FoxyAI Significantly Improves Large Regional iBuyer’s AVM.

The FoxyAI score enhanced a large regional iBuyer’s AVM, allowing for better Real Estate investment decisioning.


Improvement in PPE10 (Percent Predicted Error within 10%)


Increase in individual property values


Improvement in portfolio value


Improvement in Median Absolute Percentage Error (MdAPE)

All Case Studies
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Fello is a real estate company that simplifies the rental process through an online platform connecting tenants with properties and providing a seamless user experience.

Real Estate Investment

Automated Valuation Model (AVM)

FoxyAI Computer Vision Models

Executive Summary

In the original pilot using 19,105 properties and 400,000 photos Fello, an iBuyer found that the FoxyAI-enhanced AVM “significantly improved” its original model, enabling it to offer a better, more accurate product during a time of rapidly changing valuations. In addition, the iBuyer increased its brand footprint and industry recognition while adopting the latest in visual intelligence technology.

The Company

A Large Regional iBuyer, or an Instant Buyer (“iBuyer”), which is a Real Estate company that uses algorithms and technology to buy and resell homes quickly.

The FoxyAI score significantly improved our Automated Valuation Model with a 5% improvement in PPE10 offering us an edge over more traditional models allowing us the ability to make better investment decisions.

Head of Data Science,

at a Large Regional iBuyer

The Challenge

In 2021, a Large Regional iBuyer that successfully bought residential homes entirely online decided to expand its offering to brokers and agents, providing them with the tools to offer their own iBuying solution.

In creating this new offering, the Large Regional iBuyer’s leadership identified the key components necessary to execute and become more profitable. One essential component was to source the technology to create a hyper-sophisticated AVM to give its team and new B2B audience the confidence to acquire properties instantly in a low-interest-rate environment.

Consistently improving a successful iBuyer’s proven AVM is a serious challenge. The iBuyer must have created an accurate model to become successful, and to improve upon it takes the latest in AI advancements and a discerning data scientist.

This Large Regional iBuyer faced an added challenge of entering the saturated B2B market. Now, in addition to improving a demonstrated AVM, the iBuyer’s team had to attract an astute agent and broker audience and needed to find the right technology partner to help them do so.

The Solution

After vetting technology partners, this Large Regional iBuyer chose to run a
pilot AVM test with FoxyAI’s Computer Vision models. The iBuyer’s data science
team incorporated the FoxyAI Condition Score Model into its current AVM to
see if it would improve its accuracy.

They found that FoxyAI’s models:

Accurately Analyzed Property Media

The FoxyAI Condition Score Model analyzes a company or government entity’s property media and provides a continuous condition score, using a 6-point scale ranging from Brand New to Heavy Damage/Not Livable. It is based on the scoring system from the Uniform Appraisal Dataset used by Fannie Mae, Freddie Mac, and others for underwriting.

Retrained & Recomputed AVM Models

By retraining the iBuyer’s AVM with the FoxyAI Enhanced AVM, the iBuyer was able to “significantly improve” their original model, reporting a “5% improvement in PPE10 and a 2% improvement in MdAPE.”

Improved Property Value

In dollar terms, this AVM enhancement on average brought each of these 19,105 properties ~$3,000 ($2965.52) closer to the sales price, accounting for a ~$57,000,000 improvement in property value.

This Large Regional iBuyer rolled out its AVM, which was enhanced with FoxyAI’s Condition Score. The iBuyer team successfully improved
its demonstrated AVM in order to expand its product offering to a new market. In addition, the iBuyer increased its brand footprint and industry recognition while adopting the latest in visual intelligence technology.