Big Data Analytics — Opening up the opaque market of real estate
Real Estate has been at the heart of most of the financial crises in the past century. As an asset class, it is notoriously opaque, illiquid and susceptible to fraud. It is also one of the dominant parts of the global economy, with major financial exposure for investors, insurance, banks and governments.
Managing the risk and maximising the opportunity is driven by access to the right, correct and relevant information, which is where the power of data comes in. As more and more data becomes available from a wide variety of sources, it has become possible to create an accurate and comprehensive overview of the entire real estate market. By building this from the level of the individual building, data on property characteristics, location, ownership, rent levels and transactions can be used to create a timeline for a property. With the building as the smallest unit, aggregates can be generated for an area, city, portfolio or loan book.
Transaction data and rent levels can be used used to perform instant valuations at the building or portfolio level. Property risk can be assessed by measuring key metrics for location and building quality against market benchmarks. Insurance risk can be assessed by mapping elements like flood risk, incidence risk and explosion risk. Smart pattern search can be used to detect fraudulent transactions and bad loans.
OfficeRank is dedicated to exploring the potential of data analytics combined with data transparency. What this will mean for the real estate market is yet to be seen, but it will certainly drive and power a shift in culture. As more and better information becomes available globally, this will create massive change in how the market operates, who the dominant players will become. And who knows, we might even be able to predict or even prevent the next financial crisis.
Editors Notes: This entry has been submitted to the FINTECH Book, the world’s 1st globally crowd-sourced book on FINTECH. Readers that enjoyed this innitial abstract are invited to share and like it so that it may be featured in a longer version that will published in the FINTECH Book due to be released November 2015.