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.

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

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.

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.

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

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.

As of the first quarter of 2023, year-on-year real estate price increase was highest in Bengaluru and lowest in Chennai with **** and *** percent respectively. Followed by Bengaluru was Kochi and Delhi with an increase of **** and **** percent.

The India Real Estate Market Report is Segmented by Business Model (Sales and Rental), by Property Type (Residential and Commercial), by End-User (Individuals/Households, Corporates & SMEs and Others), and by City (Mumbai Metropolitan Region, Delhi NCR, Pune, Bengaluru, Hyderabad, Chennai, Kolkata, Ahmedabad, and the Rest of India). The Market Forecasts are Provided in Terms of Value (USD).

India Real Estate Market is projected to reach USD 1044.43 Billion by 2030 at a CAGR of 16.6% from 2025-2030

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.

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

The India Luxury Residential Real Estate Market Report is Segmented by Property Type (Apartments & Condominiums, and Villas & Landed Houses), by by Business Model (Sales and Rental), by Mode of Sale (Primary and Secondary), by City (Delhi NCR, Mumbai, Bengaluru, Hyderabad, Pune, Chennai, Kolkata and Other Cities). The Report Offers Market Size and Forecast Values (USD) for all the Above Segments.

The India Residential Real Estate Market is experiencing robust growth, projected to reach a market size of $227.26 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 24.77% from 2025 to 2033. This expansion is driven by several factors, including a burgeoning middle class with increasing disposable incomes, favorable government policies promoting affordable housing, and urbanization trends leading to a significant demand for residential properties across major metropolitan areas. The market is segmented into Condominiums and Apartments and Villas and Landed Houses, with both segments contributing significantly to overall growth. Key players such as DLF, Oberoi Realty, and Godrej Properties are shaping the market landscape through large-scale projects and innovative offerings. However, challenges remain, including high construction costs, regulatory complexities, and land acquisition hurdles, which could potentially moderate growth in certain regions. The forecast suggests continued market expansion, particularly in high-growth urban centers, fueled by ongoing infrastructure development and improved connectivity. The competitive landscape is intense, with both established players and new entrants vying for market share. The increasing preference for luxury apartments and sustainable housing options presents opportunities for developers to cater to evolving consumer preferences. Government initiatives focusing on affordable housing schemes are expected to further stimulate demand, particularly in the affordable housing segment. The market's trajectory suggests a positive outlook, although careful consideration of macroeconomic factors and potential risks is crucial for informed decision-making. Continued monitoring of evolving consumer preferences, technological advancements, and regulatory changes will be essential for sustained success in this dynamic market. This report provides a detailed analysis of the Indian residential real estate market, covering the historical period (2019-2024), the base year (2025), and forecasting the market's trajectory until 2033. It delves into market size, segmentation, key trends, growth drivers, challenges, and significant developments, offering valuable insights for investors, developers, and stakeholders. The report leverages data encompassing condominiums and apartments, villas and landed houses, and examines the impact of key players and regulatory changes. This in-depth analysis will help you navigate the complexities of this dynamic market and make informed decisions. Recent developments include: October 2022- Shriram Properties Ltd and ASK Property Fund agreed to establish an INR 500 crore (USD 608.98 million) investment platform to acquire housing projects. Both companies have signed an agreement to establish an investment platform to acquire residential real estate projects. Shriram and ASK will co-invest in plotted residential development projects in Bengaluru, Chennai, and Hyderabad as part of the platform agreement., October 2022- Magnolia Quality Development Corporation (MQDC), a Bangkok-based property development firm, was in talks with multiple landowners to acquire a large plot for a residential project in the NCR. The company plans to launch its flagship luxury residential real estate project in India and is discussing a possible transaction with property consultants and developers.. Key drivers for this market are: Growing urban population driving the growth of transportation infrastructure., Sultanate's Economic Diversification Plan (Vision 2040) to provide new growth to the market. Potential restraints include: Delay in project approvals, High cost of materials. Notable trends are: Increasing Demand for Big Residential Spaces Driving the Market.

Main Dataset

I scrapped data from 99acres using their (kind of) hidden API. I scrapped almost 10,000+ data using my scrapper app see here.

DESCRIPTION OF THE DATA

• Contains details of properties of Gurgaon, Hyderabad, Mumbai, Kolkata cities of India.
• All datasets of different cities contains almost 10K properties.
• In some datasets, some columns are not available. Sorry!!
• Target column: PRICE

Data Usage

This dataset can be used for various real estate-related tasks, including:

• Property price prediction.
• Market analysis to identify trends and patterns.
• Identifying popular property types and locations.
• Evaluating the impact of property attributes on price.

EXPLANATION OF EACH COLUMNS

NOTE: Not all the columns are important for you so first try to understand your problem statement and then filter this dataset accordingly.

• AGE: The age of the property in years.
• ALT_TAG: An alternative tag or description.
• AMENITIES: Describes the amenities available with the property.
• AREA: The area of the property.
• BALCONY_NUM: The number of balconies in the property.
• BATHROOM_NUM: The number of bathrooms in the property.
• BEDROOM_NUM: The number of bedrooms in the property.
• BROKERAGE: Information about the brokerage or agency associated with the property listing.
• BUILDING_ID: An integer identifier for the building.
• BUILDING_NAME: The name of the building.
• BUILTUP_SQFT: The total built-up area of the property in square feet.
• CARPET_SQFT: The total carpet area of the property in square feet.
• CITY_ID: An identifier for the city in which the property is located.
• CITY: The city where the property is located.
• CLASS_HEADING: A heading for the property class.
• CLASS_LABEL: A label representing the property class.
• CLASS: A classification label for the property.
• COMMON_FURNISHING_ATTRIBUTES: Attributes related to the furnishings and amenities commonly found in the property.
• CONTACT_COMPANY_NAME: The name of the company or agency responsible for the property listing.
• CONTACT_NAME: The name of the contact person associated with the property listing.
• DEALER_PHOTO_URL: URL to a photo or image associated with the property dealer.
• DESCRIPTION: A description of the property listing.
• EXPIRY_DATE: The date when the listing expires.
• FACING: Indicates the direction the property is facing.
• FEATURES: Describes the features of the property.
• FLOOR_NUM: The floor number of the property.
• FORMATTED_LANDMARK_DETAILS: Details of nearby landmarks.
• FORMATTED: Formatted information related to the property.
• FSL_Data: Data related to the property, possibly specific to a particular real estate agency.
• FURNISH: Indicates whether the property is furnished.
• FURNISHING_ATTRIBUTES: Attributes describing the level of furnishing in the property.
• GROUP_NAME: The name of the group or organization to which the property may belong.
• LISTING: Information about the property listing, possibly including its status and other details.
• LOCALITY_WO_CITY: The locality name without the city information.
• LOCALITY: The specific locality or neighborhood where the property is situated.
• location: Additional location information.
• MAP_DETAILS: Contains latitude and longitude information.
• MAX_AREA_SQFT: The maximum area of the property in square feet.
• MAX_PRICE: The maximum price of the property.
• MEDIUM_PHOTO_URL: URL to a medium-sized photo or image of the property.
• metadata: Additional metadata or information about the dataset.
• MIN_AREA_SQFT: The minimum area of the property in square feet.
• MIN_PRICE: The minimum price of the property.
• OWNTYPE: An integer representing the ownership type.
• PD_URL: URL to additional property details.
• PHOTO_URL: URL to photos or images associated with the property.
• POSTING_DATE: The date when the property listing was posted.
• PREFERENCE: Indicates the preference type for the property listing (e.g., "S" for sale).
• PRICE_PER_UNIT_AREA: The price per unit area of the property.
• PRICE_SQFT: The price per square foot of the property.
• PRICE: The price of the property. This is target column for ML.
• PRIMARY_TAGS: Primary tags or labels.
• PRODUCT_TYPE: The type of product listing.
• profile: Profile information related to the property or listing.
• PROJ_ID: An integer identifier for the project.
• PROP_DETAILS_URL: URL to detailed property information.
• PROP_HEADING: A heading or title for the property.
• PROP_ID: A ...

The real estate transaction value in the 'Residential Real Estate Transactions' segment of the real estate market in India was modeled to amount to ************* U.S. dollars in 2024. Following a continuous upward trend, the real estate transaction value has risen by ************* U.S. dollars since 2017. Between 2024 and 2029, the real estate transaction value will rise by ************* U.S. dollars, continuing its consistent upward trajectory.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Residential Real Estate Transactions.

The India real estate market size attained a value of USD 570.40 Billion in 2024 and is projected to expand at a CAGR of around 8.70% through 2034. Rapid smart city developments, government incentives and increased FDI inflows are propelling the market to achieve USD 1313.64 Billion by 2034.

Key Market Trends and Insights:

• The North India real estate market dominated the market in 2024 and is projected to grow at a CAGR of 9.6% over the forecast period.
• By property, the residential segment is expected to register 10.8% CAGR over the forecast period.
• By type, sales is expected to register 12.1% CAGR over the forecast period due to the affordable housing schemes and growing urban populations.

Market Size & Forecast:

• Market Size in 2024: USD 570.40 Billion
• Projected Market Size in 2034: USD 1313.64 Billion
• CAGR from 2025-2034: 8.70%
• Fastest-Growing Regional Market: North India

Rapid urbanization is driving the popularity of real estate in India, particularly in Tier 1 and Tier 2 cities. According to the United Nations, 60 million Indian residents are expected to reside in cities by 2030. Government initiatives like Smart Cities Mission, Bharatmala, and Metro rail expansions are improving urban infrastructure and enhancing real estate value in peripheral areas. Better roads, connectivity, and amenities make these regions attractive for residential and commercial development.

Policy initiatives like RERA (Real Estate Regulatory Authority), GST, Benami Transactions Act, and PMAY have brought structure and accountability to the India real estate market. RERA has increased buyer confidence by mandating project registration, timely delivery, and clear legal documentation. GST helped simplify the tax regime, though its impact varies by segment. Foreign Direct Investment (FDI) norms are encouraging global players to enter Indian real estate space. These reforms have set a more transparent, regulated environment conducive to long-term investment and sustainable growth.

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]

Irambati is a leading real estate company that provides property listings and market insights to individuals seeking to buy, sell, or rent properties in India. As a prominent player in the Indian real estate market, Irambati's website features a vast array of data on residential and commercial properties, including prices, locations, and amenities.

The company's data repository includes a wide range of property types, from apartments to independent houses, and covers major cities and towns across India. Irambati's expertise in the Indian real estate market, combined with its vast database of property listings, makes it an invaluable resource for anyone seeking to make an informed decision about their property needs.

The India luxury residential real estate market was valued at USD 36.73 Billion in 2024. The industry is expected to grow at a CAGR of 20.10% during the forecast period of 2025-2034. The expansion of wealthy population, rapid urbanisation, emergence of smart homes, and rise in housing projects have resulted in the market likely attaining a valuation of USD 229.32 Billion by 2034.

The India Office Real Estate Market Report is Segmented by Building Grade (Grade A, Grade B, Grade C), by Transaction Type (Rental, Sales), by End Use (IT & ITES, BFSI, Business Consulting & Professional Services, Other Services), and by Geography (Mumbai Metropolitan Region, Delhi NCR, Pune, Bengaluru, Hyderabad, Chennai, Kolkata, Rest of India). The Market Forecasts are Provided in Terms of Value (USD).