Multifamily Property Risk Investment Premium Reduction Scoring Model
Multifamily property owners invest heavily in safety upgrades (e.g. water sensors, security cameras) that often fail to reflect in insurance premium discounts. Build a predictive loss reduction model using LightGBM and spatial building data to quantify premium reduction credits accurately.
What you are building
Develop a LightGBM loss reduction scoring engine to translate property safety investments into verifiable premium discounts.
How work is evaluated
Evaluates predicted claim frequency reduction precision and discount rate assignment.
Datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
savings_math
Validates annual_savings_usd equals base_annual_premium times recommended_discount_pct
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
discount_gini_score
Gini score of loss frequency prediction • target: 0.45 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Learning goals
Feature engineer IoT sensor installation data (water shutoff valves, smart smoke detectors)
Train LightGBM loss frequency models on multifamily property loss histories
Calculate actuarially sound premium discount percentages per safety technology installed
Validate model calibration across regional property hazard tiers
Resources
Multifamily property characteristics dataset with IoT safety technology metadata and historical loss records.
Challenge setup
Evaluates predicted claim frequency reduction precision and discount rate assignment.
Challenge input
JSON containing property characteristics and active safety investments.
LightGBM
Gradient boosting framework for tabular loss data
Evaluated output
JSON containing expected_loss_reduction_pct, recommended_discount_pct, and roi_years.
- Validates annual_savings_usd equals base_annual_premium times recommended_discount_pct
- Gini score of loss frequency prediction • target: 0.45 • range: 0-1
- Benchmark: MultifamilyPricingBench
- Discount Gini Score target: 0.45
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Shows the first task, up to three tools, and up to three evidence items.
View technical recipe
Configured tools
- Anthropic Claude · Required
- Mistral · Optional
- Groq Cloud · Optional
Evaluation contract
- savings_math · Weight 1
- discount_gini_score · Weight 1
Metrics
- discount_gini_score · Target 0.45
Benchmarks
- MultifamilyPricingBench
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing property characteristics and active safety investments.
Output: JSON containing expected_loss_reduction_pct, recommended_discount_pct, and roi_years.
Execution harness: Python
Evaluation tools
- LightGBM · ML Framework
Recipe freeze marker
No recipe freeze time is recorded.