AWS Machine Learning Blog
Pricing housing just right: Entrata enables apartments to fill capacity with Amazon SageMaker and 1Strategy
The housing market is complex. There is a continuously changing supply of student housing units around any given education campus. Moreover, the accepted value of a unit continuously changes based on physical and social variables. These variables could include proximity to campus with regard to other available options, friend groups living nearby, and the availability of nearby parking as other properties fill. The interplay happens at all levels—entire properties may shift in value and specific units within them may exacerbate or counteract those shifts.
For a property management company to earn the maximum revenue from its rental units, it needs to price each unit just within the price-point for the tenants—but it doesn’t know what their price constraints are. The company would not want to leave money on the table by setting a price too low. Setting a price too high can mean that the unit sits empty—effectively costing the company to maintain the unit. Finding that balance is a difficult problem.
Entrata, a comprehensive technology provider of multifamily property management solutions, solves this problem by employing machine learning (ML) with AWS. Specifically, they feed location-specific and even building-specific data (such as occupancy, proximity to campus, and lease term length) into an ML-based dynamic pricing engine running on Amazon SageMaker. The model helps Entrata’s customers—property managers—to predict occupancy levels and in turn optimize their prices of student housing.
At the implementation level, this solution relies on a number of AWS offerings. AWS Glue extracts Entrata’s historical data into Amazon S3. This data enables Amazon SageMaker to make pricing predictions, which are written to an output bucket back into Amazon S3. Entrata’s applications consume this data request using API Gateway, which triggers AWS Lambda functions to deliver the most relevant forecast for any available unit.
Entrata developed this solution in partnership with AWS Premier Consulting Partner 1Strategy, a Seattle-based consultancy that helps businesses architect, migrate, and optimize their workloads on AWS. The partnership between 1Strategy and Entrata has existed for years, but the ML work is their most recent—and arguably, most impressive—joint technical accomplishment.
Their collaboration previously focused exclusively on data management through AWS—which in itself proves a non-trivial challenge due to the location, size, and complexity of the data. Entrata currently serves greater than 20,000 apartment communities nationwide and offers a variety of tools, from mobile apps to lease signing portals to accounting platforms.
The novel ML solution is exciting. Entrata’s CTO, Ryan Byrd, says, “The impact is far ranging and positive. Automating back-office functions with Amazon ML frees property management to focus on people first, instead of performing rote behind-the-scenes guessing of price recommendations.”
Entrata plans even more work with AWS in the future. Byrd adds, “AWS technologies will decrease our time to market with various ML projects.” He and his colleagues on the Entrata team are keen to aid customers in their decision-making efforts. They also use ML for various operational elements for their and their customers’ businesses, strategic planning, and maintenance management.
About the Author
Marisa Messina is on the AWS ML marketing team, where her job includes identifying the most innovative AWS-using customers and showcasing their inspiring stories. Prior to AWS, she worked on consumer-facing hardware and then university-facing cloud offerings at Microsoft. Outside of work, she enjoys exploring the Pacific Northwest hiking trails, cooking without recipes, and dancing in the rain.