From property-backed direct exposures to business-friendly versions and dbt scaffolding
Summary
The RRE version records real-estate– backed direct exposures with a solid concentrate on building , collateral , and appraisal with time Using GenAI, we can transform RRE specs right into recyclable metadata, generate ERDs and dbt scaffolding, and incorporate them into a generic financial services LDM along with AnaCredit and BIRD.
Trick Takeaways
- RRE’s stamina is specific modeling of property and evaluations in time.
- GenAI can draw out entity/attribute definitions and code lists from RRE docs right into YAML
- Associations like instrument ↔ home and building ↔ evaluation map easily into a generic LDM.
- dbt on Databricks operationalizes the version with seeds, hosting, and business sights.
- RRE complements AnaCredit (exposure/roles) and BIRD (events, safeties, scores) for a full enterprise picture.
Why RRE issues (organization lens)
Real-estate– backed financing is capital-intensive and risk-sensitive. Financial institutions require:
- Clear collateralization links between lendings and residential or commercial properties
- Updated valuations to keep track of LTV and capital competence
- Auditable family tree from regulative distributions to interior analytics
This lines up with my debate for a Business-Shaped Combination Layer — a secure foundation in between raw resources and consumption views. See:
The core of RRE (model lens)
- Property/Asset — type, usage, area (geo/admin coding)
- Instrument (Exposure) — the loan/facility safeguarded by the residential property
- Instrument– Residential Property Link — organization capturing collateralization and protection %
- Evaluation — time-bound assessments (date, method, quantity)
- Counterparty — borrower/owner relationships
This is an all-natural fit for our model-driven method and metadata-first reasoning:
GenAI process for RRE → metadata → code
- Remove entities/attributes/code checklists from RRE PDFs.
- Normalize names + meanings; line up with AnaCredit/BIRD (e.g., tool synonymy).
- Emit YAML specifications for entities, FKs, and recommendation lists (building kind, usage, valuation approach).
- Produce ERDs (Mermaid/diagrams. internet).
- Scaffold dbt sources/staging/business sights and tests.
This mirrors your Business-Friendly Mapping pattern:
Example YAML (RRE piece)
entities:
property:
keys: [property_id]
features:
- property_type_code
- usage_code
- location_code
instrument_property_link:
tricks: [instrument_id, property_id]
attributes:
- collateral_type_code
- coverage_pct
assessment:
tricks: [valuation_id]
fks: [property_id]
features:
- valuation_date
- market_value
- valuation_method_code
recommendations:
property_type: {code: string, tag: string}
usage: {code: string, tag: string}
valuation_method: {code: string, tag: string}
ERD
dbt scaffold (hosting instances)
-- stg_rre __ property.sql
select
property_identifier as property_id,
property_type_code,
usage_code,
location_code
from {{resource('rre','residential property')}};
-- stg_rre __ valuation.sql
select
valuation_identifier as valuation_id,
property_identifier as property_id,
valuation_date,
appraised_value as market_value,
method_code as valuation_method_code
from {{resource('rre','appraisal')}};
-- stg_rre __ instrument_property_link. sql
select
instrument_identifier as instrument_id,
property_identifier as property_id,
collateral_type_code,
coverage_percentage as coverage_pct
from {{resource('rre','instrument_property')}};
This ties right into our exposure/consumption view thinking and temporal techniques:
Exactly how RRE enriches the common LDM
- Presents Residential or commercial property as a superior entity.
- Adds Evaluation as a time-variant characteristic , which plays wonderfully with our Dual SCD 2 attitude.
- Makes clear collateralization with a specific Tool– Residential property link.
Along With AnaCredit (roles/exposures) and BIRD (events, placements, rankings), RRE fills an essential void for collateral and assessment semantics.
What’s next
Next up: BIRD deep dive — parties/groups, securities/positions, ratings, and just how BIRD assists round out a common LDM for the financial institution. I’ll additionally show how GenAI suggests cross-model mappings (AnaCredit ↔ RRE ↔ BIRD) and creates business-friendly mapping sheets
✍ Created by Jaco van der Laan
Lead Data Modeler, Option Designer, and supporter for Model-Driven Information Engineering, Business-Oriented Information Modeling, Business-Friendly Mapping, and Universal Information Versions.
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