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Integrating Land Use in a Hedonic Price Model Using GIS. URISA 2001 Yan Kestens Marius Thériault François Des Rosiers Centre de Recherche en Aménagement et Développement Laval University, Québec, Canada. Presentation Outline. Introduction Objective Method Results Conclusion. - PowerPoint PPT Presentation
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Integrating Land Use in a Hedonic Price Model Using
GIS
URISA 2001
Yan Kestens
Marius Thériault
François Des Rosiers
Centre de Recherche en Aménagement et Développement
Laval University, Québec, Canada
Presentation Outline
• Introduction
• Objective
• Method
• Results
• Conclusion
Introduction
- Achieving a better understanding of the spatial and temporal dynamics of the Quebec City Region
- Hedonic Modeling using an important database describing over 30,000 transactions covering the 1986-1996 period
- Integrating land use characteristics using GIS
Introduction
What is Hedonic Modelling?
• Calculate the specific contribution of an attribute
• Explanatory power
• Predictive power
• Method based on multiple regression analysis (MRA)
Sale price = 0+1Var1+ 2Var2+…+nVarn+Additive form:
Multiplicative form:
Introduction
What is Hedonic Modelling?
DatabasesStatistical Techniques
GIS
• A largely used method in property assessment: CAMA
• Which has taken advantage from the development of computer technology and the GIS domain
Introduction
Model specifications: the explanatory variables
• Accessibility (car travel time/distance to services, etc.)
• Property-specific attributes (living area, lot size, nb of bathrooms, etc.)
• Socio-economic attributes (census data)
• Location (Euclidean distance to externalities)
• Environmental attributes (noise, vegetation, view, etc.)
Previous Work
• Numerous hedonic models but only few of which integrate the environmental dimension
• Sight is the most important sense in our sensitive experience with our environment
• Those few hedonic models which do integrate environmental dimensions use variables resulting from ground surveys which are money- and time-consuming
• The environmental dimension plays a significant role in the determination of house prices
• Vegetation has an overall positive impact on preference
Previous Work
• Morales 76: Manchester, Connecticut: +6 to 9% for houses with a good tree cover
• Seila et al. 82: +7% for new built houses with trees
• Anderson and Cordell, 88, Athen, Georgia, +3 to +5% for houses with trees
• Luttik 2000; 8 cities in the Netherlands, positive impact of green areas and presence of trees significant in 2 cities out of 8 : +7 and +8% on sale price.
• Dombrow et al. 2000; positive contribution of the presence of mature trees of +2%.
Previous Work
• Criticism: method of data collection
use of front view photographs
on-field surveying of the properties
• Consequence: biases
bias related to fraction of vegetation from only one point of view
bias related to subjectivity of surveyors
Previous work
Quebec City Region
Impact of high-voltage power lines, schools, shopping centers, landscaping attributes
Explorations with GIS tools and statistical methods: PCA, Trend Surface Analysis, Kriging techniques, Spatial Autocorrelation measures, interactive variables.
However, significant spatial structure in residuals.
Objective
• Improving the hedonic price models by adding environmental data
• Improving the explicative and predictive power of the models
• Reducing heteroskedasticity and spatial autocorrelation in the residuals
• Using an inexpensive and efficient method to obtain data for the whole area of study: using GIS tools
Method
n=1,392
Data Modelling procedure
Sub-sample of 10%
Range:
$50,000-$250,000
Mean price:
$112,000
Method
• 75 physical attributes
Data Modelling procedure
• Over 1,400 potential interactive variables
• 48 environmental variables
• 14 location variables
• 36 census variables
• 40 accessibility estimators
Method
• Extraction of land use information using color areal photographs
Data Modelling procedure
• continuity
• availability
• low price
Method
• 124 areal color photographs covering the area of study
Data Modelling procedure
Method
Scanning: raster images
Data Modelling procedure
Spatial referencing using road network, hydrology and buildings from topographic map (Geographic Transformer)
Building of a mosaic (Arc View)
Method
• Categorization using ISODATA technique
Data Modelling procedure
• Tree coverage
• Lawn surfaces
• Barren land
• Mineral surfaces
MethodData Modelling procedure
• Computing of land use information using buffer functions
Method
• Multiplicative form, explained variable Ln of selling price
Data Modelling procedure
• Three steps*:- Model A: Property-specific attributes- Model B: location, accessibility and census variables added- Model C: land use data added
* Regression specification: OLS, stepwise procedure
• Controlling for multicolinearity (VIF), spatial autocorrelation (I of Moran), heteroskedasticity (Goldfeld-Quandt test).
Results
Spatial Autocorrelation
Model A Model B Model C
Coefficientsa
11.09964 .112 99.273 .000
-.26279 .013 -.291 -20.174 .000 1.111
.00392 .000 .401 23.859 .000 1.505
-.13928 .010 -.228 -13.722 .000 1.469
.06457 .011 .094 5.734 .000 1.445
.04876 .011 .073 4.431 .000 1.451
.07913 .012 .103 6.480 .000 1.347
-.09493 .018 -.082 -5.227 .000 1.301
-.12145 .021 -.089 -5.680 .000 1.302
.06184 .012 .079 5.207 .000 1.227
.12325 .023 .086 5.255 .000 1.434
.07896 .018 .064 4.311 .000 1.160
.10251 .012 .135 8.587 .000 1.324
.05992 .013 .069 4.578 .000 1.197
.05082 .018 .041 2.790 .005 1.163
.06215 .022 .040 2.849 .004 1.065
.07130 .018 .056 3.862 .000 1.121
.15197 .031 .073 4.906 .000 1.191
.04334 .012 .056 3.723 .000 1.193
(Constant)
LTAXRATE
LIVAREA
LNAPPAGE
FIREPLCE
LNLOTSIZ
SUPFLOOR
INFCEILQ
QUALINF
STONBR51
STOREY
BASESEFH
BASEFINH
DETGARAG
ATTGARAG
CARPORT
EXCAPOOL
WATRSEWR
DISHWASH
Model
A
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. VIF
CollinearityStatistics
Dependent Variable: LNSPRICEa.
Number ofcases
Independentvariables
Adj. R-square
SEE F ratio Prob MaximumVIF value
Model A 1253 18 0.765 0.1830 227.2 0.000 1.469
-0.20
0.00
0.20
0.40
0.60
0.80
1.00
12-298 m 300-599 m 600-898 m 900-1199 m 1200-1499 m
Lag
Mo
ran
's I
Model A LnSprice Sig 0.05
Results
Spatial Autocorrelation
Number ofcases
Independentvariables
Adj. R-square
SEE F ratio Prob MaximumVIF value
Model A 1253 18 0.765 0.1830 227.2 0.000 1.469Model B 1253 22 0.834 0.1538 286.4 0.000 2.585
Coefficientsa
10.66245 .100 106.561 .000
-.08214 .015 -.091 -5.651 .000 1.957
.00318 .000 .325 22.259 .000 1.605
-.20821 .011 -.341 -18.579 .000 2.534
.04975 .010 .073 5.141 .000 1.509
.12120 .010 .182 11.876 .000 1.765
.05812 .010 .076 5.596 .000 1.378
-.05581 .015 -.048 -3.631 .000 1.318
-.08219 .018 -.060 -4.514 .000 1.336
.03624 .010 .046 3.599 .000 1.248
.05625 .020 .039 2.818 .005 1.469
.04855 .015 .039 3.136 .002 1.172
.07279 .010 .096 7.165 .000 1.356
.04812 .011 .055 4.328 .000 1.221
.05179 .015 .042 3.347 .001 1.187
.05166 .018 .033 2.794 .005 1.082
.09030 .016 .071 5.773 .000 1.139
.11569 .026 .056 4.371 .000 1.229
.04568 .010 .059 4.654 .000 1.200
.00176 .001 .057 3.249 .001 2.351
.00580 .000 .262 14.665 .000 2.409
.00191 .000 .093 5.386 .000 2.269
-.01133 .002 -.139 -7.528 .000 2.585
(Constant)
LTAXRATE
LIVAREA
LNAPPAGE
FIREPLCE
LNLOTSIZ
SUPFLOOR
INFCEILQ
QUALINF
STONBR51
STOREY
BASESEFH
BASEFINH
DETGARAG
ATTGARAG
CARPORT
EXCAPOOL
WATRSEWR
DISHWASH
SINGLHLD
UNIVDEGR
DW46_60
ULAVLCTM
Model
B
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. VIF
CollinearityStatistics
Dependent Variable: LNSPRICEa.
Model A Model B Model C
-0.20
0.00
0.20
0.40
0.60
0.80
1.00
12-298 m 300-599 m 600-898 m 900-1199 m 1200-1499 m
Lag
Mo
ran
's I
Model A Model B
LnSprice Sig 0.05
ResultsModel A Model B Model C
Results
Model C
Coefficients
10.744 .099 108.331 .000
-.06206 .014 -.069 -4.344 .000 2.014
.00314 .000 .321 22.530 .000 1.633
-.20795 .011 -.340 -19.186 .000 2.524
.11905 .010 .179 11.945 .000 1.792
.04983 .010 .065 4.919 .000 1.396
-.06358 .015 -.055 -4.258 .000 1.325
-.07864 .018 -.058 -4.455 .000 1.338
.04306 .010 .055 4.410 .000 1.249
.04159 .015 .034 2.766 .006 1.177
.07504 .010 .099 7.612 .000 1.360
.04252 .019 .030 2.183 .029 1.490
.04397 .011 .050 4.054 .000 1.238
.04229 .015 .034 2.754 .006 1.245
.04912 .018 .032 2.737 .006 1.085
.08957 .015 .070 5.924 .000 1.133
.04793 .009 .070 5.074 .000 1.532
.11147 .026 .054 4.331 .000 1.238
.04282 .010 .055 4.491 .000 1.205
.00547 .000 .247 13.222 .000 2.803
.00089 .000 .043 2.318 .021 2.811
-.00992 .002 -.122 -6.596 .000 2.747
.00452 .001 .090 4.899 .000 2.697
-.11325 .023 -.075 -4.899 .000 1.876
-.01514 .003 -.068 -5.008 .000 1.478
.15721 .040 .056 3.978 .000 1.600
.00532 .002 .027 2.307 .021 1.100
-.00041 .000 -.035 -2.779 .006 1.238
2.26220 .450 .077 5.027 .000 1.866
(Constant)
LTAXRATE
LIVAREA
LNAPPAGE
LNLOTSIZ
SUPFLOOR
INFCEILQ
QUALINF
STONBR51
BASESEFH
BASEFINH
STOREY
DETGARAG
ATTGARAG
CARPORT
EXCAPOOL
FIREPLCE
WATRSEWR
DISHWASH
UNIVDEGR
DW46_60
ULAVLCTM
AGE65_UP
BarrenLand100
BarrenLand100 * Age65
Lawn300 * InvTimeUniv
Lawn500 * ATTGARAG
Tree/MinRatio080 *Age65
Vegetation060 *InvTimeUniv
Model
C
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. VIF
CollinearityStatistics
Results
Spatial Autocorrelation
Model A Model B Model C
-0.20
0.00
0.20
0.40
0.60
0.80
1.00
12-298 m 300-599 m 600-898 m 900-1199 m 1200-1499 m
Lag
Mo
ran
's I
Model A Model B Model C
LnSprice Sig 0.05
Validation
Results
•Validation of the final model with the sub-sample of 10%
Calculated predicted error of 10.9%
Adj. R-square: 0.829
Std. Error of estimate: 0.159
Results
C [BarrenLand100, BarrenLand100*Age 65] = (e ((-0.11325*BarrenLand100)-(0.01514*(BarrenLand100-0.6597)*(Age65-8.258)))) - 1
Standardized Coefficients t Sig.
Collinearity Statistics
B Std. Error Beta VIFBarrenLand100 -0.11325 0.023 -0.075 -4.899 0.000 1.876BarrenLand100 * Age65 -0.01514 0.003 -0.068 -5.008 0.000 1.478
Unstandardized Coefficients
Effect of Barren land cover in a 100 m radius
• Lawn in poor condition increases the feeling of insecurity (Kuo 1998)
50 2.5
Kilometers
Spatial Coefficient+2 STDD
10.920.885480.850.73
Map 2: Marginal Effect on House Value of 2 Stdd over Mean Surface of Barren Land in a 100 m Radius (1253 Cottages)
Road Network
Hydrography
Municipalities
Main Roads
Highways
Lake / River
River
Stream
Boundaries
Sc [BarrenLand100+2STDD] =e (-0.10685*1.16)+(-0.01079(1.16-0.66)(Age65-8.258))+(0.26927(1.16-0.66)*(Qualinf-0.083))
Results
Negative effect of trees at a local scale for aged population
Standardized Coefficients t Sig.
Collinearity Statistics
B Std. Error Beta VIFTree/MinRatio080 * Age65 -0.00041 0 -0.035 -2.779 0.006 1.238
Unstandardized Coefficients
In accordance with previous findings by Des Rosiers et al. (2001)
Effect of Tree vs Mineral cover in a 80 m radius
Positive effect of trees at a local scale for younger population
Results
Standardized Coefficients t Sig.
Collinearity Statistics
B Std. Error Beta VIFLawn500 * ATTGARAG 0.00532 0.002 0.027 2.307 0.021 1.1
Unstandardized Coefficients
Sc [Attgarag, Lawn500*Attgarag] = e (0.04229*1)8(0.00532*(Lawn500-22.35)*(1-0.105)
50 2.5
Kilometers
Map 1: Marginal Effect of Presence of an Attached Garage Considering the Variation in Lawn Cover (1253 Cottages)
Spatial Coefficient
Net Effect of an Attached Garage
1.16131.10881.05631.00380.9513
Road Network
Hydrography
Municipalities
Main Roads
Highways
Lake / River
River
Stream
Boundaries
Sc [ATTGARAG/Lawn500] =e (0.04201)+(0.00591*(Lawn500-22.3529)*(1-0.105))Effect of an attached garage considering lawn area in a 500 m radius
Conclusion
• Model performance: slight increase in explained variance, but important drop of spatial autocorrelation
• Use of interactive variables: the effect of an environmental attribute is not constant over space
• Use of areal photographs integrated in a GIS proved to be efficient and low cost
Integrating Land Use in a Hedonic Price Model Using
GIS
URISA 2001
Yan Kestens
Marius Thériault
François Des Rosiers
Urban and Regional Planning Research Centre
Laval University, Québec, Canada
ResultsEffect of Time Distance considering lawn areas in a 300 m radius and vegetation cover in a 60 m radius
Standardized Coefficients t Sig.
Collinearity Statistics
B Std. Error Beta VIFVegetation060 * InvTimeUniv 2.2622 0.45 0.077 5.027 0 1.866Lawn300 * InvTimeUniv 0.15721 0.04 0.056 3.978 0 1.6ULAVLCTM -0.00992 0.002 -0.122 -6.596 0 2.747
Unstandardized Coefficients
Sc [Ulavlctm, Ulavlctm*Lawn300, Ulavlctm*Veg060] =
e(-0.0092*Ulavlctm)*(0.15721*((1/Ulavlctm)-0.1016)*(v2_r300-7.866))+(2.2622*((1/Ulavlctm)-0.1016)*(vg_060-0.648276))