Author: Scott Matthews. This article first appeared in the AFR.

One of the big questions being asked in Australia's property sector at the moment is whether machines are starting to take over the task of valuing homes.

Chess, played since the 7th century, is a mastermind game steeped in tradition and focused on strategy, but over the years that strategy has become the focus of attempts to build a machine that can outplay the game's best.

In 1997 IBM's Deep Blue supercomputer famously defeated chess Grand Master Kasparov. More recently Google's AlphaZero which, equipped with no human input apart from the fundamental rules of chess, was able to self-learn and defeat the current incumbent in less than four hours.

In the case of chess, while AlphaZero's efforts are undoubtedly impressive, chess is a discrete task. The rules are clear; the basic moves of the game are clearly defined.

Real world problems, however, are far more complex as the rules are not always clear, or for that matter, well understood.

Take the task faced at CoreLogic each day, estimating market value of residential properties with increasing accuracy and speed.


The task we have is to use the price signal information we receive, coupled with property attribute, spatial and market information to estimate what an arms-length transaction for a property in today's market might fetch.

Automated Valuation Models (AVMs) have been used for some time as a means to value collateral held against a residential home loan and advanced to the point where, for a large portion of properties, a precise estimate of market value can be generated.

Increasingly, AVMs are incorporating machine learning and AI approaches to help improve these predictions.

At CoreLogic, our newest AVM IntelliVal does exactly this.

Moving down the AI learning path has allowed us to understand many things about these approaches – their strengths and importantly their weaknesses.

For AI to work well it has to be well informed as to the problem it is solving – what are the rules of the game? What matters when it comes to valuing property? In other words – "how do people think about property and estimating its value?"

These approaches also need well-curated and cleansed data, something CoreLogic is well experienced at and where we have become experts over many years of managing property data. The questions though are these: How do we best capture the rules of the game and how do we best understand how humans think about property and interact in the market?

The answers lie in those humans who are expert at valuing property, valuers.

Seeing the unseeable

Valuers have spent years understanding how the property market works and what to consider when valuing a property in a given location and where in many instances a valuer is able to do what an AVM cannot – that is to observe and interpret that which is not observed in the data we capture.

Quality is an esoteric element that is difficult to quantify and capture, but something that a valuer can intuitively estimate.

Models, on the other hand, are able to assimilate a larger volume of price signal information in understanding likely market price based on previous sales, and potential market risks.

The question is: How can models, AI and humans work together to better estimate market value?

Good AI, done well, provides an intuitive experience for the human with the outcome improving over time (think of the Google experience).

With valuing property, it is possible for valuers to inform the models and importantly for the model to inform valuers.

Both working together are likely to give the best outcomes – better accuracy, faster turnaround times, and greater efficiency.


As is the case with chess, before the self-learning AlphaZeros of today, advances were made with "centaurs", that is part machine, part man, improving on the solely machine-based approaches.

The humans were able to help inform the machine of things it could not observe directly itself and the machine able to see moves that the human player had not considered.

Of course, now the machines are able to understand themselves the dynamics of the game.

With property, the "game" is far more complex, the rules less defined. The machines need human input to help better understand how the game is played, and importantly, the machines can help humans better understand market dynamics that may not be obvious.

At CoreLogic, we have an active R&D program that is yielding interesting results in improving AVM accuracy by using property imagery and descriptions to improve the estimation of quality. We are also looking into approaches whereby we can assist valuers in better understanding the dynamics of an area and considering their feedback to better understand the local market. This is not to replace the work of valuers with a machine, but to enhance the role of both in performing the task in a more efficient and effective way.

The path we are treading is about enlightening both the man and machine as to what is driving the market so we can better estimate property value and ultimately provide a superior customer experience and management of collateral risks for the financial institutions lending against this property.

Machines are not poised to completely take over this space, but they are sufficiently advanced to interact with valuers to deliver a better result for everyone.

Scott Matthews is CoreLogic's head architect, data & analytics

Author: Scott Matthews. This article first appeared in the AFR.