Major Real Estate Provider - Rental Price Optimization
AAARL created a three-layer computational model that optimized rental prices based on a variety of different dynamic factors. The client saw a rental price increase of 4% and a vacancy cost decrease of 30%.
A large apartment company with multiple buildings and approximately 2,000 rental units was finding their approach to rental rates outdated. An early adopter to an advanced revenue management tool, the system had become difficult to understand and use and was starting to provide inconsistent results. They were looking for a fresh approach.
The challenges were threefold.
1. To introduce a scientific based pricing model to take advantage of the latest in analytic capabilities.
2. Ensure a seamless transition in the process.
3. Educate and gain the confidence of the leasing agents who are the end-users of the system.
Advanced Analytics and Research Lab developed a new system based on academic and operational research to optimize pricing based on competitive factors of location, building age, neighbourhood vacancies, unit size, amenities, known pipeline of tenants, and other factors directly driving the desirability of the individual unit.
The specific technology includes a three-layer computation model that includes automation, prediction and optimization as outlined in the graphic below.
The development process by AAARL incorporated input from dedicated real estate professionals to improve both the interface and the confidence of future users.
The results were staggering. Even though the implementation took place during the market downturn of 2020, the net result was an improvement of overall rental price of 4% relative to the other system. As well, vacancy cost decreased at the same time by close to 30%.
Further, the senior management felt they had better transparency into pricing and were able to make strategic decisions across the portfolio as the team no longer needed to “guess” the prices. Another major efficiency gain was that with a standard pricing process, hours of time and process was cut down.
AAARL conducted a variety of training sessions for the leasing agents and was subsequently retained to refine and fine-tune the algorithms in the model and provide ongoing insights into the portfolio.