Rental Trends Dashboard – USA, UAE & India (A Complete Technical Tutorial)

Introduction

Rent prices are changing faster than ever.

Platforms like Zillow (USA), Apartments.com (USA), Bayut (UAE), Property Finder (UAE), 99acres (India), Magicbricks (India) update rental listings daily and influence:

• rental affordability
• occupancy rates
• landlord pricing decisions
• relocation & migration patterns
• investment decisions
• rental yield projections

This tutorial shows how to build a complete Rental Trends Dashboard using:

• Selenium
• Requests
• BeautifulSoup
• Pandas
• Matplotlib / Plotly

You’ll learn how to scrape rental listings from USA, UAE, and India, clean the data, extract key indicators, and build a functional dashboard dataset.

This is the same workflow Actowiz Solutions uses in large-scale rental intelligence projects.

Step 1: Tools & Libraries You Need

pip install selenium
pip install beautifulsoup4
pip install requests
pip install pandas
pip install matplotlib
pip install plotly
pip install undetected-chromedriver

We will scrape:

• Zillow & Apartments.com (USA)
• Bayut / Property Finder (UAE)
• 99acres / MagicBricks (India)

And then merge them into a unified rental intelligence table.

Step 2: USA Rental Scraping (Zillow Example)

Zillow rental pages show:

• price
• beds/baths
• square footage
• address
• availability
• property type

Example URL:

https://www.zillow.com/homes/for_rent/San-Francisco,-CA_rb/

2.1 Start Undetected Chrome

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.zillow.com/homes/for_rent/San-Francisco,-CA_rb/")
sleep(5)

2.2 Scroll Page to Load Listings

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

2.3 Extract Rental Listing Cards

cards = browser.find_elements(By.XPATH, '//article')
usa_rentals = []

2.4 Parse Rental Details

for card in cards:
    try:
        address = card.find_element(By.CLASS_NAME, "list-card-addr").text
    except:
        address = ""

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

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

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

    usa_rentals.append({
        "country": "USA",
        "platform": "Zillow",
        "address": address,
        "price_raw": price,
        "beds_raw": beds,
        "url": url
    })

Step 3: UAE Rental Scraping (Bayut Example)

Example URL:

https://www.bayut.com/to-rent/apartments/dubai/

3.1 Open Bayut

browser.get("https://www.bayut.com/to-rent/apartments/dubai/")
sleep(5)

3.2 Scroll

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

3.3 Extract Listings

uae_rentals = []

items = browser.find_elements(By.XPATH, '//article[contains(@class,"ee734f1a")]')

3.4 Extract Details

for item in items:
    try:
        title = item.find_element(By.CLASS_NAME, "_7afabd84").text
    except:
        title = ""

    try:
        price = item.find_element(By.CLASS_NAME, "_105b8a67").text
    except:
        price = ""

    try:
        location = item.find_element(By.CLASS_NAME, "_162e6467").text
    except:
        location = ""

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

    uae_rentals.append({
        "country": "UAE",
        "platform": "Bayut",
        "location": location,
        "price_raw": price,
        "title": title,
        "url": url
    })

Step 4: India Rental Scraping (99acres Example)

Example link:

https://www.99acres.com/search/rent/residential-apartments/bangalore

4.1 Open 99acres

browser.get("https://www.99acres.com/search/rent/residential-apartments/bangalore")
sleep(5)

4.2 Extract Cards

india_rentals = []

cards = browser.find_elements(By.XPATH, '//div[contains(@class,"boxWrap")]')

4.3 Extract Rental Data

for c in cards:
    try:
        price = c.find_element(By.CLASS_NAME, "srpRentPrice").text
    except:
        price = ""

    try:
        details = c.find_element(By.CLASS_NAME, "srpDataWrap").text
    except:
        details = ""

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

    india_rentals.append({
        "country": "India",
        "platform": "99acres",
        "details": details,
        "price_raw": price,
        "url": url
    })

Step 5: Combine All Countries Into One Dataset

import pandas as pd

df = pd.DataFrame(usa_rentals + uae_rentals + india_rentals)
df.head()

Step 6: Normalize Prices (USD / AED / INR)

Remove unwanted characters:

import re

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

    val = val.replace(",", "").replace("AED","").replace("₹","").replace("$","")
    nums = re.findall(r"\d+", val)
    return int(nums[0]) if nums else None

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

Step 7: Convert All Prices Into a Single Currency (USD)

from currency_converter import CurrencyConverter
c = CurrencyConverter()

def convert_to_usd(row):
    if row["country"] == "USA":
        return row["price_num"]
    if row["country"] == "UAE":
        return c.convert(row["price_num"], "AED", "USD")
    if row["country"] == "India":
        return c.convert(row["price_num"], "INR", "USD")

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

Step 8: Extract Beds/Baths for All Platforms

USA (Zillow beds) comes in text like "2 bds | 1 ba | 850 sqft".

UAE (Bayut) includes "2 Beds • 3 Baths".

India (99acres) includes "2 BHK".

Let's extract:

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

df["beds"] = df["beds_raw"].fillna("") + df["title"].fillna("") + df["details"].fillna("")
df["beds"] = df["beds"].apply(extract_beds)

Step 9: Calculate Median Rent by Country & City

median_rent = df.groupby(["country"])["price_usd"].median()
print(median_rent)

Step 10: Build a Rental Trends Graph

Using Plotly:

import plotly.express as px

fig = px.box(df, x="country", y="price_usd", title="Rental Price Distribution (USA vs UAE vs India)")
fig.show()

Step 11: Build “Rental Index” for Dashboard

Actowiz Solutions often builds:

• Rent Score
• Affordability Index
• Price Pressure Score

Example:

df["rent_index"] = df["price_usd"] / df["beds"]

Step 12: Export Dashboard Data

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

Step 13: Future Enhancements

• Geo-mapping (lat/long extraction)
• Sentiment from reviews
• Time-series rental tracking
• Daily/weekly scraping automation
• Predictive modeling via ML

Limitations of Rental Scraping

• Zillow aggressively blocks bots
  Use rotating proxies.
• Bayut sometimes obfuscates prices
  JS rendering must be handled properly.
• 99acres uses anti-bot patterns
  Delay + random scroll required.
• Beds/baths format varies heavily
  Requires complex regex cleaning.
• Currency conversion fluctuates
  Realtime FX API ideal.

When to Use Actowiz Solutions

Use Actowiz if you need:

• Daily rental price monitoring across multiple countries
• API-based rental intelligence
• City-level & zip-level dashboards
• Seasonality analysis
• Occupancy insights
• Professional rental forecasting
• Automated pipelines (ETL + storage + dashboards)

We support:

• USA
• UAE
• India
• UK
• Singapore
• Europe

With scalable extraction across:

• Zillow
• Realtor
• Apartments.com
• Bayut
• Property Finder
• MagicBricks
• 99acres

Conclusion

In this tutorial, you learned how to:

• scrape USA rentals from Zillow
• scrape UAE rentals from Bayut
• scrape India rentals from 99acres
• normalize rent values
• standardize attributes
• convert prices into USD
• build a rental trends dataset
• visualize rent distribution
• export dashboard-ready data

Congratulations — you now have a complete Rental Trends Dashboard engine.

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