Dataset from Urban Redevelopment Authority. For more information, visit https://data.gov.sg/datasets/d_f65e490a8ad430f60a9a3d9df2bff2a0/view

As of the last quarter of 2022, the residential non-landed property index value amounted to *****, which means that property prices increased by **** percent since the first quarter of 2009. The index shows how the property prices changed in those years, compared to the base value from the first quarter of 2009, when the index value was equal to 100.

Housing Index in Singapore increased to 210.70 points in the first quarter of 2025 from 209.40 points in the fourth quarter of 2024. This dataset provides the latest reported value for - Singapore Property Price Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Dataset from Urban Redevelopment Authority. For more information, visit https://data.gov.sg/datasets/d_c0c26484c655113b0ab5abaa0a49952b/view

Singapore Property Price Index: Private Residential (PR): All data was reported at 211.100 1Q2009=100 in Mar 2025. This records an increase from the previous number of 209.400 1Q2009=100 for Dec 2024. Singapore Property Price Index: Private Residential (PR): All data is updated quarterly, averaging 87.600 1Q2009=100 from Mar 1975 (Median) to Mar 2025, with 201 observations. The data reached an all-time high of 211.100 1Q2009=100 in Mar 2025 and a record low of 8.900 1Q2009=100 in Mar 1975. Singapore Property Price Index: Private Residential (PR): All data remains active status in CEIC and is reported by Urban Redevelopment Authority. The data is categorized under Global Database’s Singapore – Table SG.EB002: Property Price Index. [COVID-19-IMPACT]

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_da00b36ca8c831322fa0bb2a3378a476/view

Singapore Property Price Index: PR: Non Landed (NL): Core Central Region data was reported at 199.500 4Q1998=100 in Dec 2014. This records a decrease from the previous number of 201.300 4Q1998=100 for Sep 2014. Singapore Property Price Index: PR: Non Landed (NL): Core Central Region data is updated quarterly, averaging 192.150 4Q1998=100 from Mar 2004 (Median) to Dec 2014, with 44 observations. The data reached an all-time high of 213.500 4Q1998=100 in Mar 2013 and a record low of 114.900 4Q1998=100 in Mar 2004. Singapore Property Price Index: PR: Non Landed (NL): Core Central Region data remains active status in CEIC and is reported by Urban Redevelopment Authority. The data is categorized under Global Database’s Singapore – Table SG.EB002: Property Price Index.

Singapore Property Price Index: PR: Non Landed data was reported at 200.200 4Q1998=100 in Dec 2014. This records a decrease from the previous number of 202.200 4Q1998=100 for Sep 2014. Singapore Property Price Index: PR: Non Landed data is updated quarterly, averaging 109.900 4Q1998=100 from Mar 1975 (Median) to Dec 2014, with 160 observations. The data reached an all-time high of 209.200 4Q1998=100 in Sep 2013 and a record low of 14.800 4Q1998=100 in Mar 1975. Singapore Property Price Index: PR: Non Landed data remains active status in CEIC and is reported by Urban Redevelopment Authority. The data is categorized under Global Database’s Singapore – Table SG.EB002: Property Price Index. Rebased from 4Q1998=100 to 1Q2009=100. Replacement series ID: 367030317

Singapore Property Rental Index: PR: Landed data was reported at 101.000 1Q2009=100 in Sep 2018. This records an increase from the previous number of 100.500 1Q2009=100 for Jun 2018. Singapore Property Rental Index: PR: Landed data is updated quarterly, averaging 103.400 1Q2009=100 from Mar 2004 (Median) to Sep 2018, with 59 observations. The data reached an all-time high of 120.000 1Q2009=100 in Sep 2013 and a record low of 66.100 1Q2009=100 in Jun 2004. Singapore Property Rental Index: PR: Landed data remains active status in CEIC and is reported by Urban Redevelopment Authority. The data is categorized under Global Database’s Singapore – Table SG.EB004: Property Rental Index.

Singapore Luxury Residential Real Estate Market Report is Segmented by Property Type (Apartments & Condominiums, Villas & Landed Houses), by Business Model (Sales and Rental), by Mode of Sale (Primary (New-Build) and Secondary (Existing-Home Resale)), and by District (Central Business District (CBD), Orchard Road and More). The Report Offers Market Size and Forecasts in Value (USD) for all the Above Segments.

The Singapore real estate market, valued at $46.58 billion in 2025, is projected to experience robust growth, exhibiting a Compound Annual Growth Rate (CAGR) of 6.57% from 2025 to 2033. This positive trajectory is driven by several key factors. Singapore's strong economic fundamentals, a stable political environment, and a consistently high demand for residential and commercial properties contribute significantly to market expansion. Furthermore, government initiatives aimed at improving infrastructure and attracting foreign investment fuel this growth. The increasing affluence of the population, coupled with limited land availability, continues to exert upward pressure on property prices, particularly in prime locations. However, the market is not without its challenges. Rising interest rates and potential regulatory changes could act as restraints, potentially moderating growth in the coming years. Nevertheless, the long-term outlook remains optimistic, particularly given the ongoing demand fueled by a growing population and a robust economy. The market is segmented into various property types, including residential (condominiums, apartments, landed properties), commercial (office spaces, retail malls), and industrial (warehouses, factories), each exhibiting its own growth dynamics. Key players such as UOL Group Limited, CapitaLand, GuocoLand Limited, and City Developments Limited, along with several others, compete within this dynamic landscape. The historical period (2019-2024) likely saw fluctuations influenced by global economic events and local policy adjustments. Considering the 2025 market value and projected CAGR, a reasonable estimation for market size progression would show consistent growth, potentially experiencing some year-on-year variance based on economic cycles and policy changes. While specific regional data is unavailable, Singapore's relatively compact geography suggests a less pronounced regional disparity in market share compared to larger countries. The continued emphasis on urban planning and development will likely see a sustained high demand for properties across different segments and locations throughout the forecast period. The competitive landscape, marked by both established giants and emerging developers, is likely to remain dynamic, influenced by mergers and acquisitions, and innovation in property development and management. Recent developments include: April 2024: Two historical buildings in the Pearl’s Hill vicinity are set to be demolished to make way for new housing developments. The government plans to build 6,000 new homes in the area over the next decade. The third housing site is located at the intersection of Chin Swee and Outram roads, while the white site sits primarily atop the underground Outram Park MRT station. The 2.9 ha white site, with a plot ratio of 6.3, has condominium units and long-term serviced apartments., March 2024: To meet the demand for homes, the government decided to launch a new housing area in Yishun and may develop a new residential neighborhood at Gillman Barracks. About 10,000 homes will be built in the new Yishun estate of Chencharu, situated near Khatib MRT station. At least 80% will be public housing, with the first Build-to-Order (BTO) project comprising 1,200 units of two-room Flexi to five-room flats to be launched in 2024.. Key drivers for this market are: Increasing Economic Growth, High Demand for Property Boosting the Market. Potential restraints include: Increasing Economic Growth, High Demand for Property Boosting the Market. Notable trends are: Rise in the Residential Segment of the Singapore Real Estate Market.

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Singapore Real Estate Market Size 2025-2029

The singapore real estate market size is forecast to increase by USD 62.6 billion at a CAGR of 4.6% between 2024 and 2029.

The market is witnessing significant growth, driven primarily by the burgeoning demand for industrial infrastructure. This trend is fueled by the country's status as a global business hub, attracting numerous multinational corporations seeking to establish a presence. Concurrently, marketing initiatives in the real estate industry are gaining momentum, with developers increasingly adopting innovative strategies to differentiate their offerings and cater to diverse customer segments. However, this market landscape is not without challenges. Regulatory uncertainty looms large, with ongoing debates surrounding potential changes to property cooling measures and land use regulations. These uncertainties could deter investors and developers, potentially hindering market growth. As such, navigating the complex regulatory environment and staying abreast of policy developments will be crucial for companies looking to capitalize on opportunities and mitigate risks in the Singapore Real Estate market.

What will be the size of the Singapore Real Estate Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The Singapore real estate market exhibits dynamic activity in various sectors. The sub-sale market experiences continuous fluctuations, influenced by property valuation models and market forecasting. Property law plays a crucial role in real estate financing and collective sales, including en bloc and strata title transactions. Property investment funds and real estate syndication provide financing options for freehold and leasehold properties. Real estate litigation arises from property disputes, necessitating ethical conduct in property management services. Proptech adoption streamlines property search engines and portfolio management, while property tax incentives stimulate investment. Rental management services and property insurance mitigate risks in the diverse real estate landscape. Property market trends encompass master plans, property crowdfunding, and real estate marketing strategies.

How is this market segmented?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. AreaResidentialCommercialIndustrialMode Of BookingSalesRental and leaseTypeLanded houses and villasOffice spaceApartments and condominiumsStore spaceOthersPriceMid-tierEntry-levelLuxuryGeographyAPACSingapore

By Area Insights

The residential segment is estimated to witness significant growth during the forecast period.

The Singapore real estate market encompasses various sectors, including residential, commercial, and industrial properties. The residential segment, comprised of apartments, condominiums, single-family homes, and other living arrangements, experiences significant demand due to population growth and the country's robust economy. Urban renewal projects and sustainable development initiatives contribute to the transformation of the property market. Commercial real estate, including office buildings and retail spaces, benefit from the thriving economy and increasing business activities. Property management companies employ technology, such as virtual and augmented reality, to enhance the property buying and selling experience. Real estate investment trusts and funds provide opportunities for investors seeking capital appreciation and rental income. Property prices have been on an upward trend due to high demand and limited supply, with vacancy rates remaining relatively low. Property taxes, stamp duty, and government policies influence the market dynamics. Urban planning and infrastructure development are essential for economic growth and smart city initiatives. Real estate developers and proptech startups leverage technology, including artificial intelligence and big data, to streamline property transactions and enhance property management. The rental market, property valuation, and property development are shaped by various factors, including rental yield, housing affordability, and market sentiment. Land use planning and regulations play a crucial role in shaping the real estate landscape. Capital appreciation and rental income continue to attract investors to the market, with mortgage rates influencing affordability. Smart home technologies and green building standards add value to both residential and commercial properties.

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The Residential segment was valued at USD 100.30 billion in 2019 and showed a gradual increase during the forecast period.

Market Dynamics

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Market research data and analysis for 2024 Landed Report: Strong Upgrader Demand Drives Landed Home Transaction, Trend Expected to Continue in 2025

Market research data and analysis for 3Q 2025 URA Private Residential Report: Private Home Demand Driven by Surge of New Launches

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_65f5bf43d377db3939d93eb6f744d950/view

Background

I've been sitting on this for quite a while and it's a project that I'm glad that I attempted because it forced me to learn new things through trial and error in order to enrich the dataset. Like mapping variables based on set/dict of values for the CPI, lease and GNI adjustment columns. Like trying to scrape Google Maps source code and getting blocked from their maps for a while before finding out how to use other geocoders responsibly. Like running multiple simulations of different linreg algorithms and analysing their variance contributions.

Anyway, this dataset can be used to create rich data visualisations or you can try using it for machine learning because of the large sample size in a small geographic area.

Dataset contents

ALL Prices 1990-2021 Mar.csv

The largest file by far, ALL Prices 1990-2021 Mar.csv, contains over 800k rows of transactions of Singapore Housing Development Board (HDB) resale flats. As the name implies, BTO, SERS, HUDC and private housing are not included, though resale DBSS flat transactions are treated as ordinary HDB flat transactions. Many of the columns in the file are calculated columns or mapped columns (based on supplementary information) like the CPI index and lease percentage columns. For the full metadata/glossary of how I derived these terms, see the bottom of this description and/or the file and column descriptions.

Balas Table.csv

Balas Table.csv contains the ratios of leasehold land value to freehold land value for each year of remaining lease, from 1 to 99. This table is used by Singapore Land Authority (SLA) in determining land valuations which affect property value since most land in Singapore is leasehold. As there are some mistakes/anomalies in the dataset with 100 and 101 years, I used the maximum values of 96% ratio when mapping in the 2nd version so please don't use the old version.

MAS Core Inflation.csv

This file contains SIngapore's core CPI index value for each month from January 1990 to February 2021 as compiled by MAS. For March 2021 and subsequent future transactions, you have to make estimates and also update this table based on new releases by MAS. For this dataset, I used March 2021=100.4.

complete.csv

Contains all UNIQUE BLOCK addresses, along with their geocoder-supplied full address (inconsistent, many missing) and more importantly, their latitude and longitude coordinates. As there are 9000+ addresses, they were first geocoded using a mix of Photon and Google Maps source code scraping (more accurate but doesn't give full address for quick checking). Then, I manually looked through the addresses and coordinates to find and update all blatantly wrong (outside Singapore or wrong neighbourhood) and most slightly inaccurate (correct neighbourhood and/or street but tagged to wrong block number) for a total of around 1600 addresses, many in Whampoa/Boon Keng, Sengkang, Yishun and Woodlands.

gni per capita.csv

As the name implies, this file contains Singapore's GNI per capita in nominal S$ for the years 1990-2020. For 2021, you have to make an estimate based on the projected economic recovery from COVID-19 until the actual value is released. For this dataset, I used 2021=75000.

HDB machine learning.xlsx

This file is my own basic analysis of (numerical) variables that potentially help to determine the final resale price (measured by inflation-adjusted price per square metre). I used 5 linear regression algorithms and tested each variable individually as well as tried to maximise the R^2 using multiple linear regression with as many relevant variables as possible. I also included the correlation matrix between all the variables and that for relevant variables which helps in calculating the incremental contribution to variance for each variable.

Usefulness in training models?

As i've shown in the "HDB machine learning.xlsx" workbook, some variables are more influential than others but even the amount of variance contributed changes depending on the conditions applied. Various multiple linear regression models i've tried can only post up to 0.60+ in combined R^2, which means that up to 40% of variance in the inflation-adjusted price per square metre/foot flat prices is essentially just random noise or could have another hidden variable! Perhaps you can try to find another strongly related variable? Some ideas are proximity to MRTs/bus stops, ratio of HDB to private housing, average household size, other housing to population ratio indicators? It's important to consider whether correlated factors are causes or effects as well.

More importantly, do you think you can train a model to post much better numbers than multiple linear regression?

Metadata for ALL Prices file

Original columns: `month, town, flat_type, block, street_name, storey_range, area_sqm, flat_model...

物业价格指数：PR：非接地（NL）在12-01-2014达200.2001998年4季度=100，相较于09-01-2014的202.2001998年4季度=100有所下降。物业价格指数：PR：非接地（NL）数据按季更新，03-01-1975至12-01-2014期间平均值为109.9001998年4季度=100，共160份观测结果。该数据的历史最高值出现于09-01-2013，达209.2001998年4季度=100，而历史最低值则出现于03-01-1975，为14.8001998年4季度=100。CEIC提供的物业价格指数：PR：非接地（NL）数据处于定期更新的状态，数据来源于Urban Redevelopment Authority，数据归类于Global Database的新加坡 – 表 SG.E005：物业价格指数。