Great question — the 10-year U.S. Treasury Note (T-Note) is one of the most important benchmarks in finance, and it’s tightly linked to interest rates. Here’s a breakdown of how it works and why it matters: 1. What the 10-Year Treasury Is It’s a bond issued by the U.S. government with a maturity of 10 years. Investors buy it, loaning money to the government in exchange for: Semiannual coupon payments (interest), and The face value back at maturity. Because it’s backed by the U.S. government, it’s considered one of the safest investments in the world. 2. Yield vs. Price The yield is the effective return investors earn on the bond. The yield moves inversely with the bond’s price: If demand is high and price goes up → yield goes down. If demand falls and price goes down → yield goes up. 3. Connection to Interest Rates The 10-year Treasury yield reflects investor expectations about: Future Federal Reserve policy (Fed funds rate). Inflation (higher inflation expectations push yields higher). Economic growth (slower growth often pushes yields lower). While the Fed directly controls only the short-term Fed funds rate, the 10-year yield is market-driven and often moves in anticipation of where the Fed will go. 4. Why It’s So Important Mortgage rates & lending costs: 30-year mortgage rates generally move in step with the 10-year yield (plus a spread). If the 10-year goes up, mortgage rates usually rise. Benchmark for global finance: Companies, governments, and banks often price loans and bonds based on the 10-year yield. Risk sentiment: Investors flock to Treasuries in times of uncertainty, driving yields down (“flight to safety”). 5. Practical Example Suppose the Fed raises short-term rates to fight inflation. Investors expect tighter policy and possibly lower inflation later. If they believe inflation will fall, demand for 10-years might rise → yields drop. But if they fear inflation will stay high, demand falls → yields rise. Mortgage rates, business loans, and even stock valuations all adjust accordingly. ✅ In short: The 10-year Treasury is the bridge between Fed policy and real-world borrowing costs. It signals market expectations for growth, inflation, and Fed moves, making it a crucial guide for interest rates across the economy. tune in and learn https://www.ddamortgage.com/blog didier malagies nmls#212566 dda mortgage nmls#324329 

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