It’s no secret that commercial real estate is driven by documents and data. Whether it’s an acquisition, lease, or rent roll, it’s driven by paper. From financial statements and contracts to leases and loan documents, these files typically are stored in email attachments, computer folders, or file cabinets.
For real estate teams operating in today’s market, speed, precision, and transparency are requirements. Stakeholders need documents immediately, teams need to summarize and analyze data, and missing critical information can cost a company a significant amount of money. Unfortunately, most of this important information is unstructured and not centralized. Often, real estate teams can’t easily access a dashboard that synthesizes the necessary data. Instead, specific information may be contained in a random email, in a buried provision in a lease, or hidden in an overlooked footnote.
With the advent of AI and machine learning, tools like KeyDocs can unlock the underlying value of this unstructured data. The result is static documents are transformed into centralized, structured, and actionable data.
What Is Unstructured Data in Real Estate?
Unstructured data is information that is unorganized, inconsistent, and often unsearchable. For example, a pile of lease agreements, a scanned zoning report, or a PDF operating report are all considered unstructured. While they may contain important information or key terms such as rent increases, renewal options, maintenance responsibilities, or termination clauses, they often require manual review to be organized in a way that allows real estate teams to extract value.
Unstructured data differs from structured data, which is clean and categorized, often in databases or spreadsheets. For example, structured data may include monthly rents, lease start dates, and expense reimbursements. With structured data, real estate teams can run reports, easily analyze data, and conduct financial modeling. However, to create structured data from unstructured data usually requires a manual process. For example, a junior analyst manually searches leases or contracts, and then enters key data into Excel. Unsurprisingly, this process is time-consuming and prone to error. This process is time-consuming and prone to errors, with critical details often missed, misunderstood, or inaccurately transcribed.
Why Unstructured Data Doesn’t Work
Lenders, investors, and asset managers value speed and efficiency; they can’t wait weeks to discover a lease termination clause that was overlooked or miss an expense buried in a contract that could impact underwriting. Missing a material clause could cancel a financing commitment or overstate a financial profile. Put simply, real estate stakeholders can’t afford errors, which could derail a transaction.
An over reliance on PDF documents and manual summaries creates unnecessary risk for real estate teams. Manual processes lead to inconsistencies and reduced confidence in data. One analyst might summarize a lease one way, while her colleague chooses a different approach. Another analyst identifies an important liability, while another analyst misses it. Inconsistency like this may hurt acquisitions or portfolio management, creating risk, compliance issues, or heightened financial or legal exposure.
How AI Structures the Unstructured
Tools like KeyDocs leverage AI to review, organize, and structure documents at scale. AI can take unstructured data and convert it into a structured, coordinated, and centralized source of truth. By identifying specific language patterns, AI can help extract important information such as rent amount, lease dates, rent increases, renewal rights, maintenance obligations, and aggregate that data in one location with clean data. Once extracted, the structured data is centralized and standardized, which makes it easier to search, filter, analyze, and leverage. The result is better reporting, monitoring, and consistency.
Here are several ways that AI can structure unstructured data:
1. Acquisitions
For any acquisition, time is short and accuracy is required. Buyers need to understand exactly what they are buying and what liabilities, if any, they’re assuming. AI accelerates due diligence once documents have been shared, which allows investors to base underwriting on actual assets, rights, and liabilities rather than incomplete assumptions.
2. Financing
Lenders want detailed and verifiable lease data. AI can create clean lease data that is auditable, which increases confidence and accelerates the approval process.
3. Portfolio Management
Asset managers need structured lease data that can be used for portfolio monitoring after an acquisition closes. Property managers and asset managers can track tenant risk, lease rollover schedules, or compliance milestones. They can also run portfolio-wide reports to inform capital planning or re-leasing strategies.
Conclusion
The future of real estate is information and data. Unstructured data must be unlocked to extract value, streamline decision-making, save time, and reduce errors. Static documents must be replaced with centralized information that can be queried. Tools like KeyDocs empower real estate teams to extract, structure, and centralize data at scale, creating new opportunities for investors, lenders, and asset managers. Real estate teams who make structured data essential to their business will flourish, while teams that rely on unstructured data will continue to face challenges.
