Implementing an Adaptive 

Approach to Collect Acreage on 

the American Community 

Survey

Ariel Binder

US Census Bureau

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Any opinions and conclusions expressed herein are those of the author(s) and 

do not reflect the views of the U.S. Census Bureau. All results have been 

reviewed to ensure that no confidential information is disclosed, release 

authorization numbers CBDRB-FY23-CES019-014.
• Incorporating administrative records and third party data into survey 

production is an important Census Bureau priority

• The American Community Survey’s housing questions have been 

identified as candidate test cases for these efforts

• First test case: lot size/acreage

• This presentation:

• Summarize third party data quality

• Results from simulated partial replacement 

• Plans for adaptive design implementation

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Background
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The Acreage Item (ACR): Background Information

• Sample universe: 

o Mobile homes (BLD==1)

o Single-family detached homes 

(BLD==2)

o Single-family attached homes 

(BLD==3)

• Use cases:

o HUD: excludes 3s in its 

construction of Fair Market 

Rents.

o Pathing for agricultural sales 

(AGS) question used by BEA: 

universe excludes 1s.

o PUMS file for researchers.
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Property Tax Data

• Information on parcels and their characteristics (e.g. lot size) are collected by localities for the 

purposes of property tax administration

• Several data aggregators collect these county level data and make harmonized national datasets 

available, Census has a contract with one such aggregator

• Two different ways to link property tax data to the ACS

• Address-based linkage via MAFIDs

• Geospatial linkage using parcel boundary shapefiles and GIS methods

• GS coverage rate > AB coverage rate

• GS data are less reliable than AB data for mobile homes and attached homes, but as reliable as AB 

data for detached homes. 

• Assessment data are meant to be a complete panel.

• However, we have found that a modest share of parcels in one year do not appear in a subsequent or 

previous year (beyond what we might expect due to new construction / demolition).

• Our approach is to use multiple years of data, and both Geospatial and Address linkages to maximize 

coverage
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Analyzing Property Tax Data

• How do acreage information in property tax records compare to the ACS?

• Compare 2019 and 2021 property tax data to 2019 ACS

• Construct composite property tax variable by pooling years and information type (geospatial 

or address-based) together and merging to ACS. Recode to ACR scale.

• Coverage: for each ACS acreage response, is there a corresponding property tax value?

• Agreement: conditional on property tax coverage, does the property tax value match the ACS 

value?
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National Coverage
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National Agreement
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County Coverage/Agreement
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Simulating Partial Replacement

• The property tax data is high quality and fit for use in the survey, although it does 

not have complete coverage

• Complete replacement is probably not possible while maintaining quality 

standards

• We instead simulate a partial replacement of the Acreage question

• Parameters/assumptions:

• Paper mode is unchanged

• Web and CAPI do partial replacement: we replace responses with Black 

Knight data on lot size except for low coverage counties

• Hot deck imputation (and weighting) uses the Black Knight observations as 

donors
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Simulation Results: ACR==1 
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Simulation Results: ACR==2 
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Simulation Results: ACR==3 
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Simulation Results 
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Implementing an Adaptive Design for DY 2024

• ACS sample is drawn twice per survey year (Sept. 2023 and Apr. 2024).

• We will combine property tax deliveries 2023 and 2022 to create composite adrec variables

that we will pass to the survey instrument.

• Outline:

1. Construct two composite adrec variables by using delivery 2023 info if it exists and 2022 info if it 

does not. (Data are missing if a value does not exist for either year.)

2. Blank out the adrec variables for a list of counties that have unreliable data, pre-determined 

based on prior research.

3. Add on a flag variable that will indicate whether the acreage question should be asked of the 

given CMID, and if not, which adrec value to populate the field.

4. Pass this composite file to the instrument

5. For web and CAPI responses, respondents with an adrec value will not be asked the lot size 

question. Lot size will remain on paper questionnaire.

6. Post-collection, adrec and respondent provided values will be used as donors in hot deck 

imputation
• We expect this approach to yield 

substantial burden reduction

• In 2019, nearly 80 percent of 

responses were from Web or 

CAPI

• Assuming future coverage and 

mode share stays constant, this 

implies the adaptive design 

approach will yield a 70% 

burden reduction

Less than 

1 Acre 1-10 Acres 10+ Acres Overall

Paper 0.193 0.2429 0.2717 0.2049

CATI/TQA 0.007 0.0083 0.0081 0.0073

CAPI 0.3904 0.3277 0.4015 0.3804

Web 0.4096 0.4211 0.3187 0.4075

Web+CAPI 0.8 0.7488 0.7202 0.7879

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Expected Burden Reduction
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Questions?

Contact information

• Email: ariel.j.binder@census.gov
Backup Slides

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State Coverage
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State Agreement
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Simulation Results: Confusion Matrix

Simulation value

missing <1 Acre

1-10 

Acres 10+ Acres

Production 

value

<1 Acre 0.122 0.633 0.024 0.008

1-10 Acres 0.024 0.035 0.102 0.006

10+ Acres 0.009 0.002 0.007 0.027