Methodology | OFI, AWS & Lender DNA

1. OFI — Overlay Friction Index

OFI measures real-world lender tightness — the gap between official mortgage guidelines and actual underwriting behavior. It captures the "overlay layer" that lenders apply on top of FHA, VA, and conventional standards.

Model: OFI = 4.81 × 30yr_mortgage_rate + 20.17
Correlation: r = 0.902
Observations: n = 44 quarterly (2014Q1 – 2024Q4)
Validation: Leave-One-Out MAE = 1.23
Direction accuracy: 72.1%
Current value: 47 (Q2 2026)

The base model uses the 30-year fixed mortgage rate as the primary predictor, derived from FRED series MORTGAGE30US. Higher rates correlate strongly with tighter overlays — lenders become more selective as financing costs rise.

State-level OFI applies regional modifiers based on delinquency rates, HPI trends, and local employment data from Bureau of Labor Statistics and CFPB state-level reports.

2. AWS — Approval Window Score

AWS forecasts whether current market conditions favor mortgage submission — updated weekly via GitHub Actions using FRED API data.

Formula: AWS = base_score + trend_adjustment × income_sensitivity
Base score: 100 − normalize(OFI, 20, 90)
Trend adjustment: −delta_60d × sensitivity_coefficient

Income sensitivity coefficients:
W2: 0.3 (low sensitivity)
Retirement: 0.2 (very low)
SSDI: 0.5 (moderate)
1099: 0.7 (high)
Self-Employed: 0.8 (highest)

The 6 forward-looking FRED indicators and their weights:

Indicator	FRED Series	Weight	Lead Time
30yr Mortgage Rate Momentum	MORTGAGE30US	35%	0–4 weeks
Building Permits	PERMIT	20%	4–6 months
Housing Starts	HOUST	15%	2–3 months
Consumer Sentiment	UMCSENT	15%	1–3 months
Weekly Jobless Claims	ICSA	10%	1–2 weeks
Fed Funds Direction	FEDFUNDS	5%	Policy cycle

AWS outputs a probabilistic signal — not a guarantee. Window states: OPEN / NARROWING / DETERIORATING / IMPROVING.

3. Lender DNA — HMDA Data

All lender denial rates are calculated directly from CFPB HMDA Public LAR Snapshot data, manually verified via the HMDA Data Browser.

Source: CFPB HMDA Data Browser
URL: ffiec.cfpb.gov/data-browser
Years: 2018 – 2024 (7 years)
Loan type filter: FHA only (loan_type = 2)
Action taken: Denied (3) + Originated (1)
Formula: Denial Rate = Denied ÷ (Denied + Originated)
Verification: Each data point manually pulled and calculated

Withdrawn applications and incomplete files are excluded. Only "Application denied" and "Loan Originated" are counted. This methodology matches standard HMDA analysis practice.

About the Founder

FinanceRateCalc was built by Ziya Yetiş — 23 years in banking and mortgage, based in Bellview, TX. The OFI model, lender DNA analysis, and AWS framework are original work derived from publicly available federal data.

Contact: [email protected]

4. Lender Cycle Sensitivity Score

The Lender Cycle Sensitivity Score measures how dependent a lender's denial volume distribution is on the interest rate cycle. It is a derived behavioral index — not an official measure — built from CFPB HMDA public data.

Formula: CSS = (Refi_Volatility × 0.4) + (Purchase_Sensitivity × 0.3) + (CashOut_Instability × 0.3) × 3

Where:
Refi_Volatility = stdev(refi_ratio across 7 years) × 100
Purchase_Sensitivity = (max_purchase_ratio − min_purchase_ratio) × 100
CashOut_Instability = stdev(cash_out_ratio across 7 years) × 100

Score range: 0–100 (higher = more cycle-dependent)
Segmentation: <35 = Stable | 35–60 = Balanced | >60 = Rate-Driven

Normalization note: ratios (not raw volumes) are used to correct for
size differences between lenders. A small lender with stable purchase
focus correctly scores lower than a large refi-dependent lender.

Limitations: This score reflects historical behavior only. A lender's future strategy may diverge from past patterns. Channel mix (broker vs. retail vs. correspondent) is not isolated — this may distort scores for lenders with mixed distribution models. CrossCountry's low score reflects genuine purchase stability but also lower overall volume, which reduces variance mathematically.

5. Mortgage Beta (Rate Simulator)

Mortgage Beta measures a lender's volume sensitivity to changes in the 30-year mortgage rate — analogous to beta in equity markets, where beta measures sensitivity to market movements.

Formula: β = −slope × 10

Where slope is derived from OLS linear regression:
y = refi_ratio (annual)
x = 30-year mortgage rate (FRED MORTGAGE30US annual avg)

Rate proxy used:
2018: 4.5% | 2019: 3.9% | 2020: 3.1% | 2021: 3.0%
2022: 5.3% | 2023: 7.0% | 2024: 6.9%

Prediction: volume_change% = refi_elasticity × rate_delta × base_refi_ratio × 100
Capped at ±60% to prevent extrapolation errors.

β > 1.0 = high rate sensitivity (Rate-Driven)
β 0.5–1.0 = moderate sensitivity (Balanced)
β < 0.5 = low sensitivity (Stable)

Limitations: Linear regression on 7 data points is statistically limited (low degrees of freedom). Predictions should be treated as directional signals, not precise forecasts. The model does not capture structural changes in lender strategy, regulatory changes, or M&A activity. Rate proxies are annual averages — intra-year volatility is not captured.

6. Data Limitations & Transparency

All models on this site have inherent limitations that users should understand:

1. HMDA data reflects denial/origination counts — not profitability or margin
2. Lender channel mix (broker/retail/correspondent) is not isolated
3. Volume data includes all loan types unless FHA filter is explicitly applied
4. Loan purpose data excludes "Other Purpose" and "Not Applicable" categories
5. Small lenders (CrossCountry, Guild, Planet Home) have lower variance
mathematically — this may overstate their stability
6. M&A activity (e.g. Caliber → NewRez) creates discontinuities
7. OFI base model: n=44 quarterly observations — statistically meaningful
but not large-sample
8. All forecasts are probabilistic signals, not guarantees

We publish these limitations because transparency is the foundation of credibility. If you find errors or methodological issues, contact [email protected].

Disclaimer: FinanceRateCalc is an educational and analytical tool. OFI, AWS, and Zai outputs are statistical signals based on historical and current market data — not guarantees of mortgage approval or denial. Individual outcomes depend on many factors not captured by these models. This is not financial, legal, or mortgage advice. Always consult a licensed mortgage professional before making application decisions.