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Remote Monitoring and the Future of Proactive Property Management

Property management is typically a reactive affair. Service requests for major systems such as HVAC units or water heaters are typically only made at a tenant’s or owner’s request, and after the onset of a system failure. For HVAC, heavy seasonality results in the clustering of problems in peak cooling or heating seasons. Failures during these busy times are especially problematic. For busy contractors, meeting required service-levels and response times are difficult to achieve in periods of high seasonal demand. In fact, service requests at these peak times are often met with extended delays that inconvenience owners and tenants alike.

A proactive approach in which system issues and pending system failures can be identified and addressed before they happen leads to increased customer satisfaction, and a reduction in capital expenditure. Remote monitoring technology, supplemented by a robust service network leads to fewer unplanned replacements, reducing unnecessary stress on limited operational resources.

Remote monitoring is not a new concept. Indeed, it is often utilized in high-end commercial and multi-family properties in the context of complex building management systems (BMS). A BMS is used to manage energy demand, provide access control, perform security monitoring, and implement disaster recovery as well as detect service issues.

In residential settings, security monitoring is gaining strong adoption. According to Parks Associates, in mid-2018 more than 23 million US homes have professional monitoring services. The evolution to a full smart home solution encompassing more than security is also growing fast. S&P Global Market Intelligence estimates the number of smart homes will grow from 24.3 million in 2018 more than 35 million by 2021.

Considering this robust growth, the opportunity to extend these concepts to include ‘closed-loop’ monitoring of critical building systems such as HVAC and water heaters in light commercial and residential settings is substantial.

Let’s start with an overview of a closed-loop process and discuss selection of system components. –

Closed-Loop Remote Monitoring Blueprint

The closed loop solution will typically be comprised of several components (see Fig. 1).

home monitoring

Fig. 1: Closed-Loop Remote Monitoring Blueprint

On-premises Data Collection. Closed-loop remote monitoring solutions start with the collection of on-premises sensor data and system settings. The specific sensors deployed will be determined by the type of equipment being monitored, as well as service level considerations and budget.

For example, in the case of HVAC system monitoring, the most basic solution would encompass ambient temperature readings, system temperature set points, and run-time data. Typically, the sensor data is collected on-premise via a mesh network leveraging industry communication standard such as Z-Wave, ZigBee or Thread.

Data Center. Although in-device, on-premises diagnostics are valuable, there is great utility in capturing the raw data in a centralized reporting and analytic facility serving a portfolio of properties. A single centralized system offers improved reporting, information management and decision-making.  Wi-Fi or cellular data transmission capabilities are needed to transmit data captured on-premises to what is usually a cloud-based data center.

The nature of analysis can be either descriptive or predictive. Descriptive analytics focus on detecting system issues that have already manifested. An example would be detecting an ambient temperature deviation beyond a pre-programmed range. Excessive run time and the inability for an HVAC unit to hit a set point temperature is another example. There is considerable value in these type of alerts in that hours or days may be removed from the time typically incurred for an alert to make its way from tenant to property manager to service provider.

The desired future state will make full use of predictive analytics. Predictive analytics use historical data to identify likely failures before they occur. Generally speaking, additional sensors are needed to both predict and accurately localize a pending failure. Additional HVAC data could be collected from vibration, pressure and humidity sensors. Machine learning techniques leveraging sensor data and information about the system itself (such as age, prior repair history) are then deployed to predict failures. Once a plausible failure is detected, the solution is then to perform preventative maintenance or repair.

Service Network. Identification of both manifest problems or predicted failures is not enough to deliver a proactive level of service. There must be a tight coupling of event detection with the service network. A technology platform that ingests the alerts generated by the data center and initiates and manages a service request is needed. A well-trained contractor network and equipment supply chain solution are also key components to ensure high service levels.

Our firm, Motili, has developed a nationwide service and distribution network solution  which manages the interactions between clients, contractors and equipment/supply chain sourcing equipment and supplies by leveraging ERP-level supply chain integration and inventory access across more than 2,000 nationwide distribution locations.

Case Study

Building36, a developer of home automation solutions, engaged in a pilot test of the closed-loop approach outlined above. Nearly 3,000 homes in and around the City of Boston were remotely monitored to detect HVAC service issues. The initial focus of the pilot was focused on detecting problems using descriptive analytics, ambient temperature, temperature set point and run-time.

In almost all cases tracked in the pilot study, the homeowner or tenant had not yet reported the issue before the remote the monitoring system sent an alert and dispatched a service technician to fix the problem.

An example of the Building36 data center reporting is shown below:

Remote monitoring
Fig. 2: Building36 Data Center – Reporting Dashboard

In the example highlighted in Fig. 2, the blue line corresponds to the 68oF temperature set point. The green line is the ambient indoor temperature. The blue shading of the graph indicates run-time. Average outdoor temperature during the reporting window is also shown. As can be seen, despite mild outdoor temperatures and running essentially non-stop, the HVAC unit could not reach the desired set point, and a service technician was dispatched.

The simple addition of refrigerant solved the problem, in this case.

The issue was identified ahead of the peak summer cooling season, which allowed it to be addressed with minimal impact on, and with no discomfort for, the homeowner.

Ongoing work in predictive analytics promises to further enhance the quality of closed-loop remote monitoring.


Proactive repair, replacement, and maintenance of key building systems is the ‘Holy Grail’ of residential property management, and advances in closed-loop remote monitoring are making it a reality. As we have learned, this approach offers substantial benefits to tenants, property owners and managers alike.

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