Automate leasing and lease abstraction with CRE tech

Automating leasing and the lease abstraction process can enable commercial real estate firms to better manage data across the business and effectively integrate data across multiple systems. Today, as the commercial real estate industry navigates uncertain times due to the COVID-19 pandemic, one thing is certain: continuing to rely on what worked yesterday no longer works today. Modern commercial real estate organizations should be looking for new technologies that enable them to shape innovative strategies, work more efficiently, and stay competitive during and post pandemic.

Problems that can arise during manual workflows

In a recent webinar, industry experts from MRI Software, Leverton, and VTS discussed the challenges of manual processes and how technology can streamline leasing and lease abstraction.

Manual leasing processes have several obvious disadvantages including time-consuming work for the in-house lease admin team, the expenses associated with lawyers, and outsourced abstraction teams. However, there could also be profound effects on your organization that may not be immediately apparent in your every-day process, but can hold your organization back nonetheless.

1) Disconnect between the pipeline and in-place leases. When inputting new in-place leases, it may take some time to get your systems updated for a variety of reasons, and when the new lease is finally uploaded, information may have changed or might get entered incorrectly, which could lead to errors. The longer this process takes, the longer your reporting could be inaccurate.

2) Missed opportunities through lack of data-driven insights. There are many strategic advances that you can make by surfacing the data buried within documents and folders, and users often don’t even know it’s there. By missing these critical data sets, your organization can miss out on key opportunities.

3) Manual data migration. Oftentimes, double data entry can occur when manually uploading data into an ERP system, which then leads to errors and inconsistencies. Getting data out of a contract and into a document that is then added to an ERP can lead to discrepancies that affect revenue. With that, there’s also limited or no data analysis in an ERP system, or aggregation with other data sources.

Using technology to automate leasing processes for commercial organizations

Automating your leasing processes with technology from VTS, a leasing and asset management platform that focuses on workflows and functionality, can make reporting easier and more trustworthy. With so many stakeholders handling the lease data at the center of your business, the data is at risk of becoming disparate and unreliable. Do you trust that the data in your reports is correct? Do you trust that it’s been verified? Do you have data coming from the source system? Technology that integrates with an integrated ecosystem can pull from one source of data, providing you and your stakeholders with trustworthy reports.

Additionally, automating your lease abstraction with MRI Lease Intelligence, an AI tool powered by Leverton, An MRI Company, can establish good data governance within your organization. Automating lease abstraction cuts out the manual effort of pulling data from leases and manually uploading them into target system. Instead, it pulls specific data sets, including those that a manual lookover might not have found, into one central source for all of your lease data.

Using automated leasing solutions can help ensure the accuracy of your data and eliminate manual, error-prone data aggregation and entry. They also give users the ability to make more informed, data-driven decisions by utilizing source data rather than conjecture or stale information. With these benefits, commercial owners and operators can utilize automated lease tools to bolster their businesses in uncertain times and gain a competitive edge, even in challenging circumstances. Learn more about how automating your lease processes can prepare you for the future in this webinar.