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Articles Tagged with: General

Surge in Cash-Out Refis Pushes VQI Sharply Higher

A sharp uptick in cash-out refinancing pushed RiskSpan’s Vintage Quality Index (VQI) to its highest level since the first quarter of 2019.

RiskSpan’s Vintage Quality Index computes and aggregates the percentage of Agency originations each month with one or more “risk factors” (low-FICO, high DTI, high LTV, cash-out refi, investment properties, etc.). Months with relatively few originations characterized by these risk factors are associated with lower VQI ratings. As the historical chart above shows, the index maxed out (i.e., had an unusually high number of loans with risk factors) leading up to the 2008 crisis.

RiskSpan uses the index principally to fine-tune its in-house credit and prepayment models by accounting for shifts in loan composition by monthly cohort.

Rising Rates Mean More Cash-Out Refis (and more risk)

As the following charts plotting the individual VQI components illustrate, a spike in cash-out refinance activity (as a percentage of all originations) accounted for more of the rise in overall VQI than did any other risk factor.

This comes as little surprise given the rising rate environment that has come to define the first quarter of 2022, a trend that is likely to persist for the foreseeable future.

As we demonstrated in this recent post, the quickly vanishing number of borrowers who are in the money for a rate-and-term refinance means that the action will increasingly turn to so-called “serial cash-out refinancers” who repeatedly tap into their home equity even when doing so means refinancing into a mortgage with a higher rate. The VQI can be expected to push ever higher to the extent this trend continues.

An increase in the percentage of loans with high debt-to-income ratios (over 45) and low credit scores (under 660) also contributed to the rising VQI, as did continued upticks in loans on investment and multi-unit properties as well as mortgages with only one borrower.

Population assumptions:

  • Monthly data for Fannie Mae and Freddie Mac.
  • Loans originated more than three months prior to issuance are excluded because the index is meant to reflect current market conditions.
  • Loans likely to have been originated through the HARP program, as identified by LTV, MI coverage percentage, and loan purpose, are also excluded. These loans do not represent credit availability in the market as they likely would not have been originated today but for the existence of HARP.

Data assumptions:

  • Freddie Mac data goes back to 12/2005. Fannie Mae only back to 12/2014.
  • Certain fields for Freddie Mac data were missing prior to 6/2008.

GSE historical loan performance data release in support of GSE Risk Transfer activities was used to help back-fill data where it was missing.

An outline of our approach to data imputation can be found in our VQI Blog Post from October 28, 2015.

Data Source: Fannie Mae PoolTalk®-Loan Level Disclosure


EDGE: Cash-Out Refi Speeds 

Mortgage rates have risen nearly 200bp from the final quarter of 2021, squelching the most recent refinancing wave and leaving the majority of mortgage holders with rates below the prevailing rate of roughly 5% (see chart below). For most homeowners, it no longer makes sense to refinance an existing 30yr mortgage into another 30yr mortgage.

Vintage/Note Rate Distribution 30yr Conventional Mortgages

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But, as we noted back in February, the rapid rise in home prices has left nearly all households with significant, untapped gains in their household balance sheets. For homeowners with consumer debt at significantly higher rates than today’s mortgage rates, it can make economic sense to consolidate debt using a cash-out refi loan against their primary residence. As we saw during 2002-2003, cash-out refinancing can drive speeds on discount mortgages significantly higher than turnover alone. Homeowners can also become “serial cash-out refinancers,” tapping additional equity multiple times.  

In this analysis, we review prepayment speeds on cash-out refis, focusing on discount MBS, i.e., mortgages whose note rates are equal to or below today’s prevailing rates. 

The volume of cash-out refis has grown steadily but modestly since the start of the pandemic, whereas rate/term refis surged and fell dramatically in response to changing interest rates. Despite rising rates, the substantial run-up in home prices and increased staffing at originators from the recent refi boom has left the market ripe for stronger cash-out activity. 

The pivot to cash-out issuance is evidenced by the chart below, illustrating how the issuance of cash-out refi loans (the black line below) in the first quarter of this year was comparable with issuance in the summer of 2021, when rates near historic lows, while rate/term refis (blue line) have plunged over the same period. 

Quarterly Issuance of FN/FH Mortgages

With cash-out activity set to account for a larger share of the mortgage market, we thought it worthwhile to compare some recent cash-out activity trends. For this analysis, the graphs consist of truncated S-curves, showing only the left-hand (out-of-the-money) side of the curve to focus on discount mortgage behavior in a rising rate environment where activity is more likely to be influenced by serial cash-out activity. 

This first chart compares recent performance of out-of-the money mortgages by loan purpose, comparing speeds for purchase loans (black) with both cash-out refis (blue) and rate/term refis (green). Notably, cash-out refis offer 1-2 CPR upside over rate/term refis, only converging to no cash out refis when 100bp out of the money.[1] 

S-curves by Loan Purpose

Next, we compare cash-out speeds by servicer type, grouping mortgages that are serviced by banks (blue) versus mortgages serviced by non-bank servicers (green). Non-bank servicers produce significantly faster prepay speeds, an advantage over bank-serviced loans for MBS priced at a discount. 

Cash-out Refi Performance by Servicer Type

Finally, we drill deeper into the faster non-bank-serviced discount speeds for cash-out refis. This chart isolates Quicken (red) from other non-bank servicers (green). While Quicken’s speeds converge with those of other non-banks at the money, Quicken-serviced cash-out refis are substantially faster when out of the money than both their non-bank counterparts and the cash-out universe as a whole.[2]

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Cash out Refi Performance, by Servicer

We suspect the faster out-of-the-money speeds are being driven by serial cash-out behavior, with one servicer in particular (Quicken) encouraging current mortgage holders to tap home equity as housing prices continue to rise. 

This analysis illustrates how pools with the highest concentration of Quicken-serviced cash-out loans may produce substantially higher out-of-the-money speeds relative to the universe of non-spec pools. To find such pools, users can enter a list of pools into the Edge platform and simultaneously filter for both Quicken and cash-out refi. The resultant query will show each pool’s UPB for this combination of characteristics. 

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EDGE: Recent Performance of GNMA RG Pools

In early 2021, GNMA began issuing a new class of custom pools with prefix “RG.” These pools are re-securitizations of previously delinquent loans which were repurchased from pools during the pandemic.[1] Loans in these pools are unmodified, keeping the original rate and term of the mortgage note. In the analysis below, we review the recent performance of these pools at loan-level detail. The first RG pools were issued in February 2021, growing steadily to an average rate of $2B per month from Q2 onward, with a total outstanding of $21 billion. 

 
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The majority of RG issuance has included loans that are two to seven years seasoned and represent a consistent 2-3% of the total GNMA market for those vintages, dashed line below.

Distribution of RG Loans by Age

Coupons of RG pools are primarily concentrated between 3.0s through 4.5s, with the top-10 Issuers of RG pools account for nearly 90% of the issuance.

EDGE - GNMA RG POOL PERFORMANCE

Below, we compare speeds on GNMA RG pools under various conditions. First, we compare speeds on loans in RG pools (black) versus same-age multi-lender pools (red) over the last twelve months. When out of the money, RG pools are 4-5 CPR slower than comparably aged multi-lender pools but provide a significantly flatter S-curve when in-the-money.

GN RG VS Multi-lender S-Curve

Next, we plot the S-curve for all GNMA RG loans with overlays for loans that are serviced by banks (green) and non-banks (blue). Bank-serviced RG loans prepay significantly slower than non-banks by an average of 9 CPR weighted across all incentives. Further, this difference is caused by voluntary prepays, with buyouts averaging a steady 4% CBR, plus or minus 1 CBR, for both banks and non-banks with no discernable difference between the two (second graph). GNMA RG S-curves GNMA_RG_Buyouts-graph

Finally, we analyzed the loan-level transition matrix by following each RG loan through its various delinquency states over the past year. We note that the transition rate from Current to 30-day delinquent for RG loans is 1.6%, only marginally worse than that of the entire universe of GNMA loans at 1.1%. RG loans transitioned back from 30->Current at similar rates to the wider Ginnie universe (32.3%) and the 30->60 transition rate for RG loans was marginally worse than the Ginnie universe, 30.8% versus  24.0%.[2]

Monthly Transition Rates for Loans in GNMA RG Pools: EDGE-GNMA-RG-Pool-Perform-Current-State In summary, loans in RG pools have shown a substantial level of voluntary prepayments and comparatively low buyouts, somewhat unexpected especially in light of their recent delinquency. Further, their overall transition rates to higher delinquency states, while greater than the GNMA universe, is markedly better than that of reperforming loans just prior to the outbreak of COVID.

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Incorporating Climate Risk into ERM: A Mortgage Risk Manager’s Guide

Climate risk is becoming impossible to ignore in the mortgage space.

President Biden’s May 2021 Executive Order makes clear that quantifying and mitigating climate risk will be a priority for the federal government’s housing finance agencies (HUD, FHFA, FHA, VA). It’s just a matter of time before the increased emphasis on this risk makes its way to others in the eco-system (Government-Sponsored Enterprises, Servicers, Lenders, Investors). The SEC will be coming out with climate-related requirements for the securities markets. In early 2021, a proposed rule amendment “to enhance registrant disclosures regarding issuers’ climate-related risks and opportunities” was added to their regulatory agenda with an expected release in 2022. Other agencies, including the OCC, are issuing draft guidance, or requesting feedback on climate-related risks.  Boards are taking notice, and, if you haven’t heard from yours on the topic, you will soon.

But where can you start?

Bear in mind there are a couple of critical questions you need to think about regarding your organizational response to climate risk. Most executives and boards are now familiar with the concepts of physical and transition risks of climate change, but how will these risks manifest in your organization through business, asset, regulatory, legal, and reputation risk? How will these risks impact residential housing prices, attractiveness of communities, building codes, insurance costs, and zoning laws, and the valuation of mortgages and other financial instruments that are a derivative value of residential properties and the economic strength of communities? What will be the response from homeowners, insurers, builders, investors, and public policy of local, state, and federal governments that could impact asset valuation? It’s not an easy problem to solve!

A growing body of academic literature has developed around home price dynamics, mortgage performance, and the general perception of climate risk as a market influencer. Published findings focus primarily on the effect of physical risks on mortgage performance and home prices. A recurring theme in the literature is that while individual climate events can be highly disruptive on local real estate and mortgage markets, values tend to rebound quickly (Bin and Landry, 2013) with the specter of another such event not appearing to weigh down prices significantly. On top of that, short-run effects of supply issues and competitive effects, such as attractive housing features and locations, complicate housing price dynamics. People still want to live on coasts and rivers, in hot and dry desert locations, and in earthquake- and wildfire-exposed areas that are prone to natural catastrophes and increasing impacts from climate change. So attractive are these areas, the marginal effect of a home being in an area that is projected to be underwater may actually increase home prices, without controlling for distance to the shore. This may be a consequence of the premium value associated with waterfront views (Baldauf et al., 2020). But just because impacts so far have been minimal, does not mean future impacts will follow the same trend.

While prices have rebounded quickly after events in the past and housing prices still command a premium for waterfront views, there is evidence that buyers are starting to discount values for coastal properties exposed to sea level rise (Bernstein et al. 2018).  In the future, where there is increasing chances that climate change will cause permanent change to usable land due to any number of hazards without effective resilience improvements, there may be a smaller or no rebound in prices leaving the holders of exposed real and financial assets with a loss. Or, conversely, the value of waterfront homes may even begin to experience a rapid decline if mortgage holders begin to suspect that the value (and usability) of their properties could decline substantially over the life of their mortgages.

Further discussion of the academic literature and a bibliography can be found in the note at the end of this article.

Significant uncertainty exists about how climate change will occur, over what timeframe these changes 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. What is known is that global temperatures will continue to warm over the next 50 years regardless of the actions people and governments take, and the impacts of that warming will accumulate and become more severe and frequent over time, requiring a definitive action plan for dealing with this issue.


Little differentiation in scenarios in 20 years. Risks will manifest differently over different timeframes.Global surface temperature change relative to 1850-1900


 

The standards by which organizations will be expected to deal with climate risk will evolve as the climate continues to change and more capabilities are developed to address these issues. An important first step is the need to contextualize these risks with respect to other risks to your business. One immediate need is to address near-term board and regulatory reporting requirements, as well as voluntary public disclosure, as pressure by stakeholders to understand what actions are being taken by companies to address climate change builds.

There is no easy answer, but we offer a way to bring the issue into focus and plan for a thoughtful response as the risks and standards evolve. We are tackling the problem by understanding the risks the organization faces and evaluate those through scenarios and sensitivity analysis. We recommend against over-engineering a solution; instead, design a framework that allows you to monitor and track risk over time. We propose a practical approach, one that’s incrementally phased and integrates risk management through time, enabling pause, adjustment, assessment, and changes in course as needed.


Suggested Approach for Incorporating Climate Risk into ERMSuggested Approach for Incorporating Climate Risk into ERM


We present five key components to consider when incorporating a climate and natural hazard risk dimension into an existing ERM framework.

Evaluate the Risk Landscape

As a starting point, evaluating the risk landscape entails identifying which climate-related risks have the potential to affect investment return. Climate-related financial risks can be categorized into physical and transition risks.

Physical risks can be acute or chronic. Acute physical risks include extreme events like hurricane, floods, and wildfire. Chronic physical risks refer to a property’s exposure to sea level rise, excessive heat, or drought, for example. Investors who understand these terms and scenarios – including how uncertainty is modeled, emphasizing the directional relationship and order of magnitude of changes rather than exact quantification — are at a competitive advantage.

Transition risks and the secondary effects of physical risks can arise from changes in policy, legal, technology, or market actions that come about from a movement to reduce carbon emissions.

Some important and guiding questions for both physical and transition risk include:

What are the acute and chronic physical hazard types that pose a financial risk?

How will these risks manifest as potential financial loss to mortgage investments?

How material are the possible losses?

How might these risks evolve over time?

Note that climate science continues to evolve, especially as it relates to longer-term impacts, and there is limited historical data to understand how the effects of climate change will trickle into the housing market. Risk assessments must be based on a range of scenarios and include plausible narratives that are not bound by historical observations. The scenario approach applies to studying both acute and chronic physical risks, and the scenarios used in assessing acute or chronic risks may be conceptualized differently.

Select Climate-Related Risks that Impact Mortgage Finance

Visualizing the exposure of various mortgage stakeholders to different forms of climate risk can be accomplished using a table like the following.


Figure


Establish Risk Measurement Approach

Quantifying the financial impact of physical and transition risk is critical to evaluating a portfolio’s potential exposure. From a mortgage loan perspective, loan-level and portfolio-level analyses provide both standalone and marginal views of risk.

Translating hazard risk into a view of financial loss on a mortgage instrument can be accomplished within traditional mortgage model estimations using 1) a combination of property-specific damage estimates from natural hazard and climate risk models, and 2) formulated macroeconomic scenarios guided by academic research and regulatory impacts. And because chronic effects can affect how acute risks manifest, a more nuanced view of how acute risks and chronic risks relate to one another is necessary to answer questions about financial risk.

Mortgage investors can better understand natural hazard risk measures by taking a page from how property insurers account for it. For example, the worst-case “tail loss” potential of a given portfolio is often put in context of the type of events that are at the tail of risk for the industry as a whole – in other words, a 1-in-100-year loss to the portfolio versus a loss to portfolio for a 1-in-100-year industry event. Extending this view to mortgages entails considering the type of events that could occur over the average life of a loan.

To address chronic and transition risk, selecting appropriate macroeconomic scenarios also provides a financial view of the possible impact on a mortgage portfolio. These scenarios may be grounded in published climate projections, asset-specific data collection, or different scenario narratives outlining how these risks could manifest locally.

Defining a Risk Appetite Framework

Inventorying the complete range of potential climate-related risks provides structure and organization around which risks have the largest or most severe impact and creates a framework for ranking them by appropriate criteria. A risk appetite and limit framework defines the type and quantity of natural catastrophe and climate change risk that an enterprise is willing to hold in relation to equity, assets, and other financial exposure measures at a selected probability of occurrence.  The operational usefulness of these frameworks are enhanced when defining the appetite and limits in reference to the risk measures the company selects in addition to straight notional values.

The loss exposure for a particular risk will drive operations differently across business lines based on risk preferences. From the viewpoint of mortgage activities, these operations include origination, servicing, structuring, and pricing. For instance, it may be undesirable to have more than $100 million of asset valuation at risk across the enterprise and apportion that limit to business units based upon the return of the asset in relation to the risk generated from business activity. In this way, the organization has a quantitative way for balancing business goals with risk management goals.

The framework can also target appropriate remediation and hedging strategies in light of the risk priorities. Selecting a remediation strategy requires risk reporting and monitoring across different lines of business and a knowledge of the cost and benefits attributed to physical and transition risks.

Incorporate Findings into Risk Governance

Entities can adapt policies, processes, and responsibilities in the existing ERM framework based on their quantified, prioritized, and articulated risk. This could come in the form of changes to stakeholder reporting from internal management committees, board, and board committees to external financial, investor, public, and regulatory reporting.

Because regulatory requirements and industry best practices are still being formed, it is important to continuously monitor these and ensure that policies align with evolving guidance.

Monitor and Manage Risk Within Risk Appetite and Limits

Implementation of an ERM framework with considerations for natural catastrophe and climate risk may appear different across different lines of businesses and risk management processes. For this reason, it is important that dashboards, reporting frameworks, and exposure control processes be designed to fit in with current reporting within individual lines of businesses.

A practical first step is to establish monitoring specifically to detect adverse selections issues—i.e., ensuring that you are not acquiring a book of business with disproportionately high levels of climate risk or one that adds risk to areas of existing exposure within your portfolio. The object is to manage the portfolio, so risk remains within the agreed appetite and limit framework.  This type of monitoring will become increasingly critical as other market participants start to incorporate climate risk into their own asset screening and pricing decisions. Firms that fail to monitor for climate risk will ultimately be the firms that bear it.

All of this ultimately comes down to identifying natural catastrophe and climate risks, quantifying them through property and loan-specific modeling and scenarios, ranking the risks along different criteria, and tailoring reporting to different operations in the enterprise with an eye for changing regulatory requirements and risk governance policies. An enterprise view is needed given climate risks correlate across multiple asset classes, and where it is determined that differences in risk tolerance are desired, the framework described provides a coherent and quantitative basis for differences.  Successfully negotiating these elements is more easily described than actually carried out, particularly in large financial institutions consisting of businesses with widely divergent risk tolerances.  But we appear to be reaching a point where further deferral is no longer an option. The time to begin planning and implementing these frameworks is now.

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Note on academic research and works referenced

Some empirical research has been conducted examining outcomes following natural hazard events, specifically their impact on mortgage loan performance. Kousky et al. (2020) show evidence that property damage from an extreme event increases short-term mortgage delinquencies and forbearance rates. This effect is mitigated by the presence of flood insurance, which enables borrowers to use insurance proceeds to pay off loans or sell damaged homes once they’ve received compensation and move away from the impacted area. A rebound effect, observed in home prices, occurs in loan performance as well. Delinquencies, while elevated just after the disaster, tend to quickly revert to pre-disaster levels (Fannie Mae, 2017). Extending beyond single-event analysis, delinquencies in hurricane-prone areas have been shown to be higher than delinquency rates in other areas, controlling for other risk factors (Rossi, 2020). The projected rise in hurricane intensity and incidence can therefore lead to higher default risk, which in turn leads to higher losses to investors in mortgage credit risk.

Studies on chronic risks like sea level rise reveal the risk to have a moderate effect on housing prices, stratified by climate “denier” and climate “believer” borrowers (Baldauf et al., 2020). All else equal, areas with owners who perceive a climate threat to their properties may demand a discount on prices. Similarly, Bernstein et al. (2018) show housing price discounts of up to 7% for counties more worried about sea level rise than unworried counties. Risk perception for climate change is subject to a number of biases (Kousky et al., 2020). As such, distortion created by these biases can contribute to inaccurate home pricing. Evidence suggests that regulatory floodplain properties are overvalued, but pricing is inconsistent. Borrowers who are well-informed and sophisticated may fully reflect flood risk information in their pricing (Hino and Burke, 2021). These effects can vary by consumer disclosure requirements as well, which lead to discussion about information gaps on climate risk.

Yet, there is notable research on the salience of events, where house prices following the occurrence of an extreme event have been shown to have persistent effects on home prices. Ortega and Taspinar (2018) show a permanent price decline in the 5 years following Hurricane Sandy for properties in flood zones, regardless of the damage experienced. While properties damaged by the hurricane showed a rebound in home prices right after the event, all properties affected by the storm converged to the same home price penalty. Eichholtz et al. (2019) primarily study commercial real estate properties in New York, with corroborating studies in Boston and Chicago, and find negative price effects from flood-risk exposure post-Hurricane Sandy due to sophisticated investors adjusting their valuations downward. Increased attention to climate change from the occurrence of extreme events may cause long-term price effects as communities begin evaluating the possible risks they face after weathering a catastrophic event.


For further reading, see:

Markus Baldauf, Lorenzo Garlappi, Constantine Yannelis, Does Climate Change Affect Real Estate Prices? Only If You Believe In It, The Review of Financial Studies, Volume 33, Issue 3, March 2020, Pages 1256–1295, https://doi.org/10.1093/rfs/hhz073

Eichholtz, Piet M. A.; Steiner, Eva; Yönder, Erkan “Where, When, and How Do Sophisticated Investors Respond to Flood Risk?,” June 2019. PDF

Bernstein, Asaf and Gustafson, Matthew and Lewis, Ryan, Disaster on the Horizon: The Price Effect of Sea Level Rise (May 4, 2018). Journal of Financial Economics (JFE), Forthcoming, Available at SSRN: https://ssrn.com/abstract=3073842 

Bin, O., & Landry, C. E. (2013). Changes in implicit flood risk premiums: Empirical evidence from the housing market. Journal of Environmental Economics and Management, 65(3), 361–376. HYPERLINK “https://protect-us.mimecast.com/s/SL58C5ylW5F05NOpXUzgQhi?domain=doi.org

Hinoa and Burke, The effect of information about climate risk on
property values (March 18, 2021). PDF

Ortega, Francesc and Taspinar, Suleyman, Rising Sea Levels and Sinking Property Values: The Effects of Hurricane Sandy on New York’s Housing Market (March 29, 2018). Available at SSRN: https://ssrn.com/abstract=3074762 or http://dx.doi.org/10.2139/ssrn.3074762

Clifford Rossi. “Assessing the impact of hurricane frequency and intensity on mortgage default risk,” June 2020. PDF

Markus Baldauf, Lorenzo Garlappi, Constantine Yannelis, Does Climate Change Affect Real Estate Prices? Only If You Believe In It, The Review of Financial Studies, Volume 33, Issue 3, March 2020, Pages 1256–1295, https://doi.org/10.1093/rfs/hhz073

Carolyn Kousky, Howard Kunreuther, Michael LaCour-Little & Susan Wachter (2020) Flood Risk and the U.S. Housing Market, Journal of Housing Research, 29:sup1, S3-S24, DOI: 10.1080/10527001.2020.1836915

Carolyn Kousky, Mark Palim & Ying Pan (2020) Flood Damage and Mortgage Credit Risk: A Case Study of Hurricane Harvey, Journal of Housing Research, 29:sup1, S86-S120, DOI: 10.1080/10527001.2020.1840131

Verisk 2021: How Current Market Conditions Could Impact U.S. Hurricane Season 2021

RiskSpan 2018: Houston Strong: Communities Recover from Hurricanes. Do Mortgages?


Asset Managers Improving Yields With Resi Whole Loans

An unmistakable transformation is underway among asset managers and insurance companies with respect to whole loan investments. Whereas residential mortgage loan investing has historically been the exclusive province of commercial banks, a growing number of other institutional investors – notably life insurance companies and third-party asset managers – have shifted their attention toward this often-overlooked asset class.

Life companies and other asset managers with primarily long-term, risk-sensitive objectives are no strangers to residential mortgages. Their exposure, however, has traditionally been in the form of mortgage-backed securities, generally taking refuge in the highest-rated bonds. Investors accustomed to the AAA and AA tranches may understandably be leery of whole-loan credit exposure. Infrastructure investments necessary for managing a loan portfolio and the related credit-focused surveillance can also seem burdensome. But a new generation of tech is alleviating more of the burden than ever before and making this less familiar and sometimes misunderstood asset class increasingly accessible to a growing cadre of investors.

Maximizing Yield

Following a period of low interest rates, life companies and other investment managers are increasingly embracing residential whole-loan mortgages as they seek assets with higher returns relative to traditional fixed-income investments (see chart below). As highlighted in the chart below, residential mortgage portfolios, on a loss-adjusted basis, consistently outperform other investments, such as corporate bonds, and look increasingly attractive relative to private-label residential mortgage-backed securities as well.

Nearly one-third of the $12 trillion in U.S. residential mortgage debt outstanding is currently held in the form of loans.

And while most whole loans continue to be held in commercial bank portfolios, a growing number of third-party asset managers have entered the fray as well, often on behalf of their life insurance company clients.

Investing in loans introduces a dimension of credit risk that investors do need to understand and manage through thoughtful surveillance practices. As the chart below (generated using RiskSpan’s Edge Platform) highlights, when evaluating yields on a loss-adjusted basis, resi whole loans routinely generate yield.

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In addition to higher yields, whole loans investments offer investors other key advantages over securities. Notably:

Data Transparency

Although transparency into private label RMBS has improved dramatically since the 2008 crisis, nothing compares to the degree of loan-level detail afforded whole-loan investors. Loan investors typically have access to complete loan files and therefore complete loan-level datasets. This allows for running analytics based on virtually any borrower, property, or loan characteristic and contributes to a better risk management environment overall. The deeper analysis enabled by loan-level and property-specific information also permits investors to delve into ESG matters and better assess climate risk.

Daily Servicer Updates

Advancements in investor reporting are increasingly granting whole loan investors access to daily updates on their portfolio performance. Daily updating provides investors near real-time updates on prepayments and curtailments as well as details regarding problem loans that are seriously delinquent or in foreclosure and loss mitigation strategies. Eliminating the various “middlemen” between primary servicers and investors (many of the additional costs of securitization outlined below—master servicers, trustees, various deal and data “agents,” etc.—have the added negative effect of adding layers between security investors and the underlying loans) is one of the things that makes daily updates possible.

Lower Transaction Costs

Driven largely by a lack of trust in the system and lack of transparency into the underlying loan collateral, private-label securities investments incur a series of yield-eroding transactions costs that whole-loan investors can largely avoid. Consider the following transaction costs in a typical securitization:

  • Loan Data Agent costs: The concept of a loan data agent is unique to securitization. Data agents function essentially as middlemen responsible for validating the performance of other vendors (such as the Trustee). The fee for this service is avoided entirely by whole loan investors, which generally do not require an intermediary to get regularly updated loan-level data from servicers.
  • Securities Administrator/Custodian/Trustee costs: These roles present yet another layer of intermediary costs between the borrower/servicer and securities investors that are not incurred in whole loan investing.
  • Deal Agent costs: Deal agents are third party vendors typically charged with enhancing transparency in a mortgage security and ensuring that all parties’ interests are protected. The deal agent typically performs a surveillance role and charges investors ongoing annual fees plus additional fees for individual loan file reviews. These costs are not borne by whole loan investors.
  • Due diligence costs: While due diligence costs factor into loan and security investments alike, the additional layers of review required for agency ratings tends to drive these costs higher for securities. While individual file reviews are also required for both types of investments, purchasing loans only from trusted originators allows investors to get comfortable with reviewing a smaller sample of new loans. This can push due diligence costs on loan portfolios to much lower levels when compared to securities.
  • Servicing costs: Mortgage servicing costs are largely unavoidable regardless of how the asset is held. Loan investors, however, tend to have more options at their disposal. Servicing fees for securities vary from transaction to transaction with little negotiating power by the security investors. Further, securities investors incur master servicing fees which is generally not a required function for managing whole loan investments.

Emerging technology is streamlining the process of data cleansing, normalization and aggregation, greatly reducing the operational burden of these processes, particularly for whole loan investors, who can cut out many of these intermediary parties entirely.

Overcoming Operational Hurdles

Much of investor reluctance to delve into loans has historically stemmed from the operational challenges (real and perceived) associated with having to manage and make sense of the underlying mountain of loan, borrower, and property data tied to each individual loan. But forward-thinking asset managers are increasingly finding it possible to offload and outsource much of this burden to cloud-native solutions purpose built to store, manage, and provide analytics on loan-level mortgage data, such as RiskSpan’s Edge Platform supporting loan data management and analytics. RiskSpan solutions make it easy to mine available loan portfolios for profitable sub-cohorts, spot risky loans for exclusion, apply a host of credit and prepay scenario analyses, and parse static and performance data in any way imaginable.

At an increasing number of institutions, demonstrating the power of analytical tools and the feasibility of applying them to the operational and risk management challenges at hand will solve many if not most of the hurdles standing in the way of obtaining asset class approval for mortgage loans. The barriers to access are coming down, and the future is brighter than ever for this fascinating, dynamic and profitable asset class.


RiskSpan a Winner of 2022 HousingWire’s Tech100 Mortgage Award

RiskSpan named to HousingWire’s Tech100 for a fourth consecutive year — recognition of the firm’s continuous commitment to advancing mortagage, technology, data and analytics.

Our cloud-native data and predictive modeling analytical platform uncovers insights and mitigates risks for loans and structured products.

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EDGE: Extension Protection in a Rising Rate Environment

With the Fed starting their tightening cycle and reducing balance sheet, mortgage rates have begun rising. Since late summer, 30-year conforming rates have risen more than 100bp, with 75bp of that occurring since the end of December. The recent flight-to-quality rally has temporarily eased that, but the overall trend remains in place for higher mortgage rates.

With this pivot, mortgage investors have switched from focusing on prepayment protection to mitigating extension risk. In this post, we offer analysis on extension risk and turnover speeds for various out-of-the-money Fannie and Freddie cohorts.[1]

In the chart below, we first focus on out-of-the-money prepays on lower loan balance loans. For this analysis, we analyzed speeds on loans that were 24 to 48 months seasoned. We further grouped the loan balance stories into meta-groups, as the traditional groupings of “85k-Max”, etc, showed little difference in out-of-the-money speeds. When compared to loans with balances above 250k, speeds on lower loan balance loans were a scant 1-2 CPR faster than borrowers with larger loan balances, when prevailing rates were 25bp to 100bp higher than the borrower’s note rate.

We next compare borrowers in low FICO pools, high LTV pools, and 100% investor pools. Speeds on low-FICO pools (blue) offer some extension protection due to higher involuntary speeds. At the other end, loans in 100% investor pools were dramatically slower than non-spec pools when out-of-the money.

Finally, we look at the behavior of borrowers in non-spec pools segregated by loan purpose, again controlling for loan age. Borrowers with refi loans pay significantly faster than purchase loans when only slightly out-of-the money. As rates continue to rise, refi speeds converge to purchase loans at 75bp out of the money and pay slower when 75-100bp out of the money, presumably due to a stronger lock-in effect.

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We also separated these non-spec borrowers by originators, grouping the largest banks and non-bank originators together. Out-of-the-money speeds on refi loans were significantly faster for loans originated by non-bank originators (blue and green) versus those originated by banks (red and orange). Speeds on purchase loans were only 1-2 CPR faster for non-banks versus banks and were omitted from this graph for readability.

In the current geopolitical climate, rates may continue to drop over the short term. But given the Fed’s tightening bias, it’s prudent to consider extension risk when looking at MBS pools, in both specified and non-specified pools.

[1] For investors interested in GNMA analysis, please contact RiskSpan


RiskSpan Chosen “Best Company for Diversity and Inclusion” Category Winner in WatersTechnology’s Women In Technology & Data Awards 2022 Rankings

RiskSpan Chosen “Best Company for Diversity and Inclusion” by WatersTechnology Women In Technology & Data Awards 2022


Women in Technology & Data

Learn More about riskspan's Dei initiatives

The award reflects RiskSpan’s major commitment to making DEI a priority and empowering employees from every background and at every level of the company to contribute to our mutual success.

Recent initiatives have included:

  • A broad expansion and formalization of a mentorship program pairing every non-management employee with a senior company leader.
  • Regular anonymous surveys designed to gauge employee perceptions of inclusion and identify opportunities for improvement.
  • Establishment of a Women’s Employee Resource Group featuring forums, dinners, and other social events.
  • Active participation in industry DEI committees and events.
  • Development of training frameworks open to all employees seeking help obtaining certifications and customized training programs.

These and related efforts all aim to create a close-knit organization by maximizing opportunities for communication among staff across the organization and creating more opportunities to get to know one another in smaller groups outside of assigned projects and teams.


EDGE: The Fed’s MBS, Distribution and Prepayments

Since the Great Financial Crisis of 2008, the Federal Reserve Bank of New York has been the largest and most influential participant in the mortgage-backed securities market. In the past 14 years, the Fed’s holdings of conventional and GNMA pools has grown from zero to $2.7 trillion, representing roughly a third of the outstanding market. With inflation spiking, the Fed has announced an end to MBS purchases and will shift into balance-sheet-reduction mode. In this short post, we review the Fed’s holdings, their distribution across coupon and vintage, and their potential paydowns as rates rise.

The New York Fed publishes its pool holdings here. The pools are updated weekly and have been loaded into RiskSpan’s Edge Platform. The chart below summarizes the Fed’s 30yr Fannie/Freddie holdings by vintage and net coupon.

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We further categorize the Fed’s holdings by vintage and borrower note rate (gross WAC) at the loan level. Using loan-level data (rather than weighted-average statistics published on Fed-held Supers or their constituent pools [1]) provides a more accurate view of the Fed’s distribution of note rates and hence prepayment exposure.

Not surprisingly, the recent and largest quantitative easing has left the Fed holding MBS with gross WACs below the current mortgage rate. Roughly 85% of the mortgages held by the Fed are out-of-the-money, and the remaining in-the-money mortgages are several years seasoned. These older pools are beginning to exhibit burnout, with the sizable refinancing wave over the last two years having limited these moderately seasoned loans mainly to borrowers who are less reactive to savings from refinancing.

With most of the Fed’s portfolio at below-market rates and the remaining MBS moderately burned out, market participants expect the Fed’s MBS runoff to continue to slow. At current rates, we estimate that Fed paydowns will continue to decline and stabilize around $25B per month in the second quarter, just shy of 1% of its current MBS holdings.

With these low levels of paydowns, we anticipate the Fed will need to sell MBS if they want to make any sizable reduction in their balance sheet. Whether the Fed feels compelled to do this, or in what manner sales will occur, is an unsettled question. But paydowns alone will not significantly reduce the Fed’s holdings of MBS over the near term.


[1] FNMA publishes loan-level data for pools securitized in 2013 onward. For Fed holdings that were securitized before 2013, we used FNMA pool data.  


EDGE: Measuring the Potential for Another Cash-out Refi Wave

With significant home price gains over the last two years, U.S. homeowners are sitting on vast, mostly untapped wealth. Nationally, home prices are up an aggregate of 28% over the last two years, with some regions performing even better. But unlike other periods of strong home price gains, cash-out refinancings lagged overall refinancings during the pandemic rate-rally. In this short article, we look at cash-out refinancings over time, and their potential impact on prepayments, especially on discount cohorts.

A historical perspective

In the early 2000s, mortgage rates fell nearly 200bp, triggering a massive refinancing wave as well as a rally in home prices that lasted well into 2005.

Edge Housing Gains and Cash out Refis

During this early millennium rally, the market saw significant cash-out refi activity with homeowners borrowing at then-historically low rates to free up cash. The market even saw refinancing activity in mortgages with note rates below the prevailing market rate. In 2002, CPRs on some discount cohorts hit the low to middle teens, which many participants attributed to cash-out refinancing. Resetting a mortgage 50 basis points higher can nevertheless often lead to overall lower debt servicing when borrowers use cash-out refis to consolidate auto loans, credit cards and other higher-rate unsecured borrowings.[1] In the early 2000s, this cash-out refinancing activity led to overall faster speeds and a higher S-curve for out-of-the-money cohorts. How does 2002-03 cash-out refi activity compare to today? In the early 2000s, issuance of cash-out mortgages, as a percentage of the total market, varied between 1% and 2.5% of the outstanding mortgage universe each month.

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Since the onset of the pandemic, that figure has not experienced the same kind of spike, hovering around just 0.9%.[2]

In 2002-03, most of these cash-out borrowers refinanced into lower rates, but a sufficient number took out mortgages at same or higher rates to drive prepayments on discount MBS into the low teens CPR (see black s-curve below). By comparison, out-of-the money speeds today (the blue s-curve) are approximately 4 CPR slower.

The nearly 30% rally in home prices during the pandemic has further strengthened a solid housing market. Today’s borrowers have substantial equity in their homes, leaving many homeowners with untapped borrowing power, shown in the market-implied LTVs below. From an origination standpoint, mortgage lenders have sufficient capacity to support any uptick in cash-out refinancing as rate-term refinancing volumes decline.

Any growth in cash-out refi issuance is likely to come on loans with note rates close to the prevailing mortgage rate. If a homeowner needs to generate cash for a large purchase, it can make economic sense to refinance an existing loan into a new loan with rates as much as 25bp or 50bp higher, rather than incur even higher (and shorter-term) interest rates on credit cards or personal loans. Therefore, any uptick in cash-out refinancing will likely have a larger effect on prepayment speeds for MBS that are either at-the-money or slightly out-of-the-money. This uptick may mitigate some of the extension risk in near-discount mortgages, especially in non-spec cohorts where refinancing frictions are lower. While the past two years have seen substantial changes, positive and negative, in overall refinancings, cash-out refis have largely not followed suit. But a significant home price rally, coupled with strong economic activity and excess originator capacity, could change that trend in the upcoming year.



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