What Is Computer Vision & How Will It Revolutionize Real Estate?

Computer Vision for Real Estate?

You have probably heard about artificial intelligence, but have you heard about computer vision? Computer vision can change how some areas of the real estate industry function to create amazing productivity, and it is already in action with real-world examples discussed below. 

Amid the recent surge in demand for homes, many real estate companies are looking to future technologies to increase efficiency and provide a better customer experience. A single piece of technology that can support this improvement is computer vision.

To understand computer vision, we must first define the following:

• Artificial Intelligence (AI): The ability for a computer program or machine to imitate human intelligence;
• Machine Learning (ML): A subset of AI that trains a computer how to learn;
• Neural Networks: A computer system modeled after the human brain & nervous system;
• Deep Learning: A powerful set of techniques for learning in neural networks
  Computer vision (CV) is a subset of AI that uses deep learning, a type of ML and neural networks to help computers make predictions rapidly, at scale. It runs analyses of visual data (pictures, videos, etc.) over and over until it detects distinctions and recognizes images. If AI allows a computer to think, a CV allows them to see. Today, we are seeing many uses as the world becomes digitized and consumers are in search of fast, exact property data.

Examples of how CV is Transforming Real Estate

Today, there are robust solutions to supply accurate valuations for appraisals, one of them being Automated Valuation Models. AVMs supply real estate property valuation using mathematical modeling of public and confidential data. The downside? They don’t consider property conditions and property quality. That is where the relationship between AVMs and Computer Vision can supercharge appraisers and agents to help save time and money and resources.

CV can help consider data such as design, contextual and environmental factors to aid with property valuations. FoxyAI is a leading PropTech company that uses CV in its visual property intelligence models. They have worked in conjunction with a large regional real estate company to integrate their AVM tech and FoxyAI’s CV products. The result? A 5% improvement in accuracy valuation to be within 10% of the actual sale price¹, thus resulting in an estimated $57,000,000 in realized property value.

Other benefits of CV include:

• Automate condition and quality scoring;
  • Condition scoring used in evaluating conditions of properties from brand new to heavy damage and unlivable;
• Detection of general damage such as water stains, mold and gutter damage;
• Assist in estimation of renovation and repair costs;
• Easy classification of room types, object tagging and aiding SEO;
• Simplify property listings by generating property descriptions.

¹ Using PPE10: percentage of time the AVM is within 10% of the benchmark

How AI and CV Are Addressing Fraudulent Listings

Thousands of listings are uploaded daily, ensuring that every property’s images are within regulatory guidelines seems like a never-ending battle. Every listing on a website’s portal runs the risk of fraudulent listings appearing. This demeans the user experience and trust in the data. To mitigate these issues, some real estate companies hire a team of people to comb through data, but this is costly and time consuming. If a human was tasked with looking through and manually reviewing/recording every listing, imagine the resources required. Instead, we can automate or assist these types of operations by having a computer “see” the errors and record the data using CV.

Restb.ai, an AI and CV company for real estate, helps solve the issue of duplicate listings and more. They process over 10 million photos daily and provide real-time photo monitoring to aid companies by:

• Automating compliance reviews of agent, appraiser and user uploaded imagery;
• Enriching listing details by auto-populating features extracted from photos during listing add/edit;
• Enabling users to find properties based on features or that are similar to photos they upload;
• Providing condition-adjusted comparable properties to evaluate homes at their “as-is” state and at their theoretical “post-renovation” value;
• Performing trend analysis by looking at home’s interior and exterior styles, kitchen colors and layouts and more.