residential choice: household-level analysis and hedonic modelling yan kestens, marius thériault...

Residential Choice: Household-Level Analysis and Hedonic Modelling

Yan Kestens, Marius Thériault & François Des Rosiers

Université Laval

MCRI Student Caucus

“OUR FUTURE DIRECTIONS: UNDERSTANDING INDIVIDUAL AND SOCIAL

PROCESSES IN URBAN CANADA”

ITS Centre – University of Toronto

13-14 September 2003

Residential Choice & Hedonic Modelling

Context of the study

Hedonic modelling widely used for analysing property prices

• Considering various geographical externalities (schools, high-voltage powerlines, vegetation, landscaping)

• Spatial-sensitive methods measuring the "drift" in the coefficients

Spatial Expansion Method (Casetti, 1972)

Geographically Weighted Regression (Fotheringham 1997)

Absence of spatial-sensitive modelling at the household level

• could determine if the impact of an amenity is homogenous among the sample or varies with the context (household profile)


Objectives

Gather household-level data for modelling purposes (11,000 transactions)

Obtain information about the household profile (age, income, educational attainment, previous tenure status…)

Obtain information about the choice criteria and the motivations for moving

• Reasons for moving

• Neighborhood choice criteria

• Property choice criteria


Databank & Modelling procedure

Phone survey carried out between 2000 and 2003, single-family property buyers who bought their houses between 1993-2001 (mainly 1993-1996)

• Moving motivations• Neighborhood Choice (location)• Property Choice• «Free » survey, no proposed answers, unlimited number of responses

2521 answered calls, 1134 acceptations (45%), 1347 refuses (55%), 774 complete answers (…including income)



1) Why do families move and what do they choose ?

Improve our understanding of residential choice…

Frequency analysis and correspondence analysis of responses (place-proximity-space (Filion 1999) and place-identity (Proshansky, 1978; Feldman, 1990) conceptual frameworks)

… by modelling the odds-ratio of mentioning a criterion depending on the household profile and location

Series of logistic regressions, modelling the propensity to mention a criteria depending on the household profile (previous tenure status (first-time vs former owner), age, household type, educational attainment, income)



2) Residential hedonic modelling: Are implicit prices homogeneous considering household profiles?

According to Starret (1981):

- the capitalization of an attribute is complete if

(1) there is enough variation within the variable – e.g. in order to measure the effect of proximity to power lines, it is important there are also people living at such distance to prevent an effect on house prices

(2) the residents' preferences are homogenous.

“Whereas the first condition can easily be controlled, the second has been the object of little research. If the preferences are heterogeneous, capitalization is only partial” (Tyrvainen, 1997, p.220)

..or capitalization is complete but heterogeneous depending on preferences…



2) Residential hedonic modelling: Are implicit prices homogeneous considering household profiles?

Spatial-sensitive hedonic modelling with introduction of household profile data into the hedonic function

Casetti-type expansion variables: measures the variability of a previously defined "fixed" coefficients depending on the context (household profile)

Geographically Weighted Regressions (Fotheringham, 1997 & 2002)

Local Indicators of Spatial Association (LISA)

Residential Choice & Hedonic ModellingWhy do families move and what do they choose?

0% 10% 20% 30% 40%

OwnershipSup. Housing

Housld Size ChangeP roximity to Work

New J obLandowner

Larger HomeP roximity to Services

Quiet NbhdP rox. to School

DivorceChange

Reducing CommutingP roximity Family

More Secure NbhdBirth placeP rox CBD

SmallerHomeLivly Nbhd

P rop. Better ConditionRetirement

Percentage

Moving motivations


Moving motivations

New/Ex-Owners

0% 10% 20% 30%

*** Ownership* SupHousing

Housld Size Change*** P rox. to Work

*** New J ob*** Landowner

** Larger Home*** P rox. to Services

Quiet Nbhd*P rox. to School

*** Divorce** Change

Reduc. Commuting** P rox. Family

More Secure Nbhd*Birth place

** P rox CBD** SmallerHome

** Livly NbhdP rop. Better

** Retirement

Percentage

Ex-Owners New Owners

70%


Property and Neigborhood Choice Criteria

Group

List of criteria in the group

At least one of the criteria cited by ...% of the respondents

Size Lot size, house size, number of rooms

48%

Interior Interior architecture, floor

quality, functionality, interior decoration, garage

37%

Style Architectural style, condition

36%

Property choice

Environment Trees, landscaping 15%

Accessibility proximity to services, job,

school, highway, CBD, public transit system

60%

Socio-economic context quietness, young nbhd, security, lively

43%

Attachment Sense of belonging 27%

Neighbourhood choice

Aesthetics cachet, trees 25%


Property and Neigborhood Choice Criteria

Correspondence analysis

(similar to Principal Component Analysis, but applied to binary variables)

COMPONENTS 1 2 3 4 5 6 7 8

Eigenvalues 0.306 0.295 0.284 0.273 0.254 0.235 0.23 0.214

% of expl. variance 7.06 6.82 6.57 6.31 5.86 5.42 5.31 4.95

Cumulative % 7.1 13.9 20.4 26.8 32.6 38.0 43.3 48.3

Prop Price 0.900

Prop Lotsize

Prop Design 0.916

Prop Style -0.635

Prop Size 0.689 1.386

Prop Nb Rooms -0.579

Prop Condition 0.632

Prop Trees -0.868

Prop Floor Quality -1.025 1.401 1.671 -0.615

Prop Functionality 0.662

Prop Inter. Deco. 1.080 -0.943

Prop Landscaping -1.171 3.173

Nbhd Services 0.457

Nbhd Quietness

Nbhd Attachment 1.212

Nbhd Work 1.098 1.476

Nbhd School -0.545

Nbhd Cachet -1.086 1.585

Nbhd Trees -1.095

Nbhd Highway 1.452

Nbhd CBD -1.068

Nbhd Young -1.154 -0.995

Nbhd Trans. Network 1.365

Interpretation Proximity /

Cachet trade off

Objective (Prop. Qual.

and Neighbd) vs Subjective

criteria (Attachment)

Landscaping & Trees /

Price trade off

Property quality and

size

Young neighbourhood

/ Work proximity and prop. quality (life cycle)

Centrality / Size trade

off

Highway proximity / Property quality

Interior / Exterior Quality

In the context of theoretical models

(PPS and Place- Identity)

Place / Proximity trade off

Place-Identity Place Place Proximity Place /

Space trade off

Place / Proximity trade off

Place

Logistic regression example: Likelihood to cite the school as a neighbourhood choice criteria

A household with children is 4.625 times more likely to cite the school as a neighborhood criteria


A household with children is 4.625 times more likely to cite the school as a neighborhood criteria

Logistic regression example: Likelihood to cite the school as a neighbourhood choice criteria

New oweners are 1/0.646=1.55 times less likely to cite the school as a neighborhood criteria



Moving Motivations: getting closer to a school

Prox i m i ty tos ch oo l

3 0 - 3 9 o ld w ithc o lleg e d eg r ee v s

< 3 0 w ith u n iv .d eg r ee

W ith c h ild r en into w n c en ter v s n o

c h ild r en o u ters u b u r b s

W ith c h ild r en in o lds u b u r b s v s n oc h ild r en o u ter

s u b u r b s

5 .8 8

5 .2 1

7 .9 3

H o u s e h o ld fa cto rsL o ca t io n fa cto rs

H o u s e h o ld a n d lo ca t io n fa cto rs

Nu m be rs in a rro ws a re o dd-ra t io s g iv e n by lo g is t ic re g re s s io n


Neighborhood Choice: Aesthetic criteria

A e s th e t icsAttac h ed to th en eig h b o r h o o d

Up p er to w n v sm ix ed s u b u r b s

W ith c h ild r en in o lds u b u r b s v s n oc h ild r en n ew

s u b u r b s

0 .5 6 41 .8 4

0.48

8

H o u s e h o ld fa cto rsL o ca t io n fa cto rs

H o u s e h o ld a n d lo ca t io n fa cto rs

I n c o m e ( p erad d it io n a l 1 0 0 0 0 $ )

1 .1 2 2

Nu m be rs in a rro ws a re o dd-ra t io s g iv e n by lo g is t ic re g re s s io n


Why do families move and what do they choose?

Moving motivationsPrevious tenure status

AgeHousehold

TypeEducational Attainment

Income Location Attachment

Ownership X X X XNew Job X X X XProximity to Work X X XProximity to School X X X XProximity to Family X X X X XHousehold Size Change X X XHousehold Separation X X XSecure Neighborhd X XSize Group X X XJob Group X X X XProximity Group X X X X


Neighborhood Choice

Previous tenure status

AgeHousehold



Services X X X XPublic Transit System X X XSchool X X X XJob X X XCachet X XTrees X X XAttachment X X XProximity Group X X X XSocio-Economic Group X X X XAesthetic Group X X X X


Property ChoicePrevious tenure status

AgeHousehold



Price X X XSize X X XNb Rooms X X X XLot Size X XStyle X X X XTrees X X XSize Group X X XInterior Group X X X X X

Residential Choice & Hedonic ModellingImplicit Prices – Hedonic Modelling and Household Profiles

Some findings…

Accessibility – location rent: Significant interaction between car-time to CBD and household income

Effect o f C ar-T im e D istance to M ain Activ ity C enters

C onsidering the H ousehold Incom e

5

20,000

-30 %

-20 %

-10 %

0 %

40,000

60,000

80,000

100,000

9

13

17

20


Some findings…

The percentage of university degree holders in the Census tract has a global positive effect on the property value, each additional 10% adding 4.41% to the property value

Dependent Variable: LnSpriceB (t-value) Sig VIF

(Constant) 10.2443 -91.49 *** --- -- -- -- --% of Univ. Degree Holders in theCensus tract 0.0044 -9.17 *** 3.5% of Univ. Degree Holders in theCensus tract * Households with Univ.Degree. Holders 0.0018 -3.14 *** 1.1

Model N3

Implicit Prices – Hedonic Modelling and Household Profiles

Residential Choice & Hedonic ModellingImplicit Prices – Household Profiles

Some findings…

Additionally, the interaction with the household-level binary variable “Holding a university degree” proved significant, with a positive premium of 1.8%.

high-educated people are ready to pay a premium for living next to people with similar educational attainment

Dependent Variable: LnSpriceB (t-value) Sig VIF

(Constant) 10.2443 -91.49 *** --- -- -- -- --% of Univ. Degree Holders in theCensus tract 0.0044 -9.17 *** 3.5% of Univ. Degree Holders in theCensus tract * Households with Univ.Degree. Holders 0.0018 -3.14 *** 1.1

Model N3

Residential Choice & Hedonic ModellingImplicit Prices – Household Profiles

The introduction of household-level data into the hedonic function had a very positive effect on Local Spatial Autocorrelation

Final model: only 24 significant zG*i statistics (among 761, that is, less than 5%)

Residential Choice & Hedonic ModellingConclusions

Detailed household surveys betters our understanding of the residential choice process

Household-level data introduced within the hedonic framework improves explanation power while diminishing local spatial autocorrelation

Furthers the understanding of the spatial structure of the residential market, that is, the heterogeneity of the implicit prices

Residential Choice & Hedonic ModellingConclusions

Further research: analysing the complex intertwine between residential, work and family career with individual data (space-time dynamics)

Linkages between market and people

- from individual behaviour to global processes (scale)

- policies, planning

Residential Choice: Household-Level Analysis and Hedonic Modelling

Yan Kestens, Marius Thériault & François Des Rosiers

Université Laval

MCRI Student Caucus

“OUR FUTURE DIRECTIONS: UNDERSTANDING INDIVIDUAL AND SOCIAL

PROCESSES IN URBAN CANADA”

ITS Centre – University of Toronto

13-14 September 2003

residential choice: household-level analysis and hedonic modelling yan kestens, marius thériault...

Documents

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sensitive modelling

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household level

householdlevel analysis

household profile age