per cent of searches take place on
Sustainability 2018,10, 3142 25 of 44
smartphone apps; 52 per cent of millennials use such apps for this purpose, 48 per cent of generation X
and 33 per cent of young boomers. By contrast, 94 per cent of realtors reported using mobile phones
daily, fuelling the attraction of these technologies to inverstors.
Various smartphone apps have been developed over time to promote real estate. “Juwai” is
an app for a Chinese audience. It has been integrated with a Chinese social channel and has online
Chinese social media features. Not surprisingly, it attracts a big audience among both Chinese and
English speakers [
180
]. What makes it stand out is its multi-language feature, which has been clustered
with firewall architecture to allow access from outside China, unlike local websites. However, there are
clear improvements that could be made to the service to attract a larger audience, given for starters the
absence of virtual tours and big data analytics.
“HAYBOL” is an android-based apartment locator app that aims at being used by a particular
community, specifically students, as they search for boarding houses or apartments [
181
]. It presents
convenient and affordable housing based on location-specific parameters. StereoCam3D is an android
smartphone app that enables the capture of real-time 3D pictures and videos using VR and image
stitching [182].
Tablet PCs are also offering distinct advantages. For instance, a realtor from John L. Scott Real
Estate reported using a tablet PC to bring his office closer to its clients. He held meetings with
clients in coffee shops near their locations. Using the wireless network provided at the coffee shop,
the realtor could pull all the relevant information needed to answer consumer questions and promote
the business [
183
]. So far, hundreds of apps have been developed for both real estate agents and
consumers. Some of the key apps for agents or owners include Premier Agent
®
, OpenHome Pro
®
,
DotLoop
®
, RPR Mobile
®
, Spotio
®
, DocuSign
®
, GoConnect
®
, EverNote
®
and Buffer
®
. For end users, there
are apps such as Zillow.com,Trulia.com,Apartments.com,Realor.com,RealEstate.com.au,Hotpads.com,
Domain.com.au and Rent.com. Although these websites and apps offer quality services, Technology
Adoption and its usage in disseminating information to consumers is limited. If these apps were
fine-tuned to provide reliable and abundant information based on the Big9 technologies, consumers
would gain reliable and accurate information, make better decisions as a result and be happy with
those decisions. Apps related to property valuation that offer abundant and accurate information
could also help to improve the decision-making process and reduce consumer regrets.
6.3. The Role of Social Media in Disseminating Information to Real Estate Consumers
Social media includes computer and internet-mediated tools and devices that allow the creation,
sharing or exchange of information over networks and in virtual communities [
184
]. Social media
and communications influence both private and business interactions. With the introduction of online
marketplaces such as Facebook Market, the role of social media in online information dissemination
and business has increased. Social media platforms such as Facebook, Twitter, YouTube and WhatsApp
are disrupting and revolutionising the online real estate business [
50
]. As a result, all major real estate
service providers are ensuring they have a social media presence through Facebook pages, YouTube
channels, WhatsApp groups, LinkedIn and Twitter. This has given rise to the concept of online quotes
and a real-time presence for agents, either in the form of a human or of a bot that can answer frequently
asked questions.
According to Lu [
185
], social media presence can enhance a buyer’s trust by 51 per cent and
increase their purchase intention by 44 per cent. Shelton [
186
] used two years of tweets from two places
in the US to develop a novel conceptual model based on social media that predicts the interaction,
movement and mobility of people in subsequent real estate development. Zamani and Schwartz [
187
]
used Twitter language to predict real estate market outcomes and conclude that real estate business
(marketing and commerce) is the most affected domain. Technology is the most effective component
of real estate development.
Rauniar [
188
] highlights the considerable increase in consumers’ trust and intention to buy as
well as the perceived usefulness of a product by its presence on social media, specifically Facebook.
Sustainability 2018,10, 3142 26 of 44
Facebook hosts various groups that buy and sell properties. These are particularly well known to
younger generations and students who use the internet.
WhatsApp groups and LinkedIn also host real estate offerings such as neighbourhood sales
groups and bulk property postings. Another key introduction agent is ResearchGate, which is
akin to social media for researchers and provides a space for new ideas in research to be shared
among real estate researchers. A key advantage of social media platforms such as Facebook
and YouTube is their flexibility in hosting lengthy and informative videos produced at a high
quality. Such video-based immersive information can better inform consumers about their potential
investments. These visualisations and other forms of immersion have the likelihood of reducing
purchase or rent regrets. YouTube offers 360-degree videos that enable consumers to visit and feel
properties expansively from their home computers or mobile phones. These provide a great deal more
information than 2D photographs, which can hide defects. High-quality 360-degree videos and the
flexibility they offer consumers to see the whole picture of a property before its purchase and before
an in-person tour can reduce consumer regrets.
Pertinent studies show that online technologies are available but their rate of adoption and
incorporation into business-oriented real estate is low. To increase the adoption rate, it is imperative to
improve information dissemination mechanisms, by introducing technology-derived analytics such
as big data-based informatics and neighbourhood analytics. According to Wang et al. [
189
], a key
barrier to the adoption of such technology is the risk-averse nature of organisations. Traditional
organisations prefer “playing it safe” and are usually closed to innovation; risks associated with
new technologies are exaggerated instead of being viewed as hurdles associated with any normal
process. Other actors hindering adoption are government policies and the risk perception of external
stakeholders. Similarly, utility expectations, status gains and loss avoidance, external influences,
associated costs and quality-related concerns hinder the adoption process [
190
]. Other concerns
include data usability, privacy and security that are generally associated with innovation and
open-mindedness [
191
]. To increase the rate of adoption, a broader awareness in tandem with policy
change is required, while addressing security and privacy concerns. In this context, the introduction of
FAA laws in the US, whereby locals can use drones for photography up to a specified height and in
certain areas, is a positive step. The implications of this policy and its potential downsides are yet to
be observed due to its relative newness. Nevertheless, similar policy changes are needed to pave the
way for technology adoption by real estate websites.
7. Technology Adoption Models (TAM)
This section reviews the literature to identify key factors that may affect a consumer’s decision
to accept and continuously use SRE technology. The TAM, introduced by Fred Davis in 1989, is an
information systems theory that models the use and acceptance of technologies by users [
36
]. Davis [
37
]
introduced five key components of TAM: perceived usefulness, perceived ease of use, user satisfaction,
behavioural intention to use, and actual use. According to Davis, perceived usefulness is the degree
to which the use of a system can increase the user’s job performance. Perceived ease of use is the
perception that a system is free of effort. User satisfaction is the accomplishment of a user through
functions of the system. Behavioural intention to use is the change in behaviour of the user towards
more use of the system. Finally, actual use is the increased use of a system after change in behaviour
and satisfaction.
TAMs have been used in various studies of information technology and integrated disciplines.
They have been used in supply chain management to investigate the resources and theoretical factors
of operations [
192
]. In agriculture, they have been used to measure the effects of social networks [
193
].
Nguyen [
194
] used them to explore why the incorporation of technology adoption drivers failed in
small businesses. Song [
190
] used them to explore and augment smartphone technology adoption in
China. Sepasgozar [
195
] used them in relation to construction equipment technologies to investigate
the link between customer decision-making processes and the use of construction equipment.
Sustainability 2018,10, 3142 27 of 44
Although extensive applications of TAM exist in global industries, exploration of it in real estate
has not been thoroughly reported. In fact, the application of TAM to online retail, websites and
e-commerce is the closest reported use of TAM for online real estate management. The key terms and
pertinent factors to TAM have been defined in Table 12.
Table 12. A list of selected technology acceptance factors and definitions.
Criteria Definition Factors
Information quality. Reliable and consistent information that inclines
a user to use the service [196].
Familiar technology, information novelty, 3D
models, accurate information,
updated information.
Systems quality. Efficient, ethical and smooth systems for
delivering and disseminating information [197].
Page location, loading speed, loading info
structure, website evaluation, website design.
Self-efficacy. The completeness of a platform in terms of more
features, more options and filters [198]. Content richness, search filters, sorting, maps.
Service quality. Fast, efficient, reliable and responsive services
made available to the end user [199].
Hyperlinks, customisation, response time,
consistent graphics
Playfulness and usability.
Offering more interactivity, immersion and
gaming attributes to keep the user more involved
and enhance use of the platform by attracting
more customers [200].
Easy return, navigation tools, finding
information, learning website.
Perceived enjoyment.
The feeling of ease and services at finger tips
including neighbourhood aspects for a better
lifestyle [201].
Data analytics, crime rates, neighbourhood
insights, distances to parks, virtual tours.
In general, however, the applications of TAM have been generously explored, with considerable
research put into identifying additional factors intrinsic to it, thus augmenting the original TAM
model. For example, Venkatesh [
202
] integrated control, intrinsic motivation and emotions with TAM.
Hornbæk and Hertzum [
201
] report a perceived enjoyment, absorption, beauty, flow, goodness, trust,
distancing, perceived control, playfulness, usability, self-efficacy and other attributes as key additions
to TAM. Jasperson [
197
] reported systems quality; Lu [
199
] reported service quality, and Benbasat and
Barki [
196
] reported information quality as key expansions of TAM. Taking a leap from these useful
studies, the current study considers six key expansions of TAM: three each for perceived usefulness and
perceived ease of use. These are: information quality, systems quality and self-efficacy for perceived
usefulness; and service quality, playfulness and usability, and perceived enjoyment for perceived ease
of use.
8. Stakeholder Analysis
As part of categorisation 2 of the methodology, stakeholder analysis was performed on the
retrieved publications. To systematise this analysis, four key stakeholders were established: consumers
(buyers/sellers), agents and associations (AA), government and regulatory authorities (GRA),
and complementary industries (CI). The analysis aimed to identify the basic and secondary needs of
key stakeholders and observe their interactions. This aids observations of potential areas in which
Big9 technologies may be applied to address stakeholder needs. Table 2provides the definition of
these stakeholders considering the reviewed literature.
The identified stakeholders interact in various ways with one another, but the consumer is the
central focus of the system, as shown in Figure 6. Both AA and CI provide services to consumers in
exchange for revenues. These two stakeholder groups facilitate one another through referrals to keep
each other in business. CI provides services to AA in return for revenues. Similarly, GRA protects
consumers against unethical and unprofessional practices by AA, CI and others. In return, consumers
pay taxes to oil the GRA wheels [
44
,
45
]. GRA also supports CI in the form of loans, regulations and
protections in return for taxes. AA are supported by GRA and in return are taxed, and compliance
with codes, regulations and public safety is ensured [
42
,
43
]. Figure 6shows the interactions of the key
stakeholders and how they facilitate one another.
Sustainability 2018,10, 3142 28 of 44
After identifying and defining key stakeholders, the next step in systematic synthesis is the
identification of stakeholder needs. The needs of the identified stakeholders are highlighted and
presented in Figure 7. Both basic (shown in bold) and key secondary needs were identified.
For example, a consumer’s basic need is to buy or sell property. A key secondary need is living
in a desirable neighbourhood, with a preference for a suitable and livable neighbourhood that provides
desired facilities and amenities [
170
]. Such preferences vary according to the consumer. For example,
one consumer may esteem travel time and distance to his workplace over the nearby presence of
restaurants and markets. One may prefer bus stations and hospitals in the neighbourhood; another
might be more concerned about the crime rate. These needs are dynamic and change from consumer to
consumer, so it becomes not only difficult to formulate a consistent mechanism for facilitating all types
of consumers, but their input also becomes vital to the regular updating of services provided [
171
].
Other consumer needs include, but are not limited to, pricing, mortgages, website search filters and
the co-ordination of stakeholders [177].
GRA’s basic need is providing the protection of citizens. Citizens are not limited to consumers,
but include AA, CI and others. Thus, GRA protects all stakeholders in return for taxes. To safeguard
against the misuse of business power and maintain ethical processes, GRA have such secondary needs
as the formulation of standards, ethical codes, and regulations [
42
,
43
]. These are made available to
AA and CI. Public copies of these codes are also available to consumers as part of the Global Right to
Information Act, so an interested consumer can read their rights. The economic growth of the state
and political support are other needs of GRA, but the people are the key pillars of a government’s
make-or-break scenarios. Therefore, a critical balance must be sought by government that avoids
imposing onerous and impossible-to-follow regulations on agencies that could result in the loss of
business while eschewing such leniency that leads to the exploitation of consumers by AA or CI.
Similarly, keeping the taxes optimal and payable is a key requirement for GRA [175].
Sustainability 2018, 10, x FOR PEER REVIEW 29 of 44
the loss of business while eschewing such leniency that leads to the exploitation of consumers by AA
or CI. Similarly, keeping the taxes optimal and payable is a key requirement for GRA [175].
Figure 6. Key real estate stakeholders’ interactions, * Note: AA: agents and associations; GRA:
government and regulatory authorities; CI: complementary industries.
Figure 7. Basic and other needs of key real estate stakeholders, * Note: AA: agents and associations;
GRA: government and regulatory authorities; CI: complementary industries.
A basic need of AA is profit, which is generated by services for consumers. Services include
meetings with consumers, keeping track of listings and ensuring that sellers and buyers are matched
according to their availability and demands. These needs give rise to secondary requirements such
as networking. For example, a consumer may demand that an AA provide him or her with legal
assistance. For this, the AA uses his or her network and may refer to a suitable and reliable law firm
from CI. Thus, networking and cross referrals are also needs of AA. Other needs include support
from GRA and associations, work ethics, and raising their business reputation to increase profitability
[40,41].
Figure 6.
Key real estate stakeholders’ interactions, * Note: AA: agents and associations; GRA: government
and regulatory authorities; CI: complementary industries.
Sustainability 2018,10, 3142 29 of 44
Sustainability 2018, 10, x FOR PEER REVIEW 29 of 44
the loss of business while eschewing such leniency that leads to the exploitation of consumers by AA
or CI. Similarly, keeping the taxes optimal and payable is a key requirement for GRA [175].
Figure 6. Key real estate stakeholders’ interactions, * Note: AA: agents and associations; GRA:
government and regulatory authorities; CI: complementary industries.
Figure 7. Basic and other needs of key real estate stakeholders, * Note: AA: agents and associations;
GRA: government and regulatory authorities; CI: complementary industries.
A basic need of AA is profit, which is generated by services for consumers. Services include
meetings with consumers, keeping track of listings and ensuring that sellers and buyers are matched
according to their availability and demands. These needs give rise to secondary requirements such
as networking. For example, a consumer may demand that an AA provide him or her with legal
assistance. For this, the AA uses his or her network and may refer to a suitable and reliable law firm
from CI. Thus, networking and cross referrals are also needs of AA. Other needs include support
from GRA and associations, work ethics, and raising their business reputation to increase profitability
[40,41].
Figure 7.
Basic and other needs of key real estate stakeholders, * Note: AA: agents and associations;
GRA: government and regulatory authorities; CI: complementary industries.
A basic need of AA is profit, which is generated by services for consumers. Services include
meetings with consumers, keeping track of listings and ensuring that sellers and buyers are matched
according to their availability and demands. These needs give rise to secondary requirements such as
networking. For example, a consumer may demand that an AA provide him or her with legal assistance.
For this, the AA uses his or her network and may refer to a suitable and reliable law firm from CI.
Thus, networking and cross referrals are also needs of AA. Other needs include support from GRA
and associations, work ethics, and raising their business reputation to increase profitability [40,41].
CI, like AA, has a basic need to make a profit by facilitating consumers and AA. For this to
succeed, the secondary needs of networking and referrals play a key role. For example, the two-way
process of referral between CI and AA helps both stakeholders to increase their business and profits,
since both have profit as their basic need [
176
]. CI keeps good networking and business relations with
AA and consumers, whereas support to them is provided by GRA in return for tax [177].
9. Stakeholder Synthesis
Having identified key stakeholders and established their needs, the last and most important
step in synthesis for stakeholder analysis is the exploration of what, who and how for the identified
Big9 Disruptive Technologies, as shown in Table 13. In this part, stakeholders’ needs are synthesised
according to the impacts of disruptive technologies using a what, who and how analysis. This is done
in the light of how SRE can be facilitated through technology, with key questions being: what kind of
technology, who as a stakeholder is affected directly by it and how are these stakeholders affected in
terms of processes and dissemination mechanisms? This information, if disseminated properly, has the
potential to reduce regrets among consumers that arise from a lack of information and its associated bad
decision-making. Three processes are considered in this study—buying, selling and facilitating—since
consumers are at the core of this study and they can either buy or sell something or be facilitated for it
by other stakeholders, as shown in Table 13. Further resources used for disseminating technologies
and making them available to key stakeholders are also part of the analysis. These are categorised into
two parts: technology, and its dissemination. They are further divided into two sub parts: detection
and storage for technology, and control and output for dissemination. Big data, cloud, SaaS and IoT
are classified as storage technologies; drones, 3D scanning, wearable tech, VR and AR and AI and
robotics are classified as detection technologies.
Sustainability 2018,10, 3142 30 of 44
As an example, consider big data from Table 13 as a disruptive technology. It affects AA,
consumers, GRA and CI directly, due to the needs of business, profits and networking. The use of big
data has yielded considerable uplift for websites such as Zillow.com, where key big data-based statistics
such as market price fluctuations, crime rates and average time spent on a property by consumers
are presented [
78
]. These statistics help consumers by providing market awareness, increasing
understanding of the process, and presenting features and requirement needs [
49
]. Consumers
thus become more market aware and tend to make more decisions about buying or renting. Such
awareness eliminates regrets related to bad decision-making that may ensue from a lack of information.
For GRA, ethics and regulatory challenges are presented by big data disruption: the fine line between
what is personal and should not be used by AA or CI for big data statistics and what can be used must
be revisited repeatedly to ensure that no citizen’s privacy or security rights are violated [
78
,
79
]. For CI,
big data increases referrals as contributions by a specific CI tilts user tendencies towards their services.
Thus, in all its operations, big data affects stakeholders through three key processes: buying,
selling and facilitating. In terms of dissemination, big data is mainly shared through statistical and
graphical outputs. Online media such as websites, social media platforms and electronic gadgets are
used as a source of buyer demands and requirements collected through various search engines, page
views, transaction records and other sources. Since the statistics are collected from consumer inputs,
big data necessarily takes consumer requirements into account and provides pertinent analytics that
address consumer needs. Such analytics may eliminate consumer regrets.
Big data has been used by Zillow.com to evaluate property prices and crime rates. Market leading
real estate companies such as Prudential Real Estate Investors and USAA Real Estate Company have
used it to generate performance databases. Other key components of data mining are AI and robotics,
which affect AA, GRA and CI using websites, apps and gadgets. These draw on search histories,
speech recognition and sensors and are used in various real estate processes. All the Big9 technologies
that can address stakeholder needs, as well as dissemination mechanisms, are shown in Table 13.
Table 13. Who, what and how analysis of disruptive technologies for SRE.
What Who How
Tech Stakeholders
Affected Directly Needs Addressed Directly Primary Dissemination
Mechanism Resources Used
Big data
AA Business, profit, networking Websites, social media
and gadgets to facilitate
buy, rent or sell
Land resources, realties, buyer’s
requirement, owner’s info,
buyer’s demands, transaction
records, page views
Consumers
Market awareness, Understanding
process
Features and requirements
GRA Ethics, regulations
CI Referrals
Cloud
AA Business, profit, networking, referrals,
reputation Websites, apps, gadgets
and social media to
facilitate buy, rent or sell
Internet connected devices, shared
storage, Recent searches,
Stakeholder preferences,
High-speed internet, Remote
access servers
Consumer Buy or sell, price, stakeholder
co-ordination, online searching and filter
CI Networking, profit, referrals
GRA Ethics, regulations
SaaS AA Business, profit, networking, referrals,
reputation Websites, apps, gadgets
and social media to
facilitate buy, rent or sell
Computer software, High speed
internet, remote access servers,
shared storage
CI Networking, profit, referrals
IoT
AA Business, profit, networking
Websites and gadgets to
facilitate sales
Telemetry, sensors, local networks,
remote access servers, consumer
habits
Consumer
Online searching and filters,
Understanding process, market
awareness
CI Networking
GRA Ethics, public safety, regulations
Drones
AA Business, profit, ethics
Websites and gadgets to
facilitate buy, rent or sell
UAVs, flight routes, wi-fi or
bluetooth connectivity
Consumer Neighborhood preference, features and
requirements, buy/sell
CI Profit
GRA Ethics, public safety, regulations
3D scanning
AA Business Gadgets to facilitate buy,
rent or sell
Lasers, building drawings,
training
CI Profit
GRA Ethics, public safety, regulations
Sustainability 2018,10, 3142 31 of 44
Table 13. Cont.
What Who How
Tech Stakeholders
Affected Directly Needs Addressed Directly Primary Dissemination
Mechanism Resources Used
Wearable tech
AA Business Gadgets and apps to
facilitate buy, rent or sell
Human resources,
Bluetooth/Wi-Fi connectivity,
smart processors
CI Profit, referrals
GRA Public safety, regulations
VR & AR
AA Business Gadgets, websites and
apps to facilitate buy,
rent or sell
VR AR gadgets, Bluetooth or
Wi-Fi connectivity, high speed
internet, building drawings or
plans
CI Profit, referrals
Consumer
Neighborhood preference, features and
requirements, Buy or sell, online
searching and filters, price
GRA Regulations
AI and robotics
AA Business, profit Websites, apps and
gadgets to facilitate buy,
rent or sell
Speech recognition, search history,
page views, buyer’s demands and
info, sensors
Consumer Market awareness, features and
requirements
GRA Ethics, regulations
Note: AA: agents and associations; CI: complementary industries; GRA: government and regulatory authorities; AI:
artificial intelligence, SaaS: software as a service, IoT: internet of things; VR and AR: virtual reality and augmented
reality; UAV: unmanned aerial vehicle.
Figure 8displays the schematics for technology-based detection and dissemination, based on
Table 13. It indicates detection hardware installed inside an apartment for sale and how potential buyers
can access the information. It also presents the roles of key stakeholders. For example, consider two
types of drones: aerial drones that collect information based on distances to places and that click aerial
photographs; and wall-mounted drones that can produce high-quality images for internal views as well
as generate 3D images. 3D scanners are also present as they can generate 3D drawings for maintenance
data and 360-degree images for enhanced viewing. Other gadgets with applications include wearable
tech and VR glasses, which people inside the house can use to collect VR-related data for simulations
and to receive alerts about maintenance or other requirements. All these technologies are connected
online to databases and storage servers where cross-communications take place. The databases are
equipped with AI and use big data algorithms to generate information. These databases are connected
to servers and receive and send information using clouds. These servers act as information hubs for
data collected by the technological gadgets and are controlled by web administrators at real estate
agencies and associations.
Sustainability 2018, 10, x FOR PEER REVIEW 32 of 44
Note: AA: agents and associations; CI: complementary industries; GRA: government and regulatory
authorities; AI: artificial intelligence, SaaS: software as a service, IoT: internet of things; VR and AR:
virtual reality and augmented reality; UAV: unmanned aerial vehicle.
Figure 8 displays the schematics for technology-based detection and dissemination, based on
Table 13. It indicates detection hardware installed inside an apartment for sale and how potential
buyers can access the information. It also presents the roles of key stakeholders. For example,
consider two types of drones: aerial drones that collect information based on distances to places and
that click aerial photographs; and wall-mounted drones that can produce high-quality images for
internal views as well as generate 3D images. 3D scanners are also present as they can generate 3D
drawings for maintenance data and 360-degree images for enhanced viewing. Other gadgets with
applications include wearable tech and VR glasses, which people inside the house can use to collect
VR-related data for simulations and to receive alerts about maintenance or other requirements. All
these technologies are connected online to databases and storage servers where cross-
communications take place. The databases are equipped with AI and use big data algorithms to
generate information. These databases are connected to servers and receive and send information
using clouds. These servers act as information hubs for data collected by the technological gadgets
and are controlled by web administrators at real estate agencies and associations.
Figure 8. Schematics of information detection and dissemination using disruptive technologies. *
Note: VR and AR: virtual and augmented realities; AI: artificial intelligence.
This process involves installing gadgets and data collection tools in the apartment or house that
is for sale. These detect and collect information through built-in functions and scanning. This
information is sent to databases that are equipped with artificial intelligence and use big data and
IoT-based algorithms to separate useful data from the set. This information is stored on servers that
use clouds and SaaS, so that consumers and agents can manage and control the output. CI has partial
access to this data and can help agents through referrals; they also benefit from cross referrals.
Information is passed through ethics checks and government regulations and regulatory authorities
before it is released to buyers. Finally, the refined data is made available to potential consumers to
aid their real estate decision. Thus, the displayed data occupies a critical position in the buy/rent
decision. Such information, after passing through defined checks and regulations, is not only
abundant and accurate but also reliable. As a consequence, consumer regret is less likely to arise from
poor quality information. Such enhanced decision-making can bridge the gap between real estate
consumers and service providers such as agents, website managers and agencies.
Owing to the importance of information and the use of Big9 technologies for its generation, a
framework is needed that integrates technologies using a TAM model and so adds value to real estate
Figure 8.
Schematics of information detection and dissemination using disruptive technologies. * Note:
VR and AR: virtual and augmented realities; AI: artificial intelligence.
Sustainability 2018,10, 3142 32 of 44
This process involves installing gadgets and data collection tools in the apartment or house that is
for sale. These detect and collect information through built-in functions and scanning. This information
is sent to databases that are equipped with artificial intelligence and use big data and IoT-based
algorithms to separate useful data from the set. This information is stored on servers that use clouds
and SaaS, so that consumers and agents can manage and control the output. CI has partial access to
this data and can help agents through referrals; they also benefit from cross referrals. Information
is passed through ethics checks and government regulations and regulatory authorities before it is
released to buyers. Finally, the refined data is made available to potential consumers to aid their
real estate decision. Thus, the displayed data occupies a critical position in the buy/rent decision.
Such information, after passing through defined checks and regulations, is not only abundant and
accurate but also reliable. As a consequence, consumer regret is less likely to arise from poor quality
information. Such enhanced decision-making can bridge the gap between real estate consumers and
service providers such as agents, website managers and agencies.
Owing to the importance of information and the use of Big9 technologies for its generation,
a framework is needed that integrates technologies using a TAM model and so adds value to real estate
buy/rent decisions. This is likely to eliminate consumer regrets because the information generated
will be based on technology and free of human interference and manipulation. The information can be
made available directly to consumers, after passing ethics tests and regulation checks. Furthermore,
the position of the key stakeholders who use the technology is not jeopardised but strengthened.
For example, real estate agencies and associations control the servers and databases. Complementary
industries benefit from referrals and cross referrals. Government provides ethics checks on servers,
regulations for agents and agencies, and supports the complementary industries to secure business.
Every stakeholder therefore has control over information, yet the level of information made available
to the consumer increases. This has the additional benefit of increasing trust between real estate
consumers and other stakeholders, which will benefit business and eliminate post-purchase regrets.
The hypothesised TAM framework should operate in such a way that technology is used to
upload data to the online platform in line with the needs of the potential consumer. The consumer,
who has particular needs, can use the technological applications available to obtain the desired results
online. These enhanced results will attract more consumers and enhance their level of satisfaction,
which in turn paves the way for more technological adoption in real estate, along with reducing
post-purchase regrets. For example, one of the technologies that can be used by CI is wearable tech.
It can be used for labour and equipment tracking [
156
], which opens up more avenues for business as
more equipment is sold and demand rises due to the published effectiveness of the gadgets. The need
for increased profits by securing more business is addressed through such tracking and monitoring,
which in turn provides reliable real-time information and so enhances the information component of
the TAM, leading to more perceived usefulness and actual use. Similarly, SaaS, a technology used by
both AA and CI, has multiple applications in building maintenance and hazard alerts. It addresses
the needs of gaining more business and profit, business networking and referrals and cross referrals
between two congruent industries. Increased business enhances the information quality of TAM,
whereas the remaining two enhance system quality, thus increasing the perceived usefulness of the
technologies and online platforms. Such information is useful for consumers as it enables better
decision-making and so eliminates potential regrets related to a lack of accuracy in online information.
Some key needs of consumers, such as crime indices, can be delivered by big data [
176
].
These indices are computed through big data and IoT-based smart computing and record keeping.
An increase or decrease in crime can have an impact on buy/sell decisions, with the data assisting in
the filtering process according to neighbourhood preference. This in turns enhances the “perceived
enjoyment” aspect of the TAM’s perceived ease of use; the potential buyer thinks more of the enjoyment
and quality family time they will spend due to less fear of crime. The user is satisfied and more inclined
to purchase the apartment or house. Such enjoyment increases happiness and reduces regrets related
to renting or buying properties.
Sustainability 2018,10, 3142 33 of 44
IoT, likewise, can help meet the needs of GRA, which is more concerned with rules and regulations
and public safety where information and privacy are concerned. IoT-based intelligent communities
offer exclusive access to GRA, which can monitor the entire process and check conformity with business
regulations, thus enhancing public safety through the settling of information and privacy concerns.
This addresses the service quality aspect of ease of use, and thus paves the way for greater use of
the platforms and services. IoT-based data acquisition and provision to consumers can tackle the
requirement for abundant data to reduce regrets.
The self-efficacy aspect of a TAM’s perceived usefulness is addressed by the facility of online
searching and filters for refining results, features and requirements. These are core concerns of
consumers that influence their buy/sell decisions. They can be enhanced by the use of IoT, big data
and clouds. Similarly, the aspect of playfulness and usability is enhanced by addressing such needs as
understanding the buying/selling process through the uptake of playful technologies such as virtual
simulations and game-based interactions. Through simulations and games, potential consumers can
walk through an apartment and understand the implications of their future decisions—as well as
the associated costs—by virtually adding or removing components. Such playful activities enlighten
consumers about a building’s potential usability and enhance their perceived ease of use; the greater
the perceived ease of use, the better the consumer’s buy/sell decision and the greater the use of the
platforms. Such decisions are more informed than otherwise due to the abundance and accuracy of
information provided. This may reduce real estate regrets and bring more business to agencies and
agents through recommendations from satisfied consumers.
10. Conclusions
This paper introduced the concept of SRE and defined and presented its core components as
sustainability, user centredness, and innovative technologies. Among the innovative technologies,
it targeted the Big9 disruptive technologies for disseminating information to real estate consumers.
To this end, a total of three disseminating platforms—websites, social media and smartphone
applications—were reviewed. The SRE stakeholders in focus were consumers, agents and associations,
complementary industries and government and regulatory authorities.
This paper explored the potentials of Big9 technologies in SRE. A comprehensive and systematic
review of the Big9 was performed to highlight the fact that the needs of the four key real estate
stakeholders could be met by these Big9 technologies. Specifically, the vast potential of the Big9
technologies needs to be transferred to end users, or consumers, who are the actual payers for the
technologies and industrial upgrades.
But consumers often have regrets about their buy/rent decisions, and the majority of these relate
to a lack of information provided by online channels. In terms of the Big9 technologies, big data-based
analytics can offer neighbourhood and locality insights such as crime rates, travel and sale rates,
and deliver this data-mined information to consumers according to their queries and requirements,
thus helping them to make more informed decisions. This may eliminate their regrets. AI can link
consumers to their dream homes because of its predictive analytics and intelligent matching, which not
only save time but also provide relevant and detailed property options to consumers, eliminating
human error-based regrets. Chat bots and voice recognition tools based on AI may eliminate consumer
regrets because they personalise matching and query responses. Such bots also provide financial
benefits as they charge 2 per cent commission rather than the 6 per cent that agents charge. Clouds can
tackle communication-related regrets by granting consumers access to property details, maintenance
schedules, and financial details that increase their access to information. Furthermore, clouds can
bridge the gap between agents, consumers and service providers by providing a reliable and active
communication link. SaaS-based access to property leases, tenancy and contract documents, security
issues and work orders, among other things, can increase a sense of attachment between consumers and
their property matters, and also reduce information-related regrets. IoT-based immersive tools make
consumers feel more involved and attached to their buildings and properties as they receive alerts and
Sustainability 2018,10, 3142 34 of 44
can remotely control their property’s tools, resulting in a sense of ownership and happiness rather
than regret, and promoting a positive air between stakeholders. Home automation devices such as
Domotics, smart kitchens and smart walls provide ease of use and a relaxing environment. drone-based
3D and 360-degree pictures provide unique, wider angles, detailed and comprehensive pictures and
videos that show external details such as sun paths, rooftop conditions, nearby greenery, distances to
amenities as well as finer internal details such as fungus growth, crumbling paints, wet corners and
others. Such details help consumers to make better decisions and so eliminate information-related
regrets. 3D scanning technology provides building and property layouts and drawings that can be
merged with building management models to obtain a smart match to space allocation, ensuring
a property is utilised well. Similarly, scanned images and layouts can offer consumers the luxury of
planning how they will use their property and what changes they desire. This feeling of connection
and flexibility can reduce post-purchase regrets that arise among consumers who are not aware of the
“fit for purpose” aspect and come to regret their purchase when they discover their property cannot be
used as they intended. Wearable techs provide luxury and flexibility to consumers through gadgets
and sensors installed at their property. Not only do they provide remote access, but they also gather
data based on daily interactions that, if made available to subsequent tenants or buyers, could provide
accurate and reliable information to avert purchase regrets. At the same time, the current tenant can
obtain visual alerts related to gadgets, equipment, fire hazards, security breaches and maintenance
requirements that elevate the sense of ownership and eliminate regrets. Lastly, VR- and AR-based
immersive visualisations and 3D tours can help consumers to make better decisions about the purchase
or rent of their properties. Such playful attributes and the sense of enjoyment and ability to make
changes make consumers more satisfied and eliminate their regrets.
In terms of barriers to the implementation of the Big9 technologies, the traditional rigid
mindset of managers, agents and service providers, whereby information is withheld, is a key
factor. A rigidity against accepting innovation and an inflexibility towards change among agents,
associations and managers is holding the real estate industry back from adopting the Big9 technologies.
These technologies can be disseminated to consumers through websites, social media platforms
or smartphone apps. Each dissemination mechanism provides resources and platforms for sharing
reliable, detailed and accurate information with consumers so that they can make better, more informed
decisions. The benefits of the Big9 technologies as outlined in the current study will open discussion
and avenues to their adoption, and result in providing more accurate and reliable information to
consumers. Based on such high-quality information, consumers can make better decisions and will
likely have fewer regrets, if any.
Implications, Limitaions and Future Directions
The current study has practical and theoretical implications. Practically, it provides a mechanism
for detecting and disseminating Big9 technology-based information to key stakeholders, especially
real estate consumers. In this context, using the information detection and distribution schematics
provided in the study, real estate agents and managers can install Big9 technology compatible devices
in the properties. This will help collect key information, photos, any discrepancy as well as provide
an immersive playful experience to the potential customers to be connected to their future properties
through proper information dissemination and immersion using the Big9 technologies. This may
increase the sales for the real estate agencies and enhance the trust levels of consumers leading to
more consumer satisfaction. Theoretically, it links consumer satisfaction and stakeholder needs to
the TAM model, whereby the disruptive technologies can be harnessed to reduce increasing levels
of regret among real estate consumers. The framework hypothesised in this study aims to target the
gap that exists between the Big9 technologies and consumers through TAM and needs assessment.
Each technology addresses core components of TAM, such as perceived ease of use, enjoyment and
playfulness whereby consumers are inclined to make behavioural changes to accept and subsequently
adopt the technologies. Following the framework as a guideline, web-based programs and extensions
Sustainability 2018,10, 3142 35 of 44
and smartphone apps should be provided on online real estate platforms. Such extensions and
apps can fill the information void for consumers and equip them with real-time analytics and
updates on the localities of their intended properties. Thus, not only is the knowledge gap addressed,
but post-purchase regrets can also be controlled as a consequence of the sufficiency and accuracy of
the information provided. Furthermore, the holistic considerations of key stakeholders’ needs make
the framework a win-win tool for addressing stakeholder needs and business requirements.
This study takes a first step towards adopting disruptive technologies in the real estate industry.
It is limited to the Big9 disruptive technologies. Another limitation is the absence of research on such
technologies within the industry, which has been compensated by online reports. In future, following
similar lines, it will be possible to evaluate the impact of Big9 technologies in terms of how they
enhance the industry according to specific stakeholder relations such as the buyer/seller interaction.
Based on the needs of buyers, a technology-based framework can be formulated to help sellers
sell their property more conveniently and hence increase its value, thereby increasing overall real
estate valuations. Computer programs and web extensions can enhance the “value” aspect of real
estate as well as displaying the traditional price and location.
Author Contributions:
Conceptualization, F.U. and S.M.E.S.; Methodology, F.U. and S.M.E.S; Software, F.U.;
Validation, F.U. and S.M.E.S.; Formal Analysis, F.U.; Investigation, F.U., S.M.E.S. and C.W.; Resources, F.U. and
S.M.E.S.; Data Curation, F.U.; Writing—Original Draft Preparation, F.U. and S.M.E.S; Writing—Review & Editing,
F.U. and S.M.E.S.; Visualization, F.U. and S.M.E.S.; Supervision, S.M.E.S. and C.W; Project Administration, F.U.
and S.M.E.S; Funding Acquisition, S.M.E.S.
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflict of interest.
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