Simulate Dynamic Multifamily Yield Negotiations with AutoGen
Multifamily revenue management systems struggle to balance tenant inquiry conversion speed with dynamic yield optimization during leasing surges. Build an AutoGen multi-agent system to simulate tenant-landlord lease negotiations and optimize occupancy rates within 5% of target yield targets.
What you are building
Construct an AutoGen multi-agent simulation framework with dynamic pricing feedback loops between tenant agents and property management pricing agents.
How work is evaluated
Evaluates negotiation completion rate, price convergence, and target yield compliance.
Datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
Price Range Boundary Test
Verifies final rent is within min_acceptable_rent and tenant_max_budget bounds.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Yield Optimization Alignment
Absolute variance from mathematical optimal yield point. • target: 0.05 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Learning goals
Build conversational multi-agent simulation loops in AutoGen between Tenant and Landlord agents.
Integrate dynamic pricing tools into the Landlord agent to optimize lease term lengths and concession packages.
Evaluate conversation turn thresholds and market equilibrium price convergence.
Resources
Multifamily demand elasticity records with tenant price sensitivity profiles and property manager revenue targets.
Challenge setup
Evaluates negotiation completion rate, price convergence, and target yield compliance.
Challenge input
JSON with unit_asking_rent, min_acceptable_rent, tenant_max_budget, target_lease_months
AutoGen
Assigned framework for multi-agent negotiation dynamics.
Evaluated output
JSON with final_agreed_rent, agreed_lease_months, deal_closed, variance_from_asking
- Verifies final rent is within min_acceptable_rent and tenant_max_budget bounds.
- Absolute variance from mathematical optimal yield point. • target: 0.05 • range: 0-1
- Benchmark: Agent_Negotiation_Eval
- Yield Optimization Alignment target: 0.05
- 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
- AutoGen · Required
- Aim · Optional
- Sarvam AI · Optional
Evaluation contract
- Price Range Boundary Test · Weight 1
- Yield Optimization Alignment · Weight 1
Metrics
- Yield Optimization Alignment · Target 0.05
Benchmarks
- Agent_Negotiation_Eval
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON with unit_asking_rent, min_acceptable_rent, tenant_max_budget, target_lease_months
Output: JSON with final_agreed_rent, agreed_lease_months, deal_closed, variance_from_asking
Execution harness: Python
Evaluation tools
- AutoGen · Multi-Agent Framework
Recipe freeze marker
No recipe freeze time is recorded.