Articles on: Market Reports & Analytics

Real-Time Market Statistics Implementation Guide

Our Real Estate API offers a powerful feature that allows you to request real-time market statistics. This capability is invaluable for creating data visualizations, providing users with deep market insights to make more informed decisions. The feature offers a high level of granularity, enabling you to request specific scopes of market data just as you would when filtering listings.


How to Request Statistics


Step 1: Provide the Scope of Data


Begin by defining the scope of the data you wish to analyze using the GET /listings endpoint. This scope is defined in the same way you would filter for listings. For example, if you want statistics for 4-bedroom homes in New York City that have sold in the past 2 years, your request URL will look like this:


https://api.repliers.io/listings?city=New York&minBeds=4&maxBeds=4&status=U&lastStatus=Sld&minSoldDate=2022-07-01


Important: Status Parameter for Statistics


When using our API, the status parameter defaults to 'A' (Active) if not specified. However, many statistics require data from unavailable listings to be accurate.


Why this matters: Statistics like average sold price can only be calculated using sold listings, which have a status of 'U' (Unavailable). Active listings don't contain sold price information since they haven't been sold yet.


What to do: When requesting statistics that depend on sold or completed listings, always include status=U in your API request. Additionally, consider filtering by lastStatus since unavailable listings can have different outcomes:


  • Sld - Contains sold price data
  • Ter - Removed by owner
  • Exp - Listing period ended


Example:


  • ❌ Without status (defaults to Active): Limited statistics available
  • ✅ With status=U: Access to all unavailable listings
  • ✅ With status=U + lastStatus=Sld: Most accurate for sold price statistics


Step 2: Specify the Statistics


To retrieve specific statistics, use the statistics parameter. For example, if you're interested in the average sold price, add &statistics=avg-soldPrice to your request. The full request URL would be:


https://api.repliers.io/listings?city=New York&minBeds=4&maxBeds=4&status=U&lastStatus=Sld&minSoldDate=2022-07-01&statistics=avg-soldPrice


This request will return all relevant listings along with a statistics object containing the average sold price:


{
  "statistics": {
    "soldPrice": {
      "avg": 823702
    }
  }
}


Requesting Only Statistics


If you do not need the listings and only require the statistics, add &listings=false to your request. This can significantly reduce response time. For instance:


https://api.repliers.io/listings?city=New York&minBeds=4&maxBeds=4&status=U&lastStatus=Sld&minSoldDate=2022-07-01&statistics=avg-soldPrice&listings=false


Supported Statistics


Supported statistics enable comprehensive market analysis by providing both central tendencies (averages and medians) and measures of variability (standard deviations and min/max values) across key real estate metrics.


Tax Statistics
  • avg-tax - The average annual property tax amount across all properties in the dataset
  • med-tax - The median annual property tax amount, representing the middle value when all tax amounts are sorted


Price Performance
  • pct-aboveBelowList - The percentage of properties that sold above or below their listing price, providing insight into market competitiveness


Price per Square Foot
  • avg-priceSqft - The average price per square foot, calculated by dividing the price by the property's square footage


Property Counts
  • cnt-available - The total count of properties currently available for sale or lease
  • cnt-new - The total count of newly listed properties within the specified timeframe
  • cnt-closed - The total count of properties that have completed transactions (sold or leased)


Days on Market Statistics
  • sd-daysOnMarket -The standard deviation of days on market, showing the variability in how long properties stay listed
  • med-daysOnMarket - The median number of days properties remain on the market before selling
  • avg-daysOnMarket - The average number of days properties stay on the market
  • sum-daysOnMarket - The total cumulative days all properties have been on the market
  • min-daysOnMarket - The shortest time any property spent on the market
  • max-daysOnMarket - The longest time any property spent on the market


List Price Statistics
  • sd-listPrice - The standard deviation of listing prices, indicating price variability in the market
  • med-listPrice - The median listing price, representing the middle value of all listing prices
  • avg-listPrice - The average listing price across all properties
  • sum-listPrice - The total sum of all listing prices
  • min-listPrice - The lowest listing price in the dataset
  • max-listPrice - The highest listing price in the dataset


Sold Price Statistics
  • sd-soldPrice - The standard deviation of sold prices, showing the variability in final sale prices
  • med-soldPrice - The median sold price, representing the middle value of all completed transactions
  • avg-soldPrice - The average sold price across all completed transactions
  • sum-soldPrice - The total sum of all sold prices
  • min-soldPrice - The lowest sold price in the dataset
  • max-soldPrice - The highest sold price in the dataset


Maintenance Fee Statistics
  • avg-maintenanceFee - The average monthly maintenance fee for properties that have this cost (typically condos and co-ops)
  • med-maintenanceFee - The median monthly maintenance fee across applicable properties
  • avg-maintenanceFeePerSqft - The average maintenance fee per square foot, providing a normalized comparison metric
  • med-maintenanceFeePerSqft - The median maintenance fee per square foot across applicable properties


Grouping Statistics


You can group statistics by different time periods to analyze trends. For example, to see how the average sold price has changed month-to-month over the past 2 years, add grp-mth as a comma-separated value to the statistics parameter:


https://api.repliers.io/listings?city=New York&minBeds=4&maxBeds=4&status=U&lastStatus=Sld&minSoldDate=2022-07-01&statistics=avg-soldPrice,grp-mth&listings=false


The response will include monthly data points:


{
  "statistics": {
    "soldPrice": {
      "avg": 823702,
      "mth": {
        "2022-07": {
          "avg": 743897,
          "count": 13303
        },
        "2022-08": {
          "avg": 759084,
          "count": 25539
        },
        "2022-09": {
          "avg": 739097,
          "count": 22919
        },
        ...
      }
    }
  }
}


Supported Groupings


  • grp-day - By day
  • grp-mth - By month
  • grp-yr - By year
  • grp-{x}-days - Rolling statistics, where x is the number of days for each grouping


Multiple Groupings


You can get multiple groupings in a single request. For example, if you wanted to group statistics by both month and year, you can add both to your request (comma-separated):


https://api.repliers.io/listings?city=New York&minBeds=4&maxBeds=4&status=U&lastStatus=Sld&minSoldDate=2022-07-01&statistics=avg-soldPrice,grp-mth,grp-yr&listings=false


Rolling Statistics


Rolling statistics provide a moving average over a specified period, smoothing out fluctuations to reveal underlying trends. For instance, to get a rolling average of the sold price over 30 days:


https://api.repliers.io/listings?city=New York&minBeds=4&maxBeds=4&status=U&lastStatus=Sld&minSoldDate=2022-07-01&statistics=avg-soldPrice,grp-30-days&listings=false


Requesting Multiple Statistics


You can request multiple statistics in a single API call. For example, to retrieve both the average sold price and average days on market you can do so by comma-separating the values avg-daysOnMarket and avg-soldPrice in the statistics parameter:



https://api.repliers.io/listings?city=New York&minBeds=4&maxBeds=4&status=U&lastStatus=Sld&minSoldDate=2022-07-01&statistics=avg-daysOnMarket,avg-soldPrice,grp-mth&listings=false


Aggregating Statistics


Aggregating statistics allows you to compare different segments within your data. To enable aggregation, add &aggregateStatistics=true and specify the field to aggregate by. For instance, to compare average sold prices by neighborhood:


https://api.repliers.io/listings?city=New York&minBeds=4&maxBeds=4&status=U&lastStatus=Sld&minSoldDate=2022-07-01&statistics=avg-soldPrice,grp-mth&listings=false&aggregateStatistics=true&aggregates=address.neighborhood


The response will be grouped by neighborhood:


{
  "statistics": {
    "soldPrice": {
      "avg": 986486,
      "mth": {
        "2022-07": {
          "avg": 949437,
          "count": 4600,
          "aggregates": {
            "address": {
              "neighborhood": {
                "Waterfront Communities C1": {
                  "count": 74,
                  "avg": 805367
                },
               "Waterfront Communities C2": {
                  "count": 60,
                  "avg": 709361
                },
                ...
              }
            }
          }
        }
      }
    }
  }
}


You can aggregate statistics by various fields. For example, to compare sold prices across different property types:


https://api.repliers.io/listings?city=New York&minBeds=4&maxBeds=4&status=U&lastStatus=Sld&minSoldDate=2022-07-01&statistics=avg-soldPrice,grp-mth&listings=false&aggregateStatistics=true&aggregates=details.propertyType


The response will include data segmented by property type:


{
  "statistics": {
    "soldPrice": {
      "avg": 986486,
      "mth": {
        "2022-07": {
          "avg": 949437,
          "count": 4600,
          "aggregates": {
            "details": {
              "propertyType": {
                "Detached": {
                  "count": 2526,
                  "avg": 1090041
                },
                "Condo Apt": {
                  "count": 844,
                  "avg": 696919
                },
                ...
              }
            }
          }
        }
      }
    }
  }
}


Summary


Our real-time market statistics feature empowers you to create insightful data visualizations that help users understand market trends and make informed decisions. By leveraging granular filters, diverse statistics, and powerful aggregation capabilities, you can customize data to suit specific needs. For detailed documentation and further customization options, please refer to our API Reference.

Updated on: 13/08/2025

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