NYC, 7 November 2014
Empirics of housing, land use,
and location choices
Gilles Duranton
Wharton
Housing
• At the heart of extremely large allocations
• Closely related to other large allocations
• Several traditions:
– Housing as an asset
– The macroeconomic impact of housing
– Social / public policy
– Location choice
• Advantages of focusing on housing as a location problem
– The demand and supply of housing is about locations
– Housing as part of a broader set of urban issues
– Connected to transport and commercial real estate
2
Roadmap
• A few illustrations
• Theory reminder: housing and accessibility
• The empirics of gradients
• Sorting within cities
• Suburbanisation
• Job decentralisation
• Housing and the regulation of land use
Mostly based on our recent Handbook chapter with Diego Puga
3
Land use in Paris
Multi-family residential
Single-family residential
Commercial
Transport
Open space
4
Land use in Paris
100%
Share of land by use
Open space
Transport
75%
50%
Built−up
25%
0%
−30
−20
−10
0
10
20
Distance to Notre Dame (km.)
5
30
Land use in Paris
100%
Share of built−up land by use
Commercial
75%
50%
Single−family
residential
25%
Multi−family residential
0%
−30
−20
−10
0
10
20
Distance to Notre Dame (km.)
6
30
Land prices in Berlin
Source: Ahlfeldt, Redding, Sturm, and Wolf (2014)
7
ds before World War I, declined sharply during the war and
e interwar period. In the second half of the 20th century,
per year on average.Housing prices over time: France
2012.
Source: Knoll, Schularick, and Steger (2014)
8
Figure 6: France,
1870–2012.
rowth of a little more than 1 percent. Note that this is lower than average annual GD
capita growth of about 1.8 percent for the sample average. That is to say, house pric
e risen significantly over the past 140 years relative to the consumer prices but have lagg
Housing
prices
over
time:
World
me growth in most countries.
We will
return
to this
point
later.
Source: Knoll, Schularick, and Steger (2014)
Figure 15: Mean and median real house prices, 14 countries.
9
A quick refresher on accessibility models
• Accessibility is a key determinant of the demand for housing
– Accessibility has value
– It implies other location characteristics that residents value
• The most famous accessibility model is the monocentric model
originally developed by Alonso (1964), Mills (1967), and Muth
(1969)
• With amm, accessibility is modelled in a very simple but useful
way:
– ‘Reasonable’ first-order description of cities
– Models key disadvantages of bigger cities (more expensive
housing, longer commutes)
– Structures our examination of the empirical literature
10
The accessibility - housing price tradeoff
• Provided location preferences are not fully idiosyncratic,
locations with better accessibility will be more desirable
• As a result, floorspace in these locations will fetch a higher
price
• There will be tradeoff between household travel costs and
housing prices: as accessibility declines housing price should
decline to offset the increase in travel costs exactly
• General models of accessibility are hard because the location
choice of everyone depends on that of everybody else,
including employers
• The monocentric model simplifies the problem by imposing an
exogenous location of jobs (at the centre) and by reducing the
travel problem to commutes
11
Main predictions
Five gradients:
1. Housing prices decrease with distance to the cbd
2. Because of lower prices, housing consumption by residents
increase with distance to the cbd
3. Profit maximisation by competitive constant-returns builders
leads land prices to decrease with distance to the cbd
4. This also leads the capital/land ratio to decrease with distance
to the cbd
5. In turn, the previous gradients imply that population density
decreases with distance to the cbd
12
The ‘forgotten’ predictions
The monocentric model also offers a number of quantitative
predictions
1. The Alonso-Muth condition for housing prices
2. An equivalent Alonso-Muth condition for land prices that
adjusts for density
3. Housing development should be proportional to the ratio of
land price gradients to housing price gradients
4. Differential land rent at the cbd should be proportional to city
population
5. Proportionality between transport costs and differential land
rent aggregates
13
Main challenges to the monocentric model
1. Household heterogeneity
2. Housing is durable
3. The location of jobs is endogenous
4. Land and housing markets are subjects to frictions and
regulated
14
The empirics of gradients: population density
• Large literature starting with Clark (1951)
• Rings vs area approaches
• Generally negative density gradients except in some heavily
constrained cities
• Gradients become flat in far suburbs
• Richer patterns at a micro-scale (subcenters, etc)
15
The empirics of gradients: housing prices, land prices,
development intensity and housing consumption
• Some work on housing price
• Often has a hard time to evidence gradients
• Work by Cheshire and Sheppard (E 1995) and Ahlfeldt (JRS
2011) is most advanced
• Some work on land prices
• Again supportive of negative gradients
• Close to no work on the intensity of development (except
McMillen bc 2006)
• Even less on housing consumption
16
Limited knowledge: data availability
• Historically a binding constraint
• Usually a small number of cities (often one)
• Highly selected cities
• A lot of within country variation in gradients mostly
unexplored
• Some work in ‘time series’ but limited (McMillen JUE 1996)
17
Limited knowledge: methods
• How to define centres?
• How to define subcentre? (McMillen JUE 2001)
• Residential density is not area density (or is it?)
• Unit housing prices are hard to obtain and hedonics are
potentially problematic
– Standard specifications can be derived from the amm model
using an approximation that is true only locally
– The model expects house characteristics to be determined
jointly with distance to the cbd
• Endless debates about functional forms for the spatial decay
18
A misconstrued debate
• Joint test of the (assumed) monocentric assumption and the
Alonso-Muth tradeoff
• But no city is truly monocentric
• The interesting questions regard the Alonso-Muth tradeoff
• Some work that considers accessibility but
– It imposes arbitrary accessibility indices
– Or model-based indices that are never validated by looking
at actual travel behaviour
19
More work is obviously needed
• Take theory more seriously
– Assess the Alonso-Muth condition without imposing
monocentricty
– Take accessibility seriously
• Assess the quantitative predictions of amm models (Combes,
Duranton and Gobillon, 2013)
• Develop tools to assess how monocentric cities are (?)
• More ‘quantitative’ work as in Rappaport (2014)?
• More structural work like Ahfeldt, Redding, Sturm, and Wolf
(2014)?
20
Household heterogeneity
• The monocentric city model can easily be extended to have
multiple income groups
• If commuting is paid in numeraire, the rich will live farther
from the cbd provided housing is a normal good
• With commuting paid in time, the rich will live farther from
the cbd provided the income elasticity of demand for land is
greater than the income elasticity of the value of commuting
time
• Richer forms of heterogeneity have not been explored much
(including parcel heterogeneity)
21
Patterns of location choices: sorting and segregation
Some facts for the us
• Historically (LeRoy and Sonstelie JUE 1983, Lee and Lin, 2014):
– Rich in the center
– Decentralisation of the rich with the streetcar and the car
– Recentralisation since the 1970s
– Less decentralisation in centres with strong amenities
• Today (Brueckner and Rosenthal REStat 2009 and others):
– Generally positive income gradients
– Bell-shaped beyond some distance
– Discontinuities at the boundaries of the main municipality
– Considerable income overlap
– Some rich us centres are exceptions
– Opposite patterns in Europe
22
Can the accessibility / land price tradeoff explain
these patterns?
• Need to estimate the income elasticity of the demand for land
(/housing)...
• ... and the income elasticity of commuting costs
• Wheaton (AER 1977)
– Use results from the San Francisco bart surveys
– Find both elasticities to be about 0.25
23
• Glaeser, Kahn, and Rappaport (JUE 2008)
– Estimate demand with log parcel size = c + a log income + e
– ols income elasticity of the demand for land about 0.1
– Imputing land consumption for apartments raises it to 0.3
– Land price is missing from the regression: downward bias
– Instrument income with education raises the elasticity to 0.5
– Parcel size may not capture true land consumption. Using
log area per person also raises the elasticity to 0.5 with ols
– Income elasticity of commuting costs argued to be 1
– But travel is not only time. A simple back of the envelop
suggests about 0.5 for poorer households
– GKR conclude about the importance of transit
Since the two elasticities may be close, the am tradeoff may be a
weak determinant of weak patterns (?)
24
Alternative explanations
• Amenities (Brueckner, Thisse, and Zenou EER 1999)
– Contrast Paris with Detroit
– But no real empirics (and real empirics are particularly hard
on this)
– Although appealing as an explanation, amenities may
generate many patterns depending on shape of the demand
for land, etc
25
• Housing durability (Brueckner and Rosenthal, REStat 2009)
– Controlling for the age of the housing stock explains away
the income gradient away in us cities
– The age of the housing stock is endogenous
– Instrumenting using 20-year lags confirm the findings
• ‘Flight from blight’
– Crime (Cullen and Levitt REStat 1999)
– Racial preferences (Boustan QJE 2010)
– Changes in the school system (Baum-Snow and Lutz AER
2011)
• Fiscal motives / Tiebout sorting (Inman AER 1995, de
Bartolome and Ross JUE 2003)
Not much evidence except for recent structural work (Bayer
and McMillan 2012)
26
Patterns of residential land development
The sprawl ‘confusion’:
• Low density
• Scattered development
• Causes (car-based travel)
• Consequences
27
Facts about residential decentralisation
In us metropolitan areas
• Central city share of residents (Boustan and Shertzer, 2012)
– 1940: 56%
– 2000: 32%
• Flattening of population gradients
• Constant scatteredness of development: 1976-1992 (Burchfield,
Overman, Puga, and Turner, 2006)
• Similar evidence for a sample of world cities (Angel, Parent,
and Civco, 2012)
28
A fall in commuting costs to explain residential
decentralisation?
• Early evidence is ambiguous (e.g., Brueckner and Fansler,
1983)
• Main issues: choice of measure of commuting costs and
endogeneity of these measures
• Baum-Snow (QJE 2007)
– Counts radial rays of us Interstate Highways
– Instruments them with a corresponding measure from the
1947 highway plan
– Finds strong evidence that highways led to residential
decentralisation: -9% in central city population for each
extra ray
29
• Puzzle: the monocentric model predicts decentralisation but
not a central city decline
(cheaper travel should lead to growth for central cities, albeit
less than suburbs)
• ‘Blight factors" might be at play
• Rising income may also have played a big role (Margo JUE
1992)
30
What explains new development fragmentation?
Burchfield, Overman, Puga, and Turner (2006)
• Look at the share of open space within 1 km of new
developments over 1992-2006 in us metropolitan areas
• Find evidence about:
– Car-based cities
– Uncertainty (as predicted by models of uncertain land
developments)
– Low past growth
– Temperate climate
– More decentralised production sectors (manufacturing)
– Hills (but not mountains)
– Aquifers
– Municipal fragmentation
31
Consequences of sprawl
• More driving? (Bento, Cropper, Mobarak, and Vinha, REStat
2005 and a large planning literature)
– But the effects are modest
– Not well identified
• Reduced social interactions?
– Again, poorly identified
– The better evidence (Glaeser and Gottlieb US 2006, or
Brueckner and Largey JUE 2008) says the opposite
• Obesity?
– Still poorly identified in most cases
– Eid, Overman, Puga, and Turner (JUE 2008) using panel
data find no effect
32
Facts about employment decentralisation
In us metropolitan areas
• Only 24% of jobs within 5 km of the cbd in 1996
• Share of central city jobs went from 61% in 1960 to 34% in 2000
• Employment decentralisation has been considerable but less
than residential decentralisation
• Employment decentralisation started later but is highly
correlated with residential decentralisation
• Skilled jobs have decentralised less, manufacturing jobs have
decentralised more
• Considerable heterogeneity across cities
33
Diffuse decentralisation or movement towards
subcentres?
• Some evidence about subcentres (McMillen and Smith JUE
2003)
• No measure of "diffuseness" of decentralisation?
• Glaeser and Kahn (BWPUA 2001) claim diffuse for the us
• Garcia-Lopez, Hemet and Viladecans (2015) provide evidence
of subcentre growth and emergence for Greater Paris
34
Wage gradients
According to models of endogenous business locations (eg Ogawa
and Fujita, 1980):
• Firms tradeoff land costs vs. agglomeration
• But also get closer to their workers when they decentralise
• And may thus pay them less → wage gradient
• This gradient is expected to be weak relative to the housing
price gradient
35
Evidence about wage gradients
• Workers with greater commutes have higher wages
• But this is a prediction of the monocentric model as residents
with higher wages live further from the cbd
• When firms have market power firms will pay workers with
longer commutes more even in absence of gradients (Manning,
LE 2003)
• Also: workers commute much more than the travel
minimising solution suggests (‘wasteful commuting’)
• Also: commute times have remained fairly stable (‘commuting
time paradox’)
36
Another implication of job decentralisation: spatial
mismatch
• First proposed by Kain (1968) to explain low minority
employment rates in us central cities
• Fact: Access to jobs strongly declined for minority residents in
central cities
• A variety of mechanisms possibly at play:
– Search is more difficult
– Wages net of commuting costs may get below a reservation
level
– More discrimination in white suburbs
37
• Typical regression 1: neighbourhood labour market
participation on access to jobs and controls
• Typical regression 2: neighbourhood employment on race
• Identification issue: neighbourhood choice is not exogenous
• Raphael (JUE 1998) focuses on young workers more likely to
live with their parents
• Policy solutions
– Move people where the jobs are?
(mto results are not encouraging)
– Move jobs where workers are?
(Literature on placed-based policies raises a lot of doubts)
– Subsidise travel?
38
Causes of job decentralisation
• Residential decentralisation?
• Transportation (Baum-Snow, 2014)
• Crime, housing stock decay, racial preferences, changes in the
school system
• Changes in communication technology allowing firms to
separate different production units
39
Some continuity despite considerable
decentralisation
• Subcentre continuity in Los Angeles (Redfearn RSUE 2009)
• Transit continuity in Los Angeles (Brooks and Lutz unp 2013)
• Urban continuity (Bleakley and Lin QJE 2012, Davis and
Weinstein JRS 2008)
• But major changes within cities when the stock of housing
disappears (Siodla 2014, Hornbeck and Keniston 2014)
40
Land use regulations
• Perhaps the single most important determinant of the supply
of new housing
• Regulations limit both the type and intensity of development
• A challenging empirical topic
• But not really a challenge for the monocentric model
41
Measuring land use regulations
First, some measurement issues need to be discussed
• We can measure land cover
• Land use is much harder
• There are many forms of land use regulations, particularly in
the us
• Should we focus on land use regulations or on the building
code?
42
development. Gyourko and Saiz (2006) document a large degree of heterogeneity in structure
production costs across local markets, which is correlated with a number of supply shifters
including the extent of construction worker unionization, the level of local wages, local
topography as reflected in the presence of high hills and mountains, and the local regulatory
environment as measured by an index of Internet chatter on construction regulations.
.8
1
1.2
Index, 1980 = 1
1.4 1.6 1.8
2
2.2
2.4
Real Construction Costs and House Prices Over Time
1980
1985
1990
1995
2000
2005
2010
2015
year
Real House Price
Real Construction Cost
Source: Gyourko and Molloy (2014). us Construction costs are the cost of an
Note. Construction
the costCompany
of an Economy
Quality
from R.S. Price
Economy Quality
home fromcosts
R.S.are
Means
deflated
byhome
the Consumer
Means Company deflated by the Consumer Price Index. House prices are the
Index. House
prices are the repeat-sales index published by CoreLogic deflated
repeat-sales index published by CoreLogic deflated by the price index for
excluding housing
personal services.
consumption expenditures excluding housing services. The deflator
was calculated by the authors from data published by the Bureau of Economic
Analysis.
43
Land price as the difference.
How to measure land use regulations
Three main approaches
1. Use simple economics:
• Glaeser, Gyourko and Saks (JLawEc 2005) measure the
difference between the price and marginal cost of an extra
floor in Manhattan condos and call this gap the regulatory
tax
• Finding: Manhattan condo c. 2000 were at least 50% more
expensive than in absence of regulation
• GGS also estimate the regulatory tax for single family
homes for a cross-section of cities ; it goes from zero in
Birmingham, Cincinnati, and Houston to nearly 20% in
Boston, over 30% in Los Angeles, and upwards of 50% in
the San Francisco Bay Area
44
2. ‘Deep but narrow’
• Glaeser, Shuetz, and Ward (2006) and Glaeser and Ward
(JUE 2009) use extremely detailed data for municipalities in
the Boston area including prohibitions of
irregularly-shaped lots, extensive wetlands restrictions,
septic system regulations, etc
• They also collect detailed data on undeveloped land...
• ... and on building activity over nearly 100 years
• They draw a strong connection between lack of
development and minimum lot size
45
3. ‘Shallow but wide’:
• Gyourko, Saiz, and Summers (US 2008) collected survey
information for 2,611 us communities
• They estimate indices of the stringency of the local land use
environment (wrluri index)
• Strong correlations across the component indices
• Saiz (QJE 2010) develops measures of land availability for
major us urban areas, excluding wetlands, water bodies,
slopes above 15 degree
• He finds a strong correlation with the wrluri index
• That allows him to estimate housing supply elasticities that
have been widely used in subsequent work
46
Motives for land use regulations: non-conforming
uses
• Stull (AER 1974)
• Provides a strong rationale for separating users (zoning)
• A similar case can be made in case of positive externality
across similar users (e.g., Ogawa and Fujita JRS 1980)
• Preferences for open space can also justify open space
protection (Turner, JUE 2005)
47
• Makes a good case for open space protection
• But we do not know how much space should remain
undeveloped nor whether one big park is better than two
small ones (and land prices may offer a poor guide for action)
• Outside of the separation of dirty manufacturing, it is hard to
justify full Euclidian zoning
• Externalities from non-conforming uses also make it hard to
justify restrictions that provide a ceiling on development for a
given type of users (e.g., minimum lot size)
48
Motives for land use regulations: congestion
• Perhaps large congestion externalities in cities
• More general issue behind: how much land should be
allocated to roads and parking
• Once a subject of interest (Solow Vickrey JET 1971)
• Land use regulation can be used to implement the first-best
(Pines and Sadka JUE 1985)
• But informationally very demanding for a benevolent planner
• Many other instruments seem more appropriate (tolls, parking
pricing, etc)
• No empirical knowledge, in part because we do not know
about land use in cities
49
Motives for land use regulations: Tiebout
• Tiebout equilibrium requires head taxes
• But not generally feasible
• Many countries use a property tax
• But it induces the poor to chase the rich
• Fiscal zoning can be used to re-establish a Tiebout equilibrium
• Exclusionary zoning may not be only fiscal (e.g., racism)
• Review of the evidence by Ihlanfeldt (2004): some support for
the fiscal motive but the evidence for other forms of
exclusionary zoning is mixed today
• In any case, separating sorting by income (fiscal) and sorting
by race (purely exclusionary) is difficult
50
Motives for land use regulations: Political economy
• Several variants:
1. Under limited mobility or with idiosyncracy in preferences
for specific locations, ‘monopoly’ local landowners may
want to restrict the supply of housing (Fischel 1985)
2. Although some developments may increase local land
values they are risky and local residents may be risk-averse
(Fischel 2001, Ortalo-Magne and Prat AEJ 2014)
3. Conflict between owners of developed vs. underdeveloped
land (Hilber and Robert-Nicoud JUE 2013)
• They all point to the role of local homeowners
• This rings true in the us, less clear in other countries
51
• Regressing the stringency of land use regulation on
homeownership raises an obvious simultaneity issue
• Hilber and Robert-Nicoud use the fraction of childless married
couples as instrument for the share of homeowners
• Rationale: These couples are more likely to have stable high
disposable incomes making them eligible for mortgages
• Exclusion restriction: they should not otherwise affect
regulations
• Overall, they find small effects
• Glaeser and Ward (JUE 2009) instrument current with lagged
homeownership (1940 or 1970)
• This avoids reverse-causation but maybe not omitted variables
• They also finds small effects
52
Do restrictive regulations raise land prices?
Theoretically ambiguous
• If the demand for locations is perfectly elastic it cannot
• If demand is imperfectly elastic, restricting quantities will raise
prices
53
Large empirical literature on restrictiveness and land prices:
• Mostly in cross-section
• Survey by Quigley and Rosenthal (2005): more restrictive
regulations lead to higher prices and the effects are often large
• For instance Mayer and Somerville (JUE 2000): A one month
increase in approval time for a new development reduces
building permits by 10 percent.
• Glaeser and Ward (JUE 2009) study Massachusetts townships
over 1980-2002. The measured negative effects of wetlands
bylaws and subdivision rules on construction are larger in
panel than in cross-section (restrictions are implemented in
areas with more-than-average construction activity)
54
Non-price and non-quantity effects of regulations:
• Bertaud and Brueckner (JUE 2005) show that building height
restrictions lead to more sprawl and longer commutes
• Geshkov and DeSalvo (JRS 2012): minimum lot sizes and
height restrictions contribute to sprawl, while maximum lot
sizes, limits on construction, minimum density requirements
and impact fees reduce sprawl
55
Welfare effects of regulations: Glaeser, Gyourko and
Saks (2005)
• They estimate crowding cost for housing in NY by looking at
price differentials across floors
• They also estimate a broad congestion externality by looking at
rents as a function of city population and income in a broad
cross-section of cities
• The costs of these two externalities are small relative to the
regulatory tax
• They also estimate a positive fiscal contribution of new
residents
• They conclude at negative welfare effects
56
Turner, Haughwout, and van der Klaauw (E 2014)
• Distinguish three effects of land use regulation
1. Own-lot effect: the direct cost of regulatory constraints on
how land is used
2. External effect: value of regulation for owners of nearby
land (assumed positive following an externality argument)
3. Supply effect: regulation reduces the supply of developable
land.
• Data:
– Cost-Star land price data
– wrluri data to measure regulations
– 2006 National Land Cover Database to measure
development
57
• Estimation
– They use border discontinuities between municipalities
(with different regulations)
– Identifying assumption: unobserved traits of land are
uncorrelated with regulations across borders
– Control for many covariates and use only straight borders
• Main result: a one standard deviation increase in the index of
regulation reduces land prices by 36 percent ; welfare would
be improved by relaxing land use regulations
• See also Cheshire and Sheppard (E 2002)
58
Conclusion
The empirical literature on land use is underdeveloped. Time is
ripe for a new effort:
• Big improvements in data availability
• But this is not enough:
– Better connection to theory is needed
– Identification concerns must be dealt with
• Land use is much broader than just ‘land’
59