The effect of commuting time

The role of geographic distance might be lowered by commuting costs. Even if distance will still capture part of the effect of commuting costs, I intend to control for the latter using original measures of commuting time and costs between each suburban jurisdiction and the center of the city. In that respect, I use information on the railway network provided by the French national railway company (SNCF). The railway network in the Paris region encompasses around 300 train stations directly connected to the city of Paris.

For each train station in the Paris region, the open dataset provides information on its location, the type of connections, the number of trains in peak hours, and the commuting time to the closest Parisian station. The following map represents the location of these train stations in the Paris region.

Figure 3.9: Location of train stations in the Paris area

The importance of public transport infrastructures – including the railway network – is particularly important in the Paris area. Figure 3.10 shows the relative prevalence of each transport mode for French commuters. While the car is the privileged mode of transportation for most French commuters (73% of daily commuting are made with cars), public transports are chosen by a majority of Parisian commuters. Indeed, public transportation is used by 44% of Parisian commuters, against 7.5% in the rest of the country. Therefore, data on railway networks (including access or travel time) are relevant – yet incomplete – for studying commuting patterns in the Paris metropolitan area.

In Table 3.9, I use a full set of indicators to control for the role of commuting costs in explaining housing vacancies. A correlation table between the various indicators used in this section is provided in Appendix D. In the first column, I simply include a dummy variable that indicates whether a train station with a direct connection to Paris is located in the municipality. All the variables used in the previous baseline regression are included in these estimations. The coefficient associated with this variable is insignificant. This

Figure 3.10: Commuting modes Data: Insee, recensement de la population (2009)

Sample: Workers occupied outside current residence.

measure of access to transport to the capital city does not affect the share of vacancies in the jurisdiction¹⁵. Besides, the coefficient on geographic distance to the CBD is not affected. This variable has a positive and highly significant effect on a dwelling’s prob-ability of being vacant. In column (2), I simply take into account the fact 29 bordering municipalities are directly connected to the city of Paris through the subway system.

Adding this transport mode does not change the previous finding.

In columns (3) to (8), I focus on municipalities that are directly connected to the central city of Paris (the sample now include 296 municipalities only). In column (3), I evaluate whether the minimum time required to commute to Paris can explain the spa-tial disparities in terms of housing vacancy rates across these suburban jurisdictions. In column (4), I use another proxy for access to Paris, namely the number of train stations in the municipality. Since some municipalities have several train stations, this may ease the access to Paris in these jurisdictions. Similarly in column (5), I use the number of trains going to Paris in peak hours. Finally in column (6), I use these three indicators jointly to evaluate whether a better access to Paris may affect the vacancy rate in a ju-risdiction. The results show that any of these covariates statistically matter in explaining housing vacancies – except for the number of train stations which is significant at the 10%

significance level. A F-test of joint significance shows that these variables altogether do not play a significant role in explaining housing vacancy rates. In contrast, the coefficient on geographic distance is still positive and highly significant. Such results seem to be consistent with the conclusions of the baseline estimations. According to the theoretical model, geographical distance to the CBD seems to play a role in affecting the matching process between renters and buyers, while differences in commuting time does not con-tribute to explain the equilibrium number of vacancies in each jurisdiction. Similarly in column (7), I include four dummy variables that describe the type of connection (through subway, RER, suburban or regional trains). These variables are statistically unimportant in explaining housing vacancy rates.

15Similarly, I estimate the model with an interaction term between this dummy variable and the distance to CBD. The coefficient on the interaction term turns out to be insignificant. Therefore, the results of this estimation are not reported.

The cost of using the railway network varies according to the location. The Paris region is indeed divided into five charging zones with an increasing price as the distance to Paris rises. I then include a variable that describes in which of these five zones a municipality is located. The coefficient on this variable – denoted Commuting cost – shows that a rise in the price of commuting is associated with lower vacancy rates. This result is in line with the predictions of the model as formulated in Proposition 3. Similarly, the coefficient on the main variable of interest is not affected by this measure of commuting costs.

One step further is taken by working on the full sample of municipalities and by computing the distance to the closest train station for all of them. As residents use the public railway network even if their municipality of residence does not have a direct access to a train station, this measure better reflects workers’ access to the network in the entire territory. This measure shows that each suburban municipality is located on average four kilometers away from a train station with a direct connection to the city of Paris. This distance ranges from 0 to 25 kilometers. In column (9), I include this variable to the rest of the covariates using the entire sample of suburban municipalities. The results remain similar to the previous findings. Commuting time or access to the capital city has a negative impact on housing vacancies, but this effect is insignificant. By contrast, geographic distanceper se still matters in explaining the extent of housing vacancy in the suburbs.

Finally, I approximate the time required to go to the central city of Paris from each municipality. I simply multiply the distance from each jurisdiction to the closest train station by 13 – the average time required to walk one kilometer – and add the travel time to Paris by train. By definition, this measure is imperfect as workers may also join the closest train stations with another transport mode. However, I expect this measure to reflect the overall accessibility of the city of Paris for suburban residents. The results are provided in the last column of Table 3.9. Once again, the overall findings remain similar. This proxy for commuting time does not statistically affect the extent of vacancies, and leave the estimated impact of geographic distance unaffected. As a result, it seems that distance mostly affects the matching process between renters and landlords (or buyers and sellers).

According to the model, this channel prevails over the negative impact that distance may have on commuting costs. To summarize, these empirical estimations uniformly show that (1) geographical distance from the main job center of the metropolitan area tends to increase the share of vacant dwellings in a municipality and (2) that this relationship is not altered by the inclusion of a full set of indicators reflecting the cost of commuting to the CBD. Quantitatively, all estimates show that a rise in distance to the CBD by ten kilometers is associated with an increase in the housing vacancy rate by 0.3 percentage point. Hence, a change from the median municipality to a city contiguous to Paris is associated with a fall in the vacancy rate by 1.1 percentage point.

Table3.9:Regressionwithcommuting (1)(2)(3)(4)(5)(6)(7)(8)(9)(10) Sampleofmunicipalities:AllAllMunicipalitieswithdirectconnexiontoParisAllAll DistancetotheCBD0.303***0.304***0.463***0.493***0.471***0.469***0.451***0.480***0.314***0.317*** (0.058)(0.058)(0.094)(0.083)(0.084)(0.099)(0.090)(0.085)(0.057)(0.059) Directconnection0.007-0.025 (0.142)(0.150) Commutingtime0.0030.003 (0.010)(0.011) Nb.Oftrainstations0.170*0.176* (0.094)(0.094) Nb.Oftrains-0.021-0.021 (0.018)(0.019) Subway-0.300 (0.404) RegionalExpressRail0.004 (0.273) Suburbantrain-0.392 (0.262) Regionaltrain0.426 (0.311) Commutingcost-0.035*** (0.005) Dist.Tocloseststation-0.014 (0.021) Totalcommutingtime-0.000 (0.002) ControlsAllAllAllAllAllAllAllAllAllAll Observations1,2471,2472962962962962962961,2471,247 Correlation(Y,ˆY)0.5190.5190.6960.6980.6970.6990.7090.6990.5190.519 R-squaredGLM0.2690.2690.4840.4870.4860.4890.5030.4890.2690.269 Robuststandarderrorsinparentheses ***Significantatthe1percentlevel,**5percentlevel,*10percentlevel

3.6 Conclusion

How can we explain the relatively high vacancy rates observed in many urban areas and their variation across time and space? In many large cities, problems of access to housing are considered as critical. However, a closer look at the data shows that the number of housing vacancies is very high in most urbanized areas, and little is known about these empty dwellings. Even if we cannot expect housing vacancy rates to be nil, such high and persistent vacancy rates remain to be explained.

I examine the extent of housing vacancies in the Paris metropolitan area and investi-gate how distance and commuting time to the central city of Paris influence the number of vacant dwellings in surrounding municipalities. Such an analysis enables investigating the determinants of housing vacancy, controlling for economic and labor market conditions that are common within cities.

The first part of the paper is dedicated to describing the disparities and evolution of owner-occupancy and housing vacancy rates across suburban municipalities. While problems of housing supply and land use regulations are widely emphasized by scholars and urban planners, less is known about the 327’000 unoccupied dwellings that exist in the Paris metropolitan area. To understand why local housing markets do not necessarily clear – even in the long-run – I develop a theoretical model that establishes a relationship between commuting time and vacancy rates. I extend Pissarides (2000)’s classical model of job search to analyzing the housing market. In a standard monocentric city model, I introduce search frictions in the matching process between buyers and sellers, and com-muting costs incurred by renters to go to work. Finally, I empirically test the main predictions of this theoretical model. In that respect, I use information on the railway network in the Paris area. The data precisely describe the geographic location of train stations, the frequency and the commuting time to the closest Parisian train stations.

Both theoretical and empirical analyses show that distance to the city center tends to increase the share of vacant units in the total stock of housing. In Paris, a rise in distance to the center by ten kilometers is associated with an increase in the vacancy rate by 0.3 percentage point. Therefore, a change from the median municipality to a jurisdiction contiguous to Paris is associated with a fall in the share of vacant dwellings by one percentage point. According to the model, this result might be explained a negative effect of distance on the matching between renters and landlords. Therefore, distance related costs in the housing market seem to act like sunk costs rather than variable costs.