IDEAS home Printed from https://ideas.repec.org/p/fip/fedfcw/91631.html

Neighborhood Change and Residential Instability in Oakland

Author

Listed:

Abstract

Affordable housing is critical to ensuring healthy and resilient communities and broad access to economic opportunity. In this report, we examine neighborhood change and residential instability in the City of Oakland over the past two decades. We employ multiple data sources, including individual-level data from the Federal Reserve Bank of New York Consumer Credit Panel/Equifax data. We analyze historical and contemporary data to understand patterns of residential instability, and we identify which residents and areas are most likely to experience heightened challenges in the context of the COVID-19 pandemic. Our results show that lower-SES residents experience residential instability in different ways in different parts of Oakland, suggesting the need for more geographically targeted strategies that focus on stabilizing lower-SES residents and address the multiple ways in which lower-SES residents navigate limited affordable housing.

Suggested Citation

  • Vineet Gupta & Jackelyn Hwang & Bina Shrimali, 2021. "Neighborhood Change and Residential Instability in Oakland," Community Development Working Paper 2021-01, Federal Reserve Bank of San Francisco.
    • Handle: RePEc:fip:fedfcw:91631
    DOI: 10.24148/cdwp2021-01
    as

    Download full text from publisher

    File URL: https://www.frbsf.org/community-development/wp-content/uploads/sites/3/neighborhood-change-residential-instability-oakland.pdf
    File Function: Full-text PDF
    Download Restriction: no
    File URL: https://libkey.io/10.24148/cdwp2021-01?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Anderson, Raymond, 2007. "The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision Automation," OUP Catalogue, Oxford University Press, number 9780199226405.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. A?da Kammoun & Imen Triki, 2016. "Credit Scoring Models for a Tunisian Microfinance Institution: Comparison between Artificial Neural Network and Logistic Regression," Review of Economics & Finance, Better Advances Press, Canada, vol. 6, pages 61-78, February.
    2. Crone, Sven F. & Finlay, Steven, 2012. "Instance sampling in credit scoring: An empirical study of sample size and balancing," International Journal of Forecasting, Elsevier, vol. 28(1), pages 224-238.
    3. Singh, Ramendra Pratap & Singh, Ramendra & Mishra, Prashant, 2021. "Does managing customer accounts receivable impact customer relationships, and sales performance? An empirical investigation," Journal of Retailing and Consumer Services, Elsevier, vol. 60(C).
    4. Ha-Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," EconomiX Working Papers 2015-1, University of Paris Nanterre, EconomiX.
    5. Ha-Thu Nguyen, 2014. "Default Predictors in Credit Scoring - Evidence from France’s Retail Banking Institution," EconomiX Working Papers 2014-26, University of Paris Nanterre, EconomiX.
    6. Jackelyn Hwang & Elizabeth Kneebone & Vasudha Kumar, 2023. "Recent Findings on Residential Instability in Oakland," Community Development Research Brief, Federal Reserve Bank of San Francisco, vol. 2023(02), pages 1-33, February.
    7. Hussein A. Abdou & John Pointon, 2011. "Credit Scoring, Statistical Techniques And Evaluation Criteria: A Review Of The Literature," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(2-3), pages 59-88, April.
    8. Zongxiao Wu & Ran Liu & Jiang Dai & Dan Luo, 2026. "Multimodal Insights into Credit Risk Modelling: Integrating Climate and Text Data for Default Prediction," Papers 2601.00478, arXiv.org.
    9. Rais Ahmad Itoo & A. Selvarasu & José António Filipe, 2015. "Loan Products and Credit Scoring by Commercial Banks (India)," International Journal of Finance, Insurance and Risk Management, International Journal of Finance, Insurance and Risk Management, vol. 5(1), pages 851-851.
    10. Bátiz-Zuk Enrique & Mohamed Abdulkadir & Sánchez-Cajal Fátima, 2021. "Exploring the sources of loan default clustering using survival analysis with frailty," Working Papers 2021-14, Banco de México.
    11. Galina A. Timofeeva & Yana A. Bozhalkina, 2018. "Dependence of a Loan Portfolio Structure on a Cut-Off Level in a Scoring Model," Journal of New Economy, Ural State University of Economics, vol. 19(2), pages 24-35, April.
    12. Jairaj Gupta & Nicholas Wilson & Andros Gregoriou & Jerome Healy, 2014. "The value of operating cash flow in modelling credit risk for SMEs," Applied Financial Economics, Taylor & Francis Journals, vol. 24(9), pages 649-660, May.
    13. Li, Zhiyong & Li, Aimin & Bellotti, Anthony & Yao, Xiao, 2023. "The profitability of online loans: A competing risks analysis on default and prepayment," European Journal of Operational Research, Elsevier, vol. 306(2), pages 968-985.
    14. Sergio Edwin Torrico Salamanca, "undated". "Macro credit scoring como propuesta para cuantificar el riesgo de crédito," Investigación & Desarrollo 0814, Universidad Privada Boliviana.
    15. Andrea Bedin & Monica Billio & Michele Costola & Loriana Pelizzon, 2019. "Credit Scoring in SME Asset-Backed Securities: An Italian Case Study," JRFM, MDPI, vol. 12(2), pages 1-28, May.
    16. Rafał Balina & Marta Idasz-Balina, 2021. "Drivers of Individual Credit Risk of Retail Customers—A Case Study on the Example of the Polish Cooperative Banking Sector," Risks, MDPI, vol. 9(12), pages 1-26, December.
    17. Martin Řezáč & Lukáš Toma, 2013. "Indeterminate values of target variable in development of credit scoring models," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 61(7), pages 2709-2716.
    18. Gupta, Jairaj & Wilson, Nicholas & Gregoriou, Andros & Healy, Jerome, 2014. "The effect of internationalisation on modelling credit risk for SMEs: Evidence from UK market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 31(C), pages 397-413.
    19. Lei Ding, 2017. "Borrower credit access and credit performance after loan modifications," Empirical Economics, Springer, vol. 52(3), pages 977-1005, May.
    20. Matuszyk, Anna & So, Mee Chi & Mues, Christophe & Moore, Angela, 2016. "Modelling repayment patterns in the collections process for unsecured consumer debt: A case studyAuthor-Name: Thomas, Lyn C," European Journal of Operational Research, Elsevier, vol. 249(2), pages 476-486.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:fip:fedfcw:91631. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Federal Reserve Bank of San Francisco Research Library (email available below). General contact details of provider: https://edirc.repec.org/data/frbsfus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.