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Cracking the Code for a Gender-Equal Future:  Strategies for addressing unconscious gender bias

Bias is real and we all have it – both men and women. It’s often hard to recognize, as bias is the result of cultural and societal norms that have existed for decades or more. Thus, the challenge for changing subtle behaviors requires intentional action. Earlier this week, RiskSpan co-hosted with the Structured Finance Association a lively panel discussion focused on taking intentional action towards achieving a gender-equal future.

The heart of the discussion focused on unconscious gender bias in the workplace and strategies to effect change. The group discussed the importance of intentionality to drive the mission forward and the required participation of men. Part of the dialogue addressed how women and men interact in the workplace (having lunch is completely appropriate), and the need to sometimes get comfortable being uncomfortable. The panel included a sole male representative, but the room was filled with roughly 25% men. One important take-away was that the goal of gender equality is simply unattainable without the commitment and sustained effort from both women and men, and a continued dialogue on the subject is essential. 

Further, to effect social change, economic incentives have to be sustainable and aligned with the social mission. With women graduating from college at higher rates than ever before, we are beginning to unleash half of our country’s brainpower into many fields, including business, finance and tech, that continue to be dominated by men. Women in these fields continue to face obstacles stemming from gender bias. These biases need to be addressed head-on with intentional strategies for change.

Common Gender Biases and Strategies for Change

Myth No. 1: There are plenty of women in the C-suite, so what’s the problem?

Although there has been progress with better representation of women in the C-suite, the pay gap continues to exist and is significant. Part of this relates to the fact that there are more women in C-level jobs that are considered “less risky.” These less-risky jobs include legal, compliance, or accounting, as opposed to jobs such as Chief Investment Officer, Head of Capital Markets or CEO.  

Earning potential increases not only with experience and qualification, but with a bold ambition that tends to be encouraged more often among men than women (think competitive sports). This may contribute to a more heightened fear of failure among women — the business world is no exception to that.   

Strategy for change:
Assure all members of your team, regardless of gender, that mistakes are inevitable and recoverable. What’s important is how you react to a mistake or challenge. Further, encourage women to challenge themselves – invite women to lead the next challenging project and client pitch. A favorite quote of mine serves as a reminder of this:

“I always go back to my grandmother’s advice the first time I fell and hurt myself. She said, ‘Honey, at least falling on your face is a forward movement.’ You have to be willing to be brave enough to risk falling on your face, to risk failing… Everything we do is about taking risks.”

Pat Mitchell

Myth No. 2: The workplace is (solely) a meritocracy

The fact is advancement happens not only via hard work and merit, but via personal relationships and connections. This is not to say that qualifications don’t matter – of course they do. But it is human nature to consider the people you know best, have met face-to-face, or have worked with. Others may be qualified, but if you don’t know them, you’re simply not going to consider them. This is particularly difficult for women who often expect to be recognized and rewarded for their great work. However, they are missing a big part of the equation – networking and relationship building and sponsorship.  

Strategy for change:
Find a sponsor that will advocate for you when you’re not in the room. For managers, reach out to the next generation of women and make the needed introduction to help expand her network (and invite her to coffee – it’s totally appropriate). Include women in regular networking events and create a networking practice that is naturally welcoming for women to participate in.

Myth No. 3: It is inappropriate for a man and woman to have an unchaperoned business lunch

A 2017 New York Times article reported that most people (women as well as men) thought it inappropriate for a person to have a drink with someone of the opposite sex other than their spouse.

Let’s face it — It can be awkward for a man to ask a female colleague (particularly a subordinate) to join him for coffee, lunch, or a drink. However, this significant social barrier disadvantages women and hinders their opportunity for advancement. It is virtually impossible to level the playing field if women and men can’t develop a professional relationship that includes socializing over coffee or a meal.

Business communities incorporate professional socializing to foster relationships and partnerships. This extends to advancement opportunities. You typically select the people to promote from a short list of people you know well and feel comfortable with. The people you are having lunch with are the ones who are most likely to make the list. 

Strategy for change:
Start with coffee – invite her for a 1:1 conversation. The only way to break with this social norm may be to get comfortable being uncomfortable. This means it’s ok for a woman to ask a man to coffee or lunch or visa-versa (particularly between a senior and a subordinate). Treat everyone with respect and professionalism, and never withhold an invitation simply on the basis of gender.

Myth No. 4: Women with children don’t want to travel

Managers sometimes make this assumption and withhold assignments from women that require travel – be it for a conference or a critical client engagement. Although the intention might be noble, the impact is detrimental. Doing so deprives women of the same opportunities as men to engage with important clients and others in the industry. It undermines her ability to make decisions and may adversely impact how she is viewed and valued by her peers. 

Strategy for change:
Always offer travel opportunities equally among team members regardless of gender, marital status, or motherhood/fatherhood and allow the choice to be theirs. Resist reinforcing stereotypes that consequentially keep mothers at home. 

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.

Blockchain and Structured Finance

Blockchain has the potential to revolutionize the financial services industry, in particular structured finance, and is rapidly becoming more of a when than an if. A main reason for the failure of the private-label residential mortgage-backed securities market to return to pre-crisis levels is due to a failure in trust, but this stalled market is ripe for innovations.

Why Blockchain?

Today’s model for mortgage data exchange is based on an outdated notion of what is technologically feasible. The servicer’s database is still thought of as a stand alone system-of-record and the investor’s database as a downstream applications that needs to rely on, reconcile, and make sense of loan-level ‘tapes generated by the system-of-record.

This model of a single system-of-record housed with the servicer could be transformed into a blockchain, with every detail of every mortgage and all subsequent transactions captured and distributed to investors. With this new model, investor reporting as it exists today would cease to exist.

This new method would instantly update investors with borrow activity, such as refinancing, prepayment, and rejected payments. On a blockchain, these transactions are a sequence that everyone can decipher.

Using Blockchain to Garner Trust in the PLS Market

Information asymmetry is consistently a problem for many in the PLS space, with many transactions having 10 or more parties contributing to verifying and validating data, documents, or cash flows in some way. Blockchain can help to overcome this asymmetry and among other challenges, share loan-level data with investors, re-envision the due diligence process, and modernize document custody, by allowing private blockchains to share information and document access with relevant parties.

The current steps for the due-diligence process are representative of the lack of trust in the PLS market. Increased transparency, using blockchain technology, could help to restore some trust and make the process run with less resistance.  Automation can streamline the due-diligence process, taking out the 100% file review that is currently required, and adding this to a secure blockchain only available to select parties. If reconciliations are deemed necessary for an individual loan file, the results could be automated and added to this blockchain.

Blockchain and Consensus

Talk about implementing blockchain into the realm of structured finance cannot ignore the issue of consensus, something at the heart of all distributed-ledger systems. Private (or ‘permissioned’) blockchains are designed for a specific business purpose, so achieving consensus requires data posted to the blockchain to be verified in an automated way by all parties relevant to the transaction.

Much of blockchain’s appeal is bound up in the promise of an environment in which deal participants can gain reasonable assurance that their counterparts are disclosing information that is both accurate and comprehensive. Visibility is an important component of this, but ultimately, achieving consensus that what is being done is what ought to be done will be necessary in order to fully eliminate redundant functions in business processes and overcome information asymmetry in the private markets. Sophisticated, well-conceived algorithms that enable private parties to arrive at this consensus in real time will be key.


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Permissioned Blockchains–A Quest for Consensus


Conspicuously absent from all the chatter around blockchain’s potential place in structured finance has been much discussion around the thorny matter of consensus. Consensus is at the heart of all distributed ledger networks and is what enables them to function without a trusted central authority. Consensus algorithms are designed to prevent fraud and error. With large, public blockchains, achieving consensus—ensuring that all new information has been examined before is universally accepted—is relatively straightforward. It is achieved either by performing large amounts of work or simply by members who collectively hold a majority stake in the blockchain.

However, when it comes to private (or “permissioned”) blockchains with a relatively small number of interested parties—the kind of blockchains that are currently poised for adoption in the structured finance space—the question of how to obtain consensus takes on an added layer of complexity. Restricting membership greatly reduces the need for elaborate algorithms to prevent fraud on permissioned blockchains. Instead, these applications must ensure that complex workflows and transactions are implemented correctly. They must provide a framework for having members agree to the very structure of the transaction itself. Consensus algorithms complement this by ensuring that the steps performed in verifying transaction data is agreed upon and verified.

With widespread adoption of blockchain in structured finance appearing more and more to be a question of when rather than if, SmartLink Labs, a RiskSpan fintech affiliate, recently embarked on a proof of concept designed to identify and measure the impact of the technology across the structured finance life cycle. The project took a holistic approach, looking at everything from deal issuance to bondholder payments. We sought to understand the benefits, how various roles would change, and the extent to which certain functions might be eliminated altogether. At the heart of virtually every lesson we learned along the way was a common, overriding principle: consensus is hard.

Why is Consensus Hard?

Much of blockchain’s appeal to those of us in the structured finance arena has to do with its potential to lend visibility and transparency to complicated payment rules that govern deals along with dynamic borrower- and collateral-level details that evolve over the lives of the underlying loans. Distributed ledgers facilitate the real-time sharing of these details across all relevant parties—including loan originators, asset servicers, and bond administrators—from deal issuance through the final payment on the transaction. The ledger transactions are synchronized to ensure that ledgers only update when the appropriate participants approve transactions. This is the essence of consensus, and it seems like it ought to be straightforward.

Imagine our surprise when one of the most significant challenges our test implementation encountered was designing the consensus algorithm. Unlike with public blockchains, consensus in a private, or “permissioned,” blockchain is designed for a specific business purpose where the counterparties are known. However, to achieve consensus, the data posted to the blockchain must be verified in an automated manner by the relevant parties to the transaction. One of the challenges with the data and rules that govern most structured transactions is that it is (at best) only partially digital. We approached our project with the premise that most business terms can be translated into a series of logical statements in the form of computer code. Translating unstructured data into structured data in a fully transparent way is problematic, however, and limitations to transparency represent a significant barrier to achieving consensus. In order for a distributed ledger to work in this context, all transaction parties need to reach consensus around how the cash will flow and numerous other business rules throughout the process.


A Potential Solution for Structured Finance

To this end, our initial prototype seeks to test our consensus algorithm on the deal waterfall model. If the industry can move to a process where consensus of the deal waterfall model is achieved at deal issuance, the model posted to the blockchain can then serve as an agreed-upon source of truth and perpetuate through the life of the security—from loan administration to master servicer aggregation and bondholder payments. This business function alone could save the industry countless hours and effectively eliminate all of today’s costs associated with having to model and remodel each transaction multiple times.

Those of us who have been in the structured finance business for 25 years or more know how little the fundamental business processes have evolved. They remain manual, governed largely by paper documents, and prone to human error.

The mortgage industry has proven to be particularly problematic. Little to no transparency in the process has fostered a culture of information asymmetry and general mistrust which has predictably given rise to the need to have multiple unrelated parties double-checking data, performing due diligence reviews on virtually all loan files, validating and re-validating cash flow models, and requiring costly layers of legal payment verification. Ten or more parties might contribute in one way or another to verifying and validating data, documents, or cash flow models for a single deal. Upfront consensus via blockchain holds the potential to dramatically reduce or even eliminate almost all of this redundancy.

Transparency and Real-Time Investor Reporting

The issuance process, of course, is only the beginning. The need for consensus does not end when the cash flow model is agreed to and the deal is finalized. Once we complete a verified deal, the focus of our proof of concept will shift to the monthly process of investor reporting and corresponding payments to the bond holders.

The immutability of transactions posted to the ledger is particularly valuable because of the unmistakable audit trail it creates. Rather than compelling master servicers to rely on a monthly servicing snapshot “tape” to try and figure out what happened to a severely delinquent loan with four instances of non-sufficient funds, a partial payment in suspense, and an interest rate change somewhere in the middle. Putting all these transactions on a blockchain creates a relatively straightforward sequence of transactions that everyone can decipher.

Posting borrower payments to a blockchain in real time will also require consensus among transaction parties. Once this is achieved, the antiquated notion of monthly investor reporting will become obsolete. The potential ramifications of this extend to timing of payments to bond holders. No longer needing to wait until the next month to find out what borrowers did the month before means that payments to investors might be accelerated and, in the private-label security markets, perhaps even more often than monthly. With real-time consensus comes the possibility of far more flexibility for issuers and investors in designing the timing of cash flows should they elect to pursue it.

This envisioned future state is not without its detractors. Some ask why servicers would opt for more transparency when they already encounter more scrutiny and criticism than they would like. In many cases, however, it is the lack of transparency, more than a servicer’s actions themselves, that invite the unwanted scrutiny. Servicers that move beyond reporting monthly snapshots and post comprehensive loan activity to a blockchain stand to reap significant competitive advantages. Because of the real-time consensus and sharing of dynamic loan reporting data (and perhaps of accelerated bond payments, as suggested above) investors will quickly gravitate toward deals that are administered by blockchain-enabled servicers. Sooner or later, servicers who fail to adapt will find themselves on the outside looking in.

Less Redundancy; More Trust

Much of blockchain’s appeal is bound up in the promise of an environment in which deal participants can gain reasonable assurance that their counterparts are disclosing information that is both accurate and comprehensive. Visibility is an important component of this, but ultimately, achieving consensus that what is being done is what ought to be done will be necessary in order to fully eliminate redundant functions in business processes and overcome information asymmetry in the private markets.  Sophisticated, well-conceived algorithms that enable private parties to arrive at this consensus in real time will be key.

One of the enduring lessons of our structured finance proof of concept is that consensus is necessary throughout a transaction’s life. The market (i.e., issuers, investors, servicers, and bond administrators) will ultimately determine what gets posted to a blockchain and what remains off-chain, and more than one business model will likely evolve. As data becomes more structured and more reliable, however, competitive advantages will increasingly accrue to those who adopt consensus algorithms capable of infusing trust into the process. The failure of the private-label MBS market to regain its pre-crisis footing is, in large measure, a failure of trust. Nothing repairs trust like consensus.

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