Real estate leases corresponds to the dataset describing leases on real estate items as recorded for registration purposes by the FPS Finance. This dataset is composed of seven classes. The first class shows, at the national level, for each type of lease within the meaning of the Code of Registration Duties, the number of leases taking effects during the term, the nature of the parties concerned by the contract and the median rent and expenses and the percentiles 25 and 75 of the rent and expenses of the properties concerned. The second class includes this information at the level of the three regions. The following classes do the same at the level of provinces, arrondissements, municipalities, cadastral divisions and statistical sectors. The dataset can be freely downloaded as a zipped CSV.

Characteristics of cadastral parcels - Concentration of cadastral income corresponds to the dataset measuring the concentration of cadastral income for parcels of a housing-like nature. This dataset is composed of seven classes. The first class shows, at the national level, for each category of parcels dedicated to housing, the number of parcels and the total cadastral income of the parcels by cadastral income bracket. The second class includes this information at the level of the three regions. The following classes do the same at the level of provinces, arrondissements, municipalities, cadastral divisions and statistical sectors. The dataset can be freely downloaded as a zipped CSV.

Characteristics of the cadastral parcels - Rights of natural persons corresponds to the dataset describing the natural persons holding real rights on the parcels such as recorded by the FPS Finance for registration purposes. This dataset is composed of seven classes. The first class shows, at the national level, for each cadastral nature and for each type of real right, the number of parcels, their total cadastral income, as well as the number of parcels on which natural persons of the age and sex concerned hold the concerned real rights. The second class includes this information at the level of the three regions. The following classes do the same at the level of provinces, arrondissements, municipalities, cadastral divisions and statistical sectors. The dataset can be freely downloaded as a zipped CSV.

Share of the cadastral surface area that is not artificially developed in the cadastral and not unknown surface area to the administrative entity (region, province, district and municipality) for Wallonia

Owner occupants corresponds to the dataset relating to persons domiciled in a property over which they, or a member of their family, exercise a right in rem such as recorded by the FPS Finance for registration purposes. This dataset is composed of seven classes. The first class mentions, at national level, for each age-gender combination, by type of right in rem, the number of natural persons having such a right on their home. The lack of right in rem corresponds here to a fictitious right in rem in order to identify individuals who do not have rights on their home. The second class includes this information at the level of the three regions. The following classes do the same at the level of provinces, districts, municipalities, land register divisions and statistical sectors. The dataset can be freely downloaded as a zipped CSV.

Foreign real estate - Property characteristics corresponds to the dataset of foreign real estate held by belgian taxpayers as declared to the FPS Finance (only the holders of rights allowing the enjoyment of the property are taken into account). This dataset consists of one class. This class includes, for each foreign country or territory, the number of properties in each category, the total cadastral income of these properties, the median cadastral income of each category, as well as the 25 and 75 percentiles of the cadastral income of each category.

Distribution of real estate assets - real estate companies corresponds to the dataset relating to the distribution of real estate assets owned by legal entities such as recorded by the FPS Finance for tax purposes. This dataset is composed of eight classes. The first class mentions, at national level, for each cadastral income bracket, the number of legal entities with real estate activity (that is to say, registered under the code L of NACE nomenclature) and with real estate capital valued in the relevant bracket as well as the total and median cadastral income of the parcels held by these entities. The assessment of the cadastral income takes into account the shares actually held. The second class includes this information at the level of the three regions. The following classes do the same at the level of provinces, districts, municipalities, land register divisions and statistical sectors. The last class includes this information at the level of the countries for the legal entities which are not settled in Belgium but wich are nevertheless reigstred at the Carrefour Banque of the Entreprises.The dataset can be freely downloaded as a zipped CSV.

Share of the cadastral surface area that is artificially developed in the cadastral surface area and not unknown to the administrative entity (region, province, district and municipality) for Wallonia

From multiple observations during the day with UV spectrophotometers a daily value of the ozone column is calculated. This product is not publically available yet.

Characteristics of the cadastral parcels - Exemption from withholding tax on immovable property corresponds to the dataset describing the parcels exempted from withholding tax on immovable property. The first class shows, at the national level, the number of parcels per exemption category, the total amount of cadastral income of these parcels, their surface area as well as the median cadastral income of each category. The second class includes this information at the level of the three regions. The following classes do the same at the level of provinces, arrondissements, municipalities, cadastral divisions and statistical sectors. The dataset can be freely downloaded as a zipped CSV.