historical-cost-analyzer

# Historical Cost Analyzer for Construction

## Overview

Analyze historical construction cost data for benchmarking, escalation tracking, and estimating calibration. Compare similar projects, identify cost drivers, and improve future estimates.

## Business Case

Historical cost analysis enables:
- **Benchmarking**: Compare current estimates to past projects
- **Calibration**: Improve estimating accuracy using actual data
- **Trends**: Track cost escalation and market changes
- **Risk Assessment**: Identify cost drivers and overrun patterns

## Technical Implementation

```python
from dataclasses import dataclass, field
from typing import List, Dict, Any, Optional, Tuple
import pandas as pd
import numpy as np
from datetime import datetime
from scipy import stats

@dataclass
class CostBenchmark:
    metric_name: str
    value: float
    unit: str
    percentile_25: float
    percentile_50: float
    percentile_75: float
    sample_size: int
    project_types: List[str]

@dataclass
class EscalationAnalysis:
    from_year: int
    to_year: int
    annual_rate: float
    total_change: float
    category: str
    confidence: float

@dataclass
class CostDriver:
    factor: str
    impact_percentage: float
    correlation: float
    description: str

class HistoricalCostAnalyzer:
    """Analyze historical construction costs."""

    # RSMeans City Cost Indexes (sample - would be loaded from database)
    LOCATION_FACTORS = {
        'New York': 1.32, 'San Francisco': 1.28, 'Los Angeles': 1.15,
        'Chicago': 1.12, 'Houston': 0.92, 'Dallas': 0.89,
        'Phoenix': 0.93, 'Atlanta': 0.91, 'Denver': 1.02,
        'Seattle': 1.08, 'National Average': 1.00
    }

    # Historical cost indices by year
    COST_INDICES = {
        2015: 100.0, 2016: 102.1, 2017: 105.3, 2018: 109.2,
        2019: 112.5, 2020: 114.8, 2021: 121.4, 2022: 135.6,
        2023: 142.3, 2024: 148.7, 2025: 154.2, 2026: 160.0
    }...