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Pakistan Real Estate Listing Data
Data Science and Analytics
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About
The data provides deep insights into the rapidly expanding property sector in Pakistan. It is specifically designed to facilitate machine learning tasks, such as applying feature engineering techniques to accurately predict both rental and house prices, aiming for minimal prediction error. The resource allows users to closely examine pricing movements and trends across various provinces, major cities, and distinct housing sectors within the country.
Columns
The dataset includes 18 distinct fields detailing property characteristics and listing information:
- property_id: A unique identifier assigned to the property by Zameen.com.
- location_id: An identifier associated with the specific location of the property listing.
- page_url: The exact hyperlink from the website where the listing data was scraped.
- property_type: Categorises the listing, such as Flat, House, or Penthouse.
- price: The recorded price, which may be either a rental fee or a sale price.
- location: The specific block name or sub-locality within the city (e.g., DHA Defence).
- city: One of five major, densely populated cities in Pakistan included in the data (e.g., Karachi, Lahore).
- province_name: Indicates the province, covering Punjab, Sindh, and the federal area.
- latitude: The geo-coordinate identifying the property's location.
- longitude: The geo-coordinate identifying the property's location.
- baths: Represents the number of washrooms and toilets in the property.
- purpose: Specifies if the listing is available 'For Sale' or 'For Rent'.
- bedrooms: The total number of rooms within the listed property.
- date_added: The specific date the listing was published on Zameen.com.
- agency: The name of the property dealing company involved (if provided).
- agent: The name of the specific property dealer (if provided).
- Total_Area: The overall area of the place, measured in cubic feet.
Distribution
The data file is typically supplied in a CSV format and contains approximately 168,000 unique records. The raw file size is about 47.2 MB. All columns boast 100% validity with no missing values, except for the
agency and agent fields, which have around 26% missing data.Usage
This dataset is optimally suited for:
- Developing machine learning models for house price and rental prediction, particularly utilising regression techniques.
- Conducting socio-economic research focused on Pakistan's housing affordability and market dynamics.
- Performing geospatial analysis to map price variation across different neighbourhoods and cities using latitude and longitude coordinates.
- Creating detailed reports on real estate trends, including examining the prevalence of property types (e.g., House vs Flat) and listing purposes (Sale vs Rent).
Coverage
The data covers listings geographically spread across key areas in Pakistan, specifically focusing on five of the most populated cities within the major provinces of Punjab and Sindh, alongside the federal area. The listings included were added to the Zameen.com platform between August 2018 and July 2019. The overall data structure was compiled between May 2020 and April 2021.
License
CC0: Public Domain
Who Can Use It
- Data Scientists: For training and evaluating advanced regression models to predict property values.
- Economists and Market Researchers: To gain detailed insights into the Pakistani real estate sector's performance and regional disparities.
- Academics and Students: For educational projects requiring real-world, structured data on housing markets.
Dataset Name Suggestions
- Pakistan Real Estate Listing Data
- Zameen Property Price Predictor
- Major City Housing Data Pakistan
Attributes
Original Data Source: Pakistan Real Estate Listing Data
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