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# Using Historical MLS® Data for Off-Market Property Estimates

## Overview

Off-market properties typically require manual data entry through the 'Create Estimate' endpoint to generate property valuations. However, you can streamline this process and create a better user experience by leveraging historical MLS® data when available, providing users with instant estimates without requiring manual property detail input.

## Solution: Automated Payload Population

Instead of requiring users to manually enter property details for off-market properties, you can check for historical MLS® data and use it to automatically populate your estimate payload.

### Implementation Steps

1. **Check for Historical Data**
    * Query the Repliers API for any MLS® history 
    * Use the [historical listings endpoint](https://help.repliers.com/en/article/accessing-historical-listings-for-an-address-1kfsor7/#4-history-for-a-specific-address) to retrieve property data
    * If historical data exists, proceed with automated estimate generation

2. **Use Historical Data as Payload**
    * When historical MLS® data is found, use this information directly as your payload in the `POST /estimates` endpoint
    * This eliminates the need for manual user input while maintaining estimate accuracy

3. **Handle Property Tax Updates**
    * Property taxes are a critical component that likely changed since the last listing
    * Calculate updated property tax values using market statistics

### Property Tax Approximation Method

Since property taxes are an important valuation factor and likely outdated in historical data, use the Repliers API statistics to calculate current tax estimates for the same city as the property:

1. **Get Market Tax Statistics**
```
https://api.repliers.io/listings?statistics=med-tax,grp-yr&city=New York&type=sale&listings=false&status=U&status=A
```
    * Query the listings endpoint with statistics parameters to get median tax data by year:
    * This returns median tax values grouped by year for the specific city

2. **Calculate Tax Increase Rate**
    * Compare the median tax from the property's last sale year to the current year
    * **Example**: If a property last sold in 2023 when median tax was $3,117, and current median tax is $3,744:
        * Tax increase rate = $3,744 ÷ $3,117 = 1.201 (20.1% increase)

3. **Apply Adjustment to Historical Tax**
    * Multiply the property's historical tax amount by the calculated increase rate
    * **Example**: If the property's 2023 taxes were $10,000:
        * Updated tax estimate = $10,000 × 1.201 = $12,010
        * Pass `12010` as the tax amount in your estimate payload

### Sample API Response

```json
{
    "statistics": {
        "tax": {
            "med": 2517,
            "yr": {
                "2022": {
                    "med": 3019,
                    "count": 62557
                },
                "2023": {
                    "med": 3117,
                    "count": 62405
                },
                "2024": {
                    "med": 3261,
                    "count": 71073
                },
                "2025": {
                    "med": 3744,
                    "count": 38222
                }
            }
        }
    }
}
```

## Benefits

* **Seamless User Experience**: Users receive instant estimates without data entry
* **Improved Accuracy**: Leverages actual property characteristics from MLS® history
* **Automated Workflow**: Reduces manual processes while maintaining estimate quality
* **Market-Adjusted Values**: Incorporates current market conditions for key financial metrics

## When to Use This Approach

* Off-market properties with MLS® history 
* Scenarios where user experience speed is prioritized

## Implementation Considerations

* Ensure your historical data includes all necessary property attributes
* Validate data quality before using as estimate payload
* Use the [real-time market statistics](https://help.repliers.com/en/article/real-time-market-statistics-implementation-guide-l3b1uy/) to keep tax calculations current
* Consider implementing fallback mechanisms for properties without sufficient historical data

# What questions does this article answer?

* How can I generate estimates for off-market properties without manual data entry?  
* How do I use historical MLS® data to auto-populate the estimate payload?  
* Which endpoint should I call to retrieve historical listing data for an off-market property?  
* How can I adjust outdated property tax amounts using Repliers statistics?  
* How do I compute a tax growth factor from median tax statistics over time?  
* When is this automated historical-data approach most useful, and what are its limitations?  
* What implementation considerations and fallbacks should I keep in mind?