RateAPI Data Methodology

RateAPI aggregates mortgage rate data from credit union websites across America. We focus on the data points that matter for rate comparison and financial planning.

Products

• Mortgage: 30-year fixed, 15-year fixed, adjustable-rate mortgages
• HELOC: Home equity lines of credit (coming soon)
• Auto: New and used vehicle financing (coming soon)

Fields Captured

• rate — Base interest rate before fees
• apr — Annual Percentage Rate including fees and points
• points — Discount points as a percentage of loan amount
• term — Loan term in months
• productType — Canonical product category
• productName — Original product name from source
• institution — Credit union or lender name
• url — Source URL where rate was observed

Source Types

• HTML pages: Standard rate tables and product pages
• PDFs: Rate sheets published as downloadable documents
• Embedded widgets: Third-party mortgage calculators

We maintain two distinct timestamp concepts to provide maximum transparency about data freshness.

Observed vs Effective

• observed_at — When RateAPI retrieved and parsed the source page
• effective_at — When the lender states the rate is effective (if disclosed)

When a source doesn't disclose an effective date, we only provide observed_at. We never invent timestamps.

Credit unions name their products inconsistently. We normalize to canonical categories so you can compare apples to apples.

Product Type Mapping

• "30 Year Fixed Rate", "30-Yr Fixed", "Thirty Year Mortgage" → 30-year-fixed
• "15 Year Fixed", "15-Year Fixed Rate" → 15-year-fixed
• "5/1 ARM", "5-Year Adjustable" → 5-1-arm

Term Normalization

All terms are converted to months. "30 years" becomes 360 months.

APR Handling

• When APR is provided by the source, we use it directly
• When APR is missing, we mark the field as null — we don't calculate or estimate
• We preserve the original productName alongside our normalized productType

Data quality matters. When we find errors, we fix them transparently.

We do not silently overwrite historical observations. If a parsing error is detected, we append a correction record with a reason code. This preserves full audit history.

Correction Reason Codes

Not all data points are equally trustworthy. We provide confidence signals at two levels.

Offer Ranking

We built this algorithm because we've been burned too many times by "low rates" that came with thousands in hidden fees. A 6.25% APR sounds great—until you discover the 2.5 points and $3,000 in origination fees that make it more expensive than a 6.75% rate with zero points.

So we rank offers the way we wish every rate site did: by what you'll actually pay, not what looks best in a headline. Every offer gets scored on APR, points, fees, data freshness, and source reliability. The lowest advertised rate doesn't always rank first—and that's intentional.

We also penalize lenders who exhibit deceptive bait-and-switch behavior—like advertising ultra-low rates that are rarely available or burying qualification requirements. Unlike affiliate-driven comparison sites that profit from promoting specific lenders, we have no financial incentive to rank any offer higher than it deserves.

Automated checks flag suspicious data before it reaches the API.

Flagged records enter a review queue before publication. We'd rather delay than publish bad data.

RateAPI is built toward a fiduciary standard: neutral ordering, transparent methodology, and no pay-to-rank.

• We don't sell leads. Your users' data stays yours.
• We don't accept placement fees. Ordering is by APR (or your chosen sort), not by who paid more.
• We don't have affiliate relationships with the credit unions we track.

Our incentives are aligned with yours: accurate, timely data that you can trust to power your products.

This section provides detailed technical documentation for developers and AI agents who need to understand the exact structure and behavior of rate data.

Timestamp Fields

Canonical Product Types

Correction Reason Codes

Confidence Score Ranges

• 0.9 - 1.0: Excellent — highly reliable data
• 0.7 - 0.9: Good — minor caveats possible
• 0.5 - 0.7: Fair — use with caution
• 0.0 - 0.5: Low — verify independently

API Query Examples

# Get personalized mortgage recommendations
curl -X POST "https://api.rateapi.dev/v1/decisions" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "decision_type": "financing",
    "context": { "geo": { "state": "CA" } },
    "product_request": {
      "product_type": "mortgage",
      "amount": 500000,
      "term_months": 360
    }
  }'

Proper attribution helps users verify data sources and supports transparent financial journalism. Use the format appropriate for your context.

Academic & Journalistic Citation

For research papers, news articles, and formal publications:

API Partner Attribution

If you build products using RateAPI data, include visible attribution:

• Visible link: Include "Rates powered by RateAPI" or "Data: RateAPI" near where rates are displayed
• Methodology link: Link to rateapi.dev/methodology so users can verify data collection practices

Widget Attribution

The RateAPI embed widget includes built-in attribution that must remain visible. See the Widget Documentation for complete requirements.

• "Source: RateAPI" link in the widget footer
• "Methodology" link for data transparency

LLM & AI Agent Citation

When AI agents or language models cite RateAPI data, include:

For MCP-connected agents, the lastUpdated timestamp is included in every API response. Always surface this timestamp when presenting rate data to users.