News & Insights

Machine Learning for the Real Estate Industry

11/10/2017 16:09:00 (GMT)

There has been a lot of talk about Machine Learning over the last few years with it gaining in popularity across all industries, but without going too in depth what is it? Well, it can be viewed as the intersection of data and computer algorithms. We all know that the amount of data being generated every day has increased significantly and shows no sign of slowing down either. We also know that having a resource sitting around and not working for your business is not a productive approach, worse than that you are probably already also paying for the gathering and storage of that data. Machine Learning is the approach of taking that data and creating a model with it that algorithms can run on top of. After time the machine gains in intelligence as more and more data is passed through it. Ultimately this can provide a better use for all that data you have collected. This article will talk a little bit about some of those uses that could be applied to the Real Estate Industry and also some of the likely sources of the relevant data needed.

Image Recognition

One of the most recognisable applications of Machine Learning is the understanding of images by the machine itself. Once a machine has understood an image it can then do things that a human could also do. For example:

  • Finding visually similar homes or properties.
  • Classifying room types such as kitchen, bedroom or bathroom.
  • Feature extraction such as this room contains a sink, toilet, shower but no bath.

Operational Optimisation

Another great use of Machine Learning is to help identify patterns in a large set of data that perhaps a human would not be able to see so easily. Once any subtle patterns have been identified they can then help optimise certain aspects of your business. For example:

  • Discover what agents are better at closing certain deals based on a set of input parameters. This would allow you to match up agents with specific clients or deals that had traits that would otherwise had gone undetected. Thus, increasing the chance of everything going smoothly.
  • Finding patters in buying habits could help match clients with homes or neighbourhoods that would otherwise not be in the agents' minds to show them but are likely to be a very good fit. This is also useful if your company is a Real Estate purchasing business to help identify otherwise unexplored sites.

Future Predictions

Possibly the most exciting element of Machine Learning and something that is definitely at the forefront of what is being developed is the ability for the machine to make accurate forecasts and predictions based off a set of input data. A couple of instances where this could be useful to the Real Estate industry are:

  • Forecast where house prices are likely to go in the future. This could be nationally, regionally or perhaps even more specific again. This use for a set of data is also closely related to Automated Valuation Models that many companies use currently to value a property at today's prices. After all being able to predict future prices requires the machine to understand an accurate valuation for today as well.
  • If your business invests in property then it might be of use to be able to predict where the next effective economic corridors will emerge between suburbs and industrial areas. This would give your organisation an advantage over others and allow you to get in early while prices are still relatively cheap.

Data Sources

All of this sounds wonderful, but what if you don't already have much data and are concerned that you will be left behind by organisations that do? Well, data can be obtained from a huge variety of sources. On top of that, most data can be found at relatively low costs or even free as well if you know where to look. Here are a few suggestions of possible data sources that might be useful for the Real Estate sector:

  • Demographics
  • Traffic
  • Planning Department Data
  • Property Listings
  • Website Usage Data
  • CRM Data
  • Social Media
  • Housing Sales Data
  • Infrastructure Data