Articles on: Property Estimates

Building a Step by Step Property Estimate Tool for homeowners and sellers

Overview


A Property Value Estimator is a highly effective lead generation tool. It invites users—typically homeowners curious about their equity—to input their address and receive an immediate market valuation generated by the AI Powered Property-Estimates engine (RESTimates)


Such a tool is, often, informally called: “What’s my home worth?”


To maximize the utility of this tool, it is essential that it supports off-market properties. Many users checking their home's value are not currently listed for sale. If your application relies solely on active listing databases (MLS®) for address search, these users may be unable to find their property.


This guide outlines a robust workflow that separates address identification from market data, ensuring every valid residential address can be processed.


Typical Workflow


The optimal architecture follows a "Locate first, enrich second" approach:


  • Universal Identification: Use a global map provider to validate the address and retrieve coordinates.
  • Intelligent Enrichment: Query historical MLS® data to pre-fill known property details (Repliers Listings API).
  • Valuation: Generate the price based on location and attributes (AI Powered Property-Estimates engine).
  • Visualization: Present the estimate with context (ranges, confidence, and comparables).



Step 1: Universal Address Identification


Goal: Recognize any real-world address and establish its geospatial location.


This is the most critical step for supporting off-market properties. An MLS® database is, by definition, a record of listings, not a record of all existing infrastructure. Therefore, searching the MLS® directly will only return properties that are currently or historically active.


To capture the "curious homeowner" whose property hasn't been sold in decades, you must look outside the MLS®.



Instead of querying the Repliers API for the address, utilize a dedicated geolocation service.


Recommended APIs:

  • Google Places API (Autocomplete) & Google Geocoding API (Used in our Proof of Concept)
  • Alternatives: Mapbox Geocoding, Radar, Bing Maps, or OpenStreetMap.


Note on Service Providers


While this reference implementation utilizes the Google Maps Platform for demonstration purposes, this architecture is provider-agnostic.


The AI Powered Property-Estimates engine does not require a specific map provider; it simply requires accurate latitude and longitude coordinates. You are free to swap Google for Mapbox, Bing Maps, Radar, or OpenStreetMap.


How to Adapt: If you choose a different provider, you simply need to ensure your frontend Autocomplete component captures the selected address's coordinates. You will then map those coordinates to the map.latitude and map.longitude fields in the Repliers payload described in Step 3.


Implementation Logic


  • The Search Bar: Connect your user interface to your chosen Autocomplete provider. This ensures that as the user types, they are querying a global index of addresses.
  • Benefit: The user finds their home successfully, even if it has never been listed on an MLS® system.
  • The Handshake: When the user selects their address, request the specific details from the Geocoding service.
  • The Output: Extract the precise latitude and longitude.


Key Takeaway: By obtaining the coordinates ($lat, $lng) from a third party, you establish a universal "anchor" for the property. The AI Powered Property-Estimates engine uses this geospatial anchor to analyze the surrounding neighborhood, allowing it to value a home simply by knowing where it is, without needing a listing ID.



Step 2: Intelligent Attribute Enrichment


Goal: Reduce user friction by auto-populating property details (beds, baths, etc.).

Once you have identified the location (Step 1), the next challenge is obtaining the physical attributes required for valuation. 


While the user could be asked to manually enter everything, it is a better experience to "enrich" the form with data Repliers might already have.


API Call: Historical Data Check


Endpoint: 

GET https://api.repliers.io/listings


Use the address details from Step 1 to check the Repliers database for historical records.


GET /listings?address=123 Maple Drive&city=Burlington



The Enrichment Strategy


  • Scenario A: The Property has History If the home was sold in the past (e.g., 2018), Repliers will return the old listing. You can programmatically extract fields such as numBedrooms, numBathrooms, style, sqft, and lot.
  • Action: Pre-fill the form and ask the user, "Here is what we have on file. Has anything changed?"


  • Scenario B: The Property is Truly Off-Market If the home is new or has not sold in a long time, the query may return no results.
  • Action: Seamlessly present a clean, empty form. Because you successfully identified the address in Step 1, the user simply needs to tell you about the house (e.g., "It has 3 bedrooms and 2 baths") to proceed.



Step 3: Generating the Estimate


Goal: Submit the location and attributes to the AI Powered Property-Estimates engine.

At this stage, your application has a complete picture: it knows where the property is (from your Map provider) and what the property is (from Repliers history or user input).


API Call: Create Estimate


Endpoint: 

POST https://api.repliers.io/estimates


You will now construct the valuation request. Note that the map object is populated by your Geocoding result, while the details object is populated by the user/enrichment step.


Example Payload:

JSON
{
  "class": "Residential",
  "type": "Sale",
  "address": {
    "city": "Burlington",
    "state": "ON",
    "zip": "L7S 1W2"
  },
  "details": {
    "numBedrooms": 3,
    "numBathrooms": 2,
    "sqft": 1800,
    "style": "Detached"
  },
  "map": {
    "latitude": 43.3255,
    "longitude": -79.7990
  }
}



Step 4: Visualizing the Value


Goal: Display the valuation with transparency to build trust.

The AI Powered Property-Estimates engine returns a detailed response object designed to help you manage user expectations.


Example Response:


{
  "estimate": 1450000,
  "estimateRange": {
    "low": 1380000,
    "high": 1520000
  },
  "confidence": 88,
  "comparables": [ ... ]
}


Best Practices for Display


  • Prioritize the Range: Displaying the estimateRange (e.g., "$1.38M – $1.52M") is generally more helpful than a specific price point, as it acknowledges market fluidity.
  • Contextualize with Comps: The response includes a comparables array—a list of nearby sold properties used to calculate the value.
    • Feature Idea: Plot these comparables on your map around the subject property. This visually demonstrates that the valuation is based on real, local market activity.
  • Confidence Meter: Display the confidence score (0–100). If the score is high, it reassures the user. If the score is modest (perhaps due to a lack of recent sales in a rural area), it helps the user understand that the estimate is an approximation based on available data.



Reference Implementation


To assist with your integration, we have published a functional Proof of Concept (POC) that demonstrates this "Universal Search" architecture.


You can review the code to see exactly how the map service works in tandem with the AI Powered Property-Estimates engine to create a seamless flow for any address.


Additionally, our open source portal project provides an implementation for the same concept.



Updated on: 06/01/2026

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