Play 81
Property Valuation AI
High✅ Ready
Automated property appraisal with comparable sales analysis and satellite imagery assessment.
Automated property appraisal combining comparable sales analysis, market trend prediction, neighborhood scoring, and satellite imagery assessment for real estate valuation and mortgage underwriting. AI Search indexes comparable sales databases, OpenAI performs valuation analysis and report generation, Cosmos DB stores property records with geospatial indexing, Machine Learning runs market trend prediction models, and Functions orchestrate the appraisal workflow.
Architecture Pattern
RAG-driven valuation: comparable search - market prediction - neighborhood scoring - appraisal report
Azure Services
Azure OpenAIAzure AI SearchAzure Cosmos DBAzure Machine LearningAzure Functions
DevKit (.github Agentic OS)
- agent.md — root orchestrator with builder→reviewer→tuner handoffs
- 3 agents — Valuation Builder (gpt-4o), Reviewer (gpt-4o-mini), Tuner (gpt-4o-mini)
- 3 skills — deploy (208 lines), evaluate (131 lines), tune (231 lines)
- 4 prompts — /deploy, /test, /review, /evaluate with agent routing
- .vscode/mcp.json — FrootAI MCP with OpenAI + Maps inputs + envFile
TuneKit (AI Config)
- config/openai.json - valuation analysis and report generation prompts
- config/valuation.json - comparable radius, market windows, scoring weights
- config/guardrails.json - valuation accuracy thresholds, bias detection
- evaluation/eval.py - Appraisal accuracy >95%, Market prediction >85%
Tuning Parameters
Comparable radiusMarket trend windowNeighborhood score weightsConfidence intervalAppraisal methodology
Estimated Cost
Dev/Test
$80-200/mo
Production
$2K-8K/mo