September 20, 2021

How to Solve the 5 Big Data Challenges Holding CRE Back

GlobeSt Industry News | Shared data challenges solved with PropTech

Businesses are looking at how to better tap the potential of big data and PropTech as data is becoming an indispensable aspect of decision making in commercial real estate.

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Leasing automation, centralized leasing departments, providing customer service through bots, smart-home IoT devices and using mapping technology to replace spreadsheets are among the forward steps the apartment industry has been taking to handle data since the pandemic.

Yet many companies are facing challenges in gathering, analyzing and using data. Oftentimes datasets provide an incomplete picture, limiting the value of insights gleaned, while setting up systems across different countries can be a regulatory minefield.

“Without good data practices that result in high-quality data and insights, companies are at a disadvantage when it comes to reducing costs, preparing for risks and finding opportunities.”

Michael Thompson

Head of BI & Data Analytics, Americas, at JLL Technologies

Here are some common hurdles that businesses are facing when making the most of PropTech, according to JLL’s Transform with Technology report. A few available solutions are included, too.

1. Outdated Systems

Many companies use legacy tools such as spreadsheets to collate data, risking not only manual errors but also keeping other relevant data separate. Such data silos often make it harder for teams to share and use information, ultimately impacting the quality of insights.

“Quality of information and the completeness of datasets are common problems,” says HoChun Ho, Head of Enterprise Data Governance at JLL. “To embrace the Internet of Things, companies have to be able to process large volumes of data, which requires machine learning and automation tools.”

Establishing a data governance policy – and hiring for data governance skills – is key for employees to know how to collect and understand data, and where to access it. “Data governance enables companies to know they can trust their data,” Ho says.

Tools from companies such as Engrain are making data processing and interpretation easier by displaying it more visually rather than via a spreadsheet. Asset Intelligence, a technology provided by Engrain and built on Unit Map, is an interactive mapping API that acts as a foundation for other property technology. Unit Map is made available at no charge to any property management company that wants to use it for their own dashboards or within their other vendors’ products.

Asset Intelligence places data about pricing, apartment units, floor plans and overall floors onto a visual map that helps leasing staff and prospective residents get a visual presentation of data and important trends. It’s designed to make the task more intuitive, easily understood and efficient.

2. Limited Data Skills

A lack of staff with the right skills is a common limitation in collecting and analyzing data efficiently.

For example, many companies use dashboard software that assimilate and analyze all data collected across a company’s operations. But employees often require training to get the most out of such tools.

“The practice of gathering and using data needs to be integrated into workflows,” Thompson says. “While hiring data specialists supports the shift towards more data-centric decision making, good data practices rely on all employees being able to use data in their daily work.”

Management companies such as Catalyst created its own Catalyst Innovation Lab. It is focused on incubating, piloting and scaling innovative solutions, tools and partnerships that drive operating margins, building efficiencies and portfolio sustainability throughout the multifamily sector. The lab comprises more than a dozen young companies.

Hiring for data analytics is also on the upswing. Crystal Martin, Director of Operations, Leon Capital Group, said her company is seeking analysts who can come in and aggregate the data and present it in actionable ways on dashboards shared portfolio-wide with the team.

3. Inconsistent Standards

Companies in the midst of digitizing operations often have varying processes for collecting data in different teams. This can result in incompatible data formats, requiring time-consuming standardization that stymies information sharing, analysis and adoption of the new technologies.

For businesses starting to invest in PropTech, the wide array of IoT devices and vendors can also appear a minefield, with potential incompatibility issues a barrier to further investment.

Some companies such as Madera Residential have created proprietary IoT systems, using advancing technology that saves money and is more reliable. Companies such as iApartments has developed an IoT system that does not require a dedicated network, which also saves owners a great deal.

4. Complex Privacy Regulations

PropTech data, which provides insight into human behavior, is subject to data privacy regulations that differ between jurisdictions, from general data protection laws in Europe, Brazil or Singapore, to varying U.S. state privacy laws. For companies trying to comply with requirements in multiple countries, this can be a major roadblock.

“All these different technologies have to be able to support increasingly strict privacy requirements, while different systems within a company’s network may contain more private data that needs greater protection,” Ho says.

Projects often stall because stakeholders are unsure about compliance around data collection that would drive decision-making, adds Thompson, highlighting the need to train – or hire – for expertise in data privacy law.

5. Lack of a Holistic Data Strategy

With an expanding range of technologies that monitor environmental, occupancy and operational data, integrating all these data points can be a challenge. Fortunately, many newer property management companies such as AppFolio and Knock are creating and refining their CRMs (customer relationship management) platforms that simplify this process for onsite staff.

Companies require a holistic strategy that defines every data stream and how it interacts with other building data, says Thompson. “A robust data strategy needs to define, check, organize and distribute data to the places it needs to go,” he says.

By investing in datahub tools like dashboards that assimilate information across different departments, and even with the office space itself, companies can gain insights into how to improve every aspect of their business.

“Every company needs to understand data to create their competitive edge,” Ewert says. “Occupiers are now focused on how to enable the right hybrid workplace, and investors on how to revamp existing buildings to meet future demands. Good data empowers these decisions.”

Bot technology is helping to deliver unmistakably consistent answers to apartment prospects’ questions, through pre-programmed, AI-driven data that the bots use when interacting with a website visitor.

UDR has found that using virtual leasing assistants such as ACE from LeaseHawk is a first step toward centralized leasing offices, which are gaining some momentum in operations. It’s a component of “autonomous leasing,” whereas the bots can handle many of the functions that previously were the responsibility of onsite leasing staff from looking to leasing to living.

If a prospect schedules a tour through the bot and takes it, the bot can ask, “How would you rate your tour?” If it’s a 4 or higher on a 5-point scale, the bot can ask, “Would you like me to send you an application?” Or, “Can we do a credit check with you?” This is on top of the bot already confirming and reminding the leasing agent about the appointment.

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Written by Paul Bergeron, GlobeSt