AI Recommendation System for Real Estate: How to Find Properties Quicker

Imagine a world where your property search is as personalized and efficient as your Netflix recommendations. This level of sophistication, tailored to your preferences and behavior, can now be harnessed to revolutionize the way we find and choose properties in the real estate market.

In real estate, the traditional approach of endless property listing portals with vast amounts of information is becoming outdated. Consumers don’t need more data; they need the right data. This shift in mindset is where AI recommendation systems come into play, reshaping how property portals deliver value to users.

An AI recommendation system is an artificial intelligence algorithm that recommends products or services to users based on their preferences and behavior and then presents the recommended items in a ranked list. In this article, we further delve into what AI recommendation systems are, how they work, and describe the real-life use cases of an AI recommendation engine in the real estate industry.

What is an AI Recommendation System in Real Estate?

An AI recommendation system in real estate is the use of artificial intelligence (AI) to provide the most relevant property suggestions to users based on their preferences, behavior, and past interactions with the platform. This system analyzes a wide range of data points related to properties, user preferences, and historical transactions to generate tailored recommendations that match the user’s specific criteria.

How Is AI Recommendation System Used in Real Estate?

AI recommendation systems are actively used in the real estate industry to enhance the property search experience, improve matching between properties and clients, and streamline operations. Some ways how AI recommendation engines are used in real estate are:

• Personalized property recommendations for marketplace users;
• Smart property matching for brokers;
• Targeted marketing and advertising;
• Relevant properties faster discovery.

How Does AI Recommendation Engine in Real Estate Work?

AI recommender systems can be incredibly valuable in the real estate industry. We at Ascendix see the biggest potential of an AI recommendation engine in developing innovative solutions for marketplace users and brokers in the first place. The possibility to analyze a tenant’s preferences, such as location, amenities, budget, and past rental history means recommending to tenants the properties that best match their needs. This would ultimately lead to faster property rental, more closed deals, and higher customer satisfaction.

AI Recommendations in Real Estate

AI Recommendation System for Marketplaces: Tenant Property Matching

Searching for the perfect property can be a daunting and time-consuming process. AI can simplify and enhance this experience for tenants. With AI recommendation engines, future real estate tenants can discover properties that align with their preferences and lifestyle the most.

What Does the Tenant See?

The process of property search with AI recommender systems is as easy as any other traditional real estate search. Future tenants create an account on the marketplace, insert essential information about themselves and their property preferences. After this, users start browsing marketplace properties using filters, keywords, or the natural language search. As they do it, an AI recommendation engine continuously analyzes user data, including past searches, saved properties, time spent on listings, search filters used, and any feedback or ratings.

Based on this analysis, AI generates personalized recommendations about the properties that may be the most relevant for the user and shows them to future tenants. Users may provide feedback on the recommendations received, such as rating the relevance or liking or saving recommended properties. With AI real estate recommendations, marketplace users discover relevant properties tailored to their preferences, which makes the property search on marketplaces more efficient and effective.

How Can AI Tenant Property Matching Be Implemented in the Marketplace?

1. Data Collection. The first step in AI recommendations for AI property search involves gathering a vast amount of data related to property characteristics and user preferences and behavior. This information is collected from various sources such as user profiles, property listings, and past interactions with the platform. If the user is new to the platform, they need to mention their real estate preferences for the AI recommendation system to work well.

The information collected may be:

• User data inserted during registration: location, occupation, age, etc.
• User preferences, inserted during the property search itself, regardless of search type (natural language search or filters/sorting): number of rooms, preferred location, nearby amenities, price, property type, pet-friendly apartment, size, parking, etc.
• User behavior during the property search. AI recommendation engine gathers various data about user preferences during search as well: which properties the user clicked on, which were added to “Favorites,” which the user spent the most time on, and many more factors.
• Property listings data: the information about the available properties in the marketplace and all the essential data about them are required in real estate AI recommendation systems to make a relevant prediction.
• Past interaction history: the properties saved or viewed during previous searches may influence the results shown during further searches of real estate.

2. Data Clusters Identification. The collected data is segmented into clusters based on clients’ preferences, property types, and locations. Data clusters are basically the data points that share similar characteristics or patterns. Such segmentation helps the AI recommendation engine to identify patterns and similarities within the real estate data, enabling more targeted analysis and recommendations.

3. Data Enrichment. The data might be enriched if the information collected in the previous steps is insufficient. When integrated with data enrichment sources like local public databases, the AI recommendation system can gather additional information to improve the property recommendations. The enrichment data is extracted in advance, at the stage of AI model training. Data enrichment AI lets the system improve the precision of recommendations.

4. AI Prediction Model Training. The real estate prediction model is then trained using each data segment (like property type or location data). The training process involves analyzing past transactions, client feedback, and property features to identify patterns and preferences. This way, the AI property matching model can assess the likelihood of a tenant with specific preferences choosing and renting a particular property.

5. Integration in Real Estate Marketplace. After the model is trained, it’s time to integrate it into the marketplace where it could be utilized and help in tenant property matching. As soon as the AI recommendation system gets access to available real estate data and customer profiles, increasing the chance of successful AI property search for tenants.

AI Recommendation System for Brokerage Firms

For brokers aiming to enhance operations and efficiency in tenant property matching, real estate AI recommendation engines are also a transformative solution. The AI recommender systems analyze tenants’ behavior, preferences, and interactions with property listings to deliver fast and accurate AI property recommendations.

By integrating AI technology into their everyday workflow, brokerage firms can streamline processes and create a more user-oriented AI real estate search experience, revolutionizing how properties are matched with tenants in the real estate market.

The AI-based recommendation system for real estate brokers works quite similar to the one on marketplaces, however, there are some differences in the source of data collected. In this case, the technology acts as an AI broker, matching tenants with the properties and making the process much more efficient and faster.

How Does AI Recommendation Engine Look for Real Estate Brokers and Tenants?

For brokers and tenants, the process starts with user registration as well. However, there is a slight difference. Tenants input information about their real estate preferences into the AI property search tool, while brokers need to insert information about all their successful property deals and information about previous clients’ property preferences. This is essential to ensure that the client gets exactly what is the most relevant to them.

All the data can be taken from the broker’s CRM system or any existing database. After this, the AI recommendation engine starts to analyze user data and compare it to the information from the real estate broker’s CRM, investigating the properties that are most likely to be suitable for a certain client. Then, based on this data, the AI recommender system generates personalized recommendations for properties that may be the most relevant to the brokers’ client preferences and needs.

As a result, the properties that align with the client’s criteria and interests the best are shown to the broker and tenant, so that the latter can choose only from relevant and suitable options. This way, the client gets the most personalized and tailored properties, and the broker saves loads of time without scrolling through myriads of properties trying to find the one that would suit the client.

Therefore, an AI recommendation engine, if trained on a real estate broker’s CRM, can streamline the property search experience by providing personalized and relevant recommendations, enhancing customer loyalty and satisfaction, and increasing the broker’s efficiency.

How to Implement AI Recommendation Engine for Real Estate Brokers?

1. Data Collection. The process starts with the data collection. The data entered by the client appears in the broker’s CRM. Then the AI recommendation system is trained based on the data directly from the CRM, like the relevant information about clients’ property preferences, budget constraints, location preferences, preferred amenities, past property transactions, and communication history. The information about past property transactions that are already in the CRM is also collected for AI to learn from them and predict which properties might be interesting for future tenants.
2. Data Clusters Identification. The next step is to identify data clusters and segment the clients’ data from the CRM. The data might be the clients’ preferences, behavior, transaction patterns, and other factors important for the AI recommendation engine. These data clusters serve as focal points for the AI based recommendation system, enabling it to identify patterns and similarities, facilitating targeted and relevant real estate recommendations.
3. Data Enrichment. If there is not enough information about past successful transactions, the raw CRM data from the broker’s database might need to be enriched. This means the AI recommendation engine gathers some additional information from external sources or real estate public databases. This AI data enrichment process enhances the accuracy and completeness of client profiles and preferences.
4. Model Training. The real estate AI recommendation system undergoes training using segmented enriched data. During the training, the AI based recommendation system analyzes the data about past successful rent or sale deals, any other previous transactions, client feedback, deal results, and property features. With this information, the AI tenant property matching system can assess the likelihood of tenants with specific preferences choosing and renting particular properties.
5. Integration. The predictive model is integrated into the broker’s property sorting and recommendation engine or CRM, prioritizing properties with higher likelihoods of selection based on the client’s profile and past deal successes. This results in more efficient and successful deals for brokers and more satisfied clients.

Challenges of Implementing an AI Recommendation System in Real Estate

While AI real estate recommendations sound like an easy and all-mighty solution, there are still some challenges that must be considered before integrating this tool into your business.

• Data Quality. The success of the AI recommendation engine for real estate fully depends on the quality of the data the model is trained with. Real estate data can be complex and heterogeneous, and may include various unstructured elements like property features, market trends, user preferences, and transaction history. Cleaning, standardizing, and verifying this data’s accuracy may be challenging, but it is essential for getting a high-quality tool.
• Data Quantity. The quantity of data defines how well the AI model will be trained and ultimately how relevant the results of AI property search will be. Real estate businesses must have vast amounts of data to be able to train their own model. Acquiring and managing large datasets from multiple sources while ensuring the data is valid, actual, relevant, and up-to-date can be challenging. Also, scaling the system to handle increasing data quantities efficiently may present another issue. Therefore, only big marketplaces and firms with a lot of resources would be able to get the best property recommendations.
• Privacy and Security. Handling sensitive user data and ensuring data privacy and security are particularly important in an AI based recommendation system. Real estate transactions involve personal information, financial details, and property preferences, meaning businesses must have top-notch privacy measures and compliance with data protection regulations. Safeguarding against data breaches and unauthorized access is critical in implementing AI recommendation systems.
• ML and AI Expertise. Development of a custom AI tool for property recommendations is not the easiest task, and it will require a team of development experts proficient in AI and ML, experienced in machine learning and tailored AI model training. Only a highly skilled team of AI specialists, data scientists, and developers can succeed in building accurate recommendation algorithms, fine-tuning models, addressing bias and fairness issues, and optimizing system performance.
• Implementation Cost. The cost of developing, implementing and operating an AI recommendation engine can be substantial. It involves expenses related to data acquisition, infrastructure, software development, hiring a team with AI expertise, and ongoing maintenance. Before development, the expenses and the benefits of the system should be considered carefully to find out if the profit would outweigh the investment.
• User Trust. Gaining user trust and acceptance of AI real estate recommendations is crucial for the success of its implementation. Users may have concerns about privacy, data usage, algorithm transparency, and bias in recommendations. Therefore, demonstrating value and ensuring ethical AI practices are essential for earning and maintaining user trust.

Benefits of AI Property Recommendations

AI recommendation systems in real estate property portals operate on the principle of quality over quantity. Instead of bombarding users with endless property listings, these systems focus on curating a refined selection of properties based on factors that truly matter to the consumer.

• Relevant Efficient Recommendations. Similar to how Netflix learns your viewing habits and preferences, an AI-based recommendation system in real estate analyzes your interactions, preferences, and past searches to deliver personalized property recommendations. This ensures that every property suggestion aligns with your unique needs and preferences.
• Intelligent Data Analysis. AI algorithms in real estate recommendations go beyond basic filtering options to analyze complex data points such as property features, location preferences, budget constraints, and lifestyle requirements. By understanding the nuances of each user’s preferences, the recommendation system will display properties that are not only relevant but also highly desirable and can be the best match for the tenant or buyer.
• Enhanced User Experience. The ultimate goal of AI real estate recommendations is to enhance the user experience by streamlining the property search process. Instead of sifting through hundreds of listings, users are presented with a list of high-quality properties that meet their specific criteria, saving time and effort.
• Impact on Decision-Making. With the real estate AI recommendation system, users can make more informed decisions about property selection. The system considers not just the property itself but also factors like market trends, historical data, and user feedback, providing a comprehensive view that empowers users to make confident choices.

Future Gaze on Predictive Analytics Real Estate

As technology continues to evolve, the future of real estate recommendations lies in harnessing AI to deliver intelligent, data-driven insights. By prioritizing relevance, personalization, and quality, AI recommendation systems will soon revolutionize how we navigate the vast landscape of real estate options, ensuring that every property search is efficient, tailored, and ultimately rewarding for consumers.

Ascendix – AI Expert in the Real Estate World

As the demand for AI solutions continues to rise within the real estate industry, Ascendix emerges as an experienced provider of custom AI tools designed to optimize operations and enhance efficiency. Our extensive industry experience enables us to develop customized AI solutions that align with the unique requirements of real estate businesses, streamlining processes and boosting overall productivity.

What Can Ascendix Offer Your Business?

• Tailored Custom AI Solutions for Real Estate. Ascendix designs bespoke real estate AI solutions used to improve operational efficiency and productivity for real estate professionals.
• Enhancing Your Software with AI. With our expertise in real estate AI development, Ascendix seamlessly integrates and customizes AI solutions into existing real estate software, ensuring a smooth user experience without compromising preferred features.
• AI-Powered Lease Abstraction and Property Search Tools. Having built our own frameworks for performing common real estate tasks, Ascendix specializes in developing AI-driven lease abstraction and AI property search tools that cater to the specific needs and objectives of real estate professionals.

Why Choose Ascendix for Your AI Implementation Needs?

• Trusted Partner of Major Real Estate Firms. With a track record of reliability and success, Ascendix has earned the trust of leading real estate companies such as JLL, Colliers, and Hanna Commercial, effectively optimizing their business operations with tailored software solutions.
• Deep Industry Expertise. With over two decades of experience in real estate and a dedicated focus on proptech development for 16 years, Ascendix possesses unparalleled industry knowledge and insight.
• Specialization in Software Development and AI Integration. Our skilled team specializes in addressing the unique requirements of the real estate sector, combining expertise in software development and AI integration to deliver cutting-edge solutions tailored to our clients’ needs.
• Global Presence. Headquartered in Texas, Ascendix maintains a global presence with five offices, enabling efficient collaboration across continents for seamless project delivery and support.

Hire Ascendix and elevate your real estate business with the power of AI. Contact us now and get a free consultation on how to best implement AI into your operations.