WWW.THESES.XLIBX.INFO
FREE ELECTRONIC LIBRARY - Theses, dissertations, documentation
 
<< HOME
CONTACTS



Pages:     | 1 |   ...   | 45 | 46 || 48 | 49 |   ...   | 56 |

«Moving to opportunity voluMe 14, nuMber 2 • 2012 U.S. Department of Housing and Urban Development | Office of Policy Development and Research ...»

-- [ Page 47 ] --

The loans CoreLogic assigns to its subprime database are either serviced by institutions that specialize in servicing subprime loans or identified as subprime by the servicing institution. Despite the recent demise of most subprime-specializing institutions, the subprime database continues to track active subprime loan performance because the servicing of these loans has largely transferred to other institutions that contribute to the database. In contrast to CoreLogic’s more commonly used, loan-level subprime securities database, the subprime-servicing database provides information on loans retained in bank portfolios as well as those in securities.

Although adverse neighborhood effects generally are associated with properties in later stages of foreclosure and REO, we favor including all loans 60 or more days past due in our analysis of delinquency patterns, for several reasons. First, foreclosure moratoria and loan modification programs have artificially slowed the transition through foreclosure into REO, so that our measure may be a better indicator of actual “facts on the ground.” Second, our measure is somewhat forward looking, because most loans in early stages of delinquency as of the analysis date will move into later stages of foreclosure and REO, given the relatively low cure rates associated with the mortgage crisis. Third, early stages of delinquency are relevant when considering effective policy responses. Moreover, because the 60-plus-days-past-due measure is dominated by longer term delinquent loans that are in foreclosure or REO, and because neighborhoods with lower delinquency rates in general will also have higher cure rates, we would not expect classifications based on longer term delinquency to be much different from those arising from our cluster analysis.

Although CoreLogic takes steps to eliminate duplication, some duplicate reporting of loans may occur in the data obtained from Fannie Mae and Freddie Mac by the servicers of these loans. In some ZIP Codes, we observe excess counts and adjust these counts, as well.

246 Refereed Papers Geographic Patterns of Serious Mortgage Delinquency: Cross-MSA Comparisons Thus, we develop estimates of active loan counts, prime and subprime, as of October 2008, by ZIP Code. We aggregate the estimated prime and subprime delinquency rates and active loan counts to obtain estimates of overall mortgage delinquency rates by ZIP Code.

We use additional data sources to obtain explanatory variables for the regression analysis of metropolitan-area delinquency characteristics. We use 2005, 2006, and 2007 HMDA data to construct variables descriptive of the mortgage market in a metropolitan area, such as share of home purchase loans by occupancy type (owner vs. nonowner occupied). We rely on Economy.com for data describing local economic and housing market conditions from 2005 through 2008, including annual house price appreciation rates, annual changes in housing starts, affordability index, and unemployment rates by MSA.

Estimating Active Loan Counts by ZIP Code As discussed previously, we adjust the active loan counts from the CoreLogic servicing data by comparing 2005 and 2006 origination counts in the CoreLogic data with origination counts from HMDA data. Because the CoreLogic data provide the state, county, and ZIP Code associated with a mortgage, whereas HMDA data indicate the state, county, and census tract, not the ZIP Code, we first map state, county, and census tract into ZIP Code(s).11 We apply separate adjustments to prime and subprime loan counts, associating high-cost mortgages in HMDA data (those with a reported above-prime rate spread) with subprime.

Let nj denote the number of originations reported in the CoreLogic subprime servicing data, and let Nj denote the number of subprime (high-cost) originations in HMDA data, for ZIP Code j in 2005 through 2006. Our adjustment factor is then the ratio αj = nj /Nj. We multiply the 2008 active loan count in the CoreLogic subprime servicing data by αj to obtain the estimated active subprime loan count for ZIP Code j. We apply the analogous procedure to estimate active prime loan counts. ZIP Codes with fewer than 50 estimated total (prime plus subprime) active loans are excluded from the study.12 Note that this procedure assumes that the within-ZIP delinquency rates observed for subprime loans included in the CoreLogic subprime servicing data are representative of the aggregate (observed and unobserved) within-ZIP delinquency rate; we make the same assumption regarding the prime data. Likewise, this procedure assumes that the servicing databases are representative with respect to within-ZIP proportions of 2005-to-2006 originations that remain active in 2008. Although assessing the accuracy of these assumptions is not possible, the fact that we are holding constant both geographic (ZIP Code) location and risk category (prime versus subprime) provides some assurance that the observed quantities will be reasonable approximations. At the least, correcting for the undercounts is preferable to not doing so.

Where a census tract traversed more than one ZIP Code, we allocated the mortgages across the ZIP Codes in proportion to the loan counts observed in Freddie Mac internal data.

We also exclude ZIP Codes where αj is implausibly large or small. In addition, we apply consistency checks for the prime active counts using Freddie Mac internal data. For instance, if the estimated active prime loan count for a ZIP Code is less than the number of active loans in Freddie Mac data, we use the active loan count and delinquency rate from Freddie Mac data instead.





–  –  –

MSA Selection As defined by the Office of Management and Budget, 371 MSAs were in the United States, as of December 2006.13 To limit the scope of this study to major cities and to ensure the statistical relevance of the measures calculated at the ZIP Code level, we select the 88 MSAs with at least 50 ZIP Codes or 100,000 active mortgages in our data. We include an additional 3, marginally smaller MSAs (Knoxville, Tennessee, Boise, Idaho, and Sioux Falls, South Dakota) to achieve better geographic representation. In Appendix A, we provide the complete list of selected MSAs and the number of ZIP Codes and active mortgages in each.

Large MSAs usually contain several cities along with the suburban areas around the cities. For simplicity, we abbreviate the full name of an individual MSA in the following text by referring to the major city in the MSA. For example, we refer to the New York-Northern New Jersey-Long Island MSA as “New York.” Note that we include as part of an MSA any ZIP Codes that extend beyond the MSA boundary into adjacent non-MSA areas.

Geospatial Characterization In this article, we address how delinquent loans, as of October 2008, in individual MSAs were distributed in relation to neighborhood delinquency rate, and whether any generalized patterns emerge across MSAs. Using the ZIP Code-level data described previously, we calculate eight MSA distributional statistics to quantify the patterns in a standardized way. These distributional statistics become the basis of cross-MSA comparisons and analysis.

Note that the focus is the distribution of delinquent loans in relation to neighborhood delinquency rate, not the distribution of the overall population of mortgage borrowers, homeowners, or households in relation to neighborhood delinquency rate. Although these distributions will tend to be similar, we view the former as more relevant for policy analysis addressing the mortgage crisis. For example, the share of a city’s delinquent mortgages contained in high-delinquency neighborhoods is a more important consideration for judging the relevance of the neighborhood dimension than the share of the city’s population located in these neighborhoods.

From a policy perspective, characterizing the shape of the distribution is of interest; for example, knowing whether neighborhoods with extremely high delinquency rates comprise a long tail may be important. Initially, we attempted to fit metropolitan-area delinquency distributions to twoparameter lognormal or beta functional forms. In many cases, however, the data do not conform to these distributions and require greater flexibility in fitting the mean, standard deviation, and shape characteristics (skewness and kurtosis) of the distributions. Therefore, we calculate four descriptive statistics characterizing how the delinquent mortgages in an MSA are distributed in relation to the neighborhood delinquency rate: mean, standard deviation, skewness, and kurtosis.

These moments characterize the delinquent loan distributions across individual ZIP Codes but have no spatial component. The extent to which high-delinquency neighborhoods are spatially isolated, dispersed, or clustered is also of interest from a policy perspective. For example,

–  –  –

248 Refereed Papers Geographic Patterns of Serious Mortgage Delinquency: Cross-MSA Comparisons clustering may imply that delinquency problems are contained (or containable) within a limited geographical area and likely require neighborhood-specific responses. Therefore, we also calculate four gradient and spatial autocorrelation measures, which indicate spatial aspects of the neighborhood delinquency distribution.

We calculate the mean as the mean neighborhood delinquency rate for all the delinquent loans in the MSA. Because our data are at the ZIP Code level, we represent neighborhood by ZIP Code and calculate the mean as the weighted average ZIP Code delinquency rate, weighting by number of delinquent loans in the ZIP Code. Note that this is not equivalent to the overall measured delinquency rate for the MSA, which we would obtain by weighting by number of active loans.

We use the same weighting concept to calculate standard deviation, skewness, and kurtosis. Note that the standard deviation from this calculation is small because each loan in the same ZIP Code is assigned the same delinquency rate. Therefore, the deviation among delinquent loans in the same ZIP Code is 0; the measure captures only the deviation among the ZIP Codes.

–  –  –

Gradient. We calculate two measures of gradient—greatest rate of change in delinquency rate between the ZIP Code with the highest (peak) delinquency rate and neighboring ZIP Codes.15 When we restrict attention to ZIP Codes directly adjacent to the peak-delinquency ZIP Code, we obtain what we call the “first-layer gradient.” We obtain the “second-layer gradient” by focusing on those ZIP Codes adjacent to the directly adjacent ZIP Codes (those that touch the boundaries of the first layer). Specifically, FirstLayerGradient = Max( Di − D Max )/ D Max, (3) i = 1....n

–  –  –

where DMax is the highest ZIP Code delinquency rate in the MSA, Di is the delinquency rate of the n ZIP Codes adjacent to the ZIP Code with the highest delinquency rate, and Dj is the delinquency rate of the k ZIP Codes adjacent to the n first-layer ZIP Codes.

See “The Univariate Procedure—Descriptive Statistics” from SAS 9.1.3 Online Documentation (The SAS Institute, 2003) at http://support.sas.com/onlinedoc/913/docMainpage.jsp.

In calculus, the gradient of a vector field is the vectors that point in the direction of the greatest rate of increase, with magnitude equal to the greatest rate of change.

–  –  –

A steep gradient suggests that high-delinquency neighborhoods are more isolated or extreme. An MSA with flat first- and second-layer gradients is likely to have a broad region of high-delinquency neighborhoods. An MSA without any high-delinquency-rate areas will have low gradient measures.16 Spatial Autocorrelation Spatial autocorrelation refers to the degree to which observations from nearby locations (in our context, nearby ZIP Codes) are more likely to have similar magnitude (similar delinquency rate) than by chance alone (Fortin, Dale, and ver Hoef, 2002). We calculate two spatial autocorrelation measures: Moran’s I and Geary’s C.17

–  –  –

It varies from 0 for perfect positive autocorrelation to about 2 for a strong negative autocorrelation.

If correlation is absent, the expected value equals 1.

A low value of Geary’s C corresponds to a high value of Moran’s I, both indicating a high degree of spatial autocorrelation. Moran’s I is a global indicator, whereas Geary’s C is more sensitive to local differences across neighborhood pairs. In general, Moran’s I and Geary’s C will agree on the existence of spatial autocorrelation, but not necessarily on the magnitude.

Exhibit 1 shows the summary statistics for the eight analysis variables. The mean value across the 91 MSAs of the MSA mean variable is about 0.08, and the mean skewness is about 1.6, consistent with substantial positive skewness for most MSAs.

The gradient measures apply only to the neighborhoods surrounding the ZIP Code with the highest delinquency rate. If a large MSA has multiple pockets of high-delinquency areas, the gradient measures will describe only one of them. Also, the ZIP Code size may affect the gradient measure, as does the delinquency rate differential across neighborhoods; for instance, larger ZIP Codes may mask substantial within-ZIP variation. Nevertheless, the results of our cluster analysis that follows suggest that the gradient measure is an effective tool for identifying metropolitan areas where high-delinquency neighborhoods tend to be more isolated.

Much of our discussion of these spatial autocorrelation measures is drawn from Fortin, Dale, and ver Hoef (2002) and Lembo (2008).

250 Refereed Papers Geographic Patterns of Serious Mortgage Delinquency: Cross-MSA Comparisons

–  –  –

The mean values of the spatial autocorrelation measures (0.14 for Moran’s I and 0.95 for Geary’s C) suggest that spatial autocorrelation in each city, in general, is not high. These values may be somewhat misleading, however, because we define neighborhoods rather broadly, at the ZIP Code level.



Pages:     | 1 |   ...   | 45 | 46 || 48 | 49 |   ...   | 56 |


Similar works:

«10401's IDI L,lb. IDRC C11D 4C CANA DA THE IMPACT OF INFORMA T/ON ON POLICY FORMULA TION Latin America and the Caribbean Fay Durrant Senior Program Specialist International Development Research Centre Regional Office for Latin America and the Caribbean 1335 Plaza Cagancha, Montevideo, Uruguay Phone: 5982 922038-41 Fax : 5982 920223 Internet: fdurrant@idre.ca ( r ', THE IMPACT OF INFORMATION ON POLICY FORMULATION Latin America and the Caribbean ABSTRACT This presentation will discuss the...»

«FISH AND WILDLIFE SERVICE VOLUNTEERS Volunteers Part 150 Volunteer Services Program Chapter 1 Policy, Procedures, and Responsibilities for Volunteers 150 FW 1 1.1 What is the purpose of this chapter? This chapter establishes U.S. Fish and Wildlife Service (Service) policies, procedures, and responsibilities for working with volunteers. 1.2 What is the scope of this chapter? This chapter applies to personnel working with individuals and groups who volunteer by dedicating their time and skills to...»

«The administrative working procedures of smaller states in the decision-making process of the EU by Dr. Baldur Thorhallsson University of Iceland e-mail: baldurt@hi.is Abstract This paper argues that the size and characteristics of administrations is a significant variable in explaining the behaviour of smaller states in the decision-making process of the European Union (EU) in the areas of the Common Agricultural Policy (CAP) and the Regional Policy. It argues that administrative working...»

«International Journal of Computer Networks & Communications (IJCNC) Vol.4, No.2, March 2012 DYNAMIC POLICY MANAGEMENT IN MOBILE GRID ENVIRONMENTS Tariq Alwada’n1, Hamza Aldabbas1, Helge Janicke1,Thair Khdour2, Omer Aldabbas3, Faculty of Technology, De Montfort University, UK {tariq,heljanic,hamza}@dmu.ac.uk Department of Information Technology, AlBalqa Applied University, Jordan khdour@bau.edu.jo Department of Engineering, AlBalqa Applied University, Jordan omer_aldabbas@yahoo.com ABSTRACT...»

«NOT IN THE FINE PRINT: RECOMMENDED CHANGES TO LIFE INSURANCE POLICY DISCLOSURES REGARDING RETAINED ASSET ACCOUNTS MICHAEL A. BARRESE* I. INTRODUCTION Tom is the primary breadwinner of his family. In order to protect his wife and children financially in the event that he passes away, he goes online and researches life insurance policies. After becoming familiar with the different forms of life insurance, Tom purchases a $250,000 life insurance policy from a large insurance company. When he...»

«34 Forestry Policy and Institutions Working Paper Scenario Development to Strengthen National Forest Policies and Programmes A review of future-oriented tools and approaches that support policy-making Scenario Development to Strengthen National Forest Policies and Programmes A review of future-oriented tools and approaches that support policy-making Michael den Herder,1 Chiranjeewee Khadka,2 Päivi Pelli,1 Bernhard Wolfslehner,3 Marieke Sandker,4 Marcus Lindner,1 Lauri Hetemäki,1 Ewald...»

«Chapter 2 Perceptions of Policy Conceptualisations of policy vary across the field of education policy research, and sometimes even within a particular study (Ozga 1990). While understandings of policy have certainly developed and expanded over time, this is not to declare that there is a unified view on what policy ‘is’. Older ideas are not automatically supplanted by newer concepts as they emerge. Rather, a range of older and newer definitions are at work in contemporary education...»

«2014 HANDBOOK OF IMF FACILITIES FOR LOW-INCOME February 2015 COUNTRIES IMF staff regularly produces papers proposing new IMF policies, exploring options for reform, or reviewing existing IMF policies and operations. The following document has been released and is included in this package: The Policy Paper on 2014 Handbook of IMF Facilities for Low-Income Countries, prepared  by IMF staff and completed in July 2014 and sent to the Executive Board for information on August 28, 2014. The policy...»

«ACCREDITATION POLICY AND PROCEDURE MANUAL Effective for Evaluations During the 2010-2011 Accreditation Cycle Incorporates all changes approved by the ABET Board of Directors as of October 31, 2009 Applied Science Accreditation Commission Computing Accreditation Commission Engineering Accreditation Commission Technology Accreditation Commission ABET, Inc. 111 Market Place, Suite 1050 Baltimore, MD 21202 Telephone: 410-347-7700 Fax: 410-625-2238 E-mail: accreditation@abet.org Website:...»

«Journal of Catalan Studies 2011 Modernising Melodrama: From Douglas Sirk to Isabel Coixet Jennie Rothwell Trinity College Dublin Since the death of Franco, Spain has enjoyed a revival of the melodramatic tradition in the cinema, spearheaded by Pedro Almodóvar with films like Qué he hecho yo para merecer esto (1984) and more recently Volver (2006). As Nuria Triana Toribio notes, ‘the most prominent filmmakers of the early 1990s advocated an aesthetic and thematic break with the politically...»

«STATE OF INDIANA Child Care and Development Fund Voucher Program Policy and Procedure Manual The Office of Early Childhood and Out of School Learning Family and Social Service Administration Effective February 28, 2016 TABLE OF CONTENTS General Information 4 1.0 Definitions and Acronyms 9 1.1 Definitions 10 1.2 Acronyms 23 2.0 CCDF Eligibility 26 2.1 Determining Eligibility 27 2.2 CCDF Waiting List 28 2.3 Physical Custody 32 2.4 Residency 36 2.5 Child Eligibility 40 2.6 CCDF Household Members...»

«CHAPTER 5 The Underlying Assumptions, Theory, and Practice of Neoliberal Land Policies Saturnino M. Borras Jr. In the early 1990s, neoliberal land policies emerged within, and became an important aspect of, mainstream thinking and development policy agendas. These policies have increased in prevalence since their inception at the end of the Cold War. They deal with both public and private lands, and have manifested in four broad policy types: (1) privatization and individualization of...»





 
<<  HOME   |    CONTACTS
2016 www.theses.xlibx.info - Theses, dissertations, documentation

Materials of this site are available for review, all rights belong to their respective owners.
If you do not agree with the fact that your material is placed on this site, please, email us, we will within 1-2 business days delete him.