The property industry is not the first to be permeated by artificial intelligence, and it is far from being the last. Machine learning is working its way into various sectors, but it’s proving to be of great use particularly in the property sector, providing a helping hand for humans to help reach their professional goals. One of the ways in which AI is being used successfully within the property industry is to analyse data more efficiently. This is particularly significant, as collection and analysis of data can often be time and resource-consuming and difficult to navigate; with the help of intelligent software, however, it is easier to infer the “story” or direction that a batch of data is pointing in, and consequently come to a clearer conclusion or evaluation.
Take the narrative element provided by data mentioned above – a path or a plot drawn out by an artificially intelligent machine proves to be enormously pivotal in implementing AI strategies in the property industry. Home automation tech brand, Nest, showcases this exact technique in the form of their AI-powered thermostat, collecting data that is taken by the user’s habitual use over a period of time, getting used to this user’s way of life and preferences, and eventually transforming this information into a learned way of operating. Not only does this method optimise tenant comfort, it also saves energy and money by learning specific points throughout the day when its user doesn’t need heating or hot water, for example.
Energy-saving tactics are a hugely beneficial consequence of AI technology being implemented in residential and commercial buildings. Energy management software, BuildingIQ, uses predictive control to forecast how a building will respond to its energy saving management systems, and then balances this against its optimisation model for tenant comfort. The result is a system that adheres to the desired energy consumption target of a building with as little impact as possible on the resident, made possible through an intelligent analysis of data. An additional feature offered by BuildingIQ is predictive measures taken to areas of a building that will likely need maintenance in the near future – so, if your pipes are about to burst and there’s no way you would know about it, this software will tell you about it before it happens by learning about your building’s tendencies and susceptibility to damage. Other companies implementing AI for “deep-learning” and building automation include PointGrab, which works by using sensors to extract significant data about how tenants are using their building, and how to improve energy-saving methods.
We’ve seen how self-programming thermostats and other responsive home and building systems can provide useful data to provide comfort, save energy and predict problems, ultimately helping both residents and property managers. While the opportunity for savings is huge here, we’re not quite at the stage of being able to create a home that “thinks” for itself without the need for human involvement, and the issue of hacking into building management systems could pose a serious threat.
It is logical that gaining visibility into customer’s preferences is key to successful property management, agency sales and home searching. AI can achieve this in the above-mentioned spheres, driving prospect conversions and improving outcomes for business development in property companies. Take Kylie, an AI-powered sales rep who starts off by writing template emails which are then approved by humans, after which the software eventually learns what the correct structure, tone and content of the email would be. Commercial property management platform VTS follows in a similar vein, centralising important data in order to help landlords and brokers through every step of the sales and management processes – attract, secure and retain tenants.
Home searching is another area of the property industry where AI can analyse data to help drive savings. Intelligent software is able to collect data from a user’s habits and preferences to advise on what area of a city would be a perfect match, or analyse your daily routine to suggest optimal rental space based on your needs. Airbnb similarly uses AI to set pricing on locations based on what previous customers have liked the most about a property (for example, the photos or the geographical location).
Data-driven optimisation, sales and savings are pivotal to the property sector, and AI has made it possible to enhance the way we find, collect and break down data to make it a streamlined process that allows room to focus on the tenant.
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Nick Riesel is Managing Director of commercial property brokerage Free Office Finder
March 8, 2018
Data, AI and the commercial property sector – what’s the connection?
by Nick Riesel • Comment, Property, Technology
Take the narrative element provided by data mentioned above – a path or a plot drawn out by an artificially intelligent machine proves to be enormously pivotal in implementing AI strategies in the property industry. Home automation tech brand, Nest, showcases this exact technique in the form of their AI-powered thermostat, collecting data that is taken by the user’s habitual use over a period of time, getting used to this user’s way of life and preferences, and eventually transforming this information into a learned way of operating. Not only does this method optimise tenant comfort, it also saves energy and money by learning specific points throughout the day when its user doesn’t need heating or hot water, for example.
Energy-saving tactics are a hugely beneficial consequence of AI technology being implemented in residential and commercial buildings. Energy management software, BuildingIQ, uses predictive control to forecast how a building will respond to its energy saving management systems, and then balances this against its optimisation model for tenant comfort. The result is a system that adheres to the desired energy consumption target of a building with as little impact as possible on the resident, made possible through an intelligent analysis of data. An additional feature offered by BuildingIQ is predictive measures taken to areas of a building that will likely need maintenance in the near future – so, if your pipes are about to burst and there’s no way you would know about it, this software will tell you about it before it happens by learning about your building’s tendencies and susceptibility to damage. Other companies implementing AI for “deep-learning” and building automation include PointGrab, which works by using sensors to extract significant data about how tenants are using their building, and how to improve energy-saving methods.
We’ve seen how self-programming thermostats and other responsive home and building systems can provide useful data to provide comfort, save energy and predict problems, ultimately helping both residents and property managers. While the opportunity for savings is huge here, we’re not quite at the stage of being able to create a home that “thinks” for itself without the need for human involvement, and the issue of hacking into building management systems could pose a serious threat.
It is logical that gaining visibility into customer’s preferences is key to successful property management, agency sales and home searching. AI can achieve this in the above-mentioned spheres, driving prospect conversions and improving outcomes for business development in property companies. Take Kylie, an AI-powered sales rep who starts off by writing template emails which are then approved by humans, after which the software eventually learns what the correct structure, tone and content of the email would be. Commercial property management platform VTS follows in a similar vein, centralising important data in order to help landlords and brokers through every step of the sales and management processes – attract, secure and retain tenants.
Home searching is another area of the property industry where AI can analyse data to help drive savings. Intelligent software is able to collect data from a user’s habits and preferences to advise on what area of a city would be a perfect match, or analyse your daily routine to suggest optimal rental space based on your needs. Airbnb similarly uses AI to set pricing on locations based on what previous customers have liked the most about a property (for example, the photos or the geographical location).
Data-driven optimisation, sales and savings are pivotal to the property sector, and AI has made it possible to enhance the way we find, collect and break down data to make it a streamlined process that allows room to focus on the tenant.
_________________________________