Bill Roy from LoanPASS: Why Legacy Pricing Engines are Holding Lenders Back
Bill Roy is Founder & CEO of LoanPASS, Miami
For decades, lenders have relied on first-generation product and pricing engines (PPEs) to price loans and determine eligibility—but this is 2024, and lenders need a pricing solution built to work for loan teams, not around them. The mortgage industry has evolved, driven by significant regulatory reforms, an increasingly diverse pool of homebuyers and rapid fintech innovations. From AI-powered document classification to digital loan closings, innovation has transformed every aspect of the loan origination process—yet PPEs have largely failed to adapt.
Cracks in the crown
Of all the ways inflexible, legacy PPEs are causing problems for lenders, three are especially damning:
They’re too costly.
Legacy PPEs are known for locking customers into long-term agreements based on seat licenses, a pricing approach that can feel especially unfair to lenders during times of industry contraction, when headcount may be much lower than it was at the time of contract negotiation.
In fact, a legacy PPE can be one of the biggest technology line items on a lender’s balance sheet, second only to their loan origination system. Despite this, if a lender needs any customizations, it can expect to pay a hefty ad hoc fee that’s not included in its subscription agreement.
Their outdated infrastructure is inefficient, error-prone and slow.
Legacy PPEs are built on old technology frameworks that have very slow response times and are prone to pricing inaccuracies. These old systems are also unable to pivot quickly in response to volatile markets where pricing and LLPA changes might occur several times a day. And, because much of the existing infrastructure is hard coded, it becomes incredibly difficult to add new niche or custom products.
They don’t support the range of products lenders need to stay competitive.
When interest rates curb demand for traditional purchase and refi mortgages, lenders must look to product diversification to keep pipelines flowing and protect profit margins. These additional product options could include non-qualified (non-QM) mortgages, fixed seconds, HELOCs, bridge loans, fix & flips, unique adjustable-rate options, fixed rates with temporary buydown options and reverse mortgages. Lenders are also increasingly interested in affordable home financing products geared toward properties in low-to-moderate income and majority-minority census tracts. But because of the unique nature of these lending products, requiring additional input fields, calculations and rules, most PPEs do not support them.
Lenders standing by for an update at the whim of their legacy PPE could find themselves waiting a very long time, indeed. That’s because making a change to enable one lender to offer a specific product is often impossible without affecting dozens of other users. Lenders are left with no choice but to manually price specialty loans, which adds time and money to the loan process and threatens pull-through.
These shortcomings turn pricing into an unexpected Achilles heel within the loan process that adds cost, introduces risky gaps in underwriting support and lowers the quality of customer experiences.
Heir Apparent
Traditional PPEs simply were not built for the modern marketplace, but with no alternatives available, lenders have been forced to continue relying on outdated solutions built for another time.
Except there is an alternative. Unlike traditional PPEs, which are inflexible because they were programmed to follow a hard-coded sequence of procedures, modern, rules-based decisioning engines are built to be adaptive. Lenders have the flexibility to modify or update the rules governing the pricing of their specialty products as needed, all without requiring complex modifications to static code. This allows for smooth adjustments that respond to market changes or unique product features, ensuring pricing remains dynamic and easily manageable.
Example:
Acme wants to change the price of mortgage insurance on its portfolio loan products, but only on mortgage loans for single-family residences above a specified loan amount.
Using a traditional PPE, this would be an intensive process, requiring the PPE to build code for an entirely new set of calculations to deliver the correct results without applying the same changes to every other lender using their solution. It’s likely to take days or weeks, if it’s even possible at all.
Using a configurable, rules-based decisioning engine, however, the lender’s administrative user could simply add a rule: If a single-family residential mortgage loan has a rate higher than X, then increase the price of the mortgage insurance by X. This is then automatically included for any applicable loans, allowing originators to trust that the pricing of each loan accurately reflects the lender’s rules and guidelines for each product despite the adaptations necessary for specialty products. The whole process would take less than 30 minutes to implement, and no coding would be necessary.
Similarly, many lenders offer loans in more than one vertical. Many PPEs, however, only provide pricing in specific markets. Lenders are then forced to use multiple solutions for pricing across different verticals. On the other hand, a lender leveraging a rules-based decisioning engine could price literally any loan offering, without all the hassle of getting a traditional PPE to dig in and code up unique solutions. This flexibility and adaptability represent the cornerstone of the decisioning engine architecture, extending well beyond product pricing to enhance various aspects of business operations.
Some folks would say that the final letter in ‘PPE’ doesn’t stand for engine, it stands for eligibility, which is the area of functionality where PPEs have fallen the farthest behind the curve.
A PPE provides lenders with valuable tools to effectively price loan products in accordance with GSE guidelines, which helps reduce risk on the secondary market. Most PPEs focus on the “pricing” part of the PPE acronym, running eligibility checks against only a few of the primary guidelines used in the secondary market when providing pricing options and comparisons to originators. This lack of evaluative capabilities leaves underwriters with little support when checking eligibility and creates gaps that can significantly slow down and complicate the lending process—problems that only get worse when evaluating risk-on, non-standard products.
But, a lender might say, I don’t need to expand my evaluative capabilities! The GSEs handle eligibility for me!
Yes, most lenders are familiar with a specific pair of decisioning engines known as automated underwriting systems (AUS): Fannie Mae’s Desktop Underwriter and Freddie Mac’s Loan Product Advisor. These are prime examples of decisioning engines purpose-built for the secondary market. Both DU and LPA look at the information and pricing of a mortgage product, evaluate dozens of granular data points and return a result quantifying the resell eligibility of the loan in question. This helps lenders reduce risk and take advantage of various rep and warrant relief options provided by the GSEs on qualified loans.
DU and LPA exist to help lenders evaluate whether a file meets the conforming loan guidelines set by the Federal Housing Finance Agency. They can also help inform underwriting for jumbo and government products, but that’s about the limit of their utility. A rules-based decisioning engine, on the other hand, can check a loan file against conforming loan eligibility guidelines while also supporting niche products destined for non-GSE investors and portfolio loans not intended for any secondary market investor at all. By running every loan through the same automated decisioning engine instead of relying on manual guideline reviews, lenders can save their underwriting teams a great deal of time while reducing the occurrence of errors.
Using one decisioning engine to evaluate all products at once is the most optimal solution. This flexibility allows lenders to enforce their own guidelines and rules to reduce risk on any loan product they offer. Rather than use multiple solutions, a rules-based decisioning and pricing engine allows lenders to unify their processes for both secondary market and portfolio products, reducing friction and complexity.
Current methods of managing portfolio and other internally-serviced products can be time-consuming and manually intensive. Many lenders are left adrift, trying to track and enforce their own pricing and product rules and guidelines without support from PPEs. This often creates a heavy reliance on individual internal experts (potentially creating single points of failure) or extensive networks of spreadsheets and intensive manual tracking and evaluation. Most of the time, this is a non-issue.
Until it’s time to update the guidelines or pricing. All it takes is one missed email, your underwriter taking a vacation day or an offline copy of your spreadsheet, and suddenly you have a loan team working entirely outside the expected bounds of the product. This can also leave third-party originators like your broker partners and correspondents out of the loop entirely.
And it’s not just specialty products that encounter this problem. Most PPEs also fail to accommodate short-term and promotional pricing and struggle to integrate and account for down payment assistance products and programs, both critical to maintaining a competitive edge in an expensive housing market that is constantly in flux.
The potential for your internal fulfillment teams to fall out of alignment with overall company goals and expectations isn’t the only problem presented by PPE’s inflexible pricing calculations. The larger problem is falling out of alignment with your borrower. I’m sure you’ve had the experience of telling a borrower that hey, the price on the screen is incorrect, but the cheaper, promotional pricing will absolutely be applied later.
I’m not suggesting that Millennials and Gen Zers inherently have trust issues, but it’s understandable why one might be skeptical when an incorrectly listed price on a screen is met with assurances that the promotional pricing will apply later, especially on a purchase as big as your first home. A decisioning engine’s configurable rule set makes it very easy to increase your internal alignment by rolling out guideline changes efficiently, which in turn increases borrower trust.
For example, let’s imagine you decided on September 30 to offer 0.02% lower interest rates on single-family mortgages for houses with purple doors, but only during the month of October.
Using a traditional PPE, you simply can’t include this type of pricing promotion in your pricing calculations on such short notice. You would have to manually calculate the price and hope your borrower trusts you to deliver. Even with more time, there’s a chance they can’t do it, and it will take extensive testing to code the change effectively. In the end, it will cost you more money to implement this change than the promotion itself was expected to achieve.
Using a rules-based decisioning engine, however, the lender’s administrative user could simply log in and add a custom field for door color, and then set up the new rule: If a single-family residential mortgage loan has a purple door, then decrease the price of the mortgage insurance by 0.02% between October 1 and October 30. Every originator who prices a loan and checks the dropdown box for a house with a purple door will see the promotion, and so will their borrowers.
For years, traditional product and pricing engines have ruled the landscape of mortgage technology, providing lenders with a fairly reliable framework during a period of stability and predictability. However, as market dynamics evolve, this once-dominant king of the castle is beginning to show its limitations, struggling to adapt to the complexities of today’s lending environment.
In an industry that’s evolving at breakneck speed, it’s crucial for lenders to adopt solutions that keep pace with these changes. A rules-based decisioning engine offers the adaptability, efficiency and precision that traditional PPEs lack. It will not only streamline operations but also enhance the borrower experience. The time has come to leave outdated systems behind and embrace a future where rules-based decisioning engines lead the way in delivering tailored, reliable pricing solutions for every lending scenario.
Traditional PPEs were built in a different era that no longer applies today. It is time to embrace a modern, rules-based approach to loan decisioning technology as the heir to the PPE throne.
A rules-based decisioning engine produces error-free code, delivers results in milliseconds and provides a flexible platform lenders can use to manage guidelines themselves. Lenders who upgrade to a rules-based decisioning engine report lower costs, greater efficiency, lowered risk and greater flexibility—all critical to excelling in turbulent market conditions.
(Views expressed in this article do not necessarily reflect policies of the Mortgage Bankers Association, nor do they connote an MBA endorsement of a specific company, product or service. MBA NewsLink welcomes submissions from member firms. Inquiries can be sent to Editor Michael Tucker or Editorial Manager Anneliese Mahoney.)
