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Real Estate Pakistan 2023
Data Science and Analytics
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About
This dataset focuses on house prices in Pakistan for 2023, offering a detailed view of the property market. Sourced from Zameen.com, it provides essential information for understanding real estate trends and property valuations across major Pakistani cities. The dataset serves as a valuable resource for market analysis, price prediction, and urban development studies.
Columns
- Property id: A unique identifier for each individual property listing.
- Location id: A unique identifier for each specific location, categorised by city sub-areas.
- Page URL: The web address where the property was originally published.
- Property type: Categorises properties into six distinct types: House, FarmHouse, Upper Portion, Lower Portion, Flat, and Room. 'House' and 'Flat' are the most common types.
- Price: The asking price of the property, serving as a key dependent variable within the dataset.
- City: The city where the property is located. The dataset includes listings from five major cities: Lahore, Karachi, Faisalabad, Rawalpindi, and Islamabad, with Karachi and Lahore being the most frequently occurring.
- Province: Indicates the province or state in which the city is situated.
- Location: Provides detailed information on specific locations within each city. 'DHA Defence' and 'Bahria Town Karachi' are prominent locations.
- Latitude: The geographical latitude coordinate for the property's city.
- Longitude: The geographical longitude coordinate for the property's city.
- baths: The number of bathrooms available in the property, typically ranging from 1 to 7.
- purpose: Specifies the transaction type for the property, either 'For Sale' or 'For Rent', with the majority of listings being 'For Sale'.
- bedrooms: The number of bedrooms in the property, generally ranging from 0 to 6.
- Area_in_Marla: The area of the property measured in Marla, an indigenous unit of land area.
Distribution
The dataset is provided as a CSV file named 'Cleaned_data_for_model.csv', with a file size of 6.04 MB. It contains 9 columns and approximately 99,500 records, making it a substantial collection for analysis. Data integrity appears high, with minimal missing or mismatched values across key columns.
Usage
This dataset is ideal for:
- Real estate market analysis: Identifying trends, price fluctuations, and regional differences.
- Predictive modelling: Building models to forecast property prices based on various features.
- Urban planning: Understanding property distribution and development patterns in major cities.
- Investment analysis: Informing decisions for property investors and developers.
- Academic research: Supporting studies on housing economics and property markets in Pakistan.
Coverage
The dataset primarily covers Pakistan, focusing on properties within five key cities: Lahore, Karachi, Faisalabad, Rawalpindi, and Islamabad. The data reflects house prices for 2023 and is expected to be updated annually. No specific demographic notes are provided, but the data represents the general property market in these urban centres.
License
CC0: Public Domain
Who Can Use It
- Real Estate Professionals: For market insights, valuation, and competitive analysis.
- Data Scientists and Analysts: For developing machine learning models for price prediction and trend identification.
- Researchers: For academic studies on housing markets, urbanisation, and economic indicators in Pakistan.
- Property Developers: For identifying potential investment areas and understanding market demand.
- Government Agencies: For policy-making related to housing and urban development.
Dataset Name Suggestions
- Pakistan Property Prices 2023
- Pakistani Housing Market Data
- Zameen.com Pakistan Listings
- Real Estate Pakistan 2023
- Urban Property Data Pakistan
Attributes
Original Data Source: Real Estate Pakistan 2023
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