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Bangkok Condominium Market Trends and Pricing Data
Comodities & Real Estate
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About
Bangkok stands as a premier tourist destination in Southeast Asia with a rapidly expanding condominium market. This collection aggregates web-scraped data from hipflat.com to facilitate the analysis and prediction of housing prices within the city. Providing a granular view of the real estate landscape, the data encompasses essential variables such as project area, unit density, and building age, alongside critical location-based metrics like proximity to hospitals, schools, shops, and mass transit systems. It serves as a vital resource for determining investment potential and identifying price trends in the Thai capital.
Columns
- district: The specific district in Bangkok where the condominium is situated.
- latitude: Geographic latitude coordinate.
- longitude: Geographic longitude coordinate.
- price_sqm: The price per square metre.
- year_built: The year the building was constructed.
- bld_age: Age of the condominium building.
- proj_area: Total project area.
- nbr_buildings: Total number of buildings in the project.
- nbr_floors: Number of floors in the building.
- units: Total number of units available.
- hospital: Distance to the nearest hospital.
- dist_shop_1 - dist_shop_5: Distances to the five nearest shops.
- dist_school_1 - dist_school_5: Distances to the five nearest schools.
- dist_food_1 - dist_food_5: Distances to the five nearest food outlets.
- dist_tran_1 - dist_tran_5: Distances to the five nearest transportation hubs.
- tran_type / tran_name: Type and name of transportation options available.
- Facilities (Binary/Count): Includes Elevator, Parking, Security, CCTV, Pool, Sauna, Gym, Garden, Playground, Shop, Restaurant, and Wifi.
- Financial Metrics: Includes
rental_yield,change_last_q(quarterly price change),change_last_y(yearly price change), andprice_hist(price history).
Distribution
The data is provided in CSV format, split across multiple files for different stages of analysis, such as
df_cleaned_for_ML_regression.csv for machine learning tasks and df_completed.csv for full analysis. It includes feature importance files like GBoost_best_feature_importances_.csv. The expected update frequency is set to 'Never'.Usage
- Real Estate Valuation: Training machine learning models to predict condominium prices based on physical attributes and location.
- Investment Analysis: Calculating rental yields and historical price changes to identify high-potential investment opportunities.
- Urban Planning: Analysing the relationship between residential costs and proximity to public amenities like transport, healthcare, and education.
- Market Trend Identification: Determining which features (e.g., proximity to mass transit vs. building amenities) most significantly impact property value in Bangkok.
Coverage
- Geographic: Bangkok, Thailand.
- Demographic: Condominium projects and potential buyers/investors.
- Temporal: Includes construction dates (
year_built) and data collection dates (date). - Scope: Covers physical building specs, amenity counts, and geospatial distance calculations to urban infrastructure.
License
CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
Who Can Use It
- Data Scientists: For developing regression models and predictive algorithms.
- Real Estate Investors: For comparative market analysis and ROI estimation.
- Property Developers: For benchmarking new projects against existing market data.
- Urban Researchers: For studying the impact of infrastructure on housing costs.
Dataset Name Suggestions
- Bangkok Condominium Market Trends and Pricing Data
- Thai Capital Real Estate Prediction Set
- Bangkok Housing Features and Amenities Registry
- Hipflat Condo Investment Analytics Data
Attributes
Original Data Source: Bangkok Condominium Market Trends and Pricing Data
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