hmda-analyzer 0.3.0

HMDA mortgage lending disparity analyzer — denial rates, racial disparities, lending deserts, and lender benchmarking

hmda-analyzer 📊

HMDA mortgage lending disparity analyzer.

Compute denial rate disparities by race, identify lending deserts, benchmark lenders against peers, and generate fair lending analysis reports — using CFPB HMDA LAR data. Free public API, no authentication required.

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Why hmda-analyzer?

HMDA data covers 10+ million mortgage applications per year with borrower demographics, denial rates, loan amounts, and census tract locations. It is the most powerful public dataset for analyzing mortgage lending disparities — but it requires significant engineering to use. hmda-analyzer makes it accessible in Python.

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Installation

pip install hmda-analyzer

Both of these import styles work after installation:

from hmdaanalyzer import denial_rate_by_race   # canonical form
from hmda_analyzer import denial_rate_by_race   # pip-name convention alias

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Quickstart

from hmdaanalyzer import (
    load_sample, denial_rate_by_race, disparity_ratio,
    lending_by_tract, lending_desert_score, lender_summary,
    generate_disparity_report,
)

# Load sample data (no API required)
df = load_sample(n=5000)

# Or load from CFPB API — streams and stops at limit rows
# df = load_from_api(year=2023, state="IL", limit=10_000)

# Denial rates by race
rates = denial_rate_by_race(df)
print(rates)

# Disparity ratios vs White applicants
disparities = disparity_ratio(df)
print(disparities)

# Geographic analysis — lending activity by census tract
tracts = lending_by_tract(df)
print(tracts.head())

# Lending desert identification — tracts with abnormally low application volume
deserts = lending_desert_score(df)
print(deserts.head())

# Lender analysis
summary = lender_summary(df, lei="LEI000001")

# Full disparity report
report = generate_disparity_report(df, title="Illinois Mortgage Market 2023")
print(report)

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Analyses Supported

• Denial rate by race and ethnicity
• Disparity ratios vs reference group (default: White applicants)
• Denial rate by income band
• Denial reasons by race
• Lending activity by census tract, county, and state
• Lending desert identification (low application volume tracts)
• Lender vs market comparison
• Top lenders by origination volume

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Error Handling

If you pass a DataFrame that is missing a column an analysis requires, the function raises MissingColumnError (importable from hmdaanalyzer or hmda_analyzer) instead of silently returning an empty result. In a fair-lending context a silent empty result can read as "no disparity," so a schema problem fails loudly:

from hmdaanalyzer import MissingColumnError, lending_by_state

try:
    lending_by_state(df)            # df has 'state' but not 'state_code'
except MissingColumnError as e:
    print(e)                        # names the function and the missing column

MissingColumnError subclasses ValueError, so existing except ValueError handlers keep working. This applies to the analysis functions and to filtering arguments: passing lei=... or state=... when that column is absent raises rather than silently computing whole-market results. A well-formed query that simply matches no rows is not an error — e.g. lender_summary(df, lei=...) with a valid schema but an unknown LEI still returns an empty {}, and generate_disparity_report(df, lei=...) returns a clean no-records report.

Breaking change in 0.3.0: functions that previously returned an empty result on a missing column now raise MissingColumnError. See the CHANGELOG for the full list.

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Disparity Ratio Thresholds

Based on CFPB fair lending examination standards:

• = 2.0x — HIGH disparity (triggers regulatory scrutiny)

• = 1.5x — MODERATE disparity

• < 1.5x — LOW disparity
• < 1.0x — FAVORABLE (group has lower denial rate than reference)

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Data Sources

CFPB HMDA Data Browser API — free, no API key required. 2024 data covers 4,908 institutions and millions of loan applications.

https://ffiec.cfpb.gov/data-browser/

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Running Tests

PYTHONPATH=. pytest tests/ -v

35 tests across all modules.

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Who This Is For

• Fair lending analysts and compliance teams at banks and CDFIs
• Community reinvestment researchers studying mortgage disparities
• Journalists covering housing discrimination and redlining
• Regulators and examiners analyzing lender performance
• Academics studying racial wealth gaps and homeownership barriers

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License

MIT 2026 Jaypatel1511