Modern Artificial Intelligence for Real Estate Professionals
What the tools sold to brokerages as "AI" actually are, and why that decides how you check them. This module separates rules, machine learning and generative AI, explains how language, image and voice models produce output, opens up retrieval, tools and agents, and gives a vendor-neutral field guide to real estate AI tools and what surveys of agents record.
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Rules, Machine Learning and Generative AI in Brokerage Work
35 min
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How Language, Image and Voice Models Produce Output
40 min
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Retrieval, Tools and Agents Inside Real Estate Products
40 min
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A Vendor-Neutral Field Guide to Real Estate AI Tools
40 min
What AI Can and Cannot Reliably Do With Property and Market Information
Why fluent output about homes and markets can be wrong. This module covers confabulated features, rules and citations, listing data the MLS publishes without verifying, stale rules and market figures, what research records about automated value estimate error across neighbourhoods, forecasting error at scale, and the automation bias that lets errors through.
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Confabulated Property Features, Rules and Citations
35 min
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Unverified Listing Data, Stale Rules and Old Market Figures
35 min
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Automated Value Estimates, Neighbourhood Disparities and Forecast Error
40 min
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The Jagged Frontier, Automation Bias and the Discipline of Doubt
35 min
Client Confidentiality, Transaction Data and Privacy
What client and transaction information must be protected, where AI tools put it, what regulators in South Carolina, North Carolina and California advise, how California privacy and breach law work as a worked example of state law, and a repeatable method for deciding whether a piece of information may go into a particular tool.
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What Counts as Confidential Client and Transaction Information
35 min
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Where AI Tools Put Your Data: Storage, Changing Terms and Photo Metadata
35 min
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What Regulators Advise, and California Privacy Law as a Worked Example
45 min
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A Decision Method: Can I Put This Into This Tool?
35 min
Licence Law, Broker Supervision and Your Duties When AI Helps
The duties that stay with the licensee and the broker when AI helps: South Carolina's statute on AI-assisted work product, California and North Carolina regulator guidance, Texas broker supervision rules and canons, the NAR Code for REALTOR® members, and how to find your own commission's rules and verify a licence.
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Your Licence, Your Work Product: Responsibility When AI Helps
35 min
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Broker Supervision of People, Teams and AI Tools
40 min
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Honesty, Competence and Advertising Duties AI Does Not Change
40 min
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Finding Your Own Commission's Rules and Verifying a Licence
35 min
Fair Housing Foundations for Every AI-Assisted Task
The Fair Housing Act and HUD's advertising regulation applied to AI output: statements, images and audience selection, availability, blockbusting, steering and accommodation requests, why owner exemptions never cover advertising, a GPT-4 steering audit, and which federal guidance is withdrawn or only proposed for change while the law still binds.
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The Fair Housing Act Provisions That Reach AI Output
40 min
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Words, Images and Audience Selection in AI-Assisted Advertising
40 min
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Steering, Neighbourhood Questions and Accommodation Requests When AI Answers
40 min
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Withdrawn Guidance, Proposed Rules and the Fair Housing Law That Still Binds
35 min
Verifying Property Facts, Rules and Market Data Produced by AI
A verification workflow from question to client-ready answer. The assistant is never the source: check property facts against the listing record and seller documents, rules against the issuing body's text, and market figures against their named origin and period. Learn why a standard data field is not an accurate value, and record what was checked.
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A Verification Workflow From Question to Client-Ready Answer
40 min
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Checking Property Facts Against the Listing Record and Seller Documents
40 min
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Checking Rules, Deadlines and Licence Facts Against the Issuing Body
40 min
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Checking Market Figures and Recording What Was Checked
40 min
Prompt Engineering for Real Estate Professionals
How to write prompts for brokerage work that supply verified facts instead of asking a model to recall them, name the state, MLS and transaction side, build in fair housing and licence identification constraints, and end with a licensee's review. Patterns are taught as best practice, never as a substitute for review.
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The Anatomy of a Structured Brokerage Prompt
40 min
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Iterating on a Draft Without Losing the Facts
35 min
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Prompt Patterns for Listings, Client Updates, Showing Feedback and Follow-Up
40 min
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Pasted Text, Hidden Instructions and Constraints Built Into the Prompt
40 min
Listing Content, Photos and Advertising With AI
Drafting listing content and ads from verified facts; licence and brokerage identification in California, Texas and Florida; the NAR true-picture standards for members; photographs under HUD's advertising rule; AI-altered images under California's law, Wisconsin's not-yet-effective law and New York's alert; fake reviews under the FTC rule; and the approval record.
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Listing Descriptions and Ads Drafted From Verified Facts
40 min
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Licence and Brokerage Identification in AI-Made Ads: California, Texas and Florida
35 min
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AI-Altered Photos, Virtual Staging and Listing Images
45 min
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Reviews, Testimonials and the Advertising Approval Record
35 min
Pricing Conversations, CMAs and the Boundaries of Value Opinions
Where AI helps with comparables and market summaries and where it must stop; CMAs and broker price opinions versus appraisals, with Texas's required statement and NAR's Standard of Practice 11-1 for members; valuation independence under Regulation Z; who the AVM quality control rule binds; and fair housing in brokering and appraising.
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Where AI Helps With Comparables and Market Summaries, and Where It Stops
40 min
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CMAs, Broker Price Opinions and Appraisals: Different Work, Different Rules
40 min
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Valuation Independence: What You May and May Not Ask of a Valuer
35 min
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Automated Valuation Rules, Fair Housing and the Pricing Conversation
40 min
Agreements, Disclosures and the Line Into Law Practice
Where AI help with agreements and disclosures has to stop. This module covers written buyer agreements under NAR's practice changes and Texas law, compensation terms and the negotiability statement, the seller's disclosure and the agent's own duties, and the line between explaining a form and practising law.
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Written Buyer Agreements Before Touring: NAR Practice Changes and Texas Law
40 min
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Compensation Terms, the Negotiability Statement and Listing Filters
35 min
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Seller Disclosures, Lead-Based Paint and the Agent's Own Duties
40 min
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Explaining a Form Versus Practising Law When AI Drafts
35 min
AI for Lead Response, Consumer Contact and Brokerage Operations
The consent and conduct rules that govern AI-assisted texts, calls, emails and chat. This module covers TCPA and FCC consent rules, AI-generated voices, what chatbots may say about availability and neighbourhoods, referrals and things of value under Regulation X, rental screening reports and adverse action, and a method for ranking operational uses by risk.
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Consent Rules for AI-Assisted Texts, Calls and Emails
40 min
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AI Voices, Chatbots and What They Tell Consumers About Homes
40 min
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Referrals, Lead Routing and Things of Value Under Regulation X
35 min
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Rental Screening Reports, Adverse Action and Ranking Operational Uses by Risk
40 min
Repeatable Workflows and AI Agents in a Brokerage
How to turn ad hoc chatbot use into a controlled brokerage process. This module builds a six-stage workflow ending in licensee sign-off and a record, explains what an AI agent is and how to rank what it can touch, sets human control points and stop switches, and designs around indirect prompt injection as a risk that remains.
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From Chatbot to Controlled Workflow: Six Stages Ending in Licensee Sign-Off
40 min
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What an AI Agent Is and How to Rank What It Can Touch
35 min
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Human Control Points, Stop Switches and Excessive Agency
40 min
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Indirect Prompt Injection in Enquiries, Listing Remarks and Documents
40 min
Wire Fraud, Cybersecurity and Vendor Evaluation
How email compromise reaches real estate closings and what IC3's figures do and do not count, how to verify payment instructions through a separate channel, which multifactor methods CISA ranks strongest, how to read an AI vendor's security and data-use claims, and how listing content licences, contracts, breach notice and exit belong in the deal.
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Business Email Compromise in Real Estate Closings: What IC3 Reports and What It Counts
40 min
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Verifying Payment Instructions Through a Second Channel and Choosing Stronger Multifactor Authentication
40 min
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Small-Brokerage Security Fundamentals and Reading an AI Vendor's Security and Data-Use Claims
40 min
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Listing Content Licences, Scraping Concerns, Vendor Contracts and Exit
40 min
The Brokerage AI Policy and Supervision System
How to build a brokerage AI policy that a broker can supervise: what it covers, which tools and data are allowed, how it maps to Texas's written-policies duty and California's supervision and advertising expectations, what regulators in North and South Carolina advise, and how verification, fair housing review, records, audits and review cycles keep it alive.
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Why a Brokerage Needs an AI Policy and What It Must Cover
40 min
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Approved Tools, Prohibited Data and Acceptable Use
40 min
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Mapping the Policy to Texas and California Supervision and Advertising Review Duties
45 min
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Verification, Records, Incidents, Internal Audits and Review Cycles
40 min
Practical Real Estate AI Lab
Four hands-on labs on fictional listings and clients: find the invented features and fair housing problems in an AI-drafted listing and ad, expose an invented rule and rebuild the weak prompt behind it, review AI-altered photos under California's rule and a CMA under Texas's rule, and grade a vendor's data terms before writing a brokerage AI use policy.
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Lab: Find the Invented Features and Fair Housing Problems in an AI-Drafted Listing and Ad
45 min
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Lab: Find the Invented Rule, Then Fix the Weak Prompt
45 min
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Lab: Review AI-Altered Photos Under California's Rule and a CMA Summary Under Texas's Rule
45 min
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Lab: Evaluate a Fictional Vendor's Data Terms and Build a Brokerage AI Use Policy
45 min
Capstone: The Brokerage AI Implementation Plan
Write a sixteen-section AI implementation plan for one brokerage task: choose a checkable first use with a baseline from your own records, set verification, fair housing review, vendor and record controls, name the reviewing licensee and supervising broker, then run a bounded pilot with metrics, stop signals and checks that catch rubber-stamp review.
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Choosing the Problem and Scoping the Brokerage AI Plan
45 min
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Controls, Fair Housing Review, Vendor Evidence and Documentation in the Plan
45 min
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Naming the Reviewing Licensee and the Supervising Broker
40 min
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Pilot, Metrics, Rubber-Stamp Review and Continuous Improvement
45 min