AI-Powered Property Estimates Explained

Overview

RESTimates is our advanced artificial intelligence feature that provides property value estimates using machine learning technology. Our system analyzes market data to deliver reliable predictions for both on-market and off-market properties.

How RESTimates Works

Machine Learning Foundation

RESTimates uses a sophisticated machine learning model trained on comprehensive market data. The system continuously learns from real estate transactions to provide the most current and accurate estimates possible.

Weekly Model Updates

Our AI model is automatically retrained every Sunday using the latest transaction data. This weekly refresh ensures that RESTimates adapts quickly to changing market conditions, seasonal trends, and emerging patterns in real estate pricing.

Feature Impact Analysis

The model intelligently determines which property characteristics have the greatest influence on selling price by testing thousands of feature variations. Through rigorous accuracy testing, the system identifies the most predictive factors, which typically include:

Square footage - Total living space and lot size
Location factors - Neighborhood, school districts, proximity to amenities
Property type - Single-family, condo, townhouse, etc.
Property age and condition
Market timing and seasonality
Comparable sales data

The model's feature selection process is entirely data-driven, ensuring that estimates are based on the factors that truly matter for property values in your specific market.

Two Model Types

RESTimates provides two distinct models to serve different needs:

On-Market Model

Best for: Properties currently listed for sale

The on-market model delivers higher accuracy by incorporating the property's current list price into its analysis. This model is ideal when:

Properties are actively listed
List price information is current and available

Off-Market Model

Best for: Properties not currently for sale

The off-market model provides valuable estimates without requiring list price data. This model is particularly useful when:

Homeowners want to know their property's current value
Properties are not actively listed
You need an objective valuation independent of listing price

Why Use Both Models for Listed Properties

For properties currently on the market, we recommend considering both model predictions. Here's why:

List Price Impact on Accuracy

The on-market model's reliance on list price can be both a strength and a limitation:

Strength: When properties are priced accurately, the on-market model provides better precision.
Limitation: When properties are mispriced, the model's accuracy can be affected.

Common Pricing Scenarios

Strategic low pricing: Some agents price below market value to generate interest and bidding wars
Overpricing: Properties may be listed above market value due to seller expectations or agent inexperience
Market timing: Pricing strategies may not reflect current market conditions

Getting the Complete Picture

By comparing both on-market and off-market predictions for listed properties, you can:

Identify potential pricing discrepancies
Gain confidence in estimates when both models align
Spot opportunities where list price may not reflect true market value
Provide more comprehensive guidance to clients

Best Practices

For Listed Properties

Review both on-market and off-market estimates
Consider the difference between estimates and list price
Use discrepancies to identify potential pricing opportunities

For Off-Market Properties

Rely on the off-market model for objective valuations
Consider recent market trends in your interpretation
Remember that actual sale prices can vary based on marketing, timing, and negotiation

General Guidelines

RESTimates are most effective when combined with local market knowledge
Consider the model's confidence level and data availability for each property
Use estimates as a starting point for deeper market analysis

Frequently Asked Questions

Q: How often is the model updated? A: The model is automatically retrained every Sunday with the latest market data.
Q: Which model should I use for a property that just came on the market? A: For newly listed properties, compare both models to ensure the list price aligns with market expectations.
Q: Why might the two models give different estimates? A: Differences often indicate pricing strategies or market perceptions that don't align with fundamental property values.
Q: How does RESTimates handle unique properties? A: The model analyzes thousands of features to find comparable patterns, but highly unique properties may require additional professional assessment.

Technical Notes

Models are trained using supervised learning techniques
Feature importance is determined through statistical accuracy testing
The system processes both structured data (property characteristics) and market dynamics
All updates are automated to ensure consistency and timeliness

For additional support or questions about RESTimates, please contact our technical support team.

Updated on: 04/06/2025