Smart cities?

Originally published 24 March 2017. AI polished for a better reading experience: 11 August 2026.

Under the “smart cities” label we usually mean the use of digital technology and big data in municipal management. Today that toolkit still solves relatively easy problems: traffic congestion (cameras and statistics), allocation of scarce capacity in public services (queue management), or real-time control of assets (IoT on lighting systems, for example). These are events that unfold in “real time” and that IT systems can forecast and manage well. Statistics reveal trends; managers project figures forward; when a known shock is coming, the forecast can be adjusted. Quantitative analysis looks simple.

Real-time problems, though, are only a slice of a city’s agenda. Cities are among the longest-lived institutions we still have. People organised shared resources long before countries or private companies existed; some urban settlements have endured for millennia. That longevity also means mid- and long-term problems that demand equal attention.

Housing is one of them. Availability and affordability are crucial for sustainable urban development. Look at global top municipalities such as London, New York, or Hong Kong: rapid population and economic growth came with a sharp rise in housing prices. Living near the historic core became prohibitive. Inadequate dwellings of around 20 m² became common—and even those command large sums.

Madrid tells a similar story. Rents have climbed quickly in recent years, yet roughly 10% of homes still stand empty. On the face of it, those two facts do not fit a clean market rationale.

What can the modern digital economy offer to ease affordability? How do we make cities livable again on ordinary incomes? Many of today’s digital tools are not helping the underlying problem; they are intensifying it.

Services like Airbnb sidestep pieces of urban planning and create pockets of tourist housing in neighbourhoods never designed for that intensity of turnover. Returns from short-term tourist rentals often exceed long-term rent, risk can look lower, and operating “the Airbnb way” has often meant avoiding licences that hotels and hostels must hold. Booking.com and similar platforms have also expanded into tourist apartments. These products are useful for travellers—but planning exists so a city remains comfortable for everyone who lives there. When large services run outside that frame, the unplanned shift changes the city’s equilibrium and seeds mid- to long-term strain.

Online real-estate portals are another pressure. On them, people and firms negotiate long-term contracts in near real time. In effect they create a real-time market for assets with very low liquidity: multi-year leases and purchases financed over decades. That mismatch—transaction speed versus asset liquidity—makes speculation easier and can inflate prices by manufacturing a sense of scarcity. If you own a house and prices are rising (as they usually have), you have an incentive to hold and wait for more capital gain. That logic helps explain empty stock such as Madrid’s vacant homes. When things reverse, prices can fall so fast that owners have little time to react.

Modern cities need tools to manage empty property and to discourage behaviour that undermines healthy competition. Without care, real-estate platforms can facilitate cartel-like patterns and anti-competitive outcomes: supply is locally limited, and demand rarely disappears.

Municipalities should therefore expose API-like access to their urban management systems so policy can respond automatically as conditions change. In practice that could mean:

Allowing Airbnb-style short-term rentals only through a municipal API layer, so the city can match offers to current planning rules and limits and collect the right taxes.

Identifying, via API and a single cadastral identifier, the full stock of urban real estate so empty homes held mainly for speculation can be addressed. Housing is a private asset, but it is also a city-level resource (in the economist’s sense, not the accountant’s) and needs public management through smart taxation that penalises pure hold-out behaviour—so markets reflect genuine demand and supply.

Using data from tourist and residential transactions to plan future development, transport upgrades, and land-use rules with clearer evidence.

Like many regulations, urban planning can be expressed as algorithms. By opening those algorithms through APIs, cities can keep digital transformation inside the law and on a more sustainable path—rather than letting platforms rewrite the city by default.