1

Diving Deep into Commercial, Industrial, and Institutional Water

Usage: A Benchmarking, Water Efficiency, and Market Analysis Study

in Irvine Ranch Water District

Prepared for Irvine Ranch Water District

Marilynn Margarita Alvarado, Henri Jose Fernandez, Kyle Cedric Gainey, Enrique Garduno Cortes, Ally Joy Howe, Shivani Suvarna Pampati

Advisor: Newsha Ajami

Public Policy Practicum Spring 2018

2

Acknowledgements

We would first like to thank our advisor, Dr. Newsha Ajami, for her support, encouragement, and expertise during this project along with the Public Policy Program

for this very special opportunity. Additionally, we are extremely grateful for Kim Quesnel for her support with our data and constant guidance throughout this process.

Also, a special gratitude goes out to Jose Bolorinos (Pepe) for helping and providing us with data insights. Furthermore, we would like to thank Amy McNulty and Enrique

Zanetti from the Irvine Ranch Water District for their collaboration in this project and their assistance with our data collection. Thanks for all your encouragement!

3

Executive Summary

The 2011-2015 California drought has increased both water conservation efforts and water demand strategies for water districts throughout the state. Water agencies such as Irvine Ranch Water District (IRWD) have implemented strategies for reducing residential water use. However, demand management strategies for the Commercial, Industrial, and Institutional (CII) sector have yet to be analyzed. Thus, it is likely that there is water saving potential in the CII sector due to this lack of analysis and focus within the sector.

The Stanford Public Policy Team worked with IRWD to analyze and evaluate their water efficiency programs, village water use, and water use by industry composition for their CII clients. We utilized Yelp, IRWD, and publically available data sets to analyze the following areas of interest:

● Yearly CII industry water use by income, race, and unemployment demographics ● Monthly CII industry water use by rebate participation ● Yearly CII industry water use by budget rate programs ● Yearly CII industry water use by Yelp ratings and price ranges ● 2017 IRWD school water use by income, race, and unemployment demographics

The team utilized linear regression models, GIS modeling, and qualitative analysis of

IRWD, Yelp data, and publically available data sets in order to provide a clearer and deeper understanding of the CII sector and answer the following research questions:

● How do IRWD CII businesses use water by industry? How do demographics affect their yearly water use?

● Are rebate programs an effective method of making water use more efficient? ● Is a four-tiered budget rate system more effective than a two-tiered system? Is the

IRWD budget rate program an effective method for making water use more efficient than a fixed rate system?

● How do Yelp ratings and Yelp pricing predict water use within specific industries?

● What is the water use per student across comparable schools? Are newer schools more efficient than older ones? How do demographics affect school water use?

To address the policy questions of interest, our methodology included GIS mapping for

demographic characteristics and Ordinary Least Squares (OLS) and Difference-In-Difference (DID) for our regression models. We created four subsets of data to answer our specific policy questions above. Three data sets (demographic, budget rate, Yelp) span 2010 to 2017, while our school case study data set only analyzes 2017. The demographic data set includes yearly demographics by village and average yearly water use per industry per village. Using this

4

dataset, we answered our policy questions regarding socioeconomic and budget rate effects. The rebate data set only included clients who participated in rebate programs, which we used to evaluate the effects of rebates on water use. The Yelp dataset was strictly for CII clients who have Yelp data associated with them. In this data set, we evaluated the effects of Yelp pricing and Yelp ratings. The final dataset we created was industry specific, concentrating on schools within the IRWD district. We used this final dataset to examine the effects of average building age, school enrollment, and demographics have on school water use.

Our significant findings include that the average age of a village is associated with an increase in water use. Secondly, we found that there was too little data to make concrete associations with IRWD’s rebate program. When analyzing budget rates, we found that the switch to a budget rate system from prior pay rate systems had little effect on the majority of industries in the CII sector. When examining the relationship between Yelp data and water use, we found that higher Yelp ratings are associated with decreased water use, while Yelp pricing had varied effects. In our school case study we affirmed the intuition that as schools increase in size, water use increases. Additionally, in our school analysis, we find that higher income in school districts and older meters associated with specific schools were positively associated with total water use.

Based on these findings, we recommend that Irvine Ranch Water District target older villages for water conservation programs and attempt to foster relationships with businesses in those villages. We recommend that IRWD be proactive about advertising their rebate programs and educating their CII clients on water efficiency benefits, conducting a longitudinal case study on a current participant, showcasing their story, continuing to track rebate participation, and conducting further studies on the effects. We also recommend IRWD adopt stricter monthly allocation methods for budget rates. Furthermore, we recommend that IRWD advertise that higher Yelp ratings are correlated with lower water use, communicate with companies that have high Yelp ratings and low water use, and encourage them to become a certified water efficient business (WaterStar!). Finally, we recommend replacing all schools’ meters within IRWD with “Smart Meters” for more precise water measurements.

5

Executive Summary 3

Introduction 6

Background 7 Water Use in the Commercial, Industrial, and Institutional Sector 7 Irvine Ranch Water District 9

Methodology 11

Data 11 1. Background of IRWD: Village Water use and Demographics (2017) 11 2. IRWD Case Study: School Analysis 19 3. Industry Composition of CII Sector 19 4. Yelp Data and Yelp Ratings 23 5. Normalized Water Demand 26 6. Rebate Programs 31 7. Budget Rates 33

Qualitative Analysis 33

Quantitative Analysis 34 Regression 1: Effect of Income, Unemployment, Village Age and Race on Yearly Water Use 35 Regression 2: Effect of Rebates on Monthly Industry Water Use 37 Regression 3: Effect of a Tier System Budget Rate Change on Yearly Water Use; Effect of a Budget Rate Switch on Lake Forest Yearly Water Use 39 Regression 4: Effect of Yelp Rating and Yelp Price Range on Yearly Water Use 45 Regression 5: Effect of Average Building Age, School Enrollment, Population Density, and Income on School Yearly Water Use 49

Limitations 54

Findings 55

Recommendations for Irvine Ranch Water District 55 1. Implement “Smart” meters across schools to help promote more accurate water readings 55 2. Promote rebates through more proactive methods to help better educate clients on water efficiency benefits 56 3. Create water conservation programs for more older CII villages 56 4. Advertise: lower water use correlates with higher Yelp ratings 57 5. Conduct further analysis into budget rate implementation 57 6. Collect more intensive data on industry characteristics with CII clients 57

Works Cited 58

6

Introduction

The severe California drought from 2011-2015 has led to an increased focus on the functionality of water districts within the state and has impacted the water use of homes, businesses, and other operations. Efforts to address these concerns are seen in Figure 1.1 below, which spans from 2008 to 2017. When taking into consideration the functionality of water districts, Irvine Ranch Water District (IRWD) provides an interesting case study. With a greater daytime than service population and a diverse water supply portfolio, it is necessary to evaluate IRWD’s demand management strategies offered to their water users.

Figure 1.1: California Drought Timeline

California Drought Timeline with information gathered from article “Water Deeply: An Interactive Timeline.” NewsDeeply, 2017. 1

Our team worked with IRWD to assess their CII sector water use by industry in an

attempt to evaluate trends over time. The overarching objective of this project is to assess how CII water use has been evolving over time and to what extent IRWD’s demand management strategies have been affecting water use in the CII sector. In order to better comprehend these strategies, we considered the following research questions:

● How do IRWD CII businesses use water by industry? How do demographics affect their yearly water use?

1 All points of the timeline were gathered from the NewsDeeply Interactive Timeline. 

7

● Are rebate programs an effective method of making water use more efficient? Is a four-tiered budget rate system more effective than a two-tiered system?

● Is the IRWD budget rate program an effective method for making water use more efficient?

● How do Yelp ratings and Yelp pricing predict water use within specific industries?

● What is the water use per student across comparable schools? Are newer schools more efficient than older ones? How do demographics affect school water use?

In order to answer these questions, our team worked closely with IRWD to obtain the

necessary data in addition to publicly available data sources such as Yelp to supplement our analysis. IRWD provided us with data sets on their historical water use by account, rebate program participation, parcel size and location, leak adjustment data, budget rate history, NAICS codes associated with each address, longevity of a current customer at a location, ages of buildings, and spatial identifiers. We plan on compiling and matching IRWD and Yelp data sets to perform regression analysis. Through these regressions, we believe we will gain a better understanding of IRWD’s CII sector water use and how to minimize their use to maximize water savings. The intent of this report is to provide IRWD with a comprehensive overview of their CII clients’ water use. We will provide recommendations for incentivizing efficient water use within the sector.

Background Water Use in the Commercial, Industrial, and Institutional Sector

The Commercial, Industrial, and Institutional (CII) water use sector has gone

understudied in the past due to the complexities of analyzing a heterogeneous client base. The CII sector includes a diverse range of business models with varying water use between industries. For example, laundromats and restaurants utilize more water compared to an electronics store. To provide the basis for our analysis, we examined past studies on the CII sector.

The two main focuses of this project are benchmarking water use and measuring

efficiency in the nonresidential, commercial, industrial, and institutional sectors. This has proven deeply complicated in the past primarily due to a lack of standardized classification systems for CII water users. Studies have grouped CII clients by industry and have employed popular methods such as ordinary least squares (OLS) and data envelopment methods to benchmark water use and determine an efficient range. These studies have found it effective to 2

group CII clients according to the North American Industry Classification System (NAICS)

2 Benchmarking Nonresidential Water Use Efficiency Using Parcel Level Data, 2015 

8

using public API’s. With this information, water agencies could make more informed and 3

targeted conservation policies. The continued study of water use in the CII sector is important for water conservation

efforts because estimates indicate that potential savings range from 710,000 to 1.3 million acre-feet per year. Some studies have found that the potential for conservation and efficiency 4

improvements in California is so large that, even when taking into consideration future population and industry growth, no new water sources are required to sustain the state’s demands. Estimates indicate that 2.3 million acre-feet of California’s urban water use can be 5

saved with existing technology. Meanwhile, other studies have found that commercial water use can be reduced by 30 to 50 percent, while industrial use can be reduced by 25 to 50 percent. This would save an estimated 0.74 to 1.6 million acre-feet per year. From these statistics, it is certain 6

that the CII sector has great water saving potential. Figure 2.1 below shows urban water use per capita in California and total urban water use

by hydrologic region from 2001 to 2010. Even though the South Coast hydrologic region did not use the most water per capita per day, the South Coast of California utilized the most urban water out of any hydrologic region in the state. The South Coast hydrologic region that includes the metropolitan hubs of Los Angeles County, Orange County, and San Diego County used 4,200 gallons of water per thousand acre feet per year. The South Coast hydrologic region is the most populated area in the state of California, and therefore, has great water saving potential.

3 Large-Scale Analysis of Water Efficiency in California 4 Increasing Water Efficiency in California’s Commercial, Industrial, and Institutional (CII) Sector 5 Waste Not, Want Not - Pacific Institute Report 6 Urban Water Conservation and Efficiency Potential in California - Pacific Institute Report 

9

Figure 2.1: Urban Water Use per Capita (in gallons per capita per day) and Total Water

Urban Water Use (in thousands acre-feet per day) by hydrologic region, averaged for years 2001-2010

Chart from Urban Water Conservation and Efficiency Potential in California - Pacific Institute Report 7

Irvine Ranch Water District

Irvine Ranch Water District (IRWD) is an independent water district serving Central Orange County since 1961. IRWD provides high-quality drinking water, reliable wastewater collection and treatment, groundbreaking recycled water programs, and environmentally sound urban runoff treatment. As seen below in Figure 2.2, the district includes the cities of Irvine, Tustin, Lake Forest, Newport Beach, and Costa Mesa. IRWD serves a total daytime population of 500,000 and 380,000 residential customers. IRWD supplies 27 drinking wells, 27 urban runoff treatment sites, and 36 drinking water reservoirs. Additionally, IRWD has two recycled water treatment plants, 110,000 service connections, 525 miles of recycled water pipelines, 1,760 miles of drinking water pipelines, and 1,070 miles of sewage collections pipelines. IRWD’s 8

water portfolio in 2013 consisted of 31% clear groundwater, 25% recycled water (14% in 1990), 22% imported water (66% in 1990), 19% treated groundwater, and 3% local surface water. As shown below in Figure 2.3, IRWD’s water use breakdown among clients is composed of four types of customers: residential (57.4%), CII (30.7%), landscape (9.9%), and other (2%).

7 Urban Water Conservation and Efficiency Potential in California - Pacific Institute Report 8 IRWD Fact Sheet

10

Although CII clients use 30.7% of water resources, they represent only 5.5% of IRWD’s client base. Their allocation-based rate structure rewards conservation and sends a strong price signal to inefficient water use. IRWD’s rates are based on individual customer and property needs, landscaped area, and weather conditions, ensuring maximized efficiency.2

Figure 2.2: Irvine Ranch Water District Overview

Chart from Irvine Ranch Water District About Us 9

9 Irvine Ranch Water District About Us

11

Figure 2.3: IRWD Customer Water Use Breakdown Chart from Irvine Ranch Water District About Us 10

Methodology

In order to answer our policy questions, we are using a combined data set from IRWD and Yelp. This data set will be used for qualitative and quantitative analyses. Our analysis will allow us to evaluate trends in water demand among IRWD’s CII clients by industry and determine the impact of key events and programs on water demand among CII clients.

Data 1. Background of IRWD: Village Water use and Demographics (2017)

In order to conduct our analysis for this section, the team gathered demographic data from SimplyAnalytics, a web-based analytics and data visualization application that creates interactive maps and reports. We then utilized Geographic Information System (GIS) to model our specific demographic variables of interest.

10 Irvine Ranch Water District About Us

12

The team’s first step in studying IRWD’s CII water use was to analyze water use within its small communities, also referred to as villages, and see if the demographics of those communities affected their water use. Demographics such as cultural and socio-economic backgrounds are important to take into consideration because they can affect the way people interact with commercial industries such as restaurants, laundromats, and other common businesses. Since people are often geographically grouped by demographic characteristics, including income and race, we believe these factors could affect CII water use per village.

IRWD is composed of 78 villages, which are each defined as a small community where

IRWD aggregates and analyzes water use at the communal scale. Despite villages being smaller than towns, each village typically has their own small businesses that cater to the community. Since each community in IRWD has a different demographic, the businesses in each village tailor to that specific demographic. Given that these businesses are usually run and operated by village residents, we hypothesize that there must be socioeconomic factors that affect the total water use of each business. For example, a business that is operated in a predominantly Asian community might utilize water differently than a business that is run in a predominantly Black community.

Figure 3.1 below shows a map of IRWD village boundaries, which vary in size and span

from the coast to the mountainous inland. It is important to note that some villages in IRWD’s coverage area have the same name. For example, there are two villages named IIC East and Newport Beach. Since none of the IRWD accounts were located in the southern IIC East, for the purpose of this analysis, we dropped the southern IIC East village from our study. For the two villages named Newport Beach, we matched each customer to the specific Newport Beach and named the villages Newport Beach 1 and Newport Beach 2.

13

Figure 3.1: IRWD Village Map Chart from Irvine Ranch Water District

To better understand the social fabric of the region, we analyzed six variables: population density (per sq. mile), percentage of unemployment, median household income, and percentage of Hispanic, Asian, Black, and White populations. We chose these variables because we believe

14

that there will be correlations between water use by village and population density (per sq. mile), income, race (percentage), and unemployment status (percentage). All of these variables were collected for our years of analysis, 2010 to 2017.

Table 3.2 below shows the total CII water use for 2017 by IRWD village. Our team

measured water use in CCF, a common unit of water use that equates to hundreds of cubic feet. As seen in this table, villages in IRWD have a large range of CII water use. For example, while IIC West had the highest water use in 2017 out of all villages, utilizing 3,073,764 CCF, Peppertree had the lowest 2017 water use, utilizing only 87 CCF. In totality, the average water use for all the villages was 99,190 CCF, with a standard deviation of 414,747 CCF in 2017. We believe that this large range of water use per village is due to the vast demographic diversity in IRWD.

Table 3.2: Total CII Water Use (CCF) and Number of Businesses for 2017 by Village

Village Water Use

(CCF) Number of Businesses Village

Water Use (CCF)

Number of Businesses

Baker Ranch 2,119 11 Peppertree 87 1

Cal Homes 2,794 4 Portola Hills 3,449 7

College Park 3,389 9 Portola Springs 2,845 11

Crystal Cove 20,892 17 Quail Hill 12,762 40

Culverdale 1,466 2 R San Joaquin 4,745 5

Cypress Village 5,017 19 Riviera 997 1

Deerfield 4,889 12 Santa Ana Heights 97,786 142

Eastwood 332 4 Santiago 9207 4

Foothill Ranch 159,936 159 Shady Canyon 7,716 12

Greentree 674 1 Stonegate 2,397 14

Heritage Fields Area 48,450 5 Stonegate East 889 4

Heritage Park 35,286 31 The Colony 11,658 31

Hidden Canyon 199 1 The Ranch 10,539 29

IIC East 725,260 1,045 Turtle Ridge 15,976 29

IIC West 3,073,764 968 Turtle Rock 43,953 43

Lake Forest 263,588 586 Tustin Industry 112,561 178

Laurelwood 117 1 Tustin Legacy 71,745 78

Los Olivos 3,627 15 Tustin Ranch 79,589 117

Newport Beach 1 37,592 10 Tustin Ranch North 3,562 11

Newport Beach 2 10,182 16 University Town

Center 49,769 101

15

Newport Coast 146,425 197 University Park 17,808 35

Village Water Use

(CCF) Number of Businesses Village

Water Use (CCF)

Number of Businesses

Northpark 27,893 65 West Irvine 56,987 70

Northwood 48,862 169 Westpark 66,887 116

Oak Creek 9,558 36 Willows 12,715 7

Orangetree 15,064 19 Windwood 36,558 34

Orchard Hills 10,692 32 Woodbridge 356,693 143

Parkwest Apts 3,214 6 Woodbury 38,722 90

Parkwood Apts 7,374 12 Woodbury East 105.58 2

Figures 3.3-3.9 below are a collection of maps showing the different ranges of

demographic variables by village borders. The ranges were determined by local census tract ranges in Orange County. From these maps, it is evident that there is a large range for each of our demographic variables of interest. Specifically, in Figure 3.3, the population density has a range of 16 to 21,000 residents per square mile. Similarly, each village has very different income and race demographics, as seen in Figures 3.6-3.9. The only demographic variable that has little change is unemployment, where all of the villages have below 4% unemployment rates. Despite this minor variability in unemployment, we believe that the large ranges in our other demographic variables of interest explain the differences in water use by village.

Figure 3.3: Population Density per Square Mile (2017)

16

Figure 3.4: Percentage of Unemployment in Villages (2017)

Figure 3.5: Median Household Income (2017)

17

Figure 3.6: Percentage of Hispanic Population (2017)

Figure 3.7: Percentage of Asian Population (2017)

18

Figure 3.8: Percentage of Black Population (2017)

Figure 3.9 Percentage of White Population (2017)

19

2. IRWD Case Study: School Analysis

Schools have high water uses and are a large client to target for implementing water efficiency policies. There are 57 schools in IRWD’s villages. Although these schools include both public and private institutions, we have concentrated our analysis on the four public school districts in IRWD for 2017. These four districts include the Irvine Unified School District, the Newport Mesa Unified School District, the Saddleback Valley Unified School District, and the Tustin Unified School District. Since school and water districts do not overlap, there are some schools represented in these districts that are not covered by IRWD. Therefore, we have left them out of our analysis. We have compiled data from IRWD accounts and California Ed Data. 11

California Ed Data is a government database that aggregates information on all public schools in the state of California. The team has gathered data on normalized water use per school, building age per school, school enrollment, school type, school village, and demographic data for each school village. However, the analysis is centered on water use per school district and school type. 3. Industry Composition of CII Sector

Since industries with different services tend to have different water uses, we found it important to analyze the industry breakdown of IRWD’s CII clients. In order to have a better understanding of how IRWD’s CII customer bills were broken up by industry, we used the North American Industry Classification Systems (NAICS) code for each client. For our analysis, we categorized IRWD’s CII clients into 25 different industries by NAICS codes, with our principal industry categories being mostly identical to the principal categories that NAICS identifiers. 12

Within the data provided by IRWD, there is a variable “Confidence Codes”, which represents an integer between one and ten that IRWD assigned a business depending on the district’s confidence that account was accurate. A confidence code of one means that IRWD was not at all confident in the accuracy of the NAICS code, while a confidence code of ten means that IRWD was extremely confident. Furthermore, IRWD believed that any reading with a confidence code that was six or below was too inaccurate to include in the dataset. Thus, we dropped observations that had a confidence code rating of six and below or missing. Prior to dropping these observations we were able to retain the accounts whose addresses were matched to Yelp data. Of those with a confidence code of zero to six or missing, around 18.4% were matched, retained, and categorized using Yelp. 94.3% of the accounts were matched to an industry classification using IRWD provided NAICS codes and about 5.7% were matched to an industry classification using Yelp data.

Moreover, to ensure that each principal industry category contains clients with similar end water use, we broke down certain NAICS principal categories in the following way: Real Estate Rental and Leasing into two classifications, Accommodation and Food Services into two classifications, and Other Services (except Public Administration) into four classifications. Real Estate Rental and Leasing was broken up into Real Estate Rental and Leasing (11) and Vehicle

11 California Ed Data 12 North American Industry Classification Systems

20

and Equipment Rental and Leasing (12). Accommodation and Food Services was broken up into Accommodation (19) and Food Services (20). Lastly, Other Services (except for Public Administration), was broken up into Automotive Mechanical and Electrical Repair and Maintenance (21); Personal and Household Goods Repair and Maintenance (22), Personal Services (23), and Organizations (24). Additionally, we encountered bill data that was not associated with a NAICS code or matched using Yelp data and dropped those observations.

Figure 4.1 below shows the percentages of customer bills in the dataset whose associated NAICS codes fall into the 25 industry classifications we have identified. Among the CIIs within the jurisdiction of the IRWD, we see that the top five most prevalent industries are the following: Real Estate Rental and Leasing (19.7%), Manufacturing (10.4%), Organizations (8.0%), Retail Trade (7.5%), and Professional, Scientific, and Technical Services (7.0%). In particular, our group believes that the Real Estate Rental and Leasing industry has the highest percentage of customer bills for the CII sector when compared with the other industries because of increased demand for buildings. With Southern California being such a desirable place to live in the U.S. because of its consistently warm weather, adventurous activity options, and opportunities for careers, people of all races typically want to live in the area, which increases business and revenue for real estate.

It is important to note that in Figure 4.1, the exact percentages for industry classifications Agriculture, Forestry, and Fishing (1), Mining (2), Utilities (3), and Management Companies and Enterprises (14) have been omitted because their associated values are extremely low: 0.03%, 0.12%, 0.72%, and 0.21% respectively. However, these industries are still included in our qualitative and quantitative analyses.

21

Figure 4.1: Industry Composition of the CII Sector (%)

Legend for Figure 5.1 (each number represents an industry category as follows): 1: Agriculture, Forestry, and Fishing; 2: Mining; 3: Utilities; 4: Construction; 5: Manufacturing; 6: Wholesale Trade; 7: Retail Trade; 8: Transportation and Warehouse; 9: Information; 10: Finance and Insurance; 11: Real Estate Rental and Leasing; 12: Vehicle and Equipment Rental and Leasing; 13: Professional, Scientific, and Technical Services; 14: Management Companies and Enterprises; 15: Administrative and Support and Waste Management and Remediation Services; 16: Educational Services; 17: Health Care and Social Assistance; 18: Arts, Entertainment, and Recreation; 19: Accommodation; 20: Food Services; 21: Automotive Mechanical and Electrical Repair and Maintenance; 22: Personal and Household Goods Repair and Maintenance; 23: Personal Services; 24: Organizations; 25: Public Administration.

Figure 5.2 and Figure 5.3 below show how the industry composition of IRWD’s CII clients has changed from 2010 to 2017. Figure 5.2 shows what part of the CII clients fall within each of the first thirteen industries from 2010-2017, while Figure 5.3 shows what part of the CII clients fall within each of the last twelve industries from 2010-2017. As seen in both of these graphs, the industry composition of IRWD’s CII clients has remained relatively constant since 2010. However, there are certain industry classifications that are exceptions to this generalization, as discussed below.

22

In Figure 4.2, the industry with the greatest change in the percentage of total CII clients from 2010 to 2017 is Real Estate Rental and Leasing, which decreased slightly from 21.13% in 2010 to 19.72% in 2017. Likewise, in Figure 4.3, the Organization, Accommodation, and Food Services industries experienced the greatest change in percentages. While Organization and Accommodation experienced 1.12% and 0.73% decreases respectively, Food Services experienced a 0.82% increase. Although these changes are minor, our team believes that the increase in Food Services could be due to growing populations across different demographics in the Irvine area, and the need to supply more food to more people.

Figure 4.2: Changes in Industry Composition of CII Sector (Industries 1-13)

23

Figure 4.3: Changes in Industry Composition of CII Sector (Industries 14-25) 4. Yelp Data and Yelp Ratings

Yelp is a popular application that organizes different companies by industry type, popularity, cost, and other useful characteristics. Their dataset has been made publically available online for research and analysis purposes. With regards to our project, Yelp can be used to yield useful, specific, and insightful information about a business that may not be seen within the IRWD provided data alone. Specifically, we utilized the Yelp to complement the missing data we gathered from NAICS, as discussed in Section 3 (Industry Composition of CII Sector) above. This Yelp data can be useful in understanding whether or not there is an association between highly rated business and water efficiency.

The team also analyzed the “dollar ranges” on Yelp to see if there was any significance between price range and monthly water use. In order to analyze these “dollar ranges”, the team converted them into dollar amounts per product; one “$” equates to establishments ranging from $0 to $10; two “$$” equates to establishments ranging from $11 to $30; three “$$$” equates to establishments ranging from $31 to $60; four “$$$$” equates to establishments ranging from $61 or higher. Furthermore, we compared which establishments with the same rating use water more efficiently and why that phenomenon may exist. We achieved this by controlling for basic characteristics of these specific establishments within the same ratings group.

24

We decided to use data from the water meter readings with confidence codes that were six or below in order to get better insight into the industries associated with these specific readings. When we conducted the analysis, 280 of the meters matched with the Yelp data obtained. 1,141 meters matched with the Yelp data that had confident codes of seven or higher, but 632 (~10%) of these matches were unique and not matched to several Yelp entries within the dataset.Figure 5.1 below shows the average Yelp rating per industry for industries four through thirteen, while Figure 5.2 shows the average Yelp rating per industry for industries 14 through 25. Industries one, two, and three were omitted from Figure 5.1 because their Yelp ratings were not found. As seen in both figures, most of the average Yelp ratings by industry are similar, ranging from 3.15 (Personal and Household Goods Repair and Maintenance) to 4.34 (Organization). Although it is challenging for the team to speculate why every industry has the Yelp rating that it does, a high Organization rating suggests that the work of health care, human rights, and other organizations is perceived positively by society.

Figure 5.1: Average Yelp Rating by Industry (Industries 4-13)

Legend for Figure 6.1 (each number represents an industry category as follows): 4: Construction; 5: Manufacturing; 6: Wholesale Trade; 7: Retail Trade; 8: Transportation and Warehouse; 9: Information; 10: Finance and Insurance; 11: Real Estate Rental and Leasing; 12: Vehicle and Equipment Rental and Leasing; and 13: Professional, Scientific, and Technical Services

25

Figure 5.2: Average Yelp Rating by Industry (Industries 14-25)

Legend for Figure 6.2 (each number represents an industry category as follows): 14: Management Companies and Enterprises; 15: Administrative and Support and Waste Management and Remediation Services; 16: Educational Services; 17: Health Care and Social Assistance; 18: Arts, Entertainment, and Recreation; 19: Accommodation; 20: Food Services; 21: Automotive Mechanical and Electrical Repair and Maintenance; 22: Personal and Household Goods Repair and Maintenance; 23: Personal Services; 24: Organizations; and 25: Public Administration.

Figure 5.3 below shows the average Yelp rating by pricing level, as detailed above. It is interesting to note that as the pricing level increases, the average Yelp rating increases, with the exception of the highest pricing level. In fact, this level has the lowest average Yelp rating out of all of the price levels, suggesting that more expensive businesses are not highly regarded by their consumers. We believe these low ratings could be due to many factors such as poor service or products relative to expectation and excessive waste of resources (including water), etc.

26

Figure 5.3: Average Yelp Rating by Pricing Level

5. Normalized Water Demand

We used customer bill dates and historical water use per account provided by IRWD to normalize water demand data by month. The purpose of evaluating monthly use consumption is to identify trends in water demand and compare water use between industries. The normalization process is necessary because the length of billing dates are not consistent across businesses or industries.

Within each month, there are four possible scenarios in terms of the start and end dates of billings overlapping:

1) A single reading per month 2) Multiple readings in a month and the bill being looked at is the first reading 3) Multiple readings in a month and the bill being looked at is the last reading 4) Multiple readings in a month and the bill being looked at is not the first nor the

last reading

27

We are specifically analyzing CII water use by month in order to account for differences between seasons. Furthermore, the team wanted to get a more accurate measure of the direct effects that IRWD’s program and policy changes have had on the CII sector.

Although we believed that there could be water use peaks and valleys during certain periods in the year due to seasonality, we found within the data that this is generally not the case. Therefore, we decided not to de-trend our data. This is shown in Figure 6.1 below which illustrates the average water use for all CII clients across the calendar months from 2010 to 2017.

Figure 6.1: Mean Normalized Water use by Month for all CII Clients (CCF)

In Figure 6.2 below, water use medians among CII clients vary by industry. While Agriculture, Fishing, and Forestry has the largest median, Personal and Household Goods Repair and Maintenance has the smallest median. It is important to note that some of the ranges in water use for each industry are relatively high. While Arts, Entertainment, and Recreation has the largest range, Personal and Household Goods Repair and Maintenance has the smallest range. We expect this larger variance in Arts, Entertainment, and Recreation to be due to the large number of businesses that make up this industry including anything from movie theaters to yoga centers. Additionally, popularity within these businesses varies significantly. This has an effect on water use, assuming greater popularity is correlated with greater water use.

For the purposes of illustrating normalized monthly water use of CII clients, we eliminated outliers from Figure 6.2. However, these data points are included in our analyses.

28

Figure 6.2: Normalized Water use of CII Clients by Industry (CCF)

Legend for Figure 6.2 (each number represents an industry category as follows): 1: Agriculture, Forestry, and Fishing; 2: Mining; 3: Utilities; 4: Construction; 5: Manufacturing; 6: Wholesale Trade; 7: Retail Trade; 8: Transportation and Warehouse; 9: Information; 10: Finance and Insurance; 11:Real Estate Rental and Leasing; 12: Vehicle and Equipment Rental and Leasing; 13: Professional, Scientific, and Technical Services; 14: Management Companies and Enterprises; 15: Administrative and Support and Waste Management and Remediation Services; 16: Educational Services; 17: Health Care and Social Assistance; 18: Arts, Entertainment, and Recreation; 19: Accommodation; 20: Food Services; 21:Automotive Mechanical and Electrical Repair and Maintenance; 22: Personal and Household Goods Repair and Maintenance; 23: Personal Services; 24: Organizations; 25: Public Administration.

Figures 6.3, 6.4, and 6.5 below show the changes in mean normalized water use by industry from 2010 to 2017. Figure 6.3 shows the means for industries one through four and six through thirteen, while Figure 6.5 shows the means for industries 14-25. The mean normalized water use for manufacturing, as represented by Figure 6.4, had to be made separate because the means across this time period were significantly above the means for the other industries in our analysis. For example, while the lowest mean for Manufacturing was 381.92 CCF in 2015, this mean was still higher than the highest mean for any other industry listed in any of the years.

29

Overall, most industries show fairly constant levels for mean normalized monthly water use across years, with some industries having slight increases and others having slight decreases. Amongst all of the industries, Manufacturing had the largest change in water use from 2010 to 2017, at 148.2 CCF. It is also important to note that in 2014, almost every industry, with the exception of Accommodations, experienced a decrease in monthly water use. Our team believes this nearly unanimous decrease occurred as a result of Governor Brown declaring the California drought a “state of emergency” in early January of 2014. As a result, most industries might have felt compelled, though not legally required, to minimize their water uses in an effort to improve the conditions of the drought. Although we initially believed the large increase Accommodations experienced in monthly water use in 2014 was connected to the economy, we concluded that this increase was an error in the data we were given.

Figure 6.3: Changes in Mean Normalized Water use (Industries 1-4 and 6-13)

30

Figure 6.4: Changes in Mean Normalized Water Use (Industry 5)

Figure 6.5: Changes in Mean Normalized Water Use (Industries 14-25)

31

6. Rebate Programs

The rebate programs implemented by IRWD are a critical piece of their drought management strategies. Understanding the true effect of these rebate programs is essential to evaluate the impact and importance of IRWD’s drought management strategies. For each program, we looked at participation levels for clients within a single industry to evaluate the true effects of rebates within the CII sector. We incorporated the rebate data into our regressions to evaluate whether businesses in IRWD that use rebate programs have more efficient water use than those who do not. In our analysis of rebate programs, in order to ensure that the monthly normalized water use data used was accurate, we only used water meters that were not dropped because of low meter confidence codes. Thus, we analyzed the effect of rebate programs on water use using a total of 25 instances of rebate program use by 22 different SPID’s. Of the 22 different SPID’s, 3 of the SPID’s took advantage of 2 different rebate measures. This group of clients are self-selecting and thus, the results from this data cannot be generalized to the entire CII population.

As seen in Figure 7.1 below, the rebate measures made available by IRWD include Cooling Towers, High Efficiency Clothes Washers (HECW), High Efficiency Toilets (HET), High Efficiency Toilet Flushing (HETF), (RES Premium HET), Rotating Nozzles, Ultra Low and Zero Water Urinals (ULWU), Weather Based Irrigation Controls (WBIC), and Zero Water Urinals (ZWU). The most common rebates in IRWD are High Efficiency Toilets (HET), Zero Water Urinals (ZWU), and High Efficiency Clothes Washers (HECW). HET, ZWU, and HECW combined account for 56% of the total rebate participation. On the other hand, Cooling Towers, Rotating Nozzles, and Ultra Low and Zero Water Urinals (ULWU) were the least utilized rebates.

When analyzing city and rebate use in Figure 7.2, we found that the majority of the

SPID’s participating in rebates programs are in Irvine, about 68%. Foothill Ranch, Lake Forest, and Tustin all have similarly low rebate participation amounts.

32

Figure 7.1: Rebate Participation by Measure

●

Figure 7.2: Rebate Participation by City

33

7. Budget Rates

Budget Rates are a way for IRWD to guide their CII clients to be more efficient in their water use. In our analysis, we evaluated how the budget rate based pay structure has impacted water use among CII clients. We gathered the necessary budget rate data from IRWD. IRWD switched in 2014 from a four-tiered budget rate system to a two-tiered budget rate system. This change in budget rate tiers could cause industries to respond in a change in their monthly water use. The team also conducted a second analysis to examine whether industries in villages like Lake Forest and Santiago Canyon changed their monthly water use when the village entered onto a budget rate system.

Qualitative Analysis

Our qualitative analysis identifies collinearity between our independent and dependent variables. This analysis was conducted in order to isolate the effect of our variables of interest on the dependent variable, water demand. Therefore, our qualitative analysis allows us to draw conclusions on trends in water demand over time, efficient IRWD policies, and their CII clients’ water saving potential.

For our control variables, we studied the Variable Inflation Factor (VIF). The VIF demonstrates how much the variance of the coefficient estimate is inflated by multicollinearity. For example, if the VIF variable is above two, the variance is collinear. Running this code enabled us to avoid possible instances of colinearity within our regressions. Furthermore, the greater the multicollinearity, the greater the standard errors. When high multicollinearity is present, confidence intervals for coefficients tend to be large and t-statistics tend to be small. To avoid this, we removed our Asian variable and used Black and Hispanic variables as representations of the minority population. Additionally, since population and race are highly correlated, we removed our population variable. We applied this model to each of our regressions. As shown in Table 8.1 below, our VIF values are less than two, indicating that multicollinearity is not present.

34

Table 8.1: Control Variable VIF

Quantitative Analysis

We conducted our quantitative analysis using STATA, a data analysis and statistical software. The data analysis methods we used were ordinary least squares (OLS) regression and difference-in-differences (DID) regressions. We created four different datasets to conduct our regressions: 1) average yearly water use per industry per village, 2) monthly water use per Spatial Identifier for rebate participants, 3) yearly water use per industry per village for customers with Yelp information, and 4) year-long water use for schools. We dropped industries with fewer than 30 observations because these industries are not statistically robust.

Regression 1: Dataset 1 Effect of Income, Unemployment, Village Age and Race on Yearly Water Use (OLS) Regression 2: Dataset 2 Effect of Rebates on Monthly Industry Water Use Regression 3: Dataset 1 Effect of a Tier System Budget Rate Change on Yearly Water Use Effect of a Budget Rate Switch on Lake Forest Yearly Water Use Regression 4: Dataset 3 Effect of Yelp Rating and Yelp Price Range on Yearly Water Use

Regression 5: Dataset 4 Effect of Yelp Rating and Yelp Price Range on Yearly Water Use

35

Regression 1: Effect of Income, Unemployment, Village Age and Race on Yearly Water Use

NormalizedWaterit = 𝝱 0 + 𝝱 1Incomeit + 𝝱 2Unemploymentit + 𝝱3VillageAgeit + 𝝱4Raceit

To begin our analysis, we started with the baseline regression above. This regression focuses on how different socioeconomic characteristics between villages can affect CII sector water use. CII monthly water use is denoted as “normalized water” in the regression model above. We look at the correlations between CII sector monthly water use per industry and income, race, village age, and unemployment over the 2010 to 2017 time period. Village age represents the average age of buildings in a village. This variable was generated by averaging the point of connection (SP_ID) install dates in a village. SP_ID install date is the date at which an SP_ID was entered into the IRWD database and represents the date of the first meter in that area. Our goal with including several independent variables was to specifically examine what factors have significant effects on CII water use. Tables 9.1 and 9.2 below show the correlation coefficients for these variables of interest.

Table 9.1: Effect of Income, Unemployment, Village Age and Race on Yearly Water Use

36

Table 9.2: Effect of Income, Unemployment, Village Age and Race on Yearly Water Use

We ran our baseline regression to understand how our control demographic variables such as unemployment (percentages), village age (years), income (dollars), and race (percentages) affect yearly water use by industry. From this industry specific regression, we found that certain control variables have a statistically significant impact on the yearly water use for specific CII clients. There were varying results for different demographic variables. We found that generally as village age increases, water use increases, regardless of industry at the 99% confidence interval. It is reasonable that as a village gets older, its pipes and fixtures likely get older too. This would lead to more leakage and increased water use. Also, changes to the plumbing code overtime have led to more efficient fixtures. Newer villages likely have more efficient plumbing fixtures from the beginning.

We found mixed results for our race variables. In the Utilities industry, higher concentration of minority races led to increased water use at the 95% confidence interval. However, in the Wholesale Trade business, higher concentrations of minority races led to decreased water use at the 95% confidence interval. We also found that higher percentages of

37

unemployment led to decreased water use in the Vehicle and Equipment Rental and Leasing industry at the 95% confidence interval. We believe this is the case because as unemployment increases, less people lease cars, which decreases business within that industry. Additionally, we found that an increase in a village’s income led to few statistically significant results within industry water use. For Vehicle and Equipment Rental and Leasing water use decreased as income increased at the 99% confidence interval. This result is surprising because we expect that as income increases, water use would also increase. People with higher incomes have less of a financial burden, and therefore, do not have as many constraints with their water use. As we continue through our regressions and add other explanatory variables, we observe how the effects of demographic variables on industries change.

Regression 2: Effect of Rebates on Monthly Industry Water Use

NormalizedWaterit = 𝝱 0 + 𝝱 1 Rebatesit + 𝛌ncontrolsit

Controls: Unemployment, Income, Village Age, Hispanic Population, Black Population

Rebates: Zero Water Urinals (ZWU), High Efficiency Clothes Washer (HECW), High Efficiency Toilets (HET), High Efficiency Toilet Flushing(HETF), Rotating Nozzles

After understanding how socio-economic characteristics affect normalized water use, we evaluated IRWD specific policies that could influence CII water use. The first policy evaluated focuses on how IRWD’s rebate programs influence CII water use. IRWD’s rebates are offered to each of their clients, but are managed based on an opt in strategy. We analyzed how rebates affect normalized water use for each of the rebate programs listed above. Specifically, we analyzed the effect an individual rebate had on water use based on the date the client into the program. From this analysis, we want to understand how influential the rebate program is in reducing water use among IRWD’s CII clients. However, since the rebate programs were self-selecting, the findings regarding rebate participants are not representative of the entire group of CII clients.

38

Table 9.3: Effect of Rebates on Monthly Industry Water Use

39

Table 9.4: Effect of Rebates on Monthly Industry Water Use

Once we understood whether or not our control variables had an effect on water use, we ran a regression to understand the relationship between IRWD’s rebates and CII monthly water use per industry. The three rebate programs with the largest observations were Zero Water Urinals (ZWU), High Efficiency Toilets (HET), and High Efficiency Toilet Flushing (HETF). Before running our regressions, we predicted that each individual rebate would decrease CII monthly water use. However, when aggregating the rebates, we found no statistically significant values. This could be due to low rebate participation levels, given that only 22 CII clients participated in these programs.

Regression 3: Effect of a Tier System Budget Rate Change on Yearly Water Use; Effect of a Budget Rate Switch on Lake Forest Yearly Water Use

A second internal policy that could impact water use among IRWD’s CII clients is the district’s pricing mechanism. For this analysis, we analyze budget rates from two perspectives:

40

IRWD’s switch from a four-tiered pricing system (base rate tier, inefficient tier, excessive tier, and wasteful tier) to a two-tiered system (base rate tier and excessive rate tier) and whether the budget rate system has an effect on reducing water use among CII clients compared to a previously fixed rate system. Second, we examine Lake Forest, a community that switched from the fixed rate system to the budget rate system in 2011, to observe the effects of this change.

Part 1: Effect of a Tier System Budget Rate Change on Yearly Water Use

NormalizedWaterit = 𝝱 0 + 𝝱 1 Post_Tierit + 𝛌ncontrolsit

Controls: Unemployment, Income, Village Age, Hispanic Population, Black Population

Post_Tier: A variable that signifies the time at which the switch in the tier system occurred

This regression shows whether the switch from a four to a two-tiered system has a significant effect on water use. We hypothesized that this switch has a significant effect on CII water use since a two-tiered system is a less forgiving way of pricing during the company’s allocation periods. We conduct this regression at the industry level to see if there is a statistically significant response to the change.

Table 9.5: Effect of a Tier System Budget Rate Change on Yearly Water Use

41

Table 9.6: Effect of a Tier System Budget Rate Change on Yearly Water Use

Tables 9.5 and 9.6 above evaluate the effects that switching from a forgiving four-tier system to a rigid two-tier system have on monthly water use. Overall, we found that this switch did not have a statistically significant impact on water use across industries, except in the Public Administration industry. In this industry, we found that switching over to the two-tier system resulted in a statistically significant decrease in water use at the 95% confidence level. We believe this is the case because the Public Administration industry may be more knowledgeable about the decreased punitiveness that a new two-tiered system possesses in comparison to other industries. Public Administration may also be more knowledgeable than other industries about the implications of the budget rate change because they may have a closer connection to IRWD. Although in this regression we added the Post_Tier variable as an extra explanatory variable to the Impact of Socioeconomic Characteristics by Industry regression, according to the R squared values, we were only able to achieve a very small increase in model performance.

42

Part 2: Effect of a Budget Rate Switch on Lake Forest Yearly Water Use

NormalizedWaterit = 𝝱 0 + 𝝱 1 LakeForestit + 𝝱2LFtreatmentit + 𝝱3 LFtreatment*LakeForestit + 𝛌ncontrolsit

Note: For this regression Santiago Canyon will be left out of the analysis to prevent any manipulation of the data.

Lake Forest: This variable denotes a “one” for all companies that have an address within this village.

LF Treatment: This variable denotes a “one” for all years after 2009 when Lake Forest switched from a fixed rate to a budget rate.

LF Treatment*LakeForest: This interaction variable represents all the observations that are within Lake Forest and are after the year Lake Forest switched from a fixed rate to a budget rate.

Controls: Unemployment, Income, Village Age, Hispanic Population, and Black Population

The second part of our budget rate analysis focuses on the impact of having a tiered budget rate versus a fixed rate system. It is important to note that Lake Forest joined IRWD’s budget rate system later than the rest of the villages in the district. In this analysis, we use clients that live in this village as our community of interest and all other clients as our control to see how water use varied in years following the switch. This analysis has been done both on the village level and industry level. We ran the regression for each industry to examine change across industries.

43

Table 9.7: Effect of a Budget Rate Switch on Lake Forest Yearly Water Use

44

Table 9.8: Effect of a Budget Rate Switch on Lake Forest Yearly Water Use

To see how Lake Forest’s businesses’ water use changed with the switch in 2011, we ran the regression above. Although the switch from a fixed rate to a budget rate occurred in 2009, we utilized 2010 as the treatment year due to lack of data in 2009. In Tables 9.7 and 9.8 above, we found that on average, most industries in Lake Forest after the switch did not experience a statistically significant change in water use. However, Vehicle and Equipment Rental and Leasing showed statistically significant changes. This finding may be a result of limited data points for each industry within Lake Forest. There was an increase in water use after the change that was significant at the 95% confidence level for Vehicle and Equipment Rental and Leasing

45

in Lake Forest. The increase in water use may be due to the fact that clients in that industry have enough revenue and going over their water budget is not a financial burden.

Regression 4: Effect of Yelp Rating and Yelp Price Range on Yearly Water Use

Yearly Water Use = 𝝱 0 + 𝝱 1 YelpRatingit + 𝝱 2YelpPrice_10it +𝝱3YelpPrice_11to30it+ 𝝱 4YelpPrice_31to60it + 𝛌ncontrolsit

YelpRating: The ratings as denoted on Yelp for each establishment

YelpPrice_10: Establishments with pricing from $0 to $10

YelpPrice_11to30: Establishments with pricing from $11 to $30

YelpPrice_31to60: Establishments with pricing from $31 to $60

YelpPrice_61plus: Establishments with pricing from $61 or higher (Omitted category)

Controls: Unemployment, Income, Village Age, Hispanic Population, Black Population

We are unable to make any statistically significant claims about how the size of a CII client affects water use. We hypothesized that the Yelp regression could yield statistically significant results to help us better understand how popularity and price range of an establishment affects their water use. Within our dataset, we were able to match a subset of the IRWD CII clients (about 1,400 clients of the 6,000 examined) to Yelp data in order to examine the establishment’s ratings and pricings. We chose to analyze these effects with an OLS model regression, as written above.

Tables 9.9, 9.10, and 9.11 above show the industries for which the regression model best fits the data. From our results, we see that the industries for which Yelp ratings’ effects were statistically significant at the 95% confidence level or more were Wholesale Trade, Information, Finance and Insurance, and Vehicle and Equipment Leasing, among others. Of those with statistical significance, Arts, Entertainment, and Recreation, Accommodation, and Personal Services were the industries with the largest effect on yearly water use associated with Yelp ratings. Of the total CII clients, we found that 25.5% of them show a correlation between Yelp ratings and water use. All but one industry had an inverse relationship between water use and Yelp Ratings; the only industry for which both water use and Yelp ratings rise is the Finance and Insurance industry. Other than that, most industries depicted a decrease in water use as Yelp Ratings increased. One possible explanation for this is that consumers prefer establishments that show water conservation efforts, which are depicted in the reviews that customers leave on Yelp. While another explanation may be that as California businesses become more aware of water use due to the recent droughts and ensuing legislation, an excellent business is somehow associated with a conscious consumption of water.

46

Table 9.9: Effect of Yelp Rating and Yelp Price Range on Yearly Water Use

47

Table 9.10: Effect of Yelp Rating and Yelp Price Range on Yearly Water Use

48

Table 9.11: Effect of Yelp Rating and Yelp Price Range on Yearly Water Use

We also examine whether pricing has an effect on an establishment’s water use. For example, we were interested to see if a more expensive establishment consumed more water than

49

a less expensive establishment. When running our regressions, some of the price categories were irrelevant for the industries, and STATA omitted categories from the regression. Thus, the results are mixed for the different industries. While some industries seem to use more water as they move up the Yelp Price brackets (Administrative Support and Waste Management and Remediation Services), others seem to decrease water use as they move up the Yelp Price brackets (Personal Services) and the rest show a mix of positive and negative association with movement up the price bracket (Manufacturing). Based on these results, it is probable that there are unobservable traits that go undetected in our data, thus causing omitted variable bias. Another explanation for the variability may be that more expensive establishments are more likely to spend water on cleanliness than to less expensive establishments.

A limitation of this analysis is that Yelp ratings are subjective and no clear metric exists for the quantity of stars each business should receive based on customer experience. Further, it is possible that businesses may inflate their ratings by offering promotions or discounts in exchange for positive reviews. However, we feel that such instances are limited and that this analysis could provide useful information for IRWD in tracking efficient water use among their CII clients.

Regression 5: Effect of Average Building Age, School Enrollment, Population Density, and Income on School Yearly Water Use

Yearly Water Use i = 𝝱 0 + 𝝱 1Average Building Agei + 𝝱 2School Enrollment i + 𝝱 3PopulationDensityi + 𝝱 4Incomei

Average Building Age: The average year of construction for school buildings deduced from SP_ID install date

School Enrollment: Total enrollment in each school

Population Density: Population density per square mile in the school district service area for each school

Income: Median Income in the school district district service area for each school

For our case study, we were interested in focusing on how schools’ water use is affected by demographics, in contrast to the village level demographics that we used for our previous regressions. School districts can serve a more diverse community compared to villages since service areas tend to be much larger than the village sub-divisions of IRWD. Additionally, we were interested in seeing how the average building age of schools affected water use. Schools in IRWD have relatively old building age. The average year of construction for a building is 1994 with individual school districts having construction dates ranging from 1988 to 1999.

IRWD has a wide range of yearly water use per school district. As seen below in Figure 10.1, Irvine Unified School District utilizes the most water with 86,975 CCF. The Irvine School District is also the largest out of the four districts within IRWD, shown in Figure 10.2 below.

50

The Newport-Mesa School District utilizes the least amount of water using 7,598 CCF. Figure 10.2 shows that they are also the smallest school district with 703 students.

Figure 10.1: Total Water Use (CCF) by School District (2017)

Figure 10.2: Total Enrollment by School District (2017)

51

Figure 10.3: Total Water Use by School Type (2017)

Figure 10.4: Total Enrollment by School Type (2017)

52

Figure 10.5: Total Enrollment by School Type (2017)

Furthemore, IRWD sees a wide range of water use per school type. When analyzing

water use per student in Figure 10.5 above, high schools maintained the highest total water use per student with 4.47 CCF per student in 2017. High schools in IRWD tend to have large campuses filled with sporting facilities that need significant water maintenance. Middle schools had the second highest total water use per student in 2017 with 2.07 CCF. Middle schools also have similar campuses and facilities as high schools, but usually at a smaller scale. Therefore, it is understandable that they have the second highest water use per student. Elementary schools utilize the lowest water on a per student basis with 1.97 CCF.

As seen in Figure 10.2 above and Figure 10.6 below, Newport-Mesa Unified School District had the lowest enrollment out of all the school districts with 703 students, even though it utilized the most water per student in 2017 with 10.81 CCF. The Irvine School District utilized the second most water per student with 3.06 CCF. The Irvine School District has the largest enrollment within IRWD with 28,466 students, justifying that they utilize the most water between the four districts.

53

Figure 10.6: Total Enrollment by School Type (2017)

In order to create our regression analysis, the team gathered zip codes from each school district’s service area and kept only the zip codes within IRWD. We averaged the demographic variables from each zip code in order to obtain the demographic composition for the school district within IRWD’s borders.

Similar to our previous regressions, we checked for collinearity within our model to ensure that the regression results were significant. We dropped all of the demographics relating to race because they were highly correlated. The demographic variables included in our regression serve as proxies for the omitted race variables and other socioeconomic variables (such as unemployment). As can be seen in Table 10.7 below, income and school enrollment are associated with an increase in water use. This association is significant at the 99% confidence level for both income and school enrollment. The positive association with income may be due to the fact that areas with higher income may have more facilities (sport fields, pools, labs), which as a result create a larger demand for water. Meanwhile, the increase in water associated with the increase in enrollment is intuitive; more students require more water.

However, the building age associated with the school seems to have an inverse relationship with the total yearly consumption in 2017. This is statistically significant at the 95% confidence level and it seems to be larger than the per unit impact of school enrollment and income. This association in the data may be due to the presence of older insufficient plumbing fixture or older meters being used at smaller schools or school types.

54

Table 10.7: Effect of Average Building-Age, School Enrollment, Population Density, and Income on School Yearly Water Use

Limitations Even though we are confident about our analyses, we recognized that there are limitations

to our study. Our models of analysis are based off various leading papers within the field where research has been limited and where many models are still being improved. It would be beneficial to research more robust models that fit certain districts or industristies uniquely. Secondly, we did not have many industry specific characteristics such as employee count, square footage etc. We only had village age (deduced by SP_ID install date) associated with of the industries in a village. Industry specific characteristics are commonly used to benchmark water use within the CII industry and because of this limitation, it altered our analysis with certain models. Another issue is omitted variable bias within some of our regressions. There are variables that we did not have access to in our analysis which can also impact water use. For example, pipe quality per village, urbanization, and climate uncertainty can each effect water use and are correlated with our variables of interest. Additionally, the sample size for our rebate regressions significantly limited our analysis of unique variables within our different models. In our analysis, we accounted for all of these various limitations within our data. With regards to our Yelp regressions, ratings are a subjective measure and may not be a perfectly unbiased indication of popularity. Lastly, future economic and water conditions are unknown and could alter our recommendations because they are based on past monthly and yearly measures.

55

Findings

From our analysis, we found that socioeconomic controls, like unemployment, positively affect yearly water use in Agriculture, Forestry & Fishing, but negatively affect yearly water use in Mining and Vehicle and Leasing and Equipment Rental and Leasing. These increases and decreases could be caused by different industries reacting differently to the labor supply. Furthermore, our rebate regressions show that they are correlated with lower CII water use across all industries. However, none were statistically significant due to threshold levels and rebate participation having a small sample size. From our Yelp regression, we found that Yelp ratings were inversely correlated: the higher the Yelp rating, the lower CII water use across industries. One possible explanation for this is that consumers prefer establishments that show water conservation efforts, which is reflected in Yelp reviews. Lastly, when analyzing our school regression, we find that older school meters are correlated with lower CII water use, which intuitively is hard to explain. However, one reason for these results could be that older water meters are assigned to smaller schools. We offer below several recommendations to further the effectiveness of Irvine Water Ranch District’s water efficiency efforts and encourage future analyses. Recommendations for Irvine Ranch Water District 1. Implement “Smart” meters across schools to help promote more accurate water readings

Our school regression shows that older buildings are correlated with lower water use, which intuitively should be reversed. Schools within IRWD have relatively old SP_ID install dates, with the average year being 1994. The newest average SP_ID install date is from the Tustin Unified School District in 1999, which means that the youngest building is, on average, 18 years old. The Tustin Unified School District also utilized the lowest water use per student at 2.56 CCF in 2017, while having the third highest enrollment out of the four districts. Considering that average building age is directly connected to overall CII water use, it is imperative that schools should adopt more efficient meters. Furthermore, efficient water meters are critical to installing appropriate billing systems, which is beneficial for tracking leaks and water consumption.

In particular, we recommend that IRWD replace their current meters with new “Smart”

water meters in order to achieve higher water saving potential. We recommend water meters be replaced every ten to fifteen years. Typically, meters are read bi-monthly or monthly by a 13

representative. However, “Smart” meters can be read remotely. Even though “Smart” meters 14

are two to three times more expensive than traditional meters, “Smart” meters provide many benefits such as eliminating labor costs from meter visits and having a more efficient leak detection tool. According to a 2010 survey conducted by the Association of California Water

13 Los Angeles Times 14 Alliance for Water Efficiency

56

Agencies, about 60% of the 70 agencies surveyed were considering or had facilitated plans to install smart meters. Water agencies in the nearby areas of Glendale and Burbank have already implemented “Smart” meters. 15

Another key factor associated with “Smart” meters are the costs and overall battery life

associated with their installment. According to most manufacturers, batteries generally last between ten and fifteen years and cost around $312 for each installation, costing about $10.5 million to complete in Glendale, CA. Even though “Smart” meters cost two to three times more than the traditional meter, IRWD should consider replacing their school meters with new meter technology that will soon be implemented in years to come. We believe that this focus on 16

installment of “Smart” meters in schools would result in more efficient water use across schools in IRWD and further improve water conservation efforts. 2. Promote rebates through more proactive methods to help better educate clients on water efficiency benefits

We found that water rebate programs are imperative for districts’ water conservation efforts. Rebates enable CII clients to take advantage of innovative technologies that reduce water use, thus leading to improved water efficiency across industries. Through our rebate regression analysis, we find that rebates across all of the industries are correlated to lower water use. However, due to the low sample size of CII clients participating in one of IRWD’s rebate programs, none of the results were statistically significant. Moreover, we recommend that IRWD promote their rebates through more proactive measures in order to better educate their CII clients on potential water efficiency benefits. Additionally, we believe that IRWD should begin to track rebate participation levels by industry and continue to invest in further studies on their water use efficiency. This can be achieved through target marketing and outreach to villages with older meters to encourage involvement in certain rebate programs. Considering there were only 22 unique clients out of 6,000 who participated in a rebate, there is a market available for potential rebate growth. By implementing this recommendation, IRWD can further the scope of their current water conservation efforts. 3. Create water conservation programs for more older CII villages

We find that older villages are correlated to higher levels of water use regardless of industry in our socioeconomic analysis. This is intuitive because older inefficient plumbing fixtures may still be present and as a village gets older, their pipes get older, leading to more leakage and increased water use. Therefore, we recommend that IRWD create water conservation programs for older CII villages to help promote better methods of water conservation. The best way to encourage older villages is to increase public exposure about water conservation benefits. We believe that targeting these older villages within IRWD can further untap potential water conservation benefits.

15 Los Angeles Times 16 Los Angeles Times

57

4. Advertise: lower water use correlates with higher Yelp ratings

One of the most significant results from our analysis was that lower water use correlates with higher Yelp ratings. This correlation can be a powerful marketing tool. We recommend that IRWD use the correlation between high Yelp Ratings and water efficiency to market future efforts in the CII Sector. The “WaterStar!” Program seems to promote and reward efficient water use among businesses. IRWD businesses with low water use and high Yelp ratings could serve as examples for the rest of the CII sector to follow. IRWD should continue to market the “WaterStar!” and add Yelp as an incentive to better help promote excellent businesses as water efficient businesses. 5. Conduct further analysis into budget rate implementation

Our budget rate analysis found that switching to a two tier system did not have a statistically significant impact on water use across CII industries. Since Lake Forest joined IRWD’s budget rate system later than other villages, we used the village as our baseline for change. We found that on average, most industries in Lake Forest did not experience a statistically significant change in water use after the switch to a budget rate in 2011. These budget rate results did not match the expected water use decline associated with the new implemented two-tiered system. Therefore, we recommend IRWD revisit their current budget system and conduct further analysis with more observations for all villages or specific villages of interest. 6. Collect more intensive data on industry characteristics with CII clients

One of the toughest tasks we faced with our data was finding and applying industry specific characteristics for our regression models. These data limitations hampered our analysis because many of the industries we separated by NAICS lacked characteristic distinctions. Therefore, we recommend that IRWD collect more intensive data on industry specific characteristics with their CII clients. Specifically, it would be beneficial for IRWD to design surveys for CII clients in order to collect deeper data. We feel that implementing this change will benefit further research within the CII sector for IRWD.

58

Works Cited Burt, Charles M. Irrigation Water Conservation - Benefits and Tradeoffs. USCID Water

Management Seminar, Oct. 1995, www.itrc.org/papers/pdf/irrwaterconservation.pdf.

Cai, Yue et al. “Large-Scale Analysis of Water Efficiency in California” NYU Center for Urban

Science and Progress, URL 2017

Cohen, Ronnie, et al. “Increasing Water Efficiency in California’s Commercial, Industrial, and

Institutional (CII) Sector .” NRDC, May 2009, URL, 5501.1.

Gleick, Peter et al. “Waste Not, Want Not: The Potential for Urban Water Conservation in

California” Pacific Institute, URL \ 2003

Goldenstein, Taylor. “Smart Water Meters Help Users, Agencies Gauge Usage.” Los Angeles

Times, Los Angeles Times, 5 May 2015,

www.latimes.com/local/california/la-me-smart-meter-explainer-20150505-story.html.

Heberger, Matthew “Urban Water Conservation and Efficiency Potential in California” Pacific

Institute, URL June 2014.

IRWD. “About Us.” IRWD, www.irwd.com/about-us.

Morales, Miguel, and James Heaney. “Benchmarking Nonresidential Water Use Efficiency

Using Parcel-Level Data.” Journal of Water Resources Planning and Management, vol.

142, no. 3, 2016, p. 04015064., doi:10.1061/(asce)wr.1943-5452.0000616.

NewsDeeply. “Water Deeply - American West Water News.” Oceans, News Deeply, 2017,

www.newsdeeply.com/water/background/timeline.

Sanchez, Fiona. “Drought, Water, and Policy Infrastructure.” LinkedIn, 31 Oct. 2014, URL

Tsai, Yushiou, et al. “The Impacts of Water Conservation Strategies on Water Use: Four

Case Studies1.” Freshwater Biology, Wiley/Blackwell (10.1111), 11 Apr. 2011,

59

onlinelibrary.wiley.com/doi/full/10.1111/j.1752-1688.2011.00534.x.

Water and Energy. Public Policy Institute of California, Oct. 2016,

www.ppic.org/content/pubs/report/R_1016AER.pdf.

Yelp Dataset Challenge

https://www.yelp.com/dataset/challenge