tools-only Auto Estimate Generator
Business Case
Problem Statement
Manual estimate creation challenges:
- Time-consuming quantity mapping
- Inconsistent pricing rules
- Errors in calculations
- Difficulty updating estimates
Solution
Automated estimate generation from BIM/QTO data using configurable pricing rules and assembly mappings.
Technical Implementation
import pandas as pd
from typing import Dict, Any, List, Optional, Callable
from dataclasses import dataclass, field
from enum import Enum
class ElementType(Enum):
WALL = "wall"
FLOOR = "floor"
CEILING = "ceiling"
DOOR = "door"
WINDOW = "window"
COLUMN = "column"
BEAM = "beam"
FOUNDATION = "foundation"
ROOF = "roof"
STAIR = "stair"
MEP = "mep"
@dataclass
class QTOItem:
element_id: str
element_type: ElementType
name: str
quantity: float
unit: str
properties: Dict[str, Any] = field(default_factory=dict)
@dataclass
class PricingRule:
rule_id: str
name: str
element_type: ElementType
conditions: Dict[str, Any] = field(default_factory=dict)
unit_cost: float = 0
assembly_code: str = ""
cost_breakdown: Dict[str, float] = field(default_factory=dict)
@dataclass
class EstimateItem:
qto_element_id: str
description: str
quantity: float
unit: str
unit_cost: float
total_cost: float
rule_applied: str
wbs_code: str = ""
class AutoEstimateGenerator:
"""Generate estimates from QTO data automatically."""
def __init__(self, project_name: str):
self.project_name = project_name
self.pricing_rules: List[PricingRule] = []
self.qto_items: List[QTOItem] = []
self.estimate_items: List[EstimateItem] = []
self.unmapped_items: List[QTOItem] = []
def add_pricing_rule(self, rule: PricingRule):
"""Add pricing rule."""
self.pricing_rules.append(rule)
def load_pricing_rules_from_df(self, df: pd.DataFrame):
"""Load pricing rules from DataFrame."""
for _, row in df.iterrows():
conditions = {}
if 'material' in row:
conditions['material'] = row['material']
if 'thickness_min' in row:
conditions['thickness_min'] = row['thickness_min']
if 'thickness_max' in row:
conditions['thickness_max'] = row['thickness_max']
rule = PricingRule(
rule_id=row['rule_id'],
name=row['name'],
element_type=ElementType(row['element_type'].lower()),
conditions=conditions,
unit_cost=float(row['unit_cost']),
assembly_code=row.get('assembly_code', ''),
cost_breakdown={
'labor': float(row.get('labor_pct', 0.4)),
'material': float(row.get('material_pct', 0.5)),
'equipment': float(row.get('equipment_pct', 0.1))
}
)
self.add_pricing_rule(rule)
def load_qto_from_df(self, df: pd.DataFrame):
"""Load QTO items from DataFrame."""
for _, row in df.iterrows():
properties = {}
for col in df.columns:
if col not in ['element_id', 'element_type', 'name', 'quantity', 'unit']:
properties[col] = row[col]
qto = QTOItem(
element_id=str(row['element_id']),
element_type=ElementType(row['element_type'].lower()),
name=row['name'],
quantity=float(row['quantity']),
unit=row['unit'],
properties=properties
)
self.qto_items.append(qto)
def find_matching_rule(self, qto_item: QTOItem) -> Optional[PricingRule]:
"""Find pricing rule that matches QTO item."""
matching_rules = []
for rule in self.pricing_rules:
if rule.element_type != qto_item.element_type:
continue
# Check conditions
match = True
for key, value in rule.conditions.items():
if key.endswith('_min'):
prop_name = key[:-4]
if prop_name in qto_item.properties:
if qto_item.properties[prop_name] < value:
match = False
elif key.endswith('_max'):
prop_name = key[:-4]
if prop_name in qto_item.properties:
if qto_item.properties[prop_name] > value:
match = False
else:
if key in qto_item.properties:
if qto_item.properties[key] != value:
match = False
if match:
matching_rules.append(rule)
# Return most specific rule (most conditions)
if matching_rules:
return max(matching_rules, key=lambda r: len(r.conditions))
return None
def generate_estimate(self) -> Dict[str, Any]:
"""Generate estimate from QTO items."""
self.estimate_items = []
self.unmapped_items = []
total_cost = 0
for qto in self.qto_items:
rule = self.find_matching_rule(qto)
if rule:
item_cost = qto.quantity * rule.unit_cost
self.estimate_items.append(EstimateItem(
qto_element_id=qto.element_id,
description=f"{qto.name} ({rule.name})",
quantity=qto.quantity,
unit=qto.unit,
unit_cost=rule.unit_cost,
total_cost=round(item_cost, 2),
rule_applied=rule.rule_id,
wbs_code=rule.assembly_code
))
total_cost += item_cost
else:
self.unmapped_items.append(qto)
return {
'project': self.project_name,
'total_qto_items': len(self.qto_items),
'mapped_items': len(self.estimate_items),
'unmapped_items': len(self.unmapped_items),
'mapping_rate': round(len(self.estimate_items) / len(self.qto_items) * 100, 1) if self.qto_items else 0,
'total_cost': round(total_cost, 2),
'items': self.estimate_items
}
def get_cost_by_element_type(self) -> Dict[str, float]:
"""Get cost breakdown by element type."""
by_type = {}
for qto in self.qto_items:
for est_item in self.estimate_items:
if est_item.qto_element_id == qto.element_id:
type_name = qto.element_type.value
by_type[type_name] = by_type.get(type_name, 0) + est_item.total_cost
return {k: round(v, 2) for k, v in by_type.items()}
def get_unmapped_summary(self) -> pd.DataFrame:
"""Get summary of unmapped items."""
if not self.unmapped_items:
return pd.DataFrame()
data = []
for item in self.unmapped_items:
data.append({
'Element ID': item.element_id,
'Type': item.element_type.value,
'Name': item.name,
'Quantity': item.quantity,
'Unit': item.unit,
'Properties': str(item.properties)
})
return pd.DataFrame(data)
def export_to_excel(self, output_path: str) -> str:
"""Export estimate to Excel."""
result = self.generate_estimate()
with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
# Summary
summary_df = pd.DataFrame([{
'Project': self.project_name,
'Total QTO Items': result['total_qto_items'],
'Mapped Items': result['mapped_items'],
'Unmapped Items': result['unmapped_items'],
'Mapping Rate %': result['mapping_rate'],
'Total Cost': result['total_cost']
}])
summary_df.to_excel(writer, sheet_name='Summary', index=False)
# Estimate items
items_df = pd.DataFrame([{
'Element ID': item.qto_element_id,
'Description': item.description,
'Quantity': item.quantity,
'Unit': item.unit,
'Unit Cost': item.unit_cost,
'Total Cost': item.total_cost,
'WBS': item.wbs_code,
'Rule': item.rule_applied
} for item in self.estimate_items])
items_df.to_excel(writer, sheet_name='Estimate', index=False)
# By element type
by_type_df = pd.DataFrame([
{'Element Type': k, 'Cost': v}
for k, v in self.get_cost_by_element_type().items()
])
by_type_df.to_excel(writer, sheet_name='By Type', index=False)
# Unmapped items
unmapped_df = self.get_unmapped_summary()
if not unmapped_df.empty:
unmapped_df.to_excel(writer, sheet_name='Unmapped', index=False)
return output_path
def suggest_missing_rules(self) -> List[Dict[str, Any]]:
"""Suggest pricing rules for unmapped items."""
suggestions = []
seen_types = set()
for item in self.unmapped_items:
key = (item.element_type.value, str(item.properties))
if key not in seen_types:
seen_types.add(key)
suggestions.append({
'element_type': item.element_type.value,
'sample_name': item.name,
'properties': item.properties,
'count': sum(1 for i in self.unmapped_items
if i.element_type == item.element_type
and str(i.properties) == str(item.properties))
})
return sorted(suggestions, key=lambda x: x['count'], reverse=True)
Quick Start
# Initialize generator
generator = AutoEstimateGenerator("Office Building A")
# Add pricing rules
generator.add_pricing_rule(PricingRule(
rule_id="W-001",
name="Interior Wall - Drywall",
element_type=ElementType.WALL,
conditions={"material": "Drywall"},
unit_cost=45.00,
assembly_code="09.29.10"
))
generator.add_pricing_rule(PricingRule(
rule_id="W-002",
name="Exterior Wall - Masonry",
element_type=ElementType.WALL,
conditions={"material": "Masonry"},
unit_cost=125.00,
assembly_code="04.21.13"
))
# Load QTO data
generator.qto_items = [
QTOItem("W-001", ElementType.WALL, "Interior Wall L1", 500, "SF", {"material": "Drywall"}),
QTOItem("W-002", ElementType.WALL, "Exterior Wall", 1200, "SF", {"material": "Masonry"})
]
# Generate estimate
result = generator.generate_estimate()
print(f"Total Cost: ${result['total_cost']:,.2f}")
print(f"Mapping Rate: {result['mapping_rate']}%")
Common Use Cases
1. Cost by Element Type
by_type = generator.get_cost_by_element_type()
for element_type, cost in by_type.items():
print(f"{element_type}: ${cost:,.2f}")
2. Unmapped Items
unmapped = generator.get_unmapped_summary()
print(unmapped)
3. Rule Suggestions
suggestions = generator.suggest_missing_rules()
for s in suggestions:
print(f"Need rule for: {s['element_type']} ({s['count']} items)")
Resources
- DDC Book: Chapter 3.2 - QTO and Automated Estimates
- Website: https://datadrivenconstruction.io