1. 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

2. 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)

3. Residential Choice & Hedonic Modelling 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

4. Residential Choice & Hedonic Modelling 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)

5. Residential Choice & Hedonic Modelling Databank & Modelling procedure 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)

6. Residential Choice & Hedonic Modelling Databank & Modelling procedure 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…

7. Residential Choice & Hedonic Modelling Databank & Modelling procedure 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)

8. Residential Choice & Hedonic Modelling Why do families move and what do they choose? Moving motivations

9. Residential Choice & Hedonic Modelling Why do families move and what do they choose? Moving motivations 70%

10. Residential Choice & Hedonic Modelling Why do families move and what do they choose? Property and Neigborhood Choice Criteria

11. Residential Choice & Hedonic Modelling Why do families move and what do they choose? Property and Neigborhood Choice Criteria  Correspondence analysis (similar to Principal Component Analysis, but applied to binary variables)

14. 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 Residential Choice & Hedonic Modelling Why do families move and what do they choose?

15. 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 Residential Choice & Hedonic Modelling Why do families move and what do they choose?

16. Residential Choice & Hedonic Modelling Why do families move and what do they choose? Moving Motivations: getting closer to a school

17. Residential Choice & Hedonic Modelling Why do families move and what do they choose? Neighborhood Choice: Aesthetic criteria

18. Residential Choice & Hedonic Modelling Why do families move and what do they choose?

19. Residential Choice & Hedonic Modelling Why do families move and what do they choose?

20. Residential Choice & Hedonic Modelling Why do families move and what do they choose?

21. Residential Choice & Hedonic Modelling Implicit Prices – Hedonic Modelling and Household Profiles Some findings… Accessibility – location rent: Significant interaction between car- time to CBD and household income

22. Residential Choice & Hedonic Modelling 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 Implicit Prices – Hedonic Modelling and Household Profiles

23. Residential Choice & Hedonic Modelling Implicit 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

24. Residential Choice & Hedonic Modelling Implicit 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%)

25. Residential Choice & Hedonic Modelling Conclusions 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

26. Residential Choice & Hedonic Modelling Conclusions 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

27. 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