Dark Mode
India Housing Characteristics Data
Stock & Market Data
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This dataset offers a detailed exploration of the Indian housing market, providing insights into living conditions and property characteristics across India. It encompasses crucial information such as the number of bedrooms, kitchens, and washrooms, alongside furnishing status, locality details, and parking availability. A key feature of the dataset is its focus on understanding the relationship between property prices and various other attributes, making it valuable for analysing housing preferences and market dynamics. The data has undergone exploratory analysis and feature engineering to enhance its usability, allowing for easier understanding through visualisation methods.
Columns
- Area: Indicates the general geographic area within India. This column has 1259 valid entries.
- BHK: Describes the configuration of the house, representing the number of Bedroom, Kitchen, and House units. Values typically range from 1 to 10, with a mean of 2.8.
- Bathroom: Specifies the number of bathrooms available in the property. Values generally range from 1 to 7, with a mean of 2.56. Two entries are missing from this column.
- Furnishing: Details the furnishing status of the property, primarily categorised as Semi-Furnished (56%), Unfurnished (29%), or Other (15%). Five entries are missing.
- Locality: Provides specific local area information, with examples including Lajpat Nagar 3 and Lajpat Nagar 2. This column features 365 unique localities.
- Parking: Describes the availability and type of parking facilities. Values vary, with a mean of 1.94. Thirty-three entries are missing.
- Price: Represents the property price. Values range from 1,000,000 to 240,000,000, with a mean of 21.3 million.
- Status: Indicates the current status of the property, predominantly Ready_to_move (94%) or Almost_ready (6%).
- Transaction: Specifies the nature of the property transaction, either Resale (62%) or New_Property (38%).
- Type: Categorises the property type, primarily Builder_Floor (53%) or Apartment (47%). Five entries are missing.
- Per_Sqft: Provides the price per square foot. Values range from 1,259 to 183,000, with a mean of 15,700. Two hundred forty-one entries are missing.
Distribution
This dataset is provided in a CSV file format, named "IndianHouses.csv", with a file size of 157.58 kB. It consists of 11 columns and, for most fields, includes 1259 records. As noted in the column descriptions, some fields contain a small percentage of missing values.
Usage
This dataset is ideal for:
- Conducting in-depth analyses of the Indian housing market.
- Understanding the factors influencing property prices.
- Exploring regional housing trends and preferences.
- Developing predictive models for property valuation.
- Analysing living standards and facility requirements in Indian households.
Coverage
The dataset specifically covers housing information within India, focusing on various localities, including detailed statistics for areas such as Lajpat Nagar. It captures different aspects of property characteristics and resident needs, reflecting a broad demographic scope of Indian people. While a specific time range for data collection is not provided, the data reflects housing market conditions. Records total 1259 values, with some columns showing a small percentage of missing data.
License
CC0: Public Domain
Who Can Use It
This dataset is particularly useful for:
- Data Scientists and Analysts: For exploratory data analysis, feature engineering, and predictive modelling in real estate.
- Researchers: Studying social issues related to housing, urban development, and living standards in India.
- Real Estate Professionals: Gaining market insights, understanding property trends, and assessing valuation factors.
- Policymakers: Informing decisions related to urban planning and housing policies.
Dataset Name Suggestions
- Indian Property Market Analysis
- India Housing Characteristics Data
- Indian Residential Property Insights
- India Home Ownership Patterns
- Indian Real Estate Demographics
Attributes
Original Data Source: India Housing Characteristics Data
QUALITY