9 datasets found
  1. Rental Pricing Dataset, Malaysia

    • kaggle.com
    zip
    Updated Mar 21, 2023
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    ariewijaya (2023). Rental Pricing Dataset, Malaysia [Dataset]. https://www.kaggle.com/datasets/ariewijaya/rent-pricing-kuala-lumpur-malaysi
    Explore at:
    zip(642175 bytes)Available download formats
    Dataset updated
    Mar 21, 2023
    Authors
    ariewijaya
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Malaysia
    Description

    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/ property
    • completion_year: completion/ established year of the property
    • monthly_rent: monthly rent in ringgit malaysia (RM)
    • location: property location in Kuala Lumpur region
    • property_type:property type such as apartment, condominium, flat, duplex, studio, etc
    • rooms: number of rooms in the unit
    • parking: number of parking space for the unit
    • bathroom: number of bathrooms in the unit
    • size: total area of the unit in square feet
    • furnished: furnishing status of the unit (fully, partial, non-furnished)
    • facilities: main facilities available
    • additional_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:

    • What are the biggest factor affecting the unit/rent pricing?
    • Which location in Kuala Lumpur/ Selangor region that has the highest rent price? etc?
  2. Reasons to continue renting a house among consumers in Malaysia 2024

    • statista.com
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    Statista, Reasons to continue renting a house among consumers in Malaysia 2024 [Dataset]. https://www.statista.com/statistics/1447497/malaysia-reasons-to-rent/
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    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jun 4, 2024 - Jun 26, 2024
    Area covered
    Malaysia
    Description

    According 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.

  3. House Prices in Malaysia (2025)

    • kaggle.com
    zip
    Updated Jan 3, 2025
    + more versions
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    Jien Weng (2025). House Prices in Malaysia (2025) [Dataset]. https://www.kaggle.com/datasets/lyhatt/house-prices-in-malaysia-2025
    Explore at:
    zip(39697 bytes)Available download formats
    Dataset updated
    Jan 3, 2025
    Authors
    Jien Weng
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Area covered
    Malaysia
    Description

    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">

    Data Columns (Total 8 Columns):

    1. Township: The specific township where the property is located (e.g., Cheras, Subang Jaya).
    2. Area: The locality or broader area encompassing the township (e.g., Klang Valley, Penang Island).
    3. State: The Malaysian state where the property is situated (e.g., Selangor, Johor, Penang).
    4. Tenure: The property ownership type (e.g., Freehold, Leasehold).
    5. Type: The category of property (e.g., Terrace, Condominium, Semi-Detached).
    6. Median_Price: The median price (in MYR) for properties in the specified township or area.
    7. Median_PSF: The median price per square foot (in MYR) for properties.
    8. Transactions: The number of recorded property transactions.

    Future Plans:

    • Expanded Coverage: This dataset will be regularly updated with additional property data to make it even more versatile.
    • Enhanced Features: Future updates may include rental prices, amenities, or property-specific details to offer deeper insights into Malaysia’s housing market.
  4. House-price-to-income ratio in selected countries worldwide 2024

    • statista.com
    Updated Nov 29, 2025
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    Statista (2025). House-price-to-income ratio in selected countries worldwide 2024 [Dataset]. https://www.statista.com/statistics/237529/price-to-income-ratio-of-housing-worldwide/
    Explore at:
    Dataset updated
    Nov 29, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2024
    Area covered
    Worldwide
    Description

    Portugal, 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.

  5. a

    Kuala Lumpur, Airbnb Revenue Data 2025: Average Income & ROI

    • airbtics.com
    Updated Oct 3, 2025
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    Airbtics (2025). Kuala Lumpur, Airbnb Revenue Data 2025: Average Income & ROI [Dataset]. https://airbtics.com/annual-airbnb-revenue-in-kuala-lumpur-malaysia/
    Explore at:
    Dataset updated
    Oct 3, 2025
    Dataset authored and provided by
    Airbtics
    Time period covered
    Sep 2024 - Aug 2025
    Area covered
    Federal Territory of Kuala Lumpur
    Variables measured
    yield, annualRevenue, occupancyRate, averageDailyRate, numberOfListings, regulationStatus
    Description

    See 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.

  6. a

    Kota Kinabalu, Airbnb Revenue Data 2025: Average Income & ROI

    • airbtics.com
    Updated Oct 3, 2025
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    Airbtics (2025). Kota Kinabalu, Airbnb Revenue Data 2025: Average Income & ROI [Dataset]. https://airbtics.com/annual-airbnb-revenue-in-kota-kinabalu-malaysia/
    Explore at:
    Dataset updated
    Oct 3, 2025
    Dataset authored and provided by
    Airbtics
    Time period covered
    Sep 2024 - Aug 2025
    Area covered
    Kota Kinabalu
    Variables measured
    yield, annualRevenue, occupancyRate, averageDailyRate, numberOfListings, regulationStatus
    Description

    See 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.

  7. a

    Johor Bahru, Airbnb Revenue Data 2025: Average Income & ROI

    • airbtics.com
    Updated Oct 3, 2025
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    Airbtics (2025). Johor Bahru, Airbnb Revenue Data 2025: Average Income & ROI [Dataset]. https://airbtics.com/annual-airbnb-revenue-in-johor-bahru-malaysia/
    Explore at:
    Dataset updated
    Oct 3, 2025
    Dataset authored and provided by
    Airbtics
    Time period covered
    Sep 2024 - Aug 2025
    Area covered
    Johor Bahru
    Variables measured
    yield, annualRevenue, occupancyRate, averageDailyRate, numberOfListings, regulationStatus
    Description

    See 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.

  8. a

    Petaling Jaya, Airbnb Revenue Data 2025: Average Income & ROI

    • airbtics.com
    Updated Oct 3, 2025
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    Airbtics (2025). Petaling Jaya, Airbnb Revenue Data 2025: Average Income & ROI [Dataset]. https://airbtics.com/annual-airbnb-revenue-in-petaling-jaya-malaysia/
    Explore at:
    Dataset updated
    Oct 3, 2025
    Dataset authored and provided by
    Airbtics
    Time period covered
    Sep 2024 - Aug 2025
    Area covered
    Petaling Jaya, Petaling District
    Variables measured
    yield, annualRevenue, occupancyRate, averageDailyRate, numberOfListings, regulationStatus
    Description

    See 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.

  9. a

    Melaka, Airbnb Revenue Data 2025: Average Income & ROI

    • airbtics.com
    Updated Oct 3, 2025
    Share
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    Airbtics (2025). Melaka, Airbnb Revenue Data 2025: Average Income & ROI [Dataset]. https://airbtics.com/annual-airbnb-revenue-in-melaka-malaysia/
    Explore at:
    Dataset updated
    Oct 3, 2025
    Dataset authored and provided by
    Airbtics
    Time period covered
    Sep 2024 - Aug 2025
    Area covered
    Malacca
    Variables measured
    yield, annualRevenue, occupancyRate, averageDailyRate, numberOfListings, regulationStatus
    Description

    See the average Airbnb revenue & other vacation rental data in Melaka in 2025 by property type & size, powered by Airbtics. Find top locations for investing.

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Click to copy link
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ariewijaya (2023). Rental Pricing Dataset, Malaysia [Dataset]. https://www.kaggle.com/datasets/ariewijaya/rent-pricing-kuala-lumpur-malaysi
Organization logo

Rental Pricing Dataset, Malaysia

About 10k data on rental pricing surrounding Kuala Lumpur and Selangor, Malaysia

Explore at:
zip(642175 bytes)Available download formats
Dataset updated
Mar 21, 2023
Authors
ariewijaya
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Area covered
Malaysia
Description

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/ property
  • completion_year: completion/ established year of the property
  • monthly_rent: monthly rent in ringgit malaysia (RM)
  • location: property location in Kuala Lumpur region
  • property_type:property type such as apartment, condominium, flat, duplex, studio, etc
  • rooms: number of rooms in the unit
  • parking: number of parking space for the unit
  • bathroom: number of bathrooms in the unit
  • size: total area of the unit in square feet
  • furnished: furnishing status of the unit (fully, partial, non-furnished)
  • facilities: main facilities available
  • additional_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:

  • What are the biggest factor affecting the unit/rent pricing?
  • Which location in Kuala Lumpur/ Selangor region that has the highest rent price? etc?
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