Segmenting the Paris residential market according to temporal evolution and housing attributes

Segmenting the Paris residential market according to temporal evolution

and housing attributes

Michel Baroni, ESSEC Business School, France

Fabrice Barthélémy, Univ. de Cergy-Pontoise, France

François Des Rosiers, Laval University, Canada

Paper presented at the 2009 ERES International Conference,Stockholm, Sweden, June 24-27

Research partly funded by

Objective and Context of Research

This study aims at testing the existence of similarities and differences in the pricing of housing characteristics among the twenty “arrondissements” of Paris, France.

The complexity of metropolitan residential markets makes it most relevant to assume that hedonic prices are not homogeneous over time and space.

If so, various submarkets may be generated based on selected housing attributes affecting both the level and evolution of prices.

This market differentiation issue is all the more relevant in a rapidly changing real estate context and when looked upon from the investor’s perspective.2

Literature Review – Market Segmentation and House Price Appreciation

Several authors have investigated the heterogeneity-of-attributes and market segmentation issues (Bajic, 1985; Can & Megbolugbe, 1997; Goodman & Thibodeau, 1998 and 2003; Thériault et al., 2003; Bourassa, Hoesli & Peng, 2003; Des Rosiers et al., 2007) as they affect the shaping and interpretation of hedonic prices and question a major assumption of the HP model (Rosen, 1974).

In that context, the price appreciation issue has been extensively addressed (Case & Quigley, 1991; Quigley, 1995; Knight, Dombrow and Sirmans, 1995; Meese & Wallace, 2003, for Paris dataset; Bourassa, Hoesli & Sun, 2006; Bourassa et al., 2009).

3

Literature Review – Market Segmentation and House Price Appreciation

Past research suggests that…:

Hedonic prices of housing attributes may vary over space and time according to submarket specifics and structure as well as to property buyers’ profiles;

Houses will appreciate at different rates depending on property characteristics, the relative bargaining power of agents and the strength of the local submarket;

Reliable estimates of the willingness-to-pay for housing attributes may be derived from the hedonic price (HP) framework in spite of the heterogeneity problem

4

Overall Analytical Approach

Step 1: Building a global hedonic price model for Paris as a whole, with a focus on the marginal contribution of time (Price Index), living area, building period and location (“arrondissements” dummies) on values.

Step 2: Performing a series of Principal Component Analyses (PCA) on selected cluster criteria using either level or change variables, depending on the context.

Step 3: Based on the interpretation of findings, homogeneous submarkets are generated and discussed.

5

The Database

The database (BIEN) is provided by the Chambre des Notaires de France and includes, after filtering, some 252,000 apartment sales spread over a 17 year period, that is from 1990 to 2006.

Housing descriptors include, among other things: Building age (construction period); Apartment size and number of rooms; Floor location in building; Number of bathrooms Presence of a garage; Type of street and access to building (blvd, square, alley, etc.); Location dummy variables standing for the 20 “arrondissements”

and 80 “neighbourhoods” (“quartiers”); Time dummy variables for sale year and month.6

Map 1: The Twenty Paris « Arrondissements »

Paris “Arrondissements” are structured according to a clockwise, spiral design starting in the central core of the city, on the north shore of the River Seine (Arr. 1) and ending up with Arr. 20, in the north-east area.

7

Descriptive Statistics

Number of cases by arrondissement and by nb. of rooms

24,65%

37,46%

22,20%

10,44%

4,19%

1,07%

0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

0,4

1 room 2 rooms 3 rooms 4 rooms 5 rooms 6 rooms

80

2500

5000

7500

10000

12500

15000

17500

20000

22500

25000

27500

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

18,3% of cases

81,7%of cases


Price (Euros) and Surface Area (m2) distributions

0

1

2

3

4

5

6

9


Number of cases by year of transaction

10

Main Regression Findings – Global Model / Price Index

11

Variable Parameter estimates

P value

1991 0.01550 0.0004

1992 -0.11145 <.0001

1993 -0.19251 <.0001

1994 -0.20110 <.0001

1995 -0.25941 <.0001

1996 -0.35339 <.0001

1997 -0.37273 <.0001

1998 -0.33591 <.0001

1999 -0.25001 <.0001

2000 -0.12115 <.0001

2001 -0.03100 <.0001

2002 0.06281 <.0001

2003 0.19801 <.0001

2004 0.33591 <.0001

2005 0.48493 <.0001

2006 0.59460 <.000111

Number of Obs.: 252,772 Dep. Variable: Ln Sale Price

R-Square: 0.9174 Mean Sale Price: 172,270 €

Main Regression Findings – Global Model / Price Index

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P value

1991 0.01550 0.0004

1992 -0.11145 <.0001

1993 -0.19251 <.0001

1994 -0.20110 <.0001

1995 -0.25941 <.0001

1996 -0.35339 <.0001

1997 -0.37273 <.0001

1998 -0.33591 <.0001

1999 -0.25001 <.0001

2000 -0.12115 <.0001

2001 -0.03100 <.0001

2002 0.06281 <.0001

2003 0.19801 <.0001

2004 0.33591 <.0001

2005 0.48493 <.0001

2006 0.59460 <.0001

SLUMP (P1 )

RECOVERY (P2)

BO

OM

(P3)

Number of Obs.: 252,772 Dep. Variable: Ln Sale Price

R-Square: 0.9174 Mean Sale Price: 172,270 €

Main Regression Findings –Global Model / Surface Area*Nb. of Rooms

0,9

0,95

1

1,05

1,1

-0,5

-0,4

-0,3

-0,2

-0,1

0

0,1

0,2

0,3

0,4


P value

Surface x 1 room 0.98290 <.0001

Surface x 2 rooms 1.09367 <.0001





1 room Reference

2 rooms -0.38918 <.0001

3 rooms -0.33777 <.0001

4 rooms -0.12130 <.0001

5 rooms -0.06535 0.1891

6 rooms 0.14882 0.14261313

Main Regression Findings –Global Model / Building Period

-0,16

-0,14

-0,12

-0,1

-0,08

-0,06

-0,04

-0,02

0


P value

epG (after 1991) reference

epF (1981-1991) -0.03797 <.0001

epE (1970-1980) -0.07804 <.0001

epD (1948-1969) -0.12034 <.0001

epC (1914-1947) -0.12756 <.0001

epB (1850-1913) -0.11485 <.0001

epA (before 1850) -0.09991 <.0001

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The post-WW II period (epD) is characterized by a sharp decline in prices while a market premium is assigned to both Haussmannian (epB) and historic (epA) buildings.

Main Regression Findings –Global Model / Location

15

According to « quartier » According to « arrondissement »dummies, grouped by arrt. dummies

0,55

0,75

0,95

1,15

1,35

1,55

1,75

1,95

2,15

2,35

2,55Arr1

Arr2

Arr3

Arr4

Arr5

Arr6

Arr7

Arr8

Arr9

Arr10

Arr11

Arr12

Arr13

Arr14

Arr15

Arr16

Arr17

Arr18

Arr19

Arr20

Main Regression Findings –Hedonic Price Index by « Arrondissement »

16

The graph shows differences among arrondissements:

- The 2nd arrondissement (at the top) ranks first (110% price rise) while the 16th (at the bottom) ranks last (40% rise)

- The 18th, 19th and 20th (relatively low-priced) arrondissements show a higher increase after 2003.

SLUMP (P1)

RECOVERY (P2)

BOOM (P3)

Resorting to PCA For Sorting Out Specific Residential Submarkets

The principal components method (PCA) is applied to each set of estimated effects of attributes.

The method essentially involves an orthogonal transformation of a set of variables (x1, x2, ..., xm) into a new set of mutually independent components, or factors (y1, y2, ..., ym) (King 1969), each of which consisting of a linear combination of all initial variables with weights that vary among components.

The first component, which captures the highest variance among the “m” set of components, also contributes most to the phenomenon under analysis.17

Main Findings From PCA – Price Index (1st & 2nd arrts, 1991 & 1992 excluded)

PC1 reflects the size effect: index level is maintained over time

PC2 reflects price volatility of arrondissements: above-average

decreases (1993-1997) vs. above-average increases (1998-2002)

PC3 reflects the trend: under-performance during the boom period

(2003-2006)

Correlations between Principal Components and years

Eigenvalues Cumulated %

1 9.697 0.6927

2 2.852 0.8964

3 0.903 0.961018 SLUMP RECOVERY BOOM

Main Findings From PCA – Price Index

19• PC1: The 16th arrondissement prices show a specific behaviour

• PC2: The central arrondissements prices are more volatile than the outlying ones

PC

2

PC 1


20PC 1

PC

3

Overall below-average indexduring the slump Overall above-average

index (specially during the slump)

Over-performance during the boom


21 PC 2

PC

3

Below-average P1

Above-average P2

Below-average P3

Above-average P1

Below-average P2

Below-average P3

Above-average P1

Below-average P2

Above-average P3

Below-average P1

Above-average P2

Above-average P3

0,55

0,75

0,95

1,15

1,35

1,55

1,75

1,95Arr10

Arr11

Arr12

Arr13

Arr14

Arr15

Arr16

Arr17

Arr18

Arr19

Arr20


22

Outlying arrondissements

0,55

0,75

0,95

1,15

1,35

1,55

1,75

1,95 Arr1

Arr2

Arr3

Arr4

Arr5

Arr6

Arr7

Arr8

Arr9

Centralarrondissements


230,65

0,85

1,05

1,25

1,45

1,65

1,85

2,05

Arr3

Arr4

Arr5

Arr6

Arr9

0,55

0,75

0,95

1,15

1,35

1,55

1,75

1,95

Arr7

Arr8

Central arrondissements

23

0,55

0,75

0,95

1,15

1,35

1,55

1,75

1,95

Arr10

Arr18

Arr19

Arr20


240,55

0,75

0,95

1,15

1,35

1,55

1,75

1,95

Arr12

Arr13

Arr14

Arr15

Arr17

Arr19

Outlying arrondissements

24


25

1

1,1

1,2

1,3

1,4

1,5

1,6

1,7

1

1,1

1,2

1,3

1,4

1,5

1,6

1,7

1,8

1,9

SLUMP RECOVERY BOOM

By and large, medium-size apartments (2 & 3 rooms) tend to display price elasticities that are both more similar and more stable among arrondissements than either smaller or larger apartments do.

The 6-room apartments have been excluded from the PCA computation.

Main Findings From PCA – Price Elasticities of Living Area* Nb. Rooms

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0,8

0,85

0,9

0,95

1

1,05

1,1

1,15

1,2

1,25

Surface x 1 room Surface x 2 rooms Surface x 3 rooms

Surface x 4 rooms Surface x 5 rooms

Ratio > 1 = greater-than-average elasticity.

For smaller apartments (1-3 rooms), elasticities move in the same way and are similar.

Relative elasticities for smaller and larger apartments move inversely and are more pronounced for the 5-room apartments.

Relative elasticities for the 4-room apartments tend to vary in phase with those of the 5-room apartments, but with a lower magnitude.


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Relative elasticity (e divided by average e) for a given number of rooms

Eigenvalues of the Covariance Matrix

Eigenvalues Difference Proportion Cumulated %

1 0.0157 0.007503 0.5513 0.5513

2 0.0082 0.004516 0.2891 0.8404

3 0.0037 0.003299 0.1312 0.9716


PC 1 opposes smaller apartments (1-room and, to a lesser extent, 2 and 3-room apartments) to larger ones (5-room and, to a lesser extent, 4-room apartments).

PC 2 accounts for the size effect and sorts out the arrondissements with below-average elasticities from those with above-average elasticities.

PC 3 parts the 2-room apartments (above-average e) from the 4-room apartments (below-average e). 28

Principal components description

Main Findings From PCA – Price Elasticities of Living Area*Nb. Rooms

Relatively strong elasticity for the large apartments (4-5 rooms)

Relatively strong elasticity for the small apartments (1-3 rooms)

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Pereire (Giffen good) effect?

Concluding Comments and Suggestions for Further Research

Based on the above findings, it is possible to assert that, while some housing attributes may display stable hedonic prices over space and time, others don’t.

This paves the way for structuring specific housing submarkets in Paris around price indices, price elasticities of living area, building period, etc.

In particular, a major contribution of this research is to highlight the existence of a twofold residential dynamics in the Paris region, with the central « arrondissements » clearly parting from outlying ones with respect to apartment price appreciation over time.30

Concluding Comments and Suggestions for Further Research

Furthermore, preliminary research findings also suggest that hedonic prices of various housing attributes also differ among Paris « quartiers », which implies that the « arrondissements », although currently serving as the basic spatial entity for administrative purposes, may not be as homogeneous as generally considered.

Finally, while this research uses Paris as a case study, its conclusions extend well beyond any particular context and may be assumed to apply to most metropolitan urban areas in Europe and elsewhere.

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References

Bajic, V. (1985). Housing Market Segmentation And Demand For Housing Attributes: Some Empirical Findings, AREUEA Journal, 13(1), 58-75.

Bourassa, S.C., Hoesli, M. and Peng, V.S. (2003). Do Housing Submarkets Really Matter?, Journal of Housing Economics, 12: 12-28.

Bourassa, S.C., Hoesli, M. and Sun, J. (2006). A Simple Alternative House Price Index Method, Journal of Housing Economics, 15: 80-97.

Bourassa, S. C., Haurin, D., Haurin, J. L. and Hoesli, M. (2009). House Price Changes and Idiosyncratic Risk: The Impact of Property Characteristics, Real Estate Economics, forthcoming.

Can, A. et Megbolugbe, I. (1997). Spatial Dependence and House Price Index Construction, Journal of Real Estate Finance and Economics, 14(1-2): 203-222.

Case, B. and Quigley, J.M. (1991). The Dynamics of Real Estate Prices, Review of Economics and Statistics, 73(1): 50-58.

Des Rosiers, F., M. Thériault, Y. Kestens and P-Y. Villeneuve. 2007. Landscaping Attributes and Property Buyers’ Profiles: Their Joint Effects on House Prices, Journal of Housing Studies, 22:6, 945-964.

Goodman, A.C. et Thibodeau, T.G. (1998). Housing Market Segmentation, Journal of Housing Economics, 7(2): 121-143.

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References

Goodman, A.C. et Thibodeau, T.G. (2003). Housing Market Segmentation and Hedonic Prediction Accuracy, Journal of Housing Economics, 12(3): 181-201.

King, Leslie J. (1969). King, 1969. Statistical Analysis in Geography, Prentice-Hall, Englewood Cliffs, N.J.

Knight, J.R., Dombrow, J. and Sirmans, C.F. (1995). A Varying Parameters Approach to Constructing House Price Indexes, Real Estate Economics, 23(2): 187-205.

Meese, R. and Wallace, N. (2003). House Price Dynamics and Market Fundamentals: The Parisian Housing Market, Urban Studies, 40:1027-1045.

Quigley, J.M. (1995). A Simple Hybrid Model for Estimating Real Estate Price Indices, Journal of Housing Economics, 4(12): 1-12.

Rosen, S. (1974). Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition, Journal of Political Economy, 82: 34-55.

Thériault, M., Des Rosiers, F., Villeneuve, P. et Kestens Y. (2003) « Modelling Interactions of Location with Specific Value of Housing Attributes ». Property Management, 21 (1): 25-62.

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Appendices : Price Index Robustness

Pi = arrondissement i relative to Paris

Arri = arrondissement i alone

34


35


High robustness except for 1991-1992 and arrondissement 1 & 2.

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Documents

Segmenting the Paris residential market according to temporal evolution and housing attributes