Using a predictive model to find undervalued properties.

[David D.]

I am curious if anyone has employed the simple strategy I was thinking of for investing in any kind of property:

1. Use something like ATTOM API to get historical sales snapshots, or just all the historical sales of the property class for a metropolitan area. 
2. Train a regression model using square footage, bathroom count, or more advanced features to predict the price of the sales. 
3. Get the error of your predicted housing price (if you're using price per square foot, convert to housing price) down to $10K-$40K or so. 
4. Find properties with characteristics that predict they should be selling for 2 standard deviations above what their actual price/value is (or basically just properties that are selling for way below what the model predicts they should sell for).

The challenge here is of course step 3. I just started experimenting with Downtown Detroit with ATTOM's "sales snapshots" and I haven't gotten that kind of performance yet. However, I'm sure many people have, especially if they have images or floor plans of the houses/other features and lots of data. 

I apologize if this is all just fairly standard financial modeling. Very key thing here, I don't have a finance/economics background, and am just getting started in this area, but this struck me as a good strategy to use. Also looking at neighborhood trends. My worry is that this wouldn't work because any undervalued property has its price for a "reason", e.g. it is already at its equilibrium price by the time you've determined that its "undervalued", and in fact you won't be able to flip it for whatever reason for a huge profit.

I don't understand how that could be the case though. What seems more intuitive for me is that these are the homes that just happen to have so far been ignored by other investors, or else passed over for better deals, since a small group of investors cannot snatch up all of the deals (otherwise why would this forum exist haha).

Another argument might be that you can never tell when a neighborhood is going to crash in its property values, or when people will migrate to a nearby one that is flourishing. If this were the case, then your model wouldn't be effective anyway. Presumably you can also protect against this with the aforementioned neighborhood growth trends.