7 ms·

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

by tabeth 9y ago

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 there are hundreds of other features that have a clear correlation to the sell price (amount of people who attend the open house, crime, proximity to busy street, etc.) that I'm not including. I guess the problem is, there's not any money in having an accurate prediction.

mattkrause 9y ago
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-techniques https://www.kaggle.com/c/house-prices-advanced-regression-te... b) http://www.bis.org/statistics/pp_detailed.htm http://www.bis.org/statistics/pp_detailed.htm c) https://archive.ics.uci.edu/ml/datasets/housing 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.
- tyingq 9y ago
  I imagine it's tricky because value per square foot varies quite a bit in adjoining neighborhoods. And sometimes within a neighborhood. And neighborhoods aren't necessarily well defined geographically. And, some neighborhoods go long periods of time without any houses being listed or sold. So the whole idea of "comparable sales" is shaky.
  - natoliniak 9y ago
    > value per square foot varies quite a bit in adjoining neighborhoods. And sometimes within a neighborhood The quality or rather "reputation" of the assigned public school is probably the best predictor of this variance. The problem is that the school's reputation can diverge from the official grades, so this is difficult to quantify.
    - tyingq 9y ago
      Sure, but there are other drivers that would cause different value per square foot. A house with 12 foot ceilings, granite countertops, stone floors, wainscoting, built-ins, high-end appliances, tile roof, and so on...could be 100 yards from house with the exact same square footage, and none of that. It trips up the Zestimates around here. Especially in areas with larger lots, where bulldozing the old plain ranch house and replacing it with a higher end house is common. The per-square foot values are very different. Edit: also, near power lines, near water tower, on cul de sac, next door to elementary school, etc.
    - beamatronic 9y ago
      Common deal-breakers around here are - being on a T intersection - recent deaths in the home - Located under a high-tension power line I don't think these attributes are accounted for in any MLS, Zillow, or Redfin database schema
- tabeth 9y ago
  That might be a good project actually. Try to predict previous houses prices from historical data. A retrospective analysis would be fun since you know what the "right answer" is and can adjust your model accordingly. It seems this and predicting the stock market have some similarities, but I think real estate is less volatile.
  - azernik 9y ago
    Make sure to separate your training and validation data sets! :-)
- sidlls 9y ago
  Real estate markets are rarely liquid. Such models would have to take that into account, of course, and that isn't something I'd think is straightforward at all.
  - stupidhn 9y ago
    >Such models would have to take that into account I believe the liquidity of the asset is inherently taken into account in the price at which the exchange is made.
    - sidlls 9y ago
      Why do you believe that, out of curiosity? The reason I think it isn't straightforward to account for is based on several things: it isn't fungible, the purchase date can have little or no relationship to the price, and the means of purchase typically carry other time-based constraints that are also not necessarily related to price (although they can be). Furthermore, the liquidity in a given market may change fairly quickly and without any (apparent) other reason. I haven't tried to develop a model for this, so this is just sort of a gut feel for how difficult feature engineering to account for how a market behaves in this respect might be. And that "gut feel" is mainly informed by my experience purchasing and selling property (for personal and rental use).
      - stupidhn 9y ago
        >Why do you believe that, out of curiosity? Because the liquidity of an asset is a form of risk. >The reason I think it isn't straightforward I don't think it's straightforward either, but that only means individual's valuation might be way off.
        sidlls 9y ago
        I'm not convinced that risk as used in this context contributes to price significantly in the residential real estate market. People in this market don't (typically) view transactions as investments and they don't view the home as assets. Lenders do, so the availability of mortgage funds may be linked to price and liquidity but I suspect that relationship is tenuous at best.
- rockinghigh 9y ago
  The house would have to be mispriced by more than the transaction costs (5-10%).
- conanbatt 9y ago
  >... 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.
- Houshalter 9y ago
  There is a kaggle competition right now to predict house prices in russia. If you can do it better than anyone else, you can win tens of thousands of dollars.
- coredog64 9y ago
  Isn't that similar to the model for Opendoor? They buy houses from owners at a fixed price and then need to turn a profit on it. Therefore they need to have a good idea of what the eventual selling price will be.
x0x0 9y ago
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 can be worth far less. That's going to be really hard to observe at scale. Does the backyard, if any, get full sun? What is the soil like? Have reasonable suspicions there's asbestos in the building, even if not formally acknowledged, and a $50k+ remediate would be required? How much sunlight does it get, and from what side? How old is the interior woodworking and how well has it been cared for? How good is the internal soundproofing? How much renovation is required? Do you still have knob & tube? Even silly things like how good is the cell service. Which can vary just by moving 30 feet. How good is the water pressure and how much $$ would it cost to upgrade it. etc etc
- mrfusion 9y ago
  You could run a computer vision algorithm on the included photos to get a sense of the view. If a house has a good view they're going to include a picture of it.
hehheh 9y ago
There's plenty of money in it. Imagine if you could price the house correctly out of the gate, reducing the amount of time the home stays on the market. It is indeed a hard problem to solve using ML. You'd need a few other things: 8. Floor count 9. Size and location of the bedrooms, bathrooms, kitchen 10. In-unit laundry (mostly for condos) 11. Proximity to neighbors 12. Existing tree coverage and other landscaping 13. Proximity to trails, transit, and other mostly-fixed landmarks ... I'm sure I'm missing many more.
- tehlike 9y ago
  Opendoor does exactly this?
  - deleted 9y ago
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- beamatronic 9y ago
  In the Bay Area at least, you really cannot discount the Feng Shui attributes of a home [1] and I don't think there is any MLS type of database that takes this into account. [1] http://blog.pacificunion.com/proper-home-design-crucial-chinese-buyers/ http://blog.pacificunion.com/proper-home-design-crucial-chin...
- jdross 9y ago
  It's significantly more complicated, but at Opendoor we act as a market maker for real estate by doing this. We sell our homes far faster than the market in Phoenix, without sacrificing on price.
- bkjelden 9y ago
  Real estate is not liquid enough that a 'correctly' priced house will sell immediately. You have to wait for a buyer that wants the exact featureset that house is offering. Real estate has sort of a weird duality where it's viewed as a commodity in aggregate - but it is definitely not a commodity good to the individual buyers looking at houses.
AndrewKemendo 9y ago
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.
- Uhhrrr 9y ago
  In my case I'm pretty sure a horrible yellow-brown shag carpet was the reason I paid less than list for my house (I removed it as soon as I got the key and was delighted to find hardwood floors underneath).
- ensignavenger 9y ago
  In most places, for most types of appraisals, an appraiser is unlikely to do more than glance at the sink, and primarily to determine the finish /trim level of the house, not determine whether or not it is leaking. (of course, they may catch an obvious leak and document it, but they aren't going to look too hard.)
malandrew 9y ago
There are also non-tangibles like knowing your buyer and how much you can get from them. My mother passed away last year and I handled the home sale as I was the executor of the estate. The buyer was a neighbor who heard about my mother passing away and saw it as an opportunity to buy the house for so his mother could live less than 100 meters from him and help him and his wife with raising their kids. He knew we'd be eager to sell and thought he'd get a deal, but I realized that the reason for him wanting my mother's house was because it was ideal for his particular circumstance. This buyer was a real estate agent in my mom's office (she was also a real estate agent). At the end of the day, I got about $40k premium over the appraised value (almost ~10% premium) just because I knew I had a motivated buyer. Situations with motivated buyers and sellers happen all the time and adds a lot of volatility in sale prices, and is a confounding factor that I would expect to really complicate machine learning approaches to appraising the value of a home.
- wuliwong 9y ago
  Usually this is mitigated by the appraisal. Lenders generally aren't willing lending more than a home is appraised for because of the increased risk. I'm sure this varies from state to state but I think this holds mostly true in general. That being said, the appraisals themselves are quite subjective and only based off a few similar properties sold recently, so I still agree it's a difficult problem.
  - EpicEng 9y ago
    Appraisals magically come in at the exact offer price unless the offer is just completely off the charts insane. Appraisals are often times BS or, at best, just whatever you need them to be within some rather large margin (+/- 10%? 20%?).
    - peter_mcrae 9y ago
      Sadly, my appraisal came in "magically" at list price, so I had to put down a lot more at close. It was 3 years ago so we've done well regardless. I think you are right, I'm pretty sure the appraisal is just a guess. Most people I know had the appraisal come in way high, so maybe I got the one honest person.
BeetleB 9y ago
>And the list goes on... Is it that difficult to predict? They do likely have a ML algorithm in the background. They even offer an Excel sheet where, for any county, you can see what % of sold homes are within, say, 20% or 10% of the Zestimate (it can be quite bad). And 20% is a huge error. Even 10% off is huge for most people. What is hard to capture: 1. Condition of the house 2. Design of the house 3. Color of the house 4. Neighbors being annoying 5. Alignment of incentives for both party's real estate agents. Both profit more from a high price. And so on.
- tyingq 9y ago
  >And 20% is a huge error. Even 10% off is huge for most people. Found a screenshot that on nerdwallet that shows some of that for Zestimates in a few large cities: https://assets.nerdwallet.com/blog/wp-content/uploads/2015/11/Zestimate.png https://assets.nerdwallet.com/blog/wp-content/uploads/2015/1... Basically showing they are only within 20% of the actual sales prices ~80% of the time. That's pretty lousy.
- encoderer 9y ago
  You think estimating the value of every home in the country within 10% is bad? Lots of criticism of zestimate but I'm curious: is anybody doing AVM better and for free?
  - BeetleB 9y ago
    >You think estimating the value of every home in the country within 10% is bad? It may be good from an "interesting problem" standpoint. But if your house's Zestimate at $300K, 10% means it's real value is somewhere between $273K and $334K. Not very helpful, is it?
    - flubert 9y ago
      Does anyone have a source of data for the variance of the home prices in the U.S.? Getting within 10% sounds spectacular, considering that I think you could get close to 10% price differences between different combinations of buyers and sellers on the exact same property. If someone were to come up with an algorithm for predicting the price of a steak dinner at a restaurant, using spotty 5 year old data, I think everyone would be impressed at getting it correct within $3 of $30.
strictnein 9y ago
I'm sure this varies by state and/or locale but one major thing missing from public data in my area is basement size, which can account for 25% or more of a house's size. Our city/county/state (not sure which one does it, really) only records above ground sqft, even if you have a walk out basement.
desdiv 9y ago
"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.
- saimiam 9y ago
  Is this true tho? If a product is always under priced, what incentive does a seller have to put it up for sale? For any market to work consistently, the market maker has to be basically neutral, imho.
  - desdiv 9y ago
    It's basically the same strategy as Yelp. When there's a string of bad reviews on Yelp, it's basically Yelp's way of telling the shop owner: "Gee, doesn't it suck that these spammers are bad mouthing your business without actually patronizing it? Well, if you work with us I'm sure we can weed out the spammers and only keep the honest (read: positive) reviews." In this case, it's: "Gee, it's regrettable that our Zestimate for your house is so low. Only if we had a better source of data. Well, if you work with us I'm sure we can get a better (read: higher) estimate that's more in line with the true value of your house." The buyers will flock to whichever market maker that advertises the lowest price, in this case Zillow. This in turn forces the seller to choose Zillow as well since that's where most of the buyers are.
- flubert 9y ago
  >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.
vineet 9y ago
Here is a company that is focusing on just that - estimating real estate values (and selling those estimates): https://www.housecanary.com/ https://www.housecanary.com/
- jdross 9y ago
  At Opendoor we go a step further and actually purchase and sell the homes based on the prices our algorithms produce
Spooky23 9y ago
You don't really need machine learning to do comps. There are literally dozens of tools to do this accurately. Zillow just sucks for a variety of reasons that have more to do with Zillow than anything else.
- wuliwong 9y ago
  I think the difficulty is scaling the "art" of estimating prices. Any single home I could sit down and give a good estimate but to come up with a fully automated way to do that seems pretty tough still. I'm also not sure that the really hard parts are solved with ML as well.
  - jdross 9y ago
    I challenge you to come to Opendoor and beat the algorithm over a large set of homes :)
mikeryan 9y ago
In some areas where houses were developed around similar times to similar plans this works. But, for example, here in Berkeley the price per square foot can vary wildly based on the condition of the homes. We have 100 year old homes that are barely habitable next to completely remodeled ones that can sell for a difference of $200 per square foot.
- sjg007 9y ago
  This is why each property is valued on a sq. foot cost. It naturally includes the "depreciation" of the structure and the land cost. 1. empty buildable lot (with zoning restrictions on max # buildable sq. feet) 2. tear down (add demo fees and maybe requirement to retain existing foundation to avoid extra taxes). 3. rehab 4. new construction Which is normalized by location... rehab may cost more then new construction depending on the location and if it was a flip. A $1m tear down can become a $2m+ house, or 4-5 $500k condos.
mywittyname 9y ago
ML is the perfect application to this problem. The big thing Zillow is missing is buyer information. I will personally value a home differently than you will because we have varying desires. For example, I put a low value on bedrooms past the second and multiple stories, but a high value on a large garage and big kitchen. You might value things completely differently. What Zillow should do (or maybe I should) is gather information about the buyers and calculate a personalized Zestimate that reflects what the seller thinks the actual value of the home is. As a side-effect, this approach would probably be shielded from lawsuits because it's not an appraisal, but a personalized estimate of premium over market value.
rch 9y ago
Consider credit scores in the neighborhood as well...
mozumder 9y ago
How is the machine learning going to actually inspect the house?
- beamatronic 9y ago
  With this [1] or this [2] [1] https://www.google.com/streetview/trekker/ https://www.google.com/streetview/trekker/ [2] https://matterport.com/ https://matterport.com/
  - mozumder 9y ago
    How's that going to find rotting wood underneath the porch or a broken AC? You know, what inspectors actually do...
    - beamatronic 9y ago
      Now you are hitting a very interesting idea which would be to standardize home inspection reports in a machine readable format.
    - djrogers 9y ago
      Inspectors and appraisers do different jobs - appraisers don't look for rotting wood, sometimes they don't even enter the home (i.e. if they know the trim level used for a particular development)..
EpicEng 9y ago
So many intangibles. People's emotions (on both sides) are one, but then you have the house with the nice kitchen, or that other house with a not-so-nice kitchen, but a flatter yard. And on and on. Zillow uses plenty of ML, but what's their advertised accuracy? iirc it's something like within 20% 80% of the time?
- jdross 9y ago
  Roughly 5% median absolute error. 90% within 20% nationwide.
mannykannot 9y ago
Having been through a tax reassessment in our home town, I can say it is not that easy. We found there are many fine-grained location dependencies, and not that many sales, with the latter issue complicated by the after-effects of the 2008 debacle.
11thEarlOfMar 9y ago
Take a look at an actual appraisal document[0] for clues into what impacts a home's market value. The key is in selecting the 'Comps', homes that have sold recently and which are materially comparable to the home under appraisal. Other random attributes that adjust the appraisal: - Does the home have a view? Of what? - What is the quality of construction? - Garage or Carport? - Pool? - basement? There are many variables, least of which is how active the market is today. [0] http://sacramentoappraisalblog.com/tag/reading-the-appraisal-report/ http://sacramentoappraisalblog.com/tag/reading-the-appraisal...
tobyjsullivan 9y ago
I tried once. The challenges came down to too many variables relative to sample size. You gave just a small subset of variables which can impact price by over 20%. The most important when I looked seemed to be: - Neighbourhood (even street in some cases) - Number of Bedrooms - Year of construction - Year of last renovation - Floor (apartment) - View (especially for apartments) - Number of Bathrooms - Year of sale That last one, year of sale, is important because it actually impacts your training data. Do you use five years of data even though local real estate prices have been increasing at 10-20%/year? Or can that be normalized even though some neighbourhoods are changing at different rates than others? I restricted my research to 1BR apartments in the city of Vancouver. There are some unique challenges for real estate in Canada, but basically the best sample size I could get was about 2000 sales (I think this was all the 1BR sales in a given year). Very few had information on all of the variables I listed above. Suffice it to say, you can't train a model of any quality on that data.
conanbatt 9y ago
There are multiple startups working on real estate appraisals 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.
openmosix 9y ago
This totally reminds me of https://www.xkcd.com/1831/ https://www.xkcd.com/1831/
- abritinthebay 9y ago
  The first thing I thought of when that comic was posted was HN threads
guelo 9y ago
But important variables are missing such as the views, invisible neighborhood borders, ugly house features, and many other subjective features.
Yen 9y ago
Disclaimer: I'm a former employee of Zillow Group, and still hold stock. These online real estate listing sites are using machine learning to try and predict sale price. The main difficulties are: 1. 'Machine Learning' is a broad term, with lots of potential approaches, some of which could give more or less accuracy. 2. If you put too much effort into getting the prediction accurate, you're prone to overfitting. 2. The data is messy. Really messy. Really really messy. The major players in the field often have a vested interest in not rectifying the situation. Even when they do, the software that runs most MLS data exchanges is, well, old. To a certain extent, it doesn't matter if one particular house has accurate data, if all the surrounding data is inaccurate. 3. On the scale of things, home sales are actually pretty rare. Any particular home might only change hands half a dozen times, while the market demand for that home might swing wildly. Any given home sale has a pretty big influence on the valuations of nearby homes, and if that sale is 'inaccurate', i.e., the seller over- or under-paid what others would consider the fair price, it can have an outsized effect on algorithmic estimates of value. 4. It's much easier to tell when a machine-learned valuation is wrong, and by how much, compared with other common applications. If Netflix is telling you a movie is 4 stars, when you think it's more of a 3.5, it doesn't feel that "off" to you. If text-to-speech mistakes a word for another similar-sounding word, it feels understandable. When Zillow says you have $30k less than you think you do, it's more quantifiable, and has more emotional impact. Tangentially, fun fact: while there's hundreds and thousands of potential features, if you do a PCA on it, basically 80% of a home's price is just "price per square foot in the nearby area". one feature. Of course, if you end up 20% over or 20% under based on that logic, you get sued.
- csours 9y ago
  Was the model aware of it's own error rate? For instance, could you say that this home is worth $500-650 thousand dollars? Obviously, this is harder to convey to a homebuyer and for various reasons "hard" numbers are prefered, not least because they cause the illusion that you know what you are talking about.
- deleted 9y ago
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djrogers 9y ago
Aside from the long list of unobservable attributes your ML algorithm can't learn, the biggest problem would be the small size of the dataset available, especially in the hottest markets (few sellers). Many neighborhoods (not cities, or zip codes - too much variance in god that large) have on the order of single digits a dozen sales per year, meaning that in some cases the most recent 'comps' can be a month or two old. In a fast rising or dropping market, there's nowhere near enough data to train an algorithm before the prices have changed again.

by tabeth 9y ago

mattkrause 9y ago

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-techniques https://www.kaggle.com/c/house-prices-advanced-regression-te... b) http://www.bis.org/statistics/pp_detailed.htm http://www.bis.org/statistics/pp_detailed.htm c) https://archive.ics.uci.edu/ml/datasets/housing 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.

tyingq 9y ago
I imagine it's tricky because value per square foot varies quite a bit in adjoining neighborhoods. And sometimes within a neighborhood. And neighborhoods aren't necessarily well defined geographically. And, some neighborhoods go long periods of time without any houses being listed or sold. So the whole idea of "comparable sales" is shaky.
- natoliniak 9y ago
  > value per square foot varies quite a bit in adjoining neighborhoods. And sometimes within a neighborhood The quality or rather "reputation" of the assigned public school is probably the best predictor of this variance. The problem is that the school's reputation can diverge from the official grades, so this is difficult to quantify.
  - tyingq 9y ago
    Sure, but there are other drivers that would cause different value per square foot. A house with 12 foot ceilings, granite countertops, stone floors, wainscoting, built-ins, high-end appliances, tile roof, and so on...could be 100 yards from house with the exact same square footage, and none of that. It trips up the Zestimates around here. Especially in areas with larger lots, where bulldozing the old plain ranch house and replacing it with a higher end house is common. The per-square foot values are very different. Edit: also, near power lines, near water tower, on cul de sac, next door to elementary school, etc.
  - beamatronic 9y ago
    Common deal-breakers around here are - being on a T intersection - recent deaths in the home - Located under a high-tension power line I don't think these attributes are accounted for in any MLS, Zillow, or Redfin database schema
tabeth 9y ago
That might be a good project actually. Try to predict previous houses prices from historical data. A retrospective analysis would be fun since you know what the "right answer" is and can adjust your model accordingly. It seems this and predicting the stock market have some similarities, but I think real estate is less volatile.
- azernik 9y ago
  Make sure to separate your training and validation data sets! :-)
sidlls 9y ago
Real estate markets are rarely liquid. Such models would have to take that into account, of course, and that isn't something I'd think is straightforward at all.
- stupidhn 9y ago
  >Such models would have to take that into account I believe the liquidity of the asset is inherently taken into account in the price at which the exchange is made.
  - sidlls 9y ago
    Why do you believe that, out of curiosity? The reason I think it isn't straightforward to account for is based on several things: it isn't fungible, the purchase date can have little or no relationship to the price, and the means of purchase typically carry other time-based constraints that are also not necessarily related to price (although they can be). Furthermore, the liquidity in a given market may change fairly quickly and without any (apparent) other reason. I haven't tried to develop a model for this, so this is just sort of a gut feel for how difficult feature engineering to account for how a market behaves in this respect might be. And that "gut feel" is mainly informed by my experience purchasing and selling property (for personal and rental use).
    - stupidhn 9y ago
      >Why do you believe that, out of curiosity? Because the liquidity of an asset is a form of risk. >The reason I think it isn't straightforward I don't think it's straightforward either, but that only means individual's valuation might be way off.
      - sidlls 9y ago
        I'm not convinced that risk as used in this context contributes to price significantly in the residential real estate market. People in this market don't (typically) view transactions as investments and they don't view the home as assets. Lenders do, so the availability of mortgage funds may be linked to price and liquidity but I suspect that relationship is tenuous at best.
rockinghigh 9y ago
The house would have to be mispriced by more than the transaction costs (5-10%).
conanbatt 9y ago
>... 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.
Houshalter 9y ago
There is a kaggle competition right now to predict house prices in russia. If you can do it better than anyone else, you can win tens of thousands of dollars.
coredog64 9y ago
Isn't that similar to the model for Opendoor? They buy houses from owners at a fixed price and then need to turn a profit on it. Therefore they need to have a good idea of what the eventual selling price will be.

x0x0 9y ago

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 can be worth far less. That's going to be really hard to observe at scale. Does the backyard, if any, get full sun? What is the soil like? Have reasonable suspicions there's asbestos in the building, even if not formally acknowledged, and a $50k+ remediate would be required? How much sunlight does it get, and from what side? How old is the interior woodworking and how well has it been cared for? How good is the internal soundproofing? How much renovation is required? Do you still have knob & tube? Even silly things like how good is the cell service. Which can vary just by moving 30 feet. How good is the water pressure and how much $$ would it cost to upgrade it. etc etc

mrfusion 9y ago
You could run a computer vision algorithm on the included photos to get a sense of the view. If a house has a good view they're going to include a picture of it.

hehheh 9y ago

There's plenty of money in it. Imagine if you could price the house correctly out of the gate, reducing the amount of time the home stays on the market. It is indeed a hard problem to solve using ML. You'd need a few other things: 8. Floor count 9. Size and location of the bedrooms, bathrooms, kitchen 10. In-unit laundry (mostly for condos) 11. Proximity to neighbors 12. Existing tree coverage and other landscaping 13. Proximity to trails, transit, and other mostly-fixed landmarks ... I'm sure I'm missing many more.

tehlike 9y ago
Opendoor does exactly this?
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beamatronic 9y ago
In the Bay Area at least, you really cannot discount the Feng Shui attributes of a home [1] and I don't think there is any MLS type of database that takes this into account. [1] http://blog.pacificunion.com/proper-home-design-crucial-chinese-buyers/ http://blog.pacificunion.com/proper-home-design-crucial-chin...
jdross 9y ago
It's significantly more complicated, but at Opendoor we act as a market maker for real estate by doing this. We sell our homes far faster than the market in Phoenix, without sacrificing on price.
bkjelden 9y ago
Real estate is not liquid enough that a 'correctly' priced house will sell immediately. You have to wait for a buyer that wants the exact featureset that house is offering. Real estate has sort of a weird duality where it's viewed as a commodity in aggregate - but it is definitely not a commodity good to the individual buyers looking at houses.

AndrewKemendo 9y ago

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.

Uhhrrr 9y ago
In my case I'm pretty sure a horrible yellow-brown shag carpet was the reason I paid less than list for my house (I removed it as soon as I got the key and was delighted to find hardwood floors underneath).
ensignavenger 9y ago
In most places, for most types of appraisals, an appraiser is unlikely to do more than glance at the sink, and primarily to determine the finish /trim level of the house, not determine whether or not it is leaking. (of course, they may catch an obvious leak and document it, but they aren't going to look too hard.)

malandrew 9y ago

There are also non-tangibles like knowing your buyer and how much you can get from them. My mother passed away last year and I handled the home sale as I was the executor of the estate. The buyer was a neighbor who heard about my mother passing away and saw it as an opportunity to buy the house for so his mother could live less than 100 meters from him and help him and his wife with raising their kids. He knew we'd be eager to sell and thought he'd get a deal, but I realized that the reason for him wanting my mother's house was because it was ideal for his particular circumstance. This buyer was a real estate agent in my mom's office (she was also a real estate agent). At the end of the day, I got about $40k premium over the appraised value (almost ~10% premium) just because I knew I had a motivated buyer. Situations with motivated buyers and sellers happen all the time and adds a lot of volatility in sale prices, and is a confounding factor that I would expect to really complicate machine learning approaches to appraising the value of a home.

wuliwong 9y ago
Usually this is mitigated by the appraisal. Lenders generally aren't willing lending more than a home is appraised for because of the increased risk. I'm sure this varies from state to state but I think this holds mostly true in general. That being said, the appraisals themselves are quite subjective and only based off a few similar properties sold recently, so I still agree it's a difficult problem.
- EpicEng 9y ago
  Appraisals magically come in at the exact offer price unless the offer is just completely off the charts insane. Appraisals are often times BS or, at best, just whatever you need them to be within some rather large margin (+/- 10%? 20%?).
  - peter_mcrae 9y ago
    Sadly, my appraisal came in "magically" at list price, so I had to put down a lot more at close. It was 3 years ago so we've done well regardless. I think you are right, I'm pretty sure the appraisal is just a guess. Most people I know had the appraisal come in way high, so maybe I got the one honest person.

BeetleB 9y ago

>And the list goes on... Is it that difficult to predict? They do likely have a ML algorithm in the background. They even offer an Excel sheet where, for any county, you can see what % of sold homes are within, say, 20% or 10% of the Zestimate (it can be quite bad). And 20% is a huge error. Even 10% off is huge for most people. What is hard to capture: 1. Condition of the house 2. Design of the house 3. Color of the house 4. Neighbors being annoying 5. Alignment of incentives for both party's real estate agents. Both profit more from a high price. And so on.

tyingq 9y ago
>And 20% is a huge error. Even 10% off is huge for most people. Found a screenshot that on nerdwallet that shows some of that for Zestimates in a few large cities: https://assets.nerdwallet.com/blog/wp-content/uploads/2015/11/Zestimate.png https://assets.nerdwallet.com/blog/wp-content/uploads/2015/1... Basically showing they are only within 20% of the actual sales prices ~80% of the time. That's pretty lousy.
encoderer 9y ago
You think estimating the value of every home in the country within 10% is bad? Lots of criticism of zestimate but I'm curious: is anybody doing AVM better and for free?
- BeetleB 9y ago
  >You think estimating the value of every home in the country within 10% is bad? It may be good from an "interesting problem" standpoint. But if your house's Zestimate at $300K, 10% means it's real value is somewhere between $273K and $334K. Not very helpful, is it?
  - flubert 9y ago
    Does anyone have a source of data for the variance of the home prices in the U.S.? Getting within 10% sounds spectacular, considering that I think you could get close to 10% price differences between different combinations of buyers and sellers on the exact same property. If someone were to come up with an algorithm for predicting the price of a steak dinner at a restaurant, using spotty 5 year old data, I think everyone would be impressed at getting it correct within $3 of $30.

strictnein 9y ago

I'm sure this varies by state and/or locale but one major thing missing from public data in my area is basement size, which can account for 25% or more of a house's size. Our city/county/state (not sure which one does it, really) only records above ground sqft, even if you have a walk out basement.

desdiv 9y ago

"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.

saimiam 9y ago
Is this true tho? If a product is always under priced, what incentive does a seller have to put it up for sale? For any market to work consistently, the market maker has to be basically neutral, imho.
- desdiv 9y ago
  It's basically the same strategy as Yelp. When there's a string of bad reviews on Yelp, it's basically Yelp's way of telling the shop owner: "Gee, doesn't it suck that these spammers are bad mouthing your business without actually patronizing it? Well, if you work with us I'm sure we can weed out the spammers and only keep the honest (read: positive) reviews." In this case, it's: "Gee, it's regrettable that our Zestimate for your house is so low. Only if we had a better source of data. Well, if you work with us I'm sure we can get a better (read: higher) estimate that's more in line with the true value of your house." The buyers will flock to whichever market maker that advertises the lowest price, in this case Zillow. This in turn forces the seller to choose Zillow as well since that's where most of the buyers are.
flubert 9y ago
>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.

vineet 9y ago

Here is a company that is focusing on just that - estimating real estate values (and selling those estimates): https://www.housecanary.com/ https://www.housecanary.com/

jdross 9y ago
At Opendoor we go a step further and actually purchase and sell the homes based on the prices our algorithms produce

Spooky23 9y ago

You don't really need machine learning to do comps. There are literally dozens of tools to do this accurately. Zillow just sucks for a variety of reasons that have more to do with Zillow than anything else.

wuliwong 9y ago
I think the difficulty is scaling the "art" of estimating prices. Any single home I could sit down and give a good estimate but to come up with a fully automated way to do that seems pretty tough still. I'm also not sure that the really hard parts are solved with ML as well.
- jdross 9y ago
  I challenge you to come to Opendoor and beat the algorithm over a large set of homes :)

mikeryan 9y ago

In some areas where houses were developed around similar times to similar plans this works. But, for example, here in Berkeley the price per square foot can vary wildly based on the condition of the homes. We have 100 year old homes that are barely habitable next to completely remodeled ones that can sell for a difference of $200 per square foot.

sjg007 9y ago
This is why each property is valued on a sq. foot cost. It naturally includes the "depreciation" of the structure and the land cost. 1. empty buildable lot (with zoning restrictions on max # buildable sq. feet) 2. tear down (add demo fees and maybe requirement to retain existing foundation to avoid extra taxes). 3. rehab 4. new construction Which is normalized by location... rehab may cost more then new construction depending on the location and if it was a flip. A $1m tear down can become a $2m+ house, or 4-5 $500k condos.

mywittyname 9y ago

ML is the perfect application to this problem. The big thing Zillow is missing is buyer information. I will personally value a home differently than you will because we have varying desires. For example, I put a low value on bedrooms past the second and multiple stories, but a high value on a large garage and big kitchen. You might value things completely differently. What Zillow should do (or maybe I should) is gather information about the buyers and calculate a personalized Zestimate that reflects what the seller thinks the actual value of the home is. As a side-effect, this approach would probably be shielded from lawsuits because it's not an appraisal, but a personalized estimate of premium over market value.

rch 9y ago

Consider credit scores in the neighborhood as well...

mozumder 9y ago

How is the machine learning going to actually inspect the house?

beamatronic 9y ago
With this [1] or this [2] [1] https://www.google.com/streetview/trekker/ https://www.google.com/streetview/trekker/ [2] https://matterport.com/ https://matterport.com/
- mozumder 9y ago
  How's that going to find rotting wood underneath the porch or a broken AC? You know, what inspectors actually do...
  - beamatronic 9y ago
    Now you are hitting a very interesting idea which would be to standardize home inspection reports in a machine readable format.
  - djrogers 9y ago
    Inspectors and appraisers do different jobs - appraisers don't look for rotting wood, sometimes they don't even enter the home (i.e. if they know the trim level used for a particular development)..

EpicEng 9y ago

So many intangibles. People's emotions (on both sides) are one, but then you have the house with the nice kitchen, or that other house with a not-so-nice kitchen, but a flatter yard. And on and on. Zillow uses plenty of ML, but what's their advertised accuracy? iirc it's something like within 20% 80% of the time?

jdross 9y ago
Roughly 5% median absolute error. 90% within 20% nationwide.

mannykannot 9y ago

Having been through a tax reassessment in our home town, I can say it is not that easy. We found there are many fine-grained location dependencies, and not that many sales, with the latter issue complicated by the after-effects of the 2008 debacle.

11thEarlOfMar 9y ago

Take a look at an actual appraisal document[0] for clues into what impacts a home's market value. The key is in selecting the 'Comps', homes that have sold recently and which are materially comparable to the home under appraisal. Other random attributes that adjust the appraisal: - Does the home have a view? Of what? - What is the quality of construction? - Garage or Carport? - Pool? - basement? There are many variables, least of which is how active the market is today. [0] http://sacramentoappraisalblog.com/tag/reading-the-appraisal-report/ http://sacramentoappraisalblog.com/tag/reading-the-appraisal...

tobyjsullivan 9y ago

I tried once. The challenges came down to too many variables relative to sample size. You gave just a small subset of variables which can impact price by over 20%. The most important when I looked seemed to be: - Neighbourhood (even street in some cases) - Number of Bedrooms - Year of construction - Year of last renovation - Floor (apartment) - View (especially for apartments) - Number of Bathrooms - Year of sale That last one, year of sale, is important because it actually impacts your training data. Do you use five years of data even though local real estate prices have been increasing at 10-20%/year? Or can that be normalized even though some neighbourhoods are changing at different rates than others? I restricted my research to 1BR apartments in the city of Vancouver. There are some unique challenges for real estate in Canada, but basically the best sample size I could get was about 2000 sales (I think this was all the 1BR sales in a given year). Very few had information on all of the variables I listed above. Suffice it to say, you can't train a model of any quality on that data.

conanbatt 9y ago

There are multiple startups working on real estate appraisals 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.

openmosix 9y ago

This totally reminds me of https://www.xkcd.com/1831/ https://www.xkcd.com/1831/

abritinthebay 9y ago
The first thing I thought of when that comic was posted was HN threads

guelo 9y ago

But important variables are missing such as the views, invisible neighborhood borders, ugly house features, and many other subjective features.

Yen 9y ago

Disclaimer: I'm a former employee of Zillow Group, and still hold stock. These online real estate listing sites are using machine learning to try and predict sale price. The main difficulties are: 1. 'Machine Learning' is a broad term, with lots of potential approaches, some of which could give more or less accuracy. 2. If you put too much effort into getting the prediction accurate, you're prone to overfitting. 2. The data is messy. Really messy. Really really messy. The major players in the field often have a vested interest in not rectifying the situation. Even when they do, the software that runs most MLS data exchanges is, well, old. To a certain extent, it doesn't matter if one particular house has accurate data, if all the surrounding data is inaccurate. 3. On the scale of things, home sales are actually pretty rare. Any particular home might only change hands half a dozen times, while the market demand for that home might swing wildly. Any given home sale has a pretty big influence on the valuations of nearby homes, and if that sale is 'inaccurate', i.e., the seller over- or under-paid what others would consider the fair price, it can have an outsized effect on algorithmic estimates of value. 4. It's much easier to tell when a machine-learned valuation is wrong, and by how much, compared with other common applications. If Netflix is telling you a movie is 4 stars, when you think it's more of a 3.5, it doesn't feel that "off" to you. If text-to-speech mistakes a word for another similar-sounding word, it feels understandable. When Zillow says you have $30k less than you think you do, it's more quantifiable, and has more emotional impact. Tangentially, fun fact: while there's hundreds and thousands of potential features, if you do a PCA on it, basically 80% of a home's price is just "price per square foot in the nearby area". one feature. Of course, if you end up 20% over or 20% under based on that logic, you get sued.

csours 9y ago
Was the model aware of it's own error rate? For instance, could you say that this home is worth $500-650 thousand dollars? Obviously, this is harder to convey to a homebuyer and for various reasons "hard" numbers are prefered, not least because they cause the illusion that you know what you are talking about.
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djrogers 9y ago

Aside from the long list of unobservable attributes your ML algorithm can't learn, the biggest problem would be the small size of the dataset available, especially in the hottest markets (few sellers). Many neighborhoods (not cities, or zip codes - too much variance in god that large) have on the order of single digits a dozen sales per year, meaning that in some cases the most recent 'comps' can be a month or two old. In a fast rising or dropping market, there's nowhere near enough data to train an algorithm before the prices have changed again.