Understanding Processes of Neighborhood Change using Property Text Analytics

使用属性文本分析了解社区变化的过程

• 批准号: 2314726
• 负责人: Isabelle Nilsson
• 金额: $ 38.2万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2314726&HistoricalAwards=false
• 关键词: Understanding; Processes; Neighborhood; Change; using

This project investigates the interconnected role of amenities, residential preferences, mortgage lending practices, and real estate advertisements in neighborhood change processes. In this project, the researchers analyze how the language used in real estate advertisement text has evolved over time and varies by the race and income of anticipated neighborhood mortgage applicants. The use of real-time real estate listings at a point-level spatial resolution offers the potential to predict changes before they become too entrenched, enabling public policies to adapt timely. Finally, the project promotes public engagement and the use of science and technology in public policy by developing an online textbook for integrating natural language processing (NLP) in spatial analyses and by teaching K-12 girls enrolled in a STEM Camp about NLP methods and applications.Housing market professionals, including realtors and mortgage lenders, have played a significant role in shaping neighborhoods by aiding in establishing and maintaining observed patterns of racial and income segregation across US cities. This project uses a combination of novel, theory-guided NLP, machine learning, and classic statistical methods to predict the racial and income composition of anticipated mortgage applicants in a neighborhood over time-based on the words used in property advertisements. It also investigates trends in mortgage denial rates as advertised housing and neighborhood amenities have shifted. Finally, the project develops new methodological approaches for examining housing dynamics at a fine spatial and temporal resolution.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目调查了邻里变革过程中便利设施，住宅偏好，抵押贷款实践和房地产广告的相互联系的作用。在这个项目中，研究人员分析了房地产广告文本中使用的语言如何随着时间的流逝而演变，并且随着预期的邻里抵押贷款申请人的种族和收入而有所不同。在点级的空间分辨率上使用实时房地产清单提供了预测变化的潜力，然后才能使公共政策及时适应公共政策。最后，该项目通过开发一本用于在空间分析中整合自然语言处理（NLP）的在线教科书以及在STEM营中入学的K-12女孩来促进公众参与和在公共政策中使用科学和技术在美国城市隔离。该项目结合了新颖，理论指导的NLP，机器学习和经典统计方法，以预测社区中预期的抵押贷款申请人的种族和收入成分，这些申请人随着时间的基于时间广告中所用的单词。它还调查了抵押贷款率的趋势，因为广告范围的住房和邻里便利设施发生了变化。最后，该项目开发了新的方法论方法，用于以良好的空间和时间分辨率来检查住房动态。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响评估标准，被认为值得通过评估来提供支持。