Home Prices in France Over the Long Run - CGEDD
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Home Prices
in France
Over the Long Run
Jacques Friggit, CGEDD, French Ministry in charge of Housing. Presentation, June, 2012.
The analyses and points of view expressed are the author’s, and, in particular, not necessarily CGEDD’s or the
government’s.
http://www.cgedd.developpement-durable.gouv.fr/rubrique.php3?id_rubrique=137 1Preliminary
• « CGEDD » = “Conseil général de l’environnement et du
développement durable” = internal audit and prospective
department common to the ministries in charge of the
environment, sustainable development, energy,
transportation, etc. and housing
• Accent on long term perspective
• Various papers, presentations, data series, sources, monthly
updates may be downloaded on
http://www.cgedd.developpement-durable.gouv.fr/rubrique.php3?id_rubrique=137
2PLAN
1. Home prices in France, a Historical
perspective
2. Comparison with Other Assets
3. Several Important Properties of Home Prices
4. How can we Explain the 2000-2010 Rise?
5. Home Price Prospective
3Home price indices in Paris since 1200
10
WW I and II
Home price indices in Paris since 1200 Rent controls
Inflation
Constant currency
Basis 2000=1
1
1200 1300 1400 1500 1600 1700 1800 1900 2000
1348
Great Plague
Division
0,1 by 15
Slope +0,6% per year
on average over 800 years
100 year war Little significance
0,01 Division
by 4
Centre of Paris 1200-1790
Centre of Paris 1790-1850 before adjustment for obsolescence
Paris 1840-2009 before adjustment for obsolescence
Paris 1840-2009 after adjustment for obsolescence
0,001
Source: CGEDD after d’Avenel, Duon, INSEE, indices Notaires-INSEE and notaries’ databases 41840-2011: the 1914-1965 depression
10
Home price indices, France and Paris
Constant currency, basis 2000=1
France Rent controls
Exit from
rent
Paris + inflation
controls
1 1870 1914
1930-35
« refuge » 1948 1965
defeat
1800 1850 1900 out of 1950 2000
stocks 1940-44
« refuge »
against
inflation
0,1
1840-1914 1914-1965 1965-2011
0,01 5
Source: CGEDD after INSEE, notaries’ databases, Notaires-INSEE SA indices, Duon, Toutain and Villa (CEPII).What are we talking about?
Beware quality/structural effects!
Disposable income per household (average value, stock)
Existing-home price index, France (index, flow)
Housing expense per household (from National accounts)(average value, stock) 3.12
Rent index, France (index, stock)
3 Construction cost index (index, flow) 3.08
Constant currency, basis 1965=1
2
1.68
1.44
1 1.04
0
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Source: CGEDD after INSEE and notaries’ databases 6Example of quality effect: surface
Nb de
Nbr of personnes
persons
Surface Surfaceper
Surface pardwelling,
logement, nombreofde
number personnes
persons per par logement
dwelling par logement
per dwelling
100 et surface par personne
and surface per person 5,0
92
90
89
87
90 85 4,5
83
79
80 4,0
73
Surface
Surfacepar
perlogement
dwelling (m²)
(m²)
70
70 Surface
Surfacepar
perpersonne (m²)
person (m²) 3,5
3,1 Numberde
Nombre of personnes
persons per pardwelling
logement(right scale)
(échelle de droite)
2,9
60 2,8 3,0
2,7 2,6
2,5 2,5
2,4
50 2,3 2,5
40 2,0
40
38
36
30 33
34 1,5
31
28
25
20 23 1,0
10 0,5
0 0,0
1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Year of the housing survey
Source: CGEDD after housing surveys 7Stability from 1965 to 2000 then take off of
the home price index relative to income per
household
2
1,9
Home price index
1,8 relative to disposable income per household
France, basis 1965=1
1,7
Home price index relative to disposable income per
1,6
household, basis 1965=1
Auxtunnel 0
1,5
1,4
1,3
1,2
1,1
Tunnel
1
0,9
0,8
1/1 1965 1/1 1970 1/1 1975 1/1 1980 1/1 1985 1/1 1990 1/1 1995 1/1 2000 1/1 2005 1/1 2010 1/1 2015 1/1 2020
Source: CGEDD after INSEE, notaries’ databases, Notaires-INSEE indices. 8Local differentiation
2,6
2.54 (Paris, Q1 12)
2,5
2,4 France
Home price index
Paris
2,3 Region of Paris
relative to disposable income per household Province
2,2
2,1
Differentiation Paris / Region of Paris / Province 2.14 (Paris Region, Q1 12)
Base 1965=1
2
1,9
1,8 1.83 (France, Q1 12)
NB: the divider of all four ratios is the disposable
1,7 income per household over all of France 1.69 (Province, Q1 12)
1,6 Exception = « crisis »
1,5
1,4
1,3
1,2
1,1
Tunnel
1
0,9
0,8
0,7
1/1 1965 1/1 1970 1/1 1975 1/1 1980 1/1 1985 1/1 1990 1/1 1995 1/1 2000 1/1 2005 1/1 2010 1/1 2015 1/1 2020
Source: CGEDD after INSEE, notaires’databases, Notaires-INSEE indices. 92000-2010: heterogeneity of home price
growth in the various « departments »
The extremes (growth from 2000 to 2010 in the existing-home price indices):
•The 3 smallest increases: Territoire de Belfort: +59%; Haut-Rhin (deptt of
Colmar): +64%, Moselle +70%
•The 3 biggest increases: Bouches-du-Rhône (=departt of Marseilles), Paris,
Alpes-Maritimes (departt of Nice): (in a draw) +138%
•(France: + 107%)
(Source: Notaires-INSEE indices and Perval)
Higher 2000-2010 growth if:
-More secondary residences
-More private rented principal residences
-Fewer owner-occupied principal residences
-Lower construction rate (elasticity ~ -1 or -2) 2010 home price index
-Higher population growth (elasticity ~ 1 or 2) Variation
Basis 2000=1totale
1,056 à 1,381 (30)
-Higher income growth (elasticity ~ 1) 0,906 à 1,056 (32)
-Lower unemployment growth 0,587 à 0,906 (32)
-Results not robust with respect to the period studied
Details in the paper:
http://www.cgedd.developpement-durable.gouv.fr/IMG/pdf/difference-variation-prix-immobilier-par-departement_cle76a2da.pdf
102000-2010: inversion of apartments /
indiv. houses differentiation
Relative to indiv. houses, 1,3
Apartment price index
apartments have: 1,25 relative to
detached house price index
Basis Q4 2000=1
•appreciated from 1950 to 1,2
Region of Paris minus Paris
1965 (exit from rent controls, which 1,15
Province
• had impacted apartments more than individual houses )
•depreciated from 1965 to 1,1
2000 (while occupants were 1,05
getting poorer)
•appreciated from 2001 1
(while occupants went on getting
poorer) 0,95
1/1 1995 1/1 2000 1/1 2005 1/1 2010 1/1 2015
Source: CGEDD d’après indices Notaires-INSEE. 11International comparison (1)
France, USA and UK:
similar long run trends over 1965-2000
2
International comparison:
home price index relative to France
USA (FHFA)
disposable income per household USA (S&P/C-S)
UK (DCLG)
Basis 2000=1 UK (Halifax)
Aux 0,9
1,5
1,1
1
0,9
0,5
1/1 1965 1/1 1970 1/1 1975 1/1 1980 1/1 1985 1/1 1990 1/1 1995 1/1 2000 1/1 2005 1/1 2010 1/1 2015 1/1 2020
Source: CGEDD after INSEE, notaries’ databases, Notaires-INSEE SA indices, Freddie Mac, FHFA, R.Shiller, US Bureau of Economic Analysis, Census Bureau,
UK DCLG, UK National Statistics, Halifax 12International comparison (2)
Diversity since 1995
Home price indices
relative to disposable income per household
International comparaison
Basis 2000=1
1,5 France
USA
UK
Germany
Spain
Netherlands
1
0,5
1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Source: CGEDD after INSEE, notaries’ databases, Notaires-INSEE SA indices, Freddie Mac, FHFA, R.Shiller, US Bureau of Economic Analysis, Census Bureau,
UK DCLG, UK National Statistics, Destatis, Gewos, Central Bureau voor de Statistiek, Instituto Nacional de Estadística, R. Vergès. 13International comparaison (3)
France-Germany:
Relative to income per household, from 2000 to 2010,
• home price indices diverge
• but rent indices remain flat in both countries
(impact of German households’ very high long term debt in 2000)
Home sale price Rents
(existing-home price index) ( « rent » component of the consumer price index)
1,8 1,8
Home price index
1,7 relative to income per household 1,7
Rent indices
Comparison France-Germany relative to income per household
1,6 Basis 2000=1 1,6 Comparison France-Germany
Basis 2000=1
1,5 1,5
France
1,4 1,4 France
Germany
1,3 1,3 Germany
1,2 1,2
1,1 1,1
1 1
0,9 0,9
0,8 0,8
1990 1995 2000 2005 2010 1990 1995 2000 2005 2010
Source: CGEDD after INSEE, notaries’ databases, Notaires-INSEE SA indices, Destatis, Gewos. 142000-2010: rental return (=rent / price)
1,8
collapses
Home price index and rent index
1,7 relative to disposable income per household
France, basis 2000=1
1,6
Home price index relative to disposable
1,5
income per household, basis 2000=1 NB1: the rent index and the price
Rent index relative to disposable
index have different perimeters =>
income per household, basis 2000=1
Auxtunnel 0 bias => dividing the former by the
1,4 latter does not provide an index of
gross rental income.
NB2: the «income per household»
1,3 used as divider relates is that of all
households, whether tenants or
owner-occupier. Tenants’ income
1,2
grows slower than owner-occupiers’
(by ~1% per year).
1,1
NB3: depends on location
NB4: many structure effects
1
0,9
0,8
1/1 1965 1/1 1970 1/1 1975 1/1 1980 1/1 1985 1/1 1990 1/1 1995 1/1 2000 1/1 2005 1/1 2010 1/1 2015 1/1 2020
Source: CGEDD after INSEE, Notaires-INSEE indices and notaries’ databases 15Resilience of the 6% gross rental return • In 1900-1910: – inflation +0,3%/year, – income per household +1,6%/year, – Paris home price indexs +1,1%/year, – Gov’t debt interest rate 3,1%/year • The gross rent of the (residential) properties purchased by La Fourmi Immobilière (a property company) (from 1899 to 1913) was worth 6 to 7% of their price (quoted by F. Simmonet, « La Fourmi Immobilière »). • « The average gross rental return in Paris would be 6,36% (from 5,13% in the 16th arrondissement to 7,76% in the 20th arrondissement) », of which 33% expenses must be substracted (P. Leroy-Beaulieu, « L’art de placer et gérer sa fortune », 1906). • 6% gross rental return « a residential building is worth 200 monthly rents » 16
•Which rates (nominal, or net of past
Interest rates are at a
inflation, or net of expected inflation)?
historical low
…but their decrease had •Low with respect to which reference?
begun much earlier than 2000 Average 1965-
2000
2000 2008 Fin 2010
20%
Interest rate 9,2% 5,5% 4,3% 2,8%
Inflation 5,6% 1,7% 2,8% 1,6%
Interest rate 3,6% 3,8% 1,5% 1,2%
minus inflation
15%
Long Term Interest Rate and Inflation
Interest rate
10% Inflation
Interest rate minus inflation
5%
0%
1/1 1965 1/1 1970 1/1 1975 1/1 1980 1/1 1985 1/1 1990 1/1 1995 1/1 2000 1/1 2005 1/1 2010 1/1 2015 1/1 2020
-5% 17
Source: CGEDD after Ixis, Banque de France and INSEE2000-2010: increase of mortgage initial length
25
Mortgage initial length
Purchase of a principal home by an owner-occupier
(years)
20
15
10 Ancien
Neuf
5
0
1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Source: CGEDD after housing surveys before 2005 then OFL 18« Affordability », « property purchasing power », etc.
indices: what are we talking about?
• Which period? (ex: 2000-2010 or 1990-2010 or 1965-2010)
• Which area? (ex: Paris or France)
• Which price? (« constant quality » index or average price? new or existing?)
• Which income? (buyers’? borrowers’? all households’? « disposable » or net
taxable or gross taxable income?)
• Which financing conditions?
– Which interest rate? (several series, more or less consistent and continuous
and reliable; what with adjustable rates, capped adjustable rates?)
– From 1973 to 1985, how do we factor in inflation, progressive monthly
payments?
– How do we take into account changes in mortgage length?
– How do we take into account changes in downpayment?
• Other variables: transaction costs, interaction with the gov’t (subsidies
and taxes)
• Other points of view: purchasing power in rent years??
19Example: impact on « affordability » of nominal /
‘real’(=net of inflation) interest rate, and of mortgage length
1,6
Affordability index: comparison
constant (15 years) and actual mortgage length
nominal or 'real' (=net of inflation) interest rate
1,5
France, basis 2000=1
Affordability at nominal interest rate, constant (15 years) mortgage length
1,4 Constant mortgage length
Affordability at 'real' interest rate, constant (15 years) mortgage length
Actual mortgage length (= Affordability at nominal interest rate, actual mortgage length
1,3
"forgetting" households will
have to pay for more years) Affordability at 'real' interest rate, actual mortgage length
1,2
1,1
1
0,9
0,8
High inflation
0,7
1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Source: CGEDD after INSEE, notaries’ databases, Notaires-INSEE SA indices, Banque de France, Ixis, housing surveys, OFL. 202000-2010: decrease in the
affordability index
at nominal and ‘real’ interest rate
1,5
1,4 Affordability index
Basis 1965=1
1,3
1,2
1,1
1
0,9
0,8
Affordability index at nominal interest rate
Affordability index at 'real' interest rate
0,7 Auxtunnel 1
0,6
1/1 1965 1/1 1970 1/1 1975 1/1 1980 1/1 1985 1/1 1990 1/1 1995 1/1 2000 1/1 2005 1/1 2010 1/1 2015 1/1 2020
Source: CGEDD after INSEE, notaries’ databases, Notaires-INSEE SA indices, Banque de France. 212000-2010: increase in the mortgage
length necessary to purchase the same
dwelling everything else being equal
Mortgage length to purchase the same existing dwelling with the same deposit to
45 years income and initial payment to income ratios, basis 1965=15 years
40 years
35 years 34,9, Q1 2012
30 years
25 years
20 years
15 years
10 years Mortgage length 'at nominal interest rate'
Mortgage length 'at real interest rate'
5 years
0 years
1/1 1965 1/1 1970 1/1 1975 1/1 1980 1/1 1985 1/1 1990 1/1 1995 1/1 2000 1/1 2005 1/1 2010 1/1 2015 1/1 2020
Source: CGEDD after INSEE, notaries’ databases, Notaires-INSEE SA indices, Banque de France. 222000-2010: New versus existing-home price
•The average price has grown faster for existing homes
than for new homes
•We have no price index (i.e. at constant quality) for new
homes for now (maybe it’s going to change: INSEE is working on it) so
we don’t know whether a new-home index would grow
faster or slower than the existing-home price index
232000-2010: the number of existing-home sales has
remained quite constant
Cumul. Cumul.
last 12 months last 3 months
1 200 000 1 178 000 300 000
(Apr. 12)
Number of property sales
taxed at the regular transaction tax rate
1 000 000 France All types of 250 000
property
818 000
800 000 (Mar. 12) 200 000
Of which
600 000 existing-homes 150 000
Number of property sales taxed at the
400 000 regular transaction tax rate, all types of 100 000
property: existing-homes, existing Of which existing-homes
commercial property and VAT-exempt land
(Left scale) Cumul. last 12 months Cumul last 12 months
200 000 50 000
(Right scale) Cumul. last 3 months (deseasonalized) Cumul. last 3 months (deseasonalized)
0 0
12/ 1990 12/ 1995 12/ 2000 12/ 2005 12/ 2010 12/ 2015
Source: CGEDD after CGDD/SOeS(Existan), DGFiP (MEDOC and Fidji), notaries’ databases 242000-2010: construction of new dwellings:
no excesses
Construction of dwellings
500 000 and change in the number of households
400 000
Apr. 2012
376 731
300 000
Series change
in January 2011
200 000
Number of ordinary dwellings started, cumulated on last 12 months
100 000
12 month change in the number of households
Metropolitan France
0
1/1 1950 1/1 1960 1/1 1970 1/1 1980 1/1 1990 1/1 2000 1/1 2010 1/1 2020
Source: CGEDD after CGDD/SOeS and INSEE 25Sales of new dwellings by developers (NB: only 1/3 of new dwellings)
Quarterly # of
Quarterly number of dwellings sold by developers
dwellings
40 000
Put on sale
Sold
30 000
20 000
10 000
0
déc-80 déc-85 déc-90 déc-95 déc-00 déc-05 déc-10 déc-15
Source: CGEDD after CGDD/SOeS (ECLN) 26Developers’ inventory: reasonable but picking up
Developers' inventory in months of sales
25 # of months of sales
Total inventory
Of which under construction or completed
20 Of which completed
15
10
5
0
déc-80 déc-85 déc-90 déc-95 déc-00 déc-05 déc-10 déc-15
Source: CGEDD after CGDD/SOeS (ECLN) 272000-2010: increase in the amount of
property sales relative to GDP
Apr. 2012: 14.2%
14% Total amount
of property sales
12%
as a % of gross domestic product
sales of any type of property
(residential and commercial, old and new, incl. land)
1844
subject to transaction tax, cumulated on 12 months
1990
Apr. 2012: 10.2%
10% 1881
Total
1853 Of w hich taxed at the current regular rate and antecedent rates
Of w hich existing homes
Auxabscisse
Apr. 2012: 8.1%
8% 1831
1980
6% 1848
1995
1871 1984
4%
2%
1948
0% 1915
1/1 1/1 1/1 1/1 1/1 1/1
1800 1850 1900 1950 2000 2050
Source: CGEDD after DGFiP, INSEE and Toutain 282000-2010: households’ outstanding mortgage
debt doubles with respect to their income
mortgages
Mortgage debt
outstanding
Households' new mortgages
New
and mortgage debt outstanding
as a % of households' gross disposable income
14% 70%
Apr. 12
64%
12% 60%
10% 50%
8% 40%
6% 30%
4% 20%
2% 10%
New mortgages (12-month cum.)
Mortgage debt outstanding (right scale)
0% 0%
1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1
1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020
Source: CGEDD after Banque de France and INSEE 292000-2010: households’ outstanding
mortgage debt – international comparison
140% Households' outstanding mortgage debt
as a % of households'disposable income
120%
France
100% USA
UK
Germany
80%
60%
40%
20%
0%
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Source: CGEDD after natioanl institutes of statistics and central banks 30Consequences of the increase of
home prices 2000-2010 (1)
•Increase in incomes indexed on home prices (realtors, notaries,
etc.) and in departments’ receipts in transactions taxes
•Wealth creation for owners (but almost no « equity withdrawals » in France)
•Compared to 2000, cash transfer
•From net buyers to net sellers
•From (not too) poor to rich
•From younger than 56 years to older than 56 years
31Net buyers and net sellers:
the 56 year threshold
Number of dwellings bought or built, or sold, Dwellings bought or built
Dwellings sold
as a % of the number of households Balance bought or built minus sold600 000
15%
as a function of age of the householder Number of households (right-hand scale)
Year 2006
% 500 000
Effect of the
of the number baby-boom Number
of households of households
10% 400 000
300 000
56 years
5% 200 000
100 000
0% 0
Age of the householder
20 30 40 50 60 70 80 90 100
-100 000
Average age of age brackets which are Average age of age brackets which are
net buyers: 34 years net sellers: 74 years
-5% -200 000
Source: CGEDD after DGFiP, SOeS, notaries’ databases, EPTB, Filocom 32Consequences of the increase of
home prices 2000-2010 (2)
•Injection of the cash provided by increased mortgages
(cumulated over 2000-2010: 15 to 20% of GDP) into
•(a little) construction
•(a little) financial savings
•(mostly) consumption => GDP growth, households’ income, increase in tax
receipts, increase in trade deficit – no gain in competitivity
•Future increase in cash disbursements by borrowers (because of
increased mortgage length)
•Buyers increased their debt to purchase an asset
•which does not provide any additional income (non productive asset)
•the price of which is higher today but will (as we argue thereafter) revert to its past
trend level with respect to income per household
33PLAN
1. Home prices in France, a Historical
perspective
2. Comparison with Other Assets
3. Several Important Properties of Home Prices
4. How can we Explain the 2000-2010 Rise?
5. Home Price Prospective
34Price of gold net of inflation:
100
constant in the long run
Gold price index, constant currency
basis 2000=1
in constant French currency
In constant $
In constant £
10
1
1800 1850 1900 1950 2000
0,1
Source: CGEDD after INSEE, Banque de France, World Gold Council, (Officer, 2002). 35Fixed income:
long term interest rate = inflation + 3% + wide fluctuations
30%
Long term interest rates net of inflation
20%
10%
0%
1800 1850 1900 1950 2000
-10%
-20% France
USA
-30% UK
-40%
-50%
-60%
Source: CGEDD after Vaslin, Loutchitch, Ixis, Chabert, Lévy-Leboyer, Homer & Sylla, national institutes of statistics and central banks. 36Stocks have been providing a 6.6% trend total return
above inflation over two centuries (except catastrophic wars)
10
Value of an investment in
1
US, French and British stocks,
constant local currency,
dividends reinvested, basis 2000=1
0,1
0,01
Tunnel
Slope =+6,6% /an
0,001
y = 9E-58e 0,0654x
R2 = 0,9944 US stocks
0,0001
French stocks
British stocks
0,00001
Trend of US stocks, constant $
Tunnel (« Siegel’s tunnel »)
0,000001 Slope =+6,6% /an (« Siegel’s constant »)
1800 1850 1900 1950 2000
Source: CGEDD after (Arbulu 1998), SGF, Euronext, (Chabert, 1949), (Lévy-Leboyer & Bourguignon, 1985), INSEE, (Schwert 1990), (Shiller 2000), S&P, STAT-USA,
US Bureau of Labor, (Dimson, Marsh & Staunton, 2001), UK Office of National Statistics 37Value of an investment in stocks (dividends reinvested)
relative to long term trend
1000 Value of investments in
US, French and British stocks US stocks
relative to US stocks long term trend
French stocks
100
Tunnel British stocks
10
2
Tunnel 1
0,5
Distribution:
-bimodal (?)
-Standar deviation slightly (and less and less) growing
(« mean reversion ») - platikurtic
0,1
1800 1850 1900 1950 2000
Source: CGEDD after (Arbulu 1998), SGF, Euronext, (Chabert, 1949), (Lévy-Leboyer & Bourguignon, 1985), INSEE, (Schwert 1990), (Shiller 2000), S&P, STAT-USA, US
Bureau of Labor, (Dimson, Marsh & Staunton, 2001), UK Office of National Statistics 38Value of an investment (total return: dividends reinvested),
French
1000
stocks relative to US stocks (both in constant local currencies)
Value of an investment in French stocks, div. reinv'd, constant French currency
_____________________________________________________________
Value of an investment in US stocks, dividends reinvested, constant $
100
Basis: average 1965-2005=1
10
1
01/ 01/ 01/ 01/ 01/ 01/ 01/ 01/ 01/ 01/ 01/ 01/ 01/ 01/ 01/ 01/ 01/ 01/ 01/ 01/ 01/ 01/ 01/
3800 3810 3820 3830 3840 3850 3860 3870 3880 3890 3900 3910 3920 3930 3940 3950 3960 3970 3980 3990 4000 4010 4020
Date + 2000 years
0,1
Source: CGEDD after (Arbulu 1998), SGF, Euronext, (Chabert, 1949), (Lévy-Leboyer & Bourguignon, 1985), INSEE, (Schwert 1990), (Shiller 2000), S&P, STAT-USA,
US Bureau of Labor 39Other yardsticks for stocks
• Price/earnings ratios [which earnings: past (over
which duration)? future (over which duration)?]
• Price / dividends ratio [which dividends? past (over
which duration)? future (over which duration)?]
• If the ratio departs from its long terme average, will
it revert to it by the numerator, the denominator or
both?
40Which yardstick: « Siegel’s tunnel » or PER (earnings smoothed
over 10 years)? They have been diverging since the 1990’s
3
Comparison (1) Total return index / Siegel's long term trend
(2) Shiller's 10 year adjusted PER / 15
Total return LT trend (2) / (1)
versus 0,5
Sep. Dec.
adjusted PER 1929 1999
2 End-of-month values, USA
Jun.
Nov. Jun. 1881
1802 1835
Dec.
1852 Feb.
1937
Nov. Aug.
1916 1987
1
0,9
0,8
0,7
Jun.
0,6 Dec. 1877
1814 Mar.
1842
Jun.
0,5 1949
Apr.
Oct. 1857 Feb. 2009
1942
Dec. 1920 Aug. 1982
0,4 Jun.
1932
1/1 1/1 1/1 1/1 1/1
1800 1850 1900 1950 2000
Source: CGEDD after (Schwert 1990), (Shiller 2000), S&P, STAT-USA, US Bureau of Labor 41Value of various investments (yearly returns reinvested)
1E+02 Value of various investments, French constant currency,
basis 2000=1
1E+01
1E+00
1800 1850 1900 1950 2000
1E-01
1E-02
1E-03 1914-1965
Gold
1E-04 Money market
Bonds
1E-05 French stocks
US stocks
1E-06 Rented residential property in Paris
Source: Source: CGEDD after Arbulu, Euronext, Vaslin, Loutchitch, Ixis, Banque de France, ECB, notaries’ databases, Notaires-INSEE indices, Duon, INSEE, Schwert,
Shiller, S&P, World Gold Council, Officer. 42Trend total return of an investment in
housing on the basis of 1965- 2000
Somewhat too high?
•(a) Capital gain:
GDP growth inflation + 2,5%
Households’ disposable income growth idem: inflation + 2,5%
Minus growth in the number of households - 1,2%
Equals: growth in income per household inflation + 1,3%
Capital gain idem: inflation + 1,3%
•(b) Net rental income:
Gross rental income 6,0%
Minus expenses 37% (incl. heavy repairs) - 2,2%
Minus purchase expenses (11%) depreciated over 20 years - 0,5%
Equals: net rental income 3,3%
•Total return = (a)+(b) inflation + 4,6%
43100%
Return X volatility: 1840-1914
5 year return 1840-1914
volatility
80%
60%
Inflation
40%
US stocks
French stocks (ASIS)
20%
French bonds Housing Paris
Fr. stocks (LB, dep. 1854)
French money market Yearly return
0% Gold
0% 5% 10% 15% 20% 25%
Source: CGEDD after Arbulu, Le Bris & Hautcoeur, Euronext, Vaslin, Loutchitch, Ixis, Banque de France, ECB, notaries’ databases, Notaires-INSEE indices, Duon,
INSEE, Schwert, Shiller, S&P, World Gold Council, Officer. NB : ASIS = Arbulu-SGF-INSEE-SBF250 series. LB = Le Bris series 44100%
Return X volatility: 1914-1965
5 year return Gold
1914-1965
volatility
80%
US stocks
60%
French stocks (LB) French stocks (ASIS)
40% Housing Paris
20% Inflation
French bonds
French money market Yearly return
0%
0% 5% 10% 15% 20% 25%
Source: CGEDD after Arbulu, Le Bris & Hautcoeur, Euronext, Vaslin, Loutchitch, Ixis, Banque de France, ECB, notaries’ databases, Notaires-INSEE indices, Duon, INSEE,
Schwert, Shiller, S&P, World Gold Council, Officer. NB : ASIS = Arbulu-SGF-INSEE-SBF250 series. LB = Le Bris series 45100%
Return X volatility: 1965-2011
5 year return 1965-2011
volatility
80%
60%
Gold
US stocks
French stocks (LB) French stocks (ASIS)
40%
Housing Paris
20% French bonds
Inflation
French money market
Yearly return
0%
0% 5% 10% 15% 20% 25%
Source: CGEDD after Arbulu, Le Bris & Hautcoeur, Euronext, Vaslin, Loutchitch, Ixis, Banque de France, ECB, notaries’ databases, Notaires-INSEE indices, Duon, INSEE,
Schwert, Shiller, S&P, World Gold Council, Officer. NB : ASIS = Arbulu-SGF-INSEE-SBF250 series. LB = Le Bris series 46Trend return X volatility
100%
5 year return Trend
volatility
80% (assumption: inflation 2%)
(return , volatility)
60%
US stocks (Inflation+6,6% , 50%)
Gold?? (Inflatn+0,0%, ?)
Inflation
40% French stocks (Inflation+6,6% , 42%)
Housing Paris (Inflation+4,6% , 28%) (NB: without leverage)
20% French Bonds (Inflation+3,0% , 20%) (NB: not protected against unexpected inflation
French money market (Inflation+2,0% , 8%)
Yearly return
0%
0% 5% 10% 15% 20% 25%
Source: CGEDD after Arbulu, Euronext, Vaslin, Loutchitch, Ixis, Banque de France, ECB, notaries’ databases, Notaires-INSEE indices, Duon, INSEE, Schwert, Shiller,
S&P, World Gold Council, Officer. 47Other aspects of investing
•Leverage
•Management costs and transaction costs
•Taxes
•Risks other than price volatility
(valued differently depending on the investor)
•Diversifying power
48Now
Relative to their respective long term
trend,
- stock prices are low(*)
- gold, bonds(**) and housing prices are
high
(*) although a decrease is possible in the short term
(**) except sustained very low inflation
49PLAN
1. Home prices in France, a Historical
perspective
2. Comparison with Other Assets
3. Several Important Properties of Home Prices
4. How can we Explain the 2000-2010 Price Rise?
5. Home Price Prospective
50Several properties of house prices (1)
•Series are short => limits the significance of
results (incl. problem of lack of robustness of results)
•High autocorrelation of 1 year price changes
=>strong cyclicity (in the sense of high autocorrelation)
Deciders’ « myopia » (= expectations based on the recent past = « short
memory »), self-reinforcing phenomenon
Conversely, no short term autocorrelation for stocks ( ~ random walk)
•No periodicity other than seasonality
51Seasonality
3% Seasonal variation coefficients
for existing-home prices
2%
1%
Quarter of the sale
0%
Q1 Q3 Q3 Q4
-1% France - apartments
Paris - apartments
Paris region except Paris - apartments
-2% France minus Paris region - apartments
France - individual houses
Paris region - individual houses
-3% France minus Paris region - individual houses
Source: CGEDD after Notaires-INSEE indices. 52Several properties of house prices (2)
•Link with households’ income :
•in time: intuitive in appearence, empiric in
reality
•and in space
53Link home price X income per household:
by city in the Paris region
800 000
Home price (€)
700 000
Average home price (apartments and individual houses) y = 6,63x - 38958,29
2
as a function of gross taxable income per household R = 0,81
600 000
2006, 1000 biggest cities in Paris region
(averages exclude the 10% extreme values)
500 000
400 000
300 000
200 000
The surface of the circle is proportional
to the number of households in the city
100 000
Gross taxable income per household (€)
0
0 10 000 20 000 30 000 40 000 50 000 60 000 70 000 80 000 90 000 100 000
Source: CGEDD after notaries’ databases and Filocom (DGFiP) 54Idem by urban area (impact of secondary homes)
350 000
Antibes y = 8,24x - 97272
Hom e price (€) R2 = 0,55
Chantilly
300 000 Menton
Fréjus La Teste
Bayonne Paris
La Ciotat
Nice
250 000 Annemasse
Cavaillon Rambouillet
Dinard Annecy
Saint-Nazaire Thonon
Carpentras
Toulon Draguignan
Istres Fontainebleau
Saint-Malo
Avignon
200 000 Miramas La Rochelle
Martigues
Alès Arles
Lunel Saint-Louis
Cluses
Orléans
150 000 The surface of the circle is proportional
Dijon Haguenau
Colmar Nancy to the number of households in the urban area
Longw y Mulhouse
Lens Rodez
Saint-Avold Average home price (apartments and individual houses)
100 000
Maubeuge
Epinal
as a function of gross taxable income per household
Vierzon Montceau-les-Mines
by urban area, year 2006
Gross taxable incom e per household (€)
50 000
20 000 25 000 30 000 35 000 40 000 45 000 50 000 55 000
Source: CGEDD after notaries( databases and Filocom (DGFiP) 55Interpretation of the link price X income Households’ asset (1) -100% of users are households (housing expense = 1/5 of their income) •29 million households, 34 millions dwellings of which 1/10 secondary homes (differences = vacancy) -95% of buyers are households (a household’ biggest purchase during its existence) -8 dwellings out of 10 are owned by households •difference =8/10 « social housing » (« HLM ») + 2/10 owned by other non-individuals - ¾ of households own a dwelling at least once in their existence, 63% of households own at least one dwelling, 59% of households own their principal residence Source: estimates by CGEDD after Housing surveys and Filocom, SOeS and various sources 56
Households’ asset (2)
-Out of 10 households:
-4 own no dwelling
-4 own at least 1 dwelling
-2 own more than 1 dwelling (on average 2.8 dwellings, incl. their principal residence)
By comparison only 2 households out of 10 own stocks (and only 5% own a significant amount
of stocks)
-Out of 10 households:
-4 are tenants (of which 2 get a housing benefit)
-2 are owner-occupier and reimburse a mortgage (« accédants à la propriété »)
-4 are owner-occupier and don’t reimburse a mortgage (« propriétaires non
accédants »)
-Out of 3 dwellings purchased
–1 is the first principal residence of the buyer
–1 is a principal residence of rank >1 (the 2nd one, the 3rd one, etc.) of the buyer
–1 is a rental investment or a secondary home (of which: 2/3 rental investments
and et 1/3 secondary residence)
-A household purchases on average 2,5 dwellings during its existence
Source: estimates by CGEDD after Housing surveys, Filocom, SOeS and various sources 57Households’ asset (3): number of dwellings
getting into or out of an individual’s
ownership during his lifetime
During the lifetime of an individual as part Number of
dwellings
of a household
Dwellings purchased or built 2,5
(of which purchased existing) (1,8)
With (0,7)
(of which purchased new or built)
payment
Entry into 1,8
or exit from
Dwellings sold
ownership Difference purchased or built minus sold 0,7
Net balance of dwellings given or received as 0,0
Without donations
payment
Dwellings inherited 0,4
Dwellings owned at death 1,1
Source: CGEDD estimates after various sources 58Several properties of house prices (3)
•Low univariate correlation of house price changes
and interest rate changes
•counter-intuitive but is the basis of the diversifying power of housing investment with
respect to bonds (maybe different for whole buildings owned by large investors?)
•=> one has to factor out many other phenomena to see the sensitivity of home prices with
respect to interest rates
•Link by the return to the hierarchy of trend return-risk couples but this return is not
immediate
• No univariate correlation of house price changes
with stock investment [nevertheless stock krachs have often (1929, 1987,
2000, 2008) but not always (1882) been followed by an increase in house prices (particularly
rented house prices)]
•Time-series analyses (with autoregression) methods don’t yield better results
•Multivariate analyses (incl. changes in offer and supply, income) impaired by
the series brevity (at most 46 years)]
=> Diversifying power of housing investment with respect to financial investments 59Several properties of house prices (4)
A fundamental property: the elasticity of
housing price with respect to housing
supply seems in the -1 or -2 range, maybe
(?) slightly more (-3?) in the Paris region
Details in the paper:
http://www.cgedd.developpement-durable.gouv.fr/IMG/pdf/elasticite-prix-immobilier-nombre_cle093f5d.pdf
60Elasticity of housing price with respect
to housing supply (complement 1)
• Quite few works
• Complicated because
– Many variables must be taken into account
– Reverse effects
– Time lags
– Time series are short => results not robust with respect to the
period studied
– Analyses in space may compensate the lack of memory… but few
local data
• Many more works on a reverse problem: sensitivity of
construction to housing price changes
61Elasticity of housing price with respect
to housing supply (complement 2)
• Barker report + Oxford team: e=-2 in the UK
• Murphy, Duca & al. (Oxford + Fed Reserve):e=-1 in the
USA
• Other references: widely dispersed results (by a factor 8 for
the USA!)
• Often, economists’ assumptions may be seriously
debatable (incl.: arbitrage is not instantaneous)
• Result robustness is rarely mentioned
• Economists may be wrong: cf. (McQuinn, 2004) about Ireland,
(OECD: Girouard, Kennedy & al., 2006) about the USA
• But results form a cloud centered around an order of size
of -1or -2
62Elasticity of housing price with respect to
housing supply (complement 3)
References
Ref erence
Country, period Elasticity
-0,007 (ancien), -2,0 (neuf) (OCDE, 2006)
Ireland, 1977-2004
(M cQuinn, 2004)
Ireland, 1980-2002 -0,5
(OCDE, 2004)
Netherlands, 1970-2002 -0,5
(Duca, M uellbauer & M urphy, 2009)
USA, 1979-2007 -1
(Verbruggen & al., 2006)
Netherlands, 1980-2003 -1,4
(Jacobsen & Naug, 2005)
Norway, 1990-2004 -1,7
(M een, 2002)
UK, 1969-1996 -1,9
(Cameron, M uellbauer & M urphy, 2006)
UK, 1972-2003 -2
(Barker, 2004)
Idem Idem
(Wagner, 2005)
Danmark, 1984-2005 -2,9
(M cCarthy & Peach, 2004)
USA, 1981-2003 -3,2
(Bessone, Heitz & Boissinot, 2005)
Paris, 1986-2004 -3,6
(Abelson & al., 2005)
Australia, 1975-2003 -3,6
(OCDE, 2005)
Spain, 1989-2003 -6,9 à -8,1
(M een, 2002)
USA, 1981-1998 -7,9
Source: CGEDD after table 3 of (OCDE: Girouard, Kennedy et al., 2006), as well as (Duca, Muellbauer & Murphy, 2009) , (Cameron, Muellbauer & Murphy, 2006). 63Elasticity of housing price with respect
to housing supply (complement 4)
• Our contribution
– Comparison France / UK over 1970-2005: elasticity =
minus a few units
– Comparison of the various French departments (1994-
2010) ( multiple regression of housing price change with respect to change in
income, population, number of dwellings, etc.)
• Confirms an order of -1 or -2 ,
• maybe (?) slightly more (-3?) in the Paris region
• Results are sensitive to the subperiod studied (problem of
robustness)
64PLAN
1. Home prices in France, a Historical
perspective
2. Comparison with Other Assets
3. Several Important Properties of Home Prices
4. How Can We Explain the 2000-2010 Price Rise?
5. Home Price Prospective
65How can we explain the 2000-2010
price rise?
•Supply-demand of housing service?
•Inflationary impact of housing subsidies?
•Other explanations except financial
environment ?
•Financial environnement?
•for the investor
•for the buyer of his own principal residence
66How can we explain the 2000-2010 price rise?
•Supply-demand of housing service?
No because:
•Elasticity price/supply too low (-1 or -2)
•No rent rise beyond historical trend
•Qualitative effects?
•Decrease in household size?
•Ageing?
•Foreigners?
no or not at the scale of the problem
67How can we explain the 2000-2010 price rise?
•Decrease in household size: is not new
and goes on at the previous pace
3,3
Number
Nombre of persons
de personnes per
par ménage
France métropolitaine
3,2 household
3,1
3,0
2,9
2,8
2,7
2,6
2,5
2,4
2,3
1960 1970 1980 1990 2000 2010
Source: CGEDD after INSEE 68How can we explain the 2000-2010 price rise? •Ageing: impact >0 or rather
How can we explain the 2000-2010 price rise?
• (Net) purchases by foreigners? Too few bar exceptions
Purchases of existing dwellings net of sales
by foreigners as a % of the number of sales
Moy 94-99 2000 2002 2004 2006 2008 Evol.
All foreigners 2,3% 2,6% 3,2% 3,2% 2,4% 2,1%
Britons 0,5% 1,3% 1,7% 0,9% 0,4%
Other 2,1% 1,9% 1,4% 1,5% 1,8%
Of which(mostly
MATT resident)
0,6% 0,7% 0,5% 0,5% 0,6%
Of which Portuguese 0,3% 0,2% 0,2% 0,2% 0,3%
(mostly resident)
Of which Germans 0,1% 0,0% 0,0% -0,1% -0,1%
Of which others 1,1% 1,0% 0,8% 0,9% 1,0%
Source: CGEDD after notaries’ databases 70How can we explain the 2000-2010 price rise?
•Inflationary impact of subsidies?
Not at the scale of the 70% to be explained
-Households’ housing expense = 15% of GDP
-Amount of dwellings purchased or built by households = 250
Billion € = 13% of GDP in 2007 (at its highest)
-Amount of property inflation generated in 2007 by the 70%
increase in home prices relative to income: ~100 Billion €
To be compared to
- Transfers organized by gov’t in favor of housing = 1 to 2% of
GDP (depends on how one counts)
- Leeway on these transfers = ten times smaller
- « PTZ »: around 2,5 Billion € equivalent-subsidy(at its maximum)
71How can we explain the 2000-2010 price rise?
• Other explanations except financial environmt? (1)
- Resale financed the price rise?
By nature resale feeds rises (and falls: reversible effect) but
a) Resale financed the same % (21%) of purchases (of principal residences by owner-
occupiers) in housing surveys 2002 and 2006
b) The number of existing-home sales remained constant (800 000/year) from
2000 to 2007 => the « rotation speed » of the housing stock did not increase
(rather it decreased)
c) Departments where home price rised most were those where there were the
fewest owner-occupied principal residences as a % of all dwellings
- Inheritance and donations finance (and will finance) the price rise?
Not that much and not more than previously
- One inherits from parents around 55
- Donations financed a low (3%) and constant % of purchases (of principal
residences by owner-occupiers) in housing surveys 2002 and 2006
- Lagged and reversible effect
72How can we explain the 2000-2010 price rise?
• Others explanations except financial envirnt? (2)
- The price rise since 2000 results from land price rise and
scarcity? No
Land price
- The market price of constructible land is determined by the price of existing
dwellings in its neighborhood => the rise in home prices caused the rise in
land prices, not the reverse.
- The average price of land used for building individual houses did not grow
faster than the average price of existing homes, whereas the construction
cost index grew much slower.
Land « scarcity »
- The elasticity of housing price with respect to housing supply being around -
1 or -2, an increase in the supply of constructible land parcels (by regulatory
changes or by sellers’decision to sell) decreases housing prices only slightly.
- When, from 2004, construction grew from 300 000 to more than 400 000 per
year, finding land was not a problem.
73How can we explain the 2000-2010 price rise?
• Other explanations except financial environmt? (3)
- The price rise is just a continuation of the increase in the
weight of housing expense as in households’ budgets
experienced since 1965? No since the increase in housing expenses from
1965 to 2000 took place while the house price index was constant relative to
households’ income: it resulted from an increase in the quality of housing, without
any equivalent in the 2000-2010 period.
74How can we explain the 2000-2010 price rise?
• Financial environment?
A. Housing as an investment: arbitrage against
other assets (risk X expected return)
•« Rational » investors value housing as a rent-indexed perpetual bond (Rnet
initial~r-i+k-a
where r= interest rate, i = expected inflation, k= risk premium, a= expected growth rate of rent, net of
expenses and inflation)
•In 2010 interest rates were low (relative to their trend level) => could justify
housing prices in spite of low rental returns…: housing investment was
competitive with respect to bonds, which probided low expected returns too
(but isn’t it risky to finance an indexed perpetual bond by a 25 year bond?)
•…but stocks were low (relative to their trend level) and their expected return was
high in the long term
•Certainly many households don’t arbitrage housing against stocks in any way
• but only « myopia » (after the 2000 stock krach) can explain that the others didn’t move to stocks.
•Parallel with 1930-1935
=> The 2000 - 2010 home price rise can be explained by arbitrage
only if one assumes deciders’ «myopia »
75How can we explain the 2000-2010 price rise?
• Financial environment?
B. Housing as principal residence (=majority) :
what can one buy for a given monthly payment?
•Lower interest rates impact owner-occupiers less than investors (15-20
year mortgage less sensitive than perpetual bond to interest rate)
•Longer mortgage length
•In the short term: to be relativised (increased the amount purchased by
12% to 15% everything else being equal),
•In the long term: repayment takes longer (=> from which budget will
households take the cash?)
•Downpayment as a % of price has decreased from 2000 to 2006 (consequence
of increase in indebtness allowed by longer mortgages and lower interest rates)
•Other conditions have fluctuated as they have since 1965 – may have contributed moderately
to the price rise
•Conclusion: for owner-occupyers, lower (net) interest rates relative to the
1965-2000 reference (3.6%) have not compensated the price increase, even
taking into account longer mortgages
76How can we explain the 2000-2010 price rise?
To summarize:
• Mortgage conditions (rate + length) favored some price
increase,
• + deciders’ « myopia » (mainly investors)
These factors impact rental investment more than purchases by owner occupiers
=> explains that since 2000, prices have been growing faster
•for apartments (3/4 rented) than for individual houses (3/4 owner-occupied)
•In departments with lower % of owner-occupied principal residences
The same factors seem to have caused the rebound in home prices in 2009-2010:
- Additional fall in ‘real’ interest rates (not sustainable)
-Deciders’ « myopia », even more so after the 2nd stock crash (2008 after 2000) (reversible)
-Higher rebound in larger urban areas (where more rented dwellings)
-Difference 2008-2010 France – UK - USA:
-Few housing investors + « subprimes » and repossessions in the USA
-Fewer investors + (adjustable!) interest rates fell by more in the UK,
77PLAN
1. Home prices in France, a Historical
perspective
2. Comparison with Other Assets
3. Several Important Properties of Home Prices
4. How can we Explain the 2000-2010 Price Rise?
5. Home Price Prospective
78Home Price Prospective
F Divergence
E
2
D Level change
C
1,1
1 tunnel Return to
0,9 A B the tunnel
0,8
0,7
0,6
0,5 Indice du
Home prix des
price logements
index rapporté
relative to au
0,4
revenu disponible
disposable incomepar ménage
per household
France, base 1965=1
0,3
France, basis 1965=1
0,2
Not useful to anticipate the future
(impact of rent controls)
0,1
1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1
1930 1940 1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 79
Source : CGEDD after notaries’databases, Notaires-INSEE indices and INSEEScenario F (divergence) looks unlikely
• Rental returns can’t decrease indefinitely
• => after a certain while, rents would be disproportionate
relative to households’ incomes
• => Scénario F is unlikely
• => the home price index will probably resume a progression
parallel to income per household
80Will the « tunnel » change level?
(= scenarios C, D et E)
Does the 2000-2010 price rise signal permanent « level
change » resulting from irreversible phenomena?
• Will the causes of the 2000-2010 price rise have a
permanent impact?
– Explanations by « supply / demand »: have been rejected
– Explanations by inflationary impact of subsidies: have been rejected
– Other explanations other than financial environment: have been rejected
(or are reversible)
– Remains: financial environment and deciders’ « myopia »
• New phenomenon: level of public debt
81Financial environment:
A. Housing as an investment
• Interest rates will revert to the trend level = 3% plus inflation
• Risk - return couples will revert to their trend levels for other investments
• Arbitrage => idem for housing investment: total return will revert to its trend
• « Level » => housing investment’s capital gains will be at its trend level
=> Rental return L/P (total return minus capital gains) will revert to its trend level (net ~ 3,5%)
Rents L should not grow faster than income per household:
• Have grown at the same pace up to now
• Low elasticity / supply and demand
• Tenants can not pay much more than they do as a % of their income
• Tenants’ income grows slower than average households’ income
=> Housing price P should revert to its past trend level relative to
income per household
• In addition deciders’ « myopia » (the impact of which is temporary and reversible by nature)
with respect to stocks will end and invert its impact at some point
• => « Return to the tunnel » 82Financial environment
B. Housing as principal residence
• Interest rates will revert to the trend level = 3% plus inflation
• Mortgage length
– In the long term,
• Reverse effect of additional monthly payments on the amount of further
purchases [where will people find the cash? Lower housing purchases?
Lower other housing expenses? Lower other expenses (cars, vacations,
etc.)?]
• Part of the increase in average price will be an increase in quality (cf.
mortgage lengthening of 1965-1975) => further reduces the impact on the
house price index
• In the USA, mortgage lengthening in the (21 years in 1963, 27 years in
1980) has not coincided with a « level change » (rather the opposite)
• Other: no reason not to assume past constants will change + reversibility
in some cases
=> No cause for a significant level change in the long run.
83Government deleveraging
Will impact in some way households’ housing purchases
Households' mortgage debt
and Maastricht government debt
100%
Households'mortgage debt as a % of
90% households'disposable income
Dec. 11
Maastricht general government debt as 86%
80% a % of gross domestic product
70%
Apr.12
64%
60%
50%
40%
30%
20%
10%
0% Source : CGEDD after
1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 Banque de France
1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 and INSEE 84As a conclusion: a « level change » looks unlikely
• Rejection of explanations by « supply-demand »
• Rejection of explanations by subsidies and misc.
• Reversion to « trend » interest rates (3% plus inflation)
• For the investor: stability of the risk/return hierarchy of the various
investments (return to rental returns of years prior to 2000) and end of « myopia »
• For the owner-occupier: reverse impact of mortgage lengthening
• Government deleveraging
⇒ a« level change », if any, should be small: we reject
scenarios C, D and E
⇒ Remain: scenarios A and B: « return to the tunnel »
85How fast shall we revert to the tunnel?
A (fast) =
2
nominal prices
fall by 35 to
1,1
40% in 5 to 8
1
0,9
tunnel
A B
years
0,8
0,7
0,6 B (slow)=
0,5 Home
Indice duprice index
prix des relative
logements toau
rapporté
nominal prices
0,4 disposable income
revenu disponible parper household
ménage
France, base 1965=1
France, basis 1965=1 constant for
0,3
15 to 20 ans
0,2 (« Japanese
scenario »)
0,1
1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1
1930 1940 1950 1960 1970 1980 1990 2000 2010 2020 2030 2040
Source : CGEDD after notaries’databases, Notaires-INSEE indices and INSEE 86How fast shall we revert to the tunnel?
•Based on years 1965-2000, scenario A (fast) est likelier
than scenario B (slow) which may not be excluded
nevertheless
•Low interest rates lessen the likelihood of scenario A
until deciders’ « myopia » reverses its effect
•Local scenarios may differ (depending on the 2000-2010 change in
home price and on the past and prospective change in income, unemployment,
population, number of dwellings, % of secondary residences, etc.)
87Prospective:households’ mortgage debt
projected to 2030
Macroeconomic consequences
Households' mortgage debt of an increase in households’
100% as a % of their disposable income mortgage debt?
80%
60%
40%
USA, observed
20% France, observed
France, projected, scenario D
France, projected, scenario A
France, projected, scenario B
0%
1970 1980 1990 2000 2010 2020 2030
Source: CGEDD after (up to 2011) Banque de France, INSEE, Federal Reserve, Bureau of Economic Analysis
Paper: http://www.cgedd.developpement-durable.gouv.fr/IMG/pdf/dette-immobiliere-2030-friggit_cle7496ca.pdf 88You can also read
