Private Residential Property Price Index By Type Of Property, (1st Quarter 2009 = 100), Quarterly
What this data shows
Published by Singapore Department of Statistics. 3 records. Fields include Data Series, 2026 2Q, 2026 1Q, 2025 4Q (207 columns in total). Covers 1975-01-01 to 2026-06-30. Last updated 2026-09-08.
Description
Long-run quarterly private residential property price indices for Singapore from 1975 through 2026, rebased to 1Q 2009 = 100 and published by URA via SingStat in wide CSV format by property type. From 2015 onward indices use stratified hedonic regression on caveat and stamp-duty transactions. Property analysts track condo and landed segments and compare cycles across decades.
Official description
Source: URBAN REDEVELOPMENT AUTHORITY Data Last Updated: 24/07/2026 Update Frequency: Quarterly Footnotes: From 1Q 2015, the private residential property price indices are computed using a stratified hedonic regression method. Under this method, variations in the attributes of private residential properties transacted such as age and unit size are controlled for by using hedonic regressions and price movements are aggregated using 5-quarter fixed weights to derive the aggregate price change. Data are compiled from caveats lodged at Singapore Land Authority, stamp duty data from the Inland Revenue Authority of Singapore, as well as data provided by developers. Adapted from: https://tablebuilder.singstat.gov.sg/table/TS/M212261
- Last updated
- Coverage
- 1975-01-01 – 2026-06-30
- Rows
- 3
- Columns
- 207
Chart
Values are summed where multiple rows share the same category.
Data
| Data Series | 2026 2Q | 2026 1Q | 2025 4Q | 2025 3Q | 2025 2Q | 2025 1Q | 2024 4Q | 2024 3Q | 2024 2Q | 2024 1Q | 2023 4Q |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Residential Properties | 219.400000 | 218.300000 | 216.400000 | 215.100000 | 213.200000 | 211.100000 | 209.400000 | 204.700000 | 206.100000 | 204.300000 | 201.500000 |
| Landed | 258.400000 | 252.100000 | 253.100000 | 244.800000 | 241.400000 | 236.200000 | 235.300000 | 235.500000 | 243.800000 | 239.300000 | 233.200000 |
| Non-Landed | 210.600000 | 210.800000 | 208.100000 | 208.500000 | 206.800000 | 205.400000 | 203.400000 | 197.500000 | 197.300000 | 196.100000 | 194.200000 |
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 |
| 2026 2Q | Numeric | No |
| 2026 1Q | Numeric | No |
| 2025 4Q | Numeric | 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 |
Showing the first 60 of 207 columns. Download the CSV for the full schema.