A CRE platform that uses technology and data analytics can help both the property owner and the tenant. The property and location specific data helps an owner to unravel the unique advantages of a space and thereby reach out to right brands. Similarly, a brand looking out to rent a place finds it incredibly helpful to shortlist properties with the help of relevant and updated data.

An obvious question at this stage is “what are the technologies or data analytics that we are talking about” in the context of commercial leasing. The involvement of technology starts even before the first step is taken to either offer a property for leasing or searching for a property to lease. A CRE platform using technology helps a property owner understand the characteristics of the property better and therefore supports him to decide on the potential renter as well as the rental range. Similarly, availability of various demographic data points specific to a location helps a brand short-list properties to lease.

A salient feature of technology in the commercial leasing process is to transparently connect all the stakeholders for effective and fast decision making. It reduces the time required at each step by updating everyone with most appropriate insights. An update of any information being shared on a real-time basis ensures that all those involved in the lease-finalisation process have the same information and can build-in their perspectives effectively. 

Modern commercial leasing platforms also use data analytics and artificial intelligence. Use of these advancements can help investors in their existing acquisition, disposition, and portfolio management processes to manage rising risks and complexities more effectively and mitigate vacancy and foreclosure risks. The ability to capture data in last few years has increased tremendously resulting in loads of data available to determine variables. Information such as net effective rents, lease periods, lease components, market demand, and tenant information have now become much more accessible and granular thanks to modern analytics tools.

The use of technology is not only about automating traditional data collection. One can capture data related to the whole gamut of activities using the most sophisticated technologies, and analyse the same to bring out guidance for the future. Datasets from IoT sensors, social media, geospatial information, and satellite imagery are increasingly being used to assess potential properties and thereby to arrive at optimal rental values and other lease terms. Machine learning based new algorithms can help property owners as well as brands to harness this deep data to make more informed decisions faster and more accurately.

For instance, retail mall investors can use traditional property data around footfalls, combine it with alternative retail sales data from mobile sensors, social media, and physical store sales, and use machine learning algorithms to analyze consumer buying behavior for a geography or to profile retail tenants. Brands also find it easier to commit to spaces with compelling data around these decision points and the entire process of leasing is simplified drastically.

Use of technology and data analytics help in taking the guess-work out of the decision-making process. The use of alternative and traditional datasets and the application of advanced analytics can help reduce human bias through more objective decision-making. It can result in faster decision-making, lower transaction and operational costs, and more rigorous risk management and property optimization. Some of the data analytics-driven information repositories, insights, and efficiencies could allow savvy property investors to uncover untapped value in their current and potential CRE investments.

Machine learning algorithms have helped stitch together the data obtained from various sources and across highly disparate variables. It makes it significantly easier to aggregate and interpret varied sources of data. Technology solutions automate the data collection by accessing application programming interfaces and connecting various databases before preparing the data for analysis. After all, it is not the raw data that creates value, but the ability to extract patterns and forecasts and use those predictions to decide actions by negotiating parties while closing a leasing deal.

A successful data-driven approach can yield powerful insights. For example, an application combining a large database of traditional and nontraditional data can be used to forecast the forward rent per square foot in a specific building for a particular location. These machine-learning models can predict rents with a high accuracy as the algorithms can rapidly combine macro and hyperlocal forecasts to prioritize cities and neighborhoods with the best future outlook. This allows the property owners to also suitably adjust their earnings forecasts and actions with respect to their commercial leasing portfolios.

The use of data analytics has brought to the fore the power of non-traditional data. Perhaps unsurprisingly, variables related to traditional data sources—for instance, vacancy rates—correlated with future values. But variables related to nontraditional data, such as proximity to a highly rated restaurant or changes in the number of nearby apparel stores, drafting in the oft-missed perspective.

At times, two buildings that are seemingly identical when evaluated by traditional metrics can ultimately experience very different growth trajectories. It is easy to imagine how this disparity at the individual building level, when applied across a series of investments, can drive dramatic results at the portfolio level.

These solutions will evolve at a rapid clip—progress in artificial intelligence is frequently exponential rather than linear—and companies must consider them as realistic supplements to their decision making in commercial leasing.

An often ignored yet important role of technology is to engage with the right tenants for long-term association. CRE analytics platform can provide insight into tenant engagement and sentiment to help property owners make the strategic decisions with regards to occupier selection. For instance, analytics dashboards can sort tenants by rentable square footage as well as lease expiration dates to uncover churn risks among key tenants and their relative impact of meeting cash-flow objectives.

Technology and data analytics doesn’t only highlight the at-risk tenants who regularly show signs of dissatisfaction, but also assist in pin-pointing the cause of such behaviour. These solutions empower property owners to learn what a particular tenant cares about, and what changes one can make to the building, amenities, or programming to improve tenant retention. Similarly, an agglomeration of interactions with property owners helps the property occupier to gauge in advance the challenges that could come in continuing the lease.

ImpactR is a Delhi-based, leading data-driven commercial leasing platform working with brands and property owners in the region, tracking the latest trends in commercial leasing and helping its partners adapt to those ahead of time. It aims to provide a solution that improves information availability, consistency, scalability, and quality while reducing potential for human bias. It works with unique data-sets with both traditional and non-traditional variables, using analytics to optimise the leasing decision making.