Data Science’s Influence on Commercial Real Estate
Abstract
The idea behind "Data science" was to create new and better ways to prepare, present, and model data and push the boundaries of statistics beyond theory and practice. For a real estate company to be able to run models on properties, all of the company's data has to be documented in the exact same way. For small queries, data can always be double-checked by a human, but the point of data science is to enable statistics at scale to get the most out of a model. Since the average salary of a data scientist in the U.S. is over $140,000, only the largest property firms have the resources to put together the kind of data science team that would be necessary to utilize modern data science techniques. "Managing data scientists is hard enough, but when they don't even have data that is clean enough to be useful, it can be a very costly exercise," said Ron Bekkerman, Strategic Advisor of real estate data platform Cherre. What makes property data so challenging to utilize properly, from a data science perspective, isn't just its lack of standardization; when it comes to property information, some of the most important pieces of data are not even available. "You need to connect it to other things like lenders, owners, tenants, and everything else in order for it to be helpful." Data science is not just about understanding certain data fields.