The Real Deal ZA
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The Real Deal ZA

Self-driving cars and the future real estate transaction

I can watch this for hours. It gives a bit of visual insight into the amount of computing power that goes into self-driving tech for safer roads. An incredible amount of data points are collected into a single model that makes every mile driven exponentially safer:

Driving is unsafe but that will change. The car company of the future is a software company that can deploy autonomy at scale.The end user doesn’t know how all of this is put together, they don’t need to. The end user wants to push a button that takes them from point A to B as safely and quickly as possible:

The same correlation can be made with the real estate transaction. What are the friction points?

This leads to stress, just like driving can be stressful.

The real estate company of the future, is the one that can deliver a pain free transaction.

Real estate collects data points just like self-driving cars. There is a ton of history with every house and every transaction concluded. We’ve seen the launch of AVM’s like the controversial Zestimate while many startups take a more narrow view by solving specific pain points of the real estate transaction. E.g. my company Entegral offers a listing syndication service that saves the agent capturing time (= faster marketing) while leads are also centralised (= transparency and quicker follow up). Others offer rental guarantees while Matterport is eliminating travel time for viewings with 3D virtual tours. The sum of all parts is a smoother and more predictable property transaction. Real estate transactions have, much like cars, been improved gradually since the days when agents had exclusive ownership of data with printed ‘listing cards’ (gulp!).

Predictable real estate transactions

Autonomous vehicles are redefining the automotive world, causing a mind shift in how we look at cars. Do we even have to own cars in future?

Will we see the same shift in the home ownership process? If we feed it with all possible use cases and data points can we make software intelligent enough to guide everyone through a painless transaction? I think we are perhaps starting to see this, with companies like Zillow and Opendoor who are making waves with their instant cash offers:

  • The time it will take to sell is known
  • The price you will get is known

They’ve solved 2 major pain points for sellers. Even if the price they offer is too low, it brings in an alternative option and provides a base offer for sellers to work from. Build a real estate company around that, with the instant cash offer as the enabler and you can tie in related services like mortgages and own the complete transaction.

Simulated property transactions

We underestimate the exponential rate of technology change. This tweet is from July 2018 from Waymo (owned by Google) and shows how the rate at which self-driving miles are clocked:

Only a year later and they’ve doubled their simulation miles from 5 billion to 10 billion:

Why can’t we simulate property transactions in a similar way? Too many unquantifiable data points to consider? We thought the same thing about self-driving cars. What prevents a company from building a model based on simulated property transactions?

This is why I believe AVM’s will get more accurate the more data points we throw at it. We will see image analysis that can help identify the perceived and real world value of a pool or a kitchen and combine it with current suburb price trends, traffic flow, crime trends and more for a more accurate price estimate. It is here where expert agents will help refine that model and point out anomalies to help refine that model over time. The agent of the future is perhaps a more analytical consultant.

Emotional property search

Search will get more advanced and bring in the emotional needs of buyers. Property websites are concentrated on text input at the moment, but buying a house is an emotional decision for most, i.e. do you like what you see and can I afford it? It is here where voice search can help to be more descriptive (think OK Google or Hey Siri) and collect more data points quicker. E.g. the buyer can perhaps describe their ideal kitchen “Find me a home with a French-style kitchen with gas oven” which allows machine learning algorithms to analyse current photos of homes on the market.

Tie that in with a financial profile of the buyer, their hobbies, work profile and family metrics and you can build more emotionally matched property search results. This is one of the reasons why I believe thousands of property portals are ripe for disruption.

There is no doubt that the property transaction of the future will be better, but we may just underestimate how quickly this will happen.




Promoting opinion, insights, transparency and challenges in the South African and International real estate industries

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Adriaan Grové

Adriaan Grové

I’m the CEO of, I love working with my remote team to solve real estate problems. Questions everything.

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