GoVertical presents

Vertical ML/AI Startup Creation Weekend

Hosted by Madrona Venture Labs & TiE Seattle

As a free benefit for participants, we would like to extend an invitation to the Amazon SageMaker workshop on Tue, Apr 24 from 2:30-4:30pm.

LEARN MORE

Real estate resources

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!

Panel videos

The following videos were recording during the April 19 Panel event. You may wish to reference them in preparation of the weekend ML event.

ML Panel moderated by Dan Weld. Panelists: Xin Luna Dong, Yejin Choi & Kevin Jamieson

WATCH VIDEO

VC Panel moderated by Jay Bartot. Panelists: Tim Porter, Mike Miller, Pradeep Rathinam & Ankur Teredesai

WATCH VIDEO

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?

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

What current trends are driving change in this category?  

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

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?

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.

Sample Data Sets

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

Resources