market heterogeneity and the determinants of paris apartment prices: a quantile regression...
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Market Heterogeneity and The Determinants of Paris Apartment Prices: A Quantile Regression Approach. Fabrice Barthélémy, Univ . de Cergy-Pontoise, France François Des Rosiers, Laval University, Canada Michel Baroni , ESSEC Business School , France - PowerPoint PPT PresentationTRANSCRIPT
Market Heterogeneity and The Determinants of Paris Apartment Prices:
A Quantile Regression Approach
Fabrice Barthélémy, Univ. de Cergy-Pontoise, FranceFrançois Des Rosiers, Laval University, CanadaMichel Baroni, ESSEC Business School, France
Paper presented at the 2013 ERES Conference,Vienna, Austria, July 3 - 6
Objective and Context of Research
This study aims at segmenting the Paris apartment market to better understand the structure of prices over the 2000-2006 period through quantile regression.The complexity of metropolitan residential markets makes it most relevant to assume that hedonic prices are not homogeneous over time and space and that various submarkets may be generated based on selected housing attributes.Indeed, while sale prices have globally followed a positive trend for 30 years in Paris, analyzing how housing attributes are being valued by buyers upon sale may lead to useful insights into the proper dynamics of the different market segments.2
Literature Review – Market Segmentation and House Price Structure
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, heterogeneity in housing attribute hedonic prices can be addressed through Quantile Regression (Koenker and Bassett, 1982; Koenker and Hallock, 2001; Ziets et al.,2008; Benoit and Van den Poel, 2009; Farmer and Lipscomb, 2010; Liao and Wang, 2010).3
Literature Review – Market Segmentation and House Price Structure
Past research suggests that…: By keeping in all the information available from the
dataset, QR provides the analyst with better in-depth insights into the effects of the covariates than would a series of independent standard linear regressions;
QR is relevant for adequately handling the selective heterogeneity of hedonic coefficients with regard to prices;
Homebuyers’ requirements and preferences for specific housing attributes vary greatly across different price quantiles.4
Overall Analytical Approach
Step 1: The most significant descriptors are determined by an OLS regression on the transaction prices.
Step 2: Quantile regression (performed on deciles and centiles) is applied on selected price segments for testing the relative impact of descriptors.
Step 3: Conclusions are drawn on the variability of housing attribute hedonic prices with respect to quantiles.5
The Database
The database (BIEN) is provided by the Chambre des Notaires de France and includes, after filtering, some 159,000 apartment sales spread over a 7 year period, that is from 2000 to 2006.
Housing descriptors include, among other things: Building age (construction period); Apartment size (surface) and number of rooms; Floor location in building; Number of bathrooms; Presence of a garage; Presence of a lift; Type of street and access to building (blvd, square, alley, etc.); Location dummy variables standing for the 20 “arrondissements”
and 80 “neighbourhoods” (“quartiers”);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.
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0
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
19,2% of cases
80,8%of cases
Descriptive Statistics
Number of cases by « arrondissement » and by number of rooms
24,68%
36,00%
21,66%
10,57%
4,66%1,53%
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
8
Descriptive Statistics
Distribution of cases by period of construction
9
Distribution of cases by floor
Ground
floor
1st floor
2nd floor
3rd floor
4th floor
5th floor
6th floor
7th floor
8th floor
9th floor
10th floor
0.00%
2.00%
4.00%
6.00%
8.00%
10.00%
12.00%
14.00%
16.00%
18.00%
Before
1850
From 18
50 to
1913
From 19
14 to
1947
From 19
48 to
1969
From 19
70 to
1980
From 19
81 to
1991
From 19
92 to
2000
From 2
001
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
40.00%
45.00%
50.00%
Number of cases by year of transaction
0
2500
5000
7500
10000
12500
15000
17500
20000
22500
25000
27500
2000 2001 2002 2003 2004 2005 200610
Descriptive Statistics
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Year sold Mean Median
Number of Obs.: 159 074 Dep. Variable: Ln Sale Price
R-Square: 0.9146 Root MSE: 0.2354
Main Regression Findings – OLSTransaction year/ Size-elasticity of sale price
2000 Reference
Reference
2001 0.0944** 0.0918**
2002 0.1848** 0.1830**
2003 0.3193** 0.3149**
2004 0.4557** 0.4524**
2005 0.5999** 0.5886**
2006 0.7231** 0.7109**
- Results are similarusing the Mean or theMedian
- Overall, apartment prices in Paris have experienced a 71% growth over the 2000-2006 period
- Size-elasticity of sale price is > 1 and averages 1.04
The p-values are less than 1% (*) and 0.01% (**)
Size-elasticity
1.0430** 1.0394**
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Number of rooms
Mean Median
Main Regression Findings – OLSNumber of rooms / service rooms
1 room Reference Reference
2 rooms -0.0042 -0.0071*
3 rooms 0.0137** 0.0092*
4 rooms 0.0197** 0.0164**
5 rooms 0.0217** 0.0158*
6 rooms -0.0042 -0.0138
7 rooms -0.0352* -0.0497**
8 rooms -0.0484 -0.0733**
9 rooms 0.0147 -0.0273
0 service room Reference Reference
1 Service room 0.0566** 0.0539**
2 Service rooms 0.0714** 0.0670**
3 Service rooms 0.1140** 0.1197**
4 Service rooms 0.1062* 0.1686*
5 Service rooms = 0.2734** 0.1549
Number of service rooms
Mean Median
- For a similar surface, an additional room commands a premium for 3, 4 or 5 rooms and a discount for more rooms (room size becomes too small)
- The presence of “service rooms” increases the price significantly (rental opportunities)
The p-values are less than 1% (*) and 0.01% (**)
Main Regression Findings – OLS (median)Other attributes
Construction period (Haussmannian as the reference):
Buildings built between 1914 and 1947: 1.8% discount;
Between 1948 and 1969 (post-WWII period): more than 2.5% discount (construction quality);
Most recent buildings (1991 or later): 13% market premium
Floor level (Ground floor & Lift as the reference)
As expected, the higher the floor, the higher the price. Thus, the market premium stands at around 5% for the first floor, 8% for the second floor and raises to between 11% and 12% for upper floors (6th to 9th)
Number of bathrooms(One bathroom as the reference):
Second bathroom adds 1.4% to the price;
Units without a bathroom sell at a 5% to 10% discount, depending on the number of rooms
Buildings with and without a lift
The absence of a lift leads to a price discount of 2% to 3% compared to the same apartment with a lift
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Main Regression Findings – OLS (median)Other attributes
Parking, Mezzanine & Garden Market premium of 6% and
12% for one and two parking places;
A mezzanine adds 12%; A garden adds 15%.
Location features Compared to a ‘street’, a
‘Boulevard’ (-4%) or an ‘Alley’ (-14%) location has a negative impact on prices;
On the contrary, a ‘Place’ (+5%) or a ‘Quay’ (+9%) increases significantly the value of an apartment (mixed effect of unobstructed view and social image).
14 71727374
75777840 3776687970503839476941465144454243354836336785734565455960497661585259106566321225531 3064136263311531811144192827172023
29 2625
212224
-0,4
-0,2
0
0,2
0,4
0,6
0,8
1
0 10 20 30 40 50 60 70 80
Mean location premium by district
Mean5-6-7 & 8th arrondissements
17-18 & 19th arrondissements
15
Main Regression Findings – Quantile Regression
The p-values are less than 1% (*) and 0.01% (**)
Parameters 0.1 quantile
0.2 quantile
0.3 quantile
0.4 quantile
0.5 quantile
0.6 quantile
0.7 quantile
0.8 quantile
0.9 quantile
Pseudo R2 0.662 0.694 0.712 0.725 0.736 0.741 0.758 0.757 0.762
Dependent Variable : Natural logarithm of sale price
Intercept 6.9497** 7.1372** 7.2515** 7.3493** 7.4459** 7.5246** 7.6029** 7.6906** 7.8211**
Surface (in log) 1.0704** 1.0585** 1.0515** 1.0455** 1.0395** 1.0372** 1.0349** 1.0322** 1.0264**
16
Quantile Regression Findings -Surface-elasticity coefficients of Sale Price by decile
17
Quantile Regression Findings -Floor Level (Ground floor with lift as the reference)
18
Quantile Regression Findings -Construction period (Haussmannian period as the reference)
19
Quantile Regression Findings -Bathroom & Toilet(One bathroom as the reference)
20
Quantile Regression Findings -Basement, Parking places & Balcony
21
Quantile Regression Findings -Miscellaneous features
22
Quantile Regression Findings -Location attributes(Street as the reference)
Conclusion
This study provides strong evidence that the QR method allows to bring out marked variations in the magnitude, and even direction, of housing attribute influences on price depending on the price range, which the standard OLS regression method does not.
While not all attribute implicit prices are found to vary along the value spectrum, several do: this is notably the case, in this research at least, for the price index, the apartment size, the number of rooms, service rooms and bathrooms, the type of housing unit, the floor level, parking places and some location attributes.23
Conclusion
Among other findings, the elasticity coefficient of the size variable, which stands at 1.07 for the first price decile (cheapest units), is down to 1.03 for units belonging to the ninth one (dearest units).Apartment prices do not evolve at the same pace along the value range, the annual growth rate for lower-priced properties standing at 9.8% over the six year period, as opposed to only 8.9% for luxury apartments. Finally, market premiums (+) or price discounts (-) assigned to some location attributes may also vary widely across deciles, following either an upward (alley-, avenue+) or downward (hamlet+) trend.24
Discussion
Whatever the urban context, specific submarkets may be targeted for commercial marketing, public policy or mortgage lending purposes, in which case a reliable assessment of property values turns out to be of paramount importance.
This is where the QR approach, which lends itself to a variety of methodological adaptations, has a clear advantage over the standard OLS method, although it should be viewed as a complement, rather than as a substitute, to the latter.
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