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Malaysian Housing Attributes and Rent Data
Data Science and Analytics
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About
This collection offers detailed information on rent pricing surrounding the Kuala Lumpur and Selangor regions in Malaysia. The raw data was compiled via scraping from the popular classifieds platform, mudah.my. It is structured to facilitate market analysis, containing 13 distinct features, one unique listing identifier (
ads_id), and one key target feature (monthly_rent). The product is designed to enable users to determine the major factors influencing unit rental costs and identify geographical areas experiencing the highest average prices.Columns
The dataset includes the following key fields:
- ads_id: A unique identifier for the property listing.
- prop_name: The official name of the property or building.
- completion_year: The year the property was established or completed.
- monthly_rent: The rental amount charged per month, denominated in Ringgit Malaysia (RM).
- location: The specific sub-location of the property within the Kuala Lumpur or Selangor region.
- property_type: Classification of the dwelling, such as condominium, apartment, studio, flat, or service residence.
- rooms: The total number of rooms in the rental unit.
- parking: The quantity of designated parking spaces available for the unit.
- bathroom: The number of bathrooms available in the unit.
- size: The total area of the unit, measured in square feet.
- furnished: Describes the state of furnishing (e.g., fully furnished, partially furnished, non-furnished).
- facilities: A list of the main amenities provided (e.g., security, pool, gymnasium).
- additional_facilities: Notes on extra amenities, potentially detailing proximity to attractions or inclusion of appliances like air conditioning.
- region: Categorises the listing into the primary areas of Kuala Lumpur or Selangor.
Distribution
The data is provided in a tabular format, typically a CSV file (e.g.,
mudah-apartment-kl-selangor.csv), with a file size of approximately 5.01 MB. The structure consists of 14 columns and contains an estimated 20,000 records. Users should note that the dataset is static; the expected update frequency is listed as "Never."Usage
This data product is ideally suited for various real estate and economic research applications, including:
- Predictive Analytics: Developing machine learning models to forecast monthly rental prices based on property features and location.
- Market Research: Analysing which attributes (e.g., size, number of rooms, facilities) have the largest influence on rental cost.
- Geographic Analysis: Mapping and comparing rental price variations across different locations in the Kuala Lumpur and Selangor areas.
Coverage
The geographic scope is focused exclusively on the Kuala Lumpur and Selangor regions of Malaysia. The time span of the properties included is wide, covering buildings with completion years ranging from the late 1970s up to listings for properties established in the 2020s.
License
Attribution 4.0 International (CC BY 4.0)
Who Can Use It
This product serves a diverse audience, including:
- Data Scientists: For training and evaluating regression models focused on price prediction.
- Real Estate Developers and Investors: To gain insight into current market trends, pricing strategies, and regional demand dynamics.
- Academics and Students: For economic studies focused on the Malaysian housing market, urban economics, and geographic influences on housing costs.
Dataset Name Suggestions
- Kuala Lumpur and Selangor Rental Price Index
- Malaysia Residential Property Rental Rates
- mudah.my Scraped Rental Listings (KL/Selangor)
- Malaysian Housing Attributes and Rent Data
Attributes
Original Data Source: Malaysian Housing Attributes and Rent Data
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