1 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Thesia I. Garner and Juan Munoz Henao

Bureau of Labor Statistics

Discussant: Trudi Renwick, U.S. Census Bureau

2018 Federal Committee on Statistical Methodology Research and Policy Conference, 

Concurrent Session C-3

Washington, DC

March 7, 2018

Controlling for Prices before Estimating 

SPM Thresholds and the Impact on SPM Poverty 

Statistics

Disclaimer: This presentation reports the results of research and analysis undertaken by researchers within the Bureau of Labor Statistics

(BLS). Any views expressed are those of the authors and not necessarily those of the BLS. Results are not to be quoted without authors’

permission.
2 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

The Role of Prices in SPM Thresholds

 2A+2C Thresholds for 2014

 Owners with mortgages

 Owners without Mortgages

 Renters

Over Time to “Year” from National to Geographic 

Areas

2010Q2-

2011Q1

2011Q2-

2012Q1

2012Q2-

2013Q1

2013Q2-

2014Q1

2014Q2-

2015Q1

FCSU in 2014$$
3 — U.S. BUREAU OF LABOR STATISTICS TATISTICS • bls.gov bls.gov

The Role of Prices

 Currently... 

1. Converting 5 years of expenditures to threshold year dollars using All Urban Consumers (CPI-U) for 

the U.S. City Average at CU level , prices across time

2. Creating geographic area thresholds using Median Rent Index (MRI) applied at threshold level to 

allow for differences in prices across area

 But, spatial differences in shelter and utility costs are already embedded in the 2A+2C SPM 

thresholds (Bishop, Lee, and Zeager 2017)

 As currently published, no attempt to account for spatial differences in housing costs 

before producing “national average” SPM thresholds 

 Owners with mortgages

 Owners without mortgages

 Renters

 This Study 

 Is this a problem?

 If yes, how to account for these differences before producing the thresholds?
4 — U.S. BUREAU OF LABOR STATISTICS TATISTICS • bls.gov bls.gov

FCSUi,yr = Fi+Ci+Si+Ui

FCSUi,2014 =

CPI2014

CPIyr

∗ FCSUi,yr

Thresholds Production

 At the Consumer Unit Level

 Equivalize 2-Child FCSUi,2014 expenditures to 2 Adults+2 Children (2A+2C) expenditures

 Rank CUs by equivalized 2A+2C FCSUi,2014 expenditures

 At 2A+2C Level produce housing tenure-specific thresholds based on means within 

30th-36th percentile range of FCSUi,2014

SPMj,2014 = 1.2 ∗ FCSUA,2014 − SUA + SUj

SUj

SPMj

=αj= housing share of 2A+2C SPM j threshold

 At threshold level, apply geographical price adjustment (MRI) for sub-national thresholds

SPMsgj,2014= [(αj*MRIsg) +(1- αj)]*SPMj,2014
5 — U.S. BUREAU OF LABOR STATISTICS TATISTICS • bls.gov bls.gov

Proposal: Adjust for Spatial Differences in 

Housing Costs at the CU Level

 At Consumer Unit Level, move telephone to Fi + Ci and out of housing (Si + Ui)

 At Housing Group j Level for All CUs, produce quality-adjusted normalized housing prices (as 

owner or renter) for (Si + Ui) for areas a (QANPa,j) 

 At Consumer Unit Level, adjust housing expenditures to reflect “national” dollars

FCSU′i,yr = Fi + Ci + Telei +

Si+Ui

QANPa,j

FCSU′i,2014 =

CPI2014

CPIyr

∗ FCSU′

i,yr

Add Step before Thresholds Production

Continue as before....
6 — U.S. BUREAU OF LABOR STATISTICS TATISTICS • bls.gov bls.gov

Plan

 At BLS

 Estimate regression models to produce quality-adjustment normalized prices 

(expenditures) for housing units j

– Renter: rents + utilities

– Owner with mortgage: shelter expenditures including for mortgage+ utilities

– Owner without mortgage: shelter expenditures + utilities

 Produce new “national average” 2A+2C SPM thresholds

 At Census Bureau (Trudi)

 Produce subnational geographic areas thresholds using MRI 

(plus for other CU types)

 Compare poverty rates with and without “price adjustment” at CU level
7 — U.S. BUREAU OF LABOR STATISTICS TATISTICS • bls.gov bls.gov

Advantages of Using CE Data for Initial 

Adjustment to CU-level S+U

 Quality-adjusted normalized prices based on same data as SPM thresholds

 Consumer units

 Housing units

 Expenditures

 Geographic areas

 Out-of-pocket expenditures, as basis of price adjustment, consistent with 

SPM concept of spending

 Quality adjustment based on large number of shelter unit characteristics

 Able to produce separate quality-adjusted normalized prices for

 Owners with mortgages

 Owners without mortgages

 Renters
8 — U.S. BUREAU OF LABOR STATISTICS TATISTICS • bls.gov bls.gov

Data and Methods

 CE Interview Survey data 2010Q2-2015Q1

 Hedonic log housing (S+U) expenditures model with 42 areas (self-representing PSUs with 

other areas regrouped) and shelter unit characteristics

 Based on model and approach of Martin, Aten, Figueroa (MAF, 2011) analyzing CPI Housing Survey 

and ACS data of rent and same geographic areas, first stage for RPPs

 Separate models for owners with and without mortgages and renters

 Model specification

Aij set of area dummies

Zij

n

set of shelter unit characteristics

i=1,...M geographic areas

j=1,..., J(n) classifications

n=1,...,N characteristics

 Quality-adjusted S+U prices are function of a0 and ai; controlling for characteristics 

(~ holding shelter characteristics at average values); geometric means

 Quality-adjusted normalized S+U prices for each area with respective to U.S. Average 

( = 1.0) based on consumer unit population weights

lnPij = a0 + ai

M

i=1

Aij + Bj

n

J n 

j=1

N

n=1

Zij

n + eij
9 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Areas for which CE Quality-Adjusted Normalized Prices Produced

In CPI Housing Survey Sample and CE Sample In CPI Housing Survey Sample and CE Sample

A102 Philadelphia-Wilmington-Atlantic City, PA-NJ-DE-MD D200 Midwest nonmetropolitan urban 

A103 Boston-Brockton-Nashua, MA-NH-ME-CT D300 South nonmetropolitan urban

A104 Pittsburgh, PA D400 West nonmetropolitan urban 

A109 New York City X100 Northeast small metroplitan 

A110 New York-Connecticut Suburbs X200 Midwest small metropolitan 

A111 New Jersey-Pennsylvania Suburbs X300 South small metropolitan 

A207 Chicago-Gary-Kenosha, IL-IN-WI X499 West small metropolitan 

A208 Detroit-Ann Arbor-Flint, MI

A209 St. Louis, MO-IL In CE Sample Only 

A210 Cleveland-Akron, OH R100 Northeast rural

A211 Minneapolis-St. Paul, MN-WI R200 Midwest rural

A212 Milwaukee-Racine, WI R300 South rural

A213 Cincinnati-Hamilton, OH-KY-IN R400 West rural

A214 Kansas City, MO-KS 

A312 Washington, DC-MD-VA-WV 

A313 Baltimore, MD 

A316 Dallas-Fort Worth, TX 

A318 Houston-Galveston-Brazoria, TX 

A319 Atlanta, GA 

A320 Miami-Fort Lauderdale, FL 

A321 Tampa-St. Petersburg-Clearwater, FL 

A419 Los Angeles-Long Beach, CA 

A420 Los Angeles Suburbs, CA 

A422 San Francisco-Oakland-San Jose, CA 

A423 Seattle-Tacoma-Bremerton, WA 

A424 San Diego, CA 

A425 Portland-Salem, OR-WA 

A426 Honolulu, HI 

A427 Anchorage, AK 

A429 Phoenix-Mesa, AZ 

A433 Denver-Boulder-Greeley, CO
10 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Housing Unit Characteristics

 Type of structure

 Number of bedrooms

 Number of full baths

 Number of half baths

 Total number of rooms

 Dwelling year of construction

 Central AC

 Off-street parking

 Survey years

 Energy utilities in rent

 Water, trash pickup in rent

 Public housing

 Subsidy received

 Rent as pay

Renter and Owner Models Renter Model Only

Alternative Owner with Mortgage Model

 Number of mortgages

 Max number of months remaining to pay
11 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Regression Results and 

Quality-Adjusted 

Normalized “Prices”
12 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

All Consumer Units

Dependent Variable R Square Un-weighted Observations

Rent plus utilities 0.424 44,457

Owner with mortgages plus 

utilities 0.372 46,638

Owner without mortgages plus 

utilities 0.316 32,236

Consumer Units with 2 Children

Dependent Variable R Square Un-weighted Observations

Rent plus utilities 0.509 5,123

Owner with mortgages plus 

utilities 0.448 8,092

Owner without mortgages plus 

utilities 0.481 1,471

Overall Fit of Log-Linear Weight Regression 

Models Using CE Pooled Data 2010Q2-2015Q1

Due to sample size concerns, use quality-adjusted normalized prices based on All CUs for thresholds
13 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Comparing Quality-Adjusted Normalized “Prices” 

This Study for 2014 MAF (2011) Renwick 

(2017)

Renter S+U Owner with 

Mortgage S+U

Owner without 

Mortgage S+U

CPI Housing 

Survey Rents 

(2005-2009)

ACS Rents 

(2005-2009) MRI 2014a

Maximum 1.79 1.78 2.29 1.67 1.50 1.78

Minimum 0.61 0.72 0.68 0.60 0.62 0.59

Range 1.18 1.06 1.61 1.06 0.88 1.19

Ratio of Max to 

Min 2.93 2.47 3.36 2.78 2.42 3.02

a Based on 5-year American Community Survey median rents for 2-bedroom apartments with complete kitchens and full baths (Renwick 2017).
14 — U.S. BUREAU OF LABOR STATISTICS TATISTICS • bls.gov bls.gov

Example: Applying CE Normalized Quality-Adjusted 

Prices to Housing Expenditures at CU Level for 2A+2C

Monthly Housing Expenditures 

for CUs with 2 Children

CE Quality-Adjusted 

Normalized “Prices” (all) Unadjusted Adjusted

Washington, DC-MD-VA-WV

Renter 1.461 $1,419 $971 

Owner with Mortgage 1.211 $2,544 $2,101 

Owner without Mortgage 1.234 $734 $595 

Rural South

Renter 0.615 $487 $792 

Owner with Mortgage 0.721 $932 $1,293 

Owner without Mortgage 0.683 $294 $430 
15 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Monthly Housing 

Expenditures

F+C+Telep 

Expenditures FCSU i

Unadjusted Adjusted Unadjusted Unadjusted With Adjusted SU

Washington, DC-MD-VA-WV

Renter $1,419 $971 $500 $1,919 $1,471 

Owner with 

Mortgage $2,544 $2,101 $500 $3,044 $2,601 

Owner without 

Mortgage $734 $595 $500 $1,234 $1,095 

Rural South

Renter $487 $792 $500 $987 $1,292 

Owner with 

Mortgage $932 $1,293 $500 $1,432 $1,793 

Owner without 

Mortgage $294 $430 $500 $794 $930 

Example: Using CE Normalized Quality-Adjusted Prices 

to Adjust Housing Expenditures at CU Level for 2A+2C

FCSU′i,yr = Fi + Ci + Telei +

Si+Ui

QANPa,j
16 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Thresholds and Housing 

Shares
17 — U.S. BUREAU OF LABOR STATISTICS TATISTICS • bls.gov bls.gov

Published Threshold

Published Housing 

Share Alternative Threshold

Alternative Housing 

Share

Owners with Mortgages $25,844 $25,840

shelter 34.1% 34.1%

utilities 16.6% 11.0%

housing total 50.7% 45.2%

Renters $25,460 $25,534

shelter 36.4% 36.3%

utilities 13.6% 8.2%

housing total 50.0% 44.5%

Owners without Mortgages $21,380 $21,070

shelter 18.3% 18.5%

utilities 22.2% 14.2%

housing total 40.5% 32.8%

Alternative: SPMj,2014 = 1.2 ∗ FCTSUA,2014 − SUA,2014 + SUj,2014

Published: SPMj,2014 = 1.2 ∗ FCSUA,2014 − SUtA,2014 + SUtj,2014

Impact of not Including Telephone in Housing on 

2014 2A+2C SPM Thresholds and Housing Shares

 Important for Census Bureau geographic (MRI) adjustment for sub-national thresholds
18 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

$25,844 $25,460

$21,380

$25,840 $25,534

$21,070

$26,327 $25,724

$21,992

$0

$5,000

$10,000

$15,000

$20,000

$25,000

$30,000

$35,000

2014 2 Adults with 2 Children SPM Thresholds with and without 

Quality-Adjusted Normalized “Prices” Applied to Si+Ui

published tele. expend. not U S+U (not t) CE_adj_all

Owners with Mortgages Renters

Owners without Mortgages

SPM′

j, 2014 =1.2∗FCTSU ′A,2014−SU′A,2014+SU′j,2014
19 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

2014 SPM 2A+2C Thresholds Housing Expenditure Shares for 2014 2A+2C: Published and When Shelter 

and Utilities Price-Adjusted at CU Level

Published

for Thresholds with S+U Adjusted at CU Level

Telephone in Housing 

Share

Telephone not in Housing 

Share

Owners with Mortgages

shelter 34.1% 34.1% 34.1%

utilities 16.6% 16.6% 11.1%

housing total 50.7% 50.6% 45.1%

Renters

shelter 36.4% 35.5% 35.5%

utilities 13.6% 13.9% 8.3%

housing total 50.0% 49.5% 43.8%

Owners without mortgages

shelter 18.3% 17.9% 17.9%

utilities 22.2% 23.0% 16.4%

housing total 40.4% 40.9% 34.3%

Impact on Housing Shares 

of Adjusting S+U at CU Level

 Important for Census Bureau geographic (MRI) adjustment for sub-national thresholds
20 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Poverty Rates
21 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

13.5

13.0

20.1

15.7

18.0

15.9

12.0

14.5

26.3

12.8

8.0

12.8

13.1

20.2

15.8

18.4

15.6

11.8

14.7

26.1

13.0

8.1

0.0 5.0 10.0 15.0 20.0 25.0 30.0

Outside metropolitan statistical areas

 Outside principal cities

 Inside principal cities

Inside metropolitan statistical ares

West

South

Midwest

Northeast

Renter

Owner/no mortgage/rentfree

Owner/mortgage

Residence Region Tenure

Percentage of SPM Poor Based on Published SPM Thresholds vs. 

Thresholds with Telephone not in Housing Share (no CE_adj): 2014

Published 15.3 Telephone not in Housing Share 15.3

Special thanks to Trudi Renwick for producing these poverty rates (12_1_17)
22 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

13.9

13.5

20.6

16.2

18.4

16.5

12.5

15.0

26.7

13.7

8.4

13.3

13.7

20.6

16.3

18.7

16.3

12.2

15.1

26.6

13.7

8.4

12.8

13.1

20.2

15.8

18.4

15.6

11.8

14.7

26.1

13.0

8.1

0.0 5.0 10.0 15.0 20.0 25.0 30.0

Outside metropolitan statistical areas

Outside principal cities

Inside principal cities

Inside metropolitan statistical ares

West

South

Midwest

Northeast

Renter

Owner/no mortgage/rentfree

Owner/mortgage

Residence Region Tenure

Percentage of SPM Poor Based on Published SPM Thresholds vs. 

Thresholds with S+U Adjusted at CU Level Before Thresholds 

Calculated: 2014

Published 15.3 CE-Adj FCSU with Tele in Housing Shares 15.8 CE-Adj FCSU with Tele not in Housing Shares 15.8
23 — U.S. BUREAU OF LABOR STATISTICS TATISTICS • bls.gov bls.gov

Summary

 Question: Do spatial differences in shelter and utility costs are already embedded in 

the 2A+2C SPM thresholds matter?

 Answer: Results from this study suggests that the answer is “yes”

 Question, if “yes”: How to account for these differences across areas and across 

housing tenure before producing thresholds?

 Answer: Proposal presented in this study

 Recommendations

 Remove telephone expenditures out of housing share for Census Bureau adjustment to 

derive geographic SPM thresholds

 Develop methods to account for spatial differences in shelter and utilities before estimating 

SPM thresholds

 Thoughts for the future regarding prices

 Develop out-of-pocket or payments based indexes for across time and across area 

adjustments that match concept underlying the SPM, particularly issue for owners

 For across time indexes, see experimental Household Costs Indices produced by UK Office 

for National Statistics (2017) with justification that out-of-pocket expenditures or payments 

“better reflect price changes as understood and experienced by the household” [New Zealand 

and Australia]
Contact Information

24 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Thesia I. Garner

Supervisory Research Economist

Division of Price and Index Number Research, OPLC

202-691-6576

garner.thesia@bls.gov
25 — U.S. BUREAU OF LABOR STATISTICS TATISTICS • bls.gov bls.gov

Geographic Price Adjustment Applied to 

“National” Thresholds

 At 2A+2C Threshold Level

 Adjust S+U share αj of j thresholds for differences in prices across areas 

SPMsgj,2014= [(αj*MRIsg) +(1- αj)]*SPMj,2014

where

αj = housing (S+U) share of j 2A+2C SPM threshold

s = state

g = specific metro area, other metro, or non-metro area

j = owner with mortgage, owners without mortgage, renter

MRI = Median rent index based on American Community Survey data (ACS) 

based on median rents plus utilities for 2-bedroom apartments with 

complete kitchens and full bath

 Example: Renter Threshold for San Jose-Sunnyvale-Santa Clara, CA: αR=0.5 and MRI=1.81

SPMR,SJ,2014= [(0.5*1.81) +(1-0.5)]*SPMj,2014
26 — U.S. BUREAU OF LABOR STATISTICS TATISTICS • bls.gov bls.gov

Inspiration and Guidance

 Bishop, Lee, and Zeager (2017): noted potential problem 

 Renwick (2011 and other): Median Rent Index for 

“constant quality” rental unit based on American 

Community Survey

 Martin, Aten, and Figueroa (MAF, 2011): production of 

quality-adjusted normalized rent prices using CPI Housing 

Sample and ACS (2005-2009) –first stage for RPPs

 Renwick (2014): should there be a separate index for each 

of the three thresholds

 Garner and Verbrugge (2009): owner out-of-pocket 

expenditures and rents (rental equivalence) move 

differently

 UK Office for National Statistics (2017): out-of-pocket 

expenditures or payments “better reflect price changes as 

understood and experienced by the household” 

(Household Cost Index) [New Zealand and Australia]

 Topic to examine

 Quality-adjusted “prices” relative 

to national average prices

 Log linear regression model with 

area dummies and housing unit 

characteristics

 Produce separate “prices” for 

owners with and without 

mortgages and renters

 Use out-of-pocket expenditures 

for renters and owners
27 — U.S. BUREAU OF LABOR STATISTICS TATISTICS • bls.gov bls.gov

 Shelter for primary residence

 For renters 

– Rents 

– Maintenance and repairs 

– Tenants insurance

 For owners without mortgages

– Property taxes

– Home insurance

– Maintenance and repairs

 For owners with mortgages

– Same as for owners without mortgages plus

– Mortgage interest

– Principal repayments

 Utilities for primary residence 

 Energy: natural gas, electricity, fuel oil, and other fuels

 Water and other public services

 Telephone (do not include in utilities when producing CE-quality 

adjusted normalized prices)

Shelter and Utilities
28 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Correlations of CE Quality-Adjusted Normalized 

“Prices”: All CUs versus CUs with 2 Children

All Consumer Units

Renter S+U Owner with 

Mortgage S+U

Owner without 

Mortgage S+U

Consumer Units 

with 2 Children

Renter S+U 0.960

Owner with Mortgage 

S+U 0.869

Owner without 

Mortgage S+U 0.976

Due to sample size concerns, use quality-adjusted normalized prices

based on All CUs for thresholds
29 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

MAF (2011) Quality-Adjusted Normalized 

Rent Prices

CE Quality-Adjusted Normalized 

“Prices” (2010-2014)

CPI Housing Survey 

(2005-2009) ACS (2005-2009)

Renter S+U

0.951 0.931

Owner with Mortgage S+U

0.913 0.861

Owner without Mortgage S+U

0.633 0.546

Correlations of CE Quality-Adjusted Normalized 

“Prices” with CPI and ACS Normalized Rents