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))