The pandemic has disrupted how we occupy and use spaces, making occupancy analytics more important than ever before. Even before the pandemic, there was a push to better understand the true occupancy of spaces. Occupancy data can help businesses make strategic decisions regarding a variety of important operational matters. For example, occupancy can be used to avoid unnecessary use of energy. Occupancy analytics can also be used to optimize maintenance and floor layout to ensure occupant satisfaction and reduced operating costs.
The occupancy analytics industry is growing at a healthy rate. We spoke to over 100 real estate owners and operators including CTOs, CIOs, CEOs, and COOs in commercial, retail, and residential real estate as well as commercial real estate tenants from October 2020 to February 2021 to learn more about what it is that they desire when it comes to occupancy analytics. Even with the pandemic making the need for data around space utilization more important than ever before, ROI still drives the decision to adopt proptech solutions.
To better understand the ideal ROI, this article discusses the use cases around occupancy. Moreover, the benefits and disadvantages of the various ways to track occupancy including low tech (surveys and manual counting) and high tech solutions (sensors, cameras, and wifi) are explored. This piece concludes by providing tips on how the ROI around occupancy analytics can be assessed.
Depending on the quality of the occupancy data, whether an estimate, a heat map, or live data, there are many use cases. Occupancy data can be used to reduce operational costs, create value, enhance safety, and improve occupant experience. The more accurate and systematically analyzed the data, the easier it is to understand how spaces are used.
Increasing Property Value
Property value reflects what a user is willing to pay for a given space. By making a space desirable, the property value can also be increased. There are several ways through which occupancy data can increase property value. First, occupancy data can be used to improve the interior layout of common areas to make a building attractive. Secondly, occupancy data can be used to create better management of shared spaces such as elevators, gyms, and lobbies using historical data to predict occupant behavior. Thirdly, occupancy data can be used to demonstrate footfall in order to justify retail rent prices.
Improving Occupant Experience
Today, one thing is clear, the five days a week sitting at a desk in the office is a thing of the past. The future of work requires a reimagination of space utilization as well as cleaning and maintenance schedules. Understanding how employees use various spaces such as desks, conferences, and common areas to better improve office layouts and services. Such data when integrated into apps is also useful to the occupants, as people will know what kind of environment they are going to be walking into. A busy office, a long line, or a nice peaceful corner office.
Reduce Energy Spending
Building efficiency has two components: energy savings and performance. Money spent on energy can quickly add up as the average cost of energy for a building is $2.14 per square foot. Energy waste in commercial and industrial sectors amounts to an estimated $60 billion annually. HVAC systems account for 45% of total energy usage in commercial buildings. Occupancy analytics solutions that offer APIs can be used to communicate with the HVAC system regarding the number of occupants, their whereabouts, and density. Such integration can enable the HVAC system to accurately control humidity, heat, and airflow.
Save on Maintenance & Labor Costs
With the uncertainty around post-Covid occupancy, using historical data to predict the potential occupancy of spaces on an hourly and daily basis can greatly reduce operating costs. Historical data can be used to customize cleaning schedules and labor allocation. To enhance the use of occupancy data here, heat maps are also useful as they can demonstrate the most highly trafficked areas that may require extra cleaning.
Food waste in corporate America amounts to $160 billion every year. Historical data can be used by facility managers to reduce food waste on days where the office is expected to have lower occupancy. Here, a variety of occupancy tools with the exception of surveys can be used to predict potential occupancy.
Occupiers of office spaces are focused on establishing the ideal real estate portfolio size for their needs. Occupancy data can help those in charge of real estate and the workplace reduce the cost per occupant. Many companies from all sectors of the market are now selling real estate or ending leases in order to reduce their real estate costs. To effectively use occupancy data to reduce real estate footprint.
Ways to Track Occupancy
Manual Counting & Surveys
Manual counting and surveys are low-tech solutions to understanding space utilization. However, these methods are often inaccurate, prone to human error, extremely time-consuming, and unsuitable for scaling across a large portfolio. Moreover, manual counting and surveys only capture, albeit inaccurately, occupancy, and space utilization at a single point in time, and cannot reflect any future or previous data points.
Lan & Wifi
When a device is connected to a network using a physical cable or a docking station, data can be collected using LAN. Since LAN requires a physical connection, measurements are precise. However, requiring a physical connection also means that this method cannot account for people who are on-premise, but not connected using LAN.
Wifi as opposed to LAN does not require a physical connection and relies on wireless access points to assess a device’s location using trilateration. For this method to work, at least three access points are needed. Therefore, accuracy depends on access point placement and density making the use of Wifi to detect occupancy expensive and not precise.
There are a number of sensors that can be used to predict occupancy, such as PIR sensors, environmental sensors, ultrasonic sensors, and object detection sensors. Sensors are mounted on various locations through a building. Sensors ensure the privacy of the occupants, as they do not recognize faces nor record personally identifiable information. However, sensors are known to malfunction/show inaccuracies. For example, sensors can have a hard time detecting stationary occupants. Moreover, the installation and upkeep of sensors come at a high cost, which means roll out over a large real estate portfolio can be a hard sell.
Computer Vision (Cameras)
Cameras are the most accurate at observing occupancy and occupant movement. Traditionally, to reap the benefits of camera analytics, there was the need to purchase smart cameras. Today, thanks to the advancement of computer vision, existing cameras can be turned into smart cameras, thereby, reducing hardware costs. Moreover, the software used to detect occupants can be coded to blur faces and aggregate data to ensure privacy. However, like sensors, cameras cannot predict occupancy in spaces where they are not installed, therefore, scaling such solutions in spaces without a camera infrastructure can be time-consuming.
Conclusion: The ROI for Occupancy Analytics
Getting to the ROI of occupancy analytics is key to convincing decision-makers that data is gold. As discussed above, there are multiple use cases around occupancy data; therefore, the right ROI will depend on the use case of the data. If a business does not know how to use occupancy data, then there is no value. That is why conversations around use cases are key to creating an ROI. To have an ROI, you need to know your problem and the type of data you need to solve the problem. Based on that you need to pick a technology that can help you solve the problem. It is ill-advised to track occupancy if the data is not used extensively to reduce costs and create value. Luckily, many occupancy analytics providers are offering API integrations to ensure that the data can also be integrated into third-party software seamlessly including HVAC, facilities management, and meeting room and desk booking systems. If a business is unsure of the data, the math is simple, if tech is cheaper than the problem then the ROI is positive.