mortgagemath 0.7.1

Cent-accurate mortgage amortization for Python — validated against 46 published sources across 6 countries. Zero dependencies.

mortgagemath

Cent-accurate mortgage amortization for Python. Every payment, interest charge, and balance is computed with Decimal arithmetic and validated against 46 published worked examples from government regulators, bank servicing guides, and academic textbooks across six countries. Zero runtime dependencies.

Why mortgagemath?

Most mortgage libraries get the monthly payment right but diverge from real lender statements by 1–4 cents per row. That drift compounds over the schedule, breaks reconciliation against actual bank statements, and makes audit work painful. mortgagemath is built around the rounding and accounting conventions that actual lenders use:

• Decimal arithmetic end-to-end (no float drift)
• Configurable rounding — ROUND_UP, ROUND_DOWN, ROUND_HALF_UP, ROUND_HALF_EVEN
• Two balance-tracking modes — ROUND_EACH (US lender statements) and CARRY_PRECISION (Excel / CRE textbooks)
• Both day-count conventions — 30/360 and Actual/360
• International compounding — monthly (US), semi-annual (Canadian Interest Act j₂), and effective-annual
• Flexible payment frequency — monthly, bi-weekly, weekly, quarterly, semi-monthly, or annual
• Adjustable-rate mortgages with rate schedules, payment caps, and negative amortization
• Interest-only periods with automatic recast
• Flat per-period fees for French assurance emprunteur and similar insurance loadings
• Currency unit precision — Decimal("0.01") for cents or Decimal("1") for yen/won
• Exact zero ending balance — the final row trues up so the schedule lands at exactly zero
• 46 validated fixtures from the US, Canada, France, Japan, Italy, and South Korea

Installation

pip install mortgagemath

Requires Python 3.11+. Zero runtime dependencies.

python -m mortgagemath   # self-check against reference values

Quick example

A standard 30-year fixed-rate mortgage from the command line:

mortgagemath summary --principal 300000 --rate 6.5 --term-months 360

Or generate the full payment-by-payment schedule:

mortgagemath schedule --principal 300000 --rate 6.5 --term-months 360

The same loan from Python:

from mortgagemath import us_30_year_fixed, periodic_payment, amortization_schedule

loan = us_30_year_fixed("300000", "6.5")

print(periodic_payment(loan))         # Decimal("1896.21")
sched = amortization_schedule(loan)
print(sched[1].interest)              # Decimal("1625.00")  — payment #1
print(sched[1].principal)             # Decimal("271.21")
print(sched[-1].balance)              # Decimal("0.00")     — exact zero
print(sched[-1].total_interest)       # Decimal("382628.90") — lifetime interest

The schedule is a plain list — sched[0] is the initial balance (no payment), sched[1] through sched[360] are the monthly payments.

For a quick summary without iterating the schedule:

from mortgagemath import us_30_year_fixed, loan_summary

s = loan_summary(us_30_year_fixed("300000", "6.5"))
print(s.periodic_payment)             # Decimal("1896.21")
print(s.total_interest)               # Decimal("382628.90")
print(s.total_paid)                   # Decimal("682628.90")
print(s.num_payments)                 # 360

Convenience constructors like us_30_year_fixed, us_15_year_fixed, canada_fixed_j2, canada_accelerated_biweekly, and us_actual_360_commercial handle common cases. The full LoanParams dataclass is available when you need every parameter explicitly:

from decimal import Decimal
from mortgagemath import LoanParams, PaymentRounding, amortization_schedule

loan = LoanParams(
    principal=Decimal("162000"),
    annual_rate=Decimal("3.875"),
    term_months=360,
    payment_rounding=PaymentRounding.ROUND_HALF_UP,
    interest_rounding=PaymentRounding.ROUND_HALF_UP,
)
sched = amortization_schedule(loan)
print(sched[1].payment)             # Decimal("761.78")

For Canadian j₂ mortgages, ARMs with rate and payment caps, commercial Actual/360 with balloon, interest-only periods, fee-loaded French schedules, and Japanese yen-precision loans, see the Worked examples vignette.

Pandas and Matplotlib integration

mortgagemath returns pure Python dataclasses, so converting to a pandas.DataFrame for analysis or plotting is one line:

import pandas as pd
from mortgagemath import us_30_year_fixed, amortization_schedule

loan = us_30_year_fixed("300000", "6.5")
df = pd.DataFrame(amortization_schedule(loan))

See the Pandas and Data Visualization vignette for plotting examples and scenario analysis.

What's validated

46 published amortization tables exercised by a test suite of more than 400 tests that runs on every push and release. Every committed fixture cell reproduces its source value to the cent (or yen). The sources span six countries and a wide range of loan structures:

• US regulatory — CFPB sample disclosures, Reg Z Appendix H ARM with 15 years of historical rate adjustments, FHLBB 1935 direct-reduction plan
• US commercial — Fannie Mae Multifamily Actual/360 with balloon, Geltner CRE carry-precision
• US textbooks — OpenStax, Goldstein, Skinner (1913), Arcones SOA Exam FM, Las Positas, Mississippi State Extension
• Canada — Olivier and eCampus Ontario semi-annual j₂ mortgages (monthly and quarterly), RBC accelerated bi-weekly
• France — MoneyVox tableau d'amortissement with assurance emprunteur fee loading
• Japan — JHF Flat 35 and LoanKeisan full 360-row schedule with yen-precision truncation rounding
• Italy — Solution Bank and BCC Brescia regulatory transparency documents
• South Korea — Tistory worked mortgage with won-precision
• Reference works — TI BA II Plus official guidebook, Wikipedia mortgage calculator
• Synthetic — half-cent rounding boundaries, zero-interest edge case

See the Validation vignette for the full 46-fixture parameter matrix and bibliography.

Documentation

Resource	Audience
Read the Docs	Installation, quickstart, full API reference, changelog
At a glance	60-second orientation
Validation	Audit / risk review — full fixture matrix + bibliography
Worked examples	Picking the right configuration by country and loan type
History	Academic context — institutional and mathematical history of the level-payment mortgage
Pandas & Viz	DataFrames, plotting, and scenario analysis
HTML site	Vignettes with navigation and search

Reporting a discrepancy

Found a published example or a real lender statement that mortgagemath doesn't reproduce to the cent? Two paths:

• Reporters / users — open an issue using the Mortgage example doesn't match template. Paste the published values; no pytest needed.
• Contributors — add a paired TOML + CSV fixture under tests/schedules/. See tests/schedules/README.md for the schema and contribution workflow.

For unrelated bugs or feature requests, use the Bug or feature request template.

License

MIT