7 ms·

I suspect the 'AI software' running these companies is using linear regression to predict housing prices and one of the inputs is the price of similar houses ne

I suspect the 'AI software' running these companies is using linear regression to predict housing prices and one of the inputs is the price of similar houses nearby.

c141charlie 5y ago
What could possibly go wrong with that approach? :-)
danrocks 5y ago
Maybe some reinforcement learning algorithm running as well, where the maximum reward is winning the bid. Hence the algorithm just goes and lays waste on other bidders. Gotta get that infinite reward!
- mrfox321 5y ago
  I doubt RL is involved. That would be soooo risky. Bidding algorithms can model the price of winning the auction, you can maximize profit without fancy RL
nitwit005 5y ago
I recall in one of the previous crashes it turned out a firm had a model that couldn't handle homes going down in value. A linear model is superior to that.
jitl 5y ago
This is basically how human-lead appraisals work in most markets.
streetcat1 5y ago
should move to xgboost.
SantalBlush 5y ago
In fairness, I would think humans include nearby home prices as one of their parameters as well.
- LanceH 5y ago
  A Broker Price Opinion (BPO) is typically set by appraisal, comparable sales and comparable listings. Depending on how cookie cutter the neighborhood is, there are many different levels of "appraisal" from drive by photos to a deep inspection.
- KingMachiavelli 5y ago
  Human/realtor appraisals are often very poor. They will take 3-4 nearby & similar homes and basically add/subtract the differences from the home they are comparing. Then they basically just average the adjusted sale prices. They might add a bit onto that price since prices go up over time. So this has some obvious issues: * Areas without a ton of very similar houses that have also sold recently will basically have no 'comps' to use. * It's really easy to keep identifying differences (pros/cons) until the adjusted prices equal each other but it's hard to know if all of the meaningful differences have actually been identified. * It gives average homes and average buyers a huge advantage. This model assumes that the housing market is hot but not hot enough that someone will pay 10-15% more for some specific feature. Anything unique to a home/property is only going to be worth a fraction of the time & money it would cost to add. Anything super common (kitchen remodel, finished basement, etc.) can actually add 100% or more of it's cost to the houses value because the buyer is paying with 5x or more leverage so paying a bit extra to have it included. This is also why it's often better to fix a few things as the seller than give a discount on the sale price - the buyer will often pay a premium to get things move-in ready since it doesn't impact their monthly costs significantly.
devnull3 5y ago
lol ... this infact is the sample problem which Andrew Ng explains in his famous Machine Learning course on Coursera!
- hbarka 5y ago
  Wait, can you elaborate? I’m sincerely interested in going over that material.
davnn 5y ago
There was a Kaggle Competition in case you missed it. At least here you can see what kind of features they are using and what models people employed. See https://www.kaggle.com/c/zillow-prize-1 https://www.kaggle.com/c/zillow-prize-1
- xapata 5y ago
  Zillow Offers team was isolated from the Zestimate team. They didn't share algorithms.
  - laminarflow 5y ago
    AFAIK this is false; Offers began transacting at the Zestimate price in several markets earlier this year
    - xapata 5y ago
      Could be stale information. I talked to them a while back. Still, transacting at the Zestimate price doesn't mean they're privy to the Zestimate algorithms.

I suspect the 'AI software' running these companies is using linear regression to predict housing prices and one of the inputs is the price of similar houses nearby.

c141charlie 5y ago

What could possibly go wrong with that approach? :-)

danrocks 5y ago

Maybe some reinforcement learning algorithm running as well, where the maximum reward is winning the bid. Hence the algorithm just goes and lays waste on other bidders. Gotta get that infinite reward!

mrfox321 5y ago
I doubt RL is involved. That would be soooo risky. Bidding algorithms can model the price of winning the auction, you can maximize profit without fancy RL

nitwit005 5y ago

I recall in one of the previous crashes it turned out a firm had a model that couldn't handle homes going down in value. A linear model is superior to that.

jitl 5y ago

This is basically how human-lead appraisals work in most markets.

streetcat1 5y ago

should move to xgboost.

SantalBlush 5y ago

In fairness, I would think humans include nearby home prices as one of their parameters as well.

LanceH 5y ago
A Broker Price Opinion (BPO) is typically set by appraisal, comparable sales and comparable listings. Depending on how cookie cutter the neighborhood is, there are many different levels of "appraisal" from drive by photos to a deep inspection.
KingMachiavelli 5y ago
Human/realtor appraisals are often very poor. They will take 3-4 nearby & similar homes and basically add/subtract the differences from the home they are comparing. Then they basically just average the adjusted sale prices. They might add a bit onto that price since prices go up over time. So this has some obvious issues: * Areas without a ton of very similar houses that have also sold recently will basically have no 'comps' to use. * It's really easy to keep identifying differences (pros/cons) until the adjusted prices equal each other but it's hard to know if all of the meaningful differences have actually been identified. * It gives average homes and average buyers a huge advantage. This model assumes that the housing market is hot but not hot enough that someone will pay 10-15% more for some specific feature. Anything unique to a home/property is only going to be worth a fraction of the time & money it would cost to add. Anything super common (kitchen remodel, finished basement, etc.) can actually add 100% or more of it's cost to the houses value because the buyer is paying with 5x or more leverage so paying a bit extra to have it included. This is also why it's often better to fix a few things as the seller than give a discount on the sale price - the buyer will often pay a premium to get things move-in ready since it doesn't impact their monthly costs significantly.

devnull3 5y ago

lol ... this infact is the sample problem which Andrew Ng explains in his famous Machine Learning course on Coursera!

hbarka 5y ago
Wait, can you elaborate? I’m sincerely interested in going over that material.

davnn 5y ago

There was a Kaggle Competition in case you missed it. At least here you can see what kind of features they are using and what models people employed. See https://www.kaggle.com/c/zillow-prize-1 https://www.kaggle.com/c/zillow-prize-1

xapata 5y ago
Zillow Offers team was isolated from the Zestimate team. They didn't share algorithms.
- laminarflow 5y ago
  AFAIK this is false; Offers began transacting at the Zestimate price in several markets earlier this year
  - xapata 5y ago
    Could be stale information. I talked to them a while back. Still, transacting at the Zestimate price doesn't mean they're privy to the Zestimate algorithms.