Smart cities, smart investment?

• Smart city technologies are an emerging asset class that deliver hugely divergent payback periods. This research uses standard appraisal methods to showcase how such solutions are more like a capital allocation instead of being operational expenditure, with divergent payback horizons and prize pools. For instance, technologies that optimise digital processes like transport hub navigation in Riyadh or Hong Kong pay back in two years and generate millions in return due to lower upfront investment requirements. By contrast, solutions that optimise complex operational assets such as HVAC units or construction resources in Dubai take over six years and generate billions in savings.
  
  
• Smart city technologies have the potential to enhance local GDP from 0.8% to 6% across ten years. Because this research has modelled the societal benefits of smart city technologies, it is possible to identify which technologies can act like infrastructure and alleviate challenges unique to each city analysed. High energy prices in locations such as London or Hong Kong may necessitate AI energy analytics to enable cost control, whilst in Riyadh or Dubai the same technology can enable commercial sites to offer comfortable environments to do business.
  
  
• The GDP boost from such solutions can enable city leaders to treat smart city technology as investable infrastructure. Because the value in a use case is driven by local demand, municipal authorities can use their returns to attract investors and truly scale deployment in ways that were previously not possible. Below is a framework for getting this process started, orientated around five clear steps: 1) setting economic objectives 2) baselining city characteristics 3) investing across multiple risk profiles 4) piloting to show scalability, and 5) institutionalising the capabilities.
   

To date, smart city technology has been miscategorised and their return potential underestimated. Treated as innovation spend, it gets the latitude given to experiments and the scrutiny afforded to none. New research by JLL considers how smart city technology can be treated as a form of capital allocation instead of supplementary expenditure. This new way of financial modelling shows Return on Investment (ROI) ranging from 70% to as much as 300% in the cities that were studied, through use cases such as energy, footfall, waste and digital infrastructure optimisation across urban properties. This translates into billions in local economic growth when forecast across a ten-year period to 2037. Mapping out seven use cases using a consistent set of costs and benefits across five cities, this analysis is the first of its kind and offers a fresh understanding of how smart city projects can be evaluated and ultimately financed.

The opportunity for smart city technologies requires city leaders to pool investments toward challenges that are unique to their local socio-economic environments. For example, in London and Hong Kong, high energy prices and pollution are a drain on the economy. In New York, improvements to utilisation rates at transport hubs can add further economic value, due to the friction costs savings for high-salaried employees. In Riyadh and Dubai, huge economic gains can be realised if air conditioning units can be optimised for usage and energy consumption can then be scaled, or where construction processes can be digitised to speed up nascent transport hub developments and reduce an overreliance on private cars.

If smart city initiatives are treated as functional infrastructure that alleviate a wide array of urban challenges, sizeable prizes are available. This research outlines a framework for institutionalising smart city technology investments, so that municipal leaders can chart a path forward where the outsized gains are made possible with the right capital solutions.

Since the 1960s, smart cities have been categorised across four core dimensions. The first and most important has been from a technological perspective, with a focus on IoT data systems. Other categorisations have come from a human capital or human development index (HDI) perspective, environment, social and governance (ESG) analyses and quality of life (QOL) impact assessments. Whilst these categorisations are useful, they prioritise a focus on innovation over more foundational realities.

Smart cities are in fact a selection of investable use cases that require financial discipline to derive value. JLL has developed an alternative methodology to classify use cases that are worthy of serious investment.

Attention must be paid to the operational consequences of deploying multiple pieces of software and hardware and the associated services needed to deploy the technology. This helps set expectations about the way an investment will play out over a multi-year business or electoral cycle. For example:

• Return on investment (ROI) estimates that differ massively by technology and city, because the cost of deployment varies so much. For instance, ROI can reach heights of 300% in Riyadh for some use cases but often struggle to climb above 70% in New York due to higher operational costs.
• Payback periods can range from just over a year for technologies that mostly focus on software upgrades (seamless journeys) to nearly seven years for those that focus on hardware and optimising key electrical assets (AI for energy analytics).
• The internal rate of return (IRR) shows that performance will vary by technology and city, where shorter payback periods mean higher annual returns, and longer payback periods often means a relatively lower annual return for a larger eventual prize.
   

Framing smart city technological use cases in this way is critical if city leaders are to use them as forms of profitable capital allocation. But this framing is only half the story.

The other half of the story is the untold economic impact. An underappreciated impact of existing smart city technologies can be seen on the local gross domestic product (GDP). Using a proprietary model, the research demonstrates how across seven use cases there is a huge potential halo effect on the regional economy.

At the aggregate level over 10 years, for example, the lowest a city can generate from scaling all use cases adds 0.8% to London’s GDP (equivalent to nearly $8 billion). In Dubai it can add as much as 6% ($6.9 billion) and 3% in Riyadh (nearly $16 billion). City leaders can help generate such growth by using smart city technology to save on building energy costs, boost indoor business activity and optimise human footfall through traffic hubs, to list just a few examples.

Notably, this is not a generic trickle-down effect; it can be targeted at specific spheres of the local economy that matter most to the population. The model mapped out the divergent impacts of technology by five distinct economic value streams. These encompass:
 

• Commerce, whereby improved indoor conditions lead to higher sales and increased tax take from business expansion.
• Development, whereby technology investment raises the operational resilience of real estate assets and generates further investment as property value increases.
• Health, whereby healthier indoor environments reduce sickness and encourage socially beneficial use of disposable income on local amenities.
• Productivity, whereby less friction points in the workplace and more agile business operations cut down on inefficient use of citizen time.
• Sustainability, whereby improved building operations reduce energy consumption and carbon emission, as well as boosting social equity.

Given these dynamics, city leaders should consider how each technology interacts with the local economy to anchor long-term usage and repeatable value. For example, sustainability is the primary driver of value in London and Hong Kong due to air pollution and high energy prices. In contrast, productivity gains are the primary driver in New York as the workforce commands the highest average salary. This places a premium on technologies that can save time per worker during their commutes, for example, as it can instead be used to spend more time and disposable income in the city. Across Riyadh and Dubai, the need to address year-round high temperatures mean sustainability, health and commerce outcomes are highly important.

When the local GDP impact is taken in account, smart city projects can operate more like infrastructure investments rather than isolated innovation projects. By charting the variable effect of different technologies on cities with vastly different environments, differentiated smart technologies can work to alleviate problems unique to each city.

Investors can work with city leaders to realise smart city deployments through a multifaceted programme:
 

• Step 1: Be clear about the economic objectives, not just the payback
  In addition to assessing the financial viability of a given project, city leaders should ask how the funding will generate value in the broader, local economy. This will be a critical part of the public relations campaign and long-term commercial strategy for new ventures that address known and measurable municipal challenges, such as improving healthcare or the city’s climate resilience.
  
  
• Step 2: Baseline the city’s core socio-economic features
  Rather than investing into technologically enabled properties on a speculative basis, due diligence must be done on the core factors that drive demand in a city’s economic ecosystem. For example, the model’s outputs show that whilst AI energy analytics returns $800m in New York, it does not result in the biggest GDP booster due to relatively low energy prices. Instead, tech solutions that help boost the high salaried workforce’s time are more scalable. Understanding these dynamics will help investments evolve into more projects over multiple business and election cycles.
  
  
• Step 3: Invest across a variety of risk profiles
  Because the technologies have varying payback periods and return potential, consider mixing and matching to minimise risks. For example, Dubai’s most favourable use cases are AI energy analytics and smart construction, but both require over half a decade to pay back, indicating a high level of risk. This can be offset by bringing in sizeable returns from year one, simultaneously investing into smaller projects such as seamless journeys, citizen experience platform and indoor air quality monitoring.
  
  
• Step 4: Expect proof of scalability from pilot projects
  One of the core flaws in past smart city projects was to assume demonstrating innovative capability (like advanced data monitoring) was enough. Pilot projects should instead show proof that a technology can scale across a city, not a multitude of disparate properties. City leaders should ask that strong evidence is given about how a deployment can lead to tangible economic outcomes, such as lower electricity across business parks or more comfortable indoor conditions in large retail centres coinciding with greater sales.
  
  
• Step 5: Institutionalise smart city capabilities
  The final step is to set up the administrative apparatus to ensure long term gains can be continuously captured. Working with investors, municipalities must track how capabilities are established. For instance, at a minimal there should be plans to showcase material benefits to the public, data analytics teams to track metrics year to year and strategy reviews to course correct throughout the project lifecycle. Even if investors are not directly involved with these initiatives, they must do their due diligence and avoid a hands-off approach that could forgo the economic opportunity.

The analysis derives from a use case-driven model. Using a variety of government, academic and industry literature, the research identified and modelled the mechanisms by which each use case delivers returns to investors. To map out the impact on local GDP, various inputs were put into a proprietary financial model, such as population density, workforce size, metropolitan area, average salary and energy prices. These enabled a comparison of impacts by local context, to show the value of each technology beyond the immediate payoff. Unique to this research is how the model maps out scaled deployment using multiple solutions; in the literature no projects or initiatives undertook such an exercise in theory or practice.

End note: Instead of opting for the more traditional measure of local output, Gross Added Value (GVA), we used GDP as it includes taxes and subsidies as a form of investment, which features in our financial modelling. Moreover, given that all five cities examined are crucial to the economic health of their host nations, we again deemed local GDP a more appropriate metric.