Key takeaways from our second lecture at the MIT Center for Real Estate
Coming from academia ourselves we are pleased to contribute to the leading educational practices, reading guest lectures and providing students with an understanding of our own experience in linking data science with real estate markets.
Кey takeaways from our second lecture at the MIT Center for Real Estate:
✓ The background of Location Risk Score:
- Central place theory and centrality index that we built based on a combination of human mobility and business diversity data.
- Data-driven neighbourhoods formed by clustering the initial data split by grid cells.
- Catchment area analysis through travel time calculations and building isochrones around each and every property in the analysis.
- Massive data engineering infrastructure allowing to calculate thousands of centrality clusters and their travel time on the fly.
✓ LRS insights:
- Location Risk Score measurements help with understanding a properties location quality (level), future (trend), surroundings (context), and optimal function (best use).
✓ LRS applications:
- Pricing Evaluations (Acquisition / Disposition | Opportunity Analysis | Rent Assessment)
- Portfolio Strategy (Regional / National Location Comparison | Risk Management | Operational Optimization)
- Timing Questions (Trend Analysis | Threshold Notifications)
✓ Questions you may need an LRS-based answer for:
“Is this asset really worth that much?”
“What is the assets’ best use?”
“Where should we invest?”
“Can data vehicles help us better visualize the actual economic segmentation of our cities?” (hi Teo Nicolais!)
“We expect macroeconomic conditions to improve and are looking to increase our risk exposure…”
“Should we sell now or hold?”
✓ How to use data analytics as a financial benchmark?
Within this decision-making process, the first thing that is needed is BUILDING A POOL OF CANDIDATES / CANDIDATE FILTERING which is a time-consuming exercise needing automation. Consequently, all qualified deals get into a financial model. What is needed within this model is CALIBRATION OF THE QUALIFIED POOL (that location risk score also allows doing). After that, the location score starts working as a FINANCIAL BENCHMARK.
Location has always been a key value determinant for investing in commercial real estate (CRE). However, local market dynamics can change at extreme levels nowadays, conflicting with the idea that real estate is a fundamental safe haven. There is a need for constant monitoring of the market conditions and appropriate adaptation.
So when Ben Breslau, Chief Research Officer Americas, JLL says: “Data science is a rocket fuel ultimately for the real estate industry” in invitation to this MIT SA+P short course, we do agree with that.
