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Metropolitan India Housing Market Metrics
Comodities & Real Estate
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About
Analysing the real estate landscape across India’s major urban hubs provides vital insights into property valuation for both rental and purchase markets. This collection of records, gathered from the Magicbricks platform, details house prices alongside a vast array of 91 explanatory variables. By linking fiscal data with specific property characteristics such as available amenities—including parking, swimming pools, and gymnasiums—it offers a foundation for understanding the factors that drive housing costs in metropolitan regions. While some entries use a placeholder value to indicate missing information, the sheer volume of metrics makes it a suitable resource for exploring the nuances of the Indian residential market.
Columns
- exactPrice: The specific total cost of the house or property.
- sqftPrice: The calculated price per square foot for the listing.
- securityDeposit: The financial amount required as a deposit for the property.
- propertyType: The category of the listing, such as a multistorey apartment or a residential house.
- postedOn: The specific date when the property listing was first published.
- noOfLifts: The count of elevators or lifts accessible within the building.
- maintenanceChargesFrequency: How often maintenance fees are collected (e.g., monthly or annually).
- maintenanceCharges: The specific cost associated with the upkeep of the property.
- locality: The specific neighbourhood or district where the property is situated.
- furnishing: The level of interior fit-out provided, ranging from unfurnished to fully furnished.
Distribution
The information is delivered in a single CSV file titled
Scraped_Data.csv, with a file size of approximately 11.53 MB. It contains approximately 27,900 valid records structured across 91 different columns. The core financial fields show a 100% validity rate with no mismatched entries. This resource has a usability score of 10.00 and is a static archive, with no future updates expected.Usage
This resource is ideal for developing regression models to predict property prices based on local and structural features. It is well-suited for researchers investigating the economic impact of specific amenities on housing valuations in emerging markets. Additionally, it serves as an excellent training tool for data science students to practice feature engineering and exploratory data analysis on real-world scraped data.
Coverage
The geographic scope is focused on metropolitan areas within India. Temporally, the records capture market conditions as of mid-2023, with listing dates spanning various months. The data encompasses a wide variety of property types, though multistorey apartments and residential houses form the majority of the entries.
License
CC0: Public Domain
Who Can Use It
Real estate analysts can leverage these records to benchmark property trends across different Indian cities. Data science learners may utilise the 91 variables to practice building complex predictive algorithms for house prices. Furthermore, urban researchers can use the locality and amenity data to study the distribution of modern housing facilities in Indian metropolitan centres.
Dataset Name Suggestions
- Indian Metropolitan House Prices and Amenities (2023)
- Magicbricks Real Estate Registry: Indian Urban Housing
- India Residential Property Price and Feature Data
- Metropolitan India Housing Market Metrics
- Indian Urban Real Estate: Price Prediction and Amenity Log
Attributes
Original Data Source: Metropolitan India Housing Market Metrics
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