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Realtor Support AI Service

This AI Realtor Assistant is basically a personal AI Agent Assistant of the type I talk about here:

This could be applicable to many professionals not just Realtors.

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https://www.linkedin.com/pulse/internet-ai-envisioning-future-edge-computing-bots-peter-sigurdson?trackingId=U6MULumZwU4wZ9TzuC05Sg%3D%3D&lipi=urn%3Ali%3Apage%3Ad_flagship3_profile_view_base_recent_activity_content_view%3B%2B3iBEUnPQfCrrWmFy0HnlQ%3D%3D⁠

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https://theclose.com/best-real-estate-ai-tools/⁠

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https://builtin.com/artificial-intelligence/ai-real-estate⁠

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https://www.xara.com/blog/best-ai-real-estate-tools/⁠

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https://www.xara.com/blog/ai-for-real-estate-best-prompts/⁠

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As we see from this (and many other) website, many people are looking to process automate Real Estate.

My concept is to process automate the thinking, perceptual, and emotional energy balance states of the Realtor. To make a personal assist and friend to keep them focused and on task.

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Emphasize the focus of this product human performance augmentation rather than just being a data warehouse supplement:

Create an AI assistant for the real estate industry, it is crucial that we shift our perspective away from mechanistic thinking.

There are deep pocket players doing AI models for real estate that we don’t have the capital to compete with. Even if we did, they are not going to be long-term successful. There are structural factors in how people choose to buy a house that an App can’t fulfil.

Here are 3 representative use cases I see all the time:

Husband / wife divergent opinions on what do do.

Fear of taking action.

Sharing experienes and stories. (Read The Experience Economy).

We are not simply building a machine or creating another data aggregation tool. The real estate market is already saturated with platforms that front-end data sources and provide market analytics. More websites do not address the core challenges faced by Realtors in their day-to-day operations.

Our vision for EIRA (Estate Intelligence and Realtor Assistant) goes far beyond data processing. We must avoid the mindset that merely accessing and presenting real estate data will somehow revolutionize the industry. Instead, we need to focus on a more transformative goal: augmenting human performance in the real estate profession.

The true potential for innovation and success lies in enhancing the Realtor's cognitive, emotional, and motivational states. EIRA's primary function will be to optimize the mental and emotional landscape within which Realtors operate.

By focusing on these human elements, we can create a tool that:

1. Enhances cognitive focus, allowing Realtors to make clearer, more informed decisions 2. Manages emotional states, helping Realtors navigate the stresses and challenges of the profession 3. Provides timely encouragement, boosting confidence and motivation in critical moments

This approach recognizes that at the heart of every real estate transaction is a human relationship. The most successful Realtors are those who can maintain optimal mental states, build strong connections with clients, and navigate complex emotional terrain.

EIRA will succeed not because it can process more data faster, but because it can help Realtors perform at their peak, consistently and sustainably. It will be a partner in their professional growth, a support system for their emotional well-being, and a catalyst for their success.

As we move forward, let us keep this human-centric approach at the forefront of our development process. Our goal is not just to build an AI, but to enhance human capability in the real estate industry.

What is the unique value proposition of EIRA?

Providing a virtual personal assistant. Management of email and calendar. Scheduling appointments, showings, bookings.

Providing personalized “Just in Time, Just Enough, Just for Me” content generation for follow-ups for clients.

Providing access to conversational interactions and practices related to keeping the Principal (human user) in the right mindset. Real estate coaches charge $1000 a month for weekly “mindset” calls.

Core Services of the AI Real Estate Assistant:

Maintain a Dashboard to stay focused and on task each working hour of the day: Provide accountability. This is the BIG ONE. This is what Real Estate coaches changes 1000s $ a month for.

Be the effective Personal Assisatance: manage emails, scheduling

Generate and present content.

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https://www.linkedin.com/pulse/laura-marketing-land-introduction-gpt-4o-peter-sigurdson-a2wdc?lipi=urn%3Ali%3Apage%3Ad_flagship3_search_srp_all%3BSGn56Ij8SaqcIrAEWLoGeg%3D%3D⁠

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1. Introduction and Project Context - Real estate agent and AI professor collaboration - AI application for Realtors in Ontario, Canada

2. Initial Considerations - Avoiding automated property evaluations: There are big, deep pocket players we don’t have a chance to compete with on that. - Focus on AI platform for agents to purchase/subscribe - Market saturation and financial considerations

3. Beneficial AI Applications in Real Estate - Lead generation and qualification - Market analysis and forecasting - Personalized property recommendations - Document analysis and processing - Transaction management

4. Data Sources for Ontario Real Estate - MLS data - Municipal property assessment data - Land registry data - Census data - Municipal open data portals

5. AI Assistant Concept Development - Naming the AI assistant (EIRA)

EIRA stands for Estate Intelligence and Realtor Assistant.

E - Estate (covering both residential and commercial real estate)

I - Intelligence (emphasizing its AI capabilities)

R - Realtor (clearly indicating its target user)

A - Assistant (defining its supportive role)

The name was chosen to be professional, subtle, descriptive of its function, easy to pronounce and remember, and versatile enough to work across different markets and user preferences.

6. EIRA's Core Features - Emotional intelligence support - Process adherence tools - Personalized client management - Market intelligence - Realtor well-being features

7. Product Packaging and Service Model: Think about a Platform Service like Gmail. - Integration with email and calendaring systems - Daily dashboard concept - Comprehensive service features (communication hub, document management, etc.)

8. Core Value Proposition - Comparison to Starbucks' Deep Brew AI model - Focus on Realtor's core well-being and emotional state - Importance of confidence and clear guidance

9. Mental Intentional States - The intentional state of the practioner is a key success determinant - Optimizing mental focus for peak performance

People will pay for the experience of being taken care of.

Ontario real estate data sources:

Multiple Listing Service (MLS) Data:

Source: Canadian Real Estate Association (CREA)

Contains detailed property listings, historical sales data, and market trends

Access may require partnership with local real estate boards or CREA

Municipal Property Assessment Corporation (MPAC) Data:

Provides property assessments and characteristics for all properties in Ontario

Includes information on lot sizes, building types, and assessed values

May require special agreements for bulk data access

Land Registry Data:

Maintained by the Ontario Ministry of Government and Consumer Services

Includes property ownership information, transfer history, and liens

Access might be restricted and could require legal agreements

Statistics Canada Census Data:

Offers demographic information, income levels, and population trends

Freely available and can provide context for neighborhood analysis

Ontario Ministry of Municipal Affairs and Housing Data:

Provides information on zoning, land use planning, and housing policies

Useful for understanding development potential and restrictions

Municipal Open Data Portals:

Many Ontario cities (e.g., Toronto, Ottawa, Hamilton) have open data portals

Can include building permits, property standards violations, and local amenities

Natural Resources Canada Geospatial Data:

Offers topographic information, flood plain maps, and other geographical data

Useful for assessing property risks and features

School Board Data:

Information on school districts, rankings, and catchment areas

Often available through individual school board websites or the Ministry of Education

Real Estate Investment Network (REIN) Reports:

While not a raw dataset, these reports provide valuable market analysis

May require membership or purchase

MLS data can be deployed on brokerage websites and used as feeds for AI systems, but this usage is subject to specific terms and conditions governed by the local real estate boards and the Canadian Real Estate Association (CREA).

Here are the key points regarding the deployment options and restrictions:

1. Brokerage Websites MLS data is commonly used on brokerage websites to display property listings, historical sales data, and market trends. Real estate professionals and brokerages often utilize this data to offer clients comprehensive property search and analysis tools. However, such usage must comply with the licensing agreements and usage policies set by the local real estate boards and CREA [citation:5][citation:7].

2. AI Systems MLS data can be used as feeds for AI systems to enhance functionalities such as property valuation models, market trend analysis, and personalized property recommendations. However, using MLS data for AI systems involves additional considerations: - **Data Licensing:** There must be explicit permission and licensing agreements in place to use MLS data in this manner. This often requires negotiation with local real estate boards or CREA to ensure compliance with their data usage policies.

A way around us having to deal with this is to teach the Realtors using EIRA to apply for their own Brokerage data feeds as is done with Realtor Websites.

These are some slides from the powerpoints for AML 3304 where I also encourage the students to think about building AI LLM and integrating them into business domains. Basically what we are doing the the AI Realtors’ Assistant.

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https://www.linkedin.com/pulse/laura-marketing-land-introduction-gpt-4o-peter-sigurdson-a2wdc?trackingId=kImUtBYhnmyP1iQ07Bzkuw%3D%3D&lipi=urn%3Ali%3Apage%3Ad_flagship3_profile_view_base_recent_activity_content_view%3B7jPuBp5uQeqJe1ANcxctYA%3D%3D&utm_source=coda&utm_medium=iframely⁠

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https://www.linkedin.com/pulse/building-in-house-ai-generative-language-models-peter-sigurdson?trackingId=k5ht3Nv4Sxodg%2FY21v28Kw%3D%3D&lipi=urn%3Ali%3Apage%3Ad_flagship3_profile_view_base_recent_activity_content_view%3BjLuI27fUQ16we0t62EJOng%3D%3D⁠

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Deep Brew is Starbucks Mass Personalization AI Model. This could be a template for what we want to do:

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