This article was downloaded by: [University Town Library of Shenzhen], [YuZhou]On: 08 April 2014, At: 00:44Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH,UK

International Journal of HousingPolicyPublication details, including instructions for authorsand subscription information:http://www.tandfonline.com/loi/reuj20

The decision to purchase amanufactured home: a nestedlogit model of determinantsYu Zhoua

a HSBC Business School, Peking University ShenzhenGraduate School, Shenzhen, ChinaPublished online: 17 Jul 2013.

To cite this article: Yu Zhou (2013) The decision to purchase a manufactured home:a nested logit model of determinants, International Journal of Housing Policy, 13:3,268-287, DOI: 10.1080/14616718.2013.818784

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International Journal of Housing Policy, 2013Vol. 13, No. 3, 268–287, http://dx.doi.org/10.1080/14616718.2013.818784

The decision to purchase a manufactured home: a nested logitmodel of determinants

Yu Zhou∗

HSBC Business School, Peking University Shenzhen Graduate School, Shenzhen, China

This paper attempts to identify the drivers behind households’ decision to pur-chase a manufactured home rather than buy a traditional house or rent. A nestedlogit model is estimated using recent movers’ data from the national sample of theAmerican Housing Survey 1985–2003. Explanatory factors include both housingchoice attributes and movers’ characteristics. The results suggest that loweringthe user cost of owning a manufactured home increases the probability of choos-ing that type of dwelling. Compared to their high-income counterparts, low-and medium-income households are more likely to choose owning manufacturedhomes as a transitional stage between renting and traditional home ownership.The recent movers who previously lived in manufactured homes are more in-clined to own manufactured homes. Recent movers from older age groups, whoare married, from a bigger family, or from a white family, are less likely to ownmanufactured homes.

Keywords: manufactured housing; homeownership; nested logit; the UnitedStates

Introduction

Manufactured homes are receiving more attention in the housing literature, one reasonbeing their increasing presence in the total housing stock. According to the AmericanHousing Survey (AHS) 2007, of the approximately 128 million housing units reportedin the USA, 8.7 million are manufactured or mobile homes (6.8%). In addition,manufactured homes have potential as an affordable homeownership solution forlow- and medium-income households.1 Manufactured homes are on average muchcheaper than the traditional ones. Investigation of the AHS 1985–2003 reveals thatan owner-occupied manufactured home has a mean value of about one-third that ofa traditional home.

Unlike traditional homes, which are produced on-site, manufactured homes havetheir structures manufactured in factories, then are transported to the site, and finally,

∗Email: yuzhou@phbs.pku.edu.cn

C© 2013 Taylor & Francis

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International Journal of Housing Policy 269

Figure 1. Image of a manufactured home.

installed on designated land, either permanently or temporarily. The two main sizesare ‘single-wide’ and ‘double-wide’. The single-wide manufactured home is 18 feetor less in width and 90 feet or less in length, while the double-wide is 20 feet ormore in width and 90 feet or less in length. Figure 1 portrays a typical double-widemanufactured home.

A few researchers have studied whether manufactured housing is a good alter-native for low- and medium-income households. For example, Vermeer and Louie(1997) report that manufactured homes are now ‘of higher quality, safer, and moredurable in terms of maintenance, wind safety, fire safety, and thermal efficiency’than their predecessors. Boehm and Schlottmann (2004), using evidence from theAHS 1993–2001, found that owner-occupied manufactured housing, on average, hasa better quality ranking than rental units (their traditional alternative), and manu-factured housing also has the potential to appreciate if it occupies owned land. Themajor underlying force for quality improvements has been the Department of Hous-ing and Urban Development’s (HUD) National Manufactured Housing Constructionand Safety Standard Act released in 1976.

Unlike any other minor housing forms (mobile home, modular home, panelizedhome, trailer home, caravans, etc.), which need to meet various state and/or localbuilding codes, HUD-code manufactured homes are built to meet only one singlenational standard that is based mainly on performance of designs and constructionmaterials, rather than on material types or home dimensions. Furthermore, the HUD-code pre-empts local building regulations, allowing manufacturers to avoid the delays

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270 Y. Zhou

associated with local inspection procedures. Because of these streamlined codes,manufactured housing offers far more cost-saving efficiency and economies of scalethan any other minor housing forms. For example, every year more than 250,000manufactured homes are placed on-site, compared to fewer than 40,000 modularhomes. From this point of view, manufactured housing is the only major source ofnontraditional low-cost housing for low- and medium-income households with fewhousing alternatives.

To the author’s understanding, manufactured housing in US has a few sister formsin other countries, such as the ‘transportable houses’ in Australia, and ‘the prefabri-cated multistory housing’ in Singapore and China. However, only the ‘transportablehouses’ in Australia have similar access rules to those of the US manufactured homes.In Singapore and China, the prefabricated dwellings are subsidized public housing,and access is strongly constrained by income ceilings. Besides, to own prefabricatedpublic housing in Singapore or in China means to own the structures only. It is notunder strata title, which makes it very different from traditionally built multistoryproperties.

The current paper aims to identify the factors that prompt households to purchasea manufactured home rather than to opt for traditional home ownership or renting.As stated above, the manufactured housing has been presented as both an affordableand a good homeownership solution for low- and medium-income households whomay not have accumulated enough down payments for a traditional home but stillwish to be a homeowner. In this sense, ownership of manufactured housing couldbe a critical transitional stage for some target households in moving from rentingto traditional ownership. However, there are reasons to believe that manufacturedhouses are treated differently from traditionally site-built ones in the housing market.Manufactured homes are more likely to be treated as a personal property rather thana real property. Sometimes they are called ‘wheel estate’ instead of real estate be-cause of their evolution from recreational mobile vehicles.2 Unlike traditional houses,which are produced on-site, manufactured houses have their structures manufacturedin factories and are then transported to the site and installed on designated land,either permanently or temporarily. As a result, different property tax rates mightapply. Furthermore, they are treated differently in the mortgage market, one examplebeing higher mortgage rates may be charged for manufactured homes (the rate ofinterest charged on a mortgage, which can be either fixed or variable and ultimatelydetermines the cost of the mortgage and the amount of the monthly payment). Usingthe AHS mortgage data, this paper finds that the loan-to-value ratio (LTV) is lower,the mortgage term is shorter, and the mortgage rate is higher for financing a manufac-tured home. Unlike the well-developed traditional housing market, the prospectivepurchasers of manufactured homes have limited options with respect to designs, andthere are very few firms involved in the manufactured home business. Vermeer andLouie (1997) point out that many US states do not even have manufactured homeinstallation codes. The resale network of the manufactured housing market also is

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not as developed as the traditional housing market. Although their structures canbe mass-produced in factories and achieve higher productivity than traditional homeunits, manufactured homes in many areas are only allowed to sit on certain sites (suchas a trailer park) because of land zoning regulations.

The next section introduces an augmented housing choice model that investigatesthree housing choices among recent movers: renting, owning a manufactured home,and owning a traditional home. A nested logit model is fitted to estimate effectsfrom both the housing choice attributes and the households’ characteristics on theirownership decision. Section 3 describes the data, and Section 4 presents regressionresults. Section 5 concludes and considers the policy implications of this analysis.

A nested logit model for augmented housing choices

Housing tenure choice (owning or renting) has been extensively investigated (Deng,Ross, & Wachter, 2003; Freeman, 2005; Gabriel & Painter, 2003; Robsta, Deitzb,& McGoldrickc, 1999). These studies, however, do not differentiate between tradi-tional homeownership and manufactured homeownership. Many others discuss bothtenure choices and dwelling types, but their dwelling types rarely include manu-factured homes (Ahmad, 1994; Boehm, 1982; Borsch-Supan & Pitkin, 1988; Cho,1997; Fischer & Aufhauser, 1988; Kim, 1992; Marshall & Marsh, 2007; Quigley,1976; Skaburskis, 1999; Tu & Goldfinch, 1996; Yates & Mackay, 2006). One excep-tion is Marshall (2006), which talks about housing choices among the three owner-occupied dwelling types using a multinomial logit model; owner-occupied manufac-tured homes, owner-occupied detached homes, and owner-occupied attached homes.However, rental homes, an important housing market component, are excluded fromthat research.

A nested logit method is chosen here to study an augmented housing choicemodel with the three housing modes: renting (mode 1), owning a manufacturedhome (mode 2), and owning a traditional home (mode 3). The tree structure inFigure 2 indicates that in the upper level renting is different from owning; under theowning branch, owning manufactured homes, and owning traditional homes have

Figure 2. Nesting structure of housing choices.

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272 Y. Zhou

some (dis)similarity.3 Also note that the renting branch is degenerate because thiswork assumes that households do not care about whether their rented units are site-built or factory-built.

To the best of our knowledge, this combination of housing tenure (renting orowning) and dwelling type (traditional or manufactured) has not been investigatedby previous studies. Two sources of variation comprise the explanatory variables:characteristics of movers and attributes of housing choices. That is, some of theindependent variables indicate characteristics of the choices (for example, the usercost, which is the flow cost of owning a property), others are characteristics of thechoosers (age, income, etc.). It is assumed that a chooser makes a simultaneous (notsequential) decision on housing tenure and the dwelling type, so a full informationmaximum likelihood calibration is applied in the model estimation. The nested logitmodel is

Pr(k, h) = Prkh = Prk/h Prh (1)

Prk/h = exp(βxk/h)

exp(βxtrad/h) + exp(βxmanu/h)(2)

Prh = exp(αzh + ϕh Ih)

exp(αzrent + ϕrent Irent) + exp(αzown + ϕown Iown), (3)

where h denotes the decision branch (housing tenure), k denotes the decision twig(dwelling type), x refers to attributes of each housing option at the lower level, z refersto attributes of each branch at the upper level, and Ih is known as the inclusive valueof each branch denoting the average utility each household can expect from beloweach branch of housing tenure. The inclusive value is defined as

Ih = ln[exp(βxtrad/h) + exp(βxmanu/h)]. (4)

Each household has one set of inclusive values. The inclusive value parameter,ϕh, measures (dis)similarity among twigs under each branch. If the nested logit isthe correct model specification, ϕh should be between 0 and 1. In the current model,there are no renting-specific or owning-specific attributes.4 The above Prh is thus

Prrent = exp(ϕrent Irent)

exp(ϕrent Irent) + exp(ϕown Iown)(5)

Prown = exp(ϕown Iown)

exp(ϕrent Irent) + exp(ϕown Iown), (6)

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International Journal of Housing Policy 273

where two inclusive values are

Iown = ln[exp(βxtrad/own) + exp(βxmanu/own)] (7)

Irent = ln[exp(βxrent)] = βxrent. (8)

The probability of choosing to rent homes is then expressed as

Pr(rent) = 1 × exp(ϕrent Irent)

exp(ϕrent Irent) + exp(ϕown Iown). (9)

The probability of choosing to own a manufactured home is expressed as

Prmanu/own Prown = exp(βxmanu/own)

exp(βxtrad/own) + exp(βxmanu/own)

× exp(ϕown Iown)

exp(ϕrent Irent) + exp(ϕown Iown). (10)

Finally, the probability of choosing to own a traditional home is expressed as

Prtrad/own Prown = exp(βxtrad/own)

exp(βxtrad/own) + exp(βxmanu/own)

× exp(ϕown Iown)

exp(ϕrent Irent) + exp(ϕown Iown). (11)

The inclusive value parameters and other coefficients included in the vector, β,are estimated using the statistics analysis system nested logit program.

Data and explanatory variables

The national sample of the AHS 1985–2003 recent movers’ data is fitted into theabove nested logit model. The AHS is the largest regular national housing survey inthe USA, collected every year from 1973 through 1981 and 1983, tracking a fixedsample of over 40,000 homes, plus new construction each year. Since 1985, it has beencollected biannually, tracking another housing sample. The AHS provides substantialinformation on the characteristics of the house, its location, and its occupants.

A recent mover is defined if she or he moved into their current residence in thelast 12 months. We are attempting to identify drivers that prompt mover householdsto choose owning a manufactured home versus owning a traditional home or renting.This research aim justifies our selection of recently moved households as our sample

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rather than the whole population. However, we do admit that movers might havesome different characteristics from those who choose to stay, at least in terms oftheir demographic characteristics (Lu, 1999). The findings from this work focusedonly on recent movers might not therefore be generalizable to the whole population.Additional research could target at the whole sample either with a Heckman’s sampleselection bias test or with one more branch added to the logit model’s tree structure:to move or to stay. Obviously, there are some factors affecting households’ decisionto move or not to move, for example, the moving cost. Generalized findings fromstudying the whole households will be able to have stronger policy implications.

In each national survey of the AHS 1985–2003, we identify recent movers andobserve their actual housing choices, as well as attributes of the housing choicesand the movers’ characteristics. Recent movers from all survey years are pooled intoone regression to estimate effects of selected factors on housing decision. Unlessotherwise specified, households from this point on refer to only those who haverecently moved.

Hypothesis and attributes of housing choices and movers

Based on the literature about homeownership decisions and manufactured homes, thefollowing hypotheses were constructed and tested in this study.

Hypothesis 1: A larger user cost (larger flow cost of owning, for rented units, simplya higher rental fee) of a particular housing/tenure type reduces the probability ofchoosing that mode, all others’ user costs held constant.

A larger user cost reduces the marginal benefit–marginal cost difference of ahousing/tenure type, thus makes that combination less attractive. Per dollar usercosts for renting UCrent simply equals one, thus the yearly user cost of rentingUCOSTrent equals Prent × UCrent, where Prent is the yearly rental fee. Per dollar usercosts for owner-occupied traditional and manufactured housing (UCtrad and UCmanu,respectively) are based on the standard user cost formula below,

UCk = r (1 − LTVk)(1 − tY,k) + (LTVk)rM,k(1 − tY,k) + tP,k(1 − tY,k)

+ dk + bk − π ek , (12)

where the subscript k indicates whether it is a traditional home or a manufacturedhome; r is the interest rate; LTV is the loan-to-value ratio; tY is the household marginalincome tax rate; rM is the mortgage rate and this paper assumes all mortgages arefixed rate mortgages as is the convention in the user cost literature; r(1 − LTV)(1 −tY) is thus the opportunity cost of down payment; tP is the property tax rate; d isthe dwelling depreciation rate; b is the maintenance expenditure; and the last item

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is the expected house price appreciation rate. All these parameters are measured aspercentages of property values. Because of data limitations on LTV, this work allowsthe interest rate equals the mortgage rate, so we have the following simplified versionof per dollar user cost,

UCk = (1 − tY,k)(rM,k + tP,k) + dk + bk − π ek . (13)

The marginal income tax rate is obtained by applying movers’ income to theincome tax rate table of the corresponding year. The property tax rate is measured byrespondent-reported yearly property tax divided by the home value. The maintenanceexpenditure rate is acquired by dividing the yearly routine maintenance cost by thehome value. Homeowners’ self-reported home values, household income, yearlyproperty tax, and yearly maintenance cost are provided in AHS. There is, however,no direct information on property depreciation rates or expected appreciation ratesin AHS. Fortunately, the literature indicates that home value depreciation is highlycorrelated with home ages. For example, Malpezzi, Ozanne, and Thibodeau (1987)used high-order home age effects and found a 0.68% yearly depreciation rate. Thiswork borrows this idea and uses separate hedonic regressions for traditional andmanufactured homes to estimate home age effects (up to the third order) on homevalues, and then uses the estimated coefficients to calculate yearly depreciation rates.As for the expected appreciation rates, each home’s past years’ average real annualappreciation rate is applied. Then, the yearly user cost of owning a traditional homeUCOSTtrad is calculated as Ptrad × UCtrad, and the yearly user cost of owning amanufactured home UCOSTmanu is measured by Pmanu × UCmanu, where Ptrad andPmanu are prices for traditional and manufactured homes, respectively.

The mortgage rate is believed to vary with both property and borrowers’ charac-teristics. To retrieve the rates of the whole market, rather than only the sample selected,the observed mortgage rates are regressed on property attributes and borrowers’ char-acteristics to get the market-wide mortgage rates. Regression results are presentedin Table 1. The dependent variable is the observed mortgage rates from each survey.Among the explanatory variables, ManufHome is the manufactured home dummy,households income and home values are deflated (base year 1984) to create the Re-alIncome and HomeValue variables. NegIncome is the negative income dummy, fromsingle through Hispanic are householder’s characteristic dummy variables, grade isthe number of years of education received, 1987–2003 are year dummies (1985 beingthe reference year) to indicate from which year an observation is selected. We can seethat mortgage rates are significantly correlated with borrowers’ characteristics (race,age, etc.) and property attributes (manufactured or traditional, property value, etc.).

Table 2 summarizes the above estimated user cost components for owning amanufactured home versus owning a traditional home.

The sample is a little downsized because we require home values larger than$1000, home monthly rent larger than $50, and all user cost components with no

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Table 1. Mortgage rate regression on property and borrowers’ characteristics.

Variable Estimate SE t value

Intercept 0.12∗∗∗ 0.001 145.17ManufHome 0.01∗∗∗ 0.001 17.05HomeValue (in $100,000s) −0.003∗∗∗ 0.0003 −11.71RealIncome (in $100,000s) 0.0002 0.0004 0.61NegIncome −0.002 0.004 −0.41Single 0.001∗∗∗ 0.0002 3.21Age 0.00002∗∗∗ 0.00001 2.91Male 0.0002 0.0002 1.41Black 0.002∗∗∗ 0.0003 5.04Hispanic 0.002∗∗∗ 0.0003 4.87Grade −0.0003∗∗∗ 0.00003 −6.691987 −0.02∗∗∗ 0.001 −31.161989 −0.01∗∗∗ 0.001 −22.071991 −0.02∗∗∗ 0.001 −29.601993 −0.04∗∗∗ 0.001 −61.441995 −0.03∗∗∗ 0.001 −48.321997 −0.04∗∗∗ 0.001 −75.241999 −0.04∗∗∗ 0.001 −87.482001 −0.04∗∗∗ 0.001 −81.352003 −0.05∗∗∗ 0.001 −107.41# Obs. 16,641Adj R-Sq 0.55

Note:∗

significant at 10%;∗∗

significant at 5%; and∗∗∗

significant at 1% levels.

missing values. The yearly per dollar user cost of owning a traditional home is aboutone-half that of owning a manufactured home, which mainly results from the formerhaving a smaller mortgage rate (0.076 compared to 0.089) and a much smaller yearlyhome depreciation rate (0.008 compared to 0.05).5 The marginal income tax rateis higher for traditional homeowners. This makes sense because a traditional home

Table 2. Summary of estimated parameters in the user cost expression.

Panel A: Own-occupiedtraditional homes

Panel B: Own-occupiedmanufactured homes

Variable # Obs. Mean # Obs. Mean

rM 16,327 0.076 314 0.089tP 16,327 0.010 314 0.008tY 16,327 0.265 314 0.219d 45,201 0.008 823 0.050b 16,327 0.004 314 0.007π e 36,505 0.019 1521 0.022UC per dollar 16,327 0.056 314 0.118

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International Journal of Housing Policy 277

is more likely to be owned by a relatively higher income household. The propertytax rate also is higher for traditional homeowners. In some areas, manufacturedhomes are taxed as personal property if the wheels remain attached, but are taxedmore as real estate if the wheels are removed. The yearly maintenance cost is higherfor manufactured homes, probably coming from their relatively lower quality andthus more repair needs. However, manufactured homes have a comparable yearlyappreciation rate to traditional homes, both around 2%.6

If we observe a recent mover’s actual housing choice to own a traditional home,we can then estimate per dollar user cost for owing that traditional home accordingto the simplified user cost equation. Before moving, however, he or she also hasin mind the per dollar user cost incurred in owning a similar manufactured home.The question is how to obtain the hypothetical per dollar user cost for the otherownership alternative. The basic strategy is as follows: (1) For the marginal incometax rate, we use the same marginal income tax rate since income does not changewith housing options. (2) For the property tax rate, maintenance rate, depreciationrate, and appreciation rate, we use the corresponding means of the sample of theother owner-occupied option. (3) For the mortgage rate, we apply the coefficientscorresponding to the other owner-occupied option from the mortgage rate regressionto the same attribute values of his or her current housing option. Then, the task isto apply (Table 3) hedonic regressions to pin down home price (Pmanu or Ptrad) oryearly rental fee (Prent) for a similar dwelling either purchased as a traditional houseor purchased as a manufactured house or rented.

The dependent variable is real home value or the real yearly rent. The real homevalue (base year 1984) is $10,000 and the observations of less than $1000 are elim-inated, and the real yearly rent (base year 1984) is in dollars and observations ofless than $50 per month are eliminated.7 For example, if the mover’s actual hous-ing decision is an owned traditional home, Ptrad is directly estimated using implicitprice vector from the hedonic regression for owner-occupied traditional homes. If themover’s actual housing decision is an owned manufactured home, Ptrad is indirectlyestimated by applying the implicit price vector for owner-occupied traditional homesbut keeping its current manufactured home’s characteristic values. If the mover’sactual housing decision is a rented home, Ptrad is indirectly estimated by applying theimplicit price vector for owner-occupied traditional homes but keeping its currentrented home’s characteristic values. Pmanu and Prent have similar derivation strategyas Ptrad. Home value or rent’s explanatory variables in Table 3 include northeast,midwest, south (west being the reference category), and central city location dum-mies, bathroom (the number of bathrooms), room (total number of rooms), homeAge,garage dummy, plugs dummy (every room having working electrical plugs), UnitSF(square footage), and 1987–2003 dummies (1985 being the reference year) to indicatefrom which year an observation is selected.

Hypothesis 2: Recent movers who previously lived in manufactured homes are moreinclined to live in another manufactured home.

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Tabl

e3.

Hed

onic

sby

tenu

res:

the

pool

edre

cent

mov

ers’

data

:the

AH

S19

85–2

003.

Ren

talh

omes

Ow

n-oc

cupi

edm

anuf

actu

red

Ow

n-oc

cupi

edtr

adit

iona

l

Var

iabl

eE

stim

ate

SE

Est

imat

eS

EE

stim

ate

SE

Inte

rcep

t25

75.4

3∗∗∗

61.8

71.

38∗∗

∗0.

362.

91∗∗

∗0.

1N

orth

east

160.

27∗∗

∗23

.7−1

.01∗∗

∗0.

13−0

.57∗∗

∗0.

03S

outh

−114

0.25

∗∗∗

18.5

−1.7

9∗∗∗

0.09

−3.5

2∗∗∗

0.02

Mid

wes

t−9

36.3

2∗∗∗

21.4

8−1

.68∗∗

∗0.

12−3

.19∗∗

∗0.

02C

entr

alC

ity

−157

.25∗∗

∗14

.57

−0.3

3∗∗0.

13−0

.79∗∗

∗0.

02B

athr

oom

1205

.88∗∗

∗18

.64

0.75

∗∗∗

0.09

2.01

∗∗∗

0.01

Roo

m11

7.79

∗∗∗

6.5

0.16

∗∗∗

0.03

0.37

∗∗∗

0.01

Hom

eAge

−12.

43∗∗

∗0.

41−0

.02∗∗

∗0.

004

−0.0

1∗∗∗

0.00

1G

arag

e47

6.91

∗∗∗

16.6

0.62

∗∗∗

0.07

0.75

∗∗∗

0.02

Plu

gs21

2.45

∗∗∗

49.6

5−0

.52∗

0.31

0.36

∗∗∗

0.08

Uni

tSF

0.08

∗∗∗

0.01

0.00

1∗∗∗

0.00

010.

0004

∗∗∗

0.00

001

1987

−65.

37∗∗

32.3

30.

24∗∗

0.11

0.41

∗∗∗

0.04

1989

59.2

1∗30

.94

0.76

∗∗∗

0.14

0.88

∗∗∗

0.04

1991

−40.

5531

.61

0.68

∗∗∗

0.14

0.27

∗∗∗

0.04

1993

−46.

3930

.42

0.69

∗∗0.

140.

020.

0419

9566

.28∗∗

31.1

70.

46∗∗

∗0.

150.

16∗∗

∗0.

0419

9712

9.44

∗∗∗

46.2

31.

96∗∗

∗0.

15−0

.21∗∗

∗0.

0419

9961

7.36

∗∗∗

31.5

71.

25∗∗

∗0.

160.

1∗∗0.

0420

0167

5.75

∗∗∗

30.9

11.

69∗∗

∗0.

150.

23∗∗

∗0.

0420

0376

3.89

∗∗∗

29.6

42.

17∗∗

∗0.

160.

65∗∗

∗0.

04#

Obs

.65

,842

5021

163,

826

Adj

R-S

q0.

260.

250.

34

Not

e:∗

sign

ifica

ntat

10%

;∗∗si

gnifi

cant

at5%

;and

∗∗∗

sign

ifica

ntat

1%le

vels

.

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International Journal of Housing Policy 279

The recent movers’ characteristic variables include PreManuf dummy (whether arecent mover previously lived in manufactured homes). Research by Temkin, Hong,and Davis (2007) collected information about the households’ perception of fourhousing choices (site-built, modular, manufactured, and panelized homes) and foundthat residents who currently live in manufactured homes (not necessarily being own-ers) tend to offer a higher rating of manufactured homes than other households do.We expect this variable to have a positive effect on households’ choice of an ownedmanufactured home.

Hypothesis 3: Low income households are more likely to own a manufactured home.

The manufactured home is an affordable homeownership solution for low-incomehouseholds who might not have accumulated enough down payments for a traditionalhome but still wish be homeowners. Ownership of manufactured housing could be atransitional stage for this type of households to move from renting to traditional own-ership. We expect the variable RealIncome to have a negative effect on households’choice of an owned manufactured home.

Hypothesis 4: Recent movers from older age groups, who are married, from a biggerfamily, or from a white family, are less likely to own a manufactured home.

Older people are seeking safer and more convenient housing. When people getmarried or receive more members, they tend to switch to a bigger home. The whitepeople generally earn more income than the black people. We thus expect recentmovers’ characteristic variables age, single dummy, black dummy, and persons (fam-ily size) to have a negative effect on households’ choice of an owned manufacturedhome.

In addition, we include west, south, Midwest, and northeast region dummies ascontrol variables in the model (west as the reference group) because some researchimplies there might exist regional difference in people’s preference for manufacturedhomes.

Comparison of housing options

Table 4 presents summary statistics of variables used in the nested logit model,except for the first six variables; Prent, Pmanu, Ptrad, UCrent, UCmanu, and UCtrad, whichare factors utilized to calculate the yearly user costs of each housing choice. Forillustrative purpose, Table 4 is presented in three different panels, corresponding tothe three housing modes. The product of Prent and UCrent yields the yearly user costof renting UCOSTrent. Similarly, UCOSTmanu is the yearly user cost of owning amanufactured home and UCOSTtrad for owning a traditional home.8

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280 Y. Zhou

Tabl

e4.

Sum

mar

yst

atis

tics

ofex

plan

ator

yva

riab

les

inth

ene

sted

logi

tmod

el.

Pane

lA:R

enth

omes

Pane

lB:O

wn

man

ufac

ture

dho

mes

Pane

lC:O

wn

trad

itio

nalh

omes

Var

iabl

eM

ean

(Std

.)M

in(M

ax)

Mea

n(S

td.)

Min

(Max

)M

ean

(Std

.)M

in(M

ax)

Pre

nt45

87(1

113)

1907

(11,

115)

5026

(106

7)25

91(7

534)

5940

(129

3)23

14(1

3,26

0)P

man

u27

,75

4(1

5,14

4)10

00(1

07,08

4)32

,57

3(1

5,89

9)33

22(7

2,89

8)44

,07

2(1

6,63

5)13

55(1

33,28

9)P

trad

60,86

7(2

4,39

8)18

45(1

79,27

1)68

,31

2(2

1,86

5)22

,29

0(1

35,47

5)87

,44

3(2

6,49

9)18

,77

0(2

33,47

1)U

Cre

nt1

(0)

0(1

)1

(0)

0(1

)1

(0)

0(1

)U

Cm

anu

0.11

5(0

.013

)0.

086

(0.1

74)

0.11

8(0

.027

)0.

086

(0.3

82)

0.10

7(0

.011

)0.

086

(0.1

70)

UC

trad

0.06

3(0

.013

)0.

036

(0.1

20)

0.06

6(0

.015

)0.

044

(0.1

02)

0.05

6(0

.016

)0.

026

(0.4

94)

UC

OS

Tre

nt45

87(1

113)

1907

(11,

115)

5026

(106

7)25

91(7

534)

5940

(129

3)23

14(1

3,26

0)U

CO

ST

man

u31

06(1

596)

104

(11,

120)

3679

(172

1)45

6(8

428)

4634

(162

3)14

4(1

2,38

4)U

CO

ST

trad

3827

(161

9)12

2(1

3,25

1)44

13(1

493)

1448

(971

6)48

34(1

719)

1021

(39,

392)

Sou

th0.

37(0

.48)

0(1

)0.

53(0

.50)

0(1

)0.

36(0

.48)

0(1

)M

idw

est

0.17

(0.3

7)0

(1)

0.21

(0.4

1)0

(1)

0.24

(0.4

3)0

(1)

Nor

thea

st0.

10(0

.29)

0(1

)0.

07(0

.26)

0(1

)0.

12(0

.32)

0(1

)P

reM

anuf

0.03

(0.1

6)0

(1)

0.28

(0.4

5)0

(1)

0.03

(0.1

7)0

(1)

Rea

lInc

ome

20,63

2(2

1,76

8)−7

048

(440

,28

3)25

,06

6(1

6,22

8)−5

64(8

4,52

6)42

,02

8(3

1,03

1)−6

230

(415

,83

5)A

ge33

.16

(13.

23)

10(9

3)36

.91

(14.

46)

14(8

6)36

.00

(12.

40)

14(9

3)B

lack

0.24

0.42

)0

(1)

0.08

(0.2

7)0

(1)

0.15

(0.3

5)0

(1)

Sin

gle

0.63

(0.4

8)0

(1)

0.38

(0.4

9)0

(1)

0.35

(0.4

8)0

(1)

Pers

ons

2.70

(1.5

3)1

(14)

3.02

(1.4

4)1

(9)

3.19

(1.5

4)1

(17)

#O

bs.

34,9

9831

416

,327

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International Journal of Housing Policy 281

In panel B where the observed housing choice is an owned manufactured home,the mean user cost is the lowest for owning a manufactured home among the threehousing choices ($3679 compared to $5026 for renting and $4413 for owning atraditional home). In panel C where actual housing choice is an owned traditionalhome, the mean user cost of owning a traditional home is over $1100 lower thanthat of rental homes ($4834 compared to $5940), and only a little higher than thatof owning a manufactured home, mainly because of the relatively high price oftraditional homes. This observation shows that movers tend to pursue an ownershiptype with smaller user cost. In panel A where the observed housing choice is renting,the mean user cost is the highest for renting among the three housing choices. Thiscould be explained by the fact that most renters are wealth or income constrained andtherefore cannot afford ownership.

This could be shown by comparison of the real income across renters and owners.Renters have yearly real income of $20,632, less than one-half that of traditional homeowners ($42,028) and almost 20% less than that of owners of manufactured homes($25, 066). The variable PreManuf denotes whether the recent mover has previousexperience of living in a manufactured home. About 30% of current manufacturedhome owners have such previous experience, compared to just 3% for the occupantsof the other two types of housing.

Empirical results

The model estimation results are presented in Table 5. The dependent variable is theprobability of choosing across the three housing choices. Because all explanatoryvariables (except for the use cost LogUCOST, the natural logarithm of the usercosts) remain the same across three modes for each individual mover, in order toimport enough variation in the explanatory variables, two mode-specific dummiesare created; D2 for mode 2 and D3 for mode 3. Each original variable (except forLogUCOST) is then replaced by two interaction terms (such as PreManuf 2 andPreManuf 3) interacting mode-specific dummies with each original variable.9 Theinclusive value parameters are between 0 and 1, verifying that the nested logit modelis an acceptable specification. Almost all of the independent variables have significanteffects on households’ ownership decisions, except for a couple of regional dummies.Our analysis, however, is not based on Table 5, but on Table 6, which provides eitherthe percentage changes or changes in percentage points in probabilities of choosingacross the three housing choices (depending on the specification of changes) whenthere is exogenous shock from any of the explanatory variables. Column 1 in Table 6lists shock sources. Shock directions and magnitudes (measured from the mean) arespecified in column 2, for example, ‘↑1%’ denotes an increase by 1%; ‘↑1 year’denotes the mover being one year older; ‘0→1’ denotes the dummy variable beingpresent.

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282 Y. Zhou

Table 5. Model parameter estimation.

Variable Estimate SE t value

LogUCOST −1.08∗∗∗ 0.07 −14.46PreManuf2 2.27∗∗∗ 0.14 15.47PreManuf3 0.16∗ 0.08 1.89RealIncome 2 0.00001∗ 0.00001 1.81RealIncome 3 0.00005∗∗∗ 0.000004 12.75Single 2 −1.98∗∗∗ 0.14 −14.07Single 3 −0.91∗∗∗ 0.08 −10.74Age 2 −0.03∗∗∗ 0.004 −6.48Age 3 0.01∗∗∗ 0.001 6.56Black 2 −1.33∗∗∗ 0.28 −4.75Black 3 −0.74∗∗∗ 0.08 −9.20Persons 2 −0.25∗∗∗ 0.04 −5.82Persons 3 0.14∗∗∗ 0.01 9.68South 2 −0.16 0.12 −1.32South 3 0.52∗∗∗ 0.05 10.30Midwest 2 −0.04 0.16 −0.26Midwest 3 1.08∗∗∗ 0.08 12.16Northeast 2 −0.7∗∗∗ 0.23 −2.99Northeast 3 0.55∗∗∗ 0.06 8.25Inclusive value 1 0.47∗∗∗ 0.04 10.57Inclusive value 2 0.71∗∗∗ 0.05 12.44Number of observations 51,639Number of cases 154,917

Note:∗

significant at 10%;∗∗

significant at 5%; and∗∗∗

significant at 1% levels.

User cost

The negative coefficient of the user cost variable supports Hypothesis 1, implyingthat when the user cost increases, the probability of a recent mover choosing thatmode decreases, all other modes’ user cost held constant. For the simplicity ofinterpretation, we denote E(a1,a2) as the elasticity between housing alternative a1

and a2, specifically, the percentage change in the probability of housing option a2

when the user cost of housing option a1 increases by 1%. For example, E(rent, own)equals 0.66 in Table 6 implies that when the user cost of renting increases by 1%, theprobability of an ownership decision increases by 0.66%, while E(rent, rent) equals−0.16 implies that when the user cost of renting increases by 1%, the probability ofrenting decreases by 0.16%.

The three elasticity measures pertaining to manufactured homeownership areE(rent, own manufactured) equals 0.76, E(own manufactured, own manufactured)equals −0.76, and E(own traditional, own manufactured) equals 0.25. Putting it theother way, taking E(rent, own manufactured) as an instance, if the user cost of owning

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International Journal of Housing Policy 283

Tabl

e6.

Perc

enta

gech

ange

sor

chan

ges

inpe

rcen

tage

poin

tsin

the

prob

abil

ity

acro

ssth

ree

hous

ing

choi

ces

whe

nex

plan

ator

yva

riab

les

chan

ge.

Pane

lA:p

erce

ntag

ech

ange

sin

the

prob

abil

ity

acro

ssth

ree

hous

ing

choi

ces

Sho

ckso

urce

[1]

Sho

ckty

pe[2

]P

rob.

rent

ing

bran

chP

rob.

owni

ngbr

anch

Pro

b.ow

ning

man

ufac

ture

dP

rob.

owni

ngtr

adit

iona

l

Use

rco

stre

ntin

g↑

1%↓

0.16

%(−

0.16

)↑

0.66

%(0

.66)

↑0.

76%

(0.7

6)↑

0.63

%(0

.63)

Use

rco

stow

ning

man

ufac

ture

d↑1

%↑

0.02

%(0

.02)

↓−0

.10%

(−

0.10

)↓

−0.7

6%(−

0.76

)↑

0.01

%(0

.01)

Use

rco

stow

ning

trad

itio

nal

↑1%

↑0.

01%

(0.0

1)↓

−0.0

1%(−

0.01

)↑

0.25

%(0

.25)

↓−0

.70%

(−

0.70

)

Hou

seho

ldin

com

e↑1

%↓

−0.0

6%(−

0.06

)↑

0.22

%(0

.22)

↓−0

.08%

(−

0.08

)↑

0.30

%(0

.30)

Pane

lB:p

erce

ntag

epo

intc

hang

esin

the

prob

abil

ity

acro

ssth

ree

hous

ing

choi

ces

Pro

b.ow

ning

Pro

b.ow

ning

Pro

b.m

anuf

actu

red

trad

itio

nal

Pro

b.ow

ning

owni

ngS

hock

sour

ceS

hock

type

Pro

b.re

ntin

gP

rob.

owni

ngco

ndit

iona

lon

cond

itio

nalo

nm

anuf

actu

red

trad

itio

nal

[1]

[2]

bran

ch[3

]br

anch

[4]

owni

ng[5

]ow

ning

[6]

[7]

[8]

Hou

seho

ldag

e↑1

year

↓0.

02%

↑0.

02%

↓0.

56%

↑0.

56%

↓0.

11%

↑0.

13%

Hou

seho

ldsi

ze↑1

pers

on↓

0.88

%↑

0.88

%↓

5.55

%↑

5.55

%↓

1.00

%↑

1.88

%P

revi

ous

expe

rien

ceof

man

ufac

ture

d0→

1↓

14.2

8%↑

14.2

8%↑

47.1

2%↓

47.1

2%↑

18.8

9%↓

4.61

%

Non

sing

leto

sing

le0→

1↑

10.5

0%↓

10.5

0%↓

13.7

6%↑

13. 7

6%↓

4.06

%↓

6.44

%N

onbl

ack

tobl

ack

0→1

↑7.

98%

↓7.

98%

↓7.

86%

↑7.

86%

↓2.

58%

↓5.

40%

Not

e:In

Pane

lA

,nu

mbe

rsin

pare

nthe

ses

are

elas

tici

tym

easu

res.

InPa

nel

B,

num

bers

inea

chco

lum

nsa

tisf

y[3

]+[4

]=

0,[5

]+[6

]=

0,[7

]+[8

]=

[4],

and

[3]+

[7]+

[8]=

0.

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284 Y. Zhou

manufactured homes decreases by 1%, the probability of owning manufactured homeswill increase by 0.76%.

Other results

Compared to those without such experiences, the recent movers who have previousexperience of living in manufactured units are more likely to own and less likelyto rent. The probability of owning increases by 14.28 percentage points. Underthe owning branch, the probability of owning manufactured homes increases, whilethe probability of owning traditional homes decreases. Overall, the probability ofowning manufactured homes increases by 18.89 percentage points, if a recent moverhas previously lived in manufactured homes. This result supports Hypothesis 2.

The negative income elasticity of owning manufactured homes (−0.08) indicatesthat Hypothesis 3 is confirmed, that is, if recent movers’ real income decreases by1%, the probability of owning manufactured homes increases by 0.08%, while theprobability of owning traditional homes decreases by 0.30%. This result shows thathousehold of low and medium income are more likely to own manufactured homes.

Hypothesis 4 is supported by the following results. When households are gettingolder, they tend to be homeowners. Furthermore, they prefer traditional homes tomanufactured homes. The changes in probabilities for the three housing choicesrenting, owning a manufactured home, and owning a traditional home, are −0.02percentage points, −0.11 percentage points, and 0.13 percentage points, respectively,per year of additional age. If a household has one more member, then the changesin probabilities for the three housing choices are −0.88 percentage point (renting),−1.00 percentage point (owning manufactured homes), and 1.88 percentage points(owning traditional homes). The single and black recent movers are less likely tobecome either manufactured homeowners or traditional homeowners. They tend tobe renters. When people get married, the probability of owning increases by 10.50percentage points. The white people’s homeownership probability is 7.98 percentagepoints higher than the minority households.

Generally speaking, there exists a regional difference on people’s tendency toselect owned manufactured homes based on the estimation results in Table 5.

Conclusion

This paper has demonstrated that households’ tendency to opt for manufacturedhomes (at least among recent movers) is affected by its relative users cost, and people’scharacteristics. The recent movers’ households with the following characteristics aremore likely to own manufactured homes: low- and medium-income, relatively young,relatively small household size, having previous experience of living in manufacturedhomes, married, and white. The lower the user cost of manufactured homes, the morelikely they are to be owned.

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International Journal of Housing Policy 285

Much concern exists on how to raise the American homeownership rate. Thereare many special interest and advocacy groups that support homeownership, andgovernment intervention has been taking place, especially in recent years, to slowdown the decrease in the US homeownership rate. As an affordable and good home,manufactured housing presents an alternative homeownership solution for low- andmedium-income households. This article’s findings on the characteristics of potentialcustomers for manufactured homes, and some other determinants, could be used toinform policy discussion in local jurisdictions who want to prompt manufacturedhousing ownership. For example, the key finding of this article is that loweringthe user cost of owning manufactured homes may encourage manufactured homeownership. Should manufactured home mortgages be offered at a similar level ofmortgage rates and LTVs as loans for traditional homes? Should a similar levelproperty tax rate be applied to manufactured homes, so that the owners enjoy similartax deduction benefits to traditional home owners? Should local governments requirebetter construction and installation standards to improve manufactured homes’ qualityso as to slow down their depreciation and reduce their maintenance costs? Other pointsof policy discussion might also include, should more firms be encouraged to enterthe manufactured housing industry to provide more design options and to build up amore active resale network? Should local government relax land zoning regulationson manufactured homes? All of these actions might help to prompt demand formanufactured home ownership and thus deserve future exploration.

AcknowledgementsI thank Donald Haurin and a few anonymous reviewers for stimulating discussions and/orinsightful comments. All errors are mine.

Notes1. Manufactured housing could take the following types in terms of ownership: own both the

structure and land, own the structure but rent land, rent both the structure and land, andrent the structure but own land. The first two types are common, while the latter two arerare. In my study, I focus only on manufactured housing where both the structure and landare owned. Among all owner-occupied manufactured homes in my sample, rented landcases comprise 47%–56% in each AHS survey.

2. The AHS data do not tell whether manufactured units are wheeled or nonwheeled. In someareas, if wheels are present, the manufactured homes are treated as personal property ratherthan real property.

3. The nested logit is better than the multinomial logit because the latter might not satisfy theindependence of irrelevant alternatives assumption.

4. Brownstone and Small (1989) study a similar model applied to the arrival time patterns ofcommuters in the San Francisco Bay area.

5. Only Year 2003 AHS survey is used to estimate depreciation rate. Up to the third order ofproperty ages are included.

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286 Y. Zhou

6. The main drive of appreciation of manufactured homes is from the land appreciation. Theselected AHS sample finds very high land leverage for manufactured homes (the ratio ofland value to overall property value). On an average, the land leverage for a manufacturedhome is about 47%.

7. Log real home value and log real yearly rent could be an alternative. When the logalternative is used instead, R-Sq improves slightly to 0.35 for owner-occupied traditionalhome regression and rises to 0.29 for owner-occupied manufactured home regression,while deteriorates a little to 0.24 for rental regression.

8. In the actual model estimation, the log user cost (LogUCOST) is used.9. Detailed explanation can be requested from the author.

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