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Nexa Auto automates the process of verifying the authenticity of addresses for room service eligibility and retrieving detailed specifications across multiple websites. Utilizing Selenium for web automation and GPT for handling missing data, Nexa Auto significantly reduces manual effort in data entry tasks.

This project is a powerful and extensible scrapy-based crawler designed to extract and aggregate data from multiple real estate crowdfunding platforms. Ideal for investors, analysts and researchers interested in tracking investment opportunities, platform performance and market trends

A curated collection of top tools, platforms, and projects for real estate investors, operators, agents & tech innovators.

Advanced artificial intelligence agentic system for real estate search and scheduling, implementing a swarm architecture using LangGraph-Swarm, where specialized agents collaborate dynamically to solve complex user queries.

Automated Python scraper for extracting delinquent tax data from Lancaster County, PA's public parcel viewer. Accepts parcel list input, extracts only relevant data, and outputs clean CSV files. Built for large-scale use with request throttling and error handling.

zillow data extraction scraper

Extract property listings, prices, details

Zillow sold listings pagination API

funda business data extractor

Land.com property listings scraper

A simple tool for investors to find cash-flowing properties in Canada. Scans target cities, underwrites with a conservative cash-flow model, and ranks deals by margin of safety.

immobiliare.it property data extractor

Bezrealitky real estate listings

PHP client for scraping Yandex — places and reviews from Yandex Maps, products from Yandex Market, real estate from Yandex Realty. Typed DTOs, enums, helpers. Powered by Apify.

Type-safe way to scrape 2gis using Apify

A high-performance Zillow Scraper that scrapes zillow property data. Supports ZipCode & URL filtering.

Zillow sales history scraper

Analyse exploratoire du dataset Airbnb de New York à l’aide de Python. Ce projet vise à comprendre les facteurs influençant les prix des logements, la disponibilité, et les différences entre quartiers et types de logements, à travers le nettoyage des données, l’analyse statistique et la visualisation.