When agents and property managers list properties, their ultimate goal is to quickly find qualified tenants and buyers.
Their marketing efforts, both traditional and digital, aim to cast a net for potential buyers and to rank top in property listings.
However, this is not always the case, with property agents and sellers struggling to consistently find credible clients. Traditional property search techniques suffer several limitations, disenfranchising both buyers and sellers. For instance, their limited scope fails to capture a property's actual value and client sentiments.
Artificial intelligence(AI) is spearheading significant changes in real estate. The technology has found use cases in property valuation, management, search, and recommendation. AI techniques like computer vision, machine learning(ML), and natural language processing(NLP) provide the real estate industry with tools that are rapidly revolutionizing customer experiences.
In 2023, over 1.015 million homes were listed for sale in the US monthly. The sheer volume, market dynamics, and government policies greatly impact property transactions. As such, the industry has turned to technologies such as AI to deal with these challenges.
Here are some of the AI-powered property search and recommendation applications.
Consumers associate personalizations with positive experiences. Market surveys continue to back these sentiments, with data showing that personalized marketing reduces customer acquisition costs by up to 50% [1]. Instead of a one-size-fits-all approach, personalizing property searches leverages data to create unique recommendations.
ML algorithms, such as content and collaborative-based filtering, can analyze buyers’ preferences, such as location, interactions, past search history, and feedback. The solution profiles the client and matches them with the property they are likely interested in.
Real estate recommendation engines usually output properties using a relevance score depending on user preferences. For instance, if a user has been searching for houses in a specific locality and at a particular price, the system will match the buyer with properties with similar characteristics. The solution can use an algorithm like greedy forward selection to output a diversified but optimized list of properties for buyer consideration [2].
Conversely, it is also vital to match property agents with properties they are likely to sell. ML algorithms can recommend to agents which properties they will likely sell quickly [3]. These solutions help agents nurture leads, trusting they will have a higher winning percentage with AI-powered recommendations.
Agents can use AI to generate accurate and targeted property listings. Apart from generating leads easily, optimized listing can help prospective buyers find property easily.
There are also AI-powered apps helping homeowners find the best real estate agents to sell their property. These apps use experience, locality, success rate, and accreditation data to recommend the best agent to homeowners.
Online real estate platforms have seen increased adoption due to internet penetration, smartphone adoption, and online shopping. In 2023, there were nearly 100 million visits to the top three online real estate platforms in the US [4]. The National Association of Realtors also estimated that 97% of home buyers perform online searches [5].
Instead of rigid search filters, AI can power dynamic searches that expand the search criteria. AI can consider user preferences like commute time, monthly budget, and social amenities, reducing the time buyers view relevant properties quickly.
Additionally, solutions with image recognition algorithms can extract and tag property photos better. Such data can enhance filtering augmented by visual features, providing users with more specific property options [6].
Enterprise companies feature thousands of properties for their potential client. Sifting through property lists manually to find the perfect fit for homebuyers can be challenging.
Machine learning and pattern recognition techniques can analyze historical data and search behavior to create customized listings. These listings can highlight specific features likely to resonate with clients' preferences. For example, the solution can highlight properties near schools and libraries for a young family searching for a house in the city.
The level of personalization can extend to subtle preferences that prospective buyers have not explicitly indicated in their search. For instance, beyond keyword matching, Crexi, an online real estate marketplace, uses ML algorithms to analyze search patterns allowing users to see highly relevant properties [7].
With so much information online, including photos, videos, and text, information overload is a genuine concern for home buyers and agents. Conversational AI interacts with buyers and sellers and responds to user input using voice or text. Examples of conversational AI include virtual assistants, chatbots, and voice assistants, which use ML and NLP to respond to user input in natural language.
Here are some ways conversational AI impacts property search and recommendation.
Natural language search: AI-powered search is providing new ways for property discovery. Users can now enter natural language queries like “Three-bedroom house in Paris under $800,000.” Prospective buyers can use specific search queries that may not be possible to use with traditional search features.
The AI uses keywords and phrases associated with different customer preferences. The tool can then recommend property, elevating client experience.
Analyzing calls and messages: Conversational AI can record, transcribe, and analyze calls and text to uncover insights and customer nuances. Such tools can help agents offer better recommendations to clients.
Voice-activated searches: Development in NLP is ushering in a new era of voice-enabled searches. Online property apps can allow potential buyers and sellers to use voice commands to search.
Lead generation and qualification: Agents and sellers are constantly searching for leads. Manual analysis and sorting of leads is cumbersome and can lead to missed opportunities.
Chatbots can act as the first point of contact and gather information from potential clients. ML algorithms can sift through data to identify potential buyers and sellers. The virtual assistant can qualify and filter out promising leads, helping agents spend their resources more efficiently.
Major real estate listing platforms and proptech startups are working towards harnessing the power of AI. Property search and recommendation is where AI has been used to enhance property discovery and optimize listings.
Here are some companies using AI for real estate property search and recommendation.
The real estate brokerage company uses AI to make home buying and selling easier and less stressful. The generative AI-powered assistant answers requests from homebuyers. For instance, the Redfin mobile app can help buyers learn about a home listing. The AI tool can answer questions about amenities or what school district a listing is in [8].
Additionally, Redfin has incorporated a ChatGPT plugin that accepts natural language as input. Home buyers can describe their preferences and find listings that suit their criteria. For instance, a customer can see listings for a search term like ”under $400,000 budget. Show me updated two-bedroom townhomes with private balconies or patios in walkable areas with lots of outdoor cafes” [9].
Zillow is a leading real estate marketplace popular for its AI-powered home value estimate tool, Zestimate. Apart from what the company has achieved with AI, David Beitel, the company’s CTO, foresees unique opportunities presented by generative AI [10].
The company’s natural language search tool is also powered by AI, enabling homehunters to search for property using natural language. The company also uses ML algorithms for its recommendation engine. This feature analyzes vast datasets and matches them to buyer preferences, enhancing a customer’s home-buying journey.
This leading real estate brokerage firm uses AI to enhance agents' productivity. CompassAI leverages its vast datasets to help agents automate their workflows. Its AI-powered virtual assistant can handle communication with clients and assist with developing effective property listings [11].
Compass also runs an AI-powered video studio that helps agents develop marketing videos. The platform generates videos automatically using computer vision and AI-powered phrase recognition [12].
Videos help a lot in property search, helping customers understand property features. Agents can post property videos on different platforms to leverage the rise of video searches.
AI-powered property search and recommendation is all about delivering personalized attention to a home buyer. ML algorithms can analyze datasets to determine customer preferences. Online real estate marketplaces and brokerages use recommender systems to highlight properties with buyers' preferred features.
Leading real estate companies and startups have created tools that add a personalized touch to property searches. Advancements in NLP have made it possible for home buyers to use natural language to search for property. This makes property search more personalized and interactive, allowing users to specify their unique criteria.
Written by Baptiste Aelbrecht
