Trading Places – The Man And Machine Behind

Written by Dr. Chlump Chatkupt, Founder and CEO, & Curated by Holtby Turner

Of all of the startups that are poised to disrupt real estate, may be the most exciting. Set to revolutionise from the ground up how we make investments and decisions, manage assets, and build, develop, and run cities, has major players in the industry queuing to test its AI technologies. aims to “serve as the underlying infrastructure for all location and mobility applications”. Think beyond Uber and property platforms and almost every single brick-and-mortar business.

There’s not a huge amount online about, so we reached out to its Founder and CEO, Dr. Chlump Chatkupt, who took time to answer some questions for us.

Chlump holds a PhD in Mathematics from LSE, an MA in Philosophy from NYU, and a BA in Economics from Northwestern University. He stands out from the suits and boots in real estate with long hair and a regimental attire consisting of black jeans and a black T-shirt. This is not for PR or to create an impression. Chlump, just like Zuckerberg, simply can’t be bothered to waste brain energy on deciding what to wear. This sensibility aligns with a life philosophy of owning as little as possible. However, this doesn’t mean that the 34-year-old CEO isn’t commercially minded.

The ethos behind the founding of was to make a difference, to contribute to society and solve meaningful problems for as many people as possible. Anyone with an interest in location and mobility would find value in Chlump’s work. His mission is “to help location industries to learn from their data and to build better, more efficient, smarter industries and cities”. As it stands, that help is already fairly extensive. can, for instance, help real-estate developers and investors to identify which markets could yield optimal returns — and why, when, and by how much — in addition to helping everyone from logistics firms to city managers. develops AI technologies to solve the most challenging location and mobility problems by enabling augmented decision-making.

Described as Bloomberg or Google for location and mobility, aggregates massive amounts of heterogeneous traditional and nontraditional signals from multiple sources, yielding a unified data platform, and leverages AI and machine-learning algorithms to hunt for hidden patterns and signals. technologies enable a user easily and quickly to perform large-scale quantitative analyses and forecasting; build dynamic models; interact with data via intuitive visualisations; leverage robust indices; and exploit algorithmically generated insights, such as the identification of up-and-coming areas, the detection of undervalued assets, or the optimal distribution of establishments.

Processes that are currently inefficient, expensive, unscientific, and manual — everything from the development of investment strategy and forecasting to site selection and portfolio optimisation — can be simplified and streamlined and, in time, automated.

The result is simply, an AI-driven real-estate hedge fund in a box, a foundation upon which even more can be built.

Aggregating location and mobility data and leveraging all of that information by means of AI and machine learning — all with beauty and ease — constitute a no-brainer for the industry. Yet, could’s use of data be a global game changer that affects, not just property, but society itself?

In short, yes. A bold statement? Perhaps, but many see the city as an organism, determined and influenced by its inhabitants, but simultaneously, those inhabitants are determined and influenced by the city.

According to Chlump, applying AI and machine learning and constantly updating data-driven algorithms will give property professionals tremendous new powers in time. But Richard Moss wonders is that a good thing, or might it run a risk of segmenting society even more with home ownership ‘haves and have nots’?

“As in social, economic, and political systems, the real problem here is the concentration and asymmetric allocation of power, and our aim should be to guard against such an inequity. AI and similar technologies are merely instruments, and it is up to us to determine and control how they are used. Fortunately, these instruments have the capacity also to democratise and reallocate power, create opportunities, and promote welfare.”

In terms of commercial partnerships, where do you see fitting best in real estate?

I feel a solidarity with everyone and see everyone as a potential partner. We are always looking to incorporate new or alternative data signals and set up partnerships that pertain to data management, data exchange, and data operations. Other partnerships might involve the sharing of new technologies (such as an innovative database design), new kinds of sales channels, or the integration of services (such as those that bridge B2B and B2C).

Ultimately, our sensibility is simple: we would welcome anyone who shares our vision of leveraging AI and data to build a better world that serves all of us. The ideal partner has rich domain-specific data, such as cap rates or rents, or generates an alternative data set, such as ride-sharing patterns, that can supplement or serve as an implicit signal of domain-specific interests; industry insights that can inform the development of technologies and indices; and exposure to and extensive networks in real estate and adjacent verticals.

By 2027, what positive transformation do you hope will have made?

As celebrated AI scientist Andrew Ng recently observed: “AI is the new electricity.” AI will be pervasive, foundational, and integrated into daily life and holds the potential to upend entire industries. As AI systems improve, accelerating returns will accrue to those who effectively leverage AI, and conversely, those who fail to take advantage will face an ever widening gap.

AI will enhance performance and efficiency, reduce costs, create new markets and opportunities, enhance the built environment, and revolutionise the art of placemaking.

Some of the transformations that hopes to achieve include an AI-driven interactive computing environment that enables large-scale modelling, backtesting, forecasting, and analysis across markets; asset types; geographies; economic events; macro cycles; and time scales and that is more accessible and ubiquitous than the Bloomberg terminal as well as a location-mobility stack in the manner of Google or AWS that serves as the underlying infrastructure for all location and mobility applications.

We hope to serve all location industries, such as real estate, retail, advertising, urban planning and developing, city management, transportation, logistics, and telecoms.

By adopting a scientific data-driven approach to such industries, we aim to simplify and enhance individual and collective decision making, develop and influence socioeconomic policy (for example, affordable housing, and so on), and foster a love of and an engagement with spaces. Beyond improving businesses, AI can be used also to solve sociopolitical problems (such as gerrymandering, redlining, and so on), advance epidemiology and disease control, support the management and tracking of food supplies and harvests, and assist in humanitarian efforts, such as those that concern large-scale migration and refugees.

AI should serve all – especially the most vulnerable and disadvantaged populations.

In his PropTech 3.0 report, Andrew Baum claims, “idealism appears to infect the drive towards smart buildings, the shared economy movement and real estate FinTech”. He then questions if the shared economy is a social ideal with no future, and wonders if houses will be traded online, or if it is too risky to take out the traditional advisor.

What are your thoughts on these points? Are you an idealist?

While popular sensibilities may play a role, the general trends seem to be driven less by idealism, whatever that means, than by the prevailing needs. With climate change looming as one of our greatest existential threats, energy efficiency and sustainability more generally will become increasingly important.

The sharing economy fosters some benefits, such as the more efficient use of under-utilised resources and the promotion of a sense of community, but also bears harms, such as the exploitation of local economies with the transfer of wealth from them and individuals to corporations. Other harms include the transfer of risk from corporations to workers, the weakening of labour protections, the promotion of precarious employment, and the suppression of wages.

It is crucial to get at the fundamental questions without confusing the issues. What makes a traditional advisor necessary or not? Does the asset class exhibit the properties that make it amenable to being traded online? Whether or not a traditional advisor is needed to manage a particular asset class is orthogonal to the importance of the asset class, and whether or not an asset class is important is orthogonal to the medium in which it is transacted.

Ultimately, what we do is shaped by our constraints, our circumstances, and our values. The fundamental question in any endeavour is to determine the world that we want and then, given our constraints and circumstances, to determine how to engineer such a world, all while guarding against malignant consequences. In a word, conscience must be our counsel.

How much do data results and mathematical truths impact your own vision and the innovation that underpins

A preoccupation with innovation can keep one from producing truly radical ideas and instead tie one precisely to what innovation is meant to subvert, namely, history.

Our work, approach, and ethos resemble those of the mathematician, the physicist, or the computer scientist, and our aim is to identify and characterise the fundamental structures, patterns, and dynamics of our world and of our interactions with it.

We aggregate and study as much data and evidence as we can; take an interdisciplinary approach and borrow from mathematics, physics, computer science, and other fields; engineer features; build mathematical models; devise algorithms; test hypotheses; prove results; experiment; and iterate. Essentially, we take a scientific approach to the world of location and mobility and build the environment and tools that allow others to do the same.

The goal must always be a profounder understanding, and what counts is not so much innovation, but investigation. Understanding must come first. All of the rest, including innovation, comes later.

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