Unlocking Value for Renters in Atlanta with HelloData.ai

Published by
Tim Gamble
May 10, 2023
Unlocking Value for Renters in Atlanta with HelloData.ai

Last week, we put together data on the Atlanta rental market as part of a proof of concept with a potential client. The data included rent, availability, concessions, amenities, and many other attributes for about 16k units listed on a single day in May. It also analyzed the listing photos for each property, and included the QualityScore we generated as a variable.

I was playing around with the data a bit, and plotted a graph of apartment quality vs average rent by neighborhood in Atlanta:

A chart of the impact of quality on rent amount for atlanta appartments

The thinking is, if we can identify which neighborhoods have the highest quality housing stock at the lowest price, those neighborhoods will have the best deals for renters. Now granted, we’re not taking into account crime rates, school district ratings, etc… but it’s interesting nonetheless.

Based on the data we pulled, the top 5 neighborhoods for renters to get a high-quality unit at a reasonable price are:

A table of top neighborhood with high quality and low rent

This means that Ben Hill Terrace is in the 18th percentile for rent, pretty low, but is above the 90th percentile for housing quality – pretty high. This is a quick analysis based on one day’s worth of listing data, so that could change if we surveyed more listings.

Check how you can use our computer vision models to run those analysis for any market.

Now we’re going to try applying this over a wider timeframe and across a variety of markets. Chicago is next in line. Exciting to see the results!

Property managers, investors, brokers and appraisers all use HelloData to analyze multifamily comps, optimize rents, and increase deal flow.

Tim Gamble

Co-Founder & Head of Data Engineering at HelloData. Tim is the driving force behind data structuring and engineering initiatives. His leadership role involves developing AI and computer vision-based products and overseeing the data engineering team responsible for the firm's efficient, time-saving products.

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