Identifying the best areas to invest in, knowing what type of property is in demand in each area, whether you are looking for more rentals or sales, homes or offices, or in which segments of the population there is more demand for each type of property, is part of what can now be analyzed using modern Big Data techniques.

The real estate market is not alien to the new reality focused on analyzing large volumes of information and making business decisions based on data.
More and more alternatives are available to developers, consultants, real estate investors and real estate marketing agencies.

For example, as the real estate portal infocasas.com.uy points out, “… Developers no longer drive projects based on what they “think” can be profitable. Today they have accurate data to detect market trends and demand needs. A developer can know exactly which are the best areas to invest in, what type of property is in demand in that area, if you are looking for more rentals or sales, housing or offices, in what age group there is more demand for that type of property.”

“… Data analysis gives much stronger forecasting power than a decade ago. The ability to anticipate change enables investors to plan their investments based on objective data and not perceptions, reducing financial risks and obtaining higher returns. You don’t have to be a great investor to harness the power of Big Data projection.” See full article here.

One of our clients, a real estate development company, needed to evaluate the feasibility of a vertical housing project in a city in Central America. Using public and private sources of information, we prepared a study that included: analysis of the commercial and industrial environment; population and demographic characteristics, floating population, commercial and industrial environment, and vehicle fleet, trends and preferences of people seeking to buy residence, inventory, absorption rates, characteristics of demand and analysis of available housing supply.

Based on the information, different feasibility scenarios were presented, which included a sensitivity analysis and the modeling of demand according to price per square meter.