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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.


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

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).




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]



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.



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.



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 series 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.

 

 

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.



Climate Terms the Housing Market Needs to Understand

The impacts of climate change on housing and holders of mortgage risk are very real and growing. As the frequency and severity of perils increases, so does the associated cost – estimated to have grown from $100B in 2000 to $450B 2020 (see chart below). Many of these costs are not covered by property insurance, leaving homeowners and potential mortgage investors holding the bag. Even after adjusting for inflation and appreciation, the loss to both investors and consumers is staggering. 

Properly understanding this data might require adding some new terms to your personal lexicon. As the housing market begins to get its arms around the impact of climate change to housing, here are a few terms you will want to incorporate into your vocabulary.

  1. Natural Hazard

In partnership with climate modeling experts, RiskSpan has identified 21 different natural hazards that impact housing in the U.S. These include familiar hazards such as floods and earthquakes, along with lesser-known perils, such as drought, extreme temperatures, and other hydrological perils including mudslides and coastal erosion. The housing industry is beginning to work through how best to identify and quantify exposure and incorporate the impact of perils into risk management practices more broadly. Legacy thinking and risk management would classify these risks as covered by property insurance with little to no downstream risk to investors. However, as the frequency and severity increase, it is becoming more evident that risks are not completely covered by property & casualty insurance.

We will address some of these “hidden risks” of climate to housing in a forthcoming post.

  1. Wildland Urban Interface

The U.S. Fire Administration defines Wildland Urban Interface as “the zone of transition between unoccupied land and human development. It is the line, area, or zone where structures and other human development meet or intermingle with undeveloped wildland or vegetative fuels.” An estimated 46 million residences in 70,000 communities in the United States are at risk for WUI fires. Wildfires in California garner most of the press attention. But fire risk to WUIs is not just a west coast problem — Florida, North Carolina and Pennsylvania are among the top five states at risk. Communities adjacent to and surrounded by wildland are at varying degrees of risk from wildfires and it is important to assess these risks properly. Many of these exposed homes do not have sufficient insurance coverage to cover for losses due to wildfire.

  1. National Flood Insurance Program (NFIP) and Special Flood Hazard Area (SFHA)

The National Flood Insurance Program provides flood insurance to property owners and is managed by the Federal Emergency Management Agency (FEMA). Anyone living in a participating NFIP community may purchase flood insurance. But those in specifically designated high-risk SFPAs must obtain flood insurance to obtain a government-backed mortgage. SFHAs as currently defined, however, are widely believed to be outdated and not fully inclusive of areas that face significant flood risk. Changes are coming to the NFIP (see our recent blog post on the topic) but these may not be sufficient to cover future flood losses.

  1. Transition Risk

Transition risk refers to risks resulting from changing policies, practices or technologies that arise from a societal move to reduce its carbon footprint. While the physical risks from climate change have been discussed for many years, transition risks are a relatively new category. In the housing space, policy changes could increase the direct cost of homeownership (e.g., taxes, insurance, code compliance, etc.), increase energy and other utility costs, or cause localized employment shocks (i.e., the energy industry in Houston). Policy changes by the GSEs related to property insurance requirements could have big impacts on affected neighborhoods.

  1. Physical Risk

In housing, physical risks include the risk of loss to physical property or loss of land or land use. The risk of property loss can be the result of a discrete catastrophic event (hurricane) or of sustained negative climate trends in a given area, such as rising temperatures that could make certain areas uninhabitable or undesirable for human housing. Both pose risks to investors and homeowners with the latter posing systemic risk to home values across entire communities.

  1. Livability Risk

We define livability risk as the risk of declining home prices due to the desirability of a neighborhood. Although no standard definition of “livability” exists, it is generally understood to be the extent to which a community provides safe and affordable access to quality education, healthcare, and transportation options. In addition to these measures, homeowners also take temperature and weather into account when choosing where to live. Finding a direct correlation between livability and home prices is challenging; however, an increased frequency of extreme weather events clearly poses a risk to long-term livability and home prices.

Data and toolsets designed explicitly to measure and monitor climate related risk and its impact on the housing market are developing rapidly. RiskSpan is at the forefront of developing these tools and is working to help mortgage credit investors better understand their exposure and assess the value at risk within their businesses.

Contact us to learn more.



Why Mortgage Climate Risk is Not Just for Coastal Investors

When it comes to climate concerns for the housing market, sea level rise and its impacts on coastal communities often get top billing. But this article in yesterday’s New York Times highlights one example of far-reaching impacts in places you might not suspect.

Chicago, built on a swamp and virtually surrounded by Lake Michigan, can tie its whole existence as a city to its control and management of water. But as the Times article explains, management of that water is becoming increasingly difficult as various dynamics related to climate change are creating increasingly large and unpredictable fluctuations in the level of the lake (higher highs and lower lows). These dynamics are threatening the city with more frequency and severe flooding.

The Times article connects water management issues to housing issues in two ways: the increasing frequency of basement flooding caused by sewer overflow and the battering buildings are taking from increased storm surge off the lake. Residents face increasing costs to mitigate their exposure and fear the potentially negative impact on home prices. As one resident puts it, “If you report [basement flooding] to the city, and word gets out, people fear it’s going to devalue their home.”

These concerns — increasing peril exposure and decreasing valuations — echo fears expressed in a growing number of seaside communities and offer further evidence that mortgage investors cannot bank on escaping climate risk merely by avoiding the coasts. Portfolios everywhere are going to need to begin incorporating climate risk into their analytics.



Hurricane Season a Double-Whammy for Mortgage Prepayments

As hurricane (and wildfire) season ramps up, don’t sleep on the increase in prepayment speeds after a natural disaster event. The increase in delinquencies might get top billing, but prepays also increase after events—especially for homes that were fully insured against the risk they experienced. For a mortgage servicer with concentrated geographic exposure to the event area, this can be a double-whammy impacting their balance sheet—delinquencies increase servicing advances, prepays rolling loans off the book. Hurricane Katrina loan performance is a classic example of this dynamic.



Non-Agency Delinquencies Fall Again – Still Room for Improvement

Serious delinquencies among non-Agency residential mortgages continue marching downward during the first half of 2021 but remain elevated relative to their pre-pandemic levels.

Our analysis of more than two million loans held in private-label mortgage-backed securities found that the percentage of loans at least 60 days past due fell again in May across vintages and FICO bands. While performance differences across FICO bands were largely as expected, comparing pre-crisis vintages with mortgages originated after 2009 revealed some interesting distinctions.

The chart below plots serious delinquency rates (60+ DPD) by FICO band for post-2009 vintages. Not surprisingly, these rates begin trending upward in May and June of 2020 (two months after the economic effects of the pandemic began to be felt) with the most significant spikes coming in July and August – approaching 20 percent at the low end of the credit box and less than 5 percent among prime borrowers.

Since last August’s peak, serious delinquency rates have fallen most precipitously (nearly 8 percentage points) in the 620 – 680 FICO bucket, compared with a 5-percentage point decline in the 680 – 740 bucket and a 4 percentage point drop in the sub-620 bucket. Delinquency rates have come down the least among prime (FICO > 740) mortgages (just over 2 percentage points) but, having never cracked 5 percent, these loans also had the shortest distance to go.

Serious delinquency rates remain above January 2020 levels across all four credit buckets – approximately 7 percentage points higher in the two sub-680 FICO buckets, compared with the 680 – 740 bucket (5 percentage points higher than in January 2020) and over-740 bucket (2 percentage points higher).

So-called “legacy” vintages (consisting of mortgage originated before the 2008-2009 crisis) reflect a somewhat different performance profile, though they follow a similar pattern.

The following chart plots serious delinquency rates by FICO band for these older vintages. Probably because these rates were starting from a relatively elevated point in January 2020, their pandemic-related spike were somewhat less pronounced, particularly in the low-FICO buckets. These vintages also appear to have felt the spike about a month earlier than did the newer issue loans.

Serious delinquency rates among these “legacy” loans are considerably closer to their pre-pandemic levels than are their new-issue counterparts. This is especially true in the sub-prime buckets. Serious delinquencies in the sub-620 FICO bucket actually were 3 percentage points lower last month than they were in January 2020 (and nearly 5 percentage points lower than their peak in July 2020). These differences are less pronounced in the higher-FICO buckets but are still there.

Comparing the two graphs reveals that the pandemic had the effect of causing new-issue low-FICO loans to perform similarly to legacy low-FICO loans, while a significant gap remains between the new-issue prime buckets and their high-FICO pre-2009 counterparts. This is not surprising given the tightening that underwriting standards (beyond credit score) underwent after 2009.

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