Data to the Rescue: How Digitalization is Reshaping Infrastructure Finance Models

The argument that there isn’t sufficient capital available to invest in infrastructure has been debunked for a while.  The limitations are in public investment, but even that is the result of outdated finance models.

Recent years have seen a steady rise in the number of institutional investors allocating assets to infrastructure, as well as the establishment of infrastructure as an asset class in its own right.  Public-private partnerships are being considered but still largely are based on 20th century operational models.  The challenge then is finding the right projects and financial models to allocate capital in the context of risk and return models that meet the investment objectives. 

Currently, this risk and return is still largely based on a combination of equity ownership in the asset and cashflows from tolls, parking meters and the like.  

In the future, the data asset class will be leveraged for new efficient financing models with more limited recourse to the user’s wallet.  Enter data markets.

Infrastructure is becoming smarter resulting from the integration of IoT (internet of things) technologies and data aggregation platforms.  Yet the information derived from these systems has only recently become an input for data-driven financing models.   By rethinking infrastructure as having both a real asset and a digital twin component, the capital stack of investment blurs the line between asset ownership and data commoditization. 

Consider this.  From water quantity and quality information, and pipe pressure measurements for leak detection in water provision, to weigh-in-motion sensors and cameras in road and bridge systems, terrabytes of data are collected. Add to this the data from wearables on health and personal well being metrics, home energy efficiency measurements, and solar energy conversion efficiencies, and data analytics rapidly assumes a complexity not seen before. 

Given the complexity, varying scope and time-space resolution, what information and knowledge can be extracted and valued? 

The Center for Smart Infrastructure Finance focuses on the design, testing and benchmarking of new investment models for infrastructure and the broader real asset investment space.  IoT data can help close the financing gap of public infrastructure, make project financing more attractive by shifting the capital structure, and properly price intangibles risk in stocks and securities.

A recent meeting at Goldman Sachs in New York on ‘Industry 4.0 meets Infrastructure Finance’ illustrated is clear that new operational models, data scope and frequency, are shifting what is possible in the financing space.  Even fixed income is becoming an exciting data-driven asset class, with interest rates tied into operational asset performance.  The shift in data analytics is based on how the information is interpreted in a socio-economic or financial-regulatory context.  Data have risk and return value, resulting in new investment and business models of which we’re only seeing early exploratory implications in the capital markets. 

  1. The lower left quadrant employs engineering models in the context of socio-economic considerations to facilitate systems design specifications.  Examples include bridge, roadway, water plant, energy grid and building designs to meet economic and user demand.
  2. The upper left quadrant deploys tools such as data fusion models, network mapping and machine learning algorithms to uncover data pricing and valuation models given socio-economic boundary conditions.  Examples include pricing of tollroads or transportation congestion, or auctions of data.
  3. The lower right quadrant combines engineering models with financial and regulatory conditions to leverage the data into efficient operations models.  Here we would consider issues such as finding efficiencies in water, energy and building management to improve returns and reduce costs.
  4. The upper right quadrant is where new data-driven financing and business models result from the integration of data science tools with financial/regulatory considerations.  Data auctions, insurance and swap markets, and smart contracts to transact value across a blockchain fit in this space.

The Center is led by the Department of Civil and Environmental Engineering, and is structured in a FinTech Collaboratory with the Center on Finance, Law and Policy and the FinTech initiative in the Ross School of Business.