nmtc-mapper 🗺️

Automated NMTC eligibility checker for addresses and census tracts.

Pass a DataFrame of addresses and get back a boolean column for NMTC eligibility, distress level, poverty rate, AMI ratio, and more — using official CDFI Fund and Census Bureau data. No manual lookups required.

Why nmtc-mapper?

The CDFI Fund provides a manual web tool (CIMS) for checking NMTC eligibility one address at a time. nmtc-mapper automates this — pass 10,000 addresses and get results in seconds, using the same official data source.

Installation

pip install nmtc-mapper

Quickstart

from nmtcmapper import NMTCMapper

mapper = NMTCMapper()

# Single address (geocodes automatically)
result = mapper.check_address("1234 S Michigan Ave, Chicago, IL 60605")
result.summary()
print(result.nmtc_eligible)    # True
print(result.distress_level)   # "severe"
print(result.poverty_rate)     # 0.38

# Known census tract (no geocoding needed)
result = mapper.check_tract("17031840100")
print(result.nmtc_eligible)    # True

# Batch — enrich a DataFrame of addresses
import pandas as pd
df = pd.read_csv("projects.csv")   # must have 'address' column
df = mapper.enrich(df, address_col="address")
print(df["nmtc_eligible"].value_counts())
print(df["distress_level"].value_counts())

# If you already have census tract IDs
df = mapper.enrich(df, tract_col="tract_id")

# Summary stats
mapper.eligible_count(df)

Failure behavior & offline / demo mode

NMTCMapper() downloads the official CDFI Fund eligibility and Opportunity Zone files (cached under ~/.nmtcmapper/cache). As of 0.3.4 it fails loud: if a download or parse fails, it raises a typed error instead of silently substituting demo data. (Before 0.3.4 any failure silently fell back to a 12-tract synthetic sample, which could report a real, eligible tract as "ineligible" — see the CHANGELOG.)

from nmtcmapper import NMTCMapper, NMTCMapperError

try:
    mapper = NMTCMapper()
except NMTCMapperError as e:
    # Blocked network, moved URL, corrupt file, etc. — never a fabricated answer.
    print(f"Could not load real NMTC data: {e}")
    raise

The exception hierarchy (NMTCMapperError → EligibilityDataError / OZDataError → specific *DownloadError / *ParseError leaves) is exported from the top level, so you can catch broadly or precisely.

Explicit demo / offline data — for examples, tests, or an air-gapped demo, opt in to the synthetic sample dataset. This performs no network calls and stamps the mapper so you can tell demo answers from real ones:

from nmtcmapper import NMTCMapper, load_sample_table

mapper = NMTCMapper.from_sample()   # 12 sample tracts + 6 OZ tracts, offline
print(mapper.data_source)           # "sample"   (real data → "cdfi_fund")

df = load_sample_table()            # the raw 12-tract sample frame

⚠️ Sample data is 12 synthetic-vintage tracts for demos and tests. It is never valid for a real NMTC eligibility answer.

Eligibility Rules (2016-2020 ACS — mandatory since Sept 1, 2024)

A census tract qualifies as a Low-Income Community (LIC) if it meets ANY of:

• Poverty rate >= 20%
• Median Family Income <= 80% of metro/state AMI
• Median Family Income <= 85% of state AMI (high migration rural counties)

Distress levels:

• deep — Poverty >= 40% OR AMI <= 50% OR unemployment >= 2x national rate
• severe — Poverty >= 30% OR AMI <= 60% OR unemployment >= 1.5x national rate
• lic — NMTC eligible (meets LIC criteria)
• ineligible — Does not qualify

Data Sources

• CDFI Fund 2016-2020 ACS Low-Income Community Eligibility File https://www.cdfifund.gov/research-data
• US Census Bureau Geocoding API (free, no API key required) https://geocoding.geo.census.gov

Output Columns

After running .enrich(), your DataFrame will have:

• nmtc_eligible (bool)
• distress_level (str: deep / severe / lic / ineligible)
• poverty_rate (float)
• ami_ratio (float)
• unemployment_rate (float)
• is_non_metro (bool)
• severe_distress (bool)
• deep_distress (bool)

Running Tests

PYTHONPATH=. pytest tests/ -v

44 tests across all modules (including fail-loud and explicit-sample-mode coverage added in 0.3.4).

Who This Is For

• CDEs screening project locations for NMTC eligibility
• CDFI analysts qualifying borrower locations at scale
• Researchers analyzing geographic distribution of LIC tracts
• Anyone replacing manual CIMS lookups with automated Python

License

MIT 2026 Jaypatel1511