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DOCUMENT DE TRAVAIL 2005-020 PUBLIC TRANSIT AND URBAN ACCESSIBILITY – THE EFFECT OF THE MÉTROBUS SERVICE ON QUEBEC CITY’S HOUSE PRICE François DES ROSIERS Martin LEE-GOSSELIN Marius THÉRIAULT Patricia DIB

Version originale : Original manuscript: Version original:

ISBN – 2-89524-242-9

Série électronique mise à jour : On-line publication updated : Seria electrónica, puesta al dia

08-2005

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Paper presented at the

14th Annual International AREUEA Conference, Los Cabos, Mexico, August 3-5, 2005

by

François Des Rosiers, Ph.D., Faculty of Business Administration,

Martin Lee-Gosselin, Ph.D., ÉSAD, Marius Thériault, Ph.D., Director, Land Planning Research Center and

Patricia Dib, Master’s Student, ÉSAD

Laval University, Quebec City, Canada Contact address: François Des Rosiers, Urban & Real Estate Management, Laval University, Quebec City, Canada, G1K 7P4 Phone : 418-656-2131, Ext. 5012 Fax : 418-656-2614 E-mail : Francois.Desrosiers@fsa.ulaval.ca Co-authors: Martin Lee-Gosselin, School of Planning, Laval University, Québec, Canada, G1K 7P4

Phone: 418-656-2131 ext. 2578, E-mail: Martin.Lee-Gosselin@ame.ulalval.ca Marius Thériault, Director, Planning and Research Centre, Laval University, Québec, Canada, G1K 7P4

Phone: 418-656-2131 ext. 5899, E-mail: Marius.Theriault@crad.ulaval.ca

KEY WORDS:

Accessibility, Proximity, Public Transit, Property Values

____________________________________

ABSTRACT:

This paper investigates whether, and to what extent, the introduction of an improved, high-frequency bus service on Quebec City’s main streets in 1992 and its extension in the following years resulted in any significant change, either upwards or downwards, in the value of residential properties adjacent to the bus route or located in its vicinity. The final database consists of over 11,500 single-family detached, attached and row houses which have been subject, over the 1987-2004 period, to at least one resale following an initial sale. The resale-to-sale price ratio is used as the dependent variable and regressed against mass transit attributes, with a series of control descriptors accounting for property specifics (property age, type, size and lot size), fiscal (local tax rate) and macro-economic (regional market trend, overall inflation, real mortgage rate and real estate cycle) attributes as well as environmental (noise level), socioeconomic and

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accessibility dimensions. Independent variables consist of either state or change descriptors, depending on their nature, with the latter being expressed as resale-to-sale ratios. Three mass transit descriptors are developed: while a first ratio variable measures the change in mass transit modal share between sale and resale, two dummy variables account for the proximity to an intensive Métrobus service and the introduction of the Métrobus service between sale and resale. A semi-log functional form is resorted to. Several independent variables are also applied a logarithmic transformation so as to generate parameter estimates in the form of elasticity coefficients. While all three mass transit attributes significantly contribute to explain house price variations in Quebec City over the 1987-2004 period, findings suggest that the change in mass transit modal share between sale and resale is most influential and impacts positively on the dependent variable: doubling the local MT share translates into a 5.7% increase in house value growth. The proximity to an intensive Métrobus service also impacts significantly and positively on house price variations, with properties benefiting from this attribute selling at a premium of 5.2%. Finally, and contrary to our expectations, properties located close to a bus route on which the Métrobus service was introduced between sale and resale sell at a 2.5% discount. Such a finding suggests that the extension of the Métrobus service in mostly peripheral areas of the city has turned into a negative externality for local residents for whom mass transit is not an adequate alternative to the car.

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1. OBJECTIVE AND CONTEXT OF RESEARCH

This study aims at testing whether, and to what extent, the introduction of an improved, high-frequency

bus service on Quebec City’s main streets in 1992 and its extension in the following years resulted in any

significant change, either upwards or downwards, in the value of residential properties adjacent to the bus

route or located in its vicinity.

Located 150 miles east of Montreal, Quebec City has a population of roughly 560,000 while the Quebec

Metropolitan Area (QMA) totals about 683,000 inhabitants (2001 Canadian Census). By 2000, the

average household income stood at 50,230 Can. $ (58,630 $ for Canada as a whole) while per capita

income reached 27,939 $ (29,769 $). One of the characteristics of the QMA, which extends over 3,154

square kilometres (1,218 square miles), remains its extensive road network which greatly facilitates

motorized movements and prevents most congestion problems. At the same time though, it makes any

mass transit system increasingly difficult to support financially, the competition with the private car being

harsher than ever. As a consequence, the modal share of public transport (essentially the bus service)

steadily decreased over time1. The introduction of the Métrobus service, as described below, came as an

attempt to slow down or, eventually, reverse that trend.

Back in August of 1992, the Réseau de Transport de la Capitale (RTC)2, the public body responsible for

the provision of public transit services, initiated the Métrobus system, designed at improving the supply

of public transport where most needed. Essentially, this improvement consisted in an increased vehicle

frequency coupled with fewer bus stops along main street and road sections of the agglomeration already

served by a regular service, thereby linking more efficiently downtown areas with eastern (Beauport),

northern (Charlebourg) and western (Ste-Foy) suburbs during rush hours. In 1993, the public transit

improvement program was further strengthened by the introduction of the Métrobus Plus, which provides

an even more intensive service level between suburban locations and Laval University, located in Ste-Foy.

Map 1 illustrates the structural evolution of the Métrobus network, from its early implementation in

August 1992 until its completion, three years later. Since 1995, the Métrobus network extends over some

70 kilometers (roughly 44 miles), more than half of which being served by bus lanes3 - also used by taxis,

1 For instance, while the public transport modal share still amounted to 16% of all motorized trips in 1991, it had dropped to around 9% by 2001. 2 Until January 1st, 2002, when all 13 municipalities on the north shore of the Saint-Lawrence River were amalgamated into Quebec City, the RTC was operating under the responsibility of the Quebec Urban Community (CUQ), an administrative structure designed at managing regional issues. 3 Bus lanes are part of the street network used by motorized vehicles and, for the most part, are only reserved for buses and taxis during peak hours, that is from 7:00 to 9:00 AM and from 3:00 to 5:30 PM. The only exception to this is the high-demand corridor linking the lower city CBD to Laval University and Place Laurier shopping center (upper city, west of agglomeration); along that route, the lane is reserved from 7:00 AM to 5:30 PM.

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and has 163 stops - of which 143 are covered shelters. With an average distance of 430 meters (roughly

1,400 feet) between Métrobus shelters, bus frequency stands at 5 -10 minutes intervals during peak hours

(7.5 -15 minutes intervals during summer time, on evenings and during week-ends). Finally, additional

vehicles are assigned to some high-demand segments of the network – referred to as “double-frequency”

routes - to prevent any slowing down in the Métrobus service during peak hours mainly.

By 2001, 88% of the Métrobus clientele was composed of students (46%) and workers (42%) while more

than two thirds (68%) of it belonged to the 15-64 age bracket. Women formed 60% of users. Assuming

that an upgraded public transit service improves accessibility to jobs and urban services and should

consequently be capitalized into higher property values, the issue addressed by this paper is most relevant,

particularly for planning authorities and local decision makers in search of sound financing devices for

public transport infrastructures.

2. LITERATURE REVIEW

From an analytic point of view, land and property prices are a combination of externality effects and

location rents (Krantz et al. 1982, Hickman et al. 1984, Shefer 1986, Yinger et al. 1987, Strange 1992,

Can 1993, Dubin 1998). Hoch and Waddell (1993) point out that the overlapping of access and

neighborhood characteristics leads to highly complex influences on rent levels and values. As shown by

Des Rosiers et al. (2000), accessibility factors impact differently on location rents depending on whether

they operate at a regional or local level; moreover, measuring accessibility to urban services is no simple

task, since it involves both objective and subjective dimensions linked to household structure and

individual mobility behavior (Thériault et al. 2005). Finally, the complexity of combined access and

proximity influences is mirrored in the non-monotonicity of some of the distance functions (Des Rosiers et

al. 1996 & 2001) whereby maximal property values are reached at an optimal distance from a given

externality source, within which detrimental proximity effects largely prevail over accessibility ones. In

particular, a number of studies have looked at the effects of noise and road traffic on house prices and

estimated the willingness-to-pay for such negative externalities, (Palmquist, 1992; Hughes & Sirmans,

1992 & 1993; Powe et al., 1995). In his study on a suburb of Stockholm, Wilhelmsson (2000) found that

noisy neighborhoods could drive house prices down by as much as 30%.

Most of the academic research reporting on the effect of mass transportation on property values or rents

deals with either heavy or light rail systems. Such research includes Dewees (1976), Bajic (1983), Voith

(1993), Gatzlaff and Smith (1993), Benjamin and Sirmans (1996), McDonald and Osuji (1995), Baum-

Snow and Kahn (2000), Pagliaro and Preston (2003) and, more recently, McMillen and McDonald (2004).

The literature review by Smith and Gihring (2004) on value capture financing provides a quite extensive

picture of international related experiences: although the extent of the impacts varies from place to place,

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the implementation of a new rail transit system generally results in significant rises in both residential and

commercial property values, mainly around railway or metro stations. There are reports, though, of price

drops for dwellings adjacent to such stations.

Very few studies have been found that address the impact issue in relation to the implementation or

improvement of a bus service. So et al. (1997) examined the importance of public transport on house

prices in Hong Kong. As do the vast majority of authors, they use hedonic modeling to control for various

internal attributes and environmental characteristics, measuring transport accessibility as the distance to

nearest stop on the mass transit railway (MTR), bus or minibus route. Dummy variables are also used to

account for dwellings located within a 10 minutes walk from a transport node. While results reveal an

insignificant explanatory power for bus routes, accessibility to minibuses emerges as the most influential

effect on house prices. In Brisbane, Australia, a study by the Real Estate Institute of Queensland (2001)

shows that the median house price of properties located in suburbs both directly alongside and near the

South East Busway rose, on average, by a percentage of 10.1% between June and September Quarters

2000, as opposed to 3.0% elsewhere, thereby suggesting that commuters tend to place more emphasis on

an easy access to the CBD.

The relative scarcity of past studies dealing with the impact of a bus service on property values amply

justifies the current research. Furthermore, as part of a rather typical North-American metropolitan area,

Quebec City offers a good example of how a bus system may affect real estate values.

3. DATA BANK AND ANALYTICAL APPROACH

3.1 Data Bank Organization

This study relies on two major data sources on existing house sales in the QMA: the first one, which

covers the 1987-1996 period, is based on Quebec City’s assessment roll while the second one (1997-2004)

rests on the Quebec Real Estate Board databank. Both sources provide reliable information on sale prices

and conditions, unit location and property characteristics, although there are major differences in the

nature of the property descriptors4 as well as in the location parameters5 used. Overall, more than 60,000

sales are available for analysis over an 18-years time span.

Real estate data are processed through a regional GIS which contains extensive information on

socioeconomic, neighborhood, accessibility and mobility attributes (Des Rosiers et al. 2000; Thériault et 4Municipal assessment rolls provide information on property liveable area, but not on the number or size of rooms; in contrast, Real Estate Board data bases do provide information on room number and size, but not on liveable area. 5 In any municipal assessment roll in Quebec, each real estate unit is assigned a unique, 14-digit reference number which includes a geocode referring to a universal geo-location system (NAD 83); Real Estate Board properties, in contrast, are located by their street address.

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al. 2005). In particular, the GIS provides information on the 2001 street, road and freeway networks

(speed limits, one-ways and other supply constraints) as well as on the local bus network (bus routes, bus

stop and shelter locations, bus lanes and service frequency attributes). In addition, detailed information on

weekday commuting patterns prevailing in the “extended” Quebec City area6 is available from three

origin-destination (O-D) phone surveys conducted jointly by the RTC and the Ministry of Transport of

Quebec (MTQ) in 1991, 1996 and 2001. Such surveys are based on very large samples representing

roughly between 8% and 9% of the population of the area7 and provide information on trip purpose, mode

and schedule as well as on individual and household profiles (age, gender, occupation and car ownership).

Finally, and apart from the above-mentioned dissimilarities between the data sources, a major limitation of

our database is that it does not systematically account for the changes in housing attributes over time due,

in particular, to major renovation initiatives. Consequently, housing units are assumed to be of constant

quality throughout the period of the study (1987-2004). This being said, and considering the focus of this

paper, particular emphasis is laid upon structuring mass transit attributes designed at testing whether, and

to what extent, bus service improvements may affect residential values.

3.2 Analytical Approach

In the light of the constraints and limitations affecting the database, a combination of the repeat sales

(McMillen and McDonald 2004) and hedonic approaches is used here to track down the evolution of

house prices over time for properties located along Métrobus routes and in the vicinity of bus shelters,

with buffers being used to isolate accessibility from proximity effects. For the purpose of this study, only

single-family detached8, attached and row houses which have been subject, over the 1987-2004 period, to

at least one resale following an initial sale are selected for analysis. The initial database has been filtered

in order to discard any inconsistent information with respect to sale price, resale-to-sale price ratio, living

area, lot size as well as annual change in value between sale and resale9. After filtering, 11,715 valid cases

are left in the operational database, with some properties having been resold up to six times.

Since we are looking at the marginal impact of the Métrobus on house price changes over time, it is the

resale-to-sale price ratio which is used as the dependent variable and regressed against mass transit 6 The O-D survey territory roughly corresponds to the northern portion of the Quebec urban agglomeration, extending on the north shore of the Saint-Lawrence River; this territory slightly exceeds the limits of the recently amalgamated Quebec City. 7 For instance, the 2001 survey involved some 68,000 persons living in 28,000 households; over 174,000 weekday trips (Monday to Friday) were reported. 8 Single-family detached units include “bungalows”, defined as single-storey units, and “town-cottages”, defined as multi-storey units. 9 Filter only retains : sale or resale prices above 50,000$; resale-to-sale price ratios standing above 0.5 and below 5; living areas above 50 sq. meters; lot sizes larger than building ground size times the number of stories; and annual price changes below 100% increase or 20% decrease.

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attributes, with a series of control descriptors accounting for property specifics (property age10, type11, size

and lot size), fiscal (local tax rate) and macro-economic (regional market trend12, overall inflation, real

mortgage rate and real estate cycle13) attributes as well as environmental (noise level), socioeconomic and

accessibility dimensions. The latter two descriptors are actually PCA-derived factor scores obtained by

applying factor analysis to sets of selected neighborhood (1991 Canadian Census) and car distance-time

(1988 regional road network) variables, respectively (Des Rosiers et al. 2000; Thériault et al. 2003).

While a single component accounts for the socioeconomic status prevailing in the residential

neighborhood, accessibility to jobs and urban services is accounted for using two mutually independent

factors which mirror accessibility to regional and local services.

Independent variables consist of either state or change descriptors, depending on their nature, with the

latter being expressed as resale-to-sale ratios. Turning to mass transit attributes, three descriptors are

developed:

i. Change in Mass Transit modal share between sale and resale: This change variable measures

the modification in the overall mass transit (MT) share that might have occurred in the

neighborhood of the property between sale and resale dates, with a monthly precision. MT

modal shares are expressed as the number of weekday bus trips (excluding school-bus trips) in

the area as a proportion of all motorized (car, taxi, motorcycle) trips and are based on the 1991,

1996 and 2001 O-D survey data available for the Quebec Metropolitan Area. For that purpose,

the Quebec City territory was divided into 72 homogeneous zones, including 12 Métrobus

service 500 meters buffer zones (1 km. corridors). Pre-1991 sales and resales were assigned the

1991 MT share value while post-2001 transactions were assigned the 2001 value. Linear

extrapolation was used to assign MT share values between O-D surveys.

ii. Intensive Métrobus service: This state, dummy variable identifies properties located in an

intensive Métrobus service area, i.e. within 50 meters of both a bus lane and a double-

frequency route on peak hours (42 cases).

10 To adequately control for property aging between sale and resale, both age at the time of sale and resale-to-sale age ratio have to be considered simultaneously. 11 In order to generate more acute estimates of the marginal contribution of property type on price changes, interactive variables linking property type to number of stories in the unit are used here. 12 The market trend descriptor is expressed as the resale-to-sale median market price ratio for the whole QMA and two property type categories (detached and attached houses), based on Canada Mortgage and Housing Corporation’s (CMHC) quarterly report on housing market trends in urban Canada. 13 The real estate cycle variable assumes that the price change over the retention period may differ, cæteris paribus, whether the home-seller had bought in a buyers’ market and sells in a sellers’ market, or the reverse.

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iii. Introduction of Métrobus service: This second dummy variable identifies properties located

within 500 meters from a bus route on which the Métrobus service was introduced between the

sale and resale dates, that is, between August 1992 and August 1995 (585 cases).

The operational definition of variables used in the modeling process is detailed in Table 1. Basic

descriptive statistics for selected variables are also displayed in Table 2. As can be seen, the sale price

distribution (mean price: 92,412 $) substantially diverges from the normal one: with a range that

approaches 500,000 $, it is markedly skewed to the right (3.08) and displays a particularly strong kurtosis

(16.33). The same problem, although to a lesser extent, characterizes the dependent variable, R/S Price

Ratio (mean: 1.13), as well as several independent variables, in particular Age_SL (16 yrs.), R/S Age Ratio

(2), LivArea (108 sq. m.), LotSize (602 sq. m.) and R/S MTShare Ratio (0.90).

Some 58% of sampled properties are bungalows while cottages, attached units and row houses account for

21%, 16% and 5% of all sales/resales, respectively. Identical mean values for R/S Price Ratio and R/S

MktPrice Ratio (1.13) indicate that, on average, sampled houses command prices in line with prevailing

market trends. Property values also followed inflation, as shown by the R/S CPI Ratio descriptor (mean:

1.12). Finally, the mass transit modal share (R/S MTShare Ratio) dropped, on average, by 10% between

sales and resales (mean: 0.90).

4. FUNCTIONAL FORM, MODELING PROCESS AND RESEARCH HYPOTHESES

4.1 Functional Form and Modeling Process

Considering the dependent variable’s distribution, a semi-log functional form is used here, as is generally

the case with hedonic models. The general formulation of the hedonic function may thus be expressed as

follows:

Y = eB0 * eB1i Bldg * eB2i Fisc * eB3i MEcon * eB4i Env * eB5i SEcon * eB6i Access * eB7i MTrans * eε, where: (1)

Y = Resale-to-sale Price Ratio

Bldg = Building specifics

Fisc = Fiscal variable

MEcon = Macro-Economic variables

Env = Environmental variable

SEcon = Socioeconomic factor

Access = Accessibility factors

MTrans = Mass Transit attributes

This, in turn, can be put as:

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LnY = B0 + B1i*Bldg + B2i*Fisc + B3i*MEcon + B4i*Env + B5i*SEcon + B6i*Access + B7i*MTrans + ε (2)

A major advantage of using a semi-log functional form is that regression coefficients derived from it are

expressed as relative - rather than absolute - implicit prices, thereby allowing for a more flexible

interpretation of the marginal contribution of housing attributes to price changes over time. Moreover,

several independent variables (all ratio descriptors, living area and lot size) are also applied a logarithmic

transformation, which results in their parameter estimates being expressed as elasticity coefficients.

Two models are then tested: a first one (Model A, Table 3) that omits the mass transit attributes and a

second one (Model B, Table 4) which includes them. In each case, extreme outliers exceeding three SEE

have been deleted to avoid major parameter estimate distortions. In so doing, the number of cases is

reduced by a faint 1.2%, to just below 11,580 observations.

4.2 Research Hypotheses

Three research hypotheses are to be tested, in relation with the objective of this paper:

Hypothesis H1 : Overall, the improvement over time in the mass transit service in a given neighbourhood, as measured by the rise of the MT modal share between sale and resale, impacts positively on residential value growth;

Hypothesis H2 : Properties located in areas served by an intensive Métrobus service experience higher price increases over time than those which are not;

Hypothesis H3 : Properties located close to a bus route on which the Métrobus service was introduced between sale and resale experience higher price increases than those which are not.

5. MAIN REGRESSION FINDINGS AND DISCUSSION

5.1 Commenting Model A – Mass Transit Attributes Omitted

Regression results for Model A are reported in Table 3. With an adjusted R-Square of 0.573, a F value of

970.5 and a prediction error of roughly 12%, the overall model performance is more than honest

considering that changes in building attributes are not accounted for. Most regression coefficients display

statistical significance levels of 0.001 or below, with only one variable (Access_Reg) emerging as non-

significant at the 0.10 level. As for multi-collinearity, it remains well under control with the maximum

VIF value standing at 3.15.

5.1.1 Building Specifics: Starting with building descriptors, it can be seen that, while doubling property

age between sale and resale translates into a 2.4% price drop, older houses experience a higher

value growth than more recent ones. Such a trend probably reflects the relative advantage of

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traditional, central neighborhoods offering greater residential densities as opposed to remote

locations. This tends to be confirmed by the fact that smaller units have benefited at a higher

degree from overall growth in prices than larger ones have and that cottages, but mainly attached

and row houses, have experienced values gains significantly above those assigned to bungalows

(used as the reference). For these property types though, the more stories the higher the gains.

Finally, larger lot sizes also contribute to enhance value rises over time: considering that the mean

lot size stands at 602 square meters (roughly 6,500 square feet), a property with an above-average

lot size confers some additional bargaining power its seller.

5.1.2 Fiscal and Macro-Economic Variables: As expected, findings suggest that fiscal and macro-

economic variables are major determinants of house price changes over time, as shown by the

very high t values and Beta coefficients some descriptors generate. First, in line with previous

studies that addressed the internalization of local tax differentials on house values (Yinger et al.

1987, Thériault et al. 2003), variations in the local tax rate (Ln_R/S GTR Ratio) exert a strong

negative effect on home value growth, the pertaining elasticity coefficient reaching -0.24. As to

the regional market trend (Ln_R/S MktPrice Ratio), it unsurprisingly emerges as the most

powerful determinant of prices changes, its elasticity coefficient (0.976) clearly indicating that

sampled transactions closely follow overall metropolitan trends. Similarly, resale-to-sale house

price variations also accurately reflect the overall inflation rate (Ln_R/S CPI Ratio) in the QMA

over the period of study.

The next two variables (Ln_R/S MrtgRate Ratio and RE_Cycle) display regression coefficients

whose signs appear to be counterintuitive. In the first case, findings suggest that a rise (drop) in

the real mortgage rate, hence a deterioration (improvement) in borrowing conditions, should

impact positively (negatively) on house value growth. In the second case, buying in a low (high)

and selling in a high (low) reduces (raises) the growth in value. While no sensible argument can

be found to reconcile the former result to theoretical expectations, in the latter case, a logical

explanation may be brought forward: a seller who, for instance, bought in a high while selling in a

low will tend to wait longer to recover his initial capital and get a better price than the one he

would agree upon had he bought in a low. In contrast, a seller who managed to buy in a low while

selling in a high will tend to satisfy himself with a slightly lower return on his investment,

provided he sells more quickly.

5.1.3 Environmental, Socioeconomic and Accessibility Attributes: As expected, a high noise level

(HNoiseLVL) due to heavy traffic or/and a sloping street design will affect price variations

negatively. As for the socioeconomic status of the neighborhood, findings indicate that the highest

rises in house values occurred in areas of Quebec City which, by 1991, were characterized by

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relatively low-income, low-educated households. This is most interesting a finding since it

suggests, amongst other things, that the municipal policies and programs initiated in the early

1990s and designed at upgrading derelict neighborhoods actually managed to reduce to some

extent the socioeconomic gap that have long existed between the upper, well-off, and lower,

poorer, portions of the city.

Turning to accessibility attributes, it can be seen that accessibility to regional services

(Access_Reg), which previous research showed to be a major determinant of house prices (Des

Rosiers et al. 2000), does not play a significant role when it comes to explaining price changes

over time. Accessibility to local services (Access_Loc), in contrast, still emerges as being highly

significant, but with a negative sign. This suggests that house price increases are highest in areas

of relatively low local accessibility, which actually refers to central neighborhood.

5.2 Model B – Adding On Mass Transit Attributes

5.2.1 Impact on Model A coefficients: Adding on mass transit attributes slightly improves model

performances, with the adjusted R-Square being raised to 0.582 while the prediction error and F

value drop to 0.11868 and 850.8, respectively. The overall stability of the model is not

substantially affected by the introduction of additional descriptors: the direction of the marginal

effects remains unchanged while most regression coefficients display magnitudes that are similar

to those obtained with Model A. Some of the parameter estimates, however, are affected, either

upwards or downwards. While the elasticity coefficients of the Ln_LotSize and Ln_R/S CPI Ratio

are raised in both magnitude (in the latter case, from 0.099 to 0.185) and statistical significance,

those pertaining to the Ln_R/S Age Ratio, RE_Cycle and HNoiseLVL variables undergo a slight

decrease. Most interestingly, both accessibility factors loose all statistical significance, which

suggests that mass transit attributes capture most of the local accessibility dimension.

5.2.2 The Effect of Mass Transit Attributes on House Price Variations: As can be seen from Table 4,

all three mass transit attributes significantly contribute to explain house price variations in Quebec

City over the 1987-2004 period, without causing any additional multi-collinearity. With a t value

approaching 16 and a Beta coefficient of 0.109, the change in mass transit modal share between

sale and resale (Ln_R/S MTShare Ratio) is the most influential determinant. Findings suggest that

it impacts positively on the dependent variable: doubling the local MT share translates into a 5.7%

increase in house value growth. Therefore, research hypothesis H1 to the effect that “the

improvement over time in the mass transit service in a given neighbourhood, as measured by the

rise of the MT modal share between sale and resale, impacts positively on residential value

growth” is confirmed.

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While less influential, proximity to an intensive Métrobus service (IntensvMETROBUS) also

impacts significantly (prob.: 0.011) and positively on house price variations. Thus, houses

benefiting from this attribute sell at a premium of 5.2%, thereby confirming our second research

hypothesis H2 to the effect that “properties located in areas served by an intensive Métrobus

service experience higher price increases over time than those which are not”.

Finally, our third research hypothesis (H3) to the effect that “properties located close to a bus

route on which the Métrobus service was introduced between sale and resale experience higher

price increases than those which are not” has to be rejected. Indeed, while the coefficient of the

IntrodMETROBUS emerges as highly significant, it is negatively signed: according to findings,

properties in that category sell at a 2.5% discount. By and large then, the extension of the

Métrobus service between December 1992 and August 1995 on the northern, eastern and western

portions of the city’s T-shaped development axis (see Map 1), where low-density residential areas

are found, seems to have turned into a negative externality for local home-buyers for whom mass

transit is not an adequate alternative to the car.

6. SUMMARY OF FINDINGS AND CONCLUSION

6.1 Summary of Findings

This paper investigates whether, and to what extent, the introduction of an improved, high-frequency bus

service on Quebec City’s main streets in 1992 and its extension in the following years resulted in any

significant change, either upwards or downwards, in the value of residential properties adjacent to the bus

route or located in its vicinity. The originality and relevance of this research, which combines the repeat

sales and hedonic approaches, stems from the fact that few studies have addressed that issue in the past.

The final, filtered, database consists of over 11,500 single-family detached, attached and row houses

which have been subject, over the 1987-2004 period, to at least one resale following an initial sale. The

resale-to-sale price ratio is used as the dependent variable and regressed against mass transit attributes,

with a series of control descriptors accounting for property specifics (property age, type, size and lot size),

fiscal (local tax rate) and macro-economic (regional market trend, overall inflation, real mortgage rate and

real estate cycle) attributes as well as environmental (noise level), socioeconomic and accessibility

dimensions. Independent variables consist of either state or change descriptors, depending on their nature,

with the latter being expressed as resale-to-sale ratios. Three mass transit descriptors are developed: while

a first ratio variable measures the change in mass transit modal share between sale and resale, two dummy

variables account for the proximity to an intensive Métrobus service and the introduction of the Métrobus

service between sale and resale. A semi-log functional form is resorted to. Several independent variables

13

are also applied a logarithmic transformation so as to generate parameter estimates in the form of elasticity

coefficients.

While all three mass transit attributes significantly contribute to explain house price variations in Quebec

City over the 1987-2004 period, findings suggest that the change in mass transit modal share between sale

and resale is most influential and impacts positively on the dependent variable: doubling the local MT

share translates into a 5.7% increase in house value growth. The proximity to an intensive Métrobus

service also impacts significantly and positively on house price variations, with properties benefiting from

this attribute selling at a premium of 5.2%. Finally, and contrary to our expectations, properties located

close to a bus route on which the Métrobus service was introduced between sale and resale sell at a 2.5%

discount. Such a finding suggests that the extension of the Métrobus service in mostly peripheral areas of

the city has turned into a negative externality for local residents for whom mass transit is not an adequate

alternative to the car.

6.2 Concluding Comments

This paper addresses a strategic issue which has both economic and social implications and should

therefore interest urban planners as well as local decision-makers and politicians. The provision of an

adequate supply of mass transit facilities remains a major concern for an important part of the urban

population. For some people, urban buses offer an efficient transportation means for their daily

commuting as well as an adequate, and relatively cheap, alternative to the car; for others, the non-

motorized ones, it is an absolute necessity. This being said, the provision of a decent mass transit service

has long been challenged, in most western-world cities but particularly in North-America, by ever

increasing operating costs combined to a tottering demand as a consequence of demographics, rising

incomes and evolving consumers’ habits and preferences. The overwhelming domination of the private

car, far more comfortable and flexible than public vehicles, is further strengthened by urban sprawl which

reduces any mass transit network’s efficiency, hence desirability.

With such a challenge to meet, it is all the more important that mass transit investment decisions be

optimized. A major conclusion of this study is that improving mass transit facilities in an urban context

may be economically profitable where residential densities allow it, that is, where it translates into a

positive externality that can be captured through higher house values. In contrast, in low-density areas,

such an initiative will most probably result in a recurrent operating deficit that could jeopardize any

further improvement to the network while also causing property values to drop below what they would

have been without the service.

14

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ACKNOWLEDGEMENT

This research was funded by the Canadian SSHRC (Social Sciences and Humanities Research Council) under both Major Collaborative Research Initiative and Team grants programs. Authors are grateful to the Quebec City Assessment Division and the Quebec Real Estate Board for giving access to the assessment role and home transactions data, as well as to the Ministry of Transport of Quebec and the Réseau de Transport de la Capitale (RTC) for giving access to O-D surveys and other mass transit-related information.

16

Map 1 : METROBUS Service Corridors – Quebec City, 1992 – 1995

17

Table 1 : Operational Definition of Variables

Variable Operational Definition Type*

Dependent Variable Ln_R/S Price Ratio Natural logarithm of resale-to-sale price ratio N

Building Specifics Age_SL Age of property at time of sale (years) N

Ln_R/S Age Ratio Natural logarithm of resale-to-sale age ratio N

Bungalow*NbStory (Reference)

Number of stories times property type “Bungalow” (detached dwelling with one story)

N

Cottage*NbStory Number of stories times property type “Cottage” (detached dwelling with two stories or more)

N

Attached*NbStory Number of stories times property type “Attached” (Attached single-family unit)

N

RowHouse*NbStory Number of stories times property type “RowHouse” (Row house unit)

N

Ln_LivArea Natural Logarithm of living area (m2) N

Ln_LotSize Natural logarithm of lot size (m2) N Fiscal Variable

Ln_R/S GTR Ratio Natural logarithm of the resale-to-sale ratio of local non-standardized global tax rate (GTR)

N

Macro-Economic Variables Ln_R/S MktPrice Ratio Natural logarithm of resale-to-sale median market price

ratio for selected neighborhoods and property types N

Ln_R/S CPI Ratio Natural logarithm of resale-to-sale CPI ratio for the Quebec Metropolitan Area, based on monthly data

N

Ln_R/S MrtgRate Ratio Natural logarithm of resale-to-sale real mortgage rate ratio (3-years mortgages), based on monthly data

N

RE_Cycle Real Estate Cycle variable accounting for market trend reversion between sale and resale, from a buyers’ to sellers’ (+1) or sellers’ to buyers’ (-1) market; 0 otherwise

R

Environmental Attributes HNoiseLVL Noise level affecting properties located close to the

superior road network (50 m. from collector roads or country roads; 100 m. from main roads; 150 m. from a freeway) or on a high-slope road segment (3% slope and above), or both; values range from 0 to 2

R

Socioeconomic and Accessibility Factors Socioeconomic Status PCA-derived socioeconomic status computed from

Canada’s 1991 census data; well-educated, high-income persons located mainly in the upper town (positive values) as opposed to low-educated, low-income persons living mainly in the lower town (negative values)

N

18

Table 1 : Operational Definition of Variables (end) Access_Reg PCA-derived accessibility to regional services,

computed after the1988 regional road network; close to regional-level services (positive values), far away from regional-level services (negative values)

N

Access_Loc PCA-derived accessibility to neighborhood services, computed after the 1988 regional road network; close to local-level services (positive values), far away from local-level services (negative values)

N

Mass Transit Attributes Ln_R/S MTShare Ratio Natural logarithm of resale-to-sale mass transit modal

share ratio in the neighborhood; MT modal shares are expressed as the number of weekday bus trips (excluding school-bus trips) in the area as a proportion of all motorized (car, taxi, motorcycle) trips and are based on the 1991, 1996 and 2001 O-D survey data available for the Quebec Metropolitan Area

N

IntensvMETROBUS Property is (1)/ is not (0) located in an intensive Métrobus service area, i.e. within 50 m. of both a bus lane and a double-frequency route on peak hours (mainly in central areas)

D

IntrodMETROBUS Property is (1)/ is not (0) located within 500 m. from a bus route on which the Métrobus service was introduced between the sale and resale dates (i.e. between 1992 and 1995)

D

*Type: N: Numeric, D: Dummy, R: Rank

19

Table 2 : Descriptive Statistics

Descriptive Statistics

11,715 50,500 550,000 92,421 37,601 3.08 16.33

11,715 .52 3.10 1.13 .24 1.62 4.59

11,715 0 245 16 16 2.94 18.61

11,715 1.00 19.00 2.00 1.85 3.99 19.87

11,715 0 1 .58

11,715 0 1 .21

11,715 0 1 .16

11,715 0 1 .05

11,715 50.17 394.85 107.84 35.86 2.10 7.30

11,715 1 3 1.34 .48 .83 -.97

11,715 62.00 4,864.00 602.08 314.11 4.35 34.58

11,715 .57 1.64 .97 .11 -1.07 3.86

11,715 .93 1.83 1.13 .16 1.72 2.54

11,715 .16 4.52 .96 .57 2.14 6.92

11,715 .98 1.57 1.12 .11 1.44 2.08

11,715 -2.0104 2.7762 .1763

11,715 -2.5591 2.0614 -.3952

11,715 -7.2368 1.4578 -.1481

11,715 0 1 .04

11,715 .26 16.36 .90 1.08 11.92 150.45

11,715 0 1 .00

11,715 0 1 .05

11,715

SALE PRICE ($)

R/S Price Ratio

Age_SL

R/S Age Ratio

Bungalow

Cottage

Attached

RowHouse

LivArea (sq. m.)

NbStory

LotSize (sq. m.)

R/S GTR Ratio

R/S MktPrice Ratio

R/S MrtgRate Ratio

R/S CPI Ratio

Socio-economic status (PCA FactorScores:+ High income, - Low income)

Access_Reg (PCA Factor Scores: + Highaccessiblity, - Low accessibility)

Access_Loc (PCA Factor Scores: + Highaccessibility, - Low accessibility)

HNoiseLVL

R/S MTShare Ratio

IntensvMETROBUS (Central Areas)

IntrodMETROBUS (Mainly Peripheral Areas)

Valid N (listwise)

Statistic Statistic Statistic Statistic Statistic Statistic Statistic

N Minimum Maximum Mean Std. Dev. Skewness Kurtosis

20

Table 3 : Model A \ Excluding Mass Transit Attributes \ k = 16 \ N = 11,579

Model Summaryb

.757 .573 .573 .12011Model1

R R SquareAdjustedR Square

Std. Error ofthe Estimate

Dependent Variable: Ln_R/S Price Ratiob.

ANOVA

224.015 16 14.001 970.502 .000

166.799 11,562 .014

390.815 11,578

Regression

Residual

Total

Model1

Sum ofSquares df Mean Square F Sig.

Coefficientsa

-.048 .030 -1.621 .105

.001 .000 .078 9.756 .000 1.740

-.024 .003 -.069 -8.004 .000 2.003

.005 .002 .021 2.424 .015 2.074

.017 .004 .035 4.724 .000 1.475

.011 .002 .037 5.001 .000 1.480

-.012 .006 -.018 -1.929 .054 2.480

.012 .004 .026 3.037 .002 1.942

-.244 .012 -.162 -20.399 .000 1.713

.976 .012 .703 80.469 .000 2.070

.099 .022 .049 4.552 .000 3.148

.022 .002 .063 9.465 .000 1.189

-.021 .002 -.079 -11.134 .000 1.354

-.018 .006 -.020 -3.260 .001 1.046

-.006 .002 -.030 -3.438 .001 2.043

-.003 .002 -.014 -1.527 .127 2.171

-.010 .001 -.046 -6.918 .000 1.186

(Constant)

Age_SL

Ln_R/S Age Ratio

Cottage*NbStory

RowHouse*NbStory

Attached*NbStory

Ln_LIVAREA (sq. m.)

Ln_LotSize (sq. m.)

Ln_R/S GTR Ratio

Ln_R/S MktPrice Ratio

Ln_R/S CPI Ratio

Ln_R/S MrtgRate Ratio

RE_Cycle

HNoiseLVL

Socio-economic status (+ Highincome, - Low income)

Access_Reg (+ Highaccessiblity, - Low accessibility)

Access_Loc (+ Highaccessibility, - Low accessibility)

B Std. Error

UnstandardizedCoefficients

Beta

StandardizedCoefficients

t Sig. VIF

CollinearityStatistics

Dependent Variable: LnRatio_Price_RSL/SLa.

21

Table 4 : Model B \ Including Mass Transit Attributes \ k = 19 \ N = 11,575

Model Summaryb

.764 .583 .582 .11868Model1

R R SquareAdjustedR Square

Std. Error ofthe Estimate

Dependent Variable: Ln_R/S Price Ratiob.

ANOVA

227.671 19 11.983 850.810 .000

162.739 11,555 .014

390.410 11,574

Regression

Residual

Total

Model1

Sum ofSquares df Mean Square F Sig.

Coefficientsa

-.057 .029 -1.939 .052

.001 .000 .078 9.736 .000 1.758

-.020 .003 -.058 -6.791 .000 2.030

.004 .002 .019 2.212 .027 2.075

.017 .004 .034 4.707 .000 1.477

.011 .002 .038 5.246 .000 1.480

-.012 .006 -.019 -2.030 .042 2.483

.014 .004 .032 3.811 .000 1.954

-.240 .012 -.160 -20.181 .000 1.735

.954 .012 .689 79.294 .000 2.093

.185 .022 .092 8.310 .000 3.385

.023 .002 .065 9.808 .000 1.212

-.016 .002 -.059 -8.271 .000 1.410

-.015 .006 -.017 -2.781 .005 1.046

-.007 .002 -.038 -4.418 .000 2.054

-.001 .002 -.003 -.369 .712 2.194

-.002 .002 -.008 -1.164 .245 1.349

.057 .004 .109 15.845 .000 1.304

.052 .020 .016 2.557 .011 1.030

-.025 .006 -.029 -4.504 .000 1.118

(Constant)

Age_SL

Ln_R/S Age Ratio

Cottage*NbStory

RowHouse*NbStory

Attached*NbStory

Ln_LIVAREA (sq. m.)

Ln_LotSize (sq. m.)

Ln_R/S GTR Ratio

Ln_R/S MktPrice Ratio

Ln_R/S CPI Ratio

Ln_R/S MrtgRate Ratio

RE_Cycle

HNoiseLVL

Socio-economic status (+ Highincome, - Low income)

Access_Reg (+ High accessiblity, -Low accessibility)

Access_Loc (+ High accessibility, -Low accessibility)

Ln_R/S MTShare RatioIntensvMETROBUS (Central Areas)IntrodMETROBUS (Mainly ExcentricAreas)

B Std. Error

UnstandardizedCoefficients

Beta

StandardizedCoefficients

t Sig. VIF

CollinearityStatistics

Dependent Variable: Ln_R/S Price Ratioa.