Zillow Just Gave Us A Look At Machine Learning's Future

It's put up or shut up time for Data Science teams. When the business depends on our work for revenue growth, success is expected. Failure is fatal.

Zillow’s stock is down over 30% in 5 days and their CEO put a lot of blame on the Data Science team. That’s eye opening for our industry. 25% of their staff just got laid off in the fallout. They closed an entire business unit, something Zillow tied their revenue growth to, and a major investor walked away.

I’ve been predicting this type of scenario for a couple of years now, but even I didn’t expect something this catastrophic. I don’t know how their current CEO survives this.

Think about that. Models failed in production. As a result, 25% of Zillow employees lost their jobs and Zillow lost 30% of its value in 5 days. This is the largest and most public failure, but not the first. It also won’t be the last. What happened?

Zillow has home value estimation models. They started out showing users an estimate of home value to help them decide if they were getting a good deal. From the earliest days, real estate agents and experts were vocal about estimate quality. The company stood by their models and inference served to users.

At the end of the day, Zillow didn’t lose any money if their estimates were off by a few percentage points. Especially as home prices spent the better part of 5 years on a massive run up, no one really noticed inaccuracies. This overconfidence setup what came next.

Zillow believed their inference quality was high enough to support a new business model. They’d start buying and flipping homes. It’s a simple use case. The homeowner comes to the platform. Zillow uses its models to predict 2 things, a current price the homeowner will accept and the price of the home in 6 months. If the profit margin was high enough, the platform offered to buy the home at the current predicted price.

The business knew the margin of error was very small. They called it out early in the planning process. The models had to be exceptionally accurate for this business to be viable. The prediction of current price needed to attract a significant percentage of sellers. The 6 month price prediction needed to be dead on to generate the expected profit margins.

The CEO said repeatedly, there was a narrow window of accuracy that would create a sustainable business. Everything else would result in failure. This is the connection between model metrics and business metrics. Small inaccuracy ranges could cause cascading failure.

·         Too low on the initial home value estimations and homeowners would not accept the offer.

·         Too high on the initial estimation, margins would not be optimized.

·         Miss on the customer behavioral model, and homeowners would not accept the offers at a high enough rate.

·         Too low on the 6 month home value prediction and opportunities would be missed.

·         Too high on the 6 month estimate and margins would not be optimized or offers would be extended when they shouldn’t be.

The street warned of issues with the new business model. It was predicated upon the assumption that housing prices would continue to climb without interruption at a stable rate. They said no investment does that. The domain experts warned of issues with the predictions. The business plowed ahead anyway.

I have been there. In fact, I have almost put my clients in Zillow’s shoes. My background in software quality has saved me from calamity over the last 10 years. I have learned through watching hard failure that the simulation used to test any complex system must be dead on or every test after is unreliable. All the performance metrics gathered from those tests are unreliable. The resulting models are unreliable.

The simulations and tests Zillow ran were incomplete. The assumption of stability is baked into most statistical models. In real world complex systems involving people making decisions, stability is NEVER an assumption that will hold in the long run. As I said in a previous post about experiments, these types of models will pass early validation, even using statistical experimental methods, then fail suddenly and catastrophically.

The problem is in the dynamics of real world data. The distribution changes. Training data’s distributions have distributions. The result is a probability of a starting distribution being part of serving accurate inference. I think of this in terms of inference spaces. When I advise Data Scientists to understand Topology and Differential Geometry, this is why.

A distribution of distributions can be modeled as deformations of the inference space. Pull back the covers on large scale deep learning models and this is one reason overfitting creates miraculous accuracy. Large scale models have a primitive understanding of the deformations leading to a degree of generalization. The more the model learns about how the inference space changes over time, the better it generalizes.

For that to happen, data needs to be gathered which seems disconnected from the outcomes being predicted. The data used to build the initial models where there is some relationship to outcome, is itself built from a system. Predicting changes in that system allows Data Scientists to understand the dynamic processes that generate their training data and could cause its distributions to change.

This requires experimentation. How do we know what data to gather if the relationships between features and outcomes is obscured by distance? We run experiments to find these features and validate the distant relationships to outcomes.

This creates a chain of models, and it gets ugly to maintain. Enter causal modeling vs large scale deep learning models. One camp contends these problems can be overcome by throwing extraordinary datasets at deep learning models. The model will work out the complex interactions. It will handle multiple systems, their interactions, and emergent behaviors within the layers.

The other camp points out the long list of large scale deep learning model failures and flaws. They offer causal methods as a way forward although no one has a complete grasp on efficiently doing all the work necessary to support a causal model (creation, implementation, deployment, and support).

The resources necessary to follow either approach are beyond the reach of all but the largest, most mature tech players. I have built complex pricing models with both methodologies. It’s expensive.

The tooling and automation required to execute is a large scale project in and of itself. There is a massive tools gap here and custom built is the only way to go. Models need to be built to recommend data to be gathered and hypothesis to test. The distance between intervention and outcome makes it difficult for us to design experiments. Sifting through the data necessary to guess at those relationships is not something people can manually do in business timelines.

I have had to work with teams to automate the process of running multiple model architectures against each other with several different initial feature sets and initial model states. The system sends the outputs from each run to another model that filters in promising results for further study. As you can imagine, there are thousands of dead ends for every promising lead. Iteration times need to be reduced to days. Automation is the only option and there is no off the shelf experiment management system with this type of functionality.

Feature stores need to recommend features which could improve model accuracy and feed into an experiment management system. They need to provide data discovery capabilities to support building a data catalogue. Again, there is a gap in off the shelf products.

I have oversimplified and glossed over a lot here that I will likely explore in future posts. My point is to expose the complexity rather than comprehensively detail the problems and solutions.

The review process is grueling. Even with automation, the data gathering and experimentation required is extensive. There are no shortcuts. Most projects are either unfeasible or cannot be justified by significant ROI. Just building a team who can do this means throwing a lot of money at people.

Those investments must be sustainable and the only way to support them is through revenue growth. Machine Learning capabilities and infrastructure are recurring and increasing costs, not a onetime cost. The business model must be built to monetize machine learning.

It’s not just hard to execute on projects. It’s hard to pull the pieces together to make execution possible. Harder to make success likely. Companies like Amazon, Facebook, and Google abandoned their early efforts for all these reasons. However, they quickly realized that for them to sustain their growth trajectories, they’d have to figure it out.

Companies who compete with them have followed. While no one saw COVID coming, some saw the impacts early on. In February and March 2020, 2 of my clients saw the changes in early chain models which indicated the instabilities to come. As a result, we studied the data and retrained those models to prevent impacts to late chain models. We caught the failure before it impacted pricing model stability. Zillow didn’t.

Inflation was another example. Most of my clients have been studying the impacts of our current inflationary cycle on pricing for over 2 years. Their models are ready, and they’ve already trained their customers to be less sensitive to price increases caused by inflation. They understand the behavioral component of pricing and can PREPARE. Why?

The coming inflation cycle was obvious. The fact that it could impact buying behaviors was obvious. A model that could explain what to do about it is the competitive advantage. Price sensitivity’s driving forces and how to change them is the competitive advantage.

It’s a chain. Price->Inflation->Customer Behaviors->Churn->Price Sensitivity->Customer Perception Dynamics. With a model handling all that complexity, the complete picture is comprehensible, and the best courses of action are easier to evaluate. For over a year now companies have been preparing their customers. Zillow didn’t.

As a result, Zillow didn’t realize they needed to prepare their customers for the new business model. Their machine learning models were disconnected from their customers. Their pricing predictions were not within the margin of error. Customers only accepted Zillow’s offers 5% of the time. 95% of customers were unserved and a substantial number of them were unsatisfied. Failure here was impacting Zillow’s core business model as well as the new one.

Zillow’s estimates of 6 month home values were not modified in enough time to minimize the impacts of COVID and their margins were outside the narrow range the business model needed to succeed. They realized events impacting stability could happen again and the business lacked the capabilities to accurately predict them.

I’m pretty sure they saw the cost of addressing that shortcoming and could not justify it based on the new business model’s revenue potential. They have an exceptionally talented Data Science team who likely presented a plan outlining everything necessary to succeed. The timeline would have been too long and the price tag too high.

The business model could not be supported fast enough to stop the losses. The business would have incurred a large up front expense and sustained inconsistent returns for an indefinite amount of time. As a business, you cannot think that’s the right course of action no matter how painful the alternative is.

Zillow had a business model which could monetize machine learning. They failed because they overestimated their capabilities and underestimated the complexity of executing.

You know what happened next and the fallout. The CEO points directly to the failure of their Data Science team as the root cause. Their investors aren’t buying it and I don’t either. Zillow’s Data Science team is excellent. The business knew the risks and ignored expert advice. The initial rollout should have been far more limited and cautious.

The Data Science team didn’t go to the lengths necessary to support their models. That was their failing. However, the business is ultimately responsible for betting the farm without demanding a real world track record to support those models. Their quest for cheap, quick growth relying on unproven technology is an old story of tragedy. I don’t see their senior leadership surviving this intact.

For Data Scientists, there are several lessons. You’d better understand the science before taking on significant projects. When revenue starts getting booked against Machine Learning projects, model reliability is critical. Research methodology is essential. When revenue growth starts being built around Machine Learning capabilities, those capabilities need to be comprehensive. The body of evidence supporting core models needs to be substantial. Even when the burden of accountability rests firmly on senior leadership, it’s the Data Science team’s jobs that are on the line.

For businesses, the lesson is sobering. Zillow is at the top 10% of Machine Learning maturity and they missed. Luckily, their survival did not depend on this business model, and they will walk away with the benefits of lessons learned. It’s unlikely they fail a second time, and their future growth will come from those hard lessons.

Legacy business models are working to transform and compete in the Machine Learning driven competitive landscape. Their survival does depend on new business models because their existing model is no longer viable. For them, missing the same way means they go out of business.

Their challenge is extreme. Turn a weakness, machine learning capabilities, into a strength. Find a new business model that monetizes what is currently a weakness. Build out Machine Learning capabilities and infrastructure while transforming the business at the same time. The pitfall? The current Data Science team who delivers efficiency projects and supporting features now is not the same team who will deliver models reliable enough to support the updated business model. Legacy companies will need to execute better than a very mature business could.

All this must be done while returns from the current business model decline. The up front investment is being sustained by stagnating revenues. The new business model will take time to become cash positive and sustaining losses for even a year is difficult. It takes discipline and belief to keep an entire business aligned with a massive change that is not immediately profitable.

Businesses have put off making the jump but now understand the imperative. The next 3 years will see more Zillow like stories but unlike Zillow, those stories will end with the business in bankruptcy.

This is the direction of our field for the next 5 years. The opportunities are huge because we will finally be living up to some of our hype and delivering in a way turns businesses around. It’s fun to be the superhero. However, don’t promise to deliver if you can’t. The cost of failure is your job and a significant career setback. For Data Scientists and businesses, this is win or die.