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Inspired by the quintessential House Prices Starter Competition and the popular Melbourne Housing Dataset, this dataset captures 4K+ condominium unit listings on the Malaysian housing website mudah.my.
Like the above datasets, your job is to predict the house prices given certain parameters.
The data was scraped directly from the website using this data collection notebook. I might adapt the code to include houses as well in the future, but scraping the data takes a while due to having to wait for the website to load and having to timeout to account for CloudFlare's protections.
Note: This data is a lot less clean and organized than the data in the two datasets mentioned above. However, this is a good opportunity to practice data cleaning techniques, as this is something that is often overlooked on Kaggle. That being said, I made a starter notebook that goes through the data cleaning steps and outputs a fairly cleaned version of the dataset.
description: The full (unfiltered) description for the unit listing.Ad List: The ID of the listing on the website.Category: The category of the listing. It will most likely be Apartment / Condominium.Facilities: The facilities that the apartment has, in a comma-separated list.Building Name: The name of the building.Developer: The developer for the building.Tenure Type: The type of tenure for the building.Address: The address of the building. You can refer to this link for a description of what Malaysian addresses look like.Completion Year: The completion year of the building. If the building is still under construction, this is listed as -.# of Floors: The number of floors in the building.Total Units: The total number of units in the building.Property Type: The type of property.Bedroom: The number of bedrooms in the unit.Bathroom: The number of bathrooms in the unit.Parking Lot: The number of parking lots assigned to the unit, if any.Floor Range: The floor range for the building.Property Size: The size of the unit.Land Title: The title given to the land. This link explains what land titles are.Firm Type: The type of firm who posted the listing.Firm Number: The ID of the firm who posted the listing.REN Number: The REN number of the firm who posted the listing. Refer to this link for what REN numbers are.price: The price of the unit. This is what you are trying to predict.Nearby School/School: If there is a nearby school to the unit, which school it is.Park: If there is a nearby park to the unit, which park it is.Nearby Railway Station: If there is a nearby railway station to the unit, which railway station it is.Bus Stop: If there is a nearby bus stop to the unit, which station it is.Nearby Mall/Mall: If there is a nearby mall to the unit, which mall it is.Highway: If there is a nearby highway to the unit, which highway it is.
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The Malaysia Real Estate Market Report is Segmented by Business Model (Sales and Rental), by Property Type (Residential and Commercial), by End-User (Individuals/Households, Corporates & SMEs and Others), and by Key Cities (Kuala Lumpur, Penang, Johor Bahru, Petaling Jaya and the Rest of Malaysia). The Market Forecasts are Provided in Terms of Value (USD).
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The Malaysia Residential Real Estate Market Report is Segmented by Business Model (Sales and Rental), by Property Type (Apartments & Condominiums and Villas & Landed Houses), by Price Band (Affordable, Mid-Market and Luxury), by Mode of Sale (Primary (New-Build) and Secondary (Existing-Home Resale)), and by Key Cities (Kuala Lumpur, Penang, and More). The Market Forecasts are Provided in Terms of Value (USD).
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Housing Index in Malaysia decreased to 224.20 Index in the fourth quarter of 2024 from 228.30 Index in the third quarter of 2024. This dataset provides - Malaysia House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.
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Residential Property Prices in Malaysia increased 0.71 percent in June of 2025 over the same month in the previous year. This dataset includes a chart with historical data for Malaysia Residential Property Prices.
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Graph and download economic data for Residential Property Prices for Malaysia (QMYN628BIS) from Q1 1988 to Q2 2025 about Malaysia, residential, HPI, housing, price index, indexes, and price.
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Key information about House Prices Growth
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The Malaysian residential property market, valued at RM 22.41 billion in 2025, is projected to experience robust growth, exhibiting a Compound Annual Growth Rate (CAGR) of 5.90% from 2025 to 2033. This positive outlook is driven by several key factors. Firstly, a growing population and increasing urbanization are fueling demand for housing, particularly in major cities like Kuala Lumpur and Johor Bahru. Secondly, government initiatives aimed at improving affordability and access to housing, such as affordable housing schemes and loan programs, are stimulating market activity. Furthermore, ongoing infrastructure development, including improved transportation networks and public amenities, enhances the attractiveness of residential areas, driving property values upwards. However, challenges remain. Interest rate fluctuations and economic uncertainty can impact buyer confidence and affordability. Stringent lending regulations and rising construction costs also act as potential restraints on market expansion. Key players such as Platinum Victory, Matrix Concepts Holdings Bhd, Mah Sing Group Bhd, Sime Darby Property, IGB Berhad, IOI Properties, Glomac Bhd, SP Setia, UEM Sunrise, and Eco World Development Group Berhad are shaping the competitive landscape through diverse offerings and strategic land acquisitions. The segment analysis, while not explicitly provided, likely reflects the diverse housing types available, encompassing luxury condominiums, high-rise apartments, landed properties (e.g., bungalows, terrace houses), and affordable housing units. Future growth will depend on a careful balance between addressing affordability concerns, managing construction costs, and leveraging continued economic growth to drive sustained demand within the Malaysian residential property market. The projected market size in 2033, extrapolated from the 2025 value and CAGR, suggests significant expansion potential. Competitive differentiation, innovative project designs, and sustainable development practices will be critical for success in this dynamic market. Key drivers for this market are: 4., Increasing Residential Real Estate Demand by Young People4.; Increase in Average Housing Price in Mexico. Potential restraints include: 4., Lack of Affordable Housing Inhibiting the Growth of the Market4.; Economic Instability Affecting the Growth of the Market. Notable trends are: Increase in Urbanization Boosting Demand for Residential Real Estate.
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TwitterAccording to a survey conducted on potential home buyers in Malaysia in June 2024, ** percent of respondents said their preferred type of house was a terrace house. Meanwhile, ** percent of respondents said their preferred type of residential was a condominium.
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The Malaysia Commercial Real Estate Market Report is Segmented by Property Type (Offices, Retail, Logistics and Others), by Business Model (Sales and Rental), by End-User (Individuals/Households, Corporates & SMEs and Others), and by Geography (Kuala Lumpur, Klang, Petaling Jaya, Johor Bahru, Penang and the Rest of Malaysia). The Market Forecasts are Provided in Terms of Value (USD).
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TwitterAs of the fourth quarter of 2024, Malaysia was the country with the highest inflation-adjusted increase in house prices since 2010 among the Asia-Pacific (APAC) countries under observation. The real house price index in Malaysia reached nearly 167 index points. This means that, adjusted for inflation, house prices grew 67 percent since 2010, the baseline year when the index value was set to 100. According to the nominal house price index, which does not adjust for the effects of inflation, the price increase was higher.
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House Price Index YoY in Malaysia decreased to 1.40 percent in the fourth quarter of 2024 from 4.30 percent in the third quarter of 2024. This dataset includes a chart with historical data for Malaysia House Price Index YoY.
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TwitterAccording to a survey conducted on potential home buyers in Malaysia in June 2024, ** percent of respondents were interested in properties with a price range of ******* to ******* Malaysian ringgit. Meanwhile, **** percent of respondents had a budget of more than *** million Malaysian ringgit.
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This study analyses the effects of oil price and macroeconomic shocks on the Malaysian housing market using a SVAR framework. The specification of the baseline model is based on standard economic theory. The Gregory-Hansen (GH) cointegration tests reveal that there is no cointegration among the variables of interest. Results from performing Toda-Yamamoto (TY) non-Granger causality tests show that oil price, labor force and inflation are the leading factors causing movements in the Malaysian housing prices in the long run. The findings from estimating generalized impulse response functions (IRFs) and variance decompositions (VDCs) indicate that oil price and labor force shocks explain a substantial portion of housing market price fluctuations in Malaysia.
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Malaysia Prefabricated Housing comes with extensive industry analysis of development components, patterns, flows, and sizes. The report calculates present and past market values to forecast potential market management during the forecast period between 2025 - 2033.
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Key information about Malaysia Gold Production
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Residential: CO: LA: Cluster: MYR50001-100000 data was reported at 0.000 Unit in Mar 2018. This stayed constant from the previous number of 0.000 Unit for Dec 2017. Residential: CO: LA: Cluster: MYR50001-100000 data is updated quarterly, averaging 0.000 Unit from Dec 2003 (Median) to Mar 2018, with 58 observations. The data reached an all-time high of 504.000 Unit in Sep 2007 and a record low of 0.000 Unit in Mar 2018. Residential: CO: LA: Cluster: MYR50001-100000 data remains active status in CEIC and is reported by Valuation and Property Services Department, Ministry of Finance. The data is categorized under Global Database’s Malaysia – Table MY.EB035: Residential Property Market Status: Launched: Unit: Completed: by Type of Property & Price Range.
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Residential: CO: LA: 2 to 3 Storey Terraced: MYR300001-400000 data was reported at 4,660.000 Unit in Jun 2018. This records an increase from the previous number of 4,497.000 Unit for Mar 2018. Residential: CO: LA: 2 to 3 Storey Terraced: MYR300001-400000 data is updated quarterly, averaging 1,586.000 Unit from Jun 2013 (Median) to Jun 2018, with 21 observations. The data reached an all-time high of 4,660.000 Unit in Jun 2018 and a record low of 576.000 Unit in Jun 2013. Residential: CO: LA: 2 to 3 Storey Terraced: MYR300001-400000 data remains active status in CEIC and is reported by Valuation and Property Services Department, Ministry of Finance. The data is categorized under Global Database’s Malaysia – Table MY.EB035: Residential Property Market Status: Launched: Unit: Completed: by Type of Property & Price Range.
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The Malaysian commercial real estate market, valued at approximately RM 8.88 billion in 2025, exhibits robust growth potential. A compound annual growth rate (CAGR) of 7.65% projected from 2025 to 2033 indicates a significant expansion, driven primarily by sustained economic growth, increasing urbanization, and robust infrastructure development within key cities like Kuala Lumpur, Seberang Perai, and Kajang. The burgeoning e-commerce sector fuels demand for logistics and warehousing space, while tourism recovery boosts the hospitality segment. However, challenges persist, including potential interest rate fluctuations impacting investment decisions and ongoing global economic uncertainty potentially affecting construction timelines and overall market confidence. The market is segmented by property type (offices, retail, industrial, logistics, multi-family, hospitality) and key geographical locations, providing opportunities for targeted investment strategies. Major players like Conlay Construction Sdn Bhd, YTL Corporation Berhad, and IJM Corporation Berhad dominate the landscape, competing for projects across diverse segments. The ongoing development of integrated mixed-use projects and the government's focus on sustainable development will shape the sector's trajectory in the coming years. The forecast for the Malaysian commercial real estate market suggests continued growth through 2033, though the rate may fluctuate based on macroeconomic conditions. Specific sectors such as multi-family housing and logistics are expected to experience particularly strong growth fueled by population increases and e-commerce expansion, respectively. Potential regulatory changes regarding sustainable building practices and green initiatives may influence development patterns and investment decisions. Analyzing historical data from 2019-2024 provides crucial insights into market behavior and informs more accurate projections. However, external factors such as geopolitical events and shifts in global investment patterns could still influence the market's overall performance, necessitating continued monitoring and analysis. A diversified investment approach across various property types and locations remains advisable to mitigate potential risks and maximize returns within the Malaysian commercial real estate sector. Recent developments include: July 2023: Skyworld Development Bhd plans to launch new commercial projects in Kuala Lumpur with total estimated gross development values exceeding RM 1 Billion in the current financial year ending March 31, 2024. Skyworld will explore new growth opportunities by expanding its presence from Kuala Lumpur to the state of Selangor., January 2023: Gamuda Bhd’s unit is acquiring eight parcels of freehold lands in Rawang, collectively spanning 532 acres for RM360 million. Gamuda Land (Botanic) Sdn Bhd purchased these lands from Kundang Properties Sdn Bhd for a mixed development with a gross development value of RM3.3 billion over ten years. The group said these new lands are targeted for a 2026 launch and will contribute to the group’s earnings over the following six years as Gamuda Land continues to focus on high-value opportunities both in Malaysia and overseas, where it has established its presence, namely Vietnam, Australia, Singapore and the UK.. Key drivers for this market are: Growth trajectory with a steady pipeline of distribution and warehouse projects, Increasing investment in Greater Kuala Lumpur for Office Space. Potential restraints include: Growth trajectory with a steady pipeline of distribution and warehouse projects, Increasing investment in Greater Kuala Lumpur for Office Space. Notable trends are: Rise in growth in retail sector.
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Residential: CO: LA: Detached: MYR300001-400000 data was reported at 113.000 Unit in Mar 2018. This stayed constant from the previous number of 113.000 Unit for Dec 2017. Residential: CO: LA: Detached: MYR300001-400000 data is updated quarterly, averaging 185.500 Unit from Jun 2013 (Median) to Mar 2018, with 20 observations. The data reached an all-time high of 371.000 Unit in Sep 2013 and a record low of 59.000 Unit in Sep 2015. Residential: CO: LA: Detached: MYR300001-400000 data remains active status in CEIC and is reported by Valuation and Property Services Department, Ministry of Finance. The data is categorized under Global Database’s Malaysia – Table MY.EB035: Residential Property Market Status: Launched: Unit: Completed: by Type of Property & Price Range.
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Inspired by the quintessential House Prices Starter Competition and the popular Melbourne Housing Dataset, this dataset captures 4K+ condominium unit listings on the Malaysian housing website mudah.my.
Like the above datasets, your job is to predict the house prices given certain parameters.
The data was scraped directly from the website using this data collection notebook. I might adapt the code to include houses as well in the future, but scraping the data takes a while due to having to wait for the website to load and having to timeout to account for CloudFlare's protections.
Note: This data is a lot less clean and organized than the data in the two datasets mentioned above. However, this is a good opportunity to practice data cleaning techniques, as this is something that is often overlooked on Kaggle. That being said, I made a starter notebook that goes through the data cleaning steps and outputs a fairly cleaned version of the dataset.
description: The full (unfiltered) description for the unit listing.Ad List: The ID of the listing on the website.Category: The category of the listing. It will most likely be Apartment / Condominium.Facilities: The facilities that the apartment has, in a comma-separated list.Building Name: The name of the building.Developer: The developer for the building.Tenure Type: The type of tenure for the building.Address: The address of the building. You can refer to this link for a description of what Malaysian addresses look like.Completion Year: The completion year of the building. If the building is still under construction, this is listed as -.# of Floors: The number of floors in the building.Total Units: The total number of units in the building.Property Type: The type of property.Bedroom: The number of bedrooms in the unit.Bathroom: The number of bathrooms in the unit.Parking Lot: The number of parking lots assigned to the unit, if any.Floor Range: The floor range for the building.Property Size: The size of the unit.Land Title: The title given to the land. This link explains what land titles are.Firm Type: The type of firm who posted the listing.Firm Number: The ID of the firm who posted the listing.REN Number: The REN number of the firm who posted the listing. Refer to this link for what REN numbers are.price: The price of the unit. This is what you are trying to predict.Nearby School/School: If there is a nearby school to the unit, which school it is.Park: If there is a nearby park to the unit, which park it is.Nearby Railway Station: If there is a nearby railway station to the unit, which railway station it is.Bus Stop: If there is a nearby bus stop to the unit, which station it is.Nearby Mall/Mall: If there is a nearby mall to the unit, which mall it is.Highway: If there is a nearby highway to the unit, which highway it is.