Form 10-D: ABS Distribution Reports (CMBS)

Overview

Form 10-D is an Asset-Backed Issuer Distribution Report required under Sections 13 and 15(d) of the Securities Exchange Act of 1934. These filings disclose distribution and pool performance data for publicly offered asset-backed securities (ABS).

ABS come in many forms — credit card receivables, auto loans, student loans, residential mortgages (RMBS), commercial mortgages (CMBS), and utility securitizations. However, edgartools currently supports only CMBS (Commercial Mortgage-Backed Securities) filings that include structured XML asset data in the EX-102 attachment.

For CMBS 10-D filings, edgartools extracts: - Loan-level data: 49 fields covering origination, balance, rate, maturity, servicer, and payment status - Property-level data: 46 fields covering location, type, valuation, occupancy, financials, and debt service coverage ratios - Summary statistics: Pool metrics like total balance, average DSCR, occupancy, and property type distribution

What About Non-CMBS 10-D Filings?

For non-CMBS 10-D filings (auto loans, credit cards, student loans, etc.), filing.obj() returns None because these filings do not include standardized XML asset data. Asset data for non-CMBS filings is typically reported in separate ABS-EE filings or in HTML tables with highly variable formats.

Access Pattern

from edgar import Filing

# Get a Form 10-D filing
filing = Filing(form="10-D", ...)

# Parse into TenD object
ten_d = filing.obj()  # Returns TenD for CMBS, None for non-CMBS

# Check if CMBS data is available
if ten_d and ten_d.has_asset_data:
    loans = ten_d.loans              # pandas DataFrame
    properties = ten_d.properties    # pandas DataFrame
    summary = ten_d.asset_data.summary()  # CMBSSummary

Display

When you call filing.obj() on a CMBS Form 10-D filing, edgartools parses the filing and displays a rich panel with issuer information, distribution period, and asset data summary:

TenD Display

The panel shows: - Issuing entity (ABS trust name and CIK) - ABS type (detected as CMBS, AUTO, CREDIT_CARD, etc.) - Distribution period dates - Filing date - Depositor and sponsor entities - Security classes registered - Asset data summary (loans, properties, total balance, average rates/DSCR/occupancy, property types)

Core Data Structure

TenD (Top-Level Object)

Property	Type	Description
`filing`	`Filing`	The underlying Filing object
`form`	`str`	Form type (10-D or 10-D/A for amendments)
`company`	`str`	Company name from the filing
`filing_date`	`date`	Filing date
`accession_number`	`str`	SEC accession number
`issuing_entity`	`ABSEntity`	The ABS trust/entity (parsed from HTML header)
`depositor`	`ABSEntity`	The depositor entity
`sponsors`	`List[ABSEntity]`	Sponsor entities
`distribution_period`	`DistributionPeriod`	Period covered (start_date, end_date)
`security_classes`	`List[str]`	Classes of securities registered
`abs_type`	`ABSType`	Detected ABS type enum (CMBS, AUTO, CREDIT_CARD, RMBS, STUDENT_LOAN, UTILITY, OTHER)
`has_asset_data`	`bool`	Whether EX-102 XML exists (CMBS indicator)
`asset_data`	`Optional[CMBSAssetData]`	Parsed CMBS asset data (lazy-loaded)
`loans`	`Optional[DataFrame]`	Convenience property for `asset_data.loans`
`properties`	`Optional[DataFrame]`	Convenience property for `asset_data.properties`

Supporting Data Models

ABSEntity

Represents an entity involved in the ABS transaction (issuer, depositor, or sponsor).

Field	Type	Description
`name`	`str`	Entity name
`cik`	`Optional[str]`	SEC Central Index Key
`file_number`	`Optional[str]`	Commission file number

DistributionPeriod

The distribution period covered by the filing.

Field	Type	Description
`start_date`	`Optional[date]`	Period start date
`end_date`	`Optional[date]`	Period end date

ABSType (Enum)

Detected asset-backed security type. EdgarTools detects this by looking for EX-102 XML (CMBS) or by analyzing company/issuer name keywords.

Value	Description
`CMBS`	Commercial Mortgage-Backed Securities
`AUTO`	Auto Loan/Lease ABS
`CREDIT_CARD`	Credit Card Receivables
`RMBS`	Residential Mortgage-Backed Securities
`STUDENT_LOAN`	Student Loan ABS
`UTILITY`	Utility Securitizations
`OTHER`	Other asset types

CMBS Asset Data

CMBSAssetData

The asset_data property provides access to the structured CMBS loan and property data parsed from the EX-102 XML attachment.

Property	Type	Description
`loans`	`DataFrame`	Loan-level data (49 fields)
`properties`	DataFrame`	Property-level data (46 fields)
`summary()`	`CMBSSummary`	Aggregate statistics for the pool

Loans DataFrame (49 Fields)

The loans DataFrame contains one row per loan with the following key columns:

Column	Type	Description
`loan_id`	`str`	Prospectus loan identifier
`originator`	`str`	Loan originator name
`origination_date`	`date`	Date loan was originated
`original_amount`	`float`	Original loan amount
`original_term_months`	`int`	Original loan term in months
`maturity_date`	`date`	Loan maturity date
`original_rate`	`float`	Original interest rate
`current_rate`	`float`	Current interest rate
`actual_balance`	`float`	Current actual balance
`scheduled_balance`	`float`	Scheduled principal balance at securitization
`payment_status`	`str`	Payment status code (0=current, 1=30 days, etc.)
`is_modified`	`bool`	Whether loan has been modified
`is_balloon`	`bool`	Whether loan has balloon payment
`is_interest_only`	`bool`	Whether loan is interest-only
`primary_servicer`	`str`	Primary servicer name
`lien_position`	`str`	Lien position code
`num_properties`	`int`	Number of properties securing the loan

Additional loan fields include: loan_id_type, period_start, period_end, securitization_rate, accrual_method, rate_type, io_term_months, first_payment_date, loan_structure, payment_type, payment_frequency, num_properties_securitization, grace_days, has_prepayment_premium, has_negative_amortization, lockout_end_date, yield_maintenance_end_date, prepayment_premium_end_date, period_begin_balance, scheduled_pi_due, servicer_fee_rate, scheduled_interest, scheduled_principal, unscheduled_principal, scheduled_end_balance, paid_through_date, servicing_advance_method, pi_advances_outstanding, ti_advances_outstanding, other_advances_outstanding, subject_to_demand.

Properties DataFrame (46 Fields)

The properties DataFrame contains one or more rows per loan (one per property securing the loan) with the following key columns:

Column	Type	Description
`loan_id`	`str`	Associated loan identifier
`name`	`str`	Property name
`address`	`str`	Street address
`city`	`str`	City
`state`	`str`	State code
`zip`	`str`	ZIP code
`county`	`str`	County
`property_type`	`str`	Property type code (MF, OF, RT, etc.)
`units`	`int`	Number of units/beds/rooms
`sqft`	`int`	Net rentable square feet
`year_built`	`int`	Year property was built
`year_renovated`	`int`	Year last renovated
`valuation`	`float`	Property valuation amount
`valuation_source`	`str`	Valuation source code
`valuation_date`	`date`	Valuation date
`occupancy_securitization`	`float`	Occupancy % at securitization
`occupancy_current`	`float`	Most recent occupancy %
`revenue_securitization`	`float`	Revenue at securitization
`opex_securitization`	`float`	Operating expenses at securitization
`noi_securitization`	`float`	Net Operating Income at securitization
`ncf_securitization`	`float`	Net Cash Flow at securitization
`dscr_noi_securitization`	`float`	Debt Service Coverage Ratio (NOI) at securitization
`dscr_ncf_securitization`	`float`	Debt Service Coverage Ratio (NCF) at securitization

Additional property fields include: units_securitization, sqft_securitization, status, defeased_status, tenant_1_name, tenant_1_sqft, tenant_1_lease_exp, tenant_2_name, tenant_2_sqft, tenant_2_lease_exp, tenant_3_name, tenant_3_sqft, tenant_3_lease_exp, financials_date_securitization, financials_start_date, financials_end_date, revenue_current, opex_current, noi_current, ncf_current, debt_service_current, dscr_noi_current, dscr_ncf_current.

CMBSSummary

The summary() method aggregates the loan and property data into pool-level statistics.

Field	Type	Description
`num_loans`	`int`	Total number of loans
`num_properties`	`int`	Total number of properties
`total_loan_balance`	`float`	Sum of actual balances
`total_original_loan_amount`	`float`	Sum of original amounts
`avg_interest_rate`	`Optional[float]`	Average current interest rate
`avg_dscr`	`Optional[float]`	Average DSCR (NOI-based)
`avg_occupancy`	`Optional[float]`	Average occupancy %
`property_types`	`Dict[str, int]`	Property type code → count
`states`	`Dict[str, int]`	State → count
`delinquent_loans`	`int`	Number of loans with payment_status != '0'
`modified_loans`	`int`	Number of modified loans

Property Type Codes

Common values for property_type in the properties DataFrame:

Code	Description
`MF`	Multifamily
`OF`	Office
`RT`	Retail
`IN`	Industrial
`LO`	Lodging/Hotel
`HC`	Healthcare
`SS`	Self Storage
`MH`	Manufactured Housing
`OT`	Other

Example: Analyzing a CMBS Pool

from edgar import find

# Find a CMBS 10-D filing (BANK5 2024-5YR9)
filing = find('0001888524-25-020550')
ten_d = filing.obj()

# Display the filing
print(ten_d)
# Shows the rich panel with issuer, distribution period, and asset data summary

# Check if CMBS data is available
print(ten_d.has_asset_data)  # True
print(ten_d.abs_type)         # ABSType.CMBS

# Access loan data
loans = ten_d.loans
print(f"Number of loans: {len(loans)}")
print(loans[['loan_id', 'actual_balance', 'current_rate', 'payment_status']].head())

# Access property data
properties = ten_d.properties
print(f"Number of properties: {len(properties)}")
print(properties[['name', 'city', 'state', 'property_type', 'valuation']].head())

# Get pool summary
summary = ten_d.asset_data.summary()
print(f"Total balance: ${summary.total_loan_balance:,.0f}")
print(f"Average rate: {summary.avg_interest_rate:.2%}")
print(f"Average DSCR: {summary.avg_dscr:.2f}")
print(f"Delinquent loans: {summary.delinquent_loans}")

# Property type distribution
for prop_type, count in sorted(summary.property_types.items(), key=lambda x: -x[1]):
    print(f"  {prop_type}: {count}")

# Top states by property count
for state, count in sorted(summary.states.items(), key=lambda x: -x[1])[:5]:
    print(f"  {state}: {count}")

Example: Finding High-DSCR Properties

from edgar import find

filing = find('0001888524-25-020550')
ten_d = filing.obj()

# Filter properties with strong debt service coverage
properties = ten_d.properties
high_dscr = properties[properties['dscr_noi_securitization'] > 1.5]

print(f"Found {len(high_dscr)} properties with DSCR > 1.5")
print(high_dscr[['name', 'property_type', 'state', 'dscr_noi_securitization']].sort_values('dscr_noi_securitization', ascending=False))

Example: Tracking Delinquencies

from edgar import find

filing = find('0001888524-25-020550')
ten_d = filing.obj()

# Find delinquent loans (payment_status != '0')
loans = ten_d.loans
delinquent = loans[loans['payment_status'] != '0']

print(f"Delinquent loans: {len(delinquent)} out of {len(loans)}")

if len(delinquent) > 0:
    print(delinquent[['loan_id', 'actual_balance', 'payment_status', 'primary_servicer']])

Example: Geographic Distribution

from edgar import find
import pandas as pd

filing = find('0001888524-25-020550')
ten_d = filing.obj()

# Aggregate property value by state
properties = ten_d.properties
state_totals = properties.groupby('state').agg({
    'valuation': 'sum',
    'loan_id': 'count'
}).rename(columns={'loan_id': 'num_properties'})

state_totals = state_totals.sort_values('valuation', ascending=False)
print(state_totals.head(10))

Searching for 10-D Filings

from edgar import get_filings, Company

# Search for all recent 10-D filings
recent_10d = get_filings(form="10-D").head(20)

for filing in recent_10d:
    ten_d = filing.obj()

    # Only CMBS filings will have asset_data
    if ten_d and ten_d.has_asset_data:
        print(f"{ten_d.issuing_entity.name}: {ten_d.abs_type.value}")
        summary = ten_d.asset_data.summary()
        print(f"  {summary.num_loans} loans, ${summary.total_loan_balance:,.0f}")

# Search for 10-D filings by a specific company
company = Company("1888524")  # BANK as Depositor
filings = company.get_filings(form="10-D")

Important Notes

CMBS-only support: filing.obj() returns TenD only for CMBS filings with EX-102 XML asset data. Non-CMBS 10-D filings return None.
DataFrames are pandas DataFrames: You can use standard pandas operations to filter, aggregate, and analyze the data.
Lazy loading: The XML asset data is parsed on first access to asset_data, loans, or properties.
Missing data: Many fields can be None or NaN. Always handle missing data gracefully when doing calculations.
Distribution report HTML parsing is not supported: The narrative distribution report section has highly variable HTML formats across issuers and was found to have only ~42% extraction accuracy. EdgarTools focuses on the structured EX-102 XML data.

Data Source

The structured CMBS data comes from the EX-102 XML attachment filed with the 10-D. The XML follows the SEC EDGAR schema: http://www.sec.gov/edgar/document/absee/cmbs/assetdata

The issuer, depositor, sponsor, distribution period, and security class information is parsed from the HTML header of the 10-D filing.

Common Use Cases

Portfolio Risk Analysis

Identify loans with low DSCR or high vacancy
Track delinquency rates across servicers
Monitor properties in specific states or metros

Investment Research

Compare pool composition across issuers
Analyze property type concentrations
Evaluate geographic diversification

Compliance Monitoring

Track modified loans
Monitor payment status trends
Review servicer performance

Market Intelligence

Analyze securitization activity by sponsor
Track originator market share
Compare interest rates across pools