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.

Tracks the overall price movement of the public residential market.

The index is based on quarterly average resale price by date of registration. The index till 3Q2014 was computed using stratification method, while that from 4Q2014 onwards is computed using the stratified hedonic regression method. 1Q2009 is adopted as the new base period with index at 100. The index from 1Q1990 to 3Q2014 are rebased to the new base period at 1Q2009. Indices from 1Q1990 to 3Q2014 are re-scaled using a factor of 100 (new index in 1Q2009) / 138.3 (original index in 1Q2009) multiplied on the original index level to derive the re-based index level for the respective quarters. Due to rounding, there could be some differences in the quarterly price change compared to the RPI series before re-scaling.

Dataset from Housing & Development Board. For more information, visit https://data.gov.sg/datasets/d_14f63e595975691e7c24a27ae4c07c79/view

Dataset from Urban Redevelopment Authority. For more information, visit https://data.gov.sg/datasets/d_f333bf427c827efb484cf57a73ff700a/view

Singapore Resale Price: Avg Valuation: HDB Flats: Ang Mo Kio: 3 Room Flat data was reported at 290,000.000 SGD in Sep 2018. This stayed constant from the previous number of 290,000.000 SGD for Jun 2018. Singapore Resale Price: Avg Valuation: HDB Flats: Ang Mo Kio: 3 Room Flat data is updated quarterly, averaging 290,000.000 SGD from Sep 2002 (Median) to Sep 2018, with 65 observations. The data reached an all-time high of 368,000.000 SGD in Jun 2013 and a record low of 142,400.000 SGD in Sep 2002. Singapore Resale Price: Avg Valuation: HDB Flats: Ang Mo Kio: 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.

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_913fd3cff8b7f462cf70cf415001e02b/view

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.

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

Singapore Resale Price: Avg Valuation: HDB Flats: Clementi: 3 Room Flat data was reported at 310,500.000 SGD in Sep 2018. This records a decrease from the previous number of 320,000.000 SGD for Jun 2018. Singapore Resale Price: Avg Valuation: HDB Flats: Clementi: 3 Room Flat data is updated quarterly, averaging 312,750.000 SGD from Sep 2002 (Median) to Sep 2018, with 64 observations. The data reached an all-time high of 390,000.000 SGD in Jun 2013 and a record low of 144,400.000 SGD in Sep 2002. Singapore Resale Price: Avg Valuation: HDB Flats: Clementi: 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.

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.

Singapore Resale Price: Avg Valuation: HDB Flats: Bukit Batok: 3 Room Flat data was reported at 258,000.000 SGD in Sep 2018. This records a decrease from the previous number of 264,000.000 SGD for Jun 2018. Singapore Resale Price: Avg Valuation: HDB Flats: Bukit Batok: 3 Room Flat data is updated quarterly, averaging 265,000.000 SGD from Sep 2002 (Median) to Sep 2018, with 65 observations. The data reached an all-time high of 339,000.000 SGD in Mar 2013 and a record low of 120,800.000 SGD in Sep 2002. Singapore Resale Price: Avg Valuation: HDB Flats: Bukit Batok: 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.

HDFC Bank reported 652.8B in Sales Revenues for its fiscal quarter ending in December of 2024. Data for HDFC Bank | HDB - Sales Revenues including historical, tables and charts were last updated by Trading Economics this last July in 2025.

In 2021, there were around **** thousand units of flats reaching the Minimum Occupation Period (MOP) in Singapore. By 2025, it was expected to decrease to roughly ***** thousand units. The total sales of property in Singapore had been slower in 2018 due to the cooling measures introduced in July 2018.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

HDFC Bank (HDB) Stock Forecast: A Secure Bet for Long-Term Growth

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

HDB HDFC Bank Limited Common Stock

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

零售价格指数：住房发展委员会（2009年第1季度=100）在09-01-2018达131.6002009年1季度=100，相较于06-01-2018的131.7002009年1季度=100有所下降。零售价格指数：住房发展委员会（2009年第1季度=100）数据按季更新，03-01-1990至09-01-2018期间平均值为78.3002009年1季度=100，共115份观测结果。该数据的历史最高值出现于06-01-2013，达149.4002009年1季度=100，而历史最低值则出现于03-01-1990，为24.3002009年1季度=100。CEIC提供的零售价格指数：住房发展委员会（2009年第1季度=100）数据处于定期更新的状态，数据来源于Housing & Development Board，数据归类于Global Database的新加坡 – 表 SG.E030：转售单位统计：住房发展委员会（HDB）。

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

HDFC Bank: (HDB) Riding the Wave of India's Growth

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

零售价格指数：住房发展委员会（1998年第4季度=100）在09-01-2014达192.4001998年4季度=100，相较于06-01-2014的195.7001998年4季度=100有所下降。零售价格指数：住房发展委员会（1998年第4季度=100）数据按季更新，03-01-1990至09-01-2014期间平均值为104.9001998年4季度=100，共99份观测结果。该数据的历史最高值出现于06-01-2013，达206.6001998年4季度=100，而历史最低值则出现于03-01-1990，为33.6001998年4季度=100。CEIC提供的零售价格指数：住房发展委员会（1998年第4季度=100）数据处于定期更新的状态，数据来源于Housing & Development Board，数据归类于Global Database的新加坡 – 表 SG.E030：转售单位统计：住房发展委员会（HDB）。

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 expansion is driven by several key factors. Strong economic fundamentals, a thriving job market, and a consistently high influx of both local and foreign investment fuel demand for residential and commercial properties. Government initiatives aimed at improving infrastructure and enhancing urban living further contribute to the market's positive trajectory. The segment breakdown reveals a diversified market, encompassing apartments, condominiums, villas, and other property types, catering to diverse needs and budgets, from affordable housing to luxury residences. Key players like CapitaLand, GuocoLand, and City Developments Limited dominate the landscape, shaping market trends and influencing development strategies. While potential headwinds exist, such as rising interest rates and global economic uncertainty, the Singaporean government's proactive measures and the country's economic resilience are expected to mitigate these risks. Looking forward, the market's growth trajectory is anticipated to remain strong, primarily fueled by sustained demand for high-quality residential properties and ongoing development of commercial spaces to cater to Singapore's burgeoning economy. The ongoing diversification of the market, coupled with increasing foreign investment, will further solidify Singapore’s position as a premier real estate investment destination. Increased focus on sustainable development and smart city initiatives is also likely to play an important role in shaping the future trajectory of the market. The ongoing government support and a robust economy support predictions of continued growth in the forecast period. The luxury segment is likely to show comparatively stronger growth given the sustained high net worth individual inflow and increasing demand for premium properties. This comprehensive report provides an in-depth analysis of the Singapore real estate market, covering the historical period (2019-2024), base year (2025), and forecasting the market's trajectory until 2033. Valued at billions, the Singapore property market is a dynamic landscape shaped by government policies, economic trends, and evolving consumer preferences. This report offers crucial insights for investors, developers, and industry stakeholders seeking to navigate this complex market. Search terms like Singapore property market, Singapore real estate investment, Singapore condo prices, and Singapore HDB prices are strategically incorporated to maximize search engine visibility. 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: Experiencing Slower Growth due to Government Measures, Rising Interest Rates Affecting the Growth of the Market. Notable trends are: Rise in the Residential Segment of the Singapore Real Estate Market.