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Broad application of artificial intelligence for document classification, information extraction and predictive analytics in real estate
Abstract
Real estate represents a major share of economic activities and wealth in all economies. Due to the lack of widely acknowledged standards, however, the structuring, providing and managing of a life cycle-comprehensive building documentation yet remain challenging. Based on the empirical analysis of 8965 digital documents from 14 properties of 8 different owners, the article presents a model that will unify existing approaches and lead to the development of a document classification standard. This provides the basis for software systems to process relevant data and create timely information over the entire life cycle of a building. Further, it is shown that automated information extraction through artificial intelligence will become instrumental for enhanced and innovative business models and products in real estate such as automated data validation and data evaluation, documentation review, benchmarking and other analytical applications.
... To use information further for building operation, there is an ongoing development from pure transaction data rooms to permanent data rooms. The aim is to provide information and documents without media breaks as well as to identify information unambiguously over all life cycle phases of a property (Bodenbender et al., 2019). ...
... With different classification algorithms (Naı €ve Bayes, Support Vector Machine, Deep Learning) the error rate in recognizing and interpreting text in documents could continually be reduced. With methods of supervised learning, hit rates above 80% are already achieved (Bodenbender et al., 2019;Russell and Norvig, 2016). The next step is information extraction. ...
Purpose
This research provides fundamentals for generating (partially) automated standardized due diligence reports. Based on original digital building documents from (institutional) investors, the potential for automated information extraction through machine learning algorithms is demonstrated. Preferred sources for key information of technical due diligence reports are presented. The paper concludes with challenges towards an automated information extraction in due diligence processes.
Design/methodology/approach
The comprehensive building documentation including n = 8,339 digital documents of 14 properties and 21 technical due diligence reports serve as a basis for identifying key information. To structure documents for due diligence, 410 document classes are derived and documents principally checked for machine readability. General rules are developed for prioritized document classes according to relevance and machine readability of documents.
Findings
The analysis reveals that a substantial part of all relevant digital building documents is poorly suited for automated information extraction. The availability and content of documents vary greatly from owner to owner and between document classes. The prioritization of document classes according to machine readability reveals potentials for using artificial intelligence in due diligence processes.
Practical implications
The paper includes recommendations for improving the machine readability of documents and indicates the potential for (partially) automating due diligence processes. Therefore, document classes are derived, reviewed and prioritized. Transaction risks can be countered by an automated check for completeness of relevant documents.
Originality/value
This paper is the first published (empirical) research to specifically assess the automated digital processing of due diligence reports. The findings are helpful for improving due diligence processes and, more generally, promoting the use of machine learning in the property sector.
... To effectively utilize the data extracted from the documentation, it is crucial to ensure proper handling of these documents. Previous studies have examined the appropriate methods for handling documentation in real estate management and technical due diligence processes (Bodenbender et al., 2019;M€ uller et al., 2021). These papers provide valuable preliminary insights and frameworks to help establish best practices for document management in real estate. ...
Purpose
We demonstrate the practical application of machine learning (ML) techniques in document processing, addressing the increasing need for digitalization in the real estate industry and beyond. Our focus lies on identifying efficient algorithms for extracting individual documents from multi-page PDF files. Through the implementation of these algorithms, organizations can accelerate the digitization of paper-based files on a large scale, eliminating the laborious process of one-by- one scanning. Additionally, we showcase ML-powered methods for automating the classification of both digital and digitized documents, thereby simplifying the categorization process.
Design/methodology/approach
We compare two segmentation models that are presented in this paper to analyze the individual pages within a bulk scan, identifying the starting and ending points of each document contained in the PDF. This process involves extracting relevant features from both the textual content and page design elements, such as fonts, layouts and existing page numbers. By leveraging these features, the algorithm accurately splits multi-document PDFs into their respective components. An outlook is provided with a classification code that effectively categorizes the segmented documents into different real estate document classes.
Findings
The case study provides an overview of different ML methods employed in the development of these models while also evaluating their performance across various conditions. As a result, it offers insight into solutions and lessons learned for processing documents in real estate on a case-by-case basis. The findings presented in this study lay the groundwork for addressing this prevalent problem. The methods, for which we provide the code as open source, establish a solid foundation for expediting real estate document processing, enabling a seamless transition from scanning or inbox management to digital storage, ultimately facilitating machine-based information extraction.
Practical implications
The process of digitally managing documents in the real estate industry can be a daunting task, particularly due to the substantial volume of documents involved, whether they are paper-based, digitized or in digital formats. Our approach aims to streamline this often tedious and time-consuming process by offering two models as simplified solutions that encourage companies to embrace much-needed digitization. The methods we present in this context are crucial for digitizing all facets of real estate management, offering significant potential in advancing PropTech business cases. The open-source codes can be trained further by researchers and practitioners with access to large volumes of documents.
Originality/value
This study illustrates effective methods for processing paper-based, digitized and digital files, along with tailored ML models designed to enhance these methods, particularly within the real estate sector. The methods are showcased on two datasets, and lessons learned are discussed.
... A user-based review highlighted the effectiveness of this method and its usefulness in real-world scenarios. Reference [27] focused on the efficient planning, supply, and administration of building life cycle documentation to develop novel best practices. The authors developed a framework for document classification and demonstrate their framework with an empirical study. ...
Despite the vast economic impact of real estate markets worldwide, research on real estate brokerage markets remains limited. Specifically, there are few studies that provide a systematic, integrated, and replicable analytical methodology to analyze and benchmark a given real estate brokerage market. To this end, this paper introduces a data analytics methodology for analyzing real estate brokerage markets, integrating various statistical and analytical methods to extract insights from market data, supporting real estate investment decisions. The applicability of the methodology is demonstrated with a case study analyzing data from the top 50 real estate brokerage firms in Dubai, UAE. As shown in the case study, applying this methodology to brokerage market data enables the visual benchmarking of firms, identification of similarities between them, profiling and comparison of clusters of firms, and exploration of the impacts of various categorical and numerical attributes on performance. A notable finding for the Dubai real estate brokerage market is that it takes a minimum of 700 days for a brokerage firm to mature and advance to the next level of business success.
... Out of 6.6 million houses held for auction in Korea nationwide, about 2 million were successfully sold; on average, the auction sales price was more than 71% of appraisal values [21]. The availability of up-to-date and complete transaction documents can enhance reliable estimation, reduce safety margins and mitigate estimated risks [9]. ...
This study examines the influence of technological advancement on the real estate and construction industry. Research articles published between 2006 and 2023 on advanced technological advancements in the construction industry and real estate sector were obtained from the Web of Science analysis. A total of 155 research articles were eventually explored using the VOSviewer application. Bibliometric coupling and co-occurrence networks were employed. The findings revealed that most advanced technological applications are suitable for improving the construction industry and real estate sector. Digital transformation, artificial intelligence, smart contracts, blockchain technology, industry 4.0, and the Internet of Things positively influence the construction and real estate sectors. Advanced technology innovations revolutionised both industries and provided solutions to their long-standing problems. Most of the huge problems facing the real estate sector and construction industry are rectified with the adoption of advanced technological innovations. However, the construction and real estate industries were among the least in adopting the technology despite their huge challenges in operational functionality. This is the first paper on bibliometric analysis that draws the readers’ attention to link the strength of the relationship established among variables rather than focusing mainly on citations. The study carefully incorporates necessary bibliometric normality analysis to prevent misleading findings that could lead to unreliable investment decisions of the stakeholders in real estate and the construction industry.
... -Data Collection: AI can automate the collection of material-related data from various sources, such as product databases, material suppliers, manufacturers, and construction documents (Bodenbender et al., 2019), as well as crawl and extract relevant data from websites, documents, and other digital sources, minimising the manual effort required (Kovačević & Davidson, 2008). ...
This thesis explores the integration of Circular Economy (CE) principles of narrow, slow, close, and regenerate in the social housing practice through digital technologies. Beginning with the examination of the CE implementation in Dutch social housing organisations, the research extends its focus to the broader built environment, introducing the Circular Digital Built Environment Framework and identifying ten enabling technologies. Subsequent chapters explore realworld applications of these digital technologies in circular new built, renovation, maintenance, and demolition projects of forerunner social housing organisations. The thesis includes a comprehensive study of material passports, addressing challenges around data management and proposing a digitally-enabled framework. The thesis concludes with critical reflections on the findings and their implications and provides further recommendations for research and practical applications in advancing circularity in the building industry through digital technologies.
... The problem is that each situation has its own particularity, and it is rare that two cases are the same. In an attempt to speed up these processes, authors like Bodenbender described in [83] an approach for labeling documents related to buildings. This process is accomplished through supervised learning using ML and NLP to classify the documents, where NLP enables the recognition and processing of texts in written language. ...
The article presents a Multivocal Literature Review (MLR) on the use of Artificial Intelligence (AI) in the real estate sector, aiming to analyze existing applications, literature gaps, and current challenges. The methodology involved defining keywords, searching online repositories, and applying inclusion/exclusion criteria. In total, 185 documents were selected and reviewed. Findings underscore the significance of areas such as personalized strategies for clients, real estate recommendation systems, and process automation. A literature gap was identified regarding the analysis of performance and accuracy of these applications, indicating the need for further research and technological development to meet the demands of the real estate sector.
... Smart cities are smart city models created by technologies such as cloud computing, Internet of Things, and AI. Use advanced information methods to analyze many fields such as urban planning, urban transportation, social security, and people's living policies, and make rapid and timely responses to these fields to achieve intelligent and intelligent urban management [33]. At present, the development of many cities in China is facing problems such as air pollution, garbage pollution, water pollution, traffic congestion, and resource exhaustion [34,35]. ...
21st century has witnessed a profound metamorphosis in human civilization, primarily driven by the confluence of advanced network technologies and industrial modernization. This transformative period has expanded our understanding of the world, paving the way for innovative concepts such as the “smart city”. At its essence, a smart city harnesses the power of artificial intelligence (AI) to revolutionize urban living, presenting a paradigm shift towards more efficient service models and an elevated standard of living for its inhabitants. Integrating AI into the fabric of urban infrastructure marks a monumental leap in societal evolution, underscoring the imperative to cultivate and advance AI technologies. This paper endeavors to elucidate the multifaceted applications of AI within the domains of smart cities, illuminating its pivotal role in shaping and advancing our contemporary era. From intelligent transportation systems and energy management to public safety and healthcare, AI permeates various aspects of urban life, ushering in unprecedented efficiencies and novel solutions to age-old challenges. The symbiotic relationship between AI and smart cities is explored in detail, showcasing how AI technologies are instrumental in optimizing resource allocation, improving decision-making processes, and ultimately enhancing the overall quality of life. Furthermore, this paper delves into the imperative of fostering the development and advancement of AI technologies within the context of smart cities. It underscores the interconnectedness of technological progress and urban development, emphasizing how a concerted effort to cultivate AI capabilities can propel cities into a future marked by sustainable growth, resilience, and innovation. The exploration of challenges and opportunities in deploying AI within urban environments adds a critical dimension to the discourse, encouraging a balanced consideration of ethical, regulatory, and societal implications. In conclusion, this paper seeks to contribute to the ongoing dialogue surrounding smart cities and the transformative impact of AI. By shedding light on the diverse applications of AI within urban landscapes and emphasizing its pivotal role in shaping the trajectory of our era, it underscores the critical importance of advancing AI technology development for the continued progress of smart cities and, by extension, the broader global community.
... Combining machine learning methods (random forest) with text mining on social network data using real estate-related keywords to predict house price trends has shown successful results in [35]. [80] investigated 8.965 digital records from 14 properties owned by 8 distinct owners and provided evidence in support of the view that advanced and innovative real estate business models and services would rely on automated information retrieval through artificial intelligence. For a Swiss real estate sample, [81] demonstrated that neural networks surpass ML algorithms designed exclusively on tabular information, while visual data boost rental forecasts. ...
The advanced urban digitization enhancing a huge volume of data collected in many areas has led to the emergence of artificial intelligence (AI) based tools in decision support systems. These use various machine learning algorithms to extract valuable information for important decision-making such as house price predictions. The urban real estate investment business model is undergoing a fundamental overhaul attributed to digitization and a growing market for smart and environmentally demanding buildings. This technological breakthrough is reinforced by AI data analysis which greatly improves decision-making by anticipating price changes through predictive modelling. This issue is reinforced by the strong growth in the number of scientific papers published in recent years on this problem. Nevertheless, scarce effort has been made to assess what has been done thus far in order to identify the possibilities, the most popular or flexible techniques, the effect, and the challenges in order to expand the scope going forward. To fill this gap we evaluated 70 of the most relevant papers selected from the Scopus database. Overall, our study revealed a significant concentration of publications from the USA, China, India, Japan, and Hong Kong. These countries have the particularity not only to be very digitized and the more advanced research but also the higher stakes requiring the best decision-making. On the other hand, the data sizes used were often relatively small and the research areas of the authors would favour the strong use of simple ML methods over deep learning methods. Future research and applications should not only be enriched by the large and accurate data coming from the increased digitalization of cities and thus urban real estate but also address the explainability challenges of the models built. Addressing these non-exhaustive challenges would allow for better management of both research and business model design through a better understanding and use of intelligent decision support systems by real estate stakeholders.
... AI will continue to play an important role in the real estate industry, becoming an instrument for value-added and innovative business models and products. Examples include automated data validation and evaluation, documentation review, benchmarking, and other analytical applications [14]. The use of AI in the real estate industry will help improve the buying and selling process, making it more efficient, accurate, and cost-effective. ...
Real estate plays a crucial role in driving national economies. However, the process of transferring properties
and engaging with various stakeholders can be hindered by a lack of adequate information, complex procedures,
and excessive paperwork. The advent of digital real estate has revolutionized the industry and how stakeholders interact. The present study aims to conduct a bibliometric and systematic review of digital real estate, utilizing historical,
institutional, country, and keyword analyses for the bibliometric review and Preferred Reporting Items for Systematic
Reviews and Meta-Analysis (PRISMA) guidelines for the systematic review. Through thematic analysis, the study identified four key themes for transforming digital real estate: information communication technologies, data collection
technologies, data networking tools, and digital decision-making systems. Additionally, the study proposes a digital
real estate transformation framework that can assist stakeholders, urban planners, and decision-makers in embracing digital tools and technologies. The study concludes that digital real estate has the potential to revolutionize
future urban planning and real estate development through the use of decision support systems and advanced
technologies.
... General articles on DT relating to opportunities for and barriers to DT adoption or articles discussing new areas of application for DTs were excluded, for instance, those exploring IoT-related benefits such as tracking behavior, enhancing situational awareness, producing analytics, and automating processes (Angeles, 2019;Viriyasitavat et al., 2019). Similarly, articles analyzing the application of AI for document classification, information extraction, and predictive analytics (Bodenbender et al., 2019) or those focusing on how the volume, velocity, and variety of big data can help in addressing problems such as poverty, illness, conflict, migration, natural disasters, and so forth in developing countries were excluded (Chandy et al., 2017). ...
Digital technologies (e.g. Industry 4.0, Internet of Things, cloud computing, big data, blockchain, etc.), are profoundly affecting companies' activities and processes, thus leading to changes in firms' value creation, value delivery, and value capture mechanisms. Yet, despite significant investments in digital technologies and digital transformation, firms are struggling to yield the most out of them, thereby facing a digital paradox. This scenario has drawn the attention of academics and practitioners leading to a growing body of literature on the relationship between digital technology and business model innovation. Yet, the extant academic research in this area appears highly fragmented. Hence, this study conducts a systematic literature review to gather and synthesize the extant knowledge on this topic. The review identifies four main thematic areas, provides an interpretative framework, and suggests valuable future research directions within each thematic area. The article contributes to the theoretical and managerial discussion on digital-driven business model innovation.
... People want to live better, consume quality service and travel. Digitalization also covers such an important sector of the economy as the real estate industry (Bodenbender et al., 2019). Due to digitalization, there is a transition from the traditional economy to bioeconomics (Watanabe et al., 2019). ...
The aim of the study is to test the hypothesis that the development of the digital economy contributes to a change in the structure of the labor market, the release of part of the labor force, and the growth of demand in the field of information technology. Monographic and statistical research methods, methods of economic analysis, economic and statistical modeling, tabular, graphical methods were used in the work. The research information base was made up of official data from European and Russian statistics, data from the Ministry of Science and Higher Education of the Russian Federation for the period from 2009 to 2018. The proportion of specialists using the Internet for the period from 2009 to 2018 in the countries of the European Union increased from 43 to 55%. However, digitalization is affecting a changing labor market, with a growing percentage of people working part-time. In the EU countries, this indicator increased to 18.5% in 2018, i.e. 1.2 percentage points over ten years. The results of the correlationregression analysis showed that there is a positive relationship between the rate of computer and Internet use by employees in organizations and the percentage of part-time workers is (R² = 49%). Over the past ten years in the Russian Federation, the total labor force has increased by 6%. Digitalization through entrepreneurial education penetrates almost all spheres of the life of society and helps to increase economic efficiency and increase labor productivity, changing the structure of employment. © 2021, Economic Laboratory for Transition Research. All rights reserved.
... In the real estate sector, its application exists in property value forecasting, reality development, city management and others [10]. AI and Robotics deal with performing the complicated and intelligent function with minimum human involvement have made their way into the real estate sector through predictive analytics, blockchain taxation, and customer recommendations [11]. Cloud deals with data synchronization and networking over the internet and has applications such as information value for investors' analyses of real estate market, portfolio management and elastic utilization of resources [12]. ...
Digital disruptive technologies are an integral component of the modern world. These technologies are transforming the global industries from traditional to more innovative and adaptive. However, the state of global real estate is yet to improve and is currently lagging the technology curve. Because of this lag, useful information is either not made available to the end-users or is shared too late that is raising concerns among the online real estate platform users. This results in larger vacancy rates and post-occupancy regrets among the service consumers. The current study based on the concepts of Technology Acceptance Models (TAM), presents a conceptual Real Estate Stakeholders Technology Acceptance Model (RESTAM) for addressing the key needs of the four important stakeholders of the real estate industry including the end-users or consumers, government & regulatory authorities, agents & agencies and complementary industries. Based on comprehensive literature review of 213 articles, the needs of these stakeholders are assessed and addressed through the Big9 technologies namely drones, the internet of things (IoT), clouds, software as a service (SaaS), big data, 3D scanning, wearable technologies, virtual and augmented realities (VR & AR), and artificial intelligence and robotics. The resulting RESTAM framework with a specific focus on the online platform based real estate users are expected to lay the foundation for introducing the missing technology acceptance model for real estate stakeholders whereby these Big9 disruptive technologies are implemented in real estate industry to uplift it from traditional to smart real estate. This will reduce the post-occupancy regrets of the real estate service users and improve the relations between various real estate stakeholders.
... Examples can be seen in the research articles [6][7][8]. As in other industries, many more use cases may apply such as using machine learning in evaluating the real estate confidence index (RECI) [9] and in real estate document classification [10]. In North America, real estate listing data are stored on MLS servers owned by different regional real estate associations. ...
RETSManager is a Django based platform for retrieving, storing, and synchronizing real-estate data and images from multiple listing service (MLS) servers. This platform can be used to construct synchronized and up-to-date real-estate datasets for usage in research fields such as machine learning, image-based deep learning, and housing market statistical analysis. The platform converts and synchronizes the raw XML data originating from the MLS servers to a structure data in a PostgreSQL or SQLite database. Additionally, it supports storage and synchronization of images either on a local drive or on an Amazon Web Services (AWS) S3 bucket. The platform is production-ready and can be deployed as multiple Docker containers including a pre-configured Nginx container for web application support and Celery and Redis containers to supports scheduled periodic updates. Keywords: Django, Real estate, RETS, Machine learning
Gender inequalities in housing affordability remain a persistent and pervasive issue in urban environments, yet their manifestations in modern housing arrangements, such as co-living, remain underexplored. This study investigates the nuanced ways in which gender intersects with housing affordability in New Zealand’s co-living rental market, revealing how systemic disparities manifest in this innovative yet understudied housing typology. Analysing 4,378 rental listings, we uncover that women consistently face higher rental costs for comparable properties—a phenomenon rooted in broader socio-economic dynamics rather than overt bias. Our findings suggest that these disparities are shaped by transaction costs linked to safety, privacy, and perceived management complexities, which landlords embed in pricing strategies. Through an economic modelling framework, we show how these market mechanisms inadvertently amplify gendered inequalities, reinforcing barriers to equitable access. Co-living offers a unique lens to examine these inequalities, acting as both a reflection of and a response to systemic challenges in urban housing markets. By contextualising gender-specific pricing within broader housing and affordability policies, this research offers critical insights for fostering inclusive rental markets. Addressing these hidden costs is essential to reshaping housing systems and ensuring equitable access to secure, affordable urban living for all. JEL Classifications: R31, J16
Purpose
Modern buildings use intelligent automation for comfort, efficiency and sustainability, impacting their construction and operation. Although building automation (BA) operates via bus lines and is controlled by sensors and actuators, computer-aided facility management (CAFM) systems often handle data redundantly. Current standards fail to detail effective systems integration, with a noticeable gap in practical network models and solutions.The purpose of this paper is to design a network model that integrates building services and networks at the automation level. The goal is to enable the CAFM side to control all electrical loads (such as lighting, blinds, pumps), climate control (HVAC) and security monitoring.
Design/methodology/approach
This paper explores the automatic discovery and integration of BA devices and centralized controls into CAFM systems, focusing on innovative networking models, system data provisioning, import functions and web operation. Established technologies such as Universal Plug and Play (UPnP) and Extensible Markup Language (XML) standards are utilized to develop new solutions.
Findings
The paper introduces a solution with a database and software module enabling bidirectional web-based coupling via LAN. The UPnP standard was enhanced to include facility management (FM)–specific information for device communication. The prototype effectively controls devices through CAFM systems, setting a foundation for future improvements in web-based BA. These results are crucial for developing standards for automated data processing between CAFM systems and BA.
Practical implications
This research benefits FM, especially in maintenance, operations, energy and compliance. In addition, the need for time-consuming on-site inspections to record device master data for maintenance management, in case of commissioning or changing facility service providers, can be eliminated. The principles of the developed software module enhance CAFM systems as high-performance building control tools.
Originality/value
The paper adapts existing technologies for specific FM applications and integrates them for the first time into key FM processes.
INTRODUCTION:-In the realm of building management, safety and security stand as pillars of paramount importance. From commercial skyscrapers to residential complexes, ensuring the well-being of occupants and safeguarding valuable assets is a top priority. Fortunately, advancements in technology have ushered in a new era of safety and security measures, enabling buildings to be smarter, safer, and more secure than ever before. In this article, we explore how these advancements are reshaping the landscape of building management and fortifying our foundations.
Passports for circularity, e.g., digital product passports and material passports (MPs), have gained recognition as essential policy instruments for the Circular Economy goals of the European Union. Despite the growing number of approaches, there is a lack of knowledge about the data requirements and availabilities to create MPs for existing buildings. By deploying a mixed-method research design, this study identified the potential users and their data needs within the context of European social housing organisations. Three rounds of validation interviews with a total of 38 participants were conducted to create a data template for an MP covering maintenance , renovation, and demolition stages. This data template was then tested in a case study from the Netherlands to determine critical data gaps in creating MPs, including, but not limited to the composition of materials, presence of toxic or hazardous contents, condition assessment, and reuse and recycling potential of a product. Finally, an MP framework is proposed to address these data gaps by utilising the capabilities of enabling digital technologies (e.g., artificial intelligence and scanning systems) and supportive knowledge of human actors. This framework supports further research and innovation in data provision in creating MPs to narrow, slow, close, and regenerate the loops.
Purpose
The paper aims to provide a comprehensive analysis of artificial intelligence’s (AI) transformative impact on the real estate industry. By examining various AI applications, from property recommendations to compliance automation, this study highlights potential benefits such as increased accuracy and efficiency. At the same time, this study critically discusses potential drawbacks, like privacy concerns and job displacement. The paper's goal is to offer valuable insights to industry professionals and policy makers, aiding strategic decision-making as AI continues to reshape the landscape of the real estate sector.
Design/methodology/approach
This paper employs an extensive literature review, combined with a qualitative analysis of case studies. Various AI applications in the real estate industry are examined, including machine learning for property recommendations and valuation, VR/AR property tours, AI automation for contract and regulatory compliance, and chatbots for customer service. The study also delves into the optimisation potential of AI in building management, lead generation, and risk assessment, whilst critically discussing potential challenges such as data privacy, algorithmic bias, and job displacement. The outcomes aim to inform strategic decisions for industry professionals and policy makers.
Findings
The study finds that AI has significant potential to revolutionise the real estate industry through enhanced accuracy in property valuation, efficient automation and immersive AR/VR experiences. AI-driven chatbots and optimisation in building management also hold promise. However, this study also uncovers potential challenges, including data privacy issues, algorithmic biases, and possible job displacement due to increased automation. The insights gleaned from this study underscore the importance of strategic decision-making in harnessing the benefits of AI while mitigating potential drawbacks in the real estate sector.
Practical implications
The paper's practical implications extend to industry professionals, policy makers, and technology developers. Professionals gain insights into how AI can enhance efficiency and accuracy in the real estate sector, guiding strategic decision-making. For policy makers, understanding potential challenges like data privacy and job displacement informs regulatory measures. Technology developers can also benefit from understanding the sector-specific applications and concerns raised. Additionally, highlighting the need for addressing algorithmic bias and privacy concerns in AI systems may foster better design practices. Therefore, the paper's findings could significantly shape the future trajectory of AI integration in real estate.
Originality/value
The paper provides original value by offering a comprehensive analysis of the transformative impact of AI in the real estate industry. Its multi-faceted examination of AI applications, coupled with a critical discussion on potential challenges, provides a balanced perspective. The paper's focus on informing strategic decisions for professionals and policy makers makes it a valuable resource. Moreover, by considering both benefits and drawbacks, this study contributes to the discourse on AI's broader societal implications. In the context of rapid technological change, such comprehensive studies are rare, adding to the paper's originality.
The current practice of cadastral and forensic activities shows its subjectivity. The expert makes a resolution in each specific case and for each specific real estate object. The traditional priority of a paper document over an electronic one also plays an important role. The article deals with the problem of the EGRN data reliability, which, according to the author, consists in the lack of unified approaches to gathering and processing source documents, analyzing the set of real estate objects characteristics, as well as their verification (audit). The purpose of the study is to develop a method of multi-documentary cadastral audit of real estate objects, which consists of a set of procedures for processing and analyzing documented cadastral information presented in the form of a disparate documents set. The results of the study showed the possibility of unifying and justifying a reliable model of a real estate object at any time, which is essential for verifying the reliability of the registered characteristics in the EGRN and formalizing expert activities. The developed method of multi-documentary cadastral audit simulates the work of a specialist and enables automating expert activities in the field of real estate, which meets modern trends in automation and digitalization.
As a rapidly developing field of scientific and technological innovation, artificial intelligence has a profound impact on the development of literary works appreciation. This paper mainly studies the preliminary exploration and future prospect of literary works appreciation under the background of artificial intelligence. On the basis of extensive and in-depth analysis of relevant materials, this paper draws lessons from similar questionnaires, and designs the questionnaire scientifically and reasonably according to several years’ teaching experience. A total of 225 questionnaires were distributed and 202 valid questionnaires were collected. In this paper, a comprehensive and in-depth investigation of junior high school students’ Chinese learning, to understand their learning motivation and subjective understanding, from the standpoint of students to explore and study the feasibility and effectiveness of cultivating aesthetic appreciation ability. In this paper, anonymous survey is used to ensure the accuracy and objectivity of the survey results. Before the formal distribution of the questionnaire, a pre-test was carried out, and the consistency and reliability of the questionnaire were tested by SPSS software. The data shows that 14.8% of the students have good literary appreciation level, 68.4% of the students have a general level of literary appreciation, 14.8% of the students are in a poor state, and 2% of the students have poor literary appreciation level. The results show that artificial intelligence technology can promote the appreciation ability of literary works.
Lebenszykluskosten (LZK) sind ein wichtiges Kriterium bei der Konzeption, Planung, Errichtung und dem Betrieb von Gebäuden. Die Beeinflussbarkeit der Kosten ist in der Planungsphase am größten. Zentrales Ergebnis von LZK-SIM [BAU] ist ein öffentlich zugängliches, einfach anzuwendendes Tool zur LZK-Optimierung von Gebäuden. Der Benutzer erhält durch Eingabe weniger in der Planungsphase vorliegender Informationen eine LZK-optimierte Konfiguration für ein Gebäude. Bauherr, Planer und Architekt können durch das LZK-SIM [BAU] Tool schon während der Planungsphase substanzielle Informationen über die zu erwartenden LZK erhalten. So können Zielkonflikte bzw. Optimierungspotenziale frühzeitig identifiziert und Planungen angepasst werden. Das Tool erlaubt darüber hinaus die Optimierung der Konfiguration von Bauteilen und technischen Anlagen bezogen auf den gesamten Lebenszyklus.
Förderkennzeichen: SWD-10.08.18.7-16.18, Projektlaufzeit: 09/2016 - 10/2018. Best.-Nr. F 3117 (Kopie des Manuskripts)
Zum Download: https://www.zukunftbau.de/projekte/forschungsfoerderung/1008187-1618
Der Beitrag beschreibt die Transformation von einer analogen zu einer digitalen und lebenszyklusübergreifenden Gebäudedokumentation. Eine solche Dokumentation ist eine wesentliche Grundlage für nachhaltiges Bauen und Betreiben von Immobilien und im gemeinsamen Interesse von Eigentümern und Betreibern. Digitale Lösungen übernehmen die Bereitstellung und KI-gestützte Technologien erleichtern die Auswertung und Vernetzung von Informationen, Dokumenten und Systemen. Dies ist Teil der „Plattform-Ökonomie“, die sich in weiten Teilen der Wirtschaft entwickelt.
Moderne IT-gestützte Systeme wandeln analoge in digitale Dokumente um, die damit durch Künstliche Intelligenz analysierbar sind. Neben der Klassifizierung von Dokumenten arbeiten Unternehmen und Hochschulen daran, die Automatisierung der Informationsextraktion marktfähig zu machen. Eine interessante Anwendungsmöglichkeit bildet die Informationsextraktion aus Dokumenten z.B. zur Simulation von Lebenszykluskosten für Immobilien. Der Beitrag gibt einen Einblick in technische Möglichkeiten und laufende Forschungsprojekte an der TUK sowie bevorstehende Veröffentlichungen.
Argues that the knowledge management process can be categorized into knowledge creation, knowledge validation, knowledge presentation, knowledge distribution, and knowledge application activities. To capitalize on knowledge, an organization must be swift in balancing its knowledge management activities. In general, such a balancing act requires changes in organizational culture, technologies, and techniques. A number of organizations believe that by focusing exclusively on people, technologies, or techniques, they can manage knowledge. However, that exclusive focus on people, technologies, or techniques does not enable a firm to sustain its competitive advantages. It is, rather, the interaction between technology, techniques, and people that allow an organization to manage its knowledge effectively. By creating a nurturing and “learning-by-doing” kind of environment, an organization can sustain its competitive advantages.
This review article identifies and discusses some of main issues and potential problems - paradoxes and pathologies - around the communication of recorded information, and points to some possible solutions. The article considers the changing contexts of information communication, with some caveats about the identifi- cation of 'pathologies of information', and analyses the changes over time in the way in which issues of the quantity and quality of information available have been regarded. Two main classes of problems and issues are discussed. The first comprises issues relating to the quantity and diversity of information available: infor- mation overload, information anxiety, etc. The second comprises issues relating to the changing information environment with the advent of Web 2.0: loss of identity and authority, emphasis on micro-chunking and shallow novelty, and the impermanence of information. A final section proposes some means of solution of problems and of improvements to the situation.
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