Authors

Christian Humann, Ziming Xiong, Kamili Afra

Research Question

The project addresses a critical gap in adaptation planning: What are the measurable financial and societal benefits of protective strategies against flooding and extreme heat? While cities and industries invest in resilience infrastructure, there is limited quantification of how these investments affect real estate markets, public health, and risk exposure. This makes it difficult to incentivize funding from private investors, insurers, and public agencies. By analyzing pre- and post-intervention outcomes, the project aims to identify and quantify these benefits, helping to promote adaptation finance as a driver for more resilient, data-informed urban systems.

Background

This project focuses on building a data-driven framework for digital adaptation finance that quantifies the impact of resilience strategies for urban flooding and extreme heat. Despite growing interest in climate adaptation, the financial and social benefits of protective infrastructure remain poorly understood. This limits investment from both the public and private sectors. The project will explore how adaptation strategies, such as green infrastructure, cooling centers, or coastal barriers, affect real estate values, health outcomes, infrastructure performance and revitalizing and strengthening community cohesion. By analyzing pre- and post-intervention data across multiple indicators, students will define beneficiaries and will evaluate the financial impact of such projects. This includes estimating avoided losses, risk premiums, and changes in property market signals that could inform decisions by real estate investors, insurers, banks and public agencies.

The overarching goal is to create a generalizable and scalable framework that helps quantify the return on investment (ROI) of adaptation projects. Students will also explore how digital tools (AI, satellite imagery, remote sensing, and open data) can enhance resilience planning, especially in areas with sparse or uneven data coverage.

Findings may be shared with partner cities and the NYC Panel on Climate Change (NPCC5) to inform ongoing planning efforts. This project aligns closely with CUSP’s mission to improve urban resilience using data and evidence-based tools, bridging climate adaptation and finance.

Methodology

Climate adaptation is becoming urgent, but its financial benefits are often unclear to stakeholders. This project tackles that gap using a hybrid approach combining economic analysis, geospatial methods, and urban data science. Students will begin by gathering datasets on real estate values, flood and heat impacts, health outcomes, and protective interventions across select regions.

They will compare pre- and post-intervention metrics to quantify how protective measures, such as green infrastructure, coastal barriers, or extreme heat policies, affect urban systems. Historical analysis will be paired with predictive models to assess how future projects could influence market and public sector outcomes.

Where public data is sparse, the team will leverage open satellite data (e.g., Sentinel), FloodNet sensors, and AI-based simulation to enhance spatial coverage and robustness. Tools will include Python, ArcGIS/QGIS, and common urban data analysis frameworks. Insights will be synthesized into a scalable framework that helps decision-makers and investors quantify adaptation ROI and risk shifts. Findings may inform ongoing efforts by city agencies or NPCC5 to plan future resilience investments.

Deliverables

• Quantify the financial impacts and incentives of protective measures for flooding and extreme heat, including changes in real estate value, avoided losses, and health-related costs.
• Identify key beneficiaries, such as public agencies, private investors, insurers, banks, local communities and developers, and define how these insights can inform decision-making and resource allocation.
• Develop a scalable framework that generalizes findings across sectors and geographies, with potential deliverables including a policy brief, data analysis report, or interactive dashboard to visualize results.

Data Sources

All data either comes from public sources or is reviewed for compliance. It includes:

• Real estate market data

• Health impact data

• Historical flood and heat events

• Insurance records (e.g., NFIP)

• Satellite imagery (e.g., Sentinel) and FloodNet data will supplement gaps in resolution and coverage.

• Advanced AI tools may be used to recover or interpolate data in low-density areas.