Realtors Property Resource® (RPR®) is 100% owned and operated subsidiary of the National Association of REALTORS® and is offered as a member benefit of NAR. RPR® actively collects listing data from 96% of the MLS’s in the United States and marries that with public record data demographic data and neighborhood data to provide analysis and reporting tools to help REALTORS® service consumers in the buying and selling of their homes. RPR® is an invaluable resource for REALTORS®, and we were excited that they were able to provide our hackathon participants with a special hackathon-only version of their Sandbox environment for developers.
Our friends at RPR® had a bunch of awesome ideas about how their data, combined with artificial intelligence and machine learning, could be used to improve how our members buy and sell homes – the ideas are so good, I’m going to let RPR® give their ideas directly instead of summarizing it from the emails we had back and forth during the hackathon process.
Neighborhood Turnover Rate: For example comparing count of actual SFR properties in a geo location zip census tracts etc and the number of properties closed in a month (run multiple counts month by month for the last year). Also compared the average/median sale prices over time to track trends.
– This could help Realtors and Brokerages identify neighborhoods in which they can focus their marketing efforts for a higher ROI.
– This can also help Brokerages measure possible revenue based on sales volume (sales price volume).
Correlation between distressed properties and the last sale date or sale price: For example search for SFR properties with a distressed date (YTD) in the city of San Francisco. Compare the distressed date to the sale date track a pattern and also analyze the sold price.
– This could help Realtors locate areas and price points that are about to be impacted by foreclosures.
– This could also provide Lending and Government institutions with a timeline of when the purchases occurred so they can be linked to changes in the job market interest rates lending options at the time or lack of market price bubble etc…
A couple of our teams used RPR® data in their final projects, using the data from fifteen million public records across several states in order to analyze trends in public records and property listing data. You’ll notice a theme of several of our sponsors is to collect data from multiple places and create a more streamlined system for displaying it; artificial intelligence and machine learning represent the next step in the process, where the streamlined systems can be analyzed in order to facilitate an even easier buying/selling process for agents and their clients.