Group Title: cross-section analysis of the demand for mobile homes in Florida
Title: A cross-section analysis of the demand for mobile homes in Florida
Full Citation
Permanent Link:
 Material Information
Title: A cross-section analysis of the demand for mobile homes in Florida
Physical Description: viii, 156 leaves : ill., maps ; 28 cm.
Language: English
Creator: Strader, Max Holt, 1946-
Copyright Date: 1977
Subject: Mobile home living -- Florida   ( lcsh )
Mobile homes -- Florida   ( lcsh )
Economics thesis Ph. D
Dissertations, Academic -- Economics -- UF
Genre: bibliography   ( marcgt )
non-fiction   ( marcgt )
Statement of Responsibility: by Max Holt Strader, Jr.
Thesis: Thesis--University of Florida.
Bibliography: Bibliography: leaves 148-155.
General Note: Typescript.
General Note: Vita.
 Record Information
Bibliographic ID: UF00098662
Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: alephbibnum - 000063582
oclc - 04213155
notis - AAG8781


This item has the following downloads:

crosssectionanal00stra ( PDF )

Full Text




A DISS.TON P'S r' TO -T"E GR..A''E Co;c:L CiO

DEG1' IE; OF [.L. CT' ;', Or ,' r'iF;. :.'L'H:CP' HY

UNT IV'I-RSiT ' OF F'-'LCli:ll.h

I 9,


This work is dedicated to the glory of God who

"showed His great love for us by sending Christ to die

for us while we were still sinners" so that whatever we

do, it may be for the glory of God.

(Romans 5:8, I Corinthians 10:31, TLB)


I would like to acknowledge several people who

helped make this work possible. Sonya Strader, wife

extraordinaire, provided encouragement and typing services

for the early drafts. Dr. Jerome Milliman, acting as

chairman of my dissertation committee, helped focus work

effort into a manageable topic and provided encouragement

and prodding, when needed. Drs. Frederick Goddard, Madelyn

Lockhart, and Anthony La Greca also provided the necessary

cooperation and encouragement. The Department of Economics

and The Dureau of Economic and Business Research provided

graduate assistantships which helped make graduate work

financially feasible. Sofia Kohli did the typing of the

final draft and provided editorial expertise. Errors

remaining are the sole property of the author.

- _



ACKNOWLEDGMENTS ............................. iii

ABSTRACT .. ......... ........................ vi


I INTRODUCTION ................................ i

General Setting: Housing Needs
and Alternatives ....................... 1
Housing Seczor Demand ................ ..... 3
Research Design and Methodology........... 5
Usefulness of Mobile-Home
Demand Research.........................


First Efforts............................ .11
Convention! Housing: Time-
Series Data ............................... 18
Conv,-nt.Lonal Housing: Cross-
Section tta.............................. 24
Mobile- io1e Housin. ...................... 27
nSu ma ry. .................................. 35

III THE DELrAND FOR MOBILE HOME'S ................... 36

Descciptive Overview--Florida
and U:iteL d States. ........................ 41
Publi;.c U e SC:am ]e .......................... 45

IV iCT;''i!OD'OL'CVY A D ' IODELS ......................... 57

Models to be -:;-timat.ted ................... 58
o Adel A.............................. 59
Mode Sl .............................. G6
Model Spi c J ..cati.J .. ....................... .



V EMPIRICAL FITDINGS ......................... 97

Model Estimation: Model A............... 98
Model Estimation: Model B............... 109
Model B Estimation for
Mobile-Home Owners .............. 110
Model B Estimation for
Mobile-Home Renters.............. 124

VI SUMMIIARY AND CONCLUSIONS..................... 130

Scop of Research....... ................. 130
Specific Findings: Descriptive .......... 131
Specific Findings: Analytical........... 135
Implications and Unanswered
Questions.............................. 144

BIBLIOGRAHY.................................... 148

BIOGRAPHICAL SKETCH .................... .. 155

Abstract of Dissertation Presented to the Graduate Council
of th'e University of Florida
in Partial Fulfillment or the Requiremencs
for the Degree of Doctor of Philosophy



Max Holt Stradar, Jr.

August 1977

Chairman: Jerome W. -Milliman
Major Department: Economics

Over the years a considerable amount of economic

work has been generated which seeks to ascertain the in-

come elasticity of demand fcr housing. The work done here

builds upon this literature by applying regression analysis

to the mobile-home sector of the ma.rket--a sector

which has been ro;;ing rapidly in the last r-wo decades and

now accounts for virtually all the "low-cost" housing

currently being prcdc'cec in the tUnited Stares.

iThed eco.noitJc Jiterature on housing demand is re-

vicwoed income elastic.tice of demand for conventional

housing ar': found to ran;c-e from 0.1.5 to 2.4. Such a wide

-oange aepp;rctly .esu' t.
different metlhodol gie:, and different definitlo:'s of both

jacome and hc'usinr; ex.'cn'iit us by the iesearCb-
'. 0 c-,

Two housing demand models for mobile homes are

then developed. Doth models are estimated using data from

the 1970 Public Use Sample for Florida. The first examines

the demographic variables which influence home ownership

and mobile-hoire ownership. Generally, the same variables

are found to be significant for predicting home ownership

in general and for predicting ownership of a mobile home,

but often the influences are in opposite directions. For

instance, home ownership is found to be positively related

to observed income but mobile-home ownership is found to

be inversely related to observed income. This inverse

relationship was found for most of the variables used in

the f.i:rst mnod' d'evelo:-ed.

The second model regresses mobile home housing

expeCnse against income. Four measures of income are util-

.ize.i. One is observed income and the other three are alter-

native focr-.ulations of permanent income. Income elasticity

is found co be less than unity in all cases--never rising

above 0-50.. Elasticities for reriters of mobile homes are

iou:i: to e. thhan these for owners of mobile homes.

No bliankeL sta-T.enets the preferability of

[o.':lmrcnt income over observed incoCCe for determininI

in l-,] l- h c. h i:cr.;iusi .. e:'in.''itui ca can safet]r be made on the

ba-is -c'.. .th- -'.r .u-_l(s -if tPr-" ;.od e) s developed in this work.

I. was i;:".;, hcuev:-., thaLt i c' of pL r-ma.nent incoe-:, as the

income variable did yield higher income elasticities than

were found when observed income was the income variable

used. In fact, the income elasticity appears to be moder-

a ely sensitive to the measure of income used.

Demographic variables were not very often helpful

in explaining variation in mobile-home expenditures. Price,

income and family size were the variables which most often

were found to be of explanatory significance. Older

Floridians were found to own a large percentage of the

owner-occupied mobile homes, especially in south Florida.

Nonwhites make very little use of this form of housing,

even though mobile homes are relatively low-cost housing

aad nonwhites have below-average incomes.

The second model was also estimated for renters of

mobile homes. The results were less satisfying in a

statistical sense, but it appears that rental expenditures

are less closely related to income than are owner's expen-

ditures. It seems that renting a mobile home was a tem-

porciy housing choice for many of Lhose who were renting

in 1970.



General Setting: Housing Needs and Alternatives

The Housing and Urban Development Act of 1968 set

a national goal of providing 26 million new and rehabili-

tated housing units during the fiscal 1969-78 decade. This

goal may have been unobtainable even under the best of

conditions. In any event, the economic conditions of the

early and mid 1970s have made its achievement virtually

imzcsnible., One bright spot in recent housing experience,

however, is the growing role played by mobile homes in

providing decent housing. This relatively new housing al-

tern.atie appears to be one way of providing large numbers

of housing units at relatively low costs.

Recent evidence of the growing role of mobile homes

ii. tihe n:Atiobn's stock is found in the U.S. Depart-

m i nt of Housing and Urban Developuient.'s Newsletter of

Deco-,:rec: 2, 1974 (Vol. 5, No. 43). Referring to the use

oe mobile home s os part of the effort by IIUD under Section

VlII of the 19i,7 Housing and Co:mmunity Development Act to

make husing .availal-o for low-incomoe families, Sheldon

L'"a, .' As-sJa.not Secr!e:tary for iousi;g Production

and Mortgage Credit-Federal Housing Administration Commis-

sioner said:

Undar the new Act's provisions for leased housing,
qualified families may choose to live in mobile
homes, as well as other types of housing. As a
matter of fact, in some parts of the country, with
the use of mobile homes, families may be able to
get a decent home and a suitable living environ-
ment considerably sooner than if they wore to
wait for the availability of conventional multi-
family dwellings.

Clearly, costs of new conventional housing have risen

to such a level that more consideration needs to be directed

both to the supply of mobile homes and to the nature of

demand for such housing. In support of this view one need

note only that the median price Df all conventic-nally built

new s.incle-fam~ily homes sold. in the United in 1969

was $25,600. Median family income for the same year was

$9,566. If the rule of thumb (applied to housing) of two

and one-half times annual take home income for housing

expend-i ture is applied, it can St. seen that many families

face severe budgetary problcmas in this respect. In fdct,

the Second Annual Report on Nation:'] Hlousing Goals (1970)

estimLted that about one--h a of ail Arerican families

were unable to payo more -than 1i5, 000 for a hore. And of

the less-than-Si., 0030 :;:sin-.e--riily cousin units produced

in the late 196i s, 90 percent wee :mobile i.ue.,. This is

largely atrtribuLaible to the fact. t:ha t'h cot. per square

foot for insb' b' homes i ess than .ia.i- tt- for convcn-

t-io:.P s, rcit'rc.-. 1i1:. \.he,; l'. iJr-- were CO'Liliod,

th-e intcon.,e-housing cost disparity has widened, causing

the budgetary problems faced by many families to become

more acute.

Ilousino Sector Demand

Until now primary emphasis in the analysis of the

mobile home market has been concerned with the potential

in helping to meet the housing needs of low-income families.

It may be that this emphasis has obscured the possibility

that the mobile-home market is broader and more complex than

previously assumed. There are families which are not "poor"

who do not wish to spend one-third of their income on


The general purpose of the study is to begin a seri-

ous analysis of the market for mobile homes from the demand

y side. Clearly, policy pcescriptions relating to mobile

homes and their anticipate ed role in the nation's housing

supply should be based upon sound economic studies of the

owner:rs aild crennters of richi Ic homes and the porn!:-tial market.

We need pCecific inform'-jo about socio-economic charac-

teristics cnd about budgetary patterns. (For example,

Ihow do characteristics cf owners and2 renters of mobile homes

comparei with hos' of bhoi. e owners andi .nt!es in general?

Ai:r there-: onlv ceLtai.n vy-'jes of houiholds who use mobile


This study will build specifically upon the well-

established literature in economics which deals with the

demand for housing and will develop a cross-section analysis

of the demand for mobile homes in Florida. The problem is

an important one. The traditional housing demand litera-

ture in economics is relatively well-developed for conven-

tional types of housing, but the applicability of such

models to mobile-home housing is untested and at least

needs study and exploration. It is not known what deter-

minants figure into mobile home demand. A look at Florida's

effective demand could prove useful elsewhere to the extent

that these determinants are found elsewhere in the United


The objective of this study, then, is to estimate

the demand for mobile homes in Florida, starting from the

conceptual framework of the extensive literature in

economics which deals with the demand for conventional

housing. It has generally been assumed that since mobile

ho!;es constitute "low-cost" housing, it is primarily low-

inconme families who live in them. This is probably true,

but requires substantiation.

We neod specific information with respect to the

in'cr;e e3a.-: tcity of demand for mobile homes. Even for

cori-inti onal housing, income elasticity js not a settled

issued. Wieras Muth1 (1960) and Reid (1 062) report

ticities greater than unity with respect to permanent

income, Lee (1968) states that it is less than unity for

both his cross-sectional and time-series studies. As if

these conflicting results were not unsettling enough,

Maisel and Winnick (1960) tell us that housing consumption

is no more responsive to permanent income than to changes

in observed current income. Barth (1966) reaches a similar

conclusion in developing a model of household behavior to

predict whether a consuming unit will choose to buy a house.

Even if these issues were settled ones, there is no reason

to suppose that the findings which pertain to conventional

housing would hold for mobile homes.

Research Design and Methodology

Florida is an area where the use of mobile homes is

widespread. When[ looked at on a state-by-state basis,

X Florida is second in the number of mobile homes in use.

Floridians do, indeed, make extensive use of mobile-home

housing. To the extent that factors leading to this high

level of usage are found elsewhere, future usage elsewhere

might also he high. If the only relevant factors are

peculiar to Florida, then applica-ion of this study will be

limited. It is suspected, however, tht changing tastes

and iincietasig .,obili.y are -relevnt factors in housing

idecisi.s. If this J so. FloriJd is 1 h.l-bin"ic rather

than the exception to some rule. At any race, with such

widespread experience in the use of mobile home housing,

Florida provides an excellent opportunity for study.

The primary data source for this dissertation re-

search will be the Public Use Sample of Basic Records from

the 1970 Census. This data base, collected on magnetic

tape, is a one-in-a-hundred representative sample. For

Florida there are approximately 25,000 household observa-

tions, about 1,700 of these being mobile-home households.

Observations for states, county groups, and standard

metropolitan statistical areas (SMSA) of 250,000 or more

persons are available.' For each observation there are

approximately 125 variables available in the Public Use

Sample. The data format is such that n-dimensional cross

tabulations are possible. This arrangement allows almost

unlimited flexibility. For example, among those who live

in mobile homes in St. Peterscurg, Florida, various cross

tabulations are feasible; e.g., by age, occupation, source

of income, race, education, annual cost of water, or any

other included variable. Data are broken down so that they

are available for the entire state, for five major areas

of the state, and for fourteen subareas including sever


This d.ita base will make possible derivation of a

demand funct on anirdI an economic cross-section analysis of

the demnd

analysis of housing demand for the nation as a whole or for

a particular geographic area is a well-established technique

for conventional housing. Reid (1962) and Lee (1968) have

done the most-cited work. De Leeuw (1971) has looked at

these studies and several others in an attempt to see if

their results are consistent. He concludes that there is

more agreement about the empirical value of income elastici-

ty of demand in these works than there appears to be on the

surface. The applicability of conventional housing models

is in question at this stage, however, since no one has

specifically verified whether conventional housing factors

apply to mobile-home housing.

In this respect, it appears that demographic vari-

ables require special attention. In terms of socio-

economic factors it would seem worthwhile to differentiate

between owners and renters in order to determine what in-

fluence the life cycle (i.e., age) has upon mobile-home

consumption patterns, and to examine racial differences in

consuretpItion patterns. Have mobile homes made ownership

more feasible for low-income families? Is the mobile home

of any value as a means of dispersing minority racial

groups from the central city and hence reducing the urban

problems associated with culstering of low-income housing?

Are the housing choices of in-migrants (e.g., recently

relcrcated households) different from the potential renter

market or the home-owner market? We would want reliable

answers to these questions before formulating housing poli-

cies which would include the use of mobile homes.

The approach utilized here will begin with a study

of the relevant housing demand literature. The most im-

portant works will be considered and recent work on mobile-

home housing will also be examined. Chapter III will ex-

plore housing expenditure as a household budgetary decision.

Overall demand considerations will be introduced and a

specific look at Florida's mobile-home usage pattern will

be presented. Descriptive material will be used in making

a comparison of Florida's and the nation's use of mobile

homes. Owners will be separated from renters so that the

relevant distinctions can be noted.

Models to be estimated are constructed and explained

in Chapter IV. Model A, a tenure-choice model, and Model B,

an expenditure model, are developed. Variables to be used

in these models are introduced and the rationale for their

ccnsiderat-ion is discussed. Actual fjnaiingcs when the model

is estismated are then presented in Chapter V. Important

findings are pointed out and considered. Chapter VI then

summar.izes the study, noting relevant questions which must

!;e left unanswered until further research is directed toward

dealing with these matters.

Usefulness of iobile-Humn Den..nd Research

This research is an extension of the existing liter-

ature on housing demand. It is unique in that it deals

with a sector of the housing market which has heretofore

received almost no attention, even though it is a rapidly

growing sector of the market. As will be pointed out,

there seem to be reasons for this increased use of mobile

homes which will insure their popularity in years to come.

This is particularly true in Florida and some other parts

of the United States also. One of these factors relates

to family income, and this relationship is given special


Implications of the findings =f this study should

prove useful in considering future housing policies which

specifically include use of mobile homes. For instance,

it would te desirable to know if some segments of our

population to whom we desire to give housing assistance have

strong feelings about the suitability of a mobile home.

We already have costly experience in trying to house people

i._ environflments and housing styles which do not appeal to

them (rruitt-Igcoe is probably the prime example).

Present housing programs, especially Seccion VIII

of the 1974 Housing Act, indicate that t:obile homes will,

jncd ed, figure prominently in meeting future housing goals.


Programs to make mobile-home acquisitions by low-income

families easier could perhaps facilitate achievement of

these goals. Specific information about mobile-home demand

is needed, however, before efficient programs incorporating

their use can be drawn up. This study should supply some

of the needed information which can help in shaping future

national housing policies.


First Efforts

r-: traditionally cited as the first legitimate

'.apt at empirical analysis of household expendi-

t published by Christian Lorenz Ernst Engel in

data collected by Ducpetiaux for 153 Belgian

his base, Engel proposed a law of consumption

Si expenditures on food to a family's socio-

. .'-. He proposed that poorer families spend a

*:itage of their available assets on food than do

'; 1 lies. Carroll Wright "borrowed" a hypotheti-

:S.txony which Engel had drawn up, attributed

iT assigned expenditure figures to the three

classes, and expanded Engel's generalization

--dea!ling not only with expenditures for food,

-lothing, lodging, and sundries.* (1895) reexamined his Belgian data ac-

-"' class and concluded that the proportion

J'c detailed description of these events, see
articlee in the April 1954 Journal of Politi-

of income or total expenditure spent on housing fell as

income or total expenditures rose. Stigler (1954, p. 99)

concludes from his study of these works that "Wright's

'translation,' for which I can find no satisfactory explan-

ation, still forms the basis for most present-day statements

about 'Engel's laws'." It seemed to Stigler that the rela-

tionship between income and housing which is usually re-

ferred to as Engel's Law had not been empirically verified.

Hermann Schwabe (1868, p. 266) proposed a consump-

tion law relating specifically to housing: "The poorer any-

one is, the greater the amount relative to his income that

he must spend for housing." This law was based upon salary,

income, and rent data for 14,022 observations in Germany.

The gcnereli nation was found to hold for Leipzig Iby Hassee)

and Hamburg (by Laspeyres) and was accepted by Engel. Sub-

sequent budgetary studies considered housing expenditures,

but nothing of exceptional economic interest was generated

until well into the twentieth century.

Table 1 lists the major housing studies published

in the United States. Most of these are not discussed in

this chapter, but all did make a contribution in the

3-evelopmsent of the body of housing-demand literature. A

variety of data bases has been utilized in estimating

demand for housing, and each researcher seems to have modi-

fied hiis approach to the issue in order to utilize the data

io had. The studlit:s ar listed chronologically by data

O 0
o *

-2 H'1 5 i o
C- r H 4

4 ) 1 V 0
4 --'

'H -. a 3-1 'H

D 'H'H'H-,

a) 4J 0 -,;

(H :U C v i 'C


-1- S r-

e : i f
'H '

0j^ > -,

.14 4



m rj
3 t,,

d 0

! : I


.-, -
0 U

c. .: c'

1 1 0-i

S C0 .

V 31 -4

a 1-1 o a
LO 0 >

O f f7 C
00 -4

.C C1j 3

* 'H0 IIo 0

in 4 4>a H o


|" 5

*d( cs

< u:


'4 0

.0 44

0 0

H **H --

0 m
, C: r) "

0 0 0 0 -
Om *

. 0 4

VI 0 ,*

20 0 C I ) 4
r-l ru- F O r^ cP (

0 -H

'J 1 rj
9 0 0 0'
0 r 0

..i i


--1 >

,I!" 3


C' ~I

C> O O



e a

4 I
0 )
Cl- CO

'o-. 0

> LO

+ I
.04 J -Ii )
0' 0' a -

-i .- E- .1
>Oo 'o

S .- 0 I 4

1 4 14o 44
L -44- 0 u 0

0' 0 c 0 -2

: > 2
. C7
0 2 0 -H LI W

-l00 -4 0 0
C C H< 0
:3 4 0 0 U7
0'0'0Hv s j-
44 .0 4 0,c' *< *

r n 1

:"oi -> a
0 0 00 W '
0 0

:4 FO V0 .
. i > ; l 1

, '1 1 C" ,

Q1 o r 4j n r.i


0 C Im

G-. C;01 u
5 00 00

'- *H al .-
-0 0 U 1

C o H -o a <
40i C 004-)

44 -0 0 0 0
U *n4-'




4, 4


r; .- .
>'!i3' K'.
0 -4

0 0

E H 0
-i M Co J
- lU) 0 (1)



0 III 0 0 0

0 t E in O 0 u m r3 4J
1 ,-J -;e 00 0 J Q
M c r o I u Q, I
0 0CO 00 r- 4
0 Q) 0 o 00 '0-
r- Q> H C, 0
00 C4n B 0 0 41 0 c *
> C 0 0 -

o" c C o 0- r0) > l 4 I-

0 01
ir u -V W-
S04 0 0- rl "- 40 a >
3 C lu 0 V 3 1U E 3 -I O 7i
0 H 0 0 ) j- -4
OH )-JO'T-4 00000 4-'.

S0 o-4 > > c >,

0 O O I > H
40 0 1 0 n 0 0 4V w 1-l

0 0 (1 0 : O H 3 0 I0 O
10 0 '-0 0 >0, H 0 o *-4

Us0 > .0 0' -C 0 E 00 00 0

1 0 l
0 0 0 0 0

0 i0

3 Cm C 0
4) 1 04-'lid I I -


0 I-o' ,-'0

S-A 01 0 I 00 C
i-1 u 01 o c
m > I > ra r 0
' 00 C) 1 4 1 0 4 JJ 0 co V tn O U C>
5r O V in C m cP >-, <> -4 (v ( 1

C. vi co -o ooo a'co0
rll r! 4 4 1 Q1 U) C)
Mid 0 0 0 -cI m.0000

a! 04 -J rl 0-40 iUoo V, 4 Q: M 0
m i 0 04 -l '0 0 l If
W > l O> 0 7 0 00 O a ~

o rm* 30>j o0 0 n m C
o oy c- 3 *oo o oo o ^ c o r ^

S ~o c i ; u0. 0 '0o c. o -0 o n o)L4 o 04 .

O '0 0) *l I

1 41)

1 0,

-' j I

1.1 .4 o o
^!~rt s r S |

E M C-
4) 4-
*H 0C 01

g 0


a u 0
0 P.,

C li

Soj 0

4-1 4C -
3 0
0 0 -
4U 4 0

uI )

0 c
i .
-40C 14-'

I i C 1 0
O, 2 f, -4 N
&*~ 'a O! 0oi l
90 )r'
SC *H 0 0'

S0 0 0 >-

C -4 l3 C 4 C.
> cJ: i C) Ci.
> 4-1 ;j .r .

I "

0 40



C :C

mC 4-'
II y

i 2 Mm



I" 9

0 -4

4) -P

C' C )

0 .4
o: o

'C 3
04- ()>
.- C) -


Il, 4




c --~
l-- 1 -

4C .

0 0

o C -
O o
0 C4 4
C 4 C )'

O-4 0 -4 1 C

0 -4'0 C)



C-< C:,
H0 '0

C 0

0 C)
U3 0
4 E *0 0
o -i

0 0 4 0

04 0
-4 H 0C

41 0 E0

> i
" 11





* .
0 -

type, and note is made of the unique features of each in

the Comments column.

Although many studies of housing demand have been

published over the years, most of them have not been con-

cerned with estimating the price elasticity of demand for

housing. The majority of these studies (sixteen of twenty-

two in Table 1) were carried out using cross-section data

which simply does not lend itself to precise estimates of

price elasticity. Price differences must be measured be-

tween a standardized unit of housing and the fact that

houses are located in physically different surroundings

means that a standardized unit of housing is difficult to

find. Not only is there intracity variation in quality

(such as between the central city and suburb) but there is

also intercity variation. Accurate price data would be

needed both within and between cities on a standardized

unit of housing. These data are not readily available on

a cross-section basis. Within an area price variation is

not likry to be great enough that price elasticity can

be accurately gauged and between areas quality differences

make price comparisons difficult. For this reason almost

every cros;s-setion estimate of price elasticity has been

presented with an apology for its suspected unreliability.

?:ast stu-lici: have been focused upon the income-housing

relationship s eopress::d by the income elasticity of de-

n ad (1]).,

Conventional Housing: Time Series Data

The first widely read work which attempted to

estimate the income elasticity of demand for housing from

time-series data was published by Louis Winnick in 1955.

His conclusion, based on residential construction expendi-

tures compared to either gross national product or gross

capital formation from 1890 to 1950, is that consumers'

preferences have shifted away from housing over this time

period. He transforms aggregate data into a per-capita

value for the United States housing stock (taking into ac-

count depreciation) as well as a per-dwelling unit value.

His conclusion is then drawn from the fact that these

measures jump up and down slightly over the sixty years'

period without demorns-trating any significant upward trend.

In fact, per-dwelling unit value falls over time. The

income elasticity of demand for housing which he derives en

route to his primary conclusion is 0.5.

Gutter:tag responded to Winnick's conclusion by

questioning the premises upon which it was derived. He

specifically suggested that carrying costs are more appro-

pi Late1.y co.nidercJ than capital outlays when one wishes

to lcoo- at consumer behavior. He additionally asserted

that the diemnand for housing may not be more elastic with

respect to i.onte than with respect to price--a relationship

assu'.ed iiy W;lni-k. Winnick's "Reply" (1956) to Guttentag

is coined in tcrms of space rent and reasserts the original

conclusion. While the issue of the place of housing in

consumers' budgets may not be settled, it must be remembered

that Winnick was using a "back-door" approach by using

aggregate capital value if what he was really interested in

is income-housing expenditure relationships. Additionally,

his measure of income was observed (constant-dollars) value.

So while his conclusion should not be accepted without

these caveat's, it is not without empirical foundation. In

fact it is consistent with Winger's (1969, p. 417) conclu-

sion that "the actual amount of space acquired [is] rela-

tively invariant with respect to income. . After the

space requirements are met, apparently another set of

standards comes into view" for some families. These other

standards pertain to location and quality of the structure.

Probably the most respected and most widely referred

to work in the area of housing is that done by Richard Muth.

I:n particular his "The Demand for Non-Farm Housing" (1960)

has received r--ich attention. The study is now becoming

dated (his time-series data end in 1941), but his methodology

establ ished tihe tone of subsequent work. In estimating

the cstock-~fo?.'2,irad elasticities for housing, Muth uses aggre-

qate data from, the 191.5 to 1941 non-war years which, of

course, jI cl.ucde the Great Depression years. His first

stac;- dem.' :d equation takes the form:

. Ap + By 4- Cr

where hf end-of-year per-capita non-farm housing stock

p = Boeckh index of residential construction

costs (brick)

y = Friedman's per-capita expected-income series

r = Durand's basic yield of ten-year corporate


This equation is the one estimated when Huth assumes rapid

market adjustment to changing prices and incomes. When

slower adjustment (requiring more than a year) is assumed,

the model is re-specified:

h' = Ap + Bv + Cr + Dh
g -P

where h = beginning-of-year per-capita housing stock

The complete adjustment model yields an income elasticity

of 0.55 and the incomplete adjustment model yields 0.83 for

desired stock and a whopping 5.38 for new construction. In

contrast to the previous estimates of other researchers for

the income elasticity of housing, Muth (1960, p. 72) as-

serts: "The evidence gathered here suggests that both

[price and income elasticity] are it least equal to about

unity and may even be numericalLy larger."

Mluth's conclusion seems to have been borne out by

subs'n-~-nt work, hut. his appro=iach has been criticized on:

several grounds. The first of thesis' criticisms deals with

his assumptions. In the derivation of a "unit of housing

service," Muth equates this concept to the quantity of

service yielded by one unit of housing stock per time unit.

He then standardizes price in terms of payment for this

unit of service. In effect this procedure says that any

one unit cf housing service (regardless of the type of

structure producing it) is interchangeable with any other

unit of housing service. Hence, under this system of

measurement, distinction between housing services provided

by owned homes and those provided by rental units cannot

be made.

In addition to this problem Ohls (1971, p. 23) has

taken issue with the assumption of constant annual depre-

ciation which Muth employs. Ohis tests the plausibility

of this assumption with data found within the body of Muth's

work and finds it to be an unfounded one.

Muth additionally can he questioned on the following

issues: (1) his choice of the Boeckh Construction Index as

his price variable rmay cause problems. This Index is unable

to take into account changes in productivity or possibili-

ties for input substitution. (2) As with Winnick, capital

values may be a less desirable measure for housing prefer-

ences than some measure of carrying costs. Operating costs

di rectly tatr.ibutable to houezng are thus overlooked.

(3) No account, other than p"r-capita transformation, is

tak:-n of any demographic variation.

Tong Hun Lee has also done work of note with time-

series data. His conclusions, however, are at odds with

Muth's. "The main findings of this study are that the

income elasticity is substantially less than unity while

the price elasticity exceeds unity" (Lee, 1964, p. 83).

Lee's data, being largely that used by Muth, covers the

period from 1920 to 1941. Lee's work extends that of Muth,

however, in the area of including more appropriate credit

term variables than the long-term bond yield used by Muth.

He then uses single-equation least-squares regression

estimation to derive values for price and income elastici-

ties. For the elasticities he calculates two values--one

using gross housing construction as the dependent variable

and the other which uses price or income as dependent.

Lee (1964, p. 85) then states, "the true elasticity of price

(or income) should be bracketed between thes- two limits."

This bracketing technique is statisriically acceptable, but

Lee's bracketing is nothing more than an arithmetic mean

so that his elasticities are, in the end, averages. His

0.652 income elasticity is therefore an average of 0.336

and 0.978. Both the upper and lower limits are less than

unity, however. This elasticity is derived using observed

income, but Leo also tests the permanent-income concept.

Ti'e upper andi lower limit.- then become .283 and 0.335 with

an ,vcrage of 0.809. Tne mean is still less than unity,

but the interval includes areas on both sides of unity.

Lee (1964, p. 88) concludes:

. our tentative conclusion is that the income
elasticity of the desired demand for housing
stock is smaller than one, while its price elas-
ticity is more negative than minus one. The
permanent income hypothesis holds in the area
of housing demand, in the sense that the response
of housing demand appears greater to permanent
income changes, but the elasticity of permanent
income appears to be less than unity.

The final time-series study to be considered here

is that done by Geoffrey Carliner in 1973. His work is of

particular interest because he derives income elasticities

from regression equations specified both with and without

demographic terms. Results from these regressions show that

elasticities are higher for owners than for renters and that

elasticities are consisLently higher when demographic vari-

ables (for age, race, and sex) are included in the model.

Carliner performs his calculations using several measures

of income, ranging from one-year observations to a permanent

concept incorporating imputed rental value for house owners.

Numerically, his income elasticities range from 0.410 to

0.746, being highest when income is expressed in a permanent

form. Carliner's (1973, p. 531) summary statement expresses

a belief that "the elasticity of housing demand is around

C.6 to 0.7 for owners and 0.5 for renters." He thus ends

up in the samenr neighborhood as Lee.

Conventional Housing: Cross-Section Data

An early example of a crcss-sectional study of

housing which derives an income elasticity was published

by Ogburn in 1919. He used 200 family budgets from

Washington, D.C., and derived an elasticity of 0.93 for

renters. Subsequent work by other writers produced elas-

ticities varying from 0.15 (Du.esenberry andKistin, 1953) to 0.86

(Friend and Kravis, 1957) between 1916 and 1960.

In 1962 Margaret Reid published her Housing and

Income study. She openly challenged the validity of the

Schwaba Law of Rent which h3d been sleeping peacefully for

almost a century. She asserted, and even had empirical

evidence to verify, that the income elasticity of demand

for housing is greater than unity by a substantial amount--

being as high as 2.05.

Dr. Reid's conclusions and work are hsed upon a

permanent concept of income. She maintains that such a

measure of income is the only appropriate one since the

time horizon involved in housing-consumption decisions is

quite long and since observed annual income figures are

subject to much fluctuation and are at the mercy of random,

exogenous influe-nces. Using grouped data front several

sources (spanning three decades), she demonstrates that the

income elasticity of demn'd for housing is greater than

un.ity b)itwiee-n and within cities.

As might be expected, this work has received quite

a bit of attention in the housing literature. In fact,

a Ph.D. dissertation written by Sarah Bedrosian (1966)

addresses itself to the findings and methodology involved.

The primary criticism of Reid's work in this dissertation

is that "the coefficients are to a great extent a product

of the phenomenon of data combination, and not necessarily

a reflection of the true income elasticity of housing

demand" (Bedrosian, 1966, p. 341). Bedrosian comes to this

conclusion on the basis of Reid's having grouped household

observations by the use of instrumental variables such as

geographic area. Besides this criticism relating to

statistical methodology, Bedrcsian takes issue with the

theoretical assumptions and the data base used by Reid.

Lee has also taken issue with Reid, primarily on

the basis of her method of analysis. He says, ". . Reid's

averaging process tends to 'wash out' many relevant differ-

ences in permanent housing components that should be ex-

plained by variables other than permanent income. Reid

classified individual household observations into groups

according to census tracts and hou.sing-quali ty categories

within places, and geographical areas such as cities. For

each group she computed averages of measured incomes and

of housing cata" (Lee, 1968, pp. 487-38). Additionally,

her model 'sp-cificauion implies tiat nothing, other than

income variation, has any influence on housing expenditure.

Hence, in Lee's estimation, Margaret Reid overstates the

true income elasticity for housing. He calculates it to

be about 0.8 for owners and 0.65 for renters. It should be

noted, however, that Lee's data consisted of a four-year

reinterview survey in which some of the original respondents

moved and were not reinterviewed. His results are, there-

fore, biased to this extent.

Frank de Leeuw has summarized and compared cross-

section work by four people (Reid and Lee included) in his

1971 article. His final thoughts indicate an elasticity of

0.8 to 1.0 for renters and 0.7 to 1.5 for owners. While

his is not the final word on the subject, he has attempted

to reconcile existing differences between four widely-read

studies. In addition to the work done by Reid and Lee,

de Leenw examines that done by Muth (mentioned earlier in

this work) an-d also a study published by Winger (1968).

De Leeuw cites certain shortcomings in each of these works

and suggests how each noted "deficiency" would bias the

results that each of these four people has published. His

belief is that the original range of income elasticity re-

ported by these four researchers--0.6 to 2.1--is actually,

when corrected for the shortcomings he notes, narrowed con-

siderably. Numerically, he adjusts the other researchers'

results and narrows the range for income elasticity to

0.81 to 0.99 for renter-occupied households and to about

1.1 for owners.

Mobile-Home Housing

Economic literature dealing specifically with mobile

homes is almost nonexistent. This is probably a result of

several factors. First, mobile homes were used for permanent

housing only rarely before 1955. This is the year that ten-

foot-wide units were first produced. Use of mobile homes

as permanent housing expanded quickly thereafter. A X

second reason why mobile homes have received so little at-

tention in the professional literature is that, nationally,

they riake up such a small fraction of the total housing

stock (roughly three percent). The growth of this form of

housing is, however, undeniable. Mobile-home production 4

accounted foi almost 22 percent of all housing units con-

structed in 1970. Table 2 shows the growth in production

of mobile homes since 1947.

Pobert French and Jeffrey Hladden published an

article in iaj.d Econo lcs in 1965 which analyzes the c!iar-

acteristics of rcbile homes at a national level. Their

analysis does little more than paint a picture of the

tyv)ical ichli.e-home dweller and his unit in 1960. They

conclude that "trailers" are an urban phenomenon, a "new

kid. of subu:,`i ,;' "i F you '.oul 'd. They are utilized most

Mobile-Home Shipments and Sales,

Manufacturers' Shipments
Year to Dealers in U.S.


Retail Sales



Prior to .947, production vai
to 60,000 in -'1947.

1 0-wide

homes cram into r-is ;
hom2's camiT into mass
homes; camet into irmass

:ied from 1,300 in 1930 upward

production in 1955.
production in 1962.
production in 1969.

SOURCU: Flash -Facts, Mobile" Ifr!-. M.ainufo ct:urers Assocation,-
June 1974.





-- I'-


heavily in areas of rapid population increases and in areas

of low population density. There are generally fewer per-

sons per room in mobile homes than in conventional permanent

houses, but the rooms are also smaller. Contrary to con-

ventional wisdom, mobile homes are not "substandard" hous-

ing when gauged by either overcrowding or physical condi-

tion of the structure.

X In terms of the ages of the people who live in mo-

bile homes, they were found to be either young (couples

usually) or old (retired). French and Hadden (1965, p. 138)

suggest "that the largest group of trailer dwellers are

young lower middle-class working families who are looking

for a better way of life but cannot yet afford to buy a

permanent hcne in the suburbs." They concluded, as most

writers do, by pointing out the need for further research

in the area.

Robert Berney and Arlyn Larson, following French and

Hadden's lead, published an empirical piece of research a

year and a half later (1966). Working with a survey of 800

A-izonE mobile-hoire households, they used basically the

same approach as that of French and Hadden. Data were

collected for each household on eleven different variables,

among which were: value of unit, fa'miy income, family

assets, and tc.\'s paid on unit. (The variables were selec-

Ltd and the stu iIdy performers with anr eye to implications for

tax policy.) Their work revealed that, in Arizona, the

occupational and income distributions are almost identical

for the state's mobile-home households and all of its

households. Retired households were found to have lower

incomes and fewer assets than working households. However,

neither price nor income elasticities were calculated for

their study.

The U.S. Department of Housing and Urban Development

published a volume entitled Housing Surveys in 1968. Part

2 of this volume dealt exclusively with mobile homes. Data

collected in 1966 revealed that the overall picture of

mobile homes and their residents was much the same as that

depicted in the two above-mentioned articles. Among other

things, tnis study found the cost o4 a mobi )e home to be

roughly three-tenths that of a multiple-family structure

(per unit). It also found that the median household income

for a mobile-home family was only about 85 percent of the

median household income for the entire national population.

As far as the mobile home itself is concerned, the unit

was probably financed for seven years End the downpayment

was less than $1,000. Typically the residing family was

composed of huS'band, wife, and a young child. The adults

were generally less-educated than the general population.

The unit itself was less than half the size of the average

hous-ing .unit being sold and was located outside of a

Standard Mletropolitan Statistical Area.

Some attitudinal questions were included in the

survey form, but other than averages, almost no statisti-

cal tests were performed with any of the data collected.

No effort was made to ascertain what factors were of im-

portance in affecting demand and no elasticities were


Two books which take an encyclopedic approach to

mobile-home housing have been published in the 1970s.

Margaret Drury (1972) looks at what she calls an "unrecog-

nized revolution" in American housing. After an introduc-

tion which deals with mobile homes from an historical

perspective, she includes a chapter which is basically a

review of mobile-home literature. She covers studies in

trade-type publications such as the Mobile Home Journal

and Mobile Li-fe Magazine. As in previously mentioned

sources, the statistical approach involved hardly goes

beyond averages and percentages. We are shown a "profile"

of the mobile home resident of the 1960s. Her approach

deals with social changes leading to development and ex-

panded use of mobile homes as well as the institutional

resistance to this "new" form of housing. All in all,

Ms-. Drury's book is a quick trip through thu (non-technical)

moL'e.-homIe literature from a sociological point of view.*

*A new edition of this book was published in 1976.

The other book to be considered here is Housing

Demand: Mobile, Modular, or Conventional by Harold A.

Davidson (1973). This work is quite similar to that of

Ms. Drury, but does carry analysis a bit further. For

example, Davidson looks at mobile homes in relation to

other housing alternatives and attempts to discover the

determinan.ts of the demand for mobile homes. It is this

section of the book which will be considered here.

Davidson divides his variables influencing demand

into three groups. The first group is made up of economic

variables. It includes the income distribution of the U.S.

population, the selling price of the mobile home, financing

terms, and property tax saving. The second group of vari-

ables are demographic and social in nature. Included are

age distribution, valuation of leisure time, and impact

of changing social values. His final group of variables

is called "aesthetic and political." These include mobile

home design changes and mobile home park development.

Usi:qiI multiple regression analysis, Davidson derives

a linear r;odel to estimate parameters in several demand

equations by the ordinary least squares technique. He

estimates two demand equations for mobile homes. (These

equations are estimated on the basis of quarterly data

collecLte from a number of sources.)

(1) MHDt = -372.742 137.184MPCCt + 4.303PRt
(-5.93) (-2.27) (3.01)

0.079PDIt-1 + 0.011THHt-1 + 26.16D
(-2.32) (4.46) (5.07)

d.f. = 45 R2 = .965 0 = 6.073

(2) KrHD =-55.110 24.661VRt1 62.106STHSt-
(-1.46) (-3.51) (-2.67)

+ 0.29MFI + 26.524D
(9.81)t- (6.41)

d.f. = 46 Rz = .954 a = 6.99

where MIDt = demand for mobile homes, expressed as total

mobile home shipments

.MPCCt = a price variable, the average selling price

of a mobile home

PRt prime interest; rate

PDIt_1 = per capital disposable income

THHt- = total number of households

Dt = dummy; D = 0 all quarters before 1971 1;

D = 1 for 1971 I and later

VRt_1 = vacancy rate (expressed as a percentage

STS- = sinoie-familv housing starts
t-1 total conventional housing starts

MFt-1 = median family income

(Subscripts indicate whether observation is for same time

period or is lagged one quarter.)

The numbers i:: parentheses are t values. Equation (1) has

a K' of .9G5 and Equation (2), .954. All coefficients are

significant at the 5 percent level. Neither of these equa-

tions includes a variable representing either of the

specific age groups observed to be the primary users of

mobile homes. It would appear that such an omission is

serious. Davidson's explanation for this omission centers

around the fact that inclusion of a variable he labels ASP

(which is defined as the absolute number of people in the

20 to 29 and 65 to 74 age ranges) causes the other variables

to become insignificant. He attributes this problem to

either multicollinearity or high correlation with the depen-

dent variable. It seems that a respecification of age-

specific variables would be preferable to ignoring the

factor altoqenher.

An elasticity of mobile home demand for personal

disposable income is calculated at -1.68S. This indicates

That an increase of one percent in personal disposable in-

come will cause mobile-horce demand to decrease by 1.688

percent. Such a finding would define mobile homes as an

inferior good. While this conclusion may not seem unreason-

able, the income variable is an observed one, not a perma-

nent mcasure- of income. At any rate, a finding such as

Davidson's c'-rtainly calls for further exploration into the

inrco elasticity for mobile horm demand. This is one

particular aim of this piece of research.


While the studies just discussed are not exhaustive

of all the research that has been done on housing demand,

they do include the most significant work done in the area.

Standard techniques for adapting statistical procedures

and model building to housing data have evolved. Multiple

regression seems to be the most widely used statistical

tool and it is, indeed, a powerful one. Using this procedure,

the researchers discussed above have estimated income elas-

ticity of demand for housing. The range of estimates is

wide, from 0.15 to 2.05. This variance might leave one

bewildered as to just what the income elasticity is for

housing, but to some extent this variation is a function of

the data used and methodological differences. Perhaps, as

de Leeuw told us, there is more agreement than appears on

the surface. But these studies in the academic literature

are concerned only with conventional housing. Application

of the statistical techniques developed in the housing

demand literature has not been made to the mobile-home

sector except in Davidson's work. The fact that he took no

account of the age of the occupant, and that the income

elasticity of demand he found was negative leaves several

cuestjions unanswered, even after this attempt to analyze

the mobile-homre market.


Household budgetary patterns have been of interest

to economists for some time. How families and individuals

operate within their budget constraint is, in fact, one

of the primary issues dealt with by microeconomic theory.

As one might expect a priori, expenditures for housing

constitute a large portion of total expenditures, both at

the micro and macro levels. Outlays for housing, as with

some other expenditures, have both consumption and invest-

ment aspects. While one is consuming the housing services

rendered by a structure, the structure itself may be appre-

ciating over time. Because of such conditions, one writer

(Smith, 1958, p. 1) has even suggested that housing is not

a suitable topic for theoretical analysis:

. housing involves major non-economic com-
plexities, mainly legal, institutional, and
aes;thctic; housing is an inconvenient hybrid,
a consumeT's durable good, which ra-ons that the
economics l cannot be sure whether it helons!-
under the heading of utility maximizaticn or
savings and investment.

While there is some truth at the heart of thlse remarks,

most econclssts would agree that any economic good is de-

serving of t hooretical analysis. And an area as prominent

as that of housing attracts considerable attention, both

theoretical and otherwise.

Housing consumption studies have traditionally

looked at housing as strictly a consumption matter, ignor-

ing resale value, which might be greater than the original

purchase price. So, whereas conventional housing may appre- X

ciate over time--due primarily to inflation and a rising

site value--such is not the case with a mobile home. They

depreciate over time much like an automobile and the in-

dustry even has several publications for estimating the

current n'arket value of a mobile home--just as car dealers

have their owni industry guides.

Because of the mobile nature cf a mobile home, it is

an easy matter to separate the value of the structure from

site valiu of the land upon which it may be located. A

mobile home owner has the option of locating his unit (whe-

thzer it is valued at $4,000 or $14,000) on a small or a

large parct:l of land, close in to the city (wherever zoning

perni.,:.;s oi : a large, rural parcel. Given this possibil-

ity, .iit is :isy to separate the demand for mobilc-hol.R

nhouinq sECrvices from the demand for neighborhood quality,

hi4w(vic.r est>i: ted.

Give:: this dichotomized process whereby the housing

ci-c .r. thi location ch ice are made separately, the

.'alue of t'.a using unil.1 itself is easily separable. Fur-

thcrnore, in looking at the economics of this housing

choice, it seems obvious that it is appropriate to con-

sider the choice strictly as a consumption matter. Who

would make an investment decision knowing in advance that

the item invested in would depreciate? Only potential

tax benefits could explain such a decision, and the taxa-

tion of mobile homes in Florida is handled just as that for

an automobile, so this factor is not likely to be relevant

for mobile-home purchasing. For these reasons it seems \

appropriate to consider che purchase of a mobile home as

a case of purchasing a consumer durable good.

When a household (family or single person) enters

the housing market or makes a change within it, there are

several categorical decisions to be made. These may be

made independently or jointly, and the order in which they

are rmsde will vary from case to case.

Probably the most effective constraint in the major-

ity of housing decisions is budgetary in nature. This is

simply a variant on one of the major building blocks of

ecconomics----thc clash between unlimited wants and limited

resources. In this case many people might wish to live in

a m. nsion but have incomes sufficient for only a modest

living environment. So if income is an effective con-

straint for miost households, the ocher decisions will be

,'adu fr-ollowing a decision about maximum;. a ftordcble

expenditures. Only if this figure is sufficiently large

can conventional home ownership be a viable alternative.

So tenure choice (cwn or rent) is also a decision for some

households. If rental housing is chosen, one may rent a

conventional single-family structure, a unit in a multi-

family structure, or a mobile home. Similar alternatives

exist in the owner-occupied sector also. This study fo-

cuses on households in Florida which have made decisions

to own or rent a mobile home.

The cost of housing in the United States has

climbed over the years to the point where talking about

low-cost new housing is much like talking about the uni-

corn--if it ever existed, it is now only a imrmory. Rising

costs have prevented many families from being able to con--

sider home ownership.* If there is any low-cost housing -

still being produced, it is probably a mobile home.

Several facts operate to moke this statement defensible:

y (1) The average cost per square foot of a conventional

house was .4.65 in 1971. The compi rable figure for a

mobilee ho:.e '9.07 (Davidson, 1973, p. 119). (2) Mobile

tio:om typically ii-ave fewer square feet than conventional (3) S:i ;r.n mobile- homes can be and are often located

*For a :p-.ltial -x:;;lanation of this phienonmonon, see An-
thbony DwnS.i Urtban P:ocl,-: a 1nd rospeckt (chicago, 1976),
pp. 77-:3..

on small parcels of land (owned or rented) payment for site

value can be kept low. These factors combine to make

mobile-home housing relatively inexpensive as a housing

alternative. The only competition in terms of low monthly

expenditure would come from rental of old conventional

multi-unit structures. The market value for such units

could have fallen over time, due to physical deterioration

and/or undesirable location.

Florida's climate also lends itself to this par-

ticular type of housing, and fewer square feet to heat or

cool, even if construction quality is below conventional

housing, means lower utility bills. Taxes on mobile homes

are paid through annual license plate purchases and remain

at low levels. If one decides he is tired of his present

unit, transaction costs are low and new furniture and

appliances are normally included in a new unit.

In addition to these factors, there is another force

operating on the demand side which is especially pertinent

in Florida. Many people, retirees in particular, are not A

buying a mobile home just to get another house, but to

achieve a whole new living environment and life style. A

plush "adult mobile-homle community" is not difficult to

find, especially in south Florida~. While retajinin some

cf the benefit-s of home ow.nershiM it is also possible to

enycv some cf the bcrnfi-:! of li inc in a r'-ntal cormplx.

In summary, there are a number of factors which make

mobile-home housing a desirable alternative in Florida.

Among them are low price, low maintenance, single-family

ownership, flexibility in choice of environment (mobility),

a relatively well developed used mobile home market, and

the favorable climate. While some do, not all mobile-home

residents are living in their units because they cannot

afford anything else. This fact, while not obvious from

one year's observed income, is more readily observable

when a permanent measure of income is considered.

Descriptive Overview--Florida and United States

Before grt.ting into actual mobile hone usage within

the state, some observations comparing Florida and the

United States as a whole will be of interest. Some crucial

comparisons are highlighted in Tables 3 and 4. Table 3 fo-

cuses on an overall comparison of the United States and

X Florida for ceiotain se'ec.ed demographic characteristics.

Florida had about 3.34 percent of .he nation's population

in 1970 and 3.60 percent of the ration's year-round

housing units. As a perccnLage of this housing stock,

lhc..tver, micbile home usage in Florida is about two and a

quarte- t irr-s the 3evel observed nationwide. Florida's

population .is somewhatt ojd'r than that of the nation, as

obWserv'ed by thie difference; in median age and percentage

r" co

cl mm

m co

41 o
4j! r'- r-

C) eo

ce 0

-1 0 0
o 0 0

h --

H Ne
C'! -

o c o 0

LC LO I'D r-

N r-- r-A ,-

n r-qr H

( -! Ln (\

N r-










00 O O
C.D CC "l
ol D (N (

rl r




r- -


U ^-iE;

u (
C. &




.E 0
a) O

( 1

01 0


H,-- 1

O 9.

of population over sixty-five years of age. Educationally,

Floridians are very slightly below the national average,

perhaps due to the fact that her people are a bit older.

Relatively fewer of the sixty-five-and-over population in

Florida worked in 1970 than was true for the nation,

supporting the idea of widespread retirement to Florida.

Connected with these phenomena is the fact that Florida's

1969 median family income was about 14 percent below

the national figure.

Table 4 focuses on only families living in mobile

homes in 1970. Among these people, 8.27 percent of the

nation's mobile-home households were found in Florida.

(Remember, Florida had only 3.34 percent of total popula-

tion.) Slightly more of Florida's mobile-home households

contained only one person and considerably more were headed

by persons over sixty-five. Relatively fewer household

heads worked in Florida than in the United States, and

of the heads who were employed, a lesser percentage of

Floridians living in mobile homes held "blue-collar" jobs.

Income-wi'.e, Florida's mobile home residents received sub-

stantially less than their national counterparts. Finally,

Florida nmobile-hom.e residents are found to jb, in fact,

quite mobile.

In sumnnary, there is a heavy usac;e of mobile homes

in Florid:.. Some of the generalities about the nation's

muobil'e-Kh*.:' i.]lers are .lso true in Florida. Their in-

Characteristics of Mobile Home Households for
the United States and Floiida, 1970

United States
Owner- Renter-
occupied cccupied

1,752,577 321,417


Owner- Ren ter-
occupied occupied

147,970 23,499

One-person house-
holds (% of total
mobile home house-

% of household;
whose head is
65e years

Median school years
conmplted by head

- cf heads :.-ployc
:n L659

t of employed heads
who held "blue
collar" jobs

Mediian 1969 f,:aily
inches (to rnearse

c Cf hous':held he;;ds
iivin' in a diffeu:et
stti.c 5 years aqc



70. 0






7,800 5,800

1.3.9 26.2

SO'LCE: 1970 Cent'n:u:s of housingg anid Census of Population.











comes are relatively low, and families are typically small.

Especially in Florida, the mobile home is an apparently

attractive housing choice for older people.

Public Use Sample

The primary data source to be used in this demand

study is the Public Use Sample of Basic Records from the

1970 United States Census. This cross-sectional data

base, collected on magnetic tape, is a one-in-a-hundred

representative sample which combines both the Census of

Population and the Census of Housing to make available

records for both persons and households. Observations

at the state and Standard Metropolitan Statistical Area

(SMSA) level are available as well as for county groups

created on the nodal-function area concept developed by

the Bureau of Economic Analysis' Regional Economics Div-

sion. Figure i shows the (16) county groups for Florida

and the four-digit numeric identifier of each.

Area 31 is northeast Florida and eight southeastern

Georgia counties which are heavily influenced by the Jack-

sonvilie S SA (subarea 3101). Subarea 3102 includes

GaJ .svi lie and Ocala. Area 32 is central Florida and in-

cluddcs Orlando as subarea 3201. South Florida is Area 33

and is made up of eight subarcas which include Tampa (3303),

St. Petersburg (3304), Miai:i (3302), Fort Lauderdale-





33 '

PubJic Use Sample Areas and
Subareas for Florida


Hollywood (3301), and West Palm Beach (3305). Area 3401

is west-central Florida and includes Tallahassee, the

state's capital. Area 3501 is made up of the four

western-most Florida counties and one adjoining Alabama

county. Pensacola is the dominant city in this area.

There are approximately 125 variables available

per observation. These variables constitute data covering

persons and households. For example, there are structural

characteristics about the person's dwelling unit as well

as financial characteristics about the house value or

rental rate. At the individual level there are charac-

teristics and attributes, including data on age, race,

sex, education, and income.

For purposes of this study, data were collected

for heads of households and for wives of heads of mobile-

home households. Combining the information on these per-

son records constitutes a household record. On the one-

percent sample tape there is a total of 22,189 household

records for Florida. Of these, 15,456 are owner-occupied

and 6,733 are renter-occupied units.

Selected characteristics for the county groups are

listed in Tables 5 through 8. Data are included for heads

of all owncr-occupied and rentcr-occupied housing units

and for heads of mobile-homec households, both owned and


S-00i 0 o r- CO W "
110 f o c o rQ m C ra 0 0o

0 0 (' N ( NN 4 ( O O (( N

moi N n CD o1 -T r

0 co Go r- H m m r- r- m r
Cl ( rf- r- rHl -

C0 0T C-) T0 (n o ( n O

o ( N r- m o G 0M ID M T
'I %O I C L L n IT n Ln m) L n L

. r", : J -- Cr ", 1,- 3 % c".
Ln r- -I N x q o T Co r- cM
c co r- r- 0 cmco r- r r-(

0 .- 0 C4 10 IN -1 0 - 01
CO CO c 0 O Co c Cc rO -c M r, CO
cmC~ CC> CC)~~rWCm--C

4J :0 c a, rI 0
Gl C c:! m
4, CO CO CO C) 0C )C

n LO L3 0 0 nLD
3 Cl

* y i-; .~ 4* *) 0 )

1 -i .1t rf Nm
' I0 f 0 10

I C C) C) <-( i

u() 1N CO O- C
i- cT L c 'o co

nrN A, n r .0 r- c^
0 C0000000 C
C) N m c N 0 n 5R
l-n n -^1r.- PI I-f-

1^ r-
10 cc

1' 0
:7! tr,


o C)
z- I =1

*O ^


M 0 O

C 0 i
It -I 0

C: 1. 1


S0 1 0

H >iWa

80 .
a M

tIc C

'3 -- id

It o

J < I
C 0n
0 0
001 41
C 41 <'
2 -1t3

.& CIn
0 CI1
O ew
II 3_ i r




ct if)
s -


r- C

rHO C U Crr
LI -N T C) C m

r-N Nr- I oCi C
m L'- C C tI0 in i

. . C4 . * . ..

C1 r CI f c % D c4 -x: m r I ',co

O r( o CN L 0 CD co
cu ncU co cc r C- a) r- w Ic r- ac r- co

HI 0N H CC I m'

co 11 (N ao o r' N c o o0

SC- Ci C0 0C, 0C Oc C)C 0 i0C^10 0(TI
H1 -I H1

CI o Ct f C 00i o n C en r7%-'.

t- S ifl C 4C C. C UC I -l r"- C)
0" 0 0 0 0() 4 CO-C C0 L
0 -i i i S i's *. n C) ) C 'i


C I O Lt) N nm r^ (

N rr m rC FI N -n ri N r- N Cu -I

OIU'Cn C) C U ,oI IN U -Z t fl- r 1i r n

- -1 -1 1 r-l -

x NC IN C14OH Co w Io C In rtf CI c CC ) C',

rC CII r Ct3i Ili N' Ci 'crin in
,^ D '^o P- Ln r IDo mn ko r m LI")

Ir. H rU
OC r I Ii


Ix; Li
m O

C' i-I
o 'C


0 H0
in r^

N3 'C?
m m

0 ri


co a

.1 N'

n "

c !;*
<- *.'

01 N

co N













,q rl _qN m r i m 'T or-
000 000 00000000
HH NNN mmmmmmmm Im
mmm mm)m mmmmmmmm

C z
V) En
UU ; 3

W oO =

n 0 'o

c 0

u a

rE: o


H >,

S *H

C )


a U
4 En:

Cu s

U H.
Nr 0

m co

N N`


U) N

Ln 0





n o o

0 0 -4


U) Un 0
) U) U)


0a Uo
84 0 r 2 in o
'. .C m o Cm m
C) 0C N N ( CN N


-4 Il

iT' -t T
!:1, N1 0 X C ON
o L R 1 1 O L
' 0 *O 0 CC 0

C: Im o)n
0 0

o C

C 0 0)
j l ) N N UN ,IC)
E cr ) N C O

4 0 U '

S' Co

o c) C D co uCC in
O i' Cl C' NN CC

c; .0
c) -!


c c a N -1

r! o< in u w 1', n
01 0
O c)

C) 'u n
i- Co

C1 c .2

C' CC B g

C' r.' 'n -4 CC0

For owner-occupied housing, mobile homes constitute

between 3.65 percent of the housing stock (in area 3302)

and 19.03 percent (in area 3307). In half of the sixteen

areas, mobile homes account for more than 10 percent of

the owner-occupied housing stock. In every one of the

county groups, the percentage of mobile home owners is more

predominately white than the racial composition for all

Florida home owners. This is observed in spite of the fact

that overall, white incomes are above non-white incomes

and mobile homes are relatively low-cost housing. It is

possible that zoning restrictions on location of mobile

homes may be important in explaining why nonwhite families

do not choose to live in mobile homes.

Also without exception in each area, mobile home

families have lower average incomes than do other families

owning their own homes. In subarea 3102 mobile home owners'

mean incomes were about 80 percent of those for all

home owners but in subarea 3302 they were not much over

50 percent of the area all-home owners figure. In all

but thee of the county groups this disparity in incomes

pa.railels educational differences. In these three regions

(3103, 3401, 3501) the mobile-home families are younger

tian toe all-own rs families. In the "retirement center"

ar2'ca (3304, 3307) the mobile-home owners are significantly

older than ao all owners. And in all areas, the mobile-

home *.;.n'.TCEs ar,, indeed, more mobil--a lesser percentage


having lived in the state five years before the census in

every case.

Only 11 percent of Florida's mobile-home housing

stock is renter-occupied. In the case of a rented unit it

is not uncommon to find that the owner has previously lived

in the mobile home and has moved into conventional housing.

Renters, therefore, usually do not reside in newer units.

Demographically, the renters of mobile homes are in some

respects like other renters and in other respects like

mobile-home owners. They are generally highly mobile and

(except for south Florida) quite young. They are also

largely whire-headed families with below-average educational

attainment (except for western Florida). Their family

incomes are below other renters', below mobile home owners',

and considerably below all owners' incomes. Because of the

small number of households involved, renter-occupied mobile

homes are not disaggregated below the five major area


It appears that mobile home owners are drawn from

both the potential renter and owner markets. If income

is the relevant constraint for most families, however, it

might be concluded that, on the basis of observed 1969

family income, mobile-home owners come primarily from the

potential renter segment rather than from the potential

home owners. Also in terms of mobility, age, and family

size, mobile-home owners approximate the characteristics

of all renters. Mobile-home ownership is apparently

closely related to the life cycle. Young couples and

older people find them to be a satisfactory housing alter-

native, but middle-aged families do not make heavy use of

them as permanent housing.

Some parts of south Florida are heavily populated

by retirees. Pinellas, Manatee, Sarasota, Charlotte, and

Citrus counties had 1970 populations for which one out of

every four persons was sixty-five years of age or over.

In fact, at the state level, Florida has a higher percentage

of its population over sixty years than does any other

state. In 1970, 20.7 percent of Florida's household popu-

lation was over sixty while the comparable figure tor the

nation was 14.9 percent (Housing of Senior Citizens,

p. 487). Figure 2 focuses on the age distributions of

home owners at the national and state levels. When one

analyzes home ownership, he can expect to find certain

trends. Up to some age it night be expected that the in-

cidence of home ownership would be increasing. Very few

young people have the financial resources needed for pur-

chasing a home. This trend is noted at the national and

state levels. The greatest pcicentage of United States

homeowners is found in the 45 to 54 cohort. After that age,

owners!:p falls slightly, probably as a result of older


o ,

7/- 0
MiP EI >,


-4 ILI q "a 1
'0 Ei cEl
{ 1' 1 0r- ,, 1 u

SS I, ^7>'77*- 0
c --


people making housing adjustments in order to get away

from the necessary maintenance and the natural decline

in the size of older-age cohorts as members pass away.

In Florida, however, the heavy in-migration of older

people causes the incidence of ownership to increase with

age all the way up the age spectrum. Almost 28 percent

of Florida's home owners are at least sixty-five years


When ownership is restricted to mobile homes the

trend is quite different. Heaviest usage of this type

of housing is again by older people, but in addition to

this fact, and in contrast to conventional ownership, young

people constitute a significant proportion of mobile-home

owners. In fact, more than 30 percent of the nation's

mobile-home owners are under thirty. In the middle-age

range, where conventional home ownership peaks, the inci-

dence of mobile-home ownership is lowest. This is the

pattern for the nation. For Florida the same generaliza-

tions can be noted with certain modifications. Almost

40 percent, of Florida's mobile-home owners are over

sixty-five, Thisi housing choice is extremely popular

among Florida's older population. Florida hlas more than

her share of older citizens, and many of the.e people buy

a mobile home.


As a minimum, microeconomic theory suggests that

the demand for any good is a function of the good's price,

the price of competing goods, the incomes of potential

demaniders, and existing tastes and preferences (which are

usually assu-med to be exogenous). Besides these "economic"

factors, it is quite possible that "non-economic" factors

(which may or may not be quantifiable) may be relevant

in determiinin the level of demand. The "non-economic"

variables which will be dealt with in this research are,

to some extent, quantifiable, and may be classified as

demographic in nature.

Before rwe proceed to deal with the variables con-

sidered, a note on cross-section consumer demand studies

..s L o rider. Prais and Houthakker (1955, p. 8) have dealt

with the issue of cross-section versus time-series studies

as follows:

in an rnai.ysis of family-budget data designed
to establisi laws describing the behavior of con-
surme-is Lhe assurmption has to be made that by ob-
ecrving ccn.iumiers in different circumstances at the
sam.!: time, i !-rmatio-n may be obtained which is
rilel-In iii forecastin-u t.;he behavior o0 any par-
t l-cu r consui:mr when his circumstances change
through time-. To Lake a particular example, it
nmay b- assu' :! that it there ire Cbserva ti ns;-; on

two households enjoying different incomes and the
income of the first household is next year changed
to that of the second, then its expenditure pattern
will tend to correspond with that of the second
household as observed in the base year. In prin-
ciple, the assumption made need not be so restric-
tive as in this example, but whenever a so-called
cross-sectional study is made there must ultimately
be some assumption which allows the results to be
applied to changing situations. In general, it
is assumed that the differences which are observed
to exist are the result of the differences in cir-
cuamstances acting on consumers who react in sub-
stantially the same manner.

Cross-section data is analogous to a snapshot--a picture of

what exists at a point in time. It enjoys one particular

advantage over time-series data--serial correlation does

not have to be dealt with. Otherwise, statistical analysis

of the two types of data is undifferentiated. Whereas time-

series data require repetition in collection, such is not

the case with cross-section data. The problems of defini-

tional chances or method of collection changes which often

are found in the use of time-series numbers are not found

with cross-section data. Any observations which are not

comparable with the rest of the data may be deleted without

breaking the time series.

Models to Be Estimated

The point of this research is to analyze the demand

for :mobile homes. Total demand is the sum of demands

arisino from the owner and renter sides of the market.

Owners purchase their mobile homes and pay for them either

upon purchase or over a period of years, normally not more

than seven. Renters pay rent just as renters of conven-

tional apartments do.

It cannot be determined from the data to be analyzed

whether mobile-home occupants made their decision to live

in a mobile home first, with other housing considerations

following, or whether the budget constraint was considered

first with other choices following. It may well be that,

given these different approaches, a single model could not

describe both processes accurately since in one case the

decision to own a mobile home is exogenous and in the other

it is endogenous. For this reason, two models were developed

to estimate the demand for mobile-home housing. The first

model to be discussed (Model A) is a tenure-choice model.

It yields insight into the question "what type of family

chooses to own its own home?" This model is then modified

to deal with mobile-home ownership. The second model (Model

B) is used to estimate demand for mobile-home housing

services once the decision to own or rent a mobile home has

nbeen made.

lode l A

Tenure choice is the biggest single decision a

household r-kes when shopping for housing. This is the

decision to rent someone else's property or to purchase

one's own. Several approaches to exploring this choice

and how it is made have been attempted by a variety of

researchers. Struyk and Marshall have published an article

(1974, p. 289) which "is focused primarily on the relation-

ship between tenure choice and income." Carliner published

a similar article (1974) at approximately the same time

which examines the same issue in a very similar manner.

Both research efforts use ordinary least squares

(OLS) regression techniques to examine conventional home

ownership. What is interesting about their work, however,

is that the dependent variable in their models is discrete

in nature. The dependent variable is defined as "home

ownership." It takes on a. value of 1 if the household owns

(or is buying) its own home, and 0 otherwise. It is, in

effect, a dummy dependent variable. For example, consider

the following equation:

OWN = a + b(INCOME) + c(FAMSIZE) + d(YOUNG) + e(OLD)


OWN tenure choice; if the household lives in

its own home, OWN = 1; otherwise, OWN = 0

INCOME = family income, measured in dollars; this

figure may be observed annual income or

some measure of permanent income

FAMSSIZE = a dummrny variable for family size; if the

nurbocr of persons in a family is five or

more FAMSIZE = 1; otherwise, FAMSIZE =0

YOUNG = a dummy variable for the age of the fam-

ily's head; if the head's age is less

than 30, YOUNG = 1; otherwise, YOUNG = 0

OLD = a dummy variable for the age of the fam-

ily's head; if the head's age is more

than 65, OLD = 1; otherwise, OLD = 0

a,b,c,d,e = numerical regression coefficients cal-

culated from actual data

Income is the only variable measured continuously.

While family size and age of head can be measured as dis-

crete variables, they have been set up to define dummy vari-

ables in this example. For instance if the household head's

age is 23, YOUNG = 1 and OLD = 0 for that household obser-

vation. If the head's age is 35, YOUNG = 0 and OLD = 0 for

that household observation. If the head's age is 68, YOUNG

= 0 and OLD = 1 for that household observation.

If household data are analyzed and the regression

coefficients are calculated, we may find that:

OWN -. 0.3 + .004(INCOME) + .008(FAMSIZE)

.20(YOUNG) .15(OLD)

The dummiy variables relating to age have coefficients which

express the difference in probability of cwnership from

thei "referencn group." Since dunmy variables were estab-

lished for "young" and "old" families, the reference group

consists of families whose head is between 30 and 65 years

of age. The coefficient of -.20 for YOUNG expresses the

fact that the probability of home ownership for a "young"

family is 20 percent less than the probability of owner-

ship for a family whose head is over thirty, ceteris pari-

bus. Likewise, the family whose head is over sixty-five

is 15 percent less likely to own its own home than the

reference group.

Both Carliner's and Struyk and Marshall's studies

showed some demographic factors to be significant predic-

tors of ownership probability. Additionally, Carliner's

work estimates that the probability of home ownership (for

his entire sample) goes up 1.62 percentage points for each

$1,000 increase in observed 1966 income. That is to say,

if a family's income rises $5,000 the probability of that

family's owning its own home goes up over eight percent.

Struyk and Marshall found income elasticities ranging from

-0.276 for primary individual households where the person's

observed 1969 income was over $2C.000 to +1.90 for husband-

wife families with incomes under $4,000. So the amount

spent on housing depends not only on one's income, but also

on ;ima-ital status and other der oygraphic characteristics.

A similar model was set up for Florida. The model

is for explanation of home ownership. All types of owner-

occupied housing are included. The mode] can be written as:



b,(DUMLE25) + b,,(DUMGE65) +



b9(DEDLTHS) + b,,(DEDSC) +

bli(DEDCG) + bl2(DUMIGRAN) +


FAMINCOM = 1969 observed family income, in $100 units

DMARRIED = a dummy variable for marital status of

the family head; 0 if single, 1 if married

DUMLE25 = a dummy variable for age; 0 if head is

twenty-five or under, 1 if head is over


DUMGE65 = a dummy for age; 0 if head is under sixty-

five, 1 if head is sixty-five or over

)FEMHEAD = a dummy for sex of household head; 0 if

male, 1 if female

)FMSZLE2 = a dummy for family size; 0 if more than

two people, ] if two or one

)FMSZGE5 = a dummy for family size; 0 if less than

five people, 1 if five or more

IHDNONWH = a dummy for race of head; 0 if white, 1

if non-white








DEDLTHS = a dummy for educational attainment of

head; 0 if high-school graduate, 1 if not

a high-school graduate

DEDSC = a dummy for educational attainment of

head; 0 if head never attended college,

1 if head did attend college

DEDCG = a dummy for educational attainment of

head; 0 if head did not graduate from

college, 1 if head did graduate from col-


DUMIGRAN = a dunuy for mobility; 0 if head lived in

Florida five years ago, 1 if head moved

into Florida between 1965 and 1970

DUMAKMY = a dummy for armed services head; 0 if

civilian, 1 if head is member of armed


DSTUDENT = a dummy for current enrollment status; 0

if head is not a student, 1 if head is

enrolled in school

IENURE = a dichotomous variable which takes on a

value of 0 if the dwelling is not owned

by the family occupying it and takes on a

value of 1 if the housing unit is owner-

occupied for the ALL OWNLERESHIIP model; for

the MOBILE-HOMEN OCIWNE;RSHIP version it takes

on a value of 1 if the family owns and

lives in its own mobile home

First the equation was estimated for Florida's entire popu-

lation (as sampled in the one-in-a-hundred Public Use

Sample) by setting the dependent variable of home ownership

equal to 1 if the household owns its dwelling, whatever

type, and 0 otherwise. Fourteen independent variables were

used in the model--thirteen dummies and one income variable.

The income measure used was the 1969 observed family income,

in $100 units. The dummy independent variables included

one for marital status, two for age of head, one for sex

of head, two for family size, one for race of head, three

for head's educational attainment, one for migratory experi-

ence, one for head being employed in military service, and

one for the head being a student. A constant term was cal-

culated also, so the coefficient for each dunury variable

represents the (percentage) deviation from the reference

(unspecified) group for the specified group. For example,

the summary of the ALL OWNERSHIP regression in Table 15

(Chapter V, page 100) shows that there were three educational

groups specified--less than high-school graduate, some

college, and college graduate. This group might be thought

of as the bastt group." The coefficient for each of the

other groups (-.022, +.001, -.029, respectively) therefore

represents the deviation from the basa group for the group

in question. Families whose head is not a high-school

graduate owned their own home 2.2 percent less often than

families whose head was a high-school graduate.

The model was estimated for all owner-occupied

housing units and then for all owner-occupied mobile homes.

Tnat is, the dependent variable was assigned a value of 1

when first, the ownership criterion was met, and, in the

second version of the tenure-choice model, assigned a value

of 1 when the ownership of a mobile home criterion was met.

Estimating the all home ownership model first and comparing

the results with the mobile-home ownership model should

permit one to ascertain whether the same variables are use-

ful in explaining mobile home ownership. Results of these

estimations are discussed in Chapter V.

Model B

Once the decision to live in a mobile home has been

made, the amount to be spent on such housing has to be

determined. Also, to buy or to rent becomes an issue to be

decided,. Model B is a more conventional regression model

which is esiLmat:ed using the OLS technique. Use of this

pc.'ocedure is widely observed and it has proved to be a

statistically powerful tool. The model is used to estimate

expenditures for owner-occupied mob.ile-home services and

thor. re-c-tLpmated for e;xpendci.tures on renter-occupied mobile

hon' es.

Owner-occuited mobile hones

Dependent Variable. Most housing studies which have

estimated the demand for housing at the micro level have

used either house value or housing cost as the dependent

variable. Of the five cross-section studies of the demand

for housing which de Leeuw reviews (1971, pp. 3-6), four

use house value as their dependent variable. Most precisely,

the demand for housing is a demand for housing services

which, supposedly, any of a number of different types of

physical dwelling units may be able to satisfy. The con-

centration in this research is on one type of dwelling unit--

the mobile home. The utility provided by a mobile home

which satisfies the demand for housing services is the

basis upon which the demand for mobile homes is founded.

This utility is not directly observable or measurable,

but the dollars spent to satisfy the demand for housing

services are observable and measurable. A new or used

mobile home has a purchase price or value at the time of

its purchase. This is the amount paid for the unit, either

at the tine of purchase or over a period of years. Because

a mobile home provides housing services as long as it is

occupied, however, it was felt that housing expenditure

ove1r this pj ic'l of time was the best approximation of

actual demand for these services. Therefore the dependent

var-iab]; is d<. lars of expenditure for mobile home housing

per year. This measure of demand will take into account

not only value at the time of purchase, but also the time

period over which the unit is utilized. Expenditure will

be defined here as the estimated purchase price divided

by the time period over which the unit is occupied. The

result will be annual housing expenditure.* Value of the

mobile home will thus be needed as an input into deter-

mining annual expenditure.

Within the Public Use Sample house value has been

collected for conventional housing units, but has not

been collected for mobile homes. It was therefore neces-

sary to estimate each mobile home's purchase price in

order to derive expenditure. This would be the dollar amount

to be paid by the new, owner. Sinca this datum was not col-

lected directly, it had to be derived on the basis of data

which were collected directly.

For each household the following data, which were

collected in the Public Use Sample, were utilized to ar-

rive at an expense figure:

*The expenditure measure developed in this manner does
not necessarily correspond to that used in any other housing
study. For example, this mobile hone annual expense includes
payment for appliances and furniture since virtually all
units come equipped with these items, but does not include
utility payments. Other studies of housing expense in which
conventional struc-ures were analyzed have dealt differently
with these .matters. Sometimes the researcher will figure
expense inc usive of these items and in other cases they are
omitted. Much the same variance is found with respect to
utility payments, which art excluded in this study.

1. Number of rooms (NROOM)

2. Number of baths (NBATH)

3, Presence of air conditioning (AIRCON)

4. Presence of piped hot water (HOTWATER)

5. Presence of full plumbing (PLUMBING)

6. Type of sewerage (SEWAGE)

7. Source of water (WATERSOU)

8. Type of heating (HEATING)

9. Year in which unit was built (YRBILT)

10. Year in which family bought mobile

home (Y.RVD)

The value of a mobile home is primarily a function of its

structural characteristics and its age. Items 1 through 8

relate to the structure of a unit and items 9 and 10 relate

to a unit's age when it was purchased. Figure 3 is a sche-

matic depicting how the actual items have been used.

Determining the value of a mobile home is a fairly

straight-forward, commonplace procedure in some instances.

For a new unit the value is defined as the market price.

Also, for a used unit, its value can be ascertained as it

passes through the market. The problem in valuation of the

units involved in this present study, however, is that they

are not passing through a market at the time of Census

enumeration in 1970. And the Census Bureau did not ask for

tie owner's etimate of the value of the structure. This





0 ;)


omission is unfortunate because it makes necessary a good

deal of work to ascertain the values of enumerated units.

This valuation is almost certainly less accurate than that

which could have been obtained from the occupant who pur-

chased the mobile home. But, if one wishes to use the

wealth of information which is available from the Census, a

valuation model for mobile homes can be constructed.

Within the mobile-home industry there are several

publications used for placing a value on a used mobile home.

The procedures and presentation of the information are very

similar to those employed in the used car business. In fact,

one of the publications is the Blue Book published by Judy-

Berner and used widely by dealers. Another widely used data

source is the Unicomp Directory of Used Mobile Homes. In

these publications mobile homes are broken down by manufac-

turer, model, year built, size, and physical layout.

There are several "rules of thumb" used in the in-

dustry for depreciating a used mobile home. These "rules"

might be used by a dealer in estimating trade-in value, but

at best they are only a rough estimate of a unit's value.

For instance, a dealer may use a rule such as: ten percent

loss of value the first year and five percent per year

thereafter. This would result in loss of one-halt of original

value after nii.e years' use. The rate of depreciation would

be slower after that poitit. While such an estimating tech-

unique could be used, it was felt that actual resale ex-

perience would provide better data.

Data collected from a 1974 copy of the Unicomp

Directory revealed the depreciation pattern reflected in

Table 9. Depreciation actually computed from Unicomp data

was derived only up to nine years of age. Beyond that age

the rate of depreciation is based on the author's experience

and discussions with people working in the mobile-home


Percent Depreciation by

(Of New Price)
% Value Loss


SOUrCE: Urnicom;p DirecLo:ry
personnel .

Age of Unit

% of Original
Value Retained


and discussions wiith industry

Table 10 shows the average value of new mobile homes

produced from 1950 through 1970 and indexes average selling

price of a new unit for each year. The index is ccmputed

from industry data which are published in Flash Facts. It

is simply a way of expressing a new unit's selling price

based upon average selling price in 1970. For instance,

the 1959 index is .818 because the average new unit price

of $4,996 in 1959 is 81.8 percent of the average new unit

price of $6,110 in 1970. The year 1956 was when the ten-

foot-wide unit came onto the market and 1963 was the first

full year for the twelve-foot-wide unit.

The most significant determinant of the price of a

mobile home of given age is its size. Strictly speaking,

according to industry specifications, a mobile home must

exceed eight feet in width and thirty-two feet in length.

Anything smaller is a travel trailer. While conventional

industry sizing is on the basis of dimensions (12' x 60',

etc.), the census data is in terms of number of rooms and

number of bathrooms. This discrepancy is offset by the

fact that almost all mobile-home rooms are very nearly the

same size. Second and third bedrooms are usually a foot

or two smaller than average, and living rooms are quite

often several feet longer than average. The values listed

in Table 11, based on marginal cost of a room, were derived

for 1970. The process used basically involved translating

Average Value of New Mobile Homes by Year Built

Year Average Value Index

1970 6110 1.000
1969 6050 .990
1968 6000 .982
1967 5700 .933
1966 5700 .933
1965 5600 .917
1964 5600 .917
1963 5715 .935
1962 5602 .917
1961 5599 .916
1960 4995 .818
1959 4996 .818
1958 5000 .818
1957 4996 .818
1956 5003 .819
1955 4129 .676
1954 4276 .700
1953 4187 .685
1952 3855 .631
1951 3685 .603
1950 3423 .560

SOURCE: Flash Facts: Pocket Reference to the Mobile Home
Industry, MHMA, June 1974.

number of rooms plus number of baths data into a dollar value.

An intermediate step in the process involves matching up the

number of rooms with conventional industry sizing (number

of feet For instance, a unit with four rooms and one

bath is probably between 52 and 58 feet long, while a unit

with five rooms and one and a half baths is probably 64 or

65 feet long.

Ther-- are no m-obile homos with only one room being

produced now. They arc, included here for the purpose- of

Value of New 1970 Mobile Homes

Number of Rooms


Number of Baths


evaluating old units counted in the census. Under this num-

ber-of-rooms approach, it is assumed that a unit with more

than five rooms is more than a single unit wide. The model

developed here is based on the marginal cost of an addition-

al room or bath. As nearly as possible, this technique is

designed to coincide with the industry's conventions for

sizin g.

Other stluctural characteristics influence a unit's

value. Table .2 reflects how these factors are taken into

account in the valuation model presented here.



Characteristic Components in Mobile Home Valuation Model


1. i room air conditioner
2. 2 or more room air conditioners
3. Central air conditioning
4. Room heaters with flue
5. Room heaters without flue
6. Portable room heaters
7. No heating equipment
8. Lacks piped hot water
9. No plumbing facilities
10. No piped water
11. Water from individual well
12. Water from other nonpublic
13. Septic tank sewerage
14. Other nonpublic means of
sewerage disposal

Adjustment to Value




Items 1 through 10 are actual structural characteris-

tics of individual units. The dollar adjustments are esti-

mates of the actual cost of adding the service mentioned or

of the loss of value represented by the absence of the par-

ticular feature.

Items 11 through 14 deal with water and sewerage

which actually are not part of the unit, but which are

proxies reflecting the type of environment in which the unit

is placed. These items hopefully parallel quality differ-

ences in units. For example, it is in the "adult mobile

hene" communities that one is most likely to find custom-

built units. It is also in these parks that one is most

likely to find public or mruncipal water and sewerage sys-

teams. On the other hand, a unit placed on a rural lot

where water is from an individual well and a septic tank

handles sewerage is least likely to be a custom designed

or built unit. Some account of quality variation is the

raison d'etre for items 11 through 14.

Drawing these pieces of information together is the

next step in the valuation model. The items listed in

Tables 11 and 12 are summed to arrive at a fictional entity

called VALUE70. VALUE70 is what every mobile home would

sell for (based on its structural characteristics) if it

was built and bought in 1970. This step standardizes units

in terms of 1970 dollars. VALUE70 is then indexed for the

year in which the unit was actually built. Table 10 con-

structed from industry data, is used for this purpose. The

unit is then depreciated (in accordance with industry expe-

rience as depicted in Table 9) in accordance with its age

when it was purchased. The product of VALUE70 and INDEX

and DEPRECIATION yields COST. This is the calculated

market value of the mobile home when it was purchased by

the household under observation. For example, a four-room,

one-bath unit connected to a water and a sewerage system

would assume a VALUi70 value of $6,200. If this unit had

been built in 1966 INDEX would assume a value of .933.

Therefore, the computed value of the unit when it was con-

structed is (VALUE70) x (INDEX) = ($6,200) x (.933) = $5,785.

This is the estimated value of this new unit. If it were

bought new then it would not be depreciated to find its

purchase price. If, however, this 1966 mobile home had

been purchased by its occupants in 1963, it would have

been two years old at that time. A depreciation factor,

obtained from Table 9, would need to be used to find the

unit's value when it was purchased. This factor is .74

for a two-year-old unit. Applying .74 to the previously

computed value of $5,785 yields (($5,785) x (.74)) =$4,281.

This is the estimated cost of the mobile home when it was

purchased by its current (in 1970) occupant. Deriving

annual housing expense involves one further step.

COST is divided by the number of years which the

family has lived in the unit. If this period of time is

less than five years, it is set equal to five. This choice

of five years was made because a study published by the

Florida Mobilehome and Recreational Vehicle Association

in February of 1971 (Cubberly, 1971, p. 30) revealed that

the mean length of time that 1,978 Florida mobile-home

resident households had lived in their mobile homes was

5.3 years. The same survey (p. 31) found that the mean

length of residency at the same address for its sample of

mobile-home households was 3.7 years. So COST divided by

TIME yields annual housing EXPENSE. Ev:n though a mobile

home depreciates after it is purchased, the financial obli-

nation is fixed at the time of purchase and is not affected

by depreciation. This expense is defined and constructed

so that site value is not included in housing expense. The

owner can choose how much he wishes to spend for site value

apart from his decision of how much to spend for his hous-

ing unit. Cost of appliances and furniture for the unit is

included in EXPENSE, however. The cost of credit is not

figured in. This seems preferable since financing is a

service unto itself and need not be bought through a mobile-

home dealer. In fact, a surprisingly high percentage (85)

of families who purchased their own mobile home in Florida

have been found to owe nothing on the unit (Cubberly, 1971,

p. 29). It is for these reasons and the nature of the data

that EXPENSE is defined as just explained. "Annual .ousinig

expense for mobile home" is, therefore, the dependent vari-

able in the model to be estimated.

Independent Variables. The relationship of primary

importance in this research is that between expenditures

for mobile-honme housing and family income. This relation-

ship is measured by the concept of income elasticity of

demand which is defined as the relative change in expendi-

ture compared to the relative change in income. For example,

5f a family's incom-e increases 20 percent and its expendi-

ture on ste.k increases 25 percent, the family's income

elasticity of demr.and for steak is .25/.20, or 1.25. This

sqneral relationship has been examined extensively in the

housing literature (see Chapter II) for conventional hous-

ing--both renter and owner-occupied, but has not been

explored with mobile home housing. Because of the interest

in this relationship, definition of income is of prime im-


Income variables. It has generally been concluded

that the use of one year observed income as an explanatory

variable in demand estimation is inappropriate. Income

elasticities calculated using measured income understate

the true relationship because consumption decisions, es-

pecially for durable goods, are made on the basis of a

concept of income which is much broader than one year's re-

ceipts. Milton Friedman (1957) is the person usually given

credit for breaking ground in the area of a theoretical basis

for "permanent income." He concluded that consuming units

tend to have a three-year period in mind when evaluating

their income. It seems almost certain that housing deci-

sions are based on an even longer time horizon.

What concept of income is appropriate for use with

mobile-home demand? Several key factors cone to mind. When

housing payments are known in advance and must be met

regularly, a cash flow concept for housing service becomes

the relevant consideration. Liquidation of non-liquid

assets, while possible, is not the norm for meeting such a

rogulzr financial obligation. It is possible that such ac-

tion may take place during the early stages of long-term

debt repayment on the basis of higher expected income,

however. This might involve liquidating assets to make

a down payment, but regular debt repayment does not nor-

mally involve such portfolio management.

Most individual's incomes are directly related to

how much they earn per unit of time and how much time they

work (labor-force participation). The exception is income

from non-work sources, and this is important to persons

not in the labor force and to persons with substantial in-

vestment income. Also of importance are a person's occu-

pation, education and experience (human capital), and, his

sex and race.

In developing a concept of permanent income (which

is, itself, not directly observable) these factors should

be used as inputs. Three variants of permanent income,

each embodying different assumptions, were calculated and

tested, along with observed 1969 income, for their appro-

priateness and predictive power. These variants, YPERMFAM,

FMltTR, and INCFAM, are estimates of permanent income, each

b.ised on slightly different assumptions.

The YPERMFAM concept is "pure permanent- income" as

developed in this study. It takes no account of 1969 ex-

peLrience and is an income measure based solely on each

prro-on's occupational group, attribuLes, and human capital.

Development of the earnings model used to ascertain perma-

nent income for each person in the mobile-home sample will

now be presented.

The one-percent Public Use Sample for the state of

Florida contains 40,790 person records. Those persons who

had no 1969 income were excluded from those who formed the

basis for the earnings model being developed. Then, for

each of the twelve major occupational groups identified by

the U.S. Bureau of the Census, a regression model was esti-

mated to predict individual yearly earnings. The model for

each occupational group is in the general form:

log(earnings) = bo

education -

experience =

sexd'mmy =i

racedura'iY =-

Spainish-A Jericandumny =

4 b educationn) + b,(experience) -

b3(experience') + b (sexdumny) +

bh ( + b, (Spanish--


the highest year of school


(1969 age) (6) (education)

1 if female, 0 otherwise

1 if nonwhite, 0 otherwise

1 i f Spanish-American, 0 oth-



This form is not unlike that often used in the human capi-

tal literature. (See, for example, Mincer, 1974, pp. 91-

93, or Grossman and Benham, 1974, pp. 205-233.)

The earnings term, which is the dependent variable,

is expressed in log form so that its variance is made uni-

form. The statistical rationale for this transformation

can be found, among other places, in Mendenhall's text

(1968, p. 206) on linear models. The education term is

squared because there is evidence that the earnings-

schooling relationship is not linear. The experience terms

also are specified in a non-linear form. The expected

relationship between earnings and age is the familiar in-

verted U. Experience, rather than age, is the variable

used, however, since it appears to perform better (in terms

of R2) in some instances. For purposes of this work, ex-

perience was defined as age minus years of schooling minus

six (age at which most people start school). Additionally,

precautions were taken so that persons with little or no

schooling were not allowed to enter the labor force before

age sixteen in the model. The dummy variables take account

of racial and sex differences. The variable coefficients,

with F statistics in parentheses, are presented in Table


To di'ronstrate tite operation of the model and the

use of the calculated coefficients, examining a hypotheti-

N N -4 N co n

o o
I- r-i ro -4 r IV

(3 l N -m N I N -

0 0 0 0 0 0 0 0 0

co ao C C C C" Co

N 4I I7 4 I IN N I
m) o! a oa)0 O 'A C 'N
0o o a o o0 aT' N

a- O C O -O O I O OC N O -4 CN O
C! -44.4i Na' ON -o N .i 0 ID 00
-4 o0* -' N- o* o' C o o- o C
C o : a o o, o Co o o

I cC C Oo In o 0 n o .-
H o '-4' t-o H c, NC o o IT CD
W O aIn C C C C 0 C

r o o 0 o o o o o o

a "N <0 oN vN N 4 -44 n- 4.-4 N

-0 0) cC J4 N ,-4 N ,- I r-Z NC" 0I
0 l i ol C c2 o (" o 4 i c .r o
. o .N 4 o N c N c N D -

: i rA, I Ir I ,r I, ~ o
In 0 0 C. o o 0 o o 0 o .
Sooo oo o o 0o

S-o^ e r 0- o --- - -

o C) C o
-: CC 4 C Ci Ca O CN o c c c r n n- .

C! 00, oo D ON o0 0-) c, oo4
'D a) (- G'
2 oI O N0 o r4 o 4) 0 C

N N r) Nj 4 N N N l

3) (C
o- a -4

0 u 4-, C 0) Q 4- ) 44 10 rd
.'J 4 0 1- > 40 0 a 0 4 44 fd
4 -.4 44 "4 <4 I at t: N ^

'. J4 C,- 4 4 0 1
ILI 3F CA C C <> r ;.-


o o


Cl f

N 0

CO l-

Ln o


m O


(N e-I C
r~ m m
04 N -D
Cl (~ 0

lr 0 r-
N N Cl C

0 0 0
ON N 'n Nm

0 0 0H

LD --- (N /- 0 ---

CO ( NHm m L

OH 0 0-

0 Cl C--

Cr) LA U2
* Gt 'N O t *

0 0- 0

CO- 0- -
0 0 0 .

0 0 0
o o

o oH 00
H0 Cl. 0n

Cl N C
0 C

Si; .} C) (il 3il 0 H L'
i C C:

> >
. Tl r 1 0 3 1.

cal individual might be useful. Consider an individual with

the following characteristics in 1969: male, age 36, cauca-

sion, college graduate, an engineer by vocation. The model

can be used to generate a hypothetical income for this in-

dividual for 1969. Since he is an engineer, he would be in

the Professional and Technical occupation group and the

coefficients for that group would be used to generate his

permanent income:

log(earnings) = 3.52262 + 0.00087(162) + 0.02660(14)

0.00055(142) 0.33582(0)

+ 0.03182(0) + 0.00898(0)

= 3.52262 + 0.22272 + 0.37240 0.10780

0 + 0 + 0

= 4.00994 = $10,232

If this engineer had been a female, her generated 1969 in-

come would have been lower because the dummy variable taking

into account sex would have taken on a value of 1 (rather

than 0 for a male) and the log(earnings) value would be re-

duced by 0.33582. Earnings can be generated for the same

person over a period of years to get total income over that

time period.

This model can he used to generate incomes which can

ibe usd for construction of a permanent income concept.

In e:-;cnce this nudcl produces average incomes, with un-

usuaily high n'd urnisually low. onrs canceling one another

out to some extent. Variations such as these may well be

attributable to transitory factors--the effects of which

permanent income seeks to minimize.

Use of these models allows movement of an individual

"through time." Permanent income, for purposes of this

demand study, was derived to include the time period during

which a family had lived in its mobile home or was pro-

jected to live in it. For example, a family which bought

its mobile home in 1963 would have lived in it for seven

years in 1970. Consequently. the period of income genera-

tion relevant to the family in question is from 1963 to

1970. The model can be used, for this hypothetical family,

to generate incomes for each adult person in the family

for each year in the period. These generated annual in-

comes are then totaled and divided by the number of years

involved to get a permanent income measure for the time

period during which the family was consuming mobile-home

housing services. The permanent incomes for the household's

head and for the wife (if she exists) are then added to get

the family's permanent income.

YPL:RMFAM is one variant of permanent income. It

completely ignores an individual's own earnings experience

in favor of what thar parson's peers (in terms of occupa-

tion, education, experience, race, and sex) have experienced.

This is in line with the theoretical construct of permanent

income which seems to eliminate individual, transitory

fluctuations of income.

FMINTR is a variant of permanent income which ex-

plicitly assumes that the 1969 observation of an individual's

income was not a randomly. generated figure, but was based

on circumstances or attributes which were not of a tran-

sitory nature. As an example, consider the following hypo-

thetical family:

(A) (B) (C) (D) (E)
1969 1969
Observed Generated YPER- FMINTR
Income Income (A)/(B) MFAM (C) x (D)

Head 12,000 9,000 1.33 15,000 19,995
Wife 4,000 6,000 .67 7,500 4,995
Family 16,000 15,000 22,500 24,990

In this family, the husband earned $3,000 more in 1969 than

the earnings function outlined in the previous section had

estimated he would earn. The wife earned $2,000 less than

predicted for 1969. The ratio of observed to generated in-

comes is applied for both husband and wife and the resulting

figures are summed to get FMINTR. In this case, perhaps

the husband is a better engineer than his peers, arid hence

the 1969 income he earned is based on "permanent" factors.

Likewise, perhaps the wife worked only half-time in 1969

and this is a "permanent" employment posture for her.

FMINTR assumes that the observed 1969 experience for both

hiibandnd ad wife is not the result of temporary factors.

This variant of permanent income may be best where this

assumption holds. Otherwise, it would be a poor measure

of permanent income. The next concept introduced embodies

a different assumption.

INCFAM is another variant of permanent income. It

suns the husband's generated permanent income and the wife's

observed 1969 income. The rationale behind this concept

is that the norm for the husband is full-time employment,

but what was observed in 1969 for the wife is her norm. If

her 1969 income was low because she worked only half-time,

perhaps half-time was her regular work routine. So INCFAM

is a permanent income-observed income hybrid.

The F2AMINCOM variant of income is simply obsarvcd

1969 family income. It is not permanent income, but actual

1969 experience. It is the sum of 1969 income for the house-

hold head and the spouse, if one is present.

Price variable. As mentioned in the first section

of this chapter, economic theory suggests that the price

of a good and the prices of other "competing" goods be

included in a model of demand. In an effort to do so in

the present model, a price index was constructed. This

PRICE variable for mobile homes is actually a relative

price measure. It is defined as the average cost of a

mobile home divided by the average cost of construction cf

a conventional singlo-fmrily house for the year in which the

family purchased its mobile home. Recognizing that this

is a crude measure of prices, it should be added that data

for a more appropriate set of prices are extremely diffi-

cult to ascertain. For instance, one might suspect that

a price variable based on rental housing costs would also

be useful. Or perhaps the price variable should be based

on both the costs of ownership and of renting. Any single

price measure will necessarily be an abstraction from

reality. The further removed from real-world alternatives,

however, the less likely a price variable would be to cap-

ture the influence exerted by actual price variation among

alternatives. Depending upon one's financial capability,

the range of choices may include owning or renting new or

old property. While rising costs of new units may exert

some upward pull on the price of oldar units, an older unit

could be depreciating at a faster rate than new construc-

tion costs are rising. So ultimately the price of a unique

housing unit may be a unique price. Unfortunately no price

variable which includes rental housing cost could be found

or constructed. The problem is a lack of data. While con-

struction costs do vary from place to place, materials

are transportable, and labor costs do not vary greatly since

most carpenters are unionized. But construction costs,

even if estimiitable, are only one component of the total cost

of securing housing. Supply and demand forces are also of

primary importance, and these forces vary considerably

from city to city and from county to county. Consequently,

a standard type of rental housing might rent for signifi-

cantly different rates in different places. A price vari-

able reflecting the price of rental housing would then need

to be available by area for the years in which the sampled

families made their mobile-home housing choice. These data

are not to be found. A rental price index was developed

for each Florida county for 1960 and 1970. A price variable

for non-census years could not be constructed, however, and

the concept of a more comprehensive price variable had to

be abandoned. Table 14 shows the price variable which was

used from 1950 through 1970.

Other independent variables. The NPERSON variable

is simply the number of persons in the household. A posi-

tive correlation between family size and demand for shelter

space would seem appropriate.

DUMGE65 and DUMLE25 are two age-related dummy vari-

ables utilized as independent variables because of the

importance of the life cycle in housing demand. Two dummy

variables are used because it was felt that the bimodal

age distribution found by other researchers might be the

case in Florida also.

The DUMRACE dumiiy variable takes on a value of 1 if

tih household head is nonwhite, but is 0 otherwise. The

Relative Prices of Conventional and Mobile Homes

Avg. Cost of
Mobile Home ($)



Avg. Cost of
Conventional House ($)



SOURCE: Mobile Home Manufacturer's Association and Statisti-
cal Abstract of the U.S., various years.

purpose of its existence is to allow examination of whether

nonwhites consume less mobile-home housing services (i.e.,

demand loss) than whites do.

DUMHDSEX is a dummy that takes on a value of 1 if

the household head is female. Its presence will allow us

to determine whether female-headed households demand the

samet amount of mobile-home housing services as do male-

heo"ded households.






University of Florida Home Page
© 2004 - 2010 University of Florida George A. Smathers Libraries.
All rights reserved.

Acceptable Use, Copyright, and Disclaimer Statement
Last updated October 10, 2010 - - mvs