From archives to AI: Residential property data across three decades in Brunei DarussalamZenodo
This article introduces the first publicly available data set for analysing the Brunei housing market, covering more than 30,000 property listings from 1993 to early 2025. The data set, curated from property advertisements in newspapers and online platforms, includes key attributes such as price, lo...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925002379 |
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| author | Haziq Jamil Amira Barizah Noorosmawie Hafeezul Waezz Rabu Lutfi Abdul Razak |
| author_facet | Haziq Jamil Amira Barizah Noorosmawie Hafeezul Waezz Rabu Lutfi Abdul Razak |
| author_sort | Haziq Jamil |
| collection | DOAJ |
| description | This article introduces the first publicly available data set for analysing the Brunei housing market, covering more than 30,000 property listings from 1993 to early 2025. The data set, curated from property advertisements in newspapers and online platforms, includes key attributes such as price, location, property type, and physical characteristics, enriched with area-level spatial information. Comprehensive and historical, it complements the Brunei Darussalam Central Bank's Residential Property Price Index (RPPI), addressing the limitations of restricted access to raw RPPI data and its relatively short timeline since its inception in 2015. Data collection involved manual transcription from archival sources and automated web scraping using programmatic techniques, supported by innovative processing with Large Language Models (LLMs) to codify unstructured text. The data set enables spatial and temporal analysis, with potential applications in economics, urban planning, and real estate research. Although listing prices are only a proxy for market values and may deviate from actual sale prices due to negotiation dynamics and other factors, this data set still provides a valuable resource for quantitative analyses of housing market trends and for informing policy decisions. |
| format | Article |
| id | doaj-art-076b337adb804b0d9fa36d55548a159e |
| institution | DOAJ |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-076b337adb804b0d9fa36d55548a159e2025-08-20T03:10:41ZengElsevierData in Brief2352-34092025-06-016011150510.1016/j.dib.2025.111505From archives to AI: Residential property data across three decades in Brunei DarussalamZenodoHaziq Jamil0Amira Barizah Noorosmawie1Hafeezul Waezz Rabu2Lutfi Abdul Razak3Mathematical Sciences, Faculty of Science, Universiti Brunei Darussalam, Jalan Tungku Link, Bandar Seri Begawan BE1410, Brunei Darussalam; Corresponding author.Mathematical Sciences, Faculty of Science, Universiti Brunei Darussalam, Jalan Tungku Link, Bandar Seri Begawan BE1410, Brunei DarussalamMathematical Sciences, Faculty of Science, Universiti Brunei Darussalam, Jalan Tungku Link, Bandar Seri Begawan BE1410, Brunei DarussalamUBD School of Business and Economics, Universiti Brunei Darussalam, Jalan Tungku Link, Bandar Seri Begawan BE1410, Brunei DarussalamThis article introduces the first publicly available data set for analysing the Brunei housing market, covering more than 30,000 property listings from 1993 to early 2025. The data set, curated from property advertisements in newspapers and online platforms, includes key attributes such as price, location, property type, and physical characteristics, enriched with area-level spatial information. Comprehensive and historical, it complements the Brunei Darussalam Central Bank's Residential Property Price Index (RPPI), addressing the limitations of restricted access to raw RPPI data and its relatively short timeline since its inception in 2015. Data collection involved manual transcription from archival sources and automated web scraping using programmatic techniques, supported by innovative processing with Large Language Models (LLMs) to codify unstructured text. The data set enables spatial and temporal analysis, with potential applications in economics, urban planning, and real estate research. Although listing prices are only a proxy for market values and may deviate from actual sale prices due to negotiation dynamics and other factors, this data set still provides a valuable resource for quantitative analyses of housing market trends and for informing policy decisions.http://www.sciencedirect.com/science/article/pii/S2352340925002379Housing marketProperty listingsSpatial dataWeb scrapingLarge language modelsBrunei |
| spellingShingle | Haziq Jamil Amira Barizah Noorosmawie Hafeezul Waezz Rabu Lutfi Abdul Razak From archives to AI: Residential property data across three decades in Brunei DarussalamZenodo Data in Brief Housing market Property listings Spatial data Web scraping Large language models Brunei |
| title | From archives to AI: Residential property data across three decades in Brunei DarussalamZenodo |
| title_full | From archives to AI: Residential property data across three decades in Brunei DarussalamZenodo |
| title_fullStr | From archives to AI: Residential property data across three decades in Brunei DarussalamZenodo |
| title_full_unstemmed | From archives to AI: Residential property data across three decades in Brunei DarussalamZenodo |
| title_short | From archives to AI: Residential property data across three decades in Brunei DarussalamZenodo |
| title_sort | from archives to ai residential property data across three decades in brunei darussalamzenodo |
| topic | Housing market Property listings Spatial data Web scraping Large language models Brunei |
| url | http://www.sciencedirect.com/science/article/pii/S2352340925002379 |
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