Facebook
TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
This dataset contains information on rent pricing surrounding Kuala Lumpur and Selangor region, Malaysia. The information was scraped from mudah.my
Content
There are 13 features with one unique ids (ads_id) and one target feature (monthly_rent)
ads_id: the listing ids (unique)prop_name: name of the building/ propertycompletion_year: completion/ established year of the propertymonthly_rent: monthly rent in ringgit malaysia (RM)location: property location in Kuala Lumpur regionproperty_type:property type such as apartment, condominium, flat, duplex, studio, etcrooms: number of rooms in the unitparking: number of parking space for the unitbathroom: number of bathrooms in the unitsize: total area of the unit in square feetfurnished: furnishing status of the unit (fully, partial, non-furnished)facilities: main facilities availableadditional_facilities: additional facilities (proximity to attraction area, mall, school, shopping, railways, etc)Acknowledgements The data was scraped from mudah.my
Inspiration I have been living in Kuala Lumpur, Malaysia since 2017, and in the past there was no easy way to understand whether certain unit pricing is making sense or not. With this dataset, I wanted to be able to answer the following questions:
Facebook
TwitterAccording to a survey among consumers in Malaysia on property ownership, as of June 2024, ** percent of respondents said they continued to rent a property because they don't have enough savings to buy. Meanwhile, ** percent of respondents said they didn't feel the urgency to purchase a house now.
Facebook
TwitterMIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
This dataset contains 2,000 entries of house price data from all states in Malaysia, providing a comprehensive overview of the country’s real estate market for 2025. Sourced from Brickz, a trusted platform for property transaction insights, it includes detailed information such as property location, tenure, type, median prices, and transaction counts. This dataset is ideal for real estate market analysis, predictive modeling, and exploring trends across Malaysia’s diverse property market.
https://encrypted-tbn1.gstatic.com/licensed-image?q=tbn:ANd9GcR8ttDRWTx7dIxuUegBTsggS4a6tQrnNA6DEW_HJu2DphQNsverV0PYsSkdbSdqm4qRaRuBOh4Txbv11yXMxIKWqh-_WAkeTuQI8Diu-Q" alt="Kuala Lumpur, Malaysia">
Facebook
TwitterPortugal, Canada, and the United States were the countries with the highest house price to income ratio in 2024. In all three countries, the index exceeded 130 index points, while the average for all OECD countries stood at 116.2 index points. The index measures the development of housing affordability and is calculated by dividing nominal house price by nominal disposable income per head, with 2015 set as a base year when the index amounted to 100. An index value of 120, for example, would mean that house price growth has outpaced income growth by 20 percent since 2015. How have house prices worldwide changed since the COVID-19 pandemic? House prices started to rise gradually after the global financial crisis (2007–2008), but this trend accelerated with the pandemic. The countries with advanced economies, which usually have mature housing markets, experienced stronger growth than countries with emerging economies. Real house price growth (accounting for inflation) peaked in 2022 and has since lost some of the gain. Although, many countries experienced a decline in house prices, the global house price index shows that property prices in 2023 were still substantially higher than before COVID-19. Renting vs. buying In the past, house prices have grown faster than rents. However, the home affordability has been declining notably, with a direct impact on rental prices. As people struggle to buy a property of their own, they often turn to rental accommodation. This has resulted in a growing demand for rental apartments and soaring rental prices.
Facebook
TwitterSee the average Airbnb revenue & other vacation rental data in Kuala Lumpur in 2025 by property type & size, powered by Airbtics. Find top locations for investing.
Facebook
TwitterSee the average Airbnb revenue & other vacation rental data in Kota Kinabalu in 2025 by property type & size, powered by Airbtics. Find top locations for investing.
Facebook
TwitterSee the average Airbnb revenue & other vacation rental data in Johor Bahru in 2025 by property type & size, powered by Airbtics. Find top locations for investing.
Facebook
TwitterSee the average Airbnb revenue & other vacation rental data in Petaling Jaya in 2025 by property type & size, powered by Airbtics. Find top locations for investing.
Facebook
TwitterSee the average Airbnb revenue & other vacation rental data in Melaka in 2025 by property type & size, powered by Airbtics. Find top locations for investing.
Not seeing a result you expected?
Learn how you can add new datasets to our index.
Facebook
TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
This dataset contains information on rent pricing surrounding Kuala Lumpur and Selangor region, Malaysia. The information was scraped from mudah.my
Content
There are 13 features with one unique ids (ads_id) and one target feature (monthly_rent)
ads_id: the listing ids (unique)prop_name: name of the building/ propertycompletion_year: completion/ established year of the propertymonthly_rent: monthly rent in ringgit malaysia (RM)location: property location in Kuala Lumpur regionproperty_type:property type such as apartment, condominium, flat, duplex, studio, etcrooms: number of rooms in the unitparking: number of parking space for the unitbathroom: number of bathrooms in the unitsize: total area of the unit in square feetfurnished: furnishing status of the unit (fully, partial, non-furnished)facilities: main facilities availableadditional_facilities: additional facilities (proximity to attraction area, mall, school, shopping, railways, etc)Acknowledgements The data was scraped from mudah.my
Inspiration I have been living in Kuala Lumpur, Malaysia since 2017, and in the past there was no easy way to understand whether certain unit pricing is making sense or not. With this dataset, I wanted to be able to answer the following questions: