Rationale: 50% threshold too permissive for Israeli real estate market.
Within same room count/area, legitimate price variance typically ±30-35%, not 50%.
Example: 13,402 NIS/sqm deal (47% below 25,120 median) was passing with 50% threshold.
This is almost certainly data error or partial deal, not legitimate market variation.
Changes:
- ANALYSIS_PERCENTAGE_THRESHOLD default: 0.5 → 0.4 (40%)
- Update CLAUDE.md docs to reflect 40% threshold
Impact:
- New lower bound: median × 0.6 (was median × 0.5)
- New upper bound: median × 1.4 (was median × 1.5)
- Tighter filtering while preserving legitimate high/low-end deals
- Better aligned with actual Israeli real estate market variance
Tests: All 326 tests pass
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Change ANALYSIS_IQR_MULTIPLIER default from 1.5 to 1.0 in config.py
- Add iqr_multiplier parameter to all filtering & statistics functions
- Allow runtime override via MCP tools (get_valuation_comparables, get_deal_statistics)
- Update CLAUDE.md docs with new default & override examples
Rationale: k=1.0 catches more suspicious deals (e.g. 43% below median) while
still preserving legitimate edge cases via hard bounds. Users can override
per-call for more conservative filtering (k=1.5) if needed.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Implement configurable outlier detection and robust statistical measures to
improve analysis accuracy for real estate data. Addresses issues with data
entry errors, partial deals, and other anomalies that skew statistics.
Key Features:
- IQR-based outlier detection (moderate filtering by default, k=1.5)
- Hard bounds filtering for obvious errors (price_per_sqm, deal_amount)
- Robust volatility using IQR instead of std_dev for investment analysis
- Transparent reporting with both filtered and unfiltered statistics
Implementation:
- Add outlier_detection.py module with IQR/percent/hard bounds methods
- Add OutlierReport model and enhance DealStatistics with filtered fields
- Update calculate_deal_statistics() to support optional outlier filtering
- Update analyze_investment_potential() to use robust volatility
- Add 9 new configuration parameters for customization
- Add comprehensive test suite (24 tests) for outlier detection
- Update CLAUDE.md with usage documentation
Configuration (all via env vars):
- ANALYSIS_OUTLIER_METHOD=iqr (default, or percent/none)
- ANALYSIS_IQR_MULTIPLIER=1.5 (moderate, 3.0=conservative)
- ANALYSIS_PRICE_PER_SQM_MIN/MAX=1000/100000 (bounds in NIS/sqm)
- ANALYSIS_MIN_DEAL_AMOUNT=100000 (catches partial deals)
- ANALYSIS_USE_ROBUST_VOLATILITY=true (IQR-based CV)
- ANALYSIS_USE_ROBUST_TRENDS=true (filter before regression)
Testing:
- All existing tests pass (326 passed)
- 24 new comprehensive outlier detection tests
- Real-world scenario tests (partial deals, data errors)
Backward Compatible:
- Default behavior improves accuracy without breaking changes
- All new fields in models are optional
- Config parameters have sensible defaults
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Updated ARCHITECTURE.md:
- Changed govmap.py section to govmap/ package section
- Updated architecture diagram to show modular package structure
- Marked Phase 3 as completed (✅)
- Listed all package modules and their responsibilities
Updated CLAUDE.md:
- Updated Business Logic Layer description with package structure
- Updated Key Files section with new modular package details
- Updated development roadmap to show Phase 3 as complete
- Updated "Adding New API Endpoint Support" instructions
- Updated "Important Notes for AI Agents" with package info
- Changed test count from 27 to 34 tests (all passing)
All documentation now reflects the completed modular refactoring
while emphasizing backward compatibility.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Nitzan Pomerantz