Context

So this data set is collected for completing a college project ,which is an android app for calculating the price of houses.

Content

This data is scraped from magic bricks website between june 2021 and july 2021 .

Acknowledgements

magicbricks.com

Inspiration

With the help of the data available one can make a regression model to predict house prices.

Graph and download economic data for Real Residential Property Prices for India (QINR628BIS) from Q1 2009 to Q2 2025 about India, residential, HPI, housing, real, price index, indexes, and price.

Dataset Overview :

This dataset is created as part of a machine learning mini project on House Price Prediction in India. It includes key features commonly used to predict house prices such as:

1) Number of bedrooms 2) Property type (e.g., Apartment, House) 3) Location 4) Area in square feet 5) Price per square foot 6) Total price

Column Description :

Column	Description
bhk	Number of bedrooms
propertytype	Type of property
location	City or locality
sqft	Total built-up area in square feet
pricepersqft	Price per square foot (in INR)
totalprice	Final price of the property (in INR)

Usage :

This dataset can be used to: --> Build a house price prediction model using ML algorithms --> Perform data visualization or feature correlation --> Understand real estate pricing trends in India

Key information about House Prices Growth

• India house prices grew 2.5% YoY in Jun 2025, following an increase of 5.1% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 2011 to Jun 2025, with an average growth rate of 5.1%.
• House price data reached an all-time high of 30.6% in Mar 2011 and a record low of -11.4% in Sep 2020.

CEIC calculates House Prices Growth from quarterly House Price Index. National Housing Bank provides House Price Index with base 2017-2018=100. House Prices Growth covers Mumbai only. House Prices Growth prior to Q2 2014 is calculated from House Price Index with base 2007=100.

Real Estate / Housing Dataset

This dataset is web scrapped from a real estate website, collecting all the necessary infos on the resale and new properties. It has around 14000+ rows of data having properties from various Indian cities like Chennai, Mumbai, Bangalore, Delhi, Pune, Kolkata and Hyderabad. Columns:

Name: Property Name, Property Title: Property Ad Title, Price: Property Price Location: Property Located Locality and Region Total Area: Total SQFT of the property Price Per SQFT: Price of Per SQFT of the property Description: Small paragraph about the property Baths: Number of baths in the property Balcony: Whether the Property has balcony or not

Graph and download economic data for Residential Property Prices for India (QINN628BIS) from Q1 2009 to Q2 2025 about India, residential, HPI, housing, price index, indexes, and price.

Residential Property Prices in India increased 3.13 percent in March of 2025 over the same month in the previous year. This dataset includes a chart with historical data for India Residential Property Prices.

India's residential house prices - quarterly and annual changes in house prices across cities, expert analysis and comparison with global peers.

This dataset provides comprehensive information about rental house prices across various locations in India. It includes details such as house type, size, location, city, latitude, longitude, price, currency, number of bathrooms, number of balconies, negotiability of price, price per square foot, verification date, description of the property, security deposit, and status of furnishing (furnished, unfurnished, semi-furnished).

Note: This is Recently scraped data of April 2024.

Dataset Glossary (Column-Wise)

• House Type: Type of house (e.g., apartment, villa, duplex).
• House Size: Size of the house in square feet or square meters.
• Location: Specific area or neighborhood where the property is located.
• City: City in India where the property is situated.
• Latitude: Geographic latitude coordinates of the property location.
• Longitude: Geographic longitude coordinates of the property location.
• Price: Rental price of the house.
• Currency: Currency in which the price is denoted (e.g., INR - Indian Rupees).
• Number of Bathrooms: Total number of bathrooms in the house.
• Number of Balconies: Total number of balconies in the house.
• Negotiability: Indicates whether the price is negotiable (Yes/No).
• Price per Square Foot: Price of the house per square foot.
• Verification Date: Date when the rental information was verified.
• Description: Additional description or details about the property.
• Security Deposit: Amount of security deposit required for renting the property.
• Status: Indicates the furnishing status of the property (furnished, unfurnished, semi-furnished).

Usage

This dataset aims to provide valuable insights into the rental housing market in India, enabling analysis of rental trends, comparison of prices across different locations and property types, and understanding the impact of various factors on rental prices. Researchers, analysts, and policymakers can utilize this dataset for a wide range of applications, including real estate market analysis, urban planning, and economic research.

Acknowledgement

This Dataset is created from https://www.makaan.com/. If you want to learn more, you can visit the Website.

Cover Photo by: Playground.ai

India Housing Price Index: 2008-09Q4=100: Mumbai data was reported at 248.600 2008-09Q4=100 in Mar 2013. This records an increase from the previous number of 248.500 2008-09Q4=100 for Dec 2012. India Housing Price Index: 2008-09Q4=100: Mumbai data is updated quarterly, averaging 96.850 2008-09Q4=100 from Jun 2003 (Median) to Mar 2013, with 40 observations. The data reached an all-time high of 248.600 2008-09Q4=100 in Mar 2013 and a record low of 52.900 2008-09Q4=100 in Dec 2003. India Housing Price Index: 2008-09Q4=100: Mumbai data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under Global Database’s India – Table IN.EA001: Housing Price Index: Reserve Bank of India. Rebased from 2008-09Q4=100 to 2010-11Q1=100. Replacement series ID: 354942667

Housing Index in India increased to 122 points in the first quarter of 2025 from 120 points in the fourth quarter of 2024. This dataset provides - India NHB Residex - actual values, historical data, forecast, chart, statistics, economic calendar and news.

India IN: House Price Index: Nominal: sa data was reported at 149.020 2015=100 in Dec 2024. This records a decrease from the previous number of 149.115 2015=100 for Sep 2024. India IN: House Price Index: Nominal: sa data is updated quarterly, averaging 111.391 2015=100 from Mar 2009 (Median) to Dec 2024, with 64 observations. The data reached an all-time high of 149.115 2015=100 in Sep 2024 and a record low of 34.745 2015=100 in Mar 2009. India IN: House Price Index: Nominal: sa data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s India – Table IN.OECD.AHPI: House Price Index: Seasonally Adjusted: Non OECD Member: Quarterly. Urban areas - 10 main cities; Seasonnally adjusted by OECD, using the X-12 ARIMA method; Residential property prices, sales of newly-built and existing dwellings, all types of dwellings The source for recent figures is same as the OECD Residential Property Price Indices (RPPIs) - Headline indicators database. Sales

India Housing Price Index: 2008-09Q4=100: Delhi data was reported at 259.200 2008-09Q4=100 in Mar 2013. This records an increase from the previous number of 247.800 2008-09Q4=100 for Dec 2012. India Housing Price Index: 2008-09Q4=100: Delhi data is updated quarterly, averaging 135.200 2008-09Q4=100 from Mar 2009 (Median) to Mar 2013, with 17 observations. The data reached an all-time high of 259.200 2008-09Q4=100 in Mar 2013 and a record low of 99.700 2008-09Q4=100 in Dec 2009. India Housing Price Index: 2008-09Q4=100: Delhi data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under Global Database’s India – Table IN.EA001: Housing Price Index: Reserve Bank of India. Rebased from 2008-09Q4=100 to 2010-11Q1=100. Replacement series ID: 354942677

Dataset Description:

This dataset provides synthetic housing price data for various localities in Pune, India. The data is designed to facilitate machine learning practice and experimentation, particularly for tasks related to real estate price prediction.

Dataset Details:

Number of Entries: 100000 File Format: CSV Data Attributes: id: Unique identifier for each house. area: The neighborhood or locality where the house is located. This includes well-known areas such as Koregaon Park, Hinjewadi, Pimpri-Chinchwad, Viman Nagar, and Kalyani Nagar. square_feet: The total area of the house in square feet. num_bedrooms: The number of bedrooms in the house. num_bathrooms: The number of bathrooms in the house. year_built: The year in which the house was constructed. has_garage: Indicator of whether the house has a garage (1 for yes, 0 for no). price: The price of the house in Indian Rupees (INR). Usage:

This dataset can be used for various machine learning tasks, including:

Regression Analysis: Predicting house prices based on features like area, square footage, number of bedrooms, and other attributes. Exploratory Data Analysis (EDA): Understanding the distribution of house prices across different localities and features. Feature Engineering: Creating and testing new features to improve predictive models. Note:

The data is synthetic and intended for educational and research purposes. It does not represent real-world data but is designed to mimic the structure and attributes commonly found in housing price datasets.

Feel free to use this dataset to explore different machine learning techniques and improve your predictive modeling skills!

This description provides a clear overview of the dataset's purpose, structure, and potential uses, helping users understand its value and applications.

India Housing Price Index: 2008-09Q4=100: Lucknow data was reported at 218.800 2008-09Q4=100 in Mar 2013. This records a decrease from the previous number of 221.600 2008-09Q4=100 for Dec 2012. India Housing Price Index: 2008-09Q4=100: Lucknow data is updated quarterly, averaging 140.300 2008-09Q4=100 from Mar 2009 (Median) to Mar 2013, with 17 observations. The data reached an all-time high of 221.600 2008-09Q4=100 in Dec 2012 and a record low of 100.000 2008-09Q4=100 in Mar 2009. India Housing Price Index: 2008-09Q4=100: Lucknow data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under Global Database’s India – Table IN.EA001: Housing Price Index: Reserve Bank of India. Rebased from 2008-09Q4=100 to 2010-11Q1=100. Replacement series ID: 354942707

India Housing Price Index: 2008-09Q4=100: Jaipur data was reported at 194.000 2008-09Q4=100 in Mar 2013. This records an increase from the previous number of 179.400 2008-09Q4=100 for Dec 2012. India Housing Price Index: 2008-09Q4=100: Jaipur data is updated quarterly, averaging 157.300 2008-09Q4=100 from Mar 2009 (Median) to Mar 2013, with 17 observations. The data reached an all-time high of 194.000 2008-09Q4=100 in Mar 2013 and a record low of 99.000 2008-09Q4=100 in Jun 2009. India Housing Price Index: 2008-09Q4=100: Jaipur data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under Global Database’s India – Table IN.EA001: Housing Price Index: Reserve Bank of India. Rebased from 2008-09Q4=100 to 2010-11Q1=100. Replacement series ID: 354942737

Key information about India Gold Production

• India Real Residential Property Price Index was reported at 163.521 2010=100 in Jun 2025.
• This records an increase from the previous number of 160.894 2010=100 for Mar 2025.
• India Real Residential Property Price Index data is updated quarterly, averaging 170.267 2010=100 from Mar 2009 to Jun 2025, with 66 observations.
• The data reached an all-time high of 177.067 2010=100 in Jun 2019 and a record low of 88.901 2010=100 in Mar 2009.
• India Real Residential Property Price Index data remains active status in CEIC and is reported by Bank for International Settlements.
• The data is categorized under World Trend Plus’s Association: Property Sector – Table RK.BIS.RPPI: Selected Real Residential Property Price Index: 2010=100: Quarterly. [COVID-19-IMPACT]

The Bengaluru House Price Dataset contains detailed information on residential properties in Bengaluru, India. It includes features such as location, size (in square feet), number of bedrooms (BHK), number of bathrooms, total area, price, and additional attributes like availability and property type. This dataset is widely used for real estate market analysis, data visualization, and predictive modeling, particularly in housing price prediction using machine learning techniques.

It serves as a valuable resource for data scientists, real estate analysts, and researchers interested in exploring how various factors—such as location, amenities, and property size—affect housing prices in one of India’s fastest-growing metropolitan cities.

Housing Price Index: 2012-13=100: Odisha: Bhubaneswar data was reported at 122.000 2012-2013=100 in Mar 2018. This records a decrease from the previous number of 127.000 2012-2013=100 for Dec 2017. Housing Price Index: 2012-13=100: Odisha: Bhubaneswar data is updated quarterly, averaging 115.000 2012-2013=100 from Jun 2013 (Median) to Mar 2018, with 20 observations. The data reached an all-time high of 127.000 2012-2013=100 in Dec 2017 and a record low of 107.000 2012-2013=100 in Jun 2014. Housing Price Index: 2012-13=100: Odisha: Bhubaneswar data remains active status in CEIC and is reported by National Housing Bank. The data is categorized under Global Database’s India – Table IN.EA004: Housing Price Index: National Housing Bank: Assessment Price: 2012-2013=100: Current Quarter.

India - Home Prices - Historical chart and current data through 2025.