Predictive modeling builds complex decision-making software systems. It uses current and past data to predict classification, behavior, trends, or events in the future. This involves collecting data with known results to build a mathematical model, and then using that model to make future decisions. The goal is to analyze, understand, and exploit patterns found in the input data. For this reason, the quality and quantity of the input data is very important.
Predictive Modeling Steps
- Understand objectives
- Define model goals
- Collect data for modeling
- Enter and format the data
- Analyze the data
- Select a model type
- Train, validate, and optimize the model
- Deploy the system
Most common types of predictive models
- Classification/Recognition – Classify data into distinct classes known ahead of time.
- Cluster – Create groups in data using common attributes.
- Forecast – Analyze data that varies over time.
- Outlier – Identify anomalies (abnormal entries) in the data
Applicatiions
Predictive modeling is used everywhere large data sets exist. Real estate certainly has large data sets. Examples include
Automated Valuation Systems (AVM) used to predict the value of real property. This automates the job of an appraiser.
Customer Relationship Managers (CRM) analyze customer data to predict future behaviors like purchase likelihood, upsell potential, and attrition. This enables businesses to proactively target customers with personalized campaigns based on those predictions.
Real estate companies use predictive modeling in marketing to identify and target unknown potential buyers and sellers more effectively.
1. Lead Scoring – Analyze online behavior, demographics, and past transactions to rank based on likelihood to buy or sell.
Example: Use browsing history (e.g., frequent visits to property listings) to prioritize efforts for unknown leads.
2. Targeted Digital Advertising – Predict which online users are most likely to engage with real estate ads based on browsing history, location data, and past interactions.
Example: Serve personalized ads on social media to users who have recently searched for homes in a specific ZIP code.
3. Neighborhood Trend Analysis for Investment Targeting – Analyze population growth, employment rates, and local business activity to identify emerging real estate hot spots.
Example: Target marketing campaigns to home buyers looking for up-and-coming areas with rising property values.
4. Optimal Timing with Event-Based Triggers – Predict when life events (e.g., marriage, new child, divorce, empty nest) coupled with tenure and equity growth might lead to a home purchase or sale.
Example: Prioritize campaigns with ads for larger homes in family-friendly neighborhoods to new parents who have owned their homes for 4+ years and recently refinanced.
5. Personalized Property Recommendations – Analyze previous searches, preferences, and social media interactions to suggest properties with the highest interest likelihood.
Example: A homebuyer browsing mid-century modern homes gets an automated email with similar properties based on previous searches.
6. Geo-Targeted Direct Mail Campaigns – Identify high-probability sellers and target them with tailored postcards or brochures.
Example: A brokerage sends “Just Sold” mailers to homeowners in neighborhoods with rising home prices and high turnover rates.