*1.1. Coliving*

The residential sector is transforming quickly, accelerated by the COVID-19 crisis. Factors such as densification of cities, population growth, affordability, housing supply, demand dichotomy, rising prices [24] and lack of regulations [25] have impacted house prices and facilitated the evolution of new housing typologies. Coliving offers a more flexible leasing structure and increased engagemen<sup>t</sup> with the household to form more meaningful connections with housemates and the general community—regardless of the duration of stay [26].

Coliving operators, property managers and real estate investors have highlighted that their formula of success relies on providing the creation of fluid communities and neo-tribes [27], which are related to a state of mind and a lifestyle more than a membership or lineage [28,29]. Coliving also provides a variety of shared spaces mutualized by the whole community otherwise unavailable and unaffordable in a traditional way of living. Characteristics that made them have grown exponentially across Europe and other areas (Figure 1). For the new creative class, home means also an ideal place of work: it is mobile and social [30] with the communal spaces being key for the users [27]; additionally, the need for exterior and communal spaces, core spaces of coliving buildings, has also multiplied due to the COVID-19 crisis [18,31].

**Figure 1.** Evolution trend for the term 'coliving' Worldwide in the past 7 years 2014–2021 [32].

The rise in searches for the term 'coliving' in 2015 was likely due to an increase of these facilities in New York City and companies beginning to expand this typology outside of the tech world [33]. Spain is now the country with the fourth highest interest globally, according to Google, and the first in Europe. The current research was performed at Urban Campus, a coliving operator in Spain and France with several assets being operated under coliving and coworking and with a strong strategy for monitoring and optimizing buildings performance and user wellbeing.

One of the spatial strategies to encourage community is incrementing the shared amenities [34,35] and sharing these spaces with the whole community. The incidence of these spaces outside of the private room has promoted informal interactions, which in turn enhanced familiarity among residents and community [36].

Nevertheless, little architectural or interior design research is available to describe this emerging typology and scattered best-practices or guiding principles are appearing to aid designers in making informed decisions when designing or evaluating coliving spaces [8]. There has also been limited exploration into the houses to understand HCD approaches and climate adaptability of smart housing to meet user needs [1,37].

#### *1.2. State of the Art Data-Driven Design through Post-Occupancy Evaluation (POE)*

Using data for building design is not new ([38–40]). But incorporating qualitative and quantitative data in decision-making processes is a practice that has been recently incorporated into spatial design [41,42]. Post-Occupancy Evaluation (POE) is the methodology of obtaining feedback on the use of spaces in a building and its performance for the users [43,44].

Within this changing paradigm, a need for more advanced, digital methods to understand the use of space and improve its design has risen [45,46]; POE and other methods [47,48] are becoming essential to analyzing the current use of spaces, predict performance and ensure housing resiliency [17,49–53]. Recent studies have shown how POE could improve electricity performance predicted during the design of non-residential spaces [54]; a similar method has been applied for coliving residential spaces.

The POE is generally carried out a minimum of one year after the building is fully occupied [55] and includes several methodologies to perform holistic research of the building. The research studied a method of integrating technology and Internet of Things (IOT) data analysis as an added real-time assessment of end-users' electricity consumption patterns [56]. Remote comfort and well-being tracking systems and sensors enabled to collect and analyze data with little human intervention [28,57].

Innovation and new technologies facilitate a faster and more accurate understanding of the occupancy of and interaction with space [58,59]. IOT integrated entities of the physical world by making them addressable through the Internet and making the Internet accessible through physical objects. [60]. New ways of monitoring and data analysis have already provided real-time feedback as shown in different spaces like workplaces [46,61,62]. This paper reflects how residential spaces like coliving can undergo a similar transformation by incorporating in-built technology infrastructure POE data analysis to assess use of space.

Within this changing paradigm, a need for more advanced digital methods to understand the use of space and improve its design arose [45,46]; POE and other methods [47,48] are becoming essential to analyze the current use of spaces, predict performance and ensure housing resiliency [17,49–53]. Recent studies have shown how POE could improve electricity performance predicted during the design of non-residential spaces [54]; a similar method has been applied for coliving residential spaces.

Innovation and new technologies facilitate a faster and more accurate understanding of occupancy of and the interaction with space [58,59]. IOT integrated entities of the physical world by making them addressable through the Internet and making the Internet accessible through physical objects. [60]. New ways of monitoring and data analysis have already provided real-time feedback, as shown in different spaces like workplaces [46,61]. This paper examined how residential spaces are undergoing a similar transformation, with a special focus on managed solutions like coliving.

#### **2. Materials and Methods**

The study relied on available digital infrastructure designed for Urban Campus Colivings (smart locks, Wi-Fi, and electricity consumption devices) as data sources for operating coliving, defined as Post-Occupancy Evaluation (POE). The coliving spatial assessment evaluation framework in Table 1 sets a basis to understanding behavioral performance of spaces and to making informed decisions towards future sustainable and Human-Centered Design (HCD) of spaces. Behavioral patterns in space, experience, environmental consumption, and well-being were assessed.

In Figure 2, the 4 methodological stages were mapped based on the available IT infrastructure piloted in the study: (A) Electricity analysis, (B) Access analysis, (C) Network crosscheck, (D) Spaces profiling study. The mixing methods theory [76,77] was used to combine quantitative and qualitative inputs and was implemented during phase (D) to generate the SPs.

The data sample of the coliving spaces were collected during a 31-day period (1 May 2021–1 June 2021). Additional data were collected retroactively for the Electricity analysis for 1 year (1 June 2020–1 June 2021) in order to demonstrate the applicability of the methodology to explore and compare the use of spaces across different times and seasons. The data were extracted, cleaned, processed, and represented the data through PowerBI—a business analytics platform from Microsoft enabling user friendly visualization and interactions for behavioral analysis and sustainability decision making.

The subject group of study included the 72 residents of the coliving space with an age range of 25–40 years and coming from multiple nationalities including local Spanish colivers. For the current study, full authorization was granted by the residents and the data were treated in full compliance with the EU General Data Protection Regulation, (GDPR), being aggregated and anonymized accordingly to regulations. The analysis was performed in accordance with the principles outlined in the Declaration of Helsinki.

## *2.1. Spatial Definition*

The current study analyzed a 3000 m<sup>2</sup> coliving residence in Madrid. The building has been operated as a Coliving by Urban Campus since 2019; the methodology that enabled the carrying out of remote research developed was fully compliant with regulations and COVID-19 restrictions in place in May 2021. Four different typologies of spaces in the Coliving were categorized into Table 2. There were three cluster spaces; a cluster is a shared flat consisting of individual or double studios with a private bathroom and a shared space with a shared kitchen and living room. Both the entrance door to the cluster and the door that separates the common cluster spaces from the private studio had a digital smart lock that managed entry permissions (see Figure 3). The shared kitchens and living rooms of all the clusters were open to the entire community from 07:00–23:59 and remain accessible only to cluster inhabitants during the night.

**Figure 2.** Flow chart describing methodology and the objectives of the study.



(**a**) 

**Figure 3.** *Cont*.

(**c**)

**Figure 3.** Schemes of the different spaces studied: (**a**) Cluster apartments with studio subdivision 1C, 2I, 2D (**b**) Community Coworking space 6C; (**c**) Gym (0I).

**Table 2.** Physical classification of analyzed spaces. (Size in Net Usable Area NUA). The central cluster is the shared units that correspond to the central apartments in each floor of the building. The lateral cluster corresponds to the apartments situated at the left and right of the central apartment of each floor of the building.


#### *2.2. Phase A: Electricity Analysis*

Smart Electricity Meters (SEM), clamp-connected devices that measure the electricity consumption per apartment (cluster) and smartly classify the data of different appliances [56], were an essential adoption to guiding the transition towards sustainable use of resources such as water, electricity, and gas in residential spaces [78]. The innovation of the current research is that the information was collected to understand behavioral patterns and use of space, not only consumption dynamics. The data were then stored in a cloud

platform designed for the visualization of the electricity consumption in almost real time. For the study, the data were extracted from the platforms and filtered by days and clusters selected to the study (I, C, D), community spaces and gym (0I).

Afterwards, the SEM were trained by the Urban Campus and IT teams to identify the different domestic appliances according to the SEM patterns for identifying appliances (Air-Conditioning (AC), home appliances, lights and plugs). The isolated use of each device and electricity consumption was collected in real time and transmitted through the Application Programming Interface (API) during the night.

#### *2.3. Phase B: Access Analysis*

The smart locks (from Salto) were connected to the digital network through Wi-Fi enabling remote opening of doors and transferring information of door status (open vs. closed) and entry times in real time and retroactively from the Salto platform. The research analyzed the patterns of aggregated users' digital trails and visiting of spaces in the building. The various doors—building main entrance, access to cluster doors and common spaces doors and individual doors to private studios—were configured with different accessibility permissions depending on the use of space and privacy. Central apartments were accessible to all residents (ex: tenant living in 1I has access to 1C, 2C, 3C, ... ), while side apartments were accessible to all residents living on a floor (tenant living in 1C has access to 1I, 1C and 1D but not 2I or 2D). Table 3 shows the 3 types of locks and the credentials according to the space works as follows.

**Table 3.** Categorization of locks according to the typology, location, and access permits.


The data were downloaded from the Salto platform. The data were presented in charts that assess the use of space routines; the access analysis method enabled understanding of the use and entries but not occupancy as it does not provide information on different members accessing a space at the same time or exit time.

#### *2.4. Phase C: Network Cross-Check*

The Wi-Fi network connection structure was built using Cloudtrax software. There was one network, "Service Set Identifier" (SSID) with 1 or 2 Access Point (AP) per cluster space and per Community space—a total of 20 SSIDs in the building. Data from 5 SSIDs were analyzed (two Lateral Cluster spaces 2I and 2D, one Central Cluster space 1C and 2 Community Spaces: 1 Community Coworking (6C) and Gym (0I). The location of the APs in this study relied on original infrastructure and networks available and the places located.

The Wi-Fi network worked as a digital trail of any device that is present in the spaces and is identified by the APs without the need to be connected to the Wi-Fi. The current method implied identification of the members of the coliving space and association to the digital devices they own; each coliver has on average 2–3 devices and is then anonymized and aggregated according to GDPR laws. Analysis of any device that was not assigned to a person and mobile in space was eliminated, keeping laptops, tablets, smartphones, and smartwatches. Other devices like Chromecast and SEM were dismissed. The data collected enabled to identify patterns of use based on traffic data, number of devices connected and routines that served also as a cross-check for Phase A and Phase B.

#### *2.5. Phase D: Space Profiling (SP)*

Once the Electricity, Access, and Network analysis were concluded, the Space Profiles (SPs) were developed. An SP is a dynamic flashcard that integrates the description of spaces together with inputs from real time use of this space and users' behavior obtained through users' digital trails collected from the existing built-in IT infrastructure. The process relied on the mixing methods theory extracted from Phases A, B and C learning to conform the assessment. For the current paper, 4 SPs were developed: (A) Cluster Central (B) Cluster Lateral and (C) Community Space profiles. Assigning features and characteristics to the 3 profiles developed based on the previous phases enabled to understand how Coliving spaces worked. The potential of the SP was to understand the identity of spaces as an active space, that responds in different ways depending the user's needs and its specific design features. This methodology was tailored to the different spaces and local needs to be able to reproduce the best experience for colivers, for example reducing electricity consumption and therefore optimizing cost [79].

According to Williams [80], interaction between physical, personal and social factors has an impact on behavior, that can be used to evaluate the physical profile of shared housing facilities. The characteristics he identified include size, density, proximity, surveillance, ratio of private to communal spaces and affordances within each, and non-spatial factors such as formal and informal social factors.

The SPs were the HCD interpretation of space, adding the analyzed digital trail features to the traditional spatial space definition and working as an interface between users and spaces. For example, it is broadly understood that modifying the size of the bed or the capacity of a wardrobe changes the experience of a space; likewise, interfering with the digital network, access permissions or AC parameters also alters the experience and behavior of a coliver. Residential spaces have become something other than a bed and a kitchen; the digital dimension and how it shapes users' performance must be considered when defining spaces taxonomy.

Following Williams [80], the interaction between users and spaces Table 1 is needed to evaluate different typologies of spaces (Table 4) in order to have a complete assessment of experience in the space and be able to design future spaces. The SPs represented the standards to replicate conditions for future spaces; they are also an example of interaction with real studies to test how modifications or interfering with these spaces modifies behavior and likewise interfering with behavior affects the way we use spaces. Studying these conditions helped to better understand the community. The definition of SPs for coliving and studying evolutions was essential to improving sustainable design in the present and future of coliving spaces.


**Table 4.** Spaces classification according to William's parameters (Physical parameters). \* Studios are private spaces, 1 per Coliving each cluster is connected to 3–6 studios (In Italic to differentiate from the common spaces that will be measured).
