A project has launched in Madrid that could transform the way we view urban development. For the first time, the municipality has access to a tool that allows it to analyze and forecast the status of each neighborhood in the capital down to individual buildings. This solution opens up new opportunities to identify issues related to housing, infrastructure, and the social environment, as well as to make more precise decisions that affect residents’ quality of life.
According to El Pais, the city authorities have introduced an artificial intelligence-based platform called Simulador Estratégico de Ciudad. It consolidates data on real estate, transport, green spaces, demographics, and the age of buildings. The system operates with 190 indicators and 134 data sets from 37 official sources, including Instituto Nacional de Estadística, the cadastre, Canal de Isabel II, and Registro de la Propiedad. Previously, this information was collected in a fragmented way, making it impossible to see the full picture.
Accurate data for every district
The platform is already running in test mode for the municipality’s internal use. It enables analysis not only by district and administrative area but also by individual neighborhood. For example, officials can learn where the highest concentration of children of a certain age lives, the average age of buildings (according to El Pais, it is 1979), and where urgent renovation or the creation of new green spaces is needed. This approach helps to identify imbalances and assess the potential impact of future decisions in advance.
Among the first findings: the area of Ciudad Universitaria (Moncloa-Aravaca) has the most land dedicated to public use, while Orcasur (Usera) leads in the number of medical facilities. The largest concentration of commercial real estate is in the Sol district. Madrid has a total of 1,512,496 residential properties, with an average size of 83.44 m². The most spacious apartments are in Salamanca (103.14 m²), and the most compact ones are in Puente de Vallecas (69 m²).
Impact on city strategy
The introduction of the Simulador Estratégico de Ciudad is the first stage of a large-scale plan to modernize city management. This new tool will lay the foundation for drafting a municipal strategic plan that will replace the outdated Plan General de Ordenación Urbana of 1997. Authorities expect that accurate data analysis will shape policies in eight key areas: urban structure, society, housing, climate neutrality, economy, transportation, public spaces, culture, and heritage.
The collected data will be used to model various development scenarios. For example, it will be possible to assess in advance how the situation might change with the construction of new roads or the launch of environmental initiatives. This will help minimize errors and improve the efficiency of investments in urban infrastructure. The next step will be the implementation of specific projects and subsequent evaluation of their effectiveness based on platform forecasts.
Changes in property structure
The platform also discloses details about housing ownership structures. According to El Pais, 60.65% of apartments in Madrid are owner-occupied, with Moratalaz having the highest rate. In the central Centro district, only 44% of homes are owned, while 30.2% are rented out. This data helps identify areas with the greatest need for tenant support or incentives to purchase property.
The shift to digital city management means abandoning outdated analog methods. The new approach consolidates fragmented data into a unified system, speeding up decision-making and making it more evidence-based. In the long run, this could lead to more balanced neighborhood development and reduced social inequality.
Context and latest trends
Using digital platforms to analyze urban environments is becoming increasingly common in major European cities. In recent years, Barcelona and Valencia have also implemented urban infrastructure monitoring systems, but such a comprehensive approach as in Madrid is rare. russpain.com analysis indicates that integrating big data and artificial intelligence into urban management enables faster responses to challenges like population growth, aging housing stock, and climate change. Similar projects have already helped uncover hidden issues in green space distribution and housing availability, forming the basis for new renovation programs and support for low-income families.
