Did you know that there are a myriad of ways AI chatbots can optimize real estate operations? Take property management – a real estate domain being under constant pressure to save costs. A simple property management AI tool for rent collection or tenant inquiry handling would save hours in your working schedule. And what if you had several of such tools within your main system?
Ascendix has been diligently crafting custom AI tools for various real estate needs. We’re excited to demonstrate the basics of creating real estate AI chatbots using AI for property management as an illustration.
A real estate AI chatbot is an app that uses artificial intelligence to understand and respond to users like a human would. For instance, a property management AI chatbots can manage tenant queries by generating tickets in the system, while an AI leasing assistant on a property marketplace like Airbnb can provide information about properties, answering users’ questions.
AI for Property Management:
For Brokers and Agents:
For Buyers and Renters:
For Enhanced Communication and Operations:
For Tenant and Guest Relations:
For Maintenance and Facility Management:
For Reporting and Analytics:
For Hospitality:
Leverage our expertise in AI and real estate software development to build or integrate custom AI assistants.
Before building or integrating any kind of AI chatbot for real estate, be it property management chatbot or lease assistant, it’s key to understand what tasks you need it to perform. Generally, there are two types of real estate AI chatbots.
Real Estate AI Chatbots | Informational vs Actionable AI Property Management Chatbots
These are chatbots that can handle requests for providing information, e.g. property management chatbots where users can inquire about lease terms, conditions, and rental rates, receiving instant and accurate information about the property’s leasing details.
Informational AI chatbots for real estate are the simplest version of what we know as an AI real estate chatbot. In most cases, they can be built with a no-code approach in a few minutes because the only thing required is the company’s website integration with the chatbot.
Informational chatbots act as libraries – the data is stored in a separate vector base, and later, users access the data through intuitive queries.
For instance, when GPT is integrated with the vector base (JLL GPT, Redfin ChatGPT), it bases answers to the user’s questions on the data stored in the vector base, for instance, the company’s documentation that has been uploaded earlier.
For instance, OpenAI’s GPT-4 and Microsoft’s AI Bot Service excel at responding to user queries and showcasing property details, utilizing advanced features like natural language processing (NLP) and full-text search.
Nonetheless, you should remember that informational chatbots have limits, since their performance fully depends on the vector base data. So, if you want to build an AI real estate chatbot that can answer open-end questions and perform tasks related to property management as well as lead management in addition, you’d better consider building an actionable chatbot.
Actionable AI chatbots for real estate are tools that go beyond information retrieval and can initiate actions such as creating tickets, inputting records into CRM systems, sending messages and tracking workflows. Opting for this kind of property management chatbot is ideal when you want your solution not only to answer tenants’ questions about maintenance work status but also to automatically log these inquiries as tickets into the CRM system.
These chatbots integrate features like RAG, geo-spatial search, and hybrid search, offering a comprehensive solution for users.
However, building such chatbots requires far more resources and tech knowledge compared to informational real estate AI chatbots. For instance, to create a more intricate chatbot like the one equipped with a ticket tracking system, one needs additional API integration, such as the website integration with Zapier.
Now let’s delve deeper into building an actionable chatbot for property management that can handle informational requests, recognize property maintenance inquiries by analyzing text and log those as tickets into the CRM system takes.
Say we need to develop an actionable property management chatbot with AI component for handling informational requests AND performing some actions, like creating maintenance tickets and logging records in the system. Potential workflow would be as follows:
Introduce the AI-driven chatbot with a friendly welcome message, setting expectations on its capabilities in handling both informational requests and property maintenance tasks.
Leverage natural language processing (NLP) for understanding user intents. Distinguish between general informational queries, like lease details, and maintenance-related requests.
Guide conversations on predefined topics for informational queries, addressing questions about lease terms, property amenities, or neighborhood information.
Utilize NLP to identify maintenance-related prompts. Train the chatbot to recognize keywords or phrases indicative of a maintenance request, such as “broken,” “repair,” or “issue.”
Provide immediate solutions for common maintenance issues through predefined responses, such as troubleshooting tips or informing users about expected resolution times.
Initiate the creation of a maintenance ticket in the property management system if further assistance is needed. Collect necessary details, including the type of issue, location, and any additional information.
Verify the user’s identity if necessary before processing maintenance requests, ensuring security and privacy.
Confirm the initiation of the maintenance ticket and provide a reference number. Follow up with the user on the expected timeline for issue resolution.
Implement fallback responses for unclear user intent or unfamiliar prompts, prompting users to rephrase or offering alternative options.
Seamlessly integrate the chatbot with the property management CRM system to log maintenance tickets and relevant details automatically.
Collect user feedback on their interaction with the AI chatbot, utilizing it to continually enhance the chatbot’s performance.
Optionally, incorporate a feature allowing users to inquire about the status of their maintenance requests and receive real-time updates.
1. No-Code Approach: For streamlined tasks like creating property viewing tickets and basic CRM interactions, the no-code approach is effective. This involves integration with no-code automation platforms such as Zapier. For instance, connecting a Tars-built chatbot with Zapier to create tickets when users express interest in viewing a property.
2. Custom Integration Approach: This approach is crucial for more complex transactions within AI chatbots for property management. Develop a custom chatbot using platforms like Microsoft Bot Framework or build a dedicated solution. An example involves integrating a chatbot built with Microsoft Bot Framework with a custom CRM system to handle intricate transactions and client interactions.
Before building an AI real estate chatbot for property management you should be aware of the software development process stages.
To start, the selection of the right technology stack lays the foundation for success. Opting for a chatbot framework like Microsoft Bot Framework or Rasa AI provides the necessary customizability for tailoring the chatbot to specific real estate requirements. Enhancing language understanding is critical, and utilizing cutting-edge Natural Language Processing (NLP) tools like GPT-3.5 elevates the chatbot’s capabilities.
In terms of infrastructure, the cloud platform plays a pivotal role. Azure, with its robust infrastructure and AI services, emerges as a top choice for ensuring the scalability and reliability of the real estate AI chatbot.
Moving forward, the architecture design phase involves crafting a user-friendly conversational UI, developing bot logic with the chosen framework, and integrating advanced NLP through GPT-3. Back-end services, such as Azure Functions, contribute to scalability, while a well-structured database stores crucial information about properties, bookings, and reported issues.
Model training is a crucial step in enhancing the chatbot’s understanding of user queries. Utilizing a diverse dataset and fine-tuning the model with the help of Llama or Azure tools, especially for real-estate-related terms, ensures that the AI chatbot for real estate can comprehend nuanced queries accurately.
For this stage, you’ll need to use your own models or Azure GPT models.
Bot development follows, where the conversational flow, decision-making logic, and integration with the chosen framework are implemented.
Testing is imperative to guarantee the chatbot’s functionality and user satisfaction.
Unit testing, user testing, and performance testing assess various aspects of the chatbot’s performance.
Oh, and don’t forget to validate your model if it’s the custom one.
Once the bot is refined and functional, integration with Azure Services, such as Cognitive Services and Functions, further elevates its functionality:
You can integrate services like Text Analytics for sentiment analysis. This empowers the AI chatbot for real estate to discern user sentiments, providing a more personalized and responsive interaction;
You can integrate Azure Functions to leverage serverless functions for backend processing;
As for the Azure Database integration – it is instrumental in storing and retrieving relevant data about coworking spaces, creating a structured and accessible repository for critical information.
Additionally, you can ensure with Azure Services that your real estate AI chatbot can analyze images, recognize sounds, and can be controlled by voice.
Deployment marks a significant milestone, with cloud platforms like Azure providing a stable environment for the real estate AI chatbot to operate. Continuous integration and deployment are configured for seamless updates and improvements.
The journey doesn’t end with deployment; monitoring and optimization are ongoing processes. Setting up monitoring tools enables tracking user interactions, and user feedback becomes instrumental in optimizing the chatbot’s responses to evolving needs.
This comprehensive development process ensures that real estate AI chatbots are not just functional but continually evolving to meet the dynamic demands of the industry.
We can make your legacy & 3rd-party systems talk through well-thought-out APIs and integrations. Contact Ascendix today.
Prototyping software enables designers and developers to efficiently design, test, and refine the chatbot’s functionality and user experience before committing to full-scale development.
Unfortunately, there are inherent limitations in terms of customization, particularly regarding styling and prompting. Despite these constraints, such software proves invaluable for creating quick demos and proof of concepts, offering a glimpse into the chatbot’s potential without the need for extensive coding.
Software Examples: Notable examples in this category include tools like Wondershare Mockitt and Justinmind. The latter focuses on rapid prototyping for intricate UI/UX designs and boasts strengths such as state-driven prototyping, built-in persona creation, and extensive accessibility features, making them well-suited for efficiently conceptualizing and presenting real estate AI chatbots in their early stages of development.
All-in-one chatbot building platforms such as Botpress and Voiceflow stand out in the realm of AI chatbot development for real estate, providing a comprehensive solution that goes beyond mere prototyping.
These platforms offer a broad spectrum of opportunities, allowing developers to build anything from basic to massively complex chatbots with ease. The capabilities extend to performing various actions tailored to the real estate domain, including property search, lead qualification, appointment scheduling, and seamless integration with real estate databases.
What sets these platforms apart is their user-friendly drag-and-drop interfaces, AI integration functionalities, and the ability to deploy real estate AI chatbots effortlessly across major channels such as websites, messaging apps, and voice assistants.
While the learning curve may be steeper compared to prototyping tools, with some coding knowledge required for advanced customization, the trade-off is the ability to create robust and fully functional real estate chatbots capable of handling intricate tasks.
Software Examples: Tools like Voiceflow, Botpress, and ManyChat exemplify these all-in-one platforms, offering a comprehensive solution for the development and deployment of AI chatbots for real estate.
Advanced chatbot building tools like Zapier, Stack AI, and Make play a crucial role in crafting custom functionality for AI chatbots in the real estate sector. These tools stand out by facilitating the integration of chatbots with legacy systems, enabling seamless connectivity and automation.
Their primary focus lies in automating tasks and building workflows, utilizing pre-built connectors and AI components. In the context of real estate applications, these tools empower chatbots to connect with CRM platforms, automate email triggers for leads, integrate with scheduling tools, and leverage advanced AI capabilities like sentiment analysis.
Unlike prototyping tools, which concentrate on design and testing, and complete builders that handle full development and deployment, advanced chatbot building tools excel in automating specific tasks or functions. They can be applied at any stage of development, whether for specific workflow automations or enhancing existing chatbots.
Software Examples: Zapier, Stack AI, and Make exemplify the diverse capabilities of these advanced tools, showcasing their pivotal role in integrating AI chatbots for real estate with intricate systems and workflows.
For decades, we’ve seen our clients struggle with traditional property searches. Whether it was complex filters or the output heavily depending on the specific format of the input data, the inefficiencies in the conventional property search process have been evident. That’s why we’ve decided to alleviate customers’ challenges when it comes to property searches by developing an interactive assistant capable of understanding natural human language and conducting AI searches.
Before diving into the development stage, we’ve delved into the survey, trying to simulate real-life scenarios. So, we looked for a dataset that resembled real estate listings and had different types of information, making it easy to use different search filters.
After considering various options, we opted for a dataset from Public.opendatasoft.com, specifically Airbnb Listings, due to its comprehensive and legally clear data under the Creative Commons license. Our emphasis was on diverse global property types, allowing us to thoroughly test AI capabilities.
Further, we utilized Chat GPT-4 extensively for tasks like data analysis, format conversion, cleaning, and generating additional data.
Our AI search testing focused on core functionality, followed by data analysis handled by Business Analyst specialists.
Three main search endpoints were examined: full text search, vector search, and hybrid search. Key testing strategies included assessing common search features and incorporating AI-specific scenarios like synonyms, misspellings, multilingual capabilities, and contextual understanding.
In the end, we leveraged Chat GPT-4 for query generation, initially creating queries manually and later enhancing the process with automated generation.
During the testing phase for Chat GPT integration, our primary objective was to assess the efficiency of Chat GPT in interpreting user search queries and generating corresponding search requests. This phase played a crucial role in verifying the chat interface’s ability to handle diverse user inputs while maintaining the integrity and security of the search process.
During the Chat GPT integration testing, we’ve:
Worked on Diverse User Queries:
Formed Valid Search Queries:
Ensured Security and Contextual Integrity:
A notable challenge we faced was that modifications to system prompts, whether due to bug fixes or the introduction of new features, frequently resulted in unpredictable alterations in the system’s behavior.
Even minor bug fixes had the potential to impact a broader section of the system. In response, we began implementing automated tests utilizing Playwright. This approach enabled us to offer swift feedback, reduce the need for manual testing, and uphold a high standard of precision and efficiency in our testing procedures.
Starting with the project’s design, it was divided into two phases. The initial phase focused on a quick and straightforward design to test the idea and implement functionality. Our internal design system was used without unnecessary embellishments. The user flow for a successful case was created, along with additional screens explaining extra features.
In the second phase, the goal was to give the product a different look and feel. This phase introduces a modern design and operational changes. Notably, filters are now hidden by default, and search results are moved out of the chat to the right. Despite reducing the number of search results, we ensured they are of high quality, enhancing user efficiency in finding what they need.
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A real estate AI chatbot is a tool enhanced with artificial intelligence and used by real estate professionals for purposes like engaging with customers, conducting property searches and appraisals, managing properties, and more.
The main types of AI chatbots for real estate are informational and actionable chatbots. While informational chatbots act as libraries, storing data in a separate vector base and responding to user queries based on that data, actionable chatbots can initiate various actions, such as creating tickets, inputting records into CRM systems, sending messages, and tracking workflows.
Alina is a proptech technology expert and a storyteller at Ascendix, investigating the real estate market and sharing her insights and tips with up-and-coming proptech startups, established real estate agencies, and industry stakeholders. She talks about real estate technology, business automation, and industry news.
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