All Forum Posts by: Severin Sadjina

@Doug Elkerman There is definitely quite a bit of truth in what you are writing, but you are underestimating algorithms and overestimating humans, at least in part.

• For example, what about bias? Algorithms can surely suffer from that too, but are much less prone to it than any human is.
• And, as you point out yourself also: While a human appraiser can probably not seriously keep more than 3 comps in their mind for comparison at any given time, the algorithm has no problem understanding the highly nonlinear relationship between the variables and consider hundreds or thousands of comps.
• And speaking about nonlinear relationships: Humans simply suck at that, there is no nicer way of putting this (I absolutely include myself here despite a PhD in theoretical physics)...

But again, I definitely agree with you on the emotional part and the visuals. But even that is, in principle, absolutely not outside the reach of algorithms. Just a question of the right data (see Cambridge Analytics or Target and their pregnancy predictions).

@Juan Pardo I think you are 100% spot on, as far as I can see it.

At least my models are no substitute for a professional on-site inspection and assessment by someone who is really knowledgeable in their market (like you portrayed nicely). I see the value of such models mainly for fast and accurate enough assessments of a large number of properties (given ones specific strategy) and in decision support.

I also really appreciate the "scientificeness" I can bring into the whole thing when using rigorous statistical methods to learn about the market (like change in rents and market values or how fast places sell and where etc.)

In my experience, having transaction histories available is a big plus, no doubt. At least for my market and what I've looked at, you can get reasonable performance without it too (using asking prices).

This actually touches on another issue I've been thinking about: What I want is the "real" market value of a property. That is, the value for which I would expect a property to get sold given all other properties and their transaction prices around. This could, for example, be the median of what I get if I tried to sell the same property at the same time several times (yes, this is pretty academic now :D) Neither actual sales prices nor asking prices or appraisals of ONE single property are equal to that idealized "real" value for that ONE single property. I am not sure I explained myself well, but the point is: Technically, we have targets (market values at a given time) for which we do not have accurate data. There is always some "noise in the measurements" (as neither asking price, appraisal nor actual selling price is equal to that "real" market value in a sense).

Anyway, slight digression :D

Hey, how about we organize a virtual meetup on ML in real estate for all us to explore this topic more and exchange experiences and ideas?

Who’d be interested in joining for something like that?

Hi Kevin!

I focus mainly on condos in my local market of Ålesund in Norway (check it out btw., the region is beautiful). I’m currently achieving below 5% median relative error, that is, half the estimates are within 5% accurate or better. I very much suspect that is at least as good as what the appraisers and agents are able to do in my market. To me, that is very helpful identifying deal potential very fast and accurate.
Estimating rent seems to be much harder. I get about 9% median error rate. I think part of the problem is that well over 90% of rentals are put out by laymen in my market, so the prices themselves and the data quality is a bit all over the place. But that too is still very useful in estimating the profitability of a deal up front. 

The main factors that the ML models seems to like so far are (no really big surprises there I guess):

• 1. Living area
  2. Year built
  3. Transaction history (price and date)
  4. HOA fees
  5. Location (could be GPS and/or the numbering the municipalities and cities use internally, like lot number or something)

Again, this is for condos in Norway, so take that with a grain of salt. There are more factors too but I get very far just with those. Been playing around with the agent’s texts in the ads, for example. That can sometimes reveal conditions that can influence the price a lot (like that some of the work done is not approved or whatever).

I’d love to include the standard/conditions of the real estate better, that’s a bit more tricky.

Anyway, just some thoughts! Hope they’re useful!

Hi Kevin!

Still using Data Science, ML and computer simulations to find the best deals, analyze markets and cashflows, generate automatic reports for banks and investor, and for optimizing overall strategies wrt. risks and profit potential.

In terms of ML/DS, it all depends on the data of course. But there's much possible in these directions. I have also started exploring how to commercialize these tools. There's been quite a bit of interest in that regard actually.

What else would you like to know? And have you thought along these lines yourself? :)

@David Ripplinger Do it! Pick one task/problem you think is a good mix of fun/ROI/challenge and have at it! It's how I got started, and now I use ML and simulation to find deals (estimates for market value and estimates for rental income), find comparables (sales, rents), create automatic deal reports (for myself, investors, and the banks), and for strategic planning and portfolio optimization. (I mentioned this just for some extra input, I am fully aware I'm an all out nerd. Can't help myself :D )

Thanks for setting up the group and for the connect, @Thomas Knutsen!

Howdy!

Great to see some action here. I've been talking to some people in Tønsberg about starting a Norwegian forum/networking place. Could be similar to BP: Forum, Marketplace, paid member area for premium content, videos, podcats, how tos etc. I already shot a few videos and have concepts ready for many more.
However, everyone seems to lack time for it :D Something like this does need quite a bit of commitment. Anyway, just throwing this out.

But someone's gotta take charge and actually do something :D

@Thomas Knutsen LinkedIn is a great start, how do we join?

Howdy!

I have been developing machine learning / AI tools for the past 2 years to helps me with comps, market value estimates and rental income projections. I have started to look into raw land as well, but so far it's rather hard because the land listings on finn.no are MUCH less streamlined than the property listings are. So there is such a variability in what data is available it's very hard to extract any meaningful data.

I am not aware of any other online sources better suited, however. As Morgan says, you could try siste.eiendomspriser.no, that should work quite ok for a first guess. Otherwise I'd say call the local agent (or several) and just ask. I think that would probably be your best bet.

Good luck!