Accurately Identifying and Enumerating Multi-unit 

Housing with Remote Sensing Data for Address 

Frame Enhancement

FCSM 2023, Washington DC

Lee Fiorio

Ned English
BACKGROUND: ADDRESS LISTS FOR ABS SURVEYS 2

Area probability surveys typically require 

address lists

Main ingredient for address frame:

• Computerized Delivery Sequence file (CDS)

• USPS address file

Need supplemental addresses in low-coverage areas

• 3,096 segments in NORC National Frame

• 5% needed enhancement

• Important for reducing coverage bias
3

Address listing remotely? Integrating CDS with spatial data

BACKGROUND: NEW DATA SOURCES

Source: Zamarova (2020)

Cadastral Data

Building Footprints Data

Source: OpenStreetMaps

Street and Road Data

Source: OpenStreetMaps

Satellite Data

Source: WikiCommons
4

In-office Remote Listing

NEW APPROACH: REMOTE ADDRESS LISTING

Conceptualization for remote listing

Enhanced Listing

CDS File

Census Blocks 

w/ HU ct

Field 

Enhanced 

Listing

FINAL ADDRESS LIST

+ Confirmed CDS

+ Edited CDS

- Deleted CDS

+ Field Adds

CDS File NoStat File

Census Block 

Maps

Satellite 

Imagery

Detailed Street 

and Road Map

Property 

Data/Cadastral 

Data

In-office 

Remote 

Listing

FINAL ADDRESS LIST

+ Confirmed CDS

+ Edited CDS

- Deleted CDS

+ Remote Adds

Building 

Footprints
5

NORC Interactive Mapping Address Listing Tool

NEW APPROACH: REMOTE ADDRESS LISTING
INITIAL VALIDATION OF REMOTE LISTING: METHODOLOGY 6

Address lists produced with the remote listing tool 

were validated in a sample of blocks

Total Remote Listed

61 Segments 

3,245 Blocks

~33,500 anticipated HUs (2020 Census)

~13,500 CDS addresses (40% coverage)

-----------------------------------------------------------------

+30,500 Remote Adds 

Total coverage: 130%

Sample

8 Segments 

74 Blocks

1,185 anticipated HUs (2020 Census)

415 CDS addresses (35% coverage)

---------------------------------------------------------------

+951 Remote Adds 

Total coverage 115%
INITIAL VALIDATION OF REMOTE LISTING: RESULTS 7

Round 1 Validation: Summary Findings

Initial Address List

415 CDS lines

951 Remote Adds 

--------------------------------------------

1,366 Total addresses

Field-Validated Address List

389 validated CDS lines (accuracy 94%)

889 validated Remote Adds (accuracy 93%)

+100 Field additions (7% miss rate)

--------------------------------------------------------------

1,378 Total addresses

• In aggregate, CDS and Remote Adds were validated at a similar rate 

• Similar number of addresses added by field as deleted (i.e. marked not valid)

What is our accuracy rate with multi-unit housing?
MOTIVATION 8

Remote listing requires reconciling three distinct forms of data

USPS Addresses (CDS)

• Mailing addresses

• Problems: low coverage; not necessarily linked to a physical address

Satellite Data/Building Footprints

• Visible structures

• Problems: out buildings; multi-unit buildings; business vs. residential; no address info

Tax Parcels

• Properties

• Problems: multiple HUs on a property
MOTIVATION 9

Multi-unit problem: There is not a one-to-one relationship between 

addresses, visible structures, housing units, or properties
MOTIVATION 10

NORC’s solution: detailed protocol for handling different multi-unit 

scenarios

Scenarios

• Apartment buildings, duplexes, row homes

• Manufactured home communities

• Multi-HU parcels: e.g. trailer behind house; multiple single-family homes on a lot 

Manual review

Contextual data

• Google StreetView, real estate listings, tax records
RESEARCH QUESTIONS 11

For the 2020 National Frame, 61 segments were listed with the 

Remote Listing Tool

1. What percentage of addresses added with remote listing tool are multi-unit?

2. How well does the remote listing protocol capture multi-unit scenarios?

a. Are the multi-unit adds accurate?

b. How many multi-units are missed?

c. Is there anything systematic about the inaccuracy?
DATA AND METHODS 12

NORC Field Staff used standard NORC ‘Enhanced Listing’ 

procedure to validate 6 remote listed segments in-person

6 segments in Massachusetts, 

Pennsylvania, Indiana, Louisiana, and 

California

Two FIs reviewed lists block by block

• Starting in NW corner

• Traveling in clockwise direction

• Confirming existing addresses

• Deleting non-existent or non-valid 

addresses

• Adding missing addresses
VALIDATION OF MULTI-UNIT PROCEDURE: RESULTS 13

RQ1: What percentage of addresses added with 

remote listing tool are multi-unit?

Remote Adds

Multi-unit scenarios made up one third of all addresses added

Sample

6 Segments 

40 Blocks

808 anticipated HUs (2020 Census)

306 CDS addresses (38% coverage)

---------------------------------------------------------------

+632 Remote Adds 

Total coverage 116%

422 single HU (67%)

210 multi-unit HU (33%)
VALIDATION OF MULTI-UNIT PROCEDURE: RESULTS 14

RQ2: How well does the remote listing protocol capture 

multi-unit scenarios?

How many multi-units were 

among the misses?

Multi-unit HUs confirmed at a relatively high rate (but lower than single HUs)

Multi-unit HUs do not make up a disproportionate number of the HUs missed

How accurate were the adds?

92% single HU remote adds confirmed in 

the field 

79% multi-unit HU remote adds confirmed 

by field

Multi-unit HUs comprised 30% 

(28 of 95) of the addresses 

missed 
EXAMPLE: APARTMENTS ABOVE COMMERCIAL 15
EXAMPLE: LARGE HOUSE SPLIT INTO TWO UNITS 16
EXAMPLE: TRAILER BEHIND HOUSE 17
RESULTS: SUMMARY 18

Multi-unit HUs pose a serious challenge to remote listing...

... but this challenge is (somewhat) mitigated by having a detailed protocol for 

handling wide variety of multi-unit scenarios

In a sample of 6 segments enhanced using remote listing tool

• 33% of all remote adds were generated from a multi-HU scenario

• Multi-unit remote adds were validated at a rate of 79%

• Missed addresses were not disproportionately multi-unit
DISCUSSION 19

Benefits of Remote Listing

Considerably more cost-effective and less timeconsuming than field listing

It is easier to refresh remote lists (i.e. field lists are 

‘frozen’ for the duration of the frame)

Integrating address lists with other kinds of data, like 

property records and aerial imagery, has other useful 

applications
DISCUSSION 20

Limits of Remote Listing and Next Steps

Quality is highly dependent on accuracy of administrative 

data

• Does not work well at all in places where administrative 

data contain limited or inaccurate address information

Identify covariates associated with accuracy of remote lists

• Using segment level data from ACS on demographics and 

socio-economic characteristics to model list accuracy (if 

sample size not too small)
Lee Fiorio

fiorio-lee@norc.org