In places with zoning laws (restrictions on what kind of buildings are allowed at a given address), there is often debate on whether to relax the restrictions. This would allow new construction or enlargement of existing buildings. The renters are generally in favour of more buildings, because the increased supply of housing lowers prices at a given demand. The landlords oppose construction, because it reduces the rents they can charge. These economic arguments are already part of the debate.
Much lobbying effort (that costs time and money and may create corruption) could be avoided if the market price of housing (rents or house transactions) was used directly in the regulations. New construction is allowed if the average rent is above a cutoff and denied below. Zoning laws may be a bad thing overall, but if they are to remain, they could be made more resistant to manipulation by basing restrictions on objective indicators, not lobbying.
The good incentives created by this require interest groups to put their money where their mouth is: if landlords want to prevent new construction, they should lower the rents they charge. Only with average rents low would building be blocked. Similarly, if tenants want more housing, they should pay the landlords more. They may of course decide to pool their money and found a property development firm instead.
Property developers want to get construction permits for themselves, but deny them to other property developers (their competition). The motivation to get a permit by fair means or foul is stronger when property prices are higher. In this case, the above reliance on the market price to regulate permits does not create good incentives. If new housing is allowed when prices are high, developers are motivated to form a cartel and raise the price. Permits reward high prices. A good price-based regulation of property development would require the opposite of the rental market mechanism – a low selling price of new housing should lead to more construction permits.
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.