Coupling data science with community crowdsourcing for urban renewal policy analysis
An evaluation of Atlanta’s Anti-Displacement Tax Fund
Bibliographic Data
| ID | 21247273 |
|---|---|
| Authors | Jeremy Auerbach (0000-0003-0061-7943, Colorado State University, corresponding author), Christopher Blackburn (Georgia Institute of Technology), Christopher J Blackburn (Georgia Institute of Technology), Hayley Barton (Duke University), Amanda Meng (0000-0002-1241-0504, Georgia Institute of Technology), Ellen Zegura (0000-0002-4756-1759, Georgia Institute of Technology) |
| Year | 2020 |
| Volume | 47 |
| Issue | 6 |
| Pages | 1081-1097 |
| Publication date | 2020-07-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environment and Planning B Urban Analytics and City Science (JOURNAL) |
| Journal identifiers | ISSN: 2399-8083 • E-ISSN: 2399-8091 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/2399808318819847 |
| OpenAlex | W2906657291 |
| Language | EN |
| Citations received | 10 |
| References cited | 26 |
We estimate the cost and impact of a proposed anti-displacement program in the Westside of Atlanta (GA) with data science and machine learning techniques. This program intends to fully subsidize property tax increases for eligible residents of neighborhoods where there are two major urban renewal projects underway, a stadium and a multi-use trail. We first estimate household-level income eligibility for the program with data science and machine learning approaches applied to publicly available household-level data. We then forecast future property appreciation due to urban renewal projects using random forests with historic tax assessment data. Combining these projections with household-level eligibility, we estimate the costs of the program for different eligibility scenarios. We find that our household-level data and machine learning techniques result in fewer eligible homeowners but significantly larger program costs, due to higher property appreciation rates than the original analysis, which was based on census and city-level data. Our methods have limitations, namely incomplete data sets, the accuracy of representative income samples, the availability of characteristic training set data for the property tax appreciation model, and challenges in validating the model results. The eligibility estimates and property appreciation forecasts we generated were also incorporated into an interactive tool for residents to determine program eligibility and view their expected increases in home values. Community residents have been involved with this work and provided greater transparency, accountability, and impact of the proposed program. Data collected from residents can also correct and update the information, which would increase the accuracy of the program estimates and validate the modeling, leading to a novel application of community-driven data science
Accountability · Actuarial science · American Community Survey · Atlanta · Business · Census · Crowdsourcing · Economics · Geography · Metropolitan area · Political science · Property tax · Public economics · Sociology · Subsidized housing · Subsidy · Tax reform · Computer Science · Economic and Environmental Valuation · Flood Risk Assessment and Management · Housing Market and Economics
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Introduction and Overview
| Unique citing works | 10 |
|---|---|
| Citations per year | 1,67 |
| Citation span | 2020 - 2025 (6) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 8 |