Preweek 2021/22
Real Estate & Technology

Dr Thies Lindenthal

  • htl24@cam.ac.uk
  • Twitter: @thieslindenthal

 

Photo: "Google Data Centre, Council Bluffs Iowa" by Chad Davis

What technology?
Steel, Elevators, AC? No.

  • Real estate AND technology? Buildings ARE technology!
  • Let's focus on the impact of information technology...
  • ...or to use a buzzword: Proptech.

 

Foto: "Sunrise At Hong Kong" by johnlsl

WeWork
Focus on office data, not the sensors that generate them.

  • Most prominent provider of flexible office space: Tremendous expansion.
  • Fantastic core service: High quality, low maintenance office space at flexible terms
  • Faced massive challenges: Asset-liability mismatching, insufficient corportate control, costs of rapid growth.
  • Implosion in 2019. IPO this year?

800+ locations
Actively managed. Data on usage, preferences.

  • Massive portfolio offers sufficient scale for innovation
  • Collecting data + analyse them
  • Learn, improve, roll out to other locations
  • Imagine you had to do this for a single unit / building!

Are America’s real-estate brokers rip-offs?
Transaction costs are huge!

Figure: The Economist

The promise
Integration, automatisation

  • Market is massive, complex, highly fragmented, offline.

Figure: Opendoor Investor Presentation [PDF]

Technology & Financing
3. Soon, you can sell 20% of your home!

Technology & Property Demand
4. Tech needs space, too!






Technology & Real Estate Research
5.Data that do not fit into Excel are exciting!

Teaching computers to "see"
Infer quality or style attributes from street level images.

Tap into Google Street View
Accessing new data sources

A new map of the UK
Collecting images/feature vectors of each building in the UK.

Identifying unique buildings

Objective and subjective uniqueness influences prices.

Can we trust the model predictions?
Opening up the black-box

 

  • What are "important" areas?
  • Do they cover the home?

 

 

Figure: Wan & Lindenthal (working paper)
"Towards Accountability in ML Applications"

Machine Learning/Big Data more accessible than ever
MPhil dissertation



Emma Waterhouse (REF 2017/18): “Applying Machine Learning and Image Classification to the Built Environment: The Case of UK High Streets”