PAN India RERA Data Aggregation Tutorial

Array ( [as_domain] => amazon.com [as_name] => Amazon.com, Inc. [asn] => AS16509 [continent] => North America [continent_code] => NA [country] => United States [country_code] => US )

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State	Portal
Maharashtra	https://maharera.mahaonline.gov.in
Gujarat	https://gujrera.gujarat.gov.in
Karnataka	https://rera.karnataka.gov.in
Delhi	https://rera.delhi.gov.in
Tamil Nadu	https://www.tnrera.in
Rajasthan	https://rera.rajasthan.gov.in
Telangana	https://rera.telangana.gov.in

State

Portal

Maharashtra

https://maharera.mahaonline.gov.in

Gujarat

https://gujrera.gujarat.gov.in

Karnataka

https://rera.karnataka.gov.in

Delhi

https://rera.delhi.gov.in

Tamil Nadu

https://www.tnrera.in

Rajasthan

https://rera.rajasthan.gov.in

Telangana

https://rera.telangana.gov.in

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)

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)

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") })

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"])

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

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)

Field	Description
rera_id	Unique project registration number
project_name	Raw name
project_clean	Normalized name
promoter	Builder / developer name
promoter_clean	Normalized
state	Maharashtra, Gujarat, Delhi…
city/district	Location
start_date	As per RERA
end_date	Completion date
status	Ongoing / Completed
project_type	Residential / Commercial
document_links	List of PDFs
pdf_text	Extracted content
qpr_updates	Quarterly progress reports
litigation	Complaints if any

Field

Description

rera_id

Unique project registration number

project_name

Raw name

project_clean

Normalized name

promoter

Builder / developer name

promoter_clean

Normalized

state

Maharashtra, Gujarat, Delhi…

city/district

Location

start_date

As per RERA

end_date

Completion date

status

Ongoing / Completed

project_type

Residential / Commercial

document_links

List of PDFs

pdf_text

Extracted content

qpr_updates

Quarterly progress reports

litigation

Complaints if any

Array ( [as_domain] => amazon.com [as_name] => Amazon.com, Inc. [asn] => AS16509 [continent] => North America [continent_code] => NA [country] => United States [country_code] => US )

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