CRE office acquisition (institutional lease-by-lease DCF)
Outputs below are real: the notebook runs against the
crepack's benchmark model, which CFDL validates against an independent reference. To run it yourself, see the Python SDK guide.
A two-tenant office acquisition modeled lease-by-lease: free rent, anniversary escalations, expense recoveries over stops, TI/LC, probability-weighted rollover, and an exit on forward NOI.
This notebook uses the benchmark model that CFDL validates against an independent reference to the penny (see benchmarks/).
from pathlib import Path
import cfdl_sdk
# This notebook reads a benchmark model and the pack definitions, both of which
# live in the repository, so locate its root. Searching a bounded set of
# ancestors means running from outside a checkout fails with an explanation
# rather than looping forever at the filesystem root.
def repo_root() -> Path:
here = Path.cwd().resolve()
for candidate in (here, *here.parents):
if (candidate / "Cargo.toml").exists() and (candidate / "packs").is_dir():
return candidate
raise RuntimeError(
"No CFDL checkout found above "
f"{here}. This notebook loads a model from benchmarks/ and pack "
"definitions from packs/, so it needs to run inside a clone of "
"https://github.com/bizarc/cfdl."
)
ROOT = repo_root()
PACKS = ROOT / "packs"Compile
Compile the model directory to IR.
model_dir = ROOT / "benchmarks/cre/office_two_tenant"
model = cfdl_sdk.compile(model_dir, packs_dir=PACKS)
print("streams:", len(model.ir["streams"]))streams: 12
Run
Run with the benchmark's configuration and apply the cre pack's domain metrics.
results = model.run(
config=str(model_dir / "run.json"),
pack="cre",
)
print("status:", results.status, "| warnings:", len(results.warnings))status: ok | warnings: 0
Cash flows
The engine returns per-period signed cash flows; cashflows() gives a wide DataFrame indexed by period.
cf = results.cashflows()
print('shape:', cf.shape)
cf.head()shape: (120, 13)
model.net_cash_flow stream.cre.exit.proceeds \
period
2026-01 -263345.249537 0.0
2026-02 -63345.249537 0.0
2026-03 -63345.249537 0.0
2026-04 -23345.249537 0.0
2026-05 -23345.249537 0.0
stream.cre.property.opex stream.cre.rollover.rent.tenant_a \
period
2026-01 -25000.0 0.0
2026-02 -25000.0 0.0
2026-03 -25000.0 0.0
2026-04 -25000.0 0.0
2026-05 -25000.0 0.0
stream.cre.rollover.ti_lc.tenant_a \
period
2026-01 0.0
2026-02 0.0
2026-03 0.0
2026-04 0.0
2026-05 0.0
stream.cre.unit.base_rent.tenant_a \
period
2026-01 0.0
2026-02 0.0
2026-03 0.0
2026-04 40000.0
2026-05 40000.0
stream.cre.unit.base_rent.tenant_b \
period
2026-01 0.0
2026-02 0.0
2026-03 0.0
2026-04 0.0
2026-05 0.0
stream.cre.unit.recoveries.tenant_a \
period
2026-01 0.0
2026-02 0.0
2026-03 0.0
2026-04 0.0
2026-05 0.0
stream.cre.unit.recoveries.tenant_b stream.cre.unit.ti_lc.tenant_a \
period
2026-01 0.0 -200000.0
2026-02 0.0 0.0
2026-03 0.0 0.0
2026-04 0.0 0.0
2026-05 0.0 0.0
stream.cre.unit.ti_lc.tenant_b stream.cre.vacancy.loss \
period
2026-01 0.0 -1500.0
2026-02 0.0 -1500.0
2026-03 0.0 -1500.0
2026-04 0.0 -1500.0
2026-05 0.0 -1500.0
stream.loan.permanent_debt_service
period
2026-01 -36845.249537
2026-02 -36845.249537
2026-03 -36845.249537
2026-04 -36845.249537
2026-05 -36845.249537
# Requires the [viz] extra (pip install cfdl-sdk[viz]).
results.plot.cumulative()<Axes: xlabel='period', ylabel='cumulative amount'>
Metrics
Core metrics (NPV/IRR/MOIC/...) plus the pack's domain metrics, with their source labelled.
results.metrics_frame() metric value currency \
0 domain.cre.debt_service 4.421430e+06 USD
1 domain.cre.dscr 1.067287e+00 None
2 domain.cre.leasing_costs 5.250000e+05 USD
3 domain.cre.noi 4.718934e+06 USD
4 entity.asset.tower.total 3.009647e+06 USD
5 model.irr -1.000000e+00 None
6 model.moic 3.234175e+00 None
7 model.npv 1.434623e+06 USD
8 model.payback_periods 5.300000e+01 None
9 model.payback_years 4.416667e+00 None
10 model.total 3.009647e+06 USD
11 model.wal_years 8.442742e+00 None
12 run.annual_discount_rate 7.250000e-02 None
13 run.periods_per_year 1.200000e+01 None
14 stream.cre.exit.proceeds.total 3.237143e+06 USD
15 stream.cre.property.opex.total -3.361015e+06 USD
16 stream.cre.rollover.rent.tenant_a.total 2.782460e+06 USD
17 stream.cre.rollover.ti_lc.tenant_a.total -1.750000e+05 USD
18 stream.cre.unit.base_rent.tenant_a.total 2.428385e+06 USD
19 stream.cre.unit.base_rent.tenant_b.total 2.717075e+06 USD
20 stream.cre.unit.recoveries.tenant_a.total 3.075942e+04 USD
21 stream.cre.unit.recoveries.tenant_b.total 3.012687e+05 USD
22 stream.cre.unit.ti_lc.tenant_a.total -2.000000e+05 USD
23 stream.cre.unit.ti_lc.tenant_b.total -1.500000e+05 USD
24 stream.cre.vacancy.loss.total -1.800000e+05 USD
25 stream.loan.permanent_debt_service.total -4.421430e+06 USD
source
0 domain:cre
1 domain:cre
2 domain:cre
3 domain:cre
4 core
5 core
6 core
7 core
8 core
9 core
10 core
11 core
12 core
13 core
14 core
15 core
16 core
17 core
18 core
19 core
20 core
21 core
22 core
23 core
24 core
25 core
What-if
Inspect the derived forward-NOI exit value and the DSCR domain metric.
mf = results.metrics_frame()
mf[mf["metric"].str.contains("dscr|noi|exit", case=False)] metric value currency source
1 domain.cre.dscr 1.067287e+00 None domain:cre
3 domain.cre.noi 4.718934e+06 USD domain:cre
14 stream.cre.exit.proceeds.total 3.237143e+06 USD core