Inmuebles24 Property Listings Data Extraction (Spain + Mexico) – Full Technical Tutorial

Introduction

Real estate platforms like Inmuebles24 dominate the Spanish and Mexican property markets. Analysts, investors, rental operators, real estate agencies, and valuation firms rely on this data for:

• property pricing
• rental trends
• neighborhood demand
• property type segmentation
• investment benchmarks
• location scoring

Inmuebles24 offers rich data:

• Sale & rent prices
• Bedrooms, bathrooms
• Area in m²
• Property type (house, apartment, studio)
• Location hierarchy (City → Area → Neighborhood)
• Listing agent info
• Amenities
• Photos & videos
• Property description

This tutorial shows how to build a production-grade Inmuebles24 Data Extraction Engine using:

• Python
• Selenium
• BeautifulSoup
• Requests
• Pandas

This is the exact scraping workflow Actowiz Solutions uses to power property dashboards for Spain + Mexico.

Let’s begin.

Step 1: Install Required Dependencies

pip install selenium
pip install beautifulsoup4
pip install pandas
pip install requests
pip install lxml
pip install undetected-chromedriver

Inmuebles24 loads content dynamically → Selenium required.

Step 2: Choose Target URLs (Spain + Mexico Examples)

Mexico

https://www.inmuebles24.com/casas-en-venta-en-ciudad-de-mexico.html

Spain

https://www.inmuebles24.com/casas-en-venta-en-madrid.html

You can modify these to scrape:

• apartments
• rentals
• commercial properties
• plots
• luxury homes

Step 3: Launch Undetected Chrome (to avoid blocking)

import undetected_chromedriver as uc
from selenium.webdriver.common.by import By
from selenium.webdriver.common.keys import Keys
from time import sleep

browser = uc.Chrome()
browser.get("https://www.inmuebles24.com/casas-en-venta-en-ciudad-de-mexico.html")
sleep(5)

Step 4: Scroll Multiple Times to Load More Listings

for _ in range(12):
    browser.find_element(By.TAG_NAME, "body").send_keys(Keys.END)
    sleep(2)

Step 5: Extract Property Cards

Card CSS class often contains "posting-card". Let's locate all cards.

cards = browser.find_elements(By.XPATH, '//div[contains(@class,"posting-card")]')
mx_records = []

Step 6: Extract Basic Property Details

Each card contains:

• Title
• Price
• Bedrooms
• Bathrooms
• Parking
• Area (m²)
• Location
• URL

Let's extract these.

for card in cards:
    try:
        title = card.find_element(By.CLASS_NAME, "posting-card-title").text
    except:
        title = ""

    try:
        price = card.find_element(By.CLASS_NAME, "first-price").text
    except:
        price = ""

    try:
        location = card.find_element(By.CLASS_NAME, "posting-location").text
    except:
        location = ""

    try:
        details = card.find_element(By.CLASS_NAME, "posting-features").text
    except:
        details = ""

    try:
        url = card.find_element(By.TAG_NAME, "a").get_attribute("href")
    except:
        url = ""

    mx_records.append({
        "country": "Mexico",
        "title": title,
        "price_raw": price,
        "location": location,
        "details_raw": details,
        "url": url
    })

Step 7: Extract Detail Page Information (Deep Data)

Detail pages contain:

• description
• amenities
• agent info
• listing ID
• construction year
• maintenance fee
• property type
• images

Let’s scrape deep details.

7.1 Create Detail Scraper Function

import requests
from bs4 import BeautifulSoup

def scrape_inmuebles_detail(url):
    try:
        html = requests.get(url, timeout=10).text
        soup = BeautifulSoup(html, "lxml")

        description = soup.find("p", {"class": "posting-description-text"})
        description = description.text.strip() if description else ""

        specs = {}
        spec_blocks = soup.find_all("li", {"class": "icon-feature"})
        for sb in spec_blocks:
            key = sb.find("span").text.strip()
            val = sb.text.replace(key, "").strip()
            specs[key] = val

        amenities = []
        for li in soup.select(".amenities-list li"):
            amenities.append(li.text.strip())

        return {
            "description": description,
            "specifications": specs,
            "amenities": amenities
        }

    except Exception as e:
        return {}

7.2 Attach Deep Data to Each Card

for row in mx_records:
    row.update(scrape_inmuebles_detail(row["url"]))

Step 8: Repeat the Same Process for Spain (Madrid Example)

Open the Spain URL:

browser.get("https://www.inmuebles24.com/casas-en-venta-en-madrid.html")
sleep(5)

Scroll + extract the same way.

8.1 Scroll

for _ in range(10):
    browser.find_element(By.TAG_NAME, "body").send_keys(Keys.END)
    sleep(2)

8.2 Extract Spain Listings

cards = browser.find_elements(By.XPATH, '//div[contains(@class,"posting-card")]')
spain_records = []

8.3 Extract Spain Property Details

for card in cards:
    try:
        title = card.find_element(By.CLASS_NAME, "posting-card-title").text
    except:
        title = ""

    try:
        price = card.find_element(By.CLASS_NAME, "first-price").text
    except:
        price = ""

    try:
        location = card.find_element(By.CLASS_NAME, "posting-location").text
    except:
        location = ""

    try:
        details = card.find_element(By.CLASS_NAME, "posting-features").text
    except:
        details = ""

    try:
        url = card.find_element(By.TAG_NAME, "a").get_attribute("href")
    except:
        url = ""

    spain_records.append({
        "country": "Spain",
        "title": title,
        "price_raw": price,
        "location": location,
        "details_raw": details,
        "url": url
    })

8.4 Attach Deep Data for Spain

for row in spain_records:
    row.update(scrape_inmuebles_detail(row["url"]))

Step 9: Combine Spain + Mexico Listings

import pandas as pd

df = pd.DataFrame(mx_records + spain_records)
df.head()

Step 10: Normalize Price Values (MXN, EUR)

Prices appear like:

• MXN $4,200,000
• €320,000
• $15,000 MXN/month

Let’s clean:

import re

def clean_price(val):
    if not val:
        return None

    nums = re.findall(r"\d[\d,]*", val)
    if not nums:
        return None

    return int(nums[0].replace(",", ""))

Apply:
df["price_num"] = df["price_raw"].apply(clean_price)

Step 11: Detect Currency Type

def detect_currency(val):
    if "€" in val:
        return "EUR"
    if "MXN" in val or "$" in val:
        return "MXN"
    return None

df["currency"] = df["price_raw"].apply(detect_currency)

Step 12: Convert All Prices to USD

from currency_converter import CurrencyConverter
c = CurrencyConverter()

def convert_to_usd(row):
    try:
        return c.convert(row["price_num"], row["currency"], "USD")
    except:
        return None

df["price_usd"] = df.apply(convert_to_usd, axis=1)

Step 13: Extract Bedrooms, Bathrooms, & Area

details_raw usually includes:

• "3 Recámaras"
• "2 Baños"
• "140 m²"

Extract bedrooms:

def extract_beds(val):
    match = re.search(r"(\d+)\s?(rec|hab|bed)", val.lower())
    return int(match.group(1)) if match else None

Extract bathrooms:
def extract_baths(val):
    match = re.search(r"(\d+)\s?ba", val.lower())
    return int(match.group(1)) if match else None

Extract area:
def extract_area(val):
    match = re.search(r"(\d+)\s?m²", val.lower())
    return int(match.group(1)) if match else None

Apply:
df["beds"] = df["details_raw"].apply(extract_beds)
df["baths"] = df["details_raw"].apply(extract_baths)
df["area_m2"] = df["details_raw"].apply(extract_area)

Step 14: Build Your Property Intelligence Dataset

Final fields:

• Country
• Title
• Price USD
• Currency
• Location
• Beds
• Baths
• Area m²
• Amenities
• Specs
• URL

Step 15: Export Dataset

df.to_csv("inmuebles24_property_data.csv", index=False)

Step 16: Visualize Data (Optional)

Price distribution:

import plotly.express as px
fig = px.box(df, x="country", y="price_usd", title="Price Distribution: Spain vs Mexico")
fig.show()

Technical Challenges in Inmuebles24 Scraping

1. Infinite scroll

Requires multiple scroll loops.

2. Spanish-language text

Regex must support Spanish real estate terms.

3. Mixed currency patterns

MXN $, EUR €, USD $.

4. Detail pages have dynamic HTML

Some units load via JS only.

5. Rate limiting

Delay required to avoid blocking.

Actowiz Solutions uses:

• rotating proxies
• session management
• API reverse-engineering
• Geo-specific routing

…to deliver clean data at scale.

When to Use Actowiz Solutions?

Choose Actowiz if you need:

• Daily Spain + Mexico property updates
• Price change detection
• Neighborhood scoring
• Rental/sale supply dashboards
• Amenities extraction at scale
• Image extraction + labeling
• Multi-language parsing
• Real estate forecasting models

We support:

• Inmuebles24
• Idealista
• Fotocasa
• Habitaclia
• VivaReal
• MercadoLibre
• Zillow
• Realtor.com
• Apartments.com

Conclusion

In this tutorial, you learned how to:

• scrape listings from Inmuebles24 (Spain + Mexico)
• extract deep details (amenities, description, specs)
• normalize price and convert currencies
• extract beds/baths/area using regex
• merge multi-country datasets
• export analytics-ready files
• visualize property markets

This lays the foundation for a complete Spain + Mexico real estate intelligence platform.

You can also reach us for all your mobile app scraping, data collection, web scraping, and instant data scraper service requirements!