Vertical ML/AI Startup Creation Weekend

Sector analysis

Vertical description

Real estate is property made up of land and the buildings on it, as well as the natural resources of the land Although media often refers to the "real estate market," from the perspective of residential living, real estate can be grouped into three broad categories based on its use: residential, commercial and industrial. Examples of residential real estate include undeveloped land, houses, condominiums and townhouses; examples of commercial real estate are office buildings, warehouses and retail store buildings; and examples of industrial real estate include factories, mines and farms. All services surrounding real estate is included in this category. Buying, renting, property management, mortgage services, smart homes, and general IT in real estate to name a few.

How big an opportunity space is this, how is it growing, and what’s driving that growth?  

The global real estate industry had $3.79T in revenues in 2017 and is growing at 4%. There are an estimated 557M housing units growing at 3% YoY. The real estate sales industry will reach $155B in sales this year. Growing at 6%. The real estate IT market is projected to be worth $8.9B in 21 growing at 21%. The property management market is projected to be worth $22B by 2023 growing at 9%. Much of this is driven by urbanization and developing markets.

What are the segments/pockets?

Residential, commercial, and industrial real estate exist. You can also segment the focus area by what the real estate would be used for. (i.e. cattle, oil, retail shops, mall, apartments, homes, factor, etc.) Single family rental units are the fastest growing segment of the market. Changing demographics and evolving household preferences will fuel rental growth.

What has been the VC investing trend in this category?  

What are the proof points that success may be rewarded?

• Previous winners:
  

  • Zillow
    

  • Remax
    

  • Coldwell Banker
    

  • Redfin
    

  • Sotheby’s
    

• Recent M&A:
  

  • BlueLine Rental by United Rentals ($2.1B) - real estate equipment rental
    

  • ForRent.com by Costar ($385M) - property rental site
    

  • On-Site by Realpage ($250M) - software for renting
    

• Rising stars:
  

  • WeWork ($9B raised) transforms building into creative, focus, environments
    

  • Compass - ($1.2B raised) modern real estate platform connecting talent with tech
    

  • Opendoor - ($1.05B raised) streamlined sales process
    

  • LendingHome - ($166M raised) simple fast mortgage
    

• Early seeds:
  

  • Endpoint ($7M seed) - new real estate closing process
    

  • Divvy ($30M series A) - makes home ownership accessible
    

  • Perch ($30M Series A) - transforms how you sell and house giving you certainty
    

At a high level, what problems are there to be solved using technology?  

• How can people rent without throwing money away?
  

• How can we improve liquidity in real estate market?
  

• What can we do to use empty real estate?
  

• How to make buying a home less scary?
  

• Simplify the mortgage/selling/valuation process?
  

• Better match houses with customers?
  

• Can we not cut out middlemen brokers/agents?
  

• New ways of sharing real estate?
  

• Reduce labor intensive processes? Showings, sales calls, etc
  

What current trends are driving change in this category?  

• Amenity competition
  

• Transportation gridlock
  

• New retail
  

• Sustainability focus
  

• Smart technology
  

• Renting over owning
  

• Office evolution
  

• Sharing economy
  

• Blockchain
  

• Augmented Reality
  

• Fractional ownership
  

How specifically can ML/AI change the game in this category?  

• Automated sales
  

• Improve process efficiency
  

• Property security
  

• Accurate appraisals
  

• Predict rent, airbnb value, future valuation
  

• Record of ownership
  

Investment hypothesis / rationale

The strong economy will continue to provide opportunities in real estate. As demographics change and technology advances there will be opportunities to us ML/AI in real estate that are yet to be found. It is a vast focus area with many facets to explore for problems new technology can solve.

What adverse conditions / headwinds are there for a play in this space? What makes it difficult?

• Recession
  

• Expensive to start business with assets
  

• VC appetite may be saturated
  

Data sets

Your novel business idea should be grounded in real-world data with plausible machine-learning/analytics on top. We've compiled a collection of datasets from which to gain inspiration. Note that you are not restricted to basing your idea on the data sets below. You may discover other open source data sets that inspire your creativity or you may bring your own proprietary data sets if you wish.

Many of the datasets below are from Kaggle, Figure-Eight (Crowdflower), Data.World, etc. The advantage of these datasets is that many have been cleaned and normalized and are ready to be explored with ML and data science tools. Note that the use of these datasets is often intended for research purposes only. Be sure to read any associated license agreements to understand if there are commercial restrictions if you plan to continuing using the data after the workshop is over.

Resources

• https://www2.deloitte.com/mk/en/pages/real-estate/articles/gx-real-estate-predictions-2018.html
  

• https://www.pwc.com/us/en/asset-management/real-estate/assets/pwc-emerging-trends-in-real-estate-2019.pdf
  

• https://assets.kpmg.com/content/dam/kpmg/uk/pdf/2018/09/kpmg-global-proptech-survey.pdf
  

• https://www.forbes.com/sites/valleyvoices/2018/02/13/future-of-real-estate-tech/#7a7e07391c13