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

ERES conference 2009 Stockholm KTH 2 1. Introduction URBAN CENTRES vs. ALL TERRITORIAL UNITS in hedonic price model

ERES conference 2009 Stockholm KTH 3 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

ERES conference 2009 Stockholm KTH 4 1. Introduction Identification of urban centres 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

ERES conference 2009 Stockholm KTH 5 1. Introduction Identification of urban centres 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

ERES conference 2009 Stockholm KTH 6 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

ERES conference 2009 Stockholm KTH 7 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?

ERES conference 2009 Stockholm KTH 8 2. Identification of service employment centres The Lyon Urban Area +: 812 zones (IRISes) 3,723 sq. km 1,904 thousand inhabitants (2005)

ERES conference 2009 Stockholm KTH 9 2. Identification of service employment centres 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

ERES conference 2009 Stockholm KTH Identification of service employment centres The pre-identified service employment centres

ERES conference 2009 Stockholm KTH Centrality index – attraction of zone j (either service employment density or service employment to population ratio); – travel time from zone i to zone j; N – number of zones

ERES conference 2009 Stockholm KTH Centrality index Figure A2. Centrality index for car with service employment to population ratio Clusters of centrality index for cars with service employment density Clusters of centrality index for cars with service employment to population ratio

ERES conference 2009 Stockholm KTH Accessibility index – 50 th percentile of the observed travel time from travel survey; N – number of zones As in Thériault et al. (2005): suitability index S ij for travelling from zone i to zone j – travel time from zone i to zone j; – 90 th percentile of the observed travel time from travel survey – attraction of zone j (either service employment density or service employment to population ratio);

ERES conference 2009 Stockholm KTH Accessibility index Figure A2. Centrality index for car with service employment to population ratio Clusters of accessibility index for cars with service employment density Clusters of accessibility index for cars with service employment to population ratio

ERES conference 2009 Stockholm KTH Hedonic model of apartment prices Lyon

ERES conference 2009 Stockholm KTH Hedonic model of apartment prices Data from Perval: 4,362 apartments sold in Location: mainly in Lyon and Villeurbanne Number of rooms: 1 to 9 Apartment price per square metre, Euros

ERES conference 2009 Stockholm KTH Hedonic model of apartment prices 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 apartments state (conditions) -dummies for the quality of view -dummies for number of cellars -dummy for existence of garden -dummy for existence of terrace

ERES conference 2009 Stockholm KTH Hedonic model of apartment prices Location 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

ERES conference 2009 Stockholm KTH Hedonic model of apartment prices 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 Morans I measures spatial autocorrelation

ERES conference 2009 Stockholm KTH Hedonic model of apartment prices 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

ERES conference 2009 Stockholm KTH Hedonic model of apartment prices Extracted location variables Adjusted R 2 Coefficientt-valueMaximum VIF Moran s I TT_C_10 TT_C_ (0.896) (-0.149) (-0.104) (0.20) TT_C_10 TT_C_ (0.896) (-0.097) (-0.122) (0.20) TT_C_ (0.892) (-0.130) (0.23) CI_C_SD0.876 (0.893)0.007 (0.006) (0.21) CI_C_SP0.860 (0.891)0.010 (0.009) (0.22) AI_C_SD0.873 (0.893)0.004 (0.005) (0.22) AI_C_SP0.867 (0.892)0.003 (0.003) (0.22) Global regression and GWR for cars

ERES conference 2009 Stockholm KTH Hedonic model of apartment prices Extracted location variables Adjusted R 2 Coefficientt-valueMaximum VIF Moran s I TT_PT_10 TT_PT_ (0.892) (-0.123) (-0.074) (0.22) TT_PT_10 TT_PT_ (0.894) (-0.147) (-0.059) (0.21) TT_PT_ (0.892) (-0.134) (0.22) CI_PT_SD0.867 (0.891)0.007 (0.005) (0.22) CI_PT_SP0.859 (0.890)0.012 (0.010) (0.22) AI_PT_SD0.867 (0.891)0.004 (0.005) (0.22) AI_PT_SP0.867 (0.887)0.003 (0.003) (0.25) Global regression and GWR for public transport

ERES conference 2009 Stockholm KTH Hedonic model of apartment prices The highlighted centres: 3 – Bellecour-Sala 10 – Les Belges

ERES conference 2009 Stockholm KTH Hedonic model of apartment prices The highlighted subcentres: 6 – Jussieu 10 – Les Belges

ERES conference 2009 Stockholm KTH Hedonic model of apartment prices Application of principal component analysis Extracted location variables Factor number% of variance Adjusted R 2 for factor 1 and other variables Moran s I for factor 1 and other variables Adjusted R 2 for all factors Cars Travel times to 15 centres Travel times to 3 centres Public transport Travel times to 15 centres Travel times to 3 centres

ERES conference 2009 Stockholm KTH 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.