About Dataset

Context

The dataset consists of data that was scraped from Graana.com website. It is Pakistani top leading property buy and sell platform. Content

Geography: Pakistan

Time period: 2022

Unit of analysis: Real states Data Analysis

Dataset: The dataset contains detailed information online data available on Graana.com website . It contains propertyid,locationid,pageurl propertytype,price,location,city,provincename,latitude,longitude baths,area,purpose,bedrooms,dateadded.

Variables: The dataset contain id,purpose,type,price,size,size_unit,user_id,listing_type, bed,bath,status,custom_title,lat,lon,geotagged_by,platform,created_at,system_user_name,user_name,area_name, area_marla_size,city_name,linksubtype,link

File Type: CSV

This dataset contains over 16K+ property listings from zameen.com, a prominent online property portal in Pakistan. It includes detailed information on each property, such as city, location, price in PKR, number of bedrooms and bathrooms, and property size in square feet. This comprehensive dataset is a valuable resource for real estate analysts and professionals seeking to explore the Pakistani housing market. The data can be utilized for market and trend analysis, investment research, and other related purposes.

This data is scrapped using the zameen-com-scrapper.

Don't forget to hit the upvote🙏🙏

This dataset has been scraped from graana.com & zameen.com, Pakistan's leading real estate platforms. It provides detailed information on properties listed across all over Pakistan, focusing on houses, flats, farmhouses etc. available for sale/rent. Whether you're an analyst, a student, or a developer, this dataset offers a rich opportunity for analysis in the real estate domain. 📊

Columns Breakdown 📋:

📊 Dataset Applications:

• Market Analysis: Understand property trends in different cities and regions.
• Price Prediction: Train machine learning models to predict real estate prices.
• Investment Insights: Identify prime locations and best property types for investment.
• Demographic Studies: Analyze housing needs in urban vs. suburban areas.

🚀 Get Started Now:

Use this dataset for your next real estate analysis, machine learning project, or to explore the property market trends in Pakistan! 🏘️

Happy coding! ✨

Pakistan Market Cap: PSX: Real Estate Investment Trust data was reported at 29,531.000 PKR mn in May 2018. This records an increase from the previous number of 28,708.000 PKR mn for Apr 2018. Pakistan Market Cap: PSX: Real Estate Investment Trust data is updated monthly, averaging 24,994.000 PKR mn from Nov 2016 (Median) to May 2018, with 19 observations. The data reached an all-time high of 29,531.000 PKR mn in May 2018 and a record low of 23,794.000 PKR mn in Nov 2016. Pakistan Market Cap: PSX: Real Estate Investment Trust data remains active status in CEIC and is reported by State Bank of Pakistan. The data is categorized under Global Database’s Pakistan – Table PK.Z003: Karachi Stock Exchange: Market Capitalization (New Classification).

Description: This dataset offers a comprehensive overview of residential property prices in Pakistan, gathered through web scraping from various sources. It encompasses a wide range of housing types and covers multiple regions across the country, providing a detailed insight into the dynamic real estate market.

Key Features: - Web-scraped pricing data for residential properties in Pakistan. - Granular information on house prices, including location, size, and other relevant details. - Multiple regions covered, allowing for regional and national analysis. - Regularly updated to reflect the latest market trends and fluctuations.

Potential Use Cases: - Real estate market analysis for investors and developers. - Comparative studies on property prices in different regions of Pakistan. - Data-driven insights for homebuyers and sellers. - Machine learning and predictive modeling for housing market trends.

Note: The data has been collected ethically and adheres to the terms of use of the respective websites. Please review the dataset documentation for more details on the sources and methodology.

Explore this dataset to unlock valuable information about the housing market in Pakistan, whether you are a data scientist, researcher, or enthusiast interested in real estate trends.

Pakistan Real Estate Software Market is expected to grow during 2025-2031

Context

There's a story behind every dataset and here's your opportunity to share yours. Pakistan is the 5th most populous country and 33rd largest country. The real estate sector in Pakistan is one of the most expanding sector,, so it is of due importance to study the pricing of houses in different provinces, cities a and sectors of Pakistan to see what's the trend.

Content

The data was created between May 15, 2020, 6:13 AM (UTC-07:00) to April 4, 2021, 12:41 PM (UTC-07:00).

Acknowledgements

The data was web scraped by @huzzefakhan from Zameen.com using 'beautiful soup' python library. The dataset was originally uploaded on Open Data Pakistan (https://opendata.com.pk/dataset/property-data-for-pakistan). The author made a few changes (converting areas in marlas and kanals to cubic feet and dropping redundant columns) and reuploaded the dataset for easy usability.

👨‍💻 Author: Azhar Saleem

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Dataset Overview

This dataset encompasses a comprehensive collection of property listings from Zameen.com, Pakistan's largest real estate website. It contains detailed information on properties for sale across Pakistan, making it a vital resource for data scientists, machine learning engineers, and analysts interested in the real estate market, economic trends, or geographical data analysis.

Columns Description

• url: The webpage URL for the property listing.
• title: The title of the property listing, describing key features.
• type: The type of property (e.g., House, Apartment).
• price: The listed price of the property in PKR.
• area: The total area of the property listed in local units (Marla, Kanal).
• city: The city in which the property is located.
• address: A more specific location or address within the city.
• bedrooms: The number of bedrooms in the property.
• baths: The number of bathrooms in the property.
• area_sqft: The area of the property in square feet.
• price_per_sqft: The price of the property per square foot.
• area_sqm: The area of the property in square meters.
• price_per_sqm: The price of the property per square meter.
• Latitude: Geographical latitude of the property.
• Longitude: Geographical longitude of the property.
• date_added: The date when the property was added to the website.

This dataset is ideal for conducting various types of analysis, such as market price predictions, trend analysis, and geographical data visualization, among others.

The Pakistan Paint Market size was valued at USD 400.82 Million in 2024 and is projected to reach USD 533.88 Million by 2032 growing at a CAGR of 4.18% from 2025 to 2032.

Key Market Drivers: Growing Construction Industry: The expanding construction and real estate sector in Pakistan, driven by increasing urbanization and infrastructure development projects, creates substantial demand for architectural paints. Industrial Development: The steady growth of industrial activities across various sectors, including manufacturing, automotive and infrastructure, drives demand for industrial coatings. The expansion of the automotive sector and increasing investments in industrial infrastructure create opportunities for specialized coating solutions, supporting market growth and technological advancement in the industrial segment. Rising Middle Class: The expanding middle-class population with increasing disposable income and growing awareness of home aesthetics.

📊 Dataset Description: Pakistan House Price Data This dataset has been self-curated to capture detailed real estate listings from various regions across Pakistan. It contains 27,890 entries and 18 features, offering comprehensive data on residential properties, their prices, locations, and specifications.

🏠 Key Features: property id: Unique identifier for each property listing.

location id: Encoded ID representing the specific location.

page url: Source URL of the listing from the web (e.g., Zameen.com).

property type: Type of the property (e.g., House, Flat).

price: Listed price of the property in PKR.

location / city / province name: Detailed geographical location of the property.

latitude / longitude: Geographic coordinates of the listing.

baths / bedrooms: Number of bathrooms and bedrooms.

purpose: Sale or rental status (mostly "For Sale").

date added: Date the listing was added online.

agency / agent: Real estate agency and agent (if available).

Total Area: Total covered area of the property (in square feet/meters).

📍 Coverage: Focused mainly on Islamabad but includes multiple cities and provinces.

Useful for price prediction, property analysis, and geospatial visualization.

🧹 Notes: Some entries in agency and agent fields are missing (~27% missing).

The dataset is clean, well-structured, and suitable for both exploratory data analysis (EDA) and machine learning projects.

The Housing Prices in Pakistan 2023 Dataset is a rich resource that provides valuable insights into the real estate market. It includes a diverse range of attributes such as property ID, city, province location, number of bedrooms, number of bathrooms, area, purpose, and price. This dataset enables users to analyze and understand housing price trends, regional dynamics, and property features that impact pricing. It is a valuable tool for market analysts, investors, real estate professionals, and researchers, helping them make informed decisions based on accurate and current information. Researchers can utilize this dataset to study market trends, investors can identify lucrative investment opportunities, and real estate professionals can estimate property values. By leveraging the dataset, users can gain a deeper understanding of the factors influencing housing prices and make data-driven analyses to enhance their decision-making processes. Key Features:

Property ID: Each property in the dataset is assigned a unique identifier, allowing for easy tracking and referencing of specific properties.

City: The dataset includes the city in which each property is located. This information enables users to analyze and compare housing prices across different cities.

Province Location: The dataset provides details about the province in which each property is situated. This attribute aids in regional analysis and understanding variations in housing prices between provinces.

Number of Bedrooms: This attribute indicates the number of bedrooms present in each property. It provides valuable information about the size and capacity of the property.

Number of Bathrooms: The dataset includes the number of bathrooms available in each property. This attribute assists in assessing the convenience and functionality of the property.

Area: The area attribute specifies the size of the property in terms of square feet or square yards. It offers insights into the overall space available within each property.

Purpose: The dataset includes the purpose for which the property is listed, such as sale or rent. This attribute allows users to focus their analysis on specific purposes and their associated pricing trends.

Price: The dataset provides the listing prices for each property, presenting a comprehensive overview of market values. Prices are typically listed in the local currency, such as Pakistani Rupees.

Potential Use Cases:

Market Analysis: This dataset enables users to conduct comprehensive market analysis, including studying housing price trends, identifying areas with high growth potential, and comparing prices across cities and provinces.

Investment Decision-making: Investors and real estate professionals can utilize the dataset to make informed investment decisions. By analyzing property prices, number of bedrooms, bathrooms, and areas, they can identify properties that align with their investment goals.

Property Valuation: Real estate agents, appraisers, and property valuers can leverage the dataset to accurately assess property values. By examining similar properties in terms of location, number of bedrooms, bathrooms, and area, they can estimate fair market values for properties.

Research and Data Analysis: Researchers, academicians, and data analysts can explore the dataset to study various aspects of the real estate market. They can analyze correlations between housing prices and factors such as city, province, number of bedrooms, bathrooms, and area to gain insights into market dynamics.

Please ensure that the usage of the dataset adheres to relevant legal and ethical guidelines, maintaining privacy and confidentiality of property owners and complying with applicable data usage regulations.

Dataset Description: Pakistan Real Estate Prices (2018-2019)

Context

This dataset provides real estate price listings across various cities in Pakistan, capturing property details, pricing, locations, and listing dates. The data is valuable for market analysis, price forecasting, and inflation studies, making it a key resource for investors, researchers, and data scientists.

Source & Inspiration

The dataset is sourced from Zameen.com, Pakistan's leading real estate platform, containing 168,447 property listings from 2018 and 2019. The dataset helps analyze:

Market trends before COVID-19 Price fluctuations due to inflation Impact of location and property type on prices Forecasting future price movements Features & Data Columns Property Details: property_id, property_type, bedrooms, baths, Total_Area Location Info: location, city, province_name, latitude, longitude Financials: price (target variable), purpose (For Sale / For Rent) Time Features: date_added (listing date in YYYY-MM-DD format) Agency & Agent: agency, agent Meta: page_url (property page link)

Why This Dataset Matters?

Helps predict house prices using ML models like ARIMA, Prophet, LSTM Enables inflation tracking by observing price changes over time Provides insights into real estate investments in Pakistan

📌 Context

This dataset was created by scraping Zameen.com, Pakistan’s leading real estate platform for property listings. Zameen.com is a widely used online marketplace for renting, buying, and selling real estate across the country. The goal of this dataset is to support rental market research and real estate data analysis in Pakistan.

**📊 Content **

Geography: Pakistan -**Scrape Date**: April 25, 2025 -**Unit of Analysis**: Rental property listings -**Source**: zameen.com -**Method**: Web scraping of 1,227 pages from the rental section -**Duration**: Approx. 30 minutes -**Dataset Size**: 30,675 rows × 7 columns -**File Type**: CSV

📄 Key Variables

1. price – Rental price of the property
2. currency – Currency in which the price is listed (e.g., PKR)
3. location – Area or locality of the property 4.bedrooms – Number of bedrooms 5.washrooms – Number of bathrooms (washrooms) 6.marla_size – Size of the property in Marla (local unit) 7.details – Additional listing information or description

💡 Inspiration

Rental Market Analysis: Understand trends in rental prices by location, property type, or size

Price Prediction Models: Use the data to build machine learning models that estimate rental prices based on features like location, size, and amenities This dataset contains real-time rental listings available on Zameen.com as of April 25, 2025. It includes listings from across Pakistan and covers multiple types of rental properties including houses, apartments, plots, and commercial units.

This dataset contains detailed property listings scraped from Zameen.com, one of Pakistan's leading real estate platforms. The dataset provides insights into various property attributes, including type, price, location, and facilities, making it an excellent resource for property analysis, price trend studies, and real estate market research. Key columns include:

1. Type: Property type (e.g., house, apartment).
2. Price: Listed price in PKR.
3. Location: Geographic location of the property.
4. Bath: Number of bathrooms.
5. Area: Covered area of the property.
6. Bed: Number of bedrooms.
7. Facilities: Availability of essential utilities like electricity, water supply, sui gas, and sewerage.
8. Amenities: Proximity to schools, hospitals, shopping malls, public transport, and restaurants.
9. Security Staff: Availability of security services.

This dataset is ideal for exploratory data analysis, machine learning projects, and predictive modeling in the real estate domain.

市价总值：PSX：房地产投资与信托在05-01-2018达29,531.000百万巴基斯坦卢比，相较于04-01-2018的28,708.000百万巴基斯坦卢比有所增长。市价总值：PSX：房地产投资与信托数据按月更新，11-01-2016至05-01-2018期间平均值为24,994.000百万巴基斯坦卢比，共19份观测结果。该数据的历史最高值出现于05-01-2018，达29,531.000百万巴基斯坦卢比，而历史最低值则出现于11-01-2016，为23,794.000百万巴基斯坦卢比。CEIC提供的市价总值：PSX：房地产投资与信托数据处于定期更新的状态，数据来源于State Bank of Pakistan，数据归类于Global Database的巴基斯坦 – 表 PK.Z003：卡拉奇证券交易所：市值（新分类）。

Dataset Overview This dataset captures high-end property listings in DHA (Defence Housing Authority), Pakistan's most prestigious residential area. It includes real-time listings with details like location, size (in Marla), bedrooms, bathrooms, posting time, and price in PKR Crore.

Perfect for: ✅ Real estate investors analyzing DHA market trends ✅ Property price prediction using size, location & amenities ✅ Competitive analysis of luxury housing in Karachi ✅ Time-on-market studies based on time_added_text

Key Features 📍 Locations: Exact blocks in DHA (Phase 2, 6, 7, 9 Town) 📏 Sizes: Property area in Marla (5 to 40 Marla) 🛏️ Beds: 3 to 6 bedrooms 🚿 Baths: 4 to 6 bathrooms ⏰ Time Added: How recently listed (e.g., "3 minutes ago") 💰 Prices: In PKR Crore (e.g., PKR 2 Crore to PKR 20 Crore)

Use Cases 🔹 Price Trend Analysis: Correlate size/location with price per Marla. 🔹 Demand-Supply Gaps: Identify fast-selling vs. stagnant listings. 🔹 Luxury Benchmarking: Compare Phase 6 vs. Phase 7 pricing. 🔹 Automated Valuation Models (AVMs): Train ML models to predict property values.

Why This Dataset? 🏠 Exclusive Data: Focused on DHA’s premium market. 📊 Structured & Clean: Ready for analysis in Python/R/Excel. ⏳ Time-Sensitive: Tracks "just added" properties for freshness.

This dataset contains information on house prices in Karachi, Pakistan, as of 2023. The data was collected through various sources, including property listings and real estate agents. The dataset includes information such as the location of the house, its size, number of bedrooms, number of bathrooms, and other relevant features. The purpose of this dataset is to provide a comprehensive overview of house prices in Karachi, which can be useful for real estate agents, property developers, and researchers interested in the housing market of Pakistan's largest city. The dataset is presented in a CSV format and is available for download on Kaggle.

This dataset contains historical stock market data for the Top 50 companies listed on the Pakistan Stock Exchange. It includes daily OHLCV (Open, High, Low, Close, and Volume) data making it a valuable resource for financial analysis forecasting and time series modeling.

The dataset is particularly useful for:

. Stock trend analysis

. Volatility and risk-return studies

. Machine learning models for prediction

. Portfolio optimization & financial research

Column Descriptors

symbol : Ticker symbol of the company (e.g., MARI, NESTLE, RMPL).

date : Trading date (format: YYYY-MM-DD).

open : Opening stock price on that day.

high : Highest stock price during the trading session.

low : Lowest stock price during the trading session.

close : Closing stock price on that day.

volume : Number of shares traded.

month : Month extracted from the trading date

year : Year extracted from the trading date.

****Data Collection**: **Our project's data source is Zameen.com, one of Pakistan's most popular real estate websites. This platform provides detailed property listings with vital information such as price and other property details. We may gain valuable insights into the Lahore real estate market by using data from Zameen.com. We have scraped data from this website using Python till 26 June 2023. We have adopted seven locations of Lahore in this study. These locations are: Location Count DHA Defence 6117 Bahria Town 2103 Park View City 239 Johar Town 524 Lake City 348 Gulberg 89 Allama Iqbal Town 119 TOTAL-7 9539 INDEPENDENT VARIABLES: Location, Bedrooms, Bathrooms, Size(Marla), DEPENDENT VARIABLE: Price