WIth an increasing number of financial, legal or technical information needing to be processed, analysed and shared between departments, the consolidation and management of real estate data are becoming more and more strategic.
Drowning in volumes of data, the aim is to help you select the right data to make the right decisions and communicate them to others.
The production of real estate performance indicators, SRI, ESG or environmental labels, always requires more information on the buildings and the automation of data workflows, i.e. the reporting and processing of information.
Unify your existing and future information systems.
Stonal uses public Application Programming Interfaces to have applications communicating between one another.
The APIs allow the unified real estate data repository to transmit structured, qualified data to various stakeholders at any time.
Make centralising and processing your real estate data easier.
Extract, Transform and Load is the gateway to add data into Stonal’s repository and make an impact for your organization.
This process by which data is aggregated, extracted, translated and then loaded into a target location allows users to quickly connect to customer platforms and integrate their data, in an understandable way.
Speed up your decision-making.
With the aim of facilitating and accelerating decision-making, Stonal offers personalised Business Intelligence interfaces so organisations can have consolidated, and shareable reportings.
Each customer can have personalised dashboards.
The term “real estate big data” refers to all the voluminous and complex data generated in the real estate sector.
This data is generated from a variety of sources, such as physical asset data, plans, property transactions, geospatial data, financial information, inspection reports, valuations, market data and much more.
Real estate big data involves a huge amount of data that requires specific methods and technologies to be managed, analysed and exploited.
Real estate data refers to all information and data related to the real estate industry, including everything about real estate assets, their location, condition, value, transaction and other relevant aspects. This data is used by real estate professionals, investors, asset managers, and other stakeholders to make informed decisions about portfolio asset management, maintenance, or transactions. Here are some examples of the types of real estate data:
Financial and accounting data: This includes property purchase and rental prices, estimated values, mortgage interest rates, maintenance and operating costs, as well as rental income, taxation, etc.
Transaction data: This data documents real estate transactions, including property sales, rental contracts, commercial leases and other transactions related to the purchase, sale or rental of real estate.
Geospatial data: This concerns the location of properties and includes plans, technical specifications, etc.
Inspection data: These are inspection reports (regulatory checks, diagnostics, etc.) which detail the physical condition of properties, including structural problems, repairs required and any safety issues.
Market data: This provides information on property market trends, including price fluctuations, vacancy rates, demand for property and economic factors influencing the market.
Legal and regulatory data: This data concerns laws, regulations and restrictions relating to property ownership, such as zoning, building permits and environmental data (energy consumption, etc.).
The effective aggregation, management and analysis of this data is essential to making informed decisions in the real estate industry. The use of technologies such as artificial intelligence, natural language processing and data analytics can help make the most of the wealth of information contained in real estate data.
Data management, also known as data management, refers to the set of tools, processes and practices put in place to collect, store, organise, manage, secure and exploit data effectively within an organisation. The main aim of data management is to ensure that data is accurate, consistent, reliable and available to authorised users when it is needed. It encompasses a number of different steps and disciplines aimed at maximising the value of data.
Here are some key aspects of data management:
Data collection: This involves capturing and importing data from a variety of sources, both internal and external to the organisation.
Storage and organisation: Data needs to be stored securely and organised to allow easy and efficient access. This may include the use of databases, data management systems (DMS) and other storage tools.
Cleansing and standardisation: Before being used, data often needs to be cleansed to eliminate errors, duplications and inconsistencies. Normalisation aims to standardise data so that it is consistent and comparable.
Integration: Data often comes from different sources and may be in a variety of formats. Integration involves combining this data so that it can be used together.
Security and confidentiality: Data must be protected against unauthorised access, information leaks and cyber-attacks. Security and confidentiality protocols must be put in place to guarantee data integrity.
Data quality: Ensuring data quality means guaranteeing its accuracy, relevance and consistency. This involves constant validation, verification and monitoring processes.
Access and distribution: Users must be able to access the data they need quickly and securely.
Data governance: Data governance establishes the policies and standards for data management within the organisation, including decision making, accountability and control processes.
Analysis and exploitation: Data must be exploited to obtain usable information.
Stonal’s platform is a data management tool that ensures that real estate data is managed efficiently, consistently and securely throughout the life-cycle of an asset, in order to extract maximum value for the organisation.