Analyzing the Non-QM market requires more than national statistics. According to our estimates, the Non-QM lending reached $182 billion in 2024, representing 9% of total mortgage originations. Unlike FHA or conventional loans, there is no standard flag or universal definition for Non-QM in public data sources.
Different vendors and investors have developed their own definitions based on varying methodologies. Some focus on securitization pathways. Others use product feature proxies. Many rely on incomplete datasets.
We provide a loan-level classification methodology grounded in the complete ATR/QM regulatory framework from 12 CFR §1026.43. By applying these regulatory definitions to every HMDA record, we identify the 2024 Non-QM market at $182 billion across 558,979 loans.
This guide shows you how to analyze Non-QM using loan-level data to answer critical business questions such as where are Non-QM loans concentrated geographically?; which product features trigger volume in specific markets; how does your market share compare to competitors by MSA or county?; what are the growth trajectories in your footprint?
Loan-level HMDA data provides answers with census-tract precision.
Non-QM evolved from specialty lending into mainstream mortgage origination. The market now represents 9% of total originations by loan count and 8.9% by dollar volume. DSCR loans, bank statement programs, and investor products drive growth across different geographies.
Analyzing the Non-QM market effectively requires local market precision. Calculating market share needs competitor benchmarks. Product development demands feature-level analysis. Fair lending compliance requires census-tract visibility.
Aggregate market statistics cannot provide this granularity. Loan-level mortgage data is essential for strategic analysis.
Map Non-QM Originations by MSA, County, and Census Tract
Loan-level Non-QM data reveals concentration patterns by MSA, county, and census tract. Detroit MSA originated 3,195 Non-QM loans in 2024, representing 43% year-over-year growth. Each loan maps to a specific census tract, showing exactly where origination volume concentrates.
This geographic precision supports resource allocation. Marketing budgets follow actual demand patterns. Branch locations target high-opportunity corridors. Underwriting capacity scales to regional volume. Broker recruitment focuses on markets with demonstrated Non-QM activity.
Understand What Drives Non-QM Classification in Your Markets
Understanding what triggers Non-QM classification matters for product strategy and competitive positioning. Our analysis of 558,979 Non-QM loans in 2024 shows:
These distributions vary significantly by geography. Some markets show heavy DSCR loan concentration for investor properties. Others lean toward bank statement underwriting for self-employed borrowers or pricing-driven classifications for jumbo loans.
Product development teams can use this intelligence to design offerings that match local demand rather than national trends.
Track Lender Market Share and Fair Lending Patterns
Loan-level data identifies the top Non-QM lenders in any geography. You can track their volume trends, analyze their product characteristics, and measure market share with precision.
Census tract mapping reveals fair lending patterns. You can overlay originations on income classifications and measure exactly where your footprint has gaps relative to market opportunity.
Analyze Seven Years of Market Momentum
HMDA loan-level data covers 2018 through 2024. You can track how Non-QM evolved through regulatory changes including the Temporary GSE Patch expiration in October 2022 and the transition to price-based QM standards.
This historical view shows structural trends rather than quarterly volatility. Growth trajectories become measurable. Regional market maturity becomes visible. Product feature adoption patterns emerge clearly.
We classify every HMDA record from 2018 through 2024 using the complete ATR/QM regulatory framework from 12 CFR §1026.43.
Each loan receives evaluation against year-specific criteria:
- Price-based thresholds (APR vs. APOR by loan amount tier)
- Points and fees caps with annual adjustments
- Product feature restrictions (balloon, interest-only, negative amortization)
- Purchaser type and secondary market execution
The classification logic follows regulatory definitions precisely while remaining transparent and reproducible. Every loan's Non-QM designation can be inspected to understand the specific triggering factors.
Business-purpose loans including DSCR products are flagged separately since they fall outside ATR/QM scope but represent a significant market segment.
The 558,979 Non-QM loans originated in 2024 demonstrate these market characteristics:
These national statistics mask substantial regional variation. High-cost coastal markets show different loan amounts than Midwest metros. Investor-heavy Sun Belt regions demonstrate distinct income profiles. Markets with high self-employment concentration show different product preferences.
Understanding your specific market requires local loan-level intelligence rather than national averages.
Loan-level Non-QM analysis supports different organizational needs:
Executive leadership measures total addressable market and competitive market share at strategic geographic levels.
Product development analyzes which features drive volume and how pricing affects demand in specific segments.
Sales and marketing identify high-opportunity markets and allocate resources based on actual local demand rather than national trends.
Compliance teams demonstrate fair lending across census tract income classifications with precise geographic overlays.
Secondary market teams understand pool composition and geographic concentration before securitization.
All these applications require loan-level granularity for accurate analysis.
Loan-level data makes market share measurable rather than estimated. You can calculate your competitive position in any MSA, county, or census tract with precision instead of relying on industry estimates or samples.
Competitive intelligence becomes actionable when you can identify which lenders operate in your markets, track their origination volume trends, and analyze their Non-QM product mix and pricing strategies at the local level.
Product strategy becomes evidence-based. You can measure exactly which underwriting features and pricing approaches generate volume in your geographic footprint. This reveals whether DSCR products, bank statement programs, or interest-only features actually drive demand in your markets.
Fair lending compliance gains precision through census tract mapping. You can overlay your Non-QM originations against market benchmarks across income classifications and identify gaps in LMI (low-to-moderate income) communities before regulatory review.
Secondary market execution improves when you understand pool composition and geographic concentration before securitization. Loan-level analysis lets you benchmark your Non-QM production against market standards for investor appetite.
National market trends provide context. Local market data drives decisions.
You compete in specific geographies with specific customer segments, not nationally.
Knowing that Non-QM grew to 9% market share nationally matters less than knowing it grew 43% in Detroit while showing different patterns in other metros.
Understanding that purchaser type drives 58.7% of Non-QM classifications nationally matters less than knowing how that distribution varies in your primary markets.
Loan-level HMDA data provides the local precision that aggregate statistics cannot deliver for Non-QM market analysis.
We show how Polygon Research identifies and classifies Non-QM loans using public data, and what that reveals about market structure, pricing, competition, and borrower profiles nationally and locally.
Joined by mortgage consultant Dana Georgiou, who advises small and mid-sized lenders on Non-QM strategy and operations, we walk through a repeatable framework for analyzing Non-QM in your market—loan-level characteristics, pricing patterns, competitive positioning, and the demographic factors driving demand.
Polygon Vision provides interactive access to loan-level Non-QM intelligence across all U.S. markets in its HMDAVision and CensusVIsion. Filter by geography, lender, product features, and time period. Map census-tract concentrations. Track historical trends.
Explore our state-level Non-QM market reports →
Learn how to analyze the Non-QM market using loan-level HMDA data. Discover geographic trends, competitive positioning, product mix analysis, and growth opportunities across all U.S. markets.
Analyzing the Non-QM market requires more than national statistics. According to our estimates, the Non-QM lending reached $182 billion in 2024, representing 9% of total mortgage originations. Unlike FHA or conventional loans, there is no standard flag or universal definition for Non-QM in public data sources.
Different vendors and investors have developed their own definitions based on varying methodologies. Some focus on securitization pathways. Others use product feature proxies. Many rely on incomplete datasets.
We provide a loan-level classification methodology grounded in the complete ATR/QM regulatory framework from 12 CFR §1026.43. By applying these regulatory definitions to every HMDA record, we identify the 2024 Non-QM market at $182 billion across 558,979 loans.
This guide shows you how to analyze Non-QM using loan-level data to answer critical business questions such as where are Non-QM loans concentrated geographically?; which product features trigger volume in specific markets; how does your market share compare to competitors by MSA or county?; what are the growth trajectories in your footprint?
Loan-level HMDA data provides answers with census-tract precision.
Non-QM evolved from specialty lending into mainstream mortgage origination. The market now represents 9% of total originations by loan count and 8.9% by dollar volume. DSCR loans, bank statement programs, and investor products drive growth across different geographies.
Analyzing the Non-QM market effectively requires local market precision. Calculating market share needs competitor benchmarks. Product development demands feature-level analysis. Fair lending compliance requires census-tract visibility.
Aggregate market statistics cannot provide this granularity. Loan-level mortgage data is essential for strategic analysis.
Map Non-QM Originations by MSA, County, and Census Tract
Loan-level Non-QM data reveals concentration patterns by MSA, county, and census tract. Detroit MSA originated 3,195 Non-QM loans in 2024, representing 43% year-over-year growth. Each loan maps to a specific census tract, showing exactly where origination volume concentrates.
This geographic precision supports resource allocation. Marketing budgets follow actual demand patterns. Branch locations target high-opportunity corridors. Underwriting capacity scales to regional volume. Broker recruitment focuses on markets with demonstrated Non-QM activity.
Understand What Drives Non-QM Classification in Your Markets
Understanding what triggers Non-QM classification matters for product strategy and competitive positioning. Our analysis of 558,979 Non-QM loans in 2024 shows:
These distributions vary significantly by geography. Some markets show heavy DSCR loan concentration for investor properties. Others lean toward bank statement underwriting for self-employed borrowers or pricing-driven classifications for jumbo loans.
Product development teams can use this intelligence to design offerings that match local demand rather than national trends.
Track Lender Market Share and Fair Lending Patterns
Loan-level data identifies the top Non-QM lenders in any geography. You can track their volume trends, analyze their product characteristics, and measure market share with precision.
Census tract mapping reveals fair lending patterns. You can overlay originations on income classifications and measure exactly where your footprint has gaps relative to market opportunity.
Analyze Seven Years of Market Momentum
HMDA loan-level data covers 2018 through 2024. You can track how Non-QM evolved through regulatory changes including the Temporary GSE Patch expiration in October 2022 and the transition to price-based QM standards.
This historical view shows structural trends rather than quarterly volatility. Growth trajectories become measurable. Regional market maturity becomes visible. Product feature adoption patterns emerge clearly.
We classify every HMDA record from 2018 through 2024 using the complete ATR/QM regulatory framework from 12 CFR §1026.43.
Each loan receives evaluation against year-specific criteria:
- Price-based thresholds (APR vs. APOR by loan amount tier)
- Points and fees caps with annual adjustments
- Product feature restrictions (balloon, interest-only, negative amortization)
- Purchaser type and secondary market execution
The classification logic follows regulatory definitions precisely while remaining transparent and reproducible. Every loan's Non-QM designation can be inspected to understand the specific triggering factors.
Business-purpose loans including DSCR products are flagged separately since they fall outside ATR/QM scope but represent a significant market segment.
The 558,979 Non-QM loans originated in 2024 demonstrate these market characteristics:
These national statistics mask substantial regional variation. High-cost coastal markets show different loan amounts than Midwest metros. Investor-heavy Sun Belt regions demonstrate distinct income profiles. Markets with high self-employment concentration show different product preferences.
Understanding your specific market requires local loan-level intelligence rather than national averages.
Loan-level Non-QM analysis supports different organizational needs:
Executive leadership measures total addressable market and competitive market share at strategic geographic levels.
Product development analyzes which features drive volume and how pricing affects demand in specific segments.
Sales and marketing identify high-opportunity markets and allocate resources based on actual local demand rather than national trends.
Compliance teams demonstrate fair lending across census tract income classifications with precise geographic overlays.
Secondary market teams understand pool composition and geographic concentration before securitization.
All these applications require loan-level granularity for accurate analysis.
Loan-level data makes market share measurable rather than estimated. You can calculate your competitive position in any MSA, county, or census tract with precision instead of relying on industry estimates or samples.
Competitive intelligence becomes actionable when you can identify which lenders operate in your markets, track their origination volume trends, and analyze their Non-QM product mix and pricing strategies at the local level.
Product strategy becomes evidence-based. You can measure exactly which underwriting features and pricing approaches generate volume in your geographic footprint. This reveals whether DSCR products, bank statement programs, or interest-only features actually drive demand in your markets.
Fair lending compliance gains precision through census tract mapping. You can overlay your Non-QM originations against market benchmarks across income classifications and identify gaps in LMI (low-to-moderate income) communities before regulatory review.
Secondary market execution improves when you understand pool composition and geographic concentration before securitization. Loan-level analysis lets you benchmark your Non-QM production against market standards for investor appetite.
National market trends provide context. Local market data drives decisions.
You compete in specific geographies with specific customer segments, not nationally.
Knowing that Non-QM grew to 9% market share nationally matters less than knowing it grew 43% in Detroit while showing different patterns in other metros.
Understanding that purchaser type drives 58.7% of Non-QM classifications nationally matters less than knowing how that distribution varies in your primary markets.
Loan-level HMDA data provides the local precision that aggregate statistics cannot deliver for Non-QM market analysis.
We show how Polygon Research identifies and classifies Non-QM loans using public data, and what that reveals about market structure, pricing, competition, and borrower profiles nationally and locally.
Joined by mortgage consultant Dana Georgiou, who advises small and mid-sized lenders on Non-QM strategy and operations, we walk through a repeatable framework for analyzing Non-QM in your market—loan-level characteristics, pricing patterns, competitive positioning, and the demographic factors driving demand.
Polygon Vision provides interactive access to loan-level Non-QM intelligence across all U.S. markets in its HMDAVision and CensusVIsion. Filter by geography, lender, product features, and time period. Map census-tract concentrations. Track historical trends.
Explore our state-level Non-QM market reports →
Polygon Research identifies the 2024 Non-QM market at $182 billion across 558,979 loans, representing 9.0% of total originations by loan count and 8.9% by dollar volume. The average Non-QM loan amount was $325,366, with an average interest rate of 7.538% and average applicant income of $277,371. Of the 558,979 loans classified as Non-QM, 58.7% were driven by purchaser type (loans sold to private securitizers), 23.1% were triggered by multiple classification factors, 9.7% involved balloon payment features, and 8.5% resulted from elevated pricing above QM thresholds. These distributions vary significantly by geography DSCR loan concentration, bank statement underwriting, and pricing-driven classifications each dominate different regional markets.
Polygon Research identifies the 2024 Non-QM market at $182 billion across 558,979 loans, representing 9.0% of total originations by loan count and 8.9% by dollar volume. The average Non-QM loan amount was $325,366, with an average interest rate of 7.538% and average applicant income of $277,371. Of the 558,979 loans classified as Non-QM, 58.7% were driven by purchaser type (loans sold to private securitizers), 23.1% were triggered by multiple classification factors, 9.7% involved balloon payment features, and 8.5% resulted from elevated pricing above QM thresholds. These distributions vary significantly by geography DSCR loan concentration, bank statement underwriting, and pricing-driven classifications each dominate different regional markets.
National market share figures provide context but cannot drive resource allocation, product strategy, or competitive positioning because lenders compete in specific geographies, not nationally. The Detroit MSA, for example, originated 3,195 Non-QM loans in 2024 representing 43% year-over-year growth a pattern invisible in aggregate data. Loan-level HMDA data maps each origination to a specific census tract, revealing exactly where volume concentrates, which product features drive demand locally, and where competitors are gaining or losing ground. Understanding whether DSCR products, bank statement programs, or pricing-driven classifications dominate your market requires local loan-level intelligence that national averages cannot deliver.
Loan-level Non-QM intelligence supports every function that touches market strategy or compliance. Executive leadership can measure total addressable market and competitive share at the MSA, county, or census tract level. Product development can identify which underwriting features and pricing approaches actually generate volume in specific geographies. Sales and marketing can allocate resources based on demonstrated local demand rather than national trends. Compliance teams can overlay Non-QM originations against census tract income classifications to identify gaps in LMI communities before regulatory review. Secondary market teams can benchmark pool composition and geographic concentration against market standards before securitization. All of these applications require the loan-level granularity that aggregate statistics cannot provide.