IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this paper or follow this series

Spatial Dependence and Neighborhood Effects in Mortgage Lending: A Geographically Weighted Regression Approach

  • Duan Zhuang
Registered author(s):

    Current research on mortgage lending disparity is mostly based upon the process-based approach on discrimination, and the outcome-based approach on lending disparity and redlining. In recent years, the outcome-based model has received much attention, particularly on the relationship between intra-metropolitan geography and mortgage lending outcomes. From a policy perspective, the theoretical and empirical evidence on lending disparity is of great importance. However, there exists a mismatch between theoretical models, which focus on racial preferences, and empirical studies, which are essentially reduced form without adequate information. Besides, it may be difficult to unravel the effects of neighborhood race and other attributes. So far most studies conducted with the Home Mortgage Disclosure Act (HMDA) data ignore determinants of geographic variations in lending outcome, or simply attribute them to local variations in risk. This study intends to investigate spatial dependence and neighborhood effects of mortgage lending disparities in the Southern California Five-county Region. In so doing, it assesses indicators of primary mortgage market activity and their determinants for the region as a whole and for the sub-regions inside it. The study compiles data from the 2002 HMDA and the 2000 U.S. Census to undertake a variety of analyses, including computation, assessment, and mapping of social-economic characteristics, as well as home mortgage origination, denial rates, and secondary market purchase rates by census tracts among sampled areas and population cohorts. Cluster analysis on those social-economic and mortgage parameters show distinctive patterns of spatial clustering among tracts across the region. In observing these blueprints of spatial dependence, the study further undertakes a geographically weighted regression (GWR) to analyze the spatial non-stationarity of the determinants of variability in primary market loan denial rates across locations for the year 2002. The modeling result reveals that significant spatial non-stationarity exists between mortgage denial rates and the social-economic determinants. Specifically, the study finds that those census tract-level attributes, including income, population, age, racial composition, housing stock, etc., show significant and varying impacts on mortgage denial rate pattern by spatial clusters. In particular, higher values of spatially varying coefficients on racial composition on traditionally underserved areas, such as south-central Los Angeles, and central cities of outer counties, shed lights on the concerns of redlining. The study concludes that mortgage lending pattern is better understood by the geographically-weighted model than traditional Ordinary Least Square (OLS) regression approaches on lending outcome, which ignore the spatial correlation among local determinants.

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

    File URL: http://lusk.usc.edu/sites/default/files/working_papers/wp_2006-1007.pdf
    Download Restriction: no

    Paper provided by USC Lusk Center for Real Estate in its series Working Paper with number 8571.

    as
    in new window

    Length:
    Date of creation: 2006
    Date of revision:
    Handle: RePEc:luk:wpaper:8571
    Contact details of provider: Postal: Von KleinSmid Center 363, Los Angeles, California 90089-0626
    Phone: (213) 740-6842
    Web page: http://lusk.usc.edu/
    More information through EDIRC

    References listed on IDEAS
    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

    as in new window
    1. Alicia H. Munnell, 1992. "Mortgage lending in Boston: interpreting HMDA data," Working Papers 92-7, Federal Reserve Bank of Boston.
    2. Antonio P´┐Żez & Takashi Uchida & Kazuaki Miyamoto, 2002. "A general framework for estimation and inference of geographically weighted regression models: 1. Location-specific kernel bandwidths and a test for locational heterogeneity," Environment and Planning A, Pion Ltd, London, vol. 34(4), pages 733-754, April.
    Full references (including those not matched with items on IDEAS)

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    When requesting a correction, please mention this item's handle: RePEc:luk:wpaper:8571. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Chris Steins)

    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 references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.

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

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.