Real estate agents bear a cult of personality to engage customers. They deal with them both with personal integrity and a professional approach. But with the integration of technology in the real estate business and the invention of proptech, a new dimension has popped up. Data in real estate now plays an intricate role in developing the dynamic of the real estate business. As a result, professional agents have begun analyzing their leads from a business perspective. This means one lead data will generate multiple leads. Here are some fascinating ways of application of these data in real estate.
Geographic Information System (GIS)
The best way to integrate data in real estate is by mapping them in by using geotags and clustering units. This means that you can locate them using multiple GIS systems. Location is a very important part of analyzing factors of real estate. Henceforth GIS can help us understand, visualize, and analyze locality intelligence. Site selection, pricing, locality amenities, demography, neighborhood condition, all can be integrated into a single module using open source data domains. And this is the area where GIS becomes the real player of the data in real estate.
With the change of locations and locality, the transition of real estate performance happens dramatically. Due to the socio-cultural and macroeconomic conditions, a city’s multiple neighborhoods act in different ways. Some neighborhoods have cluster-based housing societies with specific rules and bylaws. Some have mass-market houses. The role of cluster analysis is to rigorously identify the patterns of data in real estate. This identification helps to determine, which of the properties will perform similarly and which will be diverging. Also, the cluster analysis helps in mapping the time frame of the property performance in a particular location. As a result, we get segments of data that helps the investors to decide their way forwards.
Automated Valuation Models (AVM)
The work of automated valuation models is to harness data in order to process an estimation of a property’s market value. Most of these models help to determine real-time values through performing needs assessments of buyers and sellers. The AVM models are exclusive data science tools for the synthesization of data in real estate. Using this technology, a company can automatically bid on any property, providing the owners or sellers ready liquidity for their assets.
Time Series Forecasting (TSF)
Time Series Forecasting is a futuristic technology in real estate. It helps us to understand and predict real estate activities in the future. This technology safeguards investment opportunities, development deals, and ensures a higher return on investment (ROI). Time Series Forecasting uses various models and data tools to forecast the real estate market. For example, for only one set of data in real estate, TSF uses Auto-Regressive Integrated Moving Average (ARIMA) model for short-term predictions. For multiple sets of data, which are directly or indirectly related to real estate, TSF uses a combination of Vector Auto-Regression (VAR) and Vector Error Correction Models (VECM). They help to analyze and project a broad range of factors for predicting the future of real estate.
The real estate industry has significantly shifted toward using open-source data and generating data-driven results. The future progress of the industry lies in the utilization of data in real estate with accurate rendering and processing of information.