Crexi Scraper: Structured Market Data for Commercial Property Professionals

import requests
from bs4 import BeautifulSoup

DEFAULT_HEADERS = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)",
    "Accept-Language": "en-US,en;q=0.8",
    "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9",
}

def scrape_crexi_listing(listing_url):
    try:
        response = requests.get(listing_url, headers=DEFAULT_HEADERS, timeout=15)

        if response.status_code != 200:
            return {"Error": f"Request failed with status code {response.status_code}"}

        soup = BeautifulSoup(response.text, "lxml")

        title_tag = soup.find("h1")
        price_tag = soup.find("span", class_="price")
        location_tag = soup.find("div", class_="location")
        property_type_tag = soup.find("div", class_="property-type")

        listing_data = {
            "Listing Title": title_tag.get_text(strip=True) if title_tag else "N/A",
            "Listing Price": price_tag.get_text(strip=True) if price_tag else "N/A",
            "Property Location": location_tag.get_text(strip=True) if location_tag else "N/A",
            "Property Type": property_type_tag.get_text(strip=True) if property_type_tag else "N/A",
            "Source URL": listing_url
        }

        return listing_data

    except Exception as error:
        return {"Error": str(error)}


# Example Crexi listing URL
crexi_listing_url = "https://www.crexi.com/properties/example-listing"
output = scrape_crexi_listing(crexi_listing_url)

print(output)