Understanding Processes of Neighborhood Change using Property Text Analytics

本研究探讨社区变迁过程中，便利设施、居住偏好、抵押贷款实务与真实的地产广告的相互关联作用。在这个项目中，研究人员分析了真实的房地产广告文本中使用的语言是如何随着时间的推移而演变的，并因预期的社区抵押贷款申请人的种族和收入而异。使用点级空间分辨率的实时真实的房地产列表，可以在变化变得过于根深蒂固之前预测变化，使公共政策能够及时适应。最后，该项目通过开发一本将自然语言处理（NLP）纳入空间分析的在线教科书，并通过向参加STEM夏令营的K-12女孩讲授NLP方法和应用，促进公众参与和在公共政策中使用科学技术。在塑造社区方面发挥了重要作用，帮助建立和维持了美国城市的种族和收入隔离模式。该项目结合了新颖的、理论指导的NLP、机器学习和经典的统计方法，根据房地产广告中使用的词汇，预测一个社区中预期抵押贷款申请人的种族和收入构成。它还调查了抵押贷款拒绝率的趋势，因为广告中的住房和社区设施已经发生了变化。最后，该项目开发了新的方法论方法，用于以精细的空间和时间分辨率检查住房动态。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.