Fully Utilizing Housing Cost Data in the American Community Survey PUMS Data: Identifying Issues and Proposing Solutions

The American Community Survey (ACS) is emerging as a valuable tool for analyzing annual trends and patterns in housing in the United States. Researchers often use the housing cost-to-income ratios (HCIRs) provided in the ACS Public Use Microdata Sample housing file to evaluate the level of housing c...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Cityscape (Washington, D.C.) D.C.), 2008-01, Vol.10 (2), p.331-339
Hauptverfasser: Wardrip, Keith E., Pelletiere, Danilo
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 339
container_issue 2
container_start_page 331
container_title Cityscape (Washington, D.C.)
container_volume 10
creator Wardrip, Keith E.
Pelletiere, Danilo
description The American Community Survey (ACS) is emerging as a valuable tool for analyzing annual trends and patterns in housing in the United States. Researchers often use the housing cost-to-income ratios (HCIRs) provided in the ACS Public Use Microdata Sample housing file to evaluate the level of housing cost burden for renters and owners and to estimate the proportion of households spending more than a specified level of income, often 30 percent or 50 percent, on shelter. In this article, we show that these variables should be used with caution, identifying 3.2 million households in the 2006 ACS for which the Census Bureau does not calculate an HCIR, even though useful housing cost and income data are available for these households. We also identify 2.8 million owner households for which the HCIR is underestimated because monthly costs do not include mobile home fees. This article explores these issues, explains how researchers can develop an alternative HCIR, and describes the resulting distribution of households by housing cost burden.
format Article
fullrecord <record><control><sourceid>jstor</sourceid><recordid>TN_cdi_jstor_primary_20868662</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><jstor_id>20868662</jstor_id><sourcerecordid>20868662</sourcerecordid><originalsourceid>FETCH-LOGICAL-j107t-c42e9ff3f2a22d9e43b1000d86b33c6a83b66b60ffd920b3a5c0a9b3a4e8ad1c3</originalsourceid><addsrcrecordid>eNotzN9KwzAchuEcKDinlyDkBgq_JjVLPRvVucHEQS14NtIm0ZQ2Gfkj1qt3mx69B9_Hc4FmeUlZBrB4v0LXIfQAhBPOZuh7lYZhwk00g_kx9gOvXQqnVi5E_CiiwMbi-KnwclTedMIel3FM1sQJ18l_qQnvmpf6fH3AG6lsNHo6CZsQkgpYWIl33h3cma3dkKJxNtygSy2GoG7_O0fN6umtWmfb1-dNtdxmfQ6LmHUFUaXWVBNBiCxVQdscACRnLaUdE5y2jLUMtJYlgZaK-w5EeWyhuJB5R-fo7s_tQ3R-f_BmFH7aE-CMM0boL_-RVyQ</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Fully Utilizing Housing Cost Data in the American Community Survey PUMS Data: Identifying Issues and Proposing Solutions</title><source>Jstor Complete Legacy</source><source>EZB-FREE-00999 freely available EZB journals</source><creator>Wardrip, Keith E. ; Pelletiere, Danilo</creator><creatorcontrib>Wardrip, Keith E. ; Pelletiere, Danilo</creatorcontrib><description>The American Community Survey (ACS) is emerging as a valuable tool for analyzing annual trends and patterns in housing in the United States. Researchers often use the housing cost-to-income ratios (HCIRs) provided in the ACS Public Use Microdata Sample housing file to evaluate the level of housing cost burden for renters and owners and to estimate the proportion of households spending more than a specified level of income, often 30 percent or 50 percent, on shelter. In this article, we show that these variables should be used with caution, identifying 3.2 million households in the 2006 ACS for which the Census Bureau does not calculate an HCIR, even though useful housing cost and income data are available for these households. We also identify 2.8 million owner households for which the HCIR is underestimated because monthly costs do not include mobile home fees. This article explores these issues, explains how researchers can develop an alternative HCIR, and describes the resulting distribution of households by housing cost burden.</description><identifier>ISSN: 1936-007X</identifier><language>eng</language><publisher>U.S. Department of Housing and Urban Development, Office of Policy Development and Research</publisher><subject>Cash ; Cost estimates ; Data Shop ; Fees ; Household income ; Housing ; Mobile homes ; Property taxes ; Unit costs ; Utilities costs ; Variable costs</subject><ispartof>Cityscape (Washington, D.C.), 2008-01, Vol.10 (2), p.331-339</ispartof><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/20868662$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/20868662$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,778,782,801,58000,58233</link.rule.ids></links><search><creatorcontrib>Wardrip, Keith E.</creatorcontrib><creatorcontrib>Pelletiere, Danilo</creatorcontrib><title>Fully Utilizing Housing Cost Data in the American Community Survey PUMS Data: Identifying Issues and Proposing Solutions</title><title>Cityscape (Washington, D.C.)</title><description>The American Community Survey (ACS) is emerging as a valuable tool for analyzing annual trends and patterns in housing in the United States. Researchers often use the housing cost-to-income ratios (HCIRs) provided in the ACS Public Use Microdata Sample housing file to evaluate the level of housing cost burden for renters and owners and to estimate the proportion of households spending more than a specified level of income, often 30 percent or 50 percent, on shelter. In this article, we show that these variables should be used with caution, identifying 3.2 million households in the 2006 ACS for which the Census Bureau does not calculate an HCIR, even though useful housing cost and income data are available for these households. We also identify 2.8 million owner households for which the HCIR is underestimated because monthly costs do not include mobile home fees. This article explores these issues, explains how researchers can develop an alternative HCIR, and describes the resulting distribution of households by housing cost burden.</description><subject>Cash</subject><subject>Cost estimates</subject><subject>Data Shop</subject><subject>Fees</subject><subject>Household income</subject><subject>Housing</subject><subject>Mobile homes</subject><subject>Property taxes</subject><subject>Unit costs</subject><subject>Utilities costs</subject><subject>Variable costs</subject><issn>1936-007X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><sourceid/><recordid>eNotzN9KwzAchuEcKDinlyDkBgq_JjVLPRvVucHEQS14NtIm0ZQ2Gfkj1qt3mx69B9_Hc4FmeUlZBrB4v0LXIfQAhBPOZuh7lYZhwk00g_kx9gOvXQqnVi5E_CiiwMbi-KnwclTedMIel3FM1sQJ18l_qQnvmpf6fH3AG6lsNHo6CZsQkgpYWIl33h3cma3dkKJxNtygSy2GoG7_O0fN6umtWmfb1-dNtdxmfQ6LmHUFUaXWVBNBiCxVQdscACRnLaUdE5y2jLUMtJYlgZaK-w5EeWyhuJB5R-fo7s_tQ3R-f_BmFH7aE-CMM0boL_-RVyQ</recordid><startdate>20080101</startdate><enddate>20080101</enddate><creator>Wardrip, Keith E.</creator><creator>Pelletiere, Danilo</creator><general>U.S. Department of Housing and Urban Development, Office of Policy Development and Research</general><scope/></search><sort><creationdate>20080101</creationdate><title>Fully Utilizing Housing Cost Data in the American Community Survey PUMS Data: Identifying Issues and Proposing Solutions</title><author>Wardrip, Keith E. ; Pelletiere, Danilo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-j107t-c42e9ff3f2a22d9e43b1000d86b33c6a83b66b60ffd920b3a5c0a9b3a4e8ad1c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Cash</topic><topic>Cost estimates</topic><topic>Data Shop</topic><topic>Fees</topic><topic>Household income</topic><topic>Housing</topic><topic>Mobile homes</topic><topic>Property taxes</topic><topic>Unit costs</topic><topic>Utilities costs</topic><topic>Variable costs</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wardrip, Keith E.</creatorcontrib><creatorcontrib>Pelletiere, Danilo</creatorcontrib><jtitle>Cityscape (Washington, D.C.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wardrip, Keith E.</au><au>Pelletiere, Danilo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fully Utilizing Housing Cost Data in the American Community Survey PUMS Data: Identifying Issues and Proposing Solutions</atitle><jtitle>Cityscape (Washington, D.C.)</jtitle><date>2008-01-01</date><risdate>2008</risdate><volume>10</volume><issue>2</issue><spage>331</spage><epage>339</epage><pages>331-339</pages><issn>1936-007X</issn><abstract>The American Community Survey (ACS) is emerging as a valuable tool for analyzing annual trends and patterns in housing in the United States. Researchers often use the housing cost-to-income ratios (HCIRs) provided in the ACS Public Use Microdata Sample housing file to evaluate the level of housing cost burden for renters and owners and to estimate the proportion of households spending more than a specified level of income, often 30 percent or 50 percent, on shelter. In this article, we show that these variables should be used with caution, identifying 3.2 million households in the 2006 ACS for which the Census Bureau does not calculate an HCIR, even though useful housing cost and income data are available for these households. We also identify 2.8 million owner households for which the HCIR is underestimated because monthly costs do not include mobile home fees. This article explores these issues, explains how researchers can develop an alternative HCIR, and describes the resulting distribution of households by housing cost burden.</abstract><pub>U.S. Department of Housing and Urban Development, Office of Policy Development and Research</pub><tpages>9</tpages></addata></record>
fulltext fulltext
identifier ISSN: 1936-007X
ispartof Cityscape (Washington, D.C.), 2008-01, Vol.10 (2), p.331-339
issn 1936-007X
language eng
recordid cdi_jstor_primary_20868662
source Jstor Complete Legacy; EZB-FREE-00999 freely available EZB journals
subjects Cash
Cost estimates
Data Shop
Fees
Household income
Housing
Mobile homes
Property taxes
Unit costs
Utilities costs
Variable costs
title Fully Utilizing Housing Cost Data in the American Community Survey PUMS Data: Identifying Issues and Proposing Solutions
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-16T23%3A31%3A28IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-jstor&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Fully%20Utilizing%20Housing%20Cost%20Data%20in%20the%20American%20Community%20Survey%20PUMS%20Data:%20Identifying%20Issues%20and%20Proposing%20Solutions&rft.jtitle=Cityscape%20(Washington,%20D.C.)&rft.au=Wardrip,%20Keith%20E.&rft.date=2008-01-01&rft.volume=10&rft.issue=2&rft.spage=331&rft.epage=339&rft.pages=331-339&rft.issn=1936-007X&rft_id=info:doi/&rft_dat=%3Cjstor%3E20868662%3C/jstor%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_jstor_id=20868662&rfr_iscdi=true