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Role of Location Data and Analytics in Real Estate Activity

In the coming years, it will be strange to see a real estate company not having an area dedicated to data and analytics. The companies that do not have data and technology at the center of decision-making may not exist in the next five years, says Jorge Valero, Director, Data and Digital Transformation, AEDAS Homes, Spain.

Jorge Valero
By Supriya Nayak

How important is location data for business decision-making?

We are a decentralized company with six territorial directorates comprising professionals from Catalonia and Aragon, Levante and Balearic Islands, Andalusia, Costa del Sol, Center, and North. The housing market in Spain is very heterogeneous — there are more than 8,000 municipalities and each one can have different dynamics. This decentralization is a major reason for our success. We are familiar with every market and are aware what customers want in each enclave. Our decisions are based on two key factors — in-depth local knowledge through our human resources, and data and quality indicators based on our own activity and from external sources.

In this context, location data plays a key role as it allows us to know about real estate activity in an area and the competition there, vehicle traffic, patterns of movement of people, weather, and where supermarkets or leisure and work centers are located. For example, we have mapped more than 100,000 soils and raw materials, and have identified dozens of parameters, many of which are exclusive. This allows us to have them located, categorized, and qualified, so that we have unique and quality information about the investment area. Having geolocated data flows allows us to develop solutions that facilitate geospatial analysis, which enables investment opportunity analysis teams and the commercial management to make informed decisions. Until not so long ago, data only allowed us to see what had happened. By adding more and better sources of data and applying data science, we can now understand why.

What are the main applications of location data and analytics for your company?

We have experienced an exciting evolution in the use of analytical tools. We started by generating small data marts in a local environment and connecting them to Excel files to analyze information from another prism. At present, we have a three-layered infrastructure with resources dedicated exclusively to the extracttransform- load (ETL) processes, Machine Learning and data storage and security. In the past, owning Location Intelligence (LI) tools was complicated, expensive, and required many resources. Further, a large amount of data was not geotagged. Fortunately, it has now become easy to access agile LI tools and high-quality data from external sources.

We use PowerBI with Mapbox to offer mapping solutions, and thus take advantage of using the same infrastructure for all data solutions. To do this, we have developed a tool that uses our own data of more than 100,000 plots in the main areas of Spain and combines it with the exclusive information we generate on a daily basis. This volume and diversity of information allows us not only to describe reality but to generate models that give the user unique indicators to make decisions.

Having geolocated data flows allows us to develop solutions that facilitate geospatial analysis, which enables investment opportunity analysis teams and the commercial management to make informed decisions.

Please tell us about the Business Intelligence (BI) platform that you use.

We are currently using Microsoft’s PowerBI solution. We have worked with other tools in the past, but the combination of versatility, simplicity, monthly updates with new functions, integration with the rest of the 365 suite, renewed web environment and its APP made us choose this solution last year. At a strategic level, we seek to have the greatest flexibility and therefore, we have separated ETL (Extract Transform and Load) and data science work from the visualization tool (PowerBI). This allows us to have agility when it comes to answering questions concerning business, operations, investments, and financial areas. In our data warehouse, we manage thousands of refuted indicators of both the activity of AEDAS Homes and the real estate market in areas where we have, or will have, a presence.

What kind of returns have you received from investing in location analytics?

The main range of action for us is based on two pillars: reducing decision-making time and lowering uncertainty. For our activity, we do not seek return on investment in terms of a new business or sale of services based on these tools or data. The use of such unique tools allows us to better understand an area or a client or detect significant changes in demand. This helps us differentiate ourselves from the competition, be more scalable, and make faster decisions with more quality information.

As a company, we have multiplied home deliveries by seven times in the last four years. Today, we have more than 7,000 unique buyers and over 85,000 people have expressed interest in our homes. We have more quality data that can support business growth. Despite being around for only five years, we have one of the most complete and powerful databases.

To give an example, we have identified tens of thousands of soils and have classified them to build a Location Intelligence solution that allows us to know the status of the soil in a specific area and estimate the capacity to carry out construction or obtain licenses. We combine this information with other data sources to pre-analyze an area and offer our analysts the best possible information in an easy-to-use solution.

The companies that do not have data and technology at the center of decision-making may not exist in the next five years

How has location data helped you in predictive analytics?

Until recently, we focused on describing what happens, describing it homogeneously for all locations and in an automated way. This allowed us to figure out the data we have, enrich it and understand how it can help us answer predictive questions. We have already made some progress on this front, but it is undoubtedly the biggest challenge in the coming years. When you make reliable predictive models, organizations, management teams and executives add them into their workflows. It is useless to make predictive models if they cannot be put to work and provide differential value to each team.

The real estate sector in Spain is strongly impacted by what happens in other markets and industries and, therefore, we have generated a solution that allows us to visualize the main macroeconomic indicators of a dozen countries. This ease of access to international information allows us to know what is happening at the economic and real estate level, so that we can make informed decisions.

How have you addressed some of the key challenges with localization analytics?

We analyze hundreds of spatial variables, including demographic, competition, and mobility data to quickly gain an information advantage in our processes. This allows us, for example, to cluster our stakeholders and clients to offer them a unique and differential experience, or to have a very detailed analysis and knowledge in an area where we do not yet have a presence, and the ability to analyze new investment opportunities or monitor changes in the variables that activate or deactivate the demand in each municipality. The analysis of the internal processes of the company allows us to pay attention to even small inefficiencies. With data analysis, we have significantly reduced the amount of time it takes to identify and solve incidents and improve our processes.

We have been able to overcome a major challenge through the development of a solution that allows us to analyze our raw material — the soil on which we promote our houses. Every area has a different soil type and is affected by different reasons. This local knowledge is powered by the Location Intelligence solutions.

How do you expect the real estate Location Intelligence market to grow in the next five years?

In the coming years, it will be strange to see a real estate company not having an area dedicated to data and analytics. The companies that do not have data and technology at the center of decision-making may not exist in the next five years. It will be necessary to have sufficient data, both public and private, and with sufficient quality that supports all the applicable regulations regarding data protection. In my opinion, more analytical profiles trained in data science than computer science will be required, thanks to the advancement of low-code solutions and geospatial analysis tools designed for analysts.

However, this data will represent a challenge: how do we extract stories out of it, and how do we tell those stories? In order to understand how hundreds or thousands of variables correlate, we will see how Artificial Intelligence and specifically automated, or initially supervised learning, will represent one of the most important changes when working with data. We will see how data science will be deeply driven by technology. Machine Learning has been in existence for more than 50 years but it’s now that we have the technology to support it. In conclusion, it will be about data-driven strategies and the use of AI to analyze data and automatically respond to complex and personalized problems.

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