Delhi Rental Housing Price Dataset

This dataset contains structured rental property listings including attributes such as locality, property type, number of bedrooms, area size, furnishing status, and monthly rent. The dataset has been processed through a structured exploratory data analysis workflow including duplicate removal, locality name normalization, rent value range validation, and categorical standardization. Numeric attributes such as rental price and area were validated to eliminate unrealistic entries. Outlier detection techniques were applied to identify extreme rent values. The final dataset is exported in a clean CSV format optimized for regression modeling, rental price prediction, and market analytics.

Data Quality

The dataset is organized in a normalized tabular structure with consistent column naming and validated numeric data types. Duplicate property listings have been removed to preserve integrity. Rental price values have undergone statistical validation to eliminate unrealistic outliers. Area measurements have been range-checked for logical consistency. Categorical fields such as furnishing status and property type have been standardized to ensure analytical clarity. The dataset is ready for regression modeling and real estate analytics workflows.

Use Cases

This dataset can be used for rental price prediction modeling, locality-based rent comparison, and real estate market trend analysis. Analysts can evaluate how property features such as size, number of rooms, and furnishing status influence rental prices. Data scientists can build machine learning models for rental valuation. Real estate professionals can use it for pricing strategy research and dashboard development. It also supports urban housing affordability analysis and investment decision research.

Limitations

The dataset is community-sourced and may not represent official property registry records. It may reflect listing prices rather than finalized rental transaction prices. Geographic coverage is limited to Delhi and may not represent national housing trends. The dataset is intended for analytical and research purposes and should not replace official real estate market data sources.

Methodology

The dataset was prepared using a structured validation and normalization framework including schema standardization, duplicate removal, numeric validation of rental prices and area fields, and categorical normalization of property attributes. Statistical anomaly detection was applied to identify extreme rental listings. Locality names were standardized to ensure geographic consistency. The final dataset was exported in UTF-8 encoded CSV format to ensure compatibility with machine learning libraries, statistical modeling tools, and business intelligence platforms.