Emerging Digitalization in Property/Facility Management: A State-of-the-Art Review and Future Directions

Abstract

Digitalization has become a driving force for significant advancements in property/facility management (PFM). It is necessary to identify the research gaps and future research directions, which could enable the effective development of digital technologies (DTs) in the context of PFM. This paper aims to review how DTs emerge to drive digitalization in PFM and identify gaps that need to be addressed in future research. The findings reveal that research on integrating BIM, IoT, AR, AI, and big data in sustainable transformations, real-time data, and energy optimization is limited, with challenges in data security, privacy, and system interoperability. Future research should focus on BIM for sustainability, real-time data, and AR applications, alongside IoT and blockchain integration for security. Investigating VR in maintenance, AI for energy optimization, improved prediction accuracy, and enhanced NLP for chatbots are also critical areas for exploration. This state-of-the-art review summarized the gaps from the existing literature of property management digitalization and provides an update on research gaps and directions for the digitalization in PFM.

1. Introduction

Property management (PM) and facility management (FM) play significant roles in the effective operation and maintenance of built asset management, yet both share similar concepts and encompass slightly distinct responsibilities (see

Table 1. Summary and comparisons of PM and FM.

Aspect	Property Management (PM)	Facility Management (FM)
Scope	Real estate properties	Physical assets and infrastructure within an organization
Asset type	Residential, commercial, and industrial properties	An organization’s physical assets and infrastructures
Objective	Maximizing the value and profitability of the property for the owner or investor	Creating and maintaining a safe, functional, and efficient working environment
Task	Leasing, rent collection, maintenance and repairs, tenant relations, and financial management	Maintenance and repairs, space planning, security, energy management, and environmental sustainability
Complexity	Day-to-day operational tasks of managing individual properties	Manages diverse facilities, buildings, equipment, systems, and services that support the organization’s core operation
Integration with organization	Specific to individual properties	Aligns physical assets and resources with the organization’s goals and objectives

). Originally, PM was first proposed by Octavia Hill in the mid-19th century, referring to the administration of real estate properties and primarily focusing on maximizing the value and profitability of residential, commercial, and industrial assets [1]. Key responsibilities of PM include leasing, rent collection, maintenance and repairs, tenant relations, and financial management [2]. The overarching goal of PM is to create a sustainable relationship between property owners and tenants, ensuring that properties are well maintained and financially viable [3]. On the other hand, FM encompasses a more comprehensive management scope of a facility’s physical assets and infrastructures. FM includes not only the buildings, equipment, systems, and services but also the maintenance and repairs, space planning, security, energy management, and environmental sustainability, aiming to create a safe, functional, and efficient environment for occupants [4,5,6,7]. This paper proposes the concept of property/facility management (PFM) as a term reflecting the increasing convergence of PM and FM practices in response to evolving industry trends and technological breakthroughs. The hybridization of PM and FM enables a more holistic analysis of built asset operational management and is in line with contemporary shifts in the industry, where the boundaries between PM and FM are becoming more fluid.

The application of digital technologies (DTs) has experienced increasing popularity in the latest research on PFM, incorporating the application of cutting-edge technologies and analytics into management strategies to support better decision-making and more effective operations. Examples of digitalization in PFM include building information modeling (BIM) [8,9], 5G [10], artificial intelligence (AI) [11], big data [12], immersive technologies (IT) [13,14], and the Internet of Things (IoT) [13,15]. As a result of these innovations, decision-makers can monitor and optimize the performance of built assets and respond proactively to maintenance requirements and occupant feedback. These DTs have been used in several contexts in the recent literature, underlining their possible contributions to the development of PFM, especially in public infrastructure [10,16,17]. Moreover, broad reviews of digitalization trends and challenges in FM provide valuable insights into performance measurement frameworks and regional developments [16,18]. Still, most of the existing research focuses on particular sectors or technologies, such as the public sector or healthcare. Hence, there has been no such a thorough review covering the topic of digitalization in PFM. In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework for study selection, bibliometric data were extracted from Scopus and Web of Science and further analyzed using VOSviewer (version 1.6.18). Content analysis was conducted through a manual review of abstracts and texts. Key themes and technological applications were identified and categorized through iterative coding.

Hence, this paper seeks to fill this gap by providing a comprehensive and critical review regarding the adoption of DTs in the field of PFM based on the hybridization of scientometric analysis and systematic review. Below are the objectives of this study:

(1)

To bibliometrically retrieve all the relevant literature conducted on the areas concerned with digitalization in PFM;

(2)

To present a well-rounded scientometric analysis that encompasses active countries, source analysis, co-author analysis, most cited articles, and co-occurrence of author keywords;

(3)

To elaborate a comprehensive, systematic discussion regarding the reviewed studies categorized according to the types of emerging DTs from the key domains of PFM;

(4)

To discuss and elaborate on existing research gaps and make recommendations for future research.

Throughout the remainder of this paper, the following sections are presented. Section 2 outlines the actions involved in the bibliometric survey. Section 3 elaborates on the scientometric analysis results. Section 4 presents the findings of an in-depth systematic investigation. Further, Section 5 presents both research gaps and future directions. Lastly, Section 6 provides the concluding remarks of this research.

2. Bibliometric Survey

The flowchart of the literature retrieval is shown in Figure 1. Conducting a bibliometric survey is a crucial step at the beginning of a literature review. Once the review study goal is set, keyword selection is conducted to identify the most relevant documents on the defined research topic. Since this study focuses on the digitalization in PFM, the selected keywords are “facilit* management” [8,9,10,13,15,18,19] OR “propert* management” [12] AND “digital” [18,19] OR “digitalization” [18,19] OR “digital technolog*” [18,19] OR “BIM” [8,9,13,15] OR “building information modelling” [8,9,13,14,15] OR “GIS” [20] OR “geographic information system” [20] OR “IoT” [11,15] OR “internet of things” [11,15] OR “big data” [12] OR “AI” [13] OR “artificial intelligence” [13] OR “immersive technolog*” [15,16] OR “augmented reality” [13] OR “AR” [13] OR “virtual reality” [14] OR “VR” [14] OR “5G” [10]. In addition, the selected keywords are used to search for relevant publications in peer-reviewed journal papers published in English from 1 January 2012 to 30 April 2025 in two prominent database repositories, i.e., Scopus and Web of Science (WoS). Given the multidisciplinary nature of digitalization in PFM, the selected publications focus on specific technical and management domains, spanning fields such as engineering, energy, computer science, environmental science, business, management, decision science, and social sciences. Duplicates are then eliminated, and the filtered results from the two databases are combined into one file. Forward and backward snowballing techniques are used to locate related studies. In total, 535 papers (502 research papers and 33 review papers) were first obtained; further screening turned up another 378 papers (361 research papers) within the planned study area. To pinpoint future research directions in digitalization within PFM, a subset of 116 research articles from quality journals and highly cited sources was further selected and used for the scientometric analysis and systematic review in the following sections.

3. Results of Scientometric Analysis

3.1. Publication Trends

This subsection examines the trend of the retrieved studies on the digitalization in PFM. Although digitalization was applied to PFM in the 2000s, it received limited attention until the 2010s, and only a few papers were published before that period. Since the early 2010s, there has been a significant increase in terms of the number of publications on this topic. Figure 2 shows that the number of research papers increased exponentially from 5 in 2012 to 87 in 2024. This increase indicates an increasing appreciation of the role of DTs in improving the practice of PFM. Publications have surged dramatically since 2015, reflecting a more general trend toward digital transformation across sectors. Technological advances and growing demands for more efficient management solutions are likely to continue this trend.

3.2. Contribution of Countries/Regions

The VOSviewer software was used for the quantitative analysis, including active researchers, sources, co-authors, most articles cited, and the co-occurrence of author keywords. The data were analyzed by the full-count method, and each country/region listed in the publication was fully credited, regardless of the distribution of the co-authors.

Table 2. Contribution of countries and regions.

Country/Region	Documents	Citations
United States	94	2674
United Kingdom	72	2023
China	70	797
Italy	37	611
South Korea	32	806
Malaysia	29	263
India	29	102
Australia	22	639
Germany	22	220
Hong Kong SAR	17	542

lists the total number of citations and documents of the top ten contributing countries/regions. As shown in Table 2 and Figure 3, in the United States, 94 documents have been published, and 2674 citations have been found. Furthermore, the United States has the strongest overall link strength, which considerably influences other countries’ research. The United Kingdom and China are also the second and third most productive contributors to the domain of digitalization in PFM globally. The combined outputs of these three countries (the United States, the United Kingdom, and China) account for about 40% to 50% of the papers, citations, and linkages of the documents in the search results. The concentration of research activities in these countries shows that they are leading the digital transformation of PFM. They have had a significant influence on global PFM research and development, as well as on the direction and advancement of this discipline.

3.3. Analysis of Sources

As for the journals’ contributions (see

Table 3. Contribution of journals.

Journal	Documents	Citations
Facilities	18	471
Buildings	16	220
Automation in Construction	15	1765
Journal of Facilities Management	13	218
Applied Sciences	12	416
Sustainability	9	176
Journal of Building Engineering	7	350
Engineering, Construction and Architectural Management	6	204
Journal of Information Technology in Construction	5	206
Journal of Construction Engineering and Management	3	822
International Journal of Building Pathology Adaptation	3	99

and Figure 4), Facilities ranks as the most productive research outlet with 18 published documents out of 107. With an average of 26.2 per document, 18 papers gathered 4571 references. However, the most cited document was published in the Automation in Construction, with 1765 citations on average from 15 documents. The average citation rate in the document was 117.7, which had a major impact on the digitalization of PFM. Moreover, the Journal of Construction Engineering and Management received 822 references in three articles with an average citation rate of 274. Analysis of journal contributions can help guide researchers toward the important and influential journals relevant to this topic.

3.4. Analysis of Authorship

Table 4. Analysis of authorship.

Author	Documents	Citations	Total Link Strength
Dawood N.	4	515	4
Kassem M.	3	470	4
Wang X.	6	283	7
Wang Y.	3	226	5
Wang J.	4	197	6
Issa R. R. A.	7	156	4
Liu R.	4	139	4
Miettinen R.	3	102	0
Singh V.	3	100	3
Yalcinkaya M.	3	100	3

overviews the most productive authors in the area of PFM digitalization, including the number of published documents in which each author is mentioned and the total number of citations these documents have received. Based on the overall count of citations for their work, this information helps reveal the impact of each author. Dawood N. and Kassem M. are the top two contributing researchers, having published four and three papers with 515 and 470 citations, respectively, followed by Wang, X., Wang Y., and Wang, J., successively. Their contributions are vital since they emphasize the quality and applicability of their research in forming current debates in this field.

3.5. Most Cited Articles

Table 5. Most citied articles.

Source	Citations	Links	Average Citations per Year
Becerik-Gerber et al. [21]	758	11	63.2
Pärn et al. [22]	320	2	45.7
Kang and Hong [24]	265	4	29.4
Kassem et al. [25]	238	6	26.4
Pishdad-Bozirgi et al. [23]	234	4	39
Motamedi et al. [26]	215	7	21.5
Wetzel and Thabet [27]	188	1	20.9
Wang et al. [28]	163	1	14.8
Matarneh et al. [9]	153	6	30.6
Mignard and Nicolle [29]	147	2	14.7
Patacas et al. [30]	137	3	34.25
Shalabi and Turkan [31]	123	1	17.6
Dixit et al. [32]	122	5	24.4
El Ammari and Hammad [33]	119	1	23.8
Mannino et al. [15]	116	1	38.7
Nicał and Wodyński [34]	107	2	26.8
Edirisinghe et al. [35]	101	6	14.4
Lin and Su [36]	100	1	9.1

presents a benchmark on the impact of each paper by including records taken from the VOS viewer, including more than 100 references. The most cited paper was Becerik-Gerber et al. (2012) [21], and Pärn et al. (2017) [22] came in second. Since comparing the volume of references in older papers with that of more recent papers could be misleading, the number of annual citations was computed to evaluate the impact of each document. Becerik-Gerber et al. (2012) [21] have the highest number of average annual citations (i.e., 63.2). Pärn et al. (2017) [22] and Pishdad-Bozirgi et al. (2018) [23] rank second and third with 45.7 and 39 annual citations per year, respectively. This result helps scholars pinpoint the most significant and impactful research outputs related to PFM digitalization over the past years. Figure 5 provides a network visualization of the most important and impactful papers by stressing their relevance using different font sizes and node sizes. These results show the ongoing relevance of earlier works, e.g., Becerik-Gerber et al. (2012) [21], as well as more recent research, e.g., Mannino et al. (2021) [15]. This trend implies that both established and upcoming researchers are shaping the debate on digitalization in PFM.

4. Findings from Systematic Review

4.1. BIM and GIS

A majority of the retrieved studies focus on building BIM models for task management and using self-designed sensors for real-time data collection [37]. Other applications are BIM-based solutions addressing the possible gaps between design and performance, as well as workflow integration [38]. Many researchers have focused on including BIM systems or techniques to improve building energy management (EM). Costa et al. [39] proposed an integrated toolkit to support EM across various building processes and activities, involving structured performance using BIM, performance monitoring and analysis, building operation strategy evaluation, automated faulty detection, and EM strategy integration. Similarly, Gökçe and Gökçe [40] developed a system of multidimensional energy monitoring, analysis, and optimization for energy-efficient building operation by integrating BIM and other advanced technologies, which ultimately reduced the energy consumption and enhanced building efficiency. Furthermore, more ongoing studies are focusing on integrated eco-feedback systems, using BIM for better energy efficiency in buildings [41]. In addition to energy efficiency, the existing studies have also been conducted from the perspective of energy performance analysis (i.e., extraction of energy-related information) through BIM, e.g., Ladenhauf et al. [42] and Wang et al. [43]. Some of the retrieved studies focus on enhancing the delivery of safety information, where BIM is used to examine the transfer of safety information from the design and construction phases to the PFM phase. Wetzel et al. [44] developed a safety framework to integrate interoperability and input relevant safety information into a federated BIM model. Wetzel and Thabet [45] developed a BIM-based framework to identify, categorize, process, and present relevant structured and job-specific information to improve worker safety in PFM. Furthermore, the application of BIM in PFM was explored during the COVID-19 pandemic [46].

Preventive maintenance is gaining more scholarly attention in PFM. Eskandari and Noorzai [47] used the total productive maintenance approach with BIM to improve the maintenance performance of building facility systems. Zhan et al. [48] used BIM and image classification to improve PFM inspection and repair. Moreover, the BIM-oriented indoor data model is designed to support indoor navigation by leveraging advanced geometric and semantic information in BIM. This model offers data for navigation, including room window count, wall material identification, and emergency exit locating [49]. BIM could also be used for renovations, such as using BIM to evaluate and control flat transformability [50]. BIM can also help improve coordination, productivity, visualization, and value efficiency in the housing renovation industry by creating information models, electronic passports, and 3D projects for major maintenance work [51]. Heating, ventilation, and air conditioning (HVAC) is a prominent area of application within building service systems using BIM. Relevant research primarily focuses on troubleshooting HVAC-related problems and implementing fault detection and diagnostics in HVAC systems [52,53]. BIM is also considered for fault detection and diagnostics in HVAC systems, serving as a knowledge repository and database to document evolving facility information [54].

Mechanical, electrical, and plumbing (MEP)-related data can be included in the BIM as-built model for delivery. This includes the automatic establishment of logic chains, equipment grouping and labeling, and the transformation of BIM information into a GIS map model known as the BIM-FIM model [55]. In addition, an approach that automates the task of generating logic chains for MEP systems has been examined [56]. Additionally, a rule-based scan-to-BIM mapping pipeline method for plumbing systems has been developed to automate and improve the scan-to-BIM process for property management applications in the MEP field [57].

Several studies have been dedicated to optimizing the accuracy and management of as-built BIM models to facilitate and enhance PFM. One approach involves the development of as-built BIM model management systems for owners during project closeout. This system focuses on inspecting, modifying, and confirming the final as-built BIM models to ensure accuracy during the operational phase of PFM [58]. Another initiative is the exploration of a BIM interactive collaboration platform called V3DM+, which integrates 3D models and information data to assist PFM by providing users with a platform to browse facility data, initiate discussions, and assign maintenance tasks [59]. Several other studies focus on increasing the accuracy of as-built BIM models. Stojanovic et al. [60] used point clouds to record the state of the built environment and matched them to BIM geometry as specified. Moreover, a computer method has been developed to update BIM depending on inspection data [61]. Image-based modeling and material recognition techniques are also employed to generate as-built BIM for building facades [62]. Studies have also focused on improving the application of as-built BIM models. Characteristics of BIM, such as model complexity, are explored for their impact on transforming BIM into virtual environments for PFM [63]. Furthermore, the use of open standards and technologies is investigated to help building owners and PFM professionals validate and visualize asset information models (AIMs) [64].

The integration of Lean concepts and BIM stands out as a remarkable direction in the building sector context, producing better coordination, enhanced communications, more efficient processes, and waste reduction. It also facilitates constant improvement and helps streamline scheduling processes. The Digital Obeya Room framework was developed to assist PFM in utilizing Lean principles and BIM functionality [65]. Moreover, Lean–Agile BIM methods have been developed to support PFM in obtaining a low-risk, fast return on investment and enabling ongoing development [66]. Heritage buildings are another area of interest in BIM. For example, a historical building was badly damaged by fire in 1999 in Sintra, Portugal. Machete et al. [67] explored varying approaches and levels of complexity in creating BIM models for heritage buildings, discussing the challenges and benefits associated with information transfer between different BIM instances.

In recent years, research interests in integrating BIM and GIS technologies have increased. BIM focuses on creating, managing, and sharing information about the building lifecycle, while GIS can be used to store, manage, and analyze urban environment data [20]. Integrated applications of BIM and GIS are essential for smart city applications, demanding the amalgamation of data from both buildings and the urban environment. This method maximizes the strengths of BIM and GIS to improve the management and analysis of geographic data and building data [20]. As a result, it provides a more thorough and integrated viewpoint on geographic and building data, enabling well-informed decision-making. In summary, the integration of BIM and GIS applications into PFM focuses on the following: (a) heritage protection; (b) hazard response and crisis management (CM); (c) EM; and (d) space navigation. By means of BIM and GIS, more sustainable and effective PFM practices are encouraged, improving data management, analysis, and decision-making procedures in many aspects of PFM [29].

The integration of BIM and GIS offers several important challenges. Financial constraints are a significant concern, with a total cost of ownership consuming 15–25% of initial implementation budgets annually and integration costs sometimes exceeding 30% of annual IT budgets for smaller companies [68]. As for BIM, inconsistent data standards limit scalability; as for GIS at the urban level, spatial processing limitations impose restrictions. Particularly among seasoned facility managers, skill gaps and the cognitive complexity of learning integrated platforms could cause ongoing user resistance [69,70]. Ethical questions have also been raised in relation to the contested ownership of sensitive cultural data and the possibility of surveillance via spatial tracking [71,72].

4.2. IoT, 5G, and Big Data

Improving energy efficiency in PFM systems significantly depends on the inclusion of IoT and cloud-based technologies. As illustrated in Figure 6, this process involves the real-time acquisition of building energy data through sensors, combined with the application of machine learning algorithms to predict energy consumption [73]. PFM systems integrate IoT technologies and signal processing methods to enable diverse applications across building subsystems, such as lighting, HVAC, flexible loads, occupant detection, and fault diagnostics [74]. By developing and evaluating machine learning models, particularly those utilizing image-encoded time series data, it becomes possible to accurately infer device-level information from IoT signals, thereby facilitating energy savings [75].

Enhancements in IoT system detection capabilities are achieved through the calibration and validation of IoT sensors for building occupancy monitoring [76]. Moretti et al. [77] suggested using ultrasonic sensors to maximize operation and maintenance services, improving the efficiency of building operation and maintenance. Data analytics is also explored since smart meters and IoT sensors create enormous data volumes. This data is subject to analysis using business intelligence and analytics (BI&A) techniques, supporting data-driven decision-making [78]. Additional research explores the utilization of digital signage and mobile devices with IoT technology for wireless interaction and cloud-based content, aiming to simplify and improve facility inspection management.

In the context of PFM, employing big data involves collecting and analyzing extensive datasets to enhance efficiency and functionality [79]. Moreover, data integration and analytics play a crucial role in improving operational efficiency in PFM, necessitating standardization, training, and collaboration for the full utilization of data analytics potential [80]. Furthermore, safety and security considerations in operations involve big data analysis, including cluster analysis and time series analysis, to comprehend the current status of fire occurrences and minimize property losses.

Continuous cloud and processing costs associated with big data can be substantial, while the high implementation costs, especially for IoT infrastructure and 5G coverage, usually exceed the budgets of mid-sized facilities [81]. The scalability is also limited by complicated device management, poor indoor 5G penetration, and data performance concerns [82]. User resistance stems from the low usability of data dashboards, health concerns regarding 5G, and distrust of automated systems [83,84]. Ethical issues also arise from privacy concerns related to real-time tracking and algorithmic bias [85]. Additionally, the effective usage of these advanced technologies is limited by challenges such as integration with legacy systems, device compatibility, and shortages of skilled personnel [82,84].

4.3. IT

In PFM systems, the incorporation of virtual reality (VR) environments offers promising opportunities for enhancing visualization and interaction with integrated facility information. These environments provide a three-dimensional representation of the building, as demonstrated by the FM3D prototype. This system enriches the virtual facility with detailed data on spatial configurations, equipment locations, operational status, and energy consumption while also offering user-friendly control interfaces [86], as shown in Figure 7. AR can also be utilized to develop interactive inspection systems that support the detection of environmental anomalies during daily operations and maintenance activities [87]. One challenge currently under investigation involves the misalignment between real and virtual objects in AR applications. To address this issue, a drift–vibration–threshold function has been proposed to filter sensor signals on mobile devices and improve spatial accuracy [88]. Furthermore, mixed-reality applications, such as those developed by the geospatial and environmental tools team at the University of Jaén, aim to improve indoor facility visualization by aligning virtual three-dimensional models with real-world environments [89]. Leveraging information technologies in PFM, including BIM, IoT, and AR, requires the integration of additional DTs. These technologies provide real-time data and analytical tools that collectively support a comprehensive and up-to-date understanding of facility operations [90]. This integration enables more informed decision-making in PFM and facilitates the collection of energy-related information, contributing to improved building energy efficiency [91,92].

The widespread adoption of information technologies like AR, VR, and MR in PFM is hindered by significant cost, scalability, and operational challenges. Enterprise-grade hardware, content development, and ongoing system maintenance typically account for 25% to 35% of initial implementation budgets [93]. In large-scale deployments, AR systems often experience diminished accuracy, while MR applications are constrained by bandwidth limitations in environments with high metal density. VR systems, particularly in multi-user settings, are susceptible to performance degradation. Furthermore, user acceptance remains low due to several usability concerns, including motion sickness induced by VR, the complexity of AR interfaces, and limited perceived practical value [94,95]. Ethical and privacy-related issues also present barriers. These include the potential exposure of sensitive operations through MR recordings, psychological impacts associated with behavioral monitoring during VR-based training, and the risks of biometric data misuse in AR facial recognition systems. Furthermore, the lack of robust IT infrastructure, inadequate content development capabilities, and regulatory uncertainty surrounding VR safety standards continue to impede broader implementation efforts [96,97].

4.4. AI

Numerous studies have investigated various methods and systems aimed at optimizing energy consumption. These include intelligent agent-based systems, as shown in Figure 8, data-driven models for building energy performance; integrated frameworks that combine monitoring data with BIM models; and the application of algorithms such as genetic algorithms, random forest, and polynomial regression [98,99,100,101]. The use of AI to enhance energy efficiency has also been widely explored, encompassing data mining, machine learning, supervised learning algorithms, and interpretable model-agnostic techniques to improve the transparency of prediction inference mechanisms. Additional approaches include density-based clustering and artificial neural networks for anomaly detection [99,99]. Beyond the selection of technologies and models, some studies highlight the importance of automation in embedding AI into building management processes to improve both accuracy and operational efficiency [99]. Other research has examined the role of prosumer nodes, which utilize AI to generate and implement energy supply strategies. Further contributions include cross-national case studies conducted on various types of properties [99,99] and the development of trust scores to assess the reliability of predictive outcomes.

The application of AI technologies in the O&M of PFM systems has been examined in several studies. Morales Escobar et al. [102] proposed a fuzzy logic-based, context-aware control system to support intelligent decision-making in building PFM. Marzouk and Zaher [103] investigated the use of deep learning techniques to perform tasks such as facility classification, localization, data management, image recognition, location identification, and maintenance-related information retrieval. In a related study, a system based on support vector machines was developed to predict and manage building heating and cooling operations, demonstrating its effectiveness in control optimization [104]. Additionally, the potential of conversational interfaces for information delivery in PFM has been explored. These systems, leveraging ontology-based knowledge representation and natural language processing, improve the efficiency and user-friendliness of facility information retrieval processes [105].

Beyond analyses focused on individual systems, one example is the use of the Industry Foundation Classes (IFC) Web Ontology Language (OWL) ontology to translate IFC schema into a machine-interpretable format. This integration enables indoor navigation by identifying the global position of building elements and determining destination locations based on user commands [106]. AI technologies have also been applied to PFM in response to exceptional conditions, such as those arising in the post-pandemic context. For instance, Bayesian machine learning models have been employed to adapt to evolving post-pandemic scenarios through mitigation strategies that address inconsistencies in pandemic-related data [107]. Similarly, Maki et al. [19] explored the role of AI in healthcare systems, particularly in caregiving technologies. Their findings suggest that AI can enhance patient satisfaction, reduce waiting times, improve resource utilization, and increase staff efficiency. The study also outlines a strategic roadmap to support the digital transformation of healthcare facilities.

The adoption of AI in PFM presents significant challenges, particularly for mid-sized enterprises. High implementation costs remain a major barrier, driven by the need for extensive data preparation, investment in expensive edge processors, staff retraining, and recurring fees for cloud-based inference services [108]. Scalability is another concern, as distributed systems often encounter latency issues, reduced model accuracy, and increased energy consumption when deployed outside controlled training environments [109]. User resistance to AI-driven maintenance stems from several factors, including the lack of algorithmic transparency, concerns over job security from labor unions, and limited trust in automated decision-making systems [109,110]. Ethical concerns also arise, such as racial bias in facial recognition technologies, unjustified optimization of energy usage, and accountability challenges associated with opaque AI-driven processes [111]. Additional obstacles include poor data quality, regulatory ambiguity—particularly in sensitive sectors such as healthcare and education—and the difficulty of integrating AI with existing legacy building systems [112].

4.5. Critical Reflections on Digitalization in PFM

4.5.1. Contradictory Results on DT Adoption and Application

Although BIM is widely acknowledged for its long-term benefits across the infrastructure lifecycle, several studies, particularly those focusing on underdeveloped regions, have discovered persistent challenges, such as insufficient government support, which undermine its practical implementation [113]. Likewise, although the integration of BIM with AI, IoT, and big data presents a transformative trend in the PFM field, limitations still exist from the perspectives of data standardization and interoperability [114]. Although BIM is frequently associated with supporting sustainability goals, some studies highlight a lack of comprehensive empirical evidence to substantiate these claims [113,115], whereas others emphasize its importance in promoting environmentally responsible practices. These contradictory findings have suggested that the perceived advantages of DTs in the area of PFM might be overstated and that practical implementation challenges have not yet been adequately explored.

4.5.2. Prevalence Patterns of Technology Across Contexts

The application of DTs in PFM exhibits clear geographical, economic, sectoral, and institutional disparities. Our scientometric analysis reveals that the majority of existing research is concentrated in developed countries such as the United States, the United Kingdom, China, and Australia. This trend suggests that the findings may have limited applicability in resource-constrained communities. In many developing nations, inadequate infrastructure and weak government support remain the key barriers. The level of technological implementation is usually associated with a country’s economic capacity [116]. Sector-specific trends are also evident. AI remains underutilized in some sectors, such as healthcare and education; BIM is more commonly applied in heritage management; and IoT technologies are frequently used in energy systems. The level of technology implementation is also influenced by institutional and cultural factors. For example, organizational culture and inter-organizational trust have been shown to significantly affect the success of collaborative technologies [117].

4.5.3. Adequacy of Current Research Directions

There exist numerous critical concerns based on practical solutions, such as data privacy, ethical governance, and long-term usability. Although these issues are gaining increasing attention, fundamental aspects such as data standardization, interoperability, and maintainability assessment still receive inadequate attention. Meanwhile, limited validation of technologies in real-world environments and the lack of empirical studies on usability and implementation have weakened confidence in the practical relevance of the current research [18]. Additionally, limited attention to user-centered design, operational challenges, and structured change management frameworks leaves this digital transformation underdeveloped. This neglect of socio-technical complexity raises important questions about the relevance and sustainability of current research directions in meeting practical PFM requirements. Last, a review summary of the retrieved paper is shown in

Table 6. Review summary of the retrieved papers.

Source	Type of DT						Property Type	Application Area
	BIM	GIS	IoT	Big Data	VR/AR	AI		SM	EM	O&M	CM
Abdirad and Dossick [118]	X									X
Acampa et al. [50]	X						Public housing			X
Al-Daraiseh et al. [98]						X	School		X
Ali and Aldaiyat [104]						X				X
Altohami et al. [119]	X		X							X
Alwan and Gledson [120]	X							X
Amano et al. [121]	X									X
Antonino et al. [122]	X		X				Office building			X
Bakker and Veuger [123]				X						X
Becerik-Gerber et al. [21]	X									X
Carreira et al. [86]					X					X
Chang et al. [124]	X		X				School			X
Chen et al. [125]	X									X
Chen and Tsai [105]						X				X
Costa et al. [39]	X								X
Costin and Teizer [126]	X									X
Demirdöğen et al. [127]	X		X				Healthcare facility			X
Demirdöğen et al. [78]			X				Healthcare facility			X
Edirisinghe and Woo [35]	X									X
Ergen et al. [128]	X						Office building			X
Eskandari and Noorzai [47]	X						Commercial building			X
Fan et al. [99]						X			X
Francisco et al. [41]	X								X
Gheisari and Irizarry [90]	X									X
Gökçe and Gökçe [40]	X						School		X
Golabchi et al. [54]	X									X
Gomez-Jauregui et al. [88]					X					X
González et al. [106]						X				X
Mohamed and Marzouk [129]	X					X	School			X
Halmetoja [130]	X		X	X			Office building			X
Hamledari et al. [61]	X									X
Hijazi et al. [131]	X	X							X
Hilal et al. [8]	X									X
Hu et al. [55]	X									X
Hua et al. [132]		X					Green building	X
Iddianozie and Palmes [75]			X						X
Irizarry et al. [91]					X		Healthcare facility			X
Isikdag et al. [49]	X									X
Jofre-Briceno et al. [133]	X						Port infrastructure			X
Jung et al. [134]		X									X
Jurado et al. [87]					X					X
Kameli et al. [69]	X						Soccer stadium			X
Kang and Choi [135]	X									X
Kang et al. [57]	X									X
Ladenhauf et al. [42]	X								X
Lee et al. [59]	X									X
Leite and Akinci [70]		X									X
Lin et al. [58]	X									X
Loeh et al. [38]	X							X
Machete et al. [67]	X						Heritage property			X
Maki et al. [19]						X	Healthcare facility			X
Matarneh et al. [9]	X									X
McArthur et al. [66]	X						School & Hospital			X
McArthur et al. [136]	X					X				X
Merschbrock et al. [137]	X						Healthcare facility			X
Mill et al. [138]	X									X
Mirarchi et al. [139]	X	X								X
Morales Escobar et al. [102]						X				X
Moretti et al. [77]			X							X
Mota et al. [101]						X	Office building		X
Motamedi et al. [140]	X									X
Motamedi et al. [141]	X									X
Muzi et al. [142]						X			X
de Mattos Nascimento et al. [65]	X									X
Parn et al. [143]	X						School			X
Patacas et al. [30]	X									X
Patti et al. [92]					X				X
Pavón et al. [37]	X						School	X
Pavón et al. [46]	X						Public building			X
Peng et al. [144]	X						Airport			X
Piselli et al. [145]	X						Historical property		X
Rahim and Rasam [146]		X					School				X
Seghezzi et al. [76]			X							X
Siniak et al. [51]	X									X
Stojanovic et al. [60]	X									X
Tushar et al. [74]			X						X
Valinejadshoubi et al. [147]	X		X							X
Villa et al. [148]	X		X							X
Wang et al. [43]	X								X
Wetzel and Thabet [45]	X									X
Wetzel et al. [44]	X									X
Xiao et al. [56]	X									X
Xie et al. [149]	X					X	Library			X
Xie et al. [107]						X				X
Xie et al. [87]					X					X
Yang and Ergan [53]	X									X
Yang and Ergan [52]	X									X
Yu et al. [73]			X						X
Yuan et al. [99]						X	Public building		X
Yusoff and Brahim [150]	X						Heritage property			X
Zhan et al. [48]	X									X
Zou et al. [63]	X									X

.

5. Research Gaps and Future Directions

5.1. Existing Research Gaps

The application of BIM in PFM has gained significant attention, yet several research gaps remain. A major gap lies in the integration of BIM regarding sustainability and environmental performance. While some have explored BIM’s potential in retrofitting buildings to meet energy efficiency standards, comprehensive research examining BIM’s role in driving sustainability objectives is still limited [151]. Particularly, little is known about how BIM might be combined with Building-Integrated Agriculture (BIA [152]). This research avenue has the potential to address global challenges related to food production and environmental sustainability. Additionally, the integration of BIM with emerging technologies such as the IoT, big data, and predictive maintenance systems remains relatively underdeveloped. Although the ability of these technologies to support data-driven decision-making is well acknowledged, the flawless integration of BIM with these technologies is still a major area needing more research.

Another gap in BIM application is the challenge of data standardization and interoperability. While research has focused on integrating building performance attributes into asset information models, the slow progress in overcoming the inconsistencies of data standards and formats hinders comprehensive integration. Establishing effective PFM systems depends on data standards being in line regarding several platforms. Moreover, the application of BIM in safety management and disaster preparedness remains in its infancy. Although current studies look at BIM’s ability to forecast and reduce chemical, radiological, and biological events, the investigation of disaster management systems, including both internal and external environmental elements, is still lacking [70,134]. Moreover, more comparative research is needed to grasp how BIM adoption differs depending on the type of building and in developed versus developing nations, especially in relation to PFM [51] and renovation challenges.

Even though DTs are becoming more and more accepted in PFM, several important research gaps regarding IoT, 5G, and big data still exist. First of all, even if big data can maximize PFM operations, there is a dearth of thorough models guiding service providers in their methodical use for efficiency enhancement and decision-making. Additionally, data privacy and security concerns remain inadequately addressed, requiring robust strategies to ensure ethical data governance [79]. Second, with few studies on their long-term efficacy, standardization, or integration into current facility management systems, research on the deployment of IoT sensors in PFM is still in its early phases. Moreover, it is yet unknown how data maturity levels affect PFM operations, which makes it difficult to properly apply data-driven solutions [123]. Finally, including BIM in PFM calls for the creation of lifetime performance evaluations and maintainability assessment techniques. The whole promise of digital transformation in PFM cannot be realized without filling in these research voids.

The integration of IT, such as AR and VR, presents new opportunities in PFM; however, several research gaps remain. One main discrepancy is the evolution of 3D-based PFM system interfaces. Although VR environments have been investigated for maintenance activities, more research is required to evaluate their applicability and influence on output [86]. Furthermore, the smooth integration of virtual objects into real-world surroundings calls for exact coordinates and deviations; however, current studies lack consistent approaches to guarantee correct alignment [88]. Another gap is the real-time visualization of indoor facilities under demanding conditions, such as fires or inadequate lighting, where the efficacy of depth data analysis and environmental adaptation remains underdeveloped [89]. Eventually, the combination of BIM with AR in PFM calls for more empirical research to evaluate usability, practicality, and implementation difficulties, especially in complex facility environments [90]. Maximizing the possibilities of IT-driven solutions in PFM depends on filling in these voids.

PFM has great possibilities owing to the development of AI; still, several important research gaps exist. First, although machine learning methods have been used to improve prediction models, current research lacks thorough frameworks integrating extra variables and data sources to improve accuracy and dependability [142]. Furthermore, real-world validation of AI-driven applications is scarce. Thus, it is essential to evaluate their applicability and efficiency in several types of facilities [99]. Another gap involves the evaluation of AI’s role in improving building energy efficiency, particularly in high-density urban settings like Hong Kong, where localized studies remain scarce. Few studies have also been conducted on specialized industries, including hospitals and educational institutions, where AI-driven medical technology management could increase operational efficiency [19]. PFM AI applications have mostly focused on conventional property forms. Finally, research on improving chatbot logic, automating issue classification, and optimizing knowledge bases is still lacking, even if AI-powered natural language processing (NLP) is progressively being applied in customer service [105]. Addressing these gaps is essential for maximizing AI’s impact on PFM.

5.2. Future Research Directions

Future research on the application of BIM in PFM should prioritize several critical areas that have the potential to significantly improve the safety, sustainability, and efficiency of facility operations. A key focus should be the advancement of BIM-based maintenance tools, particularly those enabling automated fault detection and real-time diagnostics, which are essential for enhancing operational efficiency [54]. With the growing adoption of VR and AR, integrating these immersive technologies with BIM can further support data-driven decision-making, especially in the context of safety management and disaster preparedness [78]. Sustainability continues to be a pressing research priority. Future studies should investigate how BIM can facilitate Building-Integrated Agriculture and support energy conservation by integrating mechanical, electrical, and plumbing (MEP) systems into the BIM environment [56]. Such integration would contribute to achieving environmental targets by improving energy management and fostering sustainable operational practices. Additionally, there is a need to explore how BIM can be utilized to support urban greening, plant growth, and ecological interventions within the built environment, thereby addressing challenges related to food security and carbon emissions. The integration of BIM with emerging technologies such as the IoT, big data, and blockchain presents promising opportunities to enhance information flows and strengthen real-time decision-making processes [119]. Research should focus on identifying optimal strategies for combining these technologies with BIM to maximize operational performance. Moreover, linking BIM models with inspection data can improve the accuracy and reliability of maintenance operations, offering a pathway to more advanced and responsive PFM techniques [61]. Meanwhile, developing frameworks that integrate multiple data sources, such as sensor inputs and real-time environmental data, into BIM platforms will enable more informed, context-sensitive facility management decisions. Another essential research direction is the application of BIM in cultural heritage conservation. When combined with AR and scan-to-BIM methods, BIM can offer innovative approaches for documenting, preserving, and restoring historic buildings [50]. However, further investigation is required to address the cultural, technical, and financial challenges that often hinder BIM adoption in heritage contexts [150]. Finally, future research should examine the use of gamification and collaborative platforms to enhance training, professional engagement, and knowledge exchange among PFM practitioners. Such tools can promote interoperability and cross-disciplinary collaboration, ultimately supporting more integrated and effective facility management practices [137]. Collectively, these research directions will reinforce the continued relevance of BIM and contribute to the development of smarter, safer, and more sustainable built environments.

Future research should prioritize the development of standardized frameworks for big data utilization to enhance operational efficiency, address security and ethical concerns, and accelerate the digital transformation of PFM [79]. Expanding studies on IoT-enabled PFM deployment will be essential for gaining a comprehensive understanding of data-driven decision-making and its impact on facility operations. In addition, exploring the relationship between PFM efficiency and data maturity levels could help identify best practices for effective implementation [123]. The integration of BIM should also be further investigated, with particular emphasis on automated lifecycle management and maintainability evaluation techniques. For real-time monitoring of indoor air quality, the combined application of IoT and cloud computing can significantly improve occupant comfort and well-being. Furthermore, emerging technologies such as 5G-enabled AR present substantial potential for enhancing service delivery in PFM and merit deeper exploration. In a word, advancing these research directions will contribute to making PFM systems more resilient, intelligent, and operationally efficient.

Researchers should focus on developing AR and VR applications with advanced 3D interfaces tailored for PFM and assess their impact on maintenance efficiency and decision-making processes [86]. Recent studies emphasize the importance of sensor calibration and vision-based tracking techniques, highlighting the need for improved integration of virtual objects into real environments [88]. Strengthening this integration can significantly enhance facility monitoring, particularly under low-visibility conditions [89]. The combination of AR with indoor navigation systems also presents promising opportunities to improve real-world usability. In this context, the integration of BIM with AR warrants further investigation, with particular attention to practical implementation challenges, user-centered design principles, and empirical validation within operational facility settings [90]. Prioritizing these areas in future research will help transform information accessibility and support more effective, robust, and user-driven PFM strategies on a global scale.

To improve AI-driven prediction models’ accuracy and suitability for PFM, future research should expand the variety of data sources and improve machine learning algorithms. Additionally, expanding real-world case studies to assess AI’s effectiveness in diverse facility settings, including hospitals and educational institutions, can provide valuable insights into its practical applications [19]. AI’s role in improving building energy efficiency in urban environments like Hong Kong should also be explored further, emphasizing localized implementation strategies [99]. Deep learning allows NLP tools in PFM to automate issue classification depending on user-generated data [105] and enhance chatbot responses. Furthermore, for predictive maintenance and improved customer service, AI tools like deep learning, neural networks, and k-nearest neighbors should be researched [104]. Last but not least, PFM could be completely transformed by combining AI with BIM and other DTs, which would optimize decision-making and service delivery [106]. The future of AI-driven PFM will be shaped by these research avenues, which will coincide with worldwide technological breakthroughs. A summary of existing research gaps and future research directions is shown in Figure 9.

6. Conclusions

This study presents a comprehensive review of digitalization in property/facility management (PFM) involving a bibliometric survey, scientometric analysis, systematic review, and the elucidation of the existing research gaps and future research directions. Firstly, a thorough bibliometric survey is conducted to collect, filter, and sort the most relevant articles regarding the digitalization of PFM. Next, based on the results of scientometric analysis, it can be concluded that the United States is the most active country contributing to this topic, followed by the United Kingdom, China, Australia, etc. There also exist multiple collaborations among researchers worldwide, such as Dawood, N., and Kassem, M., while Irizarry and Edirisinghe’s publications have received the most citations. Moreover, an in-depth systematic review is conducted to summarize different digital technologies used in different areas of PFM (i.e., SM, EM, O&M, and CM), and the existing research gaps and future research directions are discussed accordingly. BIM is found to play an important role in fostering sustainability across diverse PFM contexts, and this exploration has been extended to integration with the GIS SQL database and AR mobile applications. The integration of IoT and big data regarding PFM is a significant potential direction for future research, developing guidelines for optimizing big data to enhance operational performance and strategies to mitigate privacy and security concerns. Future potential investigations could involve integrating IoT and blockchain or focusing on security enhancement, automated payment, and real-time indoor air quality monitoring. Additionally, researchers can further examine the suitability of IoT sensors, investigate the impacts of data-driven approaches on PFM, and study the relationships between data maturity and PFM practices. Within the IT field, VR shall be investigated in terms of its impact on maintenance efficiency. More efforts are being made to improve the real–virtual alignment in AR through advanced measurement and correction techniques. A real-time indoor visualization system could also be developed by integrating with navigation systems to enhance functionality. The existing research focusing on AI applications in PFM normally integrates AI with energy consumption and emphasizes renewable energy sources. Future research could refine AI and machine learning techniques for a more precise prediction. Moreover, more efforts could be made to improve chatbot responses by incorporating natural language processing and machine learning techniques. In summary, this state-of-the-art review fills the research gaps related to the digitalization of PFM and could provide scholars, engineers, experts, and utilities with more in-depth insights into dealing with such issues, which could drive innovation, efficiency, and adaptability in PFM digitalization and address sustainability, data-driven decision-making, interoperability, and the convergence of emerging technologies.