Cwicr location factor
221 AI skills for construction: BIM analysis, cost estimation, scheduling, document control, and automation with Claude Code
npx -y skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill cwicr-location-factorAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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Apply geographic location factors to CWICR estimates. Adjust costs for regional labor rates, material prices, and market conditions.
SKILL.md
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CWICR Location Factor
Business Case
Problem Statement
Construction costs vary by location:
- Labor rates differ by region
- Material prices vary geographically
- Market conditions affect costs
- Remote locations have premiums
Solution
Apply location-based cost factors to CWICR estimates, adjusting for regional differences in labor, materials, and overall market conditions.
Business Value
- Regional accuracy - Location-specific estimates
- Market awareness - Current conditions
- Comparison support - Normalize across locations
- Planning - Multi-location projects
Technical Implementation
import pandas as pd
from typing import Dict, Any, List, Optional
from dataclasses import dataclass
from enum import Enum
class CostComponent(Enum):
"""Cost components for factors."""
LABOR = "labor"
MATERIAL = "material"
EQUIPMENT = "equipment"
TOTAL = "total"
@dataclass
class LocationFactor:
"""Location adjustment factor."""
location_code: str
location_name: str
country: str
region: str
labor_factor: float
material_factor: float
equipment_factor: float
total_factor: float
currency: str
notes: str = ""
@dataclass
class AdjustedEstimate:
"""Estimate with location adjustment."""
base_cost: float
base_location: str
target_location: str
labor_adjustment: float
material_adjustment: float
equipment_adjustment: float
total_adjustment: float
adjusted_cost: float
adjustment_percent: float
# Location factors (relative to US national average = 1.00)
LOCATION_FACTORS = {
# USA
'US-NYC': LocationFactor('US-NYC', 'New York City', 'USA', 'Northeast', 1.35, 1.15, 1.10, 1.22, 'USD'),
'US-LA': LocationFactor('US-LA', 'Los Angeles', 'USA', 'West', 1.25, 1.10, 1.05, 1.15, 'USD'),
'US-CHI': LocationFactor('US-CHI', 'Chicago', 'USA', 'Midwest', 1.20, 1.05, 1.05, 1.12, 'USD'),
'US-HOU': LocationFactor('US-HOU', 'Houston', 'USA', 'South', 0.95, 0.98, 0.95, 0.96, 'USD'),
'US-PHX': LocationFactor('US-PHX', 'Phoenix', 'USA', 'Southwest', 0.90, 0.95, 0.95, 0.93, 'USD'),
'US-DEN': LocationFactor('US-DEN', 'Denver', 'USA', 'Mountain', 1.00, 1.02, 1.00, 1.01, 'USD'),
'US-SEA': LocationFactor('US-SEA', 'Seattle', 'USA', 'Northwest', 1.18, 1.08, 1.05, 1.12, 'USD'),
'US-MIA': LocationFactor('US-MIA', 'Miami', 'USA', 'Southeast', 0.98, 1.05, 1.00, 1.01, 'USD'),
'US-ATL': LocationFactor('US-ATL', 'Atlanta', 'USA', 'Southeast', 0.92, 0.98, 0.95, 0.95, 'USD'),
'US-NAT': LocationFactor('US-NAT', 'US National Average', 'USA', 'National', 1.00, 1.00, 1.00, 1.00, 'USD'),
# Europe
'UK-LON': LocationFactor('UK-LON', 'London', 'UK', 'Southeast', 1.45, 1.20, 1.15, 1.30, 'GBP'),
'DE-BER': LocationFactor('DE-BER', 'Berlin', 'Germany', 'East', 1.15, 1.10, 1.10, 1.12, 'EUR'),
'DE-MUN': LocationFactor('DE-MUN', 'Munich', 'Germany', 'South', 1.25, 1.15, 1.12, 1.18, 'EUR'),
'FR-PAR': LocationFactor('FR-PAR', 'Paris', 'France', 'Ile-de-France', 1.30, 1.18, 1.15, 1.22, 'EUR'),
'NL-AMS': LocationFactor('NL-AMS', 'Amsterdam', 'Netherlands', 'North Holland', 1.20, 1.12, 1.10, 1.15, 'EUR'),
# Middle East
'AE-DXB': LocationFactor('AE-DXB', 'Dubai', 'UAE', 'Dubai', 0.85, 1.25, 1.10, 1.05, 'AED'),
'SA-RIY': LocationFactor('SA-RIY', 'Riyadh', 'Saudi Arabia', 'Central', 0.80, 1.20, 1.05, 1.00, 'SAR'),
'QA-DOH': LocationFactor('QA-DOH', 'Doha', 'Qatar', 'Qatar', 0.88, 1.30, 1.12, 1.08, 'QAR'),
# Asia
'SG-SIN': LocationFactor('SG-SIN', 'Singapore', 'Singapore', 'Central', 1.10, 1.15, 1.08, 1.12, 'SGD'),
'HK-HKG': LocationFactor('HK-HKG', 'Hong Kong', 'Hong Kong', 'Hong Kong', 1.20, 1.25, 1.15, 1.20, 'HKD'),
'JP-TKY': LocationFactor('JP-TKY', 'Tokyo', 'Japan', 'Kanto', 1.35, 1.20, 1.18, 1.25, 'JPY'),
# Australia
'AU-SYD': LocationFactor('AU-SYD', 'Sydney', 'Australia', 'NSW', 1.25, 1.15, 1.12, 1.18, 'AUD'),
'AU-MEL': LocationFactor('AU-MEL', 'Melbourne', 'Australia', 'Victoria', 1.20, 1.12, 1.10, 1.15, 'AUD'),
}
class CWICRLocationFactor:
"""Apply location factors to CWICR estimates."""
def __init__(self,
cwicr_data: pd.DataFrame = None,
base_location: str = 'US-NAT'):
self.cwicr = cwicr_data
self.base_location = base_location
self._factors = LOCATION_FACTORS.copy()
if cwicr_data is not None:
self._index_cwicr()
def _index_cwicr(self):
"""Index CWICR data."""
if 'work_item_code' in self.cwicr.columns:
self._cwicr_index = self.cwicr.set_index('work_item_code')
else:
self._cwicr_index = None
def get_factor(self, location_code: str) -> Optional[LocationFactor]:
"""Get location factor."""
return self._factors.get(location_code)
def list_locations(self, country: str = None) -> List[Dict[str, Any]]:
"""List available locations."""
factors = self._factors.values()
if country:
factors = [f for f in factors if f.country.lower() == country.lower()]
return [
{
'code': f.location_code,
'name': f.location_name,
'country': f.country,
'region': f.region,
'total_factor': f.total_factor,
'currency': f.currency
}
for f in factors
]
def add_location(self, factor: LocationFactor):
"""Add custom location factor."""
self._factors[factor.location_code] = factor
def adjust_cost(self,
base_cost: float,
target_location: str,
cost_breakdown: Dict[str, float] = None) -> AdjustedEstimate:
"""Adjust cost from base to target location."""
base_factor = self._factors.get(self.base_location)
target_factor = self._factors.get(target_location)
if not base_factor or not target_factor:
return AdjustedEstimate(
base_cost=base_cost,
base_location=self.base_location,
target_location=target_location,
labor_adjustment=0,
material_adjustment=0,
equipment_adjustment=0,
total_adjustment=0,
adjusted_cost=base_cost,
adjustment_percent=0
)
if cost_breakdown is None:
# Default breakdown
cost_breakdown = {
'labor': base_cost * 0.40,
'material': base_cost * 0.45,
'equipment': base_cost * 0.15
}
# Calculate relative factors
labor_rel = target_factor.labor_factor / base_factor.labor_factor
material_rel = target_factor.material_factor / base_factor.material_factor
equipment_rel = target_factor.equipment_factor / base_factor.equipment_factor
# Apply adjustments
labor_adjusted = cost_breakdown.get('labor', 0) * labor_rel
material_adjusted = cost_breakdown.get('material', 0) * material_rel
equipment_adjusted = cost_breakdown.get('equipment', 0) * equipment_rel
adjusted_total = labor_adjusted + material_adjusted + equipment_adjusted
total_adjustment = adjusted_total - base_cost
adjustment_pct = (total_adjustment / base_cost * 100) if base_cost > 0 else 0
return AdjustedEstimate(
base_cost=round(base_cost, 2),
base_location=self.base_location,
target_location=target_location,
labor_adjustment=round(labor_adjusted - cost_breakdown.get('labor', 0), 2),
material_adjustment=round(material_adjusted - cost_breakdown.get('material', 0), 2),
equipment_adjustment=round(equipment_adjusted - cost_breakdown.get('equipment', 0), 2),
total_adjustment=round(total_adjustment, 2),
adjusted_cost=round(adjusted_total, 2),
adjustment_percent=round(adjustment_pct, 1)
)
def adjust_estimate(self,
items: List[Dict[str, Any]],
target_location: str) -> Dict[str, Any]:
"""Adjust entire estimate for location."""
adjusted_items = []
total_base = 0
total_adjusted = 0
for item in items:
code = item.get('work_item_code', item.get('code'))
qty = item.get('quantity', 0)
# Get costs from CWICR
labor = 0
material = 0
equipment = 0
if self._cwicr_index is not None and code in self._cwicr_index.index:
wi = self._cwicr_index.loc[code]
labor = float(wi.get('labor_cost', 0) or 0) * qty
material = float(wi.get('material_cost', 0) or 0) * qty
equipment = float(wi.get('equipment_cost', 0) or 0) * qty
base_cost = labor + material + equipment
breakdown = {'labor': labor, 'material': material, 'equipment': equipment}
adjustment = self.adjust_cost(base_cost, target_location, breakdown)
adjusted_items.append({
'code': code,
'quantity': qty,
'base_cost': adjustment.base_cost,
'adjusted_cost': adjustment.adjusted_cost,
'adjustment': adjustment.total_adjustment
})
total_base += base_cost
total_adjusted += adjustment.adjusted_cost
return {
'items': adjusted_items,
'base_location': self.base_location,
'target_location': target_location,
'total_base': round(total_base, 2),
'total_adjusted': round(total_adjusted, 2),
'total_adjustment': round(total_adjusted - total_base, 2),
'adjustment_percent': round((total_adjusted - total_base) / total_base * 100, 1) if total_base > 0 else 0
}
def compare_locations(self,
base_cost: float,
locations: List[str]) -> pd.DataFrame:
"""Compare cost across multiple locations."""
data = []
for loc_code in locations:
adjustment = self.adjust_cost(base_cost, loc_code)
factor = self._factors.get(loc_code)
data.append({
'Location': factor.location_name if factor else loc_code,
'Code': loc_code,
'Country': factor.country if factor else '',
'Adjusted Cost': adjustment.adjusted_cost,
'Adjustment %': adjustment.adjustment_percent,
'Labor Factor': factor.labor_factor if factor else 1.0,
'Material Factor': factor.material_factor if factor else 1.0
})
return pd.DataFrame(data).sort_values('Adjusted Cost')
def normalize_to_base(self,
cost: float,
source_location: str) -> float:
"""Normalize cost from source location to base location."""
source_factor = self._factors.get(source_location)
base_factor = self._factors.get(self.base_location)
if not source_factor or not base_factor:
return cost
relative_factor = base_factor.total_factor / source_factor.total_factor
return round(cost * relative_factor, 2)
def export_factors(self, output_path: str) -> str:
"""Export location factors to Excel."""
with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
df = pd.DataFrame([
{
'Code': f.location_code,
'Name': f.location_name,
'Country': f.country,
'Region': f.region,
'Labor Factor': f.labor_factor,
'Material Factor': f.material_factor,
'Equipment Factor': f.equipment_factor,
'Total Factor': f.total_factor,
'Currency': f.currency
}
for f in self._factors.values()
])
df.to_excel(writer, sheet_name='Location Factors', index=False)
return output_path
Quick Start
# Initialize with base location
loc_factor = CWICRLocationFactor(base_location='US-NAT')
# Adjust single cost
adjustment = loc_factor.adjust_cost(
base_cost=1000000,
target_location='US-NYC'
)
print(f"Base: ${adjustment.base_cost:,.2f}")
print(f"NYC: ${adjustment.adjusted_cost:,.2f}")
print(f"Adjustment: {adjustment.adjustment_percent:+.1f}%")
Common Use Cases
1. Multi-Location Comparison
comparison = loc_factor.compare_locations(
base_cost=5000000,
locations=['US-NYC', 'US-HOU', 'US-LA', 'UK-LON', 'AE-DXB']
)
print(comparison)
2. Adjust Estimate
cwicr = pd.read_parquet("ddc_cwicr_en.parquet")
loc_factor = CWICRLocationFactor(cwicr, base_location='US-NAT')
items = [
{'work_item_code': 'CONC-001', 'quantity': 200},
{'work_item_code': 'STRL-002', 'quantity': 50}
]
dubai_estimate = loc_factor.adjust_estimate(items, 'AE-DXB')
print(f"Dubai Cost: ${dubai_estimate['total_adjusted']:,.2f}")
3. Custom Location
loc_factor.add_location(LocationFactor(
'US-REMOTE',
'Remote Alaska',
'USA',
'Alaska',
labor_factor=1.50,
material_factor=1.40,
equipment_factor=1.35,
total_factor=1.42,
currency='USD',
notes='Remote location premium'
))
Resources
- GitHub: OpenConstructionEstimate-DDC-CWICR
- DDC Book: Chapter 3.1 - Location Cost Adjustments