The Impact of Mature Trees on House Values and on Residential Location Choices in Quebec City Marius Thériault, Ph.D. Yan Kestens, Ph.D. Candidate François Des Rosiers, Ph.D. International Environmental Modelling and Software Society Symposium June 16-19, Lugano, Switzerland

Introduction (1) Making a choice of home is a complex and long-term decision taken by households trying to maximize their satisfaction and utility level Often, it implies some tradeoffs among finance (house price versus household income), accessibility to urban amenities (including travel expenses in both money and time) and quality of neighbourhood (E.g. wooded areas) The structuring of residential values remains highly dependent upon both location and neighbourhood-related issues underlying homeowners’ choices. Need to better investigate: - the accessibility to, and proximity of urban services; - the impact of environmental externalities. And their mutual relationships with households preferences and needs General context

Introduction (2) Part I: Combining Multiple Regression and GIS to Improve Modelling of Housing Markets (Hedonic Modelling) GIS tools (measuring location, distance, travel time, accessibility) –Transportation GIS to measure Euclidean distances, shortest path road network distances and travel times between homes and activity places Spatial statistics –Sorting out property-specific and neighbourhood effects –Assessing spatial autocorrelation among residuals –Using Moran’s I Interactive variables –Modelling spatial variability of amenities considering accessibility, socio-economic status of the neighbourhood (census data) and buyer’s profile

Introduction (3) Part II: Assessing Probabilities of Buying a Property with Specific Environmental Attributes (Binary Logistic Regression) On-site surveys –To assess environmental attributes (E.g. mature trees) Homeowner’s phone survey –Landscaping attributes and household profiles –Appreciation of house and neighbourhood quality Binary logistic regression: measuring the propensity to buy a house with mature trees considering marginal effects of other features (externalities, property specifics, household income, family structure, tastes, etc.)

PART I: COMBINING MULTIPLE REGRESSION AND GIS TO IMPROVE HEDONIC MODELLING OF HOUSING MARKETS

Hedonic Modelling of Housing Markets l Hedonic modeling uses stepwise multiple regression methods to calculate accurate and consistent estimates of the implicit price of housing characteristics using market data l An hedonic model estimates the sale price ( dependant variable ) in relation to significant property attributes ( building characteristics, site specifics, outbuildings, local tax rates, etc. ) and several neighbourhoods factors ( accessibility, socio-economic status, public works, services, environment, etc. ) processed by the GIS (marginal effect) l GIS and spatial statistics are needed to analyze the distribution of residuals over space, to measure the market trends and to improve the appraisal process with an evaluation of the neighbourhood and structural changes

Hedonic Modelling of House Price Integrating Property- specifics Accessibility and Socio-Economic Profiles Computing accessibility from each home to selected activity places - Minimum travelling time using TransCAD Performing factor analyses on each set of access and census variables - Reduction of 49 individual attributes to six principal components Replacing individual variables by factor scores in hedonic models - Good control of multicollinearity

Integrating Accessibility PCA on distances to services

Integrating Accessibility PCA on distances to services

Integrating Socio-Economic Profiles PCA on 1991 census attributes

Integrating Socio-Economic Profiles Neighbourhood profiles PCA Component 3 Socio-economic status + (green) High education High income - (red) Poor neighbourhoods Component 3 (16.0% of variance)

Landscaping Attributes and Homeowner’s Profile - Phone survey held on homeowners in order to obtain household-level data (incentives to move, reasons for choosing neighbourhood, preferences, appreciation, family status, economic profile). - Hedonic model integrating homeowner profiles and interactions with landscaping attributes. - Respondents were asked to identify advantages and disadvantages of their home and of their neighbourhood (13.7% were spontaneously identifying vegetation as a positive feature of their property) -In site visits were made in order to collect date about landscaping of the property and its neighbours

Hedonic Modelling of Housing Price – Variables List Dependent variable Property specifics Accessibility and socio- economic status Vegetation and buyer- related attributes

Hedonic Model of Sale Price Property specifics Accessibility and socio- economic status Vegetation and buyer- related attributes 640 single-family houses Quebec City ( ) Average price : $114,081 Multiplicative form - Adj. R-Square: SEE : % - F : 153Moran’s I among residuals : p=.242 – Good control of spatial autocorrelation and multicollinearity

Hedonic Model Results Major findings from this model - House prices can be usefully explained by a mix of property attributes and their interactions : property specifics, accessibility to services, neighbourhood quality, vegetation in interaction with various attributes of the buyer and his family - Multicollinearity and spatial autocorrelation are well under control - Using interactions between vegetation status (presence of mature trees) and buyer’s profile allows for distinguishing segments of population putting value on vegetation (families with children living in high status neighbourhoods, people holding a college degree, buyers more than 30 years old, valuation of trees is increasing with income) - Except for valuation of accessibility to regional-level services, vegetation effect has, for various segments of population, an effect of about the same magnitude as accessibility to urban amenities (Beta coefficients and t tests)

Landscaping Attributes and Homeowner’s Profile Major findings from other researches using hedonics - A scattered vegetation on the lot is valued in family households with children; - A high percentage of high shrubs in front of the property has a positive impact on value when the respondent’s partner is self- employed (and hence tends to spend more time at home), but negatively if the respondent is aged 55-64; however, homeowners aged 65 and up tend to value a fenced environment; - Young households (respondents aged 25-34) seem to appreciate a high vegetation cover in front of their house, but not that much in the immediate neighbourhood; - A hard-pack entrance is detrimental to house prices where homeowners belong to an upper-income category ($80-$100K).

PART II: Assessing Probabilities of Buying a House with Mature Trees

Modelling Behavioural Attitudes - Measuring the economic valuation of landscaping is not sufficient to fully understand the choice-setting mechanisms behind the effect of trees on residential location choices - New modelling approaches integrating behavioural concepts of attitudes, tradeoffs (accessibility versus nature) and motivations could certainly improve our understanding of people’s valuation of nature - In order to further our understanding of landscaping valuation in urban regions, economic and behavioural modelling has been combined in a two-step approach: - Hedonic approach to assess economic valuation of property specifics, location and environment - Logistic regression to model households' propensity for buying a house on a wooded lot (with mature trees measuring at least 10 metres) and in wooded neighbourhoods

Logistic Model of Propensity to Buy a Property with Mature Trees 640 single-family houses Quebec City ( ) 41.2% of properties with mature trees Binary form - Nagelkerke R-Square: McFadden R-Square: Moran’s I among residuals : p=.465 – Good control of spatial autocorrelation

Modelling Behavioural Attitudes Major findings The effect of mature trees on paid price for houses considering household of buyer, perception of benefits and socio-economic status of the neighbourhood: - Appreciation of benefits gives an overall premium of about 4%; - Families with children do not behave like childless households and adjust their appreciation to the socio-economic status of their living neighbourhood (effects ranging from ‑ 9% to 15%); - In line with previous findings, trees can have an adverse effect on house value in poorer neighbourhoods and could increase value by about 15% in high socio-economic status neighbourhoods.

Modelling Behavioural Attitudes Major findings Probability of choosing a property with mature trees (with average equipment and socio-economic status neighbourhood) according to travel time (TT) and apparent age of house (years): - Self appreciation of vegetation has a strong effect on the decision: for a house aged 25, it doubles the propensity near the city centre (27%/14%), while the odds ratio slightly decreases as commuting inconvenience grows (tree lovers are less enthusiastic – 84%/69%).

Modelling Behavioural Attitudes Major findings - In Quebec City, access to local services (E.g. high school) is not competing with mature vegetation; new developments far away from neighbourhood amenities are likely to be located in open space without tree

Modelling Behavioural Attitudes Major findings - In Quebec City, access to vegetation is compromising travel time to CBD and regional-level services - However, tradeoffs with the apparent age of the house is far more important

Conclusion - Discussion - Use of adapted statistical tools and GIS to address accessibility, neighbourhood-related issues and modeling tradeoffs to buy landscaped properties - Use of spatial statistics to test remaining spatial structure in models, which is potentially highly detrimental to their robustness and stability of regression coefficients - Importance of environmental factors in housing markets - High significance of interactions, useful to understand the complexity of behaviour and preferences - Usefulness of household-level data to fully understand valuation and behaviour