A DYNAMIC DETERMINATION OF THE RESIDUAL PRODUCT VALUE: EMPIRICAL EVIDENCE FROM THE AUTOMOTIVE INDUSTRY
[004-0144]
Matthias Holweg, Judge Business School, University of Cambridge, UK
Email: [email protected], Tel: +44 1223 760 583
Paul A. Kattuman, Judge Business School, University of Cambridge, UK
Email: [email protected] , Tel: +44 1223 764 136
Abstract Traditional management thinking and research has generally regarded the new and used
durable goods markets as independent, addressing analytical issues in each in isolation. This static perspective however is limited, as in fact the new and used markets for durable goods should be linked in a dynamic fashion, and the actions in the market for new goods will have repercussions in the used goods market. These dynamic effects which have largely been ignored in the management literature, will be illustrated by quantifying the impact that sales incentives employed in the new market have on the residual values in the used market. We present estimates of the dynamic effect on the residual value of passenger cars; of sales incentives, as well as of model life cycles and product commonality. Our findings illustrate how incentive-based sales strategies may have little adverse impact on the residual values in the short term, but have a significant detrimental long-term effect, leading to a ‘mortgage’ on the future earnings of the manufacturer’s captive leasing operations.
1 Introduction
Industries that require large capital investments in product design and manufacturing equipment face a dilemma: their long development lead-times and economies of scale require them to achieve a certain capacity utilization and production volume to succeed. Troughs in demand need to be addressed, most commonly with price adjustments. While the primary effect of such sales incentives are well described in the large literature reporting estimates of price elasticity (e.g., Berry, Levinsohn and Pakes, 2004), the secondary effects of such actions are not well understood at all. Sales and distribution strategies tend be focused on short-term gains, while the long-term effects are often omitted in managerial decision-making. For example, while sales incentives will increase sales in the short term, little is yet understood how these may affect the residual value of the good, and in fact the future overall brand value. Purohit (1992) and Benjamin and Kormendi (1974) highlight the general links between the new and used markets for durable goods, while Miller (1974) outlines how the overall firm performance stems from its combined activities in both markets. In this paper we develop this argument further by investigating the dynamic impacts of sales incentives in the new market on the residual value of products in the used market.
The automotive industry is an interesting arena for an investigation of these questions. Featuring high levels of investment in product development and manufacturing equipment, economies of scales are a key determinant of industry profitability (Maxcy and Silberston 1959; Rhys 1973). The use of sales incentives to boost demand and keep volumes up is thus not a new strategy (Jung 1960). In the light of the recent sales recession starting in 2000, prompted vehicle manufacturers across market regions to use heavy incentives on their products, rather than accept the decline in the market. GM, instigator of this policy in the US market, forced the entire industry to follow suit and soon Ford, Chrysler and, albeit to a lesser extent, the European and Japanese manufacturers were also offering sales incentives, both in the US and European market (John 2004).
These incentives take a variety of forms, from cash payments, to specification upgrades, higher trade-in prices for used vehicles, and better conditions on the finance deal. The direct detrimental effect on profitability per vehicle sold has since been widely discussed in the general and trade press, but there is a further twist to this phenomenon. Although companies like Ford and GM are known as vehicle ‘manufacturers’, in fact, they derive their profits largely from
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financing vehicles, rather than from their manufacture in the first place. Neither GM nor Ford, for example, have reported any profits from their respective car manufacturing businesses since 2001, the point at which heavy incentives were first observed (see table 1). The profit contributions from the captive financing arms are considerable, to the effect that it has been suggested that some vehicle manufacturers only build cars in order to finance them later, as making and selling cars has become inherently unprofitable for them (Garsten 2003). Table 1: Operating income for auto manufacturing and finance operations at Ford and GM, 1999-2004. 1999 2000 2001 2002 2003 2004 Ford – Auto Operating income / (loss)
before income taxes 7,275 5,323 (8,857) (1,054) (1,908) (155)
Ford – Finance Operating income / (loss) before income taxes
2,579 2,976 1,438 2,118 3,247 5,008
Ford – Total Net Profit 7,237 3,467 (5,453) (980) 495 3,487 GM – Auto Operating income / (loss)
before income taxes 6,536 4,581 (422) (633) (1,508) (3,124)
GM – Finance Operating income / (loss) before income taxes
2,511 2,583 2,876 2,971 4,489 4,316
GM - Total Net Profit 6,002 4,452 601 1,736 3,822 2,805
Note: Figures in million US$. Source: annual reports / Datastream. While organizationally separate units, the manufacturing and financing arms are closely
connected. A key feature of lease finance is the ‘residual value risk’: As lease payments are based on the depreciation of the vehicle over time, the residual value at the end of the lease needs to be estimated. Depressed residual values have serious ramifications for the financing companies. Leasing firms, captive and non-captive alike, predetermine the residual value at the end of the lease - a key determinant of the lease payments. In 2000, when new vehicle prices faced increased competition, leasing companies suddenly found drastic discrepancies between their book value and the actual prices they could achieve for their formerly leased vehicles. For example, a full-spec 1997 Ford Expedition XLT with a list price of $36,580 was expected to show a residual value of $25,606 after the three-year lease. Instead, the auction value had dropped to $16,500 in 2000 – showing a $9,106 loss for the finance operator. Equally, the used vehicle inventory at the dealerships depreciated accordingly, leading to negative margins on models that featured high incentives in the new car market. Industry-wide losses from overestimating used car values amounted to $11bn and $10bn in 2000 and 2001 in the US market, respectively (Pierce 2004).
Commonly, residual value estimation is based on the historical depreciation of the vehicle and its predecessors in the respective market segment. The static perspective of residual value estimation conceives of the two markets as separate, masking a potentially dangerous process: while the primary effects of using sales incentives is to increase sales in the new car market, the negative effect on the residual value of the product, now and in the future, is yet unknown. Purohit (1992) shows the general linkage of the new and used goods markets, and one would assume that products sold with incentives in the new market will realize lower residual values as they foster cross-market substitution in favor of the new cars (Benjamin and Kormendi 1974). A comprehensive model that captures these dynamic aspects of the residual value estimation is
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missing, and in practice, leasing operators largely rely on historic market data and the interpretation of wider economic factors.
In this paper, we are concerned with the inter-linkage between the new and used car markets, a dynamic interaction we believe is important in estimating residual product values. We are not considering consumer decision-making such as offsetting the benefits that lead consumers to lease rather than buy; our focus is on determining the strategic aspects of the residual value estimation. As the lessor is responsible for any discrepancy between residual value and the vehicle’s actual lease-end market value, accuracy in residual value prediction is critically important to the firm’s financial performance. Even when residual values are strategically inflated by a lessor to increase sales volume, any deviation from the expected value of the lease-end car will change the expected cash flow. All lessors, captive or non-captive, are concerned with predicting the future value of the vehicles in their portfolios, whether or not they intend to subsidize any of the leases they write.
The key focus of this paper is the impact of, as well as and strategic trade-off between, short- and long-term gains from sales incentives. We estimate a model of residual value that takes the dynamic effects of sales incentives into account, and thus provides companies with a guideline on balancing the short-term gains with potential long-term effects of their decisions. The paper is organized as follows: in Section 2, we will review the literature related to price elasticity and incentive strategies, and discuss the structure of the new and used markets in the automotive industry, as well the economics of vehicle leasing. Section 3 will introduce the empirical model developed to test our research propositions, and Section 4 presents and discusses the results of the regression analysis, before concluding with managerial recommendations and areas for future research in Section 5.
2 Market Organization 2.1 Demand, Price Elasticity and Incentives
There is a significant body of work measuring the price elasticity of demand for durable goods such as automobiles. Several studies have focused on estimating the demand for new vehicles (Agarwal and Ratchford 1980; see Train 1986; for a comprehensive review of the earlier demand studies, and Berry, Levinsohn and Pakes 1995, 2004; for recent estimates and the current methodology for estimating demand functions). In the case of specific models of automobiles, which constitute a vertically and horizontally differentiated durable good, short-term own, and cross-price, elasticities are expected to be high, both due to the large number of substitutes as well as the potential for the purchase to be delayed. It is evident that price is a critical choice variable for the manufacturer, and this explains the prevalence of price discounts in the automotive industry.
The active secondary used automobiles market complicates the manufacturer’s profit maximization problem. Cars of a specified model are homogeneous when new. Usage leads to depreciation, driven by two main factors: age, defined as the period since it was first registered, and the usage in terms of mileage. (Further factors include the number of registered owners, accident history, as well as the general specification of the vehicle.) Consumers are heterogeneous in terms of their willingness to pay for quality. While older cars are poor substitutes for new cars, nearly new (off-lease) cars can be attractive to many people as substitutes to new cars. For a significant proportion of consumers, the price differential between new and nearly new second hand durable goods (of specified quality) will determine the degree
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of substitution between the used and the new markets (Benjamin and Kormendi 1974; Desai and Purohit 1998). Thus, a price discount on new automobiles can be expected to have two effects: the first and immediate effect will be the switching of consumers away from used to new automobiles, which is our first proposition:
Proposition 1: Sales incentives in the new car market have an immediate detrimental effect on the residual product value in the used car market.
The second and longer term effect has not been widely discussed in the literature – a larger number of new automobiles sold today will increase the supply of used automobiles in the future that will compete for buyers of forthcoming models. The market for used automobiles is equilibrated by the auction price, ceteris paribus. Demand will be determined by prices of used automobiles, automobile features (age, color, specifications etc.), prices of new automobiles, and economic conditions. Supply of vehicles in the auction market will depend on the past sales of new automobiles. This will be determined by the demand and supply schedules for new automobiles, factoring in price discounts and other sales incentives that were offered for new cars in the market at that point. Thus we can expect that the change to the equilibrium in the new car market, through sales incentives for example, will affect the used car market in the future, which is our second proposition:
Proposition 2: Sales incentives in the new car market have a distributed lagged (detrimental) effect on the residual product value in the used car market.
2.2 The New Car Market In the new car market, selling products to end customers can take various forms. In its
most direct form, products are offered at the list price, which is paid in full or in installments by the end customer or user. For most investment goods, including automobiles, various other forms of financing those purchases have evolved, such as vehicle leasing, which we will return to in section 2.3.
Vehicles are sold to two main customer groups, private buyers and institutional buyers, the latter of which include large company car fleet operators, rental car companies and leasing firms. The former (so-called ‘users-choosers’) decide on the vehicle purchase according to a combination of economic and subjective criteria, whereas the latter tend to be largely driven by economic considerations of operating the respective vehicle fleet. In some markets such as the UK for example, favorable tax regimes mean that fleet sales can account for up to 60% of the total market. The actual sales price that the customer pays is determined by the list price, as well as the incentives that are granted by the manufacturer. These can be in the form of a price reduction, free specification upgrade, inflated trade-in prices for used vehicles, and favorable financing terms (e.g. zero-percent financing), as well as volume-based discounts for institutional buyers.
The market for new cars is supplied by a set of manufacturers that each offer a range of models under a set of brands. GM, for example, the world’s second largest vehicle manufacturer, offers vehicles under its Buick, Chevrolet, Cadillac, GMC, Holden, Hummer, Opel, Pontiac, Saturn, and Vauxhall brands, and also has stakes in manufacturers such as Isuzu, Saab, Subaru (Fuji), and Suzuki. Across its brands, GM offers a portfolio of vehicles that span all market ‘segments’ from small cars, through to compact cars, midsize sedans, SUVs, sports cars and luxury vehicles.
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A key feature of the new car market is the continuous update of the product(s). Ever since Alfred Sloan introduced the annual model change at GM in the 1920s (thereby successfully out-maneuvering Ford with its static product line-up at the time, see Child, 1996) the industry has been continuously and regularly updating its product line-up. There are three types of updates: (a) the annual model year change, which generally involves very minor cosmetic changes and specification updates, (b) the common practice of a ‘facelift’, a major update commonly halfway through the model life cycle, without changing the body shell (body-in-white), and (c) the introduction of a new model generation, which generally features both a new platform as well as a new body shell.
The introduction of a new model into the new car market leads to a competition between vintages (Levinthal and Purohit 1989), and the wider customer perception of the updated product impacts on the demand-supply equilibrium for the new product (Kamien and Schwartz 1972; Rosenberg 1976). Copeland et al. (2005) show how even the annual model year change impacts on the new vehicle inventory balance and sales prices in the new car market.
The product life cycles in the automotive industry have been steadily falling to an average five years in the Western markets since the 1960s, while at the same time the model range has increased (Holweg and Pil 2004). Thus, from an economic perspective, the novelty of a new model is eventually impaired by the arrival of the next model generation. As much as the attractiveness of a new model will create a strong demand at the start of the product life cycle, so much is the residual value likely to degenerate close to the end of the life cycle, where customers are likely to ‘hold out’ and delay purchase until the next generation of the product is available. Assuming that the product attractiveness in the market degenerates in a linear fashion, we formulate our third research proposition as:
Proposition 3: Increasing the rate of product replacement (i.e. a shorter product life cycle) reduces the attractiveness of the used product, and thus depresses its residual value.
The main consequence of the combination of falling life cycles and increasing model ranges have been declining production volumes per model and an increasing overall product variety, putting the manufacturers under considerable pressure to rationalize their product development organization (Pil and Holweg, 2004). Product variety in general is detrimental to manufacturing productivity (MacDuffie et al. 1996), so a key means to achieving this increased efficiency is by sharing components and platforms (Cusumano and Nobeoka 1998; Ramdas, et al. 2003). The main thrust of platform sharing requires additional coordination effort, yet on balance, the gains of a standardized and modular design outweigh this additional effort in coordination (Meyer and Lehnerd 1997).
From a marketing point of view however one could also argue that the product commonality reduces the product’s individuality and market attractiveness, as products under a lower-segment brand might cannibalize sales for the same product under a premium brand (Levinthal and Purohit 1989). Purohit (1992) further shows that the performance of vehicles in the used car market depends on whether the replacement vehicle is built on the same, or indeed a completely new platform. We hence propose that component and platform sharing reduces the individuality of the product, and thus its attractiveness in the used car market:
Proposition 4: Increasing platform sharing reduces the individuality of the product in question, and thus depresses the residual value of these products.
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2.3 Leasing: An Institutionalized Link between Markets Leasing provides a long-established institutional link between the new and used car
markets. The first captive leasing company was the General Motors Acceptance Corporation, which was established in 1919 in order to fend off the low-cost competition from Ford’s Model T. To this date, lease penetration (the amount of vehicles sold via third-party leasing operators) is significant, see table 2. Table 2: Lease penetration by segment in %, US market, 1990-2003. % 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 Passenger cars Budget 5.5 12.1 13.6 13.4 12.1 12 10.3 10 9.5 8.8 Small 5.3 18.9 18.5 15.4 14.8 14.4 14.2 12.1 9.7 8.9 Lower middle 12.8 26.9 27.3 28.1 27.3 27.2 25.7 24.5 18.3 17.1 Core middle 16.2 30.4 31.8 31.1 28.6 27.3 26.9 26.3 22.6 20.4 Upper middle 14.7 26.2 27.3 28.1 29.1 29.4 29.2 30 30.8 30.6 Near luxury 25.2 50.5 52.6 57.3 58.3 58.8 59.7 58.9 56.2 48.7 Luxury 52.6 62.0 64.2 65.9 65.2 57.8 51.3 55.5 51.4 45.2 Specialty 24.6 59.7 61.3 58.5 57.5 55.3 50.4 52.3 45.7 37.7 Sport 18.8 26.2 30.4 34.4 39.3 40.2 41.1 44.4 44.1 42.6 Light trucks Compact pickup 4.4 14.6 15.2 16.3 15.7 15.6 15.7 15.8 15.1 10.1 Compact sport utility 9.6 34.3 36.7 38.4 39.7 41.2 40.7 42.2 38.8 29.7 Full-size pickup 8.2 18.3 19.4 22.7 25.3 28.1 26.3 27.1 23.7 18.3 Full-size sport utility 9.3 36.9 38.2 42.1 42.7 44.4 46.5 45.9 42.1 37.8 Full-size van 12.1 20.0 21.3 22.7 22.4 21.9 21.1 21 18.8 15.4 Minivan 8.4 25.8 28.1 32.8 33.5 35.7 32.3 36.6 34.5 28.1 Market average 7.3 24.2 27.2 29.3 31.5 29.1 28.7 29.2 27.9 24.7 Source: Ward’s Yearbook (various years)
From a strategic point of view, leasing allows for the postponement of the bulk payment,
and thus is as much an accounting approach, as it is a marketing and sales strategy. In terms of method and process, lease finance involves the lessee making a monthly payment to the lessor for the right to use the automobile for a fixed period of n months (Myers et al. 1976). The lessor retains ownership of the car. The monthly payment covers two types of costs: first, the depreciation (the difference between the list price P of the car or the capitalized cost and the contracted residual value of the car Rest , i.e. what the car is estimated to be worth at the end of the fixed lease term), and second, interest i (on the price of the car for the lease term). The lessee will typically return the vehicle to the lessor at the end of the lease term, but will then have the option to purchase the vehicle for a price equal to the contracted residual value. The residual value thus determines the depreciation the consumer must pay over the lease term, and also sets the price at which the consumer can buy the vehicle at the end of the lease term. The standard leasing formula in (1) exemplifies how the variables in our regression model are linked, whereby the formula determining lease payment m is:
(1) 1)1(
)1(1−+−+⋅
⋅= nest
iRiP
nm
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Where: P = The total loan amount, or in our case, the price of the car Rest = The estimated residual value i = The monthly interest rate n = The term, or the number of months of a lease m= the monthly payment
The key risk for the lessor is the ‘residual value risk’, whereby the actual residual value
Ract realized in the used car market is lower than the estimated Rest that was used to calculate the monthly payments. Predicting the estimated residual value at product return is an essential competence, as the actual residual value is uncertain due to the way in which future market conditions can be affected by general economic conditions: competition, customer preferences, innovation and new substitute products that render the current model obsolete, and the pricing of respective substitutes all can have a significant influence. Lessors take on the risk from consumers through closed-end leases, where the lessee can return the vehicle at lease end without any responsibility for the residual value agreed upon at the time of the contract.
The critical components of future vehicle values are new product introductions, durability, and the respective customer perception of the new models. Purohit (1992) found that the redesign of models affected the price of used cars from that model and similar cars from both the same and competing manufacturers. He showed that the magnitude and timing of used car depreciation are strongly dependent on the timing and nature of new model introductions and redesigns. Likewise, durability influences used vehicle values because less durable vehicles deteriorate more quickly, reducing their worth on the secondary market. Closed-end leases with purchase options, standard in the automobile industry, only exacerbate this risk for the lessor, as they ensure that only the worst cars within a model will be returned at lease-end.
Pierce (2004) further highlights the impact of organizational structure on the performance of leasing companies. While captive lessors have an advantage in terms of prior knowledge of model replacement cycles, they are also part of the wider sales strategy of the respective vehicle manufacturer. Pierce (2004) shows that captive lessors can be ‘abused’ to boost market share for less popular models by overestimating the residual value. In this case, the monthly payment by the consumer decline with overestimation of Rest, as shown in (2):
(2) 1)1( −+
−=∂∂
nest i
iRm
Inaccurately high residual values may lead to higher numbers of leases, but it will also
lead to inevitable losses at the end of the lease terms, when the realized value falls systematically short of initial estimates. The lessor ultimately loses in the final sale of the used vehicle through auction. If the actual auction price of the car after lease is considerably lower than the predicted residual value upon which the lease payments were based, then the present value of overestimating residual value is inevitably a loss for the lessor, as the customer will not choose to purchase the vehicle, and it returns to the lessor. Reducing the new price of the car is always a better strategy than setting high residual values. Thus the lessor’s profitability depends on how accurately they predict the residual value of the leased durable goods.
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2.4 The Used Car Market
At present, motor vehicles in the main segments have an average lifespan of 12 years and show a steady but non-linear depreciation with increasing vehicle age. Most commonly, the residual value is seen as a static depreciation over the usage and age of the product (i.e. the age and mileage of a car, for example). Figure 1 shows the average depreciation of motor vehicles over their age (assuming a constant ann2ual mileage).
Figure 1: Value retention of motor vehicles over vehicle age, by segment.
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 12 18 24 30 36 42 48 54 60
Vehicle Age in Months
% o
f Lis
t Pri
ce
Superminis
Lower medium segment
Upper medium segment
Executive segment
MPV
SUV
Small SUV
Prestige segment
Luxury cars
Sports cars
Note: Data shown is aggregate auction data for the UK market of 2004. Source: proprietary. This approach assumes a constant reduction of market attractiveness of the product as it
ages. On average, motor vehicles lose between 27-42% of their value in the first 12 months, and then depreciate almost linearly, until the residual value reaches zero (or even negative values, where the cost of disposal outweighs the remaining materials value). The depreciation profile, which on an individual vehicle basis is mediated by the respective brand value, is a strong element in consumers’ decision to buy a certain vehicle. In recent cases, vehicle manufacturers have even cited their ‘residual value performance’ in their marketing efforts.
The used car market, where vehicles of all vintages are traded, is essentially created by two sources of supply: private vehicle owners that sell their vehicle (either privately, through franchised dealerships, or through auctions), and the lease finance companies that sell their off-lease vehicles, almost exclusively through auctions. In a simple visualization, the used and new car markets are shown in figure 2.
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Figure 2: The linkages between new and used car markets.
VehicleManufacturer
VehicleManufacturer
Finance Co.Finance Co. Lease Customer
Lease Customer
AuctionAuction
P, dL
Rest
m,n
Ract
PrivateCustomerPrivate
Customer
P, dP
New Car Market
Used Car Market
Ract
Direct SalesDirect Sales
R*act
The manufacturer offers a vehicle at a nominal list price P, yet at the same grants a
discount dP to its private buyers, as well as a discount to its institutional buyers dL. The leasing firm purchases the vehicle and leases it to the lessor at a monthly rate m for n periods, typically of 36 months. The depreciation of the vehicle has to be estimated at Rest, and upon return to the leasing company, the vehicle is sold in the used car (auction) market. The auction houses feature large volumes of sales. They record sales data, as well as type, age and mileage of vehicles, and therefore provide the basis for a quantitative analysis of the residual value of used vehicles, and most ‘buyers guides’ such as Glass or Parkers Guide are based on these data. Here, the vehicle realizes its current market value, Ract as it is sold alongside other vehicles in the open market. Outside of the used car auctions, a vehicle might realize a value of Ract* different from Ract, as here one-to-one negotiations are common that distort the used car price. Here, additional contractual agreements whereby for example the used car forms part of the purchase of a new vehicle (which often coincides with a better trade-in price as incentive to buy the new vehicle) distort the transaction price. It is important to note that the auction price reported by the auction house is the national average, normalized for mileage (60,000 miles), condition (no major damage), age (36 months) and precise model specification.
3 Method 3.1 Model
As discussed above, modelling the residual values of automobiles is complex because of the highly differentiated nature of the product, and the existence of contemporaneous primary and secondary markets (Purohit 1992). The overlapping life cycles further complicate dynamic analyses (Copeland et al. 2005). Befitting the complexity of the market for this highly differentiated durable good, there has been a resurgence of models of automobile supply and demand (Bresnahan, 1987; Berry, Levinsohn, and Pakes, 1995; 2004; Petrin, 2002). While we use this literature as a point of departure, our focus in this paper is not on identifying and estimating demand or supply functions, but to characterise the determination of the equilibrium residual value for three-year old automobiles in the auction market for off-lease vehicles.
The equilibrium in the auction market is determined by the supply and the demand for used cars. While it is generally not possible to determine when exactly a car that was sold as new,
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enters the used car market, the leasing system that features fixed term contracts makes it possible to trace the flow from the new to the used car market. It is also worth noting that the large proportion of cars sold under lease, and the 36-month standard contract duration, imply that supply into the auction market is dominated by off-lease cars at every date.
We circumvent adverse selection problems in the used car market by relying on the fact that, before the auction process used-automobiles are inspected and the results are documented and made available to potential buyers. With negligible asymmetry in information it is reasonable to assume that competitive prices emerge from the auction process determined by the market clearance condition for each residual quality (Wilson, 1977; Milgrom, 1981). In our analysis we take the micro-economic process of the determination of this price as given, assuming that the residual quality of a good in the used-goods market is observable to buyers and sellers. The auction house normalizes the prices realized in the month for each specific model for mileage and condition and reports the average. In other words, the auction house controls for any unobserved heterogeneity arising from the used car’s mileage and condition.
The upper bound on supply to this market for any model, i, will be the number of new cars of that model registered at date t-36: . The other arguments of the three year old used
car supply ( ) function (see (3), where u denotes ‘used’ and n denotes ‘new’) will be the price
realized in the auction, , the price paid, , and the discounts, , that were granted when the car was new:
*,36, nti
Q−
SutiQ ,,
tiactR ,, 36, −tiP 36,, −tiLd
(3) ),,,( *,36,36,,36,,,,, ntitiLtitiact
Su
Suti QdPRQQ −−−=
A model of demand for used cars can be built on the basis that in each time period, each consumer has a discrete choice set which includes various models, both used and new, as well as the option of not making a purchase either because of the utility from the vehicle the consumer already owns, of from alternative modes of transportation. The utility obtained by a specific consumer from a specific used car model will depend on the observable model characteristics (e.g. engine size, fuel efficiency) as well as unobservable (to the analyst, but observable to the consumer) model characteristics (e.g. styling, prestige). The consumer will make the choice that maximizes her utility. The demand ( ) function for used cars which derives from this discrete choice process will depend on the auction price, and due to the substitutability between used and new cars, on the price of the new car, , and any discounts, , that are on offer for it. It will depend on vehicle features. In addition, demand will also depend on macro economic conditions:
dutiQ ,,
tiP, tiLd ,,
(4) ),,,,( ,,,,,,, titiLtitiactDu
Duti dPRQQ ZX=
where is a vector denoting product features, observed and unobserved. Unobserved product features such as styling, which are conceptually included in , will of course have to be omitted from the regression, and this may be expected to lead to omitted variable bias. However, the prices of new cars for the same model will reflect these unobserved characteristics and provide the necessary control. Note that the substitution possibilities in our analyses are restricted; the consumer is considered as choosing between the used car and the new car of the same model. is a vector denoting macro economic conditions on which demand depends; these may include expectations of the future.
iX
iX
tZ
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As explained earlier, our focus is not specifically on identifying either the demand function or the supply function, but on determining the impact of sales incentives granted for new cars on the equilibrium residual value of the same cars, 36 months hence. This equilibrium auction price is characterized by equating the demand and supply functions above in terms of the residual value:
(5) ),,,,( *,36,36,,36,,, tintitiLtiacttiact QdPRR ZX−−−=
As a regression model, this specification is likely to suffer collinearity, for example, possibly between and , these being directly related via the demand and supply functions for new cars 36 months earlier. From the point of view of the specification we do not model the equilibrium in the new car market. Equilibrium prices and quantities in the new car market are assumed to be exogenous from the point of view of the used car market.
36, −tiP *,36, ntiQ −
Collinearity between and is also likely, as prices may not change very much over three years, and finally between elements of ; e.g. unemployment rate and GDP growth. These collinearities will need to be inspected and resolved empirically.
tiP , 36, −tiP
tZ
Our specific interest in estimating (5) above is on the coefficient on the discount rates which gives us the responsiveness of equilibrium residual value determined through used car demand and supply to discounts offered on new cars 36 month ago by the manufacturer. Recall that from the point of view of lease financing, of the four principal lease terms - capitalized cost, residual value, term length, and interest rate - the residual value is the only ‘variable’, in that it is estimated and fixed by the lessor in the lease contract. A higher estimated residual value lowers the depreciation the consumer must pay for and therefore lowers their monthly payment. In estimating the residual value, the lessor should be guided by the list price set by the car by the manufacturer and the discounts they offer for specific car models. Our interest is in quantifying the impact of discount on residual value.
Studying the relative performance of new vehicles at their original sales and at the point of entry into the used car market as off-lease vehicles, allows for a longitudinal analysis of the connection between the two. Approximating the flow of vehicles as continuous, it is also possible to make multiple observations across multiple product life cycles. Due to the fixed lease period, the used cars market will only feature a new model, which was introduced at t=0, at t=36. Thus, it is possible to observe multiple and overlapping generations of products in the used car market.
Figure 3: The overlapping product life cycles in the new and used car markets
New ModelIntroduction
New CarMarket
Used CarMarket
36 months
t0 t36
Model A, newModel A, new Model B, newModel B, new
Model A, usedModel A, used Model B, usedModel B, used
tLC+36
New ModelIntroduction
Life Cycle (LC)
…
…
36 months
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3.2 Dataset We use a carefully compiled dataset of used passenger car auction prices in the UK from
1996 to 2004. The data set covers 47 models of 21 vehicle manufacturers, with no manufacturer accounting for more than 4 models. The frequency of observation is monthly and while the price information in the data spans the period from January 1996 to December 2004 allowing the necessary 36 months of lag restrict analysis to 2,320 observations from January 1999. The models we consider span, in roughly equal proportions the segments classed as super-mini (e.g. Ford Fiesta), lower medium (e.g. Ford Focus), upper medium (e.g. Ford Mondeo), executive (e.g. Saab 9-3) and the prestige segments (e.g. Audi A6). We exclude the luxury segment due to the idiosyncrasies in that segment (Bresnahan and Yao 1985).
In the period under consideration, the 48 models we consider accounted for 10.24m new vehicle registrations, a 70.22% representation of the cumulative UK passenger car market for this time period. The registration data are provided by the Society of Motor Manufacturers and Traders (SMMT), while the used car prices are provided as CAP (current auction price) by Glass, a data provider that specializes in supplying monthly used car data by aggregating national auction data. These datasets on new prices stem from the manufacturers’ proprietary data on recommended sales price (MSRP). Table 3: Definitions of variables Variable Description Unit
List price Price of car, new, current month GBP List price (L) Price of car, new, 36 months ago GBP Discount A Discount on price offered on new cars, current month % Registrations Number of new cars registered in the UK, current month Units Platform sharing Total number of vehicles shared on the same platform Number of vehicles ‘Plate’ change Change in the registration system from annual to bi-annual
changes Dummy
New model/new market Month of introduction of new model in the new car market Dummy New model/used market Month of introduction of new model in the used car market Dummy
Controls UK GDP change Annual growth in real GDP % UK unemployment rate Percentage workforce unemployed % Real estate index Price index of real estate Index number,
1990=100 USD / GBP Exchange rate US Dollar to Pound Sterling USD 3 month interest rate Interest rate (Inter bank) % Oil price World oil price in $/barrel USD
The unit of observation in our data is the car model and the variable of interest is the
model-specific (off-lease) normalized average auction price after the standard three-year duration that begins in a specific month. As explained earlier, vehicles that enter the auction are inspected
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and the results are documented and made available to potential buyers. The price for a model is the average across all cars sold via auction, normalized for mileage, specification and overall condition. The data has a panel structure, and the estimation method is outlined in Section 4. The three-year value retention ratio averages 34% across all models and years, ranging from a minimum of 12% (e.g. Rover’s 800 model) to a maximum of 62% (e.g. BMW’s 3-series model). The summary statistics are given below in Table 4. Table 4: Summary statistics for automobile models sold in the UK: 1999-2004
Variable Mean Std. deviation Minimum Maximum
List price 15,522 6,344 6,045 33,609
Residual value (auction price) 5,538 2,935 1,950 18100
Discounts 7.7% 5.7% 0.2% 34%
Registrations (monthly) 2,769 3,427 0 25,011
In the next section we estimate residual values based on a fully specified model that includes sales
incentives, product strategy and general economic conditions.
4 Results and Discussion 4.1 General Market Dynamics
A priori inspection of the dynamics in the used and new car markets reveals several features that signify the underlying economic decisions of customers in these markets. Figure 4 illustrates the respective dynamics of residual values and list prices for the Mercedes E-Class model.
Figure 4: Residual values and list prices for the E-Class, 1997-2004.
£0
£5,000
£10,000
£15,000
£20,000
£25,000
£30,000
£35,000
Jan-97 Jan-98 Jan-99 Jan-00 Jan-01 Jan-02 Jan-03 Jan-04
E-Class List Price E-Class Res. Value
Note: The residual value represents the CAP value for 36-months old vehicles in the UK market for the respective month, the list price is as quoted by the vehicle manufacturer in the respective month.
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Several observations can be made inspecting the patterns across all models. First, the used car market shows a high level of month-to-month variability. We suggest that this is the result of continuous adjustment of the demand and supply in the auction process. Second, the residual values show a general seasonal pattern throughout the year, with peaks in January. This peak relates to the fact that the build year increases impact on the market value (i.e. a ‘2005’ model is worth more than a ‘2004’ model, although the actual age differences might literally be only a matter of days). Third, the residual value patterns show several step changes (increases), which coincide with a new model arriving in the used car market (i.e. three years after the launch of this model in the new car market). This can be clearly seen in the above chart as the new E-Class model (W210) replaces the previous (W124) model in the used car market in August 1999. As a result, we will test for the significance of the new model introductions in both new and used markets. Fourth, the residual values for any particular model show a general downward trend, which means that an identical vehicle (in terms of model, specification, age, mileage, etc.) will lose residual value in the used car market as the design ages. This observation provides further justification for the inclusion of the model life cycle in order to derive realistic estimations of residual values (Purohit 1992; Copeland et al. 2005). In the new car market on the other hand, manufacturers do adjust prices according to their internal policies that do not necessarily relate to market feedback alone. Here, the adjustments are made both upwards and downwards, as the list price tends to increase with the launch of a new model, or at the point when the specification levels are adjusted. Overall, we observed considerably less volatility in the new car market. 4.2 Regression Results
Relative to a pooled model it is immediately evident that automobile model and time effects are significant. In the choice between fixed and random effects estimation, the Hausman test suggests that the random effects model is subject to unobserved heterogeneity bias – the fixed effects model is preferred at the 1% level, and these results are reported in table 5. It is however worth mentioning that all exploratory models (GLS random effects as well as OLS with panel-corrected standard errors) returned coefficients that were consistent in sign, significance and order of magnitude with those reported below.
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Table 5: Regression models for residual value: fixed effects.
With current list
prices and discounts With lagged list prices
and discounts
With current and lagged list prices and
discounts Log of list price 0.17 0.196 (3.65)** (4.30)** Log of list price, lagged 36 months 0.211 0.297 (5.23)** (6.15)** Discount (%) -0.126 -0.0015 (2.05)* (2.48)* Discount (%), lagged 36 months -0.521 -0.00647 (7.52)** (8.24)** Age of design -0.003 -0.002 -0.002 (20.23)** (19.29)** (16.46)** Log of registrations 0.009 0.008 0.006 (2.00)* (2.03)* (-1.21) Platform sharing index -0.01 0.006 -0.008 (-1.4) (-0.94) (-1.04) Plate change -0.033 -0.032 -0.039 (2.39)* (2.40)* (2.94)** New model arrival in the new market -0.011 -0.012 -0.014 (-1.41) (-1.8) (-1.86) New model arrival in the used market 0.2 0.163 0.159 (12.19)** (10.86)** (9.65)** Controls GDP growth rate 0.001 0.02 0.005 (-0.06) (2.78)** (-0.66) Unemployment rate 0.017 0.032 0.032 (-0.67) (-1.32) (-1.26) Real estate index 0 0.001 0.001 (2.71)** (3.50)** (4.10)** Exchange rate: USD/GBP -0.034 -0.067 -0.022 (-0.52) (-1.15) (-0.34) Oil price -0.003 -0.004) -0.004 (3.90)** (4.99)** (4.53)** Constant 6.845 6.415 3.686 (15.10)** (15.45)** (5.49)** Year effects Yes Yes Yes Observations 2,320 2,654 2,320 R-squared 0.48 0.51 0.51
Note: Absolute value of t statistics in parentheses.*=sign. at 5%, **=sign. at 1%. Months effects omitted.
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Before we turn to the discussion of our research propositions, it is important to consider the impact of the remaining variables in the model. It is straightforward that the residual value is higher for vehicles with higher list price, current and lagged. From the demand side, the positive cross-price elasticity (19.6%) signifies substitutability between the new and the 36-month old models. The supply side effect (the coefficient on lagged list price, i.e., the price at the time of original purchase at t=0) is greater, at 29.6%. These prices are significantly correlated but despite the lower efficiency, the coefficients of both prices are individually significant when introduced together.
The impact of the model specific demand variable, registrations, is mainly due to the peaks in demand in August, and since 1999, in March and September, at which point the registration letter system changed. Demand increases due to the desire to drive ‘the latest plate’ i.e. newest letter/number possible. In terms of month effects, it can be seen that (relative to January), all months apart from September (where the registration plate is updated) and February to April are significantly negative. Clearly both the calendar year and registration changes in the spring and autumn are a significant consideration in the consumers’ decision to purchase a used car.
The macro-economic controls do not vary between models, only over time. There is potential for multi-collinearity among the macro variables considered (retail sales, GDP growth, unemployment rate, real estate index, household disposable income, dollar exchange rate, interest rates and oil price). Two of the controls, retail sales index and disposable income turned out to be highly correlated with most of the variables. Not surprisingly, the residual value is significantly positively related to the real estate index, and negatively, to oil price.
4.3 The Incentive Effect
There is considerable variation in the offered discounts, which averaged 8% and ranged from 1% to 34% of the retail price P. From the regression results, it is striking that the discount at the time of original purchase has a significant negative impact on residual value. The discount currently granted is marginally significant statistically, but has a minuscule value, adding very little to the cross-price elasticity. The discount by itself does not appear to drive substitution between used and new cars.
The general impact of sales incentives on the residual value of the product stands confirmed, as the literature suggested. In the light of our findings, this argument is a little more subtle. Sales incentives given on a new product at t=0 has a twofold impact, the minor one impacting on the residual value of the product in the used market contemporaneously. The regression coefficient indicates that a 10% discount in the new car market results in an immediate drop of residual value by only 1.5%. The second, and much stronger impact of the discount occurs only when the vehicle that was sold with a discount at t=0 enters the used car market at t=36: here, a 10% discount at t=0 results in an additional 6.5% loss in residual values at t=36. With regards to our first and second research propositions, we hence conclude that incentives have a two-fold effect on the residual values of products, the more serious of which occurs with a time lag.
For companies with a captive leasing firm, as is common with most vehicle manufacturers, this brings about a strategic tension between the sales arm and the internal leasing company: while short-term incentives do increase the sales volume in the short term, they also have an effect on the residual value of the product. As this effect is lagged, the captive leasing
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company is exposed to an increasing residual value risk in the long-term. In other words, by employing sales incentives, the manufacturer can shift combined manufacturing and finance profits with little immediate repercussions. In our view, it is this logic that fuels the present incentive-based sales strategies in the automotive industry: with little immediate penalty, sales can be increased, while the leasing operation is not immediately affected. As our results show, however, this is a dangerous strategy. The adverse impact accumulates with little immediate repercussions but ultimately result in a ‘whiplash’ in the used car market where it not only lowers the residual values for specific vehicle lines but, strategically even more dangerously, erodes the long-term value perception of the entire brand (cf. Purohit 1992). Such loss in perceived value greatly affects the purchase decision of future customers, and manufacturers like Volkswagen and Mercedes already capitalize on this relationship, touting the competition’s brand value loss in their marketing campaigns.
4.4 The Life Cycle Effect
As Kamien and Schwartz (1972) and Rosenberg (1976) show, the expectation and perception of a new product impacts on the used good market. Empirically, Purohit (1992) shows that the effect can either be a positive or negative one, depending on the new product’s reception in the market. Our findings are consistent with Purohit (1992) in so far as there is no consistent pattern in the effect of new product introduction in terms of either obsolescence or enhancement. Our analysis reveals that after controlling for all other factors, the arrival of a new model in the new car market does not yield a significant effect on residual value, though it is negative in sign. However, the new product arriving in the used car market for the first time does result in an average residual value appreciation of 15.9%.
With regards to our third research proposition, we can conclude that the age of the model design (measured as the time since the actual model had been introduced into the market) has a significant negative impact on residual value. Each year the design ages, the residual value drops by an average 2.52%. This reflects the effect of an ageing model design on the residual value, and illustrates that not it is not only new car prices that decrease over the life cycle of a vehicle (cf. Copeland et al. 2005), but that used car prices also share this characteristic. As one would expect, the further a design advances in its life cycle, or the older the design becomes, the less well it retains it value. Further inspection reveals a step change in residuals as the next model generation replaces the old one. If one compares the average residual value retention of 3-year old vehicles over their respective product life cycle against the market average, one can observe two features: first, the average residual value drops considerably with increasing age of the design, consistent with the regression results. Second, we can see that individual models have a differential performance relative to the market average, with some performing considerably above and some considerably below the average. The explanations for these differentiated performance lies both in the respective marketing and sales strategies, as well as in the perception of the market. The implications of this residual value loss over the product life cycle leads to a paradoxical situation: assuming a constant list price, a product’s value retention is greatest at product launch and lowest just prior to product replacement. Hence, the differential residual value that enters the lease is greater the older the design. As a result, mathematically the lease rate is at the highest point when the model is oldest and thus the least attractive in the market. While it is obviously not desirable to increase the lease rate proportionately as the design ages, the leasing company needs to factor in this inverse relationship in their lease term calculations. This adds a
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further degree of uncertainty to their operations, in addition to the residual value risk induced by future events in the new car market, the differential profit over the lifespan of a model need to be calculated at the introduction in order to obtain lease rates that are still competitive in the market at the time the model is phased out.
Another key finding from our research thus that there are in fact two dimensions to the depreciation of a durable product. One relates to the age and usage of the actual product, the second relates to the age of the product design. The former has been well discussed in the literature: a product with increasing age loses utility as its remaining lifespan drops, and potential maintenance costs increase. The second dimension however has not been described in the literature so far. As the design ages, the residual value of the used product diminishes. The likely reason for this argue that consumer psychology of diminishing attractiveness of an ageing design leads consumers to await the next product generation, as discussed by Schwartz (1976).
4.5 The Product Commonality Effect
Product commonality increases operational efficiency in product design and manufacturing (MacDuffie et al. 1996), yet bears the danger of compromising a product’s individuality and thus market attractiveness. Purohit (1992) found that changes to vehicles of the same vehicle manufacturer (e.g. Ford and Mercury) resulted in changes at the other brands in the group, which is closely linked to the earlier discussion on platform strategies in the automotive industry. As part of our fourth proposition, we have investigated the impact of the common practice of platform sharing in our model, but could not identify any significant effect of platform sharing on residual values of used cars. One reason might be that the automobile as a product is largely of an integral product architecture (Ulrich 1995), limiting the degree of modularization and component carry-over from one generation to the next. Nonetheless, with increasing sharing of vehicle platforms, powertrains and suspension systems, product individuality is likely to be compromised in the future, and further research seems clearly warranted to determine the links between customer perceptions and vehicle performance in the new and used car markets.
5 Conclusion
We have presented estimates of the dynamics of residual value of durable goods and have shown how the new and used markets are intricately linked. Extending the work of Purohit (1992) and Benjamin and Kormendi (1974), we have analyzed the inter-linkage within the context of the automotive industry. We found that the residual value in the used car market is indeed strongly influenced by the events in the new car market, and extrapolation on past data alone is clearly insufficient. More specifically, we show the dynamic impact of various factors on the residual value at different points in time. Sales incentives have both an immediate, as well as a more serious long-term effect on the residuals in the used car market, while the age of the design over the product life cycle has a continuous detrimental effect on residuals.
The findings have a many implications: for any leasing operation, captive or non-captive, the ability to predict residual values is a crucial capability in assessing and managing residual value risk. Leasing companies have long recognized the critical need of being able to correctly predict the residual values of the products they lease, employing various models to determine residual values. As our review of common practices and interviews with risk analysts at a leading leasing company confirmed, the historic performance of vehicles in the marketplace is generally
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employed as the main predictor, alongside wider economic factors such as household disposable income, exchange rates, and the oil price. The sales practices of the vehicle manufacturers or the replacement cycles of the vehicles do not feature in their models. This is a critical gap in the academic debate as well as in professional practice, described to the authors an area ‘of gut feel and experience, rather than science’.
Furthermore, for organizations that operate in both markets, such as vehicle manufacturers with their captive leasing operations, we have identified that it is indeed possible to shift returns between their respective operations in the new and used good markets. Desai and Purohit (1998) argue that the optimal profits of a manufacturer and its captive leasing operation depend on the rate of depreciation of the products, and that leasing ‘creates strategic issues that are different from those associated with selling’ (p.19). Pierce (2004) further points out that the leasing companies can be ‘abused’ to achieve sales targets in the new car market by encouraging overestimates of residual values. We build on these arguments and quantify this dynamic link between the new and used car market. We show that manufacturers have in fact the strategic option to shift profits in the short-term at the expense of the long-term profitability of the leasing operations by employing sales incentives. However, this shift towards short-term profits between new and used markets also has quantifiable repercussions in the long-term. Not unlike the ‘bullwhip effect’ in supply chains (cf. Lee et al. 1997), the inherent delay of the impact of sales incentives in the new car market leads to an ‘incentive whiplash’ in the used car market, where they both adversely impact on the residual value, and in consequence, the long-term brand perception.
Overall we have shown that the new and used car markets for durable goods are intricately linked. For companies that operate in both markets, globally optimal strategies thus have to consider the impacts of their actions on both respective markets. The present poor performance of the automotive industry and the prevalence of sales incentives mark a case in point. Further research should focus on modeling the dynamics more precisely, and on the development of decision models to optimize overall return to organizations that are active in multiple yet linked marketplaces.
Acknowledgements We would like to thank Brian Farrell for his invaluable comments and insights throughout this study, and gratefully acknowledge the support of the ‘Innovative Supply Chain Systems for Sustainable Competitiveness’ project at the Centre for Competitiveness and Innovation, Judge Business School, University of Cambridge.
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