WRF Webcast. Water Use in the Multi-family Housing Sector

Transcription

1 WRF Webcast Water Use in the Multi-family Housing Sector February 1, 2018 No part of this presentation may be copied, reproduced, or otherwise utilized without permission.

2 Webcast Agenda Introductions & background Maureen Hodgins 5 min Presentation Jack Kiefer 45 min Question & Answers 10 min Survey 4 questions for audience

3 WRF Research Demand Forecasting & Management Goal Help improve utility plans Drivers changing water use trends, population changes, limited supplies, and/or increasing costs $3.5 M 16 projects, 11 published, 5 ongoing

4 Check out later Published CII, 4375, 2015 Residential, 4309, 2016 Multi-family, 4554, 2018 To be published 2018 Demand patterns f/ sizing meters & service Lines, 4689, est 2018 Commercial, 4619, est 2018 Customer water use Factors impacting demand Published Climate change, 4263, 2013 Recession, 4458, 2016 Passive efficiency, 4495, est 2018 Irrigation controllers, 4227, 2016 To be published Urban landscape research needs, 4633, est 2018 Published Uncertainty & long term forecasts, 4558, 2016 Uncertainty in forecasts & planning Forecasting approaches Published Customer data, 4527, 2016 Short term, 4501, 2017 To be published Long term, 4667, est 2020 Probability mgmt, 4742, est 2020

5 Summary of demand forecasting & management research

6 Downloading the 4554 report waterrf.org search 4554 download PDF

7 FYI

8 Input your webinar questions here Q&A at end of webinar

9 Download presentation Slides and recording will be available to WRF subscribers WITHIN 24 hours after the webcast

10 Water Use in the Multi-family Housing Sector Jack C. Kiefer, Ph.D. Lisa R. Krentz No part of this presentation may be copied, reproduced, or otherwise utilized without permission.

11 Presentation Overview Background on WRF 4554 Data sources and collection methods Metrics and comparisons Examples and results of modeling variability in water use Summary and conclusions Recommendations

12 The Multi-family Housing Sector About 25% percent of housing (or about 33 million residences) in the U.S Share of multi-family dwellings increasing in some areas Multi-family housing dominant residential sector in some denser urban areas Many areas plan to direct future development or densify

13 Water Research Foundation Project 4554 Water Use in the Multi-family Housing Sector Objectives Narrow knowledge gaps Develop, demonstrate, and recommend analytical strategies for: Estimating multi-family water use Categorizing multi-family properties Forecasting water use for water use categories

14 Defining what is Multi-family Everything other than traditional single-family detached homes Any residential property w/2+ units Master-metered residential properties General practice to lump MF into general residential or commercial customer classes detached, semidetached, row house, or multi-family structures with 5 or more units. Ownership/Tenure Rental Apartments Duplex Multiplex Individually Owned Condominiums Townhouses Jointly Owned Cooperatives

15 What multi-family looks like

16 What multi-family looks like

17 What multi-family looks like

18 What multi-family looks like

19 What multi-family looks like

20 What multi-family looks like

21 Selected Research Questions To what extent does multi-family water use differ from single-family use? How does water use differ among subclasses of the multi-family sector? What factors influence water use in the multifamily sector and major sub-classes? Does greater development density lead to less use? What are the effects of given property features?

22 Data Collection Water use for multi-family properties or classes Number of dwelling units (scale measure) Sub-classifications Property characteristics Other potentially influential variables Price Income Climatic Secondary information only

23 Utility Partners Denver Water New York City Department of Environmental Protection Phoenix Water Services San Diego County Water Authority Tampa Bay Water

24 Other Key Data Sources U.S. Census American Housing Survey American Community Survey New York University Furman Center Fannie Mae

25 What the US Census tells us Increase in share of population living in multiple unit structures Rental tenure dominates Lower incomes Smaller households Younger householders Proportionally fewer (in unit) clothes washers and dish washers (especially renter-occupied)

26 30 US Counties with Largest Multi-family Housing Share (2015) County Estimated Population in Multiple Unit Structures Percent of Total Population New York County, New York 1,532, % Bronx County, New York 1,230, % Kings County, New York 2,152, % Hudson County, New Jersey 537, % Suffolk County, Massachusetts 543, % Queens County, New York 1,565, % Alexandria city, Virginia 92, % San Francisco County, California 469, % Essex County, New Jersey 426, % Arlington County, Virginia 121, % District of Columbia, District of Columbia 319, % Passaic County, New Jersey 249, % Providence County, Rhode Island 274, % Cook County, Illinois 2,312, % Westchester County, New York 417, % St. Louis city, Missouri 131, % Milwaukee County, Wisconsin 372, % Union County, New Jersey 217, % Albany County, New York 108, % Cass County, North Dakota 61, % Middlesex County, Massachusetts 564, % Fulton County, Georgia 352, % Essex County, Massachusetts 273, % Richmond city, Virginia 75, % Orleans Parish, Louisiana 135, % Grand Forks County, North Dakota 23, % Denver County, Colorado 234, % Los Angeles County, California 3,509, % Hampden County, Massachusetts 158, % Bristol County, Massachusetts 187, %

27 Selected High Population Counties with Largest Gains in Multi-family ( ) County 2015 Population Change in Population Change Single Unit Change Multiple Unit Gain in Multiple Unit Share (Percentage Points) San Diego County, California 3,213, , , , Queens County, New York 2,310,954 95,615-36, , Cook County, Illinois 5,147,733-58, ,641 80, Santa Clara County, California 1,881, , ,042 94, Kings County, New York 2,597, ,038-27, , Bexar County, Texas 1,868, , , , Hillsborough County, Florida 1,328, , ,129 72, King County, Washington 2,081, , , , Alameda County, California 1,607, , ,420 77, Suffolk County, New York 1,469,390 24, , Orange County, California 3,125, ,779 87,164 97, Middlesex County, Massachusetts 1,526, ,367 57,803 60, Nassau County, New York 1,341,365 31,289 14,298 18, Orange County, Florida 1,255, , ,064 80,

28 Top 30 Counties with Largest Gains in Multi-family ( ) County 2015 Population Gain in Multiple Unit Share (Percentage Points) Arlington County, Virginia 226, Monongalia County, West Virginia 98, Natrona County, Wyoming 80, Harrison County, Mississippi 196, Suffolk city, Virginia 87, Cattaraugus County, New York 75, Leon County, Florida 272, Polk County, Oregon 77, Grand Forks County, North Dakota 66, Vigo County, Indiana 99, Orleans Parish, Louisiana 377, Craighead County, Arkansas 100, Jefferson County, Texas 238, Bradley County, Tennessee 101, Cass County, North Dakota 166, Cumberland County, North Carolina 313, Rockland County, New York 318, Muskingum County, Ohio 84, Dorchester County, South Carolina 150, Union County, New Jersey 548, Forsyth County, North Carolina 358, Houston County, Georgia 148, Portsmouth city, Virginia 92, Erie County, Ohio 74, Adams County, Illinois 65, Elmore County, Alabama 77, Lane County, Oregon 354, Bartholomew County, Indiana 80, Monroe County, Florida 75, Armstrong County, Pennsylvania 66,

29 Comparison of Unit Usage Rates (gallons per unit per day) Utility Multi-family Sector (Composite) Units Mean (Grand) Median Denver (2014) 192, New York City (2014) 2,143,108* Phoenix (2014) 181, San Diego County (2012) Tampa Bay Water (2014) 423, n/a 280, *Excludes properties in One-Family Dwelling or Mixed Residential/Commercial Land Use Tax Classes Relatively weak association with climate Distributions skewed to the right

30 Comparison of Unit Usage Rates (gallons per unit per day) Utility Multi-family Sector (Composite) Units Mean (Grand) Single-Family Sector Median Units Mean Median Denver (2014) 192, , n/a New York City (2014) 2,143,108* , Phoenix (2014) 181, , San Diego County (2012) Tampa Bay Water (2014) MF<SF in all cases 423, n/a 670, n/a 280, , *Excludes properties designated as One-Family Dwelling or Mixed Residential/Commercial Stronger association with climate in SF sector

31 Annual Precipitation and Average Unit Use Single-Family

32 Annual Precipitation and Average Unit Use

33 Gallons per Capita per Day (GPCD) Estimated Average Annual Water Use Per Capita for Five Water Systems (gallons per capita per day) Single-Family Multifamily Denver (2014) New York City (2014) Phoenix (2014) San Diego County (2012) Tampa Bay Water (2014)

34 Gallons per Capita per Day (GPCD) Estimated Average "Min-Month" Water Use Per Capita for Five Water Systems (gallons per capita per day) Single-Family Multifamily Denver (2014) New York City (2014) Phoenix (2014) San Diego County (2012) Tampa Bay Water (2014)

35 Evaluation of Sub-Classification Do average usage patterns differ significantly based on definitional groupings? Different utilities define multi-family sector differently Different utilities sub-classify multi-family sector differently Analysis depends on use of external and linkable data Land use codes Property use codes Building types

36 Sub-classification Example-Tampa Bay Water

37 Sub-classification Example-Tampa Bay Water

38 Sub-classification Example-Tampa Bay Water

39 New York City Classifications Multi-family Two or Three Family Dwellings Multi-family Buildings Mixed Residential & Commercial Two Family Dwellings 4 additional subclasses Condominiums 8 additional subclasses Elevator Apartments 10 additional subclasses Walk-up Apartments 10 additional subclasses Residence Multiple Use 7 additional subclasses

40 Gallons per Unit per Day (GPUD) 250 New York City Mean 2014 Unit Use by Residential Land Use Tax Classes (GPUD) One Family Dwellings Mixed Residential & Commercial Buildings Multi-Family Buildings Two-Three Family Dwelling

41 Gallons per Unit per Day (GPUD) 176 New York City Mean 2014 Unit Use for Building Classes within Multi-family Buildings" Land Use Tax Class (GPUD) ELEVATOR APARTMENTS CONDOMINIUMS WALK UP APARTMENTS Multifamily Buildings Class Average

42 Gallons per Unit per Day (GPUD) New York City Mean 2014 Unit Use for Building Sub-Classes within "Elevator Apartments" Building and Multi-family Buildings" Land Use Tax Class (GPUD) Fireproof (Standard Construction Without Stores) Co-op Conversion From Loft/Warehouse Miscellaneous Semi-fireproof (Without Stores) Converted Cooperatives (Other Than Condominiums) Artists in Residence Luxury Type Multifamily Buildings - Elevator Apartments Subclass Average

43 Gallons per Unit per Day (GPUD) 250 Phoenix 2014 Multi-family Annual Average Unit Usage Rates by Major Subclass (gallons per unit per day) Apartment Condominium Multiplex Multifamily Class Average

44 Median Unit Use and Housing Density (Phoenix)

45 Seasonal Peaking and Housing Density (Phoenix)

46 Median Unit Use and Housing Density (Tampa Bay Water)

47 Median Unit Use and Housing Density (New York City)

48 Assessment of Multi-family Demand Determinants Regional water use and socioeconomic databases for San Diego County and Tampa Bay Water NYU Furman Center housing and demographic database Property-level water use and survey data from 2012 Fannie Mae Multi-family Market Research Energy and Water Survey Utility-provided databases with selected property attribute and geographic assignments Tampa Bay Water Phoenix New York City Denver

49 Multi-family Class-level Forecast Model (San Diego County) Balanced panel model of water use, socioeconomics, weather, and climate 22 agencies, 120 months each Variable Estimated Multifamily Elasticity Marginal Price for Water (inflation-adjusted) Median Household Income (inflationadjusted) Housing Density (housing units per acre) Household Size +0.56

50 Multi-family Class-level Forecast Model (San Diego County) Balanced panel model of water use, socioeconomics, weather, and climate 22 agencies, 120 months each Variable Estimated Multifamily Elasticity Estimated Single- Family Elasticity Marginal Price for Water (inflation-adjusted) < Median Household Income (inflationadjusted) < Housing Density (housing units per acre) Household Size <

51 Factor Analysis of NYC Community Districts Socioeconomic data from NYU Furman Center available for 57 metrics across 59 Community Districts (64 metrics total) 1. Aggregate premise level water use data to community district level 2. Condense demographic metrics into fewer thematic factors 3. Evaluate median water use per dwelling unit with respect to factor scores

52 Factor Analysis of NYC Community Districts Thematic Factor Affluence Poverty Property Value/Cooling Towers Severe Crowding Estimated Effect of 1 unit change in factor score -18 gpud +23 gpud +8 gpud +15 gpud

53 Property-level Assessments (Fannie Mae Survey) Sample of 955 multi-family properties for 2012 categorized by region

54 Property-level Assessments (Fannie Mae Survey) Regression analysis of cross-sectional data on multi-family property features, accounting for region (n=323) Variable Estimated Elasticity or Effect Average Cost of Water Presence of Pool +10% Tenant Pays for Water -17% Level + Mechanism Property Receives Govt Subsidy -12% Property Built >= % Senior Living Facility -18%

55 Data Available from Utility Partners for Property-level Assessments System Units Lot Size Year Built Assessed Value Presence of Pool(s) Presence of Reclaimed Water Presence of Cooling Tower(s) Denver X X (pervious area) X New York City X X X X X Phoenix X X X Tampa Bay Water X (Apt only X X X X X X X (Apt only)

56 System Denver (2015) Tampa Bay Water ( average) Phoenix ( average) New York City (2014) Class Density (Units/Acre) Elasticity Assessed Value Elasticity Effect of Pool(s) Effect of Reclaimed Water Effect of Cooling Tower(s) Age Profile Multi-family Total Multi-family Total % -20.5% +-+- Condo % -18.2% +-+ Townhouse % -18.4% +- Less than 10 Units % -17.4% or More Units % -4.7% Access to alternative supplies has significant Pools increase effect on TBW water use All density estimates Effects of income demands negative and proxy are mixed statistically significant Multi-family Total Apartment % +28.7% +-+ Condo Impact of cooling towers proportionally larger in the desert Multiplex Condominiums % +-+- Elevator Apartments % +-+- Walk-up Apartments % -+-+

57 Summary & Conclusions Water use per unit in the Multi-family sector is generally lower than water use per unit in the Single-family sector The gap between SF and MF unit usage rates narrows when accounting for household size and seasonality For a given climate, generally lower seasonal use in the MF sector relative to SF

58 Summary & Conclusions Development density is statistically important More units per acre (i.e., higher unit density) - lower unit usage rates Results are consistent with Notion of shared outdoor (and other) uses Less irrigated area

59 Summary & Conclusions Two main effects from densification Between class: MF is denser than SF Within class: more dense MF, generally less water use (per dwelling unit)

60 Summary & Conclusions Water use in the MF sector is also influenced by Climate and weather Property features (water end uses, age) Socioeconomics Price The estimated effects of water use determinants tend to vary by geographic area Underlying climate Sector/subclass structure

61 Summary & Conclusions The ability to obtain information on housing units is essential for: Accounting for scale differences Calculating densities Joins to tax appraiser data typically permits estimation of units and additional classification options Sub-classification of the MF sector matters Can affect sample statistics and modeling relationships Can t easily infer reasons for differences based on segmentation alone

62 Recommendations Patterns of urban development and re-development is a key area of future uncertainty Keep a watchful eye on housing trends Recognize potential impacts of densification and other development policies Investigate the underlying structure of the overall multifamily sector Test for statistical associations with key variables for the purposes of forecasting and profiling Seek more uniformity and consistency in classifying and sub-classifying multi-family Better metrics More robust comparisons

63 Q&A No part of this presentation may be copied, reproduced, or otherwise utilized without permission.

64 Thank you Comments or questions, please contact: For more information visit: No part of this presentation may be copied, reproduced, or otherwise utilized without permission.

65 Extra Support Slides No part of this presentation may be copied, reproduced, or otherwise utilized without permission.

66 SF Metric Value/MF Metric Value Ratio of Single-Family to Multifamily Usage Rate Estimates for Five Water Systems Ratio Annual Use per Unit Ratio Annual Use per Capita Ratio "Min-Month" Use per Capita Denver (2014) New York City (2014) Phoenix (2014) San Diego County (2012) Tampa Bay Water (2014)

67 Denver ( ) Average Seasonal Patterns Avg Peak Avg Trough = 1.89 San Diego County ( ) Phoenix ( ) Avg Peak Avg Trough = 1.23 Avg Peak Avg Trough = 1.48

68 New York City (2011-AMR Sample) Average Seasonal Patterns Avg Peak Avg Trough = 1.10 Tampa Bay Water ( ) Unique Seasonal Pattern Spring is dry season Snowbirds /Spring Break Avg Peak Avg Trough = 1.06

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71 Estimated Socioeconomic Effects for Tampa Bay Water Project data supporting Tampa Bay Water s forecast model re-development Multi-family Class Price Elasticity Income Elasticity All Multi-family Condo Townhouse Less than 10 Units or more Units Mobile Home (single unit) Mobile Home (multiple unit/parks) Single-Family to to +0.45

72 Calculated Value 25 Plot of Building Age Portion of Regression Equation = Exp( *AGE *AGE^ e-05*AGE^ e-08*AGE^4) Denver Age profile: Accounting for number of units and pervious area, housing stock built in 1986 would be simulated as using the most water per premise in Older Implied Calendar Year

73 Calculated Value Plot of Building Age Portion of Regression Equation Annual March September 5 0 Older Implied Calendar Year