Naoyuki Yoshino (Dean, ADBI)

Matthias Helble (Research Fellow, ADBI)

22/05/2015, ADB

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Disclaimer: The views expressed in this paper/presentation are the views of the author and do

not necessarily reflect the views or policies of the Asian Development Bank (ADB), or its Board

of Governors, or the governments they represent. ADB does not guarantee the accuracy of the

data included in this paper and accepts no responsibility for any consequence of their use.

Terminology used may not necessarily be consistent with ADB official terms.

Contents I. Basic facts about ADBI

II. Housing policies for emerging Asia

III. Homeownership and health

IV. Future research on housing and health

I. ADB Institute Basic facts Founded in 1996 as think tank of ADB

Located in Tokyo

Number of staff: About 60 (14 professionals)

Head: Dean Naoyuki Yoshino

Two main areas of work: Research & capacity building

Current research topics: Infrastructure (economic impact)

SME

Financial inclusion

Housing policies (flagship) started Dec. 2014

Central-local governance (flagship) starting now

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II. Housing Policies Project: Rationale

Housing as fundamental need (food, clothing & shelter)

Benefits of access to adequate housing for household: Improved health (infectious and non-infectious diseases)

Better educational achievements

Contributes to social cohesion and social security

Benefits for economy: Construction multiplier

Small business aspect (place of employment/collateral)

But: Difficulties throughout Asia in providing enough affordable and adequate housing.

Research objective: Assist in developing policies to facilitate access to affordable and adequate housing.

II. Housing Policies: Increasing urbanization

Source: UN (2014)

Figure: Urban and rural population as proportion of total population (19502050)

II. Housing Policies: Increasing urbanization

Source: UN (2014)

Figure: Urban and rural population as proportion of total population (19502050)

II. Housing Policies: Increased property prices

Figure: Price to Income Ratio in 20 most expensive cities in the world

Source: Numbeo Property Prices Index (2015)

86.25

50.36

40.67 36.83 36.53 35.14 33.06 32.54 32.15 31.1 31.1 30.57 30.5 28.8 27.86 27.59 26.68 26.02 25.82 25.79

0

10

20

30

40

50

60

70

80

90

100

Price to Income Ratio

II. Housing policies: Increasing inequality.

Source: Authors calculations based on SWIID and World Bank

Figure: Gini Coefficients vs GDP per capita in 7 most populous DMCs (19902012)

II. Housing Policies: The Context

Urbanization

Health

Employment

Environment

Legal Framework

Infrastructure

II. Housing Policies: Project Overview

Events:

Brainstorming workshop (Dec. 2014)

Inception workshop (May 2015)

Housing policies conference (Sept. 2015) focus on policy makers

Knowledge products:

Working papers (inhouse and by external experts)

Book on housing policies for emerging Asia (end 2015)

Conference volume

II. Project Overview: Book

PART 1: Overview, Modelling Framework and Context Overview

Housing Policy Matrix

Demand and Supply Model of Housing Policies

Housing Policies and Urbanization (jointly with ADB)

Housing and Health (jointly with ADB)

PART 2: Country Studies Advanced countries: Japan, Rep. of Korea, Singapore,

CH, UK, and US

Emerging economies: PRC, India, and others

II. Housing Policies for Homeowners Cash benefits for

housing /

Housing subsidy

Mortgage interest

rate reduction

Mortgage interest

deduction from

income tax

Upgrading quality

standards

Construction of

housing or

provision of

land

Demand/Supply Demand Demand/Supply Supply

Merits Lowers housing

costs for HH

Simple to

implement and to

understand for HH

Lowers financial cost

to purchase housing

Enhances

competition

Lowers housing

financing costs

Enhances living

standards and

durability

Contributes to

environmental

policies

Accelerates the

construction of

houses

Ensures quality of

houses

Demerits Cash benefits used

for other purposes

Eligibility criteria

Fiscal burden

Crowds out private

banks and investors

Increases HH debt

Fiscal burden

Less effective for

low-income groups

Increases HH debt

Fiscal burden

Implementation

costs for HH and

government

Makes housing less

affordable for low-

income groups

Overstretched

supply capacity

Lack of diversity

Fiscal burden

Country examples Germany, Rep. of

Korea, Singapore

Japan, Rep. of Korea US, Japan Japan, Rep. of

Korea

Japan, Rep. of

Korea, Singapore

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II. Housing Policies for Rental Market Rent certificate

Housing voucher Rent control Public housing Subsidy to

suppliers

Demand/Supply Demand Demand/Supply Supply

Merits Increases housing

consumption

Incentivizes

maintance (owner)

Gives HH more

choices

Incentivizes

maintance (owner)

Mitigates the

burden of rent hikes

Addresses

housing shortage

Guarantee

minimum

standard

Accelerates the

construction of

houses

Addresses housing

shortage

Demerits No incentive to find

inexpensive

housing

Fiscal burden

Subsidy might be

used for other

purposes

Fiscal burden

Excess demand

Low incentive for

new construction

Inefficient

allocation

Limits HH choice

Crowds out

private suppliers

Eligibility

Fiscal burden

Overinvestment

Fiscal burden

Country examples US Germany, Rep. of

Korea, US

US, Switzerland Japan, UK Germany, India

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II. Housing Policies: Cost-Effectiveness Analysis

Owner Occupied Housing Market:

Rental Housing Market:

Policy

Policy variable

Change Policy Cost Utility

Utility (%) From To

Owner occupied

house

Cash subsidy for buyers G1 0 0.021 0.021 0.00465 0.07354%

Housing subsidy Subsidy rate 0% 0.238% 0.021 0.00464 0.07350%

Mortgage interest rate reduction

r 5% 4.462% 0.021 0.00500 0.07921%

Mortgage interest deduction from income tax

rtyL* 0 0.021 0.021 0.00465 0.07360%

Policy Policy

variable Change

Policy Cost Utility Utility

(%) From To

Rental house Cash subsidy for tenants G1 and G2 0 0.518 1.00973 0.21612 3.34299%

Rent aid Subsidy rate 0% 10% 1.00973 0.20545 3.17805%

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Research Objective Better understand the link between home ownership and health.

Are homeowners healthier? Probably YES.

Why? Because they are generally richer and spend more on medical services.

Are homeowners healthier after controlling for age, sex, income, educational background, financial assets, housing conditions...? We find that YES.

But why?

Grossman (1972) model

Data: The Keio Households Panel Survey (KHPS) provided by Panel Data Research Centre at Keio University.

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Brief overview of literature The impact on housing conditions on health

Better housing conditions healthier

Examples: India (Nayar, 1997), Malawi (Wolff et al., 2001), US (Breysse et al., 2004), UK (Blackman and Harvey, 2001)

The impact of home ownership on health Not yet tested quantitatively.

Impact of home ownership on: Educational achievements of children (Green and White, 1996;

Haurin et al., 2001)

Incentives to invest in local amenities and social capital (DiPasquale and Glaeser, 1999)

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The Keio Households Panel Survey Yearly longitudinal survey of private households in Japan

since 2004

Latest available data: 2012

About 3,500-4,000 sample households each year

Sampling of people between 20 and 69 as of 2004, representing 2/3 of the population

Questions cover wide range of information, for example: 180 questions about employment, academic history, health,

60 questions about households income and expenditure

70 questions about housing

8 questions about mortgage

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Variables for Analysis Demographic information

Age, sex, marital status, family size ,etc.

Socio-economic status Educational background, employment ,etc.

Households asset Income, saving, securities, debt, mortgage ,etc.

Housing information Home ownership Housing conditions (floor space, yard size, housing age, etc.)

Health variables Self-assessed health, health check-ups, medical expenditure,

OTC medicine purchase, hospitalization, etc.

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Renters vs Owners: Self-assessed health

22 Source: Aizawa & Helble (2015)

Renters vs Owners: Health check-ups

23 Source: Aizawa & Helble (2015)

Renters vs Owners: Medical expenditures

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Source: Aizawa & Helble (2015)

Methodology 3 different dependent variables:

1. Self-assessed health condition (dummy)

2. Health check-ups (dummy)

3. Medical expenditure (in Yen)

Explanatory variables:

Demographic information, socio-economic statuses,

households financial situation, and home ownership Controlling for housing conditions

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Methodology Estimation 1: Do home owners assess themselves as more healthy compared to renters?

Dependent variable: Self-assessed health condition

3 types of self-assessed health:

1. Self-assessed overall health (sah)

2. Self-assessed physical health (physical)

3. Self-assessed mental health (mental)

Taking account of housing conditions (floor space and yard size per family member, age of the house, amenities for the elderly, distance to the station)

RE probit estimation

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Estimation 1: Self-assessed Health

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(1) (2) (3)

Sah

Taking account

of housing

conditions

Physical health

Taking account

of housing

conditions

Mental health

Taking account

of housing

conditions

age -0.0549*** 0.00132 -0.00212

(0.00596) (0.00288) (0.00275)

edulevel2 1.206*** 0.189* 0.0893

(0.279) (0.111) (0.106)

edulevel3 1.373*** 0.283** 0.0808

(0.285) (0.117) (0.112)

lninc -0.0156 0.0485* 0.0891***

(0.0522) (0.0295) (0.0291)

lnborrow -0.0132 -0.0233*** -0.0242***

(0.0166) (0.00867) (0.00853)

mortgage 0.00101 -0.00307 0.0694

(0.140) (0.0727) (0.0710)

own 0.407** 0.149* 0.0424

(0.165) (0.0811) (0.0791)

urban -0.376** -0.0543 -0.0804

(0.177) (0.0834) (0.0803)

lnsaving 0.0580*** 0.0234*** 0.0389***

(0.0181) (0.00904) (0.00877)

lnsecurities 0.0280 0.0101 0.0109

(0.0194) (0.00970) (0.00938)

N 11520 16098 16189 Note: Only selected variables listed

Results Estimation 1 (Self-assessed health):

General Self-assessed health (sah)

Homeowners think of themselves as healthy (p

Methodology Estimation 2: Do homeowners receive health check-ups

more often?

Dependent variable: Undergoing health check-ups

3 types of health check-ups:

1. Complete screenings (fullscreen)

2. Cancer screenings (cancerscreen)

3. Periodic screenings (companyscreen)

RE probit estimation

Compulsory screenings (Periodic screening) vs Voluntary screenings (Complete and Cancer screening)

Control for the financial support system by large companies

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Estimation 2: Health Check-ups (1) (2) (3)

Complete screening Cancer screening Periodic screening

age 0.0308*** 0.0308*** 0.00324

(0.00419) (0.00262) (0.00232)

fulltimeworker 0.203** -0.320*** 0.840***

(0.0881) (0.0526) (0.0506)

parttimeworker -0.00887 -0.201*** 0.320***

(0.102) (0.0556) (0.0558)

largecompany 0.526*** -0.00684 0.481***

(0.0742) (0.0557) (0.0476)

edulevel2 0.432*** 0.0794 0.190**

(0.166) (0.0884) (0.0904)

edulevel3 0.906*** 0.333*** 0.0853

(0.174) (0.0951) (0.0960)

lninc 0.445*** 0.0611** 0.138***

(0.0547) (0.0291) (0.0257)

lnborrow -0.00524 0.0126 0.00448

(0.0129) (0.00870) (0.00743)

mortgage 0.145 -0.125* -0.0280

(0.0990) (0.0669) (0.0593)

own 0.292*** 0.0993 0.0804

(0.111) (0.0665) (0.0608)

lnsaving 0.0478*** 0.0466*** 0.0373***

(0.0136) (0.00883) (0.00765)

lnsecurities 0.0304** 0.00201 -0.0110

(0.0122) (0.00847) (0.00824)

N 19556 19556 19556 30 Note: Only selected variables listed

Results Estimation 2 (Health check-ups)

Significant for the complete screening (p

Methodology Estimation 3: Do homeowners spend more on health care?

Dependent variable: ln(medical expenditure)

= 0 + 1,, 1 + = +

Pooled OLS, FE, RE

Possible selection bias due to the different decision making between healthy and unhealthy people

Two equation model Heckman (1979)

Possible endogeneity of home ownership (Aaronson, 2000)

HT, IV(prtlive, yard, lnroomratio)

Test the endogeneity of home ownership by the Hansen-Sargan J test and the GMM distance test

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Estimation 3: Do home owners spend more on health?

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(1) (2) (3) (4) (5) (6)

Pool FE RE Heckman HT IV

age -0.0291*** -0.0370*** -0.0277*** -0.0210** -0.0350*** -0.0466***

(0.00618) (0.0127) (0.00571) (0.00919) (0.00834) (0.0147)

agesq 0.000374*** 0.000271** 0.000347*** 0.000286*** 0.000409*** 0.000357**

(0.0000650) (0.000124) (0.0000595) (0.0000964) (0.0000847) (0.000142)

lninc 0.144*** 0.0468*** 0.106*** 0.136*** 0.0840*** 0.0383*

(0.0156) (0.0163) (0.0118) (0.0228) (0.0133) (0.0200)

lnborrow -0.00312 -0.00751* -0.00430 -0.00362 -0.00511 -0.00844*

(0.00356) (0.00414) (0.00326) (0.00526) (0.00337) (0.00476)

mortgage -0.0754*** -0.0466 -0.0744*** -0.0896** -0.0535* -0.0756

(0.0268) (0.0369) (0.0250) (0.0392) (0.0285) (0.0481)

own 0.197*** 0.159*** 0.200*** 0.157*** 0.165*** 0.268**

(0.0273) (0.0507) (0.0246) (0.0391) (0.0465) (0.127)

urban -0.00257 0.0989* -0.00245 0.0271 -0.00303 0.1000

(0.0268) (0.0566) (0.0253) (0.0416) (0.0301) (0.0658)

lnsaving -0.0119*** 0.000883 -0.00777** -0.0210*** -0.00449 0.000362

(0.00352) (0.00493) (0.00325) (0.00525) (0.00354) (0.00564)

lnsecurities 0.00662* 0.00290 0.00600* 0.00586 0.00634* 0.00324

(0.00400) (0.00526) (0.00342) (0.00549) (0.00376) (0.00614)

N 20503 20503 20503 11349 20503 15518

Note: Only selected variables listed

Results Estimation 3: Medical Expenditure

Homeowners spend more on health (p

Conclusion Limitations:

Self-assessed health = True health ?

Selection bias

Main finding: Homeowners

feel healthier.

more willing to undergo health check-ups.

spend more on health care.

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IV. Possible future joint projects Research:

Urban health and housing (empirical study)

Housing policies and health systems

...

Capacity Building:

Health insurance (central-local governance)

...

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References Aaronson,A. (2000) A Note on the Benefits of Homeownership, Journal of Urban

Economics, 47, pp.356-369 Blackman,T. and Harvey,J. (2001) Housing Renewal and Mental Health; A Case

Study,Journal of Mental Health, 10(5), pp.571-583 Breysse,P., Farr,N., Galke,W., Lanphear,B., Morley,R., Bergofsky,L. (2004) The

Relationship between Housing and Health; Children at Risk Environmental Health Perspectives, 112(15), pp.1583-1588

DiPasquale,D. and Glaeser,L. (1999) Incentives and Social Capital: Are Homeowners Better Citizens? Journal of Urban Economics, 45, pp.354-384

Green,R.K., White,M.J.(1996) Measuring the Benefits of Homeowning: Effects on Children, Journal of urban economics, 41, pp.441-461

Grossman,M.(1972) On the Concept of Health Capital and the Demand for Heatlh, Journal of Political Economy,80, pp.223-255

Grossman,M.(1999) The Human Capital Model of the Demand for Health, NBER Working paper 7078

Grossman,M.(2005) Education and Nonmarket Outcomes, NBER Working paper 11582 Grossman,M.(2008) The Relationship Between Health and Schooling, Eastern

Economic Journal, 34, pp.281-292

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References Haurin,D.R., Parcel,T.L., Haurin,R.J. (2001) The impact of Homeownership on Child

Outcomes, Low-income Homeownership Working Paper Series, LIHO-01.14. Joint Center for Housing Studies of Harvard University

Hausman,J.A., Taylor,W.E.(1981) Panel Data and Unobservable individual Effects, Econometrica, 49(6), pp.1377-1398

Jason, M. F. and David E. F. (2009) Higher Education and Health Investments: Does More Schooling Affect Preventive Health Care Use?,Journal of Human Capital, Vol. 3, No. 2 , pp. 144-176

Lindeboom,M. and van Doorslaer,E. (2004) Cut-point shift and index shift in self-reported health, Journal of Health Economics, 23, pp.1083-1099

Nayar,K.R.(1997) Housing Amenities and Health Improvement; Some Findings Economic and political Weekly,32(22), pp.1275-1279

Nicoletti,C. and Peracchi,F.(2005)A Cross-country Comparison of Survey Nonparticipation in the ECHP, Journal of the Royal Statistical Society Series A, 168, pp.361-381

Wolff,C.W, Schroender,D.G., Young,M.W.(2001) Effect of Improved Housing on Illness in Children under 5 years old in Northern Malawi: Cross Sectional Study, British Medical Journal, 322, pp.1209-1212

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Self-assessed health

41

Floor area per housing unit

Source: A Quick Look at Housing in Japan, May 2014, The Building Center for Japan, p17

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The home ownership rate

Source: A Quick Look at Housing in Japan, May 2014, The Building Center for Japan, p17

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