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

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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 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.

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.

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

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.

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:

Comprehensive dataset containing 3,049 verified Real estate developer businesses in Pakistan with complete contact information, ratings, reviews, and location data.

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.

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.

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.

Comprehensive dataset containing 390 verified Real estate rental agency businesses in Pakistan with complete contact information, ratings, reviews, and location data.

Pakistan AMI: Employed Person: Finance, Real Estate etc data was reported at 32,823.810 PKR in 2016. This records a decrease from the previous number of 45,020.240 PKR for 2014. Pakistan AMI: Employed Person: Finance, Real Estate etc data is updated yearly, averaging 15,845.435 PKR from Jun 2002 (Median) to 2016, with 8 observations. The data reached an all-time high of 45,020.240 PKR in 2014 and a record low of 8,268.470 PKR in 2002. Pakistan AMI: Employed Person: 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.

Comprehensive dataset containing 845 verified Commercial real estate agency businesses in Pakistan with complete contact information, ratings, reviews, and location data.

Pakistan AMI: Employed Person: Rural: Finance, Real Estate etc data was reported at 28,730.460 PKR in 2016. This records an increase from the previous number of 26,982.130 PKR for 2014. Pakistan AMI: Employed Person: Rural: Finance, Real Estate etc data is updated yearly, averaging 13,441.285 PKR from Jun 2002 (Median) to 2016, with 8 observations. The data reached an all-time high of 28,730.460 PKR in 2016 and a record low of 4,101.040 PKR in 2005. Pakistan AMI: Employed Person: Rural: 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 CB: Advances: PS: Real Estate, Renting & Business Activity data was reported at 142,545.900 PKR mn in Dec 2017. This records an increase from the previous number of 128,953.800 PKR mn for Jun 2017. Pakistan CB: Advances: PS: Real Estate, Renting & Business Activity data is updated semiannually, averaging 102,801.200 PKR mn from Dec 2003 (Median) to Dec 2017, with 29 observations. The data reached an all-time high of 142,545.900 PKR mn in Dec 2017 and a record low of 13,029.900 PKR mn in Dec 2003. Pakistan CB: Advances: PS: Real Estate, Renting & Business Activity 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.KA021: Advances By Borrowers: Scheduled Commercial Banks.

Context

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

Geography: Pakistan

Time period: 2021

Unit of analysis: Real states Data Analysis

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

Variables: The dataset contain propertyid,locationid,pageurl,propertytype,price,location,city,provincename,latitude,longitude baths,area,purpose,bedrooms,dateadded,agency and agent.

File Type: CSV

Inspiration

https://www.kaggle.com/getting-started/292014

Introduction

Introducing the Urdu Scripted Monologue Speech Dataset for the Real Estate Domain, a dataset designed to support the development of Urdu speech recognition and conversational AI technologies tailored for the real estate industry.

Speech Data

This dataset includes over 6,000 high-quality scripted prompt recordings in Urdu. The speech content reflects a wide range of real estate interactions to help build intelligent, domain-specific customer support systems and speech-enabled tools.

Topic and Scenario Coverage

This dataset captures a broad spectrum of use cases and conversational themes within the real estate sector, such as:

Contextual Depth

Each scripted prompt incorporates key elements to simulate realistic real estate conversations:

Transcription

To ensure precision in model training, each audio recording is paired with a verbatim text transcription:

Metadata

Each data sample is enriched with detailed metadata to enhance usability:

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