Different Data, Different Measures: Comparing Alternative Indicators of Changes in Neighborhood Home Values

IF 2.8 3区 经济学 Q2 DEVELOPMENT STUDIES
Dan Immergluck, Adria Hollis
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Moreover, self-assessments of home values might be desired if the intention is to measure the value households place on their homes or to avoid potential biases baked into market values. Comparing changes in the ACS median home value to a common market-based home price index (HPI), we find that the ACS median tends to fall more slowly than the HPI when values are falling and increase more slowly than the HPI when values rise. The differences between the measures are large and are not randomly distributed across space, tending to be larger in neighborhoods where values fall or rise more steeply. They are also related to a variety of neighborhood characteristics.Keywords: Neighborhoodneighborhood changehousinghome valuesgentrificationdisinvestmentproperty values AcknowledgementsWe would like to thank the editor and the three anonymous reviewers for their very helpful comments on this paper.Disclosure StatementNo potential conflict of interest was reported by the author(s).Notes1 Regression is used to account for varying periods between paired transactions. The variation in changes in housing values is assumed to increase with the time between transactions, because variables other than market appreciation are expected to influence the values of housing units as this period increases. For more detailed information on the general FHFA repeat-sales methods, see Calhoun (Citation1996).2 The census-tract-level FHFA Home Price Index is provided here: https://www.fhfa.gov/DataTools/Downloads/Documents/HPI/HPI_AT_BDL_tract.csv. More information on how the index is constructed is provided in Federal Housing Finance Agency (Citation2023).3 Moreover, the HPI is a relative measure of home value compared to other points in time and does not provide dollar-value estimates of median or typical value at one point in time.4 The Missouri Census Data Center release of the 2005–2009 ACS median home value variable was spatially interpolated using owner-occupied housing units as the weighting variable. More information can be found at https://mcdc.missouri.edu/data/acs2009/Variables.html.5 The two exceptions are the 2007–2012 HPI change and the initial median home value, which is taken from the 2005–09 ACS, centered on 2007.6 Tables 3–5 show a slight difference in sample size of one tract between the 2007 to 2012 period regression and the 2012–2017 period regression. 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His latest book is Red Hot City: Housing, Race, and Exclusion in Twenty-First Century Atlanta (UC Press, 2022).Adria HollisAdria Hollis is a graduate student in the Urban Studies Institute at Georgia State University. She is currently a program and policy intern for Enterprise Community Partners. She was previously a graduate research assistant in the Urban Studies Institute. 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Abstract

AbstractUrban scholars and practitioners have used changes in neighborhood-level home values to serve as indicators of neighborhood change, including gentrification and disinvestment. A common measure is the “median home value” variable from the American Community Survey (ACS). However, household-level research suggests that self-assessed home values, such as those of the ACS, differ significantly from market-based measures, and medians can be affected by changes in the mix of homes . Transaction-based home price indices are unaffected by such changes and are based on market sales rather than self-assessments, but also have limitations. Moreover, self-assessments of home values might be desired if the intention is to measure the value households place on their homes or to avoid potential biases baked into market values. Comparing changes in the ACS median home value to a common market-based home price index (HPI), we find that the ACS median tends to fall more slowly than the HPI when values are falling and increase more slowly than the HPI when values rise. The differences between the measures are large and are not randomly distributed across space, tending to be larger in neighborhoods where values fall or rise more steeply. They are also related to a variety of neighborhood characteristics.Keywords: Neighborhoodneighborhood changehousinghome valuesgentrificationdisinvestmentproperty values AcknowledgementsWe would like to thank the editor and the three anonymous reviewers for their very helpful comments on this paper.Disclosure StatementNo potential conflict of interest was reported by the author(s).Notes1 Regression is used to account for varying periods between paired transactions. The variation in changes in housing values is assumed to increase with the time between transactions, because variables other than market appreciation are expected to influence the values of housing units as this period increases. For more detailed information on the general FHFA repeat-sales methods, see Calhoun (Citation1996).2 The census-tract-level FHFA Home Price Index is provided here: https://www.fhfa.gov/DataTools/Downloads/Documents/HPI/HPI_AT_BDL_tract.csv. More information on how the index is constructed is provided in Federal Housing Finance Agency (Citation2023).3 Moreover, the HPI is a relative measure of home value compared to other points in time and does not provide dollar-value estimates of median or typical value at one point in time.4 The Missouri Census Data Center release of the 2005–2009 ACS median home value variable was spatially interpolated using owner-occupied housing units as the weighting variable. More information can be found at https://mcdc.missouri.edu/data/acs2009/Variables.html.5 The two exceptions are the 2007–2012 HPI change and the initial median home value, which is taken from the 2005–09 ACS, centered on 2007.6 Tables 3–5 show a slight difference in sample size of one tract between the 2007 to 2012 period regression and the 2012–2017 period regression. This is due to a slight difference (one tract) in the missing ACS variables for the regression across the two different time periods.7 Heteroskedastic-robust standard errors were used in both regressions. Variance inflation factors (VIFs) were calculated for both regressions. The average VIFs were under 3.0, with no variables (other than those involving polynomial terms) having VIFs larger than 7.5.Additional informationNotes on contributorsDan ImmergluckDan Immergluck is Professor of Urban Studies at Georgia State University. His research concerns housing, race, neighborhood change, gentrification, segregation, real estate markets, and urban political economy. Dr. Immergluck is the author of five books and over 120 scholarly articles, book chapters, and research reports. He has consulted for the federal agencies, philanthropic foundations, and nonprofits. His latest book is Red Hot City: Housing, Race, and Exclusion in Twenty-First Century Atlanta (UC Press, 2022).Adria HollisAdria Hollis is a graduate student in the Urban Studies Institute at Georgia State University. She is currently a program and policy intern for Enterprise Community Partners. She was previously a graduate research assistant in the Urban Studies Institute. She received her B.A. from Rust College.
不同的数据,不同的测量:比较邻里房屋价值变化的替代指标
头巾学者和实践者使用社区水平房屋价值的变化作为社区变化的指标,包括中产阶级化和撤资。一个常见的衡量标准是美国社区调查(ACS)的“房屋价值中位数”变量。然而,家庭层面的研究表明,自我评估的房屋价值,如ACS的那些,与基于市场的措施有很大不同,中位数可能受到房屋组合变化的影响。基于交易的房价指数不受这种变化的影响,而且是基于市场销售而非自我评估,但也有局限性。此外,如果目的是衡量家庭对其房屋的价值或避免潜在的市场价值偏差,则可能需要对房屋价值进行自我评估。将ACS房价中位数的变化与常见的基于市场的房价指数(HPI)进行比较,我们发现,当房价下跌时,ACS房价中位数的下降速度往往比HPI慢,而当房价上涨时,ACS房价中位数的上升速度又比HPI慢。这些测量值之间的差异很大,而且不是随机分布在不同的空间中,在房价下跌或上涨更陡的社区,差异往往更大。它们还与各种邻里特征有关。关键词:邻里邻里变化住房房屋价值中产阶级化资产价值感谢编辑和三位匿名审稿人对本文非常有帮助的评论。披露声明作者未报告潜在的利益冲突。注1回归用于解释成对事务之间的不同时期。假定住房价值变化的变化随着交易间隔时间的增加而增加,因为随着这段时间的增加,市场升值以外的变量预计会影响住房单位的价值。有关FHFA一般重复销售方法的更多详细信息,请参见Calhoun (Citation1996)全国范围内的住房住房管理局房价指数在这里提供:https://www.fhfa.gov/DataTools/Downloads/Documents/HPI/HPI_AT_BDL_tract.csv。关于指数如何构建的更多信息,请参见联邦住房金融局(Citation2023)此外,HPI是房屋价值与其他时间点相比的相对衡量标准,并不提供一个时间点的中位数或典型价值的美元价值估计密苏里州人口普查数据中心发布的2005-2009年ACS房屋价值中位数变量使用自有住房单位作为权重变量进行空间插值。更多信息可以在https://mcdc.missouri.edu/data/acs2009/Variables.html.5上找到。两个例外是2007 - 2012年的HPI变化和初始房屋中位数,这是从2005-09年的ACS中提取的,以2007年7.6为中心。表3-5显示了2007 - 2012年期间回归和2012 - 2017年期间回归的一个区域的样本量略有不同。这是由于在两个不同时间段的回归中缺失的ACS变量略有差异(一个通道)两种回归均采用异方差稳健标准误差。对两种回归计算方差膨胀因子(VIFs)。平均vif低于3.0,没有变量(除了那些涉及多项式项的变量)的vif大于7.5。作者dan Immergluck是佐治亚州立大学城市研究教授。他的研究涉及住房、种族、社区变化、中产阶级化、种族隔离、房地产市场和城市政治经济。Immergluck博士是五本书和120多篇学术文章、书籍章节和研究报告的作者。他曾为联邦机构、慈善基金会和非营利组织担任顾问。他的最新著作是《炽热的城市:21世纪亚特兰大的住房、种族和排斥》(加州大学出版社,2022年)。阿德里亚·霍利斯是乔治亚州立大学城市研究所的一名研究生。她目前是企业社区合作伙伴的项目和政策实习生。她曾在城市研究所担任研究生研究助理。她在Rust College获得学士学位。
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来源期刊
CiteScore
5.40
自引率
17.20%
发文量
68
期刊介绍: Housing Policy Debate provides a venue for original research on U.S. housing policy. Subjects include affordable housing policy, fair housing policy, land use regulations influencing housing affordability, metropolitan development trends, and linkages among housing policy and energy, environmental, and transportation policy. Housing Policy Debate is published quarterly. Most issues feature a Forum section and an Articles section. The Forum, which highlights a current debate, features a central article and responding comments that represent a range of perspectives. All articles in the Forum and Articles sections undergo a double-blind peer review process.
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