We ran correlation analyses across CFPB HMDA data (2018–2025), FHFA house price index, Fed unemployment, MBA delinquency data, and SLOOS credit standards. What we found contradicts conventional wisdom on nearly every front.
When FHA denial rates rise, FHA delinquency rates follow — exactly two years later. The lag correlation is r = +0.618, strong enough to be actionable.
| Year | FHA Denial Rate | FHA Delinquency (2 yrs later) |
|---|---|---|
| 2020 | 23.1% | 5.1% (2022) |
| 2021 | 23.4% | 8.7% (2023) |
| 2022 | 31.1% | 10.2% (2024) |
| 2023 | 30.0% | 11.5% (2025) |
Denial rising in 2022–2023 predicted the 2024–2025 delinquency surge — which reached 11.5%, the highest since 2021.
The 2025 denial rate is declining (27.1%). If the pattern holds, delinquency should ease by 2027. But FHA delinquency is still elevated — the divergence is worth watching.
Higher unemployment correlates with lower mortgage denial rates. r = −0.696. This is counterintuitive and consistent across the full dataset.
| Year | Unemployment | Avg. Denial Rate |
|---|---|---|
| 2018 | 3.9% | 35.0% |
| 2020 | 8.1% | 23.1% |
| 2021 | 5.4% | 23.4% |
| 2023 | 3.6% | 30.0% |
When unemployment is high, the Fed cuts rates to near zero. Low rates reduce the DTI burden on every borrower — applications that would have failed at 6.5% sail through at 3%. The lender doesn't get more lenient. The math just gets easier.
When home prices appreciate faster, denial rates fall. The correlation is r = −0.583 — moderate but consistent.
| Year | HPI Change | Avg. Denial Rate |
|---|---|---|
| 2020 | +11.0% | 23.1% |
| 2021 | +17.1% | 23.4% |
| 2022 | +7.2% | 31.1% |
| 2025 | +2.2% | 27.1% |
Rising home prices increase existing homeowners' equity, reducing effective LTV on refinances. They also tend to occur during low-rate periods. The 2025 HPI deceleration to +2.2% is the weakest in the dataset — if the pattern holds, denial pressure may build.
Update — June 2026: NAR's May 2026 Existing-Home Sales report confirms this deceleration. Median sales price rose just +1.3% YoY to $429,300 — consistent with the weak appreciation environment our model flagged. Months of supply held at 4.5, down only slightly from 4.6 a year earlier. The slowdown we identified in the HPI data is showing up in real-time NAR figures.
Further update: NAR's Pending Home Sales Index rose 3.8% month-over-month in May 2026 (+4.8% YoY) — the strongest signal of recovering demand in nearly four years, even as the 30-year fixed rate held in the mid-6% range. NAR Chief Economist Lawrence Yun called this "consumers' acceptance of above-6% mortgage rates as the new normal." This complicates Finding 02 (the recession paradox) — buyers are now moving despite elevated rates rather than waiting for them to fall, which may decouple future denial rates from rate-driven dynamics in ways our 2018-2025 model doesn't yet capture. Worth re-testing once 2026 HMDA data is available.
Conventional wisdom: when home values rise, homeowners cash out. The data says otherwise — cash-out ratio falls as HPI rises. r = −0.735.
| Year | HPI Change | Cash-Out Ratio |
|---|---|---|
| 2019 | +5.5% | 0.416 |
| 2020 | +11.0% | 0.208 |
| 2021 | +17.1% | 0.301 |
| 2023 | +5.7% | 0.534 |
In 2020–2021, rate-and-term refinancing exploded as rates hit historic lows. Total refi volume surged — but cash-out's share of that volume fell, even as absolute cash-out volume increased. The ratio is deceiving. This is a composition effect, not a behavioral one.
When the Fed raises rates, three lenders' denial rates move up sharply. Two major lenders are almost completely immune.
loanDepot, Freedom, and PennyMac are rate-driven underwriters. When the Fed tightens, they tighten. Rocket and CrossCountry operate on a different model — their denial behavior is nearly decoupled from Fed policy.
The Fed's Senior Loan Officer Opinion Survey (SLOOS) measures what lenders say about their standards. HMDA measures what they do. The correlation between the two is r = −0.150 — essentially zero, and in the wrong direction.
| Year | SLOOS (net tightening %) | Avg. Denial Rate |
|---|---|---|
| 2020 | +37% (major tightening) | 23.1% (low!) |
| 2021 | −8% (easing) | 23.4% |
| 2022 | +20% (tightening) | 31.1% |
| 2019 | −5% (easing) | 26.9% |
In 2020, lenders reported major tightening in SLOOS — yet denial rates hit a multi-year low. The refi boom brought in so many easy-to-approve refinancing profiles that overall denial rates fell, even as purchase standards tightened.
SLOOS measures the lender's intent. HMDA measures the applicant pool's outcome. They're different things. Regulators and analysts who use SLOOS as a proxy for what's actually happening in approval markets are measuring the wrong signal.
NAR's 2026 Generational Trends Report shows a striking pattern in homebuyer debt: 39% of Younger Millennials carry student loan debt (median $30,000), compared to just 7% of Older Boomers (median $24,000). The share with student debt declines steadily with age.
| Generation | Has Student Debt | Median Balance | Financed Purchase |
|---|---|---|---|
| Younger Millennial | 39% | $30,000 | 80% |
| Older Millennial | 27% | $40,000 | 78% |
| Gen X | 18% | $32,000 | 76% |
| Younger Boomer | 11% | $28,000 | 68% |
| Older Boomer | 7% | $24,000 | 58% |
We don't have age data in HMDA — lenders aren't required to report borrower age, so we cannot directly correlate student debt with our DTI cliff findings. But the mechanism is plausible: a $30,000-$40,000 student loan balance adds meaningfully to monthly DTI. Our Finding from the Genome Project shows several major lenders carry a DTI-44 cliff — a hard threshold where denial rates spike. Borrowers carrying significant student debt are mechanically more likely to land in that 44%+ band than debt-free borrowers with identical income.
This is a hypothesis, not a proven causal link — we're combining two separate datasets (NAR survey data and CFPB HMDA data) that don't share a borrower-level join key. But the directional logic holds: younger buyers carry more student debt, student debt raises DTI, and several lenders apply sharp denial penalties exactly at the DTI thresholds where added student debt would push a borrower.
Most research pages only show what worked. We think the graveyard is half the credibility. These hypotheses were reasonable, we tested them with real data, and they failed. We're publishing them so you don't have to trust our wins on faith.
Plausible mechanism: bankruptcies signal regional stress, lenders tighten locally. The level correlation exists (r=+0.36, n=50 states, 2023 US Courts filings per capita vs FHA denial) — but the predictive test failed: bankruptcy intensity showed no relationship with subsequent denial-rate change 2023→2025 (r=−0.12), and the lag structure added nothing (t and t+2 nearly identical, the signature of a shared static factor, not a leading signal). The level link is mostly a poverty artifact, further contaminated by Chapter 13 legal culture: Alabama and Tennessee file at high rates partly because of local bankruptcy practice, not purely economic distress. A county-level test with chapter controls remains open — but the easy version of this hypothesis is dead.
Plausible mechanism: shrinking profitability cuts risk appetite; underwriting tightens with a lag. We built a panel of the five publicly traded lenders in our dataset (Rocket, UWM, PennyMac, loanDepot, Guild) using company-reported GAAP net income 2020–2024 against our HMDA denial series. The naive pooled correlation looks alive (r=−0.30) — but it's a mirage. Demean each transition year across lenders (removing the industry-wide cycle) and the lender-specific signal collapses to r=−0.09. What the naive number was really capturing: in 2021 everyone's profit crashed and in 2022 everyone's denials rose — rates drove both. Which lender bled most did not predict which lender tightened most. The profitability→underwriting channel is a market-wide phenomenon, not a lender-idiosyncratic one. This is also a case study in why pooled panel correlations lie without fixed effects.
Plausible mechanism: the spread prices secondary-market risk appetite; underwriting should follow. Result: contemporaneous r=+0.12, one-year lag r=+0.04, and the sign flips between sample halves. At annual resolution, there is no there there. If a spread signal exists, it lives at monthly frequency — untestable with public annual HMDA.
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