solids: a combinatorial auction for real...

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Solids: A Combinatorial Auction for Real EstateDries R. Goossens, Sander Onderstal, Jan Pijnacker, Frits C. R. Spieksma

To cite this article:Dries R. Goossens, Sander Onderstal, Jan Pijnacker, Frits C. R. Spieksma (2014) Solids: A Combinatorial Auction for RealEstate. Interfaces 44(4):351-363. http://dx.doi.org/10.1287/inte.2014.0749

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Vol. 44, No. 4, July–August 2014, pp. 351–363ISSN 0092-2102 (print) � ISSN 1526-551X (online) http://dx.doi.org/10.1287/inte.2014.0749

© 2014 INFORMS

Solids: A Combinatorial Auction for Real EstateDries R. Goossens

Faculty of Economics and Business Administration, Ghent University, 9000 Ghent, Belgium; andCenter for Operations Research and Business Statistics (ORSTAT), Faculty of Business and Economics,

Katholieke Universiteit Leuven, 3000 Leuven, Belgium, [email protected]

Sander OnderstalUniversity of Amsterdam, 1018 WB Amsterdam, The Netherlands, [email protected]

Jan PijnackerStadgenoot, 1018 GW Amsterdam, The Netherlands, [email protected]

Frits C. R. SpieksmaKatholieke Universiteit Leuven, 3000 Leuven, Belgium, [email protected]

In May 2011, our collaboration with housing corporation Stadgenoot culminated in the first combinatorial auctionfor housing space in a newly erected multistory building in Amsterdam in the Netherlands. Our primary goal wasto allocate space based on the preferences of many potential users. The resulting allocation included spacefor restaurants, boutiques, a dentist, and residential users. The auction we designed maximized total rent forStadgenoot while complying with municipal and building regulations. We based our design on laboratoryexperiments that gave us guidance on choices regarding, for example, pricing, feedback, and activity rules. Thispaper describes the development and the results of this auction.

Keywords : combinatorial auction; design; bidding; allocation constraints; real estate.History : This paper was refereed. Published online in Articles in Advance May 29, 2014.

The objective of the Stadgenoot, a Dutch housing cor-poration, was to construct a building in Amsterdam

that would use a concept called “solids” and would lastfor at least 200 years. A solid is a sustainable buildingwithout a predefined purpose; it should accommo-date any (legal) functionality, and its tenants woulddecide how to allocate the space within the build-ing. Amsterdam’s squatting tradition—particularlythe freedom and flexibility with which squatters usethe space they occupy—inspired the solids concept.The concept behind a solid is that the tenants can deter-mine the use, size, configuration, and even the rentof their spaces. More specifically, Stadgenoot dividesthe floor space into lots, and tenants can combineone or more adjacent lots to create what we call asolid space. By choosing an appropriate set of lots (e.g.,on a specific floor or with a preferred orientation), atenant specifies the resulting solid space. Stadgenootis, like other housing associations in the Netherlands,a nonprofit private organization with strong links tolocal authorities. The first housing corporations werefounded in the 19th century to improve the deplorable

housing conditions for working-class families at thattime. Given this background, Stadgenoot’s positionis that solids should be open to everyone, includingpeople with limited budgets.

The challenge we address in this paper is to developa combinatorial auction to allow Stadgenoot to allocateits solids, taking into account various constraints andaccommodating the preferences of a highly diversegroup of bidders. A combinatorial auction is an auc-tion in which multiple items are sold simultaneously,and bidders can express their preferences for sets(or subsets) of the items; Cramton et al. (2005) pro-vide a detailed discussion of combinatorial auctions,integrating the contributions of many authors on inter-esting theoretical and practical aspects of these auctions.A combinatorial auction is particularly suitable forsolids, because bidders are typically interested in mul-tiple lots and may value some sets of lots higher thanthey value the sum of the values of the individuallots. Moreover, a lot may have no value as a singleitem; for example, it might not be accessible directlyfrom the hallway. These complementarity effects are

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Goossens et al.: Solids: A Combinatorial Auction for Real Estate352 Interfaces 44(4), pp. 351–363, © 2014 INFORMS

bidder specific, because bidders have different needsand preferences with respect to the space they wantto rent. Some bidders may want spacious apartmentswith sun in the evening, other bidders want smallbut practical working spaces near their shops on theground level, and still others prefer inexpensive spacesfor installing offices. A combinatorial auction allowsbidders to express their preferences (including budgetconsiderations) to a greater extent than they can forindividual items.

In this paper, we describe the first real estate combi-natorial auction of which we are aware. Traditionally,real estate is sold as follows: an owner determines aproperty’s characteristics, sets a price for the property,announces the property’s availability, and waits forpotential buyers to express interest; a private negotia-tion determines the actual selling price. In past decades,auctions have gradually become more popular in realestate markets worldwide. Initially, real estate auctionswere most commonly used to dispose of real estatein bankruptcy cases; however, recent research showsthat these auctions have earned their place alongsideprivate negotiation as a market mechanism (Eklöf andLunander 2003). Real estate auctions, however, typi-cally involve a sequential and individual auctioningof properties (Quan 1994). Occasionally, in a secondauction, bidders can make a single bid on all the prop-erties that were offered in the previous auctions; theseller then selects the auction with the highest payoff.Bayers (2000) describes a case in which 14 units in aSan Francisco condominium complex were auctionedsimultaneously, but bidders were not allowed to submitpackage bids on sets of units.

Every auction requires a set of rules that determinesthe course of the auction and the actions bidders cantake. This paper covers many auction design issues thatmust be decided; examples include how to motivatebidders to reveal their preferences early in the auction,what prices to charge to the winning bidders, and whatfeedback to provide to the bidders after each round.We settled most of these issues during meetings withStadgenoot and fine-tuned design choices in laboratoryexperiments in which we tested design options. We alsofocus on the problem of determining an allocation thatmaximizes total revenue, taking into account each bidand the constraints resulting from building regulationsand Stadgenoot’s social policy.

Stadgenoot finished the construction of the first solid,called Furore, in April 2011; the company completedtwo other solids one month later and has plans tobuild additional buildings. In this text, we focus onFurore, which offers 7,000 square meters of floor space,features a spacious roof terrace with a splendid view ofthe city, and won the RIBA European Award for greatarchitecture (Royal Institute of British Architects 2012).The auction of Furore was held in May 2011; it resultedin an allocation that is in line with the functionalfreedom that characterizes the solids concept and towhich Stadgenoot aspires. Moreover, the companywas satisfied with the revenue it received, and thesolid project received positive media attention; to asubstantial extent, this was a result of the novel way ofallocating real estate to tenants.

More About SolidsStadgenoot delivers solid space as a shell. This meansthat within the building, Stadgenoot places wallsbetween the solid spaces, and each solid space hasaccess to a shaft with provisions for ventilation,drainage, and electricity. However, within a solidspace, the tenant decides where to place partitionwalls and interior doors. Thus, the rented space canto be designed to suit a range of purposes, such asresidential, business, culture, or any combination ofthese. Stadgenoot remains the owner of the shell; thetenant rents solid space and owns the interior. A tenantwho leaves can sell the interior to the next tenant.Over time, solid spaces can increase or decrease insize (by being merged with another solid space or bybeing split) to accommodate tenants in different ways,depending on a particular tenant’s needs at the time.

A solid is divided into lots that the tenants can useas building blocks to specify solid spaces. Drawing agood allotment is a nontrivial task. The solid shouldcomprise enough lots, with varying characteristics andsizes, such that a bidder can satisfactorily combine anumber of spaces. It is also important to have enoughsubstitutability across the lots, such that bidders canadapt the composition of their spaces depending onthe course of the auction. Because building regulationsand technical issues heavily constrain the allotment, wewere not primarily involved in the allotment decision;therefore, we had to consider it as given. The Furore

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67.3 m267.3 m2

35.8 m235.8 m2

54.8 m2 54.8 m2

28.8 m228.8 m2

58.4 m258.4 m2

39.9 m2 39.9 m2

45.4 m2 42.6 m2

29.3 m2 29.3 m2

38.2 m2 38.2 m2

102.3 m2 102.3 m2

34.9 m2 34.9 m2

LiftLift LiftLift

Figure 1: The allotment of the second floor of Furore illustrates how a bidder has flexibility to combine lots into asolid space that consists of the bidder’s desired size and orientation.

solid was divided into 125 lots, distributed over sevenfloors. Figure 1 shows the allotment of the second floor,which has 22 lots, each with a given surface area.

A bid consists of a specific solid space and a pricethe bidder is willing to pay for it as a monthly rent(paying monthly rent is typical in the Netherlands).Although the bidders taking part in this auction maybe diverse, we assume that no externalities are presentin bidder’s valuations (i.e., the valuation that a bidderplaces on a space depends only on the lots it contains).Thus, bidders do not care which bidders will be theirneighbors or what their neighbors intend to do with

their spaces. Stadgenoot feels that this is in line withthe philosophy of the solids project. Indeed, if keyconcepts such as flexibility and freedom appeal tobidders, they should accept that these concepts alsoapply to their neighbors. Nevertheless, activities thatcould seriously disturb other tenants (e.g., nightclubs,heavy industry) are not allowed.

Solids suit a variety of tenants; examples include fam-ilies planning to live in the building; real estate brokerslooking for bargains; commercial bidders intending toopen businesses (e.g., hotels, offices, shops, restaurants);and students, artists, or low-income people who want

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social housing. Stadgenoot categorizes bidders as resi-dential, commercial, or social. The first group consistsof people who plan to live in the solid, the secondgroup includes people who plan to open businesses,and the third group comprises low-income people.In a combinatorial auction in its most general form,bidders can bid whatever amount they please on anysubset of the items. In the solid auction, this is not thecase. First, for social bidders, Dutch law imposes anupper bound of 650 euros on the monthly rent. Second,and most important, bidders cannot bid on any setof lots. A valid lot subset consists of either adjacentlots on a single floor or all lots on a floor. A bid ona set of lots on different floors is not valid. Figure 1shows that floors above ground level consist of twowings, separated by an open space in the middle, suchthat lots in different wings are not adjacent. On theground level, however, additional lots connect thewings; consequently, valid solid spaces spanning bothwings are possible. Furthermore, a valid subset of lotsmust have at least one door to the central hallway(denoted by black triangles in Figure 1) and access to autility shaft. The surface area of a solid space can havebounds (i.e., size limitations), depending on the typeof bidder. Because of these constraints, bidders canform only 1,214 different valid spaces from the 125 lots.In the remainder of this paper, the term “solid space”refers to sets of lots that satisfy these requirements.

Apart from restrictions on the sets of lots on whichbidders can bid, there are also constraints on theallocation itself. A bidder is allowed to provide multiplebids but generally can win at most one bid. Mostbidders can afford only one solid space, and Stadgenootdoes not want them to win bids on more lots thanthey want. Bidders who want multiple solid spacesare allowed to register in the auction using multipleidentities and can win at most one solid space peridentity. Finally, bidders can also indicate that theywant to see either all or none of their bids allocated.For example, a potential hotel owner might want thetop three floors or nothing at all.

Some allocation restrictions are based on Stadgenoot’shistorical background and philosophy. The allocationshould reserve at least 15 percent of the surface to socialbidders, residential bidders should be allocated at least25 percent of the surface, and commercial biddersshould be allocated at least 30 percent. A maximum

of three restaurants are allowed in the solid. Otherconstraints originate from municipal and buildingregulations. For example, the stairs have an emergencyrescue capacity that cannot be exceeded. The rescuecapacity needed for each bid depends on the surfacearea of the solid space and bidder’s intended functionfor the space. Each bid has requirements with respect toventilation, water, gas, and electricity (again dependingon the surface area and the intended function), andwe can only allocate bids insofar as the capacity ofthe utility shaft the space uses is not exceeded. Eachsolid space must receive all its utilities through a singleshaft; however, if a solid space contains multiple shafts,the bid winner may choose which one to use. A bidderwho has no shaft preference can let the auctioneerdecide, thus adding to the bid’s flexibility. Each shaftgoes from the ground level to the top floor; hence, itscapacity may have to be split over solid spaces ondifferent floors; the allocation should be such that thisis feasible.

One of Stadgenoot’s concerns was that a lack ofbidders (e.g., on unpopular lots) could result in someunoccupied spaces in the solid after the auction. In sucha case, the unoccupied lots should also form one ormore solid spaces, such that Stadgenoot could rentthem in the future. If the number of social bidders isinsufficient, the number of unoccupied and suitablesolid spaces should be such that when the lots are laterrented to social bidders, the required 15 percent of thesolid’s total surface can still be achieved. Furthermore,to ensure that tenants can later occupy vacant solidspaces, we reserve sufficient emergency rescue capacityand at least one shaft with enough spare capacity toprovide basic utilities.

Stadgenoot’s main objective is not to make a profitbut to obtain a balanced, functional mix of residential,commercial, and social tenants. Therefore, in our auc-tion, considering the various allocation constraints isat least as important as maximizing total rent; however,we have other considerations. The auction should beaccessible to everyone. Participation is a key concernin the design of any auction; the auction must bedesigned to attract enough bidders. Learning the auc-tion rules can pose a challenge for a nonprofessionalbidder. Hence, the auction rules should be as sim-ple as possible, the bidding process should be userfriendly, and the auction must end within a single day.

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Although buying real estate is an important decisionfor each bidder, bidders generally prefer a shorterauction, which reduces participation costs. Stadgenootbelieves that once a bidder has decided to participate,that bidder will not want a protracted process and willwant to know the results as soon as possible.

Auction DesignAlthough combinatorial auctions have been designedand implemented successfully for school meals (Epsteinet al. 2002), Mars (Hohner et al. 2003), and transporta-tion services (Ledyard et al. 2002), and to supportstrategic sourcing (Sandholm et al. 2006), these designsare often problem specific and not readily practicablein our setting. More general auction mechanisms, suchas the clock-proxy auction (Ausubel and Milgrom2005) or the PAUSE procedure (Kelly and Steinberg2000), do not answer all our above-mentioned concerns.Quan (1994) reports on the winner’s choice auctionfor auctioning multiple real estate properties. Thisauction is a classic open-outcry auction, in which thewinner has the right to pick any number of proper-ties to buy at the bid’s unit price. If any propertiesremain, a second auction with the same rules follows.Although appealing for its simplicity, this auction mech-anism neglects the numerous allocation constraints wedescribed previously.

Combinatorial auction design forces trade-offsbetween desirable properties (e.g., allocative efficiency,revenue maximization, transparency, computationaltractability) (Pekec and Rothkopf 2003). In the DesignIssues section, we report on the design issues we had toaddress: pricing rules, duration, anchor rules, activityrules, fairness, feedback, collusion, and user friendli-ness. Most of the decisions were made during severalmeetings with Stadgenoot. We fine-tuned some deci-sions based on a series of laboratory experiments inwhich we used human bidders (see the Laboratory Exper-iments section). We discuss computational tractabilityin the Winner Determination Problem section.

Design IssuesOur discussions with Stadgenoot led to a decision touse a sealed-bid first-price auction with binding bids.In a sealed-bid auction, the bidders communicate theirbids directly and solely to the auctioneer (as opposed toopen-outcry auctions, in which all bids are announced

publicly). We opted for a sealed-bid auction becauseit offers privacy and encourages participation. Forexample, a bidder with the highest valuation, but whois uncertain about the best competitive bid, may bid toogreedily and lose. Thus, bidders with a lower valuationwould still have a chance to win; consequently, theyhave an incentive to participate (Pekec and Rothkopf2003). In a first-price system, bidders pay exactly theamount they bid for the solid space they win. This wasnot an obvious choice; one reason is that in second-price auctions (i.e., the highest bidder pays the priceof the second-highest bid), a dominant strategy is foreach bidder to bid the true valuation (Vickrey 1961).Nevertheless, the transparency of the first-price concept,which considers the inexperience of the bidders in thesolid auction, and the computational complexity of theVickrey-Clarke-Groves auction (i.e., the combinatorialvariant of the second-price auction) led us to adopta first-price rule. Binding bids mean that each bidremains valid until the end of the auction; withdrawingor lowering bids is not allowed. Again, we adopted thisrule because of its simplicity. Furthermore, Stadgenootmaintains a reserve price of six euros per square meter.It would prefer to leave the space empty than rent itfor less than the reserve price.

Ideally, an auction ends when all winning biddersare satisfied with what they have won, and all losingbidders realize that they are unwilling or unable topay the amount needed to obtain what they want.In a first-price auction, it suffices that no bidders bidmore than they are willing to pay to realize the firstpart; the second part, however, cannot be guaranteed ifthe number of rounds is fixed before the start of theauction. Nevertheless, for practical reasons, announcingthe number of rounds beforehand was exactly whatStadgenoot wanted. The number of rounds needed wasless clear. We assumed one hour per round (includ-ing time for computing the allocation, processing theresults, reflecting on the next action by the bidders, andcollecting new bids). Given that Stadgenoot wantedthe auction to complete within a single day, it decidedon five rounds for half-day auctions and eight roundsfor full-day auctions. The simplicity of settling theauction in a single round also appealed to Stadgenoot.The number of rounds was eventually determinedbased on the results of laboratory experiments we con-ducted, as we describe next. Having a fixed duration,

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however, requires strong anchor and activity rules toprevent serious bids from being delayed until the finalround.

Anchor rules and activity rules fix a number of bidderdecisions at some point in the auction; hence, theynarrow bidder options from that point on to providethem with an incentive to reveal their preferences earlyin the auction (see Harsha et al. 2010). Our anchorrule restricts the solid spaces on which the biddercan submit a bid as the end of the auction nears.We consider three possibilities, in order of increasingrestrictiveness:

• B1: In any round, a bidder can submit a bid onany solid space.

• B2: In the first half of the auction, a bidder cansubmit a bid on any solid space. In the second half ofthe auction, a bidder can no longer bid on new solidspaces; therefore, that bidder can only raise previousbids on solid spaces.

• B3: In the first round, a bidder is free to bid onany solid space. In the following rounds, the biddercan only raise these bids and cannot introduce bids onnew solid spaces.

Our activity rule restricts the bids that bidders areallowed to raise, depending on their behavior in previ-ous rounds. In the first auction round, bidders are freeto bid on any space. In later rounds, the eligibility toraise bids depends on the activity rule, which does notapply to new bids (i.e., bids on solid spaces on whichthe bidder did not bid in previous rounds). In ourtesting, we investigate two types of activity rules:

• A1: In all rounds, bidders are free to bid on anyspace that the anchor rule allows. They can freelychoose the amount with which they increase their bids;not increasing a bid is also a valid option.

• A2: In a specific round, bidders are active ona space if in the last round in which they are notprovisional winners they raise their previous bids onthis space with at least a minimum bid increment of0.5 euros per square meter. Bidders are only allowed toraise bids on spaces on which they are active. Note thatthe activity of a bidder always refers to the last roundin which that bidder was not a provisional winner.That is, provisional winners in a specific round do nothave to bid to keep their eligibility to bid in the nextround. Also note that when a bidder raises a bid withless than the minimum bid increment, that bid is valid

and taken into account; however, it cannot be raisedagain in the next rounds.

Dealing with a target group that should be favored(e.g., social bidders) is an interesting issue. One idea isto give those bidders virtual money (i.e., a subsidy)to allow them to compete with the other bidders.Another idea is to simply reserve a percentage of thelots exclusively for bidders in the target group (i.e., aset-aside). Pai and Vohra (2012) study the pros and consof these two possibilities. We opted for a constraint inthe winner determination problem, which states thatthe outcome should allocate at least a given percentageof the total floor space to bidders from the target group(see the Winner Determination Problem section). Thisapproach provides a stricter guarantee than a subsidymay give and does not force Stadgenoot to decidebeforehand which parts of the solid to allocate to thetarget group.

To enable the bidders to make promising bids inthe next round, we must report feedback after eachround. Although computationally challenging, it ispossible to compute, for each solid space in whichthat bidder is interested, the price that will make ita winning bid, provided that all other bids remainunchanged. Stadgenoot did not want to do this becauseit feared that (inexperienced) bidders would perceivethis information as a kind of guaranteed winningstrategy. Therefore, in our laboratory experiments, weconsidered the following forms of limited feedback:

• F1: After each auction round, bidders learn onlywhich of their bids are currently in the winning alloca-tion (if any).

• F2: After each auction round, bidders learn whichspaces are allocated, and for what prices. Thus, eachbidder sees the winning allocations; however, informa-tion about individual bids is anonymous.

Because we can partition the solid into solid spacesin many ways, a bidder for a solid space might benefitfrom bids on complementary solid spaces. This isinherent to any combinatorial auction but may encour-age bidders to collude (e.g., “if you don’t bid on mysolid space, I won’t bid on yours”), which can impactthe total revenue generated. Although this was notStadgenoot’s greatest concern, we provide the followingmeasures to limit collusion: (1) reserve prices, whichreduces the maximum gain of collusive bidding; (2) usesealed bids, which makes violations of the collusive

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agreement undetectable until it is too late for a bidderto react, at least in the final round; and (3) use bindingbids with guaranteed bidder solvency.

Finally, we must remember that the vast majorityof the bidders are not familiar with auctions. There-fore, user friendliness is more important than it is inan auction in which professional bidders take part.An information technology company developed anintuitive user interface to allow the bidders to expresstheir preferences. This system shows them the allotmentof the solid, including the positioning of the shafts,doors, and windows, and it allows them to select lotsto form a solid space. If the selected lots do not form avalid solid space, the system immediately providesfeedback. A user can also provide the system with a setof desired characteristics (e.g., surface area, orientation),and the system will generate a list of solid spaces thatsatisfy these characteristics. This can happen weeksbefore the start of the auction; interested bidders alsohave the opportunity to visit the solid in advance tosee the space(s) on which they intend to bid.

Laboratory ExperimentsWe ran laboratory experiments at the Center forResearch in Experimental Economics and political Deci-sion making (CREED) laboratory at the University ofAmsterdam to gain insights into the effects of variousauction design factors: the number of rounds, activ-ity rules, anchor rules, and between-round feedback.We wanted to determine how particular choices forthese settings would influence the behavior of real,inexperienced bidders, and how much revenue theresulting auctions would generate. At the start ofour testing, all subjects received starting capital ofseven euros, which they could increase with additionalpayoffs (eight euros on average) depending on theirperformance in the auctions.

In all auctions in our experiment, 20 lots were offeredfor sale, corresponding to two identical floors of abuilding. The lots were sold in various combinatorialauction designs, each with a fixed number of rounds.After each round, we determined a winning allocationaccording to the formulation in the appendix; theonly allocation constraint we considered was that abidder can win at most one space. Note that the otherconstraints are not very meaningful in a setting with alimited number of lots and bidders; hence, they would

only make the various auction design factors, whichwe will specify below, more confusing to the subjects.

In each auction, 8–10 subjects competed—a numberthat corresponded to the minimum level of competition(i.e., bidders per lot) we were expecting for the realauction. We did not rematch between auctions so thateach subject competed with the same group of subjectsin each of three auctions. We assigned each subject oneof four roles—big business, small business, residentialcouple, or residential single; each had a specific targetsurface. Note that we ensured that demand alwaysexceeded supply. A subject made a bid by expressinga price for a set of adjacent lots; this price had to beat least six euros per square meter. In each auction,each bidder could have bids on at most eight spaces atany time and could never revoke or lower any bid.Furthermore, bids had to satisfy the activity rule andthe anchor rule.

In addition to the target, each subject received avalue equal to price per square meter×target surface.The values were private information for each subject(i.e., before the auction, all subjects learned their ownvalue; however, the only information each had aboutthe value of the other subjects was that all prices persquare meter were drawn independently from theuniform distribution on the interval 661127). If the spacein the bid consisted of a number of (highly) interestinglots, its value increased with a bonus of (40) 20 eurosper lot; the bonus per lot was the same for each subject.This ensured that the subjects’ preferences were notuniform for the building, and simulated the belief thatsome lots are more popular than others, which is likelyto hold true in practice. When a subject won the targetsurface (or more) in the final round, the payoff equalled305 × 4value − bid5/4target surface5. At the end of thesession, we paid the subjects their starting capital andthe sum of the payoffs collected in the auctions inwhich they participated. After each auction, we drewnew values and assigned new roles to the subjects. Toensure that the auctions were comparable, we kept thedraws of the values constant across treatments.

We ran nine sessions in which we varied (1) thenumber of auction rounds, (2) the anchor rule, (3) theactivity rule, and (4) the feedback mechanism, accord-ing to the options described in the Design Issues section.In each session, the subjects interacted in three suc-cessive auctions. Sessions had auctions with one, five,

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No. of No. ofSession rounds Activity rule Anchor rule Feedback subjects

1 1–5–8 A1–A2 B1 F1 19 410+ 952 1–5–8 A1–A2 B2 F2 18 49+ 953 1–5–8 A2–A1 B3 F1 18 49+ 954 5–8–1 A2–A2 B1 F2 16 48+ 855 5–8–1 A2–A2 B2 F1 18 49+ 956 1–8–1 A2 B3 F2 18 49+ 957 8–1–5 A2–A1 B1 F1 16 48+ 858 8–1–5 A2–A1 B2 F2 16 48+ 859 8–1–5 A1–A2 B3 F1–F2 17 49+ 85

Table 1: This table provides a summary of the experimental design.

and eight rounds. We varied the order of the numberof rounds from one session to the next. In all rounds,subjects had a fixed amount of time to submit theirbids. The first round of each auction took 10 minutes;each successive round took three minutes. Note that insettings with only one auction round, the two activityrules (A1 and A2) coincide, as do the three anchor rules(B1, B2, and B3). Similarly, feedback is irrelevant in thecase of single-round auctions. A full 2 × 2 × 3 × 2 + 1factorial design was not feasible from a practical pointof view. Therefore, we used a design in which wevaried the four treatment factors between subjects andwithin subjects; Table 1 summarizes our experimentaldesign. The second column presents the number ofrounds in the three auctions of each session. The second,third, and fourth columns include the relevant activityrules, anchor rules, and feedback rules, respectively.If multiple rules are included, the first (second) refersto the first (second) multiple-round auction. The finalcolumn shows the number of subjects that participatedin the session; the distribution of subjects in the twoauctions is in parentheses.

We evaluated the auctions’ performance on the basisof revenue as the fraction of the maximum total valuesubjects could obtain (i.e., the value in an optimalfeasible allocation of lots over subjects). To disentanglethe effect of the four auction design factors on per-formance, we ran a random-effects panel regressionin which dummies for the factors and the number ofsubjects acted as explanatory variables. Table 2 presentsthe regressions results of a random-effects model onauction revenue as a fraction of maximum surplus.The regression analysis takes bidding group-specificeffects into account, where a bidding group is the set

Variable Estimate

One round −001781 (0.0557)∗∗

Eight rounds −000598 (0.0447)Activity rule A1 −000657 (0.0460)Anchor rule B1 −001182 (0.0595)∗

Anchor rule B3 −000785 (0.0530)Feedback rule F1 000132 (0.0487)Number of bidders 000137 (0.0273)Constant 008401 (0.2382)R2 within 0.27R2 between 0.17R2 overall 0.23

Table 2: This table displays the results of a random-effects model on theauction revenue as a fraction of maximum surplus.

of 8, 9, or 10 subjects that compete against each otherin three consecutive auctions. The first column indi-cates the independent variables and the goodness-of-fitmeasures. The first five variables are dummies. Fiverounds, activity rule A2, anchor rule B2, and feedbackrule F2 are used as benchmarks. The second columncontains estimates with (robust) standard errors inparentheses, and double asterisks (a single asterisk)denote significance at the one percent (five percent)level.

The regression results suggest that if we restrict ourattention to the design features tested in the laboratory,the auction should have five rounds, activity rule A2,anchor rule B2, feedback rule F1, and as many biddersas possible. In the case of eight bidders, the modelestimates that this auction’s revenue equals about96 percent of the maximum bidder surplus. Note thataccording to this regression, the number of roundsand the anchor rule have a significant impact on theauction’s performance. If the auction takes only oneround instead of five, the regression results indicatea revenue loss of about 18 percent. If we use anchorrule B1 instead of B3, the predicted loss in revenueis about 12 percent. Both effects are statistically andeconomically significant. The results with respect tofeedback seem to contradict findings by Adomaviciuset al. (2012), who claim that (for a continuous auction)without price feedback, bidders are unable to formulateeffective bids, resulting in dead bids (i.e., bids thathave no chance of winning) of 30–40 percent. However,the effects of the feedback rule (and of the activityrule) in our testing are not very pronounced, eitherstatistically or economically.

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We should be cautious in interpreting our results.The number of (independent) observations is not par-ticularly high. Moreover, real bidders might behavedifferently than the subjects in the laboratory; becausethey were mainly students from the University ofAmsterdam, they might not represent the most typicalbidders in an actual solid auction. Still, we believe thatwe can safely advise against running a single-roundauction and against using anchor rule B1. Note thatanchor rule B1 allows a bidder to stay unnoticed untilthe auctions’ final round; therefore, this rule essentiallyresults in an auction in which all the action takes placein its final round. It appears that bidders tend to holdback if they have to make their final bids without(much) information about other bidders’ preferences.Eventually, Stadgenoot opted for the auction designthat is optimal according to the aforementioned regres-sion results; however, it used feedback rule F2 insteadof feedback rule F1.

Winner Determination ProblemClearly, the auction design must be computationallyfeasible. In particular, the problem of deciding whichbidders should win which items to maximize theauctioneer’s revenue (i.e., the winner determinationproblem) must be tractable. Although fast exact algo-rithms exist for this problem (e.g., CABOB; Sandholmet al. 2005), they are unable to cope with various allo-cation constraints that we need in our setting. Next,we describe a number of theoretical results and reporton our computational experiments.

Theoretical ResultsIn general, the winner determination problem is NP-hard (van Hoesel and Müller 2001) and does notallow good approximation results (Sandholm 2002);however, our winner determination problem is basedon a restricted topology. In the solids auction, the lotsin each wing and on each floor are arranged in tworows (see Figure 1). Notice that the lots need not bethe same size or be aligned over both rows. Moreover,bidders can only bid on sets of adjacent lots. Thus, theresulting problem is as follows: given lots arranged intwo rows, find an allocation that maximizes the totalrent, provided that each bidder can win at most onebid and each lot can be allocated at most once. We referto this problem as the solid winner determination problem,

in which each bidder can bid up to b bids (SWDP-b).SWDP-b relates to a problem known in the literature asthe job interval-selection problem (JISP1): given n pairsof intervals on the real line, select as many intervals aspossible, such that no two selected intervals intersectand at most one interval is selected from each pair.Because JISP1 is MAX SNP-hard (Spieksma 1999), itimmediately follows that SWDP-b for b ¾ 2 is NP-hard,even when all lots are arranged on a single row.

An interesting case arises when b = 1, the SWDP witha single bid per bidder (SWDP-1). Notice that this isequivalent to a setting in which bidders have multiplebids but are allowed to win more than one bid. It iswell known that if all lots are arranged in a single row,this problem is polynomially solvable. Rothkopf et al.(1998) found that the winner determination problemis solvable in polynomial time if a linear order existsamong the items and bidders can only bid on subsetsof consecutive items, even when we consider the firstitem in ordering the successor of the last (i.e., a circularorder). Orlin (2011) showed that SWDP-1 remains easyif the lots are arranged in two rows.

Computational ExperimentsWe developed a mathematical formulation for thewinner determination problem in the solid auction(see the appendix), and we implemented and solvedit using IBM ILOG CPLEX, version 12.3. To evaluateour algorithm’s performance, we performed a numberof computational experiments on randomly gener-ated instances based on Furore. Stadgenoot performedseveral studies to gain information about interestedtenants; for example, it wanted to know the solidspaces in which the potential tenants were interestedand their special needs (e.g., ventilation, electricity, gas).Stadgenoot was unsure about the number of partici-pants; however, it considered more than 1,000 biddersto be unlikely. To approximate the amounts the bidderswould be willing to bid, Stadgenoot looked at pricesfor similar nearby apartment spaces. We used thisinformation to generate realistic instances with 50,150, 500, 1,000, and 2,000 bidders, where each bidderexpressed 5, 10, 20, and 40 bids. The main goal of theseexperiments was to evaluate whether the computationtimes were reasonable. Therefore, we considered only asingle round. We solved all instances on a Windows XP-based system with two Intel Core 2.8 GHz processors.

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Bids per bidder

Bidders 5 10 20 40

100 005 (0.9) 007 (1.1) 102 (2.0) 102 (2.3)500 105 (2.4) 205 (4.8) 507 (11.8) 2002 (55.2)

1,000 202 (3.8) 502 (11.5) 3909 (89.1) 3705 (118.8)2,500 1300 (26.0) 2302 (55.9) 15706 (362.2) 49104 (836.1)

Table 3: The average and maximum (in parentheses) computation times(in seconds) illustrate that realistic instances are solvable in a reasonableamount of time.

Our experiments are available online (Goossens 2012).Table 3 summarizes the results; each line gives valuesthat we averaged over 10 similar instances.

Table 3 shows average computation times (in sec-onds), followed by maximum computation times(in parentheses) for each setting. Clearly, CPLEX effi-ciently solves these instances, although considerablecomputation time differences exist among instanceswith the same number of bidders. These computationalexperiments show that we may expect our modelto compute an optimal allocation within a range of15 minutes (900 seconds), making a discrete, iterativeauction tractable.

Outcome and EvaluationOn May 7, 2011, 114 bidders took part in the com-binatorial auction for Furore, as we describe in theprevious sections. The bidding started after the mayorof Amsterdam sounded the gong; it ended eight hoursand 725 bids later with 95 percent of the Furore rentedout (the remaining five percent was rented out ina more traditional fashion in the weeks followingthe auction). All bids are available on our website(Goossens 2012).

Table 4 summarizes the outcome of the auction.It shows that in the first round, the auctioneer received

Round 1 Round 2 Round 3 Round 4 Round 5

Number of bids 528 725 725 725 725Number of winners 38 39 43 41 39Number of new winners 38 18 16 6 4Total rent (indexed) 100 114 137 141 167Computation time (s) 0.27 2.28 0.23 0.91 1.50

Table 4: This table presents a number of statistics for each round in theFurore auction.

528 bids, followed by another 197 in the next round(recall that our anchor rule prevents bids on new solidspaces after round 2). In each round, about 40 bidderswon; as many as 82 bidders were winners at somepoint in the auction. Four bidders managed to gounnoticed until the last round; however, nine bidderswere winning bidders from the first until the lastround. A look at the (indexed) total rent in the winningallocation that was computed after each round showsthat the auction closed with a total rent that was67 percent higher than after the first round. The winnerdetermination problem was solved efficiently, with allcomputation times below three seconds, as shown inthe bottom row of the table. The computation times inthe table result from solving the model in the appendixusing the data (bids) available at each round in theFurore auction. After the final round, residential bidderswon 26 percent of the total surface, commercial bidderswon 54 percent, and social bidders were allocated15 percent, as required.

Our evaluation focuses on two aspects of the auc-tion: participation and design. The number of bidderswas on the borderline for a decent competition. Oneconsequence of this was that similar solid spaces wererented for varying prices. Several reasons influencedparticipation. First, the auction was held in unfavorableeconomic circumstances. The housing market, partic-ularly the rental market component, has followed adownward trend in recent years (Francke 2010), andthe economic and financial crisis were also negativefactors. Second, the adventurous concept behind solidsprobably frightened some of the more conservativeparticipants in the housing market. Finally, the auctionitself inevitably also formed a threshold. The solidauction is a new concept; for a nonprofessional bidder,learning this mechanism poses a challenge.

With respect to the auction design, the substantialincrease in Round 3 suggests that our anchor ruleprovided an incentive for people to raise their bids.Moreover, that almost 90 percent of the winning biddershad already made a provisionally winning bid in oneor more of the previous rounds shows that the activityrule also worked. Most bidders found the feedback theyreceived useful. Looking back at the auction, however,we believe the design could have been improved bywarning the bidders when they expressed a deadbid (i.e., a bid that can never win in the auction

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because one or more other bids dominates it). In theFurore auction, this would have been computationallyfeasible; however, it may not be feasible in larger-scaledcombinatorial auctions. Nevertheless, the solid auctionworked adequately overall and resulted in a balancedfunctional mix, which was Stadgenoot’s main objective.Moreover, Stadgenoot was pleased with the allocationand the total revenue it generated.

Managerial Implications andConclusionsIn many aspects, designing the solids auction has beena pioneer project with a unique philosophy. The conceptof renting space and then creating its entire interiordesign, in conjunction with the concept of maximalfreedom, requires open-minded tenants with somesense of initiative and adventure. Solids are also novelin the way they are allocated—that is, using a com-binatorial auction. The combinatorial auction was asuccess, and one month after the Furore auction, twomore solids were auctioned using our design.

The major managerial implications of this workare threefold. Our primary contribution is that weshowed that a combinatorial auction in a real estatemarket is practical, even if we must respect variousallocation constraints. Moreover, whereas combinatorialauctions were usually considered as too complicatedfor nonprofessional bidders, we showed that they canbe made accessible to bidders whose auction experiencedoes not exceed bidding on eBay. Although the auctionwas held during unfavorable economic circumstances,we successfully implemented our design, allocatingover 13,000 m2 at a satisfactory price, in a short period,and to many bidders.

The second implication is the understandingthat preparing the bidders is immensely important.We highly recommend organizing a mock auctionbefore the real auction to let the bidders become famil-iar with the bidding system interface and perhapsexperience the consequences of certain bid strategies.In a combinatorial auction, a lower bid is sometimesallocated at the expense of a higher bid. When allocationconstraints are present, this phenomenon occurs morefrequently. Bidders should be informed in advance thatthis may happen, preferably with specific examples thatadd to their understanding of the auction. Ignoring this

preparation will lead to frustration for some biddersand could possibly result in their filing legal suits.

Finally, when designing an auction, developing a setof simple and effective rules and carefully explainingthem to the bidders is not sufficient. We should focuson the cognitive complexity of formulating combi-natorial bids (Porter et al. 2003). In this regard, thecomplexity of participating in the auction and formulat-ing sensible bids is important. We cannot overestimatethe importance of the auction software. For example, itshould be able to provide an overview of spaces (e.g.,surface area, orientation, shape) to satisfy the bidders’needs in advance of the auction and during it. Further-more, it should alert bidders about the consequencesof their actions (e.g., with respect to the activity rule).User-friendly auction software is a key to a positiveexperience for bidders.

Despite the peculiarities of the solid project, webelieve that this auction design can easily be general-ized to other real estate settings. A key assumption isthat bids can only be expressed on adjacent lots, whichreduces the number of combinations and the complex-ity of the winner determination problem. Within thiscontext, any setting in which space is to be allocated(e.g., building land, exposition space at a fair) could behandled with an auction design similar to the designwe describe in this paper.

Appendix. Mathematical Formulation for theWinner Determination ProblemWe based the integer programming formulation that wedeveloped for our winner determination problem on a set-partitioning formulation and use the following notation.The solid, with a total surface area of A m2, is divided into aset of lots denoted by L and has a number of utility shafts,s ∈ S. Each shaft s has a ventilation capacity of Vs m3/h andoffers Gs high-capacity gas connections and Es high-currentelectricity connections. Per floor, f ∈ F above the ground level,the stairs have a rescue capacity for Of 1w persons for each ofthe two wings, w ∈W . At most, R restaurants are allowed.We use B to denote the set of all bidders, and subsets Br , Bc ,and Bs represent the set of residential, commercial, and socialbidders who should be awarded at least a fraction f r , f c , andf s of the surface area of the solid, respectively. The set Ba

includes those bidders who indicated that they want to wineither all or none of their bids. Each bid t ∈ T belongs to onebidder b4t5 and is characterized by the following parameters.We use L4t5 to represent the set of lots included in bid t, atfor the surface area of this solid space, and pt for the pricethat the bidder is willing to pay as monthly rent. The solid

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space is situated on wing w4t5 and floor f 4t5, and the utilityshaft that is to be used is given by s4t5. If the bidder does notexplicitly mention which shaft to use, we duplicate the bidfor each shaft contained in the bidder’s solid space: the setD4t5 contains bid t and its duplicates. We use ot to denote thenumber of persons for which rescue capacity is needed forbid t and vt for the required ventilation capacity. We defineT R as a subset of the set of bids T containing those bidswhere the solid space is to serve as a restaurant. We also havesubsets of T for those bids requiring a high-capacity gas (T G

s )and (or) electricity (T E

s ) connection on shaft s ∈ S. We also addtwo dummy bidders, ds1 dr y B, where the former bidder bidson each (valid) solid space small enough for social biddersand the latter bids on all other (valid) solid spaces. Bothbidders always bid a price of zero (reflecting Stadgenoot’spreference for renting out at the reserve price, rather thanleaving space unallocated in this pioneer project), and theyrequire some minimal ventilation and rescue capacity. We usethe decision variable xt , which is 1 if bid t is allocated and 0otherwise, and yb for each bidder b in Ba, which is 1 if allbidder b’s bids are allocated and 0 if none is allocated:

Maximize∑

t∈T

ptxt (1)

subject to∑

t∈T 2 l∈L4t5

xt =1 ∀ l∈L1 (2)

∑

t∈T 2b4t5=b

xt¶1 ∀b∈B\Ba1 (3)

∑

t∈T 2b4t5∈Br

atxt¾f rA1 (4)

∑

t∈T 2b4t5∈Bc

atxt¾f cA1 (5)

∑

t∈T 2b4t5∈Bs∪8ds 9

atxt¾f sA1 (6)

∑

t∈T R

xt¶R1 (7)

∑

t∈T 2w4t5=w1f 4t5=f

otxt¶Of 1w ∀w∈W1∀f ∈F 1 (8)

∑

t∈T 2s4t5=s

vtxt¶Vs ∀s∈S1 (9)

∑

t∈TGs

xt¶Gs ∀s∈S1 (10)

∑

t∈T Es

xt¶Es ∀s∈S1 (11)

∑

t∈D4t5

xt =yb ∀b∈Ba1∀ t∈T 2 b4t5=b1 (12)

yb ∈80119 ∀b∈Ba1 (13)

xt ∈80119 ∀ t∈T 0 (14)

The objective function (1) states that we should maximize thetotal rent. The first set of constraints (2) enforces the allocation

of each lot. This is necessary to ensure that unoccupied lots(i.e., lots allocated to a dummy bidder) form a valid solidspace. The second set of constraints (3) ensures that eachbidder wins at most one solid space (except for those bidderswho want to win all their bids or none at all, as well as thedummy bidders). Constraints (4)–(6) ensure that each type ofbidder acquires at least a given percentage of the total surfacearea; the next constraint prevents more than R restaurantsfrom winning a place in the solid. Constraints (8) guaranteethat the rescue requirement is available for the stairs ineach wing, and constraints (9) enforce that the ventilationcapacity is respected for each shaft. Constraints (10) and(11) enforce that per shaft, no more than the available high-capacity gas and electricity connections are used. That somebidders want all their bids allocated, or none at all, is settledwith constraints (12) and (13). The final set of constraintsensures that bids are fully accepted or not at all. Note thatthis formulation without constraints (4)–(13), and with “=”relaxed to “¶” in constraints (2), corresponds to the SWDP-bproblem.

Because we have dummy bids on all valid solid spaces,allocating the entire building to dummy bidders alwayssatisfies constraints (2). Similarly, by including the socialdummy bidder ds in constraint (6), this constraint will notcause infeasibility. However, because of constraints (4) and(5), a feasible solution may not exist. If this happens inthe first round, we recompute the round with f r loweredto 15 percent and, if necessary, again with both f r and f c

lowered to zero percent. In the latter case, the model willproduce a feasible solution (e.g., leave the whole buildingempty), and the auctioneer will announce to the biddersthat the current setting does not satisfy the conditions for avalid auction result. Bidders can use the second round tobid on other solid spaces, such that after the second round,constraints (4) and (5) are satisfied. Otherwise, the modelreturns infeasibility, and the auctioneer cancels the auction.

AcknowledgmentsThis work is supported by the Interuniversity AttractionPoles Programme initiated by the Belgian Science PolicyOffice and by the Fonds Wetenschappelijk Onderzoek [GrantG.0729.13].

References

Adomavicius G, Curley SP, Gupta A (2012) Effect of information feed-back on bidder behavior in continuous combinatorial auctions.Management Sci. 58(4):811–830.

Ausubel LM, Milgrom P (2005) The lovely but lonely Vickrey auction.Cramton P, Steinberg R, Shoham Y, eds. Combinatorial Auctions(MIT Press, Cambridge, MA), 17–40.

Bayers C (2000) Capitalist econstruction. Wired 8(3):210–212.Cramton P, Steinberg R, Shoham Y, eds. (2005) Combinatorial Auctions

(MIT Press, Cambridge, MA).Eklöf M, Lunander A (2003) Open outcry auctions with secret

reservation prices: An empirical application to executive auctions

Dow

nloa

ded

from

info

rms.

org

by [

134.

58.2

53.5

7] o

n 06

Aug

ust 2

014,

at 0

0:13

. Fo

r pe

rson

al u

se o

nly,

all

righ

ts r

eser

ved.


of tenant owners apartments in Sweden. J. Econometrics 114(2):243–260.

Epstein R, Henríquez L, Catalán J, Weintraub GY, Martínez C (2002) Acombinational auction improves school meals in Chile. Interfaces32(6):1–14.

Francke M (2010) Casametrie: De Kunst van het modelleren en hetvoorspellen van de marktwaarde van woningen (Vossiuspers UvA,Amsterdam).

Goossens D (2012) Solids: Auction and instances. Accessed June 1,2013, http://www.econ.kuleuven.be/public/NDBAE53/solids.html.

Harsha P, Barnhart C, Parkes CD, Zhang H (2010) Strong activityrules for iterative combinatorial auctions. Comput. Oper. Res.37(7):1271–1284.

Hohner G, Rich J, Ng E, Reid G, Davenport AJ, Kalagnanam JR,Lee HS, Chae A (2003) Combinatorial and quantity-discountprocurement auctions benefit Mars Incorporated and its suppliers.Interfaces 33(1):23–35.

Kelly F, Steinberg R (2000) A combinatorial auction with multiplewinners for universal service. Management Sci. 46(4):586–596.

Ledyard JO, Olson M, Porter D, Swanson JA, Torma DP (2002) Thefirst use of a combined-value auction for transportation services.Interfaces 32(5):4–12.

Orlin J (2011) Email communication to authors, November 17.Pai MM, Vohra R (2012) Auction design with fairness concerns:

Subsidies vs. set-asides. Discussion paper, Kellogg School ofManagement, Northwestern University, Evanston, IL.

Pekec A, Rothkopf MH (2003) Combinatorial auction design. Manage-ment Sci. 49(11):1485–1503.

Porter D, Rassenti S, Roopnarine A, Smith V (2003) Combinatorialauction design. Proc. Natl. Acad. Sci. USA 100(19):11153–11157.

Quan D (1994) Real estate auctions: A survey of theory and practice.J. Real Estate Finance Econom. 9(1):23–49.

Rothkopf MH, Pekec A, Harstad RM (1998) Computationallymanageable combinational auctions. Management Sci. 44(8):1131–1147.

Royal Institute of British Architects (2012) Solid 11. Accessed June 1,2013, http://www.architecture.com/Awards/RIBAAwards/2012/EUAwards/Solid11/Solid11.aspx.

Sandholm T (2002) Algorithm for optimal winner determination incombinatorial auctions. Artificial Intelligence 135(1–2):1–54.

Sandholm T, Suri S, Gilpin A, Levine D (2005) CABOB: A fastoptimal algorithm for combinatorial auctions. Management Sci.51(3):374–390.

Sandholm T, Levine D, Concordia M, Martyn P, Hughes R, Jacobs J,Begg D (2006) Changing the game in strategic sourcing at Proc-ter & Gamble: Expressive competition enabled by optimization.Interfaces 36(1):55–68.

Spieksma FCR (1999) On the approximability of an interval schedul-ing problem. J. Scheduling 2(5):215–227.

van Hoesel S, Müller R (2001) Optimization in electronic markets:Examples in combinatorial auctions. Netnomics 3(1):23–33.

Vickrey W (1961) Counterspeculation, auctions, and competitivesealed tenders. J. Finance 16(1):8–37.

Verification LetterCoosje Brijker, Project Manager Solids, Stadgenoot,Sarphatistraat 370, 1018 GW Amsterdam, the Netherlands,writes:

“In this letter I confirm that there has been an intensecollaboration between our organization Stadgenoot, and DriesGoossens and Frits Spieksma from K.U. Leuven. The goal of

this collaboration was to design and implement an auctionfor real estate in Amsterdam, real estate that was developedby and built for Stadgenoot. The cooperation started in 2008,when Dries Goossens and Frits Spieksma wrote a feasibilitystudy for us indicating the potential of a combinatorialauction for our situation. It culminated when on May 7 andJune 25, 2011 two successful auctions were held, allocating(in total) over 13,000 m2 to many bidders. As far as we areconcerned, the cooperation was quite fruitful. Looking back,the influence the K.U. Leuven team had on the design of theauction (based on experiments), as well as the optimizationmethods they employed, were instrumental in the prosperityof the auctions. The benefits include:

• Achieving a highly satisfactory revenue.• Using a transparent and fair allocation mechanism.• Guaranteeing an outcome in accordance to our policy

and building regulations.• Being able to rent out 13,000 m2 in a very short period.• Obtaining experience in a new way of allocating space.

Furthermore, we appreciate the fact that the auction designand software can also be used for future Solid projects.”

Dries R. Goossens is an assistant professor in the Facultyof Economics and Business Administration at Ghent Univer-sity. He obtained a PhD degree in applied economics, withresearch in the field of combinatorial auctions. Apart fromthis, his main research interests are in combinatorial opti-mization problems as sport scheduling, personnel scheduling,procurement, transportation, and assignment problems. In hiswork, Goossens tries to combine theoretical contributionsand practical applications.

Sander Onderstal is associate professor of economics atthe University of Amsterdam. His research interests includeauctions, industrial organization, and experimental economics.He has published widely in international journals such asthe Journal of Political Economy, Journal of Public Economics,International Economic Review, European Economic Review, andEconomic Theory. Onderstal has advised several ministries,public organizations, and private parties on auctions for realestate, gasoline stations, and radio frequencies for mobiletelecommunications.

Jan Pijnacker is an information technology (IT) engineercurrently working at the housing corporation Stadgenootin Amsterdam. For the solids project, he was the projectmanager of the online auction. His main (work) passion istranslating business needs into IT language, and vice versa.Pijnacker has a professional master’s degree in managementand information technology.

Frits C. R. Spieksma is a full professor in the Faculty ofBusiness and Economics at Katholieke Universiteit Leuven,Belgium. His main research interests lay in operationsresearch, especially combinatorial optimization problemsand applications thereof, and range from (sport) schedulingand combinatorial auctions to assignment and transportationproblems. Another recent field of interest is (computational)revealed preference, a subject in microeconomics.

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