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_8b84c4ee58e3cfc0ece0d773c8ca6abc/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.

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

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.

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.

General Information and License

• HDB Resale Price data made available by SG government through the Open Data License.
  1. https://beta.data.gov.sg/open-data-license
  2. https://beta.data.gov.sg/collections/189/datasets/d_ebc5ab87086db484f88045b47411ebc5/view
• Geodata made available by SG government through the OneMap
  1. https://www.onemap.gov.sg/apidocs/apidocs
• I claim NO rights whatsoever to this data. This data has been uploaded here for educational purposes.
• There is no personally identifiable information in this data.

Data Availability

• 1990 - 02-2012 (based on registration date)
• 03-2012 - 2023-09 (based on approval date)

Contains resale prices only because most of Singapore's population lives in (very well-made and maintained!) public housing.

Does not contain condo/landed house prices.

Special Notes

• Public housing in Singapore is generally sold on a leasehold (typically 99-year) basis.
• Prices tend to decrease as the remaining lease period decreases (see Bala's Curve).

Data Representation

Each row represents 1 transaction.

Data Dictionary

Update Frequency

• This dataset may be updated every year or so as new transaction data becomes available.

Resale Price: Avg Valuation: HDB Flats: Tampines: 3 Room Flat data was reported at 330,000.000 SGD in Jun 2018. This stayed constant from the previous number of 330,000.000 SGD for Mar 2018. Resale Price: Avg Valuation: HDB Flats: Tampines: 3 Room Flat data is updated quarterly, averaging 307,900.000 SGD from Sep 2002 (Median) to Jun 2018, with 64 observations. The data reached an all-time high of 376,500.000 SGD in Jun 2013 and a record low of 161,700.000 SGD in Sep 2002. Resale Price: Avg Valuation: HDB Flats: Tampines: 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: 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.

Singapore Resale Price: Avg Valuation: HDB Flats: Pasir Ris: 5 Room Flat data was reported at 470,000.000 SGD in Sep 2018. This records a decrease from the previous number of 480,000.000 SGD for Jun 2018. Singapore Resale Price: Avg Valuation: HDB Flats: Pasir Ris: 5 Room Flat data is updated quarterly, averaging 441,000.000 SGD from Sep 2002 (Median) to Sep 2018, with 65 observations. The data reached an all-time high of 546,800.000 SGD in Mar 2013 and a record low of 287,000.000 SGD in Mar 2007. Singapore Resale Price: Avg Valuation: HDB Flats: Pasir Ris: 5 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 Housing & Development Board. For more information, visit https://data.gov.sg/datasets/d_2d5ff9ea31397b66239f245f57751537/view

In 2020, there were around 24.7 thousand resale units of Housing and Development Board (HDB) flats. By 2022, it was expected to reach 25 thousand resale units. The price growth of resale flats is expected to be slower that year, between 5 and 8 percent, due to price resistance in some locations.

Singapore Resale Price: Avg Valuation: HDB Flats: Marine Parade: 3 Room Fl data was reported at 400,000.000 SGD in Jun 2017. This records a decrease from the previous number of 410,400.000 SGD for Dec 2014. Singapore Resale Price: Avg Valuation: HDB Flats: Marine Parade: 3 Room Fl data is updated quarterly, averaging 281,750.000 SGD from Sep 2002 (Median) to Jun 2017, with 42 observations. The data reached an all-time high of 413,500.000 SGD in Sep 2011 and a record low of 171,700.000 SGD in Dec 2002. Singapore Resale Price: Avg Valuation: HDB Flats: Marine Parade: 3 Room Fl 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.

Singapore Resale Price: Avg Valuation: HDB Flats: Kallang/Whampoa: 3 Room Flat data was reported at 300,000.000 SGD in Jun 2018. This records a decrease from the previous number of 313,000.000 SGD for Mar 2018. Singapore Resale Price: Avg Valuation: HDB Flats: Kallang/Whampoa: 3 Room Flat data is updated quarterly, averaging 297,500.000 SGD from Sep 2002 (Median) to Jun 2018, with 64 observations. The data reached an all-time high of 377,500.000 SGD in Mar 2013 and a record low of 129,900.000 SGD in Sep 2002. Singapore Resale Price: Avg Valuation: HDB Flats: Kallang/Whampoa: 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 252.63B in Selling and Administration Expenses for its fiscal quarter ending in December of 2024. Data for HDFC Bank | HDB - Selling And Administration Expenses including historical, tables and charts were last updated by Trading Economics this last July in 2025.

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.

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

HDFC Bank reported $194.23B in Market Capitalization this July of 2025, considering the latest stock price and the number of outstanding shares.Data for HDFC Bank | HDB - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Here are a few use cases for this project:

1. Infrastructure Maintenance: Use the model to inspect buildings and infrastructures for cracks, damages to windows, and irregularities at power plug points. This will ensure safety and promote prompt renovations.

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5. Insurance: Insurance companies can use this model to assess the state of insured properties. This could help in claim validation by ensuring the authenticity of claimed damages.