Project Grant 2314726
- This $399,999 Project Grant award from the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program supports a multi-method research project that investigates the prevalence and impacts of demographically coded language used in opposition to multifamily housing development. The research involves collecting and analyzing textual data from fair housing lawsuits, public meeting records, and news articles to identify patterns in the use of coded language...
- This Project Grant award, funded by the National Science Foundation's Social, Behavioral, and Economic Sciences program (CFDA 47.075), supports the construction of a new dataset from county land-use records across 20 U.S. counties over 40 years. The dataset includes information on Federal Housing Administration (FHA)-insured and Veteran's Administration (VA)-guaranteed mortgages, which will be made publicly available for evaluation by academics, policymakers, and community organizations. The...
- This Project Grant award from the National Science Foundation's Social, Behavioral, and Economic Sciences (CFDA 47.075) program constructs a new dataset from county land-use records across 20 U.S. counties over 40 years. The dataset includes information on Federal Housing Administration (FHA)-insured and Veterans Administration (VA)-guaranteed mortgages, which will be made publicly available to support academic research, policymaking, and community analysis of these important federal programs....
- This $383,941 Project Grant award from the National Science Foundation (CFDA 47.075 Social, Behavioral, and Economic Sciences) supports the development of a collaborative digital platform and research network focused on mapping the historical origins of residential segregation patterns in U.S. urban areas. The key objectives are to create an open-access digital database and GIS mapping resources that enable interdisciplinary research on the locational attainments and household-level...
- This $400,000 two-year Project Grant from the National Science Foundation's Division of Behavioral and Cognitive Sciences, under the Social, Behavioral, and Economic Sciences program (CFDA 47.075), will support Cornell University in developing natural language processing techniques to automate the extraction of key regulatory information from hundreds of local zoning codes. The University will assemble zoning codes, manually review them, and incorporate specific data into the publicly accessible...
- This Project Grant award from the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program provides $353,568 to fund a research project conducted by the National Bureau of Economic Research (NBER) to study the history of displacement across American towns from 1850 to 1940. The project will first use machine learning technology to create a comprehensive database of these historical displacement events from newspaper records, and then merge this data...
- This $185,779 National Science Foundation project grant to The University of Toledo will fund collaborative research to understand how neighborhoods at the geographical margins of an ancient city may have been socially peripheral or fully engaged in central city projects and activities. The research team will examine two neighborhoods at the physical periphery of a prehistoric city occupied for roughly three centuries by both local and non-local peoples. Through non-invasive geophysical...
- This federal Project Grant award, provided by the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program, seeks to investigate transit-induced commercial and residential gentrification and displacement through a novel integration of historical business and household microdata with street-view imagery and online data. The $396,600 award to North Carolina State University, made on August 15, 2024, will develop advanced AI techniques and tools to analyze...
- This National Science Foundation project grant under the Social, Behavioral, and Economic Sciences program (CFDA 47.075) provides $808,398 to Brown University from July 2022 to June 2024. The funding will support research investigating the origins and impacts of racial and ethnic residential segregation in 45 major U.S. cities from 1920 to 1970. Specifically, the grantee will examine the use of restrictive covenants and mortgage redlining during this period and their effects on neighborhood...
- This National Science Foundation (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) Project Grant award of $106,623 to the University of Illinois will fund collaborative archaeological research to understand the relationship between people's investment in and identification with urban neighborhoods and the historical development of cities. The research team will use non-invasive geophysical survey, soil coring, and targeted excavation to compare two peripheral neighborhoods of an...
UNDERSTANDING PROCESSES OF NEIGHBORHOOD CHANGE USING PROPERTY TEXT ANALYTICS -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.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $0 | 5/20/26 | ||
| Not listed | $382.0k | 6/26/23 |