mainamuragev

mainamuragev / nairobi_property_pricing

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A data pipeline for scraping, cleaning, and enriching Nairobi property listings. It normalizes prices, extracts bedroom counts from titles/URLs, and calculates per‑bedroom affordability. The project also generates location summaries and enriched datasets for analysis.

30
11
100% credibility
Found Feb 20, 2026 at 25 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
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AI Summary

A tool for gathering, cleaning, and mapping Nairobi property listings to reveal affordability trends by neighborhood.

How It Works

1
🏠 Discover the tool

You find this handy guide for understanding property prices in Nairobi while searching for real estate insights.

2
📥 Download everything

Grab all the ready-made files so you can start exploring right away.

3
🌐 Gather listings

Collect current property ads from popular Nairobi real estate websites to get real data.

4
🧹 Clean and sort

Tidy up the messy details like prices and bedroom counts so everything makes sense.

5
📈 Build summaries

Calculate averages like price per bedroom for each neighborhood.

6
🗺️ View the map

Open the colorful interactive map to see affordability across Nairobi at a glance.

🎉 Get smart insights

Now you know the cheapest spots and best values to hunt for your dream home or investment.

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Star Growth

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AI-Generated Review

What is nairobi_property_pricing?

This Python data pipeline scrapes Nairobi property listings from sites like BuyRentKenya and Jiji, cleans messy data by normalizing prices in KSh formats and extracting bedroom counts from titles or URLs, then enriches it with per-bedroom affordability metrics. It outputs cleaned CSV datasets, location summaries in data tables, and an interactive HTML affordability map for geographic analysis. Developers get a full data pipeline definition from raw web scrapes to actionable insights on housing costs.

Why is it gaining traction?

It stands out with smart parsing for unstructured real estate text—turning "2br Ksh45k" into clean numbers—and built-in affordability calcs like price per bedroom, skipping manual ETL hassles common in data pipeline tools. The end-to-end flow includes EDA charts and Power BI-ready summaries, plus geocode handling for Nairobi-specific spots, making it a quick win for local market analysis over generic scrapers.

Who should use this?

Real estate analysts tracking Nairobi rents, proptech devs prototyping affordability dashboards, or researchers studying urban housing trends. It's ideal for data github_user projects needing a data pipeline python starter for scraping Kenyan listings without building from scratch.

Verdict

Solid niche tool with excellent README docs and MIT license, but at 19 stars and 1.0% credibility score, it's early-stage—expect tweaks for site changes. Fork it for custom data streams or analysis if Nairobi props are your focus.

(198 words)

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