Manage construction unit price databases: update prices, track vendors, apply location factors, maintain historical records. Essential for accurate estimating.
Manage and maintain construction unit price databases. Update prices from vendors, apply location and time adjustments, track price history, and ensure estimating accuracy.
Accurate unit prices are critical for:
from dataclasses import dataclass, field
from typing import List, Dict, Any, Optional
from datetime import datetime, date
from decimal import Decimal
import pandas as pd
import json
@dataclass
class UnitPrice:
code: str
description: str
unit: str
base_price: Decimal
labor_cost: Decimal
material_cost: Decimal
equipment_cost: Decimal
effective_date: date
expiration_date: Optional[date] = None
source: str = ""
vendor: str = ""
location: str = "National Average"
notes: str = ""
tags: List[str] = field(default_factory=list)
@dataclass
class PriceUpdate:
code: str
old_price: Decimal
new_price: Decimal
change_pct: float
updated_at: datetime
updated_by: str
reason: str
@dataclass
class VendorQuote:
vendor_name: str
item_code: str
quoted_price: Decimal
quote_date: date
valid_until: date
quantity_break: Optional[int] = None
notes: str = ""
class UnitPriceDatabaseManager:
"""Manage construction unit price databases."""
# Location adjustment factors
LOCATION_FACTORS = {
'New York': 1.32, 'San Francisco': 1.28, 'Los Angeles': 1.15,
'Chicago': 1.12, 'Boston': 1.18, 'Seattle': 1.08,
'Denver': 1.02, 'National Average': 1.00,
'Houston': 0.92, 'Dallas': 0.89, 'Phoenix': 0.93,
'Atlanta': 0.91, 'Miami': 0.95
}
def __init__(self, db_path: str = None):
self.prices: Dict[str, UnitPrice] = {}
self.price_history: Dict[str, List[UnitPrice]] = {}
self.vendor_quotes: Dict[str, List[VendorQuote]] = {}
self.updates: List[PriceUpdate] = []
self.db_path = db_path
def add_price(self, price: UnitPrice) -> str:
"""Add or update a unit price."""
code = price.code
# Track history
if code in self.prices:
if code not in self.price_history:
self.price_history[code] = []
self.price_history[code].append(self.prices[code])
# Record update
old_price = self.prices[code].base_price
if old_price != price.base_price:
change_pct = float((price.base_price - old_price) / old_price * 100)
self.updates.append(PriceUpdate(
code=code,
old_price=old_price,
new_price=price.base_price,
change_pct=change_pct,
updated_at=datetime.now(),
updated_by="system",
reason="Price update"
))
self.prices[code] = price
return code
def get_price(self, code: str, location: str = None,
as_of_date: date = None) -> Optional[UnitPrice]:
"""Get unit price with optional location adjustment."""
if code not in self.prices:
return None
price = self.prices[code]
# Check date validity
if as_of_date:
if price.effective_date > as_of_date:
# Look in history
if code in self.price_history:
for hist_price in reversed(self.price_history[code]):
if hist_price.effective_date <= as_of_date:
if hist_price.expiration_date is None or hist_price.expiration_date >= as_of_date:
price = hist_price
break
if price.expiration_date and price.expiration_date < as_of_date:
return None
# Apply location factor
if location and location != price.location:
adjusted = UnitPrice(
code=price.code,
description=price.description,
unit=price.unit,
base_price=self._apply_location_factor(price.base_price, price.location, location),
labor_cost=self._apply_location_factor(price.labor_cost, price.location, location),
material_cost=price.material_cost, # Materials less location-sensitive
equipment_cost=self._apply_location_factor(price.equipment_cost, price.location, location),
effective_date=price.effective_date,
expiration_date=price.expiration_date,
source=price.source,
vendor=price.vendor,
location=location,
notes=f"Adjusted from {price.location}",
tags=price.tags
)
return adjusted
return price
def _apply_location_factor(self, amount: Decimal, from_loc: str, to_loc: str) -> Decimal:
"""Apply location adjustment factor."""
from_factor = self.LOCATION_FACTORS.get(from_loc, 1.0)
to_factor = self.LOCATION_FACTORS.get(to_loc, 1.0)
return Decimal(str(float(amount) * to_factor / from_factor))
def apply_escalation(self, percentage: float, categories: List[str] = None,
effective_date: date = None) -> int:
"""Apply escalation to prices."""
if effective_date is None:
effective_date = date.today()
count = 0
factor = Decimal(str(1 + percentage / 100))
for code, price in self.prices.items():
if categories and not any(tag in price.tags for tag in categories):
continue
old_price = price.base_price
new_price = UnitPrice(
code=price.code,
description=price.description,
unit=price.unit,
base_price=price.base_price * factor,
labor_cost=price.labor_cost * factor,
material_cost=price.material_cost * factor,
equipment_cost=price.equipment_cost * factor,
effective_date=effective_date,
source=f"Escalated {percentage}% from {price.source}",
vendor=price.vendor,
location=price.location,
tags=price.tags
)
self.add_price(new_price)
count += 1
return count
def add_vendor_quote(self, quote: VendorQuote):
"""Add a vendor quote."""
code = quote.item_code
if code not in self.vendor_quotes:
self.vendor_quotes[code] = []
self.vendor_quotes[code].append(quote)
def get_best_price(self, code: str, quantity: int = 1) -> Optional[Dict]:
"""Get best available price from vendors."""
if code not in self.vendor_quotes:
return None
valid_quotes = []
today = date.today()
for quote in self.vendor_quotes[code]:
if quote.valid_until >= today:
if quote.quantity_break is None or quantity >= quote.quantity_break:
valid_quotes.append(quote)
if not valid_quotes:
return None
best = min(valid_quotes, key=lambda q: q.quoted_price)
return {
'vendor': best.vendor_name,
'price': best.quoted_price,
'valid_until': best.valid_until,
'all_quotes': [
{'vendor': q.vendor_name, 'price': q.quoted_price}
for q in sorted(valid_quotes, key=lambda x: x.quoted_price)
]
}
def search_prices(self, query: str = None, category: str = None,
min_price: float = None, max_price: float = None) -> List[UnitPrice]:
"""Search prices by various criteria."""
results = []
for code, price in self.prices.items():
# Text search
if query:
query_lower = query.lower()
if (query_lower not in code.lower() and
query_lower not in price.description.lower()):
continue
# Category filter
if category and category not in price.tags:
continue
# Price range
if min_price and float(price.base_price) < min_price:
continue
if max_price and float(price.base_price) > max_price:
continue
results.append(price)
return results
def get_price_history(self, code: str) -> List[Dict]:
"""Get price history for an item."""
history = []
if code in self.price_history:
for price in self.price_history[code]:
history.append({
'date': price.effective_date,
'price': float(price.base_price),
'source': price.source
})
if code in self.prices:
history.append({
'date': self.prices[code].effective_date,
'price': float(self.prices[code].base_price),
'source': self.prices[code].source
})
return sorted(history, key=lambda x: x['date'])
def analyze_price_trends(self, code: str) -> Dict:
"""Analyze price trends for an item."""
history = self.get_price_history(code)
if len(history) < 2:
return {'trend': 'insufficient_data'}
prices = [h['price'] for h in history]
dates = [h['date'] for h in history]
# Calculate changes
first_price = prices[0]
last_price = prices[-1]
total_change = (last_price - first_price) / first_price * 100
# Calculate annualized rate
days = (dates[-1] - dates[0]).days
years = days / 365.25
if years > 0:
annual_rate = ((last_price / first_price) ** (1 / years) - 1) * 100
else:
annual_rate = 0
return {
'code': code,
'first_price': first_price,
'last_price': last_price,
'total_change_pct': total_change,
'annual_rate_pct': annual_rate,
'data_points': len(history),
'period_years': years,
'trend': 'increasing' if total_change > 5 else 'decreasing' if total_change < -5 else 'stable'
}
def import_from_csv(self, file_path: str) -> int:
"""Import prices from CSV file."""
df = pd.read_csv(file_path)
count = 0
for _, row in df.iterrows():
price = UnitPrice(
code=row['code'],
description=row['description'],
unit=row['unit'],
base_price=Decimal(str(row['base_price'])),
labor_cost=Decimal(str(row.get('labor_cost', 0))),
material_cost=Decimal(str(row.get('material_cost', 0))),
equipment_cost=Decimal(str(row.get('equipment_cost', 0))),
effective_date=date.today() if 'effective_date' not in row else pd.to_datetime(row['effective_date']).date(),
source=row.get('source', 'CSV Import'),
tags=row.get('tags', '').split(',') if 'tags' in row else []
)
self.add_price(price)
count += 1
return count
def export_to_csv(self, file_path: str, location: str = None) -> int:
"""Export prices to CSV file."""
data = []
for code, price in self.prices.items():
if location:
price = self.get_price(code, location)
data.append({
'code': price.code,
'description': price.description,
'unit': price.unit,
'base_price': float(price.base_price),
'labor_cost': float(price.labor_cost),
'material_cost': float(price.material_cost),
'equipment_cost': float(price.equipment_cost),
'location': price.location,
'effective_date': price.effective_date.isoformat(),
'source': price.source,
'tags': ','.join(price.tags)
})
df = pd.DataFrame(data)
df.to_csv(file_path, index=False)
return len(data)
def validate_prices(self) -> List[Dict]:
"""Validate prices for issues."""
issues = []
for code, price in self.prices.items():
# Check for expired prices
if price.expiration_date and price.expiration_date < date.today():
issues.append({
'code': code,
'issue': 'expired',
'message': f"Price expired on {price.expiration_date}"
})
# Check for old prices
age_days = (date.today() - price.effective_date).days
if age_days > 365:
issues.append({
'code': code,
'issue': 'stale',
'message': f"Price is {age_days} days old"
})
# Check for zero prices
if price.base_price <= 0:
issues.append({
'code': code,
'issue': 'invalid',
'message': "Zero or negative price"
})
# Check component breakdown
total_components = price.labor_cost + price.material_cost + price.equipment_cost
if total_components > 0 and abs(float(price.base_price - total_components)) > 0.01:
issues.append({
'code': code,
'issue': 'mismatch',
'message': f"Component costs don't match total: {total_components} vs {price.base_price}"
})
return issues
def generate_report(self) -> str:
"""Generate database status report."""
lines = ["# Unit Price Database Report", ""]
lines.append(f"**Generated:** {datetime.now().strftime('%Y-%m-%d %H:%M')}")
lines.append(f"**Total Items:** {len(self.prices):,}")
lines.append("")
# Category breakdown
categories = {}
for price in self.prices.values():
for tag in price.tags:
categories[tag] = categories.get(tag, 0) + 1
if categories:
lines.append("## Items by Category")
for cat, count in sorted(categories.items(), key=lambda x: -x[1]):
lines.append(f"- {cat}: {count}")
lines.append("")
# Recent updates
recent_updates = sorted(self.updates, key=lambda x: x.updated_at, reverse=True)[:10]
if recent_updates:
lines.append("## Recent Updates")
for update in recent_updates:
lines.append(f"- {update.code}: {update.change_pct:+.1f}% on {update.updated_at.strftime('%Y-%m-%d')}")
lines.append("")
# Validation issues
issues = self.validate_prices()
if issues:
lines.append("## Validation Issues")
lines.append(f"Total issues: {len(issues)}")
for issue in issues[:10]:
lines.append(f"- {issue['code']}: {issue['message']}")
return "\n".join(lines)
from decimal import Decimal
from datetime import date
# Initialize manager
manager = UnitPriceDatabaseManager()
# Add unit prices
manager.add_price(UnitPrice(
code="033000.10",
description="Cast-in-place concrete, 4000 PSI",
unit="CY",
base_price=Decimal("450.00"),
labor_cost=Decimal("150.00"),
material_cost=Decimal("250.00"),
equipment_cost=Decimal("50.00"),
effective_date=date(2026, 1, 1),
source="RSMeans 2026",
tags=["concrete", "structural"]
))
# Get price with location adjustment
price = manager.get_price("033000.10", location="New York")
print(f"NYC price: ${price.base_price}/CY")
# Add vendor quote
manager.add_vendor_quote(VendorQuote(
vendor_name="ABC Concrete",
item_code="033000.10",
quoted_price=Decimal("420.00"),
quote_date=date.today(),
valid_until=date(2026, 3, 31)
))
# Get best price
best = manager.get_best_price("033000.10")
print(f"Best price: ${best['price']} from {best['vendor']}")
# Apply escalation
count = manager.apply_escalation(3.5, categories=["concrete"])
print(f"Escalated {count} items by 3.5%")
# Generate report
print(manager.generate_report())
pip install pandas