6: Elasticity of housing supply

Housing Economics, REC-E3400

Autumn2020

6: Elasticity of housing supply

Dr. Elias Oikarinen

Adjunct Professor of Real Estate Economics, Aalto University

Associate Professor of Economics, Oulu Business School

Plan for this topic

To gain understanding on the important role of supply elasticity of housing for housing markets and for urban growth dynamics

• Price elasticity of housing supply – what is it about?

• Influences of more (or less) elastic supply

• Determinants of the supply elasticity

2

Learning outcomes

The aim is that after the lecture the student understands

2) The consequences of more inelastic supply 3) Factors determining the supply elasticity

4) Reasons for the substantial variation in the supply elasticity across cities 5) What is the relationship between land leverage and supply elasticity

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The price elasticity of supply of housing is a key factor for housing market dynamics and equilibrium, and for the growth prospects of cities.

It determines the capability of housing supply to respond to changes in housing demand, and therefore the extent to which increasing housing demand induces higher housing prices or greater housing stock.

Price elasticity of housing supply tells how many % supply increases when price level increases by 1%

.

The elasticity of housing supply can be influenced by local zoning and land use policies.

 Highly policy relevant issue

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Price elasticity of housing supply in brief

Relatively elastic supply (i.e. relatively large price elasticity of housing supply):

• When housing demand increases (and thus price level rises), housing supply increase is large

 Price and rental levels (i.e. housing costs) increase relatively little

Relatively inelastic supply (i.e. relatively small price elasticity of housing supply):

• When housing demand increases (and thus price level rises), housing supply increase is small

 Price and rental levels (i.e. housing costs) increase substantially

 Greater burden for households (housing costs), firms (labor supply) and public finance (social security costs, housing subsidies)

• On the other hand, fast city growth (due to flexible zoning and land use policies) induces (infrastructure) costs to municipalities sometimes creating incentives to hinder the growth of housing stock…

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Elastic vs. inelastic supply

• When demand increases:

Prices increase more and supply less in a city with more inelastic (i) supply than in a city with more elastic (e) supply.

Similarly,

Prices increase more and supply less in a given city, if supply is less elastic.

Note: More inelastic supply thus also means less space (m²) per person

Note2: Here, we refer to the long-term supply elasticity unless mentioned otherwise

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Q

P1e

P1i

P

P0

Basic demand-supply schedule and supply

elasticity

• In a more supply elastic market, the equilibrium curve in lowleft corner is steeper: there is more

construction at a given price level

 Greater supply of space

(= more space per person)

 Lower rental level

 Lower price level

(But still more construction)

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Four-quadrant model and supply elasticity

Rent (€/m²)

P = R/i D = S

Price (€/m²) Stock (m²)

F(C) = P

S = C/dr

Construction (m²)

• Relatively inelasticresponse of construction to price

increase

 relatively steep long-term supply curve in the market for space

• Demand increase yields

relatively large rent and price growth and small

construction and supply growth

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Four-quadrant model and supply elasticity

Rent (€/m²)

P = R/i D = S

Price (€/m²) Stock (m²)

F(C) = P

S = C/dr

Construction (m²)

• Relatively elastic response of construction to price increase

 relatively gentle long- term supply curve in the market for space

• Demand increase yields

relatively small rent and price growth and large construction and supply growth

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Four-quadrant model and supply elasticity

Rent (€/m²)

P = R/i D = S

Price (€/m²) Stock (m²)

F(C) = P

S = C/dr

Construction (m²)

• Helsinki: : ₁ = 0.20

• Rovaniemi: ₁ = 0.82

• Price level increases more and stock less in the area with more inelastic supply

Long-term supply elasticity:

Short-term supply elasticity:

S

* = g(P

,c

) = 

+ 

P

– 

c

98 99 100 101 102 103 104 105

90 95 100 105 110 115 120

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Stock

Price

Year

Price, Helsinki Price, Rovaniemi

-0.5 % -0.3 % -0.1 % 0.1 % 0.3 % 0.5 % 0.7 % 0.9 % 1.1 % 1.3 % 1.5 % 1.7 %

-10%

-5%

0%

5%

10%

15%

20%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Stock change

Price change

Year

Price, Helsinki

Price, Rovaniemi

Supply elasticity and shorter-term price dynamics

based on stock-flow model : ^P rice level adjusts fully and immediately

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S

– S

= µ(S – S*)

– δ * S

, if S

* > S

S

– S

= – δ * S

if S

* < S

Supply elasticity and shorter-term price dynamics based on stock-flow model : ^P rice level adjusts gradually

• When price level (as in reality) adjusts only gradually and exhibits momentum effects

(i.e. backward-looking expectations) short- term price cycles can, in principle, be greater in the more supply elastic city*

• In the long-term, price reactions are stronger in the more supply inelastic city

• E.g. Glaeser et al. (2008) model suggests that long-lasting bubbles can take place only in cities with highly inelastic housing supply

* Stock-flow model equation (4), µ = 10% here Momentum parameter here: 0.45 (app. mean of Finnish cities)

(POLL)

11 90

95 100 105 110 115 120 125

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Price

Year

Supply elasticity = 0.82 Supply elasticity = 0.2

99 100 101 102 103 104

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Stock

Year

Supply elasticity = 0.82 Supply elasticity = 0.2

Los Angeles, CA

Dallas, TX Fort Worth, TX

Indianapolis, IN y = -0,47ln(x) + 0,96

R² = 0,51

0,0 0,2 0,4 0,6 0,8 1,0 1,2 1,4 1,6 1,8

0 1 1 2 2 3 3 4 4 5

Income elasticity

Supply elasticity (Saiz, 2010)

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Supply elasticity and income elasticity of

prices across the U.S.

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Supply elasticity and price overshoots of the 2000s across the U.S.

Allentown, PA-NJ Bakersfield, CA

Fort Lauderdale, FL

Fresno, CA

Indianapolis, IN Las Vegas, NV

New Orleans, LA

Oklahoma City, OK Omaha, NE-IA

Pittsburgh, PA

Richmond, VA Riverside, CA

Salt Lake City, UT

Tulsa, OK

y = -0,24ln(x) + 0,36 R² = 0,36

0,0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8

0,0 0,5 1,0 1,5 2,0 2,5 3,0 3,5 4,0 4,5 5,0

Peak of overshoot

Supply elasticity (Saiz, 2010)

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Trends in house price-income ratio and the

supply elasticity, U.S. cities

The supply elasticity has considerable consequences for households and firms, and thereby for the performance of cities and for the economy as a whole. In particular, by causing greater cost of housing for households, lower supply elasticity has

notable impacts on the population growth and composition, income growth, income and wealth distribution, migration, and on local labor markets. Moreover, less elastic housing supply strengthens housing price cycles, which strengthens cycles in the overall economy as well.

Since more inelastic housing supply decreases the attractiveness of a city from both firms' and households' point of view hindering the growth of the city, and amplifies housing price cycles, more elastic housing supply can generally be seen as a

desirable aim.

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Consequences of inelastic housing supply:

A summary and recap

It is often argued that in a country with an abundant reserve of vacant developable land (such as Finland), housing supply should be very elastic — after all, land

availability should not restrict housing construction, as land is not a scarce resource (within the country).

However, the urban economics theory implies that the supply elasticity is largely a local phenomenon, i.e., dependent mainly on city specific factors rather than the abundance of undeveloped land at the country level.

Much more important than the abundance of land within the whole of Finland (for instance) is the availability of attractive and buildable lots within, or close to, the urban area. This is shown in Oikarinen et al. (2015) for the Finnish case.

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Factors determining the elasticity

In accordance with the theoretical considerations, empirical research provides evidence of city-level factors determining the elasticity of supply (c.f. the circular city model – factors increasing the value of land tend to decrease the supply

elasticity):

• Greater city population

• Greater population density

• Greater growth rate for the city

• Greater transportation costs

• Geographic constraints (water bodies, topographical restrictions)

• Administrational constraints (due to especially zoning and land use policies)

In line with the theory, empirical findings indicate that the supply elasticity of housing can significantly vary across cities within a single country. (POLL)

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Factors determining the elasticity

Recall the graphs on demand and price development:

Demand has increased the most in Oulu, but prices not, WHY?!

18 75

100 125 150 175 200

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Aggregate disposable income (real index, 1995=100)

Oulu Pääkaupunkiseutu Tampere

Turku Rovaniemi Lahti

Pori Kajaani Kouvola

50 100 150 200 250 300 350 400

2Q1972 2Q1974 2Q1976 2Q1978 2Q1980 2Q1982 2Q1984 2Q1986 2Q1988 2Q1990 2Q1992 2Q1994 2Q1996 2Q1998 2Q2000 2Q2002 2Q2004 2Q2006 2Q2008 2Q2010 2Q2012 2Q2014 2Q2016 2Q2018 2Q2020

Helsinki-1 Pääkaupunkiseutu Tampere Turku Kouvola Oulu

Increase in total housing space (stock;

supply) and in housing space per person 2005-2019

(Data source: Statistics Finland)

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20 25 30 35 40 45 50

0%

5%

10%

15%

20%

25%

30%

35%

Helsinki Tampere Turku Oulu Kouvola

m2 per person

Increase in housing stock (%, m2) Increase in m2 per person (%)

Housing space per person, 2019 (m2)

Helsinki Tampere

Turku Oulu

Vaasa

Lahti Pori Jyväskylä

Lappeenranta Rovaniemi

Kajaani

Kotka

Kuopio

Espoo Vantaa

y = 34,945x^-0,365 R² = 0,5868

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1

10000 100000 1000000

Price elasticity of housing supply

Average population 1987-2008 (logarithmic scale)

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Supply elasticity variations across Finland

(Oikarinen et al., 2015)

21 Helsinki

Tampere Turku

Oulu

Vaasa

Lahti Pori

Jyväskylä Lappeenranta

Rovaniemi

Kajaani

Kotka

Kuopio Espoo

Vantaa

y = 0,2157x - 0,1836 R² = 0,3769

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1

2 2,5 3 3,5 4 4,5

Price elasticity of housing supply

Regulatory index (scale 1-5)

• Regulatory index based on Valtonen (2013)

• Greater index value indicates more flexible regulation / smaller administrational

constraints to housing supply

• City size, regulation, and geographic constraints together explain 80% of the differences in supply elasticities across cities

Supply elasticity variations across Finland

(Oikarinen et al., 2015)

Helsinki Jyväskylä

Kajaani

Kotka

Kuopio

Lahti Lappeenranta Oulu

Pori Rovaniemi

Tampere Turku

Vaasa

y = -0,3986x + 1,0158 R² = 0,5564

0,0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1,0

0,5 0,7 0,9 1,1 1,3 1,5 1,7 1,9

Price elasticity of housing supply

Income elasticity of housing prices

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Supply elasticity variations across Finland

(long-run elasticities; Oikarinen, 2015)

• Less elastic supply causes prices to increase more when income (i.e.

demand) increases

Additional readings on the supply elasticity of housing

Helps in gaining a deep understanding of the topic (all are included in MyCourses):

• Glaeser E., Gyourko J. (2017) The economic implications of housing supply. Journal of Economic Perspectives 31(1): 3-30.

• Oikarinen E. (2015) Asuntotarjonnan hintajouston alueelliset erot. Kansantaloudellinen aikakauskirja, 111(4): 454-475. (in Finnish; i.e. not necessary for the exam)

• Oikarinen E., Peltola R., Valtonen E. (2015) Regional variation in the elasticity of supply of housing, and its determinants: The case of a small sparsely populated country.

Regional Science and Urban Economics 50: 18-30.

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Housing Economics, REC-E3400

Autumn 2020

7: Land Leverage

Dr. Elias Oikarinen

Adjunct Professor of Real Estate Economics, Aalto University

Associate Professor of Economics, Oulu Business School

Plan for this topic

To gain understanding on the concept of “land leverage” and its important implications

• The role of land value as a major determinant of housing prices and returns, and price volatility

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Learning outcomes

The aim is that the student

1) Knows the concept “land leverage”

2) What is the relationship between land leverage and supply elasticity 3) Understands, how and why land leverage varies across cities

4) Is familiar with the key implications of land leverage variations

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Value of a property =

Value of physical structure + value of the land upon which the structure stands

• Value of the physical structure = (rebuilding) construction cost – depreciation

• Value of land = value of location per m² * size of the site

• Variation in construction costs across regions are relatively low

• Variation of land value across locations is relatively high

→ Different prices of similar dwellings or commercial properties in different locations are mostly due to the value of land (location)

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Land leverage

“Land leverage” is greater, when the fraction of the value of land component of the overall value of housing is larger:

LL = L/P = (P-C)/P

• Land, in particular, is the component of housing prices that reacts to shocks in demand

- For instance, in monocentric city model, it is the land value that increases as city grows

→ Land prices trend upwards in a growing area, or when income levels trend upwards

• Hence:

a) Land prices trend upwards in a growing area, or when income levels trend upwards b) Land prices generally are much more volatile than structure values

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Land leverage

• Volatility of construction costs over time is relatively low

• Real construction cost are close to constant over the long run

• Volatility of land value over time is relatively high

• Real land values trend upwards when demand increases

→ Housing price movements are typically dominated by land price changes

* Privately-financed flats (SF), Construction cost index (whole of Finland; SF), tender price index for flats (includes, construction companies’ profit margins; Rapal Oy & RAKLI)

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Real housing price and construction cost indices, HMA*

50 75 100 125 150 175 200 225 250 275

1Q1975 1Q1977 1Q1979 1Q1981 1Q1983 1Q1985 1Q1987 1Q1989 1Q1991 1Q1993 1Q1995 1Q1997 1Q1999 1Q2001 1Q2003 1Q2005 1Q2007 1Q2009 1Q2011 1Q2013 1Q2015 1Q2017 1Q2019

Housing price index

Tender price index for housing construction Construction cost index

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Median price of lots zoned for single-family housing in Finnish municipalities 1985-2019

0 50 100 150 200 250

1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019

€ / m2

HMA

Over 100 000 inhabitants 20 000 - 100 000 inhabitants Less than 20 000 inhabitants Satellites to HMA

Large regional variations in price growth trends and cycle magnitudes

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Helsinki-1 = centre of Helsinki Pääkaupunkiseutu = HMA

Real price indicesof (”old”) free-market apartments, 1972Q2-2019Q2

Source: Statistics Finland Hedonic indices since 1988 50

100 150 200 250 300 350 400

2Q1972 2Q1974 2Q1976 2Q1978 2Q1980 2Q1982 2Q1984 2Q1986 2Q1988 2Q1990 2Q1992 2Q1994 2Q1996 2Q1998 2Q2000 2Q2002 2Q2004 2Q2006 2Q2008 2Q2010 2Q2012 2Q2014 2Q2016 2Q2018 2Q2020

Helsinki-1 Pääkaupunkiseutu Tampere

Turku Kouvola Oulu

Given the different characteristics of land and physical structure values, in an area with greater LL:

a) Housing prices are more volatile

b) Housing prices increase more, when demand increases c) Housing prices drop more, when demand decreases

• As illustrated by the monocentric city model, LL is greater in a bigger city (ceteris paribus)

and closer to the city centre

→ Prices tend to be more volatile (and grow faster) in bigger cities and closer to the city centre (and other areas with high value of amenities)

(POLL)

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Land leverage

Value of land is greater (ceteris paribus) when supply is more inelastic

 greater land leverage

Inelastic supply and greater land leverage have similar implications w.r.t.

housing price (and rent) response to demand shocks and thus w.r.t. housing price cycles and volatility of housing price changes.

When supply is more inelastic / land leverage is greater:

• Price and rent reactions to demand shocks are greater

• Price cycles are stronger and housing price volatility greater

Supply elasticity is a good measure at the city level, while land leverage works at the submarket level or even micro level (comparison within the city or

between dwellings etc.) when considering housing price trends and volatility.

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Supply elasticity and land leverage

Consider two suburbs within a city: a centrally located one with LL, on average, being 50%, and one close to the city boundary with average LL of 10%.

In the city*

a) demand grows so that land values rise by 10%

→ Housing price increase is 5% in A, but only 1% in B b) Demand decreases so that land values drop 20%

→ Housing price decrease is 10% in A, but only 2% in B

The same basic principle applies to two cities, with average LLs of 50% vs. 10%

* As we recall from the monocentric city model, land value % increase could also be different in different parts of the city when demand grows

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Land leverage: A simple example

Land leverage, examples

• Davis & Heathcote (2007): 36% on average in the U.S. during 1975-2006

• Davis & Palumbo (2008): In 46 largest U.S. metro areas 50% on average in 2004

• Davis & Palumbo (2008): Variation in the cities from 89% (San Francisco) to 23% (Oklahoma City); generally LL is higher in coastal cities

• Oikarinen (2010): In 2000-2007, almost 50% in HELSINKI (single-family housing)

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Additional readings on land leverage

• Bostic et al. (2007). Land Leverage: Decomposing Home Price Dynamics. Real Estate Economics, 35(2), 183–208. (In MyCourses)

Not “necessary” for the exam, but may help in gaining a deeper understanding:

• Davis and Heathcote (2007). The price and quantity of residential land in the United States. Journal of Monetary Economics, 54(8), 2595-2620. (In MyCourses)

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Additional References on topics 6-7

Davis, M. A., & Palumbo, M. G. (2008). The price of residential land in large US cities. Journal of Urban Economics, 63(1), 352–384.

Glaeser, E.L., Gyourko, J., Saiz, A. (2008) Housing supply and housing bubbles. Journal of Urban Economics 64(2), 198–217.

Oikarinen, E. (2010) An Econometric Examination on the Share of Land Value of Single-family Housing Prices in Helsinki. Research on Finnish Society, 3,. 7–18

Oikarinen E., Bourassa S., Hoesli M., Engblom J. (2018) U.S. metropolitan house price dynamics.

Journal of Urban Economics 105: 54-69.

Oikarinen E., Bourassa S., Hoesli M., Engblom J. (2020) House price-income relationship revisited.

Working paper.

Valtonen E. (2013) Asuntotarjonnan hintajouston alueelliset erot ja niiden syyt Suomessa. Master’s Thesis. Aalto University, Department of Built Environment.

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