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INSIGHTS BLOG > Sharing Economy Systems

Sharing Economy Systems

Written on 03 October 2010

Ruth Fisher, PhD. by Ruth Fisher, PhD

The Buy vs. Rent Decision

Actual vs. Potential Value of Durable Products

Characterization of Potentially Collaborative Products


There has been an increasing trend toward collaborative consumption (aka the sharing economy), which, which Wikipedia describes as follows:

The term collaborative consumption is used to describe the cultural and economic force away from 'hyper-consumption' to re-invented economic models of sharing, swapping, bartering, trading or renting that have been enabled by advances in social media and peer-to-peer online platforms.

The trend includes peer-to-peer sharing/collaborative networks:

This system is based on used or pre-owned goods being passed on from someone who does not want them to someone who does want them. This is another alternative to the more common 'reduce, reuse, recycle, repair' methods of dealing with waste.

But what I’m interested in here is the trend as it applies to commercial or product service items:

Initiatives based on a 'usage mindset' where people pay for the benefit of having access to product as opposed paying more to own it outright..

Product service systems have existed for years (e.g. Libraries and laundromats) but they are gaining new relevance and appeal because technology is enabling them to provide choice and convenience.

Specific examples of current trends in collaborative product service include

The real watershed in the development of the [carsharing] sector came in the 1990s with such larger and more structured projects as StattAuto in Germany, the two precursors of Mobility CarSharing in Switzerland, Bilkollektivet in Norway and Greenwheels in the Netherlands. Follow up developments include CommunAuto, then Co-operative Auto Network and later AutoShare in Canada, Flexcar Portland, Oregon (now Zipcar), Zipcar in Boston and WhizzGo in England nationwide and CityCarClub in England and Scotland. GoGet Carshare was first to start in Australia in June 2003, followed by Smartdrivers in mid 2007. CarSharing was introduced to Ireland as GoCar and Halifax, Nova Scotia as CarShareHFX in late 2008. By mid 2010 the largest service in the world was Zipcar with 400,000 members, 4,400 locations and 9,000 vehicles, representing 80% of the U.S market share and half of all car-sharers worldwide.

The current popularity of bike sharing is attributed by many to Paris' successful launching in 2007 of Vélib’, a network of 20,000 specially designed bicycles distributed among 1450 stations throughout Paris. Vélib’, in turn, followed Lyon's Vélo'v success and is now considered the largest system of its kind in the world. Bike sharing has spread to many other European cities and is currently enjoying surging popularity in North America. Two of more prominent launches have been a small program started in Washington D.C., and a much larger program, called Bixi, launched in Montreal in the spring of 2009.

Cloud computing describes a new supplement, consumption, and delivery model for IT services based on the Internet, and it typically involves over-the-Internet provision of dynamically scalable and often virtualized resources … This frequently takes the form of web-based tools or applications that users can access and use through a web browser as if it were a program installed locally on their own computer.

Certainly the ability to rent, rather than buy a product service is nothing new.  People have been able to rent cars, furniture, and equipment for decades.  What is new is the increasing ability to rent products either (1) that were not previously available for rental, only for purchase; or (2) that were not generally rented by mainstream users, that is, the bulk of usage was performed by owners, rather than renters.


The Buy vs. Rent Decision

Let’s consider a simplified version of the buy versus rent decision as it relates to durable products, that is, products that last more than a year.

If you buy a durable product, you pay for the product up-front (cost C), receive benefits (b) each year for the life (T years) of the product, where the rate of time preference (discount factor) is β, and the annual maintenance costs or consumable costs associated with using the product each year are c.  So the net Present Value (PV) of buying a durable product is

PV if Buy = (-C + b1 - c1) + β x (b2 - c2) + β2 x (b3 - c3) + … +  βT x (bT - cT)

If you rent a durable product, you receive benefits (b') each year for the rental term (T’ years) of the product, where the rate of time preference (discount factor) is β, and the annual rental costs are r.  So the net Present Value (PV) of renting a durable product is

PV if Rent = (b'1 - r1) + β x (b'2 - r2) + β2 x (b'3 - r3) + … + βT’ x (b'T’ – rT’)

The total nominal costs of renting a product for the life of the product (T years) will generally be greater than the nominal costs of purchase.  That is, you pay for the ability to finance a product through annual rental payments, rather than having to layout the purchase price of product up front and just pay annual maintenance fees:

Generally (r1 + r2 + … + rT) > (C + c1 + c2 + … + cT )

So now given the costs and benefits associated with renting versus buying, generally speaking, you would be better off renting under the following circumstances:

  • Rent if you have a low intensity/value of usage (b'i < bi);
  • Rent if you have a short duration of usage (T' < T);
  • Rent if you have a high cost of money (C is large);
  • Rent if the future is uncertain (β is low).

Another significant factor affecting the buy vs. rent decision is the degree of flexibility surrounding the rental agreement, specifically with respect to being able to extend or curtail the duration or scope of a lease.  To the extent that the future is uncertain and renting provides more flexibility than buying, renting will be preferred to buying.


Actual vs. Potential Value of Durable Products

Consider now the potential value a durable product could provide users versus the actual value it ends up providing. Generally speaking, the more intensely a durable product is used, the sooner it will wear out; conversely, if you use it less it will last longer.  In either case, you can say the product will provide some number H hours of service during the life of the product before it wears out or dies.  If the hours of use are not consecutive, that is, the product sits unused for periods of time, then there is the possibility the product will become obsolete before it wears out.  To account for all this, I define H as the number of hours a product can provide services before it wears out or dies when the product is used continuously.  I simplify the analysis by excluding time costs (i.e., the βs from the section above).

Actual vs. Potential Net Value of Services

If I assume the average value per hour of use of the product is v, then the total potential value the durable product could provide is:

Potential Value of Durable Product = v x H

The costs associated with the provision and use of the durable good include the costs of provision, C, plus hourly maintenance and use costs, c.  The total costs associated with the potential use of a durable product are then:

Potential Costs of Services = C + c x H

So now I have the potential net value, where net value is total value less costs, of the durable product as:

Potential Net Value of Services = (v – c) x H – C

Since the potential hours of use, H, is an upper bound on usage, the actual number of hours a product is actually used during its life, N, must be less than or equal to its potential hours of use, H: N ≤ H.  The actual net value of the services a product provides is then:

Actual Net Value of Services = (v – c) x N – C, where N ≤ H

Note that N can be interpreted as the total number of hours a product is used over its life by its multiple owners for the case in which ownership is transferred from one user to another.  (Transfer payments among owners wash out in the aggregate.)

So now I have that the unrealized potential value of a durable product is

Unrealized Potential Value of Services

= [(v – c) x H – C] – [(v – c) x N – C]

= (v – c) x (H – N)

The unrealized potential value of a durable product will be larger when

  • The value of the services the product provides is greater than the variable costs (maintenance and usage costs) of providing services, and/or
  • The actual usage of the product is much less than that which the product could potentially provide.

Actual vs. Potential Value and Economies of Scale

For most durable products, economies of scale come into play.  That is, if the product is used for N hours, then

Total Cost of Usage = (C + c x N), and

Hourly Cost of Usage = (C + c x N) / N

= C / N + c

To the extent that the actual usage is less than the potential usage, N < H, the actual hourly cost of usage is greater than the potential hourly cost:

If N < H, then (C / N + c) > (C / H + c).

In this case, the hourly benefits received during the actual hours of usage need to be higher than they would need to be if the product was used more to justify the purchase of the product.

What this means is that if a durable product is used by only one user, then to justify purchase of the product, that user must either (1) have a high hourly benefit of usage, in which case he can use the product less and still justify the cost of purchase, or (2) have a lower hourly benefit of usage but use the product for more hours to justify the cost of purchase.  Users with low hourly benefits of usage or less total usage will not generate enough value from the product to justify purchase.  So with single-user durable products, users with low hourly benefits/value or less total usage may not end up using the products.

Conversely, if the product is shared by multiple users so that the actual usage is closer to its potential usage, then the hourly costs of usage will be lower.  In this case, each (partial) user can have a lower hourly benefit of usage and/or use the product less and still be able to cover the hourly costs of usage. So with multiple-user durable products, users with low hourly benefits/value or less total usage have a better chance of being able to use the products.

Actual vs. Potential Value and Network Effects

For some durable products, networks effects come into play.  Consider a bicycle sharing program within a city.  Bicycle pick-up/drop-off spots are located throughout the city where users can pick-up bicycles for rental and drop them off when they’re finished using them.

Providing a smaller number of bicycles and/or fewer pick-up/drop-off locations creates less value for potential users and thus attract fewer users.  With fewer users, the average (hourly) costs per bicycle are higher, so hourly rental prices must be higher.  Low value users are precluded from participating in the market, which reinforces the need for higher prices for those who do participate, which creates less net value.  A combination of

  • fewer bicycles and/or pick-up/drop-off locations = less value
  • fewer users
  • higher hourly costs per bicycle
  • higher hourly rental prices = less value

is thus self-reinforcing.

Conversely, providing a larger number of bicycles and/or more pick-up/drop-off locations creates more value for potential users and thus attract more users.  With more users, the average (hourly) costs per bicycle are lower, so hourly rental prices can be lower. Low value users are able to participate in the market, which helps reinforce the ability to provide lower prices and/or more pick-up/drop-off locations which creates more net value.  So a combination of

  • more bicycles and/or pick-up/drop-off locations = greater value
  • more users
  • lower hourly costs per bicycle
  • lower hourly rental prices = greater value

is also self-reinforcing.

By participating in a bicycle sharing network, each user not only generates value for himself (assuming the rental cost is less than the value of renting), but he also generates value for other users in the network by enabling them each to pay lower rental prices and/or to benefit from having a larger number of pick-up/drop-off locations.

More generally, for all but the most avid bicycle users, bicycles tend to become obsolete before they wear out.  Any time period during which a bicycle is not being used is effectively wasted potential value and rental income that the bike could be providing but isn’t.  To the extent that less rental income means higher prices and/or fewer pick-up/drop-off locations for other users, then the wasted potential value associated with lack of use by one user is exacerbated across other users.

What this means is that shifting from a one bike, one user system to a bicycle sharing program in which multiple low-intensity users share each bicycle creates much more value for everyone.  In particular, lower intensity users for whom it would not be cost effective to purchase their own bicycle, are able to benefit themselves in a bike sharing program by being able to use a bicycle when they need one.  At the same time, these lower intensity users provide value for other lower intensity users by paying rental fees that enable other users to benefit from lower prices and/or greater numbers of pick-up/drop-off locations.


Characterization of Potentially Collaborative Products

How would you characterize goods that would be feasible participants in a collaborative program versus those that would not?  That is, which durable products might switch from a one-person, one-product system to a collaborative system in the (near) future?

1. Durable products are more likely to be shared than non-durable products.  The more services a product can provide, either at any point in time or over a period of time, the greater the potentially wasted value there will be in a single-owner (non-shared) system.  As such,  those products with the greatest potential value should be first to transition to collaborative markets, because they provide more room for collaborative consumption.  At the same time, over time, as collaborative systems become more proven, products with less potential will also be able to benefit from collaborative systems.

2. Commodities are more likely to be shared than are products that are more tailored or customized to particular individuals’ needs.  By definition, more commoditized products can provide benefits to a larger number of different users than can more tailored products.  There is thus a greater potential for collaboration for more commoditized products than there is for more customized products.  Again, though, in the future, increasingly customized products may become feasible participants in collaborative programs.

3. Easily transportable/accessible products are more conducive to collaborative systems than are less transportable/accessible products.  This means products that are mobile or virtual (accessible through the Internet) are more likely candidates for collaborative consumption.   At the same time, over time, if transportation and communications costs continue to decrease, products that were previously less mobile or accessible might eventually become candidates for collaborative systems.

4. Products who use by one user does not degrade the value or use by other users are more likely candidates for collaborative consumption.  Products that are subject to congestion effects (slowdowns) or degradation (wear, contamination, etc.) by other users are less likely candidates for collaborative systems.

5. Products that are less intensely used, either during a given period or across periods, are more likely candidates for collaborative consumption.  The lower a product’s capacity utilization is either at a point in time or over time, the greater will be its wasted potential value, and so the greater will be the potential benefits from collaborative consumption.