Voice-Enabled Affordability Stress-Test Agent
Design an autonomous 'Market Advisor' agent using the OpenAI Agents SDK that performs affordability stress tests for different metros. The agent will analyze Zillow Observed Rent Index (ZORI), Zillow Home Value Index (ZHVI), and Redfin market temperature data (like inventory and price drops). You will integrate ElevenLabs to provide a high-quality, conversational voice interface that allows users to ask questions like 'How much would a 2% interest rate hike impact affordability in Austin?' The agent must identify rental-market anomalies where rent growth exceeds income growth or price drops are spiking unusually. By combining OpenAI's function-calling capabilities with real-world real estate metrics, the agent will simulate 'stress scenarios' (e.g., rising mortgage rates or unemployment) and communicate the impact via natural, synthesized speech. You will focus on building robust tools that fetch CSV data from Zillow and Redfin and calculate the housing cost burden for local residents.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a conversational real estate advisor using OpenAI Agents SDK for logic and ElevenLabs for voice interaction to analyze housing affordability stress.
How work is evaluated
Evaluation is based on the agent's ability to accurately calculate housing cost burdens and the successful integration of the voice interface.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
ElevenLabs Integration
Verifies that the agent successfully sends text to ElevenLabs and receives an audio response.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Anomaly Detection Precision
Precision of the agent in identifying rental markets with >2 standard deviation growth. • target: 0.9 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Implement a multi-turn conversation agent using the OpenAI Agents SDK
Integrate ElevenLabs API for real-time text-to-speech feedback of market analysis
Develop a stress-test calculator that adjusts housing costs based on variable mortgage rates
Create a rental anomaly detector that identifies statistical outliers in Zillow's rent-to-value ratios
Reference links and supporting material
Sample data for 1 tasks
Challenge setup
Evaluation is based on the agent's ability to accurately calculate housing cost burdens and the successful integration of the voice interface.
Challenge input
CSV data snippet from Zillow (ZHVI) and a target interest rate
ElevenLabs
AI voice synthesis platform
OpenAI
OpenAI AI model provider
Zed
High-performance code editor
Evaluated output
Float representing the monthly payment
- Verifies that the agent successfully sends text to ElevenLabs and receives an audio response.
- Precision of the agent in identifying rental markets with >2 standard deviation growth. • target: 0.9 • ran...
- Anomaly Detection Precision target: 0.9
- 1 public reference case
- Python execution harness
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- ElevenLabs · Required
- OpenAI · Optional
- Zed · Optional
Evaluation contract
- ElevenLabs Integration · Weight 1
- Anomaly Detection Precision · Weight 1
Metrics
- Anomaly Detection Precision · Target 0.9
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: CSV data snippet from Zillow (ZHVI) and a target interest rate
Output: Float representing the monthly payment
Execution harness: Python
Recipe freeze marker
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