Small and mid-market commercial properties make up the majority of real estate transactions in the United States. These properties include medical offices, retail strip centers, industrial buildings, mixed-use properties, and small multifamily assets that trade between five and $20million dollars. Although these assets represent large investments opportunities, they are often difficult to underwrite. Why? Many assets lack detailed marketing packages. Broker data is often incomplete or inconsistent. Financials may be incomplete or lightly summarized. Market information can be challenging to acquire, especially in secondary or tertiary cities.
These gaps slow down underwriting, increase uncertainty, and make it challenging for investors to pursue multiple deals at once. Large institutional investors rely on research teams to fill these information gaps, but smaller and mid-sized investors often do not have that luxury. This is where AI is beginning to reshape the underwriting process. By organizing public data, extracting insights from documents, and modeling outcomes more efficiently, AI helps investors evaluate smaller deals faster, with greater clarity and lower risk.
Why Small and Mid-Market Deals Are Harder to Underwrite
Underwriting a large institutional asset is often easier than underwriting a $10 million dollar property. Larger deals typically come with professional offering memoranda, full rent rolls, audited financials, and detailed historical operating statements. There is usually a robust set of comparable properties, solid market data, and multiple brokers providing well-researched perspectives.
Small and mid-market deals are different, however. Brokers often prepare limited materials. Financial statements may be incomplete. Rent rolls might be outdated or missing details on lease expirations or pass-through obligations. Market comps are harder to identify, especially when few properties trade in the area. The buyer is often forced to make assumptions without data.
AI helps close these gaps by structuring unstructured information, validating assumptions, and standardizing analysis across multiple deals.
How AI Structures Documents
Lease review is a major pain point in underwriting. Small properties may have multiple leases, each with different terms, side letters, or amendments. Manually reviewing them takes time and increases the likelihood of errors.
AI tools like KeyDocs can read leases, amendments, and financial documents and convert them into structured formats. Instead of spending hours searching for escalation clauses, renewal options, operating expense responsibilities, or termination rights, investors can access a clean, consistent summary across all tenants.
This matters because small and mid-market pricing often hinges on details hidden inside leases. A termination clause oran unrecorded rent discount can shift valuation significantly. AI reduces the risk of missing those details and helps investors make accurate cash flow projections earlier in the process.
Sourcing Market Data in Secondary Locations
Market data can be sparse for small asset underwriting. Many secondary or rural markets have limited brokerage coverage, and public listing sites may be incomplete or outdated. Without a baseline for market rent, vacancy trends, or comparable sales, investors either overpay or walk away from viable opportunities.
AI tools like KeyComps can identify rental comps, sale comps, and property-level attributes by scanning public datasources and standardizing results. KeyComps allows investors to benchmark rent, occupancy, and pricing against hundreds of similar assets instead of relying on a handful of broker opinions.
This is especially important for medical office, small industrial, and service-retail assets where national data sets are limited. By comparing an asset to a larger and more accurate dataset, AI reduces the uncertainty that typically surrounds smaller properties.
AI for Due Diligence Without Increasing Risk
Small deals often require quick decisions since competitive buyers move quickly and sellers expect short diligence windows. Investors sometimes pass on deals simply because they cannot complete underwriting in time. AI accelerates the slowest parts of due diligence: document review, data validation, and financial analysis. When leases or operating statements are structured automatically, investors can focus on the decisions that matter rather than administrative tasks.
AI highlights inconsistencies and missing data in documents, allowing investors to resolve issues early. The combination of speed and clarity makes small asset transactions more manageable, reducing the risk of missing critical information in a tight timeline.
Scaling Deal Flow for Investors Who Want Volume
Many investors want to scale, but underwriting capacity limits how many deals they can evaluate. Traditional underwriting might allow an investor to review five or 10 deals a month. With AI, that number can double or triple.
Instead of spending hours gathering documents or searching for comps, teams can review standardized outputs and focus on decision-making. This shift enables investors to screen far more opportunities, which increases the likelihood of finding high-quality deals. For lenders and asset managers, AI underwriting translates to better visibility into borrower assumptions, stronger credit packages, and reduced downside risk.
Why AI Matters Most for Assets Under $20 Million Dollars
Large institutional assets attract significant attention, data, and resources. Since smaller assets do not due to lack of transparency creating pricing inefficiencies. Some investors avoid these deals due to information asymmetry, whereas others overpay because they rely on incomplete data.
AI levels the playing field by bringing structure to markets that have been underserved. AI also reduces friction in transactions, increases certainty of execution, and gives investors confidence to pursue deals they might have overlooked. As more real estate teams incorporate AI into underwriting, the small and mid-market segment will become more competitive and efficient.
Conclusion
Underwriting small and mid-market real estate deals has always been challenging. Limited broker coverage, inconsistent documentation, and sparse market data often slow the process and increase uncertainty. AI is changing that by structuring leases, extracting financial data, and identifying comps.
Investors, lenders, asset managers and property managers can evaluate more deals with higher precision and less manual work. As AI becomes more integrated into real estate workflows, the small and mid-market segment will benefit the most.
