AI, Machine Learning, Big Data are not only IT industry buzz words but are established technologies with real-life applications that have solved several everyday problems in the recent decade. For example, Google Maps gets you from point A to B via an optimal route avoiding high traffic areas using Big Data and Machine Learning. Turn navigation feature was introduced by Google in 2009 and is now widely used by commuters throughout the world.
We feel that the real estate industry is lagging in terms of innovation and there are a number of opportunities for application of Machine Learning and Big Data. One of these opportunities is leveraging these technologies for Property Market modelling and forecasts. We are able to model and forecast (Machine Learning) property market trends with a high degree of accuracy thanks to our large dataset (Big Data), which is continuously updated by our crawler, that systematically browses major real estate portals and agent sites in the country and indexes past sales, current listing and rental data.
It is true that there is no “crystal ball” technology to predict the future, but as explained in Google Maps example above, it is possible to model it with a high degree of accuracy. I.e. Google Maps models the traffic for the particular day and hour of your travel, relying on real-time data it receives from the devices of other commuters around you. So, the combination of large volumes of historical data for the traffic patterns seen in recent days/weeks/months as well as its’ real-time inputs allows the Machine Learning algorithm to model the traffic and lay out an optimal route for your particular journey accordingly. Although the semantics of the property market are different from traffic routing, the same principles can be applied.
It is almost impossible to account for all of the factors that influence supply and demand in a particular property market (and in turn drive price growth or decline). Some advisers call for as many as 90 metrics to be taken into account when assessing the future price trends in a particular area. However, the need to rely on these external metrics disappears when Big Data and Machine Learning are thrown into the mix. For instance, Google Maps does not need to take into account the number of lanes, traffic lights or pedestrian crossings along the route. These factors are inherently built into the measurement of speed and frequency of stops collected from thousands of commuters’ devices on previous days and then coupled/analysed with the same data on the hour/day of your travel.
As our model also trains on historical data with new data inputted in a near-real-time manner, it inherently takes all external factors into account when producing the forecast. To continue the analogy, new traffic lanes can be compared to new dwellings, while micro- and macro- economic slumps can be compared to existing and newly introduced traffic lights and so on and so forth. This exemplifies the multitude of parameters whose implications have already been encapsulated in the thousands of recorded past and recent sales in our dataset.
It is also important to note that traffic data changes every minute/hour during the day, but similar patterns (cycles) are observed from one day to another along the route of your commute. In the same way, property prices fluctuate quarter to quarter, but evolve in 7 to 10-year cycles, with different peaks and troughs depending on the location. As such, whilst there are multiple external factors that define the amplitude of the cycle in a particular area, there is a high degree of similarity in the pattern of cycles between areas that are in close proximity to each other. For example, council areas in the Greater Sydney region share the same peaks and troughs, however, the Sydney cycle is very different to that of Perth.
Property market data from neighbouring areas (and therefore their cycle parameters) is inputted into our algorithm and is also taken into account when the forecasts are produced. As such, in the same way, that traffic data from nearby roads and streets is taken into account to lay out the route by Google Maps, cyclical patterns of neighbouring areas serve as an input metric providing for a more accurate forecast, while also delineating the overall market dynamic ‘landscape’.