It’s hard to tell if this is an article about mortgage tech or just me ranting about how expensive my kids are.
Bill Maher joked, “I don’t have any kids. I made a different choice in life and decided to have money.” As a parent of three, I found that to be pretty damn funny. Don’t get me wrong; I love my children and wouldn’t trade even one of them for a trillion dollars. But I also wouldn’t pay a dollar to have another one.
To kick off summer break, we took the kids to Disneyland in Anaheim. There are very few companies I have experienced as a consumer that have mastered price elasticity the way Disney has.
That place is a masterclass in squeezing every penny out of every guest at every income level. Harvard should do an entire module on it.
My guess is that somewhere behind Mickey Mouse’s deceptively cheerful eyes is a full floor of applied math PhDs analyzing macroeconomic data, geopolitical shifts, historical attendance, school calendars, weather forecasts, competitor pricing, family demographics (income, geography, age, etc.), and inflows/outflows throughout the day and all run through endless regression models.
And on the next floor? Probably behavioral psychologists conducting psychological warfare to drive FOMO from guest to guest, and guest to follower, across every social media platform.
Let’s just break down the options I remember (I’m sure I missed a few):
Standard single-park tickets (pricing varies by day and weather)
Park Hopper (access to both Disneyland and California Adventure)
Lightning Lane ($30/person/day to schedule line skips)
Lightning Lane Premier ($499 to skip lines without time restrictions)
VIP Tour ($500/hour, 7-hour minimum, includes personal guide and zero lines)
Disney Hotel early access
Club 33 (country-club status: $100K buy-in, $25K annual dues)
Then there’s merch, from $20 Mickey ears to $500 lightsabers. The same goes for food.
Everything is priced just enough that I find myself saying, “Fine, but this is the one and only thing I’m buying for you so make sure you really want it.”
Now let’s talk about mortgages—a space where pricing often still starts with the old “lick-your-finger-and-stick-it-in-the-air” method instead of a floor of statisticians.
Even if a lender could afford a floor of PhDs, the mortgage industry is so fragmented that small mom-and-pop shops still regularly outcompete the country’s biggest banks. There are enough pricing levers to make that possible but even with the “perfect” margin, staffing, and volume mix, a competitor pricing 25 bps thinner (intentionally or not) can change the game.
Add to that the sheer volume of macro and micro variables we’d need to analyze to confidently say lowering margins by 25 bps actually increased volume or if you just made less money on the same loans you would’ve locked anyway.
Is your lock volume tied to unemployment or underemployment? Consumer sentiment or inflation? Real estate cycles or interest rates? Tax policy, global conflict, or Fed direction? Buyer psychology by age bracket? Societal views on homeownership? The list goes on…
All these factors make it challenging for lenders to capture more margin or volume through borrower price elasticity. New products, offers, services, or more granular margin strategies can be a good start. But to truly move the needle, lenders need to pair these efforts with strong analytics to monitor and measure impact, much like Disney has mastered with its customer base. Still, even the most well-executed and data-driven plans can run into unforeseen barriers.
I’m on the pricing engine side of the business now, but I spent the first 18 years of my career in capital markets—across a small IMB, a large IMB, a big bank, and eventually a large wholesaler. Like most in capital markets, I started on the lock desk and eventually moved into hedging, trading, and FP&A.
When I was on the WL, MBS, and CRAWLS trade desk, any time I spotted an outlier trade, I used to say:
“It doesn’t matter if the person on the other side of this trade is the smartest or dumbest person in the room, they’re going to win this pool.”
A lot of the mortgage industry still works this way.
IMBs and brokers rely on pricing engines like Polly and take a best-execution approach. Larger lenders monitor competitive analytics and either go toe-to-toe… or throw their hands up and say, “If they want it that bad, they can have it.”
At some point, we’ll see a true AI revolution in analytics and pricing. And I don’t mean just margin optimization. I’m talking about a future where borrowers use AI to shop for the lowest rate, and lenders use AI to respond and compete—with both sides communicating in some hyper-efficient, AI-native language.
For those unfamiliar, Polly (where I work) operates a product and pricing engine, data analytics suite, API, and loan trading platform. We’ve already started incorporating AI into our platform and while we’re first-to-market with this functionality, the entire industry is moving fast given how much capital is being thrown at AI.
Sam Altman recently said Meta offered $100M signing bonuses to OpenAI employees. I can only hope Zuckerberg one day makes a similar offer to a mid-40s mortgage banker, aka yours truly.
Bottom line: The tech that got you where you are may not be the tech that keeps you competitive for the next decade.
If you're curious about our platform or want to explore what other PPE vendors are up to, feel free to reach out to one of our AEs to schedule a demo: sales@polly.io.
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Marcus Lam is Head of Solutions Engineering at Polly, operator of the mortgage industry’s first vertically integrated, data-driven capital markets software platform. For more information, follow Polly on LinkedIn or visit www.polly.io.