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Growing importance of data science in property research

We’ve seen a steady rise in the power of property data analytics over the past few years. The demand for professionals who can analyse data at a sophisticated level has never been higher. Big data allows organisations to understand what is happening in the real estate market as well as looking at wider, macroeconomic factors in a way that has never been possible before.

The amount of data that exists is growing at a rapid rate. In fact, it has been predicted that by the year 2020, about 1.7 megabytes of new data will be created every second for every human on the planet. Data science is where the future lies in both real estate and other industries. Real estate companies can benefit from having a data analyst or data science team. It’s also vital that professionals in property research have a strong understanding of data analytics and showcase an active interest in the area. Two data languages that are in high demand are SQL and R, respectively.

There are huge benefits of SQL databases. Structured Query Language (SQL) is one of the basic languages of data science. It is used for many popular databases such Oracle, MySQL, and big database solutions like HIVE. There are a number of courses professionals can take to improve their knowledge and learn the fundamentals of SQL for data science. SQL is the standard means of manipulating and storing data. For property professionals, what matters most is SQL databases can harness large amounts of data that can be easily transferred to an Excel sheet. SQL is efficient and enables property researchers to quickly compile relevant data. They can help real estate teams better understand information about property trends, the neighbourhood and its demographic.

Another one of the increasingly popular data languages is R. It’s an open source system, which has strong ties to academia, which enables data manipulation and offers rich functionality. For property research, R’s advantages include the potential for analysts to build their own tools and methodology, as well as use the program for forecasting. Zillow, the leading real estate firm in the US, uses R to estimate residential real estate values. As an open source, people have the freedom to tailor it to their own individual needs.

Property research professionals looking to get ahead of the curve should try and increase their knowledge and technical understanding of data science. With the increasing amount of data at our fingertips, more and more roles are opening in the ‘technical’ side of the sector, such as data analytics roles and programme builders. Look into taking courses into data science if you’d like to stand out from the crowd and pursue a career in this growing sector.

If you are interested in a career in property research and real estate data science, or you’re a company looking to hire, please contact Cobalt for more information.

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