Scraping Rental Market Data: Analyzing Rent Trends by City

Understanding rental market trends helps renters negotiate better deals and helps investors identify opportunities. Let's build a rental data scraper that tracks prices across major cities. The Rental Data Landscape Sites like Zillow, Apartments.com, and Craigslist have massive rental listing datasets. By scraping and analyzing this data over time, we can identify trends like seasonal price drops, neighborhood gentrification signals, and fair market rent for any area. Setting Up the Scraper pip install requests beautifulsoup4 pandas matplotlib We'll use ScraperAPI for reliable access to rental sites that have aggressive bot detection: import requests from bs4 import BeautifulSoup import pandas as pd import json API_KEY = " YOUR_SCRAPERAPI_KEY " def scrape_listings ( city , state , max_pages = 5 ): """ Scrape rental listings for a given city. """ listings = [] for page in range ( 1 , max_pages + 1 ): url = ( f " https://www.apartments.com/ " f " { city } - { state } / { page } / " ) res