1. 20th ERES Annual Conference,
Housing supply price-elasticity by physical characteristics. Another view .
Paloma Taltavull de La Paz
University of Alicante, Spain
20th ERES Annual Conference,
Vienna, July, 2013

2. Work in progress PRESENTATION INDEX Motivation The model Data
Empirical evidence and results
Discussion
Work in progress

3. Motivation
Increasing interest of how supply responses to housing price changes.
Effects on housing prices
Macro effects: monetary policy channels, wealth effects
Most explanation concentrate in general variables
Cost of finance, construction costs… (Blackley, 1999, Goodman, 2005 and others)
Population affect the supply elasticities of houses , Green, Malpezzi and Mayo, 2005
Most studies analyses how quantity supplied (stock and new housing) respond to price changes… supply elasticity … Different interpretations
New supply … developers reaction (Arnott, 1987)
Total supply … homeowner responses
Increasing interest. Because the effect on over valuation: higher elasticities, lower overvaluation. (2) because it is known as a key variable in the transmission mechanism of Monetary Policy and (3) Reflects the developers responses .. Sentiments taking decissions of house investment and (4) Reflects the ownership responses to prices, that is, the way to own and enjoy a house.. Diferent housing needs and the way to cover. Interpretation depends on the estimated elasticity

4. Motivation House characteristics change period to period
Different tastes
New construccion change the ‘house type’
House attributes determine differences on prices
Hedonic models… but … also price responses?
Reactions could be different under expansion rather than during recession,
and change along different cycle phases (Glaeser et al, 2005)
With lagged reactions (DiPasquale, 1999, Meen, 2002, Topel and Rosen, 1988, Quigley, 1997)
Changing supply slope curve (Pryce, 1999, Bramley, 1993, 2003, Malpezzi and Vandel, 2002)
House price vary across Space
What we know

5. Motivation
How much physical characteristics affect house price reaction?.
Some (few?) evidence:
Construction characteristics tend to be similar in each housing sub-market … ‘Housing type’
Larger house price volatility in secondary homes market rather than in primary homes mkt.
Non-linearity between prices and size: Smaller houses shows higher price by sqm

6. Aim of the paper Several hypothesis:
Evaluate whether or not housing supply change on time?.. H01
Stability on parameters?
Malpezzi, 1999 and Harter-Dreiman, 2004,… takes years
Heterogeneity in houses. .. ………………………………………….. H02
Higher elasticities in the suburban than in central (Goodman, 2005)
Role of specific physical characteristic………………………….. H03
Size, construction quality, age,
Speed returning to equilibrium ..………………………….……. H04

7. The model (1) Qts = f(PH,t, Ct ,Ht-1 , Gtk )
Conventional housing supply model
(1) Qts = f(PH,t, Ct ,Ht-1 , Gtk )
where:
Qts refers to housing supply in units
PH,t corresponds to housing prices in real terms
Cmt corresponds to the costs associated with construction materials
Cst is an indicator of the payment of salaries to construction workers
it reflects the real interest rates paid by developers for building credits
Ht-1 is the existing housing stock at the previous moment
Gtk is a set of the regional market characteristics
et is a random term
a1..8 are the estimated parameters. Since models are often defined directly as logarithmic functions, the a are measures of supply elasticity with respect to the different determinants.

8. The model Existing homes:
(2) Qts = a+ bPHt + [(Si=1n giAit )+ dGtk ] + mt
Qts is now the quantity of characteristic ‘i’
(Si=1n gAit ) is the hedonic structure of each property
(dGtk) is idiosincratic regional effect .

9. The model
The model is controlled by characteristics with an hedonic definition
Space specifities appears controlling by urban level
Time different reactions are captured estimating year to year model

10. The model The paper focuses in the following characteristics:
Size .. Total effect and the extreme segmented effect (25% of size and lower distribution, 75% and up size distribution.
Quality of construction
Type of house, first area and second resident area differences

11. Data and methodology Data base with 2.350 millions observations
Yearly based,
House information collected to be used as comparables for valuation purposes
Around 30 attributes by property: city, neighbourhood and house level
Asking prices
Most provinces in Spain but significant for 8 perhaps 9 (Madrid, Barcelona and Valencia included)

12. Data and methodology 25 characteristics are used as attributes control
4 levels of information: city, neighbourhood, building, house
2SLS method in panel analysis,
Instrumental method
to takle existing endogeneity in housing characteristics and prices
Control by some remaining correlation

13. Basic statistics

14. Data

15. Results Several models by three dimensions
Size & age
Quality & urban area
Use (1st and 2nd residence) & age
Age distinguish between new (<=2 years old) and existing (>2 years old)
Estimated elasticities are consistent in all models
Highly significant
Model explanatory power is modest (0,25-0,60)

16. Sample results

17. Results. Model by size Elasticity full period: 0,18 By age:
--- new : 0,258
--- Existing: 0,15

18. Physical Attribute 1. Size supply by time

19. Physical Attribute 1: Size Existing houses

20. Physical Attribute 1: Size New houses

21. Results. Model by size in the extreme queues of the distribution
Dataset segmented by properties with size
A.- Falling in the lower 25% of the distribution
80 m2 and less
B.- Falling in the upper 25%, so with a size in 75% of distribution or more… 118,16 m2

22. Physical Attribute 1: Size- First cuartil

23. Physical Attribute 1: Size- First cuartil* Existing houses
All properties fall in the first 25% of distribution by size. Average size= 80 m2

24. Physical Attribute 1: Size- First cuartil* New houses
All properties fall in the first 25% of distribution by size. Average size= 80 M2

25. Physical Attribute 1: Size- Last cuartil* Elasticities by time
All properties fall in the last 25% of distribution by size (up to 75%). Average size= m2.

26. Physical Attribute 1: Size- Last cuartil* Existing houses
All properties fall in the last 25% of distribution by size (up to 75%). Average size= m2.

27. Physical Attribute 1: Size- Last cuartil* New houses
All properties fall in the last 25% of distribution by size (up to 75%). Average size= m2.

28. Physical Attribute 2: Quality of Construction
Share the database
Low quality ..
Cathegories 1 to 3 (17,2%)
405,516 observations
High quality…
Cathegories 5 to 6th (22,6%)
532,456 observations
Segmented by urban area: location

29. Physical Attribute 2: Quality of Construction

30. Physical Attribute 2: Quality of Construction

31. Physical Attribute 2: Quality of Construction. Dependent areas

32. Physical Attribute 2: Quality of Construction. Autonomous cities

33. Physical Attribute 2: Quality of Construction- County capitals

34. Physical Attribute 2: Quality of Construction. Province capitals

35. Physical Attribute 3: Use. 1st and 2nd residence área
Physical Attribute 3: Use. 1st and 2nd residence área. Elasticities by time
Dataset segmented by those houses located in Fists residence area or Second homes areas.
Excluding mix areas and other uses
Elasticities:
1st residence: -0,256
2nd homes: 0,231
News…
1st residence: -0,283
2nd homes: 0,263
Existing
1st residence: -0,176
2nd residence : 0,148

36. Physical Attribute 3: Use. 1st and 2nd residence área
Physical Attribute 3: Use. 1st and 2nd residence área. Elasticities by time

37. Physical Attribute 3 : Use. 1st residence area. Existing houses

38. Physical Attribute 3 : Use. 1st residence area. New houses

39. Physical Attribute 3 : Use. 2nd residence area. Existing houses

40. Physical Attribute 3 : Use. 2nd residence area. New houses

41. Results and discussion
1.- The elasticity values in all cases moves around a limit range of values.
Showing a limited change in slope.
Elasticities tend to reduce their value along the period.
2.- Elasticities change with time but in most cases shows a return to the initial equilibrium.
elasticity shows a return to equilibrium moving with the cycle.
This is according to previous evidence.
3.- Elasticities ‘jump’ during and in few cases during
Suggesting the existence of change in market conditions in that year (structural change).

42. Results and discussion(2)
4.- Elasticities show lowering value in expansion period and rising in recession or recovery (recovery could be defined between ) period.
This is contrary to the evidence that support that show elastic responses during boom and inelastic during bust.
5.- All elasticities (they are one-year elasticities so short term) give inelastic values
6. New houses shows higher size-price elasticity than existing houses in aggregated model.
The smaller houses shows a negative price reaction suggesting that a 1% of increase on size is associated to a diminishing elastically the price.
This is consistent with hypothesis of non-linearity of size and prices.
In this cases, new houses show to be more sensible to prices.
In the case of larger houses, the elasticity is positive and elastic, with values falling from 4.5 to 1.5 along the period.

43. Results and discussion(3)
6.- Elasticities change by urban location.
With larger price effect in those dependent urban areas rather than in independent cities and capitals at both levels.
However, price elasticity in province capitals become more volatile since 2004.
7.- Low quality units show a more sensibility of prices
with special impact on capitals.
The lower quality, the lower price
Strong effect, specially in capitals.
Cluster by quality is a key issue to analyse the lower quality stock.
8.- Houses located in first or secondary urban areas show different price responses,
Negative to first residence region and positive to secondary homes.
Distinguish between use is a key variable.