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The HomeReady and Home Possible take-up gap: 62% of eligible borrowers don't use them

 ·  Mortgage Tape Team

Written by Mortgage Tape Team — a group of industry analysts leveraging our proprietary mortgage-domain language models to synthesize and decode housing data.

Our previous piece established that HomeReady and Home Possible held their share of the GSE conforming purchase market through the rate shock — around 15% of conventional purchase originations landed in these programs in 2024, near the pre-COVID baseline. This piece answers the natural follow-up: for every borrower who does use HomeReady or Home Possible, how many equally income-eligible borrowers don’t? The answer, from HMDA loan-level data, is 62%. Twenty-six percent of conventional conforming purchase borrowers meet the 80% AMI income ceiling that governs eligibility. Only 38% of that pool ends up in an HR or HP loan. The remaining 62% — roughly 343,000 borrowers per year — land in standard conventional financing and never get the pricing or leverage advantages the programs were built to provide.

📌 Executive takeaways by role

  • Originators & LO marketing teams: Your addressable market is roughly 2.6x the size of what your HR + HP volume suggests. In 2024, 550,000 conventional purchase borrowers were income-eligible for these programs; only 207,000 got them. That’s a 343,000-loan gap per year. States like Iowa (44.5% eligibility, only 30% take-up), Minnesota (44%, 39%), and Connecticut (38%, 26%) have huge pools of eligible borrowers who are being routed into standard conventional loans instead. Jump to originator takeaway.
  • Capital markets & MBS investors: The take-up gap has widened structurally. Pre-COVID (2018–19), HR + HP reached about 47% of its eligible pool. Post-shock (2022–24), it reaches 22–38%. Even after the rate-shock recovery documented in Article 1, HR + HP is capturing a materially smaller share of its addressable market than it did five years ago. Recent-vintage HR + HP pool composition (per Article 1: lower LTV, more repeat buyers) reflects this narrowing — the borrowers actually landing in HR + HP are becoming an increasingly self-selected slice of the eligible population. Jump to capital-markets takeaway.
  • Program stakeholders & policy analysts: The programs are functioning as designed on eligibility — 26% of the conventional purchase market qualifies, and the distribution around the 80% AMI ceiling is smooth (no gaming). But the delivery system is missing 60%+ of the eligible population. That’s a distribution problem, not an eligibility-design problem. Policy conversations about expanding income limits to high-cost areas should first address why the existing eligible population isn’t being reached. Jump to policy takeaway.

The eligible population is bigger than the programs reach

HMDA carries borrower income at the loan level along with the FFIEC-published MSA/MD median family income (AMI). That combination lets us compute, for every 2024 conventional conforming purchase-primary origination, whether the borrower’s income put them below the 80% AMI ceiling that governs HR and HP eligibility. The distribution across the entire 2.10 million-loan conventional purchase market:

📊 Income-to-AMI distribution for conventional purchase, 2024

The chart below shows how borrower incomes distribute against their local MSA’s AMI. Every bar to the left of the 80% ceiling line represents borrowers who would qualify for HR or HP on the income test.

Key finding: The eligible pool is roughly 26% of the conventional purchase market. The distribution around the eligibility ceiling is smooth — no bunching at the 80% line, no evidence of borrower income being reported under the cap to game eligibility.

Three observations from the shape:

First, the eligible pool is large and stable. The 26% figure holds within ~1 percentage point across every year from 2018 to 2024. The rate shock changed a lot about who was buying, but it did not change what fraction of the buying population sat below 80% AMI.

Second, the distribution is smooth across the 80% line. The 70–80% AMI bin holds 7.4% of the market. The 80–90% bin holds 6.8%. If borrowers were being systematically “steered” to look income-eligible, we would expect either a spike just below 80% (borrowers barely making the cut) or a suspicious cliff at 80% (borrowers ineligible clustering just above). Neither shape appears. The eligibility test looks clean.

Third, the distribution has a long right tail — 8% of conventional purchase borrowers earn more than three times their MSA’s AMI. These are borrowers stretching to buy in expensive markets or high-income households buying in low-AMI markets. Neither of those groups is HR/HP-relevant, but they’re relevant to understanding why the median conv-purchase borrower sits at 118% AMI — the upper end of the market is skewed high.

The take-up gap has widened

Comparing the HMDA-derived eligible pool against actual HR + HP volume (from the same GSE data used in Article 1) gives us the annual take-up rate. The trajectory is more revealing than the point-in-time gap:

📉 Annual take-up trajectory: eligible pool vs actual HR + HP volume

The chart below tracks two things side-by-side each year — the size of the income-eligible pool (from HMDA) and the actual count of HR + HP originations that landed in it (from GSE). The teal diamond series shows the take-up rate.

Key finding: Pre-COVID take-up was ~47%. Post-shock take-up sits at ~38%. Even in the recovery, the programs are reaching a materially smaller share of their eligible market than they used to.

The trajectory has three phases:

  • 2018–19: peak take-up. HR + HP reached 44–47% of their eligible pool. The programs were mature, the delivery channels were established, LOs had learned the products. That’s the reference point for what the programs can achieve when the market is not distorted.

  • 2020–22: refi-wave collapse. The eligible pool ballooned to 828K–876K as low rates pulled a broader set of purchase borrowers into the market. Absolute HR + HP volume stayed flat at ~295–333K. So the take-up rate collapsed to 34–38%, then to 22% during the 2022 rate-shock trough. The programs weren’t shrinking — they just failed to grow with the expanded eligible pool.

  • 2023–24: partial recovery. The eligible pool contracted back down to ~550K as total purchase volume fell. HR + HP absolute volume also fell, and the take-up rate has recovered only partway — from 22% at the 2022 low to 38% in 2024. Still 9 percentage points below the 2018–19 peak.

The structural read: even now, with the market normalized and HR + HP’s share of conforming purchase back near the pre-COVID baseline (Article 1’s finding), the programs are reaching a materially smaller fraction of their eligible pool than they used to. Article 1 showed that share is stable and composition changed. Article 2 shows that the raw reach of the programs into their intended market has narrowed.

Where the gap concentrates

State-level take-up varies from 15% (Mississippi) to 59% (Maryland) — nearly a 4x spread. This is not driven by eligibility — the eligible pool exists in every state. It’s driven by delivery: whether local lenders, LOs, and marketing channels are actually routing eligible borrowers into these programs.

🗺 State-level take-up rate: where the programs reach vs miss

The chart below shows the top 12 states by take-up rate (teal — programs reach a large share of their eligible pool) and the bottom 12 (rust — programs reach only a small share).

Key finding: The take-up rate varies 4x by state. High-take-up states include MD (59%), OH (48%), IN (45%), IL (45%). Low-take-up states include MS (15%), ID (18%), NM (19%), CA (22%). The pattern is not straightforward — some Article 1 “loser” states (CA, ID, NV) also have low take-up on their smaller eligible pools; some Article 1 “gainer” states (IA, CT) have moderate take-up despite huge eligible pools.

Three geographic observations worth calling out:

The Iowa paradox. Iowa has the highest eligibility rate in the country at 44.5% — nearly one in every two conventional purchase borrowers there is income-eligible for HR or HP. But Iowa’s take-up rate is only 30.5%. That means ~8,400 income-eligible Iowa borrowers per year land in standard conventional financing when they could have used an HR/HP loan. Iowa is a top-of-list opportunity for any lender with midwest presence.

The Connecticut paradox. CT was a top gainer in Article 1’s rotation table (+5.2 pp affordable share, 2019 → 2024). Take-up rate is only 26% — meaning even during CT’s affordable-share growth, the programs were missing most of the eligible borrowers. The share gain came against a growing eligible pool, not from higher penetration into it.

The coastal read-through. California’s take-up rate is only 22%, but its eligible pool is small to begin with (10% of the market, the smallest in the country). So the absolute take-up gap is 13,000 CA loans per year — meaningful in dollar terms but proportionally smaller than the heartland’s opportunity. The bigger absolute-count take-up gaps are in high-population high-eligibility states — TX (23K/year), FL (17K), IL (17K), OH (16K), MI (16K), PA (16K), NC (16K), MN (14K), NY (13K), CA (13K).

The high-take-up states form a coherent block: Maryland, Illinois, Ohio, Indiana, Michigan, Wisconsin, Kansas, Pennsylvania. This is the traditional midwest affordability corridor where affordable-lending infrastructure is established — HFAs are active, community lenders are engaged, credit unions push HR/HP as a first-line product. The low-take-up states form a different block: Mississippi, Idaho, New Mexico, California, Maine — a mix of markets where either the local lender ecosystem doesn’t lean into affordable products or where the eligible borrower population is small enough that LOs don’t develop the workflow expertise.

Why does the gap exist?

We can’t answer this definitively from the data alone. But four hypotheses are consistent with what we observe:

  1. LO awareness and workflow gaps. HR and HP require slightly different underwriting workflows (income documentation for the AMI test, homeownership education certification, borrower contribution rules). An LO who processes standard conventional 10x per week and HR/HP once a quarter has neither the muscle memory nor the incentive to route a borderline case to HR/HP. In markets where affordable-lending share is small, this compounds — the workflow expertise is thinner and the routing rarer.

  2. Compensation drift. Standard conventional loans with LLPA hits at high LTV / lower FICO carry higher rates, which support higher gain-on-sale and often (in retail channels) higher LO comp. An HR/HP loan at the same borrower profile carries a lower rate (that’s the LLPA advantage), which can translate to lower comp on that transaction. This is not universal — many LOs are compensated on volume rather than margin, and many wholesale channels neutralize the effect — but where compensation structure biases against HR/HP, the routing shifts. This is a soft-touch fair-lending concern that HUD and CFPB have flagged before.

  3. Income variability at the eligibility line. A borrower with steady $75K W-2 income at the 80% AMI cutoff is clearly eligible. A borrower with $70K base and $15K variable bonus is at the cutoff on paper, over the cutoff on averaged income, and requires more LO judgment. Loans at the eligibility line take more effort to underwrite as HR/HP than as standard conventional, so LOs opt for the easier path.

  4. Borrower preference for “just conventional.” Some borrowers, especially in higher-income eligible households, actively prefer the standard-conventional label over the “affordable-program” label — a lingering stigma effect. LOs report this occasionally. It doesn’t explain the 62% gap on its own, but it likely explains some fraction, especially at the upper end of the eligible pool (70–80% AMI).

Distinguishing which of these dominates would require survey data or LO channel analysis we don’t have. But the shape of the problem — a large, stable, geographically-uneven gap — is consistent with a delivery-system issue rather than an eligibility-design issue.

Cross-link to Article 1: the mechanism completes

Article 1 documented two facts:

  1. HR + HP’s share of GSE conforming purchase held stable at ~15% through the rate shock.
  2. The geographic footprint rotated — heartland states gained share; coastal and mountain-west boom markets lost.

Article 2 adds the missing mechanism:

  1. The geographic rotation was driven by the eligible pool shrinking on the coasts, not by better lender penetration on the heartland. Article 1’s “gainer” states (IN, IA, MN, OH) have 36–44% eligibility rates. Article 1’s “loser” states (CA, ID, NV) have 10–20% eligibility rates. The rotation shape follows the eligible-pool shape.

  2. Even where the pool grew (heartland), the take-up rate did not. Iowa gained affordable-lending share in Article 1’s data, but its take-up rate is only 30% — the same programs are still missing 70% of Iowa’s eligible pool. The share gain reflects that Iowa’s eligible pool held up better than California’s, not that Iowa’s HR + HP infrastructure improved.

The combined narrative: the programs work as designed on eligibility. They rotate geographically with where eligible borrowers live. And they consistently reach about a third of that pool — down from about half pre-COVID. For any lender or policymaker, the 62% take-up gap is a bigger addressable opportunity than the 3-percentage-point share drift Article 1 flagged.

Operational takeaways: originators, capital markets, and policy

💼 For originators and LO marketing teams: capture the 343,000-loan opportunity

Your true addressable market for HomeReady and Home Possible is 2.6x larger than current production indicates. In 2024, ~550,000 conventional purchase applicants met the 80% AMI income requirement, but only 207,000 were placed into HR/HP loans.

Actionable steps: 1. Automate pre-qual tagging — systematically flag applicants earning 80% AMI or less at the pre-qualification stage using automated LOS zip-code and FFIEC lookup rules. 2. Target high-gap heartland markets — focus sales channels on states with massive eligible pools but low take-up rates, such as Iowa (44.5% eligible, 30.5% take-up) and Connecticut (37.6% eligible, 26.0% take-up). 3. Mitigate LO workflow friction — simplify income-verification protocols for variable/bonus income cases near the cutoff so LOs don’t default to standard conventional financing out of convenience.

📊 For capital markets and MBS investors: price the self-selection shift

The 9-percentage-point decline in program take-up (from 47% pre-COVID down to 38% today) indicates that borrowers landing in HR + HP pools are an increasingly self-selected cohort. These loans disproportionately originate from markets with mature affordable-lending channels (midwest / mid-Atlantic) or from borrowers actively shopping for pricing discounts.

Combined with Article 1’s finding of lower average LTVs (85%) and higher repeat-buyer share (26%), 2023+ HR/HP paper carries higher credit quality and different prepayment dynamics. Portfolio managers should model these pools using vintage-specific CPR and CDR curves rather than legacy baseline performance models.

🏛️ For policy analysts and housing-finance executives: address delivery over design

The data shows that the 80% AMI eligibility cap is functioning properly — no artificial income bunching or systemic gaming is present. However, the delivery infrastructure fails to reach 62% of the qualified target population.

Policy recommendations: 1. Prioritize reach over threshold expansion — before debating income-limit increases in high-cost areas, focus on why six out of ten eligible borrowers in existing markets are routed into higher-cost standard conventional loans. 2. Examine loan-officer compensation and channel friction — evaluate whether LO compensation structures or underwriting complexities disincentivize routing eligible low-to-moderate-income borrowers into LLPA-discounted products. 3. Evaluate fair-lending disparities — conduct demographic analyses (ECOA / HMDA) on the 62% uncaptured pool to ensure non-take-up does not disproportionately impact minority or underserved borrower segments. HMDA carries race, ethnicity, sex, and age at loan level; whether the 343,000 eligible-but-not-in-program borrowers look demographically distinct from the 207,000 who do land in HR + HP is a natural CFPB / HUD-scope follow-up study.

Methodology and caveats

Data sources:

  • Numerator (eligible pool): mortgage.hmda_lar conventional (loan_type=‘1’) conforming purchase (loan_purpose=‘1’) primary-residence (occupancy_type=‘1’) originations (action_taken=‘1’). Income filtered to income > 0 and ffiec_msa_md_median_family_income > 0 (100% coverage on the 2.10M 2024 cohort). Eligibility test: (income × 1000) / ffiec_msa_md_median_family_income × 100 ≤ 80. Note that HMDA income is denominated in thousands of dollars per column-specification convention.
  • Denominator (actual HR + HP): dedup’d Fannie SFP (special_eligibility_program='H') + Freddie SLLD (program_indicator='H'), purchase-primary, same annual bucketization used in Article 1.

Assumptions and caveats:

  • HR + HP have additional non-income eligibility requirements — CLTV limits, borrower contribution rules, homeownership education certification, occupancy verification, condo warrantability, and (for Home Possible) a maximum LTV of 97%. Not every income-eligible borrower will qualify on those other tests. Our “62% take-up gap” therefore includes some fraction of borrowers who would fail non-income eligibility. We do not have the granular data to separate them; the true “reachable” gap is probably 55–60% rather than 62%.
  • HMDA universe vs GSE universe. HMDA reports every mortgage origination reported to the GSEs, FHLBs, banks, credit unions, IMB portfolios, and some private-label channels. The GSE-delivered subset (Fannie SFP + Freddie SLLD) is a proper subset. Some borrowers in the HMDA-eligible pool end up in FHA/VA, in bank portfolio, or in non-conforming financing — they were never candidates for HR + HP by the time they closed. This inflates the eligible-pool denominator slightly. A tighter test would require limiting the HMDA cohort to loans reported as sold to Fannie or Freddie in the loan-sale-purchaser field; that filter would reduce the eligible pool ~15% but would still leave the 350K+ gap intact.
  • Take-up rate for 2020–21 is depressed by refi wave denominator effects. The eligible pool during those years included borrowers pulled into the market by rate-and-term refis (which are not HR/HP-eligible on the refi side but are counted here as purchase). This is why the trajectory shows a temporary dip — worth acknowledging but not the primary story.
  • State-level take-up rates use 2024 HMDA vs 2024 GSE HR + HP. Both sides of the ratio use origination-year buckets (Fannie MMYYYY parsed; Freddie first_payment_date year with 1–2 month lag). The state cohort is HMDA state_code; the GSE state is property_state. Both are 2-letter codes with 100% coverage.
  • HFA Preferred / HFA Advantage (special_eligibility_program = ‘F’) are excluded from the HR + HP numerator. They’re a separate program family with different eligibility. Including them would raise 2024 total volume by ~30K.

Follow-up work worth doing:

  • HMDA demographic breakout of the take-up gap. Does the 62% who don’t land in HR + HP look demographically different from the 38% who do? Race, ethnicity, gender, and age are all populated in HMDA. If there’s a systematic disparity, that’s a fair-lending finding.
  • Lender-level take-up rates. HMDA reports LEI at the origination level. We can identify which lenders route their eligible borrowers into HR + HP consistently and which don’t. The highest-volume and lowest-volume names by take-up rate would be the natural third piece in this series.
  • Non-income eligibility filtering. Loading a CLTV distribution filter and a borrower-contribution filter on top of the income test would give a tighter “reachable” denominator — probably reducing the 62% gap to 50–55% but keeping the shape intact.

Chart 1 is a Plotly bar histogram showing the 10-pp AMI bins with a vertical 80% AMI line and callout. Chart 2 is a dual-axis Plotly grouped bar (eligible pool + actual volume) with a take-up-rate line on the secondary axis. Chart 3 is a side-by-side horizontal bar comparison of the top-12 and bottom-12 states by take-up rate.

Data pulled 2026-08-02. The underlying preflight is in 2026-07-10-hr-hp-ami-targeting-preflight-spec.md (article 2 series doc).