ESG Archives

Quantifying the Impact of Climate Risk on Housing Finance

When people speak of the risk climate poses to housing, they typically do so in qualitative and relative terms. A Florida home is at greater risk of hurricane damage than an Iowa home. Wildfires generally threaten homes in northern California more than they threaten homes in New Hampshire. And because of climate change, the risk these and other perils pose to any individual geographical area are largely viewed as higher than they were 25 years ago.

People feel comfortable speaking in these general terms. But qualitative estimates are of little practical use to mortgage investors seeking to fine-tune their pricing, prepayment, and default models. These analytical frameworks require not just reliable data but the means to translate them into actionable risk metrics.

Physical risks and transition risks

Broadly speaking, climate risk manifests itself as a combination of physical risks and transition risks. Physical risks include “acute” disaster events, such as hurricanes, tornadoes, wildfires, and floods. Chronic risks, such as sea level rise, extreme temperatures, and drought, are experienced over a longer period. Transition risks relate to costs resulting from regulations promulgated to combat climate change and from the need to invest in new technologies designed either to combat climate change directly or mitigate its effects.

Some of the ways in which these risks impact mortgage assets are self-evident. Acute events that damage or destroy homes have an obvious effect on the performance of the underlying mortgages. Other mechanisms are more latent but no less real. Increasing costs of homeownership, caused by required investment in climate-change-mitigating technologies, can be a source of financial stress for some borrowers and affect mortgage performance. Likewise, as flood and other hazard insurance premiums adjust to better reflect the reality of certain geographies’ increasing exposure to natural disaster risk, demand for real estate in these areas could decrease, increasing the pressure on existing homeowners who may not have much cushion in their LTVs to begin with.

Mortgage portfolio risk management

At the individual loan level, these risks translate to higher delinquency risks, probability of default, loss given default, spreads, and advance expenses. At the portfolio level, the impact is felt in asset valuation, concentration risk (what percentage of homes in the portfolio are located in high-risk areas), VaR, and catastrophic tail risk.

VaR can be computed using natural hazard risk models designed to forecast the probability of individual perils for a given geography and using that probability to compute the worst property loss (total physical loss and loss net of insurance proceeds) that can be expected during the portfolio’s expected life at the 99 percent (or 95 percent) confidence level. The following figure illustrates how this works for a portfolio covering multiple geographies with varying types and likelihoods of natural hazard risk.

These analyses can look at the exposure of an entire portfolio to all perils combined:

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Or they can look at the exposure of a single geographic area to one peril in particular:

Accounting for climate risk when bidding on whole loans

The risks quantified above pertain to properties that secure mortgages and therefore only indirectly to the mortgage assets themselves. Investors seeking to build whole-loan portfolios that are resilient to climate risk should consider climate risk in the context of other risk factors. Such a “property-level climate risk” approach takes into account factors such as:

Whether the property is insured against the peril in question
The estimate expected risk (and tail risk) of property damage by the peril in question
Loan-to-value ratio

The most prudent course of action includes a screening mechanism that includes pricing and concentration limits tied to LTV ratios. Investors may choose to invest in areas of high climate risk but only in loans with low LTV ratios. Bids should be adjusted to account for climate risk, but the amount of the adjustment can be a function of the LTV. Concentration limits should be adjusted accordingly:

Conclusion

When assessing the impact of climate risk on a mortgage portfolio, investors need to consider and seek to quantify not just how natural hazard events will affect home values but also how they will affect borrower behavior, specifically in terms of prepayments, delinquencies, and defaults.

We are already beginning to see climate factors working their way into the secondary mortgage markets via pricing adjustments and concentration screening. It is only a matter of time before these considerations move further up into the origination process and begin to manifest themselves in pricing and underwriting policy (as flood insurance requirements already have today).

Investors looking for a place to start can begin by incorporating a climate risk score into their existing credit box/pricing grid, as illustrated above. This will help provide at least a modicum of comfort to investors that they are being compensated for these hidden risks and (at least as important) will ensure that portfolios do not become overly concentrated in at-risk areas.

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Rising Rates; Rising Temperatures: What Higher Interest Rates Portend for Mortgage Climate Risk — An interview with Janet Jozwik

Janet Jozwik leads RiskSpan’s sustainability analytics (climate risk and ESG) team. She is also an expert in mortgage credit risk and a recognized industry thought leader on incorporating climate risk into credit modeling. We sat down with Janet to get her views on whether the current macroeconomic environment should impact how mortgage investors prioritize their climate risk mitigation strategies.

You contend that higher interest rates are exposing mortgage lenders and investors to increased climate risk. Why is that?

JJ: My concern is primarily around the impact of higher rates on credit risk overall, of which climate risk is merely a subset – a largely overlooked and underappreciated subset, to be sure, and one with potentially devastating consequences, but ultimately one of many. The simple reason is that, because interest rates are up, loans are going to remain on your books longer. The MBA’s recent announcement of refinance applications (and mortgage originations overall) hitting their lowest levels since 2000 is stark evidence of this.

And because these loans are going to be lasting longer, borrowers will have more opportunities to get into trouble (be it a loss of income or a natural disaster) and everybody should be taking credit risk more seriously. One of the biggest challenges posed by a high-rate environment is borrowers don’t have a lot of the “outs” available to them as they do when they encounter stress during more favorable macroeconomic environments. They can no longer simply refi into a lower rate. Modification options become more complicated. They might have no option other than to sell the home – and even that isn’t going to be as easy as it was, say, a year ago. So, we’ve entered this phase where credit risk analytics, both at origination and life of loan, really need to be taken seriously. And credit risk includes climate risk.

So longer durations mean more exposure to credit risk – more time for borrowers to run into trouble and experience credit events. What does climate have to do with it? Doesn’t homeowners’ insurance mitigate most of this risk anyway?

JJ: Each additional month or year that a mortgage loan remains outstanding is another month or year that the underlying property is exposed to some form of natural disaster risk (hurricane, flood, wildfire, earthquake, etc.). When you look at a portfolio in aggregate – one whose weighted average life has suddenly ballooned from four years to, say eight years – it is going to experience more events, more things happening to it. Credit risk is the risk of a borrower failing to make contractual payments. And having a home get blown down or flooded by a hurricane tends to have a dampening effect on timely payment of principal and interest.

As for insurance, yes, insurance mitigates portfolio exposure to catastrophic loss to some degree. But remember that not everyone has flood insurance, and many loans don’t require it. Hurricane-specific policies often come with very high deductibles and don’t always cover all the damage. Many properties lack wildfire insurance or the coverage may not be adequate. Insurance is important and valuable but should not be viewed as a panacea or a substitute for good credit-risk management or taking climate into account when making credit decisions.

But the disaster is going to hit when the disaster is going to hit, isn’t it? How should I be thinking about this if I am a lender who recaptures a considerable portion of my refis? Haven’t I just effectively replaced three shorter-lived assets with a single longer-lived one? Either way, my portfolio’s going to take a hit, right?

JJ: That is true as far as it goes. And if in the steady state that you are envisioning, one where you’re just churning through your portfolio, prepaying existing loans with refis that look exactly like the loans they’re replacing, then, yes, the risk will be similar, irrespective of expected duration.

But do not forget that each time a loan turns over, a lender is afforded an opportunity to reassess pricing (or even reassess the whole credit box). Every refi is an opportunity to take climate and other credit risks into account and price them in. But in a high-rate environment, you’re essentially stuck with your credit decisions for the long haul.

Do home prices play any role in this?

JJ: Near-zero interest rates fueled a run-up in home prices like nothing we’ve ever seen before. This arguably made disciplined credit-risk management less important because, worst case, all the new equity in a property served as a buffer against loss.

But at some level, we all had to know that these home prices were not universally sustainable. And now that interest rates are back up, existing home prices are suddenly starting to look a little iffy. Suddenly, with cash-out refis off the table and virtually no one in the money for rate and term refis, weighted average lives have nowhere to go but up. This is great, of course, if your only exposure is prepayment risk. But credit risk is a different story.

And so, extremely low interest rates over an extended period played a significant role in unsustainably high home values. But the pandemic had a lot to do with it, as well. It’s well documented that the mass influx of home buyers into cities like Boise from larger, traditionally more expensive markets drove prices in those smaller cities to astronomical levels. Some of these markets (like Boise) have not only reached an equilibrium point but are starting to see property values decline. Lenders with excessive exposure to these traditionally smaller markets that experienced the sharpest home price increases during the pandemic will need to take a hard look at their credit models’ HPI assumptions (in addition to those properties’ climate risk exposure).

What actions should lenders and investors be considering today?

JJ: If you are looking for a silver lining in the fact that origination volumes have fallen off a cliff, it has afforded the market an opportunity to catch its breath and reassess where it stands risk-wise. Resources that had been fully deployed in an effort simply to keep up with the volume can now be reallocated to taking a hard look at where the portfolio stands in terms of credit risk generally and climate risk in particular.

This includes assessing where the risks and concentrations are in mortgage portfolios and, first, making sure not to further exacerbate existing concentration risks by continuing to acquire new assets in overly exposed geographies. Investors may be wise to go so far even to think about selling certain assets if they feel like they have too much risk in problematic areas.

Above all, this is a time when lenders need to be taking a hard look at the fundamentals underpinning their underwriting standards. We are coming up on 15 years since the start of the “Great Recession” – the last time mortgage underwriting was really “tight.” For the past decade, the industry has had nothing but calm waters – rising home values and historically low interest rates. It’s been like tech stocks in the ‘90s. Lenders couldn’t help but make money.

I am concerned that this has allowed complacency to take hold. We’re in a new world now. One with shaky home prices and more realistic interest rates. The temptation will be to loosen underwriting standards in order to wring whatever volume might be available out of the economy. But in reality, they need to be doing precisely the opposite. Underwriting standards are going to have tighten a bit in order effectively manage the increased credit (and climate) risks inherent to longer-duration lending.

It’s okay for lenders and investors to be taking these new risks on. They just need to be doing it with their eyes wide open and they need to be pricing for it.

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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.

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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.

Webinar Recording: How Much Will That MSR Portfolio Really Cost You?

Recorded: June 8th | 1:00 p.m. ET

Accurately valuing a mortgage servicing rights portfolio requires accurately projecting MSR cash flows. And accurately projecting MSR cash flows requires a reliable forecast of servicing costs. Trouble is, servicing costs vary extensively from loan to loan. While the marginal cost of servicing a loan that always pays on time is next to nothing, seriously delinquent loans can easily cost hundreds, if not thousands, of dollars per year.

The best way to account for this is to forecast and assign servicing costs at the loan level – a once infeasible concept that cloud-native technology has now brought within reach. Our panelists present a novel, granular approach to servicing cost analytics and how to get to a truly loan-by-loan MSR valuation (without resorting to rep lines).

Featured Speakers

Venkat Mullur

SVP, Capital Markets, Ocwen

Paul Gross

Senior Quantitative Analyst, New Residential Investment Corp.

Dan Fleishman

Managing Director, RiskSpan

Joe Makepeace

Director, RiskSpan

Striking a Proper Balance: ESG for Structured Finance

The securitization market continues to wrestle with the myriad of approaches and lack of standards in identifying and reporting ESG factors in transactions and asset classes. But much needed guidance is on the way as industry leaders work toward a consensus on the best way to report ESG for structured finance.

RiskSpan gathered with other key industry players tackling these challenges at this month’s third annual Structured Finance Association ESG symposium in New York City. The event identified a number of significant strides taken toward shaping an industry-standard ESG framework and guidelines.

Robust and engaging discussions across a variety of topics illustrated the critical need for a thoughtful approach to framework development. We observed a broad consensus around the notion that market acceptance would require any solution to be data supported and fully transparent.

Much of the discussion revolved around three recurring themes: Finding a workable balance between the institutional desire for portfolio-specific measures based on raw data and the market need for a standardized scoring mechanism that everybody understands, maintaining data privacy, and assessing tradeoffs between the societal benefits of ESG investing and the added risk it can pose to a portfolio.

Striking the Right Balance: Institution-Specific Measures vs. Industry-Standard Asset Scoring

When it comes to disclosure and reporting, one point on a spectrum does not fit all. Investors and asset managers vary in their ultimate reporting needs and approach to assessing ESG and impact investing. On the one hand, having raw data to apply their own analysis or specific standards can be more worthwhile to individual institutions. On the other, having well defined standards or third-party ESG scoring systems for assets provides greater certainty and understanding to the market as a whole.

Both approaches have value.

Everyone wants access to data and control over how they view the assets in their portfolio. But the need for guidance on what ESG impacts are material and relevant to structured finance remains prominent. Scores, labels, methodologies, and standards can give investors assurance a security contributes to meeting their ESG goals. Investors want to know where their money is going and if it is meaningful.

Methodologies also have to be explainable. Though there was agreement that labeled transactions are not always necessary (or achievable), integration of ESG factors in the decision process is. Reporting systems will need to link underlying collateral to external data sources to calculate key metrics required by a framework while giving users the ability to drill down to meet specific and granular analytical needs.

Data Privacy

Detailed analysis of underlying asset data, however, highlights a second key issue: the tradeoff between transparency and privacy, particularly for consumer-related assets. Fiduciary and regulatory responsibility to protect disclosure of non-public personally identifiable information limits investor ability to access loan-level data.

While property addresses provide the greatest insight to climate risk and other environmental factors, concerns persist over methods that allow data providers to triangulate and match data from various sources to identify addresses. This in turn makes it possible to link sensitive credit information to specific borrowers.

The responsibility to summarize and disclose metrics required by the framework falls to issuers. The largest residential issuers already appreciate this burden. These issuers have expressed a desire to solve these issues and are actively looking at what they can do to help the market without sacrificing privacy. Data providers, reporting systems, and users will all need to consider the guardrails needed to adhere to source data terms of use.

Assessing Impact versus Risk

Another theme arising in nearly all discussions centered on assessing ESG investment decisions from the two sometimes competing dimensions of impact and risk and considering whether tradeoffs are needed to meet a wide variety of investment goals. Knowing the impact the investment is making—such as funding affordable housing or the reduction of greenhouse gas emissions—is fundamental to asset selection or understanding the overall ESG position.

But what risks/costs does the investment create for the portfolio? What is the likely influence on performance?

The credit aspect of a deal is distinct from its ESG impact. For example, a CMBS may be socially positive but rent regulation can create thin margins. Ideally, all would like to maximize positive impact but not at the cost of performance, a strategy that may be contributing now to an erosion in greeniums. Disclosures and reporting capabilities should be able to support investment analyses on these dimensions.

A disclosure framework vetted and aligned by industry stakeholders, combined with robust reporting and analytics and access to as much underlying data as possible, will give investors and asset managers certainty as well as flexibility to meet their ESG goals.

Why Climate Risk Matters for Mortgage Loan & MSR Investors

The time has come for mortgage investors to start paying attention to climate risk.

Until recently, mortgage loan and MSR investors felt that they were largely insulated from climate risk. Notwithstanding the inherent risk natural hazard events pose to housing and the anticipated increased frequency of these events due to climate change, it seemed safe to assume that property insurers and other parties in higher loss position were bearing those risks.

In reality, these risks are often underinsured. And even in cases where property insurance is adequate, the fallout has the potential to hit investor cash flows in a variety of ways. Acute climate events like hurricanes create short-term delinquency and prepayment spikes in affected areas. Chronic risks such as sea level rise and increased wildfire risk can depress housing values in areas most susceptible to these events. Potential impacts to property insurance costs, utility costs (water and electricity in areas prone to excessive heat and drought, for example) and property taxes used to fund climate-mitigating infrastructure projects all contribute to uncertainty in loan and MSR modeling.

Moreover, dismissing climate risk “because we are in fourth loss position” should be antithetical to any investor claiming to espouse ESG principles. After all, consider who is almost always in the first loan position – the borrower. Any mortgage investment strategy purporting to be ESG friendly must necessarily take borrower welfare into account. Dismissing climate risk because borrowers will bear most of the impact is hardly a socially responsible mindset. This is particularly true when a disproportionate number of borrowers prone to natural hazard risk are disadvantaged to begin with.

Hazard and flood insurers typically occupy the loss positions between borrowers and investors. Few tears are shed when insurers absorb losses. But society at large ultimately pays the price when losses invariably lead to higher premiums for everybody.

Evaluating Climate Exposure

For these and other reasons, natural hazards pose a systemic risk to the entire housing system. For mortgage loan and MSR investors, it raises a host of questions. Among them:

What percentage of the loans in my portfolio are susceptible to flood risk but uninsured because flood maps are out of date?
How geographically concentrated is my portfolio? What percentage of my portfolio is at risk of being adversely impacted by just one or two extreme events?
What would the true valuation of my servicing portfolio be if climate risk were factored into the modeling?
What will the regulatory landscape look like in coming years? To what extent will I be required to disclose the extent to which my portfolio is exposed to climate risk? Will I even know how to compute it, and if so, what will it mean for my balance sheet?

Incorporating Climate Data into Investment Decision Making

Forward-thinking mortgage servicers are at the forefront of efforts to get their arms around the necessary data and analytics. Once servicers have acquired a portfolio, they assess and triage their loans to identify which properties are at greatest risk. Servicers also contemplate how to work with borrowers to mitigate their risk.

For investors seeking to purchase MSR portfolios, climate assessment is making its way into the due diligence process. This helps would-be investors ensure that they are not falling victim to adverse selection. As investors increasingly do this, climate assessment will eventually make its way further upstream, into appraisal and underwriting processes.

Reliably modeling climate risk first requires getting a handle on how frequently natural hazard events are likely to occur and how severe they are likely to be.

In a recent virtual industrial roundtable co-hosted by RiskSpan and Housing Finance Strategies, representatives of Freddie Mac, Mr. Cooper, and Verisk Analytics (a leading data and analytics firm that models a wide range of natural and man-made perils) gathered to discuss why understanding climate risk should be top of mind for mortgage investors and introduced a framework for approaching it.

WATCH THE ENTIRE ROUNDTABLE

Building the Framework

The framework begins by identifying the specific hazards relevant to individual properties, building simulated catalogs of thousands of years’ worth of simulated events, computing likely events simulating damage based on property construction and calculating likely losses. These forecasted property losses are then factored into mortgage performance scenarios and used to model default risk, prepayment speeds and home price impacts.

Connecting to Mortgage Performance Analysis

Responsibility to Borrowers

One member of the panel, Kurt Johnson, CRO of mega-servicer Mr. Cooper, spoke specifically of the operational complexities presented by climate risk. He cited as one example the need to speak daily with borrowers as catastrophic events are increasingly impacting borrowers in ways for which they were not adequately prepared. He also referred to the increasing number of borrowers incurring flood damage in areas that do not require flood insurance and spoke to how critical it is for servicers to know how many of their borrowers are in a similar position.

Johnson likened the concept of credit risk layering to climate risk exposure. The risk of one event happening on the heels of another event can cause the second event to be more devastating than it would have been had it occurred in a vacuum. As an example, he mentioned how the spike in delinquencies at the beginning of the covid pandemic was twice as large among borrowers who had just recovered from Hurricane Harvey 15 months earlier than it was among borrowers who had not been affected by the storm. He spoke of the responsibility he feels as a servicer to educate borrowers about what they can do to protect their properties in adverse scenarios.

Industry Virtual Roundtable: The Intersection of Climate Risk Management with Mortgage Loan & MSR Investing

April 14th | 2:00-3:15 p.m. ET

With both the public and private sectors increasingly making climate risk management a priority, attention in our industry is turning to what it means for mortgage loan and MSR investors.

Industry experts join RiskSpan and Housing Finance Strategies for a roundtable event where they engage in a discussion on the latest approaches and technology for mitigating climate risk management in mortgage portfolios.

The loan-level cash flows discussed in this webinar were generated using RiskSpan’s Edge Platform.

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Agenda (all times Eastern)

2:00-2:05 pm | WELCOME AND PROGRAM OVERVIEW

Faith Schwartz, Founder & CEO, Housing Finance Strategies

2:05-2:20 pm | CLIMATE RISK’S IMPACT ON MORTGAGE FINANCE AND TOOLS TO MANAGE RISK

Janet Jozwik, Senior Managing Director and Head of Climate Risk, RiskSpan
Dan Raizman, Global Resilience Manager, Verisk Analytics

2:20-3:00 pm | PANEL DISCUSSION: CLIMATE RISK IN HOUSING FINANCE—RISK MANAGEMENT AND REGULATORY PERSPECTIVES

Faith Schwartz, Moderator
Mark Hanson, SVP, Freddie Mac
Kurt Johnson, CRO, Mr. Cooper
Sean Becketti, former Freddie Mac
Bernadette Kogler, CEO, RiskSpan

3:00-3:15 pm | QUESTIONS AND DISCUSSION OF POLLING RESULTS

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 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.

GET STARTED

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

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?

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

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.
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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.

Webinar: Geocoding Mortgage Data for ESG and Climate Risk Analysis

Recorded: February 16th | 1:00 p.m. ET

Geocoding remains a particularly vexing challenge for the mortgage industry. Lenders, servicers, and loan/MSR investors know the addresses of the properties securing their mortgage assets. But most data pertaining to climate and other ESG considerations is available only by matching to a census tract or latitude/longitude.

And if you have ever tried mapping addresses, you know this exercise can be a lot harder than it looks. Fortunately, a growing body of geocoding tools and techniques is emerging to make the process more manageable than ever, even with less than perfect address data.

Our panel presents a how-to guide on geocoding logic and its specific application to the mortgage space. You will learn a useful waterfall approach for linking census-tract-level, geo-specific data for climate risk and ESG to the property addresses in your portfolio.

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