Historically, only about 30% of borrowers who could lower their rate by 100 basis points actually refinanced in a given year, and fewer than half even bothered to get a quote from more than one lender, according to researchers at Morgan Stanley. That inertia has been the quiet engine behind decades of relatively predictable prepayment behavior on agency MBS. However, AI is positioned to radically transform that inertia through massive efficiency gains. Mortgage researchers foresee a day when an AI agent on a homeowner’s phone will be able to continuously monitor rates, canvass dozens of lenders for quotes, assemble documentation and enable the borrower to refinance their mortgage with something close to a few clicks. That is a far cry from today’s process, where getting multiple quotes and trying to find the best execution rate requires real effort — effort most borrowers never make. The industry currently averages 40-45 days to close a mortgage, but the largest, most AI-invested originators already close loans in 12-20 days. Large digital lenders are already pushing some stages of that timeline even further. Rocket Mortgage, for example, has said a borrower on its platform can already get from application to rate lock in about 30 minutes, and it is targeting to cut that timeframe to just 10 minutes. The Government-Sponsored Enterprises (GSEs), Fannie Mae and Freddie Mac, are beginning to respond as well, now requiring lenders to formally govern the use of AI across underwriting, pricing, verification and servicing. Even so, the real-world impact remains largely untested. Thirty-year mortgage rates have held stubbornly above 6.5% through much of 2026, muting refinance demand regardless of how efficient the process becomes. The true test will come once rates fall enough to spark a genuine refinancing wave. When that happens, the same speed and efficiency expected to benefit consumers and originators will have a more complicated impact on the investment side of the market. What It May Mean for MBS Investors Mortgage investors are effectively “short” the option to refinance. That is, they get paid (via a wider spread) for bearing the risk that a borrower will exercise that option and prepay their mortgage. A borrower who refinances faster exercises that option more efficiently, which means the option is worth more, and investors will demand higher compensation for selling it. Taken far enough, some analysts suggest agency MBS could begin to behave less like a traditional pass-through and more like a pure callable bond. It follows that prepayment models built on today’s less efficient borrower behavior are likely to understate future prepayments in an AI-empowered world. Morgan Stanley analysts estimate that a 100-basis-point rate rally could push refinance volumes roughly 40% higher than today’s models suggest, due to AI, which could widen mortgage spreads by 10 to 20 basis points. The practical effect is mortgages that exhibit greater negative convexity, shorter durations and wider spreads. Reinvestment risk also stands to compound further on the investment side. As loans prepay faster, investors get their principal back sooner than expected. This typically happens as rates fall, forcing investors to reinvest that cash at lower yields than the MBS they were originally holding. In other words, prepayment risk, the defining risk MBS investors face, could increase as AI efficiencies increase. Not every industry voice is convinced this transformation will be as swift or as large as the bull case suggests. Skeptics note that the industry has made similarly bold promises before — optical character recognition, automated phone calling — that proved more incremental than revolutionary. Others point out that sizable upfront closing costs, such as appraisals, legal fees and mortgage taxes, still discourage refinancing, no matter how quickly the paperwork moves. None of these limitations, however, alter the fundamentals of owning agency MBS. Coupon, structure and collateral attributes still drive outcomes. But it does raise the bar on prepayment diligence. Loan-level characteristics that have always slowed prepayments and dampened negative convexity (low loan balance, seasoning, geography, etc.) become more valuable as the “slow, inattentive borrower” discount shrinks. Institutions will need to pay closer attention to prepayment assumptions, asking how models are recalibrated for a market where the borrower on the other side of the trade is likely to become much more efficient. The Baker Group is one of the nation’s largest independently owned securities firms specializing in investment portfolio management for community financial institutions. Since 1979, we’ve helped our clients improve decision-making, manage interest rate risk and maximize investment portfolio performance. Our proven approach to total resource integration combines software and products developed by Baker’s Software Solutions* with the firm’s investment experience and advice. For more information, contact Andrea Pringle at The Baker Group by calling (800) 937-2257. *The Baker Group LP is the sole authorized distributor for the products and services developed and provided by The Baker Group Software Solutions Inc. 27 NEBRASKA BANKER
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