Buy Proxies for Real Estate

# Python Real Estate Data Aggregation
import requests
from datetime import datetime
import pandas as pd

class RealEstateAggregator:
    def __init__(self):
        # PlainProxies residential configuration with city targeting
        self.proxy = {
            'http': 'http://user:[email protected]:8080',
            'https': 'https://user:[email protected]:8080'
        }
        self.headers = {
            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64)',
            'Accept': 'application/json'
        }
    
    def get_property_data(self, region):
        """Aggregate property listings from multiple platforms"""
        session = requests.Session()
        session.proxies.update(self.proxy)
        
        # Collect from global real estate platforms
        platforms = [
            f'https://zillow.com/api/listings?region={region}',  # USA
            f'https://rightmove.co.uk/api/properties?area={region}',  # UK
            f'https://idealista.com/api/listings?location={region}',  # Spain/Italy
            f'https://propertyguru.com.sg/api/properties?region={region}',  # SE Asia
            f'https://domain.com.au/api/listings?area={region}'  # Australia
        ]
        
        all_listings = []
        for platform_url in platforms:
            response = session.get(platform_url, headers=self.headers)
            if response.status_code == 200:
                all_listings.extend(response.json()['listings'])
        
        return all_listings
    
    def analyze_investment(self, property_data):
        """Calculate investment metrics"""
        metrics = {
            'cap_rate': (property_data['noi'] / property_data['price']) * 100,
            'cash_flow': property_data['rental_income'] - property_data['expenses'],
            'roi': ((property_data['appreciation'] + property_data['cash_flow']) 
                   / property_data['investment']) * 100
        }
        return metrics

# Initialize aggregator
aggregator = RealEstateAggregator()

# Collect data from global markets
markets = ['london-uk', 'barcelona-es', 'singapore', 'sydney-au', 'dubai-uae']
market_data = {}

for market in markets:
    print(f"Aggregating {market} data...")
    listings = aggregator.get_property_data(market)
    market_data[market] = pd.DataFrame(listings)
    
    # Investment analysis
    for idx, property in market_data[market].iterrows():
        metrics = aggregator.analyze_investment(property)
        print(f"Property {property['id']}: Cap Rate {metrics['cap_rate']:.2f}%")