Motivated Seller Real Estate Dashboard

@farhanyoussef18

Youssef, Full-Time Freelancer with Python Programming expertise, I'm excited about your Motivated Seller Real Estate Dashboard project. My strong background in web scraping, automation, and data extraction, utilizing tools like Playwright, Selenium, and BeautifulSoup, is ideal for this. I will develop a Python-based system to automatically collect property listings daily from MercadoLibre Inmuebles and optionally InfoCasas Uruguay. This will accurately extract, organize, and track all specified data fields, including price changes and days on market, delivering it in a structured format for your Excel analysis dashboard, avoiding duplicates. I have significant experience building similar data collection and analysis systems and am confident in delivering your market radar.

@ShaikhAneesa

Hello, I have thoroughly reviewed the project requirements for the Motivated Seller Real Estate Dashboard, focusing on automating data collection from real estate portals in Uruguay to identify undervalued properties and motivated sellers. Let's chat and discuss it further. To handle your project, I will start with developing a Python scraper using tools like Selenium and BeautifulSoup to extract property listings from MercadoLibre Inmuebles and InfoCasas Uruguay. I will then organize the data into a structured dataset stored in Excel, enabling easy analysis and filtering by property type, price range, and neighborhood. The deliverables will include a working scraper, a structured dataset in Excel format, an Excel dashboard for market analysis, and clear instructions for running the scraper. Before signing-off my bid, I would like to ask a question, i.e., How frequently do website structure changes occur on the real estate portals you are targeting? Best Regards, Aneesa.

@divumanocha

Hello, Thank you so much for posting this opportunity. It sounds like a great fit, and I’d love to be part of it! I’ve worked on similar projects before, and I’m confident I can bring real value to your project. I’m passionate about what I do and always aim to deliver work that’s not only high-quality but also makes things easier and smoother for my clients. Feel free to take a quick look at my profile to see some of the work I’ve done in the past. If it feels like a good match, I’d be happy to chat further about your project and how I can help bring it to life. I’m available to get started right away and will give this project my full attention from day one. Let’s connect and see how we can make this a success together! Looking forward to hearing from you soon. With Regards!

@DeyaaAhmed

Hi, I can build your Uruguay real estate "market radar" using Python with Playwright to reliably scrape MercadoLibre and InfoCasas, handling their dynamic content and anti-bot measures effectively. The system will run daily on your machine, collecting all key fields and storing them in a structured CSV that feeds an Excel dashboard designed to instantly calculate price-per-square-meter trends and flag motivated sellers (price drops, long market time). I will implement a unique ID tracking system to monitor listing history, ensuring accurate detection of price changes and days on market without duplicates. To handle site updates, the scraper will use a modular config file, allowing quick selector adjustments without code rewrites. You'll receive the full script, the analysis-ready Excel file, and simple setup instructions. I also offer FREE post-delivery support to monitor the first week of automated runs, tweak the motivation algorithms, and adjust the scrapers if the target sites change their layout. Let's discuss the project in more details.

@OutsourceMan

⭐⭐⭐⭐⭐ CnELIndia, led by Raman Ladhani, can design a robust Python-based scraper using Scrapy and Selenium to extract listings from MercadoLibre Inmuebles and InfoCasas Uruguay daily. Implement data cleaning, deduplication, and historical tracking to ensure each property’s price changes, days on market, and multiple reductions are recorded. Structure the dataset in Excel/CSV with fields: listing ID, price, location, size, property type, bedrooms, URL, and original publication date, enabling seamless filtering and analysis. Build an Excel dashboard with calculated metrics like price/m², neighborhood median comparison, and flags for potential motivated sellers. Set up automated alerts for price reductions and properties active beyond 60–90 days to identify negotiation opportunities. Provide step-by-step instructions for running the scraper locally and incorporate error handling for site structure changes and anti-scraping measures. Leverage experience from prior web scraping and real estate data projects to ensure stable, maintainable, and scalable workflow.

@artakharutyun7

Hi, I can build a "reliable Python-based scraping and analysis system" for your Uruguay real estate “market radar”. "Technology:" Python with "Requests/Selenium + BeautifulSoup", "Pandas" for data processing, and "schedule/cron" for daily execution. Data will be stored in "CSV/Excel", making it easy to analyze and filter. The scraper will collect listings daily from "MercadoLibre Inmuebles (primary)" and optionally "InfoCasas", capturing fields such as "listing ID, price, location, property size (m²), property type, bedrooms, listing URL, and publication date". A "unique listing ID system" will prevent duplicates and allow historical tracking. To handle "website structure changes and anti-scraping", I’ll design the scraper with "flexible selectors, retry logic, request throttling, and optional Selenium fallback" if dynamic content changes. I’ve built similar "Python scraping and market-monitoring tools" for pricing and listing analysis. Best regards. Artak

@mohjib092

Hello, I can build a Python scraper for MercadoLibre Inmuebles (and optionally InfoCasas) that collects listings daily, tracks price changes, days on market, and identifies potential motivated sellers. I’ve done similar real estate scraping projects and can ensure stable, maintainable code. Best regards, Muhammad Jibran

@FaizalShaik80

Hi, hope you are well. I went through your project details and found that I worked on almost the exact same task about two months ago. I am a skilled freelancer with 6+ years of experience in Python, Excel, Web Scraping and I can deliver the results as quickly as possible. You can visit my profile to check my latest work and recent reviews. Connect in chat to discuss details and next steps. Warm regards.

@pvsysgroupinc

As an experienced professional with over 16+ years of proven success, including operating my own direct company for the past 33 years in the global market, I bring extensive competence and creativity to this project. My specialization in Python and data analysis has propelled me to deliver exceptional results in similar web scraping projects. I have successfully created myriad automated data workflows that collect, organize, and analyze large volumes of data just like you need for your real estate dashboard. My technical approach, using tools such as Selenium, BeautifulSoup, and Scrapy aligns perfectly with the preferred ones listed in your project description. These have proven to be stable solutions time and again. In terms of handling changes in website structure and anti-scraping measures, I bring comprehensive understanding to find workarounds and sustainable strategies so that system stays relevant irrespective of the changes on websites or potential anti-scraping measures. Lastly, I'd like to emphasize my dedication to our project's success. I am not merely another freelancer; I am the CEO of PVSYS GROUP looking forward for a long-term collaboration with you! With around-the-clock availability as per your convenience across any timezone and the skillset needed to deliver an accurate, robust undervalued property and motivated seller radar system within your budget of $400 - $800, I believe we are a match made in heaven! Let's build this together!

@hotanloc

With my core skill Python or JavaScript, I have finished Web Scrapting project which extract unique ID, product price, product date and product selling number and save this to excel or google sheet for uploading into client's google driver a few days ago. I have rich experienced in Web Scraping, Data Extraction, Machine Learning (ML), Python and Artificial Intelligence. I have full experience to develop python code for extracting the specific data from web site with python, HTML, CSS and JavaScript. If you give me your project, You can get best result with shortest time and best quality result. I am sure for your project and i can complete your project perfectly on time and with high quality. Please send me your message to discuss more about your project. I am waiting your reply now. Thanks.

@usamae

This is a solid project spec. I've built similar property scraping pipelines before - MercadoLibre uses a semi-structured API under the hood which makes it cleaner to scrape than pure HTML parsing. My approach: Python scraper (Scrapy or requests + BeautifulSoup) running daily via cron, deduplication by listing ID, tracking price history in a SQLite or CSV store, and outputting clean Excel files with the motivated seller indicators you described (days on market, price drops, below-median price/m2). The InfoCasas secondary source is straightforward to add once the primary pipeline is solid. I can have the core MercadoLibre scraper + Excel output running within a week, with the analysis features following right after. - Usama

@Julian29

I HAVE DEVELOPED AUTOMATED REAL ESTATE DATA SCRAPERS AND MARKET ANALYTICS SYSTEMS THAT TRACK LISTINGS, PRICE CHANGES, AND INVESTMENT OPPORTUNITIES — READY TO BUILD YOUR URUGUAY MARKET RADAR. Hello, I can create a fully automated property data collection and analysis platform that pulls listings daily from MercadoLibre Inmuebles (primary) and InfoCasas Uruguay (optional). The system will collect essential fields: listing ID, price, location, size, property type, bedrooms, listing URL, and publication date. Historical tracking will monitor first detection, days on market, price changes, and multiple reductions to identify motivated sellers. User roles include: Admin: manages scraper settings, oversees data, and exports datasets. Analyst/User: filters and analyzes listings, monitors price trends, and identifies opportunities. The scraper will run automatically once per day, avoid duplicates, and feed a structured dataset in Excel/CSV or Google Sheets, accompanied by a lightweight dashboard for filtering, price-per-m² analysis, neighborhood comparisons, and motivated seller alerts. I will deliver complete source code, setup instructions, and a fully functional system, along with 2 years of free ongoing support for updates, bug fixes, and enhancements. This solution ensures reliable daily data collection, actionable market insights, and a production-ready tool for tracking undervalued properties in Uruguay.

@KarthikTechCon

Hello, I’m Karthik, a Full-Stack Engineer with 15+ years of experience in data engineering, web scraping, and analytics dashboards. I have built multiple automated scraping and market intelligence systems for real estate, eCommerce, and price monitoring platforms. For your Uruguay real estate market radar, I will build a reliable Python-based scraper that collects listings from MercadoLibre Inmuebles (and InfoCasas if needed) and stores the data in a clean structured dataset (Excel/CSV/Google Sheets) for easy analysis. Proposed approach: • Python + Scrapy/BeautifulSoup + Selenium for stable data extraction • Scheduled daily scraping with duplicate detection and historical tracking • Capture fields: ID, price, location, m², property type, bedrooms, URL, and publication date • Track price changes, days on market, and listing history • Build an Excel dashboard with filters for neighborhood, property type, and price range • Automatic calculation of price per m² and comparison with neighborhood medians To handle site structure changes or anti-scraping, I implement modular scraping logic, retry systems, and adaptable selectors to maintain stability. You will also receive clear setup instructions so the scraper can run easily on your computer. I’d be happy to share examples of previous scraping and analytics projects and discuss the best implementation for long-term reliability. Best regards, Karthik 15+ Years | Data Engineering | Web Scraping | Analytics Systems

@RadoslavPompura

Hello, I can build this as a daily listing-tracking and market analysis system, not just a simple scraper. The workflow will collect MercadoLibre property listings once per day, avoid duplicates, track listing history, calculate price per m², compare against neighborhood median values, and flag possible motivated sellers based on price reductions and long days on market. My approach: - Python scraper - Historical tracking and deduplication - Excel-ready dataset and dashboard - Clear setup instructions for local execution I can also design the scraper to better handle website changes using modular parsing, logging, retry handling, and careful request pacing. Delivery: 7 days Bid: USD 600 Best regards

@KirtikaKataria

Hi there, I understand you need a real estate market radar for Uruguay that automatically scrapes property listings, tracks historical data, and organizes it into a structured dataset for analysis, highlighting undervalued properties and potentially motivated sellers. I am confident I can build a robust workflow that meets these requirements. My approach will begin by developing a Python scraper using Scrapy or Selenium with BeautifulSoup to collect listings from MercadoLibre Inmuebles and optionally InfoCasas. The scraper will capture all required fields; listing ID, price, location, size, property type, bedrooms, URL, and publication date while avoiding duplicates and storing historical changes for price and days-on-market tracking. Next, I will structure the Excel dataset and dashboard with calculated fields such as price per m², neighborhood median comparisons, and filters for property type, price, and location. Conditional formatting will highlight price reductions and long-standing listings to help identify motivated sellers. Finally, I will provide clear instructions for running the scraper daily, ensuring the dataset updates automatically while remaining easy to analyze and maintain. Deliverable: Python scraper, structured Excel dataset, interactive dashboard, and user instructions. QUESTION: Should the scraper capture daily snapshots of all listings, or only update records when price or listing details change? Regards, Shehwani.

@deepanshug38

Hello, I can build this as a daily real-estate tracking pipeline for Uruguay that collects listings, stores historical snapshots, and outputs an Excel dashboard to identify undervalued properties and potential motivated sellers. My approach is to build more than a scraper: the key is tracking listing history over time so you can measure price/m², compare to neighborhood medians, and flag listings with price reductions, multiple reductions, or long days on market. Tech stack: Python BeautifulSoup/requests for lightweight scraping Selenium or Playwright only if dynamic pages require it SQLite/CSV for historical tracking Excel dashboard for filtering and opportunity review What I’ll deliver: working daily scraper structured dataset with duplicate control historical tracking by listing calculations for price/m², first seen date, days on market, and price changes Excel file/dashboard with filters by neighborhood, property type, and price range setup instructions so it can run on your computer I build scrapers in a modular way so site changes are easier to maintain, with logging, validation checks, and careful request pacing for stability. A couple of points to confirm: Should version 1 focus only on sale listings, or sales + rentals? Do you want raw-history sheets included in Excel, or only the final analysis dashboard? I’d focus on delivering a reliable first version that runs daily and makes negotiation opportunities easy to spot.

@arslanshahid386

Hi there, It sounds like you're looking to build a robust real estate dashboard for Uruguay that can automatically gather and analyze property data. With 4+ years of experience in web scraping and data analysis, I can create a system that pulls listings from MercadoLibre Inmuebles and, if needed, InfoCasas Uruguay. I would use Python with libraries like BeautifulSoup or Scrapy to ensure the scraper runs smoothly every day and collects all the necessary information, while avoiding duplicates and tracking listings over time. To tackle potential website structure changes or anti-scraping measures, I would implement strategies like rotating user agents and using proxies. This way, the scraper can adapt and continue functioning effectively. Just to clarify, how would you like to prioritize the features for motivated seller detection? Best regards, Arslan Shahid

@asmasattar4

I’m an experienced developer specializing in Python web scraping and data pipelines. I can build a reliable scraper using Python (Scrapy/BeautifulSoup + Selenium if needed) to collect listings from MercadoLibre Inmuebles and InfoCasas, store them in a structured dataset, track historical changes, and calculate metrics like price/m², days on market, and price reductions. The system will run automatically once per day, prevent duplicates, and export clean data to Excel/CSV with a dashboard for filtering and opportunity detection. I also design scrapers with error handling and adaptable selectors to handle site structure changes and basic anti-scraping protections. Happy to share similar scraping projects and discuss the best architecture for your budget and long-term reliability.

@jesica46

Hello, I hope you’re having a great day. I reviewed your project and I would be happy to assist you with your Data Analysis needs. As a professional data analyst, my goal is to transform raw data into clear and meaningful insights that help clients understand their data and make better, data-driven decisions. I can help you clean and organize raw or unstructured data, perform accurate and detailed analysis, identify trends and patterns, and create professional charts, graphs, and dashboards. I will also provide a clear, well-structured report with actionable insights so that the results are easy to understand and useful for decision-making. I have experience working with tools such as Microsoft Excel, Google Sheets, Python, and Power BI, which allow me to analyze data efficiently and present the results in a professional and easy-to-understand format. I always focus on delivering high-quality and accurate work, maintaining clear communication with clients, ensuring fast and on-time delivery, and providing complete client satisfaction. I would love to learn more about your project. Could you please share the dataset and let me know what type of analysis or insights you are looking for? Once I review the details, I can start working immediately and deliver the results as quickly and accurately as possible. Thank you for your time and consideration. I look forward to working with you. Best regards,