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.

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).

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

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 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.

Pakistan AMI: Employed Person: Urban: Finance, Real Estate etc data was reported at 34,574.990 PKR in 2016. This records a decrease from the previous number of 53,933.800 PKR for 2014. Pakistan AMI: Employed Person: Urban: Finance, Real Estate etc data is updated yearly, averaging 17,825.930 PKR from Jun 2002 (Median) to 2016, with 8 observations. The data reached an all-time high of 53,933.800 PKR in 2014 and a record low of 9,066.760 PKR in 2002. Pakistan AMI: Employed Person: Urban: Finance, Real Estate etc data remains active status in CEIC and is reported by Pakistan Bureau of Statistics. The data is categorized under Global Database’s Pakistan – Table PK.H007: Household Integrated Economic Survey: Average Monthly Income: Employed Person: By Industry.

Pakistan: Housing and utilities price index, world average = 100: The latest value from 2021 is 33.26 index points, an increase from 32.722 index points in 2017. In comparison, the world average is 77.639 index points, based on data from 165 countries. Historically, the average for Pakistan from 2017 to 2021 is 32.991 index points. The minimum value, 32.722 index points, was reached in 2017 while the maximum of 33.26 index points was recorded in 2021.

👨‍💻 Author: Azhar Saleem

"https://github.com/azharsaleem18" target="_blank"> https://img.shields.io/badge/GitHub-Profile-blue?style=for-the-badge&logo=github" alt="GitHub Profile"> "https://www.kaggle.com/azharsaleem" target="_blank"> https://img.shields.io/badge/Kaggle-Profile-blue?style=for-the-badge&logo=kaggle" alt="Kaggle Profile"> "https://www.linkedin.com/in/azhar-saleem/" target="_blank"> https://img.shields.io/badge/LinkedIn-Profile-blue?style=for-the-badge&logo=linkedin" alt="LinkedIn Profile">
"https://www.youtube.com/@AzharSaleem19" target="_blank"> https://img.shields.io/badge/YouTube-Profile-red?style=for-the-badge&logo=youtube" alt="YouTube Profile"> "https://www.facebook.com/azhar.saleem1472/" target="_blank"> https://img.shields.io/badge/Facebook-Profile-blue?style=for-the-badge&logo=facebook" alt="Facebook Profile"> "https://www.tiktok.com/@azhar_saleem18" target="_blank"> https://img.shields.io/badge/TikTok-Profile-blue?style=for-the-badge&logo=tiktok" alt="TikTok Profile">
"https://twitter.com/azhar_saleem18" target="_blank"> https://img.shields.io/badge/Twitter-Profile-blue?style=for-the-badge&logo=twitter" alt="Twitter Profile"> "https://www.instagram.com/azhar_saleem18/" target="_blank"> https://img.shields.io/badge/Instagram-Profile-blue?style=for-the-badge&logo=instagram" alt="Instagram Profile"> "mailto:azharsaleem6@gmail.com"> https://img.shields.io/badge/Email-Contact%20Me-red?style=for-the-badge&logo=gmail" alt="Email Contact">

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.

Introduction

This Urdu Call Center Speech Dataset for the Real Estate industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Urdu -speaking Real Estate customers. With over 30 hours of unscripted, real-world audio, this dataset captures authentic conversations between customers and real estate agents ideal for building robust ASR models.

Curated by FutureBeeAI, this dataset equips voice AI developers, real estate tech platforms, and NLP researchers with the data needed to create high-accuracy, production-ready models for property-focused use cases.

Speech Data

The dataset features 30 hours of dual-channel call center recordings between native Urdu speakers. Captured in realistic real estate consultation and support contexts, these conversations span a wide array of property-related topics from inquiries to investment advice offering deep domain coverage for AI model development.

Topic Diversity

This speech corpus includes both inbound and outbound calls, featuring positive, neutral, and negative outcomes across a wide range of real estate scenarios.

Such domain-rich variety ensures model generalization across common real estate support conversations.

Transcription

All recordings are accompanied by precise, manually verified transcriptions in JSON format.

These transcriptions streamline ASR and NLP development for Urdu real estate voice applications.

Metadata

Detailed metadata accompanies each participant and conversation:

This enables smart filtering, dialect-focused model training, and structured dataset exploration.

Usage and Applications

This dataset is ideal for voice AI and NLP systems built for the real estate sector:

Pakistan GDP: GVA: Services: Real Estate Activities data was reported at 973,003.428 PKR mn in Dec 2024. This records an increase from the previous number of 963,548.811 PKR mn for Sep 2024. Pakistan GDP: GVA: Services: Real Estate Activities data is updated quarterly, averaging 656,425.572 PKR mn from Sep 2015 (Median) to Dec 2024, with 38 observations. The data reached an all-time high of 973,003.428 PKR mn in Dec 2024 and a record low of 422,447.634 PKR mn in Sep 2015. Pakistan GDP: GVA: Services: Real Estate Activities data remains active status in CEIC and is reported by Pakistan Bureau of Statistics. The data is categorized under Global Database’s Pakistan – Table PK.A006: SNA08: 2015-16 Base: Gross Domestic Product by Industry: Current Price.

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.

The data includes: - Property Id - Location Id - property page URL (where you can extract other information as well) - property type - price - location - city - province name - latitude - longitude

In 2017, imports of throat pastilles and cough drops (not containing medicinal properties) in Pakistan amounted to X tons, falling by -X% against the previous year. Overall, imports of throat pastilles and cough drops (not containing medicinal properties) continue to indicate a prominent increase. The pace of growth was the most pronounced in 2012, when the imports increased by X% year-to-year.

Pakistan Number of Job Postings: Removed: Real Estate Rental and Leasing data was reported at 13.000 Unit in 05 May 2025. This records an increase from the previous number of 9.000 Unit for 28 Apr 2025. Pakistan Number of Job Postings: Removed: Real Estate Rental and Leasing data is updated weekly, averaging 0.000 Unit from Jan 2008 (Median) to 05 May 2025, with 905 observations. The data reached an all-time high of 204.000 Unit in 16 May 2022 and a record low of 0.000 Unit in 02 Nov 2020. Pakistan Number of Job Postings: Removed: Real Estate Rental and Leasing data remains active status in CEIC and is reported by Revelio Labs, Inc.. The data is categorized under Global Database’s Pakistan – Table PK.RL.JP: Number of Job Postings: Removed: by Industry.

Pakistan Number of Job Postings: New: Real Estate Rental and Leasing data was reported at 63.000 Unit in 17 Nov 2025. This records a decrease from the previous number of 76.000 Unit for 10 Nov 2025. Pakistan Number of Job Postings: New: Real Estate Rental and Leasing data is updated weekly, averaging 0.000 Unit from Jan 2008 (Median) to 17 Nov 2025, with 933 observations. The data reached an all-time high of 304.000 Unit in 07 Nov 2022 and a record low of 0.000 Unit in 07 Dec 2020. Pakistan Number of Job Postings: New: Real Estate Rental and Leasing data remains active status in CEIC and is reported by Revelio Labs, Inc.. The data is categorized under Global Database’s Pakistan – Table PK.RL.JP: Number of Job Postings: New: by Industry.

In 2023, overseas purchases of bearing housings not incorporating ball or roller bearings, plain shaft bearings decreased by -6.4% to 448 tons, falling for the second consecutive year after two years of growth.

In 2024, the Pakistani market for bearing housings not incorporating ball or roller bearings, plain shaft bearings increased by 7.4% to $14M for the first time since 2021, thus ending a two-year declining trend. Over the period under review, consumption, however, showed a pronounced decrease. As a result, consumption attained the peak level of $22M. From 2022 to 2024, the growth of the market remained at a lower figure.