Data-Driven Asset Investment Strategies
There is a gap between infrastructure needs and available financing opportunities. Annual world-wide infrastructure investment by multilateral institutions, public investment, public-private partnerships and private investment is estimated at $1.7 trn./year, leaving a funding gap of over $1tn. At the same time, smart infrastructures and other smart real assets are lauded to be the path forward for sustainable growth, resiliency, and value creation for investors and owners.
But how are they financed and who will pay?
Private sector investment and institutional investor capital are often raised as possible solutions for filling the financing gap provided projects can be structured that align risk and return expectations with stakeholder incentives. The valuation of smart systems plays out in three areas:
- New operational models,
- Valuation of co-benefits, and
- Derivative value from adjacencies
The coupling of data and real asset IoT with financial technologies is unlocking new efficient financing models, smart contracts and blockchain technologies.
Vision and Mission
The UM CSIF is the only domestic academic Center where smart infrastructure and real asset data are systematically integrated with data science, finance and policy in a research, education and industry engagement model to solve sustainable financing problems across application domains (energy, water, real estate, transportation, agriculture and forestry) and asset classes.
- Equities: Valuing ESG (environmental, social & governance) data with new indexing models
- Municipal and green bonds: Pricing linked to IoT-based performance measurements
- Farmland real asset investments: capturing value from water efficiency
- Green real estate: Focused on energy optimization, grid adaptation and cost of operations
Our vision is to develop and test financial models to accelerate investment, design and scalability of smart, resilient, and sustainable infrastructures through real asset investment strategies:
- Data (fusion) platforms to price ESG risk for sustainable indexing strategies
- Fractionalization and tokenization of real asset valuation with blockchain infrastructure
- Application of web scraping, natural language processing and machine learning tools
- Actuarial modeling and testing of risk transfer models (swaps, options, insurance)
- Debt securitization of loans against smart asset performance data
- Business-to-Business (B2B) market solutions using data sales and auctions models
- Engineering modeling support for design of variable interest rate (impact) bonds
Engage with Us
Financially-supported by individuals and corporates, the CSIF is a co-founder of the UM-FinTech Collaboratory, in collaboration with the Ross School of Business, the Center on Finance Law and Policy, and the Mathematical Finance Program. Our engagement model is based on three pillars:
- Short term ideation projects: Teams of work on a specific client issues or opportunities related to financing instruments for real assets. Engagement is 1 month (renewable)
- Long term-collaborative research projects: Exploratory research engagements (1-2 years) on real asset investment problems aligned between corporate partner and CSIF.
- Sandbox leader: A 3-year engagement designed to shape the future of smart infrastructure design and financing models. Pooled investments may be allocated using a process co-developed by faculty and industry/government sandbox leads
The Center leverages a broad network of expertise in smart engineering systems, data science, finance and cyberinfrastructure across multiple schools and partnering institutions.
Peter Adriaens, PhD, Director: Infrastructure finance, multi-asset strategies, fintech, sustainable investing
Jerome Lynch, PhD, CEE Chair: Sensors and actuators, smart infrastructure systems
Branko Kerkez, PhD: Sensors and actuators, control systems, smart (storm)water infrastructure
SangHyun Lee, PhD: Wearable sensors, blockchain applications, construction supply chain
Carol Menassa, PhD: Green buildings, real options, life cycle costing, decision tools
Yafeng Yin, PhD: Transportation mobility, electric and autonomous vehicles, pricing strategies
UM Collaborator Network
Michael Barr, PhD: Director, Center on Finance, Law and Policy: Big data, fintech and the capital markets
Andrew Wu, PhD: Financial technology, blockchain and cryptocurrency models, payment processing
Robert Dittmar, PhD: Financial technology, pricing models, derivatives structures, information efficiency
Ravi Anupindi, PhD: Digital supply chains, risk pricing strategies, data-driven efficiencies
Thomas Finholt, PhD: Dean, School of Information, Computational mediation of trust, virtual organizations
Paul Resnick, PhD: Social computational systems, web scraping for indexing strategies
Mingyan Liu, PhD: Chair, Electrical Engineering and Computer Science; cyberinfrastructure
Alfred Hero, PhD: Data collection, analysis and visualization, statistical machine learning; optimization
Liad Wagman, PhD (Illinois Institute of Technology): Game theory; data mining; mechanism design
Matthew Dixon, PhD (Illinois Institute of Technology): Actuarial financial models, securitization, swaps
Yuri Lawryshyn, PhD (University of Toronto): Real options, water infrastructure, cyberinfrastructure
Newsha Ajami, PhD (Stanford University): Data-driven business models, value chain analytics and finance
Corporate and Financial Institution Network
Ripple (SFO), Nuveen (NY), MSCI (NY), Silicon Valley Bank (SF), Nephila Climate (SF), SEB Bank (NY), Goldman Sachs (NY), Credit Suisse (NY, Zürich), S-Network Global Indexes (NY), Limeyard (Zürich), UBS (Zürich), Dana Investment Advisors (WI), IBM (Chicago), KPMG (Chicago), Deloitte (Detroit),
Disclaimer. The Center for Smart Infrastructure Finance, and its home institution, The University of Michigan, are non-profit institutions and are not licensed to provide financial advisory services. All products, data platforms and all derivative services are used for inquiry only and are not intended for use in investment decisions, asset allocation strategies, or other derivative services subject to the laws and regulations of the Securities and Exchange Commission and affiliated regulatory bodies. The University of Michigan cannot be held responsible for derivative financial uses of the data and models developed by the CSIF or its faculty, students and academic partners.