AI is changing the real estate landscape in many ways. From predictive analytics to helping investors make smarter decisions, here are some of the most impactful applications of AI in the real estate industry.
Property valuation used to be a manual process that lacked consistency and accuracy. Now, it’s automated and fair with AI-driven algorithms that leverage public records, crime statistics, school district ratings and more to ensure a fair market value.
It’s Helping Buyers
AI-powered tools make it easier for home buyers to sort through listings. For instance, AI gathers information about a property’s square footage, bedrooms, bathrooms, and views and places it at the fingertips of a home buyer.
AI also suggests similar properties to a home buyer, making the process easier and faster. Similarly, Zillow’s Zestimate algorithm uses big data to determine a home’s value and sends the information to potential buyers.
In addition, AI makes it possible for real estate agents to generate high-quality leads for their clients. This may sound like a straightforward task, but it can be a difficult one for real estate professionals who spend hours pursuing leads that do not convert into sales.
In addition, AI can help speed up processes such as generating property documents and landlord accounting. It can also eliminate the margin for error by digitizing receipts, invoices and other documentation. This can greatly reduce the amount of time that a real estate agent needs to devote to managing their portfolio.
It’s Helping Sellers
AI is helping sellers with a variety of tasks, from predicting sales to improving their pipeline. This technology can help them be more efficient with time and money while also boosting engagement by giving them real-time information about their prospects.
For instance, a smart AI tool can recommend the perfect home security system for a specific buyer. This helps ensure the product is a good fit for the buyer’s needs, saving them money in the long run.
The technology can also help automate tedious, repetitive tasks. For example, a chatbot can be used to solve customer support tickets in minutes.
A clever AI program can also predict which marketing ad will be most effective for a particular search query. This can help brands get more clicks on their ads and drive higher conversion rates. This is all made possible through the power of machine learning, which uses progressive algorithms to find structure and regularities in data.
It’s Helping Agents
The real estate industry is one of the last sectors to implement technology, but AI is starting to change that. Using AI to complement and automate operations helps agencies reduce labor costs and streamline processes.
The use of AI also allows agencies to focus on the important aspects of their business, such as marketing and lead generation. AI-based software can generate targeted ad campaigns that increase lead conversions.
It also provides real-time property valuations that are accurate and fair. It draws data from public records, school district ratings, crime statistics, access to local amenities, and buying trends to assess a property’s value.
AI is also changing the way lenders do mortgage lending. It can automatically sift through bank statements and other documents to determine a borrower’s creditworthiness and loan risk. It can also automate the credit application process and ensure legal compliance.
It’s Helping Investors
AI-driven property search is delivering better outcomes for buyers and sellers alike. It helps users scan across parameters like amenities, connectivity, pricing, security and more.
This enables real estate portals to customize their listing based on the preferences of their users and deliver near-perfect results. Similarly, it helps agents to respond to their clients more efficiently.
It also enables brokerages to precisely market their listings. For example, Zillow’s Zestimates have a median error rate of just 1.9 percent.
Moreover, it helps investors evaluate properties before making an offer. This enables them to decide whether they are able to meet their bottom line or not.
However, the technology is still in its early stages. It requires a large data pool and frequent updates. It also cannot fully replace human labor in some tasks.