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Use Case: RiskSpan’s Agentic AI for MBS Data Tool 

Breaking Down VA vs FHA Prepayment Speeds 

This use case demonstrates how agentic interaction with the MBS Data Tool helps investors move from a general observation (“VA speeds are fast”) to a data-backed, multidimensional explanation tied directly to credit, LTV, and program structure. 

Business Question 

Why are VA loans — particularly those with low FICO and high LTV profiles — prepaying so much faster than historically, and faster than FHA loans with comparable characteristics? 

For the full output of our AI MBS Agent, read on or watch our two-minute demo video:

Workflow with Agentic AI 

1. Baseline Comparison 

Based on the data, I can now provide a comprehensive analysis of VA vs FHA loan prepayment trends since 2020:

Key Findings:

1. Current Prepayment Speeds (as of September 2025):

– VA loans: 11.74% CPR

– FHA loans: 8.68% CPR

– VA loans are currently prepaying about 3 percentage points faster than FHA loans

2. Historical Trends:

2020-2021 (Refinance Wave):

– Both loan types experienced very high prepayment speeds

– Peak CPRs:

* FHA: 38.48% (April 2021)

* VA: ~30-31% (Late 2021)

– VA loans consistently showed higher prepayment sensitivity during this period

2022-2023 (Rising Rate Environment):

– Both loan types saw significant slowdown in prepayment speeds

– Lowest CPRs:

* FHA: 4.42% (February 2023)

* VA: 5.07% (February 2023)

– The spread between VA and FHA prepayment speeds narrowed but remained positive

2024-2025 (Current Environment):

– Moderate prepayment speeds for both loan types

– VA loans maintain faster prepayment speeds

– Recent range:

* FHA: 5-10% CPR

* VA: 7-14% CPR

3. Key Observations:

– VA loans consistently prepay faster than FHA loans across different rate environments

– The spread between VA and FHA prepayment speeds tends to widen in more favorable refinancing environments

– Both loan types show similar seasonal patterns but with different magnitudes

– VA loans show greater prepayment sensitivity to market conditions

4. Portfolio Sizes:

– FHA: Currently ~7.45 million loans with $1.47 trillion in balance

– VA: Currently ~3.77 million loans with $1.03 trillion in balance

This analysis suggests that investors should expect consistently faster prepayment speeds from VA loans compared to FHA loans, particularly during periods of favorable refinancing opportunities. The higher prepayment sensitivity of VA loans likely reflects the generally better credit quality of VA borrowers and their greater ability to refinance when rates are favorable.

2. High-Credit Borrowers 

Based on the analysis of high-FICO (740+) VA and FHA loans, here are the key findings:

1. Current Prepayment Speeds (September 2025):

– High-FICO VA: 9.37% CPR

– High-FICO FHA: 5.96% CPR

– Spread: 3.41% faster for VA

2. Portfolio Characteristics (as of September 2025):

High-FICO VA Loans:

– Average FICO: 779

– Average WAC: 3.85%

– Average CLTV: 58.1%

– Balance: $443.4B (43% of VA portfolio)

– Loan Count: 1.47M loans

High-FICO FHA Loans:

– Average FICO: 769

– Average WAC: 4.55%

– Average CLTV: 58.4%

– Balance: $188.0B (13.8% of FHA portfolio)

– Loan Count: 879K loans

3. Key Trends (2024-2025):

Prepayment Speed Range:

– High-FICO VA: 4.26% to 14.76% CPR

– High-FICO FHA: 3.56% to 8.68% CPR

Seasonal Patterns:

– Both show seasonal increases in spring/summer months

– VA loans show more pronounced seasonal effects

– Peak prepayment typically occurs in October/November

4. Notable Observations:

– The prepayment speed differential between VA and FHA remains significant even at high FICO levels

– High-FICO VA loans have consistently lower WACs (about 70bps lower)

– Similar CLTV profiles between high-FICO VA and FHA

– VA has a much larger proportion of high-FICO borrowers (43% vs 13.8% for FHA)

– High-FICO VA loans show greater prepayment sensitivity to market conditions

5. Recent Trends:

– Both loan types show increasing prepayment speeds in 2025

– The spread between VA and FHA prepayment speeds has remained relatively stable

– High-FICO VA loans maintain faster prepayment speeds even in rising rate environments

This analysis suggests that even when controlling for credit quality (FICO ≥ 740), VA loans consistently prepay faster than FHA loans. This is likely due to:

1. Lower average note rates (WAC)

2. VA’s no-down-payment benefit making it easier to refinance

3. Generally better borrower demographics and income profiles

4. More streamlined refinancing options for VA loans

Investors should expect continued faster prepayment speeds from high-FICO VA loans compared to high-FICO FHA loans, particularly during favorable rate environments or seasonal peaks.

3. Low-Credit Borrowers 

Based on the analysis of low-FICO (<640) VA and FHA loans, here are the key findings:

1. Current Prepayment Speeds (September 2025):

– Low-FICO VA: 21.04% CPR

– Low-FICO FHA: 12.07% CPR

– Spread: 8.97% faster for VA

2. Portfolio Characteristics (as of September 2025):

Low-FICO VA Loans:

– Average FICO: 611 – Average WAC: 4.77%

– Average CLTV: 66.2%

– Average Age: 52.3 months

– Balance: $77.5B (8.3% of VA portfolio)

– Loan Count: 316K loans

Low-FICO FHA Loans:

– Average FICO: 609

– Average WAC: 4.83%

– Average CLTV: 60.4%

– Average Age: 62.8 months

– Balance: $290.7B (21.3% of FHA portfolio)

– Loan Count: 1.49M loans

3. Key Trends (2024-2025):

Prepayment Speed Range:

– Low-FICO VA: 9.08% to 27.54% CPR

– Low-FICO FHA: 6.31% to 12.73% CPR

Notable Patterns:

– Much higher volatility in VA prepayment speeds

– Larger seasonal effects in VA loans

– Consistently wider spread between VA and FHA vs high-FICO segment

4. Key Differences:

Portfolio Composition:

– FHA has a much larger concentration in low-FICO loans (21.3% vs 8.3% for VA)

– FHA’s low-FICO portfolio is nearly 4x larger than VA’s by balance

Performance Characteristics:

– VA loans show significantly higher prepayment speeds despite similar credit profiles

– VA loans exhibit greater prepayment volatility

– VA loans have slightly better average FICO scores within the low-FICO segment

– VA loans have higher CLTVs but lower WACs

5. Recent Trends:

– Both loan types show increasing prepayment speeds in 2025

– VA prepayment speeds are consistently higher and more volatile

– Seasonal patterns are more pronounced in VA loans

– The spread between VA and FHA prepayment speeds has remained wide

6. Notable Observations:

– The prepayment speed differential between VA and FHA is actually larger in the low-FICO segment than in the high-FICO segment

– Low-FICO VA loans show surprisingly high prepayment speeds despite lower credit scores

– FHA loans show more consistent, less volatile prepayment patterns

– VA loans maintain faster speeds despite higher CLTVs

This analysis reveals that the prepayment behavior difference between VA and FHA loans is even more pronounced in the low-FICO segment than in the high-FICO segment. This could be due to:

1. VA’s stronger refinancing program benefits for lower-credit borrowers

2. More flexible VA streamline refinance requirements

3. Better servicing support for VA borrowers

4. Lower average note rates in the VA program

5. Potentially different borrower demographics within the same FICO band

Investors should expect:

– Higher and more volatile prepayment speeds from low-FICO VA loans

– More stable, lower prepayment speeds from low-FICO FHA loans

– Larger seasonal effects in VA loans

– Greater market sensitivity in VA loans

4. High-LTV Loans 

Based on the analysis of high-LTV (≥95%) VA and FHA loans, here are the key findings:

1. Current Prepayment Speeds (September 2025):

– High-LTV VA: 12.37% CPR

– High-LTV FHA: 7.77% CPR

– Spread: 4.60% faster for VA

2. Portfolio Characteristics (as of September 2025):

High-LTV VA Loans:

– Average FICO: 714

– Average WAC: 4.33%

– Average CLTV: 70.9%

– Average Original LTV: 100.3%

– Average Age: 52.9 months

– Balance: $607.2B (61.1% of VA portfolio)

– Loan Count: 2.11M loans

High-LTV FHA Loans:

– Average FICO: 679

– Average WAC: 4.61%

– Average CLTV: 62.9%

– Average Original LTV: 97.6%

– Average Age: 71.7 months

– Balance: $1.04T (72.0% of FHA portfolio)

– Loan Count: 5.08M loans

3. Key Trends (2024-2025):

Prepayment Speed Range:

– High-LTV VA: 5.60% to 19.73% CPR

– High-LTV FHA: 4.50% to 8.89% CPR

Notable Patterns:

– Higher volatility in VA prepayment speeds

– Strong seasonal patterns in VA prepayments

– More stable FHA prepayment patterns

– Consistent spread between VA and FHA

4. Key Differences:

Portfolio Composition:

– Both programs have significant high-LTV concentration

– FHA has larger absolute exposure but similar relative exposure

– VA allows higher original LTV (100%+) compared to FHA

Performance Characteristics:

– VA loans consistently prepay faster despite higher LTVs

– VA borrowers have notably higher FICO scores (+35 points)

– VA loans have lower WACs (-28bps)

– VA loans are significantly younger (-19 months)

5. Recent Trends:

– Both loan types show increasing prepayment speeds in 2025

– VA prepayment speeds remain more volatile and responsive to seasonality

– Peak prepayment periods occur in spring/summer months

– The spread between VA and FHA has remained relatively stable

6. Notable Observations:

– High-LTV concentration is a defining characteristic of both programs

– VA’s higher prepayment speeds persist despite higher LTVs

– Better credit quality of VA borrowers likely contributes to faster speeds

– Younger average loan age in VA portfolio may influence prepayment behavior

– Lower WACs in VA portfolio suggest more efficient refinancing history

Implications for Investors:

1. Portfolio Management:

– Expect faster and more volatile prepayments from high-LTV VA loans

– Consider seasonal patterns in VA prepayments for timing decisions

– Factor in the significant WAC differential when evaluating prepayment risk

2. Risk Considerations:

– Higher prepayment risk in VA despite similar LTV profiles

– Greater market sensitivity in VA portfolio

– More stable, predictable prepayment patterns in FHA

3. Valuation Impact:

– Need to apply larger prepayment speed differentials for VA vs FHA

– Consider stronger seasonal effects in VA valuations

– Account for credit quality differences within high-LTV segment

The analysis suggests that even in the high-LTV segment, VA loans maintain significantly faster prepayment speeds than FHA loans, driven by:

1. Better borrower credit quality

2. Lower note rates

3. More flexible refinancing options

4. Younger loan age

5. More efficient VA refinancing program

These factors appear to overcome the theoretical constraints of high LTV ratios on refinancing ability.

5. Program Features as Drivers

The data strongly supports that program features drive faster VA prepayment speeds. Here’s the detailed analysis:

1. Early Prepayment Behavior (0-24 months):

– VA loans age 13-24 months: 25.29% CPR

– FHA loans age 13-24 months: 17.04% CPR

– The 8.25% difference reflects VA’s more flexible refinancing rules

– No seasoning requirement for VA vs 210 days for FHA

2. Refinance Incentive Response:

– High incentive (>100bp) VA: 50.43% CPR

– High incentive (>100bp) FHA: 26.55% CPR

– The dramatic difference (23.88%) shows VA borrowers can more easily act on rate incentives

– Even medium incentive (0-100bp) shows large gap:

* VA: 29.59% CPR

* FHA: 18.37% CPR

3. Credit Quality Impact:

– VA average FICO: 715-724 across age buckets

– FHA average FICO: 672-681 across age buckets

– Better VA credit quality + easier program rules = faster speeds

4. Cost Structure Effects:

VA Advantages:

– Lower funding fee (0.5%)

– No ongoing MIP

– Can include closing costs

– Results in lower all-in refinancing costs FHA Constraints:

– Higher upfront MIP (1.75%)

– Ongoing annual MIP

– More restricted closing cost inclusion

– Higher total refinancing costs

5. LTV Considerations:

– VA has no maximum LTV for IRRRL

– FHA generally limited to 97.75% LTV

– Current data shows:

* VA high-LTV loans (>95%): 12.37% CPR

* FHA high-LTV loans (>95%): 7.77% CPR

6. Program Flexibility Evidence:

Response to Rate Environment:

– VA loans show stronger reaction to refi incentives

– Higher CPRs across all incentive buckets

– Faster response to market opportunities

Seasoning Effects:

– VA shows earlier prepayment spikes

– More pronounced early-age prepayment behavior

– Less constrained by seasoning rules

7. Servicer Impact:

– VA servicers may be more proactive due to program ease

– Lower documentation requirements reduce operational friction

– Simpler approval process enables faster execution

The data demonstrates that VA’s program features create a more efficient refinancing mechanism:

1. Lower Costs:

– Reduced fees

– No ongoing MIP

– More included costs

2. Fewer Restrictions:

– No LTV limits

– No seasoning requirement

– Limited credit checks

3. Simpler Process:

– Less documentation

– Faster approval

– Lower operational burden

These advantages manifest in:

– Consistently faster prepayment speeds

– Stronger response to rate incentives

– Earlier refinancing activity

– Higher refinancing success rates

For investors, this means:

1. Expect faster VA prepayments across market cycles

2. Anticipate stronger response to refinancing opportunities

3. Factor in program differences when valuing VA vs FHA MBS

4. Consider seasoning rules when evaluating young loans

The combination of program features and data evidence clearly shows that VA’s more flexible refinancing rules are a primary driver of faster prepayment speeds compared to FHA.

6. Scenario Stress Test 

Based on the current portfolio characteristics and historical refinancing behavior, here’s how a 100bps rate drop would likely impact VA vs FHA prepayment speeds:

1. Current Portfolio Rate Distribution:

VA Portfolio:

– 7.5%+ WAC: $4.6B (0.4%)

– 7.0-7.49% WAC: $18.5B (1.8%)

– 6.5-6.99% WAC: $71.1B (6.9%)

– 6.0-6.49% WAC: $105.3B (10.2%)

– 5.5-5.99% WAC: $118.7B (11.5%)

– <5.5% WAC: $713.0B (69.2%)

FHA Portfolio:

– 7.5%+ WAC: $23.6B (1.6%)

– 7.0-7.49% WAC: $68.5B (4.7%)

– 6.5-6.99% WAC: $163.8B (11.2%)

– 6.0-6.49% WAC: $158.4B (10.8%)

– 5.5-5.99% WAC: $149.0B (10.2%)

– <5.5% WAC: $903.5B (61.5%)

2. Projected Prepayment Speeds by Refi Incentive:

Based on current behavior with similar incentives:

High Incentive (>100bps):

– VA: 50-55% CPR

– FHA: 25-27% CPR

– Differential: ~25-28% faster for VA

Medium Incentive (50-100bps):

– VA: 35-40% CPR

– FHA: 20-25% CPR

– Differential: ~15% faster for VA

Low Incentive (0-50bps):

– VA: 25-30% CPR – FHA: 15-18% CPR

– Differential: ~10-12% faster for VA

3. Portfolio Impact of 100bps Rate Drop:

VA Portfolio:

– ~$200B would move to >100bps incentive

– ~$119B would move to 50-100bps incentive

– Weighted average CPR projection: 32-37% CPR

FHA Portfolio:

– ~$256B would move to >100bps incentive

– ~$149B would move to 50-100bps incentive

– Weighted average CPR projection: 18-22% CPR

4. Key Factors Driving Divergence:

Program Features:

– VA IRRRL has no LTV restrictions

– VA has lower funding fees

– VA has simpler documentation requirements

– VA has no seasoning requirement

Borrower Characteristics:

– VA has higher average FICOs (714 vs 675)

– VA has lower average WACs

– VA has younger average loan age

5. Projected Timeline of Response:

Months 1-3:

– VA speeds increase rapidly to 30-35% CPR

– FHA speeds lag, reaching 15-18% CPR

– Differential: 15-17% CPR

Months 4-6:

– VA peaks at 35-40% CPR – FHA reaches 20-22% CPR

– Differential: 15-18% CPR

Months 7-12:

– VA moderates to 30-35% CPR

– FHA stabilizes at 18-20% CPR

– Differential: 12-15% CPR

6. Risk Factors to Consider:

Upside Risks (faster speeds):

– Pent-up refinance demand

– Improved technology/processing

– Competitive lender environment

Downside Risks (slower speeds):

– Housing market uncertainty

– Credit tightening

– Operational capacity constraints

7. Investment Implications:

1. Premium Pools:

– Expect significantly faster VA prepayments

– Greater price impact on VA premium pools

– Wider VA-FHA OAS spreads

2. Current Coupon Pools:

– Moderate prepayment differential

– Less price impact

– Smaller OAS spread widening

3. Discount Pools:

– Minimal impact on speeds

– Limited price effect

– Stable OAS spreads

Recommendations:

1. Portfolio Positioning:

– Reduce exposure to premium VA pools

– Consider FHA alternatives in premium segment

– Maintain VA exposure in current coupon/discount segments

2. Risk Management:

– Increase prepayment protection in VA holdings

– Consider specified pool stories

– Monitor servicer behavior closely

3. Relative Value:

– Look for opportunities in FHA premium pools

– Consider VA-FHA switches in premium segment

– Evaluate specified pool pay-ups

The analysis suggests a significant divergence in VA vs FHA prepayment speeds following a 100bps rate drop, with VA speeds likely to be 12-25% CPR faster depending on the segment and timing. This differential is driven by both program features and borrower characteristics, with the impact most pronounced in premium pools.

Questions or Need Support? 

Our team is standing by to help you make the most of these new features. Contact us at techsupport@riskspan.com or reach out to your dedicated account manager. 

Interested in a Complimentary Trial or Demo?

Contact us here.

Loans & MSRs: Managing model assumptions and tuners the easy way

One of the things that makes modeling loan and MSR cash flows hard is appropriately applying assumptions to individual loans. Creating appropriate assumptions for each loan or MSR segment is crucial to estimating realistic performance scenarios, stress testing, hedging, and valuation. However, manually creating and maintaining such assumptions can be time-consuming, error-prone, and inconsistent across different segments and portfolios.

Fortunately, hidden among some of the Edge Platform’s better-known features is a powerful and flexible way of running loan-level analytics on a portfolio using the Platform’s segment builder and loan model assumptions features.

These sometimes-overlooked features allow users to create and apply granular and customized modeling assumptions to a particular loan portfolio, based on its various, unique loan characteristics. Assumptions can be saved and reused for future analysis on different loans tapes.  This feature allows clients to effectively build and manage a complex system of models adjustment and tuners for granular sub-segments.

Applying the segment builder and loan model assumptions features, loan investors can:

    • Decouple how they run and aggregate results from how they assign modeling assumptions, and seamlessly assign different assumptions to various segments of the portfolio, based on user-defined criteria and preferences. For example, investors can assign different prepayment, default, and severity assumptions to loans based on their state, LTV, UPB, occupancy, purpose, delinquency status, loan type, collateral features, or virtually any other loan characteristic.

 

    • Choose from a variety of models and inputs, including RiskSpan models and vector inputs for things like CPR and CDR. Investors can define their own vector inputs as an aging curves by loan age or based on the forecast month, and apply them to different segments of the portfolio. For example, they can define their own CDR and CPR curves for consumer or C&I loans, based on the age of the loans.

    • Set up and save modeling assumptions one time, and then reference them over and over again whenever new loan tapes are uploaded. This saves time and effort and ensures consistency and accuracy in the analysis.

This hidden feature enables investors to customize their analysis and projections for different asset classes and scenarios, and to leverage the Edge Platform’s embedded cash flow, prepayment and credit models without compromising the granularity and accuracy of the results. Users can create and save multiple sets of loan model assumptions that include either static inputs, aging curves, or RiskSpan models, and apply them to any loan tape they upload and run in the forecasting UI.

Contact us and request a free demo or trial to learn more about how to use these and other exciting hidden (and non-hidden) features and how they can enhance your loan analytics.

CONTACT US

Enriching Pre-Issue Intex CDI Files with [Actual, Good] Loan-Level Data

The way RMBS dealers communicate loan-level details to prospective investors today leaves a lot to be desired.

Any investor who has ever had to work with pre-issue Intex CDI files can attest to the problematic nature of the loan data they contain. Some are better than others, but virtually all of them lack information about any number of important loan features.

Investors can typically glean enough basic information about balances and average note rates from preliminary CDI files to run simple, static CPR/CDR scenarios. But information needed to run complex models — FICO scores, property characteristics and geography, and LTV ratios to name a few — is typically lacking. MBS investors who want to run to run more sophisticated prepayment and credit models – models that rely on more comprehensive loan-level datasets to run deeper analytics and scenarios – can be left holding the bag when these details are missing from the CDI file.

The loan-level detail exists – it’s just not in the CDI file. Loan-level detail often accompanies the CDI file in a separate spreadsheet (still quaintly referred to in the 21st Century as a “loan tape”). Having this data separate from the CDI file requires investors to run the loan tape through their various credit and prepayment models and then manually feed those results back into the Intex CDI file to fully visualize the deal structure and expected cash flows.

This convoluted, multi-step workaround adds both time and the potential for error to the pre-trade analytics process.

A Better Way

Investors using RiskSpan’s Edge Platform can streamline the process of evaluating a deal’s structure alongside the expected performance of its underlying mortgage loans into a single step.

EDGEPLATFORM

Here is how it works.

As illustrated above, when investors set up their analytical runs on Edge, RiskSpan’s proprietary credit and prepayment models automatically extract all the required loan-level data from the tape and then connect the modeling results to the appropriate corresponding deal tranche in the CDI file. This seamlessness reduces all the elements of the pre-trade analytics process down to a matter of just a few clicks.

Making all this possible is the Edge Platform’s Smart Mapper ETL solution, which allows it to read and process loan tapes in virtually any format. Using AI, the Platform recognizes every data element it needs to run the underlying analytics regardless of the order in which the data elements are arranged and irrespective of how (or even whether) column headers are used.

Contact us to learn more about how RMBS investors are reaping the benefits of consolidating all of their data analytics on a single cloud-native platform.

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A Practical Approach to Climate Risk
Assessment for Mortgage Finance

Note: The following is the introduction from RiskSpan’s contribution to a series of essays on Climate Risk and the Housing Market published this month by the Mortgage Bankers Association’s Research Institute for Housing America.

Significant uncertainty exists about how climate change will occur, how all levels of government will intervene or react to chronic risks like sea level rise, and how households, companies, and financial markets will respond to various signals that will create movements in prices, demographics, and economic activity even before climate risk manifests. This paper lays out a pragmatic framework for assessing these risks from the perspective of a mortgage company. We evaluate available public and proprietary data sources and address data limitations, such as different sources providing a different view of risk for a particular property. We propose a sensitivity analysis approach to quantify risk and mitigate the uncertainties in measuring and responding to climate change.

Global temperatures will continue to increase over the next 50 years regardless of the actions people and governments take. The impacts of that warming are expected to accumulate and become more severe and frequent over time, causing stress throughout our economy. Regulators are clearly signaling that climate risk analysis will need to become a regular part of risk management activities. But detailed, industry-specific guidance has not been defined. FHFA and the regulated entities have yet to release a climate risk framework. They clearly recognize the threat to the housing finance system, however, and are actively working towards accounting for these risks.

Most executives and boards have become conceptually familiar with the physical and transition risks of climate change. But significant questions remain around how these concepts translate into specific, quantifiable business, asset, regulatory, legal, and reputation risks in the housing finance industry. Further complicating matters, climate science continues to evolve and there is limited historical data to understand how the effects of climate change will trickle into the housing market.

Sean Becketti1 describes the myriad ways climate change and natural hazard risk can permeate the housing and housing finance industries as well as some of the ways to mitigate its effects. However, quantifying these risks and inserting them into mortgage credit and prepayment models comes with significant challenges. No “best practices” have emerged for incorporating these into traditional model frameworks.

This paper puts forth a practical framework to incorporate climate risk into existing enterprise risk management practices for the housing finance industry. The framework incorporates suggestions to prepare for coming regulatory requirements on climate risk and, more importantly, proactively managing and mitigating this risk. Our approach is based on over two years of research and field work RiskSpan has conducted with its clients, and the resulting models RiskSpan has developed to deliver insights into these risks.

The paper is organized into two main sections:

  1. Prescribed Climate Scenarios and Emerging Regulatory Requirements
  2. A Practical Approach to Climate Risk Assessment for Mortgage Finance

Layering climate risk into enterprise risk management is likely to be a multiyear process. This paper focuses on steps to take in the initial one to two years after climate risk has been prioritized for investment of time and resources by corporate leadership. As explained in an MBA white paper from June 2022,2 “Existing risk management practices, structures, and relationships are already capturing potential risks from climate change.” The aim of this paper is to investigate specific ways in which existing credit, operational, and market risk frameworks can be leveraged to address this challenge, rather than seeking to reinvent the wheel.

Read the entire white paper at mba.org

How Do You Rate on Fannie Mae’s New Social Index?

Quick take-aways

  • HMDA data contains nearly every factor needed to replicate Fannie Mae’s Single Family Social Index. We use this data to explore how the methodology would look if the Fannie Mae Social Index were applied to other market participants.
  • The Agencies and Ginnie Mae are not the only game in town when it comes socially responsible lending. Non-agency loans would also perform reasonably well under Fannie Mae’s proposed Social Index.
  • Not surprisingly, Ginnie Mae outperforms all other “purchaser types” under the framework, buoyed by its focus on low-income borrowers and underserved communities. The gap between Ginnie and the rest of the market can be expected to expand in low-refi environments.
  • With a few refinements to account for socially responsible lending beyond low-income borrowers, Fannie Mae’s framework can work as a universally applicable social measure across the industry.

Fannie Mae’s new “Single Family Social Index”

Last week, Fannie Mae released a proposed methodology for its “Single Family Social Index.” The index is designed to provide “socially conscious investors” a means of “allocat[ing] capital in support of affordable housing and to provide access to credit for underserved individuals.”

The underlying methodology is simple enough. Each pool of mortgages receives a score based on how many of its loans meet one or more specified “social criteria” across three dimensions: borrower income, borrower characteristics and property location/type. Fannie Mae succinctly illustrates the defined criteria and framework in the following overview deck slide.

Social Index Figure 1: Source: Designing for Impact — A Proposed Methodology for Single-Family Social Disclosure

Each of the criteria is binary (yes/no) which facilitates the scoring. Individual loans are simply rated based on the number of boxes they check. Pools are measured in two ways: 1) a “Social Criteria Share,” which identifies the percentage of loans that meet any of the criteria, and 2) a “Social Density Score,” which assigns a “Social Score” of 0 thru 3 to each individual loan based on how many of the three dimensions (borrower income, borrower characteristics, and property characteristics) it covers and then averaging that score across all the loans in the pool.

If other issuers adopt this methodology, what would it look like?

The figure below is one of many charts and tables provided by Fannie Mae that illustrate how the Index works. This figure shows the share of acquisitions meeting one or more of the Social Index criteria (i.e., the overall “Social Criteria Share.” We have drawn a box approximately around the 2020 vintage,[1] which appears to have a Social Criteria Share of about 52% by loan count. We will refer back to this value later as we seek to triangulate in on a Social Criteria Share for other market participants.

SPEAK TO AN EXPERT

Graph Figure 2: Source: Designing for Impact — A Proposed Methodology for Single-Family Social Disclosure

We can get a sense of other issuers’ Social Criteria Share by looking at HMDA data. This dataset provides everything we need to re-create the Index at a high-level, with the exception of a flag for first time home buyers. The process involves some data manipulation as several Index criteria require us to connect to two census-tract level data sources published by FHFA.

HMDA allows us break down the loan population by purchaser type, which gives us an idea of each loan’s ultimate destination—Fannie, Freddie, Ginnie, etc. The purchaser type does not capture this for every loan, however, because originators are only obligated to report loans that are closed and sold during the same calendar year.  

The two tables below reflect two different approaches to approximating the population of Fannie, Freddie, and Ginnie loans. The left-hand table compares the 2020 origination loan count based on HMDA’s Purchaser Type field with loan counts based on MBS disclosure data pulled from RiskSpan’s Edge Platform.

The right-hand table enhances this definition by first re-categorizing as Ginnie Mae all FHA/VA/USDA loans with non-agency purchaser types. It also looks at the Automated Underwriting System field and re-maps all owner-occupied loans previously classified as “Other or NA” to Fannie (DU AUS) or Freddie (LP/LPA AUS).

Social Index


The adjusted purchaser type approach used in the right-hand table reallocates a considerable number of “Other or NA” loans from the left-hand table. The approach clearly overshoots the Fannie Mae population, as some loans underwritten using Fannie’s automated underwriting system likely wind up at Freddie and other segments of the market. This limitation notwithstanding, we believe this approximation lends a more accurate view of the market landscape than does the unadjusted purchaser type approach. We consequently rely primarily on the adjusted approach in this analysis.

Given the shortcomings in aligning the exact population, the idea here is not to get an exact calculation of the Social Index metrics via HMDA, but to use HMDA to give us a rough indication of how the landscape would look if other issuers adopted Fannie’s methodology. We expect this to provide a rough rank-order understanding of where the richest pools of ‘Social’ loans (according to Fannie’s methodology) ultimately wind up. Because the ultimate success of a social scoring methodology can truly be measured only to the extent it is adopted by other issuers, having a universally useful framework is crucial.

The table below estimates the Social Criteria Share by adjusted purchaser using seven of Fannie Mae’s eight social index criteria.[2] Not surprisingly, Ginnie, Fannie, and Freddie boast the highest overall shares. It is encouraging to note, however, that other purchaser types also originate significant percentages of socially responsible loans. This suggests that Fannie’s methodology could indeed be applied more universally. The table looks at each factor separately and could warrant its own blog post entirely to dissect, so take a closer look at the dynamics.[3]

Social Index

Ginnie Mae’s strong performance on the Index comes as no surprise. Ginnie pools, after all, consist primarily of FHA loans, which skew toward the lower end of the income spectrum, first-time borrowers, and traditionally underserved communities. Indeed, more than 56 percent of Ginnie Mae loans tick at least one box on the Index. And this does not include first-time homebuyers, which would likely push that percentage even higher.

Income’s Outsized Impact

Household income contributes directly or indirectly to most components of Fannie’s Index. Beyond the “Low-income” criterion (borrowers below 80 percent of adjusted median income), nearly every other factor favors income levels be below 120 percent of AMI. Measuring income is tricky, especially outside of the Agency/Ginnie space. The non-Agency segment serves many self-employed borrowers, borrowers who qualify based on asset (rather than income) levels, and foreign national borrowers. Nailing down precise income has historically proven challenging with these groups.

Given these dynamics, one could reasonably posit that the 18 percent of PLS classified as “low-income” is actually inflated by self-employed or wealthier borrowers whose mortgage applications do not necessarily reflect all of their income. Further refinements may be needed to fairly apply the Index framework to this and market segments that pursue social goals beyond expanding credit opportunities for low-income borrowers. This could just be further definitions on how to calculate income (or alternatives to the income metric when not available) and certain exclusions from the framework altogether (foreign national borrowers, although these may be excluded already based on the screen for second homes).

Positive effects of a purchase market

The Social Criteria Share is positively correlated with purchase loans as a percentage of total origination volume (even before accounting for the FTHB factor). This relationship is apparent in Fannie Mae’s time series chart near the top of this post. Shares clearly drop during refi waves.

Our analysis focuses on 2020 only. We made this choice because of HMDA reporting lags and the inherent facility of dealing with a single year of data. The table below breaks down the HMDA analysis (referenced earlier) by loan purpose to give us a sense for what our current low-refi environment could look like. (Rate/term refis are grouped together with cash-out refis.) As the table below indicates, Ginnie Mae’s SCS for refi loans is about the same as it is for GSE refi loans — it’s really on purchase loans where Ginnie shines. This implies that Ginnie’s SCS will improve even further in a purchase rate environment.

Social Index

Accounting for First-time Homebuyers

As described above, our methodology for estimating the Social Criteria Share omits loans to first-time homebuyers (because the HMDA data does not capture it). This likely accounts for the roughly 6 percentage point difference between our estimate of Fannie’s overall Social Criteria Share for 2020 (approximately 46 percent) and Fannie Mae’s own calculation (approximately 52 percent).

To back into the impact of the FTHB factor, we can pull in data about the share of FTHBs from RiskSpan’s Edge platform. The chart above that looks a Purchase vs. Refi tells us the SCS share without the FTHB factor for purchase loans. Using MBS data sources, we can obtain the share of 2020 originations that were FTHBs. If we assume that FTHB loans look the same as purchase loans overall in terms of how many other Social Index boxes they check, then we can back into the overall SCS incorporating all factors in Fannie’s methodology.

Applying this approach to Ginnie Mae, we conclude that, because 29 percent of Ginnie’s purchase loans (one minus 71 percent) do not tick any of the Index’s boxes, 29 percent of FTHB loans (which account for 33 percent of Ginnie’s overall population) also do not tick any Index boxes. Taking 29 percent of this 33 percent results in an additional 9.6 percent that should be tacked on to Ginnie Mae’s pre-FTHB share, bringing it up to 66 percent.

Social Index

Validating this estimation approach is the fact it increases Fannie Mae’s share from 46 percent (pre-FTHB) to 52 percent, which is consistent with the historical graph supplied by Fannie Mae (see Figure 2, above). Our FTHB approach implies that 92 percent of Ginnie Mae purchase loans meet one or more of the Index criteria. One could reasonably contend that Ginnie Mae FTHB loans might be more likely than Ginnie purchase loans overall to satisfy other social criteria (i.e., that 92 percent is a bit rich), in which case the 66 percent share for Ginnie Mae in 2020 might be overstated. Even if we mute this FTHB impact on Ginnie, however, layering FTHB loans on top of a rising purchase-loan environment would likely put today’s Ginnie Mae SCS in the low 80s.



[1] The chart is organized by acquisition month, our analysis of HMDA looks at 2020 originations, so we’ve tried to push the box slightly to the right to reflect the 1–3-month lag between origination and acquisition. Additionally, we think the chart and numbers throughout Fannie’s document are just Fixed Rate 30 loans, our analysis includes all loans. We did investigate what our numbers would look like if filtered to Fixed 30 and it would only increase the SCS slightly across the board.

[2] As noted above, we are unable to discern first-time homebuyer information from the HMDA data.

[3] We can compare the Fannie numbers for each factor to published rates in their documentation representing the time period 2017 forward. The only metric where we stand out as being meaningfully off is the percentage of loans in minority census tracts. We took this flag from FHFA’s Low-Income Area File for 2020 which defines a minority census tract having a ‘…minority population of at least 30 percent and a median income of less than 100 percent of the AMI.’ It is not 100% clear that this is what Fannie Mae is using in its definition.

Improving MSR Pricing Using Cloud-Based Loan-Level Analytics — Part II: Addressing Climate Risk

Modeling Climate Risk and Property Valuation Stability

Part I of this white paper seriesKey Takeaways introduced the case for why loan-level (as opposed to rep-line level) analytics are increasingly indispensable when it comes to effectively pricing an MSR portfolio. Rep-lines are an effective means for classifying loans across many important categories. But certain loan, borrower, and property characteristics simply cannot be “rolled up” to the rep-line level as easily as UPB, loan age, interest rate, LTV, credit score, and other factors. This is especially true when it comes to modeling based on available information about a mortgage’s subject property.

Assume for the sake of simplicity that human and automated appraisers do a perfect job of assigning property values for the purpose of computing origination and updated LTVs (they do not, of course, but let’s assume they do). Prudent MSR investors should be less interested in a property’s current value than in what is likely to happen to that value over the expected life of their investment. In other words, how stable is the valuation? How likely are property values within a given zip code, or neighborhood, or street to hold?

The stability of any given property’s value is tied to the macroeconomic prospects of its surrounding community. Historical and forecast trends of the local unemployment rate can be used as a rough proxy for this and are already built into existing credit and prepayment models. But increasingly, a second category of factors is emerging as an important predictor of home price stability, the property’s exposure to climate risk and natural hazard events.

Climate exposure is becoming increasingly difficult to ignore when it comes to property valuation. And accounting for it is more complicated than simply applying a premium to coastal properties. Climate risk is not just about hurricanes and storm surges anymore. A growing number of inland properties are being identified as at risk not just to wind and water hazards, but to wildfire and other perils as well. The diversity of climate risks means that the problem of quantifying and understanding them will not be solved simply by fixing out-of-date flood plain maps.

MSR investors are exposed to climate risk in ways that whole loan or securities investors are not. When climate events force borrowers into forbearance or other repayment plans, MSR investors not only forego the cash flows associated with missed interest payments that will never be made, but also incur the additional costs of administering the loss mitigation programs and making necessary P&I and escrow advances.

Overlaying climate scenario analysis on top of traditional credit modeling is unquestionably the future of quantifying mortgage asset exposure. And in many respects, the future is already here. Regulatory guidance is forthcoming requiring public companies to quantify their exposure to climate risk across three categories: acute physical risk, chronic physical risk, and economic transition risk.

Acute Risk

Acute climate risk describes a property’s exposure to individual catastrophic events. As a result of climate change, these events are expected to increase in frequency and severity. The property insurance space already has analytical tools in place to quantify property damage to hazard risks such as:

  • Hurricane, including wind, storm surge, and precipitation-induced flooding
  • Flooding, including “fluvial” and “pluvial” – on- and off-plan flooding
  • Wildfire
  • Severe thunderstorm, including exposure to tornadoes, hail, and straight-line wind, and
  • Earthquake – though not tied to climate change, earthquakes remain a massively underinsured risk that can impact MSR holders

Acute risks are of particular concern for MSR holders as disaster events have proven to increase both mortgage delinquency and prepayment. The chart below illustrates these impacts after hurricane Katrina.

Chronic Risk

Chronic risk characterizes a property’s exposure to adverse conditions brought on by longer-term concerns. These include frequent flooding, sea level rise, drought hazards, heat stress, and water shortages. These effects could erode home values or put entire communities at risk over a longer period. Models currently in use forecast these risks over 20- and 25-year periods.

Transition Risk

Transition risk describes exposure to changing policies, practices or technologies that arise from a broader societal move to reduce its carbon footprint. These include increases in the direct cost of homeownership (e.g., taxes, insurance, code compliance, etc.), increased energy and other utility costs, and localized employment shocks as businesses and industry leave high-risk areas. Changing property insurance requirements (by the GSEs, for example) could further impact property valuations in affected neighborhoods.

———–

Converting acute, chronic and transition risks into mortgage modeling scenarios can only be done effectively at the loan level. Rep-lines cannot adequately capture them. As with most prepayment and credit modeling, accounting for climate risk is an exercise in scenario analysis. Building realistic scenarios involves taking several factors into account.

Scenario Analysis

Quantifying physical risks (whether acute or chronic) entails identifying:

  • Which physical hazard types the property is exposed to
  • How each hazard type threatens the property[1]
  • The materiality of each hazard; and
  • The most likely timeframes over which these hazards could manifest

Factoring climate risk into MSR pricing requires translating the answers to the questions above into mortgage modeling scenarios that function as credit and prepayment model inputs. The following table is an example of how RiskSpan overlays the impact of an acute event – specifically a category 5 hurricane in South Florida — on home price, delinquency, turnover and macroeconomic conditions.

 

Chart

 

Chart

Applying this framework to an MSR portfolio requires integration with an MSR cash flow engine. MSR cash flows and the resulting valuation are driven by the manner in which the underlying delinquency and prepayment models are affected. However, at least two other factors affect servicing cash flows beyond simply the probability of the asset remaining on the books. Both of these are likely impacted by climate risk.

  • Servicing Costs: Rising delinquency rates are always accompanied by corresponding increases in the cost of servicing. An example of the extent to which delinquencies can affect servicing costs was presented in our previous paper. MSR pricing models take this into account by applying a different cost of servicing to delinquent loans. Some believe, however, that servicing loans that enter delinquency in response to a natural disaster can be even more expensive (all else equal) than servicing a loan that enters delinquency for other reasons. Reasons for this range from the inherent difficulty of reaching displaced persons to the layering impact of multiple hardships such events tend to bring upon households at once.[2]
  • Recapture Rate: The data show that prepayment rates consistently spike in the wake of natural disasters. What is less clear is whether there is a meaningful difference in the recapture rate for these prepayments. Anecdotally, recapture appears lower in the case of natural disaster, but we do not have concrete data on which to base assumptions. This is clearly only relevant to MSR investors that also have an origination arm with which to capture loans that refinance.

Climate risk encompasses a wide range of perils, each of which affects MSR values in a unique way. Hurricanes, wildfires, and droughts differ not only in their geography but in the specific type of risk they pose to individual properties. Even if there were a way of assigning every property in an MSR portfolio a one-size-fits-all quantitative score, computing a “weighted average climate risk” value and applying it to a rep-line would be problematic. Such an average would be denuded of any nuance specific to individual perils. Peril-specific data is critical to being able to make the LTV, delinquency, turnover and macroeconomic assumption adjustments outlined above.

And there is no way around it. Doing all this requires a loan-by-loan analysis. RiskSpan’s Edge Platform was purpose built to analyze mortgage portfolios at the loan level and is becoming the industry’s go-to solution for measuring and managing exposures to market, credit and climate events.

Contact us to learn more.

[1] Insurability of hazards varies widely, even before insurance requirements are considered.

[2] In addition, because servicers normally staff for business-as-usual levels of delinquencies, a large acute event will create a significant spike in the demand for servicer personnel. If a servicer’s book is heavily concentrated in the Southeast, for example, a devastating storm could result in having to triple the number of people actively servicing the portfolio.

Improving MSR Pricing Using Cloud-Native Loan-Level Analytics (Part II)

Improving MSR Pricing Using Cloud-Native Loan-Level Analytics (Part II)

  1. MSR investors are more exposed to acute climate risk than whole loan or securities investors are. MSR investors are not in a favorable position to recoup cash flows lost to climate disruptions.
  2. Climate risk can be acute, chronic, or transitional. Each affects MSR values in a different way.
  3. Integrating climate scenario analysis into traditional credit and prepayment modeling – both of which are critical to modeling MSR cash flows and pricing — requires a loan-by-loan approach.
  4. Climate risk cannot be adequately expressed or modeled using a traditional rep-line approach.


An Emerging Climate Risk Consensus for Mortgages?

That climate change poses a growing—and largely unmeasured—risk to housing and mortgage investors is not news. As is often the case with looming threats whose timing and magnitude are only vaguely understood, increased natural hazard risks have most often been discussed anecdotally and in broad generalities. This, however, is beginning to change as the reality of these risks becomes increasingly clear to an increasing number of market participants and industry-sponsored research begins to emerge.

This past week’s special report by the Mortgage Bankers Association’s Research Institute for Housing America, The Impact of Climate Change on Housing and Housing Finance, raises a number of red flags about our industry’s general lack of preparedness and the need for the mortgage industry to take climate risk seriously as a part of a holistic risk management framework. Clearly this cannot happen until appropriate risk scenarios are generated and introduced into credit and prepayment models.

One of the puzzles we are focusing on here at RiskSpan is an approach to creating climate risk stress testing that can be easily incorporated into existing mortgage modeling frameworks—at the loan level—using home price projections and other stress model inputs already in use. We are also partnering with firms who have been developing climate stress scenarios for insurance companies and other related industries to help ensure that the climate risk scenarios we create are consistent with the best and most recently scientific research available.

Also on the short-term horizon is the implementation of FEMA’s new NFIP premiums for Risk Rating 2.0. Phase I of this new framework will begin applying to all new policies issued on or after October 1, 2021. (Phase II kicks in next April.) We wrote about this change back in February when these changes were slated to take effect back in the spring. Political pressure, which delayed the original implementation may also impact the October date, of course. We’ll be keeping a close eye on this and are preparing to help our clients estimate the likely impact of FEMA’s new framework on mortgages (and the properties securing them) in their portfolios.

Finally, this past week’s SEC statement detailing the commission’s expectations for climate-related 10-K disclosures is also garnering significant (and warranted) attention. By reiterating existing guidelines around disclosing material risks and applying them specifically to climate change, the SEC is issuing an unmistakable warning shot at filing companies who fail to take climate risk seriously in their disclosures.

Contact us (or just email me directly if you prefer) to talk about how we are incorporating climate risk scenarios into our in-house credit and prepayment models and how we can help incorporate this into your existing risk management framework.  

Prepayment Spikes in Ida’s Wake – What to Expect

It is, of course, impossible to view the human suffering wrought by Hurricane Ida without being reminded of Hurricane Katrina’s impact 16 years ago. Fortunately, the levees are holding and Ida’s toll appears likely to be less severe. It is nevertheless worth taking a look at what happened to mortgages in the wake of New Orleans’s last major catastrophic weather event as it is reasonable to assume that prepayments could follow a similar pattern (though likely in a more muted way).

Following Katrina, prepayment speeds for pools of mortgages located entirely in Louisiana spiked between November 2005 and June 2006. As the following graph shows, prepayment speeds on Louisiana properties (the black curve) remained elevated relative to properties nationally (the blue curve) until the end of 2006. 

Comparing S-curves of Louisiana loans (the black curve in the chart below) versus all loans (the green curve) during the spike period (Nov. 2005 to Jun. 2006) reveals speeds ranging from 10 to 20 CPR faster across all refinance incentives. The figure below depicts an S-curve for non-spec 100% Louisiana pools and all non-spec pools with a weighted average loan age of 7 to 60 months during the period indicated.

The impact of Katrina on Louisiana prepayments becomes even more apparent when we consider speeds prior to the storm. As the S-curves below show, non-specified 100% Louisiana pools (the black curve) actually paid slightly slower than all non-spec pools between November 2003 and October 2005.

As we pointed out in June, a significant majority of prepayments caused by natural disaster events are likely to be voluntary, as opposed to the result of default as one might expect. This is because mortgages on homes that are fully indemnified against these perils are likely to be prepaid using insurance proceeds. This dynamic is reflected in the charts below, which show elevated voluntary prepayment rates running considerably higher than the delinquency spike in the wake of Katrina. We are able to isolate voluntary prepayment activity by looking at the GSE Loan Level Historical Performance datasets that include detailed credit information. This enables us to confirm that the prepay spike is largely driven by voluntary prepayments. Consequently, recent covid-era policy changes that may reduce the incidence of delinquent loan buyouts from MBS are unlikely to affect the dynamics underlying the prepayment behavior described above.

RiskSpan’s Edge Platform enables users to identify Louisiana-based loans and pools by drilling down into cohort details. The example below returns over $1 billion in Louisiana-only pools and $70 billion in Louisiana loans as of the August 2021 factor month.

Edge also allows users to structure more specified queries to identify the exposure of any portfolio or portfolio subset. Edge, in fact, can be used to examine any loan characteristic to generate S-curves, aging curves, and time series.  Contact us to learn more.

Is the housing market overheated? It depends where you are.

Mortgage credit risk modeling has evolved slowly in the last few decades. While enhancements leveraging conventional and alternative data have improved underwriter insights into borrower income and assets, advances in data supporting underlying property valuations have been slow. With loan-to-value ratios being such a key driver of loan performance, the stability of a subject property’s value is arguably as important as the stability of a borrower’s income.

Most investors rely on current transaction prices to value comparable properties, largely ignoring the risks to the sustainability of those prices. Lacking the data necessary to identify crucial factors related to a property value’s long-term sustainability, investors generally have little choice but to rely on current snapshots. To address this problem, credit modelers at RiskSpan are embarking on an analytics journey to evaluate the long-term sustainability of a property’s value.

To this end, we are working to pull together a deep dataset of factors related to long-term home price resiliency. We plan to distill these factors into a framework that will enable homebuyers, underwriters, and investors to quickly assess the risk inherent to the property’s physical location. The data we are collecting falls into three broad categories:

  • Regional Economic Trends
  • Climate and Natural Hazard Risk
  • Community Factors

Although regional home price outlook sometimes factors into mortgage underwriting, the long-term sustainability of an individual home price is seldom, if ever, taken into account. The future value of a secured property is arguably of greater importance to mortgage investors than its value at origination. Shouldn’t they be taking an interest in regional economic condition, exposure to climate risk, and other contributors to a property valuation’s stability?

We plan to introduce analytics across all three of these dimensions in the coming months. We are particularly excited about the approach we’re developing to analyze climate and natural hazard risk. We will kick things off, however, with basic economic factors. We are tracking the long-term sustainability of house prices through time by tracking economic fundamentals at the regional level, starting with the ratio of home prices to median household income.

Economic Factors

Housing is hot. Home prices jumped 12.7% nationally in 2020, according to FHFA’s house price index[1]. Few economists are worried about a new housing bubble, and most attribute this rise to supply and demand dynamics. Housing supply is low and rising housing demand is a function of demography –millennials are hitting 40 and want a home of their own.

But even if the current dynamic is largely driven by low supply, there comes a certain point at which house prices deviate too much from area median household income to be sustainable. Those who bear the most significant exposure to mortgage credit risk, such as GSEs and mortgage insurers, track regional house price dynamics to monitor regions that might be pulling away from fundamentals.

Regional home-price-to-income ratio is a tried-and-true metric for judging whether a regional market is overheating or under-valued. We have scored each MSA by comparing its current home-price-to-income ratio to its long-term average. As the chart below illustrating this ratio’s trend shows, certain MSAs, such as New York, consistently have higher ratios than other, more affordable MSAs, such as Chicago.

Because comparing one MSA to another in this context is not particularly revealing, we instead compare each MSA’s current ratio to the long-term ratio for itself. MSAs where that ratio exceeds its long-term average are potentially over-heated, while MSAs under that ratio potentially have more room to grow. In the table below highlighting the top 25 MSAs based on population, we look at how the home-price-to-household-income ratio deviates from its MSA long-term average. The metric currently suggests that Dallas, Denver, Phoenix, and Portland are experiencing potential market dislocation.

Loans originated during periods of over-heating have a higher probability of default, as illustrated in the scatterplot below. This plot shows the correlation between the extent of the house-price-to-income ratio’s deviation from its long-term average and mortgage default rates. Each dot represents all loan originations in a given MSA for a given year[1]. Only regions with large deviations in house price to income ratio saw explosive default rates during the housing crisis. This metric can be a valuable tool for loan and SFR investors to flag metros to be wary of (or conversely, which metros might be a good buy).

Although admittedly a simple view of regional economic dynamics driving house prices (fundamentals such as employment, housing starts per capita, and population trends also play important roles) median income is an appropriate place to start. Median income has historically proven itself a valuable tool for spotting regional price dislocations and we expect it will continue to be. Watch this space as we continue to add these and other elements to further refine how we measure property value stability and its likely impact on mortgage credit.

[1] FHFA Purchase Only USA NSA % Change over last 4 quarters

Contact us to learn more.

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