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ChatGPT on iBuyer performance in 2022

ChatGPT has certainly made a big splash! Although it is still in its research phase, it has become one of the fastest growing apps, and to get a subscription to its “Plus” version you are now put on a wait list.  Our curiosity about ChatGPT’s prowess led us to seek some answers about the real estate market. The real estate market has been making headlines recently, first with the stupendous increase during the pandemic years and now the cool-off due to high mortgage rates. So, we asked ChatGPT: Which metros witnessed the most iBuyers purchases in 2022?

ChatGPT’s response:

“iBuyer activity is highly concentrated in a few major metropolitan areas in the United States. In 2022, the cities with the most iBuyer purchases were likely:

  1. Phoenix, AZ
  2. Las Vegas, NV
  3. Atlanta, GA
  4. Dallas-Fort Worth, TX
  5. Tampa-St. Petersburg, FL

These cities have seen significant growth in their real estate markets in recent years, which has attracted the attention of iBuyers. Additionally, the warm climates and affordable housing prices in these cities make them attractive to a wide range of homebuyers, including those who are looking for a fast and convenient home buying experience through an iBuyer.”

ChatGPT was spot on. We analyzed purchases made by a particular iBuyer in 2022. The map below shows their purchase activity in 2022. The 5 cities mentioned by ChatGPT are in our top 6 list based on the number of homes bought by the iBuyer in a metro. In addition to the 5 metros mentioned in the ChatGPT answer, our top 6 list included Houston-The Woodlands-Sugar Land, TX at the third spot. The map below shows where the iBuyer bought most properties in 2022.

But wait. We wanted to dig deeper and find out more. We had a follow-up question for ChatGPT: “In which cities did iBuyers make the most profits and in which cities did they incur losses?”

ChatGPT’s answer:

“As a language model AI, I don’t have the capability to track the specific financial performance of iBuyers in each city, but it’s safe to say that the profitability of iBuyers can vary greatly depending on the city and local real estate market conditions…”

This is where the AI tool was unable to provide us an answer. We analyzed our data which consisted of thousands of properties that were bought by the iBuyer and compared their purchase price with their list price when they put the property up for sale. We calculated the median of the percentage difference between list price and purchase price for each metro. Here is what we found. The metros with the largest losses were Austin-Round Rock, TX; Phoenix-Mesa-Scottsdale, AZ; San Francisco-Oakland-Hayward, CA; Las Vegas-Henderson-Paradise, NV; and San Diego-Carlsbad, CA, while the metros with the largest gains were Chattanooga, TN-GA; Greensboro-High Point, NC; Columbia, SC; Spartanburg, SC; and Greenville-Anderson-Mauldin, SC.

Our research and data-driven approach, based on data on thousands of properties comes out ahead when evaluating investment opportunities that would aid an investor to minimize risk and maximize profits. This analysis ties in well with the VeroFORECASTSM that “many markets which were the big housing market winners of the past year or two are now forecast to be some of the worst-performers including San Francisco, Seattle, Austin, Boise, San Jose, and Las Vegas.”

Will the AI of the future be able to analyze large sets of data and provide such detailed answers? Time will tell. Have you tried out ChatGPT? Give it a try and see if AI answers your questions.

About Reena Agrawal, Research Economist

About Reena Agrawal, Research Economist

Reena Agrawal received her PhD in Economics from Vanderbilt University and MA in Economics from The Ohio State University and has several years of industrial experience in economic research and analysis.

About Srinivasa Bhupathiraju, Senior Data Engineer

About Srinivasa Bhupathiraju, Senior Data Engineer

Srinivasa Varma Bhupathiraju has a total of 12 years of technology experience, which includes 8+ years of experience in the real estate solutions industry. He has worked in various roles, such as Data Scientist, Senior Analytics Manager, and Senior Data engineer. He has a strong understanding of the market, trends, and technologies in the real estate solutions industry. He has also been involved in developing and implementing strategies for successful real estate projects.

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