Mubawab.ma Housing Scraper

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The Moroccan Housing Dataset — an open-source Apify Actor that scrapes real estate listings from mubawab.ma and produces a flat, ML-ready dataset modelled after the classic California Housing dataset (Géron, Hands-On ML, Chapter 2).

👁 Apify Actor
LICENSE 👁 Node.js 20
👁 Playwright
👁 Open Issues

Table of Contents

• What it does
• Output dataset
• Quick start
• Input configuration
• Apify Console output
• ML usage example
• Architecture
• Cities & property types covered
• Contributing
• License

What it does

Morocco's real estate market lacks structured, machine-readable public data. This actor closes that gap by crawling mubawab.ma — Morocco's largest property portal — and extracting every listing into a single CSV/JSON dataset suitable for:

• 🏠 Price prediction models (regression)
• 📍 Geo-spatial analysis by city and neighborhood
• 📊 Market trend dashboards
• 🤖 AI / LLM-powered property assistants

The scraper uses a two-phase Playwright crawl (search results → detail pages) and persists output through the Apify storage API so you can export CSV/JSON directly from the platform or via API with zero extra tooling.

Output dataset

Every scraped listing maps to one row with these fields:

Sample record

{
"priceDh":1250000,
"pricePerM2":12500,
"surfaceM2":100,
"numRooms":3,
"numBathrooms":2,
"floor":3,
"propertyType":"appartement",
"standing":"moyen_standing",
"state":"bon_etat",
"city":"casablanca",
"neighborhood":"maârif",
"transactionType":"vente",
"url":"https://www.mubawab.ma/fr/a/12345/appartement-a-vendre-casablanca",
"title":"Appartement à vendre à Maârif, Casablanca",
"scrapedAt":"2025-03-27T14:32:00.000Z"
}

Quick start

Option A — Run on Apify (no setup needed)

1. Open the actor on the Apify Store
2. Click Try for free
3. Configure inputs in the visual form
4. Click Start → export results as CSV or JSON once the run completes

Option B — Run locally

Prerequisites: Node.js 20+, Apify CLI

# 1. Install the CLI
npminstall-g apify-cli
# 2. Clone this repo
git clone https://github.com/MuLIAICHI/Mubawab-Housing-Scraper.git
cd Mubawab-Housing-Scraper
# 3. Install dependencies
npminstall
# 4. Quick test — 10 listings only
apify run --input='{"maxListings": 10, "transactionType": "vente"}'
# 5. Full run — all 9 cities, up to 5 000 listings
apify run

Results are saved locally under storage/datasets/mubawab-housing/.

Option C — Deploy to your Apify account

apify login # Enter your Apify API token
apify push # Build & upload the actor

Then run and schedule from console.apify.com.

Input configuration

Configure the actor via the Apify Console form or by passing a JSON input:

Example input

{
"transactionType":"vente",
"cities":["casablanca","marrakech","rabat"],
"propertyTypes":["appartements","villas"],
"maxListings":1000,
"maxConcurrency":5,
"proxyConfiguration":{
"useApifyProxy":true,
"apifyProxyGroups":["RESIDENTIAL"]
}
}

Apify Console output

After a run completes, the Output tab in Apify Console shows four named links:

ML usage example (Python)

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score, mean_absolute_error
# 1. Load dataset exported from Apify as CSV (ML Dataset view)
df = pd.read_csv("mubawab_dataset.csv")
# 2. Drop rows missing the target variable
df = df.dropna(subset=["priceDh","surfaceM2"])
# 3. Encode categoricals
df = pd.get_dummies(df, columns=["propertyType","standing","state","city","transactionType"])
# 4. Feature engineering — Géron-style derived features
df["roomsPerM2"]= df["numRooms"]/ df["surfaceM2"]
feature_cols =[c for c in df.columns if c notin["priceDh","pricePerM2","neighborhood","url","title","scrapedAt"]]
X = df[feature_cols].fillna(0)
y = df["priceDh"]
# 5. Train & evaluate
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = RandomForestRegressor(n_estimators=200, random_state=42)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
print(f"R² : {r2_score(y_test, y_pred):.3f}")
print(f"MAE : {mean_absolute_error(y_test, y_pred):,.0f} MAD")

Architecture

.
├── .actor/
│ ├── actor.json ← Actor metadata + schema references
│ ├── input_schema.json ← Typed input form for Apify Console
│ ├── output_schema.json ← Output tab links (dataset + KV store)
│ ├── dataset_schema.json ← Field definitions + two table views
│ └── key_value_store_schema.json ← KV store collections (stats / snapshots)
│
├── src/
│ ├── main.js ← Entry point: reads input, seeds URLs, starts crawler
│ ├── router.js ← Crawlee router with LISTING_PAGE + DETAIL_PAGE labels
│ ├── parsers/
│ │ ├── listingPage.js ← Extracts listing URLs + next-page link from search results
│ │ └── detailPage.js ← Extracts all 15 schema fields from a property detail page
│ └── utils/
│ └── normalize.js ← Pure functions: parsePrice(), parseSurface(), normalizeCity()
│
├── Dockerfile ← Apify Playwright image (Node.js 20 + Chromium)
├── package.json
└── README.md

Crawl flow

main.js ──builds seed URLs──► LISTING_PAGE handler
 │
 ┌─────▼──────────────────────────┐
 │ Parse search result page │
 │ Extract listing URLs │
 │ Follow rel="next" pagination │
 └─────┬──────────────────────────┘
 │ enqueue detail URLs
 ┌─────▼──────────────────────────┐
 │ DETAIL_PAGE handler │
 │ detailPage.js extracts fields │
 │ normalize.js cleans values │
 │ Actor.pushData() → dataset │
 └────────────────────────────────┘

Key technical decisions

• Playwright (not Cheerio) — mubawab.ma is JS-rendered; a headless browser is required
• Multiple CSS selector fallbacks — the site uses different HTML structures for individual listings vs. project/ensemble listings
• Polite delays — 500–800 ms between requests to avoid rate-limiting
• Named dataset mubawab-housing — makes the output easy to find and retrieve via API

Cities & property types covered

Cities (default): Casablanca · Marrakech · Rabat · Agadir · Tanger · Fès · Meknès · Oujda · Tétouan

Property types: Appartements · Villas · Maisons · Riads · Terrains · Bureaux · Commerces

Pass any subset via the cities and propertyTypes input fields.

Proxy recommendation

mubawab.ma blocks datacenter IPs. Using Apify Residential Proxy (the default) is strongly recommended for production runs. A free Apify account includes a proxy trial.

Without a proxy, you will encounter CAPTCHAs and 403 errors.

Contributing

Contributions are welcome! Here is how to get started:

1. Fork this repository
2. Create a feature branch: git checkout -b feat/your-feature
3. Make your changes and run a quick local test:

   $apify run --input='{"maxListings": 5}'

4. Open a Pull Request with a clear description of what changed and why

Good first issues

• Add support for additional Moroccan cities (agadir, beni-mellal, laayoune…)
• Improve null-rate for standing and state fields on project listings
• Add listing_id extraction from the URL slug
• Write unit tests for normalize.js (Jest or Vitest)

Please open an issue before starting large changes.

License

LICENSE © 2025 Mustapha LIAICHI

Built with Crawlee · Playwright · Apify SDK