Leakage-safe CMBS special-servicing-transfer prediction dataset for the job: identify CMBS assets at elevated risk of transferring to special servicing within the next 12 months, using ordered point-in-time observations from SEC-filed CMBS loan reports.

This release is a sequence-friendly view of the same benchmark population as the flat table. Each row is one ordered step in an asset's history, with compact model inputs inside step_features.

import pandas as pd

train = pd.read_parquet("train.parquet")
asset_sequences = (
    train.sort_values(["cik", "assetnumber", "sequence_step"])
         .groupby(["cik", "assetnumber"], sort=False)
)

Listing Terms

Contact: cairn@cmdrvl.com

License: CC-BY-NC-4.0. This dataset is open source for non-commercial use only.

Commercial use: Snowflake Marketplace listing coming soon, contact cairn@cmdrvl.com.

Files

File	Rows	Positives	Positive rate	Notes
`train.parquet`	497,552	10,825	2.18%	`reporting_period_end_date <= 2022-12-31`
`test.parquet`	299,401	6,581	2.20%	`reporting_period_end_date >= 2023-07-01`
`all.parquet`	796,953	17,406	2.18%	Combined file with `split` column

The six-month embargo window from 2023-01-01 through 2023-06-30 is excluded from all published files. Rows whose full 12-month forward label window is not yet observable are also excluded rather than shipped as negatives.

Grain

One row is one CMBS asset observation step at:

cik
loannumber
assetnumber
reporting_period_end_date
observation_id

assetnumber is part of the observation grain. loannumber is retained as a descriptive loan identifier because loan numbers can repeat across deals and can cover multiple assets.

Use sequence_step to order rows within each (cik, assetnumber) sequence.

Label And Split

Target column: transfers_to_special_servicing_within_12m

The target is 1 when the asset's first observed special-servicer transfer date occurs after the observation period end date and within the next 12 months. Rows on or after that asset-level first transfer date are dropped before modeling, so the feature table contains pre-transfer observations only.

Split policy:

Train: reporting periods on or before 2022-12-31.
Embargo: 2023-01-01 through 2023-06-30, excluded.
Test: reporting periods on or after 2023-07-01.

The split is temporal, not random. The embargo is a buffer band discarded between train and test so the two sets do not touch at the boundary.

Step Features

step_features is a struct with compact per-period fields intended for sequence models:

raw payment status and workout strategy codes
balance change percentage
payment status severity rank
delinquency streak length
seasoning and months to maturity
modification flag

Example unpack:

steps = train.sort_values(["cik", "assetnumber", "sequence_step"])
first_asset = next(iter(steps.groupby(["cik", "assetnumber"], sort=False)))[1]
feature_dicts = first_asset["step_features"].tolist()
label = int(first_asset.iloc[-1]["transfers_to_special_servicing_within_12m"])

For recurrent, transformer, temporal-convolution, or pooling-based models, group by (cik, assetnumber), sort by sequence_step, encode each step_features struct, and use the last available row's label for the supervised target.

Leakage Verification

This release is gated by a machine-checkable leakage receipt before publication. The receipt checks:

No feature rows with period_end >= first_special_servicer_transfer_date.
No published rows in the six-month embargo window.
No point-in-time appointed-servicer join where the source filing date is later than the panel filing date it is joined to.
Exact split reproduction: train 497,552 with 10,825 positives, test 299,401 with 6,581 positives.

Provenance

Every published row includes source_filing_id, filing_date, and source_url. Use these fields to trace an observation back to its SEC archive context. They are provenance fields, not model features, unless the modeling task explicitly needs provenance.

Quickstart

import pandas as pd

train = pd.read_parquet("train.parquet")
test = pd.read_parquet("test.parquet")

drop_cols = [
    "observation_id",
    "cik",
    "loannumber",
    "assetnumber",
    "reporting_period_end_date",
    "special_servicing_transfer_date",
    "source_filing_id",
    "filing_date",
    "source_created_at",
    "source_url",
    "split",
]

y_train = train["transfers_to_special_servicing_within_12m"]
X_train_steps = train.drop(columns=drop_cols + ["transfers_to_special_servicing_within_12m"])

y_test = test["transfers_to_special_servicing_within_12m"]
X_test_steps = test.drop(columns=drop_cols + ["transfers_to_special_servicing_within_12m"])

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