How Data Analytics Can Help Ensure Real Estate Success

It is becoming increasingly common to use data analytics in financial transactions. In fact, some experts report that up to 80 percent of buy-sell trades are based on data-driven models. However, in real estate, each transaction is unique and no two houses are the same. Whether apartments in a single building or homes in a residential complex, prices can vary markedly.

Data analytics in real estate contributes significantly to ensuring the success of property projects. From identifying and assessing risk to analyzing consumption patterns, anticipating a property's future is now much simpler due to the vast amounts of data and insights available.

Using such information properly and aligned to business goals provides important competitive advantages for housing developers. Nowadays, the most important firms in this field already devote resources, equipment, and time to identifying the most appropriate investment opportunities based on data analytics.

Technology for the Industry

Basic decisions any developer must consider when starting a new project are location, product type, and pricing. Real estate market studies are useful to forecast construction and financial scenarios because they provide data on the supply and demand in a given area and analysis of the urban environment of each project. Similarly, they allow pricing on the basis of comparable benchmarks, understanding the risk and return of investment, knowing the profile of potential clients and the competition, and defining an appropriate commercial strategy. 

Due to advances in technology and data analytics, such market studies have evolved, allowing — through artificial intelligence and machine learning models — the study of properties or even the performance of a specific area, in virtually any part of the country, in as little as three days. 

To achieve this, one of the keys is to have access to the transactional data of the real estate market, as is the case of Intelimétrica, which has more than 50 percent of this database and more than 75 percent of data published on public websites.

Where to begin?

The first step in evaluating if a project is suitable for development is to determine if the prospective client market is willing to buy or rent a home in the proposed location. This process is known as identifying a "driver;" for example.What motivates a person to be interested in residing in a particular district, such as the presence of a hospital, schools, supermarkets, offices, shopping malls, public transportation, and urban infrastructure.

Depending on the location, each of these drivers may have a greater or lesser influence but only by understanding their importance may they be used as a competitive advantage at the moment of making the decision. 

The second challenge for developers and real estate players is to identify consumers' purchasing power. It is extremely difficult for financial institutions to collect this information year after year, so the information, if obtained at all, is outdated. 

Working with machine learning and artificial intelligence allows us to analyze vast amounts of data and infer the purchasing power of potential clients living in a certain area, which is key for decision-makers to identify whether they will have prospects with the ability to apply for a mortgage or lease in the chosen location. No doubt, this type of analysis could not have been done easily in the past. 

Maximizing Profitability 

Real estate performance can vary drastically from one location to another. Even cities with similar characteristics may have different real estate values due to changing circumstances, such as economic activity or the real estate supply in the area. Even within a city, some neighborhoods or sub-segments, such as luxury condominiums versus low-income housing, may also demonstrate  different degrees of performance.

The analysis performed through machine learning helps determine accurate values based on the strength of data sources, which, ideally, are updated on a daily basis. Further use of this analytical technique helps us determine with higher accuracy the changes resulting from public policy intervention. In other words, when regulatory changes occur in certain areas affecting prices and investment, advanced analytics can help us identify opportunities in terms of timing to set new prices or promotions.

Finally, one thing all developers share is an interest in asking prices and the accurate timing of promotions. Using the above-mentioned technology tools, the price can be calculated dynamically as is done in other industries. It is not required to visit nearby developments but rather, using automatic valuation models, you can analyze comparable properties in a few seconds and thus build a sound pricing strategy.

Using Data to Our Benefit

Machine learning technology and data science, when used to analyze a property's performance, clearly and compellingly illustrate the risks and opportunities for each project. The new generation of real estate market reports already use these tools to provide increasingly accurate information designed to bring the real estate industry to the next level.

In the KPMG Global PropTech Survey 2018, 49 percent of participants expressed that artificial intelligence, big data, and data analysis were the technologies likely to have the biggest impact on the real estate industry in the long term. Data is increasingly present in our lives, let's use it to make better business decisions.

https://towardsdatascience.com/5-ways-to-apply-data-science-to-real-estate-e18cdcd0c1a6

https://www.experfy.com/blog/the-future-of-algorithmic-trading