AI CRE Tech startup ideas ?
I am trying to see if there are big gaps in CRE for an AI startup. My strength is in computer vision, deep learning, machine learning. What are the best ideas on how CRE can be made stronger with these technologies. In other words, what are the top pain points that developers, investors, landlords and portfolio managers face today which data and intelligence can solve ? One approach is to start with gathering data about building and property information, tenants from various public and private sources. That can be joined with neighborhood and submarket data, traffic and demographic data as well as historical pricing data. Some practical constraints are lack of public data on comparable leasing/sales which brokers seem not too keen on sharing.
I would love if suggestions account for the fact that CRE community may be slow to accept change. So the biggest idea may not be the best idea to start with. May be it is just automating some of the back office. Thoughts ??

[{"id": "dls652x", "text": "If you aren't familiar with CRE honestly I'd move on to another industry.  If you are, you'd know the answers to those question and the immense problems that come with them.\n\nI don't mean to be a debby downer but I still have people send me faxes and work with people who don't email.  These people spend millions on shit.  When you start talking data and research to people spending that kind of money \"well we used AI, the computer says so\" is one hell of a pitch to make too.  CRE clients generally expect researched market info and you to know specifics when you discuss it.  \n\nThe closest analogue to what you are going at seems to be zillow with the price estimates I guess?  As it seems like you have figured out that doesn't work great in commercial land.  I constantly find myself doing research reports/graphs etc and going through the source data and excluding shit or adding notes because I know stuff is wrong or not regular.  Big transactions influence stuff immensely.  A lot of info is private, and post transaction a lot of money is spent so stuff is skewy.  Even if we back out of that and go to a residential model were more or less for 99% of transactions the public records describe it, zestimate is awful and they are spending money hand over fist on that.  The challenges with commercial are immense comparably speaking.  \n\nTheoretically, and there are problems here of course, if I was going to dump money into what you are talking about for one thing it be retail location site selection.  There was a guy here who composed something like 200 variables to predict site sales for retailers.  Then he was using this to pitch sites to them and rep them in finding sites.  If I want to sell something to McDonalds or Walgreens, they don't care.  They do more in house then you will ever do.  If I want to do a deal with nancys hair salon or bobs bbq for inline space who are going to take off 2k sf I think there is a lot there that can be automated from demos, traffic counts, etc which are widely available.  I'd work on that personally.  Your best spot is selling automated info to people who can't pay someone who knows what they are doing to personally look at their situation, and that is where it will be.\n\n\n\n\n\n", "replies": [{"id": "dlsf2aw", "text": "Completely agree with this statement. I work at one of the top firms and the fact is, we don't want anyone to have a digital version of our data. Not even internally. Its too risky. The top of the top are too good at what they do and turning their magic into something that can be passed out on thumb drives is the last possible thing they'd ever want. \n\nNo one can force them to change because they are the top producers and could be off to another firm that would probably give them around $50 mill just to switch. End of story. \n\nMy guess is you'll have to find something that has to do with forecasting by using more accessible data from at least 5 years ago, theoretical analysis based on averages or guesswork, and lastly, of you could create an algorithm for what the must efficient property to place on X plot of land. You'd be fucking rolling in it.", "replies": [{"id": "dltnhr5", "text": "How about startups like reonomy. Do you consider using their tools  ? They seem to claim access to data from some brokerages. ", "replies": [{"id": "dlvsl4w", "text": "I haven't heard of them, but doing some quick research. I would say that any company offering a subscription to their service is probably not getting the best data. There are a few companies that have a business model like this: by uploading data, you get a credit. With this credit you may download data. That's the whole incentive for anyone to give them data I'm the first place. If you could just buy the data, you'd never upload, thus the data would never grow. \n\nSomehow costar still gets data by having a research team call people. It blows my mind. Why would anyone give them info? \n\nAt one point we had access to about 5 databases and we took all the data from each, ran it through a program that combines them based on a quality scale. So database 1 would take priority then anything missing in that data fills with database 2's info and so on. After this process was complete, shooting for the best quality data possible, we deemed it about 85% accurate. Putting most of the data below 65% or lower before the combining process. If there was a database over 92% accurate of the market it would be worth the cost of all 5 of the others. \n\nIf you get a hold of something that sticks and has value, let me know! I could guarantee my firm would be interested. Good luck out there!", "replies": [], "depth": 4}], "depth": 3}], "depth": 2}, {"id": "dltng6h", "text": "Thanks for the input. For retail site selection, there are some approaches with putting a camera to count foot traffic and compare across different sites. Mark Cuban is doing a startup in this space. ", "replies": [], "depth": 2}], "depth": 1}, {"id": "dlsbr52", "text": "VOID ANALYSIS!!!", "replies": [], "depth": 1}, {"id": "dlsetq8", "text": "Interesting article in the space http://realestatetechnews.com/blog/how-artificial-intelligence-will-transform-cre\n ", "replies": [], "depth": 1}, {"id": "dlseuhf", "text": "And a startup quantierra. ", "replies": [], "depth": 1}, {"id": "dltc4c3", "text": "Artificial intelligence is needed in *many* areas of commercial real estate. Site selection, highest and best use of a property, forecasting income/investment return, lending, risk analysis, pricing, and many more. Data transparency (and data cost) is a persistent issue in the space, and one that will slow the adoption of AI vs other industries, but novel solutions can see outsized gains in this huge industry.\n\nWe've posted a few blog entries on fintech, AI, and software for CRE that might be interesting data points for you at least: https://blog.stacksource.com/", "replies": [], "depth": 1}, {"id": "dm1vuhf", "text": "Hey man, all you need is like $50mm to compete and you'll be all set.", "replies": [], "depth": 1}]