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>
The Deal model's 'rooms' field was missing the 'assetRoomNum' alias,
causing room data from the API to not be loaded into the model.
This resulted in all room-based filters returning 0 results.
Bug discovered when querying "חנקין 62 חולון" for 3-room apartments.
Query returned 0 results despite multiple 3-room deals existing in the data.
Root cause: Pydantic requires field aliases to map API field names (camelCase)
to Python attributes (snake_case). The 'rooms' field had no alias.
Fix: Added alias="assetRoomNum" to rooms field in Deal model.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Autocomplete returns different coordinates for vague queries (e.g. "תל אביב דיזנגוף"),
sometimes landing on points with no nearby polygon_ids within 30m radius.
- Try top 3 autocomplete results until one has polygons
- Fallback radius expansion (30m→200m) if no polygons found
- Fix limit validation bug (min 1 for deal queries)
Fixes: compare_addresses, get_valuation_comparables, get_deal_statistics
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
## Root Cause
After Pydantic migration, get_deals_by_radius() was attempting to validate
API responses as Deal objects. However, this endpoint returns POLYGON METADATA
(with fields: dealscount, polygon_id, settlementNameHeb), NOT individual deals.
All responses failed Pydantic validation (missing dealAmount, dealDate),
resulting in empty lists and 0 deals returned from ALL queries.
## Fixes Applied
### 1. client.py - get_deals_by_radius()
- Return type: `List[Deal]` → `List[Dict[str, Any]]`
- Remove Pydantic validation - return raw metadata dicts
- Update docstring to clarify this returns polygon metadata
- Add note to use find_recent_deals_for_address() for actual deals
### 2. client.py - find_recent_deals_for_address()
- Update to handle polygon metadata dicts (not Deal objects)
- Use dict.get('polygon_id') instead of model attribute access
- Rename variable: `nearby_deals` → `nearby_polygons` for clarity
### 3. fastmcp_server.py - get_deals_by_radius() tool
- Update to handle dict responses (not Deal objects)
- Remove strip_bloat_fields() call (not needed for metadata)
- Update docstring with WARNING about polygon metadata
- Change response keys: "deals" → "polygons", "total_deals" → "total_polygons"
### 4. Tests
- test_govmap_client.py: Update to expect dicts, not Deal objects
- test_fastmcp_tools.py: Update 3 tests to mock dict responses
## Impact
- ✅ find_recent_deals_for_address() NOW WORKS (was returning 0 deals)
- ✅ All 174 tests passing
- ✅ E2E API test confirmed working with real data
## API Behavior Documented
get_deals_by_radius endpoint design:
1. Returns polygon/area metadata (not individual deals)
2. Extract polygon_ids from metadata
3. Call get_street_deals(polygon_id) to get actual deals
4. This workflow is automated in find_recent_deals_for_address()
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Fixed all remaining test failures after Pydantic v2 migration:
Core fixes:
- Date handling: Convert date objects to ISO strings across 4 files
- Model serialization: Use model_dump(mode='json') for JSON compatibility
- Optional fields: Made time_period_months Optional[int] in models
- Dict access: Replace .get() with getattr() for dynamic attributes
Test updates:
- Updated 50+ test fixtures from dicts to Deal models
- Fixed date-based tests to use recent dates for time filtering
- Added missing imports (CoordinatePoint, MarketActivityScore, DealStatistics)
- Updated assertions from dict keys to model attributes (snake_case)
Files modified:
- nadlan_mcp/govmap/market_analysis.py
- nadlan_mcp/govmap/statistics.py
- nadlan_mcp/govmap/models.py
- nadlan_mcp/fastmcp_server.py
- tests/test_govmap_client.py
- tests/test_fastmcp_tools.py
- .cursor/plans/TEST-UPDATE-STATUS.md (comprehensive documentation)
Result: 174/174 tests passing (100%) ✅🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Nitzan P