As a free benefit for participants, we would like to extend an invitation to the Amazon SageMaker workshop on Feb 14 from 1p-5p.LEARN MORE
Welcome to the real estate vertical page! In order to make the most of the time the weekend of the event, please review our key educational materials and data sets.
Be Prepared! Start thinking through what types of data could power your business and product ideas. Often times a combination of multiple, disparate data sets can yield the most ingenious ideas and solutions!
The following videos were recording during the April 19 Panel event. You may wish to reference them in preparation of the weekend ML event.
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.
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.
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.
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
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
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
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
Renting over owning
Improve process efficiency
Predict rent, airbnb value, future valuation
Record of ownership
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.
Expensive to start business with assets
VC appetite may be saturated
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.
Which city has the highest median price or price per square foot?
Housing market data for metropolitan areas, cities, neighborhoods and zip codes across the nation
Idea: Can you build a model that analyzes past trends to determine which local real estate markets are about to heat up and which are likely to cool down?
Download a single file with all Zillow metrics
National and regional data on the number of new single-family houses sold and for sale
This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015
How is Airbnb really being used in and affecting the neighborhoods of your city?
Idea: How can all of this data be used to build a product that helps real-estate developers choose locations and building types that optimize profits/risk.
Idea 2: How can this data be used to create an addon/extension for AirBnB hosts that helps them make sure they are maximizing profit?
Contains a categorised list of links to over 300 sites providing freely available geographic datasets - all ready for loading into a Geographic Information System