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Articles Tagged with: General

Webinar: An Investor’s Guide to America’s Housing Supply Crisis

Presenters

Amy Crews Cutts

President, AC Cutts and Associates and Chief Economist, NACM

Michael Neal

Equity Scholar and 
Principal Research Associate, Urban Institute

Janet Jozwik

Senior Managing Director and Head of Climate Analytics, RiskSpan

Divas Sanwal

Managing Director and Head of Modeling, RiskSpan

Wednesday, March 29th | 1:00 p.m. ET

Join us for an informative webinar on the nation’s current “out-of-whack” housing supply and what it means for mortgage investors, homeowners, prospective homebuyers, and renters alike!

Housing economists Amy Crews Cutts and Michael Neal will join RiskSpan credit and prepayment modelers Janet Jozwik and Divas Sanwal to explore the factors that contribute to the current housing supply imbalance, including the cost of building, the impact of permits and zoning, and the emergence of the “missing middle.” They will discuss how high interest rates and rental prices are incentivizing owners who relocate to hold old on to their old properties and become landlords. They will also examine the impact of ADUs, zoning issues, and the availability of renovation financing.

Mortgage loan and security investors will learn about what housing supply means for prepay speeds. The panelists will consider the role of financing in addressing housing supply issues, including the market for low-balance loans and unconventional options like contracts for deed and lease-to-own arrangements.

Join us for an engaging discussion on the evolving housing needs of the population, including the desire to age in place, the challenges posed by multigenerational living arrangements, and the viability of several proposed solutions, including the potential for converting unused commercial properties into housing.

Don’t miss out on this informative and timely webinar!


RiskSpan Incorporates Flexible Loan Segmentation into Edge Platform

ARLINGTON, Va., March 3, 2023 — RiskSpan, a leading technology company and the most comprehensive source for data management and analytics for residential mortgage and structured products, has announced the incorporation of Flexible Loan Segmentation functionality into its award-winning Edge Platform.

The new functionality makes Edge the only analytical platform offering users the option of alternating between the speed and convenience of rep-line-level analysis and the unmatched precision of loan-level analytics, depending on the purpose of their analysis.

For years, the cloud-native Edge Platform has stood alone in its ability to offer the computational scale necessary to perform loan-level analyses and fully consider each loan’s individual contribution to a mortgage or MSR portfolio’s cash flows. This level of granularity is of paramount importance when pricing new portfolios, taking property-level considerations into account, and managing tail risks from a credit/servicing cost perspective.

Not every analytical use case justifies the computational cost of a full loan-level analysis, however. For situations where speed requirements dictate the use of rep lines (such as for daily or intra-day hedging needs), the Edge Platform’s new Flexible Loan Segmentation affords users the option to perform valuation and risk analysis at the rep line level.

Analysts, traders and investors take advantage of Edge’s flexible calculation specification to run various rate and HPI scenarios, key rate durations, and other calculation-intensive metrics in an efficient and timely manner. Segment-level results run at both loan and rep line level can be easily compared to assess the impacts of each approach. Individual rep lines are easily rolled up to quickly view results on portfolio subcomponents and on the portfolio as a whole.

Comprehensive details of this and other new capabilities are available by requesting a no-obligation demo at riskspan.com.

This new functionality is the latest in a series of enhancements that further the Edge Platform’s objective of providing frictionless insight to Agency MBS traders and investors, knocking down barriers to efficient, clear and data-driven valuation and risk assessment.

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About RiskSpan, Inc. 

RiskSpan offers cloud-native SaaS analytics for on-demand market risk, credit risk, pricing and trading. With our data science experts and technologists, we are the leader in data as a service and end-to-end solutions for loan-level data management and analytics. Our mission is to be the most trusted and comprehensive source of data and analytics for loans and structured finance investments. Learn more at www.riskspan.com.


RiskSpan’s Snowflake Tutorial Series: Ep. 1

Learn how to create a new Snowflake database and upload large loan-level datasets

The first episode of RiskSpan’s Snowflake Tutorial Series has dropped!

This six-minute tutorial succinctly demonstrates how to:

  1. Set up a new Snowflake #database
  2. Use SnowSQL to load large datasets (28 million #mortgage loans in this example)
  3. Use internal staging (without a #cloud provider)

This is this first in what is expected to be a 10-part tutorial series demonstrating how RiskSpan’s Snowflake integration makes mortgage and structured finance analytics easier than ever before.

Future topics will include:

  • Executing complex queries using python functions in Snowflake’s SQL
  • External Tables (accessing data without a database)
  • OLAP vs OLTP and hybrid tables in Snowflake
  • Time Travel functionality, clone and data replication
  • Normalizing data and creating a single materialized view
  • Dynamic tables data concepts in Snowflake
  • Data share
  • Data masking
  • Snowpark: Data analysis (pandas) functionality in Snowflake

Agency Social Indices & Prepay Speeds

Do borrowers in “socially rich” pools respond to refinance incentives differently than other borrowers? 

The decision by Fannie and Freddie to release social index disclosure data in November 2022 makes it possible for investors to direct their capital in support of first-time homebuyers, historically underserved borrowers, and people who purchase homes in traditionally underserved areas. Because socially conscious investors likely also have interest in understanding how these social pools are likely to perform, we were curious to examine and learn whether mortgage pools with higher social ratings behaved differently than pools with lower social ratings (and if a difference existed, how significant it was). To the extent that pools rich in social factors perform better (i.e., prepay more slowly) than pools generally, we expect investors to put an even higher premium on them. This in turn should result in lower rates for the borrowers whose loans contribute to pools with higher social scores. 

The data is new and we are still learning things, but we are beginning to discern some differences in prepay speeds.

Definitions 

First, a quick refresher on Fannie’s and Freddie’s social index terminology: 

  • Social Criteria Share (SCS): The percentage of loans in a given pool that meet at least one of the “social” criteria. The criteria are low-income, minority, and first-time homebuyers; homes in low-income areas, minority tracts, high-needs rural areas; homes in designated disaster areas and manufactured housing. As of December 2022, 42.12 percent of loans in the average pool satisfy at least one of these criteria. 
  • Social Density Score (SDS): A measure of how many criteria the average loan in a given pool satisfies. For simplicity, the index consolidates the criteria into three categories – those pertaining to income, those pertaining to the borrower, and those pertaining to the property. A pool’s SDS can be zero, 1, 2, or 3 depending on the number of categories within which the loan satisfies at least one criterion. The average SDS as of December 2022 is 0.62 (out of 3). 

Do social index scores impact prepay speeds? 

While it remains too early to answer this question with a great deal of certainty, historical performance data appears to show that pools with below-average social index scores prepay faster than more “social” bonds. 

We first looked at a high-level, simplistic relationship between prepayments and Social Density Score. In Figure 1, below, pools with below-average Social Density Scores (blue line) prepay faster than both pools with above-average SDS (black line) and pools with the very highest SDS (green line) when they are incentivized by interest rates to do so. (Note that very little difference exists among the curves when borrowers are out of the money to refi.)  


Fig. 1: Speeds by Prepay Incentive and Social Density Score 

See how easy RiskSpan’s Edge Platform makes it for you to do these analyses yourself.

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We note a similar trend when it comes to Social Criteria Share (see Fig. 2, below).  


Fig. 2: Speeds by Prepay Incentive and Social Criteria Share 

Social Pool Performance Relative to Spec Pools 

Investors pay up for mortgage pools with specified characteristics. We thought it worthwhile to compare how certain types of spec pools perform relative to socially rich pools with no other specified characteristics. 

Figure 3, below, compares the performance of non-spec pools with above-average Social Criteria Share (orange line) vs. spec pools for low-FICO (blue line), high-LTV (black line) and max $250k (green line) loans. 

Note that, notwithstanding a lack of any other specific characteristics that investors pay up for, the high-SCS pools exhibit a somewhat better convexity profile than the max-700 FICO and min-95 LTV pools and slightly worse convexity (in most refi incentive buckets) than max-250k pools. 


Fig. 3: Speeds by Prepay Incentive and Social Criteria Share: Socially Rich (Non-Spec) Pools vs. Selected Spec Pools

We observe a similar effect when we compare non-spec pools with an above-average Social Density Score to the same spec pools (Fig. 4, below).   


Fig. 4: Speeds by Prepay Incentive and Social Density Score: Socially Rich (Non-Spec) Pools vs. Selected Spec Pools 

See how social index scores affect speeds relative to other spec pools.

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Edge Platform Adds Fannie and Freddie Social Index Data

ARLINGTON, Va., January 18, 2023 — RiskSpan, a leading technology company and the most comprehensive source for data management and analytics for residential mortgage and structured products, has announced the incorporation of Fannie Mae’s and Freddie Mac’s Single-Family Social Index data into its award-winning Edge Platform.

Fannie and Freddie rolled out their social index disclosures in November 2022. Consisting of two measures, the Social Criteria Score and the Social Density Score, the social index discloses the share of loans in a given pool that are made to low-income, minority, and first-time homebuyers, as well as mortgages on homes in low-income areas, minority tracts, high-needs rural areas, and designated disaster areas. Manufactured housing loans also contribute to the score.

Rather than classifying each individual bond as “social” or “not social,” the new Agency data available on the Edge Platform assigns every pool two fully transparent scores – one indicating the percentage of loans in a pool that satisfy any of the defined social criteria, the other reflecting how many criteria a pool’s average loan satisfies.

Taken together, these enable Agency traders and investors to view and understand each pool along a full continuum of the social index, as opposed to simply assigning a binary social designation. Because borrowers behave differently at various places along this continuum, traders and investors fine-tune their analytics in ways never before possible to isolate pools with potentially slower prepayment speeds in a way that transcends what has traditionally been available using so-called “spec. pool” stories alone.

Comprehensive details of this and other new capabilities are available by requesting a no-obligation live demo at riskspan.com.

This new functionality is the latest in a series of enhancements that further the Edge Platform’s objective of providing frictionless insight to Agency MBS traders and investors, knocking down barriers to efficient, clear and data-driven valuation and risk assessment.

### 

About RiskSpan, Inc. 

RiskSpan offers cloud-native SaaS analytics for on-demand market risk, credit risk, pricing and trading. With our data science experts and technologists, we are the leader in data as a service and end-to-end solutions for loan-level data management and analytics.

Our mission is to be the most trusted and comprehensive source of data and analytics for loans and structured finance investments.

Rethink loan and structured finance data. Rethink your analytics. Learn more at www.riskspan.com.

Get a Demo

About RiskSpan, Inc. 

RiskSpan offers cloud-native SaaS analytics for on-demand market risk, credit risk, pricing and trading. With our data science experts and technologists, we are the leader in data as a service and end-to-end solutions for loan-level data management and analytics. 

Our mission is to be the most trusted and comprehensive source of data and analytics for loans and structured finance investments. 

Rethink loan and structured finance data. Rethink your analytics. Learn more at www.riskspan.com. 

Media contact: Timothy Willis 


Temporary Buydowns are Back. What Does This Mean for Speeds?

Mortgage buydowns are having a deja-vu moment. Some folks may recall mortgages with teaser rates in the pre-crisis period. Temporary buydowns are similar in concept. Recent declines notwithstanding, mortgage rates are still higher than they have been in years. Housing remains pricey. Would-be home buyers are looking for any help they can get. While on the other hand, with an almost non-existent refi market, mortgage originators are trying to find innovative ways to keep the production machine going. Conditions are ripe for lender and/or builder concessions that will help close the deal.

Enter the humble “temporary” mortgage interest rate buydown. A HousingWire article last month addressed the growing trend. It’s hard to turn on the TV without being bombarded with ads for Rocket Mortgage’s “Inflation Buster” program. Rocket Mortgage doesn’t use the term temporary buydown in its TV spots, but that is what it is.

Buydowns, in general, refer to when a borrower pays “points” upfront to reduce the mortgage rate to a level where they can afford the monthly payment. The mortgage rate has been “bought down” from its original rate for the entire life of the mortgage by paying a lumpsum upfront. Temporary Buydowns, on the other hand, come in various shapes and sizes, but the most common ones are a “2 – 1” (a 2-percent interest rate reduction in the first year and a 1-percent reduction in year two) and a “1 – 0” (a 1-percent interest rate reduction in the first year only). In these situations, the seller, or the builder, or the lender or a combination thereof put-up money to cover the difference in interest rate payments between the original mortgage rate and the reduced mortgage rate. In the 2-1 example above, the mortgage rate is reduced by 2% for the first year and then steps up by 1% in the second year and then steps up by another 1% in the 3rd year to reach the actual mortgage rate at origination. So, the interest portion of the monthly mortgage payments are “subsidized” for the first two years and then revert to the full monthly payment. Given the inflated rental market, these programs can make purchasing more advantageous than renting (for home seekers trying to decide between the two options). They can also make purchasing a home more affordable (temporarily, at least) for would-be buyers who can’t afford the monthly payment at the prevailing mortgage rate. It essentially buys them time to refinance into a lower rate should interest rates fall over the subsidized time frame or they may be expecting increased income (raises, business revenue) in the future which will allow them to afford the unsubsidized monthly payment.

Temporary buydowns present an interesting situation for prepayment and default modelers. Most borrowers with good credit behave similarly to refinance incentives, barring loan size and refi cost issues. While permanent buydowns tend to exhibit slower speeds when they come in the money by a small amount since the borrower needs to make a cost/benefit decision about recouping the upfront money they put down and the refi costs associated with the new loan. Their breakeven point is going to be lower by 25bps or 50bps from their existing mortgage rate. So, their response to mortgage rates dropping will be slower than borrowers with similar mortgage rates who didn’t pay points upfront. Borrowers with temporary buydowns will be very sensitive to any mortgage rate drops and will refinance at the first opportunity to lock in a lower rate before the “subsidy” expires. Hence, such mortgages are expected to prepay at higher speeds then other counterparts with similar rates. In essence, they behave like ARMs when they approach their reset dates.

When rates stay static or increase, temporary buydowns will behave like their counterparts except when they get close to the reset dates and will see faster speeds. Two factors would contribute to this phenomenon. The most obvious reason is that temporary buydown borrowers will want to refinance into the lowest rate available at the time of reset (perhaps an ARM).  The other possibility is that some of these borrowers may not be able refi because of DTI issues and may default. Such borrowers may also be deemed “weaker credits” because of the subsidy that they received. This increase in defaults would elevate their speeds (increased CBRs) relative to their counterparts.

So, for the reasons mentioned above, temporary buydown mortgages are expected to be the faster one among the same mortgage rate group. In the table below we separate borrowers with the same mortgage rate into 3 groups: 1) those that got a normal mortgage at the prevailing rate and paid no points, 2) those that paid points upfront to get a permanent lower rate and 3) those who got temporary lower rates subsidized by the seller/builder/lender. Obviously, the buydowns occurred in higher rate environments but we are considering 3 borrower groups with the same mortgage rate regardless of how they got that rate. We are assuming that all 3 groups of borrowers currently have a 6% mortgage. We present the expected prepay behavior of all 3 groups in different mortgage rate environments:

*Turnover++ means faster due to defaults or at reset
 Rate Rate Shift 6% (no pts)

Buydown to 6%(borrower-paid)

Buydown to 6% (lender-paid)  
7.00% +100 Turnover Turnover Turnover++*  
6.00% Flat Turnover Turnover Faster (at reset)  
5.75% -25 Refi Turnover Refi  
5.00% -100 Refi (Faster) Refi (Fast) Refi (Fastest)  

Overall, temporary buydowns are likely to exhibit the most rate sensitivity. As their mortgage rates reset higher, they will behave like ARMs and refi into any other lower rate option (5/1 ARM) or possibly default. In the money, they will be the quickest to refi.

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HECM Loan Data, Smart Assumptions, and Cross-Sector Trade Impact Headline New Edge Platform Functionality

ARLINGTON, Va., December 8, 2022RiskSpan, a leading technology company and the most comprehensive source for data management and analytics for residential mortgage and structured products, has announced a flurry of new functionality on its award-winning Edge Platform.

GNMA HECM Datasets and Involuntary Prepayment Breakdown: The GNMA HECM dataset is now available to subscribers in Edge’s Historical Performance module, allowing market participants to find performance differentials within FHA reverse mortgage data. As with conventional datasets available on Edge, users slice and dice by any loan attribute to create S-curves, aging curves, time series and other decision-useful analytics.

Edge users also can now parse GNMA buyout metrics by reason, based on whether individual loans were in delinquency, loss mitigation, or foreclosure when they were removed from the security.

Smart Assumptions: Rather than relying on static assumptions to back-fill missing credit scores, DTIs, LTVs and other data on loan acquisition tapes, the Edge Platform has begun employing a smart, dynamic approach to creating more educated estimates of missing assumptions based on other loan characteristics. Users have the option of accepting these assumptions or substituting their own.

Cross-Sector Trade Impact: As a provider of loan and securities analytics, RiskSpan is making it easier to forecast the combined performance of loan and securities portfolios together in a single view. This allows traders and analysts tools to evaluate the risk and return impact of not only different loan selections or bond selections but also cross-sector reallocation.

These new enhancements all further the Edge Platform’s purpose of providing frictionless insight, knocking down barriers to efficient, clear and data-driven valuation and risk assessment.

Comprehensive details of this and other new capabilities are available by requesting a no-obligation live demo at riskspan.com.

This new functionality is the latest in a series of enhancements that is making the Edge Platform increasingly indispensable for Agency MBS traders and investors.

Get a Demo

About RiskSpan, Inc. 

RiskSpan offers cloud-native SaaS analytics for on-demand market risk, credit risk, pricing and trading. With our data science experts and technologists, we are the leader in data as a service and end-to-end solutions for loan-level data management and analytics. 

Our mission is to be the most trusted and comprehensive source of data and analytics for loans and structured finance investments. 

Rethink loan and structured finance data. Rethink your analytics. Learn more at www.riskspan.com. 

Media contact: Timothy Willis 


RiskSpan Wins Risk as a Service Category for Third Consecutive Year, Rises 6 Places in RiskTech100® 2023 Ranking

ARLINGTON, Va., December 6, 2022RiskSpan’s Edge Platform, the only single solution to include data management, models, and analytics on fully scalable, cloud-native architecture, wins “Risk as a Service” category for a third consecutive year in Chartis Research’s vaunted RiskTech100® ranking of the world’s 100 top risk technology companies.

RiskSpan was also called out as a most significant mover, climbing 6 places in the overall ranking and improving its position for the fourth year in a row.

“RiskSpan’s strong innovation in data management helped drive its six-place rise in the rankings this year,’ said Sid Dash, Research Director at Chartis. ‘The company has won the RaaS award for three consecutive years, reflecting its tech-centric and pragmatic approach in a key area of the risk management space.” 

Licensed by some of the largest asset managers, broker/dealers, hedge funds, mortgage REITs and insurance companies in the U.S., the Edge Platform is a fully managed risk solution across all asset classes with specialization in residential mortgage and structured products.  

 This year’s award reflects the Edge Platform’s unique ability to help users find alpha, execute transactions with ease, and effectively manage portfolio risks,” noted Bernadette Kogler, RiskSpan’s co-founder and CEO. It is satisfying to be recognized for our continued efforts to help clients transform their business with modern workflows and operations to optimize productivity, cost, and resilience.” 

CONTACT US

About RiskSpan, Inc.  

RiskSpan offers cloud-native SaaS analytics for on-demand market risk, credit risk, pricing and trading. With our data science experts and technologists, we are the leader in data as a service and end-to-end solutions for loan-level data management and analytics. 

Our mission is to be the most trusted and comprehensive source of data and analytics for loans and structured finance investments. 

Rethink loan and structured finance data. Rethink your analytics. Learn more at www.riskspan.com. 

 About Chartis Research:  

Chartis Research is the leading provider of research and analysis on the global market for risk technology. It is part of Infopro Digital, which owns market-leading brands such as Risk and WatersTechnology. Chartis’ goal is to support enterprises as they drive business performance through improved risk management, corporate governance and compliance, and to help clients make informed technology and business decisions by providing in-depth analysis and actionable advice on virtually all aspects of risk technology.  

 Media contact:  Timothy Willis 


Incorporating Covid-Era Mortgage Data Without Skewing Your Models

What we observed during Covid represents a radical departure from what we observed pre-Covid. To what extent do these observations impact long-term trends observed for mortgage performance? Should these data fundamentally impact the way in which we think about the effects borrower, loan and macroeconomic characteristics have on mortgage performance? Or do we need to simply account for them as a short-term blip?


The process of modeling mortgage defaults and prepayments typically begins with identifying long-term trends and reference values. These aid in creating the baseline forecasts that undergird the model in its most simplistic form. Modelers then begin looking for deviations from this baseline created by specific loan, borrower, and property characteristics, as well as by key macroeconomic variables.

Identifying these relationships enables modelers to begin quantifying the extent to which micro factors like income, credit score, and loan-to-value ratios interact with macro indicators like the unemployment rate to cause prepayments and defaults to depart from their baseline. Data observations aggregated over extended periods give a comprehensive picture possible of these relationships.

In practice, the human behavior underlying these and virtually all economic models tends to change over time. Modelers account for this by making short-term corrections based on observations from the most recent time periods. This approach of tweaking long-term trends based on recent performance works reasonably well under most circumstances. One could reasonably argue, however, that tweaking existing models using performance data collected during the Covid-19 era presents a unique set of challenges.

What was observed during Covid represents a radical departure from what was observed pre-Covid. To what extent do these observations impact long-term trends and reference values. Should these data fundamentally impact the way in which we think about the effects borrower, loan and macroeconomic characteristics have on mortgage performance? Or do we need to simply account for them as a short-term blip?

SPEAK TO AN EXPERT

How Covid-era mortgage data differs

When it comes to modeling mortgage performance, we generally think of three sets of factors: 1) macroeconomic conditions, 2) loan and borrower characteristics, and 3) property characteristics. In determining how to account for Covid-era data in our modeling, we first must attempt to evaluate its impact on these factors.

Three macroeconomic factors have played an especially significant role recently. First, as reflected in the chart below, we experienced a significant home-price decline during the 2008 financial crisis but a steady increase since then.

Second, mortgage rates continued to decline for the most part during the crisis and beyond. There were brief periods when they increased, but they remained low by and large.

The third piece is the unemployment rate. Unemployment spiked to around 10 percent during the financial crisis and then slowly declined.

When home prices declined in the past, we typically saw the government attempt to respond to it by reducing interest rates. This created something of a correlation between home prices and mortgage rates. Looking at this from a purely statistical viewpoint, the only thing the historical data shows is that falling home prices bring about a decline in mortgage rates. (And rising home prices bring about higher interest rates, though to a far lesser degree.) We see something similar with unemployment. Falling unemployment is correlated with rising home prices.

But then Covid arrives and with it some things we had not observed previously. All the “known” correlations among these macroeconomic variables broke down. For example, the unemployment rate spikes to 15 percent within just a couple of months and yet has no negative impact at all on home prices. Home prices, in fact, continue to rise, supported by the very generous unemployment benefits provided during Covid pandemic.

This greatly complicates the modeling. Here we had these variable relationships that appeared steady over a period of decades, and all of our modeling was being done (knowingly or unknowingly) relying on these correlations, and suddenly all these correlations are breaking down.

What does this mean for forecasting prepayments? The following chart shows prepayments over time by vintage. We see extremely high prepayment rates between early 2020 (the start of the pandemic) and early 2022 (when rates started rising). This makes sense.

Look at what happens to our forecasts, however, when rates begin to increase. The following chart reflects the models predicting a much steeper drop-off in prepayments than what was actually observed for a July 2021 issuance Fannie Mae major of coupon 2.0. These mortgage loans with no refinance incentive are prepaying faster than what would be expected based on the historical data.

What is causing this departure?

The most plausible explanation relates to an observed increase in cash-out refinances caused by the recent run-up in home prices and resulting in many homeowners suddenly finding themselves with a lot of home equity to tap into.  Pre-Covid , cash-outs accounted for between a third and a quarter of refinances. Now, with virtually no one in the money for a rate-and-term refinance, cash-outs are accounting for over 80 percent of them.

We learn from this that we need to incorporate the amount of home equity gained by borrowers into our prepayment modeling.

 Modeling Credit Performance

Of course, Covid’s impacts were felt even more acutely in delinquency rates than in prepays. As the following chart shows, a borrower that was 1-month delinquent during Covid had a 75 percent probability of being 2-months delinquent the following month.

This is clearly way outside the norm of what was observed historically and compels us to ask some hard questions when attempting to fit a model to this data.

The long-term average of “two to worse” transitions (the percentage of 60-day delinquencies that become 90-day delinquencies (or worse) the following month) is around 40 percent. But we’re now observing something closer to 50 percent. Do we expect this to continue in the future, or do we expect it to revert back to the longer-term average. We observe a similar issue in other transitions, as illustrated below. The rates appear to be stabilizing at higher levels now relative to where they were pre-Covid. This is especially true of more serious delinquencies.

How do we respond to this? What is the best way to go about combining this pre-Covid and post-Covid data?

Principles for handling Covid-era mortgage data

One approach would be to think about Covid data as outliers that should be ignored. At the other extreme, we could simply accept the observed data and incorporate it without any special considerations. A split-the-difference third approach would have us incorporate the new data with some sort of weighting factor for use in future stress scenarios without completely casting aside the long-term reference values that had stood the test of time prior to the pandemic.

This third approach requires us to apply the following guiding principles:

  1. Assess assumed correlations between driving macro variables: For example, don’t allow the model to assume that increasing unemployment will lead to higher home prices just because it happened once during a pandemic.
  2. Choose short-term calibrations carefully. Do not allow models to be unduly influenced by blindly giving too much weight to what has happened in the past two years.
  3. Determine whether the new data in fact reflects a regime shift. How long will the new regime last?
  4. Avoid creating a model that will break down during future unusual periods.
  1. Prepare for other extremes. Incorporate what was learned into future stress testing
  1. Build models that allow sensitivity analyses and are easy to change/tune. Models need to be sufficiently flexible that they can be tuned in response to macroeconomic events in a matter of weeks, rather than taking months or years to design and build an entirely new model.

Covid-era mortgage data presents modelers with a unique challenge. How to appropriately consider it without overweighting it. These general guidelines are a good place to start. For ideas specific to your portfolio, contact a RiskSpan representative.

SPEAK TO AN EXPERT


RiskSpan Unveils New “Reverse ETL” Mortgage Data Mapping and Extract Functionality

ARLINGTON, Va., October 19, 2022 – Subscribers to RiskSpan’s Mortgage Data Management product can now not only leverage machine learning to streamline the intake of loan data from any format, but also define any target format for data extraction and sharing.

A recent enhancement to RiskSpan’s award-winning Edge Platform enables users to take in unformatted datasets from mortgage servicers, sellers and other counterparties and convert them into their preferred data format on the fly for sharing with accounting, client, and other downstream systems.

Analysts, traders, and portfolio managers have long used Edge to take in and store datasets, enabling them to analyze historical performance of custom cohorts using limitless combinations of mortgage loan characteristics and run predictive analytics on segments defined on the fly. With Edge’s novel “Reverse ETL” data extract functionality, these Platform users can now also easily and fully design an export format for exporting their data, creating the functional equivalent of a full integration node for sharing data with literally any system on or off the Edge Platform.   

Market participants tout the revolutionary technology as the end of having to share cumbersome and unformatted CSV files with counterparties. Now, the same smart mapping technology that for years has facilitated the ingestion of mortgage data onto the Edge Platform makes extracting and sharing mortgage data with downstream users just as easy.   

Comprehensive details of this and other new capabilities using RiskSpan’s Edge Platform are available by requesting a no-obligation live demo at riskspan.com.

SCHEDULE A FREE DEMO

This new functionality is the latest in a series of enhancements that is making the Edge Platform’s Data as a Service increasingly indispensable for mortgage loan and MSR traders and investors.

### 

About RiskSpan, Inc. 

RiskSpan is a leading technology company and the most comprehensive source for data management and analytics for residential mortgage and structured products. The company offers cloud-native SaaS analytics for on-demand market risk, credit risk, pricing and trading. With our data science experts and technologists, we are the leader in data as a service and end-to-end solutions for loan-level data management and analytics.

Our mission is to be the most trusted and comprehensive source of data and analytics for loans and structured finance investments.

Rethink loan and structured finance data. Rethink your analytics. Learn more at www.riskspan.com.

Media contact: Timothy Willis

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