From archives to AI: Residential property data across three decades in Brunei Darussalam

IF 1 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief Pub Date : 2025-03-23 DOI:10.1016/j.dib.2025.111505

Haziq Jamil , Amira Barizah Noorosmawie , Hafeezul Waezz Rabu , Lutfi Abdul Razak

{"title":"From archives to AI: Residential property data across three decades in Brunei Darussalam","authors":"Haziq Jamil , Amira Barizah Noorosmawie , Hafeezul Waezz Rabu , Lutfi Abdul Razak","doi":"10.1016/j.dib.2025.111505","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111505"},"PeriodicalIF":1.0000,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data in Brief","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352340925002379","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}

引用次数: 0

Abstract

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.

查看原文本刊更多论文

从档案到人工智能：文莱达鲁萨兰国三十年来的住宅物业数据

本文介绍了用于分析文莱住房市场的第一个公开可用数据集，涵盖了1993年至2025年初的30,000多个房产列表。该数据集从报纸和网络平台上的房产广告中整理出来，包括价格、位置、房产类型和物理特征等关键属性，并丰富了区域级空间信息。它是对文莱达鲁萨兰国中央银行住宅房地产价格指数（RPPI）的全面和历史性补充，解决了原始RPPI数据访问受限的局限性，以及自2015年成立以来相对较短的时间。数据收集包括从档案来源手动转录和使用编程技术的自动网络抓取，并由大型语言模型（llm）的创新处理支持，以编纂非结构化文本。该数据集可以进行空间和时间分析，在经济学、城市规划和房地产研究中具有潜在的应用。尽管挂牌价格只是市场价值的代表，可能会因谈判动态和其他因素而偏离实际销售价格，但该数据集仍然为住房市场趋势的定量分析和政策决策提供了宝贵的资源。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Data in Brief MULTIDISCIPLINARY SCIENCES-

CiteScore

3.10

自引率

0.00%

发文量

996

审稿时长

70 days

期刊介绍： Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.