GY457
Lecture
MT
Topic 1 Household Location Choices (MCM)
Topic 2 Firm Location Choices
Topic 3 Economic Agglomeration
Topic 4 Government & Public Finance
Topic 5 Land Use Regulation
Topic 6 Hedonic Analysis
Topic 7 Spatial Disparities
Topic 8 Green Building
LT
Topic 9 Estimating Housing Demand & Supply
Topic 10 Real Estate Cycle
Topic 11 Homeownership
Topic 12 Building the City
Topic 13 Transport Infrastructure
Topic 14 Immigration and Labour Market
Topic 15 City Size
Topic 16 Choice and Competition in Public Services
,Topic 1 Household Location Choices (Monocentric City Model)
Theories of Suburbanization
1. Natural Evolution Theory (Suburbanization as a self-reinforcing process)
- Employment concentrated in center → HH squeeze in center to min commuting cost →
central areas developed first & filled → development moves to suburbs (suburbanization)
- Better housing in periphery (Rich) & Old smaller housing in central (Poor)
- Process reinforced by transport innovation, high income HH with cars moves further out
- Formation of income stratified neighbourhoods (central vs suburb)
- Decentralization of residential activity creates employment decentralization
(High skilled/richer people move further out, to save wage cost firms move to suburb)
2. Fiscal Social Problems Approach (Suburbanization due to fiscal & social problems of central city)
- Problem: High tax, Low quality of public school & services, racial tension, crime, congestion, pollution
- Tiebout’s: HH vote with their feet, Group with homo preferences move to same location
e.g. Rich HH avoid higher tax by residing in income-stratified communities
- Process reinforced by exclusionary & fiscally motivated land use control
Monocentric Model: Historical Roots
Ricardo, 1817 - Founder of “Theory of compensating differences”
- Fertility determines agricultural land price (i.e. Land differs in fertility)
- Occupant is charged for the value of whatever locational advantages exist at that site (i.e. Land price)
Von Thunen, 1826 - Founder of “Urban land use theory”
- Accessibility (i.e. distance, transport cost) determines land price instead of fertility
- Develop bid rent curves in agricultural context
- Transport cost explains differences in land price (closer to mkt place, higher value the land)
Alonso, Mills, Muth Model – Monocentric model
- Generalize Ricardo’s & Von Thunen’s concepts into an urban context
- Commuting cost differences within urban area must be balanced by diff in price of living space
- Alonso: Individual consumes land directly
- Mill, Muth: More realistic model suggesting land in an factor or production for housing
Monocentric Model: Features
- Accessibility to place of employment (i.e. CBD) is the only location advantage that is considered
Not quite realistic as HP is not solely determined by commuting cost, but e.g. good school
- Commuting to CBD gives rise to Ricardian rent
Monocentric Model: Agents
1. HH: All commute to CBD
2. Firms: At one singular centre CBD (i.e. location choice is exogenously determined)
3. Gov: No role
4. Developer: No role in simplified version (Land=Housing), Complex side they profit max
,Monocentric Model: Internal Structure Prediction
1. People max utility from housing and commodity goods given budget constrain (y – tx = pq*q + c)
2. Internal Structure: People in CBD consumes less housing (qt) with higher price, vv for periphery
People living further out must be compensated for long & costly commutes
3. House prices are decreasing in commuting cost (t)
4. House prices are increasing in income
Monocentric Model: Supply Side Prediction
- 𝑃𝑟𝑜𝑓𝑖𝑡 = 𝑝 ∗ 𝐻 𝑁, 𝐿 − 𝑖 ∗ 𝑁 − 𝑟 ∗ 𝐿 N:Capital L:Land
- Capital/Land Ratio = S = N/L: Index of height of buildings (i.e. Structural density) (assume N for height)
S is decreasing in distance from CBD
- r adjusted such that profit PSM of land = 0 (max marginal revenue)
R is decreasing in distance from CBD
Monocentric Model: Population Density Prediction
- Population Density D is a decreasing function of distance from CBD
Monocentric Model: Pros and Cons of the Model
Pros Cons
1. A simple model to start with 1. Ignore other cities & ppl moving out
2. Prediction broadly consistent with 2. Unrealistic assumptions: identical HH
empirical findings preference & income, ignore LUR & tax
3. Assume only one employment centre
Equilibrium in Urban Area
1. Existence of alternative land uses: HH outbid agricultural land users [Insert Graph p4]
2. Land scarcity: Urban population L exactly fits inside the urban boundary
3. Equilibrium depends on whether city is closed or open to migration
Comparative Static Analysis (CSA)
- Equilibrium conditions of closed & open city case enable comparative static analysis
- Analyze impact of change in 1 exogenous variable on endogenous variables for both cases
,Closed City Case
- Exogenous: 𝐿, 𝑟0 , 𝑦, 𝑡 Endogenous: 𝑈, 𝑥, 𝑝, 𝑟, 𝑞, 𝑆
- Migration cannot occur
- CSA: Impact of ∆in exo var on eq of a single city using difference between pre-∆ & post-∆ outcomes
1. Increase in population L [Insert Graph p10]
67 6< 6= 6? 6@ 6Q 6R
- > 0 ; > 0; < 0; > 0; > 0 (prod sub away from land); > 0; < 0
68 68 68 68 68 68 68
- Consumption side: Excess demand bid up price thus drop in consumption
- Production side: Producer substitutes height for land when high land rent
2. Increase in agricultural rent ra [Insert Graph p11]
67 6@ 6Q 6< 6= 6?
- <0; > 0; > 0 (Within the boundary, other empty); > 0; < 0; >0
6?S 6?S 6?S 6?S 6?S 6?S
3. Increase in income y
6< 6<
- < 0 in central; > 0 in periphery
6Y 6Y
- Makes commuting cost less relevant/Increase desired housing consumption
- Lower HP at greater distance → Move out → ↑HP in periphery ↓HP in periphery
4. Increase in commuting cost per distance unit t [Insert graph p13]
67 6\
- <0; <0
6[ 6[
6< 6? 6@ 6Q 6=
- Central: > 0; >0; > 0; > 0; <0
68 68 68 68 68
6< 6? 6@ 6Q 6=
- Periphery: < 0; < 0 ; < 0; < 0; >0
68 68 68 68 68
- Special cases p16
Open City Case
- Exogenous: 𝑈, 𝑟0 , 𝑦, 𝑡 Endogenous: 𝐿, 𝑥, 𝑝, 𝑟, 𝑞, 𝑆
- Costless migration ensures equilibrium
- CSA: Impact of ∆in exo var on eq of a single city using difference between pre-∆ & post-∆ outcomes
1. Increase in agricultural rent ra [Insert Graph p19]
67 68 6@ 6Q 6< 6= 6?
- <0; < 0; = 0; = 0; = 0; = 0; =0
6?S 6?S 6?S 6?S 6?S 6?S 6?S
2. Increase in income y [Insert Graph p20]
67 68 6@ 6Q 6< 6= 6?
- >0; > 0; > 0; > 0; > 0; < 0; >0
6Y 6Y 6Y 6Y 6Y 6Y 6Y
3. Increase in commuting cost per distance unit t [Insert graph p22]
67 68 6@ 6Q 6< 6= 6?
- <0; < 0; < 0; < 0; < 0; > 0; <0
6[ 6[ 6[ 6[ 6[ 6[ 6[
How Realistic are Key Assumptions
- Assume HH has identical income & preference
- Assume exogeneity of travel cost → Congestion, endogenous! MCM didn’t take into account
- Ignore economic cost, psychological/attachment cost of moving, and political reason
- Cannot explain secondary employment centres outside CBD & edge city
- No topography (sea, mountains etc) & LPGs
How Realistic are Theoretical Predictions
68
- CC: L Exogenous & Fixed (Unrealistic!) OC: > 0 (Realistic)
6Y
6Q
- CC: >0 (Realistic) OC: = 0 (Unrealistic)
6?S
- A single model can’t explain all, two models contain mixed evidence
,[9] The Structure of Urban Equilibria (Brueckner, 1987)
- Explain building height variation among cities
- Building near centres of large urban areas appear to be taller than those near centres of small cities
- Alonso, Mills, Muth: Commuting cost differences within urban area must be balanced by differences in
the price of living space
1. Intracity Spatial Variation
- Rent equalises utilities throughout the city
- P: Price, X: Distance to CBD, Q: Housing consumption, Y: Income, T: Commuting cost per X
- P↑ X↓: Compensation for long & costly commutes
- Q↑ X↑: Decline in price leading to more consumption
- Y↑ P↑ Q↓
- T↑ P↓ Q↑
- Supply Side:
- Input of housing production: Land (L), Capital (N)
- Capital’s marginal productivity drops: The taller the building, the more non-productive uses
- Structure density/Capital-land ratio/Index of height: S = N/l
- X↑ P↓ S↓ D↓: Lower rents are to compensate producers for lower price PSF of housing
- Broadly consistent with empirical results
- Price, Rent, Structural density, Population density: Decreasing function to distance to CBD
- Dwelling size: Increasing function to distance to CBD
2. Intercity Spatial Variation
- Equilibrium conditions
- HH outbid agricultural users for all land used in housing production
- All population fits into the city boundary
- Closed city: L is exogenous, utility is balanced by demand & supply & city boundary
- Open city: L is endogenous
3. AMM Model Modification/Criticism
1. Many cities have important secondary employment centre outside the CBD
- Muth, 1969: Lessons of analysis are largely unchanged in a polycentric setting
- White, 1976: Look at what incentives lead a CBD firm to seek a suburban location
- Mills, 1972: Location of all employment within the city is endogenous & potentially decentralised
2. Unrealistically assume all individuals earn the same income
- Mills, 1972; Muth,1969: Deal with income heterogeneity
- Hartwick et al., 1976; Wheaton 1976: Analyse city equilibrium with multiple income groups
3. Unrealistically assume single housing attribute
- Houses are characterised by a vector of attributes → Rise of empirical hedonic price literatures
- Yet, modification leaves most of the important prediction unchanged
4. Unrealistically assume housing capital is perfectly malleable → New complex model
- Producers can costless adjust capital & land inputs anytime, yet construction takes time
5. Traffic congestion is endogenous
6. Ignore local public goods
,[10a] Urban Revival in America 2000 - 2010 (Couture & Handbury, 2017)
- Explains rising inclination for college-educated (CE) & younger people to locate near city centre
- Their behaviour can’t be explained by classic residential choice model
Results
1. Divergent preference of non-tradable services explain the diverging location decisions of young & CE
- Changing preference of non-tradable service account 50-80% of their growth near city centres
- Restaurants, bars, gyms, beauty salons, networking with other young professionals (Homophily)
- Improved restaurant quality contributes most to urbanisation of young & CE, larger than other grps
- Due to rising amenity quality & diversity catering youth’s tastes → drive them to downtown
- (Self note: Probably due to university located in downtown like LSE??)
2. Change in composition of young & CE might explain their urbanisation
- They are increasingly likely to live along → Stronger demand for urban living
- Solo’s expenditure on non-tradable service is 2x(restaurants) 4x(bars) as families with children
3. More leisure time & disposable income (income growth) might explain their urbanisation
- Delay family formation shifts travel & expenditure of them towards non-tradable service
4. Classic factors (jobs, crime, school, housing) struggle to explain urban revival
- Young & CE have little aversion to crime, school quality relative to other groups
- High quality non-tradable services may compensate them for high HP near city centres
- Yet these amenities fail to compensate poorer HH already living near city centre
- Poorer HH might either displace or incur high housing cost with less amenities suit their needs
,[11] The Fundamental of Land Prices & Rental Growth (Capozza & Helsley, 1987)
- When capital is durable & landowners have perfect foresight, urban land price consists of:
1. Value of agricultural land rent
- Rent outside the urban area
2. Cost of conversion
- Opportunity cost of capital in converting raw land to finished land at the urban edge
3. Value of accessibility
- Rent rises at a rate given by transportation cost inside the urban boundary
4. Growth premium (Value of expected future rent increase)
- At the urban edge (likely to be developed in the future)
- In rapidly growing cities, this may account for half of avg price of land
- Explains large gap between price of land at the boundary & value of agricultural land rent
- Changes in real after tax interest rate have more effect on prices in rapidly growing cities
Static Monocentric Model
- Lot size increases & densities decrease with distance from CBD as equilibrium land rents decrease to
offset rising cost of commuting
- Land price is proportional to land rent
- Land price at the urban boundary equals to value of agricultural land rent
Dynamic Model
- Housing is durable with myopic landowners
- Urban development is an incremental process, densities depend solely on econ condition at that time
- Land rents, prices, population densities may rise with commuting distance
- Consider housing abandonment, aging & structure replacement, mixed land use, discontinuous spatial
development patterns (Urban sprawl)
- Land price has four components
- Efficient market produces a gap between land price at boundary and value of agricultural land rent
- EE: Urban growth rates are important determinant, positively associated with price of housing
,Topic 2 Firm Location Choices
Firms Location Choices
Determinant
- Importance of factors changes over time and differs industry by industry
1. Natural amenities (e.g. Water, port): Especially relevant for 19th century, less when transport ↑
2. Proximity to infrastructure: Railway, highways, telecommunication (fast broadband)
3. Proximity to consumers
4. Proximity to labour: Avoid monopsony power, Suburbanisation of labour, Skilled labour
5. Land: Cost, availability
6. Government: Tax & subsidy, LUR, legal system
Empirical Evidence for US Cities
1. Employment becomes more & more decentralised → sub-centres (with high density)
- Explosion of office construction outside CBD since 1980 & ↑Clustering at peripheral location
2. Metro areas become more & more polycentric
3. Firm increasingly value proximity to complementary firms & access to highway system
4. CBD is still dominant employment centre with high employment density, but industrial compo change
Empirical Evidence for Europe
- Ahlfeldt & Wendland, 2013 14a
- City still look roughly monocentric
- Hysteresis effect: Second-nature geography appears to drive on going strength of historic city centre
despite first-nature geography no longer being important
(Even in econ persist to future, even after the factor lead to that event has removed)
Towards a Multicentric City Model: Illustration with MCM [Insert Graph pp21-25]
- 19th Century: Firm rent gradient > HH rent gradient as Marginal shipping cost > Marginal commuting cost
- 20th Century: Manufacturing decentralise
1. Transportation systems become more dispersed
2. Change in methods of industrial production (Horizontal assembly line) &
Storage technology (large, horizontal structure)
3. Rise of service-based economy, benefit strongly from knowledge spillovers in centre → outbid manu
- 21st Century: Sub-centre existence
1. Labour in dominant factor in production for office firm
2. As HH subur, firms locate near their residence get less expensive labour (whose utility include com $)
,Edge City
What are Edge Cities
- New & complete cities arise outside of major core cities since mid 60s
- Centred around enormous tracts of mixed use office space
- Export product is knowledge in stead of traditional manufacturing goods (In US, always high skill)
- Provides more Grade A office space than the core (Irvin v Los Angeles) → Crucial to attract key firms
- Planned & controlled with growth limits (Usually 1 developer, in China that is government)
- Developer purchases land from government and develop all the infrastructures including roads
- Not only US phenomenon: Italy (Milan 2, Milan 3), UK (Canary Wharf)
- Unlike satellite city (Only resident), Industrial parks (Only firm), Traditional suburbs
Theories of Edge Cities
1. Henderson & Mitra, 1996 16
- Developers exploit monopsony power (less competitive with CBD’s labour as proximity to core city↓)
- Production efficiency due to proximity to core city for knowledge spillover
2. Existing cities face social-fiscal problems (externality) & Capacity constrains (congestion), e.g. Tokyo
3. Redevelopment of existing core city is mostly
3. Single (profit max) developer can internalise (positive & negative) externalities, provide LPG efficiently,
Optimal future planning of infrastructure capacity (road, parking)
Theories of Edge Cities Location Choices
1. Necessary conditions: Open space, planning permission by government
2. Garreau, 1991: Random! “Edge cities are accidents of where particular highways intersect”
3. Henderson & Mitra, 1996: Chaotic (Diff regimes) not Random! Strategic choice by edge city developer
- Chaotic as different developers choose different interaction level with port city
Integration with Real Estate Market
- The 4 Quadrant Model [Insert Graph p48]
- Exogenous increase in property demand [Insert Graph p50]
↑D → ↑R → ↑P → ↑C → ↑S
- Exogenous increase in long-term interest rates [Insert Graph p51]
↑IR → ↓P → ↓C → ↓S → ↑R
- Exogenous increase in short-term rates/more restrictive zoning rules [Insert Graph p52]
↑Restrictiveness → ↓C → ↓S → ↑R →↑P
, [13] Employment Subcenters & Subsequent RE Development in Suburban Chicago
(McDonald & McMillen, 1999)
- Sprawl: New development located in increasing distances from the traditional urban core
- Many suburban areas have large subcentres
- Examine spatial patterns of (probability of) RE development in suburban areas of Chicago
- Subcentres with most rapid employment growth are new industrial/retain suburbs & edge cities
Pr of Development Industrial Commercial Suburban Residential
Closer to Airport +
Closer to Highway Interchange + -
Closer to Commuter Rail Stn + -
Closer to Downtown Chicago + +
Closer to Suburban Employment C’ - + -
More Water/Parks/Open Space -
Closer to Agricultural land +
Potentially available for development
Size - Larger if nearer to a - Smaller if further from airport
highway interchange - Larger if further from downtown
- Larger if further from rail stn
Spatial Pattern Rather Scattered
- These areas are dominated by industrial,
cannot further accommodate residential
- Resident also avoid these areas with incompatible land use
[13a] The Number of Subcentres in Large Urban Areas (McMillen & Smith, 2003)
- Filled the gap between theoretical & empirical literatures on polycentric cities of Fujita & Ogawa work
- Central prediction: Number of employment subcentres rises with population & commuting cost
CBD vs Subcentres
- CBD: Significant agg economies, but with high wages to compensate expensive ($ + time) travel
- Subcenters: Similar to small CBD, offer some benefit of agg, but reduce commuting cost, wage, land price
- Definition: Area with significantly high employment densities than surrounding areas
- Definition: Significant effect on overall spatial structure of urban area, raising local pop density, HP, LP
Theoretical Model
- Large population leads to high commuting cost, giving firms incentive to locate outside CBD
- Theoretical lectures didn’t produce empirically testable comparative static results
Results
1. Expected number of subcentres rises with population & (amplified by) commuting cost
- These two account for 80% of variation in the number of subcentres → main determinants
- More congestion level produce more Subcenters for a given population level
- Other variables (median income, other demographic variables) add little explanatory power
2. City with older stock of housing also tend to have more subcentres
2. Urban area with low congestion develops its 1st subcentre when pop = 2.68m & 2nd when pop = 6.74m
3. Large metropolitan areas with high congestion levels are virtually certain to have at least one subcentre
