PAN India RERA Data Aggregation for Property Intelligence – A Technical Guide

Introduction

India’s real estate ecosystem has become increasingly dependent on RERA (Real Estate Regulatory Authority) data. Every state maintains a separate RERA portal with:

• project registrations
• builder profiles
• completion updates
• approvals / certificates
• litigation & complaint reports
• status updates (ongoing / completed / withdrawn)
• land details & documentation
• project financial disclosures

Brands, investors, lending institutions, property portals, valuation firms, and consultants all depend on this data.

But PAN India RERA data scraping is extremely challenging because:

• Every state has a different website
• Different HTML layouts
• Different pagination & filters
• Many portals use CAPTCHA
• PDFs must be scraped, parsed, and indexed
• Builder names vary heavily
• Project names use inconsistent formats

This tutorial shows how to build a RERA Data Aggregation Engine using:

• Selenium
• Requests
• BeautifulSoup
• PDF Extractors
• NLP-based normalization
• PAN India merging logic

This is the same technical framework Actowiz Solutions deploys for clients across real estate intelligence.

Step 1: Install Dependencies

pip install selenium
pip install beautifulsoup4
pip install pandas
pip install requests
pip install pypdf
pip install tabula-py
pip install fuzzywuzzy
pip install python-Levenshtein

Step 2: Understand the RERA Portal Structure

Each state has its own portal:

Common data elements:

• Project Name
• Promoter Name
• RERA Registration ID
• District / City
• Project Type (Residential / Commercial / Mixed)
• Project Status
• Start & End Date
• Approved Plans
• Quarterly Updates (QPR)
• Financial disclosures
• Litigation / complaints
• Uploaded documents (PDFs)

Step 3: Launch Selenium and Open a RERA State Portal (Example: Maharashtra)

from selenium import webdriver
from selenium.webdriver.common.by import By
from time import sleep

browser = webdriver.Chrome()
browser.get("https://maharera.mahaonline.gov.in/SearchList/Search")
sleep(3)

Step 4: Apply Filters (e.g., Project Name or District)

Example: Select “Mumbai Suburban”

district_dropdown = browser.find_element(By.ID, "DistrictID")
district_dropdown.click()

option = browser.find_element(By.XPATH, '//option[contains(text(),"Mumbai Suburban")]')
option.click()

search_btn = browser.find_element(By.ID, "btnSearch")
search_btn.click()
sleep(4)

Step 5: Extract All Rows From the Results Table

rows = browser.find_elements(By.XPATH, '//table[@id="tblProjects"]/tbody/tr')

rera_records = []

for row in rows:
    cols = row.find_elements(By.TAG_NAME, "td")
    if len(cols) > 5:
        rera_records.append({
            "rera_id": cols[0].text,
            "project_name": cols[1].text,
            "promoter": cols[2].text,
            "district": cols[3].text,
            "type": cols[4].text,
            "status": cols[5].text,
            "details_url": cols[1].find_element(By.TAG_NAME, "a").get_attribute("href")
        })

Step 6: Visit Each Project Page and Extract Details

Each RERA portal shows deeper details inside project page:

import requests
from bs4 import BeautifulSoup

def scrape_project_details(url):
    try:
        r = requests.get(url)
        soup = BeautifulSoup(r.text, "lxml")

        table = soup.find("table", {"class": "table"})
        rows = table.find_all("tr")

        data = {}
        for tr in rows:
            tds = tr.find_all("td")
            if len(tds) == 2:
                data[tds[0].text.strip()] = tds[1].text.strip()
        return data
    except:
        return {}

for entry in rera_records:
    entry["details"] = scrape_project_details(entry["details_url"])

Step 7: Handling PDF Documents (Layouts, Maps, Certificates)

Most RERA portals provide PDF documents:

• Approved floor plans
• Allotment letters
• Legal certificates
• Building permissions
• Quarterly reports

To extract text:

from pypdf import PdfReader

def extract_pdf_text(pdf_url):
    response = requests.get(pdf_url)

    with open("temp.pdf", "wb") as f:
        f.write(response.content)

    reader = PdfReader("temp.pdf")
    text = ""
    for page in reader.pages:
        text += page.extract_text() + "\n"
    return text

Extract all PDFs linked on project page:

def get_pdfs(soup):
    pdf_links = []
    for a in soup.find_all("a", href=True):
        if ".pdf" in a["href"].lower():
            pdf_links.append(a["href"])
    return pdf_links

Step 8: Normalize Project Names & Builder Names

Every state writes names differently:

• “Lodha Casa Rio Gold”
• “Casa Rio Gold by Lodha Group”
• “Lodha Group - Casa Rio Gold”

Use fuzzy matching:

from fuzzywuzzy import fuzz

def normalize_name(name):
    name = name.lower().strip()
    unwanted = ["pvt ltd", "private limited", "llp", "developers", "group", "builders"]
    for w in unwanted:
        name = name.replace(w, "")
    return name

df["clean_project"] = df["project_name"].apply(normalize_name)
df["clean_promoter"] = df["promoter"].apply(normalize_name)

Step 9: Merge Data From Different RERA States

Once you scrape Maharashtra, Karnataka, Gujarat, etc.:

final_df = pd.concat([mh_df, ka_df, gj_df, dl_df])
final_df.reset_index(drop=True, inplace=True)

Step 10: Build a Unified Structure for Property Intelligence

Your final aggregated dataset should contain:

Step 11: Save the Final Output

final_df.to_csv("PAN_India_RERA_Data.csv", index=False)

Step 12: Visualize Data for Real Estate Intelligence

Use dashboards for:

• New projects launched per month
• Builder performance
• City-wise construction activity
• Delay tracking
• Litigation frequency
• Project type distribution

Limitations of RERA Scraping

CAPTCHA prevents automation in some states

Requires AI-based solving or manual intervention.

Inconsistent HTML

Every state portal is different.

PDFs are inconsistent

Different formats, scanned documents.

Slow portals

Some states have outdated servers.

Rate limits

Frequent scraping may get blocked.

When to Use Actowiz Solutions Instead of DIY?

DIY works for:

• small research projects
• state-level scraping
• a few hundred projects

But use Actowiz Solutions for:

• PAN India property intelligence
• Daily/weekly updates
• Large-scale RERA dashboards
• Advanced PDF parsing
• Builder-level performance analytics
• Market forecasting models
• Automated ETL pipelines
• API-based RERA data feeds

We support all 28 states & UTs with RERA coverage.

Conclusion

This tutorial taught you how to:

• Scrape RERA portals using Selenium
• Extract project listings
• Parse detailed project pages
• Download and extract PDF contents
• Normalize builder & project names
• Combine PAN India datasets
• Generate property intelligence tables

RERA data is one of the richest sources of real estate truth — and with the right pipelines, it becomes a powerful engine for analytics, compliance, and investment intelligence.

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