Urban Property Data Bangladesh

Columns

• Title: The title or description associated with the property listing.
• Bedrooms: Specifies the number of bedrooms present in the property.
• Bathrooms: Indicates the number of bathrooms available in the property.
• Floor_no: Denotes the specific floor level on which the property is situated.
• Occupancy_status: Describes whether the property is currently vacant or occupied.
• Floor_area: Represents the total floor area of the property, measured in square feet.
• City: The city where the property is located, with listings from Dhaka, Chattogram, Cumilla, Narayanganj City, and Gazipur.
• Price_in_taka: The listed price of the property, denominated in Bangladeshi Taka (৳).
• Location: The precise location or address within the respective city.

Distribution

Usage

• Market Analysis: To understand pricing trends and identify which cities or neighbourhoods are more expensive or affordable.
• Investment Decisions: To evaluate potential real estate investments by comparing properties based on price, size, and location across different cities.
• Real Estate Valuation: For property developers and agents to assess the market value of similar properties, helping to set competitive prices for new developments or resale properties.
• Price Prediction: Machine learning models can be trained using features like floor area, number of bedrooms, and location to predict property prices, offering guidance for buyers and sellers.
• Clustering: Properties can be clustered based on features like location, size, and price to identify distinct property segments or neighbourhoods with similar characteristics.
• Demand Forecasting: Analysing trends over time can help predict future demand for housing, beneficial for real estate developers and policymakers.
• Anomaly Detection: To identify properties that are significantly over- or under-priced compared to similar properties, highlighting potential market issues or opportunities.

Coverage

• Bedrooms and Bathrooms columns have approximately 26% of their data missing.
• The Floor_no column has about 18% of its data missing.
• Occupancy_status and Floor_area columns each have approximately 3% of their data missing.
• The Location column has a negligible amount of missing data (less than 1%).

License

Who Can Use It

• Market Analysts: To gain insights into pricing trends and market dynamics.
• Investors: For evaluating potential real estate investments.
• Property Developers and Agents: For assessing market values and setting competitive prices.
• Buyers and Sellers: For obtaining price guidance and market understanding.
• Policymakers: For forecasting future housing demand and making informed decisions.
• Data Scientists and Machine Learning Practitioners: For developing predictive models, clustering analyses, and anomaly detection systems.

Dataset Name Suggestions

• Bangladesh Property Listings
• Bangladeshi Housing Market Data
• Bangladesh Real Estate Price Index
• Urban Property Data Bangladesh
• Bangladesh Property Insights

Attributes