A CROSS-SECTIO:^; ANALYSIS OF
DEM'iND FOR ;-i03IL5. HOMES IN FLORI;

Bv

MAX HOLT ST SAD?; R, JR,

?. DISSoETATION PtlESCNr^'i:' TO TliE GRAOCATE COiTICIL Ci
TH^ U^NTVr?;)TT'i OP' i''LORJDA

IN PARTi;vi. FC;L~'ii,^:-T;:[ir or- VHi:: rco^jir^msnts for ohx

DEGU.^:r; of [JOCTOk or .^'alLOSOPHY

unja^rrs lt

197

DEDICATION

This work is dedicated to the glory of God who
;hov,'ed His great love for us by sending Christ to die
for us while we we.r-i. still sinners" so that whatever we

i< ^ •". ,

d

uo.

may be for the glory of God.

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

ACKNO^'TLEDGMENTS

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
chairraan of my dissertation com.mittee, helped focus work
effort into a manageable topic and provided encouragement
and prodding, when needed. Drs. Frederick Goddard, I-'adelyn
Lockhart, and Anthony La Greca also provided the necessary
cooperation and encoaragement . The Department of Economics
and The Bureau of Economic and Business Research provided
graduate assistantships v/hich helped make graduate work
financially feasible. Sofia Kohli did the typing of the
final draft and provided editorial expertise. Errors
remaiainq are the sole property of the author.

TABL3 OF CONTENTS

Page

ACKNOWLEDGMENTS iii

ABSTR/V.CT , , vi

CHAPTER

I INTRODUCTION 1

General Setting: Housing Needs

and Al Lerna tives 1

Housing Sector Demand 3

Research Design and Methodology 5

Usefulness of Mobiie-Horne

Demand Research 9

II THE LITERATURE ON ESTIMATION OF

^j---.v^ 1 x^i i 1 v^..- u^^-:^\i-iiJ J. v^r-v i;wOijj.L<vj xx

First Efforts 11

Conventional Housing: Time-
Series-; Data 18

Cou'/enL-'' onal Housing: Cress-
Section .Co ta 24

Mobil e-iiome Housir;g 27

Suiurna r y 35

III THE DEm^^ND for MOHXLE HOMES 36

Desctripuive Overview--! j.orida

and U:; i ted S La te s 41

Public Use Saraole 4 5

:i V i'lETiiODOLOGY AND MODELS , 57

Models to be E;n:i mated, 5S

Model A. . , , . . . . 59

Model B , 6 6

Model Sneoii :i_cat.ion. "^4

Page

EMPIRICAL FIKOJMGS , . . . , 97

Modol Esti-ation: Model A „ 98

Model Esti^fiJtion: Model B 109

Model B Estination for

Mobile-Home Owners 110

Mo d e 1 B E^ s t i m a t i o n for

Mobile-Home Renters 124

SUMMARY AND CONCLUSION'S 130

Scope of Research 130

Specific Findings: Descriptive 131

Specific Findings: Analytical 135

Implications and Unanswered

Questions , , 14 4

BIBLIOGRAPHY . „ 14 8

BIOGRAPHICAi, SKETCH 15 5

7ibstract of DissGrtation Y^rasented to the Graduate Council
of the University of Florida
in Partial' Fulfil Indent of the R-^quiremcnts
for the Degree of Doctor of Phiilosophy

A CROSS- SECT IvON ANALYSIS OF
THE DEMAND FOR MOBILE ?!0i"4SS IN FLOYilDA

By

Max Holt S trader, Jr.

August 19 77

Chairman: Jerome W. Milliraan
Major Departnent : Econoiuics

Over the years a cons j.derable amount of economic
v/ork has been generated which seeks to ascertain the in-
come elasticity of deraanJ for" housing. The vrork done here
builds upon this literature by applying regression analysis
to the niobile-hoiae sector of the housi.no n)arKOt--a sector
v.'hich has been grov/ing rapidly in tl'.e last r,wo decades arid
now accounts for virtually all the "lov;-cost"' housing
currently being yjrciduced in the United States.

Th.G econcr. ic Jitei'aturo or; hou.sing demand is re-
viewed' iiiJQ-ne elas tic:.i tics of deruand for conventional
housirig are found to range froni 0.15 to 2.4. Soch a wide
range apparently results frcin cne use of different aata,
different- methodologies, and different definitions of botih
inconie and housing expend;. tures ca- the individual research-

ea

Tv.'o housing dcnancl models for mobile homes are
then dcvolcp.ed. Both models arc estimated usiaq data from
th2 1970 Public Use Caniplc for Florida. The first examin
the demographic variables v.-hich influence home ov/nership
and m.obile-noire ov;norship. Generally, the same variable.^
are found to be s^ignif icant for predicting home ov/nership
in general and for predicting ownership of a m.obile home,
but often the influences are in opposite directions. For
instance, hoi;ie ov.'nership is found to be positively related
to observed incomic- but mobile-home ownership is found to
be inversely related to observed income. This inverse
rclati.ons.hip was found for most of the variables used in
the* f^-ir?*: n'odel develo.'Od.

Trie second mvcdel regresses m.obile home housing
expense against income. Four measures of income are util-
ize::. One is obsei-ved inccme and the other three are alter
nativ^e for.!\ulationG of permanent inccrae. Inccme elasticity
is fo'.ind CO be less tlian uriity in all cases — never rising
above 0.50. Elasticities for renteis of miobile homes are
found to be l?'n;er than these for ovners of mobile homes.
No blanket stateinenLs concerning the preferability of
per\iianent income over ob;";erv(:;d income for determining
r"iOu3 le-homo housing e.Krt;:n:'itur. e can safe]y be made on the
I'^-^is of the results of ti:'e mo<1ei s developed in this work.
I ;-. was foi..nd; however, tlicjit u;.e of i:>ermanent income as the

income variable did yic-ld higher income elasticities than
v/cre found when observed income v/as the income vari.able
us-ed. In fact, the income elasticity appears to be moder-
acely sensitive to the measure of income used.

Dem.ographic variables were not very often helpful
in explaining variation in mobile-home expenditures. Price,
income and family size were the variables v;hich most often
were found to be of explanatory significance. Older
Floridians v/ere found to own a large percentage of the
owner-occupied mobile hom;es, especially in south Florida.
Nonwhites make very little use of this form of housing,
even though mobile homes are relatively low-cost housing
a,;id nonwhitos have below- average incomes.

The second miodel v;as also estiruated for renters of
mobile homes. The results were less satisfying in a
statistical I'ense, but it appears that rental expenditures
are less closely related to incomic- than are owner's expen-
ditures. It scem.s that renting e; mobile home v/as a tem-
porciy housing choice for many of ':hose who were renting
in 197 0.

CHAP TEH I
INTRODUCTION

Gei'ierai Setting: Housing Neads and Alternatives

The Housing and Urban Development Act of 1968 set
a national goal of providing 26 raillion new and rehabili-
tated housing units during the fiscal 1969-78 decade, Thi:
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
imccssiblc., One bright spot in recent housing experience,
however, is the growing role played by mobile hcnies in
providing decent housing. This relatively new housing al-
ternative appears to he one way of providing large r. umbers
o f h o u s i n g u nits at r e 1 a t i ve .1 y low c o s t s .

Recent evidence of the crowing role of mobile homes
i)-. the n-.jticii's housing stock is found in the U.S. Depart-
ment of Housing and Urban Devclopraent ' s Nov/sle^tter of
Decemi.;er 2, 1974 (Vol. 5, No. 43). Referring to the use
cl mobile hcii^es as part of the effort by HUD under Section
Vill of the 1974 Housing and Community Development Act to
make housing a\'ailabie for low-ii'.come fa-iilies, Sheldon
L;;l^ar, UVD Assi-tant Secretary for Hoi,;sirq Production

and Mortgaae Credit-Federal Housing Administration Conois-
sicner said:

Under the new Act's ca'ovisions for leai'cid housing,
qualified families laay choose to live in rr.cbile
homes, as v/ell as other types of housin-.7. As a
raatter of fact, m some parts of the country, with
the use of nobile homes, fanij.ies may be able to
get a decent home and a suitable living environ-
ment considerably sooner than if they vere to
wait for the availabiJity of conventional m>uiti-
f ami 1 y d v; c 1 1 i n g s .

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 thxs view one need
note only that: the median price of all conventionally built
new single-family homes sold in tne United Stacas in 1959
was $25,600. Median famJ.ly income for the same year was
$9,5S6. If the rule of thumb (applied to housing) of two
and one-half times annual take ^lome income for housing
expenditure is applied, it can ti-. seen that many families
face severe budCjOtary problcKis in tliis respect. In fact,
the Second Annual Report on Natioiial Housing Goals (1970)
estimated that about one- h:?i.f of all Ar^erican famiiies
v/ere unable to pay more chan ?15,000 for a hopie . And of
the less- than-?]. 5 , 000 single-family housing units produced
in the late 19&iJs, 90 percent were mobile ho^.e^,. This is
largely attributable to the fact th-U t.he cost, per square
foot tor mobr'e homes is less than haJ.f thot !:or conven-
tional scruccures. Hiuce these lv70 figures were comeiled,

th>.; incoiv.e-housing cost disparity has v/idened, causing
the budgetjiry problems faced by many families to become
more acute.

Housing Sector D e n^^a nd

Untij nov; primary emphasis in the analysis of the
mobile home market has been concerned v;ith 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-homo market is broader and m.ore complex than
previously assumed. There are families v/hich are not "poor"
who do not wish to spend one-third of their inconie on
nousing .

The general purpose of the study is to begin a seri-
ous analysis of the market for mobile homes from the demand
)( side. Clearly, policy prescriptions relating to mobile
homes and tlieir anticipated role in t'ne nation's housing
supply should be based upon sound economic studies of the
owners and rente-rs cf mobile homes and ulie potc-i.tia] maricet-
We need specific information about socio-econoTaic charac-
teristics end about budgetary patterns. 'For example,
!;Ovv do characteristics of owners and rer^.ters of ir.obile homes
coiapar;.' v/ith those of home owners aiid renters ii"* general?
Are tiiere onlv certain. cv-'Os of housericlus who use mobile

Tfiis study will build specifically upon the v:ell-
established literature in economics which deals with the
demand for housing and will develop a cress-section analysis
of the demand for mobile homes in Florida. Tiie 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 vv'liat 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
St 3 tes .

The objective of this study, then, is to estim^ate
the demand for mobij.e homes in Florida, starting from the
conceptual framework of the extensive literature in
GconoT'iics which deals v.'ith the demand for convention.al
iiousing. it has generally been assumed that since mobile
hoiae:.- constitute "low-cost" housing, it is primarily low-
inconio fari\i.].ics who live in them. This is probably true,
but requires substantiation.

V-.e naed specific information with resj-^ect to the
income eja:-:ticity of demand for mobile homes. Even for
con'-'enti onal housing, income elasticity 33 not a seLtlsjd
issued. Whereas Math (I960) and Raid (1962) report elas-

ticities greater than unity with respect to peririanent
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 U'innick (1960) tell us that housing consumption
is no more responsive to permanent income than to changes
in observed current incom.e . 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 v/ere 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 .
v>'idGspread , 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 mobi].e-home
housing. To the extent that factors leading to tiiis high
level of usage are found el sewliorc, future usage elsev/hcro
might also be 'nigh. If the only relevant factors are
peculiar to Florida, tlien applici-. cion of this study will be
li.n.ited. It is suspected, iiOv?ov".:r, that changing tastes
and increasing ir.obiii.ty are relevant factors in housing
dooi;-;i.ons . If this is so, Florida is a harbin jcr rather

than the exception to some rule. At any rate, 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 formiat is such that n-dimensional cross
tabulations are possible. This arrangement allows almiost
unliraited flexibility. For example, among those who live
in mobile hornes in St. Petersuurg, Florida, various cross
tabulations are feasible; e.g., by age, occupation, source
of income, race, education, annual cost of v;ater, or any
other included variable. Data are broken down so that they
arc available for the entire state, for five major areas
of the state , and for fourteen subareas including seven

This data base, will m.ake possible derivation of a
demand function and an economic cross-sec ':ion analysis of
the c-^:m3iid for n^obile homoi^ in Florida. Cross-section

analysis of housing demand for the nation as a whole or for
a particular geographic area is a v/ell-established technique
for conventional housing. Reid (1962) and Lee (1968) have
done the riiost-cited work. De Leeuw (1971) has looked at
these studies and several others in an attempt to see if
their results are consistent. Ke 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
specif icaJ.ly verified v/hether conventional housing factors
apply to mobile-home housing.

In this respect, it appears that dem.ographic vari-
ables require special attention. In terms of socio-
economic factors it would seem worthwhile to differentiate
betweer. owners and renters in order to determine what in-
fluence the life cycle (i.e., age) has upon m.obile-home
consun-.ption patterns, and to examine racial differences in
consur.ip tion patterns. Have mobile hOxTies made ov/nership
more feasilile for low-income families? Is the mobile home
of any value as a mieans of dispersing minority racial
group'j from the central city and hence reducing the urban
prolue.v.s associated with culstering of low-income housing?
Are the housing choices of in-mig rants (e.g., recently
relocatej households) differen.t from the potential renter

mark.3t or the hooie-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 v/orks will be considered and recent work on mobile-
hom^e 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-homce 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. Ov/ners v/ill be separated from renters so that the
relevant distinctions can be noced.

Models to be estimated are constructed cind explained
in Chapter IV. Model A, a tenure-choice model, and Model B,
an expenditure model, are developed. V£Lriabl-:;s to be used
in these models are introduced and the rationale for their
considoratioa is discussed. Actual fjnciinas when the model
is estimated are then presented in Chapter V. Importaiit
findings a.re pointed oLit and considered. Chapter VI then
summarises the study, noting relevant questions which m.ust
bo left: unanswered until further research is directed toward
doal:ing v;ith these matters.

C'sof Illness of Mobile- (-1 oiT.e Do r. ■. rind Re- so arch

This research is an exterision of the existing liter-
ature on housing deraand. It is unique in that it deals
v/ith a sector of the housing ir.arket v/hich hijs heretofore
received almost no attention, even though it is a rapidly
growing sector of the raarket. As will be pointed out,
there seem to be reasons for this increased use of raobile
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
attention.

Implications of the findings sf this study should
prove useful in considering future housing policies which
specifically include use of m^obile r.oines. For instance,
it would be desirable to knov; if sorde segments of our
uopula\:ion to whom we desire to give housing assistance havi
strong feelings about the suitability of a m.obile home.
've already have costly experience in trying to hcuse people
in envi ro,;;ments and housing styles v/hich do not appeal to
thciT. (Fruitt-Igoe is prob.;ibly the prime exi\mple) .

Present housing programs, especially Seer. ion VIII
of the 1974 Housing 7^ct, indicate triat r.;obile horaes will,
indeed, figure prominiontly in meeting future h.ousing goals.

10

Programs to make mobile-home acquisitions by low-income
families easier could perhaps facilitate achievament of
these goals. Specific information about mobile-home demand
is needed, hov;ever, 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-

CHAPTER II
I'iiE LIT£R;^>.TURE on ESTli-mTICN OF

::lasticity of demand for housing

First Effort;

■rk traditionally cited as the first legitimate
.•r.pt at empirical analysis of household expendi-
■■ ' crjblished by Christian Lorenz Ernst Engel in
I daca collected by Ducpetiaux for 153 Belgian
i]i:-> base, Enge]. proposed a law of consumption
■ '. expenditures on food to a family's socio-
'*""^. He proposed that ooorer fair.il ies s^end a
■■•itaqe of their available assets on food than do
::-u]ie3. Carroll Wright "borrovved" a hypotheti-
-'■ t'axony v/hich Engel had drav;n up, attributed
•» . assigned expenditure figures to the three
>■■: classes, and expanded Engel 's generalization
•■■"- dealing not only with expenditures for food,
■-■Nothing, lodgii.g, and sundries.*
'•i*or (1895) reexamined his Belgian data ac-
■■-■•.■"ne claims and concluded that the oroDortion

detailed description of these events, see
article i.n the April ].954 Journal of Poiit;

11

12

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 axmount 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 Genrianv.
The gener^li-cltion was found to hold for Leipzig (by Kassec)
and Hamburg (by Laspeyres) and was accepted by Engel. Sub-
sequent budgetary studies considered housing expenditures,
but nothing of exceptional econcm.ic interest was generated
until v.'ell into the twentieth century.

Table 1 lists the major housing studies published
in the United States. Most of these are not discussed in
thi.s chapter, but all did make a contribution in the
developfuent of the body of housing-demand Jiterature. A
variety of data basf.>s has been utilized j.n estimating
domnrid for hor.sing, and each researcher seemiS to have modi-
fied his approach to the issue in order to utilize the data
he had. Ti-.e studit^s arc listed chronologically by data

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17

type, and note is mado of the unique features of each in
the Comments column.

Although many studies of housing demand have been
published over the years, most of thom 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
v;hich simply does not lend itself to precise estim.ates of
price elasticity. Price differences must be measured be-
tv;een 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 betv-een the central city and suburl)) but there is
also intercity variation. Accurate price data would be
needed both within and betv/een cities on a standardized
unit of housing. These data are not readily avaj.lable on
a cross-section basis. Within an area price variation is
not likrly to be great enough that price elasticity can
be accurately gauged and between areas quality differences
make price comparisons difficult. For this reason alm.ost
every cro.ss-si :■ tion estimate of price elasticity has been
prosent-rd with .■m apology for its suspected unreliabili cy .
Mo::;t studies have been focused upon the income-housirig
relationship :is expressed by the income elasticity of de-
mand (n.,) .

18

9.93y^J} tional Housi nc?2__Tjjne__S eri e s Da ta

The first widely read v;ork which attempted to
estimate the income elasticity of demand for housing from
tim.e-series data v/as published by Louis Winnick in 1955.
His concluGion, based on residential construction expendi-
tures compared to either gross national product or gross
capital form.^tion from 1890 to 1950, is that consumers'
preferences have shifted away from housing over this tim.e
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.
Kis conclusion is then drawn from, the fact that these
measures jurap up and down slightly over the sixty years'
peri^od wxtiiout demonstrating any significant upward treiid.
In fact, per-d'welling unit value falls over time. The
incom>e elasticity of demand for housing which he derives en
route to his primiary conclusion is 0.5.

GuttorrLag responded to Winnick 'g conclusion by
questioning the premises upon which it was derived. He
specifically suggested that carrying costs are m^ore appro-
pi: iatoly conridcrcJ than capital outlays when one wishes
to look at consumer behavior. He additionally as:serted
that the demand for housing may not bo more clastic with
respect to income than v/ith respect to price — a relationship
assu'ued by Viianirk. Winnick 's "Reply" (1956) to Guttentag
IS coine-j in term.s of Si-mco rent and reasserts thie original

a

19

conclusion. i-/hxle the issue of the place of housing in
consu:ners' budgets may not be settled, it nvas. be remembered
that Kinnick was using a "back-door" approach by using

ggregate capital value if what he was really interested in
is income-hous.ng expenditure relationships. Additionally,
hrs 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 percain 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.
In particul^^r his "The Demand for Non-Farm Housing" (1960)
has received ..uch attention. The study is now becoming
dated (hxs ti.:.--series data end in ]941), but his methodology

established tiic tone of much subseau'-rt work t^ ^o4-- ^•

.. u.j.,,..4u„nr- \<orK. xn estimating

^tu:-K-:,'j,u,.,nc elasticities for housing, Muth uses aggre-
gate; dat.-i fro:.' +1""-"' IQI s >-^-, i o 1 1 ^

-•.. -J.. J Jib ^.o ..9.a non-war years which, of

C..U.SO, jrcludeR the Great Depression years. His first
stork- derf,.Hnd equation takes the forni:

h^ - Ap + By^^ 4- Cr

20

where h^ - 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
bonds

This equation is the one estimated when Iluth assumes rapid
market adjustment to changing prices and incom.es. When
slower adjustntent (requiring more than a year) is assum^ed,
the model is re-specified:

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

wr.ere 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 5.3 8 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 at least equal to about
unity and may even be numerically ]arger."

Muth's conclusion seems to have been borne out by
subsonueat vrork, but his approach has been criticized on
several grounds. The first of thes- criticisms deals with
his asEu;aptions. In the derivation of a "unit of housing

21

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 terras of pa.yir.ent for this
unit of service. In effect this procedure says tnat 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
ineasureraent, 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. Ohls tests the plausioility
of this assumption with data found within the body of Muth's
v;ork and find? jt to be an unfounded one.

Muth additionally can be questioned on the following
issues: (1) his choice of the Boeckh Construction Index as
his price variable may 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 h.e a less desirable measure for housing prefer-
ences than some measure of carrying costs. Operating costs
directly actributahle to housing are thus overlooked.
(3) No account, other thari por-capita transformation, is
taken of any demographic variatj.on.

Tong Hun Lee has also done work of note with time-
series data. His conclusions, however, iire at odds v;ith
Math's. "The main findings of this study are that the
income elasticity is substantially less than unity v/hile
the price elasticity exceeds unity" (Lee, 1964, p. 83).
Lee's data, being largely that used by Muth, covers the
period frcm 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.
EJe 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 v/hich uses price or income as dependent.
Lee (19o4, p. 85) then states, " tlie true elasticity of price
(cr income;) sliould be bracketed between thes" two limits."
This bracketing technique is statistically acceptable, but
Lee's brackeLJng is nothing m.ore than an arithjnetic mean
so that his elasticities are, in the end, averages. His
0.652 iricorcc elasticity is therefore an average of 0,336
and 0.9 78. Both the upper and lower limits aro less than
UJiity, however. This eJ.asticity is derived using observed
inco-^e, but Leo a.l.so tests the permanent- income concept.
Tiie iipper an>1 lower limit:- thei\ become ].2S3 and 0.335 with
<"^.n average of 0.609. Tne mean is still ]css 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 foi- housing
stock is smaller than one, v.'hile 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 tim.e-series study to be considered here
is that done by Geoffrey Carliner in 19 73. His work is of
particular interest because he derives income elasticities
from regression equations specified both v;ith and without
demographic terms. Results from these regressions show that
elasticities are higher for owners than for renters and that
elasticities are consiscently higher wheii 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.
NumerJcally, his income elasticities range from^ 0.410 to
0.745, being high.est v.'hen income is expressed in a permanent
fcrni, Carliner' s (1973, p. 531) summary statement expresses
a belief that "the elasticity of housing dem.and is around
C.6 to 0.7 for owners and 0.5 for renters." He thus ends
up in the same neig'aborhood as Lee.

Conventional Housing: Cro ss-Section Data

An early example of a cress-sectional study of
housing which derives an inconie elasticity was published
by Ogburn in 1919. He used 2 00 family budgets froai
Washington, D.C, and derived an elasticity of 0.93 for
renters. Subsequent work by other writers produced elas-
ticities varying from 0.15 (Duesenberry andKistin, 1953) to 0.86
(Friend and Kravis , 1957) between 1916 and 1960.

In 196 2 Margaret Reid piilished her Housing and
Il^^-2£1^ study. She openly challenged the validity of the
Schwabri Lav; of Rent which had been sleeping peacefully for
almost a century. She asserted, and even had empirical
e'/idence to verify, that the income elasticity of dem.and
for housing is greater than unity by a substantial amount —
being as high as 2.05.

Dr. Raid's conclusions and work are b?sed upon a
permanent concept of income. She maintains that such a
measure of income is the only appropriate one siiice the
time horizon involved in housing-consumption decisions is
quite long and since observed annual incomie figures are
subject uo much fJ.uctuation and are at the mercy of random,
exogenous i.nfluences. Using grouped data from several
sources (spanning three decades) , she demonstrates that the
income elasticity of demand for housing is greater tha:i
un;' ty bet;'7eon and within cities.

25

As might be expected, this work has received quite
a bit of attention in the housing literature. In fact,
a Ph.D. dissertation v/ritten 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 comJDination, 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
statisti.cal methodology, Bedrosian 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. Fie sayc , "... Reid's
averaging process tends to 'v/ash out' many relevant differ-
ences in permanent housing components that should be ex-
plained by variables other than permanent income. Reid
classified individual houyehoid observations into groups
?ACCordir;a to census tracts and housing-quality categories
v/ibhin places, and geograohicaj. areas such as cities. For
each group she computed averages of measured inco;nes and
of housing data" (Lee, 1968, pp. 437-3S). Additionally,
i':er model specification i implies that nn thing, otlier than

26

income variation, has any influence on housing expenditure.
Hence, in Lee's estimation, Margaret Roid 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, hov/ever, that Lee's data consisted of a four-year
reinterview survey in which some of the original respondents
r.oved and were not reinterviewed. His results are, there-
fore, biased to this extent.

Frank de Leeuw has summarized and compared cross-
section v7ork by four people (Raid 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 ovmers. While
his is not the final v7ord on the subject, he has attempted
to reconcile existing differences between four widely-zead
studies. In addition to the work done by Re id and Lee,
de Leeuw examines that done by Muth (mentioned earlier in
this work) iiiid also a study published by A'inger (1958).
De I^eeuw cites certain shortcomings in each of these works
and suggests liow each noted "deficiency" would bias the
results t]i-5t each of these four people has publi.shed. His
belief is that the original range of income elasticity re-
ported by these four rcsearchers--0 . 6 to 2.1 — is actually,
when corrected for the shortcomings he noces, narrowed con-
siderably. Niiraerically , he adjusts the other researchers'
results and riarrows the range for ivicom^o elasticity to

27

0.81 to 0.9 9 for renter-occupied households and to about
1.1 for owners,

Mcbile-Horue Housing

Eccnomic literature dealing specifically v/ith mobile
homes is alraost nonexistent. This is probably a result of
several factors. First, mobile homes v/ere 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 hom.es have received so little at-
tention in the professional literature is that, nationally,
they i'lake up such a small fraction of -the total iiousing
stock {roughly three percent) . The grov/th of this form of
housing is, however, undenj.able. Mobile-home production ^
accouaced foi almost 22 percent of all housing units con-
structed in 1970. Table 2 shows the growth in production
of mobile hoRiis since 194 7.

Fobert- c'reach and Jeffrey Hadden published an
article iri J'''''--l':^i_£''^'"-in.9 "■i_9_f' iii 1965 v/hich analyzes thiC: cl'uir-
actcristics of r.vobile rionies at a nationiil level. Their
analysis docs little nvore than paint a picture of the
tyt^icai nicbi.l o-home dweller and his unit in 1960. They
conc;lu,do th.jit "traileis" arc an urban phenomenon., a "new
kind of subu /bi -1 , •' if you would, Tiiey are utilized most

28

TABLE 2
Mobile-Home Shipments and

;ale;

1947-1973

Year

Manufacturers' Shipmeni
to Dealers in U.S.

Retail Sales
(Estina ted)

1973
1972
1971
1970
1969
1958
1967
1956
196 5
1964
1963
1962
1961
1960
1959
1958
1957
195 6

T n c c
J- > _i _)

1954
195 3
1952
1951
1950
1949
19 48
19 4 7

566

,920

575

94 0

496

570

4 01

,190

412

69 0

317

950

240

360

217

300

216

470

191

320

150

840

lis

000

90

200

103

700

120

500

102

000

119

300

124,

330

111

900

76

000

76,

900

83

000

67

300

63

100

46.

200

85.

500

60,

000

4

,046

,382

000

4

,00 2

,783

,000

3

297

225

,00 0

2

,451

271

,0 00

2

,496

77 5

000

1

,907

700

000

1

,370

052

000

1

238

610

000

1

212

232

000

1

071

392

,000

862

064

000

661

000

00 0

505

000

000

518

000

000

602

OOG

000

510

000

000

596

000

000

522

0 0 0

coo

1 C 1

n r\ r-

coo

325

000

00 0

322

00 0

000

320

00 0

00 0

243

000,

000

216,

00 0

000

122

00 0

000

204,

000

OOG

14 6,

000,

000

Prior to 1947,
to 60,000 in 1:

;oductior: varied froru 1,300 in 193C upv>.'ard

10 -wide ho;ues Ccirae into
12-v/ido homes came into

:; production in 1955
s production in 19 62.
s P r o d a c t i c n 1 n 19 6 9

SOURCE: Flash Fact
June 19 7 4.'

iobi],e Ilori'e i-'juiufacturers Assocation

29

heavily in areas of rapid population increases and in areas
of lois' 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 hones are not "substandard" hous-
ing v;hen gauged by either overcrov/ding or physical condi-
tion of the structure.

In terms of the ages of the people who live in mo-
bile hom.es, they were found to be either young (couples
usually) or old (retired). French and Hadden (1965, p. 138)
suggest "that the largest gro;ip of trailer d\vellers are
young Icv/er middle-class working families who are looking
for a better way of life but cannot yet afford to buy a
periuanent hone in the suburbs." They concluded, as most
v;riters do, by pointing out the need for further research
in the area.

Robert Berney and Arlyn Larson, follovving French and
Hadden '.^ load, publish.ed an empirical piece of research a
year and a half later (196G) . Working v.'it'.i. a survey of 800
Ari^:on~ mobile-home households, they used bi-^sically the
san;e approao'a as that of French and Hadden. Data were
collected for each houstbold on elu.ven different variables,
among which were: value of unit, family income, family
a 5 :- ; e t s , a n d t ? v a s paid on u i n. t . ( ■ i' h o v a r i. a b 1 (.-• s w e r e selec-
ted and 1:he study perfornvad wi'ch afi eye to implications for

30

tax policy.) Their work revealed that, in Arizona, the
occvipational 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 fev,-er assets than working households. Kowever,
neither price nor income elasticities were calculated for
their study.

The U.S. Department of Housing and Urban Developm.ent
published a volum.e 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
m>obile homes and their residents was m.uch the same as that
depicted in the two above-mentioned articles. Am.ong other X
things, tnis study found the cost o^ a mobile home to be
rough.ly three- tenths that of a m.ultiple- family structure
(per unit). It also found that the median household incom.e
for a r,'.obile-home family v;as only about 85 percent of the
median househoJd incom.e for the entire national population.
As far as the -mobile home itself is concerned, the unit
was p]:obably financed for seven years and the downpaym.ent
was less than $1,000.. Typically the residing faaiily v;as
composed of hu;vband, wife, and a your\g child. The adults
were general.Ty less-educated than the gener.?.l population.
The unit itself was less than half the size of the average
housing unit being sold emd was ].ocat-.ed outside of a
Standard Metropolitan Statiscical Area.

31

SoH'e 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 wore of im-
portance in affecting demand and no elasticities were
calculated.

IVo books which take an encyclopedic approach to
racbile-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- Lype publications such as the Mobile Hom.e Jojjrnal
and M?J:iLli?..±J-_if _^<iSJ^f -Jlne . ^^ in previously --uentioned
sources, the statistical approach involved hardly coes
beyond averages and percentages. We are shov/n a "profile"
of the mobile ho^\e resident of the 19G0s. Her approach,
d-jals v.'ith social changes leading to doveicpnient and ex-
panded use of mobile homes as v>'eli as the ins LiLuL ion.al
resistance to this "new" form of hovsing . All in all,
Ms. Drury' s book is a quick trip through the (non- technical)
mobile-home liLerature from a scciclogical point of view.*

*A ru?.^: edition of this book was publish.ed in 1976.

32

The other book to be considered here is Housirxg

Demand: Mqb_ilc^ Modular, or Conventional by Harold A.

Davidson (1973). This v/ork is quite similar to that of
Ms. Drury, but does carry analysis a bit further. For
example, Davidson looks at mobile hom.es in relation to
other housing alternatives and attempts to discover the
determinants 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 m.ade up of economic
variables. It includes the income distribution of the U.S.
population, the selling price of the m.obile hom.e , fina.ncing
terms, and property tax saving. The second group of vari-
ables are demographic and social in nature. Included are
age distribution, valuation of leisure tirae, and impact
of changing social values. His final group of variables
i;3 called "aestht;tic and political." These include mobile
homo design chajigcs and mobile home park development.

Usi;Ls' multiple regression analysis, Davidson derives
a linear iv.odG'l to ostiirtate pararueters in several demand
equations by the ordinary least squares technique. He
estimates two dem.and equations for m.obile homes. (These
equations rre estiiaated on the basis of quarterly data
culliicted fro;i: a nurai:;er of sources.)

33

(1) MHD^ = -372.742 - 137.184MPCC + 4.303PR^
(-5.93) (-2.27) (3.Q1)

- 0.079PDI^._^ + O.OIITHH , + 26.16D
(-2.32) (4.46) ' (5.07)

d.f. =45 R^ = .965 a = 6.CT3

(2) MHD^ = -55.110 - 24.661VR , - 62.106STHS ^

(-1.46) (-3.51) (-2.67)

+ 0.29M?I -\- 26.524D
(9.81) (6.41)

d.f. =46 R^ - .954 a = 6.99

where MIID^ = demand for mobile homes, expressed as total

mobile home shipments

.MPCC = a price variable, the average selling price

of a mobile home

Fi-i — prxme a-ii teres t rate

PDI , = per capita disposable income

TKK , - total number of houseliolds

D = damjay; D - 0 all quarters before 1971 I;

D = 1 for 1971 I and later

VR, _ j^ - vacancy rate (expressed as a percentage)

QT'H'^ - - s i n g 1 e - f am i 1 y housirig 3_t3^t_s

""t-J. total conventional b.ovi^ing starts

MFi . _^ = median family income

(Subscripts indicate whether observation is for same tim^e

period or is lagged one quarter.)

The nv'.mijers in parentheses are t values. Equatio i (1) has
at! R'- of .9C:--' and Equation (2), .934. All coefficients are

34

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 n'oiriber 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 multicoilinearity or high correlation with the depen-
dent variable. It seems that a resoecif ication of age-
specific variables would be preferable to ignoring the
factor aitoqeiiher.

An elasticity of mobile home demand for personal
disposable income is calculated at -1.683. This indicates
that an increase of one percent in personal disposable in-
come -will cause mobile-hor'.e demand to decrease by 1.68S
percent. Such a finding v/ould define mobile homes as an
interior good. While this conclusion may not seem unreason-
able, the income variable is an observed one, not a pen;\a-
nent Bicasure of income. At any rate, a finding such as
Davidson's certainly calls for further exploration into the
income eia^cicity lor mobile home demand. This is one
particular aini of this piece of research.

35

Summary

While the studies just discuGsed are not exhaustive
of all the research that has been done on housing dem.and,
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 seem.s to be the most widely used statistical
tool and it is, indeed, a powerful one. Using this procedure,
the researcliers discussed above have estimated income elas-
ticity of* demand for housing. The range of estim.ates is
wide, from 0,15 to 2.05. This variance might leave one
bewildered as to just v;hat the income elasticity is for
I'iOusing , but to sorae extent this variation is a function of
the data used and methodological differences. Perhaps, as
de Leeu'v told us, there is more agreement than appears on
the sui-face. But these studies in the academic literature
are concerned only V7ith conventional housing. Application
of the statistical techniques developed in the housing
demand literature has not been, made to trie raobile-home
sector except in Davidson's work. The fact that he took no
account of th.e age of the occupant, and that the income
elasticity of demand he found v:as negative leaves several
questions unanswered, even after thiis attempt to analyze
the luobile-hoiTie m.arket.

CHAPTER III
THE DEMAND FOR MOBILE HOMES

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
som.e other expenditures, have both consumption and invest-
ment aspects. While one is consuming the housing services
rendered by a structure, the structure itself raay be appre-
ciating' over time. Because of such conditions, one writer
(Smith, ]9';.3, p. 1) has even sugcjested that housing is not
a suitable topic for theoretical analysis:

. . . housing involves m;"'.jor non-econcmic com-
plexiuics, mai.nly legal, institutional, and
ae.=;thotic; housing is an inconvenient hiybrid,
a consumer's durabj.e good, which mecns that the
GConomisL.-; cannot be sure whether i.t belorigs
under the heading of utility m.aximization or
savings and investm,t?nt. .

While there is son;e tiruth at the he^rL of these remarks,
most econci.usts would agree that any economic good is de-
serving of theoretical analysis. And an area as prominent

36

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, v/hich might be greater than the original
purchase price. So, v/hereas conventional housing may appra- 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 tine much like an automobile and the in-
dustry even has several publications for estimating the
current marke:: value of a mobile hom.e--just as car dealers
hav'e their own industry guides.

Because of the mobi.le nature of a, mobile hom.e , it is
an easy matter to separate the value of che structure from
site value of the land upon which .it may be located. A
mobile ho'n\e owr.ei: has thc^ option of locating his unit (who-
tlier it is valued at $4,000 or S14,CCC) on a small or a
large parcel of land, close in to trie city (v/herever zoning
perai.'::;,) or. on a large, rural parcel. Given this pcssibil--
ity, ii. is e;i:-y to se:.'arate the damand for mobile-home
housing services from the demand for neighborhood quality,
iiOtvo^Tr estimated .

Given this dichotomj.zed process whereby the housing
chciJ ca and the location choice are made sepairately, the
value of tl^e housing unit: icself is easily separable. Fur-

38

thomore, in looking at the econca'iics 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 foi
an automobile, so this factor is not likely to be relevant
for mobile-hom.e purchasing. For these reasons it seems \
appropriate to consider the purchase of a mobile home as
a case of purchasing a consumer durable good.

When a household (family or single person) enters
the hvjusinq market or m.iikes a change within it, there are
several categorical decisions to be made. These may be
made indeper.dently or jointly, and the order in which they
are m.ade wiJ.l vary from case to case.

Probably the most effective constraint in the major-
ity of houi:ing decisions is budgetary in nature. This is
simply a varia.rit on one of the major building blocks of
economicS"-the clash between unlirrited wemts and limited
resources. In this case many people might wish to live in
a mansion but have incomes sufficient for orily a modest
living onvircnmont. So if inco.T'e is an effective con-
sl:raint for most nouseholds, Lhc- oti^er decisi.ons will be
made following a decision iibouf luaximum uff(')rdcible housing

39

cxpendi tares . Only if this tirjure is sufficiently large
can conventional home ov/nership be a viable alternative.
So tenure choice (own or rent) is also a decision for some
households. If rental housing is chosen, one may rent a
conventional s ingle- family structure, a unit in a multi-
family structure, or a m.obile hom.e . Similar alternatives
exist in the owner-occupied sector also. This study fo-
cuses on households in Florida which have ni^ide decisions
to own or rent a m.obile home.

The cost of housing in the United States has
climbed over the years to the point where talking about
lov7-co3t new housing is much like talking about the uni-
corn--if it ever existed, it is now only a memory. Rising
costs h.ave prevented many families from being able to con-
sider home ownership.* If thiere is any lov;-cost housing^
St:!!! being produced, i.t is prooably a mobile hom.e.
Several facts operate to make this statement defensible:
y (i) The average cost per square foot of a conventional
house v/as SJ.4.65 in 1571. The coiaparable figure for a
vi.cbile home w^is $9.07 (Davidson, 1^73, p. 119). (2) Mobile
ho.'LOo typically iiave fevei: square feet than conventional
■■\0use5. (3; Sir, ce inobiT! e homes Ccn be and are often located

*For a partial o-xplanation of this plienomenon, see An-
i;.h.o;iy Downs, urban Proc J €:^-3 a!id Prospect '> ((.'hicaqo, 1976),

40

on sraall parcels of land (owned or rented) payment for site
value can be kept low. These factors combine to make
mobile-liome 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, mieans lower utility bills. Taxes on m.obile homes
are paid thorough annual license plate purche'ses and rem.ain
at low levels. If one decides he is tired of his present
unit, transaction costs are lov? and nev: furniture and
applif.nces are normally included in a new unit.

In addition to these factors, there is anothcir force
operating on the demand side which is especially pertinent
in Florida. Many peopde, retirees in particular, are not ^
buying a mobile home just to get another liouse, bu'u to
achiex'o a v/hole new living environment and life style. A
pliish "adulL mobile-hoiae community" is not difficult to
fiiid, especially in south. Florid;t. V;hiie retaininQ some •
of the benefit:: of home ownershi:"-/ it is also possible to
en^oy some of the benefits cif living in a r'--ntal complex.

41

In sun-'.-nary, there are a number of factors which make
inobile-houie housing a desirable alternative in Florida.
Araong them are low price, low maintenance, single- family
ov/nership, flexibility in choice of environment (rriobility) ,
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
v/hen a permanent measure of income is considered.

^-^-£IJ:£^iYJLJly^^^Xl^i — Florida and United States

Befo'^e g^^tting into actual mobile hcn-^ usaae v-ithln
the state, som.e observations comparing Florida and the
United States as a whole will be of interest. Some crucial
compariscns are highlighted in Tables 3 and 4. Table 3 fo-
cuses on an overall cojapar.Lson of the United States and
X Florida for certain selec.:-ed demographic, characteris tics.
Florida had about 3.34 percent of the nation's population
in 1^70 and 3-68 percent of the nation's year-round
liOMsing units. As. a percentage of Lhis housing stock,
hov;ever, ii:cbile home usage in Florida is about two and a
quarter, tiroes the level observed nat ionv-ide . Florida's
population is sonewhat older than that of the nation, as
observed by tiie differences in median age and percentage

42

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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 fev/er of the sixty-f ive-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.) Slight J.y more of Florida's m.obile-home households
contained only one person and considerably more were headed
by persons over sixty-five. Relatively fev/er househoJ.d
heads worked in Florida than in the United States, and
of the heads who v;ere employed, a lesser percentage of
Flo:-idi;i.ns living in mobile honi.es held "blue-collar" jobs.
Income-v/i r,e, F].orida's mobile home residents received sub-
stantially less tlian their national counterparts. Finally,
Florida mobile- horae residents are found to be, in fact,
quite mobile.

In suramary, there is a hea^'v usage of mobile homes
i 1 1 F ] o rid.?.. S o rn e o f the q e n e x: a 1 i t .i e s a b o u t t h e n a t i o n ' s
mob.i je-jioi-;o dwellers are also true in }'lorxda. Their i.n-

44

TA3LS 4
Characte;ristics of Mobile Home i!ousehold^i for
tho United States and Florid;-!, 1970

Uiii-ed States Florida

ieaQ3 ;:

■i: --^2 ^ -

Owner- Renter- O.vnor- Renter-

occupied occupied occupied occupied

Mobile-home

households 1,752,577 321,417 147,970 2 3,499

One-person house-
holds (% of total
Toobile home house-
holds) 19.4 25.6 23.4 29.1

% of household^i

vho£ve head is

65t years 18.1 12.8 39.9 21.7

Median school years

completed by head 11.8 12.0 11.3 ll.fi

;.n i.':»69 /O.O 64.2 46.9 55.2

% of eiriplcyed head^3

v/ho h.eld "blue

collar" -jobs 50.0 67.6 30.9 65.2

Median 1969 f .";u ly
incor.e (to nearest
$100) 7,800 5,800 6,300 5,200

%■ cf h/oui-iaold hc^ds

liviny rr. a di tie rent

s^ai.e 5 yoar:o a^^c 13.9 26.2 26.1 3 7.0

SOLTCd: 19'/0 Ceni;'.::; o? Housincj and Ccr-.su? of Population.

4 5

corAes are relatively lov;, 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 m;ake 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-
siori. Figure 1 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 v/b.ich are heavily influenced by the Jack-
sonville SMS7i (subarea 3101). Subarea 3102 includes
Gainesville and Ocala. Area 32 is central Florida and in-
clud'-->s Orlando as subarea 3201. South Florida is Area 3 3
and is made up of eight subareas which include Tampa (3303) ,
St. Petersburg (3304), Mi.a:ai (3302), Fort l,auderda]e-

ARZA
/////Y.'/X

/ ,■ // /\//\
\/ //\ / / ,' :}-

AK£A
33

FIGUllE 1

Public Use Sample Areas and

S ub a r e a s f c r Flo i: i d a

47

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 counLies and one adjoining Alabama
county. Pensacola is the dominant city in this area.

There are approximately 12 5 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 financi.al characteristics about the house value or
rental rate. At the individual level there are charac-
teristics and attributes, including data on ago, race,
sex, education, and income.

For purposes of this study, data were collected
for lieads of households and for wives of heads of mobile-
hone households. Ccmhining 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 tliese, 15,'^56 a.re owner-occupied
and 6,733 are renter-occupied units.

Selected characteristics for the county groups are
listed in Tables 5 through 8. Eiata are included for heads
of all ov;ncr-occapic'd and r.:'[i!;cr--occupied housing units
and for lieads of mobile-home households, both owned and
rented .

43

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For owner-occupied housing, mobile honies constitute
between 3.6 5 percent of the housing stock (in area 3 302)
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 ov/ners is more
predominately white than the racial composition for all
Florida home ov/ners. This is observed in spite of the fact
that overall, white incomes are above non-white incomes
and mobile homes are relatively lov,'-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 v/ere about 8 0 percent of those for all
home cv/ners but in subarea 3 3 02 they were not much over
50 percent of the area all-home ov/ners figure. In all
but three of the county groups this disparity in incomes
parajleln educational differences. ?_n these three regions
(3103, 3401, 3r.01) the m>obile-horae families are younger
tli.:in ttie all-ovraors faitiij. ies. In the "retii'oment center"
areas (3304, 3307) the mobile- home ov/ners are significantly
older than arc all owners. And in all areas, the mobile-
home owiior^: are, indeed, mc^re mobile---a lesser percentage

53

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.
Demographicaliy , the renters of mobile homes are in soma
respects like other renters and in orher respects like
mobile-home owners. They are generally highly m.obile and
(except for south Florida) quite young. They are also
largely white-headed fam.ilies with below-average educational
attainment (except for western Florida) . Their fam.ily
incom.es are bellow other renters', belov; mobile home owners',
and considerably below all owners' incomes. Because of the
small number of houc-^^holds involved, renter-occupied mobile
homes are not disaggregated below the five major area
groups .

It appears that m>obi.le home owners are drav/n from,
both tl'ie potential renter and cv/nor miarkets. .If incom.e
is the relevant constraJ.nt for most families, however,- it
might bo concluded that, on the basis of observed 1969
fam.ily incom.e, mobi.le-liome Ov/ners come primarily from the
potont.iaJ renter se<jment rather than from the potent.ial
hom.e ov.'ners . Also in terms of mobility, ago, and family

54

size, rnobile-home owners approximate the characteristics
of all renters. Mobile-home ov/nership is apparently
closely related to the life cycle. Young couples and
older people find them to be a satisfactory housing alter-
native, but r.'.iddle-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 vras over sixty while the comparable figure tor the
nation was 14.9 percent (Housing of Senior Citizens,
p. 43'7). Figure 2 focuses on the age distributions of
home Gv;nerE at the national and state levels. When one
analyzes hone ownership, he can expect to find certain
trends. Up to some age. it night be expected that the in-
cidence of homo ownership would be incz"easing. Very few
young people have the f iiiancial resources needed for pur-
chasing a ho;no . This trend is noted at the national and
state levels. The greatest percentage of United States
liomoowners is found in tr.e 4 5 to 54 cohort. After that age,
owiieuship fa] Is slightly, probably as a result of older

55

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people making housing adjustraents in order to get away
from the necessary m.aintenance and the natural decline
in the size of older-age cohorts as merribers 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
old.

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-hcm.e ow-ers are under thirty. In the middle-age
range, v.'here conventionale home ownership peaks, the inci-
dence of m.obile-home ownership is lowest. This is the
pattern for the nation. For Florida the same generaliza-
tions can be noted with certain miodif ications . Almost
40 percent of I'lorida's raobile-home owners are over
sixty-five; Thi.i housing choice is rrxtremely popular
among Florida's older population. Florida lias r/iore than
her shore of older citizens, and many of fnese people buy
a mobile home.

CHAPTER IV
MIilTHODOI.OGY AND MODELS

As a luinimuni, niicroeconomic theory suggests that
the demand for any good is a function of the good's price,
the price of ccr?.peting goods, the inconies of potential,
deinaaders, and existing tastes and preferences (vvhich are
usually assu-T.ed 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 determining the level of dem.and . The "non-economic"
variables './liich viJ. 1 be dealt with in this research are,
to some extent, quantifiable, and may be cla.ssified as
demographic in nature.

Before v'o proceed to deal v;ith the variables con-
sidered, a note on cross-section consumer dem.and studies
i.s :i,!i order. Fra.is and Houthakkcr (1955, p. 8) have dealt
v;i Lli the is5:;ue of cross-section versus time-series scudies
as follows:

In an analysis of family-budget data designed
to e::tablis'n lav;s dv-'Scribing uhe behavior of con-
sumers the assuTupcion has to be made that by ob-
serving consuiuers in different circumstances at the
same time, information m.ay be obtained which is
rolev.T'Jii; ill foi^ecasti "k^ the behav^Ior of any par-
ticuj ar consigner v.'iien ihis circum.s cances chan.qc
throagli time. To take a parti culiir example, it
may be assu'i;ed thai;: :i f there are fibservcit ions on

57

53

two households enjoying different incorces 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 ^v'henever a so-called
cross-sectional study is m.ade 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-
cumstances 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 v/ith. 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 changes or method of collection changes which often
are found in the use of time-series numbers are not found
v?ith cross-section data. Any observations whi.ch are not
com.parsble with the rest of the data may be deleted without
breaking the time series.

Mode 1 s _t o_ P^e^_ E stimated

Th.e point of tb.is research is to analyze the demand
for r.vobile homes. Total demand is tlie sum of demands
arising from the owner and ren,ter sides of the market.

59

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 mGbile-hom.e occupants made their decision to live
in a mobile home first, with other housing considerations
follovzing, or whether the budget constraint was considered
first with other choices follov/ing. 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 dem.and for mobile-hom.e housing. The first
model to be discussed (Model A) is a tenure-choice m.odel .
It yields insight into the question "v/hat typo of family
chooses to own its own home?" This model is then modified
to deal with mobile-home ownership. The second m.odel (Model
B) is used to escimiate demand for mobile-home housing
Gervice.'j; one;: the decision to own or rent a mobile home has
been made.

Tenvire choice is the biggest siiigle decision a
household mar.es v/her. shopping for housing. T'nis is the
decision to rent soiriL'one else's property or to purchase

60

one's own. Several approaches to exploring this choice
and how it is made have been attempted by a variety of
researchers. Struyk and Marsiiall 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
ovmersJiip. " It takes on a value of 1 if the household owns
(or is buyingl its ov/n home, and 0 otherwise. It is, in
effect, a duntray dependent variable. For example, consider
the following equation:

OWN --■= a + b(INCO:-ffi) + c(FAiMSIZE) + d (YOUNG) + e(OLD)

where

Otfjvj - tenure ch<.">ice; if the household lives in
i.ts own home, 0V7M - 1; otherv/ise, 0^'7^T - 0
INCOME - family income, measured in dollars; this
figure m.ay be observed annual income or
som.c .maasuro of permanent invroiae
FAMSIZE - a duTimy variable for family size; if the
nuitibor of persons in a family is five or

61

more FAt-lSIZE = 1; otherwise, FAMSIZE =0
YOUNG ^ a d^jiTimy 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; other\v'ise, 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
aqe 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
coof tici-ents are calculated, we may find that:

OWN - 0.3 + .004 (INCOME) + . 003 (FAMSIZE)
- ,20 (YOUNG) - .15 (OLD)

The dumuiy variables relating to age- have coefficients which
express the difference in probability of cwiiership from
thfc "referen-o group. " Since durmvy variabl<->s were estab-

62

li&hed 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-
bu^s. Likewise, the family whose head is over sixty-five
is 15 percent less likely to own its own hom.e than the
reference group.

Both Carliner's and Struyk and Marshall's studies
shov/ed some demographic factors to be significant predic-
tors of ownership probability. Additionally, Carliner's
vrork estimates that the probability of home ownership (for
his encire sample) goes up 1.62 percentage points for eacn
$1,000 increase in observed 1966 income. That is to say,
i fc" a family's income rises $5,000 the probability of that
family's ov/nj.ng its ov/n home goes up over eight percent.
Struyk and Marsha]. 1 found incorae elasticities ranging from
-0.276 for prim^ary individual households v;here the person's
observed 1969 income was over $20-000 to +1.90 for husband-
v;ife families with incomes under $4,000. So the amount
spent en hovising depends not only on one's income, but also
on m^■'.'■iial stal.us and other ueiiographic characteristics,

A sid-.ilar 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:

63

where

TENURE = bo + bj (F/J-1IMC0M) -r b - {D?-L\RRIED) +
b3(DUMLE25) + b, (DUMGE65) +
bg (DFEMHEAD) + b- (DFMSZLE2)
b7(DFMSZGE5) + bg(DHDNONWH) +
bgCDEDLTHS) 4- b]3(DEDSC) +
bi J (DEDCG) + bj 2 (DUMIGRAN) -I-
bj 3 (DUMARI4Y) + b, ^ (DSTUDENT)

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 dunimy variable for age; 0 if head is

twerity-five or under, 1 if head is over

twenty- five
DUMGE65 - a dumm.y for age; 0 if head is under sixty-
five, 1 if head is sixty- five or over
DFEMHEAD = 3 dummy for sex of household head; 0 if

male, 1 if female
DFMSZLE2 - a dunimy for family size; 0 if m.ore than

two people, ] if two or one
DFM5ZGE5 = a dumjiiy for family size; 0 if less than

five people, 1 if five or more
DHDNONWii - a dummy for race of head; 0 if wliite, 1

if non- white

54

DEDLTHS = a dummy for educa tonal attainment of

head; 0 if high-school graduate, 1 if not
a high-school graduate
DED3C - 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-
lege

DUMIGRAN = a dummy for mobilJty; 0 if head lived in
Florida five years ago, 1 if head moved
into Florida between 1965 and 1970
DU^LA.KI'1Y =^ a dummy for armed services head; 0 if
civilian, 1 if head is member of armed
forces

DSTUDENT - a daiTuTiy for current enrollment status; 0
if head is not a student, 1 if head is
enrolled in school
lENURfJ -- a dichotoraous variiible which takes on a
value of 0 if the dv/eliing is not ov;ned
by the family occupying it and takes on a
value of 1 if the housin-g unit is owner-
occupied for the ALL OVvT-jERoHi P model; for
the MOBILE-KOME OVn^^hlRSHIP version :i t takes

65

on a value of 1 if the family owns and
lives in its own rrobile 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 ownersiiip
equal to 1 if the household owns its dv/elling, 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 19 69 observed family incom.e,
in $100 units. The dummy independent variables included
one for marital status, tvro 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 duiiuuy variable
represents the (percentage) deviation from the reference
(unspecified) group for the specified group. For examtple,
th.e summary of the ALL OWNERSh'IP regression in Table 15
(Cii.-ipter V, page 100) shov/s that there v;ere three educational
groups specif ied--less than high-school graduate, some
college, and college graduate. This group raxght be thought
of as the "base group." The coefficient for each of the
other groups (-.022, -i-.OOl, -.029, respectively) tl-ierefore
represents tl\e deviation frora the base group for the group

66

in question. Families whose head i^ not a high-school
graduate owned their own home 2.2 percent less often than
f ami. lies whose head was a high-school graduate.

The model was estiraated 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 m explaining mobile home ownership. Results of these
estimations are discussed in Chapter V.

Mode 1 3

Once the decision to live in a miobile home has been
made, the amount to be spent on such housing has to be
dotermiriod. Also, to buy or to rent becomes an issue to be
decided. Model ;j i.-j a more conventional regression model
which is ebtiniated using the OLS technique. Use of this
procedure ;i s widely observed and it has proved to be a
statistically pcv-erful tool. The model is used to estimate
expenditures for ov;ner-occupo ed mobile-home services and
then Le-et>tiiiu\tod for expoviditures on rep ter-occu[)ied mobile
homes .

67

Ov/ner-occupi'?d mobile horaes

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 wliich 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 num±)er 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
v.'hich satisfies the demand for housing services is the
basis upon vv/hich the demand for mobile horaes is founded.
This util.ity 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 time of purchase or over a period of years. Because
a nioblie Jiorae provides housing services as long as it is
occupied, hc-.'V::ver, it "was felt that housing expenditure
ovt.L tills period of time was the best approximation of
actual demand for these services. Therefore the dependent
variabj i? is dollars of expenditure for mobile hom.e housing

63

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
collecced for conventional housing units, but has not
been collected for mobile homes. It was therefore neces-
sary to estim.ate each mobile home's purchase price in
order to derive expenditure. This v/ould be the dollar amount
to be paid by tl;a nev; 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, v/hich v/ere
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 Iicusing
study. For example, this mobile home annual expense includes
paym.ent for appliances and furniture since virtually all
units come equipped v/ith these iteffiS, but does not include
utility paymonts . Other studies of housing expense in which
conventional structures were analyzed have dealt differently
with these r.iatters. Sometimies the researcher will figure
expense in."]u^;ive of these items ana in other cases they are
omitted. M.uoh the same var.iance is found with respect to
ucLlity payments, which are oxcluded in this study.

69

1. Number of rooms (NROOM)

2. Number of baths (N3ATH)

3. Presence of air conditioning (AIRCON)

4. Presence of piped hot water (HOTWATER)

5. Presence of fuJ.l plumbing (PLUMBING)

6. Type of sewerage (SEWAGE)

7. Source of water (V7ATEP-S0U)

8. Type of heating (HEATING)

9. Year in which unit was built (YRBILT)
10. Year in which family bought mobile

home (YPJWD)
The vfilue 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 item.s 9 and 10 relate
to a unit's age when it was purchased. Figure 3 i.s a sche-
matic depicting how the actual items have been used.

Determining the va.lue of a mobile hom.e is a fairly
straight- forvrard, comimGnplace procedure in some instances,
r'or a new unit the value is defined as the market price.
Also, for a used unit, its value can be ascertained as it
passes tnrough. the m.arket. Th.e problem in valuation of the
units involved in this present study, hov;ever, is that they
are not passing through a market at. the time of Census
euvimeration in 1970. And the Census Bureau did not ask for
tiie owner's estimate of the value of the structure. This

70

^

M

t-i

fc)

C/5

1 o

•z.

u

^

w

(U

en

c

&i
w

en
C

-H
Cfl

O

a
g

ro O

K

S at

H XX

u, o

O
C

o

■H

(tJ
c

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 v/hich is available from the Census, a
valuation model for mobile homes can bo 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 rna unicomp Direcrory of Used Mobile Homes. In
these publications mobile homes are broken down by manufac-
turer, model, year built, size, and physical layout.

There arc 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 niay use a rule such as: ten percent
loss of value the first year and five percent per year
thereafter. This v.'ould result in loss of one-half of original
value after nii.e years' use. The rate of depreciation would
bo slower a;;t.cr that ooiat. \«'iiiIo such an estimating tech-

72

nique could be used, it v/as felt that actual resale ex-
perience would provide better data ,

Data collected from a 1974 copy of the Unicomp
Director}/' revealed the depreciation pattern reflected in
Table 9, Depreciation actually computed from Unicomp data
was derived only up to nine years of ago. Beyond that age
the rate of depreciation is based on the author's experience
and discussions with people working in the mobile-home
industry.

TABLE 9
Percent Depreciation by Age of Unit

(Of New Price) % of Original

Age % Value Loss Value Retained

1 20 80

2 6 74

3 6 68

4 6 62

5 5 57

6 5 52

7 4 4 8

8 . 4 44

9 4 40

10 4 36

11 3 33

12 3 30

13 3 27

14 2 25

15 2 23
IG 2 21
17 2 19
i& 1 18
19 1 17

1 16

9."

SOUKCP: Unicomp Pi rectorv and discussions v:ith industry
pei;:".onnel ,

73

Table 10 shows the average value of new mobile homes
produced from 1950 through 1970 and indexes average selling
price cif a new unit for each year. The index is computed
from industry data v/hich are published in Flash Facts. It
is simply a way of expressing a nevv' 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 cam.s onto the market and 1963 was the first
full year for the twelve-- foot-wide unit.

Tihe 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
nuaiber of bathrooms. This discrepancy is offset by the
fact thrit almost all mobilo-honie rooms are vei"y nearly the
same size. Second and third bedrooms are usually a foot
or two smaller th:-in average, and living rooms are quite
often several feet longer than average. The values listed
in Tabic 11, based on marginal cost of a room, vv'ere derived
for ].970. Tlie process used basically involved translating

74

T/^BLE 10
Average Value of New Mobile Hor.es by Year Buili

Year Average Value Index

1370 6110 1,000

1369 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

I960 4995 .818

1959 4996 .818

1958 5000 .318

1957 4996 .818

1956 5003 .819

1955 4129 .576

1954 4276 .700

1953 4187 .685

1952 3855 ,631

1951 3685 .603

1950 3423 .560

SOURCE: FlasVj Facts: Pocket Reference to the Mobile Home
Industry, ^'1H^!A, June 19 74.

number of rcon'.s p].us number of baths data into a dollar value
An intermediate step in the process involves matching up the
nuiTibor of roons with conventional industry sizing (number
of feet J.ong) , For instance, a unit with four rooms and one
bath is probably between 52 and 53 feet long, while a unit
'with five roouis and one and a half baths is probably 64 or
65 feet long.

Then are no inobiie homes v/.i th only one room, being
produced nov;, They arc- iiicluded hero for tl-e purpose of

75

TABLE 11
Value of New 19 70 Mobile Homes

Number of Rooms Number of Baths Value

1 1 3,200

2 1 4,200

3 1 5,400

4 1 6,200

4 1^5 7,000

5 1 8,100
5 1% 8,900

5 2 9,400

6 1 10,700
6 Ih 11,500

6 2 12,000

7 1 12,700
7 1% 13,500

7 2 14,000

8 1 12,900
8 1^5 13,700

8 2 14,200

9 1 14,300
9 1^ 15,100
9 2 15,500

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 batli. As nev'irly as possible, this technique is
designed to coincide V7ith the industry's conventions for
si v'.ing .

Other structural characteristics influence a unit's
valup. Table .12 reflects how these factors are taken, into
account in the valuation model presented here.

76

TABLE 12
Characterir3tic Components in Mobile Home Valuation Model

Ci-iaracteristic Adjustment to Value

1. J. room air conditioner +2 00

2. 2 or more room air conditioners +4 00

3. Central air conditioning +600

4. Room heaters with flue -100

5. Room heaters without flue -300

6. Portable room heaters -4 00

7. No heating equipment -400
S. Lacks piped hot v;ater -200
9. No plun-Lbing facilities -300

10. No piped water -300

11. Water from individual well -100

12. Water from other nonpublic

source -200

13. Septic tank sewerage -100

14. Other nonpublic means of

sevrerage disposal -300

Item.s i 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 wacer and sewerage
v.-hich ectuaily are not part of the unit, but which are
proxies reflecting the type of environm.ent in which the unit
is placed. These item.s hopefully parallel quality differ-
ences in units. For example, it is in the "adult mobile
heme" comjTiuni ties that oi^e is most likely to find custom-
built unirs. It is also in these parks that one is mtost
1;. }.ely to find public or municipal water and sewerage sys-

77

terns. On the other hand, a unit placed on a rural lot
whore water is from an individual v;ell 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 those 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 VAI;UE70. VAI;UE70 is what every mobile home vv'ould
sell for (based on its structural characteristics) if it
vra.s built and bought in 1970. This step standardizes units
d.n teriTis of 1970 dollars. VALUE70 is then indexed for the
year in v/iiich the unit was actually built. Table 10 con-
fjLructed from industry data, is used for this purpose. The
unit is then depreciated (in accordance with industry expe-
ri.ence as depicted in Table 9) in accordance v;ith its age
v;Vien it was purchased. The product of VALUE 7 0 and INDEX
and DETR^ICIATION yields COST. This is the calculated
market value of the mobile home when it was purchased by
the housel-iold under observation. For example, a four- room,
one- bath uniL connected to a water and a sewerage system
would assume a VAT..UE70 value of $6,200. If this unit had
been built in 19G6 INDEX v/ou]d assume a value of .933.
'ihereiorc, the coraputed value of th>e unit v.'hen it was con-
structed is (yALl)E70) X (INDEX) = (?6,200) x (.933) = $5,78 5,

78

This is the estimated valvie of this new unit. If it were
bought new then it would not be depreciated to find its
purchase price. If, however, this 1.966 mobile hom.e had
been purchased by its occupants in 196 3, 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 v/hen it was purchased. This factor is .74
for a two-year-old unit. A.pplying .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
resj.dent households had lived in their mobile homes v;as
5.3 years. The same survey (p. 31) found that the mean
length of residency at the sarae address for its sample of
mobile-home households was 3.7 years. So COST divided by
TIMi':: yields annual housing EXPENSiC. Evon though a mobile
liome depreciates after it is purchased,, the financial obli-

79

qation is fixad 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
ov/ner 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 suprisingly high percentage (85)
of families v/ho purchased their ov/n mobile home in Florida
have been found to owe nothing on the unit (Cubberly, 19 71,
p. 29) , It is for these reasons and the nature of the data
Lnac EXPEisoE _ls aeiined as jUi;t explained. "Annual housing
expense for mobile home" is, therefore, the dependent vari-
ab.le ill the model to be escimated.

I n d e p e i^. d e n t Va r i ab 1 e s . The relationship of primary
iniportance i:i this research is that betv;een expenditures
for mobile -houiG housing and family income „ This relation-
shiip is m.oasured by the concept of income elasticity of
demand v/hich is defined as the relative change in expendi-
ture ooir.pared to the relative change in income. For example,
If a family's incom.o increases 2 0 percent and its expendi-
ture on steak increases 25 percent, the family's income
elasticity of demand for steak is .25/. 20, or 1.25. This
general relationshlD has been examined extensively in. the

80

housing literature (see Chapter II) for conventional hous-
in9'~-both renter and ov/ner-occupied, but has not been
explored with mobile home housing. Because of the interest
in this relationship, definition of income is of prime im-
portance.

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 durabJ.e goods, are made on the basis of a
concept of income which is much broader than one year's re-
ceipts. Milton Friedman (195 7) is the person usually given
credit for breaking ground in the area of a theoretical basii
for "permanent income." He concluded that consuming units
tend to have a three-year period in mind when evaluating
their income. It s-aems almost certain that housing deci-
sions are based on an even longer time horizon.

Whiit concept of inco'.ie is appropriate for use with
mobile- home demand? Several key factors com^e to mand. When
housir^g payments are known in advance and must be met
re9\il3rlv, a cash flow concept for housing service be;comes
the relevant consideration. Liquidation of non-liquid
assets, v."hilc possible, is not the norni for meeting sucli a
roguI:;'r fLnancial obligation. It is possiljle that such ac-

81

tion jviay 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 dov/n 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 m^uch 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 co 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 perm^anent incorae (which
is, itself, not directly observable) these factors should
be used as inputs. Three variants of permanent income,
each emiiodying different assum.ptions, were calculated and
t;?sted, along with observed 1969 income, for their appro-
priateness and predictive power. Those variants, yPEPJ"-IFAM,
F^'.1NTR, and INCF?ul, are estimates of permanent income, eacxh.
b-:seu on slight] y differeiit assumptions.

The ypERMFAM concept is "pure permanenl income" as
develoi-'Od in this study. It takes no account of 196 9 ex-
pec ienco and is an incoiae measure based solely on each
porr:on'3 occupational group, attribuLes, and human capital.

82

Deve

lopr

ient

of

the

ecirnings

model

used

to

ascertain perma-

nent

income

fo

r each

person

in the

mobi

le-

-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 19 6 9 income v/ere 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) = b^ + bj (education^ ) + b_, (experience) +
b 3 (experience') + b^ (sexdummy) +
bj (raceduHLmy) + b.(Spanish--
Am>e r i c an Q airj7.ry )

where :

education = the highest year of school
atteiided
Gxperienco =■ (1969 age) -- (6) - (education)
scxdumriy ^ 1 if i;enale, 0 otherwise
racedur.u/iy -= 1 if nonwhit^', 0 otherv;ise
Spanish-AmericanduniR^y = 1 if Spani.sh--Amora.can, 0 oth-
erwise

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 terras
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 R^) in some instances. For purposes of this work, ex-
perience was defined as age minus years of schooling mii>us
six (age at which most people start school) . Additionally,
precautions were taken so that persons v;ith little or no
schooling v/ere not allowed to enter the labor force before
age sixteen in the model. Tiie duru.my variables take account
of racial and sex differences. The variable coefficients,
v.-ith F statistics in parentheses, are presented in Table
13.

To demonstrate trie operation of the model and the
use of tb.e calculated coefficients, examining a hypothcti-

84

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in

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85

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86

cai individual might be useful. Consider an individual with
the follcwing 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-
dj.V-idual 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(16') + 0.02660(14)
- 0.00055(14^) - 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 v.'ould have been lower becau.se the dum^my variable taking
into account sex would have taken on a value of 1 (rather
tlian 0 for a male) and the log (earnings) value would be re-
duced by C. 33582. Earnings can be generated for the same
person over a period of years to get total income over that
ti;ue period.

This model can be used to generate incom.es vvh.ich can
be used for construction of a permanent income concept.
In essence tl;is modoi produces average ir.comes, with un-
Uoually high ap.d unusually lev..- ori':>s canceling one another

out to some extent. Variations such as these vaay well be '
attributable to transitory factors--the effects of which
permanent income seeks to minindze.

Use of these models allows m.oveir.ent of an individual
"through time." Permanent income, for pux-poses 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 v/hich bought
its niobile home in 195 3 would have lived in it for seven
years in 197Q. Consequently, the period of income generii-
tion relevant to the family in question is from 1963 to
197C. The model can be used, for this hypothetical family,
to generate incom.es for each adult person in the faraily
tor each year in the period. These generated annual in-
comes are then totaled and divided by the number of years
involved to get a perm.anent income m-easure for the time
period during which the family was consuming iiio}5ile-home
housing ser^'i ces . The permanent incomes for the household's
head and for the v.'ife (if she exists) are then adaed to get
the faiaily's permanent income.

yPLKMFAM is one variant of permanent incom.e , It
completely ignores an individual's own earnings experience
in favor of what that person's peers (in terms of occupa-
tion., education, experieaco, race, and sex) have expcritjnced.
This is in line witii the tfieoretical construct of permanent

88

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 ai\ individual's
income was not a randomly, generated figi^re, 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) MFAiM (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 Iiad
estim.ated he v/ould 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 v/ife and the resulting
figures are s-aitm>ed to get FMINTR. In this case, perhaps
the h.usband is a better engineer than his peers, and hence
the 19G9 income he earned is based on "permanent" factors.
Likevv'Isc, perhaps the wife worked only half-time in 1969
and this is a "permanent" eraployiutMit posture for her.
FMINTR assuitios that the observ/ed 1969 experience for both
husband ai-.d v/ife is not the result of temporary factors.

89

This variant of permanent income may be best where this
assumption holds. Otherwise, it would be a poor m.easure
of permanent income. The next concept introduced embodies
a different assumption.

INCFAM is another variant of permanent income. It
suiris the husband's generated permanent incom.e and the wife's
observed 196 9 income. The rationale behind this concept
is that the norm for the husband is full-time employment,
but what v,'as observed in 19 69 for the wife is her norm. If
her 1969 income was low beicause she worked only half-time,
perhaps half-time was her regular work routine. So INCFAM
is a permanent income-observed incom.e hybrid.

The FAMINCOM variant of income is simply cbsarved
1969 fam.ily income. It is not perm.anent 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 v.ariable. As mentioned in the first section
of this chapt:er, economic theory suggests that the price
of a good and the prices of other "competing" goods be
included in a UiOdel of demand. In an effort to do go in
the present rnodel , a price index was constructed. This
PR3.CE variable for mobile homes is actually a relative
price measure. It is defined as the average cost of a
miObile home divi.ded by the average cost of construction of
a conventional .'■■.ingle-family iiovise foz- the year in which the

90

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
en both the costs of ov/nership 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 m^ay exert
some upward pull on the price of older units, an o].der unit
could be depreciating at a faster rate than new construc-
tion costs are rising. So ultimately the price of a unique
housitxg unit may be a unique price. Unfortunately liO price
variable v/hich includes rental housing cost could be found
or coi.strucLed. The probleai is a lack of data. vmi le con-
struction costs do vary from place to place, materials
are transportable, and labor costs do not vary greatly since
most carpeiUvrs are unionised. But construction costs,
even if esLimable, are only one component of the total cost
of securing hc^oslng. Supply and demand lo.rces are also of

91

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 frori 19 5 0 through 1970.

Other independent vari_ables. 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.

DUMGEo5 and DUMLE2 5 are two age-related dummy vari-
ables utilized as in.dependent variables because of the
importance of the life cycle in housing deraand. IVo dumray
variables are used because it was felt that the bimodal
age disi- ribution found by other researchers might be the
case in Florida also.

Th^^ D'JMRACE dumiay variable takes on a value of 1 if
the household head is nonv/hite, but is 0 otherwise. The

92

TABLE 14
Relative Prices of Conventional and Mobile Homes

Avg. Cost of Avg. Cost of Price
Year Mobile Home ($) Conventional House ($) Ratio

8,675 .3946

9,300 .3962

9,475 .4069

9,950 .4208

10,625 .4204

11,350 .3633

12,225 .4092

13,025 .3836

12,950 .3861

13,425 .3721

13,725 .3639

13,825 .4050

14,325 .3911

14,875 .3842

15,425 .3630

16,150 .3467

16,750 .3403

17,325 .3290

18,525 .3239

19,045 .3184

50URCE: Mobile Home Manufacturer's Association and Statisti-
cal /'iDstract of the U.S., various years.

purpose of its existence is to allov/ examination of whether
nonwhites consume less mobile-hom.e housing services (i.e.,
demajid less) than v/hites do.

DUMJiDSEX 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 deman.d the
same amount of m.obilo-home housing services as do male-
h c a d e d ho u s c; hoi d s .

1950

3,423

1951

3,685

1952

3,355

19 5 3

4,187

1954

4,276

1955

4,129

1956

5,003

1957

4,998

1958

5,000

1959

4,996

1960

4,995

1961

5,599

1962

5,602

1963

5,715

1964

5,500

19 65

5,600

196 6

5,700

196 7

5,700

1963

6,000

1969-

1970(1)

6,062

93

DUMIGRAN is another dummy variable. Its purpose
is to aid in determining whether recent movers to Florida
demand moze or less housing than do more permanent resi-
dents .

DUMEDUCA is a d'ommy variable which takes on a value
of one only if the household head has less than a high-
school education. Its presence is to allow examination of
the hypothesis that lesser-educated people live in mobile
homes.

Renter-occupied mobile homes

Model B was also estimated for those families in
the Florida Public Use Sample who were renting a mobile
home in 1970. The model is unchanged except that housing
expense is directly available from the data base. For
purposes here rental expense is defined as annual contract
rent. It is different from ov/ner's expense in that some
part of the rent payment goes for site value whereas mobile-
home owner's expense excludes site value payments. To ad-
just for this difference,- Model B was estimated twice for
moh>ile home renters: the first time using actual contract
rent as housing expense and th.o second time using three-
fourths of actual contract rent as housing expense. This
adjustment is an attempt to separate payment for housing
space from payment for living ciivironrnen t.

94

Model Specification

"The mathematical form of the demand equation cannot
be specified a priori in the present state of the art"
(Houthakker and Taylor, 1970, p. 8). Some of the most often-
used forms are:

1. Linear (Q = bg + bj Y)

2. Semi-logarithmic (Q - bo + b^ log Y)

3. Double-logarithmic (log Q = bg + bi log Y)

4. Inverse semi-logarithm.ic (log Q = b(j + bj Y)

Each of these specifications can be legitimately estimated.
It cannot be stated which one will predict better than the
others. The only way in wliich it can be stated with certain-
ty that one specification is superior to the others is to
have tried all the alternatives and to have chosen the one
that performs best according to the decision rules for m.ak-
ing one's choice.

There may be reasons for preferring one form, to anoth-
er other than predictive power, hov.'ever. The need to stan-
dardize the variance for all cases of the predicted depen-
dent variable, as mentioned earlier, is one such reason .
VJhat is knov.'n about the probability distribution of the
dependent variable valu:^s may be sufficient cause for making
a (logarithmic) transformation. Tne variance of the depen-

95

dent variable can frequently be stabilized by performing

such a transformation on the dependent variable. A loga-

rithinic transformation (base 10 or base e) is handy for

use with posirive-valued variables which may cover a wide

range .

In the physical sciences, there are often sound
theoretical reasons for postulating a specific
model. . . . Because social behavior is less
predictable than physical phenomena, mathemati-
cal models are a less accurate approximation of
reality in the social sciences, hence statisti-
cal methods are more necessary. ... A mathe-
matical model provides a useful structure with
v/hich an economist may perhaps better understand
and predict economic phenomena; it can hardly
be regarded as ultimate truth. In fact, in
certain instances a mathematical model may be
used even when it is known not to be exactly
right, if it is "good enough." At our present
level of knowledge, it is often better to use a
simple, more tractable model rather than a com-
plicated one — even though the latter provides
a somev/hat better fit. This is especially true
if there are no prior grounds for expecting that
the complicated model better describes the real
world (VJonnacott and V/onnacott, 1970, pp. 99-
101) .

The double log form, is used in Model B (for housing

expense and income) for several reasons. P'irst, such

s}.iecification assumes that housing-income relationships tend

to be linear v/hen these tv.'o variables are expressed in log

form. Second, this formulation yields income coefficients

which arc the income elasticity of housing expense. This

outcome retulLs because expenditure chancres and income

changes are expressed in relative, rather than absolute

tenr-s. Wh-n the functional foriu is set up in this manner

96

the relationship between housing and income is most readily
observable .

The two models just outlined, with the pool of in-
dependent variables enumerated for each, were estimated
using OLS techniques. Model A looks at the tenure decision-
wehther a family chooses to own or rent. It then is used
to get some indication of the relevant factors influencing
a decision to own a mobile home. By default, the decision
not to own usually means renting. Results of this model
should be anaj.yzed with this in mind. Model B is used for
those families who had, in 1970. chosen to live in a mobile
home. First the model is estimated for owners and then
it is estim.ated for renters. Results of both models are
presented in Chapter V.

CHAPTER V
EMPIRICAL FINDINGS

The two models to be estimated should help answer
the questions (1) what types of families are inclined
toward mobile-home ownership and (2) how does mobile-home
usage vary as incomes change? We have learned a good deal
about patterns of housing consumption for conventional
housing over the years, but the m.obile home is a product
of the second half of the twentieth century. Even though
this "new" form of housing has been gaining acceptance at
a fairly rapid peice, little scholarly work on the econoraics
of mobile-home housing has been dore. Many people still
think of the average mobile-hom.e user as a gypsy who travels
about v.'ith his "trailer" behind him. This concept is far
from, realistic for a housing alternative which m>ay be as
large as 1,700 square feet of floor space. Indeed, travel
trailers, as popular as they have become for recreational
purposes, do not qualify as m.obile homes by industry stand-
ards. Mobile hornes are permanent housiny and are an in-
creasing proporLior: of our permanent housing stock.

Two different raodels have been e.^tim.ated for the
purpose of gaining insight into the use of m.obile-home
liousing and the economics of this housing alternative. Each

97

98

of those models can stand on its ov/n , but when the results
are looked at together, a better understanding of how they
support one another is possible.

Model Estimation: Model A

The tenure model described in the previous chapter
was first estim.ated for all households in Florida which
had incom.es in 1969. This "all household" version includes
not only mobile-home owners and renters, but all households
in the Public Use Sample. The dependent variable, called
TENURE, takes on a value of 1 when the family owns its
dwelling unit and remains 0 when it is not owned. Alm.ost
wichuut exctption the alternative to owning is renting.
The list and definitions of the independent variables used
are presented in Chapter IV. The functional form^ is:

TENURE = bo + bi(FAMINCOH) + bj (Df-mRRIED) +
b 3 ( D UMI,E 25) + b ^ ( DUMGE 65) +
b 5 ( DFEMIEAD ) + b ^ ( DFMS Z LE 2 ) -f-
b .; (DFMS Z GE 5 ) -i- b , ( DHDKONWH ) +
b9(DEDLT!IS) + b,o(DEDSC) +
bj 1 (DEDCG) + b, , (DUMTGRAN) +
b, 3 (DUM^PuMY) + bj ^ (DSTUDENT)

tVhen all fourteen indepeiidc^nt variables were forced
into the model, only two wore not significant at the 5 per-

99

cent level. The results are presented in tabular form in
Table 15. Eleven of the independent variables are signifi-
cant at the one-percent level (as indicated by the F statis-
tics) and the equation's F statistic is over 300. R^ for
the equation is .16.

The relationships, as indicated by the signs of the
coefficients, are in most instances as expected for owner-
ship. The exceptions, to this writer's way of thinking,
include female-headed families (5.8 percent more likely to
own), college-graduate-headed families (2,9 percent less
likely to own), and recent movers to Florida (22.2 percent
less likely to own) . This last one mentioned is not so
surprising in its sign, but in the absolute size of the co-
efficient and its signi.f icance level.

Careful analysis of the coefficients yields some
interesting predictions. For instance, using the informa-
tion gleaned from this model, what are the characteristics
of the family m^ost likely to own its ov;n home? Marital
status of the household head would be married and the head
would therefore be a male. If the head is 65 or over the
likelihood of ownership j.s maximized. Interm.ediate family
size (either three or four persons) is conducive to owner-
ship; a] so, if the household head has some college educa-
tion, the probability of ownership is increased. And since
the income coefficient is positive, the higher the family

100

TABLE 15
Tenure Model Estimation

All Ownership M. H. Ownership

DMARRIED -0.20577 387.02** 0.01682 9.21**

DUMLE25 -0.31690 750.19** 0.01596 7.20**

DUMGE65 0.05109 45.17** 0.02079 26.98**

DFEMHEAD 0.05842 27.43** -0.00991 2.83

DFMSZLE2 -0.06074 66.81** 0.03295 71.62**

DFMSZGE5 -0.00118 0.02 -0.01272 7.13**

DHDNONWH -0.18708 392.51** -0.05214 116.71**

DEDLTHS -0.02206 9.36** 0.01108 8.51**

DEDSC 0.00119 0.02 -0.01595 9.78**

DEDCG -0.02883 6.54* -0.02195 13.55**

FAI-IINCOM 0.04300 94.14** -0.00900 71.33**

DUMIGRAN -0.22177 1065.83** 0.01632 20.48**

DUMAPJ4Y -0.15140 54.89^* 0.02757 6.45*

DSTUDENT -0.10601 35.74** 0.01678 3.66

CONSTi^i-IT 0.61229 0.04374

Equation

R^ .1616 308.37** .0317 58.11**

Critical F: 5% =3.84 1%=6.64

*Signxficant at 5%
"*Siqnif icant at 1%

lo;

income, the more likely is ownership. If the family's
1969 income had been $15,000 for instance, what would the
model predict the possibility of ownership to be?

CONSTANT + DMARRIED + DUMGE65 + (Median Size Family)

+ DEDSC + FMIINCOM =
.61229 + ,20557(1) + .05109(1) + 0.0

+ .00119(1) + (.00043) (150) = .93464

The model estimates the probability of home ovmership to be
a 9 3.46 percent chance of owning its own home for this
family. The choice of characteristics is such that, for
every categorical variable, that alternative for increasing
Ov.'nership probability vras chosen. If this same family's
income had been $25,000 instead of $15,000, the probability
of ownership would increase from 0.93464 to 0.97764, At
the other extreme, a family highly unlikely to own its ov/n
liome, according to this model, exhibits the following char-
acteristics: a single male under twenty- five years of age
who is a student and has moved to Florida since 1965. The
lower his income, the less likely he is to ov/n. This
person, with an income of $5,000, would have a probability
of ownership of -0.01090. Such a negative probability is
logically impossible since, by definition, probability must
range only between 0 and 1. The model does not operate
asymptotically so that, given a set of extreme character-

102

istics and a very low or high inccrr.e, the model will gener-
ate a solution below zero or above one.

As one might guess, there are some multicollinearity
problems presented by using several variables which catch
the same people. Correlation coefficients for the indepen-
dent variables are presented in Table 16. The most severe
confounding problems arise between the dummy variables for
"marital status" and "head of household being female." This
arises because, by census definition, a couple is headed by
the male. The variable for "some college" also picks up
"college graduate." This model possibly could, from a sta-
tistical point of view, be improved slightly by dropping out
several of the c;onfounding variables. The cost of doing so
with almost 25,000 housenold observations would be high and,
other than the relatively high correlation just noted, no
other prcblem seems to have been caused by leaving in all
categorical variables for the sake of completeness. Further
manipulation of ths model was, therefore, not undertaken
at this time.

Also suirnaarized in Table 15 are the results of the
tenure regression when the dependent variable took on a
value of 1 only when the household owned its own mobile
home. Again/ eleven of the independent variables proved
to be significant at the 5 percent ].evel--though not the
same eleveri as in t\\e first version of the model. Those
variables signj. Cicant at the one percent level are denoted

103

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by two asterisks in Table 15. Ten of the variables proved
to be significant at the 5-percent level in both the all-
household and the mobile-home versions of the model. These
are denoted by a single asterisk. Of these ten, however,
the relationship, as evidenced by the sign of the coeffi-
cient, was different for six of the ten which were signifi-
cant in both versions. Those significant variables which
had the same sign in both the all-ownership and the mobile-
home ownership versions of the tenure model are the dummy
variables for marital status, families headed by a person
over sixty- five, families headed by a nonv/hite person and
families whose head had graduated from college. These four
variables were statistically significant at least at the
five - percent level and in both versions of the model, in-
fluenced the probability of ownership in the same direction--
positively for being married and having a head over sixty-
five, and negatively for having a nonwhite family head
and having a college-graduate head. The other six variables
v/hich proved to be significant in both versions experienced
a sign change between the versions.

Five variables influenced home ownership in a nega-
tive v;ay, but mobile-home ov/nersliip positively. These are
DUiMLE25, DFMSZ.LE2, DEDLTKS , DUMIGR\N, and DUM/vRiviY. Having
a young family riead, a small family, a family head who did
not finish Iiigh school, or a mobile family (as exhibited by

106

moving to Florida since 1965 or being a menber of the armed
forces) discourages conventional home ownership but is
conducive to mobile-home ownership.

Only observed family income proved significant in
both versions, positively influencing home ov/nership but
negatively influencing m.obile-horae ownership. That is,
conventional home ownership probability varies directly
with family incomie but mobile-home ownership probability
varies inversely with family income. Generally, then, the
sam.e variables were statistically significant in the oppo-
site direction.

The mobile-home ownership version of the m.odel was
also significant at the one-percent level , but had a smaller
F statistic than the all-ownership version. R^ for the
mobile-home version was only .03, however.

Greater multicollinearity probleins appeared with
the mobile-home ownership version. Table 17 shows the
linear correlation coefficients for the independent variables
in this m>odol. The most severe problems arise with respect
to FAMIN'COM and DSTUDENT. As one might expect, students
are generally young and have low incomes. Family incom.e
is strongly related to student status, the family head's
race, and the head bei'.ig young. Of those findings v.'hich are
significant, several stand out. A family Iieaded by a person
twenty- five years of age or less is 32 percent less likely
to own its owr, home than a family headed by a person over

107

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109

twenty- five, but is 2 percent raore likely to own a mobile
horae. A family recently moved to Florida is 22 percent less
likely to ov;n its heme than a family which has been here
more than five years. But recent movers are two percent
m.ore likely to own a mobile home than families who have
lived here more than five years. Finally, the relationship
betV7een ownership and income is different for all o\'mers
and mobile-home owners. The relationship is positive in
the all-ownership case, but negative for mobile-home owner-
ship. This fact indicates that mobile-home housing is an
inferior good--since many families who can afford to move
out of mobile homes do.

Model Estimation: Mode l B

Model B, as described in Chapter IV, estimates the
demand for mobile-homie housing by regressing housing ex-
pense against a number of independent variables. Estimation
of this model is done v^fith data which were collected from
familJ.es v/hich had previously made the clioice to live in a
mobile home and were doing so in 1970. Model B is estim.ated
using data for mobile-home owners and then reestimated for
raobile-hom.e renters. The model, in its i.;ost complete form,
is

].og (EXPENSE) = bo i b, log (INCOME) + b. (PRICE) -i-
b , ( DUMLE 2 5 ) + b „ ( DUMGE 6 5) +

110

bs (DUyiEDUCA) + bg (NPERSON) +
b^lDUMIGRAN) + b„(DUMRACE) +
bg (DUMHDSEX)

It would be very surprising, however, if all of these vari-
ables proved to be significant when the model was estimated.
So the approach taken was not to try to force all of the
independent variables into any one equation estimated. The
demand equation for owners was estimated for each county
group shown in Figure 4. The model for renters was run only
for the state. Note that area 3103 contains not only nine
Florida counties, but also seven counties in Georgia. Area
3501 contains four Florida counties and one from Alabama.

Model B Estimation for Mobile-Homo Owners

The technique used for estimation is OLS regression.
The most frequently used procedui-e for b'uilaing a regression
model is stepwise regression. This step'.vise procedure may
be "forward" or "backward." In the forward stepwise regres-
sion method, the model is expanded in discrete chunks---one
or more independent variables being added at each step.
The experimenter must sp'~cify the order in Vv^hich ve.riables
are entered. UsualJy this is done on the basis of expected
significance. The "best" independent variable is entered
first and additiona] ones are entered in the order of, say.

Ill

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31

'it %

V '/■'/-<

FIGURE 4
Public Use Sa:r.plo Iwe.as
and Suh'3.rcas ror Fieri. da

112

decreasing F statistics. Once a variable is entered, it
is "locked in" for subsequent estimations.

Besides the statistical problems of using this
technique (Wonnacott and Wonnacott, 1970, pp. 309-311) there
is no reason to believe that the "best" model of any in-
dependent variable size other than one will ever result
from this stepwise technique. The interrelationships of
the variables are sometimes such that the best model v/ill
not be obtained. Stepwise regression procedures may or may
not yield the "best" model. This generalization applies
also to the "backward" technique in which one starts with
all independent variables present and drops them out in
steps of one or more. There is no assurance of obtaining
the "best" model with either the forward or backward step-
wise technique of estimation.

For these reasons, stepwise regression v/as not the

procedure followed in estimating the xnodel developed here.

A technique developed by James R, Goodnight entitled

"Maximum R^ Improvement" v/as utilized. This approach is

de 3 c r i be din A User's Guide to the Statistical Ana lysis

S};;_sj^_ni (Service, 1972, p. 128). Goodnight considers this

technicfue

superior to the stepv/ise technique and almost
as good as calculating regressions on a].l
possible subsets of the independent variables.
[Unlike stepwise techniques] this tecl'inique
does not settle on a single model. Instead
ic looks for the "best" one-variable model, th»e
"best" two- variable niodei, and so forth. It

113

finds first the one-variable model producing
the highest R^ statistic. Then another
variable, the one which would yield the
greatest increase in R^ , is added. Once this
two- variable model is obtained, each of the
variables in the model is compared to each
variable not in the model. For each, comparison
the procedure determines if removing the vari-
able in the model and replacing it with the
presently excluded variable would increase R^ .
After all the possible comparisons have been
made, the sv/itch which produces the largest
increase in R^ is made. Comparisons are
made again, and the process continues until
the procedure finds that no switch could in-
crease R- . (Service, 1972, p. 128)

Consequently, a variable entered on an earlier estimation
is not locked in for subsequent estimations. It may or may
not help produce the maximum R^ for a larger model.

As mentioned, this technique was applied to each of
the sixteen county groups for B'lorida. The model was en-
larged up to the point at which the next variable added
was not statistically significant at the ten-percent level.
Variables were shuffled in and out subject to the constraint
that only one income variable was allowed in at any stage
of the process. A suiiunary of the results of this regression
technique is presented in Tables 18 and 19. These tables
show the independent variables which met the statistical
criteric-i. for inclusion in each county group's model. The
price variable showed up in every equation and the nature
of the relationship is as expected in every instance. Mobile
homes are (relatively) low-cost housi.ng. As the disparity

114

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119

betv/cen the cost of conventional home ownership and mobile-
home ownership increases, the mobile home looks more attrac-
tive .

Income appeared frequently as a significant predic-
tor of expenditure, being present in eleven of the sixteen
models. The value of the income coefficient ranged from
0.1458 to 0.4443, indicating, in all eleven cases, an in-
come elasticity substantially below unity. Table 18 con-
centrates on the incomie variable — showing the first model
in which an income variable appeared, the measure of income
v;hich appeared at that stage, and its coefficient.

The number of persons in the household is the only
other independent variable which appeared with any regular-
ity. It was significant in nine of the sixteen cases, and
the relationship was positive in all instances. Table 19
shows actual coefficients, the F statistic for the final
equation, and R^' for that equation. Every variable appeared
in at least one model. Five of the models included only
two explanatory variables, five included four, and six in-
cluded three variables. Explanatory power, as indicated by
r' , ranged from .205 in area 3301 to .500 for area 3501.
The moan F/ for all county groups is .360.

Since the housing expenditure-income relationship
is of prin-.ary importance, a limited size verr. ion of Model B
was estimated. The equation

120

log (EXPENSE) = b^ + b^ log (INCOME)

v/as estimated for each county group with each measure of
incorae. The results from these estimations are presented
in Table 20. All coefficients are positive and in no
instance is income elasticity greater than unity. In fact,
the maximum coefficient for any measure of income for any
area is below 0.5. This precise specification attempts to
explain expenditure with only one independent variable--
income. Such a formulation is "contrived," but has been
used elsewhere in the housing literature.*

Estimation of a one-independent- variable model such
as this one is a gross abstraction from reality. In this
form, all variation in the dependent variable is attempted
to be explained by variation in the one independent variable.
Influences from all other sources are bundled up into one
measure--in this case income. As additional explanatory
variables are added to a model, the coefficient for this
one variable may increase or decrease. So the value of the
incoTie coefficient in this smallest m>odel should not be
thought of as the m.aximum incorae elasticity.

Comparison between Table IS and 20 m^ay shed some
light on the raobile-home housing expenditure- income rela-
tionship. Table 20 allows one to examine which measure of

*ror oxemple, Margaret Reid used this type of formu-
lation in Housing aiid Income.

121

TABLE 2 0

Income Elasticities Generated

vvith Abbreviated Forn of Model E

Area

YPER-MFAM

FAMINCOM

INCFA.M

FMINTR

3101

.141
(1.71)

.105
(1.9B)

.041
(1.03)

.094
(1.61)

3102

.4 97
(19.64)

.379
(14, 34)

.068
(1.53)

,376
(12-49)

3103

.186
(3.44)

.027
(0.58)

.072
(2.84)

.031
(0.73)

3201

.126
(5.42)

.111
(4.72)

.046
(1.19)

.130
(6.71)

3202

.000
(O.CO)

.066
(2.09)

.085
(4.26)

.062
(1.81)

3203

.332
(13.74)

.271
(14.83)

.110
(8.27)

.237
(10.33)

3301

.158
(4,18)

.162
(8.28)

.068
(2.98)

.150
(7.13)

3302

.381
(16.86)

.443
(24.40)

.14 9
(8.61)

.425
(19.70)

3303

.291
(12.35)

.17 9
(6.72)

.075
(4.59)

.180
(6.43)

3304

-209
(12.99)

. 2 1 0
(13.63)

.105
(11.03)

.17 0
(8.64)

3305

.405
(13.89)

.289
(12.34)

.164
(9.41)

.26 3

(9.40)

3306

.167
(14.05)

.191
(18. IG)

.067
(5.42)

.181
(16.14)

3 307

.24 9
(17.4 7)

..177
(14.65)

.no

(16.34)

. 0 6 7
(2.91)

3308

.373
(17.91j

.316
(3.1 .2 3)

. 118
(11.27)

.134
(J 7. 02)

122

TABLE 2 0 — Continued

Area

YPERMFAM

FAMINCOM

INCFAM

FMINTR

3401

.358

.268

.058

.252

(9.08)

(13.41)

(1.68)

(11.63)

3501

.426

.175

.072

.032

(14.52)

(4.80)

(3.11)

(0.424)

123

incoiT'e best predicts oxponditure when only income is used
as a predictor. Observed 1969 income works best for eight
of the sixteen county groups. A measure of permanent
income is selected in the other eight. Of these eight where
a measure of permanent income perform.s best, in six of the
country groups the purest m.easure of permanent income is
selected. Permanent incomio measures which take account of
actual family experience are selected in only two instances.
Table 18, which shows variables as they were selected
and included on the basis of constructing the "best" model,
also shows that observed 1959 family income should be
selected in eight of the 16 county groups and that a m^easure
of perraanent income should be used in the other eight. A
point to be noted, hov/ever, is that in five of the "best"
equations income was not included as an explanatory variable.
In the eleven county group models where income is used as
an independent variable, a permanent income measure of in-
come is selected in five instances and observed 1969 income
is selected in the other six. It appears that permanent
income explains more \'ariation in expenditure when income
is the only independent variable used. When other (pri-
mari.ly demographic) variables are used to help explain
variation in expenditure, observed income performs better
in about half the cases. The iniolication seems to be that
t'm explicit variables Vv'riich measare deraograph.ic character-

124

istics explain most of what permanent incomG embodies that
obser'v'ed income does not--that is, a concept of income
viewed in a long-term sense so that the influence of random
year-to-year variations is minimized appears to explain
variation that individual demographic variables also ex-
plain.

Model B Estimation for MobiJe-Home Renters

Model B was also estimated for Florida's mobile-home
ranters. Because there were so fev; observations, however,
estimation was not attempted for each county group. Model
specif icar.ion is exactly the same as for mobile home ov/ners,
except that the housing expense variable is annual contract
rent. The results of this raodel's esti. nation are presented
in Table 21. This model, estimated for the entire state,
uses annual contract rent as the housing expense m.easure.
Estimated separately with a differetit measure of income as
the independent variable each time, the overall results were
disappointing. For all but the purest measure of ponnanent
incom.e, income, when entered, v;as not statistically signifi-
cant as an independent variable. When inconse was entered,
the observed elasticity was very low, ranging from .004
for LGFMINTR to .24 0 for LGYPERMF. This model was also
modified so that only income v%'as used as a predictor of
rental mohilo-home housing expense. Those results are pre-
sented in the fii;al colurr.n of Table 21.

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126

Comparison of those results from the rental-housing
model with the results from the owner-occupied model sug-
gests that rental expenditures are less sensitive to income
changes than are ov/ners' expenditures. This is indicated
by the generally lower income elasticities for renters.
There are som.c differences between the two which may make
direct comparisons difficult, however. The primary differ-
ence is that mobile home expense for owners does not include
any payment for site value. Contract rent (expense for
renters) does include an element of site value, hov/ever.
In an attempt to m.ake these sectors more comparable, the
renter model v/as reestimated witn the dependent variable
measured in a manner to exclude that part of contract rent
which was payment for site value. The renter version of
Model B was reestimated using as the dependent variable,
not contract rent, but 7 5 pjercent of contract
rent. The assumption eml/Odied in this formulation is that
one- fourth of the contract rent is payment for site value.
Additionally, it v;a3 hoped that t'lore might be enough ob-
servations so tliat the state could be broken dovni into at
least two gc:Ogra{-)liic regions. These regions are North
Florida (areas 31, 32, 34, 35) and South Florida (area 33),
These two broad arenas contain seventy-nine and ninety-two
househvold obsc-rva tioas, reppecti\-c1 y , The roiiter model was
reestimated incr'-rpore). ting these n-odif ications with some

127

slight iraprovcmont in results. Table 22 shows these results.
Only when explaining rental expense for South Florida does
any measure of income appear in the best model. YPERMFMl,
the measure of pure permanent income used in this study,
shows up in the m.odel and has an income elasticity of .25.
Overall, Model 3, when estimated for renters is not
very satisfying. In some cases rental housing is hardly
responsive to changes in income at all. The basic model,
whxch operates fairly well for owners' expenditures, pre-
dicts rental expenditure less accurately. Even though there
is considerable variability among the characteristics of
the mobile-hom.e renters, there is less variability in con-
tract rent payments. In North Florida no measure of income
used is a relaible predictor of rental expenditure. In
South Florida, only YPERMFAM is found to be statistically
significant when entered into the m.odel. Income elastici-
ties are less for renters than for owners, and also less
statistically significant. In some instances rental ex-
penditure is almost totally unrelated to any measure of
income. This situation is probably indicative of the fact
that renting mobile-homo housing is a temporary situation.
The findings here are too much at odds 'with conventional
th.eory and lo-rrcal reasoning to be considered of permanent
■E-iqni.tiGS.nco .

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CHAPTER VI
SUM>L\RY AND CONCLUSIONS

Scope of Research

This research has sought to do several things in
analyiiing iiiobile-home usage in Florida. First the "big
picture" v/as investigated: What is the extent of usage?
Who are the users of this form of housing? Are there
distinguishing characteristics of ir.obile-home dwellers?
Following this descriptive inquiry, several models were
developed and e?:tirnated using cross-section data to ex-
plain and predict mobile-home usage. Ov/ners and renters
were examined separately, and particular attention was
given to the housing expenditure-income relationship for
both owners and renters. Different concepts of income
were utilized so that the model's sensitivity to the
definition of incor.;e could be observed. Income elasticity
of dem.and received special attcTition to determine whether
n:Obi le h.cracs are a normal or inferior good.

This research fills a gap in the existing litera-
ture. On the one hand, the type of empirical inve-itiga-
tion performed here J s simiJar to ti'iat carried out for
coju'eution il housj.ng in the acadoi-'.ij litorature. /-slmost

1 !;■'

131

without exception, empirical modeling has not been applied
to mobile-home housing--an increasing proportion of our
housing stock. This investigation bridges the gap between
the academic literature on conventional housing and the
"trade" literature on mobile homes, which has been almost
entirely descriptive rather than analytical.

Specific Findings: Descriptive

Among work which has been published, other research-
ers v/orking with miobile-home data have assumed or concluded:

1. That once a family has decided which sector
of the m.arket to enter (i.e., to own or
rent) , the price of housing in the other sec-
tor becomes irrelevant to the family's decision
process (Ohls, 1971, p. 9).

2. That "mobile-home demand is directly competi-
tive V7ith conventional single- family housing
starts as opposed to being directly competi-
tive witli multiple- fandly starts" (Davidson,
1973, p. 159) .

3. Thac heads of mobile-hom.e liouseholds are
generally younger than heads of other types
of h.ouseholdf^ (Davidson, 1973, p. 134).

4. That younger households wiio have not yet
achieved higher income levels are the primary

132

purchasers of mobile homes (Davidson, 1973,
p. 118) .
5. That mobile-home livers are a homogeneous
group: young, starter couples, blue collar,
average education. Median incomes have been
found to be low — $6,G20 for mobile-home
ov/ners compared to $7,500 for new apartment
renters and $8,000 for new home owners
(Mobile Home Market, p. 25) .
The findings from this study of Florida's mobile-home -^
occupants are different from most of these just cited.
These previously published findings were based on national
averages, and while not being challenged here, may not be
representative of data from, particular regions. The average
of California and Maine mobile-hom.e fam.ilies may be
descriptively false of both subgroups. Florida data are,
in fact, somewhat different from the above "average" find-
ings. For instance, the tenure decision appears to be a
secondary consideration for many families. The income con-
straint seeiTiS to be of primary importance, with other
dc-cisicns .;to ov/n or rent a m.obiie home) following the bud-
getary allocation for housing. Conventional single-family
homr: ownership cannct compete with r.iobile-home hovising on
the basis of price.

133

The characteristics of mobile-home owners are in
some respects (age, family size, and mobility) like renters
and in some respects like owners (race, education, and sex
of family head) . As far as housing expenditure is concerned,
however, mobile-home ownership is much more in line with
the cost of renting rather than with that of conventional
ownership. Additionally, Florida has a high percentage of
older people among its population, and these people make
extensive use of mobile-home housing. Nationally it is not
true that those states with a relatively high percentage
of older people m.ake heavy use of mobile-home housing. So
there are different factors which lead to m.obile-home
attractiveness, and Florida seem.s to combine many of these
factors v;hich are favorable to this type of housing choice.

It is not true that Florida's m.obile-horae owners are
a homogeneous group, though. They are homogeneous only
wit?i respect to racial composition, as will be discussed
later. They are relatively young in the "young" parts of
the state (areas 31, 34, and 35), but they are older than
the "all owner" or "all renter" categories in south Florida.
Their incomes are below the "all-ownor" group's, but in
some instances, higher than the "all-renter" group's. Their
edacdtional attainment is, again, between the other two
groups. Household size for m^obile-hoinc owners is generally
small .

134

For mobile-home-renter households, it was observed
that, compared with mobile-hom.e owners, they are more
mobile, younger, have lov/er incom.es, and are m^ore often
headed by a female. This combination of factors suggests
that renting a mobile home is tem.porary, as opposed to
perm.anent, housing.

'These points summarize the com.parison with other
housing groups, but within the mobile-home-owner group,
there is also wide variation. Am.ong the county groups
the variation found in Tables 5, 6, 7 and 8 is sumoiarized
in Table 23,

TABLE 2 3
Percentage Variation in Descriptive Data

Owners

Renters

All

iMob]

-le-h"ome

All

Mobile-

-Home

y 12.3

24.3

13.6

23

7

26.5

57.0

44.3

976

0

28,6

83.2

30.5

89

1

34.1

7 2.0

18.8

43

/I

Mobility
Ago of Head

Household 3;

The. numl'ori-- represf.nt the percent variation between the high
and lou' observations. For mobile- home owners the mean age
of rjie iicad is lowest in area 34 01 (36.01 years) and the
hiqhest in area 3304 (65.98 years). The variation (29.97)
is 83.2 percent oL the lowest observation (36.01). The

135

mobile-home owners are certainly not a homogeneous group.
In the retirement areas (3304, 3307), they are generally
v;hite, mobile, not as often headed by a male, older, have
lower incomes, and smaller household size. In the least
rural areas (3101, 3201, 3301, 3302, 3303, 3305) there is
a good deal of variation in most of the characteristics
and in the most rural areas (330B, 3401, 3501) there seems
to be the most homogeneity among each subgroup.

Specific Findings: Analy tical

A model to predict the probability of home ownership
was developed. The model was estimated for al], owners
and then reestimated to predict rhe probability of mobile-
home ownership. This model was called a tenure miodel since
the dependent variable deals v/ith ownership probability.
The m.odel was used to demonstrate that many of the variables
v/hich are significant for estimating the probability of home
ownership are also significant for estimating the probability
of mobile-home ownership. Quite often, however, it v/as
noted that the sign of the estimated coefficient was differ-
ent between the "all-ownership" and the "mobile-home-
ownership" versions of the tenvire model. For instance,
the coefficient of the variable DUMIGRAN v;as -0.22177 when
estim.ated in the "all-owners" version of the tenure m^odel,
but -10.016 32 when estimated in the "mobile-home-ownership"

136

version (see Table 15) , DUMIGRAN is the durririy variable
which takes on a value of one if the family had moved into
the state in the last five years before the survey, but
remains zero otherwise. The positive coefficient in the
"mobile-home-ownership" version indicates an increased
probability of mobile-home ownership if the family had
recently moved v;hi].e the negative coefficient in the "all-
ownership" version indicates that having recently moved
decreases the probability of ownership in general. Co-
efficient sign changes such as this between the two models
were observed frequently. As observed income increased,
the probability of ownership also increased (positive co-
efficient) , but the probability of mobile-hom.e ownership
decreased (negative income coefficient) . Based on this
negative incomie coefficient, the income elasticity of
demand for mobile homes would also be negative and mobile
home housing would therefore be an inferior good.

Model B, a more traditional regression m.odel, ex-
plains m.obile-home expenditure from other (independent)
variables. This m.odel wcis estimated for each county group
for mobile-home owners and for north and south IMorido.
for renters of mobile homes.

The owners version of the expense model performed
better than the renters mtodei. Even in the ovvners version,
however, dem.ographic variables, other than houseliold size,

137

sliOv/ecl up with little regularity. Income, price, and
family size were responsible for most of the explanatory
power generated. Other variables, selected on grounds of
statistical significance, were included only in one or two
instances. For example, DUMRACE appears only in the "best"
equation for area 3306. The measure of income which v/as
selected on statistical grounds did not appear to follow
any geographical pattern. Permanent income worked well in
areas 3102, 3303, and 3501--somc of the most rural areas.
Observed incom.e performed well in 3301 (urban) and 3308
(rural and urban) and 3306 (rural).

Income elasticities turned out to be low in every
area v.'ith every measure, of income tried. They ranged from
almost zero to +0.5. Permanent income usually yielded the
highest income elasticity, but not necessarily the m.ost
significant. This phenomenon can be observed in Table 20.
Pure permanent income (YPEPaMFAI>l) exhibits the highest co-
efficient in eleven of the sixteen county groups, but has
tlic higiiest "t" statistic in only six. In no instance was
income elasticity found to be negative.

The renters version of the expense model proved to
be only m-arqinally accept£\b.le . T'ne pure measure of permci-
nent income (YPERI-IFAM) again generated the higjiest. income
elasticities, but in this case they wore also the most sig-

13:

nificant statistically. (Higher coefficients are not neces-
sarily more significant than lower ones.) One difficulty
in analyzing tlie renters of raobile homes is in selecting
the appropriate income measure. If renting is a temporary
solution to one's housing needs, a short-term measur'e of
income might be preferable. To the extent that temporary
and perm.anent renters are intermingled, no measure of in-
come may perform very v/ell.

When one integrates the findings of the two models
he finds some additional information. Within the population
of mobile-home owners there is a positive relationship
between monthly housing expense and income. But as income
increases, there is a decreasing probability of mobile-homie
ov/nership. As it turns out, v/hen we look at mobile-home
families we are usually looking at a relatively low-income
subgroup in the income spectrum. If their incomes rise
tiiey are ].ikely to make a switch to conventional home owner-
ship, especially if they are not in the retiree's age range.

E\^en though we find that mobile home occupants are
relatively lov/-iucom,e households and that nonwhito house-
holds are relatively low-income households, there are very
few r!onv;hi Le families vcliJ ch ov;i^i their own mobile home or
rent suuieone else's. There is very little information in
the census data Lo ex[>jain thi3 paradoxical fact. The
reaii^on for such little use by nonwI\ites is, at this poinl:.

13 9

a matter of spoculacion. Perhaps excl'j.~ion in the credit
market is the explanii tion. It may bo that nonwhites cannot
qualify for financing. But it may bo that so little use
of mobile homes by nonv/hites is simply a matter of tastes.
Perhaps nonwhites do not care for the alternative offered
by a mobile hom^e . In considering this line of reasoning
one might ask "Where can a m.obile home occupied by a. non-
white be located in an urban area?" Discrimination may be
a factor in this connection, but whether it is or not,
mobile homes are not usually located in central cities v/here
nonwhites are often located. Mobi.le homes are located
in suburban and rural areas. In most urban areas their
use is li.mited by zoning restrictions. It seems possible
that the spatial location of mobile housing may be respon-
sible for the lov; utilization of mobile h.omes by nonv/hites.

As shown by Model B, up to 50 percent of the varia-
tion in m.obile- hom>e housing expenditure can be explained.
The least explained variation is in area 3301 (Brov/ard
County) , and the model performs best in the v.-estern part
of the state. Income is the single best predictor of ex~
penditi-ire in only three areas, but appears in eleven
"best" equations. Price appears in all area m.odols---
showing, as one might guess, that tiie primary attractive-
ao-s? of m.obile homes is the relative cost advanraqo. They
are not crlv less exoensive. to build because of being

] H 0

smaller than conventional structures, but are also less
expensive per square foot of shelter space. And this
advantage has strengthened over the last two decades.

There is no credit market term in either of the
models developed here. This is not simply an oversight,
but is the result of several relevant considerations. The
first one is the difficulty of locating data. No questions
on financing were included in the Public Use Sample.
Secondly, it is suspected that differences in credit terms
across the state would be small, even if available. Before
1970 mobile homes were financed much like automobiles and
the termi of financing did not exceed seven years. Thirdly,
a high proportion of mobile homes are purchased without
financing. Therefore, the cost of credit is not included
as a housing cost in this study. Although unsubstantiated
at this point, it is conceivable that there could be a
positive correlation between mortgage rates and the demand
for mobile h.omas. As the cost of financing a house rises,
some potential buyers could shift away from the increased
cost of ownership. While mobile-home financing is likely
to have gone up at the same tlr.ie, the amount to bo financed
wouj.d be sur^stantially less. So oven with a high rate of
interest payable on a mobile homo loan, the dollar amount
of the mont'p.ly obliqatioi-i would still be only a fraction
of that for a conventional mortgage. T'ne total effect on

141

the mobile- ho me market of this financing cost differential
v;ould be the net result of the two offsetting effects--
analogous to t!ie income and substitution effects of micro-
economic theory.

The price variable used in this study is actually
a relative price variable which takes into account the
average price of a mobile home and the average cost of a
new conventional house. As the cost of a conventioacil
house rises relative to that of a mobile home, the value of
the price variable falls. That is to say, the mobile home
becomes a more attracti\'e alternative as its relative
price decreases. Ideally a price variable for each area
of the state would be desirable, but here again, availabil-
ity of reliable data proved to be a problem. At any rate,
the nature of cross-sectional data is such that the study
of price effects is extremely difficult. Becides the
often-present lack of sufficient variation in prices, it is
extremely difficult to abstract from, quality variation.
The assumptions necessary for dealing with these factors in
order to derive price elasticities are heroic. Other rc-
search.ers have had similar difficulti.es in analyzing con-
vert t.ional housing. For those reasons, even though a price
variable was used in the expense model, price elasticity
\: 3 s not c s t i '.v.- 1 1 c d .

Another variable which 'vould seem to be appropriate
in predicting mobile-homo demand is the vacancy rate fourd

142

in otlier housirig market sectors. If vacancy rates are
high and rental rates are downwardly flexible, the demand
for mobile homes should be reduced. In this case the rela-
tive cost advantage of a mobile homo would be lessened. But,
as v/ith the other variables just mentioned, the data needed
are simply not available. Postal service vacancy surveys
are performed periodically in various locations, but these
are of little use except in specific local situations.

Local zoning ordinances are also influential with '^
respect to demand and location of mobile hom.es. Mobile-
hom.e parks do not usually win great favor with city or county
zoning boards. There is no reliable study known which has
estimated the (restrictive) influence on mobile home dem.and
of local zoning ordinances. There are, however, known cases
of governm.ental hostility to existence of mobile-home parks.*
If there is not a suitable location for one's mobile home,
one might not buy a mobile home. To the extent that zoning
boards or planning conimissions are made up of contractors
or real estate brokers, antagonism might be expected toward
mobile- home park development.

Choice of the appropriate income concept is not
settled. Tlie issue "cannot be settled by economic theory

*Conversation with official of Florida Mobilehome and
Recreational Vehicl-.-=> Association, March 19, 1976,

143

alone" (Houthakker and Taylor, 1970, p. 225). Depending
on v.'hat it is one wishes to study, the definition of income
may vary. Since permanent incom.e is not directly observ-
able, there may be as many measures of it as there are re-
searchers. The concepts used in this study were constructed
to coincide with the housing choice being investigated.
The m.easures used here might be inappropriate to study the
demand for television sets or conventional houses, though,
since the life expectancy of these items would probably
be different from that of a m.obile home. The best measure
of income is not one that coincides with some preconceived
idea of what is relevant or with what one "ought" to find
in his study. The measure of income which best "explains"
rcobile-hom.o housing expenditure is the one which perform.s
"best" on the basis of objective, statistical grou:::ds. It
v/as found in the course of this research that no blanket
statement regarding the preferability of som>e measure of
permanent income over another or even of the preferability
of permanent income over observed incom.e could be made.
It was generally found, however, that a measure of permanent
income would usually generate a higher income elasticity
of demand than would observed income.

144

Implications and Unanswered Questions

While Florida cannot be thought of as being repre-
sentative of the United States, it does provide us with
cin area where extensive use of mobile-home housing is being
made. There are other such areas. California contains
more mobile homes than does Florida. Other states have a
greater proportion of the total mobile-home stock than of
the national population (Arizona, North Carolina, Georgia,
Alabama and Indiana are only a few) . As the relative cost
of conventional housing rises (not to mention site values) ,
an increasing segiTient of our population might find the
mobile home a more palatable alternative. It seems that
the cruciaj. question in this regard is whether real incomes
or housing costs will rise faster.

The desirable aspects of mobile-home housing which
lead to its adoption as one's housing choice are present
in places other than Florida. Those aspects were mentioned
in Chapter III and include low price, low maintenance cost,
single- family ownership, flexibility in environm.ental
choice, and the well-developed market for used units. Flor-
j.da also has appeal because of its mild climate (except
for the possibility of hurricanes), but so do other geograph-
ica], areas of the United States. As the birth rate has
fallen in recent years and families are getting siiialler.

145

there will be less need for large homes. This influence ^
should be encouraging to the mobile-horrie sector of the
hcusing market as opposed to the single-family conventional
structure. The rising cost of energy (for heating and

K

cooling) should also make smaller homes in general, and
mobile homes in particular, relatively more attractive in
the future.

VJhile the ownership expense version of Model B
performed acceptably well, the renter version was less
satisfying in its performance. It appears that further
study in this area would be desirable in developing a
better performing model of mobile-home rental behavior.
It may be that these renters are such a heterogeneous
group that this task would he difficult.

As far as mobile-home ownership is concerned, this
study indicates that time m.ight be profitably spent in
several areas. For instance, v;hy, in fact, do nonv/hites ^-^
raake such minimal use of miobile homes? Might increased
use be a partial solution to the housing problems of lov;-
income m.inorities who live in the ci.ties? If so, wh.at
policy implications follow?

No single measure of income appeared as a superior
predictor of mobile-home expense. No determining prin-
ciple as to Wiiich income measure perforn^ed best under what
ci^rcumstances v.'as found, though. Further inquiry into this

14 6

question may be needed. It may be that some measure of
income not employed in this research would, in fact, pre-
dict better than those used here. For instance, asset
holdings should perhaps go into such a formulation. This
v/ould allov/ a researcher to tie permanent income more
closely to wealth.

Finally, price considerations are deserving of
further study. Price elasticity of demand for mobile
homes is, at this point, a matter of speculation. Although
a price variable was employed in this research, further
work with better price data is needed. It is possible
that future use of m.obile homes will be as much determined
by price elasticity as by income elasticity. It m.ay also
be possible to incorporate the significance of product
quality changes over time through use of good price data.

It appears that mobile homes fill a grov/ing need
in the housing market. There is no other form of housing
currently available which can provide decent shelter
space at a carrying cost competitive with a mobile hom.e .
"For many moderate- income American fam.il ies, the mobile
hom.o i.s the only kind of housing they can reasonable af-
ford."* Mobile homes are lov/-cost housing, but they are

*Hessage from the President of the United States
transmitting the Second Annual Report on National Housing \^
Goals, Comuiittee on Banki.ng and Currency (Washington: ^
uTsT'Government Printing Office, April 1, 1970).

147

utilizevi by families which are not necessarily low- income ■
families. The growth of mobile hone utilization reflects
two basic attitudes--one new and one old. First, mobile-
home living reflects a desire for a less traditional
dwelling unit whose environment is flexible. Second, the y
mobile home choice reflects something the consumer is con-
tinuously shopping for--a good buy--decent housing at an
affordable price. The mobile home will probably never
replace the conventional single-f am.ily structure or the
apartment building, but it should continue to attract those
who prefer ovrnership to renting and those who cannot or
do not v/ish to spend a high percentage of their incomes on
housing .

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BIOGRAPHICAL SKETCH

Max Holt Strader, Jr., was born in Winston-Salera,
North Carolina, on January 19, 1945. He attended Calvin
H. Wiley granimar school and graduated from R. J. Reynolds
High School in 1963. In September 1963 he entered North
Carolina State University. In September of 1966 he was
married to the former Sonya King Thom>pson, also from
V«/inston-Salem. In May of 1967 he received a B.A. degree
as a Liberal Arts major in Economics. Follov/ing gradua-
tion the Straders moved to Gainesville, Florida, v/here
Max was enrolled in the graduate program in economics and
Sonya vvas employed as a secretary in the University Per-
sonne] Department. In August of 1959 two important things
took place: Max accepted Jesus Christ as his personal
savior. Second, he was awarded a Master of Arts degree
in Economics. His thesis was entitled "Ma] thusianism and
the DevelopTiient of Economic Thought." In October 1959,
Max began his tour of active duty as a second lieutenant
with the U.S. Array at Fort Benning, Georgia. The follow-
ing February he and Sonya traveled to Anchorage, Alaska,
where Max spent his final eighteen months-, on active duty.
He taught an economics course for the University of Alaska-
Anchorage while stationed chore.

If.^

156

In the fall of 1971 it was back to North Carolina
where Max spent one year in the graduate program at the
University of North Carolina-Chapel Hill. August of 1972
found the Straders moving back to Gainesville, Florida,
v/here Max reentered the University of Florida. In the
midst of qualifying exams in Noveiriber of 1974 their son,
Gary Preston Strader, v;as born. In August of 1976 Max
accepted a faculty position at Missouri Western State
College in St. Joseph, Missouri, as Assistant Professor
of Economics. He finished the writing of his dissertation
and was av/arded the Doctor of Philosophy degree by the
University of Florida in the summer of 1977.

I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of Doctor of Philosophy.

L Un'Z'^^'-t W ' )y\ JiL

Jerbme W. Milliman, Chairman
Professor of Econom.ics

I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of Doctor of Philosophy.

VZL

>=/\.L J

Madelyn- Lockhart
Professor of Econof

I certify that I have read this study and that in my
opinion it conforms to acceptable scandards of scholarly
prv^sentaLion ap.d is fully adequate, in scope and quality, as
a. dissertation for the degree of Doctor of Philosophy.

F r c a o r 1 c: k 0 . / G o.d da r d
Associate Professor of
Economics

I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of Doctor of Philosophy.

Anthony Jy La Greca
Assistant Professor of
Sociology

This dissertation was submitted to the Graduate Faculty of
the Department of Economics in the College of Business Ad-
ministration and to the Graduate Council, and was accepted
as partial fulfillment of the requirements for the degree
of Doctor of Philosophy.

August 19 7 7

Dean, Graduate School

UNIVERSITY OF FLORIDA

3 1262 08552 9872