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Data-Driven approach to Real Estate and Proptech

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Presentation by Petteri Teikari

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Data-Driven approach to Real Estate and Proptech

  1. 1. Data driven Real Estate- Petteri Teikari P, hD petteri-teikari.com uk.linkedin.com in petteriteikari/ / version Fri 13 January 2017 Killing the ‘old school’ dinosaurs
  2. 2. Real estate metadata exploitation
  3. 3. METADATA Structuring the text as well Residence Features ● Spacious floor plans ● Direct elevator access with private and semi-private foyers leading into each residence ● Double-door entry opening to expansive views ● Floor-to-ceiling high-impact windows with sliding glass doors ● Generous balconies with glass railings ● Ocean, city and Intracoastal Waterway views ● Large master suites with stunning views ● Walk-in closets in master bedrooms ● Freestanding soaking tubs ● Toto® bidet toilet ● Gourmet kitchens with chef's island and custom-designed Italian cabinetry ● Stone countertops in kitchens and bathrooms ● Míele® induction cooktop range and concealed dishwasher ● Míele® wine cooler ● Sub-Zero® refrigerator/freezer ● Full-size washers and dryers https://london.craigslist.co.uk/reo/5938505327.html NOW DESCRIPTION as “freeform text” → Structure into database needed so it becomes usable http://textminingonline.com/getting-started-with-word2vec-and-glove
  4. 4. metadata Hook up with all the area data US People with family the most interested in near- by schools ● Why stop there, add transit times, near-by gyms, cafes, restaurants, and the liveability index of the area http://www.zillow.com/ https://maps.nyc.gov/crime/ CRIME STATISTICS
  5. 5. metadata Hook up with all the area data UK Find Properly https://www.findproperly.co.uk/ Nice start for the bot- based idea Asking whether you want to be within 15min bicycle ride of your desired location (e.g. Hoxton, and Shoreditch Old Station) And I can exclude areas for example like Whitechapel, Stepney and Shadwell
  6. 6. metadata Hook up with all the area data UK #2 https://www.police.uk/apps/
  7. 7. UK Pricing History data from land registry https://www.bsa.org.uk/statistics/mortgages-housing
  8. 8. UK Pricing rightmove and Zoopla http://developer.zoopla.com/ http://www.rightmove.co.uk/data/ http://www.zoopla.co.uk/market/uk/ The Zed-Index is the average property value in a given area based on current Zoopla Estimates. Learn more
  9. 9. UK Area Analysis What is going on in the city? The Bartlett - Centre for Advanced Spatial Analysis (CASA) http://blogs.casa.ucl.ac.uk/category/big-data/ Agent-based Modeling in Geographical Systems The Full Stack: Tools & Processes for Urban Data Scientists http://www.reades.com/2016/10/14/the-full-stack/ “Rob Kitchin opened with a talk to frame the workshop, highlighting the history of city data (see his paper on which the talk is based). We are witnessing a transformation from data-informed cities to data-driven cities. Within these data streams we can include Big Data, official data, sensors, drones and other sources. The sources also include volunteered information such as social media, mapping, and citizen science. Cities are becoming instrumented and networked and the data is assembled through urban informatics (focusing on interaction and visualisation) and urban science (which focus on modelling and analysis)” The workshop, which is part of the Programmable City project (which is funded by the European Research Council),
  10. 10. GEOLOCATION Profile the users Use profiling with user preferred locations as “Pinterest trending” for trend analysis, compare tosentimentanalysisinquantstockmarketprediction https://www.toptal.com/javascript/a-map-to-perfection-using-d3-js -to-make-beautiful-web-maps Map “Hot/COLD” areas Where people are willing to move so can we identify real estate arbitrage options, see Proportunity from London, UK for example In other words constructing this map before you see the increased demand in prices → and not just retrospectively http://www.telegraph.co.uk/property/house-prices/which-five-lon don-boroughs-are-actually-in-demand-for-homebuyers/
  11. 11. Profiling actual fine-graining London's population has soared over the past 35 years from 6.6 million in 1981 to 8.7 million today, an increase of 2.1 million. However this masks a far greater change than the headline figure suggests as London has literally be transformed from a mostly white British city to a multi-layered multi-cultural city where the population density graph now masks the reality of London now being several cities over laid upon one another which whilst all being influenced by capital flows out of the central London global money markets, nevertheless will havetheirown housepricesbullmarketdrivingepicentres. http://www.marketoracle.co.uk/Article53301.html Is ethnicity really that predictive, and how much of the variance does it explain? Have Facebook-level profiling of people and see where the hipsters are going and driving up the propery prices? When Shoreditch and Williamsburg are just so over and cannot provide good returns http://uk.businessinsider.com/facebook-data-brokers-2016-12?r=US&IR=T
  12. 12. GEOAnalysis indirect signals So for example in London, you could start doing “sentiment analysis” from Facebook, Instagram, Snapchat whatever and see where the gentrification vectors are pointing at: Where all the coffee shops are coming, where are the craft beers, amount of unconverted warehouses, illegal warehouse parties and exclusive sex orgy parties happening, etc. and Try to predict the market as in the quant stock market trading by doing sentiment analysis for the stock market and trying to find signals from Twitter (Bollen et al. 2011, cited by 2,243 articles), etc. that would predict the stock market changes http://www.forbes.com/sites/modeledbehavio r/2012/05/23/richard-florida-is-wrong-abou t-creative-cities/#edebf217e426
  13. 13. GEOAnalysis investment returns? Central London probably keeps its value rather well, but is it really the best investment location for risk-seeking investorswhowant higherreturns?London onlyfor moneylaundryinvestmentsornot? For example Bristol gave highest returns last year “Bristol, which is the fastest growing city over the last 12 months, saw growth over the last 3 months slow to 2.6% from a recent high of 5.0% in May 2016. Prices in Cambridge fell by 1% in the last quarter although over the lst 12 months prices are 7.1% higher.” https://www.hometrack.com/uk/about-us/press-room/july-2016-hometrack-uk-cities-house- price-index-figures-released/ https://www.hometrack.com/uk/about-us/press-room/july-2016-home track-uk-cities-house-price-index-figures-released/ Chinese money making Vancouver unlivable with the most overvalued housing prices (price bubble about to burst) Canadian real estate booming with a bubble about to burst for example?
  14. 14. e.g. 15 m² Only one bathroom & toilet semantic segmentation Quantitative data from the floor plan ● How many bed rooms. How big are they? How many bathrooms? ● Make this all searchable fields, so if homebuyer wants only see flats with living rooms above 15 m² for example
  15. 15. Image-based valuation Needs more fine-grained labeling Modern STYLE The definition of modern could be the best tracked by scraping from Pinterest all the time which serves as a good “trend tracker” and what people find desirable? OUTDATED STYLE Amateur photoProfessional photo EXIF classification helps but you can get crappy images with a DSLR as well. Think how good photo → horrible flat in person, vs. amateur photo → looks actually better in person affects people’s willingness to buy that. Do they feel like conned if the marketing material is too good compared to perceived quality? Think of various dimensions that could be relevant for classification/regression ● In other words http://www.tractable.io/ for assessing real estate with visual characteristics being one dimension. Interests banks and insurance companies Think of indirect labels as well. Do not directly influence the value but helps assessing the value from images. For example in bathrooms, typically less clutter (cleaner images) CLUTTERED The extra stuff is not fixed and does not correlate with actual value Non-CLUTTERED Easier to analyze room with the most financial value
  16. 16. Additional data Training set for material detection Floor material will affect valuation. Hard to do from images ● Problems of course with good-looking but cheap-feeling materials that will feel cheap in person ● Or with shabby chic ● Needs more context at coarse-level. For example warehouse conversion needs to be assessed differently from more or less similar looking farm house. http://www.aliexpress.com/store/produc t/Decoration-Building-materials-Polish ed-Crystal-Full-body-Tile-3D-floor-til es-Porcelain-Bathroom-kitchen-Non-slip /1908302_32493044896.html Ceramic tile that looks like marble https://www.alibaba.com/product-detail/c eramic-tile-that-looks-like-marble_17551 28905.html
  17. 17. Image-based valuation in practice The labelling tool need to accommodate “multi-pass” labeling ● In practice could mean in practice: 1) Naïve annotator does the current coarse-level labeling (bedroom, living room, outliers, etc.) 2) The same person most likely have no idea what makes a flat valuable for Finnish / NYC market, and we need some real estate agent doing that which needs an interface displaying the metadata (location, etc.) for that person and that expert just gives the price estimate and nothing else. - The cost of this labeling differs, so hard to get huge volumes of valuation estimates. 3) For trends, we need to add the temporal dimension also for labels as over time tastes change, Integrate the valuation part with Pinterest and interior design magazines/blogs (need to hand- pick those that one thinks that is actually driving the taste of people rather reflecting it) Think how this fits with the future, for example “quantreal estate investmenttools” Slide 129
  18. 18. QUANTITATIVE DATABASE ● Now we have the image database connected with the metadata – City, neighborhood, address, elevation, size of different rooms, number of bathrooms, internet speed, energy efficiency, near- by services such as schools and their rating, NHS healthcare trust in London fitted for your health condition, etc. other custom requests that someone might have and what can pull from open or closed data repositories) ● This allows good queries to be done from this with bunch of parameters. ● And also we can generate image based on the metadata. E.g. generate the “average” bedroom from a condo in Williamsburg with this price, or something more useful.
  19. 19. Automated home-buying USA Style fee of 6%, similar to the standard real estate commission, plus an additional fee that varies with its assessment of the riskiness of the transaction and brings the total charge to an average of 8%. It then makes fixes recommended by inspectors and tries to sell the homes for a small premium.
  20. 20. Multimodal Quantitative model for real estate investment AREA Analysis - Schools, services, commute, crime, construction projects, noise levels,NHS, etc. Sentiment analysis - Facebook/Instagram etc. - “Geoscraping”trends VISUAL analysis - Image Recognition - Visual-based valuation - Quantifyfloorplans MARKET ANALYSIS - Investwhere and tio what? China, London, NYC, etc. REGRESSION forinvestment arbitrage Same framework couldbe hookedto a real estate platformlike Zoopla and Zillow as well, andfor example provide only area analysis Zoopla could forexample do “Geoscraping” via FindProperly and function as adataprovider forreal estate investment brokers (for bigmoney)
  21. 21. Image data without associated metadata Problem now: Low-resolution texture (and point cloud) with low-cost devices, how about upsample the texture (and bump map) so that it looks “cooler” than the pixelated original Color map super-resolution: Possible to learn the “model” of interior spaces and how they should be upsampled for optimized visualization. http://blog.digitaltutors.com/understanding-difference-texture-maps/ https://arxiv.org/abs/1609.04802 Authorsfromex-MagicPony,currently TwitterCortexVx Whattodowith “Real EstateImageNet” even without the realestate specific data?
  22. 22. Super-resolution Texture Examples https://papers.nips.cc/paper/2381-a-sampled-texture-prior-for-image-supe r-resolution.pdf https://arxiv.org/abs/1612.07919 Just emerging from semi-stealth mode (and even then, only barely), Magic Pony Technology’s researchers have trained their system by exposing it to high- and low- resolution versions of images and video, letting it learn the differences between the two. MIT Tech Review was first with the story. https://techcrunch.com/2016/04/14/magic-ponys-neural-network-dreams-up-new-i magery-to-expand-an-existing-picture/ https://techcrunch.com/2016/06/20/twitter-is-buying-magic-pony-te chnology-which-uses-neural-networks-to-improve-images/
  23. 23. HALLUCINATING NEW Spaces https://arxiv.org/abs/1511.06434 https://github.com/Newmu/dcgan_code https://github.com/carpedm20/DCGAN-tensorflow https://youtu.be/QPkb5VcgXAM?t=23m57s Soumith Chintala in London Machine Learning meetup (Man AHL) https://www.youtube.com/watch?v=hnT-P3aALVE UNSUPERVISED – In practice, just aton ofphotos with NOLABELING done!
  24. 24. Interactive immersive HALLUCINations https://www.youtube.com/watch?v=9c4z6YsBGQ0&feature=youtu.be Project: https://people.eecs.berkeley.edu/~jun... Github: https://github.com/junyanz/iGAN. When you have the semantic 3D model of your flat, you could just generate the textures using for example Finnish modern priors, and then shift to outdated African priors in real-time when being immersed to that space in virtual reality. With the image dataset, one could generate the textures then (well color map at least) Hood believes VR will trickle down to lower-priced homes for everyday sales within five years. He sees the floodgates opening once the Zillow of VR is created. “A number of young tech companies are exploring an entirely in-VR experience where you enter search criteria like price, location, and number of rooms and you’re presented a number of homes and you can virtually tour,” Hood says. “Once that happens, you’ll look back and say, ‘How did we do this before?’” http://fortune.com/2015/09/09/virtual-reality-real-estate/
  25. 25. HIRING Tech ‘buzz’ and branding So with the real-estate related matierial you could train an autoencoder {or a generative model (like GAN)} and apply that some video material and make cool promotional models/video just for fun and get into cool lawsuits like Terence Broad with his Blade Runner http://www.vox.com/2016/6/1/11787262/blade-runner-neural-network-encodin
  26. 26. https://matterport.com/schematic-floor-plan/ http://geoslam.com/ https://apiant.com/connect/Point-Cloud-Library-to-New-York-Times-Real-Estate
  27. 27. Point Cloud Surveying resistance from RICS.org (UK) Old-schoolsurveyors prefer their pen, ruler andtracingpaper http://www.savills.co.uk/blog/article/192905/commercial-property/point-c loud-data-capturer-to-help-smarter-surveying-practice.aspx http://www.cbre.co.uk/uk-en/services/buildingconsultancy /building_measurement
  28. 28. Point Cloud Resistance to technology from surveyors http://www.frankham.com/service/surveying-project-management/measured-building-laser-scanning/ http://kykloud.com/the-construction-property-real-estate-technology-stack-where-do-we-fit-in/ http://kykloud.com/the-construction-property-real-estate-technology-stack-where-do-we-fit-in/ http://www.goreport.com/wp-content/upload s/2015/12/whitepaper-mobile-future-of-bui lding-surveying.pdf http://www.sisv.org.sg/
  29. 29. PropTech killing the dinosaurs in UK her-area-where-london-is-leading-the-world-a3348461.html
  30. 30. PropTech killing the dinosaurs in Singapore http://disruptproperty.com/blog/singapore-iot-fund-sensing-cities/ http://disruptproperty.com/blog/proptech-in-asia/ http://www.propertyportalwatch.com/6-signs-singapores-real- estate-tech-scene-is-on-the-rise/ http://www.property-report.com/5-proptech-companies-you-need-to-know-about/
  31. 31. PropTech killing the dinosaurs in USA http://www.nytimes.com/2010/11/14/fashi on/14eklund.html Helps being a good-looking Swedish ex- gay pornstar in real estate http://www.inman.com/hacker17/ http://dx.doi.org/10.1073/pnas.1321202111 Investors prefer entrepreneurial ventures pitched by attractive men
  32. 32. GEOAnalysis investment returns? Central London probably keeps its value rather well, but is it really the best investment location for risk-seeking investorswhowant higherreturns?London onlyfor moneylaundryinvestmentsornot? For example Bristol gave highest returns last year “Bristol, which is the fastest growing city over the last 12 months, saw growth over the last 3 months slow to 2.6% from a recent high of 5.0% in May 2016. Prices in Cambridge fell by 1% in the last quarter although over the lst 12 months prices are 7.1% higher.” https://www.hometrack.com/uk/about-us/press-room/july-2016-hometrack-uk-cities-house- price-index-figures-released/ https://www.hometrack.com/uk/about-us/press-room/july-2016-home track-uk-cities-house-price-index-figures-released/ Chinese money making Vancouver unlivable with the most overvalued housing prices (price bubble about to burst) Canadian real estate booming with a bubble about to burst for example?
  33. 33. PropTech Very conservative industry still – a lot of opportunities for Meaning https://youtu.be/hER0Qp6QJNU https://www.estateagenttoday.co.uk/breaking-news/2017/1/purplebricks- people-may-never-accept-technology-only-sales http://uk.businessinsider.com/gocardless-cofounder-ma tt-robinson-launches-proptech-startup-nested-2016-9 Europe's First PropertyTech VentureCapital Firm http://pilabs.co.uk/ Slow moving industryasmillennialsin the end are not buyingthatmuchproperty

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