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Real Estate Valuation Benchmarks for Mumbai
Comodities & Real Estate
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About
Residential real estate listings in Mumbai provide a detailed perspective on one of India’s most active and varied housing markets. Scraped from housing.com for educational use, this collection of apartment data enables the exploration of pricing strategies, market trends, and demographic shifts across the city. By capturing essential property characteristics, it serves as a foundation for building regression models to forecast property values or evaluate rental potential in a dynamic urban environment. The information helps in identifying how specific features and locations influence the cost of living in a major economic hub.
Columns
- flat_type: Indicates the configuration of the residence, such as a 1 BHK or 2 BHK flat.
- flat_price: The listed monetary value of the property, providing a target for financial modelling.
- location1: The specific neighbourhood or area within Mumbai where the flat is situated, featuring 80 unique localities like Mira Road East and Chembur.
- buildupArea_sqft: The total square footage of the property, representing its physical size.
- age_of_property: The maturity of the building, categorised into stages such as new construction or recent construction.
- furnishing: The status of internal fittings, including semi-furnished, unfurnished, or fully furnished options.
- bedrooms: The total number of bedrooms available in the unit.
- bathrooms: The number of bathrooms included in the property.
- balcony: The count of balconies associated with the flat.
- parking: A boolean value indicating the availability of parking spaces for the resident.
Distribution
The records are delivered in a CSV format titled
data_for_model.csv with a file size of 565.57 kB. The collection contains 7,483 valid records across 10 distinct columns. The data maintains a high level of integrity with 100% validity for the primary fields, showing no missing or mismatched entries. This resource is provided as a static snapshot, with the update frequency set to never.Usage
This resource is ideal for developing regression models to predict apartment prices based on physical and geographic attributes. It can be used for market trend analysis, competitive pricing benchmarks, and demographic studies focused on urban housing. Researchers and students can apply the data to practice advanced data cleaning and statistical analysis techniques within the context of the Indian real estate sector.
Coverage
The geographic scope is specific to the city of Mumbai, India, encompassing 80 unique residential areas. The data represents a wide range of flat configurations and pricing brackets found within the current market. No specific time range is explicitly provided beyond the data being scraped to represent current market dynamics at the time of collection.
License
CC0: Public Domain
Who Can Use It
Students and educators can utilise these records for practical learning in regression and predictive analytics. Real estate professionals and analysts can leverage the data to understand regional pricing variations across Mumbai's diverse neighbourhoods. Additionally, data scientists can use the structured format for benchmarking machine learning algorithms against real-world property metrics.
Dataset Name Suggestions
- Mumbai Residential Property Pricing and Regression Index
- Urban Housing Trends: Mumbai Apartment Market Data
- Mumbai Flat Listings and Geographic Price Metrics
- Real Estate Valuation Benchmarks for Mumbai
Attributes
Original Data Source: Real Estate Valuation Benchmarks for Mumbai
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