Pricing & Profitability Analytics

Developing Mortgage Pricing Models

Held for Sale (HFS) and Held for Investment (HFI):

• HFS: The pricing strategy for mortgages held for sale focuses on short-term pricing horizons, liquidity risk, and market-based spreads. It accounts for the anticipated time of sale and aligns with secondary market conditions.
• HFI: Mortgages held for investment emphasize long-term cash flow and credit risk, considering the mortgage’s lifetime profitability, spread management over funding costs, and balance sheet implications.

Key Components:

• Synthetic Portfolios: Build synthetic portfolios that represent various mortgage products across channels, including loan features such as fixed or variable rates, loan maturities, credit quality, and geographic risk exposure.
• Market Data Integration: Incorporate real-time and historical data for interest rates, credit spreads, and other market drivers to ensure responsive pricing aligned with market conditions.
• Profit Margin Optimization: Include prepayment assumptions, servicing costs, default risks, and operational costs in pricing to optimize margins while ensuring competitiveness.

Rate Sheet Generation and Distribution

Rate Sheet Design:

• Dynamic Rate Sheets: Develop rate sheets that automatically adjust based on real-time market conditions (e.g., bond yields, market volatility) and borrower-specific risk profiles.
• Channel Differentiation: Customize rate sheets for different channels (D2C, retail, third-party) to reflect channel-specific costs, risk tolerance, and profit expectations.

Governance Processes:

• Rate Governance Committee: Establish a governance framework that includes regular reviews by a pricing committee. This committee will be responsible for validating pricing models, reviewing market conditions, and ensuring pricing strategies align with the institution’s risk appetite and strategic goals.
• Approval Workflow: Implement approval processes for any rate sheet changes, ensuring compliance with internal policies and market regulations.

Distribution Strategy:

• Automated Distribution: Use automated systems to distribute rate sheets to sales teams, third-party originators, and D2C platforms. Ensure rates are delivered in real-time to maintain competitive positioning.
• Version Control: Track historical rate sheets to support audit processes and analyze pricing decisions over time.

Competitive Pricing Analysis Framework

Systematically compare the institution’s mortgage pricing with that of competitors across various products and channels (D2C, retail, third-party) and assess the pricing dynamics in the broader mortgage market.

a) Data Collection

• Public Sources: Gather competitor pricing data from publicly available sources, such as competitor websites, industry reports, rate comparison websites, and third-party aggregators. Include both advertised rates and any additional costs (origination fees, discount points, etc.).
• Leverage 3^rd party analytics: Purchase or subscribe to industry benchmark reports that provide pricing trends, competitive intelligence, and analysis from key market segments.
• Automated Competitor Monitoring Tools using web scraping: Use tools that track competitor mortgage rates and fee structures in real-time across various platforms. This enables a continuous flow of data to inform pricing adjustments as market conditions evolve.

b) Market Benchmarking

• Industry Averages: Compare your mortgage pricing against industry averages, using aggregated data from industry reports, market surveys, and regulator data. This helps establish a baseline understanding of market standards.
• Geographic Benchmarking: Analyze pricing trends in specific regions or markets where the institution competes. This is especially important if operating in diverse markets where real estate trends, housing demand, and competition differ.
• Rate Trends Analysis: Review historical rate trends in mortgage pricing to understand how competitors are reacting to changes in macroeconomic factors, such as Federal Reserve rate changes, inflation, and housing market cycles.

Dynamic Pricing and Real-Time Monitoring

Objective: To continuously monitor competitor pricing and market conditions in real-time, enabling more agile pricing decisions.

a) Real-Time Data Monitoring

• Market Intelligence Platforms: Leverage advanced market intelligence platforms that provide real-time updates on broader economic indicators (e.g., bond yields, interest rate forecasts) that influence mortgage pricing.

b) Agile Pricing Adjustments

• Rate Adjustments: Implement dynamic pricing strategies that allow you to adjust rates on a daily or even intraday basis. This is particularly useful in volatile market environments where interest rates and spreads fluctuate frequently.
• Product Offer Adjustments: Based on competitor monitoring, introduce or phase out mortgage products that align with changing customer preferences or market conditions, ensuring your offerings remain competitive.

c) Pricing Categories

• Product-Specific Analysis: Compare pricing across different mortgage products (fixed-rate, adjustable-rate, jumbo loans, FHA, VA, etc.). Analyze competitors’ product portfolios and any unique pricing models they use.
• Channel-Specific Pricing: Assess competitors’ pricing strategies across D2C, retail, and third-party channels. Look for differences in pricing by distribution method, particularly between digital-only lenders and traditional brick-and-mortar lenders.

d) Price Elasticity and Customer Segmentation

• Price Sensitivity: Identify how sensitive competitors’ customers are to price changes. Are they more attracted by lower upfront rates, or do they respond better to long-term benefits like lower fees or better customer service?
• Customer Segmentation: Determine if competitors are using segmented pricing, offering preferential rates to specific groups (e.g., first-time buyers, veterans, high-net-worth individuals, or customers with certain credit scores).

Attribution Framework for Variance Analysis

Variance Analysis:

• Actual vs. Forecast Variance: Implement an attribution framework to track variances between forecasted and actual performance. Break down variances by product type, geographic region, market condition, or sales channel.
• Key Performance Drivers: Identify key drivers of variance, such as unexpected changes in prepayment speeds, credit performance, or interest rates, and adjust models accordingly.

Data-Driven Insights:

• Machine Learning Integration: Leverage machine learning algorithms to identify trends, patterns, and potential risks affecting pricing accuracy. Use these insights to refine pricing models.
• Reporting & Dashboards: Develop comprehensive dashboards for senior management, offering real-time visibility into pricing performance, variance analysis, and profitability metrics.

Scenario Analysis for Future Strategic Decisions

What-If Analysis:

• Rate Movement Simulation: Use scenario analysis to simulate potential movements in market rates and credit spreads, helping to inform future pricing strategies.
• Portfolio Adjustments: Analyze the impact of different asset allocations or product mixes (e.g., increasing adjustable-rate mortgages vs. fixed-rate) on profitability metrics like ROE/ROA.

Stress Testing for Strategic Planning:

• Strategic Forecasting: Use stress test results to inform long-term planning, ensuring the institution’s mortgage pricing strategy is adaptable to adverse market conditions.