How AI Can Improve Financial Outcomes in the Rented Property Sector

All markets rise and fall, and some can even disappear. But as long as people need places to live (a pretty safe bet!) the rental housing market will always have demand. The market is likely to grow as more of the population become used to renting services rather than owning fixed assets – think of taking a ride-sharing service versus buying a car, for example. 

Housing associations naturally want to provide a better service for their tenants as well as to optimise the returns they can get from their rental clients.

As more and more technology finds its way into our living spaces, companies in the field of PropTech (Property Technology) have a vested interest in adding value to their services so that they too can extract better results from their customers.

These days, sensors, apps and data are everywhere. Here are just a handful of examples of relevant PropTech hardware and apps that collect data:

  • presence detection in rooms
  • smart meters for gas and electricity
  • temperature and humidity detectors to check for damp problems
  • smart doorbells
  • home environment control apps
  • CCTV

 

Putting data together from these sources and then analysing it with AI and machine learning has the potential to offer valuable business insight to those who work in the rental housing market.

What about privacy concerns?

No one wants to feel as though others are spying on them and especially not within the walls of their own home.

Having attended an interesting event called Global PropTech Demo Day (12 January 2021), we feel confident to say that an invasion of privacy isn’t what PropTech is about at all. Still, we have to recognise this possible negative perception, and help to put minds at rest. If we’re not clear about this, the sector’s growth could be stifled, which isn’t good for housing associations, tech firms or even rental clients themselves.

The good news is that knowing people’s identities isn’t relevant to the effectiveness of this technology. Statistical data is what’s relevant, and this is not personally identifiable information. There is use of demographic information such as ages and occupations but even this doesn’t identify people, especially when there are large data sets involved. To be clear: all of the data processed in any PropTech project we’re  involved in contains no personally identifiable information, meaning that we stay fully compliant with the GDPR.

There is more value in having more data to analyse, but the interesting side note here is that more data also means more privacy. That’s because it’s harder to identify an individual from a large set of data that it would be from a tiny set of data, where some smart social analysis might be able to digitally unmask an individual.

We believe that PropTech offers great potential to improve people’s lives as well as making businesses more efficient. That’s the mission we want to get behind!

What does AI have to do with PropTech?

AI can offer great value to housing associations, landlords and installers of residential data sensor tech. Why? It all comes down to data, which is the essential fuel of all successful AI projects. 

By pulling together different data sets, it’s now possible for PropTech users to get answers to questions such as:

  • How can I better understand which rental tenants will get into financial problems?
  • How can I reduce the risk of rental tenant defaults?
  • How can I predict which rental tenants will renew their lease?

Understanding such insights can reduce financial risks and increase opportunities for profit, both for landlords and for the data tech companies that sell their services to those landlords. Imagine being able to predict the general profile of who the most challenging 10% of tenants might be – or who the most profitable 10% of tenants might be?

But how is all this possible, you might ask? It comes down to the smart analysis of data. Consider the inputs that could feed into an AI model for PropTech:

  • financial data: historical information about payments made by tenants, previous late or missed payments and arrears.
  • demographic property data: property location, category and type of occupancy.
  • demographic tenant data: age, professional status and occupation.
  • sensor data: measuring occupancy rates by detecting presence in rooms (because tenants with low occupancy rates are more likely to abscond or fail to renew their leases).
  • environmental data: potential antisocial conditions, for example local graffiti and crime rates, could affect lease signings and renewals.

 

Traditional data analytics would struggle to synthesise any meaningful insight from such disparate data sets. But with AI and machine learning regression models, it’s now possible to predict, for example, whether tenants will abscond. 

All of this and other data can be pulled together and analysed by AI and machine learning to produce an even clearer picture of the user base being served by housing associations, landlords and tech providers.

Armed with such information, housing associations and landlords can take quick action to help their vulnerable tenants as well as protecting their own financial interests. Similarly, companies that install residential tech equipment could use the same knowledge to add value to the product range. For example, smart analysis of data could reveal locations where smart bins can identify where bins need to be emptied less often, allowing the business to become more competitive (or simply more profitable) in those areas.

What this looks like in practice depends of where your business fits into the property management space, and what data is accessible for us to help you gain insight. 

Want to get the best from PropTech?

 Whether you’re a housing association, landlord or technology installer for rental accommodation, there’s the potential for AI-powered data analysis to help your business reduce its financial risk and increase its profitability. 

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