JustAutoValue.com Shows how Vehicle's Trim Alone Makes a Significant Difference in Price

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All other factors remaining constant, vehicle's Trim alone makes a significant difference in comparable prices.

While the 2012 LE (in the above example) would be priced at roughly $13K (orig. MSRP $22.6K+/-), the comparable XLE-V6 would fetch roughly $17.8K (orig. MSRP $30K+/-), nonetheless pointing to a fairly similar decay in prices (meaning Current Price to MSRP).

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Pricing Pre-owned Auto Market – A Hedonic Modeling Approach (Kindle or Paperback on Amazon)

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Pricing Model for an "Older" Model -- A Toyota Camry Case Study

Pricing Model for an "Older" Model -- A 2006 Toyota Camry Case Study

While the new automobile market is regulated by the Manufacturer’s Suggested Retail Price (MSRP), the pre-owned auto market is vastly unregulated, causing significant price differences from dealer to dealer. Glitzy showrooms with large advertising budgets tend to have more pricing power than their less ostentatious counterparts, often promoting comparable pre-owned autos at 30-50% higher than the weaker competitors.

A number of online auto information and listing service companies have emerged over the last decade or so, providing very meaningful data and pricing support for the consumers. In addition to making consumers aware of the damage, accident, service history and odometer of the pre-owned cars, they have been offering independent price points as well, hopefully leading to a more structured and secure pre-owned market. In case of untoward purchases resulting from the data failure, some are even offering purchase protections.

In order to arrive at the prices independently, the info companies have been developing their proprietary pricing models, thus predicting prices efficiently and en masse. Since their model development process is not made public, even the most data-savvy consumers are left to guesswork, at best, which has enticed the author to publish this book, detailing his perspective of pricing models for the popular pre-owned vehicles (“Autos,” “Cars,” “Vehicles”). 

The methodologies used and proposed here can easily be duplicated on an existing database or with some data collections. In order to teach the readers how to model the pre-owned auto data, all modeling samples have been extracted from the current institutional listings pertaining to the most popular as well as longest-running domestic and foreign cars. Also, all mileage, damage/accident and ownership data have been verified against some 3rd party databases.

Additionally, in order to make learning easy and pleasurable, a set of structured methodologies will be utilized throughout the modeling process; for example, in examining the predictive relationship between the dealer pricing (dependent variable in the model) and the predictive variables (independent variables), a correlation matrix will be used. Similarly, the Multiple Regression Analysis (“MRA”) will be used to develop the pricing model (“Model”) and predict the line-item prices (“Model Est”). Any generic statistical software or Excel’s Data Analysis module will help perform these functions.    

Moreover, due to the changes in market dynamics, modeling the older auto models is quite different from modeling the mid-age and newer models. For instance, the competition in the older-model market centers around the dealers and private sellers, gradually shifting to dealers vs. off-lease to dealers vs. rental companies to even dealer vs. manufacturers (offering large rebates for the unsold inventory before the new models are introduced), etc. for the newer models. Obviously, considering the significantly higher prices, the demand for financing becomes more critical for the newer models than the older ones.     

Finally, in keeping with the changes in the market dynamics, the following structured format will be resorted to: 2006 lines will represent the “older” models while 2010 and 2015 models will represent the “mid-age” and “newer” models, respectively. Therefore, in line with this prescribed format, the first three chapters will be devoted to these three models: 2006 Toyota Camry (older), 2010 Honda Accord (mid-age) and 2015 Nissan Altima (newer).

Modeling 2006 Toyota Camry

Camry has been one of Toyota’s bread and butter brands in the US since it was reintroduced as a wide-bodied mid-size car. Despite having gone through a number of design and body changes, it has been the best-selling passenger vehicle here since 1997, save a year or two in between. In fact, over 90% of them are still roaming the streets. Given this extraordinary success and achievement of this brand, it is only befitting that the 2006 Camry spearheads the modeling journey.

The 2006 Camry came in three primary trims (“packages”), Standard, LE and XLE, with LE leading the production and sales. XLE represented the top of the line with a V6 engine, sunroof, upgraded audio, leather and a luxury power pack. Roughly 450K Camry units were sold in 2006, beating all prior records.  

Modeling Step 1 (Correlation Matrix)

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The above correlation matrix sets the table for modeling. Dealer Price (abbreviated here as D/Price) has the highest (negative) correlation with Miles. The negative correlation coefficient signifies that higher mileage dampens asking prices in the market.

Prior ownership (“Owner”) is the next most important predictor of the dealer price. Owner is a linearized 3-category variable with 1-owner receiving the highest rating followed by two other categories: 2-3 owners and 4-5 owners.

Accident and Warranty are the two important binary variables, though the latter represents the dealer warranty as the original manufacturer’s warranty had long elapsed. The V6 Engine, Sunroof and Upgraded Audio are all part of the XLE package, hence the discernibly high multi-collinearity, thus forcing this prospective trio out of the modeling equation and leaving Package to stand on its own.

Modeling Step 2 (Multiple Regression Analysis)

The above MRA output confirms the transition of Miles from the negative predictive relationship to the negative contribution to the predicted price. Accident is the most valuable independent variable (highest t stat and lowest P-value), followed by Warranty, Package, Owner and Miles. The model R-square – 0.93646259 – is reasonably high, with potential for much higher if the model is rerun without outliers.

To interpret the model coefficients, consumers prefer cars that have not had reported damages and accidents. Also, the cars backed by some sort of dealer warranty are in higher demand (considering the 2006 model is 10+ years old now), while the one-owner cars are preferable to those owned by multiple people. Also, better packaged models – XLE and LE – are more sought after than the baseline Standard model. As expected, a typical buyer is expected to pay a lesser price for a car with higher mileage.   

Modeling Step 3 (Analysis of Model Estimates)

The above percentile graph shows that the dealer prices and model estimates are more or less similar up to the median (50^th percentile), beyond which the model estimates start to curve down, indicating that the dealer prices on the long and outer ends of the curve are on average $400-$500 above the market. This additionally proves that the model estimates could help both consumers and dealers to quickly converge on the same page as these estimates are independently derived. This will also help private sellers as they tend to feel quite confused – often clueless – in making quantitative adjustments to the available comps (adjusting the comps to their subjects).  

While dealers are asking the highest average price for the standard model (Std), the Model nonetheless is predicting (“Model Est”) the lowest price for it, and then ascending in the proper order. The Model is pricing the standard model $1,300 lower than the average dealer price, thus sending clear warning signals to the potential buyers of the overpricing. This is the reason why a set of independent model estimates are so critically important to protect general consumers. By the same token, dealers would be alerted to the potential under-pricing of the XLEs. As indicated before, LEs – in line with the original production – comprise nearly 2/3rds of the modeling sample.

                                                    

The Accident variable provides an excellent customer protection, safeguarding those who are particularly risk-averse. Again, the Model is predicting $1,500 higher (6,894 – 5,353) for the vehicles without any reported damages/accidents. Similarly, the dealers can benefit by using the $300 higher model estimates (6,894 – 6,600) to re-price their accident-free fleet as well.

Nowadays, vehicle data reports like CarFax* and AutoCheck* are readily available in the pre-owned market, instantly alerting buyers shopping on-site of many noteworthy issues like title, safety, accident, odometer, prior ownership, etc.     

While only 18% are able to buy with a dealer warranty, they are however $1,000 (7,724 – 6,755) ahead, advantageously. Conversely, those who are buying without any warranty are still overpaying, on average, $435 (6,398 – 5,963).

Again, by using the model estimates both parties could win. While dealers are overcharging 8 out of 10 customers when selling without warranty, they are however losing big by significantly undercharging when selling with warranty. Obviously, customers are willing to pay a hefty sum for the warranty to earn some peace of mind, that is, to avoid having to deal with a lemon from the get-go. Of course, the warranty services for older vehicles are generally more expensive than their newer counterparts’.       

The Model is revealing that the dealer prices for the lower mileage ones are overpriced while their higher mileage equivalents are somewhat under-priced. Therefore, having access to the model values would help the dealers price their inventory more accurately, without having to depend on the transposed prices between these two compensating groups. The lure of lower mileage vehicles is forcing consumers to pay an unwarranted premium which could be avoided if the model estimates were also published alongside the dealer prices.

Consumers are pleasantly benefiting from the dealers’ flawed pricing of single-owner units despite having lower than the average mileage. Of course, the dealers are making up those losses by overcharging in other two categories, particularly in the 4–5 ownership category. Unfortunately, these less desirable vehicles – high mileage, accident surviving, many prior owners, etc. – are often pushed on to the consumers with less-than-perfect credit by bundling sub-prime loans.

The above table proves the presence of location arbitrage. Midwest and West Coast markets are seriously overpriced while South and Northeast are somewhat under-priced. Of course, considering that Midwest and West Coast are not the typical Camry country, this price imbalance could be temporary, resulting from (temporary) shortage of supplies.

The Model is showing an aberration in Florida markets. Though the lower mileage is expected to bump prices up, the Model nonetheless is projecting the lowest average prices for Florida. While the demographic variables are absent in the Model, demography could be playing an invisible role here.

When the Model identifies the over-priced ones, it’s pointing to a silver lining, meaning “potential” savings. If the model values were to be reported alongside the dealer prices, buyers would immediately know the extent of potential savings. SL #1, 2, 3 and 11 are comparable autos, yet the dealer price range of $7K to $10K projects a spread of $3K. Granted #1 carries a dealer warranty and warranty services are more expensive for older models due to the significantly higher exposure, but it shouldn’t make a difference of $3K. The Model is however predicting a more reasonable range of $6K to $7.7K, thereby alerting dealers of the over-pricing and arming consumers with more negotiating power.

Alternatively, when the modeling process identifies the under-priced ones, it’s pointing to some “upfront” savings for the consumers. This is an area where dealers would be most benefitted if they were to subscribe to the model estimates. Of course, the dealer prices could consciously be lower as the minor negatives are not captured in the modeling database. Nevertheless, it would be a conscious decision on the dealer’s part to ask a lower (than the model estimate) price. While SL #1 through 7 are comparable (all high mileage) vehicles, the dealer price range of $3.5K to $5K is significantly lower than the model estimates of $5.5K to $6.7K. Then again, in the pre-owned market, dealers often factor in some psychological breakpoints in pricing their fleet. For instance, the dealer might have factored in one such psychological breakpoint (200K miles) while pricing out SL# 1.

To recap, in order to develop a statistically significant pricing model for the pre-owned market nationally, a spatially distributed modeling sample with a set of predictive variables is critical. Also, time-tested and stable research tools and techniques are equally important.  

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Developing a Pricing Model for a Late Model Vehicle - A Nissan Altima Case Study

Developing a Pricing Model for a Late Model Vehicle 

-- 2014 Nissan Altima Case Study --

Altima has been Nissan’s bread and butter mid-size sedan for over a decade, competing with other popular mid-size brands like Toyota Camry, Honda Accord, Hyundai Sonata, Chevrolet Malibu, Ford Fusion, and a host of others. Nissan sold a record 335K in combined Altima brands in 2014, rising from a mere 203K at the bottom of the last recession in 2009. 

The 2014 Altima came in two primary engine configurations: 2.5L I4 16V and 3.5L V6 24V, with the SL Trim topping both lines. While the 3.5 SL represented the hallmark of luxury for Altima with V6 engine, moonroof, upgraded Bose audio, leather interior and a luxury power pack, the 2.5 complex (2.5, 2.5 S, 2.5 SV & 2.5 SL) collectively led the overall production and sales volume. Of course, the moonroof, Bose audio and leather were available as factory options for the 2.5 SL as well.

The modeling sample, therefore, comprises of the 2.5 and 3.5 SLs only.

Modeling Step 1 (Correlation Matrix)

As indicated before, the correlation matrix sets the table for modeling. As expected, the Dealer Price (abbreviated here as Dealer Pr) has the highest (negative) correlation with Miles, signifying that the higher miles generally dampen the dealers’ asking prices in the market.

Though Warranty is the next best predictive variable, it’s extremely high collinearity with Miles (-0.8792) makes it an insignificant variable, leaving the latter to solely represent the entire mile-related complex. As all warranties are primarily tied to the mileage (and secondarily to the number of years/months), their collinearity is observably inseparable. For instance, the original factory warranty covered the car bumper to bumper for 3-years/36,000 miles, Power-train warranty covered engine and transmission for 5-years/60,000 miles, and the factory Certified (for the pre-owned vehicles) warranty extended the Power-train to 7-years/100,000 miles.    

The Trim variable demonstrates the third highest predictive relationship with the dependent variable. Though Trim has a high collinearity with the Moonroof variable, the latter would still be tried in the model considering the limited pool of independent variables.

Unlike the older and mid-age models, Service history (Service) is usually an important consideration in purchasing late model vehicles as buyers are forced to pay up to 70% of the original MSRP for the well-maintained ones. Service is therefore a new variable which shows significant predictive promise, though somewhat correlated with Owner and Accident.

Accident is the other important predictive variable in the modeling queue, with low multi-collinearity, except for Service. Moonroof and the prior ownership (Owner) will be the other predictors in the MRA model. While prior ownership – particularly original ownership – is generally an important variable in predicting prices of the older and mid-age models, it is nonetheless much less significant for the late models as the vast majority of late model cars have single owners, thus making the variable more predictable and less disparate. The lack of distribution makes it significantly weaker.

The above scatter graph depicts the negative relationship between the Dealer Prices and Miles. Prices generally decrease commensurately with the increasing mileage. Of course, the fit would tighten with some outliers removed, thus paving the way for a higher R-square, perhaps up to a more customary level. 

Modeling Step 2 (Multiple Regression Analysis)

The model R-square – 0.976608347 – is reasonably high, with potential for even higher R-square if the model is rerun without the outliers.

The above MRA output confirms the negative contributory relationship between Miles and the dependent variable, meaning higher miles are negatively contributing to the predicted prices. Though the Miles coefficient is seemingly small, it will nonetheless have significant impact on cars with high mileage; for example, the predicted price of a 2014 with 70,000 miles will be reduced by -$3,221 (-0.04601892 * 70,000), as opposed to -$920 for a competing Altima with only 20,000 miles on it.

Accident, in sharp contrast to a lower correlation coefficient, stands out as the most important independent variable (highest t stat and lowest P-value) in the model followed by Trim and Miles. The reason Accident is so prominent in the model is that it provides the maximum price differentiation between the two groups of cars – accident-free vs. accident-encountered. Simply put, the future owners of near new cars are mostly risk-averse, negotiating significantly lower (discounted) prices for the cars that have encountered damages and accidents. Accidents, often resulting in physical damages, are not covered by the factory warranty which covers manufacturing faults only.

To interpret the other MRA model coefficients, the higher Trim model – 3.5 SL – is contributing more to the model estimate than the lower 2.5 SL Trim. Additionally, single ownership (Owner) and better serviced vehicles (Service) are preferred while Moonroof adds to the model estimate as well. Again, higher miles and accidents have recognizably negative impacts on model estimates.    

Modeling Step 3 (Analysis of Model Estimates)

The above percentile graph shows that the model estimates are significantly lower at the bottom end of the curve, zigzagging between the 25^th and 50^th percentile, and are confirming the dealer prices on the long end of the curve. The fact that the model has been predicting lower prices at the bottom end of the curve points to the above-the-market asking prices for the lower end units, perhaps those with accidents on record. On the other hand, this additionally proves that the model estimates could help both consumers and dealers to quickly converge on the same page as these estimates are independently derived. Likewise, the private sellers can validate their subject prices before accepting the trade-in values from the dealers.  

The model is predicting even higher (than the dealer) prices for the top-of-the-line 3.5 SL Trim, signifying that the market is ready to withstand higher prices for the more robust V6 engine with factory-installed power and cosmetic upgrades, even though the manufacturer sells a disproportionately higher volume of the lighter 2.5 SL version (assuming, of course, that the 2.5 SL’s 63% presence in this sample represents the actual rollout too). While the dealers are under-pricing the high-end low-volume 3.5 SLs, the model estimates are signaling the possible over-pricing of the 2.5 SLs.

The original average MSRPs for these two models were $27,920 and $30,820, respectively, pointing to ironic 56% and 49% decays in respective values.

Considering these are the late model cars without any significant exposure, the cars with reported damages/accidents comprise a low 13%.  While the Model is agreeing with the dealer pricing for the vehicles without any reported damages/accidents, the dealers are however way over-pricing their fleet of Altimas with the reported damages/accidents. Therefore, by having the model estimates placed alongside the dealer prices, consumers can save on average $4,549 (15,029 – 10,480), a truly wow savings and a great firewall protection from dealers’ over-pricing.

The major car rental companies usually start withdrawing their fleets as they approach 2 years and/or 30-40K miles, to avoid having to deal with questionable rentals. As of this writing (6/2017), only one major rental company had the 2014 inventory on sale (while others have been selling 2015 and 16) which is reflected in the sample. The above graphic shows the rental car sales are more aggressively priced than the competing dealer inventories. Even the model is showing an average savings of $1,500. Knowledgeable consumers are generally aware of this potential savings and use them as direct comps while negotiating with the dealers. Again, having the model estimates available side-by-side the dealer prices would protect average consumers and ease deal-making by eliminating all unnecessary price haggling back and forth.

The presage by the correlation matrix that a better serviced car is more likely to fetch a higher price is now emphatically confirmed by the model. The lesser maintained cars fetch bottom of the barrel prices. Of course, those cars tend to have higher miles (as shown above), disproportionately more accidents, lower order trims and sometimes multiple owners even during this short stint.

The above Miles table shows the price comparison by having miles broken down into two (equal) halves. Ceteris paribus, the lower mileage group is slightly over-priced, while the higher mileage group is appropriately priced. The lure of very low mileage vehicles (not shown here) is forcing consumers to pay an unwarranted premium which could be avoided if the model estimates were also published alongside the dealer prices.

The above table proves that the location arbitrage is virtually non-existent nationally, other than the fact that the West Coast market, though inadequately represented, is seriously overpriced. Of course, considering that West Coast is not the typical Altima country, this price imbalance could be temporary, resulting from (temporary) shortage of supplies.

The above graphic is suggesting why the Moonroof is such a highly sought after option. While it is standard in 3.5 SL sedans, it is an option for 2.5 SLs – needless to say, a worthwhile option indeed. The Model is predicting $3,000 lower value for the vehicles that are unequipped with Moonroofs.   

When the Model identifies the over-priced vehicles, it’s pointing to a silver-lining, uncovering “potential” savings. If the model estimates were to be reported alongside the dealer prices, buyers would immediately know the extent of those potential savings. The above data sample demonstrates that the dealers are way over-pricing the accident-free 2.5 SLs that are still under Full (3-year/36K miles bumper-to-bumper) factory warranty (e.g., SL # 1, 3, 7 and 9), followed by the factory Certified (7-year/100K Powertrain) units (e.g., SL # 8 and 10).

Alternatively, when the modeling process identifies the under-priced cars, it’s pointing to some “upfront” savings for the consumers. This is an area where dealers would be most benefitted if they were to subscribe to the model estimates. The above sample shows that the dealers are generally under-pricing the good (accident-free and under warranty) 3.5 SLs. Again, the above under-priced sample proves the accuracy of the model. As indicated in the previous chapter, now and then, the dealer prices could consciously be lower to factor in some minor negatives (not captured in the modeling database) or to address some imminent psychological breakpoints. SL # 8 could be one such psychological case where the dealer might have consciously lowered the price as the vehicle is on the verge of running out of the 60K Powertrain warranty.

Considering the ever-escalating popularity of the Nissan Altima brand, the 2014 model has been one of the most sought after mid-size late models on the market today.

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Pricing Pre-owned Auto Market – A Hedonic Modeling Approach

Pricing Pre-owned Auto Markets - A Honda Accord Case Study

Pricing a "Mid-Age" Model -  2010 Honda Accord Case Study

Alongside Toyota’s Camry, Honda’s Accord has been immensely popular in the mid-size segment, for over twenty years now. The reliability of the Accord has paid big dividends among its loyalists, always willing to lend a hand to their favorite car to remain on the top-10 lists of almost all major auto magazines ever since. Accord consistently fetches one of the highest resale values in the mid-size segment.

The 2010 Accord came in three primary trims: LX, EX and EX-L, with EX leading the production and sales. EX-L represented the top of the line with a V6 engine, sunroof, upgraded audio, leather and a luxury power pack. Even at the bottom of the Great Recession, over 280K 2010 Accords were sold, although 350K-390K used to be the norm prior to the recession. 

Modeling Step 1 (Correlation Matrix)

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The above correlation matrix sets the table for modeling. Dealer Price (abbreviated here as Dealer Pr) has the highest (negative) correlation with Miles. The negative correlation coefficient signifies that higher mileage dampens the dealers’ asking prices in the market.

The Trim-Package variable demonstrates the second highest predictive relationship with the dependent variable, i.e., the Dealer Price. The binary Sunroof variable is the next best predictor but its demonstrably high multi-collinearity with the Trim-Package forces it out of the modeling equation, leaving Trim-Package to stand on its own in the model. Accident is the other important binary variable in the modeling queue, with extremely low collinearity with the other independent variables. Finally, the prior ownership (“Owner”) is another predictor of the dealer price. Owner is a linearized 4-category variable with 1-owner receiving the highest rating followed by 2, 3 and 4 owners, respectively.

The above scatter graph depicts the negative relation between Dealer Price and Miles. Prices generally decrease commensurately with the increasing mileage. With trimming of some outliers, the fit would tighten, moving the R-square up to a more customary level. 

Modeling Step 2 (Multiple Regression Analysis)

The above MRA output confirms the transition of Miles from the negative predictive relationship in the correlation matrix to the negative contribution to the predicted price. Trim-Package is the most important independent variable (highest t stat and lowest P-value), followed by Accident, Owner and Miles. The model R-square – 0.96809697 – is reasonably high, with potential for even higher R-square if the model is rerun without the outliers.

To interpret the MRA model coefficients, better Trim-packaged models – EX-L and EX – have higher demands than the baseline LX model. Consumers prefer Accords that have not had any reported damages or accidents, while the one-owner units are preferable to those owned by multiple people. Also, as expected, a typical buyer is expected to pay a lesser price for a high mileage Accord.   

Modeling Step 3 (Analysis of Model Estimates)

The above percentile graph shows that the dealer prices and model estimates are divergent up to the 25^th percentile, beyond which the dealer prices and model estimates start to converge. The fact that the model has been predicting lower prices at the bottom end of the curve points to the above-the-market asking prices for the lower end units. On the other hand, this additionally proves that the model estimates could help both consumers and dealers to quickly converge on the same page as these estimates are independently derived. Since the private sellers frequent the mid-age market, model estimates would help them properly price their subjects as well.  

The model is predicting even higher (than the asking) prices for the top-of-the-line EX-L package, signifying that the market is ready to withstand higher prices for the more robust V6 engine with factory-installed power and cosmetic upgrades, even though the manufacturer sells a disproportionately higher volume of the lighter version (assuming, of course, that the combined EX/LX 65% in this sample represents the actual rollout too). This is a common strategy manufacturers follow to keep the MSRPs low, thereby enticing a broader base of customers. Again, by having access to model estimates, dealers would be alerted to the potential under-pricing of the EX-Ls. Conversely, they would be warned of the over-pricing the other 65%, particularly the baseline LXs.

The Accident variable provides an excellent customer protection, safeguarding those who are particularly risk-averse. While the Model is agreeing with the dealer pricing for the vehicles without any reported damages/accidents, the dealers are however way over-pricing the Accords with the reported damages/accidents. Therefore, by having the model estimates placed alongside the dealer prices, consumers can save on average $2,500 (10,995 – 8,485), a wow savings and the true protection from over-pricing.

Nowadays, vehicle data reports like CarFax* and AutoCheck* are readily available in the pre-owned market, instantly alerting buyers shopping on-site or online of the many noteworthy issues like title, safety, accident, odometer, prior ownership, etc.
     

While the Model is confirming the dealer prices for the prior 1 and 2-owner Accords, the dealer prices are nonetheless considerably higher than the model estimates for 3 and 4-owner ones, thus significantly disadvantaging average consumers. Only the extremely knowledgeable consumers would be aware of the differences in prices at this level of detail. Thus, having the model estimates available side-by-side the dealer prices would protect average consumers and ease deal-making by eliminating all unnecessary price haggling back and forth.

The above Miles table shows the price comparison by breaking down the mileage into four equal quartile groups. The Model is revealing that the dealer prices for the two lower mileage categories are significantly above the market, though their prices in the highest mileage category are well below the market. Therefore, having access to the model values would help dealers price their inventory more accurately, without having to depend on the transposed prices between these two compensating groups. The lure of lower mileage vehicles is forcing consumers to pay an unwarranted premium which could be avoided if the model estimates were also published alongside the dealer prices.

The above table proves that the location arbitrage is virtually non-existent nationally, other that the fact that the West Coast market is seriously overpriced. Of course, considering that West Coast is not the typical Accord country, this price imbalance could be temporary, resulting from (temporary) shortage of supplies. Conversely, the South and Midwest markets are somewhat under-priced.

When the Model identifies the over-priced ones, it’s pointing to a silver lining, meaning “potential” savings. If the model values were to be reported alongside the dealer prices, buyers would immediately know the extent of potential savings. Earlier, the Model had identified the factors – baseline Trim, Accident on record, multiple prior Owners and high Mileage – that significantly lower the value of this mid-age Accord. The above over-priced data sample confirms exactly that. SL # 6 is the only one without an accident report, however satisfying the other conditions. SL # 7 and 9 units, despite accidents, have managed to maintain higher values due to the top EX-L trim, single ownership and relatively low miles.

Alternatively, when the modeling process identifies the under-priced cars, it’s pointing to some “upfront” savings for the consumers. This is an area where dealers would be most benefitted if they were to subscribe to the model estimates. While dealer prices range between $12,995 and $13,998, the Model has been predicting a much higher range – low-to-mid $15K. As the Model has already identified, dealers have been under-pricing their cream of the crop Accords, meaning the one-owner, accident-free, low-mileage, top-of-the-line EX-Ls. Again, the above under-priced sample proves the accuracy of the model.  Now and then, the dealer prices could consciously be lower as the minor negatives are not captured in the modeling database. At any rate, it would be a conscious decision on the dealer’s part listing a lower (than the model estimate) price. SL # 2 and 3 – the two low-mileage top-notch EX-Ls – could fall into the aforesaid group.

Considering the high reliability and rock solid loyalty, the 2010 Honda Accord has been one of the most sought after mid-size models on the pre-owned market today. 

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Those who are into data science or modeling can learn more about it from my recent book "Pricing Pre-owned Auto Market - A Hedonic Modeling Approach" available on Amazon (search 'Sid Som's Books'). Also, try our Free and Mobile-friendly Auto Valuation site JustAutoValue.com.

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