Top 5 Examples that Combine IoT and Analytics in Real Estate

Data science and big data combined in the form of real estate analytics can change that. Analytics solutions help businesses to more effectively monitor and process relevant data, especially the raw data from broadly implemented IoT devices.

The growing availability of data science andIoT applications allows for the increased use of their capabilities in real estate data analytics. This technology is all about establishing connections and exchanging data with devices and systems operating in buildings.

Using sensors integrated with building systems allows companies to introduce numerous innovations such as process automation and extraction of actionable insights. This leads to increased efficiency and improved usability while reducing unnecessary costs in both residential and commercial real estate. The end result is a positive impact on the commercial real estate business return, competitive advantage, security, and is backed up by improved overall cost efficiency.

However, the biggest benefit that comes from using data science and IoT technology is real-time data access. It enables further creation of interactive dashboards with real estate data to support data-driven decisions with actionable data insights. The combination of data science and sensors can also enable integration with building management systems to trigger desired actions.

How is this data used in real estate analytics?

1. Predictive analytics and preventative maintenance

A very profitable implementation of IoT technology and real estate analytics is the ability to monitor key equipment and assets in a building to track failures like malfunctions of elevators or of heating, ventilation, and air conditioning (HVAC) installations.

Thanks to preventative maintenance systems based on predictive analytics, commercial real estate professionals can receive immediate alerts if any equipment is at risk of malfunction. There’s also room for automating some of the work that was previously done manually. This maintains the continuity of assets operation and can help to avoid high repair costs.

To this purpose, the most common uses of real estate data are leak detection, temperature, humidity, and tilt sensors. One of the most popular applications is to strategically place sensors and set a simple logic for when the system should send an alert. Specific examples include:

• Use of temperature and humidity sensors in server rooms to monitor important equipment sensitive to these environmental conditions.
• Installation of leak detection sensors in kitchens and bathrooms.
• Use of tilt, shock, and leak detection sensors on production machines.
• Monitoring of anomalous sensor readings.

To realize the full potential of predictive analytics, it is important to analyze sensor data and use the insights to better plan maintenance and repairs.

2. Optimized space utilization

In most cases, one of the most important and largest costs associated with commercial real estate is the cost of rent directly related to the size of the leased space. Thanks to sensors and existing systems in the buildings, real estate data analytics can count the number of people in any given space and better plan how this space can be used. Using data science and big data to collect and collate these data points can help to optimize space utilization.

This is especially important for office buildings, where companies try to make the most efficient use of space. IoT sensors and machine learning support a better estimation of space demand. In the face of pandemics, they can also control the meeting of regulations such as the maximum number of people in a building. Some of the ways to measure space utilization include:

• Swipe cards — swipe card data can show how many people have entered a building on a given day. Many companies already have a swipe card system that requires only an additional automation setup for data acquisition and analysis processes.
• Desk sensors — small sensors mounted under a desk show with high accuracy if and when the desk was used.
• Ceiling sensors — sensors mounted on the ceiling show the number of people in a given space, they can be used in meeting rooms, collaboration spaces, etc.
• People counting sensors — sensors usually mounted at entrances that give data on the number of people in a space.
• Lockers and room booking systems — sensors in lockers provide information on how many lockers are in use, and a room booking system monitors who is in which space.

In addition to the uses mentioned above, these types of sensors are also useful for building maintenance. Depending on the sensor readings, we can use data analytics to automatically change a cleaning schedule and to turn off the lighting and HVAC in specific parts of a building.

3. Improved building maintenance and operations

Another application of IoT in real estate analytics is to improve daily building operations such as building maintenance and management, including cleaning, repairs, and security. Using the right sensors, alert systems, and reporting improves the efficiency of building operations. Some interesting examples are:

• Smart cleaning — using IoT technology and data analysis to optimize cleaning schedules and plan cleaning where and when it is actually needed.
• Security — IoT sensors and real-time alerts help enforce building security policies, such as alerts when the front door is left open for too long.
• Repairs — detection of equipment failures and reporting systems based on sensors reduce repair time and support more informed decisions in real estate management.
• Building management analytics — real-time alerts and analysis of collected IoT data from multiple areas of building operations allow building managers to improve their decision-making processes.

4. More efficient building energy consumption

Monitoring of energy consumption is easily handled with proper sensors. The very first thing to do here is to use real estate analytics to analyze the power consumption of the building. It identifies devices that are energy-consuming and benchmarks them with a peer group to see if the building is energy efficient. This is the first step to reduce consumption.

The ability to predict energy consumption using data science and data analytics is also important when negotiating a contract with an energy supplier.

Other energy-related areas are HVAC systems, which account for a significant portion of total energy consumption through regulating the temperature in the building.

The simplest example is turning off the HVAC when sensors detect that there are no people on a floor or in a building, or when there’s a window or a door left open. This is particularly important from the perspective of sustainability and the responsible use of resources.

5. Boosted employee experience

After the pandemic-induced changes in office operations, a significant number of companies are choosing to encourage employees to return to the office. IoT technology and real estate data analytics can be used to simplify this transition back and provide the best possible environment for employees to work in. Several examples are:

• Ambient temperature, noise, and light control — providing the right conditions for employees impacts their efficiency and satisfaction level. Well thought through IoT sensor placement, integration with the building management system (BMS), and data analytics can improve ambient conditions to boost employees’ experience and productivity.
• Space design — occupancy sensors are able to check which parts of the office are over and underutilized and allow better space management to meet the needs of employees.
• Feedback buttons — using tactile sensors enables feedback collection on the conditions in the office and gives the ability to address emerging problems more quickly. They can be placed at the entrance to the office, in kitchens, or in common areas to deliver data for space utilization improvements.

How to start using IoT and real estate analytics in your company?

As the first step, you need to determine which areas are the highest priority for your business and what type of IoT technologies can help you solve these problems. There are many suppliers of different types of sensors for real estate markets.

It is important that all sensors are mapped to your building according to a consistent nomenclature, and that data is issued to you in the form of an API. This will give you the ability to integrate all the data in one place, set alerts, integrate with BMS systems, and use real estate data analytics with any business intelligence tool.