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.

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Digital tools and marketing have been widely adopted in various industries throughout the world. These tools have enabled companies to obtain real-time customer insights and create and communicate value to customers more effectively. This study aims at understanding the principles and practices of sustainable digital marketing in the Malaysian property development industry by investigating the extent to which digital marketing has been adopted, the impediments to its adoption, and the strategies to improve digital capabilities for the local context. Digital marketing theories, practices, and models from other industries are adopted and applied to the local property development industry to lay the foundation for making it smart and sustainable. This paper proposes a marketing technology acceptance model (MTAM) for digital marketing strategy and capability development. The key factors used in the model are ease of use, perceived usefulness, perceived cost, higher return, efficiency, digital service quality, digital information quality, digital system quality, attitude towards use, and actual use. The model and hypothetical relationships of critical factors are tested using structural modeling, reliability, and validity techniques using a sample of 279 Malaysian property development sector representatives. A quantitative approach is adopted, using an online questionnaire tool to investigate the behavior of respondents on the current digital marketing practices and capabilities of Malaysian property development companies. The results show that the sample property development companies are driven by the benefit of easily obtaining real-time customer information for creating and communicating value to customers more effectively through the company brand. Further strategies, such as creating real-time interactions, creating key performance indicators to measure digital marketing, personalization, and encouraging innovation in digital marketing are most preferred by local professionals. An adoption framework is provided based on the reviewed models and results of the current study to help transform the Malaysian property development sector into a smart and sustainable property development sector by facilitating the adoption of digital technologies. The results, based on real-time data and pertinent strategies for improvement of the local property sector, are expected to pave the way for inducing sustainable digital marketing trends, enhancing capabilities, and uplifting the state of the property development sector in developing countries.

Big data is the concept of enormous amounts of data being generated daily in different fields due to the increased use of technology and internet sources. Despite the various advancements and the hopes of better understanding, big data management and analysis remain a challenge, calling for more rigorous and detailed research, as well as the identifications of methods and ways in which big data could be tackled and put to good use. The existing research lacks in discussing and evaluating the pertinent tools and technologies to analyze big data in an efficient manner which calls for a comprehensive and holistic analysis of the published articles to summarize the concept of big data and see field-specific applications. To address this gap and keep a recent focus, research articles published in last decade, belonging to top-tier and high-impact journals, were retrieved using the search engines of Google Scholar, Scopus, and Web of Science that were narrowed down to a set of 139 relevant research articles. Different analyses were conducted on the retrieved papers including bibliometric analysis, keywords analysis, big data search trends, and authors’ names, countries, and affiliated institutes contributing the most to the field of big data. The comparative analyses show that, conceptually, big data lies at the intersection of the storage, statistics, technology, and research fields and emerged as an amalgam of these four fields with interlinked aspects such as data hosting and computing, data management, data refining, data patterns, and machine learning. The results further show that major characteristics of big data can be summarized using the seven Vs, which include variety, volume, variability, value, visualization, veracity, and velocity. Furthermore, the existing methods for big data analysis, their shortcomings, and the possible directions were also explored that could be taken for harnessing technology to ensure data analysis tools could be upgraded to be fast and efficient. The major challenges in handling big data include efficient storage, retrieval, analysis, and visualization of the large heterogeneous data, which can be tackled through authentication such as Kerberos and encrypted files, logging of attacks, secure communication through Secure Sockets Layer (SSL) and Transport Layer Security (TLS), data imputation, building learning models, dividing computations into sub-tasks, checkpoint applications for recursive tasks, and using Solid State Drives (SDD) and Phase Change Material (PCM) for storage. In terms of frameworks for big data management, two frameworks exist including Hadoop and Apache Spark, which must be used simultaneously to capture the holistic essence of the data and make the analyses meaningful, swift, and speedy. Further field-specific applications of big data in two promising and integrated fields, i.e., smart real estate and disaster management, were investigated, and a framework for field-specific applications, as well as a merger of the two areas through big data, was highlighted. The proposed frameworks show that big data can tackle the ever-present issues of customer regrets related to poor quality of information or lack of information in smart real estate to increase the customer satisfaction using an intermediate organization that can process and keep a check on the data being provided to the customers by the sellers and real estate managers. Similarly, for disaster and its risk management, data from social media, drones, multimedia, and search engines can be used to tackle natural disasters such as floods, bushfires, and earthquakes, as well as plan emergency responses. In addition, a merger framework for smart real estate and disaster risk management show that big data generated from the smart real estate in the form of occupant data, facilities management, and building integration and maintenance can be shared with the disaster risk management and emergency response teams to help prevent, prepare, respond to, or recover from the disasters.

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The real estate sector is receiving mix responses throughout the world, with some countries like USA receiving lesser and European and Asia Pacific markets receiving more transactions in recent years. Among the concerning factors, post-purchase regrets by the real estate owners or renters are on the rise, which have never been assessed to date through scholarly research. These regrets can further increase in the time of lockdowns and bans on inspections due to Corona Virus Disease 2019 (COVID-19) and social distancing rules enforced by various countries such as Australia. The current study aims at investigating the key post-purchase regret factors of real estate and property owners and renters over the last decade using published literature and online threads. Based on pertinent literature, 118 systematically identified and text-mined articles, and four online threads with 135 responses, the current study develops system dynamics models to assess and predict the increase in consumers’ regrets over the last decade. Further, a user-generated thread with 23 responses involving seven real estate managers and five agents with more than 20 years of experience, 10 buyers with at least three successful rentals or purchases, and a photographer with more than 10 years of experience, is initiated on five online discussion platforms whereby the respondents are involved in a detailed discussion to highlight the regret reasons specific to real estate purchases based on online information. General architecture for text mining (GATE) software has been utilised to mine the text from both types of threads: Published and user generated. Overall, the articles and threads published over the last decade are studied under two periods: P1 (2010–2014) and P2 (2015–2019) to highlight the post-purchase or rent-related regret reasons. The results show that regret levels of the real estate consumers based on published post-purchase data are at an alarmingly high level of 88%, which compared to 2015, has increased by 18%. Among the major cited reasons, complicated buy–sell process, lack or accuracy of information, housing costs, house size, mortgages, agents, inspections, and emotional decision making are key reasons of regret. Overall, a total of 10% and 8% increases have occurred in the regrets related to the buy–sell process and lack of inspections, respectively. On the other hand, regrets related to agents and housing costs have decreased drastically by 40% mainly due to the good return on investments in the growing markets. However, based on the current trend of over reliance on online information and more powers to the agents controlling online information coupled with lack of physical inspections, the situation can change anytime. Similarly, lack of information, housing size, and mortgage-related regrets have also decreased by 7%, 5%, and 2%, respectively, since 2019. The results are expected to encourage policy level changes for addressing the regrets and uplifting the real estate industry and moving towards a smart and sustainable real estate sector. These results and pertinent discussions may help the real estate decision makers to uplift the current state, move towards a smart real estate, and avoid futuristic regrets, especially in the COVID-hit environment where most of the industries are struggling to survive. Careful attention is required to the top regret factors identified in the study by the real estate managers, investors, and agents to pave the way for a more managed real estate and property sector whereby the consumers are more satisfied with the value they receive for their money. This win–win situation will enhance the property business and remove the stigmas of intentional and deliberate withholding of information by managers and agents from the property and real estate sectors that can help boost the business through more purchases and satisfaction of its customers.

Purpose The purpose of this paper is to investigate the level of implementation of Six Sigma (SS) in the construction industry of Pakistan along with the current state of affairs and the challenges, and opportunities for a successful implementation. Design/methodology/approach The research is purely exploratory in nature. Based on published work, critical success factors are gathered, and a number of questionnaire surveys and interviews are conducted to refine and quantify their impact. A system dynamics framework to assess the SS influence on project success is developed and case study project are simulated. Findings The construction industry of Pakistan is still functioning in a traditional way; marred with low level of awareness and ad hoc approaches, the findings point to a huge improvement opportunity. Further, when under planning projects are exposed to SS, the chances of project success improve better than under execution projects. Research limitations/implications The limited level of awareness possessed by the respondents constrains the possible outreach of this work in industrially developed contexts. However, this work may become an impetus for further research in managing quality in construction industry. Practical implications The findings can be used to improve the quality provision of construction projects. Originality/value This work may trigger an important debate over the research and implementation of SS in the construction industry of developing countries that may greatly benefit by improving the quality of their projects and rectify their diminishing reputation for project success.