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Articles Tagged with: Credit Analytics

Enriching Pre-Issue Intex CDI Files with [Actual, Good] Loan-Level Data

The way RMBS dealers communicate loan-level details to prospective investors today leaves a lot to be desired.

Any investor who has ever had to work with pre-issue Intex CDI files can attest to the problematic nature of the loan data they contain. Some are better than others, but virtually all of them lack information about any number of important loan features.

Investors can typically glean enough basic information about balances and average note rates from preliminary CDI files to run simple, static CPR/CDR scenarios. But information needed to run complex models — FICO scores, property characteristics and geography, and LTV ratios to name a few — is typically lacking. MBS investors who want to run to run more sophisticated prepayment and credit models – models that rely on more comprehensive loan-level datasets to run deeper analytics and scenarios – can be left holding the bag when these details are missing from the CDI file.

The loan-level detail exists – it’s just not in the CDI file. Loan-level detail often accompanies the CDI file in a separate spreadsheet (still quaintly referred to in the 21st Century as a “loan tape”). Having this data separate from the CDI file requires investors to run the loan tape through their various credit and prepayment models and then manually feed those results back into the Intex CDI file to fully visualize the deal structure and expected cash flows.

This convoluted, multi-step workaround adds both time and the potential for error to the pre-trade analytics process.

A Better Way

Investors using RiskSpan’s Edge Platform can streamline the process of evaluating a deal’s structure alongside the expected performance of its underlying mortgage loans into a single step.

EDGEPLATFORM

Here is how it works.

As illustrated above, when investors set up their analytical runs on Edge, RiskSpan’s proprietary credit and prepayment models automatically extract all the required loan-level data from the tape and then connect the modeling results to the appropriate corresponding deal tranche in the CDI file. This seamlessness reduces all the elements of the pre-trade analytics process down to a matter of just a few clicks.

Making all this possible is the Edge Platform’s Smart Mapper ETL solution, which allows it to read and process loan tapes in virtually any format. Using AI, the Platform recognizes every data element it needs to run the underlying analytics regardless of the order in which the data elements are arranged and irrespective of how (or even whether) column headers are used.

Contact us to learn more about how RMBS investors are reaping the benefits of consolidating all of their data analytics on a single cloud-native platform.


RiskSpan to Launch Usage-based Pricing for its Edge Platform at SFVegas 2024 

New innovative pricing model offers lower costs, transparency, and flexibility for analytics users 

RiskSpan, a top provider of cloud-based analytics solutions for loans, MSRs, structured products and private credit, announced today the launch of a usage-based pricing model for its Edge Platform. The new pricing model enables clients flexibility to pay only for the compute they use. It also gives clients access to the full platform, including data, models, and analytics, without having to license individual product modules. 

Usage-based pricing is a trend that reflects the evolving nature of analytics and the increasing demand for more flexible, transparent, and value-driven pricing models. It is especially suited for the dynamic and diverse needs of analytics users, whose data volumes, usage patterns, and analytical complexity requirements often fluctuate with the markets.

RiskSpan was an early adopter of the Amazon Web Services (AWS) cloud in 2010. Its new usage-based pricing, powered by the AWS cloud, enables RiskSpan to invoice its clients based on user-configured workloads, which can scale up or down as needed. 

“Usage-based pricing is a game-changer for our clients and the industry,” said Bernadette Kogler, CEO of RiskSpan. “It aligns our pricing with the value we deliver and the outcomes we enable for our clients. It also eliminates the waste and inefficiency of paying for unused, fixed-fee compute capacity, year after year in long-term, set price contracts. Now our clients can optimize their spending while experimenting with all the features our platform has to offer.”

“We are excited RiskSpan chose AWS to launch its new pricing model. Our values are aligned in earning trust through transparent variable pricing that allows our customers to innovate and remain agile.” said Ben Schreiner, Head of Business Innovation, at Amazon Web Services. “By leveraging the latest in AWS technology, including our generative AI services, RiskSpan is accelerating the value they deliver to their customers, and ultimately, the entire financial services industry.”

Usage-based pricing offers several benefits for RiskSpan clients, including: 

  • Lower Costs: Clients pay only for what they need, rather than being locked into an expensive contract that may not suit their current or future situation. 
  • Cost Sharing: Clients can share costs across the enterprise and better manage expense based on usage by individual functions and business units. 
  • Transparency: Clients can monitor their usage and directly link their analytics configuration and usage to their results and goals. They can also better control their spending by tracking their usage and seeing how it affects their bill. 
  • Flexibility: Clients can experiment with different features and options of RiskSpan’s Edge Platform, as they are not restricted by a predefined package or plan. 

For a free demo, visit https://riskspan.com/ubp/.

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About RiskSpan, Inc. 

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

Its 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, Dominium Advisors Announce Market Color Dashboard for Mortgage Loan Investors


ARLINGTON, Va., January 24, 2024 – RiskSpan, the leading tech provider of data management and analytics services for loans and structured products, has partnered with tech-enabled asset manager Dominium Advisors to introduce a new whole loan market color dashboard to RiskSpan’s Edge Platform.

This new dashboard combines loan-level market pricing and trading data with risk analytics for GSE-eligible and non-QM loans. It enables loan investors unprecedented visibility into where loans are currently trading and insight on how investors can currently achieve excess risk-adjusted yields.

Dashboard

The dashboard highlights Dominium’s proprietary loan investment and allocation approach, which allows investors to evaluate any set of residential loans available for bid. Leveraging RiskSpan’s collateral models and risk analytics, Dominium’s software helps investors maximize yield or spread subject to investment constraints, such as a risk budget, or management constraints, such as concentration limits.

“Our strategic partnership with RiskSpan is a key component of our residential loan asset management operating platform ,” said Peter A. Simon, Founder and CEO of Dominium Advisors. “It has enabled us to provide clients with powerful risk analytics and data management capabilities in unprecedented ways.”

“The dashboard is a perfect complement to our suite of analytical tools,” noted Janet Jozwik, Senior Managing Director and Head of Product for RiskSpan’s Edge Platform. “We are excited to be a conduit for delivering this level of market color to our mortgage investor clients.”

The market color dashboard (and other RiskSpan reporting) can be accessed by registering for a free Edge Platform login at https://riskspan.com/request-access/.

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About RiskSpan, Inc. 

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

Its 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.

About Dominium Advisors Dominium Advisors is a tech-enabled asset manager specializing in the acquisition and management of residential mortgage loans for insurance companies and other institutional investors. The firm focuses on newly originated residential mortgage loans made to high quality borrowers – GSE eligible, jumbo and non-QM. Its proprietary loan-level software makes possible the construction of loan portfolios that achieve investor defined objectives such as higher risk-adjusted yields and spreads or limited exposure to tail risk events. Learn more at dominiumadvisors.com.


Snowflake Tutorial Series: Episode 3

Using External Tables Inside Snowflake to work with Freddie Mac public data (13 million loans across 116 fields)

Using Freddie Mac public loan data as an example, this five-minute tutorial succinctly demonstrates how to:

  1. Create a storage integration
  2. Create an external stage
  3. Grant access to stage to other roles in Snowflake
  4. List objects in a stage
  5. Create a format file
  6. Read/Query data from external stage without having to create a table
  7. Create and use an external table in Snowflake

This is the third in a 10-part tutorial series demonstrating how RiskSpan’s Snowflake integration makes mortgage and structured finance analytics easier than ever before.

Episode 1, Setting Up a Database and Uploading 28 Million Mortgage Loans, is available here.

Episode 2, Using Python User-Defined Functions in Snowflake SQL, is available here.

Future topics will include:

  • 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

RiskSpan’s Snowflake Tutorial Series: Ep. 2

Learn how to use Python User-Defined Functions in Snowflake SQL

Using CPR computation for a pool of mortgage loans as an example, this six-minute tutorial succinctly demonstrates how to:

  1. Query Snowflake data using SQL
  2. Write and execute Python user-defined functions inside Snowflake
  3. Compute CDR using Python UDF inside Snowflake SQL

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

Episode 1, Setting Up a Database and Uploading 28 Million Mortgage Loans, is available here.

Future topics will include:

  • 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

RiskSpan Adds CRE, C&I Loan Analytics to Edge Platform

ARLINGTON, Va., March 23, 2023 – RiskSpan, a leading technology company and the most comprehensive source for data management and analytics for mortgage and structured products, has announced the addition of commercial real estate (CRE) and commercial and industrial (C&I) loan data intake, valuation, and risk analytics to its award-winning Edge Platform. This enhancement complements RiskSpan’s existing residential mortgage toolbox and provides clients with a comprehensive toolbox for constructing and managing diverse credit portfolios.

Now more than ever, banks and credit portfolio managers need tools to construct well diversified credit portfolios resilient to rate moves and to know the fair market values of their diverse credit assets.

The new support for CRE and C&I loans on the Edge Platform further cements RiskSpan’s position as a single-source provider for loan pricing and risk management analytics across multiple asset classes. The Edge Platform’s AI-driven Smart Mapping (tape cracking) tool lets clients easily work with CRE and C&I loan data from any format. Its forecasting tools let clients flexibly segment loan datasets and apply performance and pricing assumptions by segment to generate cash flows, pricing and risk analytics.

CRE and C&I loans have long been supported by the Edge Platform’s credit loss accounting module, where users provided such loans in the Edge standard data format. The new Smart Mapping support simplifies data intake, and the new support for valuation and risk (including market risk) analytics for these assets makes Edge a complete toolbox for constructing and managing diverse portfolios that include CRE and C&I loans. These tools include cash flow projections with loan-level precision and stress testing capabilities. They empower traders and asset managers to visualize the risks associated with their portfolios like never before and make more informed decisions about their investments.

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

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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 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

A Practical Approach to Climate Risk
Assessment for Mortgage Finance

Note: The following is the introduction from RiskSpan’s contribution to a series of essays on Climate Risk and the Housing Market published this month by the Mortgage Bankers Association’s Research Institute for Housing America.

Significant uncertainty exists about how climate change 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. This paper lays out a pragmatic framework for assessing these risks from the perspective of a mortgage company. We evaluate available public and proprietary data sources and address data limitations, such as different sources providing a different view of risk for a particular property. We propose a sensitivity analysis approach to quantify risk and mitigate the uncertainties in measuring and responding to climate change.

Global temperatures will continue to increase over the next 50 years regardless of the actions people and governments take. The impacts of that warming are expected to accumulate and become more severe and frequent over time, causing stress throughout our economy. Regulators are clearly signaling that climate risk analysis will need to become a regular part of risk management activities. But detailed, industry-specific guidance has not been defined. FHFA and the regulated entities have yet to release a climate risk framework. They clearly recognize the threat to the housing finance system, however, and are actively working towards accounting for these risks.

Most executives and boards have become conceptually familiar with the physical and transition risks of climate change. But significant questions remain around how these concepts translate into specific, quantifiable business, asset, regulatory, legal, and reputation risks in the housing finance industry. Further complicating matters, climate science continues to evolve and there is limited historical data to understand how the effects of climate change will trickle into the housing market.

Sean Becketti1 describes the myriad ways climate change and natural hazard risk can permeate the housing and housing finance industries as well as some of the ways to mitigate its effects. However, quantifying these risks and inserting them into mortgage credit and prepayment models comes with significant challenges. No “best practices” have emerged for incorporating these into traditional model frameworks.

This paper puts forth a practical framework to incorporate climate risk into existing enterprise risk management practices for the housing finance industry. The framework incorporates suggestions to prepare for coming regulatory requirements on climate risk and, more importantly, proactively managing and mitigating this risk. Our approach is based on over two years of research and field work RiskSpan has conducted with its clients, and the resulting models RiskSpan has developed to deliver insights into these risks.

The paper is organized into two main sections:

  1. Prescribed Climate Scenarios and Emerging Regulatory Requirements
  2. A Practical Approach to Climate Risk Assessment for Mortgage Finance

Layering climate risk into enterprise risk management is likely to be a multiyear process. This paper focuses on steps to take in the initial one to two years after climate risk has been prioritized for investment of time and resources by corporate leadership. As explained in an MBA white paper from June 2022,2 “Existing risk management practices, structures, and relationships are already capturing potential risks from climate change.” The aim of this paper is to investigate specific ways in which existing credit, operational, and market risk frameworks can be leveraged to address this challenge, rather than seeking to reinvent the wheel.


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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