Predictive analytics has transformed the real estate industry due to its powerful ability to deliver fast and accurate actionable insights. This has largely come about through the advent of Big Data and Geographic Information Systems (GIS) that harness the intrinsic power of real estate data.
Predictive analytics tools take this analysis to the next level to predict future outcomes based on how past and present events occurred. Consumer demographics, housing trends and property price history are some of the areas where predictive analytics represent a huge opportunity for the industry.
You may be interested in: “Big Data and the real estate market.”
Models for Real Estate Agents
Analyze local housing upgrades and improvements, identifying the type of properties buyers are willing to pay more for, or the condition of the property they expect at a given price, helping to improve the quality of buyer and seller leads, generate more homes for sale, match property buyer leads with the right listing, decrease the cost of customer acquisition etc.
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Models for Real Estate Investors
Significantly eases the search for the perfect investment property, as predictive analytics draws from huge amounts of Big Data, combined with a heat map visualization the best areas within a particular city to invest in rental properties can be predicted, helping investors choose the right properties and maximize profits.
Models to Understand the Customer Journey
User data is used to understand the buyer’s journey in a more comprehensive way, identifying consumers who are in the market to buy a home and getting the right message to them, at the right time.
Models in Real Estate Marketing
Use a prospect’s behavior and purchase history to predict what they are looking for and when they are likely to buy, helping sales reps focus on the leads that will provide the most value to the business.
At PREDIK Data Driven we support the real estate industry to begin its transition to predictive modeling and big data analytics to reduce the work associated with manual data collection and anticipating future problems.
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