As of 1st January 2025, the average resale price of a Housing Development Board (HDB) 4-room flat was at 636,259 Singapore dollars. The resale price of such flats had increased by about 200,000 Singapore dollars since 2017. HDB is responsible for managing Singapore's government housing, and cater to all income levels in Singapore. HDB flats range from 1-room apartments to large, multi-generational apartments. Around 75 percent of the Singapore population live in HDB flats. Citizens who wish to purchase a new flat would need to apply for a built-to-order (BTO) apartment.

Dataset from Housing & Development Board. For more information, visit https://data.gov.sg/datasets/d_2d5ff9ea31397b66239f245f57751537/view

As of the fourth quarter of 2024, the resale price index of residential units from the Housing Development Board (HDB) in Singapore was at *****, which means that HDB resale flat prices increased by **** percent since the first quarter of 2009. The index tracks the overall price movement of the public residential market, compared to the base value from the first quarter of 2009, when the index value was equal to 100.

The dataset consists of information about the resale prices of Singapore public housing (HDB) flats or apartments from the years 2015 to 2024.

The dataset consists of 220,971 rows, each representing 1 transaction.

There are 11 columns: - resale_price: transaction price (in Singapore dollars) - year: transaction year (range from 2015 to 2024) - town: name of town where the flat is located - flat_type: type of flat (e.g. 1-room, 2-room, 5-room, executive) - block: building number - street_name: street address - storey_range: the floor on which the apartment is located - floor_area_sqm: size of apartment in square meters - remaining_lease_years: number of years remaining in the lease (note: all Singapore HDB flats have a 99-year lease) - storey_range_category: categorical variable created based on "storey_range" (Low (01-06); Low-Mid (07-12); Mid (13-18); High (19-30); Very High (>30)) - distance_from_expressway: distance from the building to the nearest expressway (<=50m, 51-100m, 101-150m, 151-300m, 301-500m, >500m)

The dataset was created through the following sources: 1. HDB Resale Flat Prices (Jan 2015 to 2024) -- Source: https://data.gov.sg/datasets?query=resale+flat+price&resultId=189&page=1 2. National Map Line (SLA) -- Road coordinates of all 10 existing expressways -- Source: https://data.gov.sg/datasets?query=national+map+line&resultId=d_aa9129ea72a19af27998dd4c78b5fd28&page=1 3. OneMap Reverse Geocode: -- Identify HDB buildings located within a specified radius of a given coordinate along the expressway -- Source: https://www.onemap.gov.sg/apidocs/apidocs/#reverseGeocode

For more information about the data preparation process, please visit the following Github repository: https://github.com/Goh-DYA/HDB-resale-highway/tree/main

Resale transacted prices. Prior to March 2012, data is based on date of approval for the resale transactions.
For March 2012 onwards, the data is based on date of registration for the resale transactions.

Context This dataset is a record of every building or building unit (apartment, etc.) sold in the California property market along with the customer data.

Content Real estate is property consisting of land and the buildings on it, along with its natural resources such as crops, minerals or water; immovable property of this nature; an interest vested in this (also) an item of real property, (more generally) buildings or housing in general.

Inspiration

What can you discover about California real estate by looking at a year's worth of raw transaction records? Can you spot trends in the market, or build a model that predicts sale value in the future?

Geneva stands out as Europe's most expensive city for apartment purchases in early 2025, with prices reaching a staggering 15,720 euros per square meter. This Swiss city's real estate market dwarfs even high-cost locations like Zurich and London, highlighting the extreme disparities in housing affordability across the continent. The stark contrast between Geneva and more affordable cities like Nantes, France, where the price was 3,700 euros per square meter, underscores the complex factors influencing urban property markets in Europe. Rental market dynamics and affordability challenges While purchase prices vary widely, rental markets across Europe also show significant differences. London maintained its position as the continent's priciest city for apartment rentals in 2023, with the average monthly costs for a rental apartment amounting to 36.1 euros per square meter. This figure is double the rent in Lisbon, Portugal or Madrid, Spain, and substantially higher than in other major capitals like Paris and Berlin. The disparity in rental costs reflects broader economic trends, housing policies, and the intricate balance of supply and demand in urban centers. Economic factors influencing housing costs The European housing market is influenced by various economic factors, including inflation and energy costs. As of April 2025, the European Union's inflation rate stood at 2.4 percent, with significant variations among member states. Romania experienced the highest inflation at 4.9 percent, while France and Cyprus maintained lower rates. These economic pressures, coupled with rising energy costs, contribute to the overall cost of living and housing affordability across Europe. The volatility in electricity prices, particularly in countries like Italy where rates are projected to reach 153.83 euros per megawatt hour by February 2025, further impacts housing-related expenses for both homeowners and renters.

Overview: This dataset was collected and curated to support research on predicting real estate prices using machine learning algorithms, specifically Support Vector Regression (SVR) and Gradient Boosting Machine (GBM). The dataset includes comprehensive information on residential properties, enabling the development and evaluation of predictive models for accurate and transparent real estate appraisals.Data Source: The data was sourced from Department of Lands and Survey real estate listings.Features: The dataset contains the following key attributes for each property:Area (in square meters): The total living area of the property.Floor Number: The floor on which the property is located.Location: Geographic coordinates or city/region where the property is situated.Type of Apartment: The classification of the property, such as studio, one-bedroom, two-bedroom, etc.Number of Bathrooms: The total number of bathrooms in the property.Number of Bedrooms: The total number of bedrooms in the property.Property Age (in years): The number of years since the property was constructed.Property Condition: A categorical variable indicating the condition of the property (e.g., new, good, fair, needs renovation).Proximity to Amenities: The distance to nearby amenities such as schools, hospitals, shopping centers, and public transportation.Market Price (target variable): The actual sale price or listed price of the property.Data Preprocessing:Normalization: Numeric features such as area and proximity to amenities were normalized to ensure consistency and improve model performance.Categorical Encoding: Categorical features like property condition and type of apartment were encoded using one-hot encoding or label encoding, depending on the specific model requirements.Missing Values: Missing data points were handled using appropriate imputation techniques or by excluding records with significant missing information.Usage: This dataset was utilized to train and test machine learning models, aiming to predict the market price of residential properties based on the provided attributes. The models developed using this dataset demonstrated improved accuracy and transparency over traditional appraisal methods.Dataset Availability: The dataset is available for public use under the [CC BY 4.0]. Users are encouraged to cite the related publication when using the data in their research or applications.Citation: If you use this dataset in your research, please cite the following publication:[Real Estate Decision-Making: Precision in Price Prediction through Advanced Machine Learning Algorithms].

Singapore Resale Price: Avg Valuation: HDB Flats: Bukit Merah: 3 Room Flat data was reported at 355,000.000 SGD in Sep 2018. This records an increase from the previous number of 335,000.000 SGD for Jun 2018. Singapore Resale Price: Avg Valuation: HDB Flats: Bukit Merah: 3 Room Flat data is updated quarterly, averaging 333,500.000 SGD from Sep 2002 (Median) to Sep 2018, with 65 observations. The data reached an all-time high of 412,000.000 SGD in Jun 2013 and a record low of 135,300.000 SGD in Sep 2002. Singapore Resale Price: Avg Valuation: HDB Flats: Bukit Merah: 3 Room Flat data remains active status in CEIC and is reported by Housing & Development Board. The data is categorized under Global Database’s Singapore – Table SG.EB027: Resale Flat Statistics.

Montevideo, Uruguay's capital, leads Latin American cities with the highest apartment sale prices in 2024, averaging ***** U.S. dollars per square meter. This figure surpasses other major metropolitan areas like Mexico City and Buenos Aires, highlighting significant disparities in real estate markets across the region. The data underscores the varying economic conditions and housing demand in different Latin American urban centers. Regional housing market trends While Montevideo tops the list for apartment prices, other countries in Latin America have experienced notable changes in their housing markets. Chile, for instance, saw the most substantial increase in house prices since 2010, with its nominal house price index surpassing *** points in early 2024. However, when adjusted for inflation, Mexico showed the highest inflation-adjusted percentage increase in house prices, growing by nearly five percent in the first quarter of 2024, contrasting with a global decline of one percent. Home financing in Mexico The methods of home financing vary across Latin America. A breakdown of homeownership by financing method in Mexico reveals that about two-thirds of owner-occupied housing units were financed through personal resources in 2022. Nevertheless, government-backed loans such as Infonavit (Mexico’s National Housing Fund Institute), Fovissste (Housing Fund of the Institute for Social Security and Services for State Workers), and Fonhapo (National Fund for Popular Housing), play an important role for homebuyers, with just over ** percent of home purchases relying on such finance. Bank credit, which offers mortgage loans with interest rates ranging between **** and ** percent, appeared as a less popular option.

Dataset from Housing & Development Board. For more information, visit https://data.gov.sg/datasets/d_8b84c4ee58e3cfc0ece0d773c8ca6abc/view

Singapore Resale Price: Avg Valuation: HDB Flats: Sengkang: 4 Room Flat data was reported at 410,000.000 SGD in Sep 2018. This stayed constant from the previous number of 410,000.000 SGD for Jun 2018. Singapore Resale Price: Avg Valuation: HDB Flats: Sengkang: 4 Room Flat data is updated quarterly, averaging 389,500.000 SGD from Sep 2002 (Median) to Sep 2018, with 65 observations. The data reached an all-time high of 501,500.000 SGD in Jun 2013 and a record low of 210,300.000 SGD in Sep 2002. Singapore Resale Price: Avg Valuation: HDB Flats: Sengkang: 4 Room Flat data remains active status in CEIC and is reported by Housing & Development Board. The data is categorized under Global Database’s Singapore – Table SG.EB027: Resale Flat Statistics.

Graph and download economic data for Interest Rates and Price Indexes; Multi-Family Real Estate Apartment Price Index, Level (BOGZ1FL075035403Q) from Q4 1985 to Q1 2025 about multifamily, real estate, family, interest rate, interest, rate, price index, indexes, price, and USA.

Singapore Resale Price: Avg Valuation: HDB Flats: Queenstown: 3 Room Flat data was reported at 355,000.000 SGD in Sep 2018. This records an increase from the previous number of 348,400.000 SGD for Jun 2018. Singapore Resale Price: Avg Valuation: HDB Flats: Queenstown: 3 Room Flat data is updated quarterly, averaging 320,000.000 SGD from Sep 2002 (Median) to Sep 2018, with 65 observations. The data reached an all-time high of 408,000.000 SGD in Dec 2013 and a record low of 126,400.000 SGD in Sep 2002. Singapore Resale Price: Avg Valuation: HDB Flats: Queenstown: 3 Room Flat data remains active status in CEIC and is reported by Housing & Development Board. The data is categorized under Global Database’s Singapore – Table SG.EB027: Resale Flat Statistics.

Singapore Resale Price: Avg Valuation: HDB Flats: Tampines: Executive Flat data was reported at 662,500.000 SGD in Jun 2018. This records a decrease from the previous number of 689,000.000 SGD for Mar 2018. Singapore Resale Price: Avg Valuation: HDB Flats: Tampines: Executive Flat data is updated quarterly, averaging 550,000.000 SGD from Sep 2002 (Median) to Jun 2018, with 63 observations. The data reached an all-time high of 700,000.000 SGD in Sep 2013 and a record low of 376,200.000 SGD in Sep 2006. Singapore Resale Price: Avg Valuation: HDB Flats: Tampines: Executive Flat data remains active status in CEIC and is reported by Housing & Development Board. The data is categorized under Global Database’s Singapore – Table SG.EB027: Resale Flat Statistics.

Graph and download economic data for Interest Rates and Price Indexes; Multi-Family Real Estate Apartment Price Index, Level (BOGZ1FL075035403A) from 1985 to 2024 about multifamily, real estate, family, interest rate, interest, rate, price index, indexes, price, and USA.

The monthly house price index in London has increased since 2015, albeit with fluctuation. In May 2025, the index reached 99.1, which is a slight increase from the same month in 2024. Nevertheless, prices widely varied in different London boroughs, with Kensington and Chelsea being the priciest boroughs for an apartment purchase.

Housing Index in Sweden decreased to 936 points in the first quarter of 2025 from 937 points in the fourth quarter of 2024. This dataset provides - Sweden House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Dataset from Housing & Development Board. For more information, visit https://data.gov.sg/datasets/d_b51323a474ba789fb4cc3db58a3116d4/view

The borough with the highest property prices in London, Kensington and Chelsea, had an average price for a flat exceeding *** million British pounds. London is the most populous metropolitan area in the UK, and living in it comes with a price tag. Unsurprisingly, the most expensive boroughs in terms of real estate prices are located in the heart of the metropolis: Kensington and Chelsea, the City of Westminster, and the City of London. In Kensington and Chelsea, home to several museums such as the Natural History Museum, the Victoria and Albert Museum, and the Science Museum, as well as galleries and theaters, the average price of apartments was over a million British pounds. How have residential property prices developed in recent years? The average house price in England have risen notably over the past decade, despite a slight decline in 2023. While London continues to be the hottest market in the UK, price growth in the capital has moderated. Conversely, prices in the more affordable cities, such as Belfast and Liverpool, have started to rise at a faster pace. Are residential property prices in London expected to grow in the future? Despite property prices declining in 2024, the market is forecast to continue to grow in the next five years, according to a October 2024 forecast. Some of the reasons for this are the robust demand for housing, the chronic shortage of residential properties, and the anticipated decline in mortgage interest rates.