estimating the impact on housing prices brought by a light rail infrastructure

© Association For European Transport and Contributors 2011

ESTIMATING THE IMPACT ON HOUSING PRICES BROUGHT BY A LIGHT RAIL INFRASTRUCTURE IN FRANCE

Elise BOUCQ

STRATEC S.A. (Belgium)

1. INTRODUCTION The development of transport infrastructure has economic effects enjoyed by economic agents located at its proximity, measured in particular by its effect on housing prices. Based on this observation, this paper aims measuring this added value generated by the implementation of public transport infrastructure. The immediate consequence of the implementation of such an infrastructure is accessibility modifications. We focus here on the impact of accessibility on equilibrium prices but not on the effects on supply and demand taken separately. We have selected the case of the T2 light rail infrastructure, brought into service in the Hauts-de-Seine department (France) in September 1997. This line was initially a railway closed in 1993, thus the space for the tracks already existed. Its layout cuts through wealthy neighbourhoods along the river Seine. It connects two major centers (La Défense in the North, and Issy-les-Moulineaux in the South). There was no significant additional urban quality improvement associated with its conversion, but this infrastructure noticeably improved accessibility in the department. In the section 2, we present the theoretical bases used in this research: urban economics, accessibility concept and hedonic method. In the section 3, some empirical literature on links between transport infrastructures and residential property prices will be presented. In the section 4, a description of the data used is given. Section 5 explains the methodology of the construction of accessibility indicators, the choice of functional form of the hedonic function and the method to measure the light rail impact on housing prices. In the section 6, accessibility indicator to jobs will be put on maps, in level and in variation induced by the light rail. Finally, the results of hedonic models will be displayed and discussed. 2. THEORETICAL BASES Urban economic theory explains the links between housing prices and transport costs. In the basic model (Alonso, 1964), the city is monocentric, all the jobs are located in the Central Business District, and the choice of a residential location by an economic agent results from a trade-off, under budget constraint, between expenditure for land and transport cost to move to the business center. The transport cost is supposed to be linearly decreasing with the distance to it. These hypotheses imply that land rents decrease with transport cost or distance to the center. This model was largely extended (see for example Fujita (1989)): other variables may influence the choice of a location (amenities, neighbourhood externalities…), and the cities may be


polycentric. So land prices cannot be explained anymore by transport cost to the business center, and indicators of “potential” accessibility are more adapted. The concept of “potential” accessibility, initiated by Hansen (1959), is based on the hypothesis that transport is a derived demand and the trip has scarcely an interest in itself, but serves other purposes as work or consumption. Hansen (1959) developed a gravity model inspired by Newton's law of universal gravitation. Next, Wilson (1967) developed a spatial interaction model using the entropy approach, which became probabilistic. In general, indicators to “potential” accessibility to opportunities are based on spatial interaction models and are calculated by the sum of number of the considered opportunities (jobs, shops…) in a given zone, weighted by a decreasing function of transport time or distance to the geographical zone (see Makrí and Folkesson (1999) or Geurs and Ritsema van Eck (2001) for a literature review of accessibility measures). This decreasing function is often called “dissuasion function” and two functional forms are most usually used in studies: reciprocal form and exponential form, corresponding respectively to the Stewart-Warntz’s measure (Stewart and Warntz, 1958) and Hansen’s measure (Hansen, 1959). This function includes a sensitivity parameter to transport time or distance. As we assume that housing prices are influenced by accessibility, we turn to hedonic theory, which takes into account the heterogeneity of housings in the empirical estimates of their prices. Indeed, accessibility is not the only factor for the choice of a dwelling: it is a heterogeneous good, with internal characteristics (surface area, equipment, type of dwelling…) and external characteristics (environment quality, school proximity ...). This theory was initiated by Court (1939) but popularized by Lancaster (1966) and Rosen (1974). It supposes there are “implicit” competitive markets for each characteristic of heterogeneous goods, and the price of a characteristic is determined by the comparison of supply with demand for this characteristic in its implicit market. So the housing price is a function of “implicit” prices of the goods which characterize it. 3. EMPIRICAL LITERATURE

In Ryan (1999), we can find a literature review on empirical studies of the relationship between transport facilities (highways, heavy rail, and light rail systems) and property prices. There are many works on the link between transport and residential property prices, using a hedonic price model. But often in these researches, this link is represented by the nearest distance between the dwelling and the infrastructure (or distance between dwelling and the nearest station of a railway). Four studies in the United States realized in the nineties concern important interurban railway infrastructures: Gatzlaff and Smith (1995) studied the impact of a new rail system in Miami. Authors used both hedonic method and repeat-sales method, and showed the robustness of their results. Armstrong (1994) realized the same type of study on Boston, as wall as McDonald and Osuji (1995) on Chicago, and Benjamin and Sirmans (1996) on Washington.


In these four studies, it is the proximity to a station, taken in a continuous form, which serves to measure the infrastructure effects on prices. Therefore, even it exists, the negative effect of immediate proximity to a station, found by some authors (Bowes and Ihlanfeldt (2001) or Kazmierczak and Jayet (2002) for example), cannot be taken into account. Smersh and Smith (2000) and Boarnet and Chalermpong (2001) studied the impact of a road transportation network modification on individual dwellings prices and used the distance between the dwelling and the closest access to the studied infrastructure in a continuous way. Nevertheless, the first ones studied the impact of a new bridge in Jacksonville, so immediate proximity to the bridge has not necessarily a negative effect; and the seconds, when studying the impact of the construction of a toll roads network in Orange County, excluded the closest dwellings from the analysis to avoid nuisance effects due to immediate road proximity. In their study of a rail impact in Atlanta region on individual real estate prices, Bowes and Ihlanfeldt (2001) introduced the distance between the housing and the closest station with a discontinuous manner, under the form of 4 dummy zone variables, in order to take into account the non-linearity of impacts. Like them, Yiu and Wong (2005) used the shortest distance between the housing and the infrastructure under non linear form, by introducing 6 dummy zone variables, to estimate the impact on real estate prices of the construction of a tunnel in Hong Kong. Besides the introduction of the 4 dummy variables, Bowes and Ihlanfeldt (2001) used the road distance to the business center and a gravity measure of jobs accessibility, like Gordon and Richardson (1983), Cao and Hough (2007) and Ottensmann et al. (2008), taking account of the decentralization. But this last one is not significant. Franklin and Waddell (2003) and Du and Mulley (2006) introduce too gravity measures of accessibility, but there are activity-specific accessibility measures. In their study on the impact of railway lines on real estate prices in Netherlands, Debrezion et al. (2007) introduced 3 indicators of the rail effect: the distance to the stations with dummy zone variables, the rail frequency and the housing proximity to the railway line. The second indicator had a weak effect on the prices and the third one allowed taking into account the nuisances generated by the trains (principally the noise). The original characteristic of their study is the first indicator: most of the authors construct this variable of distance between the dwelling and the closest station, and here authors construct a distance variable between the dwelling and the most frequently borrowed following the postal code. With this new variable, the effect is much more significant. Indeed, the most often chosen station has additional qualities, not directly observable. In France, Gravel et al. (2002) explained the sale price of houses in 33 big cities of the region “Val d’Oise”, and their accessibility variables are the sum of transport times to go to the 33 selected cities to the center of Paris by road and by public transport, and the closest distance between the center of the city and a highway entry. These indicators are introduced in a continuous way. DREIF (2002) studied the impact of the T1 light rail in Paris on real estate prices using isochronic measures, like Cervero (2004): number of jobs and number of individuals accessible in less than 30 minutes of public transport to


the considered zone. These indicators are also introduced in a continuous way. Deymier (2005) studied the effects of the implementation of a road infrastructure in Lyon on real estate prices, and used as accessibility indicator the distance to the closest entry of the roadway. After introducing it previously in a continuous form, the author used “cubic spline” functions to take into account the non-linearity of the effects on prices. 4. STUDY PERIMETER AND DATA USED

First we defined the study perimeter as the whole Hauts-de-Seine region, in the west of Paris (see Fig. 1). We chose a wide temporal dimension, from 1993 to 2004, in order to identify possible anticipation or persistence impacts, like for instance McDonald and Osuji (1995), and Deymier (2005). Indeed, the buyers could have anticipated the implementation of the T2 light rail and incorporated the value of accessibility gains into prices before 1997. Reciprocally, the value of the accessibility gains may have impacted prices only after 1997, once the light rail was in place. The data used for the hedonic regression are sales of residential dwellings in the Hauts-de-Seine department, which include the prices and some internal characteristics of the dwellings. We restricted ourselves to transactions relating to apartments, which account for 90% of the residences in the department, given the small number of transactions relating to single family houses and the low quality of the empirical results obtained with the latter. Here we consider a polycentric urban area, which is representative of our study zone. The households move towards all the centers, and a location is characterized by the whole of the potential destinations, and more precisely by the generalized total cost of transportation towards all these destinations. Therefore we built accessibility indicators, using matrices of generalized public transport time, from station to station. We do not have the direct monetary costs, but in an urban area they are negligible compared to non-monetary costs; moreover, fares are rather flat and the season ticket holding rate is high. We deduced generalized public transport time between all the IRIS, which are zones of 2000 inhabitants issued of an administrative segmentation, and these transport times were directly used in calculations of accessibilities. We have also built these indicators with road transport times. To these data we have added neighbourhood characteristics from the Population Census and other sources that contain external characteristics, located at the geographical level of the IRIS. Fig. 1 shows the trend of the average price per square meter between 1993 and 2004. At first sight, it seems that there is no impact due to the T2 light rail, but we will see later that the results of the econometric analysis are different.


Figure 1: Evolution of the average price per square meter between 1993 and 2004

Data Source: CD-BIEN

5. METHODOLOGY 5.1 Accessibility indicators Indicators were calculated on the geographical level of the IRIS, and represent accessibility to all the IRIS of the Hauts-de-Seine. We chose potential accessibility indicators for population, jobs and firms. These indicators are based on spatial interaction models (see for example Schürmann et al. (1997) or Geurs and van Eck (2001)).

The formula is: k

ikki timefOA )( where

- i represents the IRIS taken into account, and k another IRIS in the department - timeik is the generalized transport time between the center of IRIS i and the center of IRIS k - kO represents the volume of population, jobs or firms in the IRIS of

destination k

- )( iktimef denotes the dissuasion function, decreasing in time, which we shall

assume to follow a reciprocal form

ikik timetimef /1)( or a negative exponential

form etimef iktimeik

)()( , being a sensitivity parameter to transport time,

between 0 and 1, and reducing the effect of travel time increase or decrease.


In order to choose the functional form and the parameter, we introduce the various accessibilities in a hedonic price model, and we keep the form and the

parameter giving the best explanatory capacity. Classically the sensibility parameter is estimated empirically using a spatial interaction model, such as for example in Calzada and Le Blanc (2005), but it is necessary to know emissions and receptions. This is not our case and our methodology to estimate this parameter is the same as Franklin and Waddell (2003) or Theriault et al. (2006). 5.2 The functional form of hedonic function Rosen (1974) recommends selecting the best specification of the hedonic function in an empirical way. Amongst the most common functional forms are the linear model and the logarithmic model (see for instance Follain and Jimenez (1985)). Cavailhès (2005) questions the constancy of the hedonic prices of residence characteristics, according to quantities, obtained with a linear model, “in particular because of the fixed costs of production (costs of construction) and of transaction, and because of indivisibilities for the consumer”. On the contrary, the logarithmic form implies hedonic prices depending on quantities. We can also use the Box-Cox specification (Box and Cox, 1964), in order to approximate the actual shape by maximum likelihood. 5.3 Measurement of the light rail impact on housing prices The hedonic estimates will enable us to have a function that will be used to calculate the price of the heterogeneous good, when modifying the quantities of the various characteristics which define it. Thus, if accessibility plays a role in the formation of residential property values in our study zone, we will be able to measure the effects on dwelling prices of the accessibility gains induced by the installation of the T2 light rail. To do this, we compare the dwelling prices observed with the light rail with the prices which we would have observed without the light rail, and these effects will be isolated from the influence of other factors. Notice that we only use the first step of the hedonic method of Rosen (1974), because we only measure the impact of better accessibility on equilibrium prices. We don’t explain how these effects come about, so we don’t use the second step of this method to identify demand effects and supply effects (moreover, we don’t have the necessary data to do this). 6. DESCRIPTION OF STUDY ZONE ACCESSIBILITY For accessibility by public transport, the best specification is the negative

exponential function with a low , i.e. 0.01, whatever the opportunities: so in collective transportation, people are not very sensitive to a small variation in time. This value is close to that chosen by Spiekermann and Wegener (2007), that is 0.005, for accessibility by public transport. Accessibilities by road are not significant in the hedonic price model. Accessibilities by public transport are highly correlated and, in line with urban economic theory, potential accessibility to jobs is the most significant. So we retain this indicator to estimate the hedonic housing price function.


Fig. 2 shows the absolute levels of accessibility to jobs by public transport, in the Hauts-de-Seine department, before the opening of the T2 light rail (in 1996). The most accessible zones are located in the north around La Défense (Neuilly, Levallois, Asnières, Bois-Colombes, La Garenne-Colombes) and around Issy-les-Moulineaux (Boulogne, Vanves, and in the south of Issy, up to Châtenay-Malabry), as well as in the north of the department, where the number of jobs is quite high.

Figure 2: Accessibility to jobs in 1996

Data Sources: RATP and SIRENE

Fig. 3 represents the accessibility gains due to the T2 light rail. The change in accessibility measure reflects only then change in travel time induced by the transit system (the distribution of jobs is constant). The most significant gains are located along the line. We observe a diffusion of these gains from Issy-les-Moulineaux to the south-east, to the south-west, as well as to the north/north-west. Thus the T2 light rail facilitated access to areas which were not very accessible.


Figure 3: Gains of accessibility to jobs, by public transport, induced by the T2 line

Data Sources: RATP and SIRENE

7. RESULTS Firstly, we used all the observations of real estate transactions of the department - about 91,000 observations - and we then split the department into three parts, according to the 2 terminals of the line, and we focused on the central area on which the T2 impact was undeniable. We end with 38,000 observations. We estimated several hedonic price models to explain the total housing price, differing mainly on the one hand by the functional form (by testing a Box-Cox

specification in its simplest form (

1y) for the dependent variable, the

surface and the surface per room in the hedonic regression; we obtained

parameters close to zero, which led us to adopt a logarithmic transformation of the 3 variables) and on the other hand by selecting the study area and variables (especially the form of accessibility indicators, first continuous as in most existing studies, and then discontinuous in order to capture non-linear effects of the light rail on prices). We finally chose the latter model, estimated on the area directly affected by the T2 light rail, and with discontinuous accessibility to jobs. The results are much more robust when this accessibility to jobs is taken as discontinuous variable (the accessibilities are divided into classes). The R-square for this model is 86.4%. The variables retained in this model to explain the logarithm of housing prices are:

- intrinsic features of the housing: building age, surface and surface per room;


- transaction year, in order to take into account the evolution of the housing market; but if we introduce directly this variable, a part of the effects of the T2 light rail implementation on previous years will be included in this variable, so we cross the transaction year with accessibility gains taken in classes;

- the neighbourhood characteristics of the IRIS: share of households by size and by socio-professional category of the head of the household;

- the taxation level of the municipality, represented by a synthetic qualitative variable with 2 modalities, mixing tax rates on dwellings and on the built up land value;

- 3 elements of accessibility to jobs: • logarithm of the level before the T2 light rail was in place (1996), • classes of gains induced by the T2 (between 1996 and 1997), crossed with the transaction period; • classes of gains between 1997 and the transaction year, crossed with transaction year.

This cutting of accessibility variable is necessary to isolate the effects of accessibility gains induced by the light rail on residential prices. The hedonic function has been estimated by OLS method. Some authors use spatial econometrics to estimate hedonic price functions (see for example Beron et al. (2004)) but in most cases transactions are located at the precise street address. In our case transactions are located at the IRIS level and the notion of neighborhood, crucial in spatial econometrics, cannot be correctly treated. Moreover the complexity of the model specification implies many difficulties for a spatial estimation. We have detected the presence of heteroscedasticity using the Gleisjer test, which we could not treat. So we use the White estimators (Greene, 2003) in order to estimate the variance and the t-statistics. Estimated coefficients of the hedonic regression are presented in Table 1 for internal and external variables except accessibility.


VARIABLES Coeff t-stat

(White)

Constant 6,036 21,81

year of the building

before 1914 -0,384 -59,51

1914 – 1947 -0,399 -78,88

1948 – 1969 -0,363 -83,45

1970 – 1980 -0,285 -62,07

1981 – 1991 -0,213 -39,58

after 1992 (reference) - -

logarithm of the surface 1,096 397,43

logarithm of the surface per room 0,035 5,91

distribution of the households in the

IRIS according to the size

Share of households of one person (reference) - -

Share of households of two persons -0,677 -8,89

Share of households of more than two persons -0,730 -25,01

distribution of the households in the IRIS according to

the social and economic category of the person of

reference

Share of households where the PR is independant 0,704 12,77

Share of households where the PR is an executive

(reference) - -

Share of households where the PR is in an intermediate profession -0,767 -19,45

Share of households where the PR is an employee -0,130 -4,30

Share of households where the PR is a worker -1,182 -39,41

Share of households where the PR is a pensioner 0,050 1,30

Share of households where the PR is without

occupation 0,427 3,90

city tax level low or medium (reference) - -

High -0,078 -15,58

Table 1: Coefficients of internal and external variables in the hedonic regression (except accessibility variables)

The parameters obtained for the internal and external variables are not surprising; they confirm the results already obtained in the empirical literature, like for example Cavailhès (2005), Gravel et al. (2002), Özdilek et al. (2002), Cornuel et al. (2003). The price increases with the age of the building, the total surface area and the surface area per room (evidence of higher quality buildings). The neighbourhood variables are significant too: prices are higher in IRIS where the share of one or two person households is significant, and prices are higher in IRIS where there is a significant share of households of a high social category. The price decreases with the level of municipality tax. Tables 2, 3 and 4 present the results for accessibility variables:

VARIABLE Coeff t-stat

logarithm of the accessibility to jobs in 1996 0,0938 4,9100

Table 2: Coefficient of the logarithm of accessibility before the T2 light rail was in place in the hedonic regression

VARIABLE F-stat

accessibility gains to jobs between 1996 and 1997,

crossed with the period of transaction 30,28

accessibility gains to jobs between 1997 and the transaction year, crossed with the transaction year 52,23

Table 3: Fischer statistics for the discontinuous variables of accessibility gains


0 0-500 500-1000 1000-1600 1600-2000 2000-3000 3000-7000 > 7000

1993-1995 -0,5412 -0,4816 -0,5139 -0,5243 -0,5183 -0,4781 -0,5193 -0,4866

1996-2000 -0,4776 -0,4576 -0,4102 -0,4776 -0,4611 -0,4459 -0,4063 -0,3707

2001-2004 -0,1739 -0,0748 -0,0247 -0,0636 -0,0466 -0,0859 -0,0123 0,0000

Transaction

period

Qualitative variable of accessibility gain to jobs between 1996 and 1997

Table 4: Coefficients of regression of accessibility gains induced by the T2 light rail, crossed with transaction period, when the reference is a dwelling sold between 2001 and 2004 with

the highest accessibility gain

The elasticity of accessibility to jobs (in level 1996) is about 9%, and accessibility gains are significant. We turn table 4 into the next two tables when changing the references:

0 0-500 500-1000 1000-1600 1600-2000 2000-3000 3000-7000 > 7000

1993-1995 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000

1996-2000 0,0636 0,0240 0,1037 0,0467 0,0572 0,0323 0,1129 0,1159

2001-2004 0,3673 0,4068 0,4892 0,4607 0,4717 0,3922 0,5070 0,4866

Transaction

period


Table 5: Temporal evolution of prices specific to each type of dwelling

0 0-500 500-1000 1000-1600 1600-2000 2000-3000 3000-7000 > 7000

1993-1995 0,0000 0,0596 0,0273 0,0169 0,0229 0,0631 0,0219 0,0546

1996-2000 0,0000 0,0200 0,0674 0,0000 0,0165 0,0317 0,0713 0,1069

2001-2004 0,0000 0,0990 0,1492 0,1103 0,1273 0,0880 0,1616 0,1739

Transaction

period


Table 6: Differences between types of dwellings for each period

In table 5, the reference is the transaction period between 1993 and 1995. We can see the temporal evolution for each type of dwelling according to the accessibility gains induced by T2. The price increase is higher when the accessibility gain is high. In table 6, the reference is the accessibility gain induced by T2 equal to zero (corresponding to no gain), and we can see the differences between the types of dwellings for each period: we observe very little differences before the T2 (corresponding to the period 1993-1995) and high differences after the T2 implementation, until 15% more in the zones where accessibility gains are bigger than 3,000 than in the zones with no accessibility gains. So there is a global T2 effect on real estate prices which is stable over time, and we can calculate the added values induced by the light rail. The global added value on the selected area is just over 9% of housing prices. The results differ by municipality (see Fig. 4), and gains are particularly high along the T2 line and at the south-west, where accessibility gains were very important. When comparing Fig. 4 with Fig. 1, we can see the importance of hedonic modeling: municipalities with highest gains induced by the light rail are not necessarily the same that those with highest evolution of housing prices.


Figure 4: Added values for each municipality of the selected zone, in percentage of prices Despite the variety of contexts (urban environment or not, infrastructure type, environment...), we can compare our global impacts obtained with this model to those found in other studies of effects of a transport infrastructure on residential property prices. In a general way, the studies agree on the positive effect of accessibility on prices. But the relative effects are more volatile. Table 7 presents the results of some other works; relative impacts are estimated as between 3% and 15%. So our results are comparable to those of the existing literature.

Study Infrastructure type Effects on prices

Palmquist, 1982 highway + 12% to 15%

Voith, 1991 heavy rail + 4% to 10%

Gatzlaff and Smith, 1993 heavy rail + up to 5%

Armstrong, 1994 heavy rail +6.7%

Chen et al., 1997 light rail + up to 10.5%

Smersh and Smith, 2000 bridge +8.7%

Fritsch, 2005 light rail + 3% to 9%

Debrezion et al., 2007 heavy rail +8.6%

Table 7: Effects of a transport infrastructure on residential property prices


8. CONCLUSION

We showed that the capitalization of accessibility gains represent 9% of the dwelling prices in the selected area. This result is coherent with those in the existing literature, where the effect of infrastructure on prices is between 3% and 15%, which varies depending on the level of accessibility before the new infrastructure. Nevertheless, we do not find anticipation effects, though present in the work of McDonald and Osuji (1995), Boarnet and Chalermpong (2001), Smersh and Smith (2000), Yiu and Wong (2005) or Deymier (2005). So the high percentage of added value induced by the T2 light rail is due to learning effects, once the light rail was in place. This phenomenon may be due to the urban characteristic of this infrastructure, but we don’t have elements of comparison because of the scarcity of such works in France. Bibliography

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estimating the impact on housing prices brought by a light rail infrastructure

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