Unpacking PropTechs and IoT ecosystems: European insights with a novel business model taxonomy

PropTechs are experiencing a rapid upswing, with numerous notable start-ups and strong growth. Europe is home to a considerable number of PropTechs, albeit second to the USA in terms of funding and number of start-ups (Oladiran and Dickins, 2024; Unissu, 2020). PropTechs can support the transformation of the building sector and offer various services to make buildings more sustainable, save costs, create new user experiences and improve user well-being. The EU's recent decision to mandate building automation and control systems in non-residential buildings is giving further impetus to smart building (SB) technologies and services as well as PropTechs in Europe (Directive (EU) 2024/1275, 2024).

The extant literature on PropTechs addresses fundamental topics and provides answers to questions regarding the definition of PropTechs, their classification, and the benefits and advantages they offer (Baum, 2017; Baum et al., 2020; Saiz, 2020; Starr et al., 2021; Tagliaro et al., 2021, 2024). In light of growing interest from venture capitalists, research is also investigating factors that contribute to PropTech success and encourage venture capital investment activity (Kassner et al., 2023; Kassner, 2024). Other research primarily approaches PropTechs from a specific real estate perspective as drivers of data-driven markets and platform economy (Braesemann and Baum, 2020; Müller et al., 2021; Saull et al., 2020; Shaw, 2020). Further analyses also discuss more generally influences, barriers, drivers of innovation, and the influence of PropTechs that are limited to certain technologies and regions, such as smart technologies from the Internet of Things (Schäfer and Hennig, 2025; Tan and Miller, 2023; Ullah et al., 2021; Vigren et al., 2022). Tan and Miller (2023) examine the market and general business model of sustainability-related PropTechs, particularly those involved in managing (smart) buildings, and identify barriers to the uptake of their solutions. They find that owners recognise digitalisation as an important factor in reducing energy demand and greenhouse gas emissions in buildings. Furthermore, they find a lack of multi-dimensional integration of technical solutions, as also reported by Schäfer and Hennig (2025) in relation to smart solutions for private homes. To overcome this multi-dimensional integration hurdle, we argue that we need to take a holistic view from the perspective of PropTechs and their business models. First and foremost, this is not just about technical integration into building technology systems, but also about the integration between different smart technologies and their ecosystems. In addition, integration must occur at both business model and user levels. Conversely, adopting a PropTech solution entails more than selecting an appropriate product; it requires choosing a reliable company. For customers, the key consideration is not only whether the solution addresses the problem but also whether its provider is likely to endure—so a purely product-centred perspective is insufficient. Furthermore, Vigren et al. (2022) likewise report that building owners often lack the requisite capabilities to advance digitalisation. Therefore, this study aims to provide an industry-specific and comprehensive taxonomy of PropTech business models offering solutions for the smart built environment. The taxonomy captures four meta-dimensions of observable PropTech elements and can be readily applied to build a structured, holistic understanding of individual PropTechs and their solutions. It enables both in-depth and comparative analyses, supports informed decision-making, and clarifies which elements an SB project may encompass and how PropTechs can make a contribution to the development of long-term, sustainable buildings.

The extant literature attests to an “increasing consensus that business model innovation is key to firm performance” (Zott et al., 2011, p. 1033). Digital business models—most notably platform-based models—feature prominently in the information-systems and business-model literature and have proliferated rapidly across sectors. The examination of a business model provides a perspective that highlights a company’s uniqueness beyond the technological dimension (Osterwalder and Pigneur, 2010).

With the development of the Internet of Things (IoT), physical objects have become part of the (information) network and the digital world. There is a consensus that IoT holds significant potential for business processes, as reflected in various use cases in real estate (Atzori et al., 2010; Püschel et al., 2022). In real estate, business model terms such as “IoT platforms”, “cloud platforms”, or “operating systems” have gained traction alongside the development of SBs (Skopp et al., 2023). The development of an ecosystem and the establishment of partnerships with integrated solutions offer valuable opportunities to increase adoption rates and competitive advantages (Schäfer and Hennig, 2025; Tan and Miller, 2023; Ullah et al., 2021).

Baum (2017) directly links the IoT to SBs, which can be described as nearly energy self-sufficient buildings that respond to the external environment and user needs, using a Building Energy Management System to monitor and control the building appropriately (Al Dakheel et al., 2020). Buckman et al. (2014, pp. 98–99) define SBs, based on the literature, as “buildings which integrate and account for intelligence, enterprise, control, and materials and construction as an entire building system, with adaptability, not reactivity, at the core, in order to meet the drivers for building progression: energy and efficiency, longevity, and comfort and satisfaction […]”. The terms “automated building”, “intelligent building”, and “smart building” have also been used synonymously with building management systems (BMS) or building automation systems (BAS), among others (Taboada-Orozco et al., 2024). We follow Xu et al. (2019), who identify the business model of (AI-driven) SBs as a research gap and describe SBs as a product-based model with a value chain and as a platform-based model with an ecosystem.

In the absence of a clear consensus, the above definitions of SBs are considered equally in our analysis. We also include smart products offered by PropTechs that are not directly integrated into the building system as technical components, but would lose their raison d’être if the built environment did not exist. In our view, a BMS/BAS is a subsystem within the building and does not constitute an SB. We focus exclusively on PropTechs and their solutions within the smart built environment, including smart homes (B2C).

Scholars examine business models from a range of vantage points—strategic, conceptual, procedural and elemental—so no single, universally accepted definition prevails (Al-Debei and Avison, 2010; Möller et al., 2022; Zott et al., 2011). Following Massa et al. (2017, p. 76), we interpret the business model as “attributes of real firms”, because our research seeks to identify and measure observable corporate characteristics, treating a business model as a bundle of realised, observable attributes.

A taxonomy ^[1] provides a structured representation of an emerging subject area, enabling the communication of novel technologies and specific knowledge in a way that deepens the understanding of relationships and concepts and facilitates their application (Glass and Vessey, 1995). Furthermore, a taxonomy supports theory development based on observable phenomena and classifies elements within a hierarchical structure (Nickerson et al., 2013). The extant literature provides related taxonomies for the architectural structure or business model of (I)IoT platforms as well as for smart services and smart things, particularly in the context of smart homes. These taxonomies support the understanding of the IoT landscape but they are less useful for understanding the business models of PropTechs. The taxonomy proposed by Hodapp et al. (2019) clearly outlines how core competencies of IoT platform providers can be perceived and monetised using firm-specific resources, including hardware, software, and services. Arnold et al. (2022) provide a comprehensive analysis of the architectural components found in the IIoT platform landscape, which exhibit significant overlap with those observed in the PropTech domain. The perspective on products and services is reflected in the Smart Things taxonomy of Püschel et al. (2022). Fischer et al. (2020) are even more specific about smart living to describe smart services in the private sector. However, the application of these taxonomies to PropTechs would overlook the specifics of real estate and the integration of technical building elements. We are proposing a taxonomy that incorporates the distinct components of the real estate and PropTech sectors.

Depending on the customer segment, very different approaches may be required, favouring either a product-based or a platform-based model. Accordingly, in selecting a framework for the business model of a PropTech, we draw on the literature on digital platforms (e.g. Al-Debei and Avison, 2010; El Sawy and Pereira, 2013; Osterwalder and Pigneur, 2010). The framework of a business model is described through basic, interrelated elements (Fielt, 2013) and regularly forms the starting point for analyses. Al-Debei and Avison (2010) present the V4 framework with four dimensions for business models: Value Proposition, Value Architecture, Value Network and Value Finance. This framework is our starting point for developing a clear, comprehensive, and adaptable taxonomy.

For the development of the taxonomy, we adopted the widely used iterative approach proposed by Nickerson et al. (2013), which can be considered state-of-the-art (Fischer et al., 2020; Möller et al., 2022). The aim is not to objectively create the best or only correct version, as such version cannot be objectively defined, but rather to produce a useful and applicable one. An additional advantage of this approach is its flexibility to extend the taxonomy, making it inherently an artefact that can be expanded to accommodate a dynamic environment and emerging phenomena (Nickerson et al., 2013).

This procedure is a seven-step, iterative process (see Figure 1), which begins with the determination of the meta-characteristic (step 1), in our case: components of PropTech business models for SB technologies and services. In the next step (2), the conditions that determine the end of the iterative procedure must be formulated. We adopt the objective condition proposed by Nickerson et al. (2013, p. 344): “at least one object is classified under every characteristic of every dimension”, “no new dimensions or characteristics were added in the last iteration”, “no dimensions or characteristics were merged or split in the last iteration” and “every dimension is unique and not repeated”. After each iteration, the taxonomy is evaluated according to subjective ending conditions as to whether it is sufficiently “concise”, “robust”, “comprehensive”, “extendible” and “explanatory” (Nickerson et al., 2013, pp. 341–342). Steps (3) to (7) are iterative in nature and must be carried out until the subjective and objective ending conditions are reached. Step (3) involves deciding whether the process for developing the taxonomy should be empirical-to-conceptual or conceptual-to-empirical. The empirical-to-conceptual approach generates new elements for the taxonomy development (step 6e) by identifying (step 4e) and analysing existing objects and extracting common characteristics (step 5e) that can be implicitly derived from the meta-characteristic. The conceptual-to-empirical approach is deductive in nature and is dependent on the subjective assessment and level of knowledge of the researcher (step 4c). The results of this approach are then applied to real-world objects for evaluation (step 5c) in order to subsequently create the taxonomy (step 6c). The last step (7) is the verification of the ending conditions, which eventually marks the end with the final taxonomy.

As shown in Figure 1, we conducted six iterations to develop the final taxonomy. To ensure transparency and facilitate replicability, we document the execution of these iterations and the taxonomy’s development in Appendices A–D:

1. Appendix A: List of n = 90 PropTechs examined during the iterations.

2. Appendix B: Detailed descriptions of each iteration (literature review, analysis of real-world cases, and expert interviews).

3. Appendix C: Prompt Engineering – Illustration of the novel techniques based on large language models used to automatically analyse extensive qualitative data sets, together with the underlying prompt-engineering workflow.

4. Appendix D: Summary of taxonomy dimensions considered during the iterative development process.

To test the general and consistent applicability of the taxonomy by external practitioners without a codebook, experts E4 and E9 were asked to apply the taxonomy to a randomly drawn sample of five PropTechs. Although the experts were familiar with parts of the taxonomy from the interviews and some features from a brief description, they essentially interpreted the information based on their background knowledge. The results were then checked for interrater reliability by determining the value for Cohen's (1960) kappa (for two people) and Fleiss' (1971) kappa (for more than two people). Furthermore, to illustrate the taxonomy’s application and facilitate further analysis, we classified two anonymised PropTechs according to our final taxonomy.

Figure 2 presents the final taxonomy, which is structured into three levels: meta-dimensions (MD 1 to MD 4), dimensions, and characteristics. The mutually exclusive dimensions (MEX) are labelled “Yes”, and non-exclusive dimensions are labelled “No”. The meta-dimensions correspond to the V4 framework by Al-Debei and Avison (2010). In total, the taxonomy contains 16 dimensions, each with two to five characteristics.

Al-Debei and Avison (2010) describe Value Proposition as the company's offering that provides specific added value for a specific customer segment and other stakeholders.

Core Capabilities reflect the core competence of a company to offer specific added value for its customers. Some PropTechs (e.g. EVOLY or Respory) focus on hardware and in particular on the delivery of data so that the building can be “seen” in the first place (expert E8), which expert E13 calls a “digital refurbishment” of a building. Others (e.g. Pult or THING TECHNOLOGIES) want to become the face to the user and offer a comprehensive front end with an appealing user interface (UI), which goes hand in hand with the requirement for (software) integration services. Other PropTechs (e.g. aedifion or Basking Automation) aim to derive the maximum added value from the data obtained from a building. This can be at room level to monitor and optimise space efficiency or at building level to increase the efficiency of technical building systems. The latter group often includes companies from other industries or very young PropTechs (e.g. vilisto or RYSTA). A single use case is served by offering the necessary hardware and software from a single source, e.g. optimising heating at room automation level.

Key Customer Benefit refers to the key value proposition. Climate protection is currently an obvious and major driver for change. As a building is a complex system and the owner wants to reduce and control this complexity as far as possible (expert E2), it is advantageous to limit the number of interfaces to services. It is therefore a valid value proposition to be the one-stop shop as a single point of contact, even if various solutions from different companies are used in the background (expert E9). PropTechs with their own claim to be a one-stop shop regularly form the IoT platform in a building ecosystem, though not necessarily. They perceive an opportunity to become the digital hub of a building to consolidate their business in the long term, even if scalability is initially limited to a single building’s system due to the development of other applications, including those by external parties. The one-stop shop in our context is therefore not one that claims to offer all use cases with its own solutions and resources, especially since such a PropTech does not seem to exist according to Tan and Miller (2023) and in agreement with expert E6. While the use of data regularly enables the optimisation of various processes, digital interfaces can use data to improve the user experience and the well-being and satisfaction of users. This seems to be a valid driver in times of New Work. The reporting measures and obligations required both internally and externally can be automated in the era of digital buildings.

Key Target Customer refers to the customer segment that is primarily targeted. This includes all stakeholders in the building. Building owners are a diverse and key customer group who make key decisions for the building and its ecosystem. PropTechs can offer owners marketing potential through their solutions. Expert E14 sees potential for tenants who are prepared to pay premium rents for technically advanced buildings. Service companies, such as facility, property or asset managers, are regular customers of digital solutions. Finally, building operators such as hotel, co-living, or co-working operators, and ultimately the occupants of a building, are also regular paying customers.

Building Use and Occupancy Type is part of the targeted market segment and goes hand in hand with technical requirements. The separation of smart home solutions (B2C) and other SB solutions (B2B) is key here, as smart home solutions for private individuals must meet simple technical requirements (experts E6, E9 and E12). Commercial properties have a greater variety of user types and more complex technology, meaning that simple plug-and-play solutions, even if regularly offered, are less important.

Brand integration is a priority for commercial customers who seek a digital and future-proof external solution as an extension to their own service offerings. White labelling is commonly offered for software and UI solutions as part of the value proposition.

This meta-dimension describes the totality of a company's resources and how they are organised (Al-Debei and Avison, 2010). For Value Architecture, we have drawn in particular on concepts already developed in the literature.

For the Hardware dimension, we follow Hodapp et al. (2019) to describe the different types of hardware solutions offered and supported by PropTechs. Some PropTechs develop own hardware, while others access specific hardware from partners. There are PropTechs that have a very open strategy, supporting any hardware as long as it complies with the relevant standards. The “No Hardware” characteristic has been introduced for pure software products, such as office management applications to optimise space and improve the user experience.

Physical Data Transmission describes the possibilities for the physical transmission of data (Arnold et al., 2022). Data transmission can be wired (e.g. via Ethernet), short-range wireless (e.g. via WiFi or Bluetooth), cellular, or LPWAN (e.g. via LoRaWAN or Sigfox). “No Integration on Field Level” is included for completeness.

Data Processing describes, as in Arnold et al. (2022), the location of the product's intelligence and logic, which can be “on edge/fog”. There is also the frequently used option of keeping data on the cloud of the PropTech as a provider or on the cloud of a third party, which can also be the building owner. Building owners are regularly obliged to handle sensitive information, in particular about building users, with care, and therefore to hold and process these data to the best of their knowledge and belief (expert E8). On-premise solutions, i.e. dedicated servers in the building, are often favoured.

BMS Integration takes into account the PropTech's system integration capability to incorporate the building-specific elements into its solution and is a core element in our analysis. Although there are many levels and types of integration, we have limited ourselves to a simple mapping of BMS integration for ease of management. Tan and Miller (2023) have already highlighted the importance of BMS integration, while expert E3 states that many buildings do not have or are dependent on a BMS. Some PropTechs even offer own BMS solution.

Device and Data Interaction is an important and frequently requested aspect of a technical solution from PropTechs (experts E4 and E9). It is also referred to in Püschel et al. (2022) for their more general taxonomy on smart things. A solution can only read data, i.e. receive data unidirectionally for further analysis, or both read and write, i.e. import instructions and commands bidirectionally into corresponding systems to autonomously manipulate the physical environment. Bidirectional applications can, for example, authorise access to rooms and buildings or control smart heating thermostats.

Analytics describes how data are processed. Arnold et al. (2022) explain this dimension, which we simplify to “descriptive” and “advanced”. “Advanced” is any type of data processing with advanced methods, such as machine learning. Whether data are used in real time is not relevant here.

Value Network encompasses the attitude and behaviour of a company when interacting with external third parties in different roles (Al-Debei and Avison, 2010). Technical features or strategic partnerships as well as integrations with key systems can be decisive here.

Application Programming Interfaces (APIs) are offered by PropTechs for the integration of their solution into other, external systems. It is an important component in the ecosystem of a building. There are PropTechs that make their APIs openly available and standardised, or only make them available to customers on request or for a fee. Some do not yet offer APIs.

(IoT) Ecosystem implies whether a PropTech pursues a product-based business model with a value chain or a platform-based business model with an ecosystem. Partners who, for example, take on the installation of hardware as fulfilment agents are not partners in our context. Partner systems, as we understand them, extend the depth of value creation and the scope of application of a PropTech on an equal footing. PropTechs use marketplaces as transaction platforms to build an ecosystem to present and broker different, often complementary and integrable solutions.

Third-party (software) integration describes the uneven relationship and integration of established systems that are regularly used by companies. This raises the question of whether the PropTech's solution can integrate enterprise software, such as ERP or CRM systems, or certain web services, similar to Hodapp et al. (2019). Some PropTechs offer the ability to incorporate digital building data, such as digital twins or other digital building models, into their system.

This meta-dimension covers how a company generates revenue from its offering and organises its costs (Al-Debei and Avison, 2010). Details of the cost structure are generally unavailable and thus excluded from our analysis, but can be inferred from other dimensions, such as Value Architecture. This meta-dimension also implicitly captures the fundamental service aspect of how PropTech solutions are rolled out and implemented.

Single Payment Revenues include payments that are generally non-recurring. We observed that PropTechs charge set-up fees for the initial installation of their solution and offer additional services depending on the situation. PropTechs that develop hardware also offer their equipment for sale as an option. In the case of optional set-up fees, the rollout is left to the customer, who can purchase support from the PropTech.

Continuous Revenue describes recurring income. Although, as young companies, PropTechs prefer immediate revenues for reinvestment, according to expert E7, recurring revenues serve to attract investors and (external) financing. Recurring revenues correspond to the “as a service” concept and are based on the building (e.g. rental area, number of parking spaces), the number of users, the devices or data points in use or other models that include different service packages with more functionalities or, in some cases, “pay as you save” models.

According to Landis and Koch (1977), the kappa statistic for the interrater reliability is “fair” or “moderate”. Fleiss' (1971) kappa statistic, which includes all four reviewers, yields a value of 0.390. These results are acceptable and require no further examination, but indicate that the taxonomy must be accompanied by a detailed codebook with examples in order to achieve consistent results. In particular, the dimensions “Core Capabilities”, “Key Customer Benefit” and “Key Target Customer” can vary greatly depending on the experts' views and market knowledge. To resolve these discrepancies, the dimensions can be listed as non-exclusive. However, this entails the risk of losing selectivity and missing the core of a PropTech. Instead, a “Clearly Confirmed” column (not shown in Figure 2) should be added for practical purposes.

Appendix E shows the application of two anonymised PropTechs. It demonstrates how a structured analysis of certain PropTech business models is possible while clarifying the interrelationships among its dimensions. The resulting analysis not only guides PropTechs in shaping their strategic roadmaps but also equips customers and investors with a comprehensive framework for understanding, comparing, and evaluating individual PropTechs.

The iterative approach offers diverse insights into PropTechs. While the results are implicitly incorporated into the taxonomy, we also present additional findings from our expert interviews ^[2] here, which deepen the general understanding and may serve as a reference point for further research.

Innovation in buildings is not driven only by PropTechs. Incumbents such as Siemens, Bosch, Schneider Electric, Honeywell, ABB, Cisco and Johnson Controls are also acting as innovators to gain market share. Not only do they have significant market penetration, they also have a wide range of solutions and skills. As a result, they can reduce complexity for customers by enabling extensive integration across their product portfolios. Nevertheless, PropTechs have a strong potential because of their ability to innovate and the willingness of building owners to engage less established vendors to mitigate the risks of lock-in, ensure data sovereignty and reduce costs.

Even when the various drivers and barriers to PropTech adoption are discussed in depth (including in the literature), one self-evident fact remains central and merits explicit attention. There is a consensus that PropTechs provide tangible solutions to various problems, primarily through cost savings or other added value (Schäfer and Hennig, 2025; Vigren et al., 2022). In our interviews, experts emphasised that added value—especially in B2B contexts—must be measured in monetary terms. Therefore, we did not include “cost savings” or “increased income” as taxonomy dimensions. We presuppose a monetary benefit for the customer resulting, for example, from efficiency gains or rental premiums.

Business models refer not only to the products in isolation but also to the associated services—such as roll-out and support—in terms of vertical integration. Experts identify two challenges regarding the vertical integration of PropTechs. First, they note that PropTechs often see themselves as software companies with only individual hardware components. Consequently, subsequent processes—namely product deployment in buildings and support—are insufficiently addressed.

Second, this issue is exacerbated by the lack of expertise among real estate stakeholders responsible for maintaining digital solutions in their portfolios. Facility and property managers must develop these skills over time, seeking ongoing support from PropTechs.

They identify a challenge in PropTechs’ limited resources and capacity constraints. It would even be advisable to take this a step further by involving engineers and architects and engaging in direct dialogue.

To fully engage customers and end-users, it is essential to frame and communicate the product in terms of concrete use cases. Accordingly, explicit identification of these use cases within the business model is crucial, even when the taxonomy implicitly captures them. The SmartScore certification from WiredScore, for instance, provides a user-centred perspective that can serve as a valuable complement.

The business model of the building owner poses an additional challenge. Experts describe an allocation problem, whereby responsibility for the digital building is unclear. As the key decision maker, the owner often leaves the digitisation of the space to the tenant - who then brings in their own solution– due to risk and cost considerations. However, this fragmentation results in numerous siloed solutions that undermine long-term viability. Thus, the siloed solutions stem not only from non-integrable offerings of PropTechs but also from the building owner’s business model itself.

The experts also highlight the risk of siloed solutions and the need for industry standards, particularly regarding sustainability. Network coupling and infrastructure consolidation, which determine a building’s adaptability for retrofit measures, can only be implemented in a sustainable manner if the underlying network infrastructure is valuable and recyclable.

During the development of the taxonomy, cybersecurity repeatedly emerged as a key issue, as reported by Tan and Miller (2023). Experts noted that cybersecurity is sufficiently defined by regulatory authorities. Moreover, this extensive topic is too broad and could warrant a taxonomy in its own right. Accordingly, despite its importance, it is not addressed here.

Our research identified sixteen dimensions over six iterations, grouped into Value Proposition, Value Architecture, Value Network and Value Finance. Examining the business model not only clarifies the value proposition, the technically relevant components, the strategic position of a PropTech in the ecosystem and the monetisation of a product, but also implies the specific motivations and perspectives of PropTechs. Core Capabilities and Key Customer Benefit clearly reflect fragmentations and foci. The combination of these two dimensions also implies the respective use cases that a PropTech offers. This is particularly important because both users and building owners tend to focus on discrete use cases and engage with the solution from a less technical standpoint. Value Architecture shows how hardware and software can be used to develop and integrate a range of different products and services. Value Network involves the interaction of different players and how PropTechs can map their approach to other solutions. Finally, Value Finance can be used to understand monetisation and business models.

When the prospective client is a public institution—such as a school administration—or a cost-sensitive property owner, the priority is not to install an elaborate suite of technologies that will attract tenants willing to pay premium rents. Rather, the goal is typically to cut operating costs through straightforward measures. Integration therefore becomes secondary to standalone solutions that meet a single requirement: smart heating thermostats, for example, can yield immediate savings without reliance on a broader ecosystem. Unless these PropTechs wish to expand into new customer segments, they need uncomplicated, self-contained offerings rather than complex platforms. For property developers, complexity must be minimised to deliver the project resource-efficiently, while flexibility is essential for adapting to changing conditions. Where a resilient ecosystem is in place, an appropriate solution can be identified with minimal friction. The taxonomy likewise should enable PropTechs to respond to market shifts and translate these into appropriate adaptations of their own models. Ultimately, smart solutions—where desired—must follow the built structure. A flexible ecosystem is of little value if the underlying built infrastructure does not promote adaptability.

Customers derive equal benefit from this research: a developer wishing to implement specific use cases in a building can first consult the taxonomy to gain an overview of the PropTech landscape and, in particular, of the technical breadth and depth involved in a smart-building project. As highlighted in the interviews, a smart-building initiative is less a matter of “bricks and mortar” than a software project that requires dedicated resources. The taxonomy brings these critical issues to the fore. The added value extends to other service providers—such as architects, facility managers and building engineers—by guiding them on which PropTech partners are needed and, to a lesser extent, which specific products to specify. Focusing exclusively on products risks future integration problems not only because disparate technical solutions may prove incompatible, but also because a considerable proportion of PropTechs may fail to endure owing to insufficiently resilient business models. Additional stakeholders, including venture capitalists, likewise profit from this rigorous analysis.

Further interview findings emphasise that PropTechs occupy a distinct role in the market and are indispensable. It is incumbent upon building owners to develop a robust digitalisation strategy for their assets, engaging in proactive, collaborative planning to prepare for future developments and prevent siloed solutions. PropTechs, in turn, must engage other service providers at an early stage and enhance product viability by expanding their business models through vertical integration.

When analysing PropTechs, despite a very rigorous approach, we often came across incomplete information or an unreliable and exaggerated presentation of their own capabilities. This missing information can be relevant for assessing a PropTech and can be collected through very elaborate measures, such as direct contact. The dynamism and flexibility of PropTechs also make it difficult to achieve a certain degree of selectivity. This requires experience and knowledge in order to explore core competencies in depth. A notable constraint of the present study is its exclusive focus on PropTechs from Europe. Sustainability efforts in Europe and regulatory requirements are significant drivers of innovation. This may be less significant in other regions.

PropTechs are proving to be an up-and-coming field in both practice and research. In the current market situation and in times of structural change in real estate, they have great potential to play a key role in the digital transformation and have a lasting impact on the building stock and its use. However, PropTechs and their ecosystems have been little researched, and their solutions and potential are often underestimated. Furthermore, for many stakeholders, the terminology, scope and implications of an SB project remain unclear, as does the contribution of PropTechs. When assessing adoption barriers of PropTech solutions, the principal obstacle remains the challenge of multidimensional integration. Therefore, we comprehensively and systematically mapped the business models of PropTechs offering solutions for the smart built environment into a taxonomy. The taxonomy offers a holistic, fine-grained means of classifying PropTechs active in the smart built environment. By shifting attention from individual products to the business as a whole, it is applicable across a wide range of use cases for practitioners and researchers.

The taxonomy supports well-informed, sustainable decisions that deliver a building aligned with the building owner’s expectations and capable of long-term integration. Such an approach not only increases analytical rigour, but also helps to preserve and enhance the property’s value. The results are of interest not only to building owners, service providers and venture capitalists but also to PropTechs, which themselves rely on ecosystems. By taking their clients’ processes into account, PropTechs can use the taxonomy to make concrete, strategic decisions that align their business models sustainably with those of their customers.

We also make contributes to the existing literature in the fields of business model research and information systems by combining them and including real estate. Framing their research within this structured taxonomy enables researchers to enhance the practical salience of their work. Engineering studies on SBs, for example, may draw on the taxonomy to map their findings directly to PropTech business models, thereby establishing a clear and readily comprehensible pathway to real-world implementation.

Further research could first include the perspective of different real estate organisational levels, use cases, specific services and users. Second, there is the potential to incorporate a global perspective. This may result in the emergence of new business model elements that can be situated in disparate geographical regions. The utilisation of novel methods, such as LLMs, has the potential to streamline research activities pertaining to data collection and preliminary evaluation. Third, there is potential for management research to identify the key factors that contribute to the success of PropTech business models. The development of scalable business models for affordable solutions is a key priority in this regard in order to roll-out the solutions to the broad range of building stocks.

The authors used generative AI (from ChatGPT by OpenAI) in the process of research and data preparation. The methodology section provides a detailed description and justification of the employed research process and the specific Large Language Models (LLMs) utilised. We assure that we take full responsibility for the content of the manuscript.

The supplementary material for this article can be found online.