Data-driven real estate business

What is a data-driven business?

Real estate business model is simple: raise money, buy or develop property, lease it out, collect rent, pay service fees, sell it with upside, and distribute returns to investors and creditors. But, as we like to repeat the beat-up phrase, the devil is in the details. 

Business is driven by decisions, which, in real estate, are usually based on experience and gut feeling - professionals use their business intuition to judge the information. And ‘information’ is the key word here.

The winners are those who are the first to get the most relevant information that helps spot investment opportunities faster, understand the profitability potential and risk clearer.

What is information? It’s interpreted data. Where is the data? It’s in emails and files, and multiple databases behind business software. In order to make informed decisions, businesses need tools to work with data effectively. The tools used to collect and interpret data that sits in IT systems is nowadays called a “Modern Data Stack”.

What is a data stack?

A data stack is a set of integrated software tools that can collect, store and process raw data to be interpreted as meaningful information for business decisions. Simply put, these tools turn “inedible data” (that cannot be worked with) into “edible data” (that can be worked with).

What is the difference between modern and traditional data stack?

Traditional data stack used to be an on-premise solution, meaning that each organization was responsible to maintain its own hardware and software infrastructure to store, transform and share the data. That takes a lot of human, hardware and consequently, financial resources, especially as the volume of data increases. Therefore, modern data stack is a software-as-a-service cloud solution.

The main elements of the data stack

Data Sources

These are the various systems and applications where data originates, such as internal databases, software for asset, property and facility management, customer relationship management (CRM) systems, building administration systems (BAS) or IoT devices.

Data Integration Tools

Software used to extract, transform, and load (ETL) or extract, load as-is and then transform (ELT) data from different sources into a centralized data storage. These tools often help automate the process of data extraction, ensuring data consistency and quality. There are generic integration tools like Azure Data Factory or Kafka Connect, and there are ETL tools which offer ready-made connectors to popular software, like Airbyte , Zapier , Locoia .

Data Storage

Structured and unstructured data is stored in a centralized data repository. Examples are Microsoft MSSQL, Postgres Professional PostgreSQL, MongoDB , Snowflake , Elastic .

Later we will discuss structured and unstructured data, SQL and noSQL databases, data warehouses and data lakes and the difference between them - stay tuned.

Data Transformation

Data processing usually means a transformation or conversion of source data from one format, structure, or value system to another. This is where raw data is processed for analysis and decision-making. In this case we usually talk about some methods like aggregation, matching data sets (e.g. finding all information about one particular real estate asset from different source systems and presenting it as a single document), or grouping data sets with some filtering and calculation involved (e.g. grouping all rent roll data by tenant and summing up gross rental value for each).

Data Orchestration and Analytics

When data is available in a single repository, it can be enriched with additional data and metadata. What is metadata? It’s data about data, such as how the data is collected and why, who uses the data and how, what the data is and how it can be interpreted.

Business Intelligence (BI) and visualization tools enable organizations to create interactive dashboards and reports that make it easy to explore and understand the data. Examples are MS PowerBI or MongoDB Charts.

Data Governance and Security

Data governance and security tools are essential for ensuring that data is protected, compliant with regulations, and used ethically and responsibly. This includes tools for data access control, data masking, encryption, and auditing.

Reverse ETL

This is a tool set that automates the process of moving the transformed data from the data repository out into other software, e.g. a real estate asset manager can use aggregated portfolio data to send it directly into investor reporting software or automatically produce newsletters or reports for investors or banks.

Cloud Infrastructure

The modern data stack is often deployed on cloud infrastructure, such as Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure. Cloud-based services offer scalability, reliability, and flexibility, allowing organizations to quickly provision resources and scale their infrastructure based on demand. Some modern data stack solutions are open-sourced and can be deployed on private clouds of companies in own or rented data centers.

Hopefully you are following so far :)

You might be asking yourself:

I am a business professional with no technical background, why do I need to know this?

or

We are using mainly Excel, integrations and data stack are for large enterprises, we are a small business without an IT team, how can this information help us?

Stay tuned to find out the answer to these questions, and more!

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