Predicting Home Sales: Is it Possible to Leverage AI and Data to Anticipate Your Client's Return to the Market?

Real estate agents can now use AI, big data analytics, and predictive modeling technologies to anticipate when homeowners are considering selling. This blend of tech allows for the analysis of key factors such as life changes, legal situations, market trends, financial status, and property conditions. By leveraging these advanced tools, agents can identify potential sellers early, offering timely and tailored services to meet homeowners' needs precisely when they're most open to making a move.

In 2024, one can easily identify as many as 11 key predictors that can be analyze, which are (in order of importance):

1. Market Trends: Changes in local real estate market trends, such as rising or falling home prices, can influence a homeowner's decision to sell.
2. Legal Actions: Legal actions significantly impact moving analysis by introducing urgent and sometimes involuntary motivations for selling a property. Divorce often leads to the sale of a shared home to distribute assets. Lis pendens and bankruptcy indicate financial distress, making a sale more likely to recover funds or satisfy debts. A notification of sale or death can also precipitate a sale, as heirs may decide to liquidate assets.
3. Home Equity: The amount of equity a homeowner has in their property might predict their likelihood to sell, especially if they've gained significant equity.
4. Duration of Ownership: The length of time the homeowner has lived in the property can indicate selling intentions, as people often move due to life changes after certain periods.
5. Home Improvement Activities: A spike in permits for renovations could indicate preparation for a sale.
6. Neighborhood Sales Activity: An increase in nearby homes being listed or sold can prompt others to sell.
7. Economic Indicators: Economic factors like employment rates and mortgage interest rates can impact homeowners' decisions to sell.
8. Life Events: Major life events such as marriages, divorces, births, or retirements are strong indicators of a potential sale.
9. Property Age and Condition: Older homes or those requiring significant maintenance might be closer to being listed.
10. Owner Information Changes: Changes in the homeowner's information, such as address changes (not related to the property) might indicate plans to move.
11. Social Media and Online Activity: Increasingly, data from social media or online searches related to moving, selling homes, or buying homes can be predictive.

So how do you get your crystal ball plugged in and working? A service like a Customer Relationship Management (CRM) system integrated with AI and big data analytics can synthesize the effects of key predictors for a real estate agent, determining if a homeowner is likely ready to sell. This tool analyzes data points such as life events, market trends, and property history, offering a comprehensive conclusion on a homeowner's selling readiness.

Or, you can wait on an AI model to come out but then every real estate agent in your county will have access to it in addition to you. It will pay to stay informed on the advances of AI.

The system I like now is called Title Toolbox by Benutech. It includes some very AI-like algorithms already and has more data than anything else I've seen.

The future of real estate is changing rapidly and only the informed agent will survive in this decade.