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For years, commercial real estate has relied on processes that move at the speed of paperwork, whether that is evaluating a property, reviewing financial documents, or finding a suitable lender.
In a market where a few days can determine whether an acquisition closes or falls apart, that friction is increasingly difficult to justify.
Artificial intelligence is beginning to change that equation. Market participants are increasingly using AI to analyze deal information, accelerate underwriting, identify financing options, and reduce manual work across the transaction process.
One of the more consequential applications is emerging at the earliest stage of a transaction: funding the earnest money deposit (EMD) required to secure a property. Rather than treating deposit financing as a separate, largely manual process, new technology-driven platforms are applying automation directly to this critical step.
In what follows, we will consider the specific applications of AI in CRE underwriting and how they all contribute to the speed of acquisition.
AI-powered due diligence is compressing the research cycle
Vetting a property during the due diligence stage is one of the key aspects of CRE investing. In most markets, the payment of the EMD gives the buyer an exclusive right to the property (the seller takes it off the market) during the due diligence period so they can certify that all is well with the property and the deal.
This process often requires weeks of market analysis, physical inspection, financial modeling, operational review, legal review, and tenant review. Behind all these processes are leases, financial statements, property records, market data, and other documents that need to be reviewed.
AI is changing the game.
Today, CRE investors can use AI tools (like Dealpath AI) to review thousands of documents and extract the relevant information needed to make a decision. They can also cross-reference market data and detect anomalies and risks like inflated valuations and undisclosed encumbrances.
Some models also have predictive capacities: they can estimate how the asset performs under different macroeconomic scenarios.
With this, sponsors can identify risks, compare properties, and move from initial review to a more informed decision faster.
By automating these steps, AI compresses the research cycle from a manual and document-heavy process into a data-driven and automated workflow without compromising analytical depth.
With this, investors can decide on the property quickly, without risking the EMD they have paid.
Automated underwriting is turning deal analysis into a faster workflow
While many traditional lenders are still stuck with legacy underwriting systems that hinder accessibility and speed, alternative financing solutions are automating it with AI.
Many AI systems now assign dynamic risk scores to each borrower based on dozens of variables, including the sponsor’s experience, loan-to-value ratio, asset type, asset location, among others.
Instead of relying entirely on analysts to manually input and evaluate financial information, automated underwriting systems can process property-level data, assess borrower information, model scenarios, and identify potential financing parameters.
More importantly, these systems allow alternative lenders to employ a comprehensive approach that values both the deal itself and the investor/sponsor instead of obsessing over credit scores and reports.
This allows CRE investors and sponsors to get quick feedback on the financial viability of the deal and the type of capital it will require. With capital quickly sorted, the acquisition process can proceed at a fast pace.
AI is making lender and financing matching more intelligent
Finding the right financing source for a property can be a hassle. Investors often have to evaluate different providers and choose the one that fits best based on property type, loan size, geography, leverage, pricing, and transaction requirements.
Many financing marketplaces or directories (like CommLoan and Lev) now use AI to match investors with the right financing solution based on deal size, asset class, geography, and risk appetite. Instead of relying on manual introductions or generic loan marketplaces, these AI-powered systems match sponsors to lenders whose underwriting criteria align perfectly with the deal.
Some even use a predictive fit scoring system where each potential lender is given a score based on the likelihood that it will approve financing for a given investor. This scoring system relies on historical approvals, credit metrics, and market conditions.
AI is bringing automation to earnest money deposit financing
EMD lending is one area where AI is also making inroads.
First, EMD financing companies like Duckfund use AI in their underwriting process. Instead of requesting credit reports or scores, they use AI to process information about the deal and the sponsor to make quick approval decisions (within 24 hours, for Duckfund).
Second, they use AI to understand current market trends and provide financing terms that reflect them. For example, Duckfund allows buyers in competitive markets to propose higher EMD amounts when such is needed to gain an advantage. By understanding each market’s peculiarities, they provide personalized solutions.
By quickly securing an EMD, sponsors and investors can stay ahead of the queue and get the first look in competitive markets.
The impact of AI on commercial real estate is becoming less about replacing individual tasks and more about connecting the entire deal process. From analyzing due diligence documents and accelerating underwriting to matching sponsors with lenders and streamlining earnest money deposit financing, automation is removing bottlenecks that can slow transactions down.
As these technologies mature, the competitive advantage may increasingly belong to CRE firms that can quickly turn information into decisions, and decisions into deployed capital. In that sense, AI is not simply making commercial real estate more intelligent; it is making the business of closing deals faster.
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