Investors watch their stocks and let market data inform their activity. Process engineers monitor machine feedback and respond to the data describing the state of the line. Even football managers now monitor movement and touches by their players, adjusting lineups and strategies based on data from the pitch. If you are in the property business, you must follow their example and work with real estate data.

This insight might be hard to grasp. It may seem strange to think of land and buildings as “machines.” Real estate is so different from machines on an assembly line. Yet land and the buildings on it shape processes every day for occupants. Are the buildings functioning efficiently? Are they making good use of their inputs? Machines wear out. So do buildings. Would it be possible to work with real estate data to create an understanding for where and when wear is happening? 

Modern real estate generates a vast amount of data, much thanks to the IoT – Internet of Things. The challenge is to collect, analyze and present these data in a way that leads to better decision making. 

Types of Real Estate Data

Consider the data we must process. When we work with real estate data, you will encounter constant and dynamic data. Some real estate data remain fixed over time. For example:

  • What is the size of the land?
  • Where is the land located? 
  • Do we have buildings on that land?
  • What is the footprint of each building? 
  • What is the floorplan of each building? 
  • How many windows does each building have?
  • What direction does each window face?

Then there are real estate data that change with time:

  • How many people occupy each building? 
  • What changes to equipment have we made? 
  • How much heat does each computer in the building generate?
  • What is our use of electricity, water, natural gas?
  • What is the price of each of these utilities? 
  • What is the weather at the location of each building? 

Further, these data are interrelated. For example, each utility data point must be linked both to time and to the building in which it is used. 

Work with real estate data from various sources

The raw data are generated by very different sources as well. Each was designed to serve a particular purpose. Virtually none of the data structures were designed to seamlessly integrate with other data. To look at a few:

  • Land data flows from real estate filings. It may come from government or private sources. 
  • Architectural data flows from drawings. The drawings are often closely protected by building owners. 
  • Asset data about a building (people, furniture, computers) flow from the user of the building.  
  • Utility pricing data flows from utility providers. 
  • Utility usage data flows from sensors on building sites. The sensors can be privately owned, or utility provided.
  • Weather data flows from public sources. 

Effective decisions on building management, building investment and real estate performance can only happen when these data can be accumulated and presented. The owner, or the operator wants to know “Can the data answer the question I’m asking?”. 

Integrate and work with real estate data using RealEstateCore has just a system to answer those questions.

We have designed a system from top down which defines each element of real estate and its data standards. We’ve implemented this system from the bottom up, to capture raw data efficiently. We are not inventing new data standards. Rather, we are bridging existing standards to make data applicable for decision making. We integrate financial, architectural, operational and IoT sensor data to describe complex real estate portfolios. Learn more about Real Estate Core here.

This system scales easily. The large portfolio owner can zoom in on a specific building or step back to examine all holdings. 

If this system would solve problems for you, please contact us here. We look forward to talking about your real estate and letting its data work for you. 

Dr. Erik Wallin

Chief Ecosystem Officer, and founder of ProptechOS and RealEstateCore is recognized as a leader in Building Operating Systems (BOS) and making the buildings of the world smarter. He holds an MSc and a Ph.D. in Media and Computer Science from KTH Royal Institute of Technology.

Read his full bio and information here.