urban studies dwelling unit choice in a urban studies...

Article

Urban Studies1–20� Urban Studies Journal Limited 2015Reprints and permissions:sagepub.co.uk/journalsPermissions.navDOI: 10.1177/0042098015593023usj.sagepub.com

Dwelling unit choice in acondominium complex: Analysis ofwillingness to pay and preferenceheterogeneity

Hai JiangTsinghua University, China

Shuiping ChenTsinghua University, China

AbstractWe study the choice of dwelling units in a condominium complex, a problem that has not beenthoroughly investigated in the housing research literature. We take a revealed preferenceapproach to quantify home buyers’ preference toward attributes associated with a dwelling unit.In particular, we estimate a mixed logit model using the hierarchical Bayes approach based on theMarkov Chain Monte Carlo method. We derive the willingness to pay measures for detailed attri-butes associated with a dwelling unit including its floor level, orientation, location in the complex,location on a floor level, and the type of bathrooms. We also find strong evidence for the prefer-ence heterogeneity among home buyers and conduct regression analysis to explain the prefer-ence heterogeneity using home buyers’ socioeconomic characteristics. Our results show thathome buyers with older ages and higher annual household income tend to focus more on thequality of the dwelling units, while first-time home buyers are more willing to accept dwellingunits with less-desirable attributes.

Keywordscondominium complex, dwelling unit choice, hierarchical Bayes, mixed logit, regression analysis

Received August 2013; accepted May 2015

Introduction

Condominium living is the most commonresidential choice for homeowners in China.1

It carries many desirable characteristics, suchas affordable housing, low maintenancerequirements, enhanced security featuresand, often times, easy access to restaurants,malls and other urban amenities. A

condominium complex typically consists oflow-rise and high-rise residential buildings,which are divided into a collection of private

Corresponding author:

Hai Jiang, Department of Industrial Engineering, Tsinghua

University, Shunde Building, Room 517, Beijing 100084,

China.

Email: [email protected]

at Tsinghua University on August 30, 2015usj.sagepub.comDownloaded from

http://usj.sagepub.com/

dwelling units. Each dwelling unit is ownedby and registered in the name of the purcha-ser of the unit. Real estate developers pricethese dwelling units differently based on thepopularity of their attributes, for example,size, floor level, orientation, proximity tostreet and so on. Currently, real estate devel-opers in China rely heavily on the experienceof the salespersons to set the prices of dwell-ing units (Liao, 2011). Hence, they are veryinterested in understanding how prospectivehomeowners make their choices of dwellingunits within a condominium complex, partic-ularly how they value the different attributesassociated with these units. Such knowledgecan help real estate developers better differ-entiate the prices of dwelling units and pro-vide valuable insights into the design of newresidential complexes in the future. It is alsoof interest to real estate developers to quan-tify and explain the preference heterogeneity,if any, among home buyers, so that thedevelopers can better match prospectivehome buyers with their preferred dwellingunits in the sales process.

Literature on dwelling unit choice in acondominium complex is very scarce. In aneffort to ascertain the influence of the GreenBuilding Certification Scheme on prospec-tive homeowners, Heinzle et al. (2013) con-ducted a stated preference survey to studyhome buyers’ preferences toward variousattributes of condominium complexes andthe dwelling units inside. The focus was ondetermining home buyers’ willingness to pay(WTP) for properties with the GreenBuilding Certification. Ishikawa et al. (2011)examined how individuals read and compre-hend the floor plan of a condominium unit.Subjects were asked to classify various typesof floor plans and results were analysed toidentify floor plan attributes that are impor-tant. There are also a few remotely relatedstudies investigating the choice of hometypes, for example, single-family detachedhouse, townhouse, apartment and so on.

However, they do not model the detailedcharacteristics of the dwelling units. A sam-pling of this research includes Quigley(1976), Tu and Goldfinch (1996), Skaburskis(1999) and Habib and Kockelman (2008).For a more comprehensive survey on theresidential choice literature in general, inter-ested readers are referred to Yates andMackay (2006), Barrios Garcia andRodriguez Hernandez (2007), Hoshino(2011) and Wu et al. (2013).

To the best of the authors’ knowledge,this paper is the first of its kind to investigatethe choice of dwelling units within a condo-minium complex. Studies of residential pre-ferences generally employ one of two typesof data, that is, stated preference (SP) databased on respondents’ choice under hypothe-tical situations; or revealed preference (RP)data based on actual market behaviour.Their respective strengths and limitations arethoroughly discussed in Train (2003). In thisresearch, we take a RP approach and useactual sales data from a real estate developerin Beijing to analyse dwelling unit choice.The data were collected in the second quar-ter of 2012 and contain detailed informationabout the dwelling units sold as well as thesocioeconomic characteristics of the homebuyers.

We first estimate a multinomial logitmodel, which is a widely used discrete choicemodel in the housing research literature anduse it as the base model for comparison. Theattributes we examine include location of thedwelling unit in the complex (whether it is inthe centre of the complex or adjacent to citystreets), location of the dwelling unit in theresidential building (its floor level and loca-tion on the floor), orientation, characteristicsof bathrooms, size of bay windows, size ofthe unit, purchase price and so on. We thenestimate a mixed logit model that allows usto assess the heterogeneity among home buy-ers. The model is estimated using a hierarchi-cal Bayes approach based on the Markov

2 Urban Studies



Chain Monte Carlo (MCMC) method.Results from the mixed logit model revealstrong evidence for the behavioural hetero-geneity among home buyers. Besides inter-preting the results for the mixed logit modelthrough the conventional ‘random coeffi-cients’ perspective, we also take the ‘errorcomponents’ approach (Train, 2003) to ana-lyse the substitution or correlation patternsamong dwelling units. We show that com-plex correlations exist among dwelling unitsand the mixed logit model is more appropri-ate in this problem context.

Based on the the WTP estimates pro-duced by the mixed logit model, we derivethe price adjustments real estate developersmake for dwelling units with different attri-butes and characterise the ideal dwelling unitin Chinese home buyers’ minds. We alsoidentify factors that influence home buyers’choice and discover that the floor level andorientation are the top two factors affectingdwelling unit choice, followed by the loca-tion in the complex, the location on a floorlevel and, finally, the types of bathrooms inthe dwelling unit.

To uncover the socioeconomic causes forthe preference heterogeneity among homebuyers, we estimate each individual’s WTPconditional upon his or her observed choice.We then conduct regression analysis to iden-tify the correlation between home buyers’WTP estimates and their socioeconomiccharacteristics. We find a reasonable rela-tionship between the WTP estimates and theexplanatory variables. For example, theregression results suggest that home buyerswith older ages and higher annual householdincome focus more on the quality of thedwelling units, that is, they prefer dwellingunits located on good floor levels and whoseorientation is north- and south-facing. It isalso found that first-time home buyers aremore willing to accept dwelling units withless-desirable attributes. These results canassist real estate developers in tailoring their

offerings to different customer segments toimprove customer satisfaction as well astheir profitability.

Methodology

This paper investigates dwelling unit choicein a condominium complex. To address thisdiscrete choice problem, we use the multino-mial and mixed logit models (Ben-Akiva andLerman, 1985; Train, 2003). The multino-mial logit model is a widely used discretechoice model in the housing choice litera-ture, while the mixed logit model is well sui-ted to discover behavioural heterogeneityamong decision makers.

From the discrete choice perspective, homebuyers are assumed to attain some level ofutility when purchasing a dwelling unit in thecondominium complex. Specifically, for homebuyer n n= 1, 2, � � � ,Nð Þ, let Cn be the set ofdwelling units available to him or her, that is,the set of unsold dwelling units in the condo-minium complex before the home buyerfinally purchases. Let yin take value 1 if homebuyer n chooses dwelling unit i 2 Cn; and 0,otherwise. Define yn =(y1n, y2n, � � � , yjCnjn),where jCnj is the cardinality of set Cn. Fordwelling unit i 2 Cn, its utility function forhome buyer n can be written as:

Uin =b0nxin + 2in , ð1Þ

where xin is a vector of observed attributesrelated to dwelling unit i for home buyer n,bn is the coefficient vector of these variablesfor this buyer, and 2in is a random term, rep-resenting the unobserved utility component.Let k be the length of vector bn. The prob-ability for home buyer n to choose dwellingunit i is then defined as:

Pn(ijCn)=P(Uin � Ujnjj 6¼ i, j 2 Cn): ð2Þ

Depending on the assumptions on the distri-butions of bn and 2in, we can obtain variousdiscrete choice models.

Jiang and Chen 3



The multinomial logit model

The multinomial logit model is a widelyapplied discrete choice model in the housingchoice literature because of its simplicity inestimation, its closed form specification andits robustness to the very strong Independentfrom Irrelevant Alternatives (IIA) assump-tion imposed on the error terms.

If we assume that the coefficient vector bn

is constant across home buyers (that is,bn =b, 8n) and 2in is independently identi-cally Gumbel distributed across home buyersand alternatives, we obtain the multinomiallogit model. The probability that homebuyer n chooses dwelling unit i is:

Pn(ijCn)=exp(b0xin)P

j2Cnexp(b0xjn)

: ð3Þ

The coefficient vector b is estimated bymaximising the likelihood function based onall individuals in the sample, that is,L(b)=PN

n= 1L(yn), where L(yn) is the prob-ability of home buyer n’s observed choicesand can be expressed as:

L(yn)=Yi2Cn

exp(b0xin)Pj2Cn

exp(b0xjn)

!yin

: ð4Þ

The mixed logit model

The mixed logit model relaxes the assump-tion that bn is constant across home buyersand allows us to assess the heterogeneityamong home buyers as well as the correla-tion among dwelling units (Train, 2003). Inthe mixed logit model, bn is assumed to varyover individuals to capture their taste varia-tions. That is, bn is decomposed into mean band deviation zn. Often times zn is assumedto conform to a multivariate normal distri-bution N (0,W) where W is the variance-covariance matrix. The density function ofbn can therefore be written as f(bnjb,W).

The probability that home buyer n choosesalternative i, conditional on bn, is:

Pn(ijbn,Cn)=exp(b0nxin)P

j2Cnexp(b0nxjn)

: ð5Þ

The probability of home buyer n’s observedchoices, conditional on bn is:

L(ynjbn)=Yi2Cn

exp(b0nxin)Pj2Cn

exp(b0nxjn)

!yin

: ð6Þ

The unconditional probability is thereforethe integral of L(ynjbn) over bn:

L(ynjb,W)=

ðL(ynjbn)f(bnjb,W)dbn ð7Þ

and the final likelihood function is:

L(b,W)=PNn= 1L(ynjb,W): ð8Þ

Parameters b and W can be estimated usingthe hierarchical Bayes approach based onthe MCMC method, which offers twoadvantages over the maximum simulatedlikelihood approach: (1) it does not attemptto maximise the likelihood function, whichcan be very challenging numerically andconvergence can be hard to achieve; and (2)desirable estimation properties, such as con-sistency and efficiency, can be obtainedunder more relaxed conditions (Train,2003). Let k(b,W) be the prior density func-tion for b and W, and Y be the vector(y1, y2, � � � , yN ), then the posterior distribu-tion of b and W is:

K(b,WjY)}YN

n= 1

L(ynjb,W)k(b,W): ð9Þ

It is customary to assume that b and W areindependent, the prior on b is normal withan unboundedly large variance, and theprior on W is inverted Wishart with k

degrees of freedom and scale matrix I, whichis an identity matrix with rank k. To improve

4 Urban Studies



computational efficiency, the hierarchicalBayes approach treats each bn as a para-meter (that is, a random variable) along withb and W, and the corresponding posteriorfor b,W, and bn8n is then given by:

K(b,W,bn8njY)}YNn= 1

L(ynjbn)f(bnjb,W)k(b,W):ð10Þ

In the hierarchical Bayes approach, drawingfrom K(b,W,bn8njY) can be efficientlyachieved by the Gibbs sampler, which isdetailed in Train (2003). Essentially, themethod starts with any initial values b0, W0

and b0n 8n. The r-th iteration (r � 1) consists

of the following steps:

1. Draw br from the normal distributionN (b

r�1,Wr�1), where b

r�1= 1

N

PNn= 1

br�1n ;

2. Draw Wr from the inverted Wishart dis-tribution IW (k+N , (kI+NSr�1)=(k+N )), where Sr�1 = 1

N

PNn= 1

(br�1n � br)(br�1

n � br)0;3. For each n 2 f1, 2, � � � ,Ng, draw br

n

from its posterior conditional on yn,

br�1, and Wr�1, that is, from K(bnjyn,

br�1,Wr�1)} L(ynjbn) f (bnjbr�1,Wr�1),which is achieved by applying theMetropolis-Hasting algorithm.

The above steps are repeated for many itera-tions and the resulting values converge todraws from the joint posterior of b, W andbn8n. Consequently, the mean and standarddeviation of the converged draws can be cal-culated to obtain estimates and standarderrors of the parameters.

Willingness to pay analysis

One of the key outputs of a discrete choicemodel is the WTP measurement for variousattributes of the alternatives. An advantage

of the mixed logit model is that it enables usto estimate each individual’s WTP for theseattributes. Once we obtain the individualspecific WTP estimates, we can conductregression analysis to infer the socioeco-nomic sources that cause such differencesacross individuals. This type of two-stepanalysis, that is, a mixed logit model fol-lowed by a regression analysis, was first pro-posed by Campbell (2007) to estimate theeconomic benefits associated with rurallandscape improvement. Recently, Hoshino(2011) applied this method to analyse prefer-ence heterogeneity in a residential choiceproblem.

Recall that when estimating the mixedlogit model through the hierarchical Bayesmethod, each individual’s bn is treated as arandom variable along with b and W. Basedon the joint posterior for b, W, and bn8ndefined in equationð10Þ, we can obtain theposterior distribution of bn by integratingout variables b, W, b1, b2, � � �, bn�1, bn+ 1,� � �, bN in the joint posterior:

h(bnjY)=ð ð� � �ð

K(b,W,bn8njY) db dW

db1 db2 � � � dbn�1 dbn+ 1 � � � dbN :

ð11Þ

As a result, the mean bn for individual n inthe sampled population can be written as:

bn =E bnjY½ �=ð

bnh(bnjY) d bn

=

ð ð� � �ð

bnK(b,W,bn8njY) db dW

db1 db2 � � � dbN

ð12Þ

The last equality in equationð12Þ is obtainedby plugging in the result for h(bnjY) fromequationð11Þ. The above multi-dimensionalintegral does not have a closed form and iscalculated by simulation. Note that in thehierarchical Bayes approach, we have

Jiang and Chen 5



already obtained the draws for b, W, andbn8n from their joint posterior defined inequationð10Þ. Let R be the number of drawsafter the burn-in period, and br

n be thesampled value in the r-th draw, we have:

bn =1

R

XR

r= 1

brn: ð13Þ

Let bns be the coefficient for attribute s invector bn, then home buyer n’s WTP forattribute s is given by:

WTPns = ��bns

bp

, ð14Þ

where bp is the price coefficient. To explainthe variability in WTPns, we assume thatindividual n’s WTP for attribute s can beexpressed as:

WTPns =as +g0szns +vns, ð15Þ

where as is the intercept term, which is con-stant for all individuals; zns is a vector ofindividual n’s socioeconomic characteristicsrelated to attribute s, and gs is the corre-sponding coefficient vector; and vns is theunobserved random term which models theeffect of omitted variables. The estimates forthe coefficient vector gs can then help usuncover the determinants for the preferenceheterogeneity toward attribute s.

Data description

The data used in this research come from acondominium complex in the city of Beijing.The complex comprises ten residential build-ings and covers a total area of around46,200 m2, as illustrated in Figure 1. Thecomplex is surrounded by a river on the east,a minor street on the south, and two majorstreets on the west and the north, respec-tively. Buildings are identified by the num-bers in the figure. Buildings 1, 4 and 8 are34-storey buildings; Buildings 2, 3, 5, 7, 9

and 10 are 18-storey buildings; and Building6 is a 16-storey building. Given a residentialbuilding, the number of dwelling units oneach floor can also be identified. For exam-ple, there are eight dwelling units on eachfloor of Building 6, each of which corre-sponds to a dark gray shape in the figure.Altogether there are 1092 dwelling units inthe condominium complex. Our data con-tain 818 units sold in the second quarter of2012. The sales data clearly record the dateand time each unit is sold and the detailedinformation of each dwelling unit, includingits location, floor level, floor plan, size, priceand so on. Note that if there was a changeto the price of a dwelling unit, it is alsodocumented in our data. In addition, foreach dwelling unit sold, we know the corre-sponding home buyer’s age, gender andother socioeconomic characteristics.

Based on discussions with experts in thereal estate sector and potential home buyers,we came up with an initial set of variables touse in our models. Through exploration andtesting of the multinomial logit model, thefinal set of variables are determined andsummarised in Table 1. There are eight

Figure 1. The layout of the condominiumcomplex.

6 Urban Studies



groups of explanatory variables, where thefirst seven groups describe a dwelling unitand the last group is related to the socioeco-nomic characteristics of the home buyers.

The first group of variables deals with thelocation of the dwelling unit in the residen-tial complex. Dwelling units adjacent to citystreets may suffer from excessive noise com-pared with those located in the centre of thecomplex. To capture home buyers’ prefer-ences, we classify the locations of the dwell-ing units into three categories: in the centreof the complex (Buildings 3, 4, 5 and 7),adjacent to major streets (Buildings 1, 2 and

6), or adjacent to minor streets (Buildings 8,9 and 10). In this table we also indicate thevariables used as the base during modelestimation.

The second group of variables quantifieshome buyers’ preferences toward floor levelsin a building. In general, home buyers inChina dislike living on the first floor orlower floors because of safety concerns, pri-vacy issues and lack of daylight. For exam-ple, residents on the first floor need to installburglar-proof windows and lower their cur-tains most of the day to prevent passers-byfrom seeing further into the rooms. In

Table 1. Description of variables used in the models.

Variable Description

Location of the dwelling unit in the complexBLD_CTR Binary: 1 = the dwelling unit is in a building in the centre of the complexBLD_MJR (base) Binary: 1 = the dwelling unit is in a building adjacent to major streetsBLD_MNR Binary: 1 = the dwelling unit is in a building adjacent to minor streetsFloor level of the dwelling unit in a buildingFLR_1 Binary: 1 = the dwelling unit is on the first floorFLR_2 Binary: 1 = the dwelling unit is on the second floorFLR_3 Binary: 1 = the dwelling unit is on the third floorFLR_4_5 Binary: 1 = the dwelling unit is on the fourth or the fifth floorsFLR_6_10 (base) Binary: 1 = the dwelling unit is located between the sixth and the tenth floorsFLR_11_SH Binary: 1 = the dwelling unit is located between the eleventh and the

second highest floorFLR_H Binary: 1 = the dwelling unit is located on the highest floorLocation of the dwelling unit on a floorUL_END Binary: 1 = the dwelling unit is located at the end of the floorOrientation of the dwelling unitORNT_SN (base) Binary: 1 = the orientation of the dwelling unit is south- and north-facingORNT_S Binary: 1 = the orientation of the dwelling unit is south-facingORNT_EW Binary: 1 = the orientation of the dwelling unit is east- and west-facingBathroomBATH_MW Binary: 1 = both bathrooms in the dwelling unit are MingWei bathroomsBay windowBAYWND The sum of the widths of bay windows in the dwelling unit, measured in metresSize and priceSIZE The total size of the dwelling unit, measured in square metresPRICE The purchase price of the dwelling unit, measured in 10,000 CNYSocioeconomic characteristics of the home buyerAGE The home buyer’s ageGNDR Binary: 1 = the individual is maleINC The home buyer’s annual household income in 10,000 CNYFTHB Binary: 1 = the individual is a first-time home buyer

Jiang and Chen 7



addition, residents on lower floors often needto worry about the overflow of waste waterin their bathrooms when the sewerage systemfor the building is choked. Dwelling units onmid or higher floor levels are much preferredby Chinese home buyers, because they canget more daylight and these floors offer nicerviews out of the windows. However, whenwe go to dwelling units on the highest floor,there does not seem to be a consensus. Onone hand, dwelling units on the top floorprovide homeowners with a best-in-the-building view and they are in general consid-ered as the quietest place because one isspared the sounds of children and pets run-ning around upstairs, or early risers stum-bling from their beds at 5 o’clock in themorning. On the other hand, these dwellingunits can get very hot in the summer and theelectric bill may be high. Moreover, roofwear and tear can also create leak problems,which can become a big headache. In thisresearch, several different specifications forthe floor level variable are examined andcompared (for example, linear, piecewise lin-ear, log linear and step functions), and thestep function specified in this table providesthe best fit.

The third group of variables measureshome buyers’ preferences toward dwellingunits on the same floor. Units at the end ofeach floor are in general not preferredbecause a higher percentage of the exteriorwalls of these units is exposed to outsideenvironment, often causing higher energyconsumption. For example, units at the eastend of the floor get full morning sun, whileunits at the west end get full afternoon sun.Binary variable UL_END is introduced toindicate whether a unit is at the end of itsfloor. Take Building 6 in Figure 1 as anexample. There are two dwelling units at theend of each floor: one on the east end andthe other on the west end.

The fourth group of variables describesthe floor plan of a dwelling unit. An

important attribute related to a floor plan isits orientation. Good orientation improvesthe energy efficiency of a home, making itmore comfortable to live in and cheaper torun. The orientation of a dwelling unit isdetermined primarily by the facing of itsexterior windows. There are three commontypes of orientations for dwelling units inChina: south- and north-facing, east- andwest-facing, and south-facing. When at leastone exterior window faces the south and thenorth, respectively, this dwelling unit is clas-sified as south- and north-facing, which isindicated by variable ORNT_SN. The orien-tation of the dwelling unit shown in Figure2(a) is south- and north-facing. This is themost popular orientation among Chinesehome buyers because it has good exposureand the best natural ventilation. In the win-ter days when the sun is low, the windowson the south side allow natural light to bathethe rooms throughout the day; while in thesummer days when the sun is high, only lim-ited sunlight could pass through the south-ern windows, which helps to keep the roomscool. When at least one exterior windowfaces the east and the west, respectively, thisdwelling unit is defined as east- and west-facing, which is indicated by variableORNT_EW. Figure 2(b) shows the floorplan of a dwelling unit whose orientation iseast- and west-facing. Although this type offloor plan still can offer proper natural ven-tilation, the major drawback is that roomshaving west-facing windows can get very hotin the summer because of the extended expo-sure to sunlight in the afternoon. When mostof the exterior windows face the south, thisunit is classified as south-facing, which isindicated by variable ORNT_S. The majorconcern with this type of orientation is venti-lation. Since most of the exterior windowsface the south, there is no passage to allowcool breezes to get through the rooms. Allthree types of orientation are found in thiscondominium complex.

8 Urban Studies



Fig

ure

2.

Illust

ration

offlo

or

pla

ns.

Jiang and Chen 9



The next group of variables describes animportant characteristic of bathrooms in thedwelling unit. A dwelling unit in China typi-cally has a guest bathroom accessible fromthe living room (or the dining room) and anensuite bathroom attached to the masterbedroom. Depending on whether it has anexterior window or not, a bathroom is

classified into two types, where MingWeibathrooms are those with exterior windowsand AnWei bathrooms are those with noexterior windows. For the dwelling unitshown in Figure 2(b), Bathroom 1 is aMingWei bathroom while Bathroom 2 is anAnWei bathroom. Since MingWei bath-rooms offer better ventilation and more

Table 2. Parameter estimates for the multinomial logit and mixed logit models.

Multinomial logit Mixed logit

Coefficient SE Parameter SE

(1) BLD_CTR 3.9758z 0.1663 Mean of coefficient 7.5300z 0.4295(2) – – Std. dev. of coefficient 2.5051z 0.5408(3) BLD_MNR 1.0430z 0.0981 Mean of coefficient 1.4297z 0.2049(4) – – Std. dev. of coefficient 3.0270z 0.4768(5) FLR_1 240.1278y 18.2524 Mean of coefficient 248.1156z 1.5052(6) – – Std. dev. of coefficient 9.0330z 1.9080(7) FLR_2 227.1530y 12.1833 Mean of coefficient 230.2771z 0.8542(8) – – Std. dev. of coefficient 6.6485z 1.2175(9) FLR_3 219.5892y 9.1432 Mean of coefficient 221.3956z 0.9489(10) – – Std. dev. of coefficient 6.5600z 1.3421(11) FLR_4_5 212.9468y 6.113 Mean of coefficient 213.9328z 0.6736(12) – – Std. dev. of coefficient 6.4627z 1.4136(13) FLR_11_SH 6.8563y 3.0259 Mean of coefficient 8.5487z 0.5221(14) – – Std. dev. of coefficient 3.2611z 0.8027(15) FLR_H 20.8630z 0.179 Mean of coefficient 28.2632z 1.6121(16) – – Std. dev. of coefficient 11.0139z 2.0317(17) UL_END 20.9451z 0.1551 Mean of coefficient 24.1848z 0.4597(18) – – Std. dev. of coefficient 4.1489z 0.5730(19) ORNT_S 212.3321y 5.7281 Mean of coefficient 213.1265z 0.6991(20) – – Std. dev. of coefficient 3.2699z 0.4902(21) ORNT_EW 227.4623y 12.1847 Mean of coefficient 228.8199z 0.3052(22) – – Std. dev. of coefficient 2.9164z 0.4408(23) BATH_MW 1.1013z 0.1381 Mean of coefficient 0.9504z 0.2905(24) – – Std. dev. of coefficient 2.6811z 0.5733(25) BAYWND 0.6038z 0.0429 Mean of coefficient 1.1982z 0.0880(26) – – Std. dev. of coefficient 1.0759z 0.1067(27) SIZE 10.1032y 4.9639 Mean of coefficient 10.1944z 0.0302(28) – – Std. dev. of coefficient 0.2036z 0.0195(29) PRICE 23.0926y 1.5294 Coefficient 23.0679z 0.0165

No. of observations 818 818Loglikelihood null 25284 25284Loglikelihoodat convergence

24329 24184

r2 0.181 0.208�r2 0.178 0.203

Notes: �: p-value \0:10, y : p-value \0:05, z: p-value \0:01.

10 Urban Studies



daylight, they are very appealing to homebuyers in China. Unfortunately, becausedwelling units on the same floor arearranged side by side, real estate developersoften times cannot accommodate twoMingWei bathrooms in a dwelling unit,instead they make one of them an AnWeibathroom. It is very rare to have two AnWeibathrooms in a dwelling unit. We introducedummy variable BATH_MW which takesvalue 1 if both bathrooms are MingWeibathrooms; and 0 otherwise. The two bath-rooms in the dwelling unit shown inFigure 2(a) are both MingWei bathrooms.

Bay windows project outward from theexterior walls of the dwelling unit. Theyoffer panoramic views, bring in more naturallight, provide good ventilation and give afeeling of spaciousness to a room. All ofthese factors make bay windows highlyattractive to home buyers in China. InFigure 2(b), there are three rooms with baywindows. We introduce variable BAYWNDto measure the sum of the widths of all baywindows in a dwelling unit. Presumably, thelarger the value of BAYWND, the more pre-ferred the dwelling unit when all else areequal.

The seventh group of variables includesthe size of the dwelling unit measured insquare metres and the purchase price for theunit measured in 10,000 Chinese Yuan. Forthe reader’s reference, the exchange ratebetween US Dollar (USD) and ChineseYuan (CNY) is approximately 1 USD = 6.3CNY in the second quarter of 2012.

The last group of variables correspondsto the socioeconomic characteristics of thehome buyer: age, gender, annual householdincome and whether he or she is a first-timehome buyer.

Empirical results

The estimation of the multinomial logitmodel is straightforward. For the mixed

logit model, all parameters are assumed tobe random except the parameter for variablePRICE. This treatment is recommended byTrain (2003) and Hoshino (2011) to avoidnumerical difficulty when calculating theWTP measures. While estimating the mixedlogit model, we perform 50,000 iterations inthe MCMC process, of which the first25,000 iterations are for the burn-in period.Afterwards, we retain every fifth draw toobtain a total of 5000 draws from the pos-terior density function.

Parameter estimates

The parameter estimates for both the multi-nomial logit and mixed logit models arereported in Table 2. We can find that allpositive/negative signs of the estimated coef-ficients in the multinomial logit model areconsistent with expectation. And all of themare significantly different from zero. Theparameter estimates show that home buyersprefer residential buildings located in thecentre of the complex than those locatednext to streets. Speaking of floor levels,dwelling units on the first floor are least pre-ferred. Homeowners favour dwelling unitson higher floor levels and this trend contin-ues until we reach the second highest floor.Units located at the end of each floor arenot as good as those located in the middle ofeach floor, which is suggested by the nega-tive sign for UL_END. Homeowners pay alot of attention to orientation, they preferunits that are south- and north-facing, fol-lowed by those that are south-facing, andfinally those that are east- and west-facing.Homeowners also prefer units having twoMingWei bathrooms and bigger baywindows.

Similar results are observed for para-meters in the mixed logit model. In addition,the estimated standard deviations of coeffi-cients in the mixed logit model are highlysignificant, indicating the presence of

Jiang and Chen 11



substantial preference heterogeneity indwelling unit choice behaviour. The value ofthe log-likelihood is much greater in themixed logit model than that in the multino-mial logit model. The log-likelihood ratiotest statistic is 290, which is greater than thecritical value of the chi-square distributionfor 14 degrees of freedom, that is, 29.141 atthe 0.01 level of significance.

When we compare the parameter esti-mates in the multinomial logit model to theestimated mean values of the parameters inthe mixed logit model on rows (1), (3), (5),� � �, and (29), we notice that the estimatedmean values of the parameters in the mixedlogit model are generally larger in magnitudethan those in the multinomial logit model.This phenomenon is expected because thescale of utility is determined by the normali-sation of the i.i.d. term ein. In the multino-mial logit model, all stochastic terms notaccounted for by the deterministic part isabsorbed by this single error term. However,in the mixed logit model, a portion of thevariance in the random component of utilityis captured in zn. Hence, the variance of ein

in the multinomial logit model is greaterthan that in the mixed logit model, that is,the scale of ein in the multinomial logitmodel is smaller than that in the mixed logitmodel. Since we normalise the scale of ein toone in both models, the utility (and hencethe parameters) of the multinomial logitmodel are scaled down relative to the mixedlogit model.

The mixed logit model also allows us tocalculate the probability when the coefficientfor an attribute is positive (or negative)because the density function for the coeffi-cient vector bn, f(b,W), is now known. Forexample, the probability when the coefficientfor BLD_CTR is positive can be calculatedas Pr(bBLD CTR.0) = 0.9987, suggestingalmost all home buyers prefer to live in thecentre of the complex than near majorstreets. We are particularly interested in the

coefficient for FLR_H because it is widelyaccepted by real estate developers in Chinathat home buyers do not like dwelling unitson top floors and discounts should beoffered (Liao, 2011). The mean of bFLR H isindeed negative, however, the probabilitywhen bFLR H .0 is Pr(bFLR H .0) = 0.2265,indicating that there are about one-quarterof home buyers who prefer to live on thehighest floor than on the sixth to the tenthfloors, which is the base level for floor vari-ables. To get a better understanding onhome buyers’ preference toward floorlevels, we can also calculate the followingprobabilities: Pr(bFLR 1\0) = 1.0000,Pr(bFLR 2\0)= 1.0000, Pr(bFLR 3\0) =0.9994, Pr(bFLR 4 5\0) = 0.9845, andPr(bFLR 11 SH.0)= 0.9956. Since theseprobabilities are all very close to 1.0000, wecan draw the following conclusion:

FINDING 1 Although home buyers haveunanimous agreement on the preference forfloor levels below the top floor, there are sig-nificantly different opinions on living on thetop floor. Unlike the widely accepted beliefthat home buyers dislike dwelling units on thetop floor, we find that a fair portion of homebuyers, that is, about one-quarter of them,prefer to stay on the top floor.

Substitution patterns

The multinomial logit model assumes thatalternatives are independent of each other,which leads to equal cross elasticity amongalternatives. That is, when a new alternativeis introduced to a set of existing alternatives,the new alternative will draw proportio-nately from all existing ones. The mixedlogit model, however, allows us to modelcomplex substitution patterns (or correla-tions) among alternatives. In the mixed logitmodel, following our previous notation,coefficient bn is decomposed into mean b

and deviation zn. We can re-write the utilityfunction as:

12 Urban Studies



Uin =(b0+ z0n)xin + 2in

= b0xin +(z0nxin + 2in ),ð16Þ

where b0xin is the deterministic part of thetotal utility and z0nxin + 2in is the randompart of the total utility. z0nxin + 2in intro-duces correlation among alternatives:

Cov(Uin,Ujn)=Cov(z0nxin

+2in , z0nxjn+2jn)

= x0inWxjn:

ð17Þ

This interpretation of the mixed logit modelallows us to examine the correlation or sub-stitution patterns among the alternatives. InTable 2, rows (1), (3), � � � , (29) are the meanvalues of the parameters, that is, vector band rows (2), (4), � � �, (28) are the estimatedvariances associated with zn.

Let xkin be the k-th element in xin and the

corresponding coefficient is bkn = bk + zk

n. Ifxk

in is a dummy variable, it suggests thatalternatives having the same value for xk

in arecorrelated and they are substitutable foreach other to a certain extent. This can beillustrated through the three examples shownin Table 3. In Case 1, a dwelling unit locatedin the centre of the complex (Unit 3) is intro-duced to a base situation consisting of Unit1 located in the centre of the complex andUnit 2 located near the minor street, and allthe other attributes are the same across thethree units. We know that the substitution

effect between Units 3 and 1 is strongerthan that between Units 3 and 2, becauseCov (Uunit 1 , Uunit 3 ) 2 Cov (Uunit 2 ,Uunit 3 ) = var(zBLD CTR) =2:50512 .0.Similarly, in Case 2, a dwelling unit on thefirst floor (Unit 3) is introduced to a basesituation consisting of dwelling units on thesecond and first floors, respectively (Units1 and 2), and all the other attributes are thesame across the three units. We know thatthe substitution effect between Unit 3and Unit 2 is stronger than that betweenUnit 3 and Unit 1. This is because Cov(Uunit 1;Uunit 3Þ�Cov ðUunit 2; Uunit 3Þ ¼ �varzFLR 1Þ ¼ �9:03302\ 0.

Case 3 shows a more complex situation:Unit 3 on the first floor of a residentialbuilding located in the centre of the complexis introduced to a base scenario consisting ofUnit 1 on the second floor of a residentialbuilding located in the centre of the complexand Unit 2 on the first floor of a residentialbuilding located near the minor street, andall the other attributes are the same acrossthe three units. We know that the substitu-tion effect between Unit 3 and Unit 2 isstronger than that between Unit 3 andUnit 1, because Cov (Uunit 1 , Uunit 3 ) � Cov(Uunit 2 , Uunit 3 ) = var(zBLD CTR) 2 var(zFLR1

)= 2:50512 � 9:03302\ 0.

Since the estimated variances associatedwith zn in Table 2 are all significant and theyare roughly in the same order of magnitude,

Table 3. Examples that illustrate the complex substitution patterns among dwelling units.

Case Attribute Base New

Unit 1 Unit 2 Unit 3

1 BLD_CTR 1 0 1BLD_MNR 0 1 0

2 FLR_1 0 1 1FLR_2 1 0 0

3 BLD_CTR 1 0 1BLD_MNR 0 1 0FLR_1 0 1 1FLR_2 1 0 0

Jiang and Chen 13



it clearly indicates that complex correlationexists among dwelling units along multipledimensions, including location in the com-plex, floor levels, location on the floor,orientation and so on. Hence, the mixedlogit model is more appropriate for the mod-elling of the dwelling unit choice problem.

Willingness to pay analysis

The WTP estimates based on the mixed logitmodel are summarised in Table 4. Column 1shows the names of the attributes. Column 2shows the absolute WTP estimates measuredin CNY. Column 3 shows the relative WTPestimates, which is expressed as percentagesof the average unit price. That is, Column 3is obtained by dividing Column 2 by theaverage unit price in this residential complex.Columns 4 and 5 show the standard devia-tions of WTP estimates in both absolute andrelative terms. The WTP for FLR_1 is nega-tive and its absolute value is the largestamong all attributes, indicating that homebuyers have strong objections to living onthe first floor. They have to be compensated

by 156,864 CNY if they are asked to movefrom a unit on the sixth floor (the base levelfor floor variables) to the first floor, which isabout 3.545% of the average price of thedwelling units. Besides floor levels, homebuyers place heavy weight on the orientationof a dwelling unit. For example, comparedwith a dwelling unit whose orientation issouth- and north-facing, the price of a dwell-ing unit whose orientation is east- and west-facing needs to be about 93,941 CNY (or,2.123% of the average unit price) lower.Since preferable attributes have greater WTPestimates, by examining the mean values ofthe WTP estimates (that is, Columns 2 and 3in Table 4), we can characterise what anideal dwelling unit looks like.

FINDING 2 In Chinese home buyers’minds, an ideal dwelling unit meets the follow-ing criteria: (1) located on or above the ele-venth floor but below the highest floor; (2)south- and north-facing; (3) away fromstreets; (4) not at either end of the floor level;and (5) both bathrooms are MingWei.

More importantly, the WTP estimatesreported in Table 4 can be used as guidelines

Table 4. WTP estimates under the mixed logit model. Absolute WTP estimates are reported in CNY,while relative WTP estimates are measured as percentages of the average unit price.

Attribute Mean of WTP Standard deviation of WTP

Absolute value (CNY) Relative pct Absolute value (CNY) Relative pct

(1) BLD_CTR 24,546 0.555% 4388 0.099%(2) BLD_MNR 4669 0.106% 6598 0.149%(3) FLR_1 2156,864 23.545% 21,301 0.481%(4) FLR_2 298,707 22.231% 15,768 0.356%(5) FLR_3 269,751 21.576% 15,763 0.356%(6) FLR_4_5 245,422 21.026% 15,624 0.353%(7) FLR_11_SH 27,866 0.630% 7311 0.165%(8) FLR_H 226,968 20.609% 26,563 0.600%(9) UL_END 213,644 20.308% 9134 0.206%(10) ORNT_S 24279 20.967% 11,656 0.263%(11) ORNT_EW 293,941 22.123% 12,383 0.280%(12) BATH_MW 3093 0.070% 5216 0.118%(13) BAYWND 3908 0.088% 2276 0.051%

14 Urban Studies



to price dwelling units in a condominiumcomplex:

FINDING 3 The relative percentages ofthe WTP estimates, that is, Column 3reported in Table 4, can be used to assist realestate developers in adjusting the prices ofdwelling units according to the differences intheir attributes, for example, floor levels,orientation and so on.

We use two examples to illustrate howFinding 3 can be applied. Suppose we havea dwelling unit located on the first floor of abuilding, then it should be priced 3.545%lower than a dwelling unit located on thesixth floor of the same building when all elseare held equal. Another example could bethat, all else held equal, a dwelling unit onthe first floor of a building located in thecentre of the complex should be priced3.545%–0.555% = 2.99% lower than adwelling unit on the sixth floor of a buildinglocated next to major streets.

In Table 5, we summarise the ranges ofthe mean WTP estimates according to theattribute groups defined in Table 1. Column1 shows the names of the attribute groups.For each attribute group, we show the mini-mum and maximum mean WTP estimates inColumns 2 and 3, and calculate their differ-ences in Column 4. For example, the firstattribute group describes the location of thedwelling unit in the complex and the vari-ables are BLD_CTR, BLD_MNR, andBLD_MJR (the base). Attribute BLD_MJRhas the minimum mean WTP estimate,

which is 0 CNY; while attribute BLD_CTRhas the maximum mean WTP estimate,which is 24,546 CNY. The differencebetween the maximum and minimum meanWTP estimates is 24,526 CNY. Since attri-bute groups having large values in Column 4have greater influence on home buyers, wecan immediately obtain the following result.

FINDING 4 The top two factors thataffect home buyers’ decisions are the floorlevel of the dwelling unit and its orientation,followed by its location in the complex, itslocation on a floor level, and finally, whetherboth of its bathrooms are MingWei.

The above observation is consistent withthe findings reported by Heinzle et al. (2013)where various characteristics of the condo-minium unit and the condominium complexare analysed to quantify real estate inves-tors’ preferences toward the Green BuildingCertification. Their results show that amongattributes associated with a dwelling unit,the main aspect of living room (a conceptsimilar to the orientation of a dwelling unit)and the floor level are the two most impor-tant ones.

Explaining the behavioural heterogeneity

An advantage of the mixed logit model isthat it offers considerable insights into thetaste variation among home buyers. The lasttwo columns in Table 4 show the standarddeviations of the corresponding WTP esti-mates in both absolute and relative terms

Table 5. Ranges of the WTP measures along key dimensions of a dwelling unit (in CNY).

Attribute group Mean WTP

Min Max Max-Min

Location of the dwelling unit in the complex 0 24,546 24,546Floor level of the dwelling unit in a building 2156,864 27,866 184,730Location of the dwelling unit on a floor 213,644 0 13,644Orientation of the dwelling unit 293,941 0 93,941Bathroom 0 3093 3093

Jiang and Chen 15



under the mixed logit model. A large stan-dard deviation indicates that there exists sig-nificant taste variation for this attribute.Row (1) shows the standard deviation of theWTP estimate for dwelling units located inthe centre of the complex, and its value isrelatively small (4388 CNY for BLD_CTR),indicating that there is a unanimous prefer-ence among home buyers to live in the centreof the complex, so they can get a quieterenvironment at home. Now let us look at thevalues in Rows (3) through (8) in Table 4,which quantify the taste variation towardfloor level variables. Although in section‘Willingness to pay analysis’ we show thathome buyers prefer units on higher floorsexcept the highest floor, we can now see thatthis preference is accompanied by significantbehavioural heterogeneity, which is reflectedby the relatively large standard deviations.We also notice that the standard deviationsfor variable FLR_H is the largest (which is26,563 CNY, or 0.600% of the average unitprice) among all variables, indicating thatthere are significantly different opinionstoward living on the highest floor.

Real estate developers are interested inthe socioeconomic sources that cause theheterogeneity in preference, so that they canbetter match home buyers with their pre-ferred units. For example, if real estate devel-opers know the kind of home buyers thatprefer dwelling units on the highest floor,they would then specifically target those buy-ers with units on the highest floors. Toaccomplish this goal, we follow the steps out-lined in section ‘Willingness to pay analysis’to relate each individual’s WTP estimate tohis or her socioeconomic variables. We firstestimate each home buyer’s individual WTPfor the attribute we are interested in condi-tional upon his or her actual choice. We thenfit a regression model in which the dependentvariable is a home buyer’s WTP for thisattribute and the explanatory variables arehis or her socioeconomic attributes presented

in the last four rows in Table 1. The estima-tion results are reported in Table 6 and thedetails are as follows.

� The first group of results are related tothe floor variables. We can see that theWTP regression for variable FLR_1 hasan r2 of 0.248, which suggests that about25% of the heterogeneity in WTP can beexplained by the explanatory variables.The coefficient for AGE is negative andsignificant at the 0.01 level, which sig-nifies that older home buyers dislikedwelling units on the first floor. Thecoefficient for INC is also negative andsignificant at the 0.05 level, which indi-cates that high income home buyers areless willing to buy units on the first floor.The coefficient for FTHB is positive andsignificant at the 0.05 level, whichimplies that first-time home buyers aremore prone to purchase dwelling unitson the first floor. A possible explanationis that since they are novice home buy-ers, they probably have not fully recog-nised the disadvantages associated withliving on the first floor. Similar resultsare observed for the other floor vari-ables: Home buyers with older ages andhigher income are less likely to purchasedwelling units located on poor floor lev-els, while first-time home buyers aremore willing to stay on those floors. It isworth mentioning that in the regressionmodel for variable FLR_H, the coeffi-cient of GNDR is positive and signifi-cant at the 0.05 level, indicating thatmale home buyers are more interested inliving on the top floors.

� The second group of results are relatedto the orientation of the dwelling unit. Inthe WTP regression result for attributeORNT_S, the coefficients of AGE andINCOME are negative and significant atthe 0.01 level, which suggests that homebuyers with older ages and higher annual

16 Urban Studies



Tab

le6.

Par

amet

eres

tim

ates

for

the

WT

Pre

gres

sion

model

s

Att

ribute

group

Dep

enden

tva

riab

leIn

terc

ept

AG

EG

ND

RIN

CFT

HB

r2

Floor

leve

lFL

R_1

Coef

ficie

nt

212.6

724z

20.0

668z

20.0

407

20.0

272�

0.3

008�

0.2

48

S.E.

0.8

773

0.0

153

0.0

872

0.0

132

0.1

766

FLR

_2

Coef

ficie

nt

28.0

062z

20.0

627z

20.0

250

20.0

229z

0.1

906

0.1

82

S.E.

0.6

516

0.0

114

0.0

391

0.0

201

0.1

312

FLR

_3

Coef

ficie

nt

25.2

580z

20.0

635z

20.0

180

20.0

188�

0.0

942

0.1

52

S.E.

0.6

524

0.0

132

0.0

392

0.0

144

0.1

313

FLR

_4_5

Coef

ficie

nt

22.8

582z

20.0

317z

20.0

268

20.0

094

0.0

118

0.0

69

S.E.

0.6

467

0.0

127

0.0

380

0.0

133

0.1

302

FLR

_11_SH

Coef

ficie

nt

3.1

784z

20.0

141z

0.0

166

20.0

051

20.0

557

0.0

30

S.E.

0.3

044

0.0

041

0.0

150

0.0

053

0.0

613

FLR

_H

Coef

ficie

nt

0.1

457

20.1

077z

0.0

263�

20.0

343�

0.8

377z

0.1

27

S.E.

1.0

982

0.0

287

0.0

144

0.0

191

0.2

210

Ori

enta

tion

OR

NT

_S

Coef

ficie

nt

23.1

717z

20.0

369z

20.0

355

20.0

199z

0.0

291

0.1

53

S.E.

0.2

744

0.0

032

0.0

586

0.0

048

0.0

552

OR

NT

_EW

Coef

ficie

nt

28.2

902z

20.0

312z

0.0

513

20.0

141z

20.0

476

0.1

16

S.E.

0.2

620

0.0

046

0.0

559

0.0

041

0.0

527

Loca

tion

inth

eco

mple

xBLD

_C

TR

Coef

ficie

nt

1.7

901z

0.0

155z

20.0

301

0.0

124z

20.0

373

0.0

43

S.E.

0.1

826

0.0

022

0.0

390

0.0

012

0.0

368

BLD

_M

NR

Coef

ficie

nt

0.2

229

0.0

197z

0.0

351

0.0

119

y2

0.0

580

0.0

47

S.E.

0.2

686

0.0

055

0.0

573

0.0

047

0.0

541

Loca

tion

on

aflo

or

UL_

EN

DC

oef

ficie

nt

20.8

690z

20.0

149

0.0

577

20.0

112�

0.0

312

0.0

11

S.E.

0.3

789

0.0

167

0.0

808

0.0

066

0.0

763

Bat

hro

om

BA

TH

_MW

Coef

ficie

nt

0.0

751

0.0

068�

0.0

362

20.0

026

0.0

046

0.0

05

S.E.

0.2

169

0.0

047

0.0

463

0.0

038

0.0

437

Not

es:z

:p�

valu

e\0:0

1,y

:p�

valu

e\0:0

5,�

:p�

valu

e\0:1

0.T

he

dep

enden

tva

riab

les,

that

is,th

ein

div

idual

WT

Pes

tim

ates

,are

mea

sure

din

10,

000

CN

Y.

Jiang and Chen 17



household income levels are reluctant tostay in dwelling units whose orientationis south-facing. Similar results areobserved for attribute ORNT_EW.

� The third group of results are related tothe location of the dwelling unit in theresidential complex. The coefficients forAGE and INC are positive and signifi-cant in both regressions, which suggeststhat home buyers with older ages andhigher income prefer dwelling unitslocated far from major streets so as tostay away from urban noise.

� The fourth group of results are relatedto the location of the dwelling unit on afloor. The coefficient for INC is negativeand significant at the 0.05 level, whichsuggests that high income home buyersdo not like dwelling units at either endof the floor. The coefficients for AGEand FTHB are not significant, indicatingthat they are somehow indifferent to thelocation of the dwelling unit on a floor.

� The last group of results are related tothe type of bathroom in a dwelling unit.The positive coefficient for AGE indi-cates that older home buyers prefer tohave two MingWei bathrooms.

Overall, the relationships between the coeffi-cients and the dependent variables are rea-sonable and we can summarise the aboveanalysis into the following two findings:

FINDING 5 Home buyers with older agesand higher annual household income havestronger preferences for dwelling units withgood quality, that is, dwelling units that sat-isfy the criteria specified in Finding 2; whilefirst-time home buyers are more flexible toaccept dwelling units with one or more less-desirable attributes, for example, those on lowfloor levels, or those that are not south- andnorth-facing.

FINDING 6 The gender of the homebuyer appears to have little impact on thetaste heterogeneity in dwelling unit choice; we

do, however, find evidence that male homebuyers are more interested in dwelling unitson the top floors.

It is worth noting that the explanatorypower of the independent variables differsacross models. For example, in the regres-sion model for FLR_1, we have r2 = 0:248.However, in the regression models forFLR_11_SH, the r2 is rather small, whichsuggests that only a very small portion ofthe observed preference heterogeneity isexplained. Such phenomena are alsoreported in Walker and Li (2007) andHoshino (2011). A possible explanation isthat the purchase of a dwelling unit is a col-lective decision process involving not onlythe home buyer him- or herself, but alsomembers in the home buyer’s family as wellas members of the extended family, that is,the home buyer’s spouse, parents and par-ents-in-law. Therefore, the behavioural het-erogeneity may need to be interpreted bycollecting data about the home buyer’s fam-ily members as well as his or her extendedfamily members.

Conclusions

The main purpose of this paper is to quan-tify home buyers’ preference toward dwell-ing units in a condominium complex, aproblem that has not been thoroughly inves-tigated in the housing research literature.We estimate a multinomial logit model anda mixed logit model using actual sales datafrom a condominium complex in the city ofBeijing. Results from the mixed logit modelprovide strong evidence that considerablebehavioural heterogeneity exists amonghome buyers. This also suggests that com-plex substitution patterns exist among dwell-ing units and the mixed logit model is moreappropriate for the dwelling unit choiceproblem.

Based on the WTP estimates from themixed logit model, we produce the

18 Urban Studies



appropriate price adjustments real estatedevelopers shall make for dwelling unitswith different attributes, for example, differ-ent floor levels, orientation and so on. Wecharacterise what an ideal dwelling unitlooks like in Chinese home buyers’ mindsand quantify the importance of factorsrelated to dwelling unit choice. We find thatthe top two factors that affect dwelling unitchoice are the floor level and the orientationof the unit, followed by the location in thecomplex, the location on a floor level andfinally whether both bathrooms areMingWei.

To further investigate the causes for thebehavioural heterogeneity among home buy-ers, we estimate each home buyer’s WTP forattributes where significant behaviour het-erogeneity is observed. We then conductregression analysis to explain the differencesin WTP across individuals by their socioeco-nomic characteristics. We find that the rela-tionships between the explanatory variablesand the dependent variables are reasonableand that home buyers with older ages,higher annual household income and previ-ous home ownership experience tend tofocus more on the quality of the dwellingunits, that is, they prefer to stay in units onhigher floor levels (but not on the top floor)with good orientation. Nonetheless, we alsonotice that the explanatory variables weexamine in the regression models onlyaccount for a limited portion of the prefer-ence heterogeneity. This signifies that morefactors need to be considered to account forthe preference heterogeneity in dwelling unitchoice behaviour.

A possible extension to this research is toadminister SP surveys to include broadersocioeconomic characteristics of the homebuyers as well as his or her family andextended families in the analysis. These datacan then be combined with the sales data toleverage the strengths from each data source.In this way, we can develop more effective

ways to segment home buyers according totheir preferences, which will assist real estatedevelopers in designing and pricing dwellingunits tailored toward each segment andimprove profitability.

Funding

This research received no specific grant from anyfunding agency in the public, commercial, or not-for-profit sectors.

Note

1. Note that the word ‘condominium’ can havedifferent meanings in different countries. InChina, a condominium (or a condo) refers tothe form of housing tenure and other realproperty where a specified part of a piece ofreal estate (usually of an apartment house) isindividually owned. It is an economical wayto own a home. This definition also applies tothe USA and most provinces of Canada. Inother countries such as Singapore, however, acondominium is used for housing buildingswith luxury features such as security guards,swimming pools and tennis courts.

References

Barrios Garcia JA and Rodriguez Hernandez JE

(2007) Housing and urban location decisions

in Spain: An econometric analysis with unob-

served heterogeneity. Urban Studies 44(9):

1657–1676.Ben-Akiva M and Lerman S (1985) Discrete

Choice Analysis: Theory and Application to

Travel Demand. Volume 9. Cambridge, MA:

MIT Press.Campbell D (2007) Willingness to pay for rural

landscape improvements: Combining mixed

logit and random-effects models. Journal of

Agricultural Economics 58(3): 467–483.Habib KMN and Kockelman KM (2008) Model-

ing the choice of residential location and home

type: Recent movers in Austin, Texas. Paper

presented at the 87th Annual Meeting of the

Transportation Research Board, Washington,

DC., 2008. Available at: http://www.caee.utex

as.edu/prof/kockelman/public_html/TRB08

HomeType%26Location.pdf.

Jiang and Chen 19



Heinzle SL, Boey Ying Yip A and Low Yu Xing

M (2013) The influence of green building certi-

fication schemes on real estate investor beha-

viour: Evidence from Singapore. Urban

Studies 50(10): 1970–1987.Hoshino T (2011) Estimation and analysis of pre-

ference heterogeneity in residential choice

behaviour. Urban Studies 48(2): 363–382.Ishikawa T, Nakata S and Asami Y (2011) Per-

ception and conceptualization of house floor

plans: An experimental analysis. Environment

and Behavior 43(2): 233–251.Liao Z (2011) A Practical Guide to Real Estate

Pricing. Beijing: China Architecture & Build-

ing Press.Quigley J (1976) Housing demand in the short

run: An analysis of polytomous choice. In:

NBER Explorations in Economic Research.

Volume 3(1). Cambridge, MA: NMBER,

pp. 76–102.

Skaburskis A (1999) Modelling the choice oftenure and building type. Urban Studies

36(13): 2199–2215.Train KE (2003) Discrete Choice Methods with

Simulation. Cambridge, UK: Cambridge Uni-versity Press.

Tu Y and Goldfinch J (1996) A two-stage housingchoice forecasting model. Urban Studies 33(3):517–537.

Walker J and Li J (2007) Latent lifestyle prefer-ences and household location decisions. Jour-nal of Geographical Systems 9(1): 77–101.

Wu W, Zhang W and Dong G (2013) Determi-nant of residential location choice in a transi-tional housing market: Evidence based onmicro survey from Beijing. Habitat Interna-

tional 39: 16–24.Yates J and Mackay D (2006) Discrete choice

modelling of urban housing markets: A criticalreview and an application. Urban Studies

43(3): 559.

20 Urban Studies



urban studies dwelling unit choice in a urban studies...

Documents