Socio-demographic Characteristics and The Preference of Bangkok s Condominium Location

Transcription

1 Senior Researh Soio-demographi Charateristis and The Preferene of Bangkok s Condominium Loation Rattanakorn Nitkitsomboon Advisor: Thanee Chaiwat, Ph.D. April 21, 2014 Senior Researh Submitted in Partial Fulfillment of the Requirements for the Bahelor of Arts degree in Eonomis (International Program) The Bahelor of Arts Program in Eonomis Faulty of Eonomis Chulalongkorn University Aademi Year 2013 Approve (Asso. Prof. Sothitorn Mallikamas, Ph.D.) Chairman Date of Approval

2 Abstrat This paper applied a hoie experiment tehnique to study the preferene of mid-range ondominiums loation in Bangkok, Thailand. Conditional logit models omprising loation attributes were estimated for all samples and various sub-samples regarding individuals soio-demographi harateristis suh as gender, household monthly inome, household size, urrent residential loation, and urrent workplae loation. The sub-samples aording to other individuals harateristis suh as travel harateristis and onsumer types were also onsidered in this study. The models were then used to ompute the marginal willingness to pay for eah loation attribute levels. The results found that rime rate is the most importane harateristi that affeting the ondo purhasing deision, followed by ondo zone, aessibility, and view, respetively. Further, different individuals harateristis were found to have different preferenes for ondominium loations. 1

3 Table of Contents Introdution... 4 Researh Objetives... 5 Sope and Limitation... 5 Literature review... 6 Review of housing loation hoie and housing preferene... 6 Review of stated preferene and hoie models... 8 Conlusion and ontributions... 9 Coneptual Framework Methodology Design of hoie experiment Survey and Sampling design Data Analysis Results and analyses Respondent Charateristis Condominium loation hoie model Preferene by respondent segments Conlusions Referene List Appendix Appendix A Survey Questionnaire Appendix B Regression results by segmetation

4 List of Tables Table 1: Condominium loation attributes and their levels Table 2: Choie set design Table 3: Level balane of the hoie set design Table 4: Soio-demographi harateristis of respondents Table 5: Travel harateristis of respondents Table 6: Respondents' middle-end ondominium purhasing behaviors Table 7: Marginal willingness to pay of the basi hoie model Table 8: Preditions for hoies Table 9:Soio-demographi harateristis used for segmentation Table 10: Travel harateristis used for segmentation Table 11: Condominium purhasing behaviors used for segmentation List of Figures Figure 1: An example of a hoie experiment used in the survey Figure 2: Relative importane of attributes (in perentage) Figure 3: Relative importane of attributes for onsumer types (in perentage)

5 Introdution Most of stated preferene studies employed ontingent valuation method in order to reveal the preferene. The method allows researhers to ask a respondent diretly for his or her willingness to pay for a speifi ommodity. However, it is not suitable to deal with ases of ommodities that have several attributes and where these attributes are hange at the same time, for example, in this ase is a ondo loation. As a onsequene, another stated preferene method alled hoie experiment was applied in this researh. The hoie experiment tehnique desribes goods in terms of their attributes and levels, in ontrast to ontingent valuation method where goods are desribed in terms of goods themselves. Thus, the trade-off an be expressed in terms of goods rather than in monetary value, like ontingent valuation, the willing to pay an be revealed indiretly for this kind of situation. The hoie experiment motivated this researh to study in ondominium loation sine there are few studies using the hoie experiment in real estate eonomis. Most of papers applied this method in transportation and environmental eonomis. Moreover, the ondominium market has been strongly grown in Bangkok sine the last two years, with pries rising and developers launhing new projets to trat the demand. Espeially in the areas with BTS sky train and MRT subway routes attrat more ommuters to live in. Aording the researh published by Colliers International Thailand, average pries for new ondominium projets in Bangkok inreased by 21% quarter-on-quarter. In the fist half of 2013, the number of the new launhes was 12% higher ompared with the same period of Another reason is that ondominium loation is a key fator influening onsumers buying deision and pries. Average selling pries were reorded at approximately 89,000 baht per square meter. The researh also indited that a prie delines one a ondominium is loated more than 200 meters from a mass transit station, and substantially drops one the distane is more than 1,000 meters. Furthermore, the ondo loation has various attributes or harateristis in terms of zone, aessibility, view, et. Sine the ontingent valuation tehnique is inappropriate when evaluating goods with no attribute, in order to examine the preferene of eah harateristi of a good, this paper attempts to develop the hoie experiment tehnique in order to evaluate the ondominium features in terms of the loation harateristis, in partiular, in Bangkok aording to soio-eonomi harateristis of Thai middlelass that are the major onsumers of ondo market. In addition, the findings arried out in this paper may be very useful for Bangkok ity planning and Bangkok poliy beause it provides information about the distributed preferene of residential loation regarding different soio-demographi harateristis of Bangkok residents. 4

6 Researh Objetives The main purpose of this study is to determine how the preferenes for the loation of ondominiums differ regarding to soio-demographi harateristis suh as gender, household inome, household size, urrent residential and workplae loation. This fouses partiularly on the ase study in Bangkok, Thailand. To be more speifi, this paper aims to examine the relative importane of loation fators or harateristis that influene the deision on seleting a ondominium. In addition to the willingness to pay of different features among eah loation harateristi an be ompared, and then their magnitudes an be identified in terms of the perentage. Sope and Limitation Pratially, the ondominium setor is broad with various segments. The fous of this researh is partiularly mid-range ondominiums in Bangkok, sine the ondominium segment provides the largest market share in the Bangkok s ondo market. Moreover, in fat there are many harateristis of ondominiums, for example, dwelling harateristis the number of rooms, room sizes- neighborhood harateristis, and loation harateristis. As a result of one of the key fators that influene onsumers on their ondominium buying deision is a loation as well as typially the pries of ondominiums are lassified aording to loations, this study onsiders only on the loation harateristis of ondominium. However, due to time, logistial and finanial limitation, this paper onsiders merely on the ase in Bangkok, and the sample size was limited to 375 with random sampling. The estimations of the results were assumed that the respondents in this study refer to the population in middle lass of Bangkok. Furthermore, the brand of a ondominium was not labeled in the survey question or the study. There was just neessary bakground information provided the fats for respondents to analysis. Lastly, only demand side of mid-end ondominium was onsidered in this study. 5

7 Literature review This overall review fouses mainly on areas where the existing researh most losely relates to this paper. First of all, most of reviewed literatures involve the findings of fators influening the housing loation hoie, in partiular soioeonomi fators, as well as housing loation preferene. Moreover, the seond part of this review disusses a little on the tehnique of olleting data and method used in the analysis. At the end of the literature review informs a onlusion of the methodology and ontribution of this paper. Review of housing loation hoie and housing preferene A wide range of researh disusses on the determinants of housing loation hoies, regarding housing mobility and loation hoie lassify the variables affeting housing hoies into four groups. The first group is demographi fators suh as inome, age, and family size. The seond one is soial fators suh as shool quality, ethniity, and rime. The third one is loation and neighborhood fators, namely, aessibility, density and amenity, and taxes. And the last group is housing ost and affordability. O Sullivan (2009) studied the role of household preferene and lifestyle in housing loation hoie. That is, a high-inome household is more likely than a lowinome household to hoose large, high quality housing. The reason simply is the high-inome household has a greater housing budget. At the same time, even though two households have the same inome and budget on housing, the dwelling hoies may not be alike, due to differene in their preferenes for dwelling type. Jun (2013) applied a random utility-based land use simulation model of the Seoul metropolitan area to evaluate the impat of medium- and high-inome households preferene for apartments on residential loation hoie. The findings imply that apartment preferene of medium- and high-inome groups would have ontributed to providing more apartment units, more housing unit in suburbs, and higher apartment rent premiums in wealthy ommunities than the housing market assuming under the onsideration of housing type, loation, and rents. The different between male and female on the deision of seleting housing loation has studied in various researhes. Sermon and Koppelman (2001) developed multinomial logit models of residential loation hoie for two-worker households in the San Franiso Bay Metropolitan Area to identify household harateristis in relation to the relative differenes in household sensitivity to female and male ommute time when making residential hoies. The disrete hoie approah is used to provide diret analysis of gender disparities in ommute length. The approah bases on the basis of random utility theory, whih say that households trade off various loational attributes when hoosing the loation that maximizes their utility. The results reveal that preferene of hildren, oupation of the male worker, and the relative order of the last residential hange and the female workers workplae are 6

8 important determinants of female and male ommuting time parameters in household residential loation utility funtions. The paper seems to support the study of Turner and Niemier (1997) that found that woman have ommute time less than men beause women tend to perform more of household maintenane and hild- rearing responsibilities. Their result identifies that presene of hildren is a strong fator in explaining differenes in female and male ommute times. Furthermore, Hanson and Pratt (1995) finding suggests that male workplae hanges lead to household residential loation hanges and female workplae hanges, and this pattern leads to shorter female and longer male ommuting times. Some researhes investigate the impat of loation harateristis and overall individual soioeonomi harateristis in determining the residential loation hoies of households. Wu, Zhang, & Dong (2012) used a large-sale household survey and aggregated ensus data from Beijing and the hybrid onditional logit model of residential loation hoie to estimate the impats of both loation-speifi harateristis and individual-speifi harateristis on households residential preferene. It assumes that the hoie outome of one household will not affet the neighborhood omponents, the loal publi goods distribution or even the housing market struture in the ity. The result found that loation-speifi fators, suh as distane to the entral business area, job aessibility, loal publi goods as well as soio-demographi attributes of residential zones are strongly importane in determining the loation hoie of households. However, most of these loation speifi fators, when interated with individual-speifi harateristis suh as inome, ommuting time, eduational attainment reveal substantial differential impats in residential hoie aross the urban spae. In addition, household residential loation hoie in Beijing show a strong soial and spatial differentiation with respet to variations in household inome, ommuting time, eduation bakground, lifeyle, oupation, and housing soures. Wang and Li (2006) applied state-of-the-art experimental design methods to develop hoie experiments in Guangzhou, China. Beside, they examined two hoie dimensions of housing preferene while most studies examined only one. Thus, Joint logit models omprising both neighborhood and dwelling attributes are estimated for the overall sample and for various sub-samples lassified by family inome, age, eduation, nature of employment organizations, and distrit of urrent residene. The models are then used to ompute utilities for different attribute levels, the impats of these attributes on hoie probabilities, and the relative housing pries that the respondents are willing to pay for buying a house in different distrits, with different aessibilities, of different types, et. The study shows that neighborhood and loation-related attributes are found to be more important than dwelling-related attributes in home purhase deisions. Further, fators suh as family inome, age, eduation, nature of employment organization, and urrent residential loation affet housing preferene. In omparison with the high-inome group, the low and medium inome groups show stronger preferene towards the inner ore distrits and plae 7

9 more importane on living onveniene and aessibility to publi transport. The high-inome group, however, is more willing to move to outer distrit and pay more attention to the quality of dwelling. Similarly, people with less eduation attainment show stronger preferene towards the inner ore distrits. While the age effet is generally not strong, the findings show that the younger age group partiularly favors the suburb that ontains the new entral business distrits. Lastly, people working in state work units are generally more onerned with neighborhood seurity and the internal design of the building. As have seen in the reviewed literatures, the relevant literatures show that soioeonomi or individual harateristis have an impat on housing loation hoie, for example, high- inome groups have preferenes more for housing size in suburbs and higher premiums in wealthy ommunities while low-and medium-inome groups prefer inner ore distrit, living onveniene, and aessibility to publi transport. Less eduation people are more likely to move in inner distrit whereas the younger age groups favors outer distrit. In addition, gender is found to have a differene in deision making on housing loation through female have less ommute times than male. Most researhes use the disrete hoie model and random utility theory as a onept to analyze the individuals preferene. And different types of logisti model are employed as a tool of analysis. The major differene is the data used for the analysis. Some studies either use the existing data in the market, known as reveal preferene data or non-market information known as stated preferene data. Some use both kinds of data for the analysis. And this will be disussed in the next part of this literature review. Review of stated preferene and hoie models The stated preferene method has proved to be partiularly useful where there is an absene of atual market information from whih preferenes an be revealed (Walker et al., 2002). Few studies apply stated preferene method as well as use experimental data in evaluating housing loation preferene (Timmermans and van Noortwijk, 1995; Wang and Li, 2006). Disrete hoie experiments were developed widely in the areas of transport eonomis, tourism and rereation researh, health eonomis, marketing, environmental eonomis, and teleommuniations. For example, Ryan, Bate, Eastmond, and Ludbrook (2001) applied the tehnique to rheumatology outpatient linis. Dellaert,Borgers and Timmermans (1995) introdued and tested a hoie experiment approah to modeling Duth urban tourists hoie of ativity pakages for a weekend in Paris. Kemperman, Borgers, Oppewal and Timmermans (2000) examine the hypothesis that theme-park hoie is partly influened by variety-seeking and seasonality effets in The Netherlands. Birol, Karousakis, and Koundouri, (2006) employed a hoie experiment to estimate the values of hanges in several eologial, soial and eonomi funtions that the Cheimaditida wetland provides to the Greek publi. 8

10 In a situation of non-market goods that is a ondominium in this study, hypothetial senarios an be reated in order to find the preferenes and put a willingness to pay on this. This tehnique is alled a stated preferene method (Alpizar et al, 2001; Bateman et al, 2002). It is separated into two groups: hoie modeling tehniques (CM) and ontingent valuation method (CVM). For ontingent valuation, respondents are given information on a non-market good and asked for their willingness to pay for the good. Suh a irumstane runs the risk of overestimate the WTP. One reason is that respondents are asked to valuate the goods that they might not have atual monetary value. Hene, it is diffiult for respondents to know how to value the good in the questions. Seondly, when the senario is hypothetial, respondents know that they will not be fored to pay the money in real life and therefore have no inentive to state the WTP losely to their real WTP. Moreover, the method is not suitable in the ases where hanges are multidimensional, suh as a ase where several ondo loation harateristis are altered at the same time (Hanley et al, 2001). Choie modeling (CM) is ommonly used in the survey where goods are desribed in terms of attributes and levels, in ontrast to CVM where the good is desribed as the good itself and thus WTO is stated diretly (Alpizer et al, 2001). This is the reason why the model highly has been used in marketing purposes. In addition, the trade-off an be presented in terms of goods rather than in monetary terms, like in CVM. This property an redue the risk of overestimating WTP (Hiselius, 2005). However, CM has the risk of reating a ognitive burden for the respondents. Too many attributes and levels inluded make a survey too diffiult for respondents to omprehend. Further, it is sensitive to the survey design and that the aurate levels and desriptions are inluded in the hoie sets (Bateman et al, 2002). Choie experiment (CE) is a subset of the CM tehnique and has its origin in onjoint analysis, whih is a well-known method applied in marketing studies to aess the demand for new produts. The CE method based on the random utility theory. This is in line with the onsumer demand theory developed by Lanaster. Aording to Lanaster (1966), utility is derived from attributes or harateristis of goods instead of diretly from the goods themselves. Conlusion and ontributions In fat, the full information of ondominium loation preferene in Bangkok is not available. The stated preferene method is probably the best hoie for modeling the preferene of ondominium loation. In this study purposes to employ disrete hoie models in order to model the preferenes of ondominium loation in Bangkok as well as apply disrete hoie experiment approah as a tehnique of olleting stated preferene data whih is similar to the study of Wang and Li (2006). This study has two features that distinguish it from the existing literature, in terms of the method of revealing individuals housing preferene on loation. Firstly, 9

11 many studies have presented the impat of soioeonomi harateristis on housing loation preferene using reveal preferene data or stated preferene data by using a CVM. Few have however developed CE tehnique to ollet the required information used for the analysis in the housing area. The approah has an advantage in whih it allows to imitate the real situation and respondents are required to make deisions by maximizing their utility orresponding to multi-attributes hanges in goods. This implies that ondominium loation hoie deision omprise of multiple dimensions suh as prie, zones, aessibility, senery, neighborhood, et. Ones have to trade off their utility aording to some ombination of hoies. Moreover, the tehnique avoids the problem of overestimating WTP sine the trade-off is expressed in terms of attribute hanges in goods rather than monetary value and thus WTP is referred indiretly. Lastly, this study presents the first attempt to apply hoie experiment approah in housing loation deision, speifially applied to ondominium in Bangkok, a apital ity of Thailand. 10

12 Coneptual Framework In order to evaluate the preferenes of mid-end ondominium onsumers, this study applied the hoie experiment method where respondents are presented with various hoie situations of two or more alternatives and asked to hoose the most preferred. One senario is generally defined as a status quo, for example, no buy or to stik with a default senario. Eah senario is desribed by a number of levels of a set of attributes and attributes an be qualitative or quantitative in nature. The hoie experiment (CE) is based on the random utility theory (RUT). It builds on the disrete hoie theory that used to derive onventional demand urves (Bateman et al, 2002). Aording to the RUT, the individual s indiret utility funtion (U) omposes of two parts, a deterministi element V and a stohasti element, ε. U = V + ε (1) The deterministi elements are fators or attributes that researher an observe while the stohasti elements are unobservable fators that affet hoies. The utility funtion is derived from the goods attributes rather than the goods itself. The ontribution of eah attribute or the relative importane is the part-worth of the utility. In this study, V (linear-in-parameters) expresses the relative importane of hoie attributes suh as area, aessibility, view, and rime rate. Various goods, ondos in this ase, have different attributes. The alternatives seleted by partiipants will typially maximize their utility given their inomes and others are onstant. In a hoie set C with J number of alternatives an individual will hoose alternative a over the other alternatives in a hoie set C if the utility reeived from alternative a is the highest among all alternatives. This an be desribed as following: U a > U j (2) Where U a represents the utility of alternative a. U j is the utility of other alternatives in hoie set C. The utility of an individual an be expressed as: U a = V a + ε a = βx a + ε a for alternative a (3) U j = V j + ε j = βx j + ε j for other alternatives in hoie set C Where β is the vetor of the preferene of oeffiients in relation to attribute X. Substituting (3) into (2) will provide: V a + ε a > V j + ε j (4) (V a - V j ) + (ε a - ε j ) > 0 11

13 Sine a stohasti omponent is in the utility funtion, no ertain preditions an be made and the analysis beomes the probability of hoosing alternative a. That is: Prob(U a > U j ) = Prob[(V a - V j ) + (ε a - ε j ) > 0] (5) The equation (5) is true under assumptions on a probability distribution of the error part ε are made. It is normally assumed to be independently and identially distributed with an extreme value (Gumbel) distribution. The distribution of error term implied that the probability of a being hosen as the most preferred an be expressed in terms of the logisti distribution, know as onditional logit model (Mfadden, 1973), as shown in equation (6): (6) Explanatory variables X e.g. ondo loation attributes assume different values in eah alternative and the probabilities rely on the differene in the value of the harateristis or attributes aross alternatives. When hoies among a set of ommodity are made based on their harateristis or attributes, onditional logit model is the most ommonly used (Ben-Akiva & Lerman, 1985). The seletions from a hoie set should fulfill the ondition of independene from irrelevant alternatives (IIA) property, whih stated that the relative probabilities of two options being hosen are unaffeted by the introdution or removal of other alternatives (Bateman at al, 2002). The hoie model is estimated using maximum likelihood estimation proedures and assumes liner-in-parameters funtional form for deterministi portion of the indiret utility funtion (Ben-Akiva and Lerman, 1985). The oeffiients of the variables in the utility funtion are the marginal utility of the variables. Marginal rate of substitution (MRS) is defined as the negative ratio between two attributes. When one of these is a ost attribute, marginal willingness-to-pay (WTP) or impliit pries for various ondo loation attributes an be alulated. It is onsistent with the utility maximization as long as a status quo or will not buy option is inluded (Hanley, Mourato and Wright, 2001). Thus the marginal WTP is equal to (7) 12

14 Methodology Design of hoie experiment In this researh hoie experiment is the main methodology. It is an attributebased stated preferene method or hoie-based onjoint analyses where a respondent is presented to a hoie set of alternatives and diretly asked to hoose the most preferred one. It is used in estimating the level of preferene and eonomi values of hanges in attributes or harateristis of goods. Sanko (2001) provided the guideline for stated preferene experimental design. There are three steps involved in the design of hoie experiment for this study. First, the definition of attributes and their levels are reated. Then, hoie sets of the experiment are designed. Lastly, the hoie experimental design is employed and developed in a questionnaire Condominium loation attributes and their levels An attribute is a harateristi or feature of an alternative. A level or attribute level refers the value of an attribute. One attribute an have two or more levels. For this study, there are five attributes inorporated in this experimental design, namely, ondo zone, aessibility, view, rime rate in ondo loation, and prie of mid-end ondominiums. All attributes are onsidered generi or unlabeled. Eah attribute has three levels. The identifiation of attributes and attribute levels rely on the purpose of this study. The ondominium loation attributes and attribute levels used in hoie experiment design are presented in Table 1. The lassifiation of ondo zones in this experiment is based on the information by the environmental quality ontrol and management division, Bangkok. Aording to the ity division in 2001, inner ity omprises of 21 distrits suh as Pranakorn, Pomprabsadtruprai, Sumparnthawong, Pathumwan, Bang Rak, Yannawa, Sathorn, Bang Khor Lam, Bang Sue, Phayathai, Dusit, Rathatewi, Huaykwang, Khlong toei, Chatuhak, Thonburi, Khlong Sarn, Bangkok Noi, Bangkok Yai, Din Dang, and Wattana. Urban fringe or middle ity is omposed of 18 distrits suh as Prakanong, Bang Khen, Pra-vate, Bang Kapi, Ladprao, Bueng khum, Bang Plud, Phasiharoen, Chomthong, Ratburana, Suan Luang, Bang Na, Toong Kru, Bang Kae, Wangthonglang, Khunnayao, Saparnsoong, and Saimai. Finally, suburb or outer ity omprises of 11 distrits suh as Meenburi, Don Meung, Nong Jok, Ladkabang, Talinghan, Nong Kham, Bang Khun Tien, Laksee, Khlong Sam Wa, Bang Bon, and Taweepattana. The attribute levels of aessibility are mass transit lines, workplae, and Shopping enters or onvenient store. Mass transit lines refer to BTS, MRT, and Airport rail link. Workplae in this study represents the respondents workplaes. And shopping enters or onvenient stores inlude Central, The Mall, Robinson, Teso, 13

15 BigC, Seven eleven, Max value, et. Speifially, the aessibility means that respondents an approah to one of those three plaes within 15 minutes. Table 1: Condominium loation attributes and their levels Attributes Levels Variable name Desription Condo zone (Zone) Inner ity Middle ity Inner Middle Loation of a ondominium lassified by zone Outer ity Outer Aessibility Mass transit line Workplae Shopping enters/convenient stores Mass Workplae Shopping Plae around a ondominium with aess time of 15 minutes View River Open spae River Open Senery surrounding ondominium a High building Building Crime rate (Crime) Low level Moderate level Good Avg Level of rime rate in ondo loation High level Bad Prie 150,000 baht/sqm 108,000 baht/sqm 74,000 baht/sqm View attribute onsists of river, open spae or landsape without high buildings, and high buildings. The view is defined as the senery that residents an view outside the window from their ondominiums. Crime rate represents the average numbers of rime our in a distrit per day. The ats like murder, rape, and theft are onsidered as rime. As reported by the metropolitan polie bureau in 2013, the average rime rate is 6 per day per 1 distrit in Bangkok. In this experimental design the low level of rime rate is 8 and the high level of rime rate is 4. For the prie of mid-end ondominiums, their levels were obtained from thinkofliving.om. A mid-range ondo segment an be divided aording to the prie in terms of baht per m 2 into three lasses. One is a main lass, whih widely 14

16 onstruted to attrat onsumers in mid-end segment nowadays. Its prie is in the range of 70,000 to 90,000 baht per m 2. Seond, upper lass ondominiums mostly are loated lose to mass transit links with the average prie of 90,000 to 120,000 baht per m 2. Lastly, high-lass ondominiums generally are established in very good loation or along mass transit lines. The average prie of this lass is in the range of 120,000 to 160,000 baht per m 2. Those prie levels in hoie experiment design are arbitrary set. The ranges between levels are high enough to make respondents trade off between two hoies Choie sets reation Given a design with five three-level attributes, there are = 3 5 = 243 possible ombinations or profiles of attributes levels that an be reated. Eah ombination explains one alternative. This full fatorial design is orthogonal; that is, it ensures that attributes presented to respondents are varied independently from one another. This avoids multi-olinearity between attributes, whih is a ommon problem of reveal preferene data. Also, all main effets an be estimated and are unorrelated among desired harateristis. However, suh a design is very large and not tratable in a hoie experiment. Therefore, it is neessary to redue the number of ombinations. One solution is a frational fatorial design. For this hoie experiment design, only main effets were onsidered for estimation. Although higher order interations may be better, main effet design is enough to explain the hoie made by respondents. Aording to Louviere et al. (2000), main effets ommonly aount for 70% or more of explain variane, twoway interations typially aount for 5% to 15% of explained variane, and higherorder interations explain the remaining varianes. Following the proedure desribed by Sanko (2001), the shifting design was used in this study. The shifting method uses the orthogonal main-effet array as the first alternative in eah hoie set. This method reates one additional alternative by adding a onstant to eah attribute levels of the initial alternative. In addition to it still maintains the orthogonality of the starting alternative. R statistial software was used by employing the support.ces pakage (Aizaki, 2012). The funtion rotaion.design weas applied to generate hoie sets. This ommand reates an unlabeled type of CE design that an ontain generi attributes in the utility funtion of onditional logit model. For this study, 3 bloks with 6 hoie sets eah of 2 alternatives were designed as shown in Table 2. The blok design requires the division of the hoie set into sub-sets known as Blok. This approah is in line with the assumption that the preferenes of the samples of respondents are homogeneous. Inevitably, differenes between individuals will inrease the error of the results. 15

17 Table 2: Choie set design Choie Set Blok Alternative 1 Alternative 2 Design Codes Condo Zone (1 st olumn) Inner ity Middle ity Outer ity Aessibility (2 nd olumn) Mass transit line Workplae Shopping onveniene View (3 rd olumn) River Open spae High building Crime rate (4 th olumn) Good Average Bad Prie (5 th olumn) ,000 baht/sqm 2-108,00 baht/sqm 3-74,000 baht/sqm Finally, the hoie set design was evaluated aording to four riteria of the effiient design (Huber and Zwerina, 1996): 1. Orthogonality. The variations of the attribute levels should be unorrelated for ombinations hosen in all hoie sets. With the rotation design by support.ces in R, the hoie set design maintains this ondition. 2. Level balane. The level of eah attribute should appear with equal frequeny in the design. In this design of hoie experiment, level balane was ahieved as presented in Table 3. 16

18 3. Minimal overlap. The same attribute level should not repeat in the same hoie set. Due to the shifting method, there is no overlap in this experimental design. 4. Utility balane. The utility in eah of two alternatives in a hoie set should be set equal. However, there is no good basis to estimate the values of the parameter oeffiients, this study did not onsider this riterion. Table 3: Level balane of the hoie set design Attribute Levels Number of Appearane in the Design Zone Aessibility View Crime Prie Employing the hoie experiment By the hoie set design the 18 hoie sets were divided into 3 bloks with 6 hoie sets eah. As a result, three versions of the questionnaire were onduted with the hoie experiment. The reason to reate the blok design is to not make it too demanding to respondents. Eah blok was answered by an equal number of respondents. Furthermore, the final hoie sets are omplemented with a no-hoie alternative sine foring individual to make a deision to selet between the two alternatives ould overestimate the results. Moreover, the ordering of the attributes was randomized in eah hoie set to prevent learning whih might result in biased hoies by the respondents. Another onsideration was the onsisteny of respondents. This survey inluded a test for transitivity. The seventh hoie set was dupliated the first in eah version of questionnaire, but with hange in the attribute levels in one of the alternatives. For this survey, the attribute prie was hanged in alternative 2 by reduing one level of prie. Therefore, the utility of alternative 2 is higher, whereas the utility of alternative 1 remains the same as in hoie set 1. If the respondent hose alternative 2 in hoie set 1, it should hoose 2 in the seventh as it is better. Hene, the riteria of transitivity would be fulfilled. If the respondent hose 2 in the first hoie set and hose 1 in the seventh, the answer is inonsistent. However, if the respondent hose 1 in both hoie set 1 and 7, suh a respondent annot be tested. If the individual hose the alternative Neither in hoie set 1 then hose 1 in hoie set 7, whih has less utility than 2, the person had violated the transitivity. All respondents who failed the test of onsisteny were removed from the final data set. An example of a hoie set is shown in Figure 1. 17

19 Figure 1: An example of a hoie experiment used in the survey Choie Set 1 Condominium A Condominium B Aessibility Mass transit lines Workplae Crime rate (times/day/distrit) Very Bad Bad Average Good Very Good Very Bad Bad Average Good Very Good Condo Zone Inner ity Middle ity View River Open spae Prie (million baht/45 m 2 ) Whih one do you prefer? Neither Survey and Sampling design The questionnaires were diretly distributed to random respondents by a researher. In addition to the hoie experiments, the questionnaires were addressed the data needed for the study, for example, soioeonomi and demographis information of respondents, travel behavior, and purhasing behavior. Moreover, the definition for eah attribute and attribute level is learly identified to respondents. The entire survey is found in Appendix A. Three different versions of surveys were randomly given to 125 respondents for eah, giving a total of 375 respondents. A onveniene sampling was an option of this survey. The respondents of this study were mater degree or dotorial students from the faulty of ommere and aountany and the faulty of eonomis, Chulalongkorn University. Respondents were randomly seleted on the spot to omplete the questionnaires. Data Analysis 1.4. Making a design matrix The funtion make.design.matrix in the pakage support.ces in R is able to onvert the CE design into a design matrix that is suitable for the onditional logit model. In this CE design, the variables zone, aessibility, view, and rime were treated as ategorial variables, while the variable prie was assigned as a ontinuous variable. A unit of the prie attribute is a thousand baht per m 2. The ategorial 18

20 attributes were onverted into dummy variables. For example, in a ategorial attribute zone with the three levels - inner, middle, outer - dummy variables are reated as follows: Sine the CE design is unlabeled, two dummy variables are reated, a dummy variable middle is assumed as the value 1 when the attribute zone takes middle and 0 otherwise, and a dummy variable outer is assumed as the value 1 when the attribute zone takes outer and 0 otherwise Making a data set The ommand make.dataset reates a data set suitable for a funtion logit in the pakage Survival by ombining a data set inluding information of the response to the CE questions and a data set inluding a design matrix related to these questions. The respondent data set has to ontain the variable ID, orresponding to respondent s identifiation number and the variable BLOCK, orresponding to the serial number of bloks to whih eah respondent is assigned and response variable orresponding to the answers to eah CE questions. Additionally the individual s harateristis variables are inluded in the respondent data. For the response variables, eah alternative must be assigned an integer value starting from 1. For example, if respondent answers alternative 1 in question 1 (q1), alternative 2 in question 2 (q2), and alternative neither in question 3 (q3), the response variables q1, q2, q3 - are assigned as 1, 2, 3, respetively. In this study, eah respondent was required to answer 6 CE questions with 3 alternatives inluding an opt-out option, the number of the observations in the data set reated by the design matrix and the respondent data set is equal to 18 for eah individual Choie modeling After preparing a data set that fits the hoie experiment into a onditional logit model in order to estimate the oeffiient of the utility funtion for ondominiums, the funtion logit in the pakage Survival was used. The basi utility speifiation is as follows: U = β inner Inner + β middle Middle + β outer Outer + β mass Mass + β workplae Workplae +β shopping Shopping + β river River + β open Open + β building Building + β good Good + β avg Avg + β bad Bad + β prie Prie In this model, all variables are ategorial and dummy-oded, exept for Prie variable is treated as a ontinuous variable to be onvenient for the alulation of the marginal willingness to pay. Furthermore, all the respondents who are inonsistent were removed from the data set. 19

21 Results and analyses The olleted data of 292 respondents out of 375 respondents was analyzed. All of the unompleted and inonsistent questionnaires were removed from the analysis. In the first part of the results is a desription of the samples of respondents that will be used in order to estimate the ondominium loation hoie model in the next setion of these analyses. Respondent Charateristis The respondents are lassified into three types of harateristi, that is soiodemographi harateristis, travel harateristis, and purhasing behaviors. 1. Soio-demographi Charateristis Table 4: Soio-demographi harateristis of respondents Charateristi Perentage Mean Age (years) Gender Female Male Monthly household inome (baht) Below 30,000 baht 30,000 50,000 baht 50, ,000 baht 100, ,000 baht 150, ,000 baht Over 200,000 baht Oupation Employed Self-employed/Businessman None Household Size Households with hildren 61.3% 38.7% - 5.8% 16.4% 21.2% 15.1% 14.0% 27.4% 68.8% 13.4% 17.8% % ,

22 Households without hildren 89.7% - Current residential loation Inner distrit Middle distrit Outer distrit Current workplae s loation Inner distrit Middle distrit Outer distrit 50% 28.8% 21.2% 77.7% 9.3% 13.0% The soio-demographi harateristis of the respondents are summarized in Table 4. The average age of this sample is 27.6 years. 61.3% are female and 38.7% are male. On average, the household monthly inome is 148,500 baht. 27.4% are ontributed to monthly household inome of at least 200,000 baht and 21.2% are the range between 50,001 and 100,000 baht, respetively. More than half of respondents, 68.8%, are employees whereas 13.4% are selfemployed or businessmen. The remaining have no oupation and are undergraduate Master degree students. The mean of household size is 4. As shown in Table 4, households with hildren under 15 years old are only 10.3% of samples. Half of the respondents are living in inner distrits. 28.8% and 21.2% are living in middle and outer distrits, respetively. The urrent workplaes of this sample are loated in an inner area by 77.7%. However, respondents workplaes are resorted in suburban by 13% but in middle region by 9.3%. 2. Travel Charateristis Table 5 shows the result of travel harateristis of respondents. Average travel time from respondents urrent residenes to their workplaes is 48.1 minutes. More than half (51.7%) of these samples do not use their private vehiles in order to travel to their workplaes while the remaining of the samples do. Table 5: Travel harateristis of respondents Charateristi Perentage Mean Travel time (mins) Private vehile user Yes No 48.3% 51.7%

23 3. Condominium Purhasing Behavior Respondents are lassified in terms of onsumer types as shown in Table % are ontributed to onsumers who have already bought a ondominium within the last two years, 45.2% are respondents who would like to buy a ondominium within the next five years, and 43.2% are those who neither bought a ondo nor will buy one in the future. Note that there are some respondents being both already onsumers and potential onsumers at the same time. The mean of willingness to pay for mid-range ondominiums in Bangkok is 102,600.6 baht per square meter. Most of respondents of 43.5% are willing to pay at the range of 70,000 to 90,000 baht per square meter. 32.5% of them are willing to pay 90,001 to 120,000 baht per square meter. For the middle-end ondominium with the prie over 160,000 baht per square meter is the least willing to pay by the respondents. The results reflet that most of the respondents in this study are middle lass or middle-inome onsumers. Table 6: Respondents' middle-end ondominium purhasing behaviors Consumer type Charateristi Perentage Mean Already onsumer Potential onsumer Non-onsumer Willingness to pay (baht/sqm) Below 70,000 70,000 90,000 90, , , ,000 Over 160, % 45.2% 43.2% 13.7% 43.5% 32.5% 7.5% 2.7% ,

24 Condominium loation hoie model In this setion, a basi onditional logisti model was firstly estimated, using R statistial software with the pakage support.ces and pakage Survival. In Model 1, all the explanatory variables were ategorial and dummy-oded exept for the Prie attribute was treated as a ontinuous variable. It is neessary to onvert the prie attributes to a quantitative variable in order to ompute for the willingness to pay of the respondents for speifi loation of ondominium attributes. This will provide a single oeffiient for the prie attribute. For dummy-oded attributes, one of the variables was set as the base variable. All estimated oeffiients are interpreted relative to these base variables. Quantitative variable Prie did not require a base variable. The base variable used for eah attribute were: Attribute Condo zone Aessibility View Crime rate Base variable Inner Mass transit line River Good The regression result of the basi model is shown in Model 1 as following: Model 1: Basi ondominium loation hoie model oef exp(oef) se(oef) z p ASC e+00 Middle e-03 Outer e+00 Workplae e-04 Shopping e-13 Open e-01 Building e-02 Avg e-05 Bad e+00 Prie e-14 Likelihood ratio test = 603 on 10 df, p = 0 n = 5256, number of events =

25 Rho-squared = Adjusted rho-squared = Number of oeffiients = 10 Log likelihood at start = Log likelihood at onvergene = Table 7: Marginal willingness to pay of the basi hoie model MWTP 5% 95% Middle Outer Workplae Shopping Open Building Avg Bad Results in Model 1 show that all parameters are highly signifiant at 1% signifiane level exept for the Open Spae variable. The signs of the parameters met logial expetations. For an interpretation of the regression result in logisti model, variables are ompared within eah ondo loation attribute in terms of the hanges in odds ratio (exp(oef)) relative to base variables, holding the other attributes at their mean values. However, the marginal willingness to pay shown in the Table 7 will hange with the assumption of values at whih other variables are held onstant at their urrent inadequate levels. As reported by the Model 1, ondominiums in middle and outer zones derease the possibility of a ondominium being hosen by 21.1% and 54.9%, respetively, relative to ondominiums in inner zones. This an be explained that sine the majority of offies, workplaes, or business areas are typially settled in an inner ity more than middle and outer ities in Bangkok, therefore, residents greatly prefer to live in ondos loated in entral business areas around inner zones. Table 7 shows that, on average, respondents are willing to pay an extra 26,522 baht per square meter and 89,200 baht per square meter, respetively, for a ondo in inner distrits, as ompared with buying one in middle distrits and suburban. 24

26 Condominiums loated within 15 minutes aessing to residents workplaes and shopping malls or onvenient stores derease the probability of being hosen by 25.3% and 47.2%, respetively, ompared with those resorted lose to mass transit lines. Alternatively, respondents prefer to live in a ondominium that they an easily aess to mass transit stations more than their workplaes and onveniene stores, orrespondingly. They are willing to pay more for a ondominium with quikly aessing to mass transit routes than their workplaes and onvenient stores by 32,714 baht per square meter and 71,510 baht per square mete, respetively. Condos with open spae view have greater probability of being hosen than those with river view by 0.8%. However, the open spae attribute is not signifiant. A ondo with river view has higher probability of being hosen than one with high building view by 18.6%. This an be attributed to the pereption of high buildings as obstales to a landsape surrounding a residene. Base on the results, respondents are willing to pay an additional 898 baht per square meter to trade a river view ondo for a open spae view ondo, and a redution of 22,993 baht per square meter for one with high building view. As logial expetations, ondominiums in the areas with average level of rime rate and high level of rime rate derease their odds of being hosen by 28.1% and 73.6%, respetively, relative to those in the areas with low level of rime rate. Table 7 shows that ondos with low rate of rime need a premium of 36,994 baht per square meter and 149,148 baht per square meter, orrespondingly, over those with moderate rate of rime and high rate of rime. Lastly, an additional of 1000 baht per square meter in prie of a middle-end ondominium derease the possibility of being hosen by 0.009%, in other words, the prie barely effets the probability of hoosing a mid-range ondominium. Figure 2 indiates the relative importane of eah attribute by omputing the attributes ranges. An attribute s range is the different between the highest and lowest estimated oeffiients of its levels. The ranges of all attributes are summed and the ontribution of eah attribute is alulated aording to its proportion. Figure 2: Relative importane of attributes (in perentage) Zone# Aessibility# 23%# 27%# View# 6%# Crime# 44%# 0%# 5%# 10%# 15%# 20%# 25%# 30%# 35%# 40%# 45%# 50%# 25

27 This figure demonstrates that rime rate is the most important attribute, with a relative importane of 44%. This is followed by the attributes zone and aessibility, with a relative importane of 27% and 23%, orrespondingly. The view is learly the least important attribute. The atual probability of being hosen for eah loation harateristi of a ondominium is given in Table 8. Table 8: Preditions for hoies Attribute Variable Exp(oef) Probability Zone Inner % Middle % Outer % Aessibility Mass transit line % Workplae % Shopping enter % View River % Open spae % High building % Crime rate Good % Average % Bad % 26

28 Preferene by respondent segments The basi ondominium loation hoie model was used to estimate on various segments of the respondents. The segments were made aording to soiodemographi harateristis, travel harateristis, and onsumer types. All of the regression results and the estimation of marginal WTP for eah respondent segment are presented in Appendix B. The soio-demographi harateristis used for segmentation are desribed as shown in Table 9. Table 9:Soio-demographi harateristis used for segmentation Charateristis Segments Remarks Gender Female Male Monthly household inome Below 150,000 baht/sqm Over 150,000 baht/sqm Low-inome High-inome Household Size 4 or less 5 or more Small household Large household Current residential loation Inner ity Middle ity Outer ity Current workplae loation Inner ity Middle ity Outer ity 1. Segmentation by soio-demographi harateristis 1.1 Gender The findings present that male and female have different preferene of loation of ondominium in terms of the senery. Male prefer a river view ondo than open spae view and high building view ondos, respetively, whereas female would rather hoose a ondo with open spae view than ones with river and high building views, respetively. The other loation attributes yield the same preferenes as the 27

29 basis hoie model for both male and female. Nevertheless, a female group is willing to pay an extra more than male to stay in an inner ity and in low rime rate area, relative to in middle and outer ities as well as in moderate and high rate of rime distrits, orrespondingly. Condos in inner regions ommand a premium of 37,769 baht per m 2 and 94,885 baht per m 2 by a female group over those in middle and outer distrits, respetively. These premiums are 10.7 and 1.2 times over a male group s premiums. Conversely, male are willing to pay an additional for a ondo losed to mass transit links approximately 3.3 times and 1.7 times over female, respetively, ompared to ones losed to their workplaes and onvenient stores or shopping malls. 1.2 Monthly household inome Results provide the same diretion of preferenes as the basi hoie model for a high-inome group. However, only view attributes are different from the basi model for a low-inome group in whih ondos with river view are preferred to those with open spae and high building senery. The group of high inome is willing to pay an extra of 19,873 baht per m 2 for an open spae view over a river view as well as an extra of 8,996 baht per m 2 for a river view ompared to a high building view. The low-inome group is willing to pay a prie of 5,878 baht per m 2 and 30,588 baht per m 2 higher to buy a ondo with open spae and high building seneries, orrespondingly. For the remaining loation attributes, those who have high household monthly inome are willing to pay greater for them than those who have low monthly household inome as logial expetations. 1.3 Household size The group of small family size is willing to pay an additional of 6,388 baht per m 2 and 24,633 baht per m 2, respetively, for a river view ompared with an open spae view and a high building view, while the large households is willing to pay 16,679 baht per m 2 extra in order to trade a river view ondo to an open spae view ondo, and 20,426 baht per m 2 redution for one with high building view. The small households is willing to pay a premium lower than the large households by 3,283 baht per m 2 to stay in inner zones as ompared to middle zones, but a premium higher by 8,270 baht per m 2 for suburban. Furthermore, they are willing to pay more an extra for ondos losed to mass transit lines over their workplaes, yet lower an extra as ompared to shopping onvenient stores than those who have large family size. These trade offs between the two groups are also the same for the rime rate preferene in whih ondos with low rime rate are preferred over moderate rime rate and high rime rate, respetively. 1.4 Current residential loation The overall estimation reveals the same diretions and preferenes as the basi hoie model outomes for only those who are living in middle regions, however, only one loation harateristi-view- differs from the basi model results for those who are living in inner and outer regions. That is, they favor more for river view than open spae view, and high building view, orrespondingly. Besides inner distrit residents have the greatest marginal willingness to pay to stay in inner areas over in 28

30 middle and outer areas, as ompared with the remaining residents. Middle distrit residents have the lowest marginal willingness to pay for staying in inner areas over middle area. As well as outer distrit residents have the least marginal willingness to pay for inner zone ondo over outer zone ondo. For the aessibility harateristi, inner region residents are willing to pay the highest premiums for mass transit routes following by middle and outer region residents. The results indiate that respondents seleted the ondo loation in terms of zones and mass transit links aording to their urrent residential loations. Note that the mass transit lines pratially onentrate more in inner areas than middle areas, and outer areas, respetively. Lastly, results present that residents living in inner region have the highest marginal willingness to pay for ondos with low rime rate over those with moderate and high rime rate, respetively. It is not surprise that they are the most sensitive to the rate of rime, as a result of, the rime rate is normally highest in ore region and entral business areas. 1.5 Current workplae loation The regression outomes onvey that those who work in inner areas are willing to pay greater extras for ondo zones than those who work in suburb. They prefer to buy a ondo in inner distrits than ones in middle and outer distrits, orrespondingly. While those working in middle areas are willing to pay a premium of 3,957 baht per m 2 to stay in middle distrits over in inner distrits, and 56,876 baht per m 2 to stay in outer distrits. The diretions and preferenes of the remaining attributes for workers in inner and outer regions are the same as the basi hoie model exept for view attributes. They prefer river view to open spae and high building views, respetively. Yet inner distrit workers are willing to pay muh lower premiums for river view over open spae and high building views, as ompared with outer distrit workers. For those work in middle areas, nevertheless, they ommands premiums of 3,846 baht per m 2 and 45,374 baht per m 2, respetively, for their workplaes over mass transit lines and shopping onveniene. Moreover, they are willing to pay for open spae view by 35,243 baht per m 2 extra relative to river view but 5,154 baht per m 2 redution ompared with high building view. Lastly, they prefer a ondo with moderate rime rate to one with low rime rate following by high rime rate. This an be explained that respondents hoose ondo zones aording to their urrent workplae loation. And those who work in middle zone prefer to stay lose to their workplaes than mass transit stations sine the mass transit systems are not highly aess in the middle zone ompared with the inner zone. Furthermore, the rime rate in middle areas is less than in inner areas, hene, the workers may not too onern about rime. 29

31 2. Segmentation by travel harateristis The segments of eah travel harateristi are given in Table 10. Table 10: Travel harateristis used for segmentation Charateristis Segments Remarks Travel time Private vehile user 48.1 minutes or less More than 48.1 minutes Use private vehile Do not use private vehile Short travel time Long travel time 2.1 Travel time Respondents who have short travel time from their urrent residenes to their workplaes are willing to pay premiums for the ondo region and the rime rate more than those who have long travel time. They prefer to stay in inner areas to middle and outer areas, respetively. Also, they prefer to buy ondos with low rime rate over those with average and high rime rate, orrespondingly. This an be explained that the greatest development of mass transit systems is in inner ity, people an lower their hour spending to travel from their residenes to workplaes. As a result, the majority of people who have short travel time are living in an inner area, whih has relatively high density of business ativities and generally has high rate of rime. As a onsequene, they favor a ondo in the areas losed to their workplaes and one with low rate of rime. However, both short and long travel time respondents would rather buy a ondo losed to mass transit links than those losed to their workplaes following by onvenient stores. 2.2 Private vehile user Both respondents who use and do not use their own personal vehiles for traveling to their workplaes have the same preferenes as respondents lassified by travel time. Those who do not use personal vehiles have greater MWTP for ondo region and aessibility ompared with the MWTP of those who use private vehiles. The results are orrelated with travel time. This implies that respondents who do not use their own private vehiles are more likely to have short travel time from their residenes to workplaes. Thus, they more prefer to stay in inner distrits and ondos losed to mass transit links beause the mass transit lines greatest aess to an inner zone in Bangkok. 30

32 3. Segmentation by ondominium purhasing behaviors The mid-end ondominium purhasing behaviors were identified by segments as given in Table 11. Table 11: Condominium purhasing behaviors used for segmentation Charateristis Segments Remarks Consumer type Those who have bought a ondo within the last 2 years Those who may buy a ondo within the next 5 years Those who neither bought a ondo in the past nor will buy one in the future Already onsumer Potential onsumer Non-onsumer 3.1 Consumer types The outomes show the same preferene as a basi hoie model results for all onsumers exept for the attribute rime rate of already onsumer type. Already onsumers are willing to pay an additional of 8,950 baht per m 2 to stay in average level of rime rate ondos relative to in low level of rime rate ondos. They have losely MWTP for low rime rate over high rime rate as potential onsumers but lower than non-onsumers. While potential onsumers and non-onsumers have roughly the same MWTP to buy a ondo with low rime rate ompared to buy one with average rime rate. All types of onsumer have slightly the same MWTP for a ondo in inner zones over one in suburb, but potential onsumers have more MWTP for an inner ity ondo relative to a middle ity ondo than already onsumers and non-onsumers, respetively. Potential onsumers and already onsumers have nearly the same MWTP for mass transit lines over their workplaes but higher than nononsumers. Non-onsumers have the least MWTP and already onsumers have the most MWTP for mass transit links ompared with shopping onveniene. Figure 3 shows that for already onsumers, aessibility is the most important attribute, with a relative importane of 32%. The less important attributes are rime rate, area, and view respetively. While rime rate is the most important attribute for potential onsumers and non-onsumers, with relative importane of 41% and 48%, orrespondingly. These are followed by aessibility and area attributes. Additionally, the view is the least important attribute for all onsumer types. 31

33 Figure 3: Relative importane of attributes for onsumer types (in perentage) 32

34 Conlusions The researh applied a hoie experiment approah to value loation attributes for mid-range ondominiums in Bangkok, Thailand. The study overed 292 respondents using a onvenient random sample. Four ondominium loation attributes were onsidered, whih are ondo zone, aessibility, view, and rime rate. And eah attribute onsists of three attribute levels. The onditional logit models were estimated for the entire sample and various sub-samples lassified by gender, monthly household inome, family size, urrent residential and workplae loations. There are also the estimated hoie models for the other segments lassified travel time, personal vehile user, and onsumer type. The model then use to ompute the relative importane of loation attributes as well as the distributions within eah loation attribute. The results exist that most of respondents have medium household monthly inome and are employees. The inner ity is the most onentration of residential and workplae loations for this sample. Around half of respondents use their own personal vehile traveling to work and are potential onsumers of mid-end ondominiums in Bangkok. This study reveals that people in Bangkok attah greater importane to rime rate, ondo zone, aessibility, and view, respetively. In these attributes, people plae the preferenes for inner ity over middle ity following by outer ity. The easily aessibility to mass transit lines is the most preferred following by workplaes and shopping onveniene. People like open spae view better than river view and high building view, orrespondingly. Lastly, the low rime rate loation is more likely to be seleted than average and high rime rate loation. The differenes in soio-demographi fators were found to be different in the preferene of ondominium loation. Female shows stronger preferene towards the inner ity and low rate of rime loation, where as male plae more importane for mass transit links. The high-inome groups prefer more on the inner ity, mass transit lines, and low rime rate as ompared with the-low-inome groups. The large family plaes more importane for open spae view whereas the small family prefers river view. People hoose ondo loation, aessibility to mass transit stations, and rime rate aording to their urrent residential loations. However, they selet only ondo zone aording to their workplae loations. Moreover, those who have short travel time reflet the possibility of not using their own personal vehile in order to travel to work. They have strong preferene to stay in inner distrits with easily aess to mass transit links and low rime rate loation, as ompared to those who have long travel time or use their own private vehile to travel to their workplae. Finally, potential onsumers have more preferenes for inner zone and low rime rate loation than the others. While already onsumers plae more importane for aessibility of mass transit systems than the other onsumers. 33

35 These finding suggest that middle-inome onsumers are willing to pay higher prie of ondominium in inner ore distrits than in middle zones and suburb. Additionally, the onsumers have strong preferene to stay in a ondo with easily approah to mass transit stations. Therefore, the mass transit systems in Bangkok should be expanded so that people will have less onsideration to stay in an inner area, as a onsequene the population density might be less in the inner area. Furthermore, sine the rime rate shows as the most importane fator of ondo loation, the government an support the ondo market by promoting the poliy that help reduing the rime rate. However, there is a possibility to develop this study for the further researh in order to provide more interesting findings. The study of ondominiums an be overed more segments suh as low-end and high-end ondominiums. The results would reflet greater pitures of the ondo market. The hoie model in this paper an be extended further to inlude more harateristis of ondominiums in order to inrease auray and preision of estimations. In addition, the survey designs an be developed to aount for the risk of under or overestimate WTP by onduting several versions of survey designs sine hoie experiment method faes the drawbak of a ognitive burden for the respondents. 34

36 Referene List Aizaki, H. (2012). Basi Funtions for Supporting an Implementation of Choie Experiments in R. Journal of Statistial Software, 50. Alpizar, F., Carlsson, F., & Martinsson, P. (2001). Using Choie Experiments for Non-Market Valuation. Goeteborg University, Deparment of Eonomis. Bateman, J, I., & al, e. (2002). Eonomi Valuation with State Preferene Tehniques A Manual. Cheltenham: Edward Elger Publishing Limited. Ben-Akiva, M., & Lerman, S. (1985). Disrete hoie analysis: theory and appliation to travel demand. The MIT Press. Birol, E., Karousakis, K., & Koundoun, P. (2006). Using a hoie experiment to aount for preferene heterogeneity in wetland attributes: The ase of Cheimaditida wetland in Greee. Eologial Eonmis 60, Colliers International. (2013). Retrieved Marh 3, 2014, from Colliers International: Dellaert, B., Borgers, A., & Timmermans, H. (1995). Using onjoint hoie experiments ro model urban tourists' hoie of ativity pakages. Tourism Management, Hanley, N., Mourato, S., & Wright, R. E. (2001). Choie Modelling Approahes: A superior alternative for environmental valuation? Journal of Eonomi Surveys, 15, 3. Hanson, S., & Pratt, G. (1991). Job searh and the oupational seggregation of women. Annals of the Assoiation of Amerian Geographers 81, Hiselius, W., & Lena. (2005). External Costs of Transports Imposed on Neighbours and Fellow Road Users. Lund University, Department of Eonomis. Lund Eonomi Studies. Huber, J., & Zwerina, K. (1996). The importane of Utility Balane in Effiient Choie Designs. Journal of marketing researh, Jalotjot, H. C. (2012). Determinants of Vehie Choie in Metro Manila: Consumer Preferne for Low Emission Vehiles (LEVs). Master Thesis, The University of Tokyo, Graduate Shool of Frontier Sienes. Jun, M.-J. (2013). The effets of housing preferene for an apartment on residentail loation hoie in Seoul: A random bidding land use simulation. Land Use Poliy 35,

37 Kemperman, A. D., Borgers, A. W., Oppewal, H., & Timmermans, H. J. (2000). Consumer Choie of Theme Parks; A Conjoint Choie Model of Seasonality Effets and Variety Seeking Behavior. Leisure Sienes,22, Mfadden, D. (1973). Conditional logit analysis of qualitative hoie behavior. Frontier in Eonometris, O'SulliVan, A. (2009). Urban Eonomis (7th ed.). MGraw-Hill,Irwin. Ryun, M., Bate, A., Eastmond, C., & Ludbrook, A. (2001). Use of disrete hoie experiments to eliit preferenes. Quality in Health Care, Sanko, N. (2001). Guidelines for Stated Preferene Experiment Design. Master Thesis, Eole Nationale des Ponts et Chaussées, Shool of International Management. Sermons, W. M., & Koppelman, F. S. (2001). Representing the differenes between female and male ommute behavior in residential loation hoie models. Journal of Transport Geography 9, Timmermans, H., & van Noortwijk, L. (1995). Context dependenies in housing hoie behaviour. Environment and Planning A, Tunner, T., & Niemeier, D. (1997). Travel to work and household responsibility: New evidene. Transprtation 24, Walker, B., Marsh, A., Wardman, M., & Niner, P. (2002). Modelling tenents' hoies in the publi rented setor: A stated preferene approah. Urban Studies, 39(4), Wang, D., & Li, S.-m. (2006). Soio-eonomi differentails and stated housing preferenes in Guangzhou, China. Elsevier Ltd. Wu, W., Zhang, W., & Dong, G. (2013). Determinat of residential loation hoie in a transitional housing market: Evidene based on miro survey from Beijing. Habitat International 39,

38 Appendix Appendix A Survey Questionnaire แบบสอบถาม เร อง ล กษณะทางส งคมและประชากร และความชอบต อท าเลท ต งของคอนโดม เน ยมระด บกลางในกร งเทพมหานคร ว ตถ ประสงค แบบสอบถามน จ ดท าข นเพ อเก บรวบรวมข อม ลส าหร บการศ กษาค นคว าอ สระเพ อการศ กษาของน กศ กษาระด บปร ญญาตร คณะเศรษฐศาสตร จ ฬาลงกรณ มหาว ทยาล ย โดยข อม ลในแบบสอบถามจะน าไปใช ประโยชน เพ อการศ กษาเท าน น ท งน ผ ว จ ยขอขอบพระค ณผ ตอบแบบสอบถามเป นอย างย ง ท สละเวลาและให ความร วมม อในการตอบแบบสอบถามมา ณ โอกาสน ค าช แจง 1. กร ณาท าเคร องหมาย ลงในช องส เหล ยมท ท านเล อกและเต มข อความหร อต วเลขลงในช องว างท ม ให 2. แบบสอบถามม ท งหมด 3 ส วน ด งน ส วนท 1 ข อม ลท วไปของผ ตอบแบบสอบถาม ส วนท 2 ข อม ลประกอบการตอบแบบสอบถาม ส วนท 3 แบบสอบถามการทดลองแบบทางเล อก 3. ข อม ลท ได ร บจากผ ตอบแบบสอบถามจะถ กเก บเป นความล บ ส วนท 1 ข อม ลท วไปของผ ตอบแบบสอบถาม เพศ: ชาย หญ ง อาย : ป ระด บการศ กษา: ก าล งศ กษาปร ญญาโท ก าล งศ กษาปร ญญาเอก รายได ท งครอบคร วต อเด อน: น อยกว า 30,000 บาท 30,000 50,000 บาท สถานะการจ างงาน: 50, ,000 บาท 100, ,000 บาท 150, ,000 บาท มากกว า 200,000 บาท ล กจ าง - full time ล กจ าง - Part time ว างงาน/ก าล งหางาน เจ าของก จการ จ านวนสมาช กในท อย อาศ ย: คน อ นๆ โปรดระบ จ านวนเด กอาย ต ากว า 15 ป ในท อย อาศ ย: คน ท อย อาศ ยจร งในป จจ บ น: เขต 37

39 สถานท ท างานในป จจ บ น: เขต ระยะเวลาท ใช เด นทางจากท อย ป จจ บ นไปท ท างาน/สถานศ กษา: นาท ค ณใช รถยนต หร อพาหนะส วนต วในการเด นทางไปท างาน/สถานศ กษาหร อไม ใช ไม ใช ในช วงระยะเวลา 2 ป ท ผ านมา ค ณเคยซ อคอนโดม เน ยมในกร งเทพฯใช หร อไม ใช ไม ใช ภายในระยะเวลา 5 ป ข างหน า ค ณคาดว าจะซ อคอนโดม เน ยมในกร งเทพฯใช หร อไม? ใช ไม ใช ส วนท 2 ข อม ลประกอบการตอบแบบสอบถาม กร ณาศ กษาข อม ลต อไปน เพ อประกอบการตอบค าถามในส วนท 3 5 ป จจ ยท ส งผลต อการเล อกท าเลของคอนโดม เน ยมระด บกลางในกร งเทพมหานคร 1. เขต: ต าแหน งท ต งของคอนโดม เน ยม แผนท การแบ งเขตพ นท ในกร งเทพมหานคร 2. การเข าถ ง: สามารถเด นทางจากคอนโดม เน ยมท เล อกไปย งสถานท เหล าน ได ภายใน 15 นาท การเข าถ งท ระบ ในแบบสอบถามส วนท 3 หมายถ ง ท านสามารถเด นทางไปย งสถานท ท ระบ เพ ยงท ใดท หน งเท าน นภายใน 15 นาท เช น หากในแบบสอบถามระบ การเข าถ ง ค อ รถไฟฟ า ท านจะสามารถเด นทางไปย งรถไฟฟ าได ภายใน 15 นาท แต ท านจะเด นทาง ไปย งห างสรรพส นค าและสถานท ท างานของท านมากกว า 15 นาท 38

40 3. ท ศน ยภาพ: ล กษณะท วท ศน ท มองเห นจากห องช ดในคอนโดม เน ยม ว วต กส ง ว วโล ง/ไม ม ส งก ดขวาง ว วแม น า 4. อ ตราอาชญากรรม: จ านวนคร งเฉล ยในการเก ดอาชญากรรมต อว นใน 1 เขต เช น ข มข ท าร ายร างกาย ว งล กช งปล นทร พย ส น ยาเสพต ด การพน น ค าประเวณ ในบร เวณท อย อาศ ยของท าน 5. ราคา: อ างอ งจากราคาขายในป จจ บ นของราคาของคอนโดม เน ยมระด บกลาง ม หน วยเป นล านบาทต อขนาดห อง 45 ตารางเมตร ต วอย างของคอนโดม เน ยมระด บกลาง: ป จจ บ นม ราคาเร มต นต งแต ประมาณ 1.75 ล านบาท หร อราคาเฉล ยประมาณ 70, ,000 บาทต อตารางเมตร เช น The Base, Aspire, Centri, The tree, Life, IDEO, The room, Rhythm, Condolette Light เป นต น ส วนท 3 แบบสอบถามการทดลองแบบทางเล อก (A) สมมต ว าท านม ความต องการท จะซ อคอนโดม เน ยมระด บกลางในกร งเทพมหานครเพ ออย อาศ ย โดยล กษณะของคอนโดม เน ยมท ท านเล อก o คอนโดม เน ยมใหม ส ง 30 ช น o ห องท ท านเล อกต งอย ช น 15 o ขนาดห อง 45 ตารางเมตร 15 o 1 ห องนอน 1 ห องน า 1 ห องคร ว o ไม ม เฟอร น เจอร o ม ท จอดรถ 45% ของจ านวนห องท งหมด o สระว ายน า, ฟ ตเนส, ประต Key ard, ล ฟต, กล องวงจรป ดในอาคาร, ห องร บรอง, สวนหย อม, รปภ, แม บ าน ราคาท ระบ ในแต ละช ดทางเล อกเป นราคาของคอนโดม เน ยมท ม ล กษณะท กล าวไว ข างต น ซ งราคาจะเปล ยนแปลงข นอย ก บ 4 ป จจ ยในการเล อกท าเลของคอนโดม เน ยมระด บกลางตามท อธ บายไว ในส วนท 2 ของแบบสอบถาม สมมต ให ท านต ดส นใจเล อกซ อคอนโดม เน ยมจากท าเลของคอนโดม เน ยม A หร อ คอนโดม เน ยม B โดยพ จารณาจากข อม ลท ระบ ในแต ละช ดทางเล อก จากน นท าเคร องหมาย ลงในช องส เหล ยมใต คอนโดม เน ยมท ท านเล อกเพ ยง 1 ช องเท าน 39