Real Estate Data Analysis

Real Estate Market

To clarify the unknowns of real estate transactions.

This dataset aims to provide a series of variables to clarify the unknowns of real estate transactions from the different aspects surrounding them. It not only addresses determining the price, whether for sale or rental, which is the most common question to resolve initially, but also addresses more elaborate issues, such as indicators that help us understand the real estate supply, profile its demand, and project its evolution. Ultimately, it provides a deeper and more detailed layer that allows for analyzing how and to what degree the real estate market is moving, for tasks such as Location and Geospatial Analysis, Portfolio Valuations, Drive-by or Desktop, AVM, Portfolio Segmentations, Algorithm Modeling, etc.

Returning to the data and risk thread, we could have descriptive-level Supply and Demand Indicators that establish distortions between supply and demand, or, as shown in the following image, Market Rental Stress Level Indices. This index indicates how expensive it is to access the rental market in each area in relation to the economic profitability that rent has for each owner. It shows the relationship between the average rental prices in each census section, household incomes, the percentage of the family budget represented by purchased or rented housing, sale prices, the supply/demand of housing both for rent and for sale, etc.

Dataset Real Estate Market
Rental Market Stress Level Indices in Madrid

The census section has been chosen as the spatial reference object, being a more detailed reference than the postal code, which has traditionally been used in the sector.

Energy Efficiency

Protagonist in the criteria of this first environmental taxonomy.

In light of the Sustainable Development Goals (SDGs) set by the UN in 2015 in its 2030 Agenda and the European Taxonomy, which in its first phase establishes the activities that will be considered sustainable with the decarbonization goal set by the 2030 Agenda, more and more sustainable profile parameters are influencing decision-making within the real estate sector.

At the regulatory level in Europe, in addition to the European Taxonomy, which essentially presents an exposition of which activities and under what conditions will be considered sustainable, a series of directives are being developed for the disclosure of information about financial companies, known by their English acronym SFDR (Sustainable Finance Disclosure Regulation), and non-financial companies, or NFDR (Non-Financial Disclosure Regulation), all aimed at evolving, refining, and regulating what were previously known as Sustainable and Responsible Investments (SRI).

In this regard, energy efficiency plays a key role in the criteria of this initial environmental taxonomy. As the optimization of resources within a geo-strategic sector is crucial in the lifecycle of the real estate value chain, its impact and footprint in terms of emissions and energy consumption hold a primary role in the real estate sector of tomorrow, becoming a key parameter for anyone wishing to operate within this sector.

Therefore, we decided to create an independent dataset, which, although it could have been included within the Real Estate Market, we preferred to give it the relevance it deserves for future developments due to the significant scope of this area. This dataset provides at the census section level the main parameters that give a snapshot of the actual emissions and energy consumption in the area, with aspects such as the mode letter, values, and a score. It is also used in Location and Geospatial Analysis, Portfolio Valuations, Portfolio Segmentations, Algorithm Modeling, but also in Sustainability Analysis of Real Estate Portfolios, Risk Rating, Stress Tests, etc.

In the following images, we can see the average emissions values by census section of the real estate park, as well as the energy consumption score.


Values of emissions in Madrid

Energy Consumption Score in Barcelona

Environmental Risks

To continue advancing in environmental criteria

With the aim of making further progress in environmental criteria, together with the characterization of the locations, and complementing the real estate analysis with a view to the European Taxonomy, This dataset was created to provide data on the various environmental risk factors, categorizing them and making available a score that classifies the various risks, facilitating comparison between geographical areas and conceptually simplifying the analysis, since in some cases the data originates from variables and concepts that are sometimes very specialized.

In this area, in addition to an environmental risk as a summarized risk index, we can find the details of the risks that make it up, such as risks related to air quality, maritime and river flooding, desertification, potential land erosion, or fire frequency.

The characterization of the area provides information not only about the property but also about its surrounding environment and its risks, and how these and to what probability they can affect the property. These data, as we have been saying, provide information and facilitate decision-making regarding assets. Their uses are the same as the Energy Efficiency dataset due to being information aimed at mitigating climate change..

As an example, we could observe the environmental risk of an area as in the following images, even the detail that mainly causes that environmental risk, enabling, for example, the capacity to segment a portfolio according to risk profiles that one wishes to assume.


Physical Risk Score in Valencia

Maritime Flood Risk Score in Valencia

Urban Risks

A key factor for the viability of a project.

The real estate sector’s first link in the value chain is land.

This asset presents an enormous complexity of analysis, not only because of the great legislative heterogeneity that surrounds it throughout the territory with the numerous urban laws of different granularity (national, autonomous, provincial, local and sectorial) but also because in the value structure of the same converge and intervene, in addition almost all the parameters that work within the real estate, supply, demand, expectations, macroeconomic aspects, sectorial, and a long etc..

Land is undoubtedly the main actor, the figure that makes possible or invalidates a subsequent development activity, where its price is the key to the viability of a project, transferring it to the final price of the finished product.

The management of land portfolios, as a real estate asset, is no stranger to the concepts of information and decision or, as we have already mentioned, of data and risk. This type of asset operates in a sector in which the urban planning component has a vital weight, not only because of the variables in terms of the degree of use that will cause an economic return, but also because of something much more important, the time periods in which these revenues and expenses will materialize.

The time factor, one of the variables that, if poorly estimated, most penalizes the profit and loss account of any urban development.

In order to make an approximation of the degree of risk due to the urban development factor, we have a dataset that at an aggregate level provides the main variables of income and expenses to face the development, as well as a service that at a coordinate level returns with greater precision aspects of the state of the urban development management to be carried out, as well as recommendations that are adjustable to the type of strategic management that the client wants to do with its portfolio.