Transcript Demographic Change and the Demand for Housing in England

Demographics, Human Capital,
and the Demand for Housing
Piet Eichholtz
Maastricht University
Thies Lindenthal
Maastricht University
ICPM Netspar Conference
Maastricht University, 30 October 2007
Expected change in total population, 2005-2050
Large differences across Europe
in %
> 20
15-25
15/5
5/0
0/–5
–5/–10
–10/–15
–15/–20
<–20%
Source: United Nations
Housing performance and demographic contraction
Limburg is lagging behind the national trend
250
200
150
100
50
Zuid Limburg
Netherlands
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
0
Shrinking Amsterdam
Population decline 1795-1814 drove down house prices and rents
250,00
200,00
150,00
100,00
50,00
0,00
1650
1792 1813
Rents
Prices
1914
Structure of presentation
Introduction
Method and Data
Results
Conclusion
Intention of the paper is to understand (future) housing
demand better
• How do demographic changes influence the demand for
residential real estate?
• Will demand for housing decline when population growth
slows down and societies become older?
• This paper contributes to the discussion in three ways
– Refined methodology
– Very detailed and high-quality data
– European evidence
Preview of results
• Demographics impact the demand for housing
– Human capital is one of the key drivers
• Education, income, health, employment status
– Housing demand does not decline with age, but increases
• Positive human capital effects get stronger with age
• Aging and slowdown in population growth do not
necessarily imply a decline in overall housing demand
• Education effect may even offset shrinking population
Literature Review
The first wave of research
• Mankiw and Weil (1989) started the debate claiming that
aging baby boomers will demand less housing in the
future
– They predicted house price drop of 47%
• Intense criticism by (inter alia) Peek and Wilcox (1991),
Hendershott (1992), Engelhardt and Poterba (1991)
• Green and Hendershott (1996) find housing demand to
stay constant with age
– Education is main driver of demand
International evidence
Good empirical studies are still very scarce
• England: Ermisch (1996)
– Demand partly explained by demographics
• Japan: Ohtake and Shintani (1996)
– Short run price effects of demographics, long run supply adjustment
• Sweden/OECD: Lindh and Malmberg (1999)
– Demographics explain new construction in Sweden and OECD
• Austria: Lee et al. (2001)
– Number of households is important
• The Netherlands: Neuteboom and Brounen (2007)
– Demand for housing will not go down in aging society (due to cohort
effects)
Agenda
Introduction
Method and Data
Results
Conclusion
First decompose, then predict demand for housing
Control for housing quality and the demographic profile of household
1. Decompose house into housing services
2. Investigate willingness to pay for these services
3. Investigate the role of household’s demographic situation
and human capital
4. Define a constant quality house
5. Calculate the willingness to pay for this house as a
household becomes older
6. Predict housing demand with changing demographics
Refining the methodology
Cohort variables versus life-cycle variables
• Cohort variables do not change when households grow
older
– Gender, ethnicity, education, birth-cohort
– Mankiw and Weil: these variables change as households age
• Life-cycle variables depend on the household's position in
life-cycle
– Household size, employment status, income, health of household
members
– We take age as a proxy for the position in the life-cycle
– Explicitly model income differences over time
– Green and Hendershott: these variables are constant as
households age
English Housing Condition Survey (EHCS)
Covers both housing data and demographic information
• British government collected data on the current housing
stock
– Study provides a representative cross-section of households and
their houses
– We use the 2001 cross-section
• Excellent level of detail and quality of data
– More than 900 variables, 17,500 households
– Housing characteristics and values from professional inspections
of dwellings
– Information on household based on interviews
• Subsidies distort picture: exclude all subsidized housing,
10,000 left
Agenda
Introduction
Method and Data
Results
Conclusion
Hedonic regression: dwelling related variables
How much are the components of a dwelling worth on average?
Hedonic regression: location related variables
How much are the components of a dwelling on average worth?
Demographic regression
Controlling for household size and income
Demand increases with education
Additional educational achievement drives up reservation prices
Impairments to human wealth drive down demand
Negative impact of disabilities, long-term illnesses, and children
Additional results of the demographic regression
• Importance of education increases with age
– Older university graduates willing to pay more than younger ones
• No results for age and health
– Analysis of the interaction terms for age and chronic illness, and
for age and disability does not yield any significant results
• Full time employment
– Drives down willingness to pay
Recap of the demographic regression results
• Willingness to pay for housing
– Increases with household size and income
– Decreases with children
• Human capital is key driver of demand
– Education drives up demand
– Chronic health problems and disabilities decrease demand
• Age has positive effect on demand
– Age-income effect is positive
– Age-education effect is positive
• When calculating future housing demand, the dynamics
of these variables must be considered
– Cohort variables vs. life-cycle variables
Household's willingness to pay for constant quality house
Overall, demand is upward sloping as households become older
12000
11000
10000
GBP
9000
8000
7000
6000
Demand with life-cycle effects only (Mankiw & Weill)
Combination of life-cycle and cohort effects
Demand with cohort-effects only (Green & Hendershott)
80+
75-79
70-74
65-69
60-64
55-59
50-54
45-49
40-44
35-39
30-34
25-29
20-24
5000
Demand for different dwelling types
Upward-sloping with age for all types
Detached houses and bungalows steepest increase
14000
12000
GBP
10000
8000
6000
detached house
terraced house
bungalow
apartment
80+
75-79
70-74
65-69
60-64
55-59
50-54
45-49
40-44
35-39
30-34
25-29
20-24
4000
Similar demand growth for the English population scenarios
Based on different assumptions fertility, migration, and life expectancy
1.20%
1.00%
0.80%
0.60%
0.40%
0.20%
0.00%
2005
2010
2015
young population
2020
base case
old population
2025
Will higher demand translate into higher prices?
• Malpezzi and Maclennan (2001) find supply elasticities
between 0 and 1 for post-war UK
– The range depends on the assumptions for their models
• Office of the Deputy Prime Minister projects housing
shortages if supply remains at current level
Agenda
Introduction
Method andData
Results
Conclusion
Conclusion
• Demographics influence the demand for housing
– Education, income, health, employment status, and household
size are main drivers
– Housing demand does not decline with age, but increases
• A slowdown in population growth (or even a shrinkage)
does not necessarily imply a decline in overall demand
– Human capital will keep on increasing
– Younger generations better educated
– Improving health
• Study provides analytical framework to apply to other
(European) countries and regions
• Housing remains key asset in private retirement portfolio