131–140 of 265 posts
Last October when I refinanced, it was appraised at $455k. Zillow had it at $467k-ish.
Not enough to bum me out because I also had a now former neighbor sell recently. Buyers got into a bidding war and he walked off with $15k over asking.
Seems accurate to me! ;)
Realtors have always hated the Zestimate with a passion [0]. When they publicly called out Zillow CEO for selling his own home for 40% less than the Zestimate on it, Zillow magically released a superior algorithm [2]. [0] Inaccurate Zillow 'Zestimates' a source of conflict over home prices | Feb 8, 2015 | http://www.latimes.com/business/realestate/la-fi-harney-2015... [1] Zillow CEO sold his home for 40% less than Ze…
[1] reminds of an interview with the founder of Ashley Madison. He was going on and on about how western cultures were so regressive and prude about adultery and sex in general. The interviewer then asked him if he had ever used his own service. This elicited an uncomfortable non-answer that basically boiled down to "No".
of course i'm just playing devil's advocate, because all evidence points to ashley madison being a scam, with essentially no women on the site.
You would think real estate would prove to be one of the few real life fields where machine learning would be able to predict the prices pretty accurately. You have: 1. List price 2. "Favorites" on the MLS. 3. Square footage 4. Bedrooms 5. Bathrooms 6. Sold price for nearby properties 7. Sold price for nearby properties relative to their list price And the list goes on... Is it that difficult to predict? Literally th…
The thing with RE is that its as imperfect as you can imagine. Its possible that the listing price varies from the real sales prices because its a part of the marketing campaign, which that alone puts a lot of noise into the market. (for example, putting a 100k house at 80k can get people in a bidding war ultimately irrationally or emotionally paying above 100k. Alternatively, a higher listing price might make the seller more adept and following a realtor.)
> I guess the problem is, there's not any money in having an accurate prediction.
Market fit is definitely one of the hardest parts of the problem.
Some companies have decided to actually buy the houses and sell them themselves!
Disclaimer: I'm one of them.
You would think real estate would prove to be one of the few real life fields where machine learning would be able to predict the prices pretty accurately. You have: 1. List price 2. "Favorites" on the MLS. 3. Square footage 4. Bedrooms 5. Bathrooms 6. Sold price for nearby properties 7. Sold price for nearby properties relative to their list price And the list goes on... Is it that difficult to predict? Literally th…
"It is difficult to get a man to understand something, when his salary depends upon his not understanding it!" -Upton Sinclair Zillow has an accurate estimate somewhere in their database but it's not in their financial interest to make that estimate public. By low balling, they both gain more buyers and gain more sellers.
Just looked up my house, and got a chuckle out of my wife when I told her the zestimate for our house was 61% higher than the price we paid two years ago. Needless to say, we're in not in a "hot" real estate market (i.e. city population ~60,000). The other shady thing it that Zillow says the sold price for the house we sold back then is $20,000 higher than actual sale price. I wonder what percentage of people think Zillow is underestimating vs. overestimating home prices.
You would think real estate would prove to be one of the few real life fields where machine learning would be able to predict the prices pretty accurately. You have: 1. List price 2. "Favorites" on the MLS. 3. Square footage 4. Bedrooms 5. Bathrooms 6. Sold price for nearby properties 7. Sold price for nearby properties relative to their list price And the list goes on... Is it that difficult to predict? Literally th…
If you want to try it, there are a few data sets kicking around, including a) https://www.kaggle.com/c/house-prices-advanced-regression-te... b) http://www.bis.org/statistics/pp_detailed.htm c) https://archive.ics.uci.edu/ml/datasets/housing If you had a really great model, you might be able to make some money with it--find underpriced (per the model) houses, buy them, and then try to sell them at the model's price.
At the liquidity and costs of marketing and buying-selling a property, this strategy would require you to find really high margin houses and those are snatched , marketed and sold within a week.
You would think real estate would prove to be one of the few real life fields where machine learning would be able to predict the prices pretty accurately. You have: 1. List price 2. "Favorites" on the MLS. 3. Square footage 4. Bedrooms 5. Bathrooms 6. Sold price for nearby properties 7. Sold price for nearby properties relative to their list price And the list goes on... Is it that difficult to predict? Literally th…
It makes it illegal to "develop a real estate appraisal" without a license. An appraisal is defined as simply "an opinion of value". So if you are driving down the street and say to your spouse, "Look at that house, it's gotta be worth a million bucks," you just committed a misdemeanor.
This law as it's written has got to be unconstitutional.
You would think real estate would prove to be one of the few real life fields where machine learning would be able to predict the prices pretty accurately. You have: 1. List price 2. "Favorites" on the MLS. 3. Square footage 4. Bedrooms 5. Bathrooms 6. Sold price for nearby properties 7. Sold price for nearby properties relative to their list price And the list goes on... Is it that difficult to predict? Literally th…
Except appraisals include much more than what you listed there which typically needs to be seen individually on a case by case basis and isn't public data. For example looking at if a sink is leaking.
You would think real estate would prove to be one of the few real life fields where machine learning would be able to predict the prices pretty accurately. You have: 1. List price 2. "Favorites" on the MLS. 3. Square footage 4. Bedrooms 5. Bathrooms 6. Sold price for nearby properties 7. Sold price for nearby properties relative to their list price And the list goes on... Is it that difficult to predict? Literally th…
You would think real estate would prove to be one of the few real life fields where machine learning would be able to predict the prices pretty accurately. You have: 1. List price 2. "Favorites" on the MLS. 3. Square footage 4. Bedrooms 5. Bathrooms 6. Sold price for nearby properties 7. Sold price for nearby properties relative to their list price And the list goes on... Is it that difficult to predict? Literally th…
Not really; you don't observe too much. Note you have two problems: 1 - features that you can't observe (sentiment / how much someone likes the house or area); 2 - features that are too expensive to observe Have a view of the ocean? In CA, that can be worth another $X00k or maybe even millions. Have ocean view rights, ie nobody can build between you and ocean? Even more. The neighboring property with a blocked view c…