Could Smart Cities And Data Solve The Housing Crisis?

Making a more quantified and calculated system may stop the rot


Location, location, location, that is the prime factor for setting house pricing in the world today. If you buy a house in the middle of the countryside far from transport links, it will cost considerably less than a comparable house in the middle of a popular city. For instance, a 5 bedroomed house in Belgravia in London costs £7,500,000 whilst a 5 bedroomed house in Lampeter in Wales can be below £400,000. They are essentially the same in terms of the basic price to build, but due to social constructs one is nearly 20 times the price.

Not helping this is the current issue that is being faced by London, as investors are buying houses purely for investment and leaving them empty. In some of the most desirable residential areas of the city 20% of houses bought are simply left empty. This has had knock-on effects for local businesses and services. In fact many of the UK’s leading newspapers claim that new developments are targeting foreign money that will leave locals unable to afford to stay in the area.

This leaves residential space at a premium in the UK capital, alongside some of the world’s other major metropolitan areas. This means that prices rise, and in the case of London, much quicker than wage increases, therefore local people can’t afford to live in the area and the infrastructure begins to struggle.

One way that companies and governments can help to alleviate this and improve the state of cities is through adopting smart techniques and creating smart cities.

With a growing urban population the pressure to add more housing is increasing, and by using robust data and sensors it is possible to create a market based around facts and stats, rather than assumptions. At present the valuations of housing has no strict science, with much of it about perception rather than reality. Through using smart technology, it is possible to create a fairer system and make it far easier for estate agents to value houses accurately.

For instance, if a house has a better energy rating, more space within each room and better transport links it is going to be more expensive, whilst another house which may be the same size, but not quite as efficient or close to transport links, would be less expensive. The difference in value between the two would be difficult to establish in the current setup, but with data quantifying, this price setting could be much easier and would potentially stop the rocketing house prices that we are currently seeing.

Similarly city planning could be considerably more efficient when data is used to optimize design and space used, whilst also helping to monitor energy use and pollution, this would not only increase space for humans, but improve the environment around them too. Combined with the IOT, this data could even allow difficult to reach or monotonous tasks to be automated and for maintenance to be carried out before something has broken, rather than doing so afterwards at considerably more expense. All of this would see the costs of maintaining housing decrease, whilst also allowing for the environments around them to be more pleasant.

However, the major obstacle to these being realized is the market itself, which sees estate agents, land owners and property developers make vast sums of money through current inflation. This means that implementing these changes will be difficult in privately held properties, but governments may need to look at how this could be done as the current market is unsustainable, especially in cities like London. For instance, a typical flat in some parts of London has increased in value by 138% compared to average wages actually reducing in real terms.

So could making cities smarter really make a difference? I believe that it could, but it will require brave governments to implement something that will undoubtedly benefit people in the long run, but perhaps harm their income in the short term. 


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