2005 report for the louisville water co

The Causes of Declining Residential Water Sales A Research Report for the Louisville Water Company

by Paul Coomes, Ph.D.

Professor of Economics, and National City Research Fellow

Margaret Maginnis, Senior Research Associate

Fadden Holden, Economics Student University of Louisville

December 2005

Executive Summary

he Louisville Water Company has been experiencing declining water sales among residential customers, forcing the company to raise rates to ensure the revenues needed to expand service and replace old water mains and equipment. Water use per

residential customer in both 2003 and 2004 was the lowest on record, twenty percent lower than the usage peak in 1988. Company officials attribute the decline in usage to several possible factors including wetter weather, new water-conserving appliances, changing demographics, and classification anomalies.

T We have studied the academic and industrial literature and examined historical data on water usage in order to better understand the causes of declining water use by households in the service area. In addition, we have examined the Company’s customer database to ascertain the extent to which classification procedures miss residential demand in multi-family complexes. We also fit an econometric model, using thirty years of monthly residential water use per customer, to obtain indications of the importance of key variables in causing the decline in water use. The empirical literature suggests that there is a positive relationship between household size and water usage. However, it also indicates that water use does not increase proportionately with number of persons due to economies of scale in dishwashing, laundry and other common functions. Thus, played in reverse, as the average number of persons per household declines in the Louisville market, there will be a reduction in water use per household, but at a diminishing rate. Our preliminary econometric work suggests that at least one-third of the decline in residential water use over the last fifteen years is due to a reduction in the number of persons per household. Our model also suggests that water usage per person has remained fairly stable over the last thirty years, so that declining household demand is a function of less people per household rather than less individual water use. There have been dozens of studies published that examine the sensitivity of residential water usage to price increases and decreases. While there are a wide range of estimates reported, they cluster most around a price elasticity of demand of -0.4 to -0.5, with outdoor water use much more price-sensitive than indoor use. Given that water is a necessity of life, it is not surprising that overall demand is inelastic. A policy consequence of this finding is that the Louisville Water Company could raise water rates significantly without a proportionate

decrease in sales, stimulating Company revenues as needed. Specifically, assuming this midpoint estimate of elasticity, a twenty percent increase in rates would lead to a ten percent decrease in residential water sales per customer. Company revenues would rise even though less water would be provided to the customers. A complicating issue is that the sewer bill, also based on water usage, is presented to customers jointly with the water bill. Hence, when the Metropolitan Sewer District raises its sewer charges, customers see this as an increase in water rates. Were water and sewer rates to creep up over time, and the bi-monthly bill become high enough that residential customers start to notice the impact on their budgets, customers would likely become more price-sensitive. The American Water Works Association has sponsored a very useful study of end uses of water by households that provides detailed data on water use by indoor appliances and outdoor usage. Although the study was conducted primarily in far western and southern cities across the United States, the methodology can be directly applied to Louisville, with some of their results transferable as well. We recommend a local end use study, whereby electronic data loggers are installed on the meters of a small sample of Louisville households. Water usage by appliance can then be modeled against measures of household technology along with demographic and economic factors. We believe this is the most promising and cost-effective way to finally determine the impact of new water-conserving appliances and to distinguish between indoor and outdoor water use. Since the objective of our research was to understand residential water usage in Louisville, we were curious about how many households were not classified as residential customers. Because of state tax laws and some legacy information technology issues, most apartments and other multi-family units are classified as commercial customers, and hence their water usage is not included in the residential data we examined. We investigated this issue in great detail, using a random sample of 500 commercial customers. We found that the sample include 162 premises containing 1,528 housing units. We can infer from this that, county-wide, there are nearly 44,200 housing units currently counted under the commercial classification. If the Company wants to better understand household water demand, it needs to reclassify these customers and track their usage separately from commercial customers. As part of the sampling exercise we also found a number of single-family homes classified as commercial customers. This suggests a need to clean the Company’s customer database so that it is more useful for analytical purposes. We believe the Company’s customer database is a rich and relatively untapped resource for analysis of water usage patterns and trends. Much could be learned from matching customer water use to geographic and economic data from other publicly available administrative data. The LOJIC system can be used to determine the footprint of a housing unit, the lot size, and whether a swimming pool is present. The lot size is a good indicator of sprinkler water usage during droughts and the presence of a swimming pool is obviously an important explanatory variable for outdoor water use. Customers with and without a separate meter for outdoor water use can be studied, with these important controls for yard size and swimming pools. Property Valuation Assessment records can be used to determine the age of a dwelling (an indicator of its plumbing technology) and the assessed value (an indicator of household income). Combined with results from regular end use studies discussed above, the Company could effectively zoom in on the causes of trends and fluctuations in residential water use.

Residential Water Sales, Louisville Water Company 2

Overview of the Puzzle ur team at the University of Louisville was engaged over the summer by the Louisville Water Company to study the causes of recent decline in residential water use per customer. Residential water usage per customer has fallen as the number of

residents and households continues to grow, and as household incomes continue to rise. The chart below summarizes thirty years of monthly data on average water usage per residential customer. A 12-month moving average was constructed to smooth out variations in month-to-month use due to seasonal demand and billing anomalies. It is clear that water use per customer has fallen significantly. Water usage peaked in late 1988 at around 7,000 gallons per month. Today, the average customer uses only 5,600 gallons per month, a decline of 20 percent from the peak. This has serious revenue implications for the Louisville Water Company. Stable revenues are needed to finance the capital programs required for replacing legacy water mains and extending water service to new suburban communities. Increased water rates are the most direct way to recoup revenues from falling water usage, but if the Company continues to raise water rates there will eventually be resistance from homeowners and voters. It has become increasingly urgent to understand what is causing the decline in residential water sales.

O

Water Usage per Residential Customergallons by month, 1975-2004

4,000

5,000

6,000

7,000

8,000

9,000

10,000

11,000

Jun-75

Jun-76

Jun-77

Jun-78

Jun-79

Jun-80

Jun-81

Jun-82

Jun-83

Jun-84

Jun-85

Jun-86

Jun-87

Jun-88

Jun-89

Jun-90

Jun-91

Jun-92

Jun-93

Jun-94

Jun-95

Jun-96

Jun-97

Jun-98

Jun-99

Jun-00

Jun-01

Jun-02

Jun-03

Jun-04

12-month centered moving average

Several hypotheses have been advanced to explain the reduction in residential water usage.

1. Wetter weather has reduced the need for outdoor watering. There is a clear negative relationship overall between rainfall and water usage per customer. The peak water usage period (1988) in the chart above was among the driest in thirty years. The relationship between average residential water usage and ground moisture is clear in the chart below. We focus here only on the April to September months, when outdoor watering of lawns and landscaping is most prevalent. The Palmer Drought


Severity Index provides a general measure of ground moisture for the central Kentucky region. One can easily see the negative relationship between ground moisture and residential water usage. The driest years, 1986 and 1988, were the ones with the highest water usage. The wettest years, including the last two years, have low water usage. We investigate this more carefully with an econometric model presented later in this report.

2. The average number of persons per household has been falling, thereby reducing the total water usage of the typical household. It is certainly true that the number of

persons per household has been falling in Jefferson County. The last four decennial censuses revealed a decline from 3.16 persons per household in 1970, to 2.69 in 1980, to 2.48 in 1990, and to 2.37 in 2000. This represents a twenty-five percent reduction in household size in just three decades. Industry research shows that water usage is indeed sensitive to household size, as less people means less laundry, less dishwashing, less bathing, and less toilet use per household. Our econometric work, as well as the research of others, suggests that an additional person in a household leads to between 600 and 1,100 gallons more water usage per month (depending on age). Played in reverse and applied to the local situation, a drop in average household size in Jefferson County from 2.92 to 2.35 persons during the 1975 to 2004 period, would lead to a decline in monthly water usage of between 340 to 630 gallons per residential customer. This range nicely brackets the actual net decline in average usage (525 gallons per month per customer) seen by the Louisville Water Company over the period. However, note from the first chart above that all of the decline in water usage per customer has occurred since 1985, while household size has been falling for decades. So, while falling household size has no doubt contributed greatly to declining water sales, it is evidently not the only causal factor. Something else was

Average Residential Water Use vs. Ground Moisture IndexApril to September, 1975 - 2004

30,000

35,000

40,000

45,000

50,000

-5 -4 -3 -2 -1 0 1 2 3 4 5

Palmer Drought Severity Index (-5 severe drought, +5 saturated), April to September only

Ave

rage

Res

iden

tial

Wat

er U

sage

2000

1999

1988

2004

1975

2003

1989

1986

1979


causing water usage per customer to rise in the earlier period even as there were fewer people per household each year.

3. Federal water-conservation laws have required manufacturers to make water appliances that use much less water, beginning in the mid-1990s. Most major plumbing ware manufacturers began in 1994 to produce low-volume toilets, urinals, showerheads, and faucets that comply with the Energy Policy Act of 1992 regulations1. Thus, contractors have been installing low-flow water appliances in new homes and in renovation projects for a decade now. These new appliances use on net less than half the water per use as older appliances, though it is unclear how much of this decline is offset by longer showers, multiple flushes, and second rinses in the clothes washer. The Louisville area has seen a surge in home construction, and Jefferson County has added 50,000 new housing units since 1990, accounting for over one-sixth of the current housing stock. The chart below shows the distribution of new housing (authorized) among single-family and multi-family units. Declining interest rates have particularly spurred single-family home construction since the early 1990s.

An end use modeling system would be required to understand the importance of the new water-conserving appliances on water usage by household. Data loggers would need to be installed on water meters in a sample of homes, with profiles developed on the physical characteristics of the home and the demographic and economic characteristics of the people living in the home. By controlling for these many factors, analysts could determine the incremental effects of low-flow toilets, showers, dishwashers, and clothes washers on the household’s water usage.

Housing Units Authorized, Single and Multi-FamilyJefferson County, Kentucky

1,694 1,669 1,590 1,684 1,8692,266

2,714 2,7992,480 2,567 2,508

3,087 3,0272,797 2,978

2,7493,164 3,237

1,681

1,120738 762 537

637

343

855

627871

480

1,026 1,323

1,012 599761

831 649

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Source: US Census Bureau.

Multi-Family Units

Single-Family Units

1 Source: letter from Amy Vickers and Associates to CH2M Hill Engineering, September 20, 1994.


Without an end-use study, we have only aggregate data on which to base estimates of the effects of the new water-conserving appliances. In the econometric work presented later, we develop a proxy for the introduction of water-conserving appliances in the mid-1990s. Basically, we assume that all new homes are equipped with lower-flow appliances and measure their rising share of the County’s total housing stock. This measure, while admittedly crude, is statistically significant in one model developed to explain the reduction in average water use among residential customers.

4. A large proportion of households are classified as commercial water users in the Water Company’s database. These households include apartment dwellers and condo owners. We have extensively investigated this classification issue, using a random sample of 500 Louisville Water Company ‘commercial’ customers in 2004. We found that the sample included 162 residential premises, containing 1,528 housing units. The sample results were adjusted for occupancy and applied to a County-wide estimate, suggesting there are 44,200 occupied housing units in the County counted under the commercial customer classification. This represents about one-sixth of all occupied housing units (of any type) in Jefferson County. A detailed discussion of our investigation is provided later in this report.

It is revealing to examine the growth in residential water customers and housing units in Jefferson County between the last two decennial censuses. There is a tight fit between the net growth in residential water customers and occupied housing units in the County. Between 1990 and 2000, the Water Company gained 26,400 customers classified as residential (from 193,400 to 220,800 customers). The Census Bureau reports a growth of 23,800 occupied housing units over the decade (from 264,200 to

New Housing vs. Growth in Residential Water Customers

0

1,000

2,000

3,000

4,000

5,000

6,000

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Annual growth in residential water customers, December to December

New housing units authorized in Jefferson County, single and multi-family

287,000 units). The Census figure includes both owner-occupied and renter-occupied


housing units (186,400 and 100,600 respectively in 2000), but the Census does not provide a breakout for single-family versus multi-family. Annual building permit data follow the same general pattern as new residential

ere

e

ote that if one adds the number of average residential water customers (237,800) in

he

y

ounting apartment units as commercial customers causes a reduction in measured

er of

uilding permit records indicate that there are on average about 700 multi-family l

ther water utilities around the United States are also now facing a decline in residential

ts of

,

water’s low price, and modest population growth.

customers, though the cumulative numbers do not align2. The data show that thwere 25,000 new single-family homes authorized over the decade, plus 7,500 new multi-family units. So, it appears that about 6,000 more units were built than can baccounted for by the net growth in residential customers or occupied units. Much of this discrepancy is due to demolitions, particularly around the airport and in older neighborhoods west of Interstate 65. Nthe year 2004 to our estimate of occupied housing units classified as commercial customers (44,200), you arrive at 282,000, only three percent less than the CensusBureau’s estimate of the number of households (292,300) in Jefferson County for tsame year. The difference could be due to a higher occupancy rate for apartment units than we assumed (90 percent), to sampling error, or to other Water Companclassification issues. Cresidential customers, but also a biased measure of water usage per residential customer – at least in the literal sense of the word residential. The average numbpersons living in a rental unit is less than in an owner-occupied dwelling. The 2000 Census reports 2.14 persons per rental unit versus 2.50 persons per owner-occupiedunit in Jefferson County. Given that fewer persons per unit translates directly into lower water use per unit, we can infer that if all the multi-family housing units werecounted as residential customers, residential usage per customer across the system would be even lower than now perceived. Bunits (apartments or condominiums) built in Jefferson County each year. Nearly alof these households continue to be classified as commercial customers. The mixing of households between the residential and commercial classifications makes an analysis of household water usage more difficult.

Owater use per customer, and industry analysts are beginning to focus on the causes. However, as will become evident in the next section, the literature on the determinanhousehold water usage is not very mature. Estimates vary widely of the effect of changing household size, of conservation laws, and of the response to price and income increases. Moreover, most of the relevant research has focused on water usage in the arid Southwestwhere water rationing is a common occurrence. The paucity of research on household waterusage in the Midwest is no doubt due to the region’s historically ubiquitous water supply,

2 The three spikes in the chart showing growth of residential water customers are due to the conversion of

wholesale customers into residential customers – Jeffersontown (1990), Bullitt Kentucky Turnpike #2 (2000), and Goshen and Shepherdsville in 2002-03.


Review of Industry and Academic Research on Residential Water Usage Modeling

n this section, we provide a summary of the published literature on residential water usage. We have scoured industry and academic sources to identify any studies that have looked at the issue of fluctuating water demand, with particular emphasis on quantifying

the factors that cause households to consume more or less water over time. The literature provides some studies that help us understand what is causing the decline in average residential water use in Louisville. Many variables have been used to fit demand models over the last century, including water price, household income, outdoor water use, weather, and household size. The dissemination of low-flow water appliances, prompted by the Energy Policy Act of 1992, has spurred a fresh literature that focuses on water technology as a variable also. A complete list of the studies cited is provided in a reference section at the end of this report. There are two basic methods used to analyze household water usage, econometric and end-use. Econometric models have been fit using historical data on aggregate residential water use for a system or for usage by individual households at a point in time or across time. Residential water usage per customer by month is modeled as a function of weather, water price, household demographics, technology, and other economic-demographic factors. These models are also essentially models of shifting demand. Water supply is taken to be inelastic at the given water price, regardless of quantity consumed. The quantity of water demanded by a household may be price-sensitive at very high prices per gallon, but is quite inelastic over the range of prices seen historically in the Midwest. That is, a rise in water price of ten to twenty percent would not cause residences to use much less water. And a similar drop in water prices would not cause residences to use much more water. The actual water demand, and hence usage, in a market is determined by how weather and other factors shift the demand curve, not by water prices.

The textbook supply and demand diagram above is useful as a conceptual starting point only. The market for water is more complex, particularly when considering changes over time. As with gasoline, electricity, medicine, and other necessities of life, demand for water

I

gallons of water per month

$15

6,000

Price per thousand gallons

Supply

Demand


will certainly be more price-sensitive once consumers have a period to adjust. In the short-onths), consum their housing

r

term (mcharacteristics and lifestyles cannot be changed immediately. But over several years, people would respond to higher water rates by installing more efficient appliances, fixing leaky fixtures, and reducing outdoor watering. Moreover, the supply curve is not fixed over time. The technology of water delivery is always improving, putting downward pressure on price. The flatness of the supply curve is only an approximation around the point of typical wateusage. There are great economies of scale in water production and distribution, so that costs (and therefore prices) fall dramatically as customers are added, particularly in a densely populated area.

ers have little choice but to pay higher rates, as

End-use models are inherently micro. They focus on the water usage of individual households. A housing unit is characterized by its physical and plumbing features, including

hether there w is outdoor water usage for a garden, landscaping, or a swimming pool. The household is characterized by demographic features such as number of residents and their ages, and by economic factors such as the number of working members of the household and their incomes. Special water metering devices are installed, or diaries are kept by someone in the household, to monitor water usage by day or even time of day. Statistical analysis is performed after sufficient data are acquired, to determine the differential impacts of housing and household characteristics on water usage. The most comprehensive end-use modeling reference is Residential End Uses of Water, by Mayer et al. (1999). This study was sponsored by the American Water Works Association Research Foundation. The investigators randomly selected 1,000 households from billing records in each of fourteen cities in North America, then chose a sub-sample of 100 in each for detailed data-logging. While most of the cities were in the western US, two were in Ontario and are presumably more like Louisville in terms of water availability and usage. The study reports detailed distributions and statistics on water usage in each city, including per capita daily usage for toilets, showers, baths, faucets, clothes washers, dish washers, leakages, and other indoor uses, as well as measurements of outdoor usages. We summarize the relevant findings from the major end-use and econometric studies below, organized by the key variables thought to determine household water use. Household Demographics The literature points to a positive relation between residential water demand and number of members of a household. Moreover, researchers have suggested that a change in number of people in a household causes a less than proportional change in water demanded (Howe and Linaweaver, 1967). There are economies of scale in water usage for a household, particularly for dishwashing and laundry, so that water use is not expected to be a linear function of the number of persons per household. In a recent study conducted in Spain, the elasticity of water usage with respect to family members was between 0.734 and 0.868 (Arbues and Barberan, 2004). Older estimates place the elasticity between 0.25 and 0.74 (Morgan 1973, Grimm 1972, Danielson 1979). Thesestudies implicitly assume a constant elasticity, and hence a hyperbolic relationship betweennumber of residents and household water use.


For studies fitting a linear relation between indoor water use and size of the household the elasticity is not constant. Mayer et al. (1999) use a large pooled sample of individual households to find a linear relationship as follows: (indoor water use per day) = 69.2 + 37.2 (number of people per household). So, if the average number of persons per householdwere to fall by, say, 0.5, then using this equation we would expect the average household water consumption to fall by 558 gallons per month. This represents a significant reduction from a typical base water usage of 6-7,000 gallons per month. Other research suggests that the age composition of a household is a statistically significdeterminant of w

ant ater usages (Lyman 1992, Hanke and de Mare 1982). Lyman finds that

another child would increase water usage in a home by about 2.5 times that of another “teenager and 1.4 times that of another adult”. Price Elasticity There are no substitutes for water in its basic household uses, and hence economic theory predicts that residential consumption will be very inelastic with respect to price. Moreover,water prices have historically been low enough that water bills typically account for a percentage of a household’s monthly income. Thus, consumers are often not even aware when water prices change and this makes it even less likely that consumption would change in the face of small price variations. However, there are goo

small

d a priori reasons to believe the rice elasticity of water is not zero. Beyond drinking and sanitation uses of water, much

, s ld

eek

nge in water usage allons) divided by the percentage change in water price per gallon. We say that water is

usehold goes own five percent we say that the price elasticity of demand is -0.5, or inelastic. It is

the price elasticity of demand can change dramatically over the

cases, however, but on the effect of price changes in the eighborhood of typical water rates and monthly usages.

dy

ointed to price elasticities of demand of round 0.5 (Gottlieb 1963). Howe and Linaweaver (1967) found the price elasticity to be

phousehold water usage can not be deemed a necessity. Sprinkler systems for landscapinggarden irrigating, car washing, and swimming pool refilling would all likely see reductions awater prices rose appreciably. Leaky plumbing that might be ignored under low prices woube repaired under high prices. And even some sanitary uses would be curtailed under very high prices, as many people would find that they get along fine with four showers per winstead of eight to ten. Finally, as is evident from these examples, households’ response to higher water rates will be much greater over several years than several weeks. The price elasticity of water demand is defined as the percentage cha(gprice elastic if the ratio is greater than one in absolute value, and inelastic if it is less than one. So, if water price per gallon goes up ten percent and water usage per hodimportant to recognize thattheoretical range of prices. For example, in the extreme case of very expensive water households will continue to purchase enough water to survive, and thus demand is very inelastic for further price increases. Similarly, at the other extreme, water that is approaching a zero price per gallon will not cause the typical household to consume much more water than before. The price elasticity of water is inelastic to price decreases in this case. Most analyses focus not on these extreme n There is a long literature on the sensitivity of residential water demand to changes in water prices. A 1926 article in the Journal of the American Water Works Association reported on a stuof 29 utilities, and indicated a definite reduction in water use per residence as price rose (Metcalf 1926). Studies in the 1905s and 1960s pa


about -0.4, but pointed out that this sensitivity is composed of an indoor water usage elasticity of -0.2 and a ‘sprinkler’ or outdoor water usage of -1.6 for humid eastern aras Louisville. That is, indoor water usage was found to be relatively insensitive to price, but outdoor water usage to be Espey, Espey, and Shaw (1997) preformed a meta-analysis on 30 years of research in the field of price elasticity of water. Their research concluded that

eas such

the average price elasticity of ater for residential use was -0.51 with 90% of the estimated elasticities falling between 0

spiration influenced the price elasticity stimate. A number of variables that were found to be important to determining total water

appear to effect price elasticity, including temperature, household size, and

wand -0.75. The literature includes studies with very different model specifications and estimation methods, and the focus of this paper was to investigate how the ultimate priceelasticity estimates in the literature were affected by model and variable choice. Includingvariables such as income, rainfall, and evapotranedemand did notpopulation density. Also, price elasticity estimates were not sensitive to whether the modelswere fitted with cross sectional or time series data, or with aggregated or disaggregated data. Another review article, by Arbues et al. (2003), also finds a range of price elasticity estimates. These authors examine three types of model specifications over fifty papers. The estimates range generally between -0.1 and -0.7. Like Espey et al., the findings reviewed have a midpoint elasticity estimate of around -0.5. Income Elasticity The sensitivity of water usage with respect to household income has also been analyzed through a variety of lenses, and the empirical results vary widely. At the individual household level it is usually not feasible to obtain direct measurements of income. “Assessedvalue of the property,” first used by Howe and Linaweaver (1967), is a common surrogate for household income. Real estate values are public information, easily obtaina

ble for each ddress, and are known to be highly correlated with income. Other proxies for income in the

of

, se in

e

e

utdoor Use

aliterature include the education level of the household head, age of the home, occupationhousehold head, and number of cars (Jones and Morris 1984). Howe and Linaweaver (1967) report an income elasticity of 0.35 for residential water usageimplying that a 10 percent increase in household income leads to a 3.5 percent increawater usage. In the review article by Arbues et al. (2003), income elasticities are reported between 0.15 and 7.83, a vast range. The problem for these and other researchers is toseparate the income effect from all the other income-related effects. As household incomrises, we see fewer persons per household, but more outdoor water uses (irrigated landscaping, swimming pools). Moreover, the typical water bill is a very small fraction of thincome of affluent people, suggesting lower price elasticity than for poorer households (though this was not found in the meta-study of Espey et al., 1997). O

Research focused on time of year suggests that summer water demand is more elastic than winter water demand (Arbues et al. 2003, Mayer et al.1999, Howe and Linaweaver 1967). Originally winter demand was considered non-seasonal demand, while the difference between summer demand and winter demand was categorized as seasonal demand (Howeand Linaweaver 1967); but more recent research, with access to disaggregated end-use


analysis, suggests that indoor water usage also fluctuates with the time of the year and that outdoor water use also occurs in the winter (Mayer et al. 1999). They have shown that outdoor use rises in concert with the square footage of the home and lot size. They theorizethat both exogenous variabl

thus

es serve as indicators of standard of living. Also, the outdoor

ater price elasticity, which they calculated as -0.82, is relatively elastic compared to overall se

homes which water with a hand-held hose use 33% less water outdoors than other

tility, water source displayed 25% less outdoor use than those without access

wwater price elasticity, in accord with economic theory. Other findings of outdoor water uin their detailed end-use study include:

homes with swimming pools use more than twice as much water outdoors than homes without them

homes with in-ground sprinkler systems use 35% more water outdoors than those who do not

homes that use an automatic timer to control their irrigation systems used 47% morewater outdoors than those that do not

homes with drip irrigation systems use 16% more water outdoors than those withoutthem

homes homes which maintain a garden use 30% more water outdoors than those without a

garden homes with access to another, non-u

Weather Weather has been shown to affect seasonal water demand, though results vary geographiand it is difficult to generalize. Nieswiadomy (1989) investigated the interaction of wand price elasticities, calculating the difference between potential evapotranspiration for Bermuda grass and actual rainfall. Evaportranspiration was shown to significantly alter theown-price elasticity of water. Others have used precipitation during the growing season, minutes of sunshine, and annual rainfall (Arbues et al. 2003). As measured by Miaou (1990), weather was shown to be hystere

cally eather

tic, dynamic, and state-ependent: hysteretic, the response to temperature at different temperatures is different at

all.

ddifferent times of the year; dynamic, rainfall’s effect diminishes over time; and state-dependent, the higher seasonal water use before rain “the more water use reduction can beexpected.” Weather is thought to have non-linear effects on water usage. According to Miaou’s statistical analysis the number of rainy days is a better predictor than total rainf Technology and Regulation A literature is emerging on the effects of household water technology on indoor water usage (White 2004). Most research in this area has focused on conservation, induced by the Energy Policy Act of 1992 and its regulations on plumbing-ware manufacturers. In one tudy the introduction of low-flow water technology reduced water consumption per

6%, in another 46% (Mayer et al. 2003, Mayer et al. 2004). With such shousehold by 3significant drops in usage reported in the literature, it seems likely that the introduction of water-conserving appliances has contributed to the drop in per customer usage in the Louisville area. However, as far as we know, no Louisville-specific research has been performed to determine the saturation of these appliances in the local housing stock.


Customer Classification Issues ouisville Water Company officials are well aware that many customers classified as ‘commercial’ are in fact households, not business establishments. However, until this study the extent of the classification problem was not known. This section addresse

issues of residential and commercial customer classification in the LWC database. We examined a random sample of 500 commercial customers and found that the sample contained 162 premises with 1,528 hous

s

ing units. These units were primarily apartment com ified as comm r sample results imply that about 15 percent of all housing unitcustom Company database. Interestingly, the average commercially classified hou g

We begin with a brief discussion of common approaches to customer classification within the ucustomer database. We then provide a statistical characterization of the entire Company data sappro c we identified commercial customers that actually represented housing units, and w Cla fThe water industry does not have a stan

ic research and industry officials acknowledge that

to water usage data when

o

and irrigation services. In the delivery of potable water, typically

es s or

Lplexes and condominiums, though we did discover several single-family homes class

ercial customers. Ous in Jefferson County are counted under the commercial, rather than residential,

er class in thesin unit uses more water than the average residentially classified housing unit.

ind stry, and explain the classification system used in the Louisville Water Company

ba e, showing the distribution of customers by type. Finally, we describe the sampling a h, how

ho inferences were made county-wide.

ssi ication Methods within the Industry dardized methodology for customer billing

ions. However, both academclassificatmost water companies group customers according to similar ‘use characteristics’ such as amount of water consumed, topographic constraints and service type, rather than actual property use (Dziegielewski et al. 2002)3. This approach to customer classification poses a problem in trying to understand water consumption patterns based on economic and demographic models. For example, economists analyze water demand and supply in the same way they analyze other goods and services. They use consumer theory to model

ousehold water demand. But it is difficult to apply these modelshhousehold water use is measured under a commercial classification because a business happens to own a multi-family housing complex. In practice, customer classes are influenced by service type. Service types are distinguished first by whether the water is for potable or non-potable use. Potable water is defined as water suitable for drinking, cooking and irrigating on a domestic scale. Non-potable water refers t

ater used for large area irrigation, fire, and industry. Both residential and commercial wcustomers use potable water customers are grouped into one of two broad categories, residential and nonresidential users. These categories are further divided into subsectors that vary among water companies. For example, some water companies treat all single family, multi-family units and mobile homas residential, while other companies may categorize apartment complexes, mobile home

3 The statement is also based on phone conversations with officials at the Kentucky Public Service

Commission and the Louisville Water Company.


condombusines

CustomThe Lo

iniums as commercial. This is particularly true if the account is registered to a

of

rvices

ater

Large Domestic Services

s that

d

r

s rather than an individual person (Dziegielewski et al. 2000).4

er Classifications within the Louisville Water Company uisville Water Company identifies seven customer billing classes: Residential,

Commercial, Industrial, Fire Hydrant, Fire Service, Municipal and Wholesale5. Types services offered by the Water Company include Domestic, Fire, Irrigation, Combined Residential Domestic/Fire and Combined Commercial Domestic/Fire6. The scope of this study includes only LWC customers who received domestic water sein 2004. The table below refers to the categories of domestic service available to Residential and Commercial customers as defined in the Louisville Water Company Board of WWorks Rules and Regulations. The meter sizes typically used in each category are taken fromthe distributions found in our analysis of the 2004 customer billing data.

Residential and Commercial Billing Classes Under the Louisville Water Company’s Domestic Water Services

LWC DOMESTIC W

Single Family Residential

A single family house typically uses a ¾"

domestic service for water usage. Larger size

meters are available.

Domestic serviceare larger than 4". The customer provides the

point of highest flow anthe point of lowest flow for meters over 2", so

that the optimum meteassembly can be

constructed to best serve that location.

Water Irrigation Irrigation Water

Meter sizes typically range from 5/8” to 4”




Includes two or fewer housing units, residential properties held in common such as condos and

non-residential farms.

establishments engaged in selling merchandise or rending service, construction, mining, agriculture,

and condominium units owned by developer.

Residential Commercial

A separate meter placed at a location to be used specifically for irrigation systems on the site. The irrigation meter counts the water separately and

will save the customer the MSD sewer charges in areas that are served by MSD.

ATER SERVICES

Includes non-manufacturing industries,

tionIrriga

4 See also online references: Local Water Utilities Administration, 2005; and City of Salem Finance

Department, 2005. 5 LWC online < http://www.lwcky.com/water_works/default.asp> 2005. Louisville Water Company Board of Water Works. Rates, sec.6.01 through 6.09.

ice . 6 LWC online < http://www.lwcky.com/water_works/default.asp > Service Applications/ 2005 Serv

Rules and Regulations, Sec. 1.04.1 through 1.04.5


Characteristics of the LWC Customer Database This section highlights the structure and characteristics of the billing data. The customer billing data provided by LWC for analysis included 1,486,098 individual records that

throughout Jefferson, e

days

le below provides brief explanation of each field in the database. This is followed by a more detailed

represented every bill issued to commercial and residential customers Bullitt and Oldham counties in 2004. Billing information contained within the databasincluded premise number, attachment number, account name, service address, service zipcode, mailing zip code, customer type, service type, meter size7, billing date, number ofbilled, and volume of water used during each billing period cycle. The tabaexplanation of various aspects of the billing information and their distributions.

Customer Record Fields Used in Study

Field Label Definition

PREMNUM Premise number s where water

meter (or meters) is attached. Each physical address has only ay have multiple meters.

Specific number assigned to physical addres

one premise number, although it m

ATTNUM Attachment number

ise may have more e meter, therefore more than one attachment number connected to the premise number. However, a meter has only one attachment number. This is the only unique ID field in the database.

Specific number assigned to each meter. A premthan on

ACCTNAME Account name Name of the business or individual(s) responsible for payment on the account.

SERVADD Service address Physical address of the premise. SERVSIZE Service size Size of water meter of given attachment number.

SERVTYPE Service Type Type of service, either Water or Irrigation, to given attachment number.

TAXDIST Tax District Tax District where premise is located.

RESCOMM Residential or Commercial Type of customer, either Residential of Commercial, never both.

PREMZIP Premise zip code Zip code of premise address.

ACCNTZIP Account name zip code Zip code of address of person(s) or business in whose name the account resides.

BILLDATE Date of bill Date by month, day, and year the water bill was issued.

BILLDAYS Number of days billed Number of days in the billing cycle for which the premise was billed.

USAGE Water use in billing period (000s gallons )

Amount of water used in the billing cycle, measured in one-thousand gallon increments.

7 Meter sizes were not available for 6,925 meters in Bullitt and Oldham counties.


Premise and Attachment Numbers The LWC customer billing data is based on premise numbers and attachment numbers. Each physical property with a meter issued by LWC has a premise number. In effect, premise number is connected to the site address. There is only one premise number for every address, although a premise may have more than one meter. For example, there mbe two or more meters of different sizes, or one or more meters measuring potable water and one or more measuring irrigation. Each meter on a premise is assigned a unique attachment number. Premise and attachment numbers remain a permanent record feature connected to specific physical addresses, even though the account name assigned to aaddress may change. For example, a rental property may change account names tw

the

ay

n o or more

mes in a given year, yet the premise number assigned to that address and the attachment number or numbers assi is is true for every premise, resid r commercia d or owned. M

ti

3/4"7,799

Other241

3%

613%

1%

2"

3890% 4"

1870%

6"430%

8"100%

10"1

0%

0%

108,593

1"6,7

1 1/2"1,940

1,7081%

3"5/8" X 3/4"

5/8"124,800

49%

43%

gned to the premise remain the same. Thential o l, rente

eters All water supplied by the Louisville Watemeasured by meters installed and maintaiThe Water Com e amouwater a premise uses over one or two-mobilling cycles as indicated by the on-premm ter rying sizes indiameter, anywhere from 5/8” to 5/8” X 3/4” (a low/high-flow feature) to 10” depending on the volume of wi anuf stomer whose production process depends o ume allyhave meters of at least 4” in diameter

and more likely 6” to 8” diameters while a ily re wouldn e 5/8 3/4” to 3/4” meters. Customers in the LWC

r Company is ned by LWC. nt of nth

pany calculates th

ise eters. A me can be of va

ater needed by the customer. An ndustrial m acturing cu

n large vol s of water would typic

single-fam sidential customer ormally us ”, 5/8” X

Residential and Commercial

Three-County Service Area


Customer Classes and Service Types

Irrigation 5,4252%

Commercial premises21,009

8%

Potable Water253,681

98%

LWC identifies seven customer classes including residential, commercial, industrial, fire service, fire hydrants, municipal, and wholesale. The two customer classes included in thisanalysis are residential and commercial. And while there are a number of service type classifications within the LWC billing structure, this analysis includes only two, potable wateand irrigation, both of which fall under the broader category of domestic service provided the Company.

Broken out by premises, the residential class accounts for 92% of LWC’s commercial

d residential

r by

, ancustomersand delivery of potable water Residential premises

238,118comprises 98% 92%of overall demand

in the three county area. Meter Size by Customer Class8

Although smaller meters are the norm ine si

the

10"1

0%

6"430%

4"1861%

8"100%

no meter size given4272%

3"3852%

2"1,6358%

95

3/4"1,2036%

5/8"5,52125%

531

1 1/2"1,79%

1"4,22%

5/8" X 3/4"5,27225%

3/4"6,5963%

4"1

0%

2,2301% no meter s e given

6,4483%

0%

119,27950%

43%

1"

0% 730% 3"

4

iz

1 1/2"145 2"

5/8"5/8" X 3/4"

103,321

ze of

ercial

ial Class

The pie chart at left shows the predominance of smaller meters in use among customers classified as residential.

delivery of water for domestic use, thommmeter varies, particularly among C

customers. This variation was a flag in looking for residential properties classified as commercial.

The figure to the right depicts the variance in meter sizes used for water delivery to commercial customers in the LWC service area.

Meter Size by Residential Class

Meter Size by Commerc

6,925 meters among 6, 875 premises in Bullitt and Oldham counties lacked identification by meter size. 8


Meter Size by Service Type Meter sizes vary according to service type as well as customer class. Although there is a gdeal of overlap, this analysis found that surprisingly, the larger meters were used more among customers of potable water service than of irrigation services. However, as the cbelow indicate, the typical meter size applied to the delivery of irrigation services was generally larger than the 5/8” or 5/8 X 3/4” meters that dominate in delivery of potable water.


1"6,0532%

1 1/2"1,7891%

2"1,5381%

3"3820%

no meter size given6,8423%

Other6,8943%

6"410%

8"100%

10"1

0%

4"185

5/8" X 3/4"107,505

42%"

67

5/8"124,768

49%

0%

3/44,52%

3"732

1%

2"1703%

1 1/2"1513%

Other44

no meter size given331%

/8" X 3/4"

20%

59%

1"

13%

0%4"2

6"

0%2

0%

5/8"3/4"

3,232

1%1,088 7085

reat

harts

Meters

ater

Distribu b For

Distribution of by Size For Potable W

tion of Meters y Size

Irrigation

Random Sample of Commercial Customers This section describes our analysis of a random sample of 500 commercial customers within Jefferson County. Our objective in pulling a sample was to learn how many properties classified as commercial were actually in residential use. Here we explain how the random sample was obtained and the property use identified. This is followed by a discussion odistribution of customer characteristics and water use within the sample. The results of thsample

f the e

analysis are then used to construct estimates of the total number of housing units overed by the commercial class of customers within the County.

m Sample

c Criteria for the RandoThe random sample

. The critewas pulled from a universe of 16,074 premises classified as commercial ria for forming the universe of commercial customers from which to

tract the sample were the following: each customer (premise) should have one year of ontinuous service to at least one meter on premise in 2004; use either [domestic] water or

irrigation services; be classified commercial and be located within Jefferson County.

he number of bills received in 2004 served as a proxy for one full year of service. Any tachment number that received 6 or more bills in 2004 qualified. Using SPSS 13.0, the umber of residential and commercial customers in Jefferson County was derived by ducing the original database of 1,486,098 billing records in the three-county area to only ose records whose Tax District was listed as Jefferson County. Next, we identified records ith Service Types of either Potable Water (W) or Irrigation (I), dropping all others. Finally, e identified how many bills went to each meter in 2004, and within that pool, how many remises had meters with six or more bills sent in the course of the year.

fferson County Residential and Commercial Customers

customersexc

Tatnrethwwp Je


Residential185,027

92%

Commercial16,074

8%

he number of residential and commercial premises with a continuous year of water service Jefferson County totaled 201,101, with a distribution of 92% residential customers and

me proportion found in the overall data for the three counties. ccounted for 99.9% of the delivery service type, a slightly higher proportion

rea.

Tin8% commercial, the saPotable water athan in the larger a

POTABLE WATER200,82899.9%

IRRIGATION273

0.1%

Meter Sizes by Service Type The following two charts illustrate the distribution of meters by size and service type for those residential and commercial customers in Jefferson County who received at least sixduring the course of the year. The first pie chart represents the distribution of meters used the delivery of Potable Water Service and the second chart illustrates the distribution as it applies to Irrigation Services.

Jefferson County Commercial and Residential C

1 1/2"238%

Other42

15%

4"1

0%

3"1

0%

2"40

15%

5/8"3

1%

4"0%

1"60

%5/8" X 3/4"45

16%

3/1038

22

8"6

6"160%

4"1270%

Other1490%

3"3010%3/4"

3,8522%

5/8", 102,061,

50%

5/8" X 3/4", 85,999,

43%

0%

1%

2"1,417

1 1/2"1,6271%

1"5,4223%

bills in

ustomers 1 Year of Service for Potable Water

istribution by Meter Size in 2004

D

Jefferson County Commercial and Residential Customers

1 Year of Service for Irrigation Distribution by Meter Size in 2004


Potable W15,9599

ater 1

%

Irrigation1231%

4"2

5/8"12

0%

3"112%

2"429%

1 1/2"48

10%

16% 5/8" X 3/4"

13127%

3/4"214%

3%25

1"123

2"2

33%

1 1/2"4

67%

Potable Water 49499%

Irrigation6

1%

sample was pulled from only Commercial customers in ate

Pota

Among Jefferson County Comme with One Full Year of Servic

Proportion of Potable Water and Irrigation Services Among the Random Sample

Distribution of the Random Sample by Meter Size for Potable Water Service

Distribution of the Random Sample by Meter Size for Irrigation Service

As previously stated, a randomJefferson County, a universe of 16,074 premises. The two charts immediately below illustrthe proportion of customers using Potable Water and Irrigation Services among the universe of Jefferson County Commercial premises and the random sample respectively. These are followed by two charts that represent distributions of the random sample broken out by Service Type and Meter Size. Proportion of ble Water and Irrigation Services

rcial Premises e


The map below shows the spatial distribution of the random sample overlaid on Jefferson County land use zones.


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LOUISVILLE #* Random sample of LWC commercial customers*

Residential estate

Single and two-family residential

Urban neighborhoodWATER COMPANY

0 2 41 Miles

Rural residential

Planned employment ctr.

Enterprise zone

Traditional neighborhood

Commercial residential

CBD

E

Commercial mfg.

Commercial industrial

*Random sample of 500 commercial customers in 2004

ssues of Customer ClassI he majority of commercial premises that proved to be residential in use were multi-family

W ter

iniums. For in common, while

ominium units’ are categorized as commercial if owned by the developer. The reasons he ambiguity are tw compliance with state tax laws, and second,

storage and processing.

compliance with state tax laws, the Louisville Water Company classifies apartment omplexes, some condominium groupings, and other multi-family housing units as

r’s association overseeing such properties sets up a single account for multiple rental ore served by one meter and individual water charges are passed on to the [unit] occupants as portion of monthly rental or maintenance fees. Because the real estate owner or omeowners’ association has the opportunity to earn a profit as they pass along utility costs

state requires the Water Company to levy the Kentucky six les tax on water service to these developments.

Trental or condominium properties. There are several reasons such properties may be c d b c to th a

ndlassifie commercial in the LWC data ase. Ac ording e 2005 Louisville

Company Service Rules and Regulations, the distinction between Residential a condomCommercial properties is vague in regard to apartment complexes and

xample, ‘condos’ are considered residential if they are properties helde‘condfor t o-fold: first, the need for a result of legacy information technology limitations on data

Inccommercial if the real estate company or homeowne

r condo units. In such cases, all units aahto the renters and owners, thepercent sa

Verification of Property Use in the Random Sample A line-by-linc

e examination were obviously ommercial, judging from Account Name and Water Usage. Any property registered under a

business whose water use exceeded 7,000 gallons in an average billing cycle was considered commercial. The property uses of the remaining 275 premises were identified using a variety of tools including the Internet, proprietary real estate databases, apartment rental and condominium publications available at supermarkets and drug stores, and where all else failed, windshield surveys. Two concerns were the proper identification of actual use of the premise in question, and identification of the number of residential units each premise represented. Some premise addresses represented single-family homes. Others represented multiple units of large apartment or condominium complexes, while still others represented a single building with multiple units within large complexes. There were many combinations of possibilities and unless the number of units was easily identifiable through an internet search, a real estate database search, or a commercial listings publication, we could not assume the correct number of units attached to the address. In such instances we drove to the site and counted the number of units attached. Findings from the Random Sample

of the sample revealed that 225 premises

We determined that of the 500 randomly selected premises, 162 of these were actually not businesses, but housing units. Furthermore, the premises we identified represented 1,528 individual units, either as separately addressed condominiums and apartments, or apartment and condominium complexes where residents shared one street address, or in a few cases as single family homes. The average number of housing units per commercial premise containing residential property was 9.43. Although the majority of these properties are not misclassified according to LWC rules and regu dential uses of

ecause th service they receive is officially categorized

imate water

-ent complexes is due to more

lations, they do represent resiewater that are not measured as such b

as ‘commercial’. Using this sample of ‘commercial’ premises and our inspections, we have made an estof the total number of housing units in the Louisville Water Company system whoseusage is classified under the commercial category. We assumed that all the separately addressed housing units were occupied, and assumed a 90 percent occupancy rate for units in apartment and condo complexes. This implies that there were nearly 44,200 occupied residential units among Jefferson County customers classified as commercial. This is a good approximation, though the estimate is subject to some measurement error due to our subjective judgments about which commercial customers were actually businesses, ourassessments of how many housing units were associated with each residential use, and our assumption of occupancy rates. Using this sample, we estimate that in 2004, the total volume of water used by the propertiesdesignated as commercial customers, but identified as serving housing units, was approximately 110 million gallons for the year. Over the 1,528 housing units, adjusted for an assumed 90 percent occupancy rate, this works out to 6,660 gallons per month, higher than the average water use per residential customer (5,620 gallons) in 2004. This is a surprising result, given that renter-occupied housing units have less people per household than owneroccupied units. Possibly, the additional water use in apartm


extensive landscaping and irrigation, and the higher likelihood of swimming pools. A more lve

r ousing units classified under the commercial category. This is

out 24 percent of the total commercial water use in 2004, and equivalent to 22 percent of

detailed investigation of a sample of apartment complexes would be necessary to resothis. We treat this finding as tentative until more a more detailed investigation can be made. Others have found that single-family homes use on average much more water than a dwelling unit in a multi-family building.9 Extrapolating the sample results county-wide, we estimate that 3.5 billion gallons of watewere consumed in 2004 by habannual water use now classified as residential. Clearly, this represents a major portion of theCompany’s water customers and usage, a portion that is not yet well-understood.

9 See Dziegielewski and Opitz (2002), page 5.34, though all comparisons are for households served by

California water systems.


Some Econometric Results for Louisville e have estimated a simple econometric model of average monthly residential wausage, to determine how much the identified causal factors have contributed to the decline in sales over the past three decades. We obtained monthly data o

precipitation and ground moisture, and con

ter

n structed a measure of the number of persons per

ousehold and average household income in Jefferson County over the period. A measure of

most important factor. Theoretical considerations From the literature review, we can posit some reliable theoretical considerations in modeling residential water use. Water is a necessity of life, though this consideration is important only for, say, the first twenty gallons per person per day – that used for drinking, bathing, and toiletry. Most households use around 200 gallons per day, or on average about 80 gallons per person. So, water use is not thought to be very sensitive to its price for base consumption. And because the cost of water is typically a very small fraction of household income, water is not expected to be very price sensitive over the range of use for most households. For similar reasons, indoor water usage is not very sensitive to changes in household income. However, outdoor watering is believed to be much more price sensitive, because the outdoor uses are less necessary and because the volume of water is typically much higher. Monthly water use per household in a city, then, is expected to be determined by the following factors that we attempt to measure and fit in a regression model for the Company.

1. Water use is positively related to the number of persons per household

h

W new housing stock was created to simulate the introduction of water-conserving appliancessince 1994. We also included monthly dummy variables to pick up the effects of changes inwater usage due to normal seasonal behavioral changes throughout the year. The simple model provides some insights into the causes of the decline in average residential water usage in Jefferson County. The decline in average household size appears to be the

. We expect this relationship to be quadratic, with diminishing additional water use per additional resident. We model this by including both a linear and squared term for household size.

2. Indoor water use is seasonal, with different average household water demands per month as people wash themselves and their clothes more or less due to seasonal changes in temperature, daylight, and activity, and as people attend school and take vacations, celebrate holidays, and the like throughout the year. We model this by including eleven monthly seasonal dummies, one for each month, with the constant term of the regression picking up the effect of the twelfth month.

3. Outdoor water use is a function of weather during the growing season, essentially April through October in Louisville. Dry weather induces a large spike in water use as people turn on sprinklers and use hand-held hoses to quench the thirst of their lawns and landscaping. Very dry periods induce extreme water use as households seek to keep plants alive. Wet periods reduce average outdoor water use to almost zero. Note however, that increasing rain after saturation does not reduce water use further. Hence, it is likely that the relationship between ground moisture and outdoor water use is asymmetric and possibly nonlinear. We model this using a


groundso that it provides an asymmetric measure as portrayed in the chart. We separate

mois modified the index

ose

ture index for central Kentucky10. However, we have

outdoor water use

drought Ground moisturenormal

months with below and above average ground moisture and create separate indexes. For the dry months, we create both a linear and squared index so we can fit the possible exponential increase in outdoor watering occurring during drought periods.

4. People living in new and renovated homes are expected to use less water than thliving in older homes, due to the introduction of water-conserving appliances after1994. There is little data on renovations and the introduction of new plumbingfacilities in existing homes. But there is data on household growth, as well as on

building permits for both single-family and multi-family units in Jefferson County. s in

Wehousehconstru s these ar ral Kentucalways a and new housing stocmon lspecificcoeffici This minsightsMultico le,

We use these data as a proxy for the penetration of water-conserving appliancethe County. There were approximately 237,000 households in the County in 1994, and nearly 300,000 today. We have created a measure of cumulative growth in households in the County since 1994 and use this to measure the reduction in water use per household since the new water appliances were introduced.

use ordinary least squares to fit the model, using thirty years of monthly average old water use as the dependent variable. The moisture and drought variables are cted from monthly data as well. We use only the values for April through October, ae the prime months for outdoor water usage. The Palmer Drought Index for centky was used for these measures, though we have transformed it so that the index is positive number to make interpretation easier. The household size

k variables are derived from annual measures, with an interpolation made to simulate th y growth between annual points. The regression results for several alternative

ations are provided in the accompanying table, with only statistically significant ent estimates shown.

odel relies only on aggregate data and hence cannot be expected to provide detailed into changes in the end uses of water over time or across customers. llinearity is a particular problem with such aggregate time series data. For examp

10 Palmer Drought Index, wwwagwx.ca.uky.edu/wpdanote.html.


the decbelieve hypothesis testing difficult, as the inclusion of one of these variables lowers the statistical

iables

ested, but was not statistically significant. The drought variable

Model 1 Model 2 Model 3 Model 4Constant 3,927.67 -39,575.05 5,969.45 -42,376.65

Persons per household 743.22 34,566.11 36,808.42Persons per household squared -6,547.46 -6,984.94

Gr

2

27

25.45 1,518.26September 1,621.04 1,614.75 1,651.20 1,642.38

1,082.2592.18

Dependent Variable: Average Monthly Residential Water Usage, 1975-2004Regression Results

line in household size over the period is highly correlated with other variables we are important, such as household income and new housing stock. This makes

New housing stock, post-1994 -0.03

Palmer Drought Severity Index -81.34 -62.67ound moisture index, above average months* -124.90 -122.26

precision of coefficient estimates on the other. Note that in Model 3, the inclusion of our new housing stock measure reduces the significance (to zero) of our household size variables. We were not able to fit a model in which all these theoretically important varwere statistically significant, and this is no doubt due to multicollinearity among the economic variables.

Nevertheless, the statistical results from this simple model are instructive. We find that increased ground moisture has a negative and linear effect on monthly water usage. A uadratic moisture term was t

October 1,061.77 1,050.57 1,092.04November 425.79 412.16 303.96 2December -208.75 -221.79

Adjusted r-squared 0.61 0.65 0.65 0.68

* Moisture and drought indexes for April through October only; converted drought months to positive values.All estimated coefficients significant at 95 confidence level.

Drought index, below average moisture months* -380.95 -373.34Drought index squared* 203.07 196.2

Seasonal dummy variablesFebruary

March -163.84 -154.03 -215.40 -215.9April -356.21 -350.34 -311.85 -312.0MayJune 349.97 348.04 388.38 384.01July 1,094.22 1,094.72 1,161.39 1,154.86

August 1,465.12 1,466.04 1,5

qhas an exponentially positive effect on water usage, with the quadratic term on our drought measure statistically significant in Models 3 and 4. These results can be used to explain how much of a reduction in residential water sales have been due to unusual weather.


The number of persons per household clearly has a nonlinear effect on water usage, as seein Model 4. Controlling for the other factors, water usage per household peaks at arou6,340 gallons per month for a household of 2.65 persons. The reduction in average household size between 1988 and 2004 (from 2.50 to 2.35 persons per household) is sufficient to drop household water usage by 440 gallons per month, or about seven pUsage per person is nearly unchanged over that range, and the reduction in household water use is primarily due to less people per house. Note that the variation in average houssize over the sample

n nd

ercent.

ehold period (2.92 to 2.35) is much less than the actual variation among

individual households (zero to perhaps ten persons). Hence, we would expect a much more

n examination of the regression residuals from Model 4 revealed that most of the nexplained variation in monthly water sales occurred during drought periods, particularly

iable

precise estimate of these coefficients using detailed household data (which is not yet available).

The July through October seasonal dummy variables took large estimated coefficients and were all statistically significant. There is a clear peak in September, with water usage of 1,640 gallons more than the average month.

Water Usage per Household and per Person

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

2.00 2.05 2.10 2.15 2.20 2.25 2.30 2.35 2.40 2.45 2.50 2.55 2.60 2.65 2.70 2.75 2.80 2.85 2.90

Household size (persons)

Wat

er u

se p

er m

onth

(ga

llon

s)

per household

per person

2004 1975

1988

Authe summer and fall months of 1983, 1988, 1999, and 2002. Thus, while our drought varwas highly significant in the regression, it was not able to explain the extent of water usespikes during very dry periods. The July to September period of 1988 was by far the biggest outlier, with water use per residential customer climbing to over 10,000 gallons per month in August. This suggests that further refinement of our weather measures would improve the fit of the models, and perhaps lead to more precise estimates of the coefficients on emographic and economic variables. d


Residential Water Sales, Louisville Water Company

Unexplained Water Usage per Residential Customer: Model 4gallons by month, 1975-2004

1,500

2,000

2,500

3,000

-1,500

-1,000

-500

0

500

1,000

Jun-75

Jun-76

Jun-77

Jun-78

Jun-79

Jun-80

Jun-81

Jun-82

Jun-83

Jun-84

Jun-85

Jun-86

Jun-87

Jun-88

Jun-89

Jun-90

Jun-91

Jun-92

Jun-93

Jun-94

Summer 1988

Fall 1983 Spring 1978

29

Jun-95

Jun-96

Jun-97

Jun-98

Jun-99

Jun-00

Jun-01

Jun-02

Jun-03

Jun-04

Fall 1999

Summer 2002


Summary and Recommendations e have taken a number of steps to determine why residential customers in Louisville have been reducing their average water usage. We reviewed the academic and industry literature to see

problem in particular, and water use in general. We found many econometric attempts to measure the price and income elasticities of water demand, the effect of household size, of weather, and of conservation measures. The results were uneven and sometimcontradi eve ongest findings do not all apply directly to Louisville. Perhaps the most useful research reviewed is the 1999 Residential End Uses of Water study, funded by the American Water Works Association. The authors provide handy reference tables on water use by appliance, as well as an examination of outdoor water use and its detailed determinants. Some of these coefficients and ratios could be applied to research on Louisville customers, though none of the households they studied were located in the Midwest of the United States. Their study also provides a cost-effective method for understanding water usage by appliance in individual residences locally. Its use of electronic data-loggers and analytical software could be easily applied to Louisville, to great effect. We have investigated the Company’s customer database to see if there are classification issues that might contribute to the measured reduction in residential water demand. We drew a random sample of 500 customers classified as ‘Commercial’, and found that the sample included 162 parcels containing around 1,528 housing units. We inferred from this that 44,200, or 15 percent, of occupied housing units in Jefferson County are counted under the commercial water classification. Nearly all of these housing units are apartments or condominiums. They should be included in any analysis of residential water demand. Finally, we estimated a simple econometric model of local residential water usage. The dependent variable was average monthly water use per residential customer, using thirty years of data from the Company’s database. Explanatory variables included household size, household income, new housing stock, moisture indexes, and seasonal factors. We found strong statistical relationships between water use and household size, moisture, and the seasonal dummy variables. The reduction in the number of persons per household in Jefferson County has clearly caused a reduction in water use per household. Our model suggests that the decline in household size is responsible for about one-third of the reduction in water use since 1988. Extended dry periods, as measured by the Palmer ground moisture index, explain much of the abnormal variation in monthly usage over the last three decades. We could not find a statistically significant impact of rising household incomes or of the surge in new homes over the last ten years – homes that presumably are fitted with federally-required water-conserving appliances. We suspect both of these variables are quite important, but multicollinearity among the explanatory variables prevents us from finding the independent contribution of each in such an aggregate data exercise. While all of these investigations provide good indications of where to dig for more insights, they do not provide a complete explanation for declining residential water use locally. We feel confident that declining household size and the introduction of water-conserving appliances have contributed to a decline in average water use. However, this should be partially offset by increased watering of lawns and landscapes as local incomes have risen. We recommend a research effort to resolve these and other remaining issues.

W what others have learned about this

es ctory, and n the str

RecommendWe believe the greatest long term research value for the Company is in exploiting its customer database, in combination with other publicly available databases and possibannual end use survey. In particular, the Company should begin to systematically checkreclassify as needed all its customers to reflect more precisely their water usage type. Housing units now classified as commercial should be reclassified as residential-multifamily customers, so that water usage patterns of apartment and condo dwellers can be tseparately. A more elaborate classification system needs to be developed for all customer types, one that exploits the great advances in information technology, and which is designedwith analysis (rather than just billing) in mind. The Company should use its powerful GIS tools to better understand the relationshibetween household water use and age of structure, household income, and outdoor wause. The age of structures can be inferred for most units from the ‘date of service’ fiethe Company’s customer database, cross-checked against the ‘date of structure’ entry for the housing unit in the Real Estate Master File database of the Jefferson County Property

ations own ly an

and

racked

p ter

ld in

aluation Administrator. The vintage of the housing unit is a good indicator of the

hs

be h

erging and using these databases. Address match rates between databases will not be 100 icro

d

ter use

he

s, ood inferences

ould be made on the detailed water usage of all homes in Jefferson County. By repeating

Vplumbing technology in the unit, and hence a way to model the saturation of new water-conserving appliances. Moreover, the PVA’s ‘assessed value’ of the property is an excellent proxy for household income, and should be used in econometric studies on water usage byindividual customers. Finally, the Company’s LOJIC system has digitized aerial photograpof all County structures. These can be used in conjunction with PVA databases to ascertain which residential customers have swimming pools, and special statistical studies can performed on these households. There are of course many measurement problems witmpercent. But filtering algorithms can be developed which can pull a wealth of reliable mdata for research purposes. As a way of tracking local residential use, the Company should consider a cost-effective enuse study, following the techniques described in DeOreo et al. (1996) and Mayer et al. (1999). The basic water flow information on plumbing facilities in a housing unit is generated by a data logger attached to the water meter. The Mayer et al. study used the Meter-Master 100EL, manufactured by the R.S. Brainard Company11, to monitor waby component for 100 homes in fourteen cities. The beauty of their approach is that the datalogger is attached to the home’s water meter, not individual appliances, and yet by recognizing the ‘flow signature’ of each appliance type it can record use throughout the day of any and all appliances12. Moreover, the logger recognizes outdoor water usage as well. Tdata loggers are easily installed, at a rate of five homes per hour. A mail survey of each home is required, where the respondent supplies basic information about hardware, demographicand behavior. By surveying and logging water usage on, say, 200 homes, gcthe research each year, the Company could track changes in technology, demographics, and behavior, leading to a deeper understanding of overall residential water use in the system.

11 See www.meter-master.com/ms/index?page=mm_products&v=metermaster&cat=FLOW_RECORDERS

in combination with Brainard’s Meter-Master software.

12 The Mayer et al. study used Trace Wizard, a proprietary software package by Aquacraft,


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