In 2025, Polygon Research expanded its loan-level mortgage market intelligence platform across pricing analysis, borrower behavior, market structure, risk modeling, reporting, and education.
Key releases included new pricing tiers and free trial access, new applications for natural hazard risk and branch strategy, predictive prepayment and default models, automated benchmarks, and new loan-level insights such as the Borrower Shopping Index, LLPA pricing matrices, and a consistent Non-QM framework.
Together, these additions enable lenders, credit unions, investors, and analysts to evaluate mortgage markets using comparable, loan-level evidence rather than aggregated averages or anecdotal benchmarks.
Polygon Research is an independent mortgage data science company that builds analytics solutions on loan-level analysis and other open data, such as Census PUMS (microdata) and other agency datasets. Polygon Research platform is fast and intuitive and supports analysis of:
All tools and research outputs are designed to allow users to examine mortgage markets at a level of detail that supports real-world decision-making.
Polygon Research doubled down on its microdata approach in 2025. By blending open data, agency loan-level datasets, and deep domain expertise, Polygon tools help users understand how mortgage markets function in practice.
Polygon Research enables analysis of pricing behavior, borrower decision-making, lender competition, workforce dynamics, and risk exposure at the level where those outcomes are actually determined: the individual loan.
In 2025, platform development centered on deepening this loan-level perspective and making it easier to apply consistently across markets, products, and time.
In 2025, Polygon Research introduced a new access and pricing structure designed to reflect how organizations adopt analytics tools over time.
The updated model includes a free trial, three subscription tiers (Starter, Pro, and Premium), and both monthly and annual plans across the Vision, Pulse, and Risk suites. This type of flexibility for high-end analytics is unprecedented in mortgage banking.
This structure allows teams to begin with focused use cases—such as market analysis or peer benchmarking—and expand access as analytical needs grow across strategy, production, risk, and compliance functions.
Pricing details for Vision, Pulse, and Risk are available on the Polygon Research site.
TerraVision connects mortgage activity with natural hazard risk data, allowing users to evaluate how environmental exposure intersects with lending activity across geographies.
The application supports analysis such as:
TerraVision extends mortgage analytics beyond production and pricing into risk dimensions that increasingly shape long-term strategy.
BranchVision is now included in Polygon Vision to bring branch presence and deposit trends into the same analytical environment as mortgage origination data.
This allows users to examine:
By combining lending outcomes with footprint data, BranchVision supports more informed discussions about distribution strategy and market alignment.
Polygon Research launched Polygon Risk in 2025, introducing predictive models focused on loan performance.
Initial capabilities include Conditional Prepayment Rate (CPR) and Conditional Default Rate (CDR) models, supported by interactive dashboards that allow users to explore outcomes across cohorts, markets, and lender segments.
These models are designed to support forward-looking analysis while remaining grounded in explainable, loan-level drivers. The emphasis is on transparency and consistency rather than opaque risk scoring.
The Borrower Shopping Index (BSI) measures borrower shopping behavior using loan-level outcomes. It allows users to evaluate how often borrowers shop and how competitive dynamics vary across lenders and markets.
The BSI supports analysis of:
By relying on observed outcomes rather than survey responses, the BSI provides a clearer picture of how borrowers actually behave.
In 2025, Polygon Research introduced standardized measures of mortgage loan officer turnover, tenure, and productivity, benchmarked by lender and market.
These metrics support:
The goal is to replace anecdotal assumptions about talent and retention with comparable, data-driven evidence.
The LLPA Matrix provides a structured view of mortgage pricing behavior using actual loan-level outcomes.
Pricing is organized by credit score and combined loan-to-value (CLTV) ranges, with measures such as loan counts and rate spreads. This structure allows users to compare pricing posture across peers using a consistent framework.
The matrix supports more precise discussions about how lenders compete across risk layers, rather than relying on overall averages that can obscure important differences.
Polygon Research introduced a standardized approach to identifying Qualified Mortgage (QM) and Non-QM loans across the market.
The methodology applies loan-level criteria including ATR/QM rule logic, rate spread and points-and-fees thresholds, risk layering indicators, and product feature characteristics.
This framework enables consistent analysis of Non-QM activity across lenders, products, and years, supporting use cases such as market sizing, peer comparison, and compliance-oriented analysis.
In 2025, Polygon Research expanded automated reporting capabilities, including standardized benchmarks such as ACUMA Benchmark Report and credit union leaderboards.
These tools are designed to reduce manual reporting effort, improve consistency across peer comparisons, and support clearer internal and external communication.
Automation allows teams to focus more time on interpretation and decision-making rather than report construction.
Polygon Research launched Polygon Academy to support education in mortgage data, analytics, and applied AI.
The Academy provides on-demand content focused on practical application, helping teams build shared understanding of both the data and the analytical frameworks used throughout the platform.
This investment reflects the belief that effective analytics depends as much on data literacy as on tools.
Alongside product development, Polygon Research continued to invest in independent analysis and industry education throughout 2025.
This included loan-level market analyses, reusable open-data charts, original white papers, ongoing market commentary, and participation in industry conferences and webinars.
These efforts support a broader goal of improving transparency and shared understanding across the mortgage ecosystem.
All of the capabilities described in this post are available through Polygon Research’s subscription platform, with a free trial available for evaluation.
Mortgage market intelligence is most effective when it is transparent, consistent, and grounded in loan-level evidence.
In 2025, Polygon Research expanded its loan-level mortgage market intelligence platform across pricing analysis, borrower behavior, market structure, risk modeling, reporting, and education.
Key releases included new pricing tiers and free trial access, new applications for natural hazard risk and branch strategy, predictive prepayment and default models, automated benchmarks, and new loan-level insights such as the Borrower Shopping Index, LLPA pricing matrices, and a consistent Non-QM framework.
Together, these additions enable lenders, credit unions, investors, and analysts to evaluate mortgage markets using comparable, loan-level evidence rather than aggregated averages or anecdotal benchmarks.
Polygon Research is an independent mortgage data science company that builds analytics solutions on loan-level analysis and other open data, such as Census PUMS (microdata) and other agency datasets. Polygon Research platform is fast and intuitive and supports analysis of:
All tools and research outputs are designed to allow users to examine mortgage markets at a level of detail that supports real-world decision-making.
Polygon Research doubled down on its microdata approach in 2025. By blending open data, agency loan-level datasets, and deep domain expertise, Polygon tools help users understand how mortgage markets function in practice.
Polygon Research enables analysis of pricing behavior, borrower decision-making, lender competition, workforce dynamics, and risk exposure at the level where those outcomes are actually determined: the individual loan.
In 2025, platform development centered on deepening this loan-level perspective and making it easier to apply consistently across markets, products, and time.
In 2025, Polygon Research introduced a new access and pricing structure designed to reflect how organizations adopt analytics tools over time.
The updated model includes a free trial, three subscription tiers (Starter, Pro, and Premium), and both monthly and annual plans across the Vision, Pulse, and Risk suites. This type of flexibility for high-end analytics is unprecedented in mortgage banking.
This structure allows teams to begin with focused use cases—such as market analysis or peer benchmarking—and expand access as analytical needs grow across strategy, production, risk, and compliance functions.
Pricing details for Vision, Pulse, and Risk are available on the Polygon Research site.
TerraVision connects mortgage activity with natural hazard risk data, allowing users to evaluate how environmental exposure intersects with lending activity across geographies.
The application supports analysis such as:
TerraVision extends mortgage analytics beyond production and pricing into risk dimensions that increasingly shape long-term strategy.
BranchVision is now included in Polygon Vision to bring branch presence and deposit trends into the same analytical environment as mortgage origination data.
This allows users to examine:
By combining lending outcomes with footprint data, BranchVision supports more informed discussions about distribution strategy and market alignment.
Polygon Research launched Polygon Risk in 2025, introducing predictive models focused on loan performance.
Initial capabilities include Conditional Prepayment Rate (CPR) and Conditional Default Rate (CDR) models, supported by interactive dashboards that allow users to explore outcomes across cohorts, markets, and lender segments.
These models are designed to support forward-looking analysis while remaining grounded in explainable, loan-level drivers. The emphasis is on transparency and consistency rather than opaque risk scoring.
The Borrower Shopping Index (BSI) measures borrower shopping behavior using loan-level outcomes. It allows users to evaluate how often borrowers shop and how competitive dynamics vary across lenders and markets.
The BSI supports analysis of:
By relying on observed outcomes rather than survey responses, the BSI provides a clearer picture of how borrowers actually behave.
In 2025, Polygon Research introduced standardized measures of mortgage loan officer turnover, tenure, and productivity, benchmarked by lender and market.
These metrics support:
The goal is to replace anecdotal assumptions about talent and retention with comparable, data-driven evidence.
The LLPA Matrix provides a structured view of mortgage pricing behavior using actual loan-level outcomes.
Pricing is organized by credit score and combined loan-to-value (CLTV) ranges, with measures such as loan counts and rate spreads. This structure allows users to compare pricing posture across peers using a consistent framework.
The matrix supports more precise discussions about how lenders compete across risk layers, rather than relying on overall averages that can obscure important differences.
Polygon Research introduced a standardized approach to identifying Qualified Mortgage (QM) and Non-QM loans across the market.
The methodology applies loan-level criteria including ATR/QM rule logic, rate spread and points-and-fees thresholds, risk layering indicators, and product feature characteristics.
This framework enables consistent analysis of Non-QM activity across lenders, products, and years, supporting use cases such as market sizing, peer comparison, and compliance-oriented analysis.
In 2025, Polygon Research expanded automated reporting capabilities, including standardized benchmarks such as ACUMA Benchmark Report and credit union leaderboards.
These tools are designed to reduce manual reporting effort, improve consistency across peer comparisons, and support clearer internal and external communication.
Automation allows teams to focus more time on interpretation and decision-making rather than report construction.
Polygon Research launched Polygon Academy to support education in mortgage data, analytics, and applied AI.
The Academy provides on-demand content focused on practical application, helping teams build shared understanding of both the data and the analytical frameworks used throughout the platform.
This investment reflects the belief that effective analytics depends as much on data literacy as on tools.
Alongside product development, Polygon Research continued to invest in independent analysis and industry education throughout 2025.
This included loan-level market analyses, reusable open-data charts, original white papers, ongoing market commentary, and participation in industry conferences and webinars.
These efforts support a broader goal of improving transparency and shared understanding across the mortgage ecosystem.
All of the capabilities described in this post are available through Polygon Research’s subscription platform, with a free trial available for evaluation.
Mortgage market intelligence is most effective when it is transparent, consistent, and grounded in loan-level evidence.