Pricing--the last stand: with the prospect of a more challenging economic environment ahead and the absence of alternative profit levers to pull, the financial services industry has turned to price- and revenue-management techniques to enhance profitability.

In a tough economic environment with fewer available profit levers to pull, pricing has become one of the last opportunities that business executives can use to achieve sustained profit growth. This is true also for the mortgage industry. [??] If you look up the definition of "price" you will find varied descriptions, but they ultimately lead to one definition: "The numerical monetary value assigned to a good, service or asset based on its worth." And that raises three big questions: Who assigns the "value"? How can one establish a product's real "worth"? And, more important, one must know "worth" to whom? [??] A few banks, mortgage and consumer finance companies are already answering these very questions, and tapping into substantial profit opportunities.

No sunny days ahead

The mortgage market has tightened notably since its peak in 2004. Ignited by a series of interest-rate increases, home prices have cooled, home inventory has gone up and transaction volume is off significantly.

The next few years do not look particularly rosy for lenders, many of which are waiting for the end of another mortgage market trough to put their originations operations back in the black. Indeed, the mortgage market is cyclical, as every veteran of the mortgage lending business knows. But current economic conditions suggest we may be still a few years away from an improved market, both in terms of volume and margin.

Some of the things keeping mortgage lenders awake at night in the search for growth alternatives are: 1) slower real-estate appreciation compared with recent years' performance, which reduces the amount of available equity for borrowers to tap; 2) high inventory levels of for-sale property, placing a dampening effect on house prices; and 3) a flattened yield curve, with few signs of when a normal yield curve might return.

The good news is that a few lenders are already looking at their customer base differently--be it consumer or broker. A few early adopters have shifted the way they price their products, considering not only the underlying costs, but also the demand side of the pricing equation.

Pricing and profitability management as the last frontier

Traditionally, the mortgage industry--as well as other consumer-related industries--has relied on a number of strategic initiatives to drive profitability. Cost management is the preferred and most widely adopted one. Nonetheless, lenders should not expect to continue achieving aggressive profitability targets solely through further cost improvements.

Seeking to seize efficiencies, the mortgage industry has become technologically sophisticated, and has already consolidated to a great extent.

According to data taken from the Mortgage Bankers Association's (MBA's) 2005 Mortgage Originations Summation Study Book, the top 25 originators control 84 percent of the market versus 56 percent in 2000 and 39 percent 10 years ago. Consolidation has produced bigger lenders, benefiting from economies of scale that have beefed up their production premiums. Today, most large cost-reduction synergies have been realized, and operational costs are already relatively low, thus limiting the value of subsequent cost-reduction projects.

The next preferred growth strategy has been new-product development, but this strategy is also destined to generate progressively thinner results. The fact is, there are more products out there than the ordinary customer can digest, such as option adjustable-rate mortgages (option ARMs) and the recently launched 50-year mortgages.

Buying deeper into higher-credit-risk profiles would be another alternative for lenders seeking to lock in higher margins. Yet, as the recent delinquency indicators have demonstrated and experienced lenders can attest, this strategy can be costly in the medium to long run. Consequently, marketing departments can continue to come up with more creative mortgage products. Benefits to the bottom line, however, will be marginal.

Since 2004, in order to keep the "factory" running, mortgage lenders have slashed margins, dropping prices in exchange for volume. Many mortgage managers have done the same, relying on their instincts rather than on data or quantitatively oriented approaches.

Today, the highly competitive mortgage industry finds itself in a position of having relatively low operating costs and lower margins, with many new products in the hands of, in some cases, riskier borrowers. Additionally, a slowing housing market dims prospects for new volume growth.

It is evident that traditional profit growth alternatives have almost been exhausted. The mortgage industry needs to adopt leading pricing practices currently being used in other industries if it wants to continue producing substantial financial results in the future.

Demand-based pricing

Mortgage bankers and financial services companies have historically set prices through a combination of three premises: 1) A product's price needs to cover its underlying costs; 2) A product's price is based on what managers think the product is worth; and 3) A product's price must be competitive relative to its position in the marketplace.

In the current pricing equation, very little importance is given to how much the customer is actually willing to pay for a product or service. This is where a demand-based pricing approach comes into play.

Although a number of other industries have already adopted demand-elasticity models to optimize their price offerings, financial services companies have only just begun to grasp the significance of demand-based pricing. Measuring a customer's sensitivity to price changes and being able to optimize the volume-profit trade-off are at the core of the demand-based approach.

This approach is not about raising prices across the board. It is about offering the right price to the right customer at the right time, thereby maximizing revenue and also increasing customer satisfaction.

In other words, determining the demand elasticity of a mortgage loan means quantifying the trade-off between volume and price. The elasticity of demand is the ratio between a change in price and a corresponding change in volume. The higher the elasticity, the greater the volume-response to a price change.

The purpose of a demand-based pricing approach is not to prove already-known market behaviors, but to explicitly quantify them and use this information to better price products. This is not about raising prices; it is about pricing better. In fact, it is common to have a balanced number of price increases and price decreases.

Demand-modeling and optimization techniques do not utilize individual customer demographics in setting prices; rather, these techniques consider product characteristics, volume performance, competitive environment and internal costs. Enhanced pricing and profitability techniques operate independently from underwriting decisions.

A case study

Although the nature of the following case study is for illustrative purposes only, the results presented here are derived from actual pricing work conducted by Deloitte & Touche for its clients.

"XYZ Mortgage" is a fictitious direct and indirect mortgage lender mainly focused on prime and alternative-A customers, with operations in 40 states. XYZ Mortgage's management believed there was significant profit opportunity being left on the table, and was not sure how to capture it.

Key questions XYZ management wanted to answer were:

* If rates increased or decreased by 10 basis points, what would happen to XYZ's overall origination volume? What would happen to each of XYZ's products?

* How should XYZ respond to a promotion offered by two of its top-five competitors, but not offered by the other three?

* What strategy should XYZ adopt in order to launch operations in a new state?

* Sales managers gave price exceptions on 20 percent of all loans funded. How can management quantify how much value, if any, those rate exceptions represent?

XYZ Mortgage decided to look into pricing more quantitatively through a price-optimization program. XYZ Mortgage had conducted some experiments in the past with promotional rates, but it turned out to be very difficult to correlate price reduction with volume increase due to many other factors simultaneously driving demand. The price-setting process at XYZ Mortgage was mainly driven by its secondary-market operations and the cost to produce a loan.

XYZ Mortgage's first step in its price-optimization program was to quantify price elasticities at a very granular level--at the rate-sheet cell level. This is only possible due to the advent of new technologies both in the front- and back-end operations. The superior capabilities of today's integrated data warehouses, combined with powerful computer systems, allow companies to precisely calculate price elasticities of thousands of different market segments. This required sophisticated mathematical and statistical analysis, and tools beyond the skills of XYZ Mortgage's analytical team. So it hired a consulting firm to help.

Quantifying price elasticities is not trivial, and each calculation comes with an expiration date. The complexity of mortgage or home-equity pricing derives from the multiple features of a single loan. In the case of XYZ Mortgage, 40 rate sheets, each with six FICO[R] bands, five loan-to-value (LTV) ratio bands, two types of occupancy status, two lien positions, five property types and five loan amount bands produced an astonishing 120,000 different product combinations.

Each product potentially had a different elasticity. To further complicate the pricing process, not only would these 120,000 rates require bi-weekly updates, but also the rates are subsequently given to a sales force with authority to override every single rate, within certain parameters.

With the right technical support, XYZ Mortgage was able to complete an initial elasticity study in three months. Management found that mortgage elasticity coefficients could differ by more than two times, depending on product characteristics such as LTV, lien position or even rate-lock length.

Elasticities varied by more than four times across different channels. XYZ Mortgage also learned that estimates of elasticities require constant updating as market and consumer behavior changes. This made having a self-adjusting demand model with a feedback loop essential.

With elasticities calculated at a granular level, XYZ Mortgage's second step was to optimize profit based on a new price-setting strategy. Optimization is based on a delicate balance of lowering rates on selected rate-sheet cells to reach more customers and create additional volume, while marginally increasing other rates to improve margins, without giving up considerable volume.

XYZ Mortgage was able to find that for every $1 billion it originated, there was approximately an additional $1.5 million annual profit opportunity while keeping risk profile, customer demographics and total origination volume intact. This represented almost a 20 percent increase in bottom-line profitability for XYZ Mortgage with very small investments in underlying infrastructure.

XYZ was now serving a larger, more diverse customer base by offering the customers prices they were willing to pay as a result of better assessing competing offers and overall market behavior.

Price elasticity analysis also identified which competitors mattered most in each market in which XYZ Mortgage operated. After the optimization program was complete, XYZ Mortgage was able to not only intelligently react to competitors' promotions, but also quantify the impact of such reactions on profitability.

The demand model also helped filter competitive price movements by segregating competitors that were actually responding to XYZ Mortgage's promotions from those that were changing prices based on other factors beyond XYZ Mortgage's control. What had been done based on gut feel was now supported by a scientific approach.

What about management's third question regarding the definition of optimal pricing strategy for operations in XYZ's new state? How could XYZ Mortgage calculate optimal rate sheets if there were no historical data available for that new operating location?

The answer was to use competitive, market and demographic data to identify similar patterns between the new state and the 40 states where XYZ Mortgage was currently operating. After a statistically suitable market had been found, the self-adjusting nature of the demand model took care of the rest, constantly updating itself and routinely positioning XYZ Mortgage's rates at or near its optimal point in the new market.

As the pricing program was being implemented, a few members of XYZ Mortgage's management were still skeptical about whether optimized rates would reach the borrower intact due to exceptions and discounts, especially in the wholesale channel. XYZ Mortgage had historically had a 20 percent exception rate on its prices. Nevertheless, strict compliance to rate-sheet pricing is not necessary to realize the profit benefits of an optimization program.

The most robust models take into consideration originators' and brokers' tendencies to discount rate-sheet pricing. As long as exception-rate patterns do not change dramatically overnight, the demand model and the optimization engine will incorporate such behavior when setting prices.

Further, through a "what-if" functionality embedded in the supporting analytics software, XYZ Mortgage could now quantify whether rate exceptions were actually generating the additional volume and increased profits frequently used as justification for promotions and discounts. This feature turned out to be one of the most liked components of the optimization software by XYZ management.

Capturing the pricing opportunity

Experience indicates that there is an average price improvement of 8 to 15 basis points to be found in mortgages and home-equity products, in part from adopting a pricing-optimization technology. Nonetheless, a price-management program is more than just a scientific model or software implementation; it is about cultural and procedural changes as well.

Successful pricing programs involve broad change management--not merely a systems enhancement. Programs that fail to meet expectations typically either focus solely on the technology or involve a narrow group of data analysts. Lenders willing to embrace better pricing and profitability-management techniques will need a few of the following building blocks in order to succeed:

* Two or more years of data -- This includes origination data (approved, not funded, approved and funded). Competitive, market and cost data are also needed. Although no perfect data are required, the more data and cleaner they are, the better.

* Senior management support -- Given the breadth of change required, these programs require significant changes in culture and processes.

* Alignment of incentives with organizational and performance objectives

* Superior analytical skills -- Pricing will shift from an operational set of activities to a strategic function, more analytical and forward-looking.

* Active involvement of compliance and regulatory departments

Implementing a pricing strategy requires strong project-management skills--and the correct approach. Experience has demonstrated that selecting a suboptimal pricing strategy can significantly constrain the benefits from a price-management initiative.

Additional benefits

In addition to the direct financial impact of improving profit through a more quantitative, data-driven approach to pricing, mortgage lenders that have engaged in enhanced pricing programs have found additional benefits, including the following:

* Data-driven insight into the greatest opportunities to grow business profitably, improved capability to understand volume-profit trade-off at the granular level (i.e., product, channel, etc.), and the ability to develop and evaluate strategies to diversify beyond existing footprint;

* Analytical platform for additional insight and what-if analysis capabilities beyond pricing--particularly for product analysis, such as profitability and customer-demand drivers by channel, etc.;

* Improved sales and distribution effectiveness analysis, such as broker and sales force compensation, profitability and quality measurements;

* Enhanced competitive analysis capabilities--insight into the relative importance of different competitors by geography, product, channel, etc.;

* A means to support a change-management effort to shift focus from volume to profit at all levels in the organization through the standardization and institutionalization of the profitability metric used to measure business, and an improved ability to link sales compensation to profitable growth measures;

* Superior process efficiency through automation of previously manual rate-setting and entry processes; and

* Improved price visibility, control and compliance with regulatory requirements such as the Real Estate Settlement Procedures Act (RESPA), the Truth in Lending Act, usury ceilings, etc.

What comes next?

Although price-optimization models somewhat account for rate-exception behavior, significant value can be found in price execution by providing better negotiation tools to the sales force and aligning compensation with profitability, in addition to volume. Through the combination of price-optimization and price-execution techniques, a lender can understand and measure how much and where revenue is being lost from the moment prices are set to the point when a deal is closed or an application booked.

Price-execution techniques help in explaining rate exceptions and quantifying relationships with brokers and the rest of the sales force. It provides decision-makers with regulatory ceiling prices, list prices and stretch prices, for example, linking different compensation levels to each. A comprehensive pricing program helps to improve not only profits, but also customer satisfaction by building strong, long-term, valued relationships.

Business practices are shifting and helping to shape a more comprehensive definition of price. Perhaps the next time one looks up the meaning of "price," this enhanced definition will be displayed: "The numerical monetary value assigned to a good, service or asset based on its worth and, more important, based on how much each buyer is willing to pay for that given good, service or asset at a given time."

Leandro DalleMule is a manager and Terry Kuester is a principal in New York-based Deloitte & Touche LLP's Pricing & Profitability Management practice. They specialize in the financial services industry, and can be reached at [e-mail] and [e-mail].