Modelo de evaluación para la valoración del desempeño de los

Among all the variables that were initially included in the models, number of bedrooms, distance to closest regional transit station, distance to closest freight railway, number of bus departures from the closest subway or SkyTrain station, Walk Score and Bike Score variables did not perform according to expectations.

With the exception of the final models for apartment properties in Toronto and Vancouver, the number of bedrooms variable was either wrong-sided (had a negative value) or was statistically insignificant in the rest of the models. At the same time, living space or lot size and number of bathrooms variables were statistically significant and performed according to expectation which

73 could mean that some correlation between number of bedrooms and living space or land area exists. This correlation was also reported by Brown and Li (1980) and Boarnet and Chalermpong (2001) who argued that property prices were more influenced by dwelling size than by the number of bedrooms and that their observation about the negative coefficient on number of bedrooms was indicative of the presence of higher-priced, luxury home markets, with larger homes that have relatively fewer bedrooms (Boarnet & Chalermpong, 2001; Brown & Li, 1980).

In both Toronto and Vancouver, variables representing distance to the closest regional transit stop were insignificant in some cases and contradictory in others. Although access to regional transit could be beneficial to some commuters, due to spatial or “modal mismatch” (Foth, Manaugh & El-Geneidy, 2013, pg. 4),32 _{it might not be regarded as a significant advantage for a}

lot of households residing in these two cities. Regional transit in Toronto and Vancouver has a limited schedule and operates mainly during rush hour periods on weekdays with large

headways. In Toronto, GO Stops are spatially distributed along various major arterial roads and as a result, being in the proximity of the stops could simply be viewed undesirable due to the negative externalities that are created by automobile traffic. In Vancouver, West Coast Express connects the downtown area and Mission City Centre (which is 60 km to the west of Vancouver City Centre) and provides coverage that is limited mainly to the northern part of Metro

Vancouver.

32_{The term modal mismatch has also recently entered the literature (Blumenberg and Manville, 2004; Grengs, 2010)}

and refers to the difficulty of reaching desired destinations without a car. While arguably already implicit in spatial mismatch theory, transportation and modal mismatch explicitly capture the fact that two areas in a city may not be separated by a great distance but may not be connected by reliable or viable public transit. Therefore, those reliant on public transit may not be able to access certain areas easily while car drivers can (Foth, Manaugh & El-Geneidy, 2013).

74 Although the variable representing distance to freight railway was statistically insignificant in the final models for Toronto townhouses and Vancouver apartments, proximity to freight rail was inversely correlated with asking prices of Toronto single family houses, and Vancouver single family houses. The exact reason for the difference in sensitivity of various property types to negative externalities created by freight rail could be identified using the current model.

The variable representing number of bus routes from the closest rail rapid transit station was not statistically significant in most of the models except for Toronto apartments (buffer model) and Toronto single family houses. In the case of Toronto apartments, its negative value was an indication that properties which were located closer to a TTC subway station with a higher number of bus routes had, in average, lower listing prices. Because this variable only represents the number of bus routes that a certain rail rapid station serves and not the frequency of service, this finding should be treated with care. Although in most cases stations which serve more routes would provide more accessibility than stations with fewer routes, a more accurate measure would captures number of routes and frequency together. Additionally, frequency of bus service from the closest bus stop to the property (and not at the closest rail rapid transit station) might have been a better measure of bus access.

In the case of single family houses in Toronto, the coefficient of Walk Score proved to be negative. The Bike Score variable had a positive relationship with property values in models for Toronto apartments and townhouses, while this relationship was negative in the case of

Vancouver single family houses. In their study on the impact of bicycle sharing facilities on property values, El-Geneidy et al. (2015) found that the variable Walk Score was highly correlated with distance to city center. This is consistent with findings from the current study where some degree of multicollinearity was observed between Walk Score and distance to

75 downtown. The VIF test for all of the models except for the single family properties model confirmed this multicollinearity. Additionally, although there are multiple benefits associated with walkability, current transportation planning practices tend to undervalue walking (Litman, 2017). Walkability is not as easily quantified and so tends to be undervalued in planning

decisions. This is very prevalent in low-density areas that favor automobile use (Litman, 2017). As a result, walkability and biking friendliness might not be regarded highly in suburban areas (where single family properties are usually concentrated) and this could partially explain the negative relationship between the Walk Score and Bike Score variables with single family property values in this study.