The main motivation behind HomeScraper was to save time and reduce the stress of apartment hunting. We wanted a tool that could quickly gather rental data from multiple sources, clean and standardize it, and present it in a format that’s easy to analyze and compare.
What We Learned
While building HomeScraper, we gained experience in several areas:
Web scraping & automation: Using Python libraries and ScraperGraphAi to interact with websites and extract structured data.
Data cleaning & normalization: Handling missing values, standardizing addresses, and deduplicating listings.
How We Built It
HomeScraper is built in Python, using a combination of:
API requests & scraping tools to gather listing data from sites like Redfin and Homes.com.
Data normalization functions to replace missing values (null) with -1, standardize addresses, and normalize price/bedroom information.
Deduplication algorithms to remove repeated listings, including fuzzy matching for slightly different addresses.
JSON output for easy integration with downstream analysis tools, dashboards, or further processing.
Challenges
Some of the biggest challenges we faced included:
Data inconsistencies: Listings often had missing or inconsistent fields, requiring robust cleaning logic.
Rate limiting & access restrictions: Avoiding blocks while scraping large numbers of listings.
Multi-language input: Supporting city names in various languages while keeping URLs and search queries compatible.
Despite these challenges, we were able to create a modular, reusable system that significantly reduces the time spent searching for housing, making it a valuable tool for anyone moving to a new city.
Built With
- python
- scraper
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