Using Predictive Analytics To Manage Your Spaces

Predictive analytics have emerged as a useful tool in the corporate real estate industry, changing how teams understand and forecast space utilization and usage trends. By leveraging artificial intelligence (AI) and machine learning algorithms, companies can now analyze vast amounts of data to gain insights into future workspace needs, occupancy patterns, and employee behavior. This technology enables more informed decision-making, optimizes space usage, and helps organizations create more efficient and productive work environments.  In today's blog, we'll examine how this is taking shape.

Applications of Predictive Analytics

There are already some everyday use cases for predictive analytics in workplace and real estate. While the list below isn't exhaustive, these are some of the most common use cases today. Thanks to AI, the use of this kind of technology is growing daily.

Utilization forecasting

One of the most significant applications of predictive analytics in corporate real estate is space utilization forecasting. AI models can analyze historical occupancy data, employee work patterns, and external factors to predict future space needs with increasing accuracy. This helps organizations make informed decisions about leasing, expanding, or consolidating office space.

Occupancy Pattern Predictions

Predictive analytics can forecast occupancy patterns by analyzing factors such as employee schedules, remote work trends, and seasonal variations. This information is invaluable for facilities managers in optimizing heating, cooling, and lighting systems, as well as planning cleaning and maintenance schedules.

Workspace Design Optimization

AI-powered predictive models can identify trends in how employees use different spaces, helping optimize workspace design. These insights can guide decisions on the allocation of collaborative areas, quiet zones, and meeting rooms to better meet employee needs and preferences.

Resource Allocation Analysis

Predictive analytics can forecast future demand for various office resources, such as meeting rooms, parking spaces, or shared equipment. This enables more efficient resource allocation and can help prevent bottlenecks or underutilization.

Data Sources for Predictive Corporate Real Estate Models

So, what makes all of this possible?  Well, in short, data.  Predictive analytics models rely on various data sources to generate accurate forecasts. These  include but are not limited to data from the sources below :

• Badge swipe data and access logs
• Occupancy sensors and IoT devices
• Wi-Fi and network connection data
• Calendar and room booking systems
• Employee surveys and feedback
• HR data on headcount and department growth
• External factors (e.g., local events, weather, traffic patterns)
• Energy consumption data
• Video analytics from security cameras (anonymized for privacy)

Challenges in Implementing Predictive Analytics for Utilization

While predictive analytics offers numerous benefits, implementing these systems comes with challenges.  Ensuring data privacy and security can be a specific area of focus for many procurement and IT teams.  Additionally, data integration without a platform like Trebellar can be time-consuming and costly.  If you don't already have sensors deployed in your space this can add another layer of complexity as well.  While most spaces have some combination of motion sensors for lighting controls, more robust sensors will yield more precise data and additional layers of information that can enable better analytics.  Most of these issues have standard solutions, but being informed about them is essential to get ahead of them as a team.

Integrating Predictive Analytics

If you want to get ahead of these issues and to maximize the benefits of predictive analytics, organizations should establish clear objectives for using predictive analytics in space management. This will enable the entire organization to understand the value of the investment.  In addition, teams should provide training to their teams on interpreting and using predictive insights.  An alternative path is using a platform like Trebellar that enables this out of the box.  Lastly, it's vital to use predictive insights to complement, not replace, humans in space planning.  There will always be nuances that sensors, readers, and other systems miss.  

Future Trends in AI-Powered Utilization Forecasting

The future of predictive analytics in corporate real estate looks promising, with several emerging trends, such as integrating with employee experience platforms, using augmented reality for space planning, and even better real-time optimization. It's definitely an exciting time to be in the workplace and real estate. By providing unprecedented insights into space utilization and usage trends, these tools enable companies to create more efficient, flexible, and employee-centric workspaces.

As AI technology continues to advance, the accuracy and scope of these predictions will only improve. Corporate real estate professionals who embrace and integrate these tools into their decision-making processes will be better positioned to adapt to changing work patterns and optimize their portfolios. However, it's important to remember that while predictive analytics is a powerful tool, it should be used with human expertise and judgment to achieve the best results in creating productive and engaging work environments.