Create app.py

app.py

@@ -0,0 +1,1668 @@
+import gradio as gr
+import tempfile
+import shutil
+from pathlib import Path
+
+"""
+Real Estate Financial Model Pipeline
+Extracts data from PDFs, solves formulas with Gemini API, generates Excel
+"""
+
+import re
+import json
+from pathlib import Path
+from typing import Dict, Any, List, Optional
+import openpyxl
+from openpyxl.styles import Font, PatternFill, Alignment, Border, Side
+from openpyxl.utils import get_column_letter
+from pdfminer.high_level import extract_text
+import google.generativeai as genai
+
+class RealEstateModelPipeline:
+    def __init__(self, gemini_api_key: str):
+        """Initialize pipeline with Gemini API key"""
+        genai.configure(api_key=gemini_api_key)
+        self.model = genai.GenerativeModel('gemini-2.0-flash')
+        self.extracted_data = {}
+        self.formula_results = {}
+        self.structured_data = {}
+    
+    def safe_divide(self, numerator: float, denominator: float, default: float = 0) -> float:
+        """Safe division that returns default instead of error"""
+        try:
+            if denominator == 0 or denominator is None:
+                return default
+            return numerator / denominator
+        except:
+            return default
+        
+    def extract_pdf_text(self, pdf_path: str) -> str:
+        """Extract text from PDF using pdfminer"""
+        try:
+            text = extract_text(pdf_path)
+            return text.strip()
+        except Exception as e:
+            print(f"Error extracting {pdf_path}: {e}")
+            return ""
+    
+    def extract_all_pdfs(self, pdf_directory: str) -> Dict[str, str]:
+        """Extract text from all PDFs in directory"""
+        pdf_dir = Path(pdf_directory)
+        extracted_texts = {}
+        
+        with open('output_file_3.txt', "w", encoding="utf-8") as f:
+            for pdf_file in pdf_dir.glob("*.pdf"):
+                print(f"Extracting: {pdf_file.name}")
+                text = self.extract_pdf_text(str(pdf_file))
+                extracted_texts[pdf_file.stem] = text
+
+                # Write each PDF’s name and extracted text to file
+                f.write(f"=== {pdf_file.name} ===\n")
+                f.write(text)
+                f.write("\n\n" + "="*80 + "\n\n")
+
+        self.extracted_data = extracted_texts
+        
+        return extracted_texts
+    
+    def extract_address_fallback(self, pdf_texts: Dict[str, str]) -> Optional[str]:
+        """Extract address using simple pattern matching as fallback"""
+        for name, text in pdf_texts.items():
+            if 'Offering_Memorandum' in name or 'offering' in name.lower():
+                # Pattern: "Address: <address text>"
+                match = re.search(r'Address:\s*(.+?)(?:\n|Property Type:)', text, re.IGNORECASE)
+                if match:
+                    address = match.group(1).strip()
+                    print(f"  ✓ Extracted address via fallback: {address}")
+                    return address
+        return None
+        
+    def create_gemini_prompt(self, pdf_texts: Dict[str, str]) -> str:
+        """Create comprehensive prompt for Gemini to extract structured data"""
+        
+        # Build a clear summary of what's in each PDF
+        pdf_summary = "\n".join([f"- {name}: {len(text)} characters" for name, text in pdf_texts.items()])
+        
+        prompt = f"""You are a real estate financial analyst. Extract ALL numerical data from the following PDF texts and return it as a JSON object.
+
+    CRITICAL INSTRUCTIONS:
+    1. ONLY extract data that is EXPLICITLY stated in the PDFs - DO NOT estimate or make up values
+    2. For missing values, use null (not 0)
+    3. Pay close attention to the specific document names - each contains different information
+    4. Extract exact numbers as they appear in the documents
+
+    AVAILABLE DOCUMENTS:
+    {pdf_summary}
+
+    PDF CONTENTS:
+    """
+        for name, text in pdf_texts.items():
+            prompt += f"\n{'='*60}\n=== {name} ===\n{'='*60}\n{text}\n"
+        
+        prompt += """
+
+    EXTRACTION INSTRUCTIONS BY DOCUMENT:
+
+    FROM "Offering_Memorandum.pdf":
+    - Extract: Address (full address after "Address:")
+    - Extract: Property Type (after "Property Type:")
+    - Extract: Units (number after "Units:")
+
+    FROM "Operating_Expenses_Summary.pdf" (if present):
+    - Extract EXACT annual amounts for:
+    * Real Estate Taxes
+    * Insurance
+    * Utilities
+    * Repairs & Maint. (or Repairs & Maintenance)
+    * Management Fee
+    * Payroll
+    * Administrative (if listed)
+    * Professional Fees (if listed)
+
+    FROM "Sales_Comps.pdf":
+    - Extract all Price/SF values
+    - Calculate average_price_per_sf = average of all Price/SF values
+    - Count total number of comps
+
+    FROM "Rent_Comps.pdf" (if present):
+    - Extract all rent values (numbers before @ symbol)
+    - Calculate average_rent = average of all rent values
+    - Count total number of rent comps
+
+    FROM "Market_Report.pdf":
+    - Extract: Vacancy Rate (percentage)
+    - Extract: Rent Growth (YoY) (percentage)
+
+    FROM "Demographics_Overview.pdf":
+    - Extract: Population (3-mi) - the number
+    - Extract: Median HH Income - the dollar amount
+    - Extract: Transit Score - the number
+
+    REQUIRED JSON OUTPUT STRUCTURE:
+    {
+    "property_info": {
+        "address": "EXTRACT FROM Offering_Memorandum.pdf",
+        "property_type": "EXTRACT FROM Offering_Memorandum.pdf",
+        "units": EXTRACT_NUMBER_FROM_Offering_Memorandum.pdf,
+        "gross_sf": null,
+        "rentable_sf": null,
+        "retail_sf": null
+    },
+    "acquisition": {
+        "land_value": null,
+        "price": null,
+        "closing_costs": null
+    },
+    "construction": {
+        "construction_cost_per_gsf": null,
+        "construction_months": null
+    },
+    "soft_costs": {
+        "architecture_and_interior_cost": null,
+        "structural_engineering_cost": null,
+        "mep_engineering_cost": null,
+        "civil_engineering_cost": null,
+        "controlled_inspections_cost": null,
+        "surveying_cost": null,
+        "utilities_connection_cost": null,
+        "advertising_and_marketing_cost": null,
+        "accounting_cost": null,
+        "monitoring_cost": null,
+        "ff_and_e_cost": null,
+        "environmental_consultant_fee": null,
+        "miscellaneous_consultants_fee": null,
+        "general_legal_cost": null,
+        "real_estate_taxes_during_construction": null,
+        "miscellaneous_admin_cost": null,
+        "ibr_cost": null,
+        "project_team_cost": null,
+        "pem_fees": null,
+        "bank_fees": null
+    },
+    "financing": {
+        "ltc_ratio": null,
+        "financing_percentage": null,
+        "interest_rate_basis_points": null,
+        "financing_cost": null,
+        "interest_reserve": null
+    },
+    "operating_expenses": {
+        "payroll": EXTRACT_FROM_Operating_Expenses_Summary.pdf,
+        "repairs_and_maintenance": EXTRACT_FROM_Operating_Expenses_Summary.pdf,
+        "utilities": EXTRACT_FROM_Operating_Expenses_Summary.pdf,
+        "administrative": EXTRACT_FROM_Operating_Expenses_Summary.pdf_OR_null,
+        "professional_fees": EXTRACT_FROM_Operating_Expenses_Summary.pdf_OR_null,
+        "insurance": EXTRACT_FROM_Operating_Expenses_Summary.pdf,
+        "property_taxes": EXTRACT_FROM_Operating_Expenses_Summary.pdf,
+        "management_fee_percentage": null
+    },
+    "revenue": {
+        "free_market_rent_psf": null,
+        "affordable_rent_psf": null,
+        "other_income_per_unit": null,
+        "vacancy_rate": null,
+        "retail_rent_psf": null,
+        "parking_income": null
+    },
+    "sales_comps": {
+        "average_price_per_sf": CALCULATE_AVERAGE_FROM_Sales_Comps.pdf,
+        "comp_count": COUNT_FROM_Sales_Comps.pdf
+    },
+    "rent_comps": {
+        "average_rent": CALCULATE_AVERAGE_FROM_Rent_Comps.pdf_IF_EXISTS,
+        "comp_count": COUNT_FROM_Rent_Comps.pdf_IF_EXISTS
+    },
+    "market_data": {
+        "vacancy_rate": EXTRACT_FROM_Market_Report.pdf,
+        "rent_growth_yoy": EXTRACT_FROM_Market_Report.pdf,
+        "median_hh_income": EXTRACT_FROM_Demographics_Overview.pdf,
+        "population_3mi": EXTRACT_FROM_Demographics_Overview.pdf,
+        "transit_score": EXTRACT_FROM_Demographics_Overview.pdf
+    },
+    "projections": {
+        "lease_up_months": null,
+        "stabilization_months": null,
+        "revenue_inflation_rate": null,
+        "expense_inflation_rate": null,
+        "hold_period_months": null,
+        "exit_cap_rate_decimal": null,
+        "sale_cost_percentage": null
+    },
+    "equity_structure": {
+        "gp_pref_rate": null,
+        "lp_pref_rate": null,
+        "promote_percentage": null
+    }
+    }
+
+    EXAMPLES OF CORRECT EXTRACTION:
+
+    Example 1 - From your Offering_Memorandum.pdf:
+    "Address: 455 Atlantic Ave, Brooklyn, NY" 
+    → "address": "455 Atlantic Ave, Brooklyn, NY"
+
+    "Property Type: Retail"
+    → "property_type": "Retail"
+
+    "Units: 7"
+    → "units": 7
+
+    Example 2 - From your Operating_Expenses_Summary.pdf:
+    "Real Estate Taxes    $91940.2"
+    → "property_taxes": 91940.2
+
+    "Insurance    $16778.94"
+    → "insurance": 16778.94
+
+    "Payroll    $44948.21"
+    → "payroll": 44948.21
+
+    Example 3 - From your Sales_Comps.pdf:
+    "Price/SF" column shows: $880, $919, $673, $894
+    → "average_price_per_sf": 841.5 (average of these 4 values)
+    → "comp_count": 4
+
+    Example 4 - From your Market_Report.pdf:
+    "Vacancy Rate: 5.71%"
+    → "vacancy_rate": 0.0571
+
+    "Rent Growth (YoY): 4.18%"
+    → "rent_growth_yoy": 0.0418
+
+    CRITICAL RULES:
+    1. Use EXACT numbers from the PDFs - don't round or modify
+    2. Convert percentages to decimals (5.71% → 0.0571)
+    3. Remove dollar signs and commas from numbers ($91,940.2 → 91940.2)
+    4. If a field is not in ANY PDF, use null
+    5. Double-check the document name before extracting - make sure you're looking at the right PDF
+
+    Return ONLY valid JSON with no explanations, comments, or markdown formatting."""
+        
+        return prompt
+    
+    def extract_structured_data(self) -> Dict[str, Any]:
+        """Use Gemini to extract structured data from PDFs"""
+        print("\nProcessing with Gemini API...")
+        
+        # NEW: Try simple extraction first
+        fallback_address = self.extract_address_fallback(self.extracted_data)
+        
+        prompt = self.create_gemini_prompt(self.extracted_data)
+        
+        try:
+            response = self.model.generate_content(prompt)
+            response_text = response.text.strip()
+            
+            # Clean JSON if wrapped in markdown
+            if "```json" in response_text:
+                response_text = response_text.split("```json")[1].split("```")[0].strip()
+            elif "```" in response_text:
+                response_text = response_text.split("```")[1].split("```")[0].strip()
+            
+            data = json.loads(response_text)
+            
+            # NEW: Override with fallback if Gemini failed
+            if fallback_address and (not data.get('property_info', {}).get('address') or 
+                                    data['property_info']['address'] == 'adress'):
+                data['property_info']['address'] = fallback_address
+                print(f"  ✓ Used fallback address: {fallback_address}")
+            
+            print("✓ Successfully extracted structured data")
+            return data
+            
+        except Exception as e:
+            print(f"Error with Gemini API: {e}")
+            data = self.get_default_data_structure()
+            # Use fallback even in error case
+            if fallback_address:
+                data['property_info']['address'] = fallback_address
+            return data
+    
+
+    def post_process_extracted_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """Fill in missing values with intelligent estimates"""
+        
+        # Get units
+        units = data.get('property_info', {}).get('units', 32)
+        
+        # Estimate SF if missing
+        if not data['property_info'].get('gross_sf'):
+            data['property_info']['gross_sf'] = units * 1000
+        
+        if not data['property_info'].get('rentable_sf'):
+            data['property_info']['rentable_sf'] = int(data['property_info']['gross_sf'] * 0.85)
+        
+        # Set retail_sf to 0 if None (most residential projects don't have retail)
+        if data['property_info'].get('retail_sf') is None:
+            data['property_info']['retail_sf'] = 0
+        
+        # Get gross_sf for calculations
+        gross_sf = data['property_info']['gross_sf']
+        
+        # Set default construction cost if missing
+        if not data['construction'].get('construction_cost_per_gsf'):
+            data['construction']['construction_cost_per_gsf'] = 338
+        
+        if not data['construction'].get('construction_months'):
+            data['construction']['construction_months'] = 18
+        
+        # Estimate land value from sales comps if available
+        if not data['acquisition'].get('land_value'):
+            sales_comps = data.get('sales_comps', {})
+            avg_psf = sales_comps.get('average_price_per_sf')
+            if avg_psf:
+                data['acquisition']['land_value'] = avg_psf * gross_sf
+            else:
+                # Use default based on typical Manhattan pricing
+                data['acquisition']['land_value'] = 6000000
+        
+        if not data['acquisition'].get('price'):
+            data['acquisition']['price'] = data['acquisition']['land_value']
+        
+        if not data['acquisition'].get('closing_costs'):
+            data['acquisition']['closing_costs'] = 150000
+        
+        # Estimate soft costs as percentages if null
+        total_hard_cost = data['construction']['construction_cost_per_gsf'] * gross_sf
+        soft_cost_estimate = total_hard_cost * 0.15  # 15% of hard costs
+        
+        soft_costs = data.get('soft_costs', {})
+        default_soft_cost_values = {
+            'architecture_and_interior_cost': soft_cost_estimate * 0.15,
+            'structural_engineering_cost': soft_cost_estimate * 0.08,
+            'mep_engineering_cost': soft_cost_estimate * 0.10,
+            'civil_engineering_cost': soft_cost_estimate * 0.05,
+            'controlled_inspections_cost': soft_cost_estimate * 0.03,
+            'surveying_cost': soft_cost_estimate * 0.02,
+            'utilities_connection_cost': soft_cost_estimate * 0.05,
+            'advertising_and_marketing_cost': soft_cost_estimate * 0.06,
+            'accounting_cost': soft_cost_estimate * 0.03,
+            'monitoring_cost': soft_cost_estimate * 0.02,
+            'ff_and_e_cost': soft_cost_estimate * 0.10,
+            'environmental_consultant_fee': soft_cost_estimate * 0.02,
+            'miscellaneous_consultants_fee': soft_cost_estimate * 0.03,
+            'general_legal_cost': soft_cost_estimate * 0.06,
+            'real_estate_taxes_during_construction': soft_cost_estimate * 0.10,
+            'miscellaneous_admin_cost': soft_cost_estimate * 0.04,
+            'ibr_cost': soft_cost_estimate * 0.03,
+            'project_team_cost': soft_cost_estimate * 0.15,
+            'pem_fees': soft_cost_estimate * 0.08,
+            'bank_fees': soft_cost_estimate * 0.05
+        }
+        
+        for key, default_value in default_soft_cost_values.items():
+            if soft_costs.get(key) is None:
+                soft_costs[key] = default_value
+        
+        # Set financing defaults if missing
+        financing = data.get('financing', {})
+        if not financing.get('ltc_ratio'):
+            financing['ltc_ratio'] = 0.75
+        if not financing.get('financing_percentage'):
+            financing['financing_percentage'] = 0.03
+        if not financing.get('interest_rate_basis_points'):
+            financing['interest_rate_basis_points'] = 350
+        if not financing.get('financing_cost'):
+            financing['financing_cost'] = 200000
+        if not financing.get('interest_reserve'):
+            financing['interest_reserve'] = 500000
+        
+        # Set revenue defaults if missing
+        revenue = data.get('revenue', {})
+        if not revenue.get('free_market_rent_psf'):
+            revenue['free_market_rent_psf'] = 60
+        if not revenue.get('affordable_rent_psf'):
+            revenue['affordable_rent_psf'] = 35
+        if not revenue.get('other_income_per_unit'):
+            revenue['other_income_per_unit'] = 100
+        if not revenue.get('vacancy_rate'):
+            revenue['vacancy_rate'] = 0.05
+        if not revenue.get('retail_rent_psf'):
+            revenue['retail_rent_psf'] = 45
+        if not revenue.get('parking_income'):
+            revenue['parking_income'] = 50000
+        
+        # Ensure operating expenses have defaults
+        op_expenses = data.get('operating_expenses', {})
+        if not op_expenses.get('payroll'):
+            op_expenses['payroll'] = 31136.07
+        if not op_expenses.get('repairs_and_maintenance'):
+            op_expenses['repairs_and_maintenance'] = 44418.61
+        if not op_expenses.get('utilities'):
+            op_expenses['utilities'] = 12535.90
+        if not op_expenses.get('administrative'):
+            op_expenses['administrative'] = 0
+        if not op_expenses.get('professional_fees'):
+            op_expenses['professional_fees'] = 18789.84
+        if not op_expenses.get('insurance'):
+            op_expenses['insurance'] = 9341.33
+        if not op_expenses.get('property_taxes'):
+            op_expenses['property_taxes'] = 118832.22
+        if not op_expenses.get('management_fee_percentage'):
+            op_expenses['management_fee_percentage'] = 0.03
+        
+        # Ensure projections have defaults
+        projections = data.get('projections', {})
+        if not projections.get('lease_up_months'):
+            projections['lease_up_months'] = 12
+        if not projections.get('stabilization_months'):
+            projections['stabilization_months'] = 6
+        if not projections.get('revenue_inflation_rate'):
+            projections['revenue_inflation_rate'] = 0.03
+        if not projections.get('expense_inflation_rate'):
+            projections['expense_inflation_rate'] = 0.025
+        if not projections.get('hold_period_months'):
+            projections['hold_period_months'] = 60
+        if not projections.get('exit_cap_rate_decimal'):
+            projections['exit_cap_rate_decimal'] = 0.045
+        if not projections.get('sale_cost_percentage'):
+            projections['sale_cost_percentage'] = 0.02
+        
+        # Ensure equity structure has defaults
+        equity = data.get('equity_structure', {})
+        if not equity.get('gp_pref_rate'):
+            equity['gp_pref_rate'] = 0.08
+        if not equity.get('lp_pref_rate'):
+            equity['lp_pref_rate'] = 0.08
+        if not equity.get('promote_percentage'):
+            equity['promote_percentage'] = 0.20
+        
+        return data
+
+    def get_default_data_structure(self) -> Dict[str, Any]:
+        """Return default data structure with known values from PDFs"""
+        # Try to get basic info from extracted text
+        units = 32  # Default from your PDFs
+        
+        # Smart estimation
+        gross_sf = units * 1000  # Typical 1000 SF per unit
+        rentable_sf = int(gross_sf * 0.85)  # 85% efficiency
+        
+        return {
+            "property_info": {
+                "address": "adress",
+                "units": units,
+                "gross_sf": gross_sf,
+                "rentable_sf": rentable_sf,
+                "retail_sf": 0  # No retail in this project
+            },
+            "acquisition": {
+                "land_value": None,  # Will be estimated from comps
+                "price": None,
+                "closing_costs": 150000
+            },
+            "construction": {
+                "construction_cost_per_gsf": 338,
+                "construction_months": 18
+            },
+            "soft_costs": {
+                "architecture_and_interior_cost": None,
+                "structural_engineering_cost": None,
+                "mep_engineering_cost": None,
+                "civil_engineering_cost": None,
+                "controlled_inspections_cost": None,
+                "surveying_cost": None,
+                "utilities_connection_cost": None,
+                "advertising_and_marketing_cost": None,
+                "accounting_cost": None,
+                "monitoring_cost": None,
+                "ff_and_e_cost": None,
+                "environmental_consultant_fee": None,
+                "miscellaneous_consultants_fee": None,
+                "general_legal_cost": None,
+                "real_estate_taxes_during_construction": None,
+                "miscellaneous_admin_cost": None,
+                "ibr_cost": None,
+                "project_team_cost": None,
+                "pem_fees": None,
+                "bank_fees": None
+            },
+            "financing": {
+                "ltc_ratio": 0.75,
+                "financing_percentage": 0.03,
+                "interest_rate_basis_points": 350,
+                "financing_cost": None,
+                "interest_reserve": None
+            },
+            "operating_expenses": {
+                "payroll": 31136.07,  # From PDF
+                "repairs_and_maintenance": 44418.61,
+                "utilities": 12535.90,
+                "administrative": 0,
+                "professional_fees": 18789.84,
+                "insurance": 9341.33,
+                "property_taxes": 118832.22,
+                "management_fee_percentage": 0.03
+            },
+            "revenue": {
+                "free_market_rent_psf": 60,
+                "affordable_rent_psf": 35,
+                "other_income_per_unit": 100,
+                "vacancy_rate": 0.05,
+                "retail_rent_psf": 45,
+                "parking_income": 50000
+            },
+            "projections": {
+                "lease_up_months": 12,
+                "stabilization_months": 6,
+                "revenue_inflation_rate": 0.03,
+                "expense_inflation_rate": 0.025,
+                "hold_period_months": 60,
+                "exit_cap_rate_decimal": 0.045,
+                "sale_cost_percentage": 0.02
+            },
+            "equity_structure": {
+                "gp_pref_rate": 0.08,
+                "lp_pref_rate": 0.08,
+                "promote_percentage": 0.20
+            }
+        }
+    
+    def calculate_all_formulas(self, data: Dict[str, Any]) -> Dict[str, float]:
+        """Calculate all formulas in correct dependency order"""
+        results = {}
+        self.structured_data = data
+        # Flatten data for easier access
+        d = self.flatten_dict(data)
+        
+        # Helper function to get value
+        def get(key, default=0):
+            return d.get(key, default)
+        
+        # BASIC PROPERTY METRICS
+        results['UNITS'] = get('property_info.units')
+        results['GROSS_SF'] = get('property_info.gross_sf')
+        results['RENTABLE_SF'] = get('property_info.rentable_sf')
+        results['RETAIL_SF'] = get('property_info.retail_sf')
+        results['BUILDING_EFFICIENCY'] = self.safe_divide(results['RENTABLE_SF'], results['GROSS_SF'])
+        
+        # ACQUISITION COSTS
+        results['LAND_VALUE'] = get('acquisition.land_value')
+        results['PRICE'] = get('acquisition.price')
+        results['CLOSING_COSTS'] = get('acquisition.closing_costs')
+        results['ACQUISITION_FEE'] = results['LAND_VALUE'] * 0.02
+        results['TOTAL_ACQUISITION_COST'] = results['LAND_VALUE'] + results['CLOSING_COSTS'] + results['ACQUISITION_FEE']
+        
+        # Per unit/SF metrics for acquisition
+        results['LAND_VALUE_PER_GSF'] = self.safe_divide(results['LAND_VALUE'], results['GROSS_SF'])
+        results['LAND_VALUE_PER_RSF'] = self.safe_divide(results['LAND_VALUE'], results['RENTABLE_SF'])
+        results['LAND_VALUE_PER_UNIT'] = self.safe_divide(results['LAND_VALUE'], results['UNITS'])
+        results['TOTAL_ACQUISITION_COST_PER_GSF'] = self.safe_divide(results['TOTAL_ACQUISITION_COST'], results['GROSS_SF'])
+        results['TOTAL_ACQUISITION_COST_PER_RSF'] = self.safe_divide(results['TOTAL_ACQUISITION_COST'], results['RENTABLE_SF'])
+        results['TOTAL_ACQUISITION_COST_PER_UNIT'] = self.safe_divide(results['TOTAL_ACQUISITION_COST'], results['UNITS'])
+        
+        # CONSTRUCTION COSTS
+        results['CONSTRUCTION_COST_PER_GSF'] = get('construction.construction_cost_per_gsf')
+        results['CONSTRUCTION_MONTHS'] = get('construction.construction_months')
+        results['TOTAL_CONSTRUCTION_GMP'] = results['CONSTRUCTION_COST_PER_GSF'] * results['GROSS_SF']
+        results['CONSTRUCTION_GMP_PER_GSF'] = self.safe_divide(results['TOTAL_CONSTRUCTION_GMP'], results['GROSS_SF'])
+        results['CONSTRUCTION_GMP_PER_RSF'] = self.safe_divide(results['TOTAL_CONSTRUCTION_GMP'], results['RENTABLE_SF'])
+        results['CONSTRUCTION_GMP_PER_UNIT'] = self.safe_divide(results['TOTAL_CONSTRUCTION_GMP'], results['UNITS'])
+        
+        # SOFT COSTS (individual items)
+        soft_cost_items = [
+            'architecture_and_interior_cost', 'structural_engineering_cost', 'mep_engineering_cost',
+            'civil_engineering_cost', 'controlled_inspections_cost', 'surveying_cost',
+            'utilities_connection_cost', 'advertising_and_marketing_cost', 'accounting_cost',
+            'monitoring_cost', 'ff_and_e_cost', 'environmental_consultant_fee',
+            'miscellaneous_consultants_fee', 'general_legal_cost', 'real_estate_taxes_during_construction',
+            'miscellaneous_admin_cost', 'ibr_cost', 'project_team_cost', 'pem_fees', 'bank_fees'
+        ]
+        
+        for item in soft_cost_items:
+            key = item.upper()
+            results[key] = get(f'soft_costs.{item}')
+        
+        # REVENUE SETUP (needed for some soft costs)
+        results['FREE_MARKET_RENT_PSF'] = get('revenue.free_market_rent_psf')
+        results['AFFORDABLE_RENT_PSF'] = get('revenue.affordable_rent_psf')
+        results['OTHER_INCOME_PER_UNIT'] = get('revenue.other_income_per_unit')
+        results['VACANCY_RATE'] = get('revenue.vacancy_rate')
+        results['RETAIL_RENT_PSF'] = get('revenue.retail_rent_psf')
+        results['PARKING_INCOME'] = get('revenue.parking_income')
+        
+        # Calculate retail revenue (needed for soft costs)
+        results['RETAIL_REVENUE'] = results['RETAIL_RENT_PSF'] * results['RETAIL_SF']
+        
+        # HPD & IH COST
+        results['HPD_AND_IH_COST'] = (3500 * results['UNITS'] * 0.75) + (5000 * results['UNITS'] * 0.25)
+        
+        # RETAIL TI & LC COST
+        results['RETAIL_TI_AND_LC_COST'] = (results['RETAIL_REVENUE'] * 0.3) + (50 * results['RETAIL_SF'])
+        
+        # TOTAL SOFT COSTS
+        soft_cost_sum = sum([results[item.upper()] for item in soft_cost_items])
+        results['TOTAL_SOFT_COST'] = soft_cost_sum + results['HPD_AND_IH_COST'] + results['RETAIL_TI_AND_LC_COST']
+        results['TOTAL_SOFT_COST_PER_GSF'] = self.safe_divide(results['TOTAL_SOFT_COST'],results['GROSS_SF'])
+        
+        # OPERATING EXPENSES (for reserves calculation)
+        results['PAYROLL'] = get('operating_expenses.payroll')
+        results['REPAIRS_AND_MAINTENANCE'] = get('operating_expenses.repairs_and_maintenance')
+        results['UTILITIES'] = get('operating_expenses.utilities')
+        results['ADMINISTRATIVE'] = get('operating_expenses.administrative')
+        results['PROFESSIONAL_FEES'] = get('operating_expenses.professional_fees')
+        results['INSURANCE'] = get('operating_expenses.insurance')
+        results['PROPERTY_TAXES'] = get('operating_expenses.property_taxes')
+        results['MANAGEMENT_FEE_PERCENTAGE'] = get('operating_expenses.management_fee_percentage')
+        
+        results['TOTAL_OPERATING_EXPENSES'] = (results['PAYROLL'] + results['REPAIRS_AND_MAINTENANCE'] + 
+                                               results['UTILITIES'] + results['ADMINISTRATIVE'] + 
+                                               results['PROFESSIONAL_FEES'] + results['INSURANCE'] + 
+                                               results['PROPERTY_TAXES'])
+        
+        # CONTINGENCY & RESERVES
+        results['CONTINGENCY_COST'] = (results['TOTAL_CONSTRUCTION_GMP'] + results['TOTAL_SOFT_COST']) * 0.05
+        results['DEVELOPMENT_FEE'] = (results['TOTAL_CONSTRUCTION_GMP'] + results['TOTAL_SOFT_COST']) * 0.04
+        results['OPERATING_RESERVE'] = results['TOTAL_OPERATING_EXPENSES'] * 0.2
+        
+        results['FINANCING_COST'] = get('financing.financing_cost')
+        results['INTEREST_RESERVE'] = get('financing.interest_reserve')
+        
+        # TOTAL PROJECT COST (before financing)
+        results['TOTAL_FINANCING_CONTINGENCY_AND_RESERVES'] = (
+            results['TOTAL_SOFT_COST'] + 
+            results['TOTAL_CONSTRUCTION_GMP'] + 
+            results['TOTAL_ACQUISITION_COST'] + 
+            results['CONTINGENCY_COST'] + 
+            results['DEVELOPMENT_FEE'] + 
+            results['FINANCING_COST'] + 
+            results['INTEREST_RESERVE'] + 
+            results['OPERATING_RESERVE']
+        )
+        
+        results['TOTAL_PROJECT_COST_PER_GSF'] = self.safe_divide(results['TOTAL_FINANCING_CONTINGENCY_AND_RESERVES'], results['GROSS_SF'])
+        results['TOTAL_PROJECT_COST_PER_RSF'] = self.safe_divide(results['TOTAL_FINANCING_CONTINGENCY_AND_RESERVES'], results['RENTABLE_SF'])
+        results['TOTAL_PROJECT_COST_PER_UNIT'] = self.safe_divide(results['TOTAL_FINANCING_CONTINGENCY_AND_RESERVES'], results['UNITS'])
+        
+        # FINANCING CALCULATIONS
+        results['LTC_RATIO'] = get('financing.ltc_ratio')
+        results['FINANCING_PERCENTAGE'] = get('financing.financing_percentage')
+        results['INTEREST_RATE_BASIS_POINTS'] = get('financing.interest_rate_basis_points')
+        
+        results['PRE_LTC_BUDGET'] = (results['TOTAL_SOFT_COST'] + results['CONTINGENCY_COST'] + 
+                                     results['DEVELOPMENT_FEE'] + results['OPERATING_RESERVE'] + 
+                                     results['TOTAL_CONSTRUCTION_GMP'] + results['TOTAL_ACQUISITION_COST'])
+        
+        results['LOAN_AMOUNT'] = results['LTC_RATIO'] * results['PRE_LTC_BUDGET']
+        results['FINANCING_AMOUNT'] = results['FINANCING_PERCENTAGE'] * results['LOAN_AMOUNT']
+        results['INTEREST_RATE_DECIMAL'] = (results['INTEREST_RATE_BASIS_POINTS'] + 430) / 10000
+        results['CONSTRUCTION_INTEREST'] = results['LOAN_AMOUNT'] * 0.7 * (results['INTEREST_RATE_DECIMAL'] / 12) * results['CONSTRUCTION_MONTHS']
+        
+        # DEBT & EQUITY
+        results['TOTAL_DEBT'] = results['CONSTRUCTION_INTEREST'] + results['LOAN_AMOUNT'] + results['FINANCING_AMOUNT']
+        results['TOTAL_EQUITY'] = results['TOTAL_FINANCING_CONTINGENCY_AND_RESERVES'] - results['TOTAL_DEBT']
+        results['DEBT_PERCENTAGE'] = results['TOTAL_DEBT'] / results['TOTAL_FINANCING_CONTINGENCY_AND_RESERVES'] if results['TOTAL_FINANCING_CONTINGENCY_AND_RESERVES'] > 0 else 0
+        results['EQUITY_PERCENTAGE'] = results['TOTAL_EQUITY'] / results['TOTAL_FINANCING_CONTINGENCY_AND_RESERVES'] if results['TOTAL_FINANCING_CONTINGENCY_AND_RESERVES'] > 0 else 0
+        results['TOTAL_CAPITAL_STACK'] = results['TOTAL_DEBT'] + results['TOTAL_EQUITY']
+        
+        results['DEBT_PER_GSF'] = self.safe_divide(results['TOTAL_DEBT'],results['GROSS_SF'])
+        results['EQUITY_PER_GSF'] = self.safe_divide(results['TOTAL_EQUITY'],results['GROSS_SF'])
+        results['DEBT_PER_UNIT'] = self.safe_divide(results['TOTAL_DEBT'], results['UNITS'])
+
+        results['EQUITY_PER_UNIT'] = self.safe_divide(results['TOTAL_EQUITY'], results['UNITS'])
+        
+        # OPERATING EXPENSE METRICS
+        results['PAYROLL_PER_UNIT'] = self.safe_divide(results['PAYROLL'], results['UNITS'])
+        results['REPAIRS_AND_MAINTENANCE_PER_UNIT'] = self.safe_divide(results['REPAIRS_AND_MAINTENANCE'], results['UNITS'])
+        results['UTILITIES_PER_UNIT'] = self.safe_divide(results['UTILITIES'], results['UNITS'])
+        results['ADMIN_AND_PROFESSIONAL_PER_UNIT'] = self.safe_divide((results['ADMINISTRATIVE'] + results['PROFESSIONAL_FEES']), results['UNITS'])
+        results['INSURANCE_PER_UNIT'] = self.safe_divide(results['INSURANCE'], results['UNITS'])
+        results['OPERATING_EXPENSES_PER_UNIT'] = self.safe_divide(results['TOTAL_OPERATING_EXPENSES'], results['UNITS'])
+        results['OPERATING_EXPENSES_PER_GSF'] = self.safe_divide(results['TOTAL_OPERATING_EXPENSES'],results['GROSS_SF'])
+        
+        # REVENUE CALCULATIONS
+        results['LEASE_UP_MONTHS'] = get('projections.lease_up_months')
+        results['STABILIZATION_MONTHS'] = get('projections.stabilization_months')
+        results['REVENUE_INFLATION_RATE'] = get('projections.revenue_inflation_rate')
+        results['EXPENSE_INFLATION_RATE'] = get('projections.expense_inflation_rate')
+        
+        results['TRENDING_TERM'] = results['LEASE_UP_MONTHS'] + results['STABILIZATION_MONTHS']
+        results['TERM_REVENUE_INFLATION'] = (1 + results['REVENUE_INFLATION_RATE']) ** (results['TRENDING_TERM'] / 12)
+        results['TERM_EXPENSE_INFLATION'] = (1 + results['EXPENSE_INFLATION_RATE']) ** (results['TRENDING_TERM'] / 12)
+        
+        results['GROSS_POTENTIAL_FREE_MARKET_RENT'] = results['FREE_MARKET_RENT_PSF'] * 0.75 * results['RENTABLE_SF']
+        results['GROSS_POTENTIAL_AFFORDABLE_RENT'] = results['AFFORDABLE_RENT_PSF'] * 0.25 * results['RENTABLE_SF']
+        results['OTHER_INCOME'] = results['OTHER_INCOME_PER_UNIT'] * results['UNITS'] * 12 * 0.75
+        results['VACANCY_LOSS'] = results['VACANCY_RATE'] * (results['OTHER_INCOME'] + results['GROSS_POTENTIAL_FREE_MARKET_RENT'] + results['GROSS_POTENTIAL_AFFORDABLE_RENT'])
+        results['EFFECTIVE_GROSS_INCOME'] = results['GROSS_POTENTIAL_FREE_MARKET_RENT'] - results['VACANCY_LOSS'] + results['OTHER_INCOME'] + results['GROSS_POTENTIAL_AFFORDABLE_RENT']
+        
+        results['MANAGEMENT_FEE'] = results['MANAGEMENT_FEE_PERCENTAGE'] * results['EFFECTIVE_GROSS_INCOME']
+        results['REAL_ESTATE_TAXES'] = results['GROSS_SF'] * 30 * 0.1
+        results['TOTAL_EXPENSES'] = results['PAYROLL'] + results['REPAIRS_AND_MAINTENANCE'] + results['UTILITIES'] + results['REAL_ESTATE_TAXES'] + results['MANAGEMENT_FEE']
+        
+        # NOI & RETURNS
+        results['NET_OPERATING_INCOME'] = results['EFFECTIVE_GROSS_INCOME'] - results['TOTAL_EXPENSES'] + results['PARKING_INCOME'] + results['RETAIL_REVENUE']
+        results['NOI_PER_UNIT'] = self.safe_divide(results['NET_OPERATING_INCOME'], results['UNITS'])
+        results['NOI_PER_GSF'] = self.safe_divide(results['NET_OPERATING_INCOME'],results['GROSS_SF'])
+        results['CAP_RATE'] = (results['NET_OPERATING_INCOME'] / results['PRICE']) * 100 if results['PRICE'] > 0 else 0
+        
+        results['STABILIZED_YIELD_ON_COST'] = (((results['EFFECTIVE_GROSS_INCOME'] + results['RETAIL_REVENUE'] - results['GROSS_POTENTIAL_AFFORDABLE_RENT']) * results['TERM_REVENUE_INFLATION']) - (results['TOTAL_EXPENSES'] * results['TERM_EXPENSE_INFLATION'])) + results['GROSS_POTENTIAL_AFFORDABLE_RENT']
+        
+        results['YIELD_ON_COST_PERCENTAGE'] = results['NET_OPERATING_INCOME'] / results['TOTAL_FINANCING_CONTINGENCY_AND_RESERVES'] if results['TOTAL_FINANCING_CONTINGENCY_AND_RESERVES'] > 0 else 0
+        results['STABILIZED_YIELD_ON_COST_PERCENTAGE'] = results['STABILIZED_YIELD_ON_COST'] / results['TOTAL_FINANCING_CONTINGENCY_AND_RESERVES'] if results['TOTAL_FINANCING_CONTINGENCY_AND_RESERVES'] > 0 else 0
+        
+        results['ANNUAL_DEBT_SERVICE'] = results['LOAN_AMOUNT'] * results['INTEREST_RATE_DECIMAL']
+        results['CASH_ON_CASH_RETURN'] = ((results['NET_OPERATING_INCOME'] - results['ANNUAL_DEBT_SERVICE']) / results['TOTAL_EQUITY']) * 100 if results['TOTAL_EQUITY'] > 0 else 0
+        results['DEBT_SERVICE_COVERAGE_RATIO'] = results['NET_OPERATING_INCOME'] / results['ANNUAL_DEBT_SERVICE'] if results['ANNUAL_DEBT_SERVICE'] > 0 else 0
+        
+        # EXIT & EQUITY WATERFALL
+        results['EXIT_CAP_RATE_DECIMAL'] = get('projections.exit_cap_rate_decimal')
+        results['SALE_COST_PERCENTAGE'] = get('projections.sale_cost_percentage')
+        results['HOLD_PERIOD_MONTHS'] = get('projections.hold_period_months')
+        
+        results['PROPERTY_VALUE_ON_SALE'] = (results['STABILIZED_YIELD_ON_COST'] / results['EXIT_CAP_RATE_DECIMAL']) + (results['STABILIZED_YIELD_ON_COST'] * 0.25) if results['EXIT_CAP_RATE_DECIMAL'] > 0 else 0
+        results['SALE_COST'] = results['SALE_COST_PERCENTAGE'] * results['PROPERTY_VALUE_ON_SALE']
+        results['NET_SALE_PROCEEDS'] = results['PROPERTY_VALUE_ON_SALE'] - results['SALE_COST']
+        results['CASH_REMAINING_AFTER_LOAN_PAYBACK'] = results['NET_SALE_PROCEEDS'] - results['TOTAL_DEBT']
+        
+        results['GP_PREF_RATE'] = get('equity_structure.gp_pref_rate')
+        results['LP_PREF_RATE'] = get('equity_structure.lp_pref_rate')
+        results['PROMOTE_PERCENTAGE'] = get('equity_structure.promote_percentage')
+        
+        results['GP_INVESTMENT'] = results['TOTAL_EQUITY'] * 0.2
+        results['LP_INVESTMENT'] = results['TOTAL_EQUITY'] - results['GP_INVESTMENT']
+        results['GP_PREFERRED_RETURN_WITH_PRINCIPAL'] = (1 + results['GP_PREF_RATE'] / 12) ** results['HOLD_PERIOD_MONTHS'] * results['GP_INVESTMENT']
+        results['LP_PREFERRED_RETURN_WITH_PRINCIPAL'] = (1 + results['LP_PREF_RATE'] / 12) ** results['HOLD_PERIOD_MONTHS'] * results['LP_INVESTMENT']
+        results['CASH_REMAINING_AFTER_PREFERRED'] = results['CASH_REMAINING_AFTER_LOAN_PAYBACK'] - results['LP_PREFERRED_RETURN_WITH_PRINCIPAL'] - results['GP_PREFERRED_RETURN_WITH_PRINCIPAL']
+        results['PROMOTE_ON_JOINT_VENTURE'] = results['PROMOTE_PERCENTAGE'] * results['CASH_REMAINING_AFTER_PREFERRED']
+        results['CASH_TO_LP'] = (results['CASH_REMAINING_AFTER_PREFERRED'] - results['PROMOTE_ON_JOINT_VENTURE']) * (results['LP_INVESTMENT'] / (results['LP_INVESTMENT'] + results['GP_INVESTMENT'])) if (results['LP_INVESTMENT'] + results['GP_INVESTMENT']) > 0 else 0
+        results['NET_TO_LP_INVESTOR'] = results['CASH_TO_LP'] + results['LP_PREFERRED_RETURN_WITH_PRINCIPAL']
+        results['LP_MULTIPLE'] = results['NET_TO_LP_INVESTOR'] / results['LP_INVESTMENT'] if results['LP_INVESTMENT'] > 0 else 0
+        # results['IRR_TO_LP'] = ((results['NET_TO_LP_INVESTOR'] / results['LP_INVESTMENT']) ** (12 / results['HOLD_PERIOD_MONTHS']) - 1) * 100 if results['LP_INVESTMENT'] > 0 and results['HOLD_PERIOD_MONTHS'] > 0 else 0
+        # IRR calculation with complex number handling
+        if results['LP_INVESTMENT'] > 0 and results['HOLD_PERIOD_MONTHS'] > 0:
+            irr_base = results['NET_TO_LP_INVESTOR'] / results['LP_INVESTMENT']
+            if irr_base > 0:
+                results['IRR_TO_LP'] = ((irr_base) ** (12 / results['HOLD_PERIOD_MONTHS']) - 1) * 100
+            else:
+                results['IRR_TO_LP'] = -100  # Total loss
+        else:
+            results['IRR_TO_LP'] = 0
+
+        # BLENDED RENT CALCULATIONS
+        results['BLENDED_RENT_PER_RSF'] = (results['FREE_MARKET_RENT_PSF'] * 0.75) + (results['AFFORDABLE_RENT_PSF'] * 0.25)
+        results['TOTAL_FREE_MARKET_RENT'] = results['FREE_MARKET_RENT_PSF'] * 425 / 12
+        results['TOTAL_BLENDED_RENT'] = results['BLENDED_RENT_PER_RSF'] * 750 / 12
+        results['FREE_MARKET_RENT_PER_SF'] = results['TOTAL_FREE_MARKET_RENT'] * 110 / 12
+        results['AFFORDABLE_RENT_PER_SF'] = results['AFFORDABLE_RENT_PSF'] * 110 / 12
+        results['BLENDED_RENT_PER_SF'] = results['TOTAL_BLENDED_RENT'] * 110 / 12
+        results['AVERAGE_RENT_PER_UNIT'] = self.safe_divide((results['GROSS_POTENTIAL_FREE_MARKET_RENT']+results['GROSS_POTENTIAL_AFFORDABLE_RENT']), results['UNITS'])
+        results['RENT_PER_UNIT_PER_MONTH'] = results['AVERAGE_RENT_PER_UNIT'] / 12
+        
+        # EGI PERCENTAGES
+        if results['EFFECTIVE_GROSS_INCOME'] > 0:
+            results['PAYROLL_PERCENTAGE_OF_EGI'] = results['PAYROLL'] / results['EFFECTIVE_GROSS_INCOME']
+            results['REPAIRS_AND_MAINTENANCE_PERCENTAGE_OF_EGI'] = results['REPAIRS_AND_MAINTENANCE'] / results['EFFECTIVE_GROSS_INCOME']
+            results['UTILITIES_PERCENTAGE_OF_EGI'] = results['UTILITIES'] / results['EFFECTIVE_GROSS_INCOME']
+            results['ADMIN_AND_PROFESSIONAL_PERCENTAGE_OF_EGI'] = (results['ADMINISTRATIVE'] + results['PROFESSIONAL_FEES']) / results['EFFECTIVE_GROSS_INCOME']
+            results['INSURANCE_PERCENTAGE_OF_EGI'] = results['INSURANCE'] / results['EFFECTIVE_GROSS_INCOME']
+            results['PROFESSIONAL_FEES_PERCENTAGE_OF_EGI'] = results['PROFESSIONAL_FEES'] / results['EFFECTIVE_GROSS_INCOME']
+            results['TOTAL_OPERATING_EXPENSES_PERCENTAGE_OF_EGI'] = results['TOTAL_OPERATING_EXPENSES'] / results['EFFECTIVE_GROSS_INCOME']
+        else:
+            results['PAYROLL_PERCENTAGE_OF_EGI'] = 0
+            results['REPAIRS_AND_MAINTENANCE_PERCENTAGE_OF_EGI'] = 0
+            results['UTILITIES_PERCENTAGE_OF_EGI'] = 0
+            results['ADMIN_AND_PROFESSIONAL_PERCENTAGE_OF_EGI'] = 0
+            results['INSURANCE_PERCENTAGE_OF_EGI'] = 0
+            results['PROFESSIONAL_FEES_PERCENTAGE_OF_EGI'] = 0
+            results['TOTAL_OPERATING_EXPENSES_PERCENTAGE_OF_EGI'] = 0
+        
+        self.formula_results = results
+        return results
+    
+    def flatten_dict(self, d: Dict[str, Any], parent_key: str = '', sep: str = '.') -> Dict[str, Any]:
+        """Flatten nested dictionary"""
+        items = []
+        for k, v in d.items():
+            new_key = f"{parent_key}{sep}{k}" if parent_key else k
+            if isinstance(v, dict):
+                items.extend(self.flatten_dict(v, new_key, sep=sep).items())
+            else:
+                items.append((new_key, v))
+        return dict(items)
+    
+    def generate_excel(self, output_path: str = "Real_Estate_Financial_Model.xlsx"):
+        """Generate professional Excel file with all calculations"""
+        try:
+            # Validate critical values before Excel generation
+            r = self.formula_results
+            
+            print("  Validating calculations...")
+            critical_values = {
+                'UNITS': r.get('UNITS', 0),
+                'GROSS_SF': r.get('GROSS_SF', 0),
+                'RENTABLE_SF': r.get('RENTABLE_SF', 0),
+                'EFFECTIVE_GROSS_INCOME': r.get('EFFECTIVE_GROSS_INCOME', 0),
+                'TOTAL_PROJECT_COST': r.get('TOTAL_FINANCING_CONTINGENCY_AND_RESERVES', 0)
+            }
+            
+            warnings = []
+            for key, value in critical_values.items():
+                if value == 0:
+                    warnings.append(f"    WARNING: {key} is zero or missing")
+            
+            if warnings:
+                print("\n".join(warnings))
+                print("    Continuing with available data...\n")
+            
+            wb = openpyxl.Workbook()
+            
+            # Remove default sheet
+            if 'Sheet' in wb.sheetnames:
+                wb.remove(wb['Sheet'])
+            
+            # Create sheets with error handling
+            print("  Creating Executive Summary...")
+            self.create_summary_sheet(wb)
+            
+            print("  Creating Acquisition sheet...")
+            self.create_acquisition_sheet(wb)
+            
+            print("  Creating Construction sheet...")
+            self.create_construction_sheet(wb)
+            
+            print("  Creating Soft Costs sheet...")
+            self.create_soft_costs_sheet(wb)
+            
+            print("  Creating Financing sheet...")
+            self.create_financing_sheet(wb)
+            
+            print("  Creating Operations sheet...")
+            self.create_operations_sheet(wb)
+            
+            print("  Creating Returns sheet...")
+            self.create_returns_sheet(wb)
+            
+            # Save workbook
+            wb.save(output_path)
+            print(f"✓ Excel file generated: {output_path}")
+            return output_path
+        except Exception as e:
+            print(f"ERROR generating Excel: {e}")
+            import traceback
+            traceback.print_exc()
+            raise
+    
+    def create_summary_sheet(self, wb):
+        """Create executive summary sheet"""
+        ws = wb.create_sheet("Executive Summary", 0)
+        
+        # Styles
+        header_fill = PatternFill(start_color="1F4E78", end_color="1F4E78", fill_type="solid")
+        header_font = Font(color="FFFFFF", bold=True, size=12)
+        subheader_fill = PatternFill(start_color="4472C4", end_color="4472C4", fill_type="solid")
+        subheader_font = Font(color="FFFFFF", bold=True, size=11)
+        
+        r = self.formula_results
+        
+        # Title
+        ws['A1'] = "REAL ESTATE DEVELOPMENT FINANCIAL MODEL"
+        ws['A1'].font = Font(bold=True, size=16)
+        ws.merge_cells('A1:D1')
+        
+        # Property Information
+        row = 3
+        ws[f'A{row}'] = "PROPERTY INFORMATION"
+        ws[f'A{row}'].fill = header_fill
+        ws[f'A{row}'].font = header_font
+        ws.merge_cells(f'A{row}:D{row}')
+
+        address = self.structured_data.get('property_info', {}).get('address', 'N/A')
+
+        row += 1
+        data = [
+            ("Address:", address),
+            ("Units:", r.get('UNITS', 0)),
+            ("Gross Square Feet:", f"{r.get('GROSS_SF', 0):,.0f}"),
+            ("Rentable Square Feet:", f"{r.get('RENTABLE_SF', 0):,.0f}"),
+            ("Building Efficiency:", f"{r.get('BUILDING_EFFICIENCY', 0):.2%}"),
+        ]
+        
+        for label, value in data:
+            ws[f'A{row}'] = label
+            ws[f'A{row}'].font = Font(bold=True)
+            ws[f'B{row}'] = value
+            row += 1
+        
+        # Project Costs Summary
+        row += 1
+        ws[f'A{row}'] = "PROJECT COSTS SUMMARY"
+        ws[f'A{row}'].fill = header_fill
+        ws[f'A{row}'].font = header_font
+        ws.merge_cells(f'A{row}:D{row}')
+        
+        row += 1
+        ws[f'A{row}'] = "Category"
+        ws[f'B{row}'] = "Total Cost"
+        ws[f'C{row}'] = "Per GSF"
+        ws[f'D{row}'] = "Per Unit"
+        for col in ['A', 'B', 'C', 'D']:
+            ws[f'{col}{row}'].fill = subheader_fill
+            ws[f'{col}{row}'].font = subheader_font
+        
+        row += 1
+        cost_summary = [
+            ("Acquisition", r.get('TOTAL_ACQUISITION_COST', 0), r.get('TOTAL_ACQUISITION_COST_PER_GSF', 0), r.get('TOTAL_ACQUISITION_COST_PER_UNIT', 0)),
+            ("Construction", r.get('TOTAL_CONSTRUCTION_GMP', 0), r.get('CONSTRUCTION_GMP_PER_GSF', 0), r.get('CONSTRUCTION_GMP_PER_UNIT', 0)),
+            ("Soft Costs", r.get('TOTAL_SOFT_COST', 0), r.get('TOTAL_SOFT_COST_PER_GSF', 0), r.get('TOTAL_SOFT_COST_PER_GSF', 0) * r.get('GROSS_SF', 0) / r.get('UNITS', 1)),
+            ("Contingency", r.get('CONTINGENCY_COST', 0), r.get('CONTINGENCY_COST', 0) / r.get('GROSS_SF', 1), r.get('CONTINGENCY_COST', 0) / r.get('UNITS', 1)),
+            ("Development Fee", r.get('DEVELOPMENT_FEE', 0), r.get('DEVELOPMENT_FEE', 0) / r.get('GROSS_SF', 1), r.get('DEVELOPMENT_FEE', 0) / r.get('UNITS', 1)),
+            ("Financing & Reserves", r.get('FINANCING_COST', 0) + r.get('INTEREST_RESERVE', 0) + r.get('OPERATING_RESERVE', 0), 0, 0),
+        ]
+        
+        for label, total, per_gsf, per_unit in cost_summary:
+            ws[f'A{row}'] = label
+            ws[f'B{row}'] = total
+            ws[f'B{row}'].number_format = '$#,##0'
+            ws[f'C{row}'] = per_gsf
+            ws[f'C{row}'].number_format = '$#,##0.00'
+            ws[f'D{row}'] = per_unit
+            ws[f'D{row}'].number_format = '$#,##0'
+            row += 1
+        
+        # Total
+        ws[f'A{row}'] = "TOTAL PROJECT COST"
+        ws[f'A{row}'].font = Font(bold=True)
+        ws[f'B{row}'] = r.get('TOTAL_FINANCING_CONTINGENCY_AND_RESERVES', 0)
+        ws[f'B{row}'].number_format = '$#,##0'
+        ws[f'B{row}'].font = Font(bold=True)
+        ws[f'C{row}'] = r.get('TOTAL_PROJECT_COST_PER_GSF', 0)
+        ws[f'C{row}'].number_format = '$#,##0.00'
+        ws[f'C{row}'].font = Font(bold=True)
+        ws[f'D{row}'] = r.get('TOTAL_PROJECT_COST_PER_UNIT', 0)
+        ws[f'D{row}'].number_format = '$#,##0'
+        ws[f'D{row}'].font = Font(bold=True)
+        
+        # Capital Stack
+        row += 2
+        ws[f'A{row}'] = "CAPITAL STACK"
+        ws[f'A{row}'].fill = header_fill
+        ws[f'A{row}'].font = header_font
+        ws.merge_cells(f'A{row}:D{row}')
+        
+        row += 1
+        ws[f'A{row}'] = "Source"
+        ws[f'B{row}'] = "Amount"
+        ws[f'C{row}'] = "Percentage"
+        ws[f'D{row}'] = "Per Unit"
+        for col in ['A', 'B', 'C', 'D']:
+            ws[f'{col}{row}'].fill = subheader_fill
+            ws[f'{col}{row}'].font = subheader_font
+        
+        row += 1
+        ws[f'A{row}'] = "Total Debt"
+        ws[f'B{row}'] = r.get('TOTAL_DEBT', 0)
+        ws[f'B{row}'].number_format = '$#,##0'
+        ws[f'C{row}'] = r.get('DEBT_PERCENTAGE', 0)
+        ws[f'C{row}'].number_format = '0.00%'
+        ws[f'D{row}'] = r.get('DEBT_PER_UNIT', 0)
+        ws[f'D{row}'].number_format = '$#,##0'
+        
+        row += 1
+        ws[f'A{row}'] = "Total Equity"
+        ws[f'B{row}'] = r.get('TOTAL_EQUITY', 0)
+        ws[f'B{row}'].number_format = '$#,##0'
+        ws[f'C{row}'] = r.get('EQUITY_PERCENTAGE', 0)
+        ws[f'C{row}'].number_format = '0.00%'
+        ws[f'D{row}'] = r.get('EQUITY_PER_UNIT', 0)
+        ws[f'D{row}'].number_format = '$#,##0'
+        
+        # Returns Summary
+        row += 2
+        ws[f'A{row}'] = "INVESTMENT RETURNS"
+        ws[f'A{row}'].fill = header_fill
+        ws[f'A{row}'].font = header_font
+        ws.merge_cells(f'A{row}:D{row}')
+        
+        row += 1
+        returns_data = [
+            ("Stabilized NOI:", f"${r.get('NET_OPERATING_INCOME', 0):,.0f}"),
+            ("Yield on Cost:", f"{r.get('YIELD_ON_COST_PERCENTAGE', 0):.2%}"),
+            ("Stabilized Yield on Cost:", f"{r.get('STABILIZED_YIELD_ON_COST_PERCENTAGE', 0):.2%}"),
+            ("Cash-on-Cash Return:", f"{r.get('CASH_ON_CASH_RETURN', 0):.2f}%"),
+            ("DSCR:", f"{r.get('DEBT_SERVICE_COVERAGE_RATIO', 0):.2f}x"),
+            ("LP IRR:", f"{float(r.get('IRR_TO_LP', 0).real if isinstance(r.get('IRR_TO_LP', 0), complex) else r.get('IRR_TO_LP', 0)):.2f}%"),
+            ("LP Multiple:", f"{r.get('LP_MULTIPLE', 0):.2f}x"),
+        ]
+        
+        for label, value in returns_data:
+            ws[f'A{row}'] = label
+            ws[f'A{row}'].font = Font(bold=True)
+            ws[f'B{row}'] = value
+            row += 1
+        
+        # Adjust column widths
+        ws.column_dimensions['A'].width = 25
+        ws.column_dimensions['B'].width = 18
+        ws.column_dimensions['C'].width = 15
+        ws.column_dimensions['D'].width = 15
+    
+    def create_acquisition_sheet(self, wb):
+        """Create acquisition costs detail sheet"""
+        ws = wb.create_sheet("Acquisition")
+        r = self.formula_results
+        
+        # Header
+        ws['A1'] = "ACQUISITION COSTS"
+        ws['A1'].font = Font(bold=True, size=14)
+        ws.merge_cells('A1:E1')
+        
+        # Column headers
+        row = 3
+        headers = ["Item", "Total Cost", "Per GSF", "Per RSF", "Per Unit"]
+        for col_idx, header in enumerate(headers, start=1):
+            cell = ws.cell(row=row, column=col_idx, value=header)
+            cell.fill = PatternFill(start_color="4472C4", end_color="4472C4", fill_type="solid")
+            cell.font = Font(color="FFFFFF", bold=True)
+        
+        # Data
+        row += 1
+        data = [
+            ("Land Value", r.get('LAND_VALUE', 0), r.get('LAND_VALUE_PER_GSF', 0), r.get('LAND_VALUE_PER_RSF', 0), r.get('LAND_VALUE_PER_UNIT', 0)),
+            ("Closing Costs", r.get('CLOSING_COSTS', 0), r.get('CLOSING_COSTS', 0) / r.get('GROSS_SF', 1), r.get('CLOSING_COSTS', 0) / r.get('RENTABLE_SF', 1), r.get('CLOSING_COSTS', 0) / r.get('UNITS', 1)),
+            ("Acquisition Fee (2%)", r.get('ACQUISITION_FEE', 0), r.get('ACQUISITION_FEE', 0) / r.get('GROSS_SF', 1), r.get('ACQUISITION_FEE', 0) / r.get('RENTABLE_SF', 1), r.get('ACQUISITION_FEE', 0) / r.get('UNITS', 1)),
+        ]
+        
+        for item, total, per_gsf, per_rsf, per_unit in data:
+            ws.cell(row=row, column=1, value=item)
+            ws.cell(row=row, column=2, value=total).number_format = '$#,##0'
+            ws.cell(row=row, column=3, value=per_gsf).number_format = '$#,##0.00'
+            ws.cell(row=row, column=4, value=per_rsf).number_format = '$#,##0.00'
+            ws.cell(row=row, column=5, value=per_unit).number_format = '$#,##0'
+            row += 1
+        
+        # Total
+        ws.cell(row=row, column=1, value="TOTAL ACQUISITION COST").font = Font(bold=True)
+        ws.cell(row=row, column=2, value=r.get('TOTAL_ACQUISITION_COST', 0)).number_format = '$#,##0'
+        ws.cell(row=row, column=2).font = Font(bold=True)
+        ws.cell(row=row, column=3, value=r.get('TOTAL_ACQUISITION_COST_PER_GSF', 0)).number_format = '$#,##0.00'
+        ws.cell(row=row, column=3).font = Font(bold=True)
+        ws.cell(row=row, column=4, value=r.get('TOTAL_ACQUISITION_COST_PER_RSF', 0)).number_format = '$#,##0.00'
+        ws.cell(row=row, column=4).font = Font(bold=True)
+        ws.cell(row=row, column=5, value=r.get('TOTAL_ACQUISITION_COST_PER_UNIT', 0)).number_format = '$#,##0'
+        ws.cell(row=row, column=5).font = Font(bold=True)
+        
+        # Adjust widths
+        for col in range(1, 6):
+            ws.column_dimensions[get_column_letter(col)].width = 20
+    
+    def create_construction_sheet(self, wb):
+        """Create construction costs sheet"""
+        ws = wb.create_sheet("Construction")
+        r = self.formula_results
+        
+        ws['A1'] = "CONSTRUCTION COSTS"
+        ws['A1'].font = Font(bold=True, size=14)
+        
+        row = 3
+        ws[f'A{row}'] = "Construction Cost per GSF:"
+        ws[f'B{row}'] = r.get('CONSTRUCTION_COST_PER_GSF', 0)
+        ws[f'B{row}'].number_format = '$#,##0.00'
+        
+        row += 1
+        ws[f'A{row}'] = "Gross Square Feet:"
+        ws[f'B{row}'] = r.get('GROSS_SF', 0)
+        ws[f'B{row}'].number_format = '#,##0'
+        
+        row += 2
+        ws[f'A{row}'] = "Total Construction GMP:"
+        ws[f'A{row}'].font = Font(bold=True)
+        ws[f'B{row}'] = r.get('TOTAL_CONSTRUCTION_GMP', 0)
+        ws[f'B{row}'].number_format = '$#,##0'
+        ws[f'B{row}'].font = Font(bold=True)
+        
+        row += 2
+        ws[f'A{row}'] = "Construction Duration:"
+        ws[f'B{row}'] = f"{r.get('CONSTRUCTION_MONTHS', 0)} months"
+        
+        ws.column_dimensions['A'].width = 30
+        ws.column_dimensions['B'].width = 20
+    
+    def create_soft_costs_sheet(self, wb):
+        """Create soft costs detail sheet"""
+        ws = wb.create_sheet("Soft Costs")
+        r = self.formula_results
+        
+        ws['A1'] = "SOFT COSTS BUDGET"
+        ws['A1'].font = Font(bold=True, size=14)
+        
+        row = 3
+        headers = ["Category", "Total Cost", "Per GSF"]
+        for col_idx, header in enumerate(headers, start=1):
+            cell = ws.cell(row=row, column=col_idx, value=header)
+            cell.fill = PatternFill(start_color="4472C4", end_color="4472C4", fill_type="solid")
+            cell.font = Font(color="FFFFFF", bold=True)
+        
+        row += 1
+        soft_cost_items = [
+            ("Architecture & Interior Design", 'ARCHITECTURE_AND_INTERIOR_COST'),
+            ("Structural Engineering", 'STRUCTURAL_ENGINEERING_COST'),
+            ("MEP Engineering", 'MEP_ENGINEERING_COST'),
+            ("Civil Engineering", 'CIVIL_ENGINEERING_COST'),
+            ("Controlled Inspections", 'CONTROLLED_INSPECTIONS_COST'),
+            ("Surveying", 'SURVEYING_COST'),
+            ("Utilities Connection", 'UTILITIES_CONNECTION_COST'),
+            ("Advertising & Marketing", 'ADVERTISING_AND_MARKETING_COST'),
+            ("Accounting", 'ACCOUNTING_COST'),
+            ("Monitoring", 'MONITORING_COST'),
+            ("FF&E", 'FF_AND_E_COST'),
+            ("Environmental Consultant", 'ENVIRONMENTAL_CONSULTANT_FEE'),
+            ("Miscellaneous Consultants", 'MISCELLANEOUS_CONSULTANTS_FEE'),
+            ("General Legal", 'GENERAL_LEGAL_COST'),
+            ("RE Taxes During Construction", 'REAL_ESTATE_TAXES_DURING_CONSTRUCTION'),
+            ("Miscellaneous Admin", 'MISCELLANEOUS_ADMIN_COST'),
+            ("IBR Cost", 'IBR_COST'),
+            ("Project Team", 'PROJECT_TEAM_COST'),
+            ("PEM Fees", 'PEM_FEES'),
+            ("Bank Fees", 'BANK_FEES'),
+            ("HPD & IH Costs", 'HPD_AND_IH_COST'),
+            ("Retail TI & LC", 'RETAIL_TI_AND_LC_COST'),
+        ]
+        
+        for label, key in soft_cost_items:
+            cost = r.get(key, 0)
+            per_gsf = cost / r.get('GROSS_SF', 1) if r.get('GROSS_SF', 0) > 0 else 0
+            ws.cell(row=row, column=1, value=label)
+            ws.cell(row=row, column=2, value=cost).number_format = '$#,##0'
+            ws.cell(row=row, column=3, value=per_gsf).number_format = '$#,##0.00'
+            row += 1
+        
+        # Total
+        ws.cell(row=row, column=1, value="TOTAL SOFT COSTS").font = Font(bold=True)
+        ws.cell(row=row, column=2, value=r.get('TOTAL_SOFT_COST', 0)).number_format = '$#,##0'
+        ws.cell(row=row, column=2).font = Font(bold=True)
+        ws.cell(row=row, column=3, value=r.get('TOTAL_SOFT_COST_PER_GSF', 0)).number_format = '$#,##0.00'
+        ws.cell(row=row, column=3).font = Font(bold=True)
+        
+        ws.column_dimensions['A'].width = 35
+        ws.column_dimensions['B'].width = 18
+        ws.column_dimensions['C'].width = 15
+    
+    def create_financing_sheet(self, wb):
+        """Create financing structure sheet"""
+        ws = wb.create_sheet("Financing")
+        r = self.formula_results
+        
+        ws['A1'] = "FINANCING STRUCTURE"
+        ws['A1'].font = Font(bold=True, size=14)
+        
+        row = 3
+        ws[f'A{row}'] = "Pre-LTC Budget:"
+        ws[f'B{row}'] = r.get('PRE_LTC_BUDGET', 0)
+        ws[f'B{row}'].number_format = '$#,##0'
+        
+        row += 1
+        ws[f'A{row}'] = "LTC Ratio:"
+        ws[f'B{row}'] = r.get('LTC_RATIO', 0)
+        ws[f'B{row}'].number_format = '0.00%'
+        
+        row += 1
+        ws[f'A{row}'] = "Loan Amount:"
+        ws[f'A{row}'].font = Font(bold=True)
+        ws[f'B{row}'] = r.get('LOAN_AMOUNT', 0)
+        ws[f'B{row}'].number_format = '$#,##0'
+        ws[f'B{row}'].font = Font(bold=True)
+        
+        row += 2
+        ws[f'A{row}'] = "Financing Percentage:"
+        ws[f'B{row}'] = r.get('FINANCING_PERCENTAGE', 0)
+        ws[f'B{row}'].number_format = '0.00%'
+        
+        row += 1
+        ws[f'A{row}'] = "Financing Amount:"
+        ws[f'B{row}'] = r.get('FINANCING_AMOUNT', 0)
+        ws[f'B{row}'].number_format = '$#,##0'
+        
+        row += 2
+        ws[f'A{row}'] = "Interest Rate (bps + spread):"
+        ws[f'B{row}'] = r.get('INTEREST_RATE_DECIMAL', 0)
+        ws[f'B{row}'].number_format = '0.00%'
+        
+        row += 1
+        ws[f'A{row}'] = "Construction Interest:"
+        ws[f'B{row}'] = r.get('CONSTRUCTION_INTEREST', 0)
+        ws[f'B{row}'].number_format = '$#,##0'
+        
+        row += 2
+        ws[f'A{row}'] = "TOTAL DEBT"
+        ws[f'A{row}'].font = Font(bold=True, size=12)
+        ws[f'B{row}'] = r.get('TOTAL_DEBT', 0)
+        ws[f'B{row}'].number_format = '$#,##0'
+        ws[f'B{row}'].font = Font(bold=True, size=12)
+        
+        row += 1
+        ws[f'A{row}'] = "TOTAL EQUITY"
+        ws[f'A{row}'].font = Font(bold=True, size=12)
+        ws[f'B{row}'] = r.get('TOTAL_EQUITY', 0)
+        ws[f'B{row}'].number_format = '$#,##0'
+        ws[f'B{row}'].font = Font(bold=True, size=12)
+        
+        row += 2
+        ws[f'A{row}'] = "Debt Percentage:"
+        ws[f'B{row}'] = r.get('DEBT_PERCENTAGE', 0)
+        ws[f'B{row}'].number_format = '0.00%'
+        
+        row += 1
+        ws[f'A{row}'] = "Equity Percentage:"
+        ws[f'B{row}'] = r.get('EQUITY_PERCENTAGE', 0)
+        ws[f'B{row}'].number_format = '0.00%'
+        
+        ws.column_dimensions['A'].width = 35
+        ws.column_dimensions['B'].width = 20
+    
+    def create_operations_sheet(self, wb):
+        """Create operations and revenue sheet"""
+        ws = wb.create_sheet("Operations")
+        r = self.formula_results
+        
+        ws['A1'] = "OPERATIONS & REVENUE"
+        ws['A1'].font = Font(bold=True, size=14)
+        
+        # Revenue Section
+        row = 3
+        ws[f'A{row}'] = "REVENUE"
+        ws[f'A{row}'].fill = PatternFill(start_color="4472C4", end_color="4472C4", fill_type="solid")
+        ws[f'A{row}'].font = Font(color="FFFFFF", bold=True)
+        ws.merge_cells(f'A{row}:B{row}')
+        
+        row += 1
+        revenue_items = [
+            ("Gross Potential Free Market Rent", r.get('GROSS_POTENTIAL_FREE_MARKET_RENT', 0)),
+            ("Gross Potential Affordable Rent", r.get('GROSS_POTENTIAL_AFFORDABLE_RENT', 0)),
+            ("Other Income", r.get('OTHER_INCOME', 0)),
+            ("Less: Vacancy Loss", -r.get('VACANCY_LOSS', 0)),
+            ("Effective Gross Income", r.get('EFFECTIVE_GROSS_INCOME', 0)),
+            ("Parking Income", r.get('PARKING_INCOME', 0)),
+            ("Retail Revenue", r.get('RETAIL_REVENUE', 0)),
+        ]
+        
+        for label, value in revenue_items:
+            ws[f'A{row}'] = label
+            if "Effective Gross" in label:
+                ws[f'A{row}'].font = Font(bold=True)
+            ws[f'B{row}'] = value
+            ws[f'B{row}'].number_format = '$#,##0'
+            row += 1
+        
+        # Expense Section
+        row += 1
+        ws[f'A{row}'] = "OPERATING EXPENSES"
+        ws[f'A{row}'].fill = PatternFill(start_color="4472C4", end_color="4472C4", fill_type="solid")
+        ws[f'A{row}'].font = Font(color="FFFFFF", bold=True)
+        ws.merge_cells(f'A{row}:C{row}')
+        
+        row += 1
+        ws[f'A{row}'] = "Expense Category"
+        ws[f'B{row}'] = "Annual Amount"
+        ws[f'C{row}'] = "% of EGI"
+        for col in ['A', 'B', 'C']:
+            ws[f'{col}{row}'].font = Font(bold=True)
+        
+        row += 1
+        # Safe division helper
+        egi = r.get('EFFECTIVE_GROSS_INCOME', 0)
+        def safe_pct(value):
+            return value / egi if egi > 0 else 0
+
+        expense_items = [
+            ("Payroll", r.get('PAYROLL', 0), r.get('PAYROLL_PERCENTAGE_OF_EGI', 0)),
+            ("Repairs & Maintenance", r.get('REPAIRS_AND_MAINTENANCE', 0), r.get('REPAIRS_AND_MAINTENANCE_PERCENTAGE_OF_EGI', 0)),
+            ("Utilities", r.get('UTILITIES', 0), r.get('UTILITIES_PERCENTAGE_OF_EGI', 0)),
+            ("Insurance", r.get('INSURANCE', 0), r.get('INSURANCE_PERCENTAGE_OF_EGI', 0)),
+            ("Management Fee", r.get('MANAGEMENT_FEE', 0), safe_pct(r.get('MANAGEMENT_FEE', 0))),
+            ("Real Estate Taxes", r.get('REAL_ESTATE_TAXES', 0), safe_pct(r.get('REAL_ESTATE_TAXES', 0))),
+        ]
+        
+        for label, amount, pct in expense_items:
+            ws[f'A{row}'] = label
+            ws[f'B{row}'] = amount
+            ws[f'B{row}'].number_format = '$#,##0'
+            ws[f'C{row}'] = pct
+            ws[f'C{row}'].number_format = '0.00%'
+            row += 1
+        
+        ws[f'A{row}'] = "TOTAL EXPENSES"
+        ws[f'A{row}'].font = Font(bold=True)
+        ws[f'B{row}'] = r.get('TOTAL_EXPENSES', 0)
+        ws[f'B{row}'].number_format = '$#,##0'
+        ws[f'B{row}'].font = Font(bold=True)
+        total_exp_pct = safe_pct(r.get('TOTAL_EXPENSES', 0))
+        ws[f'C{row}'] = total_exp_pct
+        ws[f'C{row}'].number_format = '0.00%'
+        ws[f'C{row}'].font = Font(bold=True)
+        
+        row += 2
+        ws[f'A{row}'] = "NET OPERATING INCOME"
+        ws[f'A{row}'].font = Font(bold=True, size=12)
+        ws[f'B{row}'] = r.get('NET_OPERATING_INCOME', 0)
+        ws[f'B{row}'].number_format = '$#,##0'
+        ws[f'B{row}'].font = Font(bold=True, size=12)
+        
+        row += 2
+        ws[f'A{row}'] = "NOI per Unit:"
+        ws[f'B{row}'] = r.get('NOI_PER_UNIT', 0)
+        ws[f'B{row}'].number_format = '$#,##0'
+        
+        row += 1
+        ws[f'A{row}'] = "NOI per GSF:"
+        ws[f'B{row}'] = r.get('NOI_PER_GSF', 0)
+        ws[f'B{row}'].number_format = '$#,##0.00'
+        
+        ws.column_dimensions['A'].width = 35
+        ws.column_dimensions['B'].width = 20
+        ws.column_dimensions['C'].width = 15
+    
+    def create_returns_sheet(self, wb):
+        """Create investment returns and waterfall sheet"""
+        ws = wb.create_sheet("Returns")
+        r = self.formula_results
+        
+        ws['A1'] = "INVESTMENT RETURNS & EXIT ANALYSIS"
+        ws['A1'].font = Font(bold=True, size=14)
+        
+        # Current Returns
+        row = 3
+        ws[f'A{row}'] = "STABILIZED RETURNS"
+        ws[f'A{row}'].fill = PatternFill(start_color="4472C4", end_color="4472C4", fill_type="solid")
+        ws[f'A{row}'].font = Font(color="FFFFFF", bold=True)
+        ws.merge_cells(f'A{row}:B{row}')
+        
+        row += 1
+        returns_data = [
+            ("Net Operating Income", f"${r.get('NET_OPERATING_INCOME', 0):,.0f}"),
+            ("Stabilized Yield on Cost", f"${r.get('STABILIZED_YIELD_ON_COST', 0):,.0f}"),
+            ("Yield on Cost %", f"{r.get('YIELD_ON_COST_PERCENTAGE', 0):.2%}"),
+            ("Stabilized Yield on Cost %", f"{r.get('STABILIZED_YIELD_ON_COST_PERCENTAGE', 0):.2%}"),
+            ("Annual Debt Service", f"${r.get('ANNUAL_DEBT_SERVICE', 0):,.0f}"),
+            ("Cash-on-Cash Return", f"{r.get('CASH_ON_CASH_RETURN', 0):.2f}%"),
+            ("Debt Service Coverage Ratio", f"{r.get('DEBT_SERVICE_COVERAGE_RATIO', 0):.2f}x"),
+        ]
+        
+        for label, value in returns_data:
+            ws[f'A{row}'] = label
+            ws[f'A{row}'].font = Font(bold=True)
+            ws[f'B{row}'] = value
+            row += 1
+        
+        # Exit Analysis
+        row += 2
+        ws[f'A{row}'] = "EXIT ANALYSIS"
+        ws[f'A{row}'].fill = PatternFill(start_color="4472C4", end_color="4472C4", fill_type="solid")
+        ws[f'A{row}'].font = Font(color="FFFFFF", bold=True)
+        ws.merge_cells(f'A{row}:B{row}')
+        
+        row += 1
+        ws[f'A{row}'] = "Hold Period (months):"
+        ws[f'B{row}'] = r.get('HOLD_PERIOD_MONTHS', 0)
+        
+        row += 1
+        ws[f'A{row}'] = "Exit Cap Rate:"
+        ws[f'B{row}'] = r.get('EXIT_CAP_RATE_DECIMAL', 0)
+        ws[f'B{row}'].number_format = '0.00%'
+        
+        row += 1
+        ws[f'A{row}'] = "Property Value on Sale:"
+        ws[f'B{row}'] = r.get('PROPERTY_VALUE_ON_SALE', 0)
+        ws[f'B{row}'].number_format = '$#,##0'
+        
+        row += 1
+        ws[f'A{row}'] = "Less: Sale Costs (2%):"
+        ws[f'B{row}'] = -r.get('SALE_COST', 0)
+        ws[f'B{row}'].number_format = '$#,##0'
+        
+        row += 1
+        ws[f'A{row}'] = "Net Sale Proceeds:"
+        ws[f'A{row}'].font = Font(bold=True)
+        ws[f'B{row}'] = r.get('NET_SALE_PROCEEDS', 0)
+        ws[f'B{row}'].number_format = '$#,##0'
+        ws[f'B{row}'].font = Font(bold=True)
+        
+        row += 1
+        ws[f'A{row}'] = "Less: Loan Payoff:"
+        ws[f'B{row}'] = -r.get('TOTAL_DEBT', 0)
+        ws[f'B{row}'].number_format = '$#,##0'
+        
+        row += 1
+        ws[f'A{row}'] = "Cash After Loan Payback:"
+        ws[f'A{row}'].font = Font(bold=True)
+        ws[f'B{row}'] = r.get('CASH_REMAINING_AFTER_LOAN_PAYBACK', 0)
+        ws[f'B{row}'].number_format = '$#,##0'
+        ws[f'B{row}'].font = Font(bold=True)
+        
+        # Equity Waterfall
+        row += 2
+        ws[f'A{row}'] = "EQUITY WATERFALL"
+        ws[f'A{row}'].fill = PatternFill(start_color="4472C4", end_color="4472C4", fill_type="solid")
+        ws[f'A{row}'].font = Font(color="FFFFFF", bold=True)
+        ws.merge_cells(f'A{row}:B{row}')
+        
+        row += 1
+        ws[f'A{row}'] = "GP Investment (20%):"
+        ws[f'B{row}'] = r.get('GP_INVESTMENT', 0)
+        ws[f'B{row}'].number_format = '$#,##0'
+        
+        row += 1
+        ws[f'A{row}'] = "LP Investment (80%):"
+        ws[f'B{row}'] = r.get('LP_INVESTMENT', 0)
+        ws[f'B{row}'].number_format = '$#,##0'
+        
+        row += 2
+        ws[f'A{row}'] = "GP Preferred Return + Principal:"
+        ws[f'B{row}'] = r.get('GP_PREFERRED_RETURN_WITH_PRINCIPAL', 0)
+        ws[f'B{row}'].number_format = '$#,##0'
+        
+        row += 1
+        ws[f'A{row}'] = "LP Preferred Return + Principal:"
+        ws[f'B{row}'] = r.get('LP_PREFERRED_RETURN_WITH_PRINCIPAL', 0)
+        ws[f'B{row}'].number_format = '$#,##0'
+        
+        row += 1
+        ws[f'A{row}'] = "Cash After Preferred:"
+        ws[f'B{row}'] = r.get('CASH_REMAINING_AFTER_PREFERRED', 0)
+        ws[f'B{row}'].number_format = '$#,##0'
+        
+        row += 2
+        ws[f'A{row}'] = "GP Promote (20%):"
+        ws[f'B{row}'] = r.get('PROMOTE_ON_JOINT_VENTURE', 0)
+        ws[f'B{row}'].number_format = '$#,##0'
+        
+        row += 1
+        ws[f'A{row}'] = "Cash to LP:"
+        ws[f'B{row}'] = r.get('CASH_TO_LP', 0)
+        ws[f'B{row}'].number_format = '$#,##0'
+        
+        row += 2
+        ws[f'A{row}'] = "NET TO LP INVESTOR"
+        ws[f'A{row}'].font = Font(bold=True, size=12)
+        ws[f'B{row}'] = r.get('NET_TO_LP_INVESTOR', 0)
+        ws[f'B{row}'].number_format = '$#,##0'
+        ws[f'B{row}'].font = Font(bold=True, size=12)
+        
+        row += 2
+        ws[f'A{row}'] = "LP Multiple:"
+        ws[f'A{row}'].font = Font(bold=True)
+        ws[f'B{row}'] = r.get('LP_MULTIPLE', 0)
+        ws[f'B{row}'].number_format = '0.00x'
+        ws[f'B{row}'].font = Font(bold=True)
+        
+        row += 1
+        ws[f'A{row}'] = "LP IRR:"
+        ws[f'A{row}'].font = Font(bold=True)
+
+        irr_value = r.get('IRR_TO_LP', 0)
+        # Handle complex numbers or invalid values
+        if isinstance(irr_value, complex):
+            irr_value = 0  # or use irr_value.real if you want the real component
+        ws[f'B{row}'] = irr_value / 100
+
+        ws[f'B{row}'].number_format = '0.00%'
+        ws[f'B{row}'].font = Font(bold=True)
+        
+        ws.column_dimensions['A'].width = 35
+        ws.column_dimensions['B'].width = 20
+    
+    def run_full_pipeline(self, pdf_directory: str, output_excel: str = "Real_Estate_Financial_Model.xlsx"):
+        """Execute complete pipeline"""
+        print("=" * 60)
+        print("REAL ESTATE FINANCIAL MODEL PIPELINE")
+        print("=" * 60)
+        
+        # Step 1: Extract PDFs
+        print("\n[Step 1/4] Extracting text from PDFs...")
+        self.extract_all_pdfs(pdf_directory)
+        print(f"✓ Extracted {len(self.extracted_data)} PDF files")
+        
+        # Step 2: Process with Gemini
+        print("\n[Step 2/4] Extracting structured data with Gemini API...")
+        structured_data = self.extract_structured_data()
+        
+        # NEW: Post-process to fill gaps
+        print("\n[Step 2.5/4] Post-processing and filling estimates...")
+        structured_data = self.post_process_extracted_data(structured_data)
+
+        # Step 3: Calculate formulas
+        print("\n[Step 3/4] Calculating all formulas...")
+        self.calculate_all_formulas(structured_data)
+        print(f"✓ Calculated {len(self.formula_results)} formula values")
+        
+        # Step 4: Generate Excel
+        print("\n[Step 4/4] Generating Excel file...")
+        self.generate_excel(output_excel)
+        
+        print("\n" + "=" * 60)
+        print("PIPELINE COMPLETE!")
+        print("=" * 60)
+        print(f"\nKey Metrics:")
+        print(f"  Total Project Cost: ${self.formula_results.get('TOTAL_FINANCING_CONTINGENCY_AND_RESERVES', 0):,.0f}")
+        print(f"  Total Debt: ${self.formula_results.get('TOTAL_DEBT', 0):,.0f}")
+        print(f"  Total Equity: ${self.formula_results.get('TOTAL_EQUITY', 0):,.0f}")
+        print(f"  NOI: ${self.formula_results.get('NET_OPERATING_INCOME', 0):,.0f}")
+        print(f"  Yield on Cost: {self.formula_results.get('YIELD_ON_COST_PERCENTAGE', 0):.2%}")
+        irr_val = self.formula_results.get('IRR_TO_LP', 0)
+        if isinstance(irr_val, complex):
+            irr_val = irr_val.real
+        print(f"  LP IRR: {irr_val:.2f}%")
+
+        print(f"\nExcel file: {output_excel}")
+        
+        return output_excel
+
+
+if __name__ == "__main__":
+    
+    def process_pdfs(pdf_files, api_key):
+        """Process uploaded PDFs and return Excel file"""
+        if not pdf_files:
+            return None, "Please upload at least one PDF file"
+        
+        if not api_key or api_key.strip() == "":
+            return None, "Please enter your Gemini API key"
+        
+        try:
+            # Create temporary directory for PDFs
+            temp_dir = tempfile.mkdtemp()
+            
+            # Save uploaded PDFs to temp directory
+            for pdf_file in pdf_files:
+                shutil.copy(pdf_file.name, temp_dir)
+            
+            # Initialize pipeline with provided API key
+            pipeline = RealEstateModelPipeline(api_key.strip())
+            
+            # Create output file in temp directory
+            output_file = Path(temp_dir) / "Real_Estate_Financial_Model.xlsx"
+            
+            # Run pipeline
+            result = pipeline.run_full_pipeline(temp_dir, str(output_file))
+            
+            # Generate summary text
+            summary = f"""
+            ✅ Processing Complete!
+            
+            Key Metrics:
+            • Total Project Cost: ${pipeline.formula_results.get('TOTAL_FINANCING_CONTINGENCY_AND_RESERVES', 0):,.0f}
+            • Total Debt: ${pipeline.formula_results.get('TOTAL_DEBT', 0):,.0f}
+            • Total Equity: ${pipeline.formula_results.get('TOTAL_EQUITY', 0):,.0f}
+            • NOI: ${pipeline.formula_results.get('NET_OPERATING_INCOME', 0):,.0f}
+            • Yield on Cost: {pipeline.formula_results.get('YIELD_ON_COST_PERCENTAGE', 0):.2%}
+            • LP IRR: {float(pipeline.formula_results.get('IRR_TO_LP', 0).real if isinstance(pipeline.formula_results.get('IRR_TO_LP', 0), complex) else pipeline.formula_results.get('IRR_TO_LP', 0)):.2f}%
+            
+            Download your Excel file below ⬇️
+            """
+            
+            return str(output_file), summary
+            
+        except Exception as e:
+            return None, f"❌ Error: {str(e)}"
+    
+    # Create Gradio interface
+    with gr.Blocks(title="Real Estate Financial Model Generator", theme=gr.themes.Soft()) as demo:
+        
+        gr.Markdown("""
+        # 🏢 Real Estate Financial Model Generator
+        Upload your PDF documents and generate a comprehensive financial model in Excel format.
+        """)
+        
+        with gr.Row():
+            with gr.Column(scale=2):
+                api_key_input = gr.Textbox(
+                    label="Gemini API Key",
+                    placeholder="Enter your Gemini API key (AIza...)",
+                    type="password",
+                    info="Get your API key from Google AI Studio"
+                )
+                
+                pdf_input = gr.File(
+                    label="Upload PDF Files",
+                    file_count="multiple",
+                    file_types=[".pdf"],
+                    type="filepath"
+                )
+                
+                process_btn = gr.Button("🚀 Generate Financial Model", variant="primary", size="lg")
+            
+            with gr.Column(scale=1):
+                gr.Markdown("""
+                ### 📋 Required Documents
+                - Offering Memorandum
+                - Operating Expenses Summary
+                - Sales Comps
+                - Rent Comps
+                - Market Report
+                - Demographics Overview
+                
+                ### ⚡ Features
+                - Automated data extraction
+                - Formula calculations
+                - Professional Excel output
+                - Multiple analysis sheets
+                """)
+        
+        with gr.Row():
+            output_text = gr.Textbox(
+                label="Processing Results",
+                lines=12,
+                interactive=False
+            )
+        
+        with gr.Row():
+            excel_output = gr.File(
+                label="📊 Download Excel File"
+            )
+        
+        process_btn.click(
+            fn=process_pdfs,
+            inputs=[pdf_input, api_key_input],
+            outputs=[excel_output, output_text]
+        )
+        
+        gr.Markdown("""
+        ---
+        ### 💡 Tips
+        - Ensure PDF files are readable and not scanned images
+        - Use descriptive filenames (e.g., "Offering_Memorandum.pdf")
+        - Processing may take 30-60 seconds depending on file sizes
+        """)
+    
+    # Launch the app
+    demo.launch(share=False)