AI-Powered Real Estate Photo Enhancement
The Problem
Professional real estate photography requires extensive post-processing: brightening window views (window pulls), replacing overcast skies, removing temporary objects, and enhancing lighting. This retouching is time-consuming and expensive, often costing $5-15 per image with turnaround times of 24-48 hours. With photographers shooting dozens of properties weekly, these costs add up quickly. The client — a real estate photography business — wanted to know if the latest generative AI models could automate this workflow while maintaining the quality standards required for MLS listings, where images must accurately represent the actual property.
The Solution
We developed a multi-step retouching pipeline using GPT Image 1.5 and other generative models. The workflow included automated window pull enhancement, sky replacement with realistic alternatives, object removal for staging items, and overall color and lighting correction. Each step was tested independently to identify which tasks the AI handled well and where it struggled. We processed a test batch of real property photos and compared results against professionally retouched versions, specifically checking for accuracy against the original scenes.
The Result
The retouching quality itself was excellent — enhanced images looked professional and visually appealing. Window pulls and sky replacements achieved great results. However, we discovered a critical limitation: generative models cannot maintain pixel-perfect accuracy of the original image. They subtly reshape objects like musical instruments, flowers, paintings, and architectural details. These "hallucinations" are unacceptable for MLS listings, which must represent actual reality for legal and ethical reasons. Our conclusion: this technology is NOT advisable for real estate at this time. However, comparing current models to those from just one year ago, the progress is enormous. The multi-step workflow we developed is sound — we're simply waiting for model accuracy to catch up. We recommend re-testing with each major model release.