‘Data is the new oil’ has been an ongoing catch phrase over the past few years. In this article, Estate Intel’s resident data scientist Emmanuel Offisong highlights the importance of data engineering to the real estate sector.

How Data Engineering Can Improve Access to Real Estate Data 

The importance of data-driven decision-making in every industry cannot be underestimated, especially in today’s world where data is king. In this vein, the demand for data professionals, such as data analysts, data scientists, and data engineers has risen to a great extent in the past decade.

Forward-thinking companies in every industry are constantly discovering ways they can implement data and insights in their businesses to gain an advantage over their competitors. One sector with high demand for data professionals is the real estate sector. 

The African real estate market remains relatively opaque due to a number of . These include the lack of easily accessible data. Generally, investors (both individuals and organizations)  require data to be available, transparent, and easily readable so that data-driven decisions can be made. Dolapo Omidire, CEO of Estate Intel noted during a recent event with  Techpoint Africa that investors and developers are unable to make forecasts or predict what should happen in the real estate market as a result of low data in the real estate market. 

In this article, we discuss the importance of data engineering and its impact on the real estate sector.

What is Data Engineering?  

Data engineering is the process of extracting data from multiple sources, transforming the data and loading it into a unified storage. Data engineers build automated data pipelines which enables continuous data flow in an organization. For example, a data engineer can build and automate a data pipeline that extracts data from an API (Application Programming Interface) source on a daily basis. 

Stages of Data Engineering

The stages of data engineering are often referred to as ETL operations, which stand for Extraction, Transformation, and Loading.

1. Data Extraction

Data extraction, or data ingestion, is the process of gathering information from multiple sources such as APIs, databases, or scraping the web for content. However, this data in its raw form cannot be used to generate meaningful insights because information from websites or APIs is poorly organized. So, the data will need to undergo some form of transformation in order to be useful.

2. Data Transformation

The next step in the ETL process is transformation of data. Transformation is the process of cleaning the data by removing duplicate values from data, standardizing outliers, and making sure the data is accurate. Data from websites and APIs tend to be dirty. By ‘dirty’, it means the data has problems such as duplicates, missing rows, or contains extreme outliers. 

The image above is an example of messy data. The data contains repeated values and no meaningful insight can be gotten from data of this structure. Hence, data transformation is necessary.

3. Data Loading

Loading is the process of storing this accurate information in a data warehouse so that it can be used to generate insights. A data warehouse is a type of data storage system mainly used for analysis. Examples are Amazon Redshift, Snowflake, Google Big Query, amongst others. This accurate data can then be tapped by data scientists, business intelligence developers, analytics engineers, and data analysts to generate insights from the data.

What type of insights can be gotten from transformed real estate data?

1. Accurate value estimates: 

Using transformed and cleaned real estate data, you can extract accurate value estimates for house and land prices across several markets. Tools like Vesper make it easy for you to do this by utilizing accurate and updated real estate data from Estate intel’s database. With Vesper, you can also get real estate investment costs and opportunities, construction cost estimates, and contractor recommendations for your construction projects.

2. Investment decisions: 

Imagine you want to invest in a property in the real estate market but you don’t know how to get started; data can be your guide. For example, Vesper can provide land and property suggestions based on data about your earnings. With Vesper, you can also get access to developers and agent information if you’d like to take immediate action.

 

3. Trend analysis:
With data, you can also discover trends in a particular area. For example, did you know that land prices in Epe have risen by over 100% in the past 5 years? We can make this deduction with the market analysis dashboards created using estate intel’s comprehensive real estate database.

You can read more about Epe land prices growth here.

4. Time series forecasting growth analysis:
Time series forecasting helps us know where the property market is headed tomorrow. This is done by studying historical trends and using advanced techniques like machine learning to forecast new trends. This helps in making better investment decisions and generating higher returns on investment. With data, you can also forecast the path of inflation, GDP and so much more.

The power of data in real estate

When the Mathematician, Clive Humby said “data is the new oil”, it turns out he knew exactly what he was talking about. When the right data is available, it creates a strong foundation for making the best informed decisions as an individual and organization. This is evident in the real estate industry as data availability can be the difference between a prosperous and mature market or an immature and opaque market.

We love your feedback! Let us know your thoughts about data engineering and its importance to the African real estate market by sending an email to [email protected].