which assumes risk is (i) normally distributed and (ii) a source of reward. For most people, however, risk looks like Theranos or the Fukushima accident or the Challenger distaster.
It's unbelievable that a machine learning model trained to predict house prices based on experience would be accurate in the face of events like the COVID-19 pandemic or what will happen when the Fed raises interest rates. You can model risks like that, but to the extent that you're working from experience you are working from a database from the 1929 Crash, South Sea Bubble, etc.
(B) Mark Levine wrote a good article about how you'd exploit such a predictive model. If you consistently gave people low offers, a few people would accept them. You would get a high rate of return but could invest little capital.
To invest more capital you have to make more offers that get accepted, that is, give better prices. Your rate of return goes down and if there is shrinkage from errors, accidents, etc. you could get a negative return.
It's that "tendency towards a declining rate of profit" that Marx warned about.
(C) The analogy with stock market market makers doesn't sound good when you consider the differing timescales.
Market makers are isolated from some risk because of the length of their holdings. Yet, they make profits by exploiting the stochastics of a stationary market (e.g. if you don't like the price at time t1, you will usually get a better price at t2) but they lose money when markets move definitively in one direction or another.
That kind of trader heads for the bathroom when things go South and in the interest of being orderly markets impose sanctions on market makers who do the natural thing and press the "STOP & UNWIND ALL POSITIONS" button when it gets tough.
In the case of Zillow I see holding times that go on for weeks or months and all kinds of real world risk like planning to do certain renovations but having to delay the work because out of 20 things you need from Home Depot they only have 16 of them.