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

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

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

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

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

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 10-12 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

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.

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 .

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

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

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)

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

Basic statistics

Data

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)

Sample results

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

Physical Attribute 1. Size supply by time

Physical Attribute 1: Size Existing houses

Physical Attribute 1: Size New houses

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

Physical Attribute 1: Size- First cuartil

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

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

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= 118.16 m2.

Physical Attribute 1: Size- Last cuartil* Existing houses All properties fall in the last 25% of distribution by size (up to 75%). Average size= 118.16 m2.

Physical Attribute 1: Size- Last cuartil* New houses All properties fall in the last 25% of distribution by size (up to 75%). Average size= 118.16 m2.

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

Physical Attribute 2: Quality of Construction

Physical Attribute 2: Quality of Construction

Physical Attribute 2: Quality of Construction. Dependent areas

Physical Attribute 2: Quality of Construction. Autonomous cities

Physical Attribute 2: Quality of Construction- County capitals

Physical Attribute 2: Quality of Construction. Province capitals

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

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

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

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

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

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

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 2004-5 and in few cases during 2000-2001. Suggesting the existence of change in market conditions in that year (structural change).

Results and discussion(2) 4.- Elasticities show lowering value in expansion period and rising in recession or recovery (recovery could be defined between 1995-1997) 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.

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.