Get a demo
Select the type of demo you'd like to experience with Indico Data.
Select the type of demo you'd like to experience with Indico Data.
Commercial real estate companies are awash in purchase and sale agreements, leases, appraisals, mortgage documents, legal contracts, and more. Many of these documents are complex and hefty, as much as 100 pages in length. Yet automating document processing in commercial real estate is now a requirement to stay in business. This automation enables real estate companies to keep lower overhead expenses and more profit as part of the digital transformation efforts that are common in the industry.
Compounding this challenge is that most of the documents in commercial real estate are unstructured, making them a challenge to deal with for workflow automation approaches that rely on robotic process automation, keywords, templates, or rule-based methods. Unstructured data requires an intelligent document processing system (or “hyperautomation” in Gartner parlance) that employs artificial intelligence technologies to essentially “read” the data just as a human would.
Related content: Gartner 2022 Market Guide for intelligent document processing solutions
Commercial real estate companies have been seeking the process automation holy grail for years, with only modest success. The reason is that these attempts have focused on technologies using rule- and template-based approaches that only work well with highly structured data, but not with the unstructured documents that dominate the commercial real estate field.
Take the task of extracting data from rent rolls. The rent rolls contain the property owner’s name, address, type of zoning, details on square footage, rooms, lot size, tenants, and more. If commercial real estate firms can automate the processing of rent rolls, they will have access to data which can help unearth trends and inform decisions around topics such as rent increases. This data will also help companies uncover red flags, such as a suddenly delinquent tenant on rent.
Real estate automation techniques that have used templates or rules to try to automate the processing of rent rolls have been ineffective because they are so complex and varied. It’s impossible to develop enough rules or templates to account for all the variations in the documents. Instead, commercial real estate firms have been forced to use employees to manually extract relevant data.
Related content: How intelligent automation delivers value through process acceleration
Effective commercial real estate automation processes involving unstructured data require a tool with artificial intelligence capabilities, such as machine learning, transfer learning, and natural language processing (NLP).
The tool needs to have enough intelligence to understand the context behind a document. The Indico Intelligent Process Automation platform, for example, is built on a database of more than 500 million labeled data points, enough to enable it to understand human language and the context behind documents and even images. It’s a key differentiator because it would take even the largest commercial real estate firm years to collect that much data and build its own AI model.
Companies can use the underlying Indico database to build custom models to automate virtually any commercial real estate process involving documents thanks to transfer learning. Using the Indico platform, it takes only a few hours to label about 200 documents and create a process automation model that’s highly accurate.
Additionally, data scientists aren’t required to build automation models. It’s business people, those who know the processes best, who train the automation models. (For more information, check out our Intelligent Process Automation page.)
Intelligent automation can also complement the robotic process automation (RPA) tools companies use to automate commercial real estate processes. RPA involves software-based robots that perform deterministic, repetitive tasks.
RPA in the real estate industry works well with structured data, such as spreadsheets, databases, and some forms. It can automate the process of extracting data from these sources and input it into downstream systems for processing and analytics.
But RPA does not have the cognitive capabilities required to “read” data that is not in a structured format (a fact that Cushman & Wakefield uncovered early in its process automation journey). You can use RPA in tandem with an intelligent automation solution that does have such capabilities. An intelligent document processing tool, for example, can “read” unstructured documents and convert the relevant data into a structured format. Then RPA can process the now-structured data.
Using intelligent automation, commercial real estate companies can also automate lease agreement processing. The idea is to extract essential information from lease agreements for entry into downstream tools, such as an ERP system. Today, that process relies on paralegals and lease administrators to read over the lease and extract details and facts to create a lease abstract, which sums up the terms in an actionable format. This manual “stare and compare” is an expensive, time-consuming process. But automated lease abstraction is possible by using an intelligent automation platform which can automate the entire process and free up that time. The Indico IPA platform classifies the images typically included in lease agreements based on property type, location, size, and other criteria. Commercial real estate firms can then perform analyses on the resulting data to uncover market trends and opportunities.
Some think of invoices as structured data that an RPA bot can handle. But when you consider that a commercial real estate firm likely gets invoices from dozens if not hundreds of companies and that each uses a different format, it quickly becomes apparent that automated invoice processing is a job for an intelligent automation platform. Such a platform could extract relevant data – such as vendor, invoice number, amount, due date – and input it into a company ERP system for processing (perhaps using RPA for that step).
Commercial real estate firms are awash in legal and contractual documents for various deals. These documents hold potentially valuable data that, to date, has been essentially out of reach of the sort of analysis that can unlock that value because it required many hours of manual effort to extract the data. Intelligent process automation enables firms to automate the “reading” of these documents and extract data for input into downstream systems. Such systems may include a contract lifecycle management tool and risk analysis tools that help manage factors like upcoming expirations, exception-based clauses, and various obligations.
Commercial real estate leases also have to be administered throughout their lifecycle to meet all contractual agreements and regulatory compliance rules. While many companies traditionally used spreadsheets or paper files, a better way is to automate the process using software designed explicitly for lease administration. However, even with such software, you’ve still got to populate the tool as each new lease comes along. That’s where intelligent automation can help, enabling you to extract the critical data from each lease and input it into your lease administration system.
Rent rolls are another example of a document containing lots of potentially valuable data if it’s unlocked. That includes primary property data, including owner name, address, and type of zoning. Rent rolls also contain details on square footage, number and type of rooms, and amenities, as well as rental income summaries, including total monthly and annual rent collected and any additional expenses, such as landscaping. They should also contain details about tenants, including the monthly rent rate, date rent is due, any problems with specific tenants, and lease dates. The ability to pull relevant data from rent rolls gives commercial real estate firms to make better judgments about their property investments. Applying analytics to data extracted from rent rolls can help companies identify trends in the properties it already owns, such as where opportunities for rent increases lie, and identify any red flags, such as a property that needs attention to maintenance. Only by automating the process of extracting such data can commercial real estate firms benefit from it.
Expand process capacity:
Automation enables commercial real estate employees from the back office to the front lines to be more productive, effectively enabling you to increase revenue without adding employees.
Increase efficiency:
Nobody enjoys mundane tasks. Process automation means freeing up employee time for more rewarding and valuable work.
Improve cycle times:
Automating commercial real estate processes enables you to get more work done faster and with increased accuracy. And only exception handling is required by staff, improving cycle times. Processes that previously required days to process can be processed in a fraction of time.
Capture industry knowledge:
Before you can automate a process, you first must codify it. For many firms, the process automation exercise is the first time anyone has attempted to define how a process is supposed to work clearly. The activity creates a formal, lasting agreement on how a process works. It also often results in streamlining processes to make them less cumbersome and more effective.
Compete effectively:
Whether you’re already a commercial real estate giant or aspire to be one, intelligent automation makes your organization more competitive, better able to identify and take advantage of opportunities.
