Location Location Location: Residential Distributions on the Shorelines of North Pond and East Pond

By Sophie Sarkarí11

Colby College Environmental Studies Program

GIS and Remote Sensing

 

ABSTRACT

 In this study I examine the spatial distribution of high value and new residences on the shoreline of East Pond and North Pond. To do so, I analyzed property tax data in ArcGIS, between lakes and within lakes, to determine whether there was spatial clustering of the following shoreline property characteristics: property values, price/meter2 of land, and house age.The results identify six potential areas between the two lakes with significantly higher property values, land values, or house ages. If these variables are effective proxies for household income and resident age, then the identified areas may be good locations for local lake associations to target stakeholder engagement projects.

INTRODUCTION

One of the main challenges that local lake conservation organizations on the Belgradelakes face is engaging residents in lake stewardship (Shannon 2010). Several studies on pro-environmental behavior have shown that certain demographic characteristics are significant determinants of whether an individual is willing to pay for and participate in environmental stewardship. These studies have shown a positive relationship between income and conservation behavior and a negative relationship between age and conservation behavior (Del Saz-Salazar et al. 2009; Steg and Vlek 2009; Fransson and Garling 1999).A recent contingent valuation survey of shoreline residents on North Pond and East Pond that I conducted in March confirmed these findings for the region.Using two of the Belgrade Lakes, East Pond and North Pond, as case studies, I attempt to locate concentrations of high land values, property values, and house age as proxies for income and resident age in an effort to help local conservation groups gain financial support for their conservation programs.

METHODS

 I constructed a database by joining per parcel tax data from the Belgrade Regional Conservation Alliance with spatial data of East Pond and North Pondís shoreline lots (McCullough 2010).The tax data included information on land and property value, while the spatial data included information on lot size, location, and the year each residence was built. Then, using ArcMap 9.3, I created a point layer out of the shoreline lot polygons, from which I generated four kernel density maps.From these maps, I selected out lots within concentrated areas of high land values (measured as price/m2), property values, and low age.I labeled these areas A-F (Figure 4), and conducted a non-parametric analysis, using the Mann-Whitney U test, to see whether these regions had significantly higher land and property values and significantly lower ages compared to the full shoreline population.North Pond and East Pond were analyzed separately.

RESULTS

In the results that follow, averages are reported in parentheses.On North Pond, Areas B ($83.13/m2), C ($82.92/m2), and D ($77.89) have significantly higher land values than the rest of the shoreline population.Area B ($112,016), however, also has significantly lower property values. The results of the analysis of house age show that Area A has significantly newer houses (18 years) and Areas C (74 years) and D (57 years) have significantly older houses.Table 1 summarizes these results.Within the East Pond shoreline population, both Areas E ($70.67/m2) and F ($20.14/m2) are concentrated with significantly higher land values than the rest of the shoreline population.Area E is also characterized by significantly higher property values ($165,808).On the other hand, Area F has significantly lower property values ($67, 814). The results of the analysis of house age show that Area E has significantly older houses (66 years) and Area F has significantly newer houses (38 years). Table 2 summarizes these results.

PRICE_LOT.emf

 

 

 

 

 

 

 

 

 

 

 

 

 

PROP_VAL.emf

 

 

 

Figure 1. A map showing concentrations of high land values along East Pond and North Pond

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

PROP_VAL.emf

 

 

 

PROP_VAL.emf

 

 

 

Figure 2. A map showing concentrations of high property values along East Pond and North Pond

 

 

 

 

 

 

 

 

AGE.emf

 

 

 

 

 

 

 

 

 

 

 

 

 

 

PROP_VAL.emf

 

 

Figure 3. A map showing concentrations of new houses along East Pond and North Pond

 

 

 

 

 

 

 

Figure 4.A map showing the five areas analyzed based on the kernel density layers above

 

 

 

Table 2: A Comparison of land values, property values, and house age between the highlighted areas E and F and the rest of the East Pond shoreline

Location on East Pond Shoreline

Price/m2

 

Property Value

Age of House

N

Mean

Mann-

Whitney

(P-value)

N

Mean

Mann-Whitney

(P-value)

N

Mean

Mann-Whitney

(P-value)

East Pond (E)

 

 

 

 

 

 

 

 

 

EP-E

13

70.67

-5.389

13

165808

-2.886

 

13

66

3.242

 

Rest of Shoreline

213

20.14

(<0.01)**

213

142528

(0.004)**

213

43

(0.001)**

 

East Pond (F)

 

 

 

 

 

 

 

 

 

EP-F

85

27

8.050

 

85

67814

8.094

 

85

38

-3.861

 

Rest of Shoreline

141

20.89

(<0.01)**

141

189297

(<0.01)**

141

48

(<0.01)**

(*)p<0.05; (**)p<0.01

Table 1: A Comparison of land values, property values, and house age between the highlighted areas A, B, C, Dand the rest of the North Pond shoreline

Location on North Pond Shoreline

Price/m2

 

Property Value

Age of House

N

Mean

Mann-

Whitney

(P-value)

N

Mean

Mann-Whitney

(P-value)

N

Mean

Mann-Whitney

(P-value)

North Pond (A)

 

 

 

 

 

 

 

 

 

 

NP-A

44

61.79

-1.485

44

140186

0.677

44

18

-5

Rest of Shoreline

206

64.96

(0.6277)

206

144824

(0.499)

206

51

(p<0.01)**

North Pond (B)

 

 

 

 

 

 

 

 

 

 

NP-B

31

83.13

-2.872

31

112016

2.876

31

52

1.213

Rest of Shoreline

219

61.75

(0.004)**

219

148498

(0.004)**

219

46

(0.225)

North Pond (C)

 

 

 

 

 

 

 

 

 

 

NP-C

38

82.92

-2.923

38

139971

-0.683

38

74

5.785

Rest of Shoreline

212

59.30

(0.004)**

212

144710

(0.495)

212

42

(p<0.01)**

North Pond (D)

 

 

 

 

 

 

 

 

 

 

NP-D

29

77.89

-2.581

29

146100

-1.465

29

57

2.289

Rest of Shoreline

221

62.62

(0.010)**

221

143717

(0.143)

221

45

(0.0221)*

(*)p<0.05; (**)p<0.01

 

DISSCUSSION

Assuming that both property and land values are good proxies for household income, and holding age constant, Area E on East Pond is probably the best target for conservation groups looking to find financial assistance for their projects. If land values alone are taken into consideration, then Areas B, C, and D on North Pond, and Areas E and F on East Pond may also be good locations to focus their residential engagement campaigns.Lastly, assuming that house age is a good proxy for resident age, and that younger residents are more willing to pay for and participate in conservation (Del Saz-Salazar et al. 2009; Steg and Vlek 2009; Fransson and Garling 1999), Area A on North Pond and Area F on East Pond would be the best locations to target engagement campaigns.The results of this study, however, are difficult to interpret for several reasons.The first is that the two different proxies used for household income were often negatively associated, and it is unclear which is a better proxy for household income.Additionally, in many areas the house age variable was positively associated with higher property and land values, and thus it remains unclear whether residents in those areas would be more likely or less likely to engage in lake stewardship.Furthermore, it is difficult to say whether house age is a good proxy for resident age without more information about household residents.

CONCLUSION

Although there do seem to be concentrated areas of high property values, high land values, and older houses on both North Pond and East Pond, it is not clear whether these variables are sufficient proxies for household income and resident age.Therefore, the results of this study are largely inconclusive.However, the structure and the methodology behind this study can be used as a model for future research with survey data that reveals more information about the residents on each lot, including household income and age.

ACKNOWLEDGEMENTS

I would like to thank Professor Philip Nyhus for the many GIS lab hours he devoted to all of his students this semester.I would also like to thank Manny Gimond for his enormous GIS wisdom, kindness, and patience.

Works Cited

Bouchard, Roy. 2010. Maine DEP Lake Assessment Program.

Del Saz-Salazar, Salvador, Francesc Hernandez-Sancho, and Ramun Sala-Garrido. 2009. The social benefits of restoring water quality in the context of the Water Framework Directive: A comparison of willingness to pay and willingness to accept. Science of The Total Environment 407 (16):4574-4583.

Fransson, N., and T. Garling. 1999. Environmental concern: conceptual definitions, measurements, methods, and research findings. Journal of Environmental Psychology 19 (369-382)

McCullough, Ian 2010. The impacts of land use and development patterns on water quality of the Belgrade Lakes. Environmetnal Studies Program. Colby College, Waterville, ME.

Michael, Holly, Kevin Boyle, and Roy Bouchard. 1996. Water Quality Affects Property Prices: A Case Study of Selected Maine Lakes. Maine Agricultural and Forest Experiment Station Misc. Report 398, Feb 1996, Univ. of Maine

MDEP 2010. Draft 2010 Integrated Water Quality Assessment Report. Maine Department of Environmental Protection.

Shannon, Maggie. 2010. Maine Congress of Lake Associations.

Steg, Linda, and Charles Vlek. 2009. Encouraging pro-environmental behaviour: An integrative review and research agenda. Journal of Environmental Psychology 29 (3):309-317.