Why Image Recognition Fails for Property Vacancy
Summary (TL;DR)
Leveraging AI image recognition to determine property vacancy by detecting furniture in photos is an unreliable method. Our analysis shows this approach is prone to significant error because real estate photos are often misleading. Key issues include the use of professional staging in empty homes, computer-generated (CGI) or “virtual” staging, and outdated imagery from previous sales. Consequently, backend data sources like tax records and sales information provide a far more accurate assessment of a property’s true occupancy status.
Why Image Recognition Fails for Property Vacancy
The application of Artificial Intelligence in real estate is expanding rapidly, and one of the most discussed uses is image recognition. The premise is straightforward: an AI analyzes property photos online, and if it detects furniture, it flags the property as “occupied.” If the rooms are empty, it’s flagged as “vacant.”
While this sounds like an innovative technological solution, our team decided to investigate its practical effectiveness. We conducted a controlled test to determine if this visual analysis could match the accuracy of traditional data-driven methods.
The Methodological Showdown
We established a comparative analysis between two distinct approaches:
- ▶ The Image Recognition Model: We developed an AI model using powerful, open-source computer vision frameworks. Its sole function was to analyze photos from 62 property listings and determine occupancy based on the presence of furniture.
- ▶ Verified Data Analysis: This approach ignored images entirely. Instead, it relied on an aggregation of authoritative backend data, including tax assessments, mortgage records, sales history, and other official documentation.
The objective was to see which method provided a more accurate picture of a property’s real-world status.
Why Visual Analysis is Fundamentally Flawed
The results were conclusive: the image recognition model was frequently incorrect, especially for higher-value properties (over $900,000) where marketing budgets are larger. The core issue is that real estate photography is designed to sell, not to provide an accurate, real-time representation of occupancy. Here are the three primary factors that misled the AI:
- 1. The Impact of Professional Staging (24% of Errors): It is standard practice to furnish a vacant property with rental furniture and decor to make it more appealing to buyers. This “staging” creates the illusion of an occupied home. The AI correctly identifies the furniture but incorrectly concludes this means the property is occupied.
- 2. The Rise of Virtual Staging and CGI (10% of Errors): Increasingly, real estate listings use computer-generated imagery (CGI) to digitally add furniture to photos of empty rooms. To the human eye, these can look nearly identical to real photos, and an AI model cannot distinguish this “virtual” furniture from physical objects without specific, complex training.
- 3. The Problem of Outdated Imagery (12% of Errors): A significant number of listings recycle photos from previous sales, sometimes from years prior when the property was genuinely occupied. The AI has no context for the photo’s age; it analyzes the pixels as if they were captured yesterday, leading to a direct mismatch with the property’s current vacant status.
Analyzing the Performance Data
The performance gap between the two methods was not minor. The following visualizations illustrate the disparity in accuracy and the reasons for the AI’s failure.
Graph 1: Accuracy Rate – Model vs. Verified Data
The data conclusively shows that verified records are vastly more reliable.
Graph 2: Root Causes of Image Recognition Errors
This breakdown confirms that the model’s errors are not random but are direct results of standard marketing practices in the real estate industry.
Conclusion: Data Integrity Over Visuals
Our test demonstrated that relying on image analysis for vacancy detection is a method with inherent and significant flaws. The integrity of the source data (the photo) is compromised by its marketing purpose. For investors, analysts, and businesses that depend on accurate occupancy data, this method presents an unacceptable risk of error. The more reliable, cost-effective, and accurate approach remains the analysis of granular backend data. These records reflect real-world transactions and legal statuses that are not subject to the visual manipulation present in property listings.
Explore the Technology
The AI we used is built with “open-source” tools, meaning their underlying code is publicly available for developers to use and modify. If you are interested in the field of computer vision, here are the foundational projects for this type of technology:
1. TensorFlow Object Detection API
What it is: A framework by Google for building models that can identify and locate multiple objects within an image. It’s a powerful tool for academic and commercial applications.
2. YOLO (You Only Look Once)
What it is: A state-of-the-art, real-time object detection system renowned for its speed and efficiency, ideal for processing live video or large batches of images quickly.
GitHub: ultralytics/yolov5
These frameworks excel at object recognition, but our findings show that identifying an object is only the first step. Understanding its context is a far more complex challenge.
The Bright Future of Image Recognition
While this article highlights a specific failure of image recognition, it’s crucial to understand that the technology itself is incredibly valuable when applied correctly. The key is using it for tasks where the visual data is reliable and provides direct insight.
For instance, companies like Yembo are successfully using AI-powered visual analysis on the prospecting and operations side of industries like moving and insurance. By analyzing user-submitted videos of their homes, they can accurately estimate the volume of belongings or assess property conditions for claims. In these cases, the visual data is current, relevant, and used for its intended purpose.
This is the direction Moovsoon is heading. We are actively training our own models on proprietary data to enhance our services. By analyzing images, we aim to improve how we identify and present commercial and renter opportunities. Furthermore, this technology will allow us to create dynamic marketing content tailored to specific property features, ensuring that our visual assets are not just appealing, but also intelligent and informative. The future isn’t about abandoning image recognition, but about deploying it smartly where it can deliver real, measurable value.
