Empty housing is wasteful from society’s point of view. Both landlords and renters would benefit from finding a suitable counterparty to contract with faster. There are already online systems for listing housing for rent and sale, and also notice boards for people seeking housing. This is a good start, but a predictive system would be better. Given enough data, computers could forecast who is a good tenant or landlord and which apartment or house suits a given person’s preferences. Less searching would be needed by all involved.

Rental agencies already have a tenant database where they exchange references for renters. A similar online system should be created for landlords and housing (distinguishing the two). Also, the rental agency or real estate bureau should be rated separately from the people working in it, otherwise bad agents may move from one employer to another and escape their reputation. A bad notoriety may even motivate a person to change their name. For good agents, the loss of a reputation not tied to their person may make it difficult to change jobs.

Instead of chancing on complaints or praise in forums, a renter could see a summary rating of many rental agencies, agents and buildings in one place. The building database should include objective measures like the distance of a building to the city centre and the nearest supermarket, the yearly electricity and heating bills, the outdoors noise level in decibels, some average air pollution measure, school catchment areas, floor plan and area, etc. This saves labour for prospective tenants, so each of them does not have to search for the same data from various sources. Information entered by past renters is hopefully objective and protects novice tenants like students from being misled by advertisements like “five minute drive to the city centre” (only at 3 am when the roads are empty, in a Formula 1 car), “short walk to the supermarket” (short compared to the Shackleton Solo expedition), “safe neighbourhood” (compared to a war zone), “quiet” (relative to a rock concert), “spacious” (roomier than a shoebox), “close to nature” (insects and rodents inside). Distances to various landmarks could be automatically downloaded from Google Maps when the building address is known. Crime, pollution and traffic density statistics could similarly be autocompleted.

Renters should be able to select the measures they consider important in the data and get a ranking of the housing on offer according to these. Once someone has rated several apartments, the system could potentially predict the housing that would please that person.