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Revenue Management for Four Single Family Rental Communities

As part of a pilot project, (a subsidiary of Advanced Analytics and Research lab focused on the Real Estate Industry) leveraged the first AI-based dynamic pricing for Single Family Rental Communities for a Large US-based residential rental owners. The goal was to help the organization increase rent revenue, decrease vacancy costs, and improve operational efficiencies for four Single Family Rental Communities.


This company, which was relatively new to the industry, was rapidly growing and quickly becoming one of the 50 largest real estate private equity investors in the world by total annual deployment — deploying more than $1 billion of capital annually in 2021 and 2022 alone.

As they build up the residential side of their portfolio, single family rental (SFR) communities have become a major focus for the organization as it is a rapidly growing asset class with increasingly strong demands across the US. Currently, while pricing units/homes at these communities, they were relying on ad-hoc competition research and “gut-feel”. Although they want to optimize how their rental units were priced and improve efficiency in their operations, there were no Revenue Management products built specifically for the unique features of the SFR asset class.


The challenges included:

  1. To conduct a pilot project on four Single family rental communities across the US (some stabilized, some in lease-up phase), introducing a sophisticated pricing model to take advantage of the latest in machine learning and analytic capabilities. The specific goals include:

    1. Decrease vacancy costs by 20%+: by decreasing the amount of time that each unit is spent vacant and/or increase lease velocity if a new build community in the lease up phase.

    2. Improve rent revenue by 2-5%: compared to market average (if market rents were going up by 5%, we want to achieve a 7-10% increase).

    3. Improve process for pricing units, decreasing time spent on data entry and decreasing data errors.

  2. Ensure a seamless transition in the process, working with over 5 stakeholder groups.

  3. Educate and gain the confidence of the leasing agents who are the end-users of the system.


AAARL introduced our innovative pricing system into four SFR communities as a Pilot project. This system is based on our highly successful Multifamily Rental Revenue Management product, combined with years of academic and operational research to create a product specifically for the nuanced single family rental asset class. The system optimally prices each rental home based on competitive factors such as location, home age, neighborhood vacancies, unit size, amenities, demand, availability, and more to try and determine the desirability of a particular home and a particular moment in time.

Specifically, the technology includes a three-layered computational model including automation, prediction, and optimization.

Beyond the implementation of this pricing system, AAARL worked with the key stakeholders to educate them on how to use and understand the system in order to increase the overall efficiency of their operation. Further, AAARL worked with the team on an on-going basis in order to provide them with weekly and monthly strategic recommendations for each individual property. Finally, AAARL provided the team with weekly and monthly dashboards, as can be seen below:


Through the pilot, the organization was able to see an increase in rent prices and improvement in vacancy costs / lease up velocity. Anecdotally, we have heard very positive results from the client as well, mentioning our service and customizability as major differentiators.

As highlighted below, we were able to achieve strong lease velocity and rents compared to the same period the previous year:

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