Speed & Efficiency AI Underwriting: Processes applications in seconds to minutes. 1.Can instantly pull data from multiple sources (credit reports, bank statements, income verification, property valuations, etc.). Ideal for high-volume, standardized cases. Human Underwriter: Takes hours to days, depending on complexity. Manually reviews documents, contacts third parties, and applies professional judgment. Slower, especially for complex or edge cases. 2. Data Handling AI: Uses algorithms and machine learning to analyze massive datasets. Can detect patterns humans might miss (e.g., spending behavior, alternative data like utility payments, even digital footprints in some markets). Human: Relies on traditional documentation (pay stubs, tax returns, appraisals). Limited by human bandwidth—can’t process as much raw data at once. 3. Consistency & Bias AI: Decisions are consistent with its rules and training data. However, if the data it’s trained on is biased, the system can replicate or even amplify those biases. Human: Brings subjective judgment. Can weigh special circumstances that don’t fit a neat rule. Risk of inconsistency—two underwriters might interpret the same file differently. May have unconscious bias, but also flexibility to override rigid criteria. 4. Risk Assessment AI: Excels at quantifiable risks (credit scores, loan-to-value ratios, historical claim data). Weak at unstructured or nuanced factors (e.g., a borrower with an unusual income stream, or a claim with unclear circumstances). Human: Strong at contextual judgment—understanding unique borrower situations, exceptions, or “gray areas.” Can pick up on red flags that an algorithm might miss (e.g., forged documents, conflicting information). 5. Regulation & Accountability AI: Regulators are still catching up. Requires transparency in decision-making (explainable AI). Hard to appeal an AI decision if it can’t explain its reasoning clearly. Human: Provides a clear chain of accountability—borrower can request explanations or escalate. Easier for compliance teams to audit decision-making. 6. Cost & Scalability AI: Scales cheaply—one system can process thousands of applications simultaneously. Lower ongoing labor costs once implemented. Human: Labor-intensive, costs grow with volume. Better suited for complex, high-value, or unusual cases rather than mass processing. ✅ Bottom line: AI underwriting is best for speed, scale, and straightforward cases. Human underwriters are best for nuanced judgment, exceptions, and handling edge cases. Most modern institutions use a hybrid model: AI handles the bulk of simple files, while humans step in for complex or flagged cases. tune in and learn https://www.ddamortgage.com/blog didier malagies nmls#212566 dda mortgage nmls#324329

A new survey from Clever Real Estate shows that 61% of baby boomer homeowners say they “never” plan to sell their homes, a jump of 7 percentage points from 2024. The main reason? More than half want to age in place. That’s a big shift. Baby boomers now make up the largest share of U.S. homeowners, and if more than 6 in 10 say they’ll “never” sell, that has ripple effects: Inventory squeeze : With fewer boomers putting homes on the market, younger buyers have less supply to choose from, which can keep prices elevated. Aging in place trend : The desire to stay put often means investing in accessibility upgrades—things like stair lifts, walk-in showers, and smart home tech for safety. Generational divide : Millennials and Gen Z face higher borrowing costs and limited starter-home availability, while boomers are holding onto larger family homes longer. Long-term planning : Some experts note that many of these homes will eventually transfer through inheritance rather than sales, changing how housing stock re-enters the market. Didier Malagies nmls212566 DDA Mortgage nmls324329