let, transport economics laboratory (cnrs, university of lyon, entpe)

Is it worth identifying service employment (sub)centres

for modelling apartment prices?

The case of Lyon, France

LET, Transport Economics Laboratory(CNRS, University of Lyon, ENTPE)

ERES conference 2009 Stockholm KTH

Marko Kryvobokov

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1. Introduction

URBAN CENTRES vs.

ALL TERRITORIAL UNITS

in hedonic price model

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1. Introduction

Identification of urban centres

- Generalization – creation of higher order objects from lower order objects

- von Thünen, Alonso, Wingo, Wendt, Harris and Ullman…

- McDonald (1987): an urban center represents a distinct zone whose employment density exceeds the density of its adjacent neighborhood and whose size is sufficiently large to potentially impact the urban land and/or property market

- McDonaln (1987): employment subcentres as secondary peaks in the employment density and the employment-population ratio

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1. Introduction


McMillen (2001), McMillen and Smith (2003): the first stage: potential subcentres have significant

residuals in the locally weighted regression of employment density on distance from the CBD;

the second stage: check if they provide significant explanatory power in a semiparametric employment density regression

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1. Introduction


Empirical examples in the real estate literature:

- Söderberg and Janssen (1999): re-estimate regression for apartment properties in Stockholm changing the precise location of the CBD with the step of 50 meters

- Sivitanidou (1996): application of the definition of McDonalds for office-commercial real estate in Los Angeles

- McDonald and McMillen (1990), McMillen (1996): land values in Chicago in 1836-1990

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1. Introduction

Accessibility and centrality

Des Rosiers and Thériault (2008):

accessibility is the ease with which persons, living at a given location, can move to reach activities and services which they consider as most important.

It is distinct from

centrality, which relies on structural features and relates to proximity to urban amenities

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1. Introduction

All territorial units

- Thériault et al. (2005) and Des Rosiers and Thériault (2008): hedonic modelling of real estate prices with centrality and accessibility indices; accessibility index, based on interview and fuzzy logic criteria, far outweigh the centrality index in Quebec city

- With fast development in GIS and transportation analysis software, in principle, all territorial units in a city can be focused. Do we still need generalization, i.e. identification of urban centres?

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2. Identification of service employment centres

0 10 205 Kilometers

The Lyon Urban Area +:

812 zones (IRISes)3,723 sq. km

1,904 thousand inhabitants (2005)

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Two origine-destination (O-D) matrices of travel times from the MOSART transportation model for the Lyon Urban Area (2007), a.m. peak:

- cars- public transport (N. Ovtracht and V. Thiebaut, LET)

As in McMillen (2001), we run a simple regression model of service employment density on travel time to Bellecour-Sala (the CBD)

15 zones have positive standardized residuals higher than 3.3

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The pre-identified service employment centres

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3

8

9

4

13

116

5

10

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1512

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2

0 0.5 10.25 Kilometers

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3. Centrality index

– attraction of zone j (either service employment density or service employment to population ratio);

N

j ij

ji tt

ACI

1

jA

ijtt – travel time from zone i to zone j;

N – number of zones

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3. Centrality index

0 6 123 Kilometers

Centrality index

466.92 - 1181.14

1181.15 - 2400.61

2400.62 - 2981.00

2981.01 - 3521.00

3521.01 - 4713.31

0 6 123 Kilometers

Centrality index

282.21

282.22 - 834.18

834.19 - 1933.40

1933.41 - 3479.00

3479.01 - 7903.88

Clusters of centrality index for cars with service employment density

Clusters of centrality index for cars with service employment to population ratio

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4. Accessibility index

– 50th percentile of the observed travel time from travel survey;

N – number of zones

As in Thériault et al. (2005): suitability index Sij for travelling from zone i to zone j1ijS 50Cttij

5090

501CC

CttS ij

ij 9050 CttC ij

0ijS 90Cttij

ijtt – travel time from zone i to zone j;

50C

– 90th percentile of the observed travel time from travel survey90C

N

jjiji ASAI

1

– attraction of zone j (either service employment density or service employment to population ratio);

jA

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4. Accessibility index

Clusters of accessibility index for cars with service employment density

Clusters of accessibility index for cars with service employment to population ratio

0 6 123 Kilometers

Accessibility index

118.03 - 2871.12

2871.13 - 5497.00

5497.01 - 7457.00

7457.01 - 9667.30

9667.31 - 14822.52

0 6 123 Kilometers

Accessibility index

20.63 - 1387.71

1387.72 - 2581.95

2581.96 - 3485.00

3485.01 - 4602.00

4602.01 - 7099.83

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5. Hedonic model of apartment prices

Lyon

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Data from Perval: 4,362 apartments sold in 1997-2008

Location: mainly in Lyon and Villeurbanne

Number of rooms: 1 to 9

Apartment priceper square metre,Euros

0 1 20.5 Kilometers

317 - 1200

1201 - 1800

1801 - 2500

2501 - 3000

3001 - 5112

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Apartment variables:

- dummies for year of transaction- apartment area- dummies for number of bathrooms- dummies for number of parking places- dummies for floor- dummies for period of construction- dummies for apartment’s state (conditions)- dummies for the quality of view- dummies for number of cellars- dummy for existence of garden- dummy for existence of terrace

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5. Hedonic model of apartment pricesLocation variables:- dummy for location within a 100 m buffer of water- dummy for location in an ad hoc district- % middle income households- % high income households- travel times by car to each of the 15 pre-identified centres- travel times by public transport to each of the 15 pre-identified

centres- centrality index for cars with service employment density- centrality index for cars with service employment to population ratio- centrality index for public transport with service employment density- centrality index for public transport with service employment to

population ratio- accessibility index for cars with service employment density- accessibility index for cars with service employment to population

ratio- accessibility index for public transport with service employment

density- accessibility index for public transport with service employment to

population ratio

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Dependent variable: log price

42 or 43 independent variables

OLS regression:- global- geographically weighted regression (GWR) (Brunsdon et al., 1996) GWR with a Gaussian error term; fixed kernel type

After the first global OLS run, observations with standardised residuals

higher than 3 were deleted. 4,308 observations remained

Variance inflationary factor (VIF) checks multicollinearity

Moran’s I measures spatial autocorrelation

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Examination of the influence of the pre-identified centres:- global model without travel times - travel time to each of the pre-identified centres is added one

at a time; fifteen global models for each transport mode- sorting their adjusted R-squared high to low, all fifteen

variables are added to the equation and then excluded one by one from the bottom until it is obtained a model with acceptable VIF

- the best global models include two centres

Global model with travel time to the CBD only

Global model with centrality index

Global model with accessibility index

GWR models for the same cases

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Extracted location variables

Adjusted R2 Coefficient t-value Maximum VIF Moran’s I

TT_C_10TT_C_3

0.880 (0.896) -0.163 (-0.149)-0.126 (-0.104)

-22.965-13.612

6.307 0.29 (0.20)

TT_C_10TT_C_6

0.880 (0.896) -0.094 (-0.097)-0.155 (-0.122)

-9.439-13.562

6.305 0.29 (0.20)

TT_C_3 0.865 (0.892) -0.196 (-0.130) -21.188 6.301 0.36 (0.23)

CI_C_SD 0.876 (0.893) 0.007 (0.006) 29.490 6.298 0.30 (0.21)

CI_C_SP 0.860 (0.891) 0.010 (0.009) 17.144 6.306 0.36 (0.22)

AI_C_SD 0.873 (0.893) 0.004 (0.005) 27.266 6.304 0.32 (0.22)

AI_C_SP 0.867 (0.892) 0.003 (0.003) 22.699 6.312 0.34 (0.22)

Global regression and GWR for cars

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Adjusted R2 Coefficient t-value Maximum VIF Moran’s I

TT_PT_10TT_PT_6

0.874 (0.892) -0.114 (-0.123)-0.132 (-0.074)

-10.737-10.238

6.307 0.31 (0.22)

TT_PT_10TT_PT_3

0.873 (0.894) -0.154 (-0.147)-0.089 (-0.059)

-17.629-8.266

6.309 0.31 (0.21)

TT_PT_3 0.864 (0.892) -0.192 (-0.134) -20.695 6.298 0.35 (0.22)

CI_PT_SD 0.867 (0.891) 0.007 (0.005) 23.514 6.299 0.33 (0.22)

CI_PT_SP 0.859 (0.890) 0.012 (0.010) 16.638 6.309 0.36 (0.22)

AI_PT_SD 0.867 (0.891) 0.004 (0.005) 23.449 6.309 0.34 (0.22)

AI_PT_SP 0.867 (0.887) 0.003 (0.003) 23.052 6.301 0.34 (0.25)

Global regression and GWR for public transport

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The highlighted centres:

3 – Bellecour-Sala10 – Les Belges

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116

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1512

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The highlighted subcentres:

6 – Jussieu10 – Les Belges

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116

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1512

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5. Hedonic model of apartment pricesApplication of principal component analysis


Factor number % of variance

Adjusted R2 for factor 1

and othervariables

Moran’s I for factor 1

and other variables

Adjusted R2 for all factors

CarsTravel times to 15

centres 1 18.6 0.880 0.29 0.606

Travel times to 3 centres 1 6.8 0.850 0.40 0.607

Public transportTravel times to 15

centres 1 22.1 0.871 0.32 0.601

Travel times to 3 centres 1 8.3 0.854 0.38 0.618

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6. Conclusions

The best results for travel times were obtained with three centres: Bellecour-Sala, Les Belges, and Jussieu.

Among them, it is difficult to find a leader.

Duocentric models are better than the monocentric one.

Centrality index and accessibility index behave differently in comparison with each other, but in most cases outperform the monocentric model.

Both global and GWR models with travel times to two centres, either with or without the CBD, are the best among all, including centrality and accessibility indices.

let, transport economics laboratory (cnrs, university of lyon, entpe)

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