Implementing Real Estate Data Solutions: The Complete Guide

Method 1: Apify Zillow Scraper

# Using Apify Python Client
from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run_input = {
    "searchType": "address",
    "search": "123 Main St, Los Angeles, CA",
    "maxItems": 1
}

run = client.actor("maxcopell/zillow-scraper").call(run_input=run_input)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"Address: {item['address']}")
    print(f"Zestimate: {item['zestimate']}")
    print(f"Beds: {item['bedrooms']}, Baths: {item['bathrooms']}")
    print(f"Sqft: {item['livingArea']}")

RapidAPI Realtor Endpoint

import requests

url = "https://realtor16.p.rapidapi.com/search"
headers = {
    "X-RapidAPI-Key": "YOUR_RAPIDAPI_KEY",
    "X-RapidAPI-Host": "realtor16.p.rapidapi.com"
}
params = {
    "location": "Dallas, TX",
    "status": "for_sale",
    "limit": "20"
}

response = requests.get(url, headers=headers, params=params)
data = response.json()

for prop in data['properties']:
    print(f"{prop['address']['line']} - {prop['list_price']}")
    print(f"  Agent: {prop['agent']['name']}")
    print(f"  Phone: {prop['agent']['phone']}")

import requests
import pandas as pd
from datetime import datetime

# Configuration
API_KEY = "your_rapidapi_key"
LOCATIONS = ["Austin, TX", "Denver, CO", "Phoenix, AZ"]

def fetch_listings(location):
    url = "https://realtor16.p.rapidapi.com/search"
    headers = {"X-RapidAPI-Key": API_KEY, "X-RapidAPI-Host": "realtor16.p.rapidapi.com"}
    params = {"location": location, "status": "for_sale", "limit": "50"}
    
    response = requests.get(url, headers=headers, params=params)
    return response.json().get('properties', [])

def process_data(properties, location):
    records = []
    for p in properties:
        records.append({
            "location": location,
            "address": p.get('address', {}).get('line', ''),
            "city": p.get('address', {}).get('city', ''),
            "price": p.get('list_price', 0),
            "beds": p.get('description', {}).get('beds', 0),
            "baths": p.get('description', {}).get('baths', 0),
            "sqft": p.get('description', {}).get('sqft', 0),
            "price_per_sqft": round(p.get('list_price', 0) / max(p.get('description', {}).get('sqft', 1), 1), 2),
            "agent_name": p.get('primary_agent', {}).get('name', ''),
            "agent_phone": p.get('primary_agent', {}).get('phone', ''),
            "scraped_at": datetime.now().isoformat()
        })
    return records

# Main Execution
all_records = []
for loc in LOCATIONS:
    print(f"Fetching {loc}...")
    listings = fetch_listings(loc)
    all_records.extend(process_data(listings, loc))

df = pd.DataFrame(all_records)
df.to_csv(f"real_estate_data_{datetime.now().strftime('%Y%m%d')}.csv", index=False)
print(f"Saved {len(df)} properties to CSV")

Workflow Steps:

1. Schedule Trigger: Run daily at 8:00 AM
2. HTTP Request: Call Apify Zillow Scraper API
3. Filter: Keep only properties under $500k
4. Google Sheets: Append new listings to spreadsheet
5. IF Condition: Check if price dropped >5%
6. Email: Send "Price Drop Alert" to client

Estimate Formula

def calculate_estimate(property_data):
    # Get comparable sales (last 6 months, within 0.5 miles)
    comps = get_comparable_sales(property_data['address'])
    
    # Average price per sqft from comps
    avg_price_per_sqft = sum(c['sold_price'] / c['sqft'] for c in comps) / len(comps)
    
    # Base estimate
    base_estimate = avg_price_per_sqft * property_data['sqft']
    
    # Adjustments
    adjustments = 0
    if property_data['renovated_year'] > 2020:
        adjustments += base_estimate * 0.05  # +5% for recent reno
    if property_data['pool']:
        adjustments += 25000  # +$25k for pool
    if property_data['garage_spaces'] > 2:
        adjustments += 10000  # +$10k for extra garage
    
    final_estimate = base_estimate + adjustments
    
    return {
        "low": round(final_estimate * 0.95),
        "mid": round(final_estimate),
        "high": round(final_estimate * 1.05),
        "comps_used": len(comps)
    }