Add statistical refinement and outlier detection system

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>

nadlan_mcp/govmap/market_analysis.py

@@ -10,7 +10,10 @@ from datetime import date, datetime, timedelta
 import logging
 from typing import Dict, List, Optional, Tuple
 
+from nadlan_mcp.config import GovmapConfig, get_config
+
 from .models import Deal, InvestmentAnalysis, LiquidityMetrics, MarketActivityScore
+from .outlier_detection import calculate_iqr, filter_deals_for_analysis
 from .statistics import calculate_std_dev
 
 logger = logging.getLogger(__name__)
@@ -201,7 +204,9 @@ def calculate_market_activity_score(
     )
 
 
-def analyze_investment_potential(deals: List[Deal]) -> InvestmentAnalysis:
+def analyze_investment_potential(
+    deals: List[Deal], config: Optional[GovmapConfig] = None
+) -> InvestmentAnalysis:
     """
     Analyze investment potential based on price trends and market stability.
 
@@ -209,8 +214,12 @@ def analyze_investment_potential(deals: List[Deal]) -> InvestmentAnalysis:
     and provides investment metrics for decision-making. The MCP provides
     data metrics; the LLM interprets them for investment advice.
 
+    With outlier filtering enabled, this function removes statistical outliers
+    before calculating volatility and trends, resulting in more accurate assessments.
+
     Args:
         deals: List of Deal model instances with price and date information
+        config: Configuration object (optional, uses global config if not provided)
 
     Returns:
         InvestmentAnalysis model containing:
@@ -229,9 +238,27 @@ def analyze_investment_potential(deals: List[Deal]) -> InvestmentAnalysis:
     if not deals:
         raise ValueError("Cannot analyze investment potential from empty deals list")
 
-    # Extract price per sqm and dates
+    if config is None:
+        config = get_config()
+
+    # Step 1: Apply outlier filtering if configured
+    deals_for_analysis = deals
+    if (
+        config.analysis_use_robust_trends
+        and config.analysis_outlier_method != "none"
+        and len(deals) >= config.analysis_min_deals_for_outlier_detection
+    ):
+        filtered_deals, _ = filter_deals_for_analysis(deals, config, metric="price_per_sqm")
+        if filtered_deals and len(filtered_deals) >= 3:  # Need at least 3 deals for analysis
+            deals_for_analysis = filtered_deals
+            logger.info(
+                f"Filtered {len(deals) - len(filtered_deals)} outliers "
+                f"from investment analysis ({len(deals)} → {len(filtered_deals)} deals)"
+            )
+
+    # Step 2: Extract price per sqm and dates from deals_for_analysis
     price_data = []
-    for deal in deals:
+    for deal in deals_for_analysis:
         price_per_sqm = deal.price_per_sqm  # Use computed field from Deal model
 
         if price_per_sqm and price_per_sqm > 0 and deal.deal_date:
@@ -292,12 +319,26 @@ def analyze_investment_potential(deals: List[Deal]) -> InvestmentAnalysis:
     else:
         price_trend = "stable"
 
-    # Calculate price volatility (coefficient of variation)
-    std_dev = calculate_std_dev(prices)
-    if avg_price_per_sqm > 0:
-        coefficient_of_variation = (std_dev / avg_price_per_sqm) * 100
+    # Step 3: Calculate price volatility
+    # Use robust volatility (IQR-based) if configured, otherwise use traditional CV
+    if config.analysis_use_robust_volatility:
+        # Robust volatility using IQR (less sensitive to outliers)
+        iqr = calculate_iqr(prices)
+        # Calculate median for robust CV
+        sorted_prices = sorted(prices)
+        median_price = sorted_prices[len(sorted_prices) // 2]
+        if median_price > 0:
+            # Robust coefficient of variation: IQR / median × 100
+            coefficient_of_variation = (iqr / median_price) * 100
+        else:
+            coefficient_of_variation = 0
     else:
-        coefficient_of_variation = 0
+        # Traditional volatility (coefficient of variation using std_dev)
+        std_dev = calculate_std_dev(prices)
+        if avg_price_per_sqm > 0:
+            coefficient_of_variation = (std_dev / avg_price_per_sqm) * 100
+        else:
+            coefficient_of_variation = 0
 
     # Convert CV to volatility score (0-100, lower is better)
     # Using defined volatility thresholds