BCPAO Data Extraction Skill

Extracts comprehensive property data from Brevard County Property Appraiser's GIS API.

When to Use This Skill

• Stage 2 of Everest Ascent pipeline (Scraping)
• Getting property valuations for ARV calculations
• Retrieving property photos for reports
• Extracting parcel details for title research

API Endpoint

Base URL:

https://gis.brevardfl.gov/gissrv/rest/services/Base_Map/Parcel_New_WKID2881/MapServer/5

Operation:

/query

Query Parameters

params = {
    'where': f"PARCEL_ID = '{parcel_id}'",  # or SITE_ADDR LIKE '%123 Main St%'
    'outFields': '*',
    'returnGeometry': 'false',
    'f': 'json'
}

Key Fields Returned

Property Identification

• PARCEL_ID

  : Unique parcel identifier (e.g., "29-37-38-00-00000.0-000010.0")

• ACCOUNT

  : Account number for photo URLs

• SITE_ADDR

  : Full property address

• LEGAL_DESC

  : Legal description

Ownership

• OWNER_NAME

  : Current owner of record

• OWNER_ADDR

  : Mailing address

• MAIL_CITY

  ,

  MAIL_STATE

  ,

  MAIL_ZIP

Valuations

• JUST_VAL

  : Just (assessed) value

• ASSESSED_VAL

  : Assessed value for tax purposes

• TAXABLE_VAL

  : Taxable value after exemptions

• LAND_VAL

  : Land value only

• BLDG_VAL

  : Building value only

Property Characteristics

• LIV_AREA

  : Living area in square feet

• YR_BLT

  : Year built

• ZONING

  : Zoning classification

• USE_CODE

  : Property use code

• TOTAL_BEDROOMS

  : Bedroom count (NOT RELIABLE - often missing)

• TOTAL_BATHROOMS

  : Bathroom count (NOT RELIABLE - often missing)

Tax Information

• TAX_DIST

  : Tax district

• EXEMPTIONS

  : Homestead/other exemptions

Photo URL Generation

Photos follow predictable pattern:

def get_photo_url(account_number):
    # Format: https://www.bcpao.us/photos/{prefix}/{account}011.jpg
    # Account: 2934567 → prefix: 293, photo: 2934567011.jpg
    
    prefix = account_number[:3]
    photo_url = f"https://www.bcpao.us/photos/{prefix}/{account_number}011.jpg"
    return photo_url

Example:

• Account:

  2934567

• Prefix:

  293

• URL:

  https://www.bcpao.us/photos/293/2934567011.jpg

Availability:

• ~13/15 properties have photos (87% success rate)
• Returns 404 if photo doesn't exist
• Fallback: Use placeholder or note "No photo available"

Data Limitations

❌ NOT Available in BCPAO API:

• Bedrooms (field exists but often null)
• Bathrooms (field exists but often null)
• Detailed property condition
• Recent sales comparables
• Interior features

✓ Available via BCPAO:

• Living area (reliable)
• Year built (reliable)
• Assessed values (reliable)
• Ownership (reliable)
• Photos (87% available)

🔍 Must Use Other Sources For:

• Beds/Baths: Zillow, Redfin, Realtor.com
• Comps: MLS, Zillow, recent sales
• Condition: Drive-by, photos, inspector
• Rental rates: Airbnb, Furnished Finder, Zillow

Example Response

{
  "features": [
    {
      "attributes": {
        "PARCEL_ID": "29-37-38-00-00000.0-000010.0",
        "ACCOUNT": "2934567",
        "SITE_ADDR": "123 MAIN ST",
        "OWNER_NAME": "SMITH JOHN & MARY",
        "JUST_VAL": 425000,
        "ASSESSED_VAL": 425000,
        "LAND_VAL": 125000,
        "BLDG_VAL": 300000,
        "LIV_AREA": 2150,
        "YR_BLT": 2005,
        "ZONING": "RES-1",
        "USE_CODE": "0100",
        "LEGAL_DESC": "LOT 10 BLOCK A SUBDIVISION PLAT..."
      }
    }
  ]
}

ARV Calculation Strategy

Since BCPAO doesn't give comps, use multi-source approach:

Step 1: BCPAO Baseline

bcpao_value = response['JUST_VAL']  # Assessed value

Step 2: Zillow/Redfin Comps

# Search Zillow for recent sales within 0.5 miles
# Similar: beds, baths, sqft, year
comparable_sales = get_recent_sales(address, radius=0.5)
avg_comp_value = mean([sale['price'] for sale in comparable_sales])

Step 3: Calculate ARV

# Weight: 40% BCPAO, 60% comps
arv = (bcpao_value * 0.4) + (avg_comp_value * 0.6)

# Conservative adjustment
arv_conservative = arv * 0.95  # 5% haircut for safety

Integration with Pipeline

# Stage 2: Scraping
bcpao_data = bcpao_extraction_skill.query(parcel_id)

property_details = {
    'address': bcpao_data['SITE_ADDR'],
    'living_area': bcpao_data['LIV_AREA'],
    'year_built': bcpao_data['YR_BLT'],
    'assessed_value': bcpao_data['JUST_VAL'],
    'photo_url': get_photo_url(bcpao_data['ACCOUNT']),
    'legal_desc': bcpao_data['LEGAL_DESC']
}

# Use for ARV calculation in Stage 8
# Use photo in Stage 10 (Report Generation)

Error Handling

try:
    response = requests.get(api_url, params=params)
    data = response.json()
    
    if not data.get('features'):
        # Parcel not found
        return None
    
    # Extract first feature
    property_data = data['features'][0]['attributes']
    
except requests.exceptions.RequestException:
    # API timeout or error
    return None

Example Usage

"Use bcpao-data-extraction-skill to get details for parcel 29-37-38-00-00000.0-000010.0"

"Extract BCPAO data for property at 123 Main St Melbourne FL"

"Get assessed value and photo for account 2934567"

Best Practices

1. Cache Results: Don't query same parcel multiple times
2. Handle Missing Photos: 13% don't have photos, use fallback
3. Don't Trust Bed/Bath: BCPAO data unreliable, use Zillow/Redfin
4. Use JUST_VAL for ARV: Most accurate baseline value
5. Verify Legal Desc: Critical for title research