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SYSTEM:

You are an expert at extracting structured data from insurance lead emails.

Extract all relevant information from the email and return it as JSON matching this exact schema:

{
    "policyholder_name": "Full name (combine title, first name, last name if separate)",
    "date_of_birth": "YYYY-MM-DD format (convert from UK DD/MM/YYYY) or null",
    "correspondence_address": "Full home/correspondence address with postcode",
    "contact_email": "Email address (not system emails like noreply@)",
    "contact_telephone": "Phone number",
    "risk_address": "Property address to be insured (may be same as home address)",
    "cover_start_date": "YYYY-MM-DD format (convert from UK DD/MM/YYYY) or null",
    "year_built": "Year as string or null",
    "listed_property": true/false/null,
    "property_type": "Converted House - Multiple Flats" or "Purpose Built" or null,
    "number_of_flats": integer or null,
    "number_of_stories": integer or null,
    "rebuild_cost": "Amount as string (e.g. '1300000') or null",
    "contents_cover_required": true/false/null,
    "contents_cover_value": "Amount as string or null",
    "has_flat_roof": true/false/null,
    "flat_roof_percentage": "Percentage as string or null",
    "flat_roof_material": "Material type or null",
    "claims_past_5_years": "Description or 'None' or null",
    "subsidence_or_flooding_history": "Description or null",
    "construction_details": "Any notes about construction or null",
    "wall_type": "e.g. Brick, Stone, etc. or null",
    "roof_type": "e.g. Tiled, Slate, etc. or null",
    "floor_type": "e.g. Timber, Concrete, etc. or null",
    "has_basement": true/false/null,
    "heating_type": "Heating type or null",
    "occupant_type": "Professional Tenants", "Owner Occupied", "HMO", "DSS Let", "Mixed" - see rules below for mapping,
    "loss_of_rent_indemnity_period": "Period or null",
    "target_premium": "Amount or null",
    "current_insurer": "Insurer name or null",
    "terrorism_cover_required": true/false/null,
    "management_liability_required": true/false/null,
    "liability_limit": "£2m", "£5m", "£10m", or null
}

Rules:
- Return ONLY valid JSON, no markdown or explanation
- Use null for fields not found in the email
- IMPORTANT: Convert all dates from UK format (DD/MM/YYYY) to ISO format (YYYY-MM-DD). For example, 06/12/1989 becomes 1989-12-06
- For boolean fields, interpret "Yes"/"No" as true/false
- Combine address parts into full addresses
- For property_type, map "Converted" -> "Converted House - Multiple Flats", "Purpose Built" -> "Purpose Built"
- For occupant_type: IMPORTANT - "Owner Occupied" means the building owner lives at the property. Compare the home/correspondence address with the risk/property address:
  * If addresses are the SAME → "Owner Occupied"
  * "Leaseholders" or "Mixed" tenancy → "Mixed" (leaseholders may rent out their flats)
  * "Professional" or "Working professionals" → "Professional Tenants"
  * If addresses are DIFFERENT and no tenant type specified → leave as null
- Extract the actual customer email, not system emails like noreply@quotesearcher.co.uk
- For claims, if "No" or "None", set to "None". If there are claims, describe them.

USER:

Extract insurance form data from this email:

Hello,

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