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Pakistan Urban Real Estate Data
Data Science and Analytics
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About
This dataset provides up-to-date real estate data for both renting and selling properties, including houses, flats, and apartments, across the top 10 largest cities in Pakistan by population. Scraped directly from Zameen.com, Pakistan's leading property buy and sell platform, this dataset is provided in a machine learning-ready CSV format. It is ideal for developing price prediction models, analysing rental yields, and understanding buying and renting trends in specific urban areas, sectors, or societies across Pakistan. It can also facilitate comparative analysis of price appreciation against historical property data.
Columns
The dataset contains 22 columns, providing detailed attributes for each property listing. Key columns include:
- Property_Id: A unique identification number assigned to each property listing.
- Purpose: Indicates whether the property is available for 'Buy' or 'Rent'.
- City: The specific city where the property is situated.
- Province: The province in which the property is located.
- Property Type: Describes the type of dwelling, such as 'House' or 'Flat/Apartment'.
- Location: The general area or neighbourhood of the property.
- Society/Sector: The specific residential society or sector where the property resides.
- Short Desc: A concise textual description of the property.
- Price: The numeric value of the property's price.
- Price in words: The property's price expressed in a textual format.
- Size (in Zameen.com): The dimensions of the property, typically given in Marla or Kanal units.
- Long Desc: A more elaborate and detailed description of the property.
- Bedrooms: The total number of bedrooms in the property.
- Baths: The total number of bathrooms in the property.
- Link: A direct URL to the original property listing on the Zameen.com website.
- Creation_date: The date when the property listing was first created.
- Updation_date: The most recent date when the property listing was updated.
Distribution
This dataset is provided as a CSV file, which is suitable for machine learning applications. It consists of 92.8 thousand rows and 22 distinct columns, offering a substantial collection of property data.
Usage
This dataset is highly versatile and can be applied to several analytical and machine learning tasks:
- Developing Machine Learning models for rental yield prediction and property price forecasting.
- Analysing buying and renting trends across various cities, sectors, and societies within Pakistan.
- Conducting comparative analysis to observe price appreciation over time or between different geographical areas.
- Performing horizontal and vertical analyses based on specific locations within cities.
Coverage
- Geographic Scope: The dataset covers property listings from the top 10 largest cities in Pakistan. This includes key urban centres such as Lahore (33%), Karachi (25%), and other cities (42%). Properties are distributed across provinces, with Punjab accounting for 50% and Sindh for 25% of the listings.
- Time Range: The data was collected on 26 June 2023.
- Property Status: Listings include properties available for Sale (63%) and Rent (37%).
- Property Types: The dataset features a majority of Houses (81%), followed by Flats/Apartments (18%), and other property types (1%).
License
CC0
Who Can Use It
This dataset is an ideal resource for:
- Data scientists and machine learning engineers interested in real estate analytics and predictive modelling.
- Real estate analysts and investors seeking insights into property market trends, rental yields, and price dynamics in Pakistan.
- Researchers and academics focusing on urban development, housing markets, or economic indicators in Pakistan.
- Anyone looking to understand buying and renting opportunities across Pakistan's major cities.
Dataset Name Suggestions
- Zameen.com Pakistan Property Listings
- Pakistan Urban Real Estate Data
- Top Cities Property Market Pakistan
- Pakistan Housing Data 2023
- Zameen.com Property Sales and Rentals
Attributes
Original Data Source: Zameen.com Property Data Pakistan (Top 10 Cities)
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