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Research ApproachBy measuring the elasticity of residential water demand to changes in price and weather through a linear regression model, a reasonable of likely changes in revenue can be provided.

The City of Goodyear is interested in understanding how changes in price and weather patterns will affect revenue from residential water use.

Research Statement Summary Statistics

Conclusions

A reliable estimate of the elasticity proved difficult due to endogeneity in the error term possibly from the omitted conservation variable. Strongly correlated predictors have made regression coefficients unstable, also negatively impacting the ability to interpret the impact of price and weather patterns on water consumption.

Future Research Further understanding of more sophisticated methods for modeling increasing block rate structure using marginal price and alternative specifications for the demand equation must be understood to complete this research project.

Acknowledgment

This material is based upon work supported by the National Science Foundation under Grant No. SES-0951366 Decision Center for a Desert City II: Urban Climate Adaption (DCDC). Any opinions, findings, and conclusions or recommendation expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF). I would like to thank City of Goodyear’s Water Resource Manager, Mark Holmes, and Associate Professor at the School of Sustainability, Rimjhim Aggarwal, for guidance and support.

Price Elasticity for Residential Water Demand in the City of Goodyear

Undergraduate Internship for Science-Practice IntegrationJeffrey W. Iles

- Consumption data cleaned to reflect proper demographics - Increasing block prices identified as function of consumption - Averaged price method used in block rate pricing calculations - Ordinary Least Squares method used for estimation of elasticities

Limitations

- Water conservation data unavailable: unaccounted effect - Household data integration unachievable due to time constraint

Analysis - The statistical software, SPSS, was used to perform an Ordinary Least Squares (OLS) regression analysis

The dependent (criterion) variable in this equation is residential water consumption *One Centum Cubic Foot (CCF) Equals 748 Gallons

The independent (predictor) variables are price, temperature, and precipitation *Continuity of Operations (COOP) Site at Litchfield Park, AZ Selected - The control variable for this model was population, data was found using the 2010 U.S. Census Report and the 2012 U.S. Census Bureau, American Community Survey report

Results

*Overall Model. R2 = .035, adjusted R2 = -.036; F (1, 59) = .492 p = .742 **Tolerance: Population .985, Price .925, Precipitation .984, Temperature .933

Research Methods

!Monthly Average of Average Daily Temperatures

(Degrees Fahrenheit) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

2010 55.06 57.7 61.48 68.8 76.03 88.07 94.63 91.81 87.79 74.55 59.53 56.39 2011 52.92 53.79 65.32 72.27 76.79 87.65 93.65 95.82 87.92 75.15 60.5 51.05 2012 56.21 57.71 63.31 72.98 81.65 90.97 92.13 93.68 86.32 75.37 64.82 53.56 2013 51.47 55.09 66.92 72.45 81.11 92.02 94.74 91.81 85.47 70.74 63.27 53.03 2014 56.6 61.62 66.61 72.4 81.08 90.42 94.08 89.45 86.1 76.27 62.27 55.18

Monthly Sum of Precipitation (Inches)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2010 3.28 1.6 1.07 0 0 0 0.91 1.21 0 0.45 0.11 1.83 2011 0 0.8 0.23 0.39 0 0 0.4 0 0.11 0.34 0.61 1.06 2012 0.03 0.02 0.67 0.03 0 0 1.18 1.59 0.14 0 0.02 1.06 2013 1.14 0.15 0.26 0.03 0 0 0.26 1.74 1.05 0.05 2.26 0.61 2014 0 0 1.23 0 0 0 0.53 2.38 5.4 0.21 0 0.74 !

!

City of Goodyear Base Rate Prices Monthly!

Meter Size (inches) 2010 2011$ 2012$ 2013$ 2014$0.75 $ 9.69 $ 9.94 $ 10.05 $ 10.23 $ 10.23 1 $ 10.95 $ 11.81 $ 12.74 $ 14.14 $ 14.14 1.5 $ 13.56 $ 16.36 $ 19.74 $ 22.86 $ 22.86 2 $ 21.47 $ 26.22 $ 32.02 $ 37.01 $ 37.01 3 $ 38.05 $ 48.43 $ 61.64 $ 70.67 $ 70.67 4 $ 69.78 $ 83.36 $ 99.58 $ 115.24 $ 115.24 6 $ 135.11 $ 158.83 $ 189.08 $ 218.71 $ 218.71

!

City of Goodyear Increasing Block Rate Prices Per 1,000 Gallons

Rate Blocks 2010 2011 2012 2013 2014 0-6,000 gal $ 1.27 $ 1.32 $ 1.46 $ 1.18 $ 1.18 6,001-12,000 gal $ 2.54 $ 2.64 $ 2.92 $ 2.36 $ 2.36 12,001-30,000 gal $ 3.81 $ 3.96 $ 4.38 $ 3.54 $ 3.54 30,001 plus gal $ 4.95 $ 5.15 $ 5.69 $ 5.69 $ 5.69

Total Residential Water Consumption Measured in Centum Cubic Feet

2010 2011 2012 2013 2014 January 102085 120964 78861 97883 103013 February 74169 78736 99657 76304 83011 March 73292 57099 84887 79141 85269 April 107443 110020 91682 93379 120085 May 101787 133366 134203 132327 109351 June 113668 77089 120647 118195 118730 July 154019 149304 124385 127192 157624 August 147992 155852 148859 160405 120745 September 81661 86156 112928 112766 110054 October 135179 137150 103899 130109 120821 November 123628 120518 126045 100302 97441 December 60065 70173 86142 81681 74878 Total 1274988 1306270 1312195 1309684 1301022 !

Model Summary and Coefficients

Zero order r B SE B b t p Population -.039 -.161 1.072 -0.20 -.150 .881 Price .175 23387.85 18059.16 .178 1.295 .201 Precipitation .044 1734.19 3866.65 .060 .449 .656 Temperature .035 -1.694 245.27 -.001 -.007 .995 !