- Email: [email protected]
A psychometric analysis of residential location
Socio-Econ Ph. Sri Vol. 15,No.6, pp.305419,1981 Printed inGreathitain
A PSYCHOMETRIC ANALYSIS OF RESIDENTIAL LOCATION JULIAN BENJAMIN The TransportationInstitute, North CarolinaA&T State University, Greensboro, NC 27411,U.S.A. and
ROBERT E. PAASWELL Department of Environmental Design and Urban Planning, School of Architecture, State University of New York at Buffalo, Buffalo, NY 14214,U.S.A. (Receiued 5 August 1980;in revised form 2@March
1981)
Abstract-Traditional housing needs studies focus on the trade-off between location and housing amenities. In most models, mode choice is viewed as conditional behavior in a given setting. New movers, however, select a new environmentwhich includesa house, neighbourhood and transportation system. By using psychometric techniques, the attitudes and preferences of new movers to the suburbs of a large northeast city were analyzed to determine the relative importance in their selection process of the transportation characteristics of the new location. It was found that local and regional transportation and public transit played little role in selecting an apartment. There was no evidence of tradeoffs between travel time and living space postulated by urban economics. Most important to the choice process of these residents were internal characteristicsof the apartmentand pricing issues. This supports the idea that suburbanites chose to be captive auto users even when equivalenthousingopportunitieswith transport alternatives are available. Implicationsfor public transit and land use planning alternativesfor the suburbsare discussed.
was the pilot in a project whose objectives were to provide developers with a much more astute picture of their market, hence increasing the probability of a match between the costs of investment in desired attributes and the demand for these attributes.
INIDODUCTION
Many traditional housing needs study focus on the tradeoffs between accessibility and value of other amenities as determinants of residential location. When a large scale development is constructed, the developer makes some assumption about the results of that trade off. He assumes that he understands the market for his housing and can provide the package of amenities and accessibility for a price that will satisfy the buyer. In suburban locations, generally it is assumed that household travel will be by car, so that model trade-offs are not considered. That is housing choices will be made with certain predetermined choices if made by the renter or buyer. There are some questions that remain about accessibility and housing location. How important is it, to the housing owner or renter to be close to work, or close to other activities? What tradeoffs are made between activity location and access to work? What tradeoffs are made between accessibility and other housing alternatives? To examine a number of these tradeoff decisions, a study has been made of factors influencing housing choice for a carefully selected sample of a population who have moved to a specific location-a suburban new-town adjacent to major metropolitan area. The sample has been controlled to determine whether in making ultimate similar housing choices, the population arrived at these choices in a similar way. Thus, many of the population variables were controlled. This made it possible to evaluate multi-variate techniques as a means of establishing determinants of housing choice. This paper examines the method used in a pilot study to assess the attributes of housing that led to the specific choice, with some emphasis on accessibility. The study
EOUSDVG LOCATION ANDACC~~~BJLITY
The locational aspects of housing, regional or sighted in a neighborhood, have been theorized for some time by urban economists. Locational models of Alonso[l] and LowryI used distance from housing to work as a specific variable used to postulate the distribution of population. In fact, the mathematical forms of their models make distance one of the most powerful variables in population location. Later work by others (Reif[2]) began to add certain neighborhood characteristics to these distance variables. Researchers fram a variety of disciplines have investigated moving behavior, housing choice and the role of transportation. These studies examine diverse factors that include regional and neighborhood characteristics and the physical exterior and interior amenities of housing. Moore and Cale[3] designed specific housing search areas, limiting the regional nature of choice. They specified that housing searches are limited to specific regions within an urban area and that considerations after that initial location choice are secondary. Sociologists such as Ericksen[4] reinforce this approach by recognizing that like groups, segregate themselves geographically. Geographers [S] have quantified the migration of like groups in urban areas, concluding that socio-economic indicators are at least one set of important characteristics of a neighborhood. 305
306
J. BENJAMIN and R. E. PAASWELL
Other studies have focused on the physical characteristics of the neighborhood. Lansing and Marans[6], Peterson[7] and Sanoff [8] investigated methods of measuring resident response to neighborhood appearance. All these studies asked residents for comparisons of neighborhoods or neighborhood characteristics. In comparing neighborhood SatisfaCtions, Peterson found that the most important factors contributing to overall resident satisfaction are upkeep, aesthetics, quietness, friendly people, similar people and places for children to play. Of these, upkeep was the most important. Wilkinson and Sigsworth[9] found that the degree of interaction and friendliness between neighbor was related to the external design. By concentrating on physical and social characteristics researchers have developed a long list of attributes important to housing location. These attributes can later be weighed against accessibility. Study of behavior inside the home is by nature more difficult. Beyer [lOI summarized work by more economists which examine interior design from a human factors viewpoint. These variables add one more component to the increasingly complex housing choice set. A general analysis that includes regional neighborhood, exterior and interior design has been only employed by a small number of researchers. In a national study by Butler et al.[l1] measures of attitudes, satisfactions and moving behavior were obtained. In the aggregate, movers preferred better neighborhood quality at the expense of accessible location and desirable dwelling unit, to a nice outside appearance. They also indicated a preference for a new house and well established neighborhoods, modem style to traditional, a one floor unit, few children in the neighborhood and large lots to small. It was found that moving intentions were most closely related to life cycle. The study made no attempt to model moving behavior and analysis was primarily cross-sectional. A total approach has also been employed to evaluate differences in satisfaction of residents in different environments. One example of this approach is a comparison of satisfactions of townhouse and garden apartment residents by Norcross[l21. This approach is area specific and is subject to variations by respondents as well as to differences in envirbnments. Both Engel[l3] and Norcross showed tie of life cycle to desired amenities specifically with regard to recreation and space. This, of course, then ties to availabIe space/$ or the important income constraint. Physical and economic choice (rent, size, heat) were described in using a variety of psychometric techniques[14]. This study was important for the analysis of measuring the trade-off, or choice decision and provided more insight into the relative importance of pre-identified attniutes in choice. Methodologically, these’studies fall short of obtaining a comprehensive understanding of location behavior, i.e. location choice as a function of choice within region, choice within neighborhood and then physical choices within the housing itself. A thorough understanding of SThese features have long been recognized by European planners who design accessibility into community planning. This minimizes the travel cost and reduces the uumber of tradeoffs necessary to be accommodated in housing choice. Middle class Americans have often assumed that mobility at low cost was always available and the housing market always responded by providing a wide range of housing in a wide range of location.
consumer needs and wants cannot be obtained by merely asking “What do you want?‘, “Are you satisfied?” or “Why did you move?” Responses to these questions will only supply a partial picture of resident opinions of existing conditions. Furthermore, because subjects are not trained architects and are unaware of all the consequences of design decisions, when asked to express likes and dislikes they would most likely ask for as much space and amenities as possible. Hence, what is required is a knowledge of tradeoffs that are made in choosing between feasible alternatives. The question then becomes: “Which designs do yau most prefer given all personal constraints and desired household characteristics?’ A complete knowledge of how choices are made can only be obtained through an understanding of tradeoffs made by individuals at the time the location decision is made. Within specific income and expenditure constraints people decide between units by selecting the unit which has desired important features. They do this by sacrificing what, for them, is not so important. This is the basis of the tradeoff analysis used by Knight and Menchik[ IS]. Knight and Menchik investigated tradeoffs between private, semi-private and common open space made by inhabitants of planned communities. Respondents were asked to compare diagrams and pictures which varied with respect to private and open space and neighborhood quality. They found respondents willing to sacrifice personal space to gain more common space. Because of the data requirements, however, significant modification would be needed to use a total housing and locational perspective. From the efforts reviewed here, it is evident that there is insufficient knowledge of the tradeoffs and compromises made in choosing housing alternatives and locations and the relative influence of transportation on these choices. This research describes the first step in the development of a strategy to measure these tradeoffs. As recognition of the important role of accessibility as an integral component of the American urban-suburban environment, planners have developed planned unit developments and new towns which include several design elements to increase accessibility of urban activities.t For example, by mixing land uses, commercial and industrial centers become accessible by car or public transit, leisure and recreational’ facilities are located within walking distance and intra-city transportation becomes centrally located. Another transportation related new town inndvation is clustered housing which permits open space and park land to be interwoven with residential areas so that it is an easy walk from all homes [ 161. It was theorized by planners that such an arrangement would be a viable and environmentally preferable alternative to conventional suburban development which is often characterized as urban sprawl. In conventional developments almost all activities are accessible by car only. Perhaps the one exception is ihe journey to work, located traditionally in the central business district or, in a suburban twon center can often be reached by public transit. Even then, the transit system is usually accessible only by car in some park and ride arrangement. Thus, the choice to live in conventional suburbs is often a choice available to car owning households only. With the notable exception of a small number of new towns, most movers to the suburbs are faced with no
A psychometricanalysis of residentiallocation choice of modal mobility; become captive drivers or stay in the city. It has been suggested by planners that a substantial segment of these movers would prefer the accessible setting and attributes that are associated with a planned community if it were made available[l7,18]. It was further suggested that accessibility to the central business district was an important element in selecting a neighborhood and that transportation did play a role in home selection. The validity of these suggestions was tested in a study that asked subjects to answer an extensive set of questions about several different apartment offerings, apartment characteristics and general questions about themselves. The approach to ths study and the findings are reported in the following section.
DESIGN OF A STUDY TO DETERMINE APARTMENT PREFEWNCES
To test these planning suggestions discussed above, a survey instrument was administered to a sample of 100 residents (50 couples) who had recently moved to the area adjacent to the proposed new town of Audubon in Amherst, New York. The respondents could be characterized as middle income households. This sample represented a first group of new movers to a small suburban development. The procedure was designed to collect 100 individual, independent responses, through the sampling of 50 couples. The majority had no children, both heads worked and held skilled or professional occupations. The results of the analysis of two pertinent parts of the questionnaire will be reported here. In one part of the instrument 10 apartment units were completely described. Respondents were asked to rank order their preference for the apartments from most preferred to least preferred. Respondents were asked to make realistic selections, acting as if these were the only apartments available at this time. Each apartment was displayed on a 10”x 20” card that contained: -An internal view -An external view -A floor plan -A neighborhood map -A regional map -Additional apartment related information, including rent, accessible transportation and nearby activities. The layout for each card is illustrated in Fig. 1. The elements of Apartment 1 are illustrated in Figs. 2-4. The information concerning each apartment was based upon methodologies that had proved successful in similar type studies [8,13,14]. In addition, the information presented was refined by discussions with local planners, realtors and advertisers. A summary of key characteristics of each apartment is presented in Table 1. A full description of each apartment is quite lengthy and can be found in Benjamin[U)]. The key apartment characteristics listed ia Table 1 were selected based both on the results of a review of previous similar studies and due to their relevance to the questions of tradeoff raised earlier. A review of the number of characteristics and the number of levels of each characteristic reveals that there are a very large number of apartments that could be created from various combinations of characteristic levels. Further, it was tThe programMDPREFwas used.
307
found that respondents required considerable time to analyze and compare cards (approx. 2min card). Thus, the experimental design made it necessary to limit the total amount of choices presented to the respondent. For this reason, only a small number of apartments were selected. The apartments were chosen to represent either existing apartments in the area or proposed new apartments in the planned community. At the sanie time, each apartment was selected to be part of the choice set that would convey all levels of each characteristics to the respondent. For this reason, only a small number of apartments were selected. The apartments were chosen to represent either existing apartments in the area or proposed new apartments in the planned community. At the same time, each apartment was selected to be part of the choice set that would convey all levels of each characteristics to the respondents. Existing apartments were represented by actual photographs, floor plans, maps and other pertinent information. Apartments in new communities were represented by taking photographs of existing facilities and locating them in new communities by using maps and other information. New and existing facilities were not identified as such.
APARTMENT A’llWWTEIMPORTANCE In the other section of the questionnaire discussed here, respondents were asked to compare apartment attributes according to their importance in apartment selection. Fifty apartment attributes were selected based on a critical analysis of the literature[21,22]. These attributes are listed in Table 2. The apartment attributes represented housing subsystem that included management, internal, semi-private, neighborhood and community characteristics. Each attribute was written on a card and the subjects were asked to sort the cards into equal groups according to its importance in selecting an apartment. Groups were identified with size restrictions as follows: not important at all-8 cards not so important -8 cards not important -9 cards important -9 cards more important -8 cards very important -8 cards. Cards were sorted in a two step procedure and respondents were asked to keep within the restrictions as much as possible. After the response, a detailed analysis of the attributes that influenced apartment selection was carried out. MULTIDIMJSNSIONALANALYSlS OF PREFERENCE RANKING
The first step in the analysis was to determine the preference of the respondents for each apartment category and then to correlate apartments perceived of as being similar by identifiable groups of the population. Preference rankings were analyzed by an internal analysis of preference[23]. In the first phase, a vector modelt of preference was calibrated using the ranking order of the apartments. In this model, it is assumed that a respondents preference for an apartment can be described using measures
J. BENJAMIN and R. E. PAASWELL
308
APARTMENT CARD LAYOUT
Outside
View
Inside View
Floor Plan
Neighborhood Map
.k!ditional Apartment Related Information
Regional Xap
Fig. 1. Apartmentcard layout.
of specific attributes of that apartment. In this case, it is assumed that groups of measures can form dimensional axes that generate a space in which the attributes of the apartments can be measured. (One dimension, e.g. might simply be rent, another, number of rooms, etc.). To locate the particular apartment in these dimensions, a vector is then necessary. The actual model has the form: pik = &,
’ x,k
(1)
Pa is the preference of subject i for apartment k; B,, is the relative importance of variable j for apartment k; and tTo facilitatecomparisonsbetween bi coeilkknts fbr different objective measures, unit vectors were formed normalizingthe b coefficients
X, is the relative position of apartment k on variable j. The MDPREF algorithm solves both the perception space and the individual preference vectors simultaneously. The muttidimensional solution consists of a common, orthogonal perceptual space for all subjects and individual preference vectors. The first two dimensions (size, price) of the solution for these-100 subjects is illustrated in Fig. 5. The dimensions of this solution can be labelled by associating the location of each apartment in tbe perceptual space with objective apartment characteristics for each apartment. The relationship between apartment characteristics and perceptual space location can be tested by the following linear model: Y = b, t b,x, + bgz t b3x3t E. where Y is the objective measure to be tested; xi is apartment location on perceptual dimension i; b, is the coefficient of Xi;t and E is an error term.
A psychometric analysis of residential location
30!4
Fig. 2. Internal and external view of Apartment 1.
Variables tested were selected from Table 1 or were composite of these variables. Normalized coefficients for the 14 variables with the highest correlation coefficients are listed in Table 3. From this analysis it was possible to identify the dimensions as: Dimension 1: SIZE, the number of rooms. Dimension 2: FAIR PRICE, i.e. proximity to the average price of a two-bedroom market rental apartment. A third dimension was found which related most to the absolute cost of the apartment. These dimensions accounted for approx. 70% of the variance in the data. The remaining seven dimensions all accounted for an average of less than 5% of the variance and were considered insignificant. It is important to note that statistically 100 respondents are ranking only ten apartments. The use of multidimensional techniques makes it possible to examine a number of attributes for each, but the final clustering and evaluation are based on these two numbers. ACCFSrBrLtTv No significant dimension was closely related to transportation. The classic tradeoff between size and accessibility was not demonstrated by these respondents. Accessibility to the Central Business District was represented by a dimension called the north-south direction. The area in which the apartments were located was approx. 10 miles north of the CBD and the dimensions of the area were approx. 10 miles by 10 miles; the CBD and all other regional activities were accessible only by an expressway located on the southern edge of suburban territory. But north-south direction was most closely related to dimension 1 in the perceptual space, and other key variables associated with Dimension 1 had higher correlation coefficients. Local activities were most highly accessible in the planned communities. Furthermore, many complexes offered some recreational activities. And yet, degree of accessibility to local activities was also not significantly related to any dimension, that is, it was not important in identification of apartments by the respondents.
APARTMENTCLUSTRRS
For further assistance in interpreting dimensions, apartments were clustered using Johnson’s Hierarchical cluster analysis[23]. In Fig. 5, three clusters were formed which, as expected, were based on size and cost considerations only. Planning and physical location within the suburban area had no effect on cluster member ship. This highlights the lack of consideration that transportation plays in the preferences of the respondents constituting the sample for the study. SURJRCTCLUSTERS
To gain further insight into personal housing preferences, subject clusters were formed based on subject ideal points combinations of attributes most preferred by each subject. These clusters can then be used to identify potential market segments. To cluster subjects, their preference must be described as a point location in the preference space (i.e. the space defined by dimensions derived from the most pertinent apartment attributes). To do this, the principles of unfolding analysis are used. It is assumed that there is some combination of underlying choice variables which is most preferred by each respondent. The “ideal point” or “person point” is estimated by using the following model: Pij =
2 U(Zj’- Xi’)* ,=I
where Pii is the preference of subject j for simulus i; I,’ is the ideal point for subject j on dimension t; Xi’ is the location of stimulus
310
J. BENJAMIN and R. E. PAASWELL
A psychometric
analysis of residential location
311
J. BENJAMIN and R. E. PAASWELL
312
1.0
f’%
@!_a
Fair
prim -1.0
Fig. 5. Apartment clusters in Dimension I and Dimension 2.
AAA --.
A
6
*)
It_,‘0
.
;ol. .
. r/
.
*C--
/‘-‘;I
e/
A
/’ /’ H
/
,$..
6.. ,.;,.=%. ..............q i............... ‘h .
1’.
\ ;-
--______-e-w-.
l\
.
1
>
I
\
>
\ .\ 1.0
4
. .’ .’ 0. .I ,Y
4 .
4UtU&ttt,i,......O
~atxbnlrtw ..,... m~atI4ulm&8tlaeaadu
-w_sg-
Ionl?oat
0
Iair
prim
..,
1.0
11 . .......... .. 11I -s-L_
Iv _._._._._
Fii. 6. Subject ideal points in Dimension 1 and Dimension 2.
>
313
A psychometric analysis of residential location Table 1. Apartment characteristics Ausrc-
Rmt
lJei:it:er
No.
Dent
$ per
IlX?Ld2d
BG-
Kc5er
EG^ril
TOtal Rc;Lxs
of
r1osr SPJCC (Sq.
ro5.15
Positfor. (E-.0'
Position (S-S)2
13. of
PJCl
Type
Neiahbxhood
T.xaIhouse Hiphrise Garden
P?XXXd
3
3
3
CJlIVeE:iOCZl Pla:Xa?.
2
1
1
yes
2
2
1
Yes
iiocrs
rc.)
y*:
7
1142
3
203
4
5i7
2
4
610
1
a0
490
C‘X.*t5tionnl CEQkX
2
4
?hlLifanfly Garden
1
2
1
2
1
y*r
Yes
2
4
5iO
Garden
Co?qlex
2
2
1
yas
,315
no
2
7
1096
Townhouse
Plszmzd
3
3
3
Y@S
275
nn
3
6
856
TOWI-
Plaor.ed
2
2
2
Yes
CCZ?LiX
L
2
1
no
C‘Xl-,?~r.
2
2
1
yes
1
3'5
no
2
2
196
Yes
1
3
2X
L3
2
4
130
"0
5
200
Yes
s
235
7 8
9e
9
ieo
Ye=
2
4
516
house GarCe?r
i0
235
‘0
2
4
61
Gzrder.
1 Bclativcposicim in .?r.=: l-msc wes~crn :hirC, 2-central:\ied, 3-ilosteastern third , I.-cenxal ehird. 3-most nce:Cetr third Bolativopisicizain <.;a: l-nsir sou:hcm third,
Table 2. Environmental attributes bv housina subsvstem ATTRIBUTE
jlOIJSINC SUBSYSTEM
xanagemat Internal
16. 36. 1. 4. 9. 14. 15. 17. 31. 33. 34. 35.
:9": 40. Sml-rrivate
18. 30. 2:: 49.
Nalgltborhood
2. 3. 6. 8. 11. 12. 13. 19. 20. 22. 23. 26. 20. 29. 32.
aesponsive manajiencnt Rent less than $200 Bedrooms set away from living room and dining room Modern interior New apartment Hodem, fully equipped kitchen, with a dishwasher Family room Plenty of storaec Entrance to apartment separated from living room wall to wall carpeting in living room, dining area and bedroom Xore than two bedrooms Dining facilities inside kitchen as well as an additional dining area Two or more floors Large rooms Tile bath Outside private patio area Craft room nearby Laundry room Adjacent garden plot Community meeting room Bnaeball field nearby No through traffic on street Close to frieods and relative8 Wooded area nearby Swimming pool nearby Clean neCahborhcod Plenty of-parking Child care center within walking distsnce Tznnia co"rtR within wetking disterre Mixture of different types of people Nodern exterior Quiet street Adjacent playground Natural envixcment. such ae many trees and shrubs Good schools nearby
J. BENJAMIN and R. E. PAASWELL
314
Table 2. (Con@. ATTRIBlPTE
HOUSING SUDSYSTFJ4
community 453:Plmned People like myself Safe neighborhood 50. 5JiTDUllity
5. 7. 10. 21. 24. ;:: 41. 42. :46: 40.
Accessibility to supermarket vocation near place of work far bend of household . Accessibility to movie theaters Accessibility to clctbes chops AccrsRibility to hixlways Accessibility to rr-.st>.crants Hunting or fishing (1ro8 within driving distance Lake nearby Accessibility to golf course Accessibility to doctor’s office Good cormnonity service such 06 fire departaent and police depnrtment Accessibility to public trsnnportncion
adjacent clusters points, where unclustered points are considered individual clusters). As a result, five subject clusters were identified. Each cluster is characterized by its position in the perception space. Two sets of cluster dimensions were used: (1) Fair price and apartment size and (2) size and cost per number of bedrooms. Apparent overlaps in Fig. 6 are due to the use of a three dimentional solution in the clustering process. By examining the positions of the clusters of subjects in preference space, the clusters could then be identified as groups preferring: Large apartments, with high cost per unit of space These subjects felt they needed plenty of space and were willing to pay for it. The centroid was nearest that of townhouses (45 subjects). Large apartments with low cost per unit of space These subjects not only were looking for large apartments, but were not willing to pay extra for what they wanted (20 subjects). Mid-sized apartments with high cost per unit of space These were respondents with some space requirements and with the desire to pay for what they wanted. They would be satisfied with a luxury apartment near the fair price (8 subjects). Large apartments with high cost per unit These subjects would be satisfied with luxury garden apartments or townhouses and were willing to deviate substantially from the fair price (6 subjects). Unclustered This group consisted of subjects who preferred a variety of different apartment types. Many of these subjects had distinct preferences which would appear on the perceptual map as outlier points. Thus, there was no one central preference for members of this group (21 subjects). The clusters were naturally determined by use of the Johnson techniques, and by using two sets of key dimensions. As expected from the dimensions of the preference space, no market segment was identified which was concerned with transportation. However, two non-clus-
tered outliers were careless, which was the only time transportation was an issue for these subjects.
ANALYSISOF APARTMENT CHARACTERIsTIcS
The results of the initial sorting of apartment attributes into groups by the respondents were analyzed using unidimensional Thurstone scales. These scales are developed under the assumption that the distance between scale values is proportional to the number of times one stimulus (apartment characteristics in this case) is rated higher than another. Because it was suggested in the literature review that life cycle and income were important determinants of housing attitudes, attitude scales were computed for separate groups based on income and family size. The results of these analyses are presented in Figs. 7 and 8. When grouped by family size, the largest group was families with no children. They account for approx. 2/3 of all respondents. It was found for this group that the highest rated attributes (according to highest ranking first) were:
Ranking for childless families : 3 4 5 6 I
! 10
Attributes
Ranking for families with $15,000-$20,000 annual income
1. Safe neighborhood 12. Clean neighborhood 14. Modern fully equipped kitchen with dishwasher 37. Laundry Room 16. Responsive management 17. Plenty of storage 33. Wall-to-wall carpeting in living room, dining area, and bedrooms 13. Plenty of parking 26. Quiet street 39. Large rooms
: 6 5 9
7 10 11
A psychometricanalysis of residentiallocation
315
Table 3. Summarytable of regressionsof objective measureson subjectivevariables SubjectiveVerieblerl Norme:izedCmfficientr F Test
ObjectiverewJ\ire
Significance
1.
2.
.
AectualRent (k listed on cerds)
-0.949
71.87 .ooo
-.03l2 7.768 .032
Pool
-0.65 2.71 .151
0.17 0.19 .676
-0.88 5.67 -0.867 254.4
3.
Number of Bedrooms
4.
Tote1 Nudmr of Rooms
. 000 5.
Number of Floore
6.
PhyeicalPosition in Area (Eeet-West)
-0.83 12.8 .012
7.
Degree of Planning
-.9% 19.20 .Ol
8.
Phyaicel Poeition it4 Area (North-South)
-0.62 41.15 .Ml
9.
n00r Spece
Test
0.04 O.Z.2 0.739 -0.739
.931 27.02 .OOl .731 2.294 .178
0.38 1.056 .344
0.29 0.597 .469
.733 2.32 .175
-0.412 59.22
0.27 25.4 .to2
.99 112.6 .oco
0.195 4.9 .069
.977 43.4 .000
-0.593 45.3 .OOl
. 000
F
Sigeificarml
^
3.485 -111
. 000
-0.781 78.7
Over*11%asuresL Multiple 1
-0.37 3.92 .095 .C1 .114 .700
-0.34
1.97 .210 .03 .706 .50
.67 .026 .026 .975 .03
-0.134 1.103 .334
0.56 19.23 .005
.95 19.63 .002
-1.11 -0.30 167.7 18.2 .ooo .005
c.31 19.8 .004
.9a 67.6 .ooo
0.34 2.07 .200
-0.885 14.1 .oo9
.866 6.024 .031
11. Adjusted Rent3 per Number of Bedrooms
-0.098 -0.685 .OR3 6.73 .783 .041
-0.45 1.77 .232
.i62 2.79 0.133
12. Adjusted Rent3 per Floor Space
0.668 -0.274 15.3 2.51 . Oil8 .160
-0.6S2 16.3 -007
.916 10.5 .ooo
10. Adjusted Rent3per tote1 Number of Room
23. Distence from
Fair
Adjusted Rent34
14. Distance frcm
Reir
Rant4
0.32 1.62 .279
o.oa 0.53 0.43
-0.804 53.9
0.55 26.3
.002
.967 29.4 .CC1
0.13 .65 .39
d.76 31.27 .OOl
0.61 18.81 .005
.5&a 17.R .302
‘Entries for eech mbjective.veriebleinclude: statisticend bottom - ai&ficence
of
F
. cm
top -- normalizedcoefficient,middle -- F
stcti’ltic.
2Entries for overall meesures consist of: top - middle R, middle - F test of function, and bottom -- significanceof F teat. 3Pdjustad rent: Rent reduced by emtimetedcosts of includedutilities. 4Fair
rent: The absolutevalue of the differencebetween the rent of the two ceuoom luxury apartmentwhich is most often preferred and the apartmentbeing considered: i.e.. D-P-R where D is distance,F is selected fair rent mnd R ir rent of apertnent.
When respondents were divided by income they demonstrated similar attitudinal scales. (These are clearly shown in Figs. 7 and 8.) Noticing preference and attribute ranking, this pilot study showed that neither
income nor number of children are good indicators of preference. The only transportation related attributes which received a high rating was “plenty of parking”. This
.r.
J. BENJAMINandR. E. PMSVIELL
316
Attribute 16. 36.
0
Responsive management. Rent less than $200.
38. 39. 40. 18. 30. 37. 47. 49.
Dutside private patio area. Craft rooms nearby. Laundry room. Adjacent garden plot. Community meeting room.
2. 3. 6. 8. 11. 12. 13. 19.
32. 43. 45.
Baseball field nearby. No through traffic on street. Close to friends and relatives. Wooded area nearby. Swimming pool nearby. Clean neighborhood. Plenty of parking. Child care center within walking distance. Tennis courts within walking distance. Mixture of different types of people. Modern exterior. Quiet street. Adjacent playground Natural environment, such as many trees and shrubs. Good schools nearby. Planned community. People like myself.
so.
Safe neighborhood.
4. 9. 14. 15. 17. 31. 33. 34. 35.
20. 22. 23. 26. 28. 29.
5. 7. 10. 21. Z: 27. 41. 42. 44. 46. 48.
Scale Value* 1.0 0.5 0.75
S......”
Bedrooms set away from living room and dining room Modern interior. New apartment. Modern, fully equipped kitchen, with a diswasher. Family room. Plenty of storage. Entrance to apartment separated from 1 iving room. Wall to wall carpeting in living room, dining area and bedrooms. More than two bedrooms. Dining facilities inside kitchen as well as an additional dining area. Two or more floors. Large rooms. Tile bath.
1.
0.25
-\
‘-9,
-9.
>
0.
9:.
s -‘~....,
_ .-•
4-_..-•
‘-
-
. l=....r_ b
.
.
.
-
.
-..*
.
.
.
‘.
-.
‘.
.
/I’
i__.-• r1’
ousing Attribute attribute
1.5
‘!. ,‘.“’
3
scale values by number
of children.
‘*
-0
__‘3
*-., l
l
_.....-.
Accessibility to supermarket. Location near place of work for head of household. Accessibility to movie theaters. Accessibility to clothes shops Accessibility to highways. Accessibility to restaurants. Hunting or fishing area within driving distance. Lake nearby. Accessibility to golf course. Accessibility to doctor’s office. Good community services such as fire department and police department. Accessibility to public transportation.
Fig. 7. Housing
1.25
--
Ratings.
l
b . . .
317
A psychometric analysisof residential location
Attribute 16. 36.
0
fi: 40. 18. 30. 37. 47. 49.
Outside private patio area. Craft rooms nearby. Laundry room. Adj acent garden plot. Community meeting room.
2. 3. 6. 8. 11. 12. 13. 19.
32. 43. 45.
Baseball field nearby. No through traffic on street. Close to friends and relatives. Wooded area nearby. Swimming pool nearby. Clean neighborhood. Plenty of parking. Child care center within walking distance . Tennis courts within walking distance. Mixture of different types of people. Modern exterior. Quiet street. Adjacent playground. Natural environment, such as many trees and shrubs’. Good schools nearby. Planned community. People like myself.
50.
Safe neighborhood.
4. 9. 14. 15. 17. 31. 33. 34. 35.
20. 22. 23. 26. 28. 29.
5. 7. 10. 21. 24. 25. 27. 41. 42. 44. 46. 48.
Scale Value* 0.5 0.75 1.0
1.25
1.5
Responsive management. Rent less than $200. Bedrooms set away from living room and dining room. Modern interior. New apartment. Modern, fully equipped kitchen, with a dishwaster. Family room. Plenty of storage; Entrance to apartment separated from living room. Wall to wall carpeting in living room, dining area and bedrooms. Wore than two bedrooms. Dining facilities inside kitchen as well as an additional dining area. Two or more floors. Large rooms. Tile bath.
1.
0.25
to supermarket. Accessibility Location near place of work for head of household. to movie theaters. Accessibility to clothes shops. Accessibility to highways. Accessibility Accessibility _. to restaurants. Hunting or fishing area within driving distance. Lake nearby. Accessibility to golf course. Accessibility to doctor’s office. Good community services such as fire department and police department. Accessibility to public transportation.
ncome
O-87,500-
$7,500-$10,000--
*Derived from Thurstone Case V Analysis
- -SlO,OOO-$lS,OOO........ of Housing Attribute
Fig. 8. Housing attribute scale values by income.
glS,OOO-$20,000 * a,,‘, -’
Ratings
J. BENJAMIN and R.E. PMSWELL
318
underscored the willingness of these respondents to travel to outside activities by automobile. Most concern was with the neighborhood and with-interior features of the home, not with accessible activities, even the journey to work. THE ROLEOF IWNWORTATION INHOUSINGCAOICE
For these subjects, transportation played little role in selecting an apartment. The internal attributes of the apartment and rent were the major factors. A comparison of results of Part A and Part B of the questionnaire show them to be consistent. In Part A, general neighborhood .attributes were unimportant in selecting apartments since all apartment neighborhoods were presented as safe and clean neighborhoods. Likewise, rent was not rated as important for most groups in the attribute ratings because only a very low level (“less than $200”) was presented. However, in neither case were public transportation or accessible activities indicated to be important in selecting an apartment. Apparently at the time these persons selected an apartment, they had decided that there was no problem in becoming captive auto users. To put it more strongly, as consistent auto users, and with above population median incomes, the advantages of car ownership and use are taken for granted. Costs associated with travel times are not seen as penalties and are probably not given a value at all. The dimensions of this research area posed no constraints on selecting an apartment site. This leads to the conclusion that for these subjects only a travel time that exceeds a specific threshold would influence apartment choice and that travel time threshold has not yet been reached in this search area. Tradeoffs between travel time and living space may occur, but these are only the results of discrete changes beyond given thresholds. The lack of concern for transportation issues among these respondents is an indicator for policy-makers who would try to make decisions regarding suburban vs central city housing location. Furthermore, any policy which are desired to affect residential location should be tested using similar research methods. The best hope for gaining any measure of the limits to suburban growth now appears to be availability (and not cost) of gasoline. These conclusions are consistent with the experiences of new town developers across the country. In a recent article in Planning[25] it was pointed out that those new towns receiving federal assistance have all had severe financial difficulties. While there are several economic reasons for this, a major reason for these di@culties is that homes in planned communities. were difficult to sell. Similar problems were evident in’Audubon, New York, and the proposed market rental housing never has been constructed. Apparently highly accessible activities are not important to any major segment of suburbanites. It would seem that a more market oriented planning strategy should be adopted in the development of major market plans for urban areas and in the operation of policies related to transportation and land use. The models described in this paper clearly show that multivariate techniques can be used to describe and represent housing choice by individuals. Individual values, stage in life cycle and income establish housing needs, and effect apartment preferences. Values and attitudes also add to the preference set, leading to a self
internal negotiation of and accommodation to individual preferences and available housing. The model used is a framework within which future research and design studies can be made. Although a pilot study, the sample size (100) and apartment choice (10) were sufficient to get statistical verification of the tie of preferences to ideal points and actual housing chosen. The results are useful in terms of their contribution to the planning and design process.
REFERENCES 1. W. Alonso, Location and Land Use-Toward A General Theory of Land Rent. Harvard University Press, Cambridge, Mass. (1964). 2. B. Reif, Models in Urban and Regional Planning. Intertest Educational Publishers, New York (1974). 3. E. Moore and S. Cale, Comments on models of occupancy patterns and neighborhood change. In Mode/s of Residential Location and Relocation in the City (Edited by E. Moore). D. 135-175.Northwestern University: Evanston,~Illinois (1972). D. Ericksen, Urban Behavior. Macmillan. New York (1954). J. L. Berry and P. J. Schwind, Information and entroov in _. migrant flows. Geograph. Anal. 3,5-14 (1970). J. Lansing and R. Marans. Evaluation of neiahborhood auality. J. Am. Znstit.Planners 35, 195-199(l%$. ’ G. L. Peterson, A model of preferences: Quantitative analysis of the perception of the visual appearence of residential neighborhoods. J. Reg. $5. 7, 19-31 (1%7). 8. H. Sanoff, Family attitudes and housing preferences. In Integrating User Needs in Environmental De&n (Edited bv H. -Sane-@, National Technical Information‘ Service, Springfield, Virginia (1973). 9. R. K. Wilkinson and E. hf. Siisworth, Attitudes to the housing environment: An analysis of private and local authority households in Batley, Leeds and York. Urban Studies 9, 193-214(1972). 10. G. H. Beyer, Housing and Society. Macmillan, New York (1965). 11. E. Butler, F. Chapin, G. Hemmens, E. Kaiser, M. Stegman and S. Weiss, Moving Behavior and Residential Choice. National Cooperative Hiway Program Research Report 81, Highway Research Board, Washinrrton. D.C. (1969). 12. W.-C. Norcross, Townhouses andC’ondomin;ums~Residential Likes and Dislikes. Urban Land Institute, Washington, D.C. (1974). 13. J. G. Engel, D. Kollat and R. Blackwell, Consumer Behavior, 2nd Edn. Dryden Press, Hinsdale, Illinois (1973). 14. D. Currv. _ I. Levin and hf. Grav. . A comoarison of additive conjoint measurement and functional measurement in a study of apartment preferences. Tech Rep. 98. Institute of Urban and Regional Research, University of Iowa (Jan. 1978). 15. R. Knight and M. Menchik, Conjoint preference estimation for residential land use policy evaluation. Rand Rep. P 5 172 (1974). 16. J. DeChiara and L. Koppelman, Manual of HousingPlanning and Design Criteria Prentice Hall, Englewood Cliffs, New Jersey (1975). 17. F. Becker, Design for Living: The Residents View of Multifamily Housing, Center for-Urban Development Research, Cornell University, Ithaca, New York (1974). 18. C. Norcross, New towns under the microscope. Urban Land 34(2),5-15 (1975). 19. R. E. Frank, Y. Wind and W. E. Massey, Market Segmentation. Prentice Hall, Englewood Cliffs, New Jersey (1972). 20. J. Benjamin, The assessment of user preferences for new apartments: an application of multidemensional scaling. Unpublished dissertation, State University of New York at Buffalo (1977).
A psychometric analysis of residential location 21. J. Kain and J. Quigley, Measuring the value of housing quality. 1. Am. Statis. Assoc. 65(S), 532-548(1970). 22. R. Paaswell and R. Stevens, “User Preferences and Housing Needs”. Proceedings, Third Int. Symposium on Lower Cost Housing, Montreal, Canada. 23. S. C. Johnson, Hierarchical clustering schemes. Psychometrika 32,241-254 (1%7). 24. J. D. Carroll, Individual differences and multidemensional
SEPS Vol. IS, No. 6-D
319
scaling (Edited by R. N. Shepard, A. K. Rommy and S. Nerlove). MultidimensionalScaling: Theory and Application in the Behauioral Sciences Vol. I. Seminar Press, New York (1972). 25. America1Planning Association, The new-town program bites the dust. Planning (Dec. 1978). 26. B. G. Hutchinson, Principles of Urban Transport Systems Planning.Scripta, Washington, D.C. (1975).
A PSYCHOMETRIC ANALYSIS OF RESIDENTIAL LOCATION JULIAN BENJAMIN The TransportationInstitute, North CarolinaA&T State University, Greensboro, NC 27411,U.S.A. and
ROBERT E. PAASWELL Department of Environmental Design and Urban Planning, School of Architecture, State University of New York at Buffalo, Buffalo, NY 14214,U.S.A. (Receiued 5 August 1980;in revised form 2@March
1981)
Abstract-Traditional housing needs studies focus on the trade-off between location and housing amenities. In most models, mode choice is viewed as conditional behavior in a given setting. New movers, however, select a new environmentwhich includesa house, neighbourhood and transportation system. By using psychometric techniques, the attitudes and preferences of new movers to the suburbs of a large northeast city were analyzed to determine the relative importance in their selection process of the transportation characteristics of the new location. It was found that local and regional transportation and public transit played little role in selecting an apartment. There was no evidence of tradeoffs between travel time and living space postulated by urban economics. Most important to the choice process of these residents were internal characteristicsof the apartmentand pricing issues. This supports the idea that suburbanites chose to be captive auto users even when equivalenthousingopportunitieswith transport alternatives are available. Implicationsfor public transit and land use planning alternativesfor the suburbsare discussed.
was the pilot in a project whose objectives were to provide developers with a much more astute picture of their market, hence increasing the probability of a match between the costs of investment in desired attributes and the demand for these attributes.
INIDODUCTION
Many traditional housing needs study focus on the tradeoffs between accessibility and value of other amenities as determinants of residential location. When a large scale development is constructed, the developer makes some assumption about the results of that trade off. He assumes that he understands the market for his housing and can provide the package of amenities and accessibility for a price that will satisfy the buyer. In suburban locations, generally it is assumed that household travel will be by car, so that model trade-offs are not considered. That is housing choices will be made with certain predetermined choices if made by the renter or buyer. There are some questions that remain about accessibility and housing location. How important is it, to the housing owner or renter to be close to work, or close to other activities? What tradeoffs are made between activity location and access to work? What tradeoffs are made between accessibility and other housing alternatives? To examine a number of these tradeoff decisions, a study has been made of factors influencing housing choice for a carefully selected sample of a population who have moved to a specific location-a suburban new-town adjacent to major metropolitan area. The sample has been controlled to determine whether in making ultimate similar housing choices, the population arrived at these choices in a similar way. Thus, many of the population variables were controlled. This made it possible to evaluate multi-variate techniques as a means of establishing determinants of housing choice. This paper examines the method used in a pilot study to assess the attributes of housing that led to the specific choice, with some emphasis on accessibility. The study
EOUSDVG LOCATION ANDACC~~~BJLITY
The locational aspects of housing, regional or sighted in a neighborhood, have been theorized for some time by urban economists. Locational models of Alonso[l] and LowryI used distance from housing to work as a specific variable used to postulate the distribution of population. In fact, the mathematical forms of their models make distance one of the most powerful variables in population location. Later work by others (Reif[2]) began to add certain neighborhood characteristics to these distance variables. Researchers fram a variety of disciplines have investigated moving behavior, housing choice and the role of transportation. These studies examine diverse factors that include regional and neighborhood characteristics and the physical exterior and interior amenities of housing. Moore and Cale[3] designed specific housing search areas, limiting the regional nature of choice. They specified that housing searches are limited to specific regions within an urban area and that considerations after that initial location choice are secondary. Sociologists such as Ericksen[4] reinforce this approach by recognizing that like groups, segregate themselves geographically. Geographers [S] have quantified the migration of like groups in urban areas, concluding that socio-economic indicators are at least one set of important characteristics of a neighborhood. 305
306
J. BENJAMIN and R. E. PAASWELL
Other studies have focused on the physical characteristics of the neighborhood. Lansing and Marans[6], Peterson[7] and Sanoff [8] investigated methods of measuring resident response to neighborhood appearance. All these studies asked residents for comparisons of neighborhoods or neighborhood characteristics. In comparing neighborhood SatisfaCtions, Peterson found that the most important factors contributing to overall resident satisfaction are upkeep, aesthetics, quietness, friendly people, similar people and places for children to play. Of these, upkeep was the most important. Wilkinson and Sigsworth[9] found that the degree of interaction and friendliness between neighbor was related to the external design. By concentrating on physical and social characteristics researchers have developed a long list of attributes important to housing location. These attributes can later be weighed against accessibility. Study of behavior inside the home is by nature more difficult. Beyer [lOI summarized work by more economists which examine interior design from a human factors viewpoint. These variables add one more component to the increasingly complex housing choice set. A general analysis that includes regional neighborhood, exterior and interior design has been only employed by a small number of researchers. In a national study by Butler et al.[l1] measures of attitudes, satisfactions and moving behavior were obtained. In the aggregate, movers preferred better neighborhood quality at the expense of accessible location and desirable dwelling unit, to a nice outside appearance. They also indicated a preference for a new house and well established neighborhoods, modem style to traditional, a one floor unit, few children in the neighborhood and large lots to small. It was found that moving intentions were most closely related to life cycle. The study made no attempt to model moving behavior and analysis was primarily cross-sectional. A total approach has also been employed to evaluate differences in satisfaction of residents in different environments. One example of this approach is a comparison of satisfactions of townhouse and garden apartment residents by Norcross[l21. This approach is area specific and is subject to variations by respondents as well as to differences in envirbnments. Both Engel[l3] and Norcross showed tie of life cycle to desired amenities specifically with regard to recreation and space. This, of course, then ties to availabIe space/$ or the important income constraint. Physical and economic choice (rent, size, heat) were described in using a variety of psychometric techniques[14]. This study was important for the analysis of measuring the trade-off, or choice decision and provided more insight into the relative importance of pre-identified attniutes in choice. Methodologically, these’studies fall short of obtaining a comprehensive understanding of location behavior, i.e. location choice as a function of choice within region, choice within neighborhood and then physical choices within the housing itself. A thorough understanding of SThese features have long been recognized by European planners who design accessibility into community planning. This minimizes the travel cost and reduces the uumber of tradeoffs necessary to be accommodated in housing choice. Middle class Americans have often assumed that mobility at low cost was always available and the housing market always responded by providing a wide range of housing in a wide range of location.
consumer needs and wants cannot be obtained by merely asking “What do you want?‘, “Are you satisfied?” or “Why did you move?” Responses to these questions will only supply a partial picture of resident opinions of existing conditions. Furthermore, because subjects are not trained architects and are unaware of all the consequences of design decisions, when asked to express likes and dislikes they would most likely ask for as much space and amenities as possible. Hence, what is required is a knowledge of tradeoffs that are made in choosing between feasible alternatives. The question then becomes: “Which designs do yau most prefer given all personal constraints and desired household characteristics?’ A complete knowledge of how choices are made can only be obtained through an understanding of tradeoffs made by individuals at the time the location decision is made. Within specific income and expenditure constraints people decide between units by selecting the unit which has desired important features. They do this by sacrificing what, for them, is not so important. This is the basis of the tradeoff analysis used by Knight and Menchik[ IS]. Knight and Menchik investigated tradeoffs between private, semi-private and common open space made by inhabitants of planned communities. Respondents were asked to compare diagrams and pictures which varied with respect to private and open space and neighborhood quality. They found respondents willing to sacrifice personal space to gain more common space. Because of the data requirements, however, significant modification would be needed to use a total housing and locational perspective. From the efforts reviewed here, it is evident that there is insufficient knowledge of the tradeoffs and compromises made in choosing housing alternatives and locations and the relative influence of transportation on these choices. This research describes the first step in the development of a strategy to measure these tradeoffs. As recognition of the important role of accessibility as an integral component of the American urban-suburban environment, planners have developed planned unit developments and new towns which include several design elements to increase accessibility of urban activities.t For example, by mixing land uses, commercial and industrial centers become accessible by car or public transit, leisure and recreational’ facilities are located within walking distance and intra-city transportation becomes centrally located. Another transportation related new town inndvation is clustered housing which permits open space and park land to be interwoven with residential areas so that it is an easy walk from all homes [ 161. It was theorized by planners that such an arrangement would be a viable and environmentally preferable alternative to conventional suburban development which is often characterized as urban sprawl. In conventional developments almost all activities are accessible by car only. Perhaps the one exception is ihe journey to work, located traditionally in the central business district or, in a suburban twon center can often be reached by public transit. Even then, the transit system is usually accessible only by car in some park and ride arrangement. Thus, the choice to live in conventional suburbs is often a choice available to car owning households only. With the notable exception of a small number of new towns, most movers to the suburbs are faced with no
A psychometricanalysis of residentiallocation choice of modal mobility; become captive drivers or stay in the city. It has been suggested by planners that a substantial segment of these movers would prefer the accessible setting and attributes that are associated with a planned community if it were made available[l7,18]. It was further suggested that accessibility to the central business district was an important element in selecting a neighborhood and that transportation did play a role in home selection. The validity of these suggestions was tested in a study that asked subjects to answer an extensive set of questions about several different apartment offerings, apartment characteristics and general questions about themselves. The approach to ths study and the findings are reported in the following section.
DESIGN OF A STUDY TO DETERMINE APARTMENT PREFEWNCES
To test these planning suggestions discussed above, a survey instrument was administered to a sample of 100 residents (50 couples) who had recently moved to the area adjacent to the proposed new town of Audubon in Amherst, New York. The respondents could be characterized as middle income households. This sample represented a first group of new movers to a small suburban development. The procedure was designed to collect 100 individual, independent responses, through the sampling of 50 couples. The majority had no children, both heads worked and held skilled or professional occupations. The results of the analysis of two pertinent parts of the questionnaire will be reported here. In one part of the instrument 10 apartment units were completely described. Respondents were asked to rank order their preference for the apartments from most preferred to least preferred. Respondents were asked to make realistic selections, acting as if these were the only apartments available at this time. Each apartment was displayed on a 10”x 20” card that contained: -An internal view -An external view -A floor plan -A neighborhood map -A regional map -Additional apartment related information, including rent, accessible transportation and nearby activities. The layout for each card is illustrated in Fig. 1. The elements of Apartment 1 are illustrated in Figs. 2-4. The information concerning each apartment was based upon methodologies that had proved successful in similar type studies [8,13,14]. In addition, the information presented was refined by discussions with local planners, realtors and advertisers. A summary of key characteristics of each apartment is presented in Table 1. A full description of each apartment is quite lengthy and can be found in Benjamin[U)]. The key apartment characteristics listed ia Table 1 were selected based both on the results of a review of previous similar studies and due to their relevance to the questions of tradeoff raised earlier. A review of the number of characteristics and the number of levels of each characteristic reveals that there are a very large number of apartments that could be created from various combinations of characteristic levels. Further, it was tThe programMDPREFwas used.
307
found that respondents required considerable time to analyze and compare cards (approx. 2min card). Thus, the experimental design made it necessary to limit the total amount of choices presented to the respondent. For this reason, only a small number of apartments were selected. The apartments were chosen to represent either existing apartments in the area or proposed new apartments in the planned community. At the sanie time, each apartment was selected to be part of the choice set that would convey all levels of each characteristics to the respondent. For this reason, only a small number of apartments were selected. The apartments were chosen to represent either existing apartments in the area or proposed new apartments in the planned community. At the same time, each apartment was selected to be part of the choice set that would convey all levels of each characteristics to the respondents. Existing apartments were represented by actual photographs, floor plans, maps and other pertinent information. Apartments in new communities were represented by taking photographs of existing facilities and locating them in new communities by using maps and other information. New and existing facilities were not identified as such.
APARTMENT A’llWWTEIMPORTANCE In the other section of the questionnaire discussed here, respondents were asked to compare apartment attributes according to their importance in apartment selection. Fifty apartment attributes were selected based on a critical analysis of the literature[21,22]. These attributes are listed in Table 2. The apartment attributes represented housing subsystem that included management, internal, semi-private, neighborhood and community characteristics. Each attribute was written on a card and the subjects were asked to sort the cards into equal groups according to its importance in selecting an apartment. Groups were identified with size restrictions as follows: not important at all-8 cards not so important -8 cards not important -9 cards important -9 cards more important -8 cards very important -8 cards. Cards were sorted in a two step procedure and respondents were asked to keep within the restrictions as much as possible. After the response, a detailed analysis of the attributes that influenced apartment selection was carried out. MULTIDIMJSNSIONALANALYSlS OF PREFERENCE RANKING
The first step in the analysis was to determine the preference of the respondents for each apartment category and then to correlate apartments perceived of as being similar by identifiable groups of the population. Preference rankings were analyzed by an internal analysis of preference[23]. In the first phase, a vector modelt of preference was calibrated using the ranking order of the apartments. In this model, it is assumed that a respondents preference for an apartment can be described using measures
J. BENJAMIN and R. E. PAASWELL
308
APARTMENT CARD LAYOUT
Outside
View
Inside View
Floor Plan
Neighborhood Map
.k!ditional Apartment Related Information
Regional Xap
Fig. 1. Apartmentcard layout.
of specific attributes of that apartment. In this case, it is assumed that groups of measures can form dimensional axes that generate a space in which the attributes of the apartments can be measured. (One dimension, e.g. might simply be rent, another, number of rooms, etc.). To locate the particular apartment in these dimensions, a vector is then necessary. The actual model has the form: pik = &,
’ x,k
(1)
Pa is the preference of subject i for apartment k; B,, is the relative importance of variable j for apartment k; and tTo facilitatecomparisonsbetween bi coeilkknts fbr different objective measures, unit vectors were formed normalizingthe b coefficients
X, is the relative position of apartment k on variable j. The MDPREF algorithm solves both the perception space and the individual preference vectors simultaneously. The muttidimensional solution consists of a common, orthogonal perceptual space for all subjects and individual preference vectors. The first two dimensions (size, price) of the solution for these-100 subjects is illustrated in Fig. 5. The dimensions of this solution can be labelled by associating the location of each apartment in tbe perceptual space with objective apartment characteristics for each apartment. The relationship between apartment characteristics and perceptual space location can be tested by the following linear model: Y = b, t b,x, + bgz t b3x3t E. where Y is the objective measure to be tested; xi is apartment location on perceptual dimension i; b, is the coefficient of Xi;t and E is an error term.
A psychometric analysis of residential location
30!4
Fig. 2. Internal and external view of Apartment 1.
Variables tested were selected from Table 1 or were composite of these variables. Normalized coefficients for the 14 variables with the highest correlation coefficients are listed in Table 3. From this analysis it was possible to identify the dimensions as: Dimension 1: SIZE, the number of rooms. Dimension 2: FAIR PRICE, i.e. proximity to the average price of a two-bedroom market rental apartment. A third dimension was found which related most to the absolute cost of the apartment. These dimensions accounted for approx. 70% of the variance in the data. The remaining seven dimensions all accounted for an average of less than 5% of the variance and were considered insignificant. It is important to note that statistically 100 respondents are ranking only ten apartments. The use of multidimensional techniques makes it possible to examine a number of attributes for each, but the final clustering and evaluation are based on these two numbers. ACCFSrBrLtTv No significant dimension was closely related to transportation. The classic tradeoff between size and accessibility was not demonstrated by these respondents. Accessibility to the Central Business District was represented by a dimension called the north-south direction. The area in which the apartments were located was approx. 10 miles north of the CBD and the dimensions of the area were approx. 10 miles by 10 miles; the CBD and all other regional activities were accessible only by an expressway located on the southern edge of suburban territory. But north-south direction was most closely related to dimension 1 in the perceptual space, and other key variables associated with Dimension 1 had higher correlation coefficients. Local activities were most highly accessible in the planned communities. Furthermore, many complexes offered some recreational activities. And yet, degree of accessibility to local activities was also not significantly related to any dimension, that is, it was not important in identification of apartments by the respondents.
APARTMENTCLUSTRRS
For further assistance in interpreting dimensions, apartments were clustered using Johnson’s Hierarchical cluster analysis[23]. In Fig. 5, three clusters were formed which, as expected, were based on size and cost considerations only. Planning and physical location within the suburban area had no effect on cluster member ship. This highlights the lack of consideration that transportation plays in the preferences of the respondents constituting the sample for the study. SURJRCTCLUSTERS
To gain further insight into personal housing preferences, subject clusters were formed based on subject ideal points combinations of attributes most preferred by each subject. These clusters can then be used to identify potential market segments. To cluster subjects, their preference must be described as a point location in the preference space (i.e. the space defined by dimensions derived from the most pertinent apartment attributes). To do this, the principles of unfolding analysis are used. It is assumed that there is some combination of underlying choice variables which is most preferred by each respondent. The “ideal point” or “person point” is estimated by using the following model: Pij =
2 U(Zj’- Xi’)* ,=I
where Pii is the preference of subject j for simulus i; I,’ is the ideal point for subject j on dimension t; Xi’ is the location of stimulus
310
J. BENJAMIN and R. E. PAASWELL
A psychometric
analysis of residential location
311
J. BENJAMIN and R. E. PAASWELL
312
1.0
f’%
@!_a
Fair
prim -1.0
Fig. 5. Apartment clusters in Dimension I and Dimension 2.
AAA --.
A
6
*)
It_,‘0
.
;ol. .
. r/
.
*C--
/‘-‘;I
e/
A
/’ /’ H
/
,$..
6.. ,.;,.=%. ..............q i............... ‘h .
1’.
\ ;-
--______-e-w-.
l\
.
1
>
I
\
>
\ .\ 1.0
4
. .’ .’ 0. .I ,Y
4 .
4UtU&ttt,i,......O
~atxbnlrtw ..,... m~atI4ulm&8tlaeaadu
-w_sg-
Ionl?oat
0
Iair
prim
..,
1.0
11 . .......... .. 11I -s-L_
Iv _._._._._
Fii. 6. Subject ideal points in Dimension 1 and Dimension 2.
>
313
A psychometric analysis of residential location Table 1. Apartment characteristics Ausrc-
Rmt
lJei:it:er
No.
Dent
$ per
IlX?Ld2d
BG-
Kc5er
EG^ril
TOtal Rc;Lxs
of
r1osr SPJCC (Sq.
ro5.15
Positfor. (E-.0'
Position (S-S)2
13. of
PJCl
Type
Neiahbxhood
T.xaIhouse Hiphrise Garden
P?XXXd
3
3
3
CJlIVeE:iOCZl Pla:Xa?.
2
1
1
yes
2
2
1
Yes
iiocrs
rc.)
y*:
7
1142
3
203
4
5i7
2
4
610
1
a0
490
C‘X.*t5tionnl CEQkX
2
4
?hlLifanfly Garden
1
2
1
2
1
y*r
Yes
2
4
5iO
Garden
Co?qlex
2
2
1
yas
,315
no
2
7
1096
Townhouse
Plszmzd
3
3
3
Y@S
275
nn
3
6
856
TOWI-
Plaor.ed
2
2
2
Yes
CCZ?LiX
L
2
1
no
C‘Xl-,?~r.
2
2
1
yes
1
3'5
no
2
2
196
Yes
1
3
2X
L3
2
4
130
"0
5
200
Yes
s
235
7 8
9e
9
ieo
Ye=
2
4
516
house GarCe?r
i0
235
‘0
2
4
61
Gzrder.
1 Bclativcposicim in .?r.=: l-msc wes~crn :hirC, 2-central:\ied, 3-ilosteastern third , I.-cenxal ehird. 3-most nce:Cetr third Bolativopisicizain <.;a: l-nsir sou:hcm third,
Table 2. Environmental attributes bv housina subsvstem ATTRIBUTE
jlOIJSINC SUBSYSTEM
xanagemat Internal
16. 36. 1. 4. 9. 14. 15. 17. 31. 33. 34. 35.
:9": 40. Sml-rrivate
18. 30. 2:: 49.
Nalgltborhood
2. 3. 6. 8. 11. 12. 13. 19. 20. 22. 23. 26. 20. 29. 32.
aesponsive manajiencnt Rent less than $200 Bedrooms set away from living room and dining room Modern interior New apartment Hodem, fully equipped kitchen, with a dishwasher Family room Plenty of storaec Entrance to apartment separated from living room wall to wall carpeting in living room, dining area and bedroom Xore than two bedrooms Dining facilities inside kitchen as well as an additional dining area Two or more floors Large rooms Tile bath Outside private patio area Craft room nearby Laundry room Adjacent garden plot Community meeting room Bnaeball field nearby No through traffic on street Close to frieods and relative8 Wooded area nearby Swimming pool nearby Clean neCahborhcod Plenty of-parking Child care center within walking distsnce Tznnia co"rtR within wetking disterre Mixture of different types of people Nodern exterior Quiet street Adjacent playground Natural envixcment. such ae many trees and shrubs Good schools nearby
J. BENJAMIN and R. E. PAASWELL
314
Table 2. (Con@. ATTRIBlPTE
HOUSING SUDSYSTFJ4
community 453:Plmned People like myself Safe neighborhood 50. 5JiTDUllity
5. 7. 10. 21. 24. ;:: 41. 42. :46: 40.
Accessibility to supermarket vocation near place of work far bend of household . Accessibility to movie theaters Accessibility to clctbes chops AccrsRibility to hixlways Accessibility to rr-.st>.crants Hunting or fishing (1ro8 within driving distance Lake nearby Accessibility to golf course Accessibility to doctor’s office Good cormnonity service such 06 fire departaent and police depnrtment Accessibility to public trsnnportncion
adjacent clusters points, where unclustered points are considered individual clusters). As a result, five subject clusters were identified. Each cluster is characterized by its position in the perception space. Two sets of cluster dimensions were used: (1) Fair price and apartment size and (2) size and cost per number of bedrooms. Apparent overlaps in Fig. 6 are due to the use of a three dimentional solution in the clustering process. By examining the positions of the clusters of subjects in preference space, the clusters could then be identified as groups preferring: Large apartments, with high cost per unit of space These subjects felt they needed plenty of space and were willing to pay for it. The centroid was nearest that of townhouses (45 subjects). Large apartments with low cost per unit of space These subjects not only were looking for large apartments, but were not willing to pay extra for what they wanted (20 subjects). Mid-sized apartments with high cost per unit of space These were respondents with some space requirements and with the desire to pay for what they wanted. They would be satisfied with a luxury apartment near the fair price (8 subjects). Large apartments with high cost per unit These subjects would be satisfied with luxury garden apartments or townhouses and were willing to deviate substantially from the fair price (6 subjects). Unclustered This group consisted of subjects who preferred a variety of different apartment types. Many of these subjects had distinct preferences which would appear on the perceptual map as outlier points. Thus, there was no one central preference for members of this group (21 subjects). The clusters were naturally determined by use of the Johnson techniques, and by using two sets of key dimensions. As expected from the dimensions of the preference space, no market segment was identified which was concerned with transportation. However, two non-clus-
tered outliers were careless, which was the only time transportation was an issue for these subjects.
ANALYSISOF APARTMENT CHARACTERIsTIcS
The results of the initial sorting of apartment attributes into groups by the respondents were analyzed using unidimensional Thurstone scales. These scales are developed under the assumption that the distance between scale values is proportional to the number of times one stimulus (apartment characteristics in this case) is rated higher than another. Because it was suggested in the literature review that life cycle and income were important determinants of housing attitudes, attitude scales were computed for separate groups based on income and family size. The results of these analyses are presented in Figs. 7 and 8. When grouped by family size, the largest group was families with no children. They account for approx. 2/3 of all respondents. It was found for this group that the highest rated attributes (according to highest ranking first) were:
Ranking for childless families : 3 4 5 6 I
! 10
Attributes
Ranking for families with $15,000-$20,000 annual income
1. Safe neighborhood 12. Clean neighborhood 14. Modern fully equipped kitchen with dishwasher 37. Laundry Room 16. Responsive management 17. Plenty of storage 33. Wall-to-wall carpeting in living room, dining area, and bedrooms 13. Plenty of parking 26. Quiet street 39. Large rooms
: 6 5 9
7 10 11
A psychometricanalysis of residentiallocation
315
Table 3. Summarytable of regressionsof objective measureson subjectivevariables SubjectiveVerieblerl Norme:izedCmfficientr F Test
ObjectiverewJ\ire
Significance
1.
2.
.
AectualRent (k listed on cerds)
-0.949
71.87 .ooo
-.03l2 7.768 .032
Pool
-0.65 2.71 .151
0.17 0.19 .676
-0.88 5.67 -0.867 254.4
3.
Number of Bedrooms
4.
Tote1 Nudmr of Rooms
. 000 5.
Number of Floore
6.
PhyeicalPosition in Area (Eeet-West)
-0.83 12.8 .012
7.
Degree of Planning
-.9% 19.20 .Ol
8.
Phyaicel Poeition it4 Area (North-South)
-0.62 41.15 .Ml
9.
n00r Spece
Test
0.04 O.Z.2 0.739 -0.739
.931 27.02 .OOl .731 2.294 .178
0.38 1.056 .344
0.29 0.597 .469
.733 2.32 .175
-0.412 59.22
0.27 25.4 .to2
.99 112.6 .oco
0.195 4.9 .069
.977 43.4 .000
-0.593 45.3 .OOl
. 000
F
Sigeificarml
^
3.485 -111
. 000
-0.781 78.7
Over*11%asuresL Multiple 1
-0.37 3.92 .095 .C1 .114 .700
-0.34
1.97 .210 .03 .706 .50
.67 .026 .026 .975 .03
-0.134 1.103 .334
0.56 19.23 .005
.95 19.63 .002
-1.11 -0.30 167.7 18.2 .ooo .005
c.31 19.8 .004
.9a 67.6 .ooo
0.34 2.07 .200
-0.885 14.1 .oo9
.866 6.024 .031
11. Adjusted Rent3 per Number of Bedrooms
-0.098 -0.685 .OR3 6.73 .783 .041
-0.45 1.77 .232
.i62 2.79 0.133
12. Adjusted Rent3 per Floor Space
0.668 -0.274 15.3 2.51 . Oil8 .160
-0.6S2 16.3 -007
.916 10.5 .ooo
10. Adjusted Rent3per tote1 Number of Room
23. Distence from
Fair
Adjusted Rent34
14. Distance frcm
Reir
Rant4
0.32 1.62 .279
o.oa 0.53 0.43
-0.804 53.9
0.55 26.3
.002
.967 29.4 .CC1
0.13 .65 .39
d.76 31.27 .OOl
0.61 18.81 .005
.5&a 17.R .302
‘Entries for eech mbjective.veriebleinclude: statisticend bottom - ai&ficence
of
F
. cm
top -- normalizedcoefficient,middle -- F
stcti’ltic.
2Entries for overall meesures consist of: top - middle R, middle - F test of function, and bottom -- significanceof F teat. 3Pdjustad rent: Rent reduced by emtimetedcosts of includedutilities. 4Fair
rent: The absolutevalue of the differencebetween the rent of the two ceuoom luxury apartmentwhich is most often preferred and the apartmentbeing considered: i.e.. D-P-R where D is distance,F is selected fair rent mnd R ir rent of apertnent.
When respondents were divided by income they demonstrated similar attitudinal scales. (These are clearly shown in Figs. 7 and 8.) Noticing preference and attribute ranking, this pilot study showed that neither
income nor number of children are good indicators of preference. The only transportation related attributes which received a high rating was “plenty of parking”. This
.r.
J. BENJAMINandR. E. PMSVIELL
316
Attribute 16. 36.
0
Responsive management. Rent less than $200.
38. 39. 40. 18. 30. 37. 47. 49.
Dutside private patio area. Craft rooms nearby. Laundry room. Adjacent garden plot. Community meeting room.
2. 3. 6. 8. 11. 12. 13. 19.
32. 43. 45.
Baseball field nearby. No through traffic on street. Close to friends and relatives. Wooded area nearby. Swimming pool nearby. Clean neighborhood. Plenty of parking. Child care center within walking distance. Tennis courts within walking distance. Mixture of different types of people. Modern exterior. Quiet street. Adjacent playground Natural environment, such as many trees and shrubs. Good schools nearby. Planned community. People like myself.
so.
Safe neighborhood.
4. 9. 14. 15. 17. 31. 33. 34. 35.
20. 22. 23. 26. 28. 29.
5. 7. 10. 21. Z: 27. 41. 42. 44. 46. 48.
Scale Value* 1.0 0.5 0.75
S......”
Bedrooms set away from living room and dining room Modern interior. New apartment. Modern, fully equipped kitchen, with a diswasher. Family room. Plenty of storage. Entrance to apartment separated from 1 iving room. Wall to wall carpeting in living room, dining area and bedrooms. More than two bedrooms. Dining facilities inside kitchen as well as an additional dining area. Two or more floors. Large rooms. Tile bath.
1.
0.25
-\
‘-9,
-9.
>
0.
9:.
s -‘~....,
_ .-•
4-_..-•
‘-
-
. l=....r_ b
.
.
.
-
.
-..*
.
.
.
‘.
-.
‘.
.
/I’
i__.-• r1’
ousing Attribute attribute
1.5
‘!. ,‘.“’
3
scale values by number
of children.
‘*
-0
__‘3
*-., l
l
_.....-.
Accessibility to supermarket. Location near place of work for head of household. Accessibility to movie theaters. Accessibility to clothes shops Accessibility to highways. Accessibility to restaurants. Hunting or fishing area within driving distance. Lake nearby. Accessibility to golf course. Accessibility to doctor’s office. Good community services such as fire department and police department. Accessibility to public transportation.
Fig. 7. Housing
1.25
--
Ratings.
l
b . . .
317
A psychometric analysisof residential location
Attribute 16. 36.
0
fi: 40. 18. 30. 37. 47. 49.
Outside private patio area. Craft rooms nearby. Laundry room. Adj acent garden plot. Community meeting room.
2. 3. 6. 8. 11. 12. 13. 19.
32. 43. 45.
Baseball field nearby. No through traffic on street. Close to friends and relatives. Wooded area nearby. Swimming pool nearby. Clean neighborhood. Plenty of parking. Child care center within walking distance . Tennis courts within walking distance. Mixture of different types of people. Modern exterior. Quiet street. Adjacent playground. Natural environment, such as many trees and shrubs’. Good schools nearby. Planned community. People like myself.
50.
Safe neighborhood.
4. 9. 14. 15. 17. 31. 33. 34. 35.
20. 22. 23. 26. 28. 29.
5. 7. 10. 21. 24. 25. 27. 41. 42. 44. 46. 48.
Scale Value* 0.5 0.75 1.0
1.25
1.5
Responsive management. Rent less than $200. Bedrooms set away from living room and dining room. Modern interior. New apartment. Modern, fully equipped kitchen, with a dishwaster. Family room. Plenty of storage; Entrance to apartment separated from living room. Wall to wall carpeting in living room, dining area and bedrooms. Wore than two bedrooms. Dining facilities inside kitchen as well as an additional dining area. Two or more floors. Large rooms. Tile bath.
1.
0.25
to supermarket. Accessibility Location near place of work for head of household. to movie theaters. Accessibility to clothes shops. Accessibility to highways. Accessibility Accessibility _. to restaurants. Hunting or fishing area within driving distance. Lake nearby. Accessibility to golf course. Accessibility to doctor’s office. Good community services such as fire department and police department. Accessibility to public transportation.
ncome
O-87,500-
$7,500-$10,000--
*Derived from Thurstone Case V Analysis
- -SlO,OOO-$lS,OOO........ of Housing Attribute
Fig. 8. Housing attribute scale values by income.
glS,OOO-$20,000 * a,,‘, -’
Ratings
J. BENJAMIN and R.E. PMSWELL
318
underscored the willingness of these respondents to travel to outside activities by automobile. Most concern was with the neighborhood and with-interior features of the home, not with accessible activities, even the journey to work. THE ROLEOF IWNWORTATION INHOUSINGCAOICE
For these subjects, transportation played little role in selecting an apartment. The internal attributes of the apartment and rent were the major factors. A comparison of results of Part A and Part B of the questionnaire show them to be consistent. In Part A, general neighborhood .attributes were unimportant in selecting apartments since all apartment neighborhoods were presented as safe and clean neighborhoods. Likewise, rent was not rated as important for most groups in the attribute ratings because only a very low level (“less than $200”) was presented. However, in neither case were public transportation or accessible activities indicated to be important in selecting an apartment. Apparently at the time these persons selected an apartment, they had decided that there was no problem in becoming captive auto users. To put it more strongly, as consistent auto users, and with above population median incomes, the advantages of car ownership and use are taken for granted. Costs associated with travel times are not seen as penalties and are probably not given a value at all. The dimensions of this research area posed no constraints on selecting an apartment site. This leads to the conclusion that for these subjects only a travel time that exceeds a specific threshold would influence apartment choice and that travel time threshold has not yet been reached in this search area. Tradeoffs between travel time and living space may occur, but these are only the results of discrete changes beyond given thresholds. The lack of concern for transportation issues among these respondents is an indicator for policy-makers who would try to make decisions regarding suburban vs central city housing location. Furthermore, any policy which are desired to affect residential location should be tested using similar research methods. The best hope for gaining any measure of the limits to suburban growth now appears to be availability (and not cost) of gasoline. These conclusions are consistent with the experiences of new town developers across the country. In a recent article in Planning[25] it was pointed out that those new towns receiving federal assistance have all had severe financial difficulties. While there are several economic reasons for this, a major reason for these di@culties is that homes in planned communities. were difficult to sell. Similar problems were evident in’Audubon, New York, and the proposed market rental housing never has been constructed. Apparently highly accessible activities are not important to any major segment of suburbanites. It would seem that a more market oriented planning strategy should be adopted in the development of major market plans for urban areas and in the operation of policies related to transportation and land use. The models described in this paper clearly show that multivariate techniques can be used to describe and represent housing choice by individuals. Individual values, stage in life cycle and income establish housing needs, and effect apartment preferences. Values and attitudes also add to the preference set, leading to a self
internal negotiation of and accommodation to individual preferences and available housing. The model used is a framework within which future research and design studies can be made. Although a pilot study, the sample size (100) and apartment choice (10) were sufficient to get statistical verification of the tie of preferences to ideal points and actual housing chosen. The results are useful in terms of their contribution to the planning and design process.
REFERENCES 1. W. Alonso, Location and Land Use-Toward A General Theory of Land Rent. Harvard University Press, Cambridge, Mass. (1964). 2. B. Reif, Models in Urban and Regional Planning. Intertest Educational Publishers, New York (1974). 3. E. Moore and S. Cale, Comments on models of occupancy patterns and neighborhood change. In Mode/s of Residential Location and Relocation in the City (Edited by E. Moore). D. 135-175.Northwestern University: Evanston,~Illinois (1972). D. Ericksen, Urban Behavior. Macmillan. New York (1954). J. L. Berry and P. J. Schwind, Information and entroov in _. migrant flows. Geograph. Anal. 3,5-14 (1970). J. Lansing and R. Marans. Evaluation of neiahborhood auality. J. Am. Znstit.Planners 35, 195-199(l%$. ’ G. L. Peterson, A model of preferences: Quantitative analysis of the perception of the visual appearence of residential neighborhoods. J. Reg. $5. 7, 19-31 (1%7). 8. H. Sanoff, Family attitudes and housing preferences. In Integrating User Needs in Environmental De&n (Edited bv H. -Sane-@, National Technical Information‘ Service, Springfield, Virginia (1973). 9. R. K. Wilkinson and E. hf. Siisworth, Attitudes to the housing environment: An analysis of private and local authority households in Batley, Leeds and York. Urban Studies 9, 193-214(1972). 10. G. H. Beyer, Housing and Society. Macmillan, New York (1965). 11. E. Butler, F. Chapin, G. Hemmens, E. Kaiser, M. Stegman and S. Weiss, Moving Behavior and Residential Choice. National Cooperative Hiway Program Research Report 81, Highway Research Board, Washinrrton. D.C. (1969). 12. W.-C. Norcross, Townhouses andC’ondomin;ums~Residential Likes and Dislikes. Urban Land Institute, Washington, D.C. (1974). 13. J. G. Engel, D. Kollat and R. Blackwell, Consumer Behavior, 2nd Edn. Dryden Press, Hinsdale, Illinois (1973). 14. D. Currv. _ I. Levin and hf. Grav. . A comoarison of additive conjoint measurement and functional measurement in a study of apartment preferences. Tech Rep. 98. Institute of Urban and Regional Research, University of Iowa (Jan. 1978). 15. R. Knight and M. Menchik, Conjoint preference estimation for residential land use policy evaluation. Rand Rep. P 5 172 (1974). 16. J. DeChiara and L. Koppelman, Manual of HousingPlanning and Design Criteria Prentice Hall, Englewood Cliffs, New Jersey (1975). 17. F. Becker, Design for Living: The Residents View of Multifamily Housing, Center for-Urban Development Research, Cornell University, Ithaca, New York (1974). 18. C. Norcross, New towns under the microscope. Urban Land 34(2),5-15 (1975). 19. R. E. Frank, Y. Wind and W. E. Massey, Market Segmentation. Prentice Hall, Englewood Cliffs, New Jersey (1972). 20. J. Benjamin, The assessment of user preferences for new apartments: an application of multidemensional scaling. Unpublished dissertation, State University of New York at Buffalo (1977).
A psychometric analysis of residential location 21. J. Kain and J. Quigley, Measuring the value of housing quality. 1. Am. Statis. Assoc. 65(S), 532-548(1970). 22. R. Paaswell and R. Stevens, “User Preferences and Housing Needs”. Proceedings, Third Int. Symposium on Lower Cost Housing, Montreal, Canada. 23. S. C. Johnson, Hierarchical clustering schemes. Psychometrika 32,241-254 (1%7). 24. J. D. Carroll, Individual differences and multidemensional
SEPS Vol. IS, No. 6-D
319
scaling (Edited by R. N. Shepard, A. K. Rommy and S. Nerlove). MultidimensionalScaling: Theory and Application in the Behauioral Sciences Vol. I. Seminar Press, New York (1972). 25. America1Planning Association, The new-town program bites the dust. Planning (Dec. 1978). 26. B. G. Hutchinson, Principles of Urban Transport Systems Planning.Scripta, Washington, D.C. (1975).