Price Indices Of Non-Landed Private Residential Properties By Locality (1st Quarter 2009 = 100), Quarterly
What this data shows
Published by Singapore Department of Statistics. 3 records. Fields include Data Series, 2025 3Q, 2025 2Q, 2025 1Q (88 columns in total). Covers 2004-01-01 to 2025-09-30. Last updated 2025-10-24.
Description
Quarterly price indices of non-landed private residential properties in Singapore by locality (1st quarter 2009 = 100) from 2004 through the third quarter of 2025, from the Urban Redevelopment Authority via SingStat. Stratified hedonic methods support regional condo and apartment price comparisons for investors and housing researchers. Adapted from SingStat TableBuilder TS/M212271.
Official description
Source: URBAN REDEVELOPMENT AUTHORITY Data Last Updated: 24/10/2025 Update Frequency: Quarterly Footnotes: Data are computed using stratified hedonic regression method. The sum of values of transactions from Q1 2014 to Q1 2015 is used as weights to compute the index. Prior to 3Q 2016, data are compiled based on transaction prices given in contracts submitted for stamp duty payment and data provided by licensed developers on new units sold. From 3Q 2016, data are compiled based on transaction prices given in contracts submitted for stamp duty payment, and data provided by both licensed and de-licensed developers on new units sold. Adapted from: https://tablebuilder.singstat.gov.sg/table/TS/M212271
- Last updated
- Coverage
- 2004-01-01 – 2025-09-30
- Rows
- 3
- Columns
- 88
Chart
Values are summed where multiple rows share the same category.
Data
| Data Series | 2025 3Q | 2025 2Q | 2025 1Q | 2024 4Q | 2024 3Q | 2024 2Q | 2024 1Q | 2023 4Q | 2023 3Q | 2023 2Q | 2023 1Q |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Core Central Region | 163.400000 | 160.700000 | 156.000000 | 154.800000 | 150.900000 | 152.600000 | 153.100000 | 148.100000 | 142.500000 | 146.500000 | 146.600000 |
| Rest Of Central Region | 225.500000 | 224.800000 | 227.300000 | 223.500000 | 217.000000 | 215.300000 | 211.900000 | 211.300000 | 213.000000 | 208.600000 | 213.900000 |
| Outside Central Region | 263.000000 | 260.900000 | 258.100000 | 257.300000 | 249.100000 | 249.100000 | 248.600000 | 248.100000 | 237.400000 | 225.000000 | 222.300000 |
Preview shows 3 rows and 12 columns. Use the table search or download the CSV for the full data.
Columns
| Column | Type | Categorical |
|---|---|---|
| Data Series | Text | No |
| 2025 3Q | Numeric | No |
| 2025 2Q | Numeric | No |
| 2025 1Q | Numeric | No |
| 2024 4Q | Numeric | No |
| 2024 3Q | Numeric | No |
| 2024 2Q | Numeric | No |
| 2024 1Q | Numeric | No |
| 2023 4Q | Numeric | No |
| 2023 3Q | Numeric | No |
| 2023 2Q | Numeric | No |
| 2023 1Q | Numeric | No |
| 2022 4Q | Numeric | No |
| 2022 3Q | Numeric | No |
| 2022 2Q | Numeric | No |
| 2022 1Q | Numeric | No |
| 2021 4Q | Numeric | No |
| 2021 3Q | Numeric | No |
| 2021 2Q | Numeric | No |
| 2021 1Q | Numeric | No |
| 2020 4Q | Numeric | No |
| 2020 3Q | Numeric | No |
| 2020 2Q | Numeric | No |
| 2020 1Q | Numeric | No |
| 2019 4Q | Numeric | No |
| 2019 3Q | Numeric | No |
| 2019 2Q | Numeric | No |
| 2019 1Q | Numeric | No |
| 2018 4Q | Numeric | No |
| 2018 3Q | Numeric | No |
| 2018 2Q | Numeric | No |
| 2018 1Q | Numeric | No |
| 2017 4Q | Numeric | No |
| 2017 3Q | Numeric | No |
| 2017 2Q | Numeric | No |
| 2017 1Q | Numeric | No |
| 2016 4Q | Numeric | No |
| 2016 3Q | Numeric | No |
| 2016 2Q | Numeric | No |
| 2016 1Q | Numeric | No |
| 2015 4Q | Numeric | No |
| 2015 3Q | Numeric | No |
| 2015 2Q | Numeric | No |
| 2015 1Q | Numeric | No |
| 2014 4Q | Numeric | No |
| 2014 3Q | Numeric | No |
| 2014 2Q | Numeric | No |
| 2014 1Q | Numeric | No |
| 2013 4Q | Numeric | No |
| 2013 3Q | Numeric | No |
| 2013 2Q | Numeric | No |
| 2013 1Q | Numeric | No |
| 2012 4Q | Numeric | No |
| 2012 3Q | Numeric | No |
| 2012 2Q | Numeric | No |
| 2012 1Q | Numeric | No |
| 2011 4Q | Numeric | No |
| 2011 3Q | Numeric | No |
| 2011 2Q | Numeric | No |
| 2011 1Q | Numeric | No |
Showing the first 60 of 88 columns. Download the CSV for the full schema.