Skip to main content

Easy modeling and scenario analytics for commercial real estate debt

Project description

cred is a Python package that provides flexible data structures for commercial real estate debt. It is designed to quickly answer common loan- and portfolio-level questions related to payment schedules, prepayment costs, dates, and scenario analytics. The package includes convenience functions for common loan structures.

The Period class represents the atomic cash flow unit. Periods have a start date, end date, reference to the previous Period, and a collection of rules that define the cash flow attributes of the Period’s schedule.

Users typically will not interact with Periods directly. Instead, users can use Borrowings to automatically create and manage a set of Periods.

The PeriodicBorrowing subclasses (specifically, FixedRateBorrowing and FloatingRateBorrowing) provide built-in interfaces for working with common fixed and floating rate debt. Calling the Borrowing.schedule() method will build and return a Pandas DataFrame with the loan schedule.

Examples

FloatingRateBorrowings require an index rate provider function that takes a datetime argument.

In this example, random numbers around 1.5% are returned for demonstration purposes.

from datetime import datetime
import numpy as np

from cred import FloatingRateBorrowing, open_repayment

def rate(dt):
    return  np.random.lognormal(0.015, 0.001) - 1

floating_borrowing = FloatingRateBorrowing(start_date=datetime(2020, 1, 1),
                                           end_date=datetime(2021, 1, 1),
                                           spread=0.03,
                                           index_rate_provider=rate,
                                           initial_principal=100)
floating_borrowing.schedule()

Result:

   start_date   end_date  bop_principal  index_rate  interest_rate  interest_payment  principal_payment  eop_principal
0  2020-01-01 2020-02-01            100    0.013134       0.043134          0.371428                  0            100
1  2020-02-01 2020-03-01            100    0.014265       0.044265          0.356579                  0            100
2  2020-03-01 2020-04-01            100    0.013389       0.043389          0.373632                  0            100
3  2020-04-01 2020-05-01            100    0.016527       0.046527          0.387729                  0            100
4  2020-05-01 2020-06-01            100    0.014282       0.044282          0.381317                  0            100
5  2020-06-01 2020-07-01            100    0.015641       0.045641          0.380339                  0            100
6  2020-07-01 2020-08-01            100    0.014892       0.044892          0.386574                  0            100
7  2020-08-01 2020-09-01            100    0.014530       0.044530          0.383454                  0            100
8  2020-09-01 2020-10-01            100    0.015665       0.045665          0.380543                  0            100
9  2020-10-01 2020-11-01            100    0.017037       0.047037          0.405038                  0            100
10 2020-11-01 2020-12-01            100    0.015731       0.045731          0.381089                  0            100
11 2020-12-01 2021-01-01            100    0.014520       0.044520          0.383367                100              0

Schedules are not stored in order to avoid accidentally accessing stale data. Instead, each call rebuilds most schedule data.

However, periods dates are calculated when the Borrowing is created and can be accessed in the Borrowing.dates attribute. This allows users to prepare rates more efficiently. For example, fetch forward LIBOR projections in one batch rather than make individual calls as each period is calculated.

FixedRateBorrowing look very similar, but do not require an interest rate provider.

The additional convenience options in the block below (e.g. repayment, frequency, day_count) can be used for FloatingRateBorrowings in the same manner.

The percent_of_principal function builds a function for calculating repayment costs based on the dates and step-downs. The default is open repayment.

Default frequency is monthly, and default day_count is actual/360.

from dateutil.relativedelta import relativedelta

from cred import FixedRateBorrowing, percent_of_principal, thirty360

step_down = percentage_of_principal(pd.date_range('2020-08-1', '2020-12-01', freq='MS'),
                                                  [0.05, 0.04, 0.03, 0.02, 0.01])

fixed_borrowing = FixedRateBorrowing(start_date=datetime(2020, 1, 1),
                               end_date=datetime(2021, 1, 1),
                               frequency=relativedelta(months=3),
                               coupon=0.05,
                               initial_principal=100,
                               repayment=step_down,
                               day_count=thirty360)
fixed_borrowing.schedule()

Result:

  start_date   end_date  bop_principal  interest_rate  interest_payment  principal_payment  eop_principal
0 2020-01-01 2020-04-01            100           0.05              1.25                  0            100
1 2020-04-01 2020-07-01            100           0.05              1.25                  0            100
2 2020-07-01 2020-10-01            100           0.05              1.25                  0            100
3 2020-10-01 2021-01-01            100           0.05              1.25                100              0

In addition to calculating schedules, Borrowings also has the following method:

  • scheduled_cash_flow(self, attr_names, start_date=None, end_date=None) : Returns cash flows defined in attr_names between (optional) start and end dates

PeriodicBorrowings have the additional methods:

  • repayment_amount(self, date) : Implemented in subclasses, returns total repayment amount for date
  • net_cash_flows(self, exit_date, pmt_attrs=[INTEREST_PAYMENT, PRINCIPAL_PAYMENT]) : Net payments through date including initial funding and repayment costs

For example, to calculate the effective borrowing cost if the previous fixed borrowing was prepaid after three quarters:

>>> import numpy as np
>>> cash_flows = fixed_borrowing.net_cash_flows(datetime(2020, 10, 1))
>>> np.irr(cash_flows) * 4
0.0670659774603255

Project details


Release history Release notifications

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for cred, version 0.0.0
Filename, size File type Python version Upload date Hashes
Filename, size cred-0.0.0-py3-none-any.whl (11.2 kB) File type Wheel Python version py3 Upload date Hashes View hashes
Filename, size cred-0.0.0.tar.gz (11.0 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page