Citation
A cross-section analysis of the demand for mobile homes in Florida

Material Information

Title:
A cross-section analysis of the demand for mobile homes in Florida
Creator:
Strader, Max Holt, 1946-
Copyright Date:
1977
Language:
English
Physical Description:
viii, 156 leaves : ill., maps ; 28 cm.

Subjects

Subjects / Keywords:
Elasticity of demand ( jstor )
Home ownership ( jstor )
Housing ( jstor )
Housing demand ( jstor )
Income elasticity of demand ( jstor )
Income mobility ( jstor )
Mathematical variables ( jstor )
Mobile homes ( jstor )
Prices ( jstor )
Statistical models ( jstor )
Dissertations, Academic -- Economics -- UF
Economics thesis Ph. D
Mobile home living -- Florida ( lcsh )
Mobile homes -- Florida ( lcsh )
Miami metropolitan area ( local )
Genre:
bibliography ( marcgt )
non-fiction ( marcgt )

Notes

Thesis:
Thesis--University of Florida.
Bibliography:
Bibliography: leaves 148-155.
General Note:
Typescript.
General Note:
Vita.
Statement of Responsibility:
by Max Holt Strader, Jr.

Record Information

Source Institution:
University of Florida
Holding Location:
University of Florida
Rights Management:
Copyright [name of dissertation author]. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Resource Identifier:
022209547 ( ALEPH )
04213155 ( OCLC )
AAG8781 ( NOTIS )

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A CROSS-SECT [ON ANALYSIS OF
TUE DEMANlD FOR iE B3IL HOMES IN FLORIDA









By

MAX HOLT STFADER, JR.














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


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









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


I 9,














DEDICATION



This work is dedicated to the glory of God who

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

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

do, it may be for the glory of God.


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















ACKNOWLEDGMENTS


I would like to acknowledge several people who

helped make this work possible. Sonya Strader, wife

extraordinaire, provided encouragement and typing services

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

chairman of my dissertation committee, helped focus work

effort into a manageable topic and provided encouragement

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

Lockhart, and Anthony La Greca also provided the necessary

cooperation and encouragement. The Department of Economics

and The Dureau of Economic and Business Research provided

graduate assistantships which helped make graduate work

financially feasible. Sofia Kohli did the typing of the

final draft and provided editorial expertise. Errors

remaining are the sole property of the author.


- _
















TABLE OF CONTENTS


Page

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

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

CHAPTER

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

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

IT THE LITERATURE ON ESTIMATION OF
ELASTICITY OF D=EAND ZOR HICUSIIG. ........... 11

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

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

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

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

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


__










Page

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

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

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

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

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

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













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



A CROSS-SECTION ANALYSIS OF
THE DEMAND FOR MOBILE I!HOkS IN FLORIDA

By

Max Holt Stradar, Jr.

August 1977

Chairman: Jerome W. -Milliman
Major Department: Economics

Over the years a considerable amount of economic

work has been generated which seeks to ascertain the in-

come elasticity of demand fcr housing. The work done here

builds upon this literature by applying regression analysis

to the mobile-home sector of the housi.no ma.rket--a sector

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

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

currently being prcdc'cec in the tUnited Stares.

iThed eco.noitJc Jiterature on housing demand is re-

vicwoed income elastic.tice of demand for conventional

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

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

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










Two housing demand models for mobile homes are

then developed. Doth models are estimated using data from

the 1970 Public Use Sample for Florida. The first examines

the demographic variables which influence home ownership

and mobile-hoire ownership. Generally, the same variables

are found to be significant for predicting home ownership

in general and for predicting ownership of a mobile home,

but often the influences are in opposite directions. For

instance, home ownership is found to be positively related

to observed income but mobile-home ownership is found to

be inversely related to observed income. This inverse

relationship was found for most of the variables used in

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

The second model regresses mobile home housing

expeCnse against income. Four measures of income are util-

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

native focr-.ulations of permanent income. Income elasticity

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

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

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

No bliankeL sta-T.enets concernr.nc the preferability of

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

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

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

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











income variable did yield higher income elasticities than

were found when observed income was the income variable

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

a ely sensitive to the measure of income used.

Demographic variables were not very often helpful

in explaining variation in mobile-home expenditures. Price,

income and family size were the variables which most often

were found to be of explanatory significance. Older

Floridians were found to own a large percentage of the

owner-occupied mobile homes, especially in south Florida.

Nonwhites make very little use of this form of housing,

even though mobile homes are relatively low-cost housing

aad nonwhites have below-average incomes.

The second model was also estimated for renters of

mobile homes. The results were less satisfying in a

statistical sense, but it appears that rental expenditures

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

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

porciy housing choice for many of Lhose who were renting

in 1970.


viii















CHAPTER I
INTRODUCTION



General Setting: Housing Needs and Alternatives


The Housing and Urban Development Act of 1968 set

a national goal of providing 26 million new and rehabili-

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

goal may have been unobtainable even under the best of

conditions. In any event, the economic conditions of the

early and mid 1970s have made its achievement virtually

imzcsnible., One bright spot in recent housing experience,

however, is the growing role played by mobile homes in

providing decent housing. This relatively new housing al-

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

of housing units at relatively low costs.

Recent evidence of the growing role of mobile homes

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

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

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

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

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

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

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











and Mortgage Credit-Federal Housing Administration Commis-

sioner said:

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

Clearly, costs of new conventional housing have risen

to such a level that more consideration needs to be directed

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

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

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

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

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

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

and one-half times annual take home income for housing

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

face severe budgetary problcmas in this respect. In fdct,

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

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

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

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

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

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

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

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










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

the budgetary problems faced by many families to become

more acute.



Ilousino Sector Demand


Until now primary emphasis in the analysis of the

mobile home market has been concerned with the potential

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

It may be that this emphasis has obscured the possibility

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

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

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

housing.

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

ous analysis of the market for mobile homes from the demand

y side. Clearly, policy pcescriptions relating to mobile

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

supply should be based upon sound economic studies of the

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

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

teristics cnd about budgetary patterns. (For example,

Ihow do characteristics cf owners and2 renters of mobile homes

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

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

hom'i.s"?"










This study will build specifically upon the well-

established literature in economics which deals with the

demand for housing and will develop a cross-section analysis

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

an important one. The traditional housing demand litera-

ture in economics is relatively well-developed for conven-

tional types of housing, but the applicability of such

models to mobile-home housing is untested and at least

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

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

effective demand could prove useful elsewhere to the extent

that these determinants are found elsewhere in the United

States.

The objective of this study, then, is to estimate

the demand for mobile homes in Florida, starting from the

conceptual framework of the extensive literature in

economics which deals with the demand for conventional

housing. It has generally been assumed that since mobile

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

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

but requires substantiation.

We neod specific information with respect to the

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

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

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











ticities greater than unity with respect to permanent

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

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

these conflicting results were not unsettling enough,

Maisel and Winnick (1960) tell us that housing consumption

is no more responsive to permanent income than to changes

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

conclusion in developing a model of household behavior to

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

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

to suppose that the findings which pertain to conventional

housing would hold for mobile homes.



Research Design and Methodology


Florida is an area where the use of mobile homes is

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

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

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

housing. To the extent that factors leading to this high

level of usage are found elsewhere, future usage elsewhere

might also he high. If the only relevant factors are

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

limited. It is suspected, however, tht changing tastes

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

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










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

widespread experience in the use of mobile home housing,

Florida provides an excellent opportunity for study.

The primary data source for this dissertation re-

search will be the Public Use Sample of Basic Records from

the 1970 Census. This data base, collected on magnetic

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

Florida there are approximately 25,000 household observa-

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

Observations for states, county groups, and standard

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

persons are available.' For each observation there are

approximately 125 variables available in the Public Use

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

tabulations are possible. This arrangement allows almost

unlimited flexibility. For example, among those who live

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

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

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

other included variable. Data are broken down so that they

are available for the entire state, for five major areas

of the state, and for fourteen subareas including sever

SAiS.s.

This d.ita base will make possible derivation of a

demand funct on anirdI an economic cross-section analysis of

the demnd










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

a particular geographic area is a well-established technique

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

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

these studies and several others in an attempt to see if

their results are consistent. He concludes that there is

more agreement about the empirical value of income elastici-

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

surface. The applicability of conventional housing models

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

specifically verified whether conventional housing factors

apply to mobile-home housing.

In this respect, it appears that demographic vari-

ables require special attention. In terms of socio-

economic factors it would seem worthwhile to differentiate

between owners and renters in order to determine what in-

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

consumption patterns, and to examine racial differences in

consuretpItion patterns. Have mobile homes made ownership

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

of any value as a means of dispersing minority racial

groups from the central city and hence reducing the urban

problems associated with culstering of low-income housing?

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

relcrcated households) different from the potential renter











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

answers to these questions before formulating housing poli-

cies which would include the use of mobile homes.

The approach utilized here will begin with a study

of the relevant housing demand literature. The most im-

portant works will be considered and recent work on mobile-

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

plore housing expenditure as a household budgetary decision.

Overall demand considerations will be introduced and a

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

be presented. Descriptive material will be used in making

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

homes. Owners will be separated from renters so that the

relevant distinctions can be noted.

Models to be estimated are constructed and explained

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

an expenditure model, are developed. Variables to be used

in these models are introduced and the rationale for their

ccnsiderat-ion is discussed. Actual fjnaiingcs when the model

is estismated are then presented in Chapter V. Important

findings are pointed out and considered. Chapter VI then

summar.izes the study, noting relevant questions which must

!;e left unanswered until further research is directed toward

dealing with these matters.










Usefulness of iobile-Humn Den..nd Research


This research is an extension of the existing liter-

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

with a sector of the housing market which has heretofore

received almost no attention, even though it is a rapidly

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

there seem to be reasons for this increased use of mobile

homes which will insure their popularity in years to come.

This is particularly true in Florida and some other parts

of the United States also. One of these factors relates

to family income, and this relationship is given special

attention.

Implications of the findings =f this study should

prove useful in considering future housing policies which

specifically include use of mobile homes. For instance,

it would te desirable to know if some segments of our

population to whom we desire to give housing assistance have

strong feelings about the suitability of a mobile home.

We already have costly experience in trying to house people

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

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

Present housing programs, especially Seccion VIII

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

jncd ed, figure prominently in meeting future housing goals.




10





Programs to make mobile-home acquisitions by low-income

families easier could perhaps facilitate achievement of

these goals. Specific information about mobile-home demand

is needed, however, before efficient programs incorporating

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

of the needed information which can help in shaping future

national housing policies.














CHAPTER II
:'!iE LITERATURE ON ESTIMATION OF
::LASTICITY OF DEMAND FOR HOUSING



First Efforts


r-: traditionally cited as the first legitimate

'.apt at empirical analysis of household expendi-

t published by Christian Lorenz Ernst Engel in

data collected by Ducpetiaux for 153 Belgian

his base, Engel proposed a law of consumption

Si expenditures on food to a family's socio-

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

*:itage of their available assets on food than do

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

:S.txony which Engel had drawn up, attributed

iT assigned expenditure figures to the three

classes, and expanded Engel's generalization

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

-lothing, lodging, and sundries.*

it.cr (1895) reexamined his Belgian data ac-

-"' class and concluded that the proportion


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









of income or total expenditure spent on housing fell as

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

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

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

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

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

tionship between income and housing which is usually re-

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

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

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

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

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

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

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

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

sequent budgetary studies considered housing expenditures,

but nothing of exceptional economic interest was generated

until well into the twentieth century.

Table 1 lists the major housing studies published

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

this chapter, but all did make a contribution in the

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

variety of data bases has been utilized in estimating

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

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

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












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

the Comments column.

Although many studies of housing demand have been

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

cerned with estimating the price elasticity of demand for

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

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

which simply does not lend itself to precise estimates of

price elasticity. Price differences must be measured be-

tween a standardized unit of housing and the fact that

houses are located in physically different surroundings

means that a standardized unit of housing is difficult to

find. Not only is there intracity variation in quality

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

also intercity variation. Accurate price data would be

needed both within and between cities on a standardized

unit of housing. These data are not readily available on

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

not likry to be great enough that price elasticity can

be accurately gauged and between areas quality differences

make price comparisons difficult. For this reason almost

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

presented with an apology for its suspected unreliability.

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

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

n ad (1]).,








Conventional Housing: Time Series Data


The first widely read work which attempted to

estimate the income elasticity of demand for housing from

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

His conclusion, based on residential construction expendi-

tures compared to either gross national product or gross

capital formation from 1890 to 1950, is that consumers'

preferences have shifted away from housing over this time

period. He transforms aggregate data into a per-capita

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

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

His conclusion is then drawn from the fact that these

measures jump up and down slightly over the sixty years'

period without demorns-trating any significant upward trend.

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

income elasticity of demand for housing which he derives en

route to his primary conclusion is 0.5.

Gutter:tag responded to Winnick's conclusion by

questioning the premises upon which it was derived. He

specifically suggested that carrying costs are more appro-

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

to lcoo- at consumer behavior. He additionally asserted

that the diemnand for housing may not be more elastic with

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

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

is coined in tcrms of space rent and reasserts the original









conclusion. While the issue of the place of housing in

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

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

aggregate capital value if what he was really interested in

is income-housing expenditure relationships. Additionally,

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

So while his conclusion should not be accepted without

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

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

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

tively invariant with respect to income. . After the

space requirements are met, apparently another set of

standards comes into view" for some families. These other

standards pertain to location and quality of the structure.

Probably the most respected and most widely referred

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

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

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

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

establ ished tihe tone of mni.ch subsequent work. In estimating

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

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

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

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


. Ap + By 4- Cr










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

p = Boeckh index of residential construction

costs (brick)

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

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

bonds


This equation is the one estimated when Huth assumes rapid

market adjustment to changing prices and incomes. When

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

the model is re-specified:


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

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


The complete adjustment model yields an income elasticity

of 0.55 and the incomplete adjustment model yields 0.83 for

desired stock and a whopping 5.38 for new construction. In

contrast to the previous estimates of other researchers for

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

serts: "The evidence gathered here suggests that both

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

unity and may even be numericalLy larger."

Mluth's conclusion seems to have been borne out by

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

several grounds. The first of thesis' criticisms deals with

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










service," Muth equates this concept to the quantity of

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

He then standardizes price in terms of payment for this

unit of service. In effect this procedure says that any

one unit cf housing service (regardless of the type of

structure producing it) is interchangeable with any other

unit of housing service. Hence, under this system of

measurement, distinction between housing services provided

by owned homes and those provided by rental units cannot

be made.

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

taken issue with the assumption of constant annual depre-

ciation which Muth employs. Ohis tests the plausibility

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

work and finds it to be an unfounded one.

Muth additionally can he questioned on the following

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

his price variable rmay cause problems. This Index is unable

to take into account changes in productivity or possibili-

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

values may be a less desirable measure for housing prefer-

ences than some measure of carrying costs. Operating costs

di rectly tatr.ibutable to houezng are thus overlooked.

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

tak:-n of any demographic variation.











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

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

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

income elasticity is substantially less than unity while

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

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

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

however, in the area of including more appropriate credit

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

He then uses single-equation least-squares regression

estimation to derive values for price and income elastici-

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

using gross housing construction as the dependent variable

and the other which uses price or income as dependent.

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

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

This bracketing technique is statisriically acceptable, but

Lee's bracketing is nothing more than an arithmetic mean

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

0.652 income elasticity is therefore an average of 0.336

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

unity, however. This elasticity is derived using observed

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

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

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









but the interval includes areas on both sides of unity.

Lee (1964, p. 88) concludes:

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

The final time-series study to be considered here

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

particular interest because he derives income elasticities

from regression equations specified both with and without

demographic terms. Results from these regressions show that

elasticities are higher for owners than for renters and that

elasticities are consisLently higher when demographic vari-

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

Carliner performs his calculations using several measures

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

concept incorporating imputed rental value for house owners.

Numerically, his income elasticities range from 0.410 to

0.746, being highest when income is expressed in a permanent

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

a belief that "the elasticity of housing demand is around

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

up in the samenr neighborhood as Lee.









Conventional Housing: Cross-Section Data


An early example of a crcss-sectional study of

housing which derives an income elasticity was published

by Ogburn in 1919. He used 200 family budgets from

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

renters. Subsequent work by other writers produced elas-

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

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

In 1962 Margaret Reid published her Housing and

Income study. She openly challenged the validity of the

Schwaba Law of Rent which h3d been sleeping peacefully for

almost a century. She asserted, and even had empirical

evidence to verify, that the income elasticity of demand

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

being as high as 2.05.

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

permanent concept of income. She maintains that such a

measure of income is the only appropriate one since the

time horizon involved in housing-consumption decisions is

quite long and since observed annual income figures are

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

exogenous influe-nces. Using grouped data front several

sources (spanning three decades), she demonstrates that the

income elasticity of demn'd for housing is greater than

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










As might be expected, this work has received quite

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

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

addresses itself to the findings and methodology involved.

The primary criticism of Reid's work in this dissertation

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

of the phenomenon of data combination, and not necessarily

a reflection of the true income elasticity of housing

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

conclusion on the basis of Reid's having grouped household

observations by the use of instrumental variables such as

geographic area. Besides this criticism relating to

statistical methodology, Bedrcsian takes issue with the

theoretical assumptions and the data base used by Reid.

Lee has also taken issue with Reid, primarily on

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

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

ences in permanent housing components that should be ex-

plained by variables other than permanent income. Reid

classified individual household observations into groups

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

within places, and geographical areas such as cities. For

each group she computed averages of measured incomes and

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

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










income variation, has any influence on housing expenditure.

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

true income elasticity for housing. He calculates it to

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

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

reinterview survey in which some of the original respondents

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

fore, biased to this extent.

Frank de Leeuw has summarized and compared cross-

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

1971 article. His final thoughts indicate an elasticity of

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

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

to reconcile existing differences between four widely-read

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

de Leenw examines that done by Muth (mentioned earlier in

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

De Leeuw cites certain shortcomings in each of these works

and suggests how each noted "deficiency" would bias the

results that each of these four people has published. His

belief is that the original range of income elasticity re-

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

when corrected for the shortcomings he notes, narrowed con-

siderably. Numerically, he adjusts the other researchers'

results and narrows the range for income elasticity to











0.81 to 0.99 for renter-occupied households and to about

1.1 for owners.



Mobile-Home Housing


Economic literature dealing specifically with mobile

homes is almost nonexistent. This is probably a result of

several factors. First, mobile homes were used for permanent

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

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

as permanent housing expanded quickly thereafter. A X

second reason why mobile homes have received so little at-

tention in the professional literature is that, nationally,

they riake up such a small fraction of the total housing

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

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

accounted foi almost 22 percent of all housing units con-

structed in 1970. Table 2 shows the growth in production

of mobile homes since 1947.

Pobert French and Jeffrey Hladden published an

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

acteristics of rcbile homes at a national level. Their

analysis does little more than paint a picture of the

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

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

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











TABLE 2
Mobile-Home Shipments and Sales,


Manufacturers' Shipments
Year to Dealers in U.S.


1947-1973


Retail Sales
(Estinated)


$4,046,382,000
4,002,783,000
3,297,225,000
2,451,271,000
2,496,775,000
1,907,700,000
1,370,052,000
1,238,610,000
1,212,232,000
1,071,392,000
862,064,000
661,000,000
505,000,000
518,000,000
602,000,000
510,000,000
596,000,000
622,000,000

325,000,000
322,000,000
320,000,000
248,000,000
216,000,000
122,000,000
204,000,000
146,000,000


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


1 0-wide
12l--wicio


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


:ied from 1,300 in 1930 upward


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


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


1973
1972
1971
1970
1969
1968
1.967
1966
1965
1964
1963
1962
1961
1960
1959
1958
1957
1-956

1954
1953
1952
1951
1950
1949
1948
1947


566,920
575,940
496,570
401,190
412,690
317,950
240,360
217,300
216,470
191,320
150,840
118,000
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


~-~---------------------


-- I'-


---------------------










heavily in areas of rapid population increases and in areas

of low population density. There are generally fewer per-

sons per room in mobile homes than in conventional permanent

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

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

ing when gauged by either overcrowding or physical condi-

tion of the structure.

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

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

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

suggest "that the largest group of trailer dwellers are

young lower middle-class working families who are looking

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

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

writers do, by pointing out the need for further research

in the area.

Robert Berney and Arlyn Larson, following French and

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

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

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

same approach as that of French and Hadden. Data were

collected for each household on eleven different variables,

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

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

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









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

occupational and income distributions are almost identical

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

households. Retired households were found to have lower

incomes and fewer assets than working households. However,

neither price nor income elasticities were calculated for

their study.

The U.S. Department of Housing and Urban Development

published a volume entitled Housing Surveys in 1968. Part

2 of this volume dealt exclusively with mobile homes. Data

collected in 1966 revealed that the overall picture of

mobile homes and their residents was much the same as that

depicted in the two above-mentioned articles. Among other

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

roughly three-tenths that of a multiple-family structure

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

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

median household income for the entire national population.

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

was probably financed for seven years End the downpayment

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

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

were generally less-educated than the general population.

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

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

Standard Mletropolitan Statistical Area.










Some attitudinal questions were included in the

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

cal tests were performed with any of the data collected.

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

portance in affecting demand and no elasticities were

calculated.

Two books which take an encyclopedic approach to

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

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

nized revolution" in American housing. After an introduc-

tion which deals with mobile homes from an historical

perspective, she includes a chapter which is basically a

review of mobile-home literature. She covers studies in

trade-type publications such as the Mobile Home Journal

and Mobile Li-fe Magazine. As in previously mentioned

sources, the statistical approach involved hardly goes

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

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

deals with social changes leading to development and ex-

panded use of mobile homes as well as the institutional

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

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

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


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











The other book to be considered here is Housing

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

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

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

example, Davidson looks at mobile homes in relation to

other housing alternatives and attempts to discover the

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

section of the book which will be considered here.

Davidson divides his variables influencing demand

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

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

population, the selling price of the mobile home, financing

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

ables are demographic and social in nature. Included are

age distribution, valuation of leisure time, and impact

of changing social values. His final group of variables

is called "aesthetic and political." These include mobile

home design changes and mobile home park development.

Usi:qiI multiple regression analysis, Davidson derives

a linear r;odel to estimate parameters in several demand

equations by the ordinary least squares technique. He

estimates two demand equations for mobile homes. (These

equations are estimated on the basis of quarterly data

collecLte from a number of sources.)









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

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

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


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

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

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


where MIDt = demand for mobile homes, expressed as total

mobile home shipments

.MPCCt = a price variable, the average selling price

of a mobile home

PRt prime interest; rate

PDIt_1 = per capital disposable income

THHt- = total number of households

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

D = 1 for 1971 I and later

VRt_1 = vacancy rate (expressed as a percentage

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

MFt-1 = median family income

(Subscripts indicate whether observation is for same time

period or is lagged one quarter.)


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

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











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

tions includes a variable representing either of the

specific age groups observed to be the primary users of

mobile homes. It would appear that such an omission is

serious. Davidson's explanation for this omission centers

around the fact that inclusion of a variable he labels ASP

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

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

to become insignificant. He attributes this problem to

either multicollinearity or high correlation with the depen-

dent variable. It seems that a respecification of age-

specific variables would be preferable to ignoring the

factor altoqenher.

An elasticity of mobile home demand for personal

disposable income is calculated at -1.68S. This indicates

That an increase of one percent in personal disposable in-

come will cause mobile-horce demand to decrease by 1.688

percent. Such a finding would define mobile homes as an

inferior good. While this conclusion may not seem unreason-

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

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

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

inrco elasticity for mobile horm demand. This is one

particular aim of this piece of research.











Summary


While the studies just discussed are not exhaustive

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

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

Standard techniques for adapting statistical procedures

and model building to housing data have evolved. Multiple

regression seems to be the most widely used statistical

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

the researchers discussed above have estimated income elas-

ticity of demand for housing. The range of estimates is

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

bewildered as to just what the income elasticity is for

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

the data used and methodological differences. Perhaps, as

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

the surface. But these studies in the academic literature

are concerned only with conventional housing. Application

of the statistical techniques developed in the housing

demand literature has not been made to the mobile-home

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

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

elasticity of demand he found was negative leaves several

cuestjions unanswered, even after this attempt to analyze

the mobile-homre market.















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

some other expenditures, have both consumption and invest-

ment aspects. While one is consuming the housing services

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

ciating over time. Because of such conditions, one writer

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

a suitable topic for theoretical analysis:

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

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

most econclssts would agree that any economic good is de-

serving of t hooretical analysis. And an area as prominent











as that of housing attracts considerable attention, both

theoretical and otherwise.

Housing consumption studies have traditionally

looked at housing as strictly a consumption matter, ignor-

ing resale value, which might be greater than the original

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

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

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

depreciate over time much like an automobile and the in-

dustry even has several publications for estimating the

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

have their owni industry guides.

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

an easy matter to separate the value of the structure from

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

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

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

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

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

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

nhouinq sECrvices from the demand for neighborhood quality,

hi4w(vic.r est>i: ted.

Give:: this dichotomized process whereby the housing

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

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











thcrnore, in looking at the economics of this housing

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

sider the choice strictly as a consumption matter. Who

would make an investment decision knowing in advance that

the item invested in would depreciate? Only potential

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

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

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

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

appropriate to consider che purchase of a mobile home as

a case of purchasing a consumer durable good.

When a household (family or single person) enters

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

several categorical decisions to be made. These may be

made independently or jointly, and the order in which they

are rmsde will vary from case to case.

Probably the most effective constraint in the major-

ity of housing decisions is budgetary in nature. This is

simply a variant on one of the major building blocks of

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

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

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

living environment. So if income is an effective con-

straint for miost households, the ocher decisions will be

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










expenditures. Only if this figure is sufficiently large

can conventional home ownership be a viable alternative.

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

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

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

family structure, or a mobile home. Similar alternatives

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

cuses on households in Florida which have made decisions

to own or rent a mobile home.

The cost of housing in the United States has

climbed over the years to the point where talking about

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

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

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

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

still being produced, it is probably a mobile home.

Several facts operate to moke this statement defensible:

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

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

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

tio:om typically ii-ave fewer square feet than conventional

hous.es. (3) S:i ;r.n mobile- homes can be and are often located


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











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

value can be kept low. These factors combine to make

mobile-home housing relatively inexpensive as a housing

alternative. The only competition in terms of low monthly

expenditure would come from rental of old conventional

multi-unit structures. The market value for such units

could have fallen over time, due to physical deterioration

and/or undesirable location.

Florida's climate also lends itself to this par-

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

cool, even if construction quality is below conventional

housing, means lower utility bills. Taxes on mobile homes

are paid through annual license plate purchases and remain

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

unit, transaction costs are low and new furniture and

appliances are normally included in a new unit.

In addition to these factors, there is another force

operating on the demand side which is especially pertinent

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

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

achieve a whole new living environment and life style. A

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

find, especially in south Florida~. While retajinin some

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

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










In summary, there are a number of factors which make

mobile-home housing a desirable alternative in Florida.

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

ownership, flexibility in choice of environment (mobility),

a relatively well developed used mobile home market, and

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

residents are living in their units because they cannot

afford anything else. This fact, while not obvious from

one year's observed income, is more readily observable

when a permanent measure of income is considered.



Descriptive Overview--Florida and United States


Before grt.ting into actual mobile hone usage within

the state, some observations comparing Florida and the

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

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

cuses on an overall comparison of the United States and

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

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

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

housing units. As a perccnLage of this housing stock,

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

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

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

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























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

Florida worked in 1970 than was true for the nation,

supporting the idea of widespread retirement to Florida.

Connected with these phenomena is the fact that Florida's

1969 median family income was about 14 percent below

the national figure.

Table 4 focuses on only families living in mobile

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

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

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

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

contained only one person and considerably more were headed

by persons over sixty-five. Relatively fewer household

heads worked in Florida than in the United States, and

of the heads who were employed, a lesser percentage of

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

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

stantially less than their national counterparts. Finally,

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

quite mobile.

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

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

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












TALE 4
Characteristics of Mobile Home Households for
the United States and Floiida, 1970


United States
Owner- Renter-
occupied cccupied


1,752,577 321,417


Mobile-home
households


Florida
Owner- Ren ter-
occupied occupied


147,970 23,499


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

% of household;
whose head is
65e years

Median school years
conmplted by head

- cf heads :.-ployc
:n L659

t of employed heads
who held "blue
collar" jobs

Mediian 1969 f,:aily
inches (to rnearse
$100;

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


18.1


11.8


70. 0




50.0


12.8


12.0


64.2




67.6


7,800 5,800




1.3.9 26.2


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


23.4




39.9


11.3


46.9




30.9




6,300




26.1


65.2




5,200




37.0










comes are relatively low, and families are typically small.

Especially in Florida, the mobile home is an apparently

attractive housing choice for older people.



Public Use Sample


The primary data source to be used in this demand

study is the Public Use Sample of Basic Records from the

1970 United States Census. This cross-sectional data

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

representative sample which combines both the Census of

Population and the Census of Housing to make available

records for both persons and households. Observations

at the state and Standard Metropolitan Statistical Area

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

created on the nodal-function area concept developed by

the Bureau of Economic Analysis' Regional Economics Div-

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

and the four-digit numeric identifier of each.

Area 31 is northeast Florida and eight southeastern

Georgia counties which are heavily influenced by the Jack-

sonvilie S SA (subarea 3101). Subarea 3102 includes

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

cluddcs Orlando as subarea 3201. South Florida is Area 33

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

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





46

























AREA 34



















'<


0-





AREA
33 '

















FIGURE 1
PubJic Use Sample Areas and
Subareas for Florida


AREA 35










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

is west-central Florida and includes Tallahassee, the

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

western-most Florida counties and one adjoining Alabama

county. Pensacola is the dominant city in this area.

There are approximately 125 variables available

per observation. These variables constitute data covering

persons and households. For example, there are structural

characteristics about the person's dwelling unit as well

as financial characteristics about the house value or

rental rate. At the individual level there are charac-

teristics and attributes, including data on age, race,

sex, education, and income.

For purposes of this study, data were collected

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

home households. Combining the information on these per-

son records constitutes a household record. On the one-

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

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

and 6,733 are renter-occupied units.

Selected characteristics for the county groups are

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

of all owncr-occupied and rentcr-occupied housing units

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

rented.






















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For owner-occupied housing, mobile homes constitute

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

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

areas, mobile homes account for more than 10 percent of

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

county groups, the percentage of mobile home owners is more

predominately white than the racial composition for all

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

that overall, white incomes are above non-white incomes

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

possible that zoning restrictions on location of mobile

homes may be important in explaining why nonwhite families

do not choose to live in mobile homes.

Also without exception in each area, mobile home

families have lower average incomes than do other families

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

mean incomes were about 80 percent of those for all

home owners but in subarea 3302 they were not much over

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

but thee of the county groups this disparity in incomes

pa.railels educational differences. In these three regions

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

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

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

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

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





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.

Demographically, the renters of mobile homes are in some

respects like other renters and in other respects like

mobile-home owners. They are generally highly mobile and

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

largely whire-headed families with below-average educational

attainment (except for western Florida). Their family

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

and considerably below all owners' incomes. Because of the

small number of households involved, renter-occupied mobile

homes are not disaggregated below the five major area

groups.

It appears that mobile home owners are drawn from

both the potential renter and owner markets. If income

is the relevant constraint for most families, however, it

might be concluded that, on the basis of observed 1969

family income, mobile-home owners come primarily from the

potential renter segment rather than from the potential

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










size, mobile-home owners approximate the characteristics

of all renters. Mobile-home ownership is apparently

closely related to the life cycle. Young couples and

older people find them to be a satisfactory housing alter-

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

them as permanent housing.

Some parts of south Florida are heavily populated

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

Citrus counties had 1970 populations for which one out of

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

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

of its population over sixty years than does any other

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

lation was over sixty while the comparable figure tor the

nation was 14.9 percent (Housing of Senior Citizens,

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

home owners at the national and state levels. When one

analyzes home ownership, he can expect to find certain

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

cidence of home ownership would be increasing. Very few

young people have the financial resources needed for pur-

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

state levels. The greatest pcicentage of United States

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

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





55








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people making housing adjustments in order to get away

from the necessary maintenance and the natural decline

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

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

people causes the incidence of ownership to increase with

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

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

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

range, where conventional home ownership peaks, the inci-

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

pattern for the nation. For Florida the same generaliza-

tions can be noted with certain modifications. Almost

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

sixty-five, Thisi housing choice is extremely popular

among Florida's older population. Florida hlas more than

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

a mobile home.
















CHAPTER IV
METHODOLOGY AND MODELS



As a minimum, microeconomic theory suggests that

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

the price of competing goods, the incomes of potential

demaniders, and existing tastes and preferences (which are

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

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

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

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

variables which will be dealt with in this research are,

to some extent, quantifiable, and may be classified as

demographic in nature.

Before rwe proceed to deal with the variables con-

sidered, a note on cross-section consumer demand studies

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

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

as follows:

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











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

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

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

advantage over time-series data--serial correlation does

not have to be dealt with. Otherwise, statistical analysis

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

series data require repetition in collection, such is not

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

tional chances or method of collection changes which often

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

with cross-section data. Any observations which are not

comparable with the rest of the data may be deleted without

breaking the time series.



Models to Be Estimated


The point of this research is to analyze the demand

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

arisino from the owner and renter sides of the market.











Owners purchase their mobile homes and pay for them either

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

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

tional apartments do.

It cannot be determined from the data to be analyzed

whether mobile-home occupants made their decision to live

in a mobile home first, with other housing considerations

following, or whether the budget constraint was considered

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

given these different approaches, a single model could not

describe both processes accurately since in one case the

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

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

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

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

It yields insight into the question "what type of family

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

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

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

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

nbeen made.


lode l A

Tenure choice is the biggest single decision a

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

decision to rent someone else's property or to purchase











one's own. Several approaches to exploring this choice

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

researchers. Struyk and Marshall have published an article

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

ship between tenure choice and income." Carliner published

a similar article (1974) at approximately the same time

which examines the same issue in a very similar manner.

Both research efforts use ordinary least squares

(OLS) regression techniques to examine conventional home

ownership. What is interesting about their work, however,

is that the dependent variable in their models is discrete

in nature. The dependent variable is defined as "home

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

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

effect, a dummy dependent variable. For example, consider

the following equation:


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


where

OWN tenure choice; if the household lives in

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

INCOME = family income, measured in dollars; this

figure may be observed annual income or

some measure of permanent income

FAMSSIZE = a dummrny variable for family size; if the

nurbocr of persons in a family is five or











more FAMSIZE = 1; otherwise, FAMSIZE =0

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

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

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

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

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

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

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

culated from actual data


Income is the only variable measured continuously.

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

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

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

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

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

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

= 0 and OLD = 1 for that household observation.

If household data are analyzed and the regression

coefficients are calculated, we may find that:


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

.20(YOUNG) .15(OLD)


The dummiy variables relating to age have coefficients which

express the difference in probability of cwnership from

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










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

consists of families whose head is between 30 and 65 years

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

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

family is 20 percent less than the probability of owner-

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

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

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

reference group.

Both Carliner's and Struyk and Marshall's studies

showed some demographic factors to be significant predic-

tors of ownership probability. Additionally, Carliner's

work estimates that the probability of home ownership (for

his entire sample) goes up 1.62 percentage points for each

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

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

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

Struyk and Marshall found income elasticities ranging from

-0.276 for primary individual households where the person's

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

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

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

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

A similar model was set up for Florida. The model

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

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











TENURE = bo


+ b (FAMINCOM) + b MARRIEDE) +

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

b (DFEMHEAD) + b,(DFMSZLE2)

b7(DFMSZGE5) + b,(DHDNONWH) +

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

bli(DEDCG) + bl2(DUMIGRAN) +

bi3 (DUMARMY) + 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 dummy variable for age; 0 if head is

twenty-five or under, 1 if head is over

twenty-five

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

five, 1 if head is sixty-five or over

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

male, 1 if female

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

two people, ] if two or one

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

five people, 1 if five or more

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

if non-white


where


I



r



D



D
L:


I

I












DEDLTHS = a dummy for educational attainment of

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

a high-school graduate

DEDSC = a dummy for educational attainment of

head; 0 if head never attended college,

1 if head did attend college

DEDCG = a dummy for educational attainment of

head; 0 if head did not graduate from

college, 1 if head did graduate from col-

lege

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

Florida five years ago, 1 if head moved

into Florida between 1965 and 1970

DUMAKMY = a dummy for armed services head; 0 if

civilian, 1 if head is member of armed

forces

DSTUDENT = a dummy for current enrollment status; 0

if head is not a student, 1 if head is

enrolled in school

IENURE = a dichotomous variable which takes on a

value of 0 if the dwelling is not owned

by the family occupying it and takes on a

value of 1 if the housing unit is owner-

occupied for the ALL OWNLERESHIIP model; for

the MOBILE-HOMEN OCIWNE;RSHIP version it takes











on a value of 1 if the family owns and

lives in its own mobile home

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

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

Sample) by setting the dependent variable of home ownership

equal to 1 if the household owns its dwelling, whatever

type, and 0 otherwise. Fourteen independent variables were

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

The income measure used was the 1969 observed family income,

in $100 units. The dummy independent variables included

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

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

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

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

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

culated also, so the coefficient for each dunury variable

represents the (percentage) deviation from the reference

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

the summary of the ALL OWNERSHIP regression in Table 15

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

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

college, and college graduate. This group might be thought

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

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

represents the deviation from the basa group for the group











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

graduate owned their own home 2.2 percent less often than

families whose head was a high-school graduate.

The model was estimated for all owner-occupied

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

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

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

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

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

Estimating the all home ownership model first and comparing

the results with the mobile-home ownership model should

permit one to ascertain whether the same variables are use-

ful in explaining mobile home ownership. Results of these

estimations are discussed in Chapter V.


Model B

Once the decision to live in a mobile home has been

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

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

decided,. Model B is a more conventional regression model

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

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

statistically powerful tool. The model is used to estimate

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

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

hon' es.










Owner-occuited mobile hones

Dependent Variable. Most housing studies which have

estimated the demand for housing at the micro level have

used either house value or housing cost as the dependent

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

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

use house value as their dependent variable. Most precisely,

the demand for housing is a demand for housing services

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

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

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

the mobile home. The utility provided by a mobile home

which satisfies the demand for housing services is the

basis upon which the demand for mobile homes is founded.

This utility is not directly observable or measurable,

but the dollars spent to satisfy the demand for housing

services are observable and measurable. A new or used

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

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

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

a mobile home provides housing services as long as it is

occupied, however, it was felt that housing expenditure

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

actual demand for these services. Therefore the dependent

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










per year. This measure of demand will take into account

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

period over which the unit is utilized. Expenditure will

be defined here as the estimated purchase price divided

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

result will be annual housing expenditure.* Value of the

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

mining annual expenditure.

Within the Public Use Sample house value has been

collected for conventional housing units, but has not

been collected for mobile homes. It was therefore neces-

sary to estimate each mobile home's purchase price in

order to derive expenditure. This would be the dollar amount

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

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

which were collected directly.

For each household the following data, which were

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

rive at an expense figure:


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











1. Number of rooms (NROOM)

2. Number of baths (NBATH)

3, Presence of air conditioning (AIRCON)

4. Presence of piped hot water (HOTWATER)

5. Presence of full plumbing (PLUMBING)

6. Type of sewerage (SEWAGE)

7. Source of water (WATERSOU)

8. Type of heating (HEATING)

9. Year in which unit was built (YRBILT)

10. Year in which family bought mobile

home (Y.RVD)

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

structural characteristics and its age. Items 1 through 8

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

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

matic depicting how the actual items have been used.

Determining the value of a mobile home is a fairly

straight-forward, commonplace procedure in some instances.

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

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

passes through the market. The problem in valuation of the

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

are not passing through a market at the time of Census

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

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






























a)
0






c
0

















0
0






-,--


in
0 ;)
O





43
a










omission is unfortunate because it makes necessary a good

deal of work to ascertain the values of enumerated units.

This valuation is almost certainly less accurate than that

which could have been obtained from the occupant who pur-

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

wealth of information which is available from the Census, a

valuation model for mobile homes can be constructed.

Within the mobile-home industry there are several

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

The procedures and presentation of the information are very

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

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

Berner and used widely by dealers. Another widely used data

source is the Unicomp Directory of Used Mobile Homes. In

these publications mobile homes are broken down by manufac-

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

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

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

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

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

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

loss of value the first year and five percent per year

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

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

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











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

perience would provide better data.

Data collected from a 1974 copy of the Unicomp

Directory revealed the depreciation pattern reflected in

Table 9. Depreciation actually computed from Unicomp data

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

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

and discussions with people working in the mobile-home

industry.


TABLE 9
Percent Depreciation by


(Of New Price)
% Value Loss

20
6
6
6
5
5
4
4
4
4
3
3
3
2
2
2
2
1
1
1


SOUrCE: Urnicom;p DirecLo:ry
personnel .


Age of Unit


% of Original
Value Retained

80
74
68
62
57
52
48
44
40
36
33
30
27
25
23
21
19
18
17
16


and discussions wiith industry











Table 10 shows the average value of new mobile homes

produced from 1950 through 1970 and indexes average selling

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

from industry data which are published in Flash Facts. It

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

based upon average selling price in 1970. For instance,

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

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

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

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

full year for the twelve-foot-wide unit.

The most significant determinant of the price of a

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

according to industry specifications, a mobile home must

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

Anything smaller is a travel trailer. While conventional

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

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

number of bathrooms. This discrepancy is offset by the

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

same size. Second and third bedrooms are usually a foot

or two smaller than average, and living rooms are quite

often several feet longer than average. The values listed

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

for 1970. The process used basically involved translating











TABLE 10
Average Value of New Mobile Homes by Year Built


Year Average Value Index

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


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


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

An intermediate step in the process involves matching up the

number of rooms with conventional industry sizing (number

of feet l.ong). For instance, a unit with four rooms and one

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

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

65 feet long.

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

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












TABLE 11
Value of New 1970 Mobile Homes


Number of Rooms

1
2
3
4
4
5
5
5
6
6
6
7
7
7
8
8
8
9
9
9


Number of Baths

1
1
1
1
1l
1
1
2
1
1
2
1
1
2
1
1
2
1
1
2


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

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

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

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

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

designed to coincide with the industry's conventions for

sizin g.

Other stluctural characteristics influence a unit's

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

account in the valuation model presented here.


Value

3,200
4,200
5,400
6,200
7,000
8,100
8,900
9,400
10,700
11,500
12,000
12,700
13,500
14,000
12,900
13,700
14,200
14,300
15,100
15,600










TABLE 12
Characteristic Components in Mobile Home Valuation Model


Characteristic

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


Adjustment to Value

+200
+400
+600
-100
-300
-400
-400
-200
-300
-300
-100

-200
-100

-300


Items 1 through 10 are actual structural characteris-

tics of individual units. The dollar adjustments are esti-

mates of the actual cost of adding the service mentioned or

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

ticular feature.

Items 11 through 14 deal with water and sewerage

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

proxies reflecting the type of environment in which the unit

is placed. These items hopefully parallel quality differ-

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

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

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

likely to find public or mruncipal water and sewerage sys-











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

where water is from an individual well and a septic tank

handles sewerage is least likely to be a custom designed

or built unit. Some account of quality variation is the

raison d'etre for items 11 through 14.

Drawing these pieces of information together is the

next step in the valuation model. The items listed in

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

called VALUE70. VALUE70 is what every mobile home would

sell for (based on its structural characteristics) if it

was built and bought in 1970. This step standardizes units

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

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

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

unit is then depreciated (in accordance with industry expe-

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

when it was purchased. The product of VALUE70 and INDEX

and DEPRECIATION yields COST. This is the calculated

market value of the mobile home when it was purchased by

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

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

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

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

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

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











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

bought new then it would not be depreciated to find its

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

been purchased by its occupants in 1963, it would have

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

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

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

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

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

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

purchased by its current (in 1970) occupant. Deriving

annual housing expense involves one further step.

COST is divided by the number of years which the

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

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

of five years was made because a study published by the

Florida Mobilehome and Recreational Vehicle Association

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

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

resident households had lived in their mobile homes was

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

length of residency at the same address for its sample of

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

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

home depreciates after it is purchased, the financial obli-










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

by depreciation. This expense is defined and constructed

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

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

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

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

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

figured in. This seems preferable since financing is a

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

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

of families who purchased their own mobile home in Florida

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

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

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

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

able in the model to be estimated.

Independent Variables. The relationship of primary

importance in this research is that between expenditures

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

ship is measured by the concept of income elasticity of

demand which is defined as the relative change in expendi-

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

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

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

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

sqneral relationship has been examined extensively in the












housing literature (see Chapter II) for conventional hous-

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

explored with mobile home housing. Because of the interest

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

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 durable goods, are made on the basis of a

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

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

credit for breaking ground in the area of a theoretical basis

for "permanent income." He concluded that consuming units

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

their income. It seems almost certain that housing deci-

sions are based on an even longer time horizon.

What concept of income is appropriate for use with

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

housing payments are known in advance and must be met

regularly, a cash flow concept for housing service becomes

the relevant consideration. Liquidation of non-liquid

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

rogulzr financial obligation. It is possible that such ac-











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

debt repayment on the basis of higher expected income,

however. This might involve liquidating assets to make

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

mally involve such portfolio management.

Most individual's incomes are directly related to

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

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

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

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

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

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

sex and race.

In developing a concept of permanent income (which

is, itself, not directly observable) these factors should

be used as inputs. Three variants of permanent income,

each embodying different assumptions, were calculated and

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

priateness and predictive power. These variants, YPERMFAM,

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

b.ised on slightly different assumptions.

The YPERMFAM concept is "pure permanent- income" as

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

peLrience and is an income measure based solely on each

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










Development of the earnings model used to ascertain perma-

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

now be presented.

The one-percent Public Use Sample for the state of

Florida contains 40,790 person records. Those persons who

had no 1969 income were excluded from those who formed the

basis for the earnings model being developed. Then, for

each of the twelve major occupational groups identified by

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

mated to predict individual yearly earnings. The model for

each occupational group is in the general form:


log(earnings) = bo


education -



experience =

sexd'mmy =i

racedura'iY =-

Spainish-A Jericandumny =


4 b educationn) + b,(experience) -

b3(experience') + b (sexdumny) +

bh (racedun.my) + b, (Spanish--

Americanaumirmy)


the highest year of school

attended

(1969 age) (6) (education)

1 if female, 0 otherwise

1 if nonwhite, 0 otherwise

1 i f Spanish-American, 0 oth-

erwise


where:












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

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

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

The earnings term, which is the dependent variable,

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

form. The statistical rationale for this transformation

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

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

squared because there is evidence that the earnings-

schooling relationship is not linear. The experience terms

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

relationship between earnings and age is the familiar in-

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

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

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

perience was defined as age minus years of schooling minus

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

precautions were taken so that persons with little or no

schooling were not allowed to enter the labor force before

age sixteen in the model. The dummy variables take account

of racial and sex differences. The variable coefficients,

with F statistics in parentheses, are presented in Table

13.

To di'ronstrate tite operation of the model and the

use of the calculated coefficients, examining a hypotheti-















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cal individual might be useful. Consider an individual with

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

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

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

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

the Professional and Technical occupation group and the

coefficients for that group would be used to generate his

permanent income:


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

0.00055(142) 0.33582(0)

+ 0.03182(0) + 0.00898(0)

= 3.52262 + 0.22272 + 0.37240 0.10780

0 + 0 + 0

= 4.00994 = $10,232


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

come would have been lower because the dummy variable taking

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

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

duced by 0.33582. Earnings can be generated for the same

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

time period.

This model can he used to generate incomes which can

ibe usd for construction of a permanent income concept.

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

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











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

attributable to transitory factors--the effects of which

permanent income seeks to minimize.

Use of these models allows movement of an individual

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

demand study, was derived to include the time period during

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

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

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

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

tion relevant to the family in question is from 1963 to

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

to generate incomes for each adult person in the family

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

comes are then totaled and divided by the number of years

involved to get a permanent income measure for the time

period during which the family was consuming mobile-home

housing services. The permanent incomes for the household's

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

the family's permanent income.

YPL:RMFAM is one variant of permanent income. It

completely ignores an individual's own earnings experience

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

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

This is in line with the theoretical construct of permanent










income which seems to eliminate individual, transitory

fluctuations of income.

FMINTR is a variant of permanent income which ex-

plicitly assumes that the 1969 observation of an individual's

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

on circumstances or attributes which were not of a tran-

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

thetical family:


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

Head 12,000 9,000 1.33 15,000 19,995
Wife 4,000 6,000 .67 7,500 4,995
Family 16,000 15,000 22,500 24,990


In this family, the husband earned $3,000 more in 1969 than

the earnings function outlined in the previous section had

estimated he would earn. The wife earned $2,000 less than

predicted for 1969. The ratio of observed to generated in-

comes is applied for both husband and wife and the resulting

figures are summed to get FMINTR. In this case, perhaps

the husband is a better engineer than his peers, arid hence

the 1969 income he earned is based on "permanent" factors.

Likewise, perhaps the wife worked only half-time in 1969

and this is a "permanent" employment posture for her.

FMINTR assumes that the observed 1969 experience for both

hiibandnd ad wife is not the result of temporary factors.











This variant of permanent income may be best where this

assumption holds. Otherwise, it would be a poor measure

of permanent income. The next concept introduced embodies

a different assumption.

INCFAM is another variant of permanent income. It

suns the husband's generated permanent income and the wife's

observed 1969 income. The rationale behind this concept

is that the norm for the husband is full-time employment,

but what was observed in 1969 for the wife is her norm. If

her 1969 income was low because she worked only half-time,

perhaps half-time was her regular work routine. So INCFAM

is a permanent income-observed income hybrid.

The F2AMINCOM variant of income is simply obsarvcd

1969 family income. It is not permanent income, but actual

1969 experience. It is the sum of 1969 income for the house-

hold head and the spouse, if one is present.

Price variable. As mentioned in the first section

of this chapter, economic theory suggests that the price

of a good and the prices of other "competing" goods be

included in a model of demand. In an effort to do so in

the present model, a price index was constructed. This

PRICE variable for mobile homes is actually a relative

price measure. It is defined as the average cost of a

mobile home divided by the average cost of construction cf

a conventional singlo-fmrily house for the year in which the











family purchased its mobile home. Recognizing that this

is a crude measure of prices, it should be added that data

for a more appropriate set of prices are extremely diffi-

cult to ascertain. For instance, one might suspect that

a price variable based on rental housing costs would also

be useful. Or perhaps the price variable should be based

on both the costs of ownership and of renting. Any single

price measure will necessarily be an abstraction from

reality. The further removed from real-world alternatives,

however, the less likely a price variable would be to cap-

ture the influence exerted by actual price variation among

alternatives. Depending upon one's financial capability,

the range of choices may include owning or renting new or

old property. While rising costs of new units may exert

some upward pull on the price of oldar units, an older unit

could be depreciating at a faster rate than new construc-

tion costs are rising. So ultimately the price of a unique

housing unit may be a unique price. Unfortunately no price

variable which includes rental housing cost could be found

or constructed. The problem is a lack of data. While con-

struction costs do vary from place to place, materials

are transportable, and labor costs do not vary greatly since

most carpenters are unionized. But construction costs,

even if estimiitable, are only one component of the total cost

of securing housing. Supply and demand forces are also of












primary importance, and these forces vary considerably

from city to city and from county to county. Consequently,

a standard type of rental housing might rent for signifi-

cantly different rates in different places. A price vari-

able reflecting the price of rental housing would then need

to be available by area for the years in which the sampled

families made their mobile-home housing choice. These data

are not to be found. A rental price index was developed

for each Florida county for 1960 and 1970. A price variable

for non-census years could not be constructed, however, and

the concept of a more comprehensive price variable had to

be abandoned. Table 14 shows the price variable which was

used from 1950 through 1970.

Other independent variables. The NPERSON variable

is simply the number of persons in the household. A posi-

tive correlation between family size and demand for shelter

space would seem appropriate.

DUMGE65 and DUMLE25 are two age-related dummy vari-

ables utilized as independent variables because of the

importance of the life cycle in housing demand. Two dummy

variables are used because it was felt that the bimodal

age distribution found by other researchers might be the

case in Florida also.

The DUMRACE dumiiy variable takes on a value of 1 if

tih household head is nonwhite, but is 0 otherwise. The












TABLE 14
Relative Prices of Conventional and Mobile Homes


Avg. Cost of
Mobile Home ($)

3,423
3,685
3,855
4,187
4,276
4,129
5,003
4,996
5,000
4,996
4,995
5,599
5,602
5,715
5,500
5,600
5,700
5,700
6,000

6,062


Avg. Cost of
Conventional House ($)

8,675
9,300
9,475
9,950
10,625
11,350
12,225
13,025
12,950
13,425
13,725
13,825
14,325
14,875
15,425
16,150
16,750
17,325
18,525

19,045


SOURCE: Mobile Home Manufacturer's Association and Statisti-
cal Abstract of the U.S., various years.


purpose of its existence is to allow examination of whether

nonwhites consume less mobile-home housing services (i.e.,

demand loss) than whites do.

DUMHDSEX is a dummy that takes on a value of 1 if

the household head is female. Its presence will allow us

to determine whether female-headed households demand the

samet amount of mobile-home housing services as do male-

heo"ded households.


Year

1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1963
1969-
1970(I)


Price
Ratio

.3946
.3962
.4069
.4208
.4204
.3638
.4092
.3836
.3861
.3721
.3639
.4050
.3911
.3842
.3630
.3467
.3403
.3290
.3239

.3184




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PAGE 1

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 orVHi:: rco^jir^msnts for ohx DEGU.^:r; of [JOCTOk or .^'alLOSOPHY unja^rrs lt 197

PAGE 2

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)

PAGE 3

ACKNO^'TLEDGMENTS I would like to acknowledge several people who helped make this work possible. Sonya Strader, wife ext raordinaire , 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.

PAGE 4

TABL3 OF CONTENTS Pag e 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
PAGE 5

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

PAGE 6

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 CROSSSECT 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 income 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 reviewed' 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, differentmethodologies, and different definitions of botih inconie and housing expend;. tures cathe individual research-

PAGE 7

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 incomicbut 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 utilize::. 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

PAGE 8

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 moderacely 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 belowaverage 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 incomicthan are owner's expenditures. It scem.s that renting e; mobile home v/as a temporciy housing choice for many of ':hose who were renting in 197 0.

PAGE 9

CHAP TEH I INTRODUCTION Gei'ierai Setting: Housi n g Neads a nd Al tern atives The Housing and Urban Development Act of 1968 set a national goal of providing 26 raillion new and rehabilitated 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 alternative 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. Department 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

PAGE 10

and Mortgaae Credit-Federal Housing Administration Conoissicner 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 environment considerably sooner than if they vere to wait for the availabiJity of conventional m>uitif 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 oneh:?i.f of all Ar^erican famiiies v/ere unable to pay more chan ?15,000 for a hopie . And of the lessthan-?]. 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 conventional scruccures. Hiuce these lv70 figures were comeiled,

PAGE 11

th>.; incoiv.e-housing cost disparity has v/idened, causing the budgetjiry problems faced by many families to become more acute. Ho using 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 serious 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] maricetWe need specific information about socio-econoTaic characteristics 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

PAGE 12

Tfiis study will build specifically upon the v:ellestablished 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 literature in economics is relatively well-developed for conventional 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 determinants 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 lowinconio 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-

PAGE 13

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. Re sea r ch Design and Methodo logy 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

PAGE 14

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 research 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 observations, 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

PAGE 15

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 elasticity 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 variables require special attention. In terms of socioeconomic factors it would seem worthwhile to differentiate betweer. owners and renters in order to determine what influence 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

PAGE 16

mark.3t or the hooie-owner market? We would want reliable answers to these questions before formulating housing policies 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 important v/orks will be considered and recent work on mobilehom^e housing will also be examined. Chapter III will explore 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.

PAGE 17

C's of Illness o f Mobile(-1 oiT. e Do r. . rind Reso arch This research is an exterision of the existing literature 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.

PAGE 18

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-

PAGE 19

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

PAGE 20

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 explanation, still forms the basis for most present-day statements about 'Engel's laws'." It seemed to Stigler that the relationship between income and housing which is usually referred to as Engel's Law had not been empirically verified. Hermann Schwabe (1868, p. 266) proposed a consumption law relating specifically to housing: "The poorer anyone 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. Subsequent 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 modified his approach to the issue in order to utilize the data he had. Ti-.e studit^s arc listed chronologically by data

PAGE 21

13

PAGE 22

14

PAGE 23

15 1' u ^ Bi O O n -J a p. ^ CI en 3:5 O o M rH

PAGE 24

16 a o C .X Q E ^ o U f3 rH

PAGE 25

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 concerned with estimating the price elasticity of demand for housing. The majority of these studies (sixteen of twentytwo 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 betv;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 demand (n.,) .

PAGE 26

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 expenditures 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 account 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 appropi: 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

PAGE 27

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) conclusion that "the actual amount of space acquired [is] relatively 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\-^-, 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 storkderf,.Hnd equation takes the forni: h^ Ap + By^^ 4Cr

PAGE 28

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) asserts: "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 thescriticisms deals with his asEu;aptions. In the derivation of a "unit of housing

PAGE 29

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 possibilities for input substitution. (2) As with Winnick, capital values may h.e a less desirable measure for housing preferences 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.

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Tong Hun Lee has also done work of note with timeseries 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 elasticities. 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 permanentincome 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.

PAGE 31

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 foihousing stock is smaller than one, v.'hile its price elasticity 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 variables (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.

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Conventi onal 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 elasticities 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.

PAGE 33

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 differences in permanent housing components that should be explained 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

PAGE 34

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, therefore, biased to this extent. Frank de Leeuw has summarized and compared crosssection 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 reported by these four rcsearchers--0 . 6 to 2.1 — is actually, when corrected for the shortcomings he noces, narrowed considerably. Niiraerically , he adjusts the other researchers' results and riarrows the range for ivicom^o elasticity to

PAGE 35

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 tenfoot-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 attention 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 constructed in 1970. Table 2 shows the growth in production of mobile hoRiis since 194 7. Fobertc'reach and Jeffrey Hadden published an article iri J'''''--l':^i_£''^'"-in.9 "i_9_f' iii 1965 v/hich analyzes thiC: cl'uiractcristics 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

PAGE 36

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

PAGE 37

29 heavily in areas of rapid population increases and in areas of lois' population density. There are generally fewer persons per room in mobile homes than in conventional permanent houses, but the rooms are also smaller. Contrary to conventional wisdom, mobile hones are not "substandard" housing v;hen gauged by either overcrov/ding or physical condition of the structure. In terms of the ages of the people who live in mobile 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 selected and 1:he study perfornvad wi'ch afi eye to implications for

PAGE 38

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 threetenths that of a m.ultiplefamily 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.

PAGE 39

31 SoH'e attitudinal questions were included in the survey form, but other than averages, almost no statistical tests were performed with any of the data collected. No effort was made to ascertain what factors wore of importance 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 "unrecognized revolution" in American housing. After an introduction 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 tradeLype publications such as the Mobi le Hom.e Jojjrnal and M?J:iLli?..±J-_if _^'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 (nontechnical) mobile-home liLerature from a scciclogical point of view.* *A ru?.^: edition of this book was publish.ed in 1976.

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32 The other book to be considered here is Housirxg De mand : Mqb_ilc^ Modula r, or Conventiona l 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 variables 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.)

PAGE 41

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 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 a m 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

PAGE 42

34 significant at the 5 percent level. Neither of these equations 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 dependent variable. It seems that a resoecif ication of agespecific 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 income -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 unreasonable, the income variable is an observed one, not a pen;\anent 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.

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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 elasticity 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.

PAGE 44

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 prio ri, 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 investment aspects. While one is consuming the housing services rendered by a structure, the structure itself raay be appreciating' 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 complexiuics, 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 deserving of theoretical analysis. And an area as prominent 36

PAGE 45

as that of housing attracts considerable attention, both theoretical and otherwise. Housing consumption studies have traditionally looked at housing as strictly a consumption matter, ignoring resale value, v/hich might be greater than the original purchase price. So, v/hereas conventional housing may appraX 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 industry 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 (whotlier 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-

PAGE 46

38 thomore, in looking at the econca'iics of this housing choice, it seems obvious that it is appropriate to consider 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 taxation 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 majority 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 consl:raint for most nouseholds, Lhcoti^er decisi.ons will be made following a decision iibouf luaximum uff(')rdcible housing

PAGE 47

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 inglefamily structure, a unit in a multifamily structure, or a m.obile hom.e . Similar alternatives exist in the owner-occupied sector also. This study focuses 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 unicorn--if it ever existed, it is now only a memory. Rising costs h.ave prevented many families from being able to consider 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 Ani;.h.o;iy Downs, urban Proc J €:^-3 a!id Prospect '> ((.'hicaqo, 1976),

PAGE 48

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 particular 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.

PAGE 49

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, singlefamily 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 focuses 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

PAGE 50

42 ItJ

PAGE 51

43 of population over sixtyfive 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 population.) 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 substantially less tlian their national counterparts. Finally, Florida mobilehorae 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-

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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 ^ OwnerRenterO.vnorRenteroccupied occupied occupied occupied Mobile-home households 1,752,577 321,417 147,970 2 3,499 One-person households (% of total Toobile home households) 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.

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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. Publi c 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 Divsiori. 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 Jacksonville SMS7i (subarea 3101). Subarea 3102 includes Gainesville and Ocala. Area 32 is central Florida and includ'-->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-

PAGE 54

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

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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 characteristics 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 mobilehone households. Ccmhining the information on these person records constitutes a household record. On the onepercent 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 .

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43 m fi r) rj ro .-n f^ CM cN rg rj v£> T t^ un 1J3 in iH n lo M O ^ ^ O rH O oi-irior-f-iroin OrHcricOrHr-cor~ aJ c/) in rv 1-0 n T CO r^ CN vo rr-~ r^ r^ rr~ CO V o t— ( LO r^ i o o o rr•~i O vD fl 1,0 IJT; o o r-j m in o O uui o i;-i KO •vr m o ra) CI 0000

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49 Q O m oil ^^ •I' o r-i rj ro ri_n ^ rj r; (N :s: o cr n (M r~CN CTi r^ CNl CTv en -^ r~ in CD (T. f^ m r~ ij2 1.0 LTi >jD M" m LTI vC (N cr CO cj o r^
PAGE 58

50

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52 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 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 mobilehome ov/ners are significantly older than arc all owners. And in all areas, the mobilehome owiior^: are, indeed, mc^re mobile---a lesser percentage

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53 having lived in the state five years before the census inevery 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
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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 alternative, 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 population 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 incidence of homo ownership would be incz"easing. Very few young people have the f iiiancial resources needed for purchasing 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

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55 O G OJ

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56 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 sixtyfive 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 incidence of m.obile-home ownership is lowest. This is the pattern for the nation. For Florida the same generalizations 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.

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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 considered, 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 consumers the assuTupcion has to be made that by observing 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 particuj 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

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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 principle, the assumption made need not be so restrictive 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 circumstances acting on consumers who react in substantially 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 timeseries data require repetition in collection, such is not the case with cross-section data. The problems of definitional 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.

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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 conventional 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

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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 relationship 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 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 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

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61 more FAt-lSIZE = 1; otherwise, FAMSIZE =0 YOUNG ^ a d^jiTimy variable for the age of the family's head; if the head's age is less than 30, YOUNG = 1; otherwise, YOUNG ^ OLD ^ a dummy variable for the age of the family's head; if the head's age is more than 65, OLD = 1; other\v'ise, OLD = a,b,c,d,e = numerical regression coefficients calculated from actual data Income is the only variable measured continuously. While family size and age of head can be measured as discrete variables, they have been set up to define dummy variables in this example. For instance if the household head's aqe is 23, YOUNG -1 and OLD for that household observation. If the head's age is 35, YOUNG = and OLD = for that household observation. If the head's age is 68, YOUNG = 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 agehave coefficients which express the difference in probability of cwiiership from thfc "referen-o group. " Since durmvy variabl<->s were estab-

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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 ownership 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 predictors 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 husbandv;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 owneroccupied housing are included. The mode] can be written as:

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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) 4b]3(DEDSC) + bi J (DEDCG) + bj 2 (DUMIGRAN) -Ibj 3 (DUMARI4Y) + b, ^ (DSTUDENT) FAMINCOM = 1969 observed family income, in $100 units DMARRIED = a dummy variable for marital status of the family head; if single, 1 if married DUMLE25 = a dunimy variable for age; if head is twerity-five or under, 1 if head is over twentyfive DUMGE65 a dumm.y for age; if head is under sixtyfive, 1 if head is sixtyfive or over DFEMHEAD = 3 dummy for sex of household head; if male, 1 if female DFMSZLE2 a dunimy for family size; if m.ore than two people, ] if two or one DFM5ZGE5 = a dumjiiy for family size; if less than five people, 1 if five or more DHDNONWii a dummy for race of head; if wliite, 1 if nonwhite

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54 DEDLTHS = a dummy for educa tonal attainment of head; if high-school graduate, 1 if not a high-school graduate DED3C a dummy for educational attainment of head; if head never attended college, 1 if head did attend college DEDCG = a dummy for educational attainment of head; if head did not graduate from college, 1 if head did graduate from college DUMIGRAN = a dummy for mobilJty; 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; if civilian, 1 if head is member of armed forces DSTUDENT a daiTuTiy for current enrollment status; if head is not a student, 1 if head is enrolled in school lENURfJ -a dichotoraous variiible which takes on a value of 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 owneroccupied for the ALL OVvT-jERoHi P model; for the MOBILE-KOME OVn^^hlRSHIP version :i t takes

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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 population (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 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 experience, one for head being employed in military service, and one for the head being a student. A constant term was calculated 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

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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 useful 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 .

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67 Ov/n er-occ upi'?d m obile h oraes Depen dent Variab le. 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 concentration 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

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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 determining 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 necessary 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 collected 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 arrive 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.

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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 schematic depicting how the actual items have been used. Determining the va.lue of a mobile hom.e is a fairly straightforvrard, 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

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70 ^

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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 purchased 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 JudyBerner and used widely by dealers. Another widely used data source is rna unicomp Direcror y of Used Mobile H omes . In these publications mobile homes are broken down by manufacturer, model, year built, size, and physical layout. There arc several "rules of thumb" used in the industry 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-

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72 nique could be used, it v/as felt that actual resale experience 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 % Va lue 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 ,

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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 Fla sh 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 tenfoot-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 thirtytwo 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

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74 T/^BLE 10 Average Value of New Mobile Hor.es by Year Buili Year Av erage 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 arciiicluded hero for tl-e purpose of

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75 TABLE 11 Value of New 19 70 Mobile Homes Nu mber of R o oms Number of Bat hs 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 number-ofrooms 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 additional 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.

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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 characteristics of individual units. The dollar adjustments are estimates of the actual cost of adding the service mentioned or of the loss of value represented by the absence of the particular 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 differences in units. For example, it is in the "adult mobile heme" comjTiuni ties that oi^e is most likely to find custombuilt unirs. It is also in these parks that one is mtost 1;. }.ely to find public or municipal water and sewerage sys-

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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 confjLructed from industry data, is used for this purpose. The unit is then depreciated (in accordance with industry experi.ence as depicted in Table 9) in accordance v;ith its age v;Vien it was purchased. The product of VALUE 7 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 fourroom, onebath 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 constructed is (yALl)E70) X (INDEX) = (?6,200) x (.933) = $5,78 5,

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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-

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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 housing 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 mobilehome 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 variab.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 relationshiip is m.oasured by the concept of income elasticity of demand v/hich is defined as the relative change in expenditure ooir.pared to the relative change in income. For example, If a family's incom.o increases 2 percent and its expenditure 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

PAGE 88

80 housing literature (see Chapter II) for conventional housin9'~-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 importance. Inco me 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, especially for durabJ.e goods, are made on the basis of a concept of income which is much broader than one year's receipts. 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 decisions are based on an even longer time horizon. Whiit concept of inco'.ie is appropriate for use with mobilehome 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-

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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 normally 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 investment income. Also of importance are a person's occupation, 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 appropriateness 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 expec ienco and is an incoiae measure based solely on each porr:on'3 occupational group, attribuLes, and human capital.

PAGE 91

This form is not unlike that often used in the human capital literature. (See, for example, Mincer, 1974, pp. 9193, 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 uniform. 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 earningsschooling 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 inverted 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, experience 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-

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o ^ 85 •o

PAGE 94

86 cai individual might be useful. Consider an individual with the follcwing characteristics in 1969: male, age 36, caucasion, college graduate, an engineer by vocation. The model can be used to generate a hypothetical income for this indj.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 + + 4.00994 $10,232 If this engineer had been a female, her generated 1969 income 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 for a male) and the log (earnings) value would be reduced 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 unUoually high ap.d unusually lev..ori':>s canceling one another

PAGE 95

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 projected 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 generiition 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 incomes 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 occupation., education, experieaco, race, and sex) have expcritjnced. This is in line witii the tfieoretical construct of permanent

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88 income which seems to eliminate individual, transitory fluctuations of income, FMINTR is a variant of permanent income which explicitly 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 transitory nature. As an example, consider the following hypothetical family : (A) (B) (C) (D) (E) 1969 1969 Observed Generated YPERFMINTR Income Income (A)/(B) MFAiM (C) x (D) Head 12,000 9,000 1.33 15,000 19,995 Wife 4,000 6, 00 .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 incomes 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.

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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 household head and the spouse, if one is present. P rice 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 fozthe year in which the

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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 difficult 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 capture 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 construction 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 construction 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

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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 significantly different rates in different places. A price variable 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 through 1970. Other independent vari_ables. The NPERSON variable is simply the number of persons in the household, A positive correlation between family size and demand for shelter space would seem appropriate. DUMGEo5 and DUMLE2 5 are two age-related dummy variables 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 disiribution 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 otherwise. The

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92 TABLE 14 Relative Prices of Conventional and Mobile Homes Avg. Cost of Avg. Cost of Price Year Mobile Home ($) C onventional House ($ ) 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 Statistical /'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 maleh c a d e d ho u s c; hoi d s . 1950

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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 residents . DUMEDUCA is a d'ommy variable which takes on a value of one only if the household head has less than a highschool education. Its presence is to allow examination of the hypothesis that lesser-educated people live in mobile homes. Renter-occupied mob il e home s 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 mobilehome owner's expense excludes site value payments. To adjust 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 threefourths of actual contract rent as housing expense. This adjustment is an attempt to separate payment for housing space from payment for living ciivironrnen t.

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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 oftenused 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 certainty 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.aking one's choice. There may be reasons for preferring one form, to another other than predictive power, hov.'ever. The need to standardize the variance for all cases of the predicted dependent 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-

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95 dent variable can frequently be stabilized by performing such a transformation on the dependent variable. A logarithinic 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, mathematical models are a less accurate approximation of reality in the social sciences, hence statistical methods are more necessary. ... A mathematical 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 complicated 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. 99101) . 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

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96 the relationship between housing and income is most readily observable . The two models just outlined, with the pool of independent variables enumerated for each, were estimated using OLS techniques. Model A looks at the tenure decisionwehther 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.

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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 standards. Mobile hornes are permanent housiny and are an increasing 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

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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 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 ) -fb .; (DFMS Z GE 5 ) -ib , ( 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-

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99 cent level. The results are presented in tabular form in Table 15. Eleven of the independent variables are significant at the one-percent level (as indicated by the F statistics) 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 ownership. 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 coefficient and its signi.f icance level. Careful analysis of the coefficients yields some interesting predictions. For instance, using the information 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 ownership; a] so, if the household head has some college education, the probability of ownership is increased. And since the income coefficient is positive, the higher the family

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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%

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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 characteristics: a single male under twentyfive 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 and 1. The model does not operate asymptotically so that, given a set of extreme character-

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102 istics and a very low or high inccrr.e, the model will generate 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 independent 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 statistical 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

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103 (Ncoco'^oooor^f— icTscTia>cor~fN f'>tHtNcorsj>^oor\joo:Hin'LTcocr\c\) c<; oocNnocoor^(NCN'3'fNiu3om CO tHO^CNO^i-HOOOCNOOOn 2; ..... U-i OOOOrHOOOOOOOOOO Q > I II III III u o vr> ^-^ rH >i J M K P < o >sOmiHOOO'3"r^-<:rc^cr^LOr-r'")'3< >x>for-oooioooo(^ritNt^4CNji— i^o fM'^cNOiHLncMmona3iHt^LncN iHn'.Donrsio^i,o*x>oocrir~~cNr~iHOiHOCNi-HrHOOOrsiOOOr^ 000 rH 00000 C 00000 I ! ! I I I i I ! I coooor--roLnrM^'cxDixiOrHij3(Mr^ OLTOCNirocouDroni^n— i(TiOv£> oTr-o'xiconcocriOisO'd'mcocor^ OrHOiH'a'OJOrHrHOCNOOOrH OOiHOOOOOOOOOOOO I II I I I I I I I i-(0;Hm,HiHcoH'«a'a>vx)c>cocNiH r-ioooror\'mMr~r~-'d'Lnij3iniH(N ^~ou^«^|^D';rLno^t--CNLnr^c^c^lr^ fNjor-nor-cNj-HoorHooonnH OJOr-!OOOOrHOOCOrHrslO OrHOOOOOOOOOOOOO I ill: I I aI

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104 ^(HVOrrOtrcNCOr-^fNOCNinvDO ncNir-'^cNrHocTic^iHoooocNio CO r^ 1,^1 fN) cNj c^ CO r^ro vx> a-. 00 CT-, o r-iOr-ir-~rnHrHOoonoooo OOOOOOOOOOOOOOrW I I I I I I ^iHOIrHiHCNr-UDCNOOCNTrcOOCN a^ro ccfNOrHOiHrsjr-icNmnoo OCMOOOOOi-ICNtHOOOOO OOOOOOOOOOOOOfHO I I I I I I I I I incoror-i— lcooom^Or^r^r-~oa^Ln ooLnvi!r-jLnO'3voooo^icrio m iTi n cc C7\ I— I -cr o uT 00 o ^ ro '^ rHOOJCNCNiH.— Ir-JCNCVOOOOn OOOOOOOOOOiHOOOO I I I I I II r^cr\r-(Tioincr>0'r}'oouDrHO(N r-i-^ior")criVDooo(NOtx>coc^niH r-g r-i V.D ro n ko 00 CO rH o CO LA 00 r-o Oi— |i-ouDCNocTi^«*or-~r-rHrHLn 0000000n^.£)0CN00l-^0 OOOOOOOOOHOOOOO III 11 M'^CTi'^rHcorHcriO'^^OLniX'ror^ o-vfCDr^mr^CNjvjDOiNnLnoorsicri oooovDojom'^0'trr^co»:rrs''^j' 00iH000rHLr)0>J3CsJ00(N0 OOCOOOOOrHOOOOOO I I I < I I I CO r-^l— lor-^rHrsir^ocoi^r^ncooo K n f-"-o' o rsj rH o o ^D o CD CO o >x> en E^ a> CTi ro 0-) VD cN CO o ;r (^ O O O "^ ^' ^D CO o o I I I H O O I : 3 O O O O I I I I I Q r-j in K in in f.^ u]

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105 by two asterisks in Table 15. Ten of the variables proved to be significant at the 5-percent level in both the allhousehold 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 coefficient, was different for six of the ten which were significant in both versions. Those significant variables which had the same sign in both the all-ownership and the mobilehome ownership versions of the tenure model are the dummy variables for marital status, families headed by a person over sixtyfive, 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, influenced the probability of ownership in the same direction-positively for being married and having a head over sixtyfive, 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 negative 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

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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 opposite 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 twentyfive years of age or less is 32 percent less likely to own its owr, home than a family headed by a person over

PAGE 115

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PAGE 116

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109 twentyfive, 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 ownership. 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 Estimatio n: Mode l B Model B, as described in Chapter IV, estimates the demand for mobile-homie housing by regressing housing expense 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) -ib , ( DUMLE 2 5 ) + b „ ( DUMGE 6 5) +

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110 bs (DUyiEDUCA) + bg (NPERSON) + b^lDUMIGRAN) + b„(DUMRACE) + bg (DUMHDSEX) It would be very surprising, however, if all of these variables 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 Estima t ion 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 regression 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.

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Ill C2-i 31 'it % V '/'/-< FIGURE 4 Public Use Sa:r.plo Iwe.as and Suh'3.rcas ror Fieri. da

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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 independent 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 stepwise 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 Stati sti cal 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" twovariable niodei, and so forth. It

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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 twovariable 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 variable 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 increase 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 enlarged 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

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114 •H X! O S U O «w c o 00 -H rH +J nJ E •H -P to W m EH W •a o 0) 0) (1) X> H (0 > 4J (U C > •H U-l c»P H O H 4-> x; 4s-p tE 4J O en O (^ H W W a' Tj (u M a' S 3 M O en O u .-0 -P c a) K H S tq P (3) OT E M O -H c G QJ (D 5-J (C Ci) 4-1 -p ^ c; CO S t4 (/5 'O (1) a' 0.) '^i .H c o Xi -H S It Q H -P 4-) M C (fi rJ o a > C) a n p.
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Ill !T5 Cj U ^ H 4-1 c« -H O C ^ CD (C

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116 y 4J

PAGE 125

117 m m •o in CTi o^ o
PAGE 126

118

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119 betv/cen the cost of conventional home ownership and mobilehome ownership increases, the mobile home looks more attractive . Income appeared frequently as a significant predictor 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 income elasticity substantially below unity. Table 18 concentrates 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 regularity. 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 included 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

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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-independentvariable 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 expenditureincome relationship. Table 20 allows one to examine which measure of *ror oxemple, Margaret Reid used this type of formulation in Housing aiid Income.

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121 TABLE 2 Income Elasticities Generated vvith Abbreviated Forn of Model E Area

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122 TABLE 2 — Continued Area

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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 income 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 (primari.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-

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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 explain. Model B Es timation 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 significant as an independent variable. When inconse was entered, the observed elasticity was very low, ranging from .004 for LGFMINTR to .24 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 presented in the fii;al colurr.n of Table 21.

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12•J) II Tj n B C tH M c >y m O M ^ > e c o c o I— I kD in r-i P -H &'+ e a o o o u ri r.1 A O S It) VI M O ^ n-l < o o

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126 Comparison of those results from the rental-housing model with the results from the owner-occupied model suggests 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 difference 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 onefourth of the contract rent is payment for site value. Additionally, it v;a3 hoped that t'lore might be enough observations 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

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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, predicts rental expenditure less accurately. Even though there is considerable variability among the characteristics of the mobile-hom.e renters, there is less variability in contract 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 elasticities are less for renters than for owners, and also less statistically significant. In some instances rental expenditure 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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12S a e o u u OJ

PAGE 137

E O O O u u ^ a> — > H D

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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 explain 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 literature. On the one hand, the type of empirical inve-itigation performed here J s simiJar to ti'iat carried out for coju'eution il housj.ng in the acadoi-'.ij litorature. /-slmost 1 !;'

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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. Specif ic Fi ndings: Descriptive Among work which has been published, other researchers 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 sector becomes irrelevant to the family's decision process (Ohls, 1971, p. 9). 2. That "mobile-home demand is directly competitive V7ith conventional singlefamily housing starts as opposed to being directly competitive witli multiplefandly 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

PAGE 140

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" findings. For instance, the tenure decision appears to be a secondary consideration for many families. The income constraint seeiTiS to be of primary importance, with other dc-cisicns .;to ov/n or rent a m.obiie home) following the budgetary allocation for housing. Conventional single-family homr: ownership cannct compete with r.iobile-home hovising on the basis of price.

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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 .

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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

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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 Findin gs: 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 mobilehome 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 different between the "all-ownership" and the "mobile-homeownership" 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"

PAGE 144

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 "allownership" version indicates that having recently moved decreases the probability of ownership in general. Coefficient sign changes such as this between the two models were observed frequently. As observed income increased, the probability of ownership also increased (positive coefficient) , 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, explains 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,

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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 coefficient 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 permcinent income (YPERI-IFAM) again generated the higjiest. income elasticities, but in this case they wore also the most sig-

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13: nificant statistically. (Higher coefficients are not necessarily 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 income 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 ownership, 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 households 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:.

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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. nonwhite 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 responsible for the lov; utilization of mobile h.omes by nonv/hites. As shown by Model B, up to 50 percent of the variation in m.obilehom>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 attractiveao-s? of m.obile homes is the relative cost advanraqo. They are not crlv less exoensive. to build because of being

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] H 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

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141 the mobileho 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 microeconomic 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, availability 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 rcsearch.ers have had similar difficulti.es in analyzing convert 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

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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 relative 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. Mobilehom.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 mobilehome 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,

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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 observable, there may be as many measures of it as there are researchers. 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.

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144 Implications and Unanswered Questions While Florida cannot be thought of as being representative 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, singlefamily ownership, flexibility in environm.ental choice, and the well-developed market for used units. Florj.da also has appeal because of its mild climate (except for the possibility of hurricanes), but so do other geographica], areas of the United States. As the birth rate has fallen in recent years and families are getting siiialler.

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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 principle as to Wiiich income measure perforn^ed best under what ci^rcumstances v.'as found, though. Further inquiry into this

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14 6 question may be needed. It may be that some measure of income not employed in this research would, in fact, predict 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 moderateincome American fam.il ies, the mobile hom.o i.s the only kind of housing they can reasonable afford."* Mobile homes are lov/-cost housing, but they are *Hessage from the President of the United States transmitting the Second Annual Report on Nation al Ho using \^ Goals, Comuiittee on Banki.ng and Currency (Washington: ^ uTsT'Government Printing Office, April 1, 1970).

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147 utilizevi by families which are not necessarily lowincome families. The growth of mobile hone utilization reflects two basic attitudes--one new and one old. First, mobilehome 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 continuously 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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BIBLIOGR?\PHY Allen, R. G. D. "Expenditure Patterns of Families of Different Sizes." Studies in Mathematical Economics and Econometrics , edited by Oscar Lange, F. Mclntyre, and Theodore O. Ynetma. Chicago: University of Chicago Press, 1942, pp. 190-207. Barth, Richard C. "A Study of the Demand for Housing." Ph.D. dissertation. University of Wisconsin, 1966. Bedrosian, Sarah G. "An Analysis of Demand for Housing with Special Reference to Margaret Reid's Hypothesis of the Housing-Incom.e Relationship." Ph.D. dissertation. University of Southern California, 1966. Borney, Robert E. , and Larson, Arlyn J. "Micro-Analysis of Mobile Horae Characteristics v.i. th Implications for Tax Policy." L and E c ono mics (November, 1966), T?^, Tr i971)V'pp. 'l10. "" "

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149 Di rector y of Used .Mob ile Hon'.cs . Libertyville , Illinois: United COiiipi lation, Inc. , various issues (published annually) . Downs, Anthony. Ur ban Problems a n d Prospects . Chicago: Rand McMally, 1976. X Drary, .Margaret J. Mobile Hom es: The Unrecognized Revolu tion in Ame rican Hous ing. Nev/ York: Praeger Pubrrshers", 19 7 2 . Duesenberry , James S. Income, Saving and the Theo ry of ConsuTTior Behav ior . Cambridge: h'arvard University Press, 1949. Duesenberry, James, and Kistin, Helen. "The Role of Demand in the Economic Structure." S tudies in the S tructur e of th e America n Economy, edited by VI. Leontief et al. 'New York: Oxford University Press, 1953. Engel, Ernst. "Die Productionsund Consumptionsverhaltnisse des Konigsreichs Gachsan." Reprinted as an appeiidix to Die Lebenskosten belgischer Arde Ardeiterfamilieu (Dresden, 1895) . Ferguson, C. E. Microeconomic Theory. Homewood, Illinois: Richard D. Irwin, Inc., 1972. Fisher, Robert M. , and Graham, John W. "Housing Demand by One-Person Households." Land E conomics (May, 1974), pp. 163-168. Flash-Facts--?oc]cet R eference to the Mobile Home Ind ustry. ChariFiTIyT'vIrginia: Mobile Homes Manufacturers Association, published annually. French, Robert .Mills, and Hadden, Jeffrey K. "An Analysis of the Distribution and Characteristics of Mobile Kor.'.es in America." Land Econo m.ics (May, 1965), pp. 131-139. Friedman , Mi 1 ton . £.^ Theory of the Consumption Fun ction . Princeton: pFinceton' University Press, 1957. Friend, Irwjn, and Kravis, Irving P.. "Consumption Patterns and Pcrm.anent. Income." The American E conondcs Rev-i ew Pa|-^ers and P roceed ings, XLVTI (i-iay, 19 57) ,pp. 53 65^5".

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150 Golfand, Jack E. "Mortgage Credit and Lower-Middle Income Housing Demand." Land Economic s (May, 1970), pp. 163170, Goulet, Peter G. "Discriminant Analysis of Low-Cost Housing Alternatives: A Study of Non-Price Factors Influencing Housing Choice Decision." Ph.D. dissertation, Ohio State University, 1971. Grossman, Michael, and Benham, Lee. "Health, Hours and Wages." The Ec onomics of Health and Med ical Ca_re_, edited by'M'ark Periman. London: The McMillan Press Ltd., 1974, pp. 205-233. Guttentag, Jack. "Winnick's Case for a Changing Attitude Tov/ard Housing: Comment." Quarterly Jo urnal o f Economics, LXX (May, 1956), pp. 314-319. Harris, P.. N. S. , Telley, G. S. , and Harrel, C. "The Residence Site Choice." The_Revi ew of E conomi cs and Statistics , L, No. 2 (May, 1968"), pp. 241-247. Eass, Bruce W. "Housing Demand: P. Lifetime Income Approach." Ph.D. dissertation. University of Colorado, 1973. Heilbrun, James. Urban Econom ics and Public Policy . Mew York: St. Martins Press, 1974. Henderson, James M. , and Quandt, Richard E. Microeconomic Theor y: A Mathematica l Approach. New York: McGrav7HfriT 1971. ' Housing o f Senior Citizens. U.S. Department of Commerce, ^Bureau of the Census Subject Report HC(7)-2. Washington: Government Printing Office, 1970. Eousiriq Surveys: Part_ 2^._ HobjLl e Hom es and bho Housing _-_-y^^ u7sT' "Department of Housing and Urban FeveTopment. VJashington: Government Printing Office, 19 63. Houthakkcr, H. S., and Taylor, L. D. ConsuJT^j:_Dj?jnaiid_J^^ th-j United States. Cambridge: Harvard University Pre'ss". "Tivdustrial Impacts of Residential Construction and Mobile Hom.e Production." -^^£_^ilP^, QL./-'^^-^'^"-' ^^ Busi ness, 50, No. 10 (October, 197^0 ), pp. 14-17. Juster, FTriomas. AnJ^._cy>atJ.Cins_an^^^ Princeton: P r i nee con Uni uors ity Pres s , 1964.

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151 Kartman, Arthur E. "A Cross-Section Analysis of Housing Demand." Ph.D. dissertation, University of Washington, 1969. Keynes, John M. The General Theory of Employment, Interest and Money . New York: Harcourt, Brace and Co., 1938. Lawrence, James D. "An Analysis of the Role of Mobile Homes in Satisfying Defined Levels of Potential Demand for Housing in the United States." Ph.D. dissertation. University of Georgia, 1971. Lee, Tong Hun. "Demand for Housing: Cross-Section Analysis," The Review of Economics and Statistic s, XLV, No. 2 (May, 1963) , pp. 190-196. Lee, Tong Hun. "Housing and Permanent Income: Tests Based on a ThreeYear Reinterview Survey." The Review of Economics and Statistics , L, No. 4 (November, 1968), pp. 480-490. Lee, Tong Kun. "The Stock Demand Elasticities of Non-Farm Housing." The Review of Economics and Stati stics , LXVI, No. l" (February, 1964), pp. 82-39. Maisel, Sherman, and Winnick, Louis. "Family Housing Expenditures: Elusive Laws and Instrusive Variances." S t udy of Consumer Expenditures, Incomes and Savings . Proceedings of the Conference on Consumption and Saving, Vol. I, edited by Irwin Friend and Robert JoT\GS. Philadelphia: University of Pennsylvania Press, 1960, pp. 49-174. Mandelker, Daniel R. , and Montgomery, Roger. Housing in Ap.ierica . Indianapolis: The Bobbs-Merrill Co., Inc., 1973. Marcin, ThoT.as C. The Ef fects of Declining PopulatJ. on Gro\vth on the Dema nd for Housing . U.S.D.A. Forest Ser'vice General Technical Report NC-11. Washington: Government Printing Office, 1974. Mayer, Lav/rence. "Mobile Homes Move into the Breach." tSlL^-'Ulll' ^^' ^^^ (March, 1970), pp. 126-130. Me n c-j cn h a 1 1 , W i Ilia u , . Intro ^"l!:lEt_ion_ to Linear Models and i !:£. I'-^^sign and Analysis o f Exper infonts . B e Imo n t : Wadoworth Publisba.ng Co., Inc., 1968.

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152 Mills, Edwin 3. Urban E c onomics . Glenview, Illinois: Scott, Foresman & Co., 1972. Mincer, Jacob. Schooling, Ex peri ence, and Earnings . Nev/ York: National Bureau of Economic Research, 1974. Mo bile Home Blue Book--Of f icial Market Repor t . Vo 1 . 38, No. 1. Westchester, Illinois: Judy-Berner Publishing Co., January, 1974. Mobile Home Flna ncing--2 0th Annual Survey . Chicago: Mobile Hom.es Manufacturers Association, 1972. Mobile Home Market . First National City Bank, A Study Prepared by Shiefman, Werba & Associates. (Undated.) Mobile Homes . U.S. Departm^ent of Commerce, Bureau of the Census Subject Report KC(7)-6. Washington: Government Printing Office, 1973. Morris, Earl W. , Woods, Margaret E. , and Jacobson, Alvin L. "The Measurement of Housing Quality." Land Economics (November, 1972), pp. 383-387. Morton, Walter A. Housing Taxation . Madison: University of Wisconsin Press, 1955. Muth, Richard F. "The Demand for Non-Farm Housing." The Demand for Dur abl e Goods , edited by Arnold C. Harberger. Chicago: University of Chicago Press, 1960, pp. 29-:6. Math, Richard F. "Moving Costs and Housing Expenditure." Center for Research in Economic Growth, Memorandum 144. Stanford: Stanford University, March, 1973. Needleman, Lionel. The Econom.ics of Hous ing. London: Staples Press, 1965. Netzer, Dick. "The Incidence of the Property Tax Revisited, National Tax Journal, XXVI, No. 4 (December, 1973), pp , 515-535. Newsletter. U.S. Depiirtuient of Housing and Urban Developxneril, 5, No. 48 (December 2, 1974). NGursc, Hugh O, The_ J^f f cct of Public Poli cy o n Housing M.irkets. Lexinaton: Lexington Books, 1973.

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15 3 Ogburn, William. "Analysis of the Standard of Living in the D. of C. in 1916." Publications of the American Statistical Association , XVI (1918-19), pp. 374-392. Ohls, James C. "A Cross-Section Study of Demand Functions for Housing and Policy Implications of the Results." Ph.D. dissertation. University of Pennsylvania, 1971. Prais, S. J., and Houthakker, H. S. The Analysis of Family Budgets . Cambridge, England: Cambridge University Press, 1955. Reid, Margaret G. Housing and Income . Chicago: University of Chicago Press, 1962. Renshaw, Edv/ard F. "The Demand for Housing in the Mid1970s." Land Economics (August, 1971), pp. 249-255. Report on Used Mobile Homes . U.S. Department of Housing and Urban Developm.ent and Housing Production and Mortgage Credit--Federal Housing Administration, August, 1975. Ricks, R. Bruce, editor. National Housing Model s. Lexington, Massachusetts: D, C. Heath & Co., 1973. Schwabe, Hermann. "Das Verhaltniss von Miethe und einkdmiP.en in Berlin." In Berlin und seine Entwickelung fur 1868 (Berlin, 1868). Second Annual Report on Na tion al Hous ing Goals. ComjTiittee on Banking and Currency. Washington: Government Printing Office, 1970. Service, Jolayne. A Use r's Guide to the Statistic al Analysis System. Raleigh: North Carolina State University, August", 19 72. Smith, Wallace. "An Outline of the Housing Market v/ith Special Reference to Low-Income Housing and Urban Rer.ev;al , " Ph.D. dissertation. University of Washington, 1958. Snyder, Elanor M. " Impact of Long-Term Structural Changes on Fami].y Expenditures: 1838-1950." Consumer Behavi or: Rese arch on Consumer Re action s, edited by Lincoln H. Clark. New York: Harper and Brothers, 1953, pp. 359-393. Stigler, George J. "T'n.e Ear]y H.i story of Empirical 3tudi.es of Cvonsumer Behavior." Journal of Political Kconomy , LXII (April, 1954), pp. 95-113.

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15^1 Straszheim, Mahlon R. "Estimation of the Demand for Urban Housing Services from Household Interview Data." '^^lg_fi-£XJrgy_°^ Eco nomics and Statistics , LV, No. 1 jFebruary, 1973), pp. 1-8. Struyk, Raymond J., and Marshall, Sue. "The Determinants of Household Home Ownership." Urban Studies , II, No. 3 (October, 1974), pp. 289-299. Walzer, Norman, and Singer, Don. "Housing Expenditures in Urban Low-Income Areas." Lan d Econom ics (August, 1974) , pp. 224-231. Wheaton, William L. C, Milgrara, Grace, and Meyerson, Margy Ellin. Urban Housing . New York: The Free Press, 1965. Williams, D. N. "Low-Cost Housing is Homeless Waif." Iron Age, CCCIV, No, 11 (September, 1969), pp. 63-69. Winger, Alan R. "Housing and Income." Western Economic Journal (June, 1968), pp. 22 6-232. Winger, Alan R. "Housing Space Demand: (February, 1962), pp. 33-41. Land Economics Winger, Alan R. "Trade-Offs in Housing." Land Economi cs_ (November, 1969) , pp. 413-417. V/innick, Louis. "Housing: Has There Been a Downv/iird Shift in Consumers' Preferences?" Quarterly J ourn al of Economics, LXIX (February, 1955), pp. 85-98. Winnick, Louis. "Reply." Quarterly Journal of Econorui£S LXX (May, 1956), pp. 319-323. VJonnacott, Ronald J., and Wonnacott, Thomas H. Econometri cs. New York: John VJiley & Sons, Inc., 1970.

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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 graduation 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 Personne] 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 following 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 AlaskaAnchorage while stationed chore. If.^

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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.

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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 MadelynLockhart 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 . / G o.d da r d Associate Professor of Economics

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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 Administration 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

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UNIVERSITY OF FLORIDA 3 1262 08552 9872