Drivly Connect required integrations: Manheim, Kelly Blue Book, CarGurus, Black Book, NADA Guides, Edmunds. *At least two book value sources are required for Drivly’s Pricing API.

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Drivly Vehicle Pricing:

The Drivly weighted average price calculation takes into account pricing information from a variety of sources, including all major pricing categories, such as MSRP, trade-in, wholesale, private party, retail, and loan values.

1

Calculate the average price for each book:

MSRP Average = (nada.msrp + blackBook.msrp) / 2

Trade Average = (cargurus.trade + edmunds.trade.clean + kelley.trade.good +
nada.trade.avgBook + blackBook.trade.totalAvg) / 5

Wholesale Average = (edmunds.wholesale.clean + kelley.wholesale.book +
nada.wholesale.avg + blackBook.wholesale.totalAvg +
manheim.lastSixMonthsPrice) / 5

Private Party Average = (cargurus.privateparty + edmunds.privateparty.clean +
kelley.privateparty.good) / 3

Retail Average = (cargurus.retail + edmunds.retail.clean + kelley.retail.book +
nada.retail.book + blackBook.retail.totalClean) / 5

Loan Average = (nada.loan.book + blackBook.loan.totalValue) / 2
2

Then calculate a weighted average for each:

Drivly Price =
(MSRP * w1 + Trade * w2 + Wholesale * w3 + Private Party * w4 + Retail * w5 + Loan * w6)
/ (w1 + w2 + w3 + w4 + w5 + w6)
SourceTrust ScoreBBB RatingUser ReviewsWeight
CarGurus9/10A+4.5/50.23
Kelley Blue Book9/10A+4.5/50.23
Edmunds8/10A+4/50.18
NADA Guides8/10A4/50.14
Black Book8/10A3.5/50.09
Manheim8/10A4/50.13

In this formula, w1, w2, ... w6 are the weights assigned to the corresponding pricing categories based on the importance, accuracy, and credibility of the sources. These weights are adjusted according overtime to reflect the market.

By calculating these weighted average prices, we normalize the data to find the best approximation of the vehicle’s value across various conditions and sources.

const data = {
  vin: 'WP0CD29929S773682',
  year: '2009',
  make: 'Porsche',
  model: '911',
  trim: 'Turbo',
  msrp: {
    nada: 139300,
    blackBook: 139300,
  },
  trade: {
    cargurus: 65262,
    edmunds: {
      outstanding: 70972,
      clean: 66809,
      average: 59872,
      rough: 50853,
    },
    kelley: {
      good: 61891,
      baseGood: 61081,
    },
    nada: {
      base: 50525,
      baseAvg: 47675,
      baseRough: 44300,
      book: 66550,
      avgBook: 63700,
      roughBook: 60325,
    },
    blackBook: {
      baseClean: 62150,
      baseAvg: 57125,
      baseRough: 48855,
      totalClean: 64875,
      totalAvg: 60025,
      totalRough: 51955,
      totalWithHAVClean: 64875,
      totalWithHAVAvg: 60025,
      totalWithHAVRough: 51955,
    },
  },
  wholesale: {
    edmunds: {
      outstanding: 59867,
      clean: 56356,
      average: 50504,
      rough: 42897,
    },
    kelley: {
      book: 69319,
      base: 68509,
      fair: 57788,
      good: 64039,
      veryGood: 67663,
      excellent: 72600,
      baseGood: 63229,
    },
    nada: {
      high: 57701,
      avg: 55476,
      low: 53276,
      baseLow: 45550,
      baseAverage: 47750,
      baseHigh: 49975,
    },
    blackBook: {
      baseClean: 61850,
      baseAvg: 56825,
      baseRough: 51975,
      totalClean: 64575,
      totalAvg: 59725,
      totalRough: 55075,
      totalWithHAVClean: 64575,
      totalWithHAVAvg: 59725,
      totalWithHAVRough: 55075,
    },
    manheim: {
      lastSixMonthsPrice: 68500,
    },
  },
  privateparty: {
    cargurus: 85801,
    edmunds: {
      outstanding: 81594,
      clean: 76612,
      average: 68310,
      rough: 57516,
    },
    kelley: {
      good: 62871,
      baseGood: 62061,
    },
  },
  retail: {
    cargurus: 88522,
    edmunds: {
      outstanding: 87681,
      clean: 82120,
      average: 72853,
      rough: 60805,
    },
    kelley: {
      book: 74852,
      base: 74042,
      msrp: 142150,
      baseFairPurchasePrice: 71909,
      totalFairPurchasePrice: 72719,
      fairPurchasePriceLow: 67907,
      fairPurchasePriceHigh: 77530,
    },
    nada: {
      base: 57250,
      book: 73275,
    },
    blackBook: {
      baseClean: 72500,
      baseAvg: 63475,
      baseRough: 57950,
      totalClean: 75225,
      totalAvg: 66375,
      totalRough: 61050,
      totalWithHAVClean: 75225,
      totalWithHAVAvg: 66375,
      totalWithHAVRough: 61050,
    },
  },
  loan: {
    nada: {
      base: 45475,
      book: 61500,
    },
    blackBook: {
      baseValue: 61825,
      totalValue: 64275,
      totalWithHAV: 64275,
    },
  },
}

function calculateWeightedAverages(prices, weights) {
  let weightedSum = 0
  let totalWeights = 0

  prices.forEach((price, index) => {
    weightedSum += price * weights[index]
    totalWeights += weights[index]
  })

  return weightedSum / totalWeights
}

const weights = [0.23, 0.18, 0.23, 0.14, 0.09, 0.13]

const msrpAverage = (data.msrp.nada + data.msrp.blackBook) / 2
const tradeAverage =
  (data.trade.cargurus +
    data.trade.edmunds.clean +
    data.trade.kelley.good +
    data.trade.nada.avgBook +
    data.trade.blackBook.totalAvg) /
  5
const wholesaleAverage =
  (data.wholesale.edmunds.clean +
    data.wholesale.kelley.book +
    data.wholesale.nada.avg +
    data.wholesale.blackBook.totalAvg +
    data.wholesale.manheim.lastSixMonthsPrice) /
  5
const privatePartyAverage =
  (data.privateparty.cargurus + data.privateparty.edmunds.clean + data.privateparty.kelley.good) / 3
const retailAverage =
  (data.retail.cargurus +
    data.retail.edmunds.clean +
    data.retail.kelley.book +
    data.retail.nada.book +
    data.retail.blackBook.totalClean) /
  5
const loanAverage = (data.loan.nada.book + data.loan.blackBook.totalValue) / 2

const averagePrices = [
  msrpAverage,
  tradeAverage,
  wholesaleAverage,
  privatePartyAverage,
  retailAverage,
  loanAverage,
]

const weightedAveragePrice = calculateWeightedAverages(averagePrices, weights)

console.log('Weighted Average Price:', weightedAveragePrice)

​
Incorporating the standard deviation to understand pricing confidence

For the Drivly Price, we can calculate the average value for each pricing category, and then take a weighted average based on the relevance of each category.

Drivly Price is a weighted average of Trade-in, Wholesale, Private Party, and
Retail values.

1

Trade-In Average:

(50,525+66,550 + 48,855+64,875 + 65,262 + (50,853+70,972)/2 + (61,081+61,891)/2) / 7
≈ 58,704
2

Wholesale Average

(45,550+57,701 + 51,975+64,575 + (42,897+59,867)/2 + (57,788+72,600)/2 + 68,500) / 7
≈ 57,889
3

Private Party Average:

(85,801 + (57,516+81,594)/2 + (62,061+62,871)/2) / 3
≈ $72,566
4

Retail Average:

(57,250+73,275 + 57,950+75,225 + 88,522 + (60,805+87,681)/2 + (67,907+77,530)/2) / 7
≈ $72,807
5

Drivly Price:

(0.4 * 58,704) + (0.2 * 57,889) + (0.2 * 72,566) + (0.2 * 72,807)
≈ $62,954

Next, we’ll calculate the weighted average considering their relevance. In this example, we assume weights of 40% for Trade-In, 20% for Wholesale, 20% for Private Party, and 20% for Retail, but these can be adjusted as required based on market data.

Drivly Price = (0.4 * 58,704) + (0.2 * 57,889) + (0.2 * 72,566) + (0.2 * 72,807) ≈ $62,954

The Drivly Price, would be approximately $62,954 for the vehicle with the given VIN WP0CD29929S773682.

  1. Calculate the mean price: (58,704 + 57,889 + 72,566 + 72,807) / 4 = $65,241.5
  2. Find the squared differences:
  • (58,704 - 65,241.5)^2 = 42,735,969.25
  • (57,889 - 65,241.5)^2 = 53,919,863.25
  • (72,566 - 65,241.5)^2 = 53,534,497.25
  • (72,807 - 65,241.5)^2 = 57,387,798.25
  1. Average the squared differences: (42,735,969.25 + 53,919,863.25 + 53,534,497.25 + 57,387,798.25) / 4 = 51,894,532
  2. Take the square root of that average: √51,894,532 ≈ $7,206

The standard deviation of the pricing data is approximately $7,206. This measure gives an idea of how widely spread or dispersed the average prices are.

CategoryNADABlack BookCarGurusEdmundsKBBManheimDrivly
MSRP139,300139,300-----
Trade-In50,525-66,55048,855-64,87565,26250,853-70,972*61,081-61,891--
Wholesale45,550-57,70151,975-64,575-42,897-59,867*57,788-72,60068,500-
Private Party--85,80157,516-81,594*62,061-62,871--
Retail57,250-73,27557,950-75,22588,52260,805-87,681*67,907-77,530--
Loan45,475-61,50061,825-64,275-----
STD------$7,206
Drivly Price------$62,954
  • For Edmunds, the range is based on the conditions: Rough, Average, Clean, and Outstanding.

​
Drivly Price with confidence interval that takes the standard deviation into account.

Using a single standard deviation (which typically accounts for about 68% of the data) to create a confidence interval:

Drivly Price: $62,954 ± $7,206

This means that we expect approximately 68% of the actual prices to fall within the range of $55,748 to $70,160. This gives users a sense of the variability and accuracy of the Drivly Price.

For higher confidence, we can use two standard deviations (about 95% of the data) for the interval. So, approximately 95% of the actual prices should fall within the range of $48,542 to $77,366.

This wider interval provides an even higher level of confidence in our pricing accuracy.

​
Drivly Price with STD and Weighted Price algorithm:

function calculateWeightedAverages(prices, weights) {
  let weightedSum = 0
  let totalWeights = 0

  prices.forEach((price, index) => {
    weightedSum += price * weights[index]
    totalWeights += weights[index]
  })

  return weightedSum / totalWeights
}

function calculateStandardDeviation(array) {
  const n = array.length
  const mean = array.reduce((sum, val) => sum + val, 0) / n
  const squaredDiff = array.map((val) => Math.pow(val - mean, 2))
  const variance = squaredDiff.reduce((sum, val) => sum + val, 0) / n
  const stdDev = Math.sqrt(variance)
  return stdDev
}

const weights = [0.23, 0.18, 0.23, 0.14, 0.09, 0.13]

const weightedAveragePrice = calculateWeightedAverages(averagePrices, weights)
const standardDeviation = calculateStandardDeviation(averagePrices)
const accuratePrice = weightedAveragePrice - standardDeviation
const pricingConfidence = (1 - standardDeviation / weightedAveragePrice) * 100

console.log('Weighted Average Price:', weightedAveragePrice)
console.log('Accurate Price:', accuratePrice)
console.log('Pricing Confidence Score:', pricingConfidence.toFixed(2) + '%')

How it works:

  1. We first calculate the average price for each pricing type (MSRP, Trade, Wholesale, Private Party, Retail, and Loan) and stores them in variables: msrpAverage, tradeAverage, wholesaleAverage, privatePartyAverage, retailAverage, and loanAverage.
  2. These average prices are then combined into an array averagePrices.
  3. The calculateWeightedAverages function computes the Weighted Average Price using the averagePrices array and the given weights.
  4. Finally, the code prints the Weighted Average Price.

Outputs:

Weighted Average Price: $64,257.955 Drivly Price: $63,984.721 *considering the standard deviation Pricing Confidence Score: 99.57% *high confidence level pricing accuracy

Drivly Price takes the Weighted Average Price into account by adjusting it based on the standard deviation (variability) of the prices from different sources. The intention of this adjustment is to provide a more reliable and realistic estimation of the car’s price, acknowledging the inherent uncertainty and variability.

These values indicate that the initial Weighted Average Price is 64,257.95,theDrivlyPrice(consideringthestandarddeviation)iscalculatedtobeapproximately64,257.95, the Drivly Price (considering the standard deviation) is calculated to be approximately 63,984.72, and the Pricing Confidence Score is 99.57%, emphasizing the high confidence level in the estimated accurate price.

Coming soon!

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Adding in more variables to improve accuracy

Expanding the dataset to include normalized Vehicle History, condition reports, OEM Build Options and high value features, and additional market data (Drivly Listings API).

  1. Vehicle History and Condition:

    • Add vehicle’s mileage, maintenance history, and wear and tear.
    • Create a function to calculate condition adjustments based on the provided data from Drivly Connect providers.
    • Modify the price estimates by applying the condition adjustments to each pricing type’s average.

  2. Options and features:

    • Include additional pricing properties in the data object to account for specific options and features.
    • Create a function to calculate adjustments for each option and feature.
    • Modify the price estimates by applying the options and features adjustments to each pricing type’s average.

  3. Market data:

    • Utilize Drivly’s Listings API and Drivly Connect providers to add regional market data like local demand, supply, and car price trends.
    • Create a function to calculate market adjustments based on the provided data.
    • Modify the price estimates by applying the market adjustments to each pricing type’s average.

Here’s an example of how we could implement part of the algorithm for condition using a mileage adjustment:

// Update data object with car's condition (mileage) and a sample condition-based adjustment
const data = {
  // ...pre-existing data...
  condition: {
    mileage: 40000,
    mileage_adjustment: {
      mileage_30000: 0.95,
      mileage_40000: 0.9,
      mileage_60000: 0.85,
      //...
    },
  },
}

function applyConditionAdjustment(basePrice, mileage, mileage_adjustment) {
  if (mileage <= 30000) {
    return basePrice * mileage_adjustment.mileage_30000
  } else if (mileage > 30000 && mileage <= 40000) {
    return basePrice * mileage_adjustment.mileage_40000
  } else {
    // Assuming mileage > 40000 and mileage <= 60000
    return basePrice * mileage_adjustment.mileage_60000
  }
}

const conditionAdjustedMSRP = applyConditionAdjustment(
  msrpAverage,
  data.condition.mileage,
  data.condition.mileage_adjustment
)
// ... Do this for other pricing types

// Update the averagePrices array with condition-adjusted prices
// ...

// Proceed with the existing algorithm to calculate the weighted average price and adjusted price