Data In Real Estate

The Real Estate industry is overflowing with data. Data about properties, data about people, data about markets and data about financials. Here are some of the ways data engineering can be used in Real Estate.

Point of Data Engineering in Real Estate

Remember this: Data engineering is NOT app development. However, it is related and does draw on a similar skill set. The main point of data engineering and data achitecture is to provide a wholistic view of the data in your real estate business.

Apps, while very useful, solves a single problem at a time, such as customer interactions with a CRM. Data Engineering gives you the full picture so you can understand how those customer interactions relate to financing, property maintenance, etc. and it can be seen together using Business Intelligence of as the source for automation and machine learning.

Avoiding The Data Silo

If you are not familiar with the data world we call a Data Silo it is any data storage that is isolated and can't be used by others. These include:

• Single department database, that isn't shared with others
• A spreadsheet on someone's coputer that is unaccessible to others
• Data from that cool app that you can't get out of the app

The by-product of a data silo is a degradaton of the quality and reliability of your organizations data. It should be avoided if at all possible.

Integrations

Apps by there very nature are data silos. All the data is contained with the app and it is difficult to get it out.

Keep in mind a real estate businesses likely has apps for CRM, accounting, marketing, GIS, property maintenance, listings and possbily more. This is dependent on the structure of the business and its size. Is it a large commercial real estate or a residential realtor in a small firm? This changes the makeup of the apps but they all have them.

Integrating the data from the variety of apps is the purpose of data engineering. Techniques used in data engineering extract the data form the apps you use and get it consitently standardized and formatted. While apps tend to show you transactions at an individual level data engineering looks at data in the aggregate so you can see patterns.

Why Is It Needed

Extracting this data allows you to do things like Business Intelligence, Automation and Machine Learning. All of these makes your business more valuable. Here are some examples:

• Business Intelligence allows you to notice things like what equipment breaks regularly and how tenent screening affects work order requests.
• Automation would notify maintenance to replace air filters extending the useful lifetime of air conditioning units.
• Machine Learning would be able to do a Linear Regression model to predict appropriate rental rates based on property features.

But you can't do any of this if you don't have quality data. That's why the Real Estate business benefits from data engineering.