6
0
Budget: 400 USD Deadline: 1 day
I will provide you with everything clearly and qualitatively, any protections are not a problem, feel free to contact me.
45
0
Budget: 400 USD Deadline: 14 days
I have solid experience in web scraping, data cleaning, and real estate market analysis using Python.
Web scraping:
Scraping major real estate portals using Python
Extracting structured fields: price, administration fee, location (neighborhood/city), socioeconomic stratum, built area, bedrooms, bathrooms, property age, listing date
Handling pagination, dynamic content, and anti-bot limitations
Setup for regular updates (scheduled scraping)
Data processing & analysis:
Data cleaning and normalization (units, currencies, formats)
Removing duplicates and inconsistent listings
Calculating:
Average price per square meter by neighborhood
Administration fee per square meter
Identifying outliers (undervalued properties vs market average)
Visualization & dashboard:
Interactive dashboard using Google Looker Studio / Power BI (your choice)
Filters by neighborhood, price range, area, stratum, etc.
Clear visual insights for investment decision-making
Deliverables:
Clean CSV / Excel database
Interactive dashboard link
Well-structured and reproducible workflow for future updates
I focus on data accuracy, scalability, and practical insights, not just raw scraping.
0
0
Budget: 400 USD Deadline: 2 days
Hello.
I was interested to learn about your project. I am confident that I can do effective and quality work that meets your requirements and expectations. I have over 8 years of experience. I am ready to discuss the details and start working. I will be waiting for your response, feel free to write and we will discuss.
1
1
Budget: 380 USD Deadline: 6 days
Hello!
I am ready to conduct a comprehensive real estate market research with regular updates: data collection from key portals, cleaning/deduplication, normalization of prices/areas, calculation of price per m² by districts (including admin fee per m²), detection of outliers, and building an interactive dashboard.
17
0
Budget: 600 USD Deadline: 14 days
Hello!
I am ready to conduct a comprehensive real estate market research with regular updates: data collection from key portals, data cleaning/deduplication, price/area normalization, calculation of price per m² by districts (including admin fee per m²), detection of outliers, and building an interactive dashboard.
Technology stack
Backend:
*Python 3.12
*Scrapy + Playwright
*Pandas
*PostgreSQL
*FastAPI
-Infrastructure / DevOps:
*Docker + Docker Compose
*VPS
*GitHub
I will be waiting for your message to discuss the stages of development and approaches to work.
Best regards, Andriy!
Budget: 390 USD Deadline: 6 days
Hello! I understand the task, I have extensive experience with web scraping, databases and Excel.
I understand algorithms and methods of visualization and filtering the necessary data. I will develop the necessary tool inexpensively, because I am still building a portfolio and am ready to start immediately! Contact me!
0
0
Budget: 500 USD Deadline: 7 days
I can build a high-performance system to handle this study end-to-end:
Automated Scraping: Extracting real-time data from major portals using Python (Playwright), including "Estrato" and age.
Data Cleaning: Removing duplicates and normalizing prices/areas for 100% consistency.
Investment Analytics: Identifying "outliers" (undervalued properties) using statistical modeling.
Interactive Dashboard: Creating a Google Looker Studio or Power BI report for easy filtering.
I have a strong background in Data Science (ex-FOREO) and experience scraping complex platforms like Facebook. Ready to start immediately.
Budget: 500 USD Deadline: 3 days
I can deliver the project in 3 days with 100% completion.
I have proven expertise in web scraping, data cleaning, statistical analysis, and dashboard visualization.
I will execute exactly as specified:
— extract all required fields;
— clean and normalize the dataset;
— calculate average price and admin fees per m² by district and identify outliers;
— build an interactive monitoring dashboard;
— deliver the final CSV/Excel file.
Confirmed acceptance of the $500 payment.
25
1
Budget: 400 USD Deadline: 7 days
Hello, I am a Python developer with over 5 years of experience. I have successfully completed projects involving parsing and competitor analysis, and I can parse a wide variety of websites without any problems collecting and structuring data. We need to discuss the websites that are sources of real estate listings, the number of listings, and the amount of parsing required to understand the full scope of the work. I will do everything with high quality, help with project deployment on the hosting, and provide support in identifying bugs. One of my projects is parsing system for car sale websites, data structure and preparing for ine telegram bot, more then 5 websites, telegram channels and all work fine. The project is interesting, and I will be happy to discuss the details and move on to cooperation. I will do everything with high quality and quickly!
0
0
Budget: 400 USD Deadline: 6 days
Hello!
I have strong experience in web scraping, data cleaning, and real estate market analysis. I can collect and normalize property data, calculate price per m² and admin fees by area, identify undervalued listings, and deliver a clean CSV/Excel file plus an interactive dashboard (Power BI / Tableau / Looker Studio).
Ready to start and provide regular updates.
1
0
Budget: 400 USD Deadline: 10 days
Hello! I have reviewed the project description — I have relevant experience in web scraping, data cleaning, and real estate market analytics, so I can fully handle this task "turnkey."
How I propose to implement the project:
1. Web scraping
Data collection from the main real estate portals in Bogotá
Regular updates (on schedule)
Data fields collection:
price
administrative fees
location (city / district)
socio-economic stratification level
area
number of bedrooms / bathrooms
age of the property
date of posting
Technically: Python + Playwright / requests (depending on website protection).
2. Data cleaning and processing
removal of duplicates
normalization of price and currency formats
unification of measurement units
verification of data completeness and accuracy
3. Analytics
calculation of price per m² by districts
calculation of administrative fees per m²
identification of investment opportunities (properties with prices significantly lower than the average)
basic statistical indicators and comparisons
4. Visualization
interactive dashboard in Power BI
filters by districts, price, area, stratification
convenient tool for market analysis and decision-making
Result
CSV / Excel with a complete cleaned database
link to the interactive dashboard
readiness for regular data updates
👉 I can provide examples of similar projects in personal messages.
Timeline and cost
Initial implementation: 10–14 days
Cost: $400-500
(depends on the number of sources and the complexity of the dashboard)
I am ready to discuss sources, update frequency, and start working immediately.
7
0
Budget: 400 USD Deadline: 1 day
I am among the top 5 developers in the category of "Artificial Intelligence and Machine Learning" among ~2100 specialists on the platform.
I guarantee:
- Fast and quality execution of the task
- Strict adherence to deadlines
- Regular communication throughout the entire process
I would be happy to discuss the details of your project in private messages.
15
0
Budget: 1200 USD Deadline: 7 days
Hi,
I would be happy to help with your real estate market study in Bogotá.
I have strong experience in real estate web scraping, data mining, and statistical analysis, using Python and R.
I have previously worked with large property datasets, extracting and cleaning data from major real estate portals and transforming it into reliable, analysis-ready databases.
What I can deliver:
• Automated web scraping of real estate portals with regular updates
• Clean and normalized datasets (CSV / Excel)
• Calculation of price per square meter and administration fee per square meter by neighborhood
• Detection of underpriced properties and statistical outliers
• Interactive dashboards (Power BI, Tableau, or Looker Studio) for easy filtering and exploration
Tools & Skills:
• Python (Scrapy, BeautifulSoup, Selenium, Pandas, NumPy)
• R (tidyverse, data.table, visualization)
• SQL for data storage and updates
• Dashboard creation and data visualization
I focus on data accuracy, scalability, and reproducibility, ensuring the solution can be updated regularly as new listings appear.
I would be glad to discuss your target areas, preferred real estate portals, and update frequency.
Best regards,
Roman
379
0
Budget: 400 USD Deadline: 21 days
I am a data analysis and web scraping expert with a proven track record in the real estate sector. I recently completed a similar comprehensive market study for the Cyprus real estate market, where I automated the extraction and analysis of thousands of listings to identify high-yield investment opportunities.
I can build a robust pipeline for the Bogotá market using the following approach:
- Smart Scraping: I use Python-based tools (Scrapy/Selenium/Playwright) capable of bypassing anti-scraping measures on major portals to ensure consistent data flow for your regular updates.
- AI-Powered Processing (LLM): Unlike traditional methods, I integrate LLMs (Large Language Models) to:
--Normalize and categorize "messy" location data and neighborhood names.
- -Extract hidden insights from property descriptions (e.g., specific amenities or seller urgency).
- -Filter out duplicate or low-quality listings with high precision.
- Statistical Depth: I will calculate price/sqm and administration/sqm metrics, using statistical methods (Z-score/IQR) to highlight undervalued "outliers" that represent the best investment opportunities.
- Interactive Visualization
I am ready to start in a week.
Looking forward to discussing how I can help you find the best deals in Bogotá!
13
0
Budget: 3000 USD Deadline: 14 days
I am ready to develop this project. I will write in Python with full data analysis and support. If you are interested in working - write to me.
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