The Death of the Mall, Why It Matters, And How Technology Can Help (by Anita Ngai)

This is a guest post by Anita Ngai, who has extensive experience in technology, retail and urban development. She worked in McKinsey for 4 years before transitioning to Real Estate in Hong Kong, and online travel. She was trained as a structural engineer. She is currently exploring a start-up idea focused on helping developers become more data-driven in their planning and leasing processes. You can contact her here.

I love that domain experts – in this case – a structural engineer cum real estate professional, are thinking about how technology can transform the way their industry works. Hope you enjoy her article as much as I did!




“The Death of the American Mall”, “Ghost Malls in China”, “Are Malls Over?”, “Is the Physical Shopping Mall Dead?”, “China’s Ghost Towns and Phantom Malls” – if you google search the term “shopping malls”, these headlines pop up. What’s interesting is that these headlines cover places as diverse as the Midwest US to the large metropolitans of China. Some reasons for this trend include:

  1. Online shopping
  2. Urbanization – higher concentration of population and/or wealth means less retail space needed in suburbs
  3. Changing demographics – deceleration of population growth, aging core group
  4. Slowing income growth/increasing inequality – weaker GDP growth; wealth more concentrated in hands of a smaller number of people
  5. Change in consumer preferences – trend that millennials prefer to live and occupy less space
  6. Overbuilding catch up – we have been overbuilding for some time, and it’s finally catching up (as New York Times quoted a real estate executive: “The mall genie was out of the bottle, and it was never going to come back.”)
  7. Poor management – bifurcation of malls into great versus terrible ones that don’t survive

The death of the malls poses serious challenges to developers and planners. Their previous paradigm, “build a mall and people will come”, no longer holds today. Instead of building new malls, developers need to focus on conversions and repurposing of existing malls and spaces.[1]


Underutilized mall spaces are not just a problem for developers – they are a waste of a city’s precious land resources. For example, in dense cities like Hong Kong, where I worked in real estate in different roles for four years, the competing demands on land are very real – retail is very much in demand by the upper-middle class and mainland Chinese tourists. On the other hand, the housing crisis is getting more and more acute because of the lack of space for new housing developments.

Instead of allowing new retail spaces to be built nearby, or even tearing down retail spaces, it makes more sense to convert and enhance existing retail spaces. Maintaining density levels in urban and suburban areas can bring socioeconomic benefits. Furthermore, the carbon footprint of retrofitting has been shown to generally be much lower than demolition and rebuild.  All this means that potential public and private investments into our built environment can be better directed, to projects with higher value to society.


In the age of Airbnb and Uber, one would think we could do better in optimizing the underutilized assets in malls. Indeed, technology holds tremendous potential in helping developers do this – both at the planning and the post-completion stages.

Planning Stage:

Collecting and analyzing data can help developers customize their projects to their potential users. In the past, developers only had blunt demographic data (population size, income levels, age composition) on which to base their plans. Now, sensors and mobile phones can capture large volumes of finer data e.g. what types of shops women between 30-40 in the geographical vicinity dwell longer and spend more money at.

Combining all this data, developers can use sophisticated statistical simulations and machine learning to predict the foot traffic, occupancy levels, and likely visitor profile (e.g. income-level) of the project if they vary the proportion of space dedicated to retail vs entertainment vs hospitality/accommodations.

Testing hundreds of scenarios of the project mix and layout would only take seconds, but is close to impossible for humans to do – both from data collection and computational analysis perspectives.

Post-completion Stage:

 After the project is built, there are decisions that developers and their leasing teams have to make continually – who should we lease each space to? How should we price each space? How long should the lease period be for each space ( the default now is 3-5 years depending on the market which works for some, but not for others).

Each of these decisions has tremendous ramifications for the mall’s utilization. For example, putting a fast-food restaurant at a certain entrance to a mall would draw a lot more footfall through that door, versus a beauty supply store or the front lobby of a three-star hotel. For each space in a mall – whether a back corner on the ground floor or center core on the third floor, a fast-fashion tenant, quick service restaurant or three to four food court stalls will each have a different footfall impact, chance of success, and likelihood of sustaining their business over the long-run.

Developers also need to be more flexible with the use of space – pop-up stores, for example, have helped ease some of the long-term vacancies or low footfall issues that landlords are seeing in their retail properties. But this is not done in a data-driven or widespread way: pop-up stores are often under the purview of marketing teams, and theleasing teams may only take a support role.

If developers collect and analyze data effectively, they will also be able to lease their spaces and re-configure their malls based on real-time data. All this boosts utilization and uses space most efficiently.


Having worked in real estate for a number of years, here are the factors that hold back these obvious innovations from taking off.

The first reason lies in how developers think about innovation. The only teams within those organizations thinking about innovation and technology – some form of a “digital” department and an incubator/VC – are not usually tasked with looking at the design process. They focus on “downstream” issues like improving customer experiences in a shopping mall or on having a bet in a start-up who will “hit it big” one day.

Second, even if a developer/owner is motivated to take a data-driven approach to design, a single company’s portfolio of property may not be large enough to yield data that is representative of the market view. Certain Asian developers come closest to controlling the ownership of an entire neighborhood or district, but worried about competition, they would not be motivated to share this data with the industry, competitors or brokerage firms.

A third reason is similar to what we have seen in many other industries: existing players will only make incremental changes, until someone new comes in to disrupt traditional practices. Tech start-ups have been active in the real-estate sector, but mainly in three areas:

  • Real estate transactions
  • IoT and smart homes/buildings/cities (the fridge that will order for you when you’re out of milk, the trash can that sends a signal when it’s full and needs to be serviced) and
  • Visualization (VR for potential buyers to walk through their unbuilt/faraway home; 3D rendering and VR experience of construction blueprints).

Unfortunately, I have not seen many start-ups work on applications that will help with the design and planning of malls. There are a few providing heat maps of where footfall is in a mall; or analyzing the type of store a given neighborhood needs, e.g. apparel, doctor’s office. Mapping start-ups are currently focused on other areas of applications, such as self-driving cars.


Retail makes up a significant portion of a city’s built space inventory: San Francisco has about 76.3 million square feet of office space versus 80.5 million square feet of retail space. It will remain a useful and desired part of city life for time to come. However, it will be a costly waste of precious city space if the trend of underutilization continues. Developers will be able to buck this trend if they use a far more data-driven approach to planning and leasing.

I sketched out the challenges above, and I believe they can be overcome if developers can take a longer-term view to invest in evolving their planning and design processes and to incorporate new data and technologies available. The benefits from using new approaches are not easily quantifiable without having tested them, so sticking strictly to ROI figures will not lead decision makers down this path.

Also, more startups and public agency collaborations such as Uber Movement and World Bank’s Open Transport Partnership would allow the immense amount of data being accumulated to become transparent for public use. Having a public agency host data from different private sources may help overcome more data privacy concerns floating around, though these agencies would likely need tech companies to help them improve on data security. Governments can play a more proactive role in facilitating progress, through regulations and test projects, and I believe the municipal level – because of smaller size and relatively less partisan impasse – will be the best testing grounds.

[1] (Of course, there are still places where there is a real growth in the population or local economy, and so new retail space is indeed needed.)

[2]A number of studies actually show that higher densities can lead to higher public expenditure per capita, though there is evidence that this is due to government management practices, e.g. higher government employee compensation. In addition, lower densities do not necessarily increase public expenditure because the costs for sewage, electricity and other infrastructure are actually priced into the new houses, i.e. bore by the residents themselves. Benefits from higher density developments are more obvious if we include quality of life metrics (e.g. traffic congestion, air pollution).



Two (Game-Changing) Ways Cities can use Technology to Fight Inequality

The story of income inequality is not new – as lower and middle-class incomes stagnate while the highest income brackets race ahead, the wealthy have access to goods and services that are increasingly out of the average person’s reach.

But we now see its detrimental effects more clearly than ever. I live in the Silicon Valley, and when news of Donald Trump’s election broke, the overwhelming feeling was disbelief. It was unimaginable. Tears of anguish were shed, yet a large part of the country celebrated. To me, that moment captured the deeper impact of inequality – fragmentation of society. Our politics become polarized, we are unable to find middle ground in our interests, and we increasingly feel like a nation of enemies, not countrymen.

While the problem gets more serious, our typical approaches to tackling inequality are reaching their limits. Redistribution is a political hot potato that pits the interests of the “haves” and “have-nots” against each other. Investing heavily in educational opportunities has diminishing marginal returns on social mobility both in the absolute sense (because the future of jobs is increasingly uncertain) and in the relative sense (because wealthier parents give their children more and more advantages).

We are in desperate need of new paradigms to fight inequality in cities. Here are two ways I believe technology can be a powerful, game-changing force – if deployed thoughtfully by cities.



First, cities should use technology to make life experiences in the city more and more independent of incomes.

 It would be impossible to close the income gap completely, short of communism. A society where incomes are totally equal is also undesirable, as it erodes the motivation to work.

However, I believe that technology can make life in the city increasingly independent of income, which can go a long way towards mitigating the daily experience of inequality.

Let me start with explaining the notion of an aspirational good – things that people wish they had money to buy. In transport, most people aspire towards owning a car. In housing, it is a condominium or a private home (American friends: as opposed to a publicly-built Housing Development Board apartment, which 80% of Singaporeans live in). In healthcare, it is a private doctor or hospital bed – at your choice and convenience. In education, it is getting into top schools and universities.

There is an unsustainable dynamic behind aspirational goods. Because these goods are limited in supply, the more people can afford it, the more expensive they get, and the further out of reach of the average citizen. Aspirational goods are the sources of a huge amount of angst in the middle class.

Technology has the potential to overturn the entire notion of an aspirational good. By creating new forms of value, it can make the alternatives so attractive that even those who have money choose not to buy the aspirational good. 

Take transportation for example. Owning a car is so attractive today because public transportation is an inferior option on many counts – the low cost cannot make up for its lack of time efficiency (it takes about twice the amount of time as a car ride), comfort (especially in humid weather), and customization (as a car owner, I know I can get a ride whenever I want).

What if public transport can be faster, more comfortable, more customized and cheaper than owning a car? With technology, this need not be a pipe dream. Imagine a day when you can wake up in the morning and your phone already knows where you need to be. It recommends the top three ways to get there. You select one, and within a minute, your ride shows up at your door – perhaps a shared car, or an electric bike if it’s sunny. It gets you to the train station just as your train pulls in. When you get out of the train, your minibus has just arrived to take you to the office. After work, you can summon a sleek designer vehicle for your dinner date. On the weekend, an autonomous jeep shows up at your door-step to take your family around for a day of fun.

You don’t need to buy multiple tickets – everything is paid through your phone. Or, you can even pay for transport just like a Netflix or Amazon Prime Subscription: a flat fee for unlimited rides. You never need to worry about parking again. With alternatives like this, how many people would still want to own a personal car? Even the wealthy may reconsider, especially if we simultaneously put in policies to make driving more inconvenient, such as no-drive zones in the city.

Just as technology brings about new forms of value (e.g. customization, flexibility) for those who don’t own a car, how can it do the same for other sectors?

  • How can technology help to transform Singapore’s public housing estates such that they offer new forms of value which private estates cannot provide? For example, how can we help HDB dwellers feel like the entire estate – with all its facilities and open spaces – is their home, one much bigger and diverse than any private estate? Digital communities and intra-town transportation may be aspects of this.
  • How can technology make a face-to-face doctors’ appointment something that people no longer seek as the “premium option”, for example, by making tele-health so attractive and pervasive?

I believe if domain experts and technologists put their minds to this, they will be able to come up with much better ideas than these! In short, technology can help catapult currently “inferior” options to equal status as “aspirational” options by creating new forms of value.

2. Second, cities should use technology to distribute scarce land and human resources more equitably.

In most countries, there is a healthy debate on how progressive and equitable the tax and redistribution regime is. However, not as much attention is paid to how other scarce city resources – land and manpower – are used. These too, must be used equitably, and technology can help cities achieve this.


Reducing the land used on roads is a great example of how we can use land more equitably. Roads and parking lots tend to be utilized disproportionately by those who own cars, who – in Singapore – tend to be wealthier. Can we cut down on roads and parking, and reallocate this land to purposes such as community facilities and public housing, which benefit a wider proportion of the population?

Yes, and technology is critical to this. How much land we need for roads and parking is determined by the concept of “peak demand” – the maximum number of vehicles on the road, ever. We can cut down peak demand by encouraging people to use shared mobility options rather than drive a private car (I write about how tech enables this here), and by investing in autonomous freight and utility so that these activities can be done at night, when roads are far emptier.

Public Sector Manpower

Similarly, we can use public sector manpower more equitably by investing in technology. Technology can significantly reduce the manpower we commit to customer services. For example, Govtech rolled out MyInfo, which enables citizens to automatically fill in their administrative information for Government schemes with the click of a button. Chatbots on Government websites will increasingly be able to answer public queries; phone lines will no longer be needed. Public sector manpower can now be dedicated to functions which are in great need of resources. One such area is social work and education. Families in the bottom rung of society often face a cocktail of challenges – divorce, low-income, lack of stable employment, cycles of incarceration and so on. Giving them (or their children) a real chance of breaking out involves an extremely high level of hand-holding and investment by social workers and schools. Resources are sorely needed here.

Access to top quality healthcare

Let’s take another scarce resource – top surgeons. People who can pay for their services access better quality care, and stand a higher chance at recovery. Technology can change this dynamic. Companies like Verb Surgical are using machine learning to propagate top surgeons’ expertise more widely. This is how it works: every time the best surgeons perform a procedure, every single action is recorded in a common machine “brain”. The “brain” is trained to associate each action with the probability of a successful surgery. As the “brain” records more and more surgeries, it gets smarter and smarter. Now, the “brain” is made accessible to ALL surgeons. At each step of their surgery, they are told what successful surgeons did. Now, the best surgical expertise is within the reach of the average citizen.

Technology that enables our scarce resources (e.g. land, public sector manpower and top surgeons) to benefit the broad population and serve those in acute need are the types of technologies that cities should invest in, and quickly enable through regulations.


If you google the “Smart City” movement, you’ll find many broad and loose definitions. Generally, it refers to how cities deploy technology to improve city life and allocate resources more efficiently, whether it is helping transport systems run more efficiently, making interactions with various Government services easier, or to adding fun to the city experience.

Unfortunately, such broad and loose definitions give cities little guidance to on what to focus on in prioritising investments and regulatory reform, which is an incredibly important conversation given the limited resources at most cities’ disposal. It also does not paint a compelling vision for why being a Smart City matters, which disengages most of the population. Personally, before I worked in tech, I felt absolutely no connection to the idea of a ‘Smart City’. Tech was cool, but I never thought it was crucial.

I believe that using technology to tackle inequality and its effects should be a Smart City’s ambitious goal and guiding force, providing focus and rallying support from its constituents. This article spelled out two ways to do so.