Algoritmos que deciden tus alquileres en España y Estados Unidos

The renewal letter arrives. The rent goes up by almost ten percent. The tenant calls the landlord and asks why. The answer is calm and almost friendly: the system says so. There is no person behind that figure. There is a program that spent the night reviewing thousands of leases, empty units and signed renewals across the same area. And it returned a number. The landlord does not negotiate because, strictly speaking, he decided nothing. The question holding this video together is exactly that. If nobody decides to raise rents, why do they all rise at once? And above all, who answers when the one responsible is a model? Understand the mechanism before the effects. These programs are called revenue management tools and they work like the ones airlines use. Large owners hand a technology company data that is not public: actual signed rents, discounts, lease lengths, occupancy building by building. The program pools information from owners who compete with each other and returns a recommended price for every unit, every day. Here lies the heart of the matter. The model does not try to fill every home. It tries to maximise total revenue. It often recommends leaving units empty for weeks rather than cutting the rent, because a cut contaminates the price of the whole building. That detail changes the nature of the market completely. In theory, a rental market works because each owner decides alone and competes for the tenant. If one asks too much, another lowers the price and captures demand. That balance disappears when everyone consults the same oracle, fed with everyone's data. Nobody meets. Nobody signs a pact. Nobody keeps minutes. And yet the outcome looks a great deal like a classic cartel. Lawyers call it tacit coordination through an intermediary. The company selling the program competes with nobody, but it sits at the centre and synchronises everyone. The illegal agreement is replaced by an apparently technical recommendation. This is where the uncomfortable comparison appears. For decades, in North American cities, official maps painted in red the neighbourhoods where mortgages should not be granted. Those neighbourhoods were, almost always, Black and immigrant. The exclusion was signed, dated and stamped by an administration. That is why it could later be exposed, documented and taken to court. The current version carries no signature. The model never says a neighbourhood is undesirable. It says its potential rent allows an upward adjustment. The researcher Cathy O'Neil warned that such systems inherit the prejudices of the data that trained them and hand them back as mathematics. Discrimination stops being an opinion and becomes an output. The most advanced case is in the United States. The Department of Justice, together with several states, sued the leading company in the sector for reducing competition between landlords in rent setting. It also sued large owners who used the program. Some have reached financial settlements without admitting fault. An official report estimated that algorithmic price setting cost tenants billions in a single year. Several cities and at least one state have directly banned setting rents on a program's recommendation. The company has sued back, arguing that banning a price recommendation amounts to censoring information. The record shows the battle is no longer about planning. It is legal and algorithmic. In Spain the phenomenon takes a different, blurrier and less documented form. There is no equivalent court case, but tenant unions make the accusation insistently. They argue that the big property portals and automated valuation tools publish price estimates that owners then adopt as a minimum reference. The effect is similar even if the mechanism is gentler. The whole market looks into the same mirror, and the mirror always suggests raising the price. Public authorities have answered with an algorithm of their own: an official reference price index that caps what corporate landlords may charge in areas declared under strain. This can be read as a telling sign. We now argue only about which model rules. Now the counterargument, and it deserves to be taken seriously. Defenders hold that these programs do not create scarcity, they only measure it. If a city lacks homes and demand is high, prices would climb anyway, program or no program. The software would be a very precise thermometer, not the fever. They add that the owner always keeps the final word and that many ignore the recommendation. They also point out that the largest company closed its main case without admitting any wrongdoing. And there is truth in all of that. No digital tool explains on its own a housing crisis built over decades of expensive land, insufficient public building and heavy financial investment. It can still be argued that the thermometer is resting on the