Household Mobility and House Values:Household Mobility and House Values:A Perceptual Approach to Modelling A Perceptual Approach to Modelling

Accessibility to Urban ServicesAccessibility to Urban Services

François Des Rosiers, Marius Thériault , Florent Joerin,Paul Villeneuve & Murtaza Haider

MCRI-ILUTE 2nd International PROCESSUS ColloquiumToronto, Canada, June 12-15, 2005

Research funded by

This paper is an attempt to bridge the gap between, on the one hand, the mobility behaviour of households and their perception of accessibility to urban amenities and, on the other hand, house price dynamicsThe impact of accessibility on house prices is empirically tested applying hedonic modelling to some 952 single-family houses sold in Quebec City between 1993 and 1996Two sets of accessibility indices are compared: the first one is based on simulated travel times to nearest

amenities aggregated through factor analysis (PCA) Second one rests on perceived accessibility indices obtained

via a fuzzy logic approach applied to observed trip patterns derived from the 2001 QMA O-D survey

A centrality index is also developed and used concurrently to perceived accessibility indices

Introduction: Context and objective (1)

The overall objective of this paper is to test whether perceptual indices of accessibility are actually internalized in housing prices

This rests upon the assumption that the perception of space is not homogeneous and that households will adjust their willingness to pay for an improved access to urban amenities to their needs, preferences and profile

Secondary objectives are:Testing for the marginal contribution to value of

centrality as opposed to accessibilityTesting for the relative superiority of “subjective”,

over “objective” accessibility indices in their ability to explain house price differences

Introduction: Context and objective (2)

Traditional urban models are currently based on the centrality concept (distance decay function) and on accessibility to the CBD (monocentric model)As cities grow in complexity though and turn polycentric, mere Euclidean distance to the CBD falls short of integrating all relevant aspects of accessibility (Jackson 1979, Dubin and Sung 1987, Niedercorn and Ammari 1987, Hoch and Waddell 1993)

Despite use of minimum travel time and walking distance (Bateman et al. 2001), the faulty specification of accessibility descriptors may explain rather poor performances

Measuring accessibility: Time vs. distance (1)

Hence the usefulness of travel surveys to analyze commuting patterns and accessibility to jobs and houses: Levinson (1996 – Washington, DC): Commuting durations

remain stable in spite of suburbanization of jobs and rising total trip volume

Helling (1996 - Atlanta): Accessibility do not affect everyone similarly while gravity indices only provide partial information

Srour et al. (2002 - Dallas-Fort Worth): Specific accessibility-to-job indices tend to perform better that overall measurements while impacting positively on residential land values

More sensitive, GIS-derived, measurements of actual road distances and travel times can improve our understanding of mobility behaviour (Thériault et al. 1999a & 1999b, Vandersmissen et al. 2003 & 2004)

Measuring accessibility: Time vs. distance (2)

Step 1: a distance and trip duration modelling procedure is applied to the Quebec Metropolitan Area (QMA) street network using the TransCAD transportation-oriented GIS software

The resulting regional network is composed of 29 035 street segments - acting as directional links - and 20 262 nodes - acting as street intersections - (Des Rosiers et al. 2000)

Each property, residential and non residential, can be easily located in the regional GIS which serves as the basic processing device for this study

Since speed limits, one-ways and impedance (crossing time) are known for all street segments, distances and access times from each home to any service centre may be computed

Designing “objective” accessibility indices using factor analysis (PCA) (1)

Step 2: compute 15 travel times (car and walking) to the nearest local & regional amenities : primary & high schools, colleges, universities; regional, neighbourhood & local shopping centres; CBD

Step 3: PCA - extract 2 principal components using Varimax rotation Access_Factor1 : access to nearest regional-level services

(42% of variance) Access_Factor2 : access to nearest local-level services

(34% of variance) PCA successfully used by Des Rosiers et al., 2000 Mutually independent factors help control multicollinearity

Step 4: Factor scores are substituted for access attributes

Designing “objective” accessibility indices using factor analysis (PCA) (2)

PCA-derived accessibility indices

Access_Factor 1Regional-level services

Access_Factor 2Local-level services

Conducted from mid-September to mid-December 2001 by the Ministry of Transport of Quebec (MTQ) and the Quebec City Transit Authority (RTC)Involves 68 121 persons ( 27 839 households) and 174 243 weekday trips (Monday to Friday)Homes and activity places are located using street addressesEach person is characterized by age, gender, occupation and ownership of a car driver licenceVarious household types are formed (lone person, childless couple, two-parent family, lone-parent family, and other households)Trip purpose, transportation mode as well as departure time, origin and destination of each trip are knownCar-based trips represent 73,3% of all trips; of these, 29 602 originate from Quebec City residential areas

Integrating QMA’s O-D survey trip patterns

Trip duration differences By Type of Person/Household

Trip purpose Group Average duration

(min.)

versus Group Average duration

(min.)

Student Sig.

Work 6 371 Women 10,30 6 576 Men 10,76 4,048 0,000

School 407 Adults 9,89 1 979 Children 7,07 8,196 0,000

Shopping – large stores 2 521 Childless households 6,81 601 Families 7,54 3,414 0,001

Shopping – small shops No significant differences among groups

Grocery 2 229 Childless households 4,52 669 Families 5,15 3,612 0,000

Leisure 1 057 Childless women 7,59 325 Mothers 8,28 2,072 0,038

1 013 Childless men 8,25 268 Fathers 9,34 2,706 0,007

1 539 Women 7,72 1 665 Men 8,33 3,132 0,002

2 663 Adults 8,10 541 Children 7,59 1,971 0,049

Health care 859 Adults 8,26 84 Children 5,76 3,781 0,000

Restaurant No significant differences among groups

Modelling “subjective” accessibility indices using fuzzy logic criteria (1)

According to Kim and Kwan (2003) accessibility measurement should consider “thresholds on activity participation time and travel time in order to identify a meaningful opportunity set when evaluating space-time accessibility”

Suitability indices (Sij ) of travelling from home i to activity j are

computed from actual O-D trip durations based on travel time satisfaction thresholds, using the following fuzzy logic criteria:

[1] any travel time < median (C50) is totally acceptable (1)

[2] a travel time > C90 is likely to be unsatisfactory (0)

[3] intermediate cases obtained using linear interpolation between 1 and 0

Ai : Raw suitability of residential location i (sum of suitable opportunities)

Sij : Suitability index of travelling from residential location i

to activity location jPj : Total number of potential activities at location j

where:

Ai* : Accessibility index of residential location i relative to the most suitable place

Modelling “subjective” accessibility indices using fuzzy logic criteria (2)

Finally, raw and relative accessibility indices are computed as follows:

mjniPSAm

jjiji ,...,1,...,1,

1

niAAAAA

n

ii ,...,1,),...,,max(100

21

*

where:

Travel time satisfaction thresholdsby trip purpose and type of person/hhld

Accessibility Index Trip purpose Type of person/household C50 C90

AWork * Women Work Women 9,5 18,6 AWork * Men Men 9,8 19,3 ASchool * Adults School Adults (16 years and more) 8,3 18,9 ASchool * Children Children (15 years and less) 4,8 16,1 ALargeShop * ChildlessHld Shopping – large stores Childless households 5,9 12,5 ALargeShop * Families Families 6,5 14,1 ASmallShops Shopping – small shops 4,8 12,6 AGrocery * ChildlessHld Grocery Childless households 3,6 9,4 AGrocery * Families Families 3,5 11,2 ALeisure * ChlWomen Leisure Childless women 6,4 14,9 ALeisure * Mother Mothers 7,1 15,0 ALeisure * ChlMen Childless men 7,3 16,2 ALeisure * Father Fathers 8,3 17,6 ALeisure * Children Children (15 years and less) 6,0 14,3 ARestaurant Restaurant 5,3 12,6 AHealthCare * Adults Health care Adults (16 years and more) 6,8 16,6 AHealthCare * Children Children (15 years and less) 4,4 11,3

Perceived Accessibility to Restaurants

0 5 10

kilometres

Restaurants

3 000

1 500

300

Accessibility Index (%)90 to 10080 to 9070 to 8060 to 7050 to 6040 to 5030 to 4020 to 3010 to 20

0 to 10

C50 : 5,3 min.C90 : 12,6 min.

Perceived Accessibility to workplace - Women

C50 : 5,3 min.C90 : 12,6 min.

0 5 10

kilometres

Work Places

13 000

6 500

1 300

Accessibility Index (%)90 to 10080 to 9070 to 8060 to 7050 to 6040 to 5030 to 4020 to 3010 to 20

0 to 10

Perceived Accessibility to groceries - Families

C50 : 5,3 min.C90 : 12,6 min.

0 5 10

kilometres

Groceries

3 000

1 500

300

Accessibility Index (%)90 to 10080 to 9070 to 8060 to 7050 to 6040 to 5030 to 4020 to 3010 to 200 to 10

Designing an urban centrality index

Gravity model of interaction flows (Tiefelsdorf, 2003) used to estimate centrality:

where: d

pd

po

ij

jiij

D

PPe

μij : Expected number of car trips between locations i and j

Pi : Total population at residential location i

Pj : Total number of potential activities at location j

Dij : Travel time by car from residential location i to activity location j (minutes)

Resulting 52 801 392 potential trips were reduced to 300 079 pairs of residential-activity places (563 residential grid cells x 533 activity cells)

Indices then normalized to 100 by comparison to observed maximum flow

Database: hedonic modelling applied to 952 single-family houses sold in Quebec City between 1993 and 1996 - sale prices range from $50 000 to $460 000 (mean price: $109 000)

Due to a high variance on sale prices (SP), we use a semi-log, multiplicative functional form (Dependent var.: ln_SP)

Phone survey conducted on sampled houses to get information about buyer’s profile (household structure, age, income, choice factors, etc.)

Survey revealed that accessibility to services, jobs, schools, highways and transit networks was an important criteria for choosing new neighbourhoods

Database & Modelling Approach (1)

Three steps: Model 1:

Ln SP = f [Property SSpecifics, IInflation, TTaxation]

Model 2:

Ln SP = f [SS, II, TT, PCAPCA of travel times to nearest amenities]

Models 3 @ 12:

Ln SP = f [SS, II, TT, PAIPAI (Perceived Accessibility Indices), Centrality Index]

Database & Modelling Approach

Main regression results (1)

Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Maximum VIF Residuals

Moran’s I Sig.

1 .858 .736 .731 .17959 3.050 0.2827 .002

2 .884 .782 .777 .16330 3.053 0.2376 .008

3 .874 .763 .758 .17040 3.060 0.2732 .003

ANOVA

Model Sum of Squares df Mean Square F Sig.

1 Regression 83.884 18 4.660 144.487 .000

Residual 30.093 933 .032

Total 113.977 951

2 Regression 89.126 20 4.456 166.951 .000

Residual 24.851 931 .027

Total 113.977 951

3 Regression 86.973 21 4.142 142.631 .000

Residual 27.004 930 .029

Total 113.977 951

All models do perform well in spite of remaining spatial autocorrelation among residuals

Model 2 performs better in all respects

Main regression results (2)

Model 1 Model 2 Model 3

B Std. Err

Beta t B Std. Err

Beta t B Std. Err

Beta t

(Constant) 11.68731 .04746 246.3 11.55619 .05250 220.1 11.50028 .05038 228.3

LotSizeSqrMetres .00003 .00002 .031 1.6 .00008 .00002 .078 4.3 .00008 .00002 .080 4.2

Bungalow * LivArea .00235 .00018 .357 13.3 .00231 .00016 .351 14.4 .00228 .00017 .346 13.6

Cottage * LivArea .00249 .00013 .569 19.4 .00250 .00012 .571 21.3 .00247 .00012 .565 20.2

Attached * LivArea .00149 .00027 .101 5.5 .00098 .00025 .067 3.9 .00112 .00026 .076 4.3

AppAge -.00387 .00057 -.138 -6.8 -.00853 .00062 -.303 -13.8 -.00662 .00060 -.235 -11.0

Washrooms .09517 .01252 .144 7.6 .07281 .01151 .110 6.3 .08150 .01197 .124 6.8

Fireplace .05082 .01209 .079 4.2 .04970 .01101 .077 4.5 .05106 .01149 .079 4.4

HardWoodStair .07454 .01623 .096 4.6 .05922 .01489 .076 4.0 .06646 .01544 .085 4.3

HighQualFloor .06689 .01295 .097 5.2 .04912 .01185 .071 4.1 .05814 .01232 .084 4.7

Terrace .12394 .04813 .045 2.6 .10813 .04382 .039 2.5 .10856 .04577 .039 2.4

Brick51FC .04567 .01420 .064 3.2 .03660 .01294 .051 2.8 .04089 .01349 .057 3.0

Clapbord51 -.05414 .01565 -.069 -3.5 -.04675 .01425 -.060 -3.3 -.05210 .01489 -.067 -3.5

SimpAttGarage .13307 .02731 .085 4.9 .11599 .02488 .074 4.7 .12187 .02598 .078 4.7

DoubAttGarage .16945 .03793 .080 4.5 .13446 .03459 .063 3.9 .15802 .03626 .074 4.4

DoubDetGarage .10959 .03132 .062 3.5 .12144 .02857 .069 4.3 .11030 .02974 .062 3.7

ExcaPool .18383 .02617 .125 7.0 .16487 .02386 .112 6.9 .16491 .02495 .112 6.6

Month93Jan -.00184 .00045 -.070 -4.0 -.00167 .00041 -.063 -4.1 -.00191 .00043 -.072 -4.5

Tax_OvUnzdRate -.25656 .01589 -.292 -16.1 -.14557 .02068 -.166 -7.0 -.25032 .01575 -.285 -15.9

Acces_Factor1 .12485 .00959 .322 13.0

Acces_Factor2 .04177 .00871 .090 4.8

AWork * NoWorkerHld .00287 .00042 .181 6.8

AWork * WorkerHld .00273 .00035 .216 7.7

Centrality Index .00173 .00051 .068 3.4

… Size and Age coefficients are strengthened.Tax rate effect

declines.This suggests

structural spatial links among these

variables and urban form.

Model 1 : All coefficients highly significant

and consistent with expectations.Prominence of age, size and

taxation

Model 2 : Factors 1 and 2

substantially improve

performances.Most other coefficients

unchanged, but…

Model 3 : Journey-to-Work coefficients highly significant

even when controlling for urban centrality.

Perceptual accessibility indices provide a more comprehensive picture of accessibility – more

related to people and less related to closest amenities.

Main regression results (3)

Models 4, 9 and 10 : Accessibility to schools and health care facilities for families as well as to restaurants exerts strong influence on pricesPerceived accessibility indices far outweigh centrality

Model Accessibility index R Square

SE Estimate

B Std. Err

Beta t VIF

4 ASchool * Family ,765 ,1678 ,00333 ,00035 ,279 9,6 3,431

Schools ASchool * ChildlessHld ,00255 ,00032 ,220 8,0 3,068

Centrality Index ,00146 ,00050 ,058 2,9 1,572

9 AHealthCare * Family ,766 ,1673 ,00342 ,00035 ,265 9,9 2,947

Health care AHealthCare * ChildlessHld ,00262 ,00033 ,199 7,9 2,574

Centrality Index ,00124 ,00050 ,049 2,4 1,618

10 ARestaurant ,768 ,1668 ,00323 ,00032 ,212 10,1 1,801

Restaurants Centrality Index ,00120 ,00050 ,047 2,4 1,608

Main regression results (4)

Model 11 : People aged 35-54 are willing to pay a substantial market premium to locate at a reasonable travel time from their work placeModel 12 : The higher the household income, the stronger the propensity to lessen work-trip duration: under an income constraint, households trade-off longer commuting trips for cheaper land

Model Accessibility index R Square

SE Estimate

B Std. Err

Beta t VIF

11 AWork * Age34less ,757 ,1704 ,00220 ,00037 ,155 5,9 2,698

Work places AWork * Age35-44 ,00301 ,00033 ,306 9,0 4,507

Age groups AWork * Age45-54 ,00324 ,00033 ,318 9,7 4,236

AWork * Age55more ,00317 ,00039 ,194 8,1 2,229

12 AWork ,771 ,1655 ,00311 ,00037 ,179 8,3 1,914

Work places AWork * Income<60K$ -,00111 ,00024 -,098 -4,7 1,811

Household AWork * Income60-80K$ -,00060 ,00024 -,050 -2,5 1,682

Income level AWork * Income80-100K$ -,00029 ,00026 -,021 -1,1 1,544

AWork * income>100K$ ,00074 ,00025 ,060 2,9 1,737

Centrality Index ,00192 ,00049 ,076 3,9 1,582

Both “objective” and “subjective” indices adequately capture the internalization of accessibility into house prices

While the former seem to perform better from a merely statistical point of view, perceptual accessibility indices are more efficient at accounting for the heterogeneous behaviour of households with respect to space and time

The different nature of centrality and accessibility is well brought out in the fact that both dimensions are shown to affect prices significantly, although perceptual accessibility indices far outweigh the centrality index

This questions the prominence of the CBD as a location criterion for households

Conclusion, Limits & Research agenda

While most useful a tool, O-D survey is limited by the fact that only week-day trips (Monday to Friday) are available for analysis, which has the side effect of underestimating various kinds of trips, like week-end shopping and leisure ones

Much remains to be known about households’ travel as well as house location choice decisions

Relating house prices to travel behaviour and accessibility to urban amenities from a space-time perspective, using for instance panel regression techniques, is certainly among the research avenues the are worth exploring

Conclusion, Limits & Research agenda