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«Urban Problems and sPatial methods VolUme 17, nUmber 1 • 2015 U.S. Department of Housing and Urban Development | Office of Policy Development and ...»

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Eggert, Kurt. 2007. “Comment on Michael A. Stegman et al.’s ‘Preventive Servicing Is Good Business and Affordable Homeownership Policy’: What Prevents Loan Modifications?” Housing Policy Debate 18 (2): 279–297.

Gelpern, Anna, and Adam J. Levitin. 2009. “Rewriting Frankenstein Contracts: The Workout

Prohibition in Residential Mortgage-Backed Securities,” Southern California Law Review 82:

1077–1152.

Goodman, Laurie, and Pamela Lee. 2014. “OASIS: A Securitization Born From MSR Transfers.” Housing Finance Policy Center Commentary, March 31. Washington, DC: Urban Institute.

Government Accountability Office (GAO). 2014. Troubled Asset Relief Program: More Efforts Needed on Fair Lending Controls and Access for Non-English Speakers in Housing Programs. GAOWashington, DC: Government Accountability Office.

Haughwout, Andrew, Ebiere Okah, and Joseph S. Tracy. 2010. Second Chances: Subprime Mortgage Modification and Re-Default. Staff Report No. 417. New York: Federal Reserve Bank of New York.

Levitin, Adam J., and Tara Twomey. 2011. “Mortgage Servicing,” Yale Journal on Regulation 28 (1): 1–90.

Mallach, Alan. 2011. Building Sustainable Ownership: Rethinking Public Policy Toward LowerIncome Homeownership. Discussion papers: Community Development Studies and Education.

Philadelphia: Federal Reserve Bank of Philadelphia.

Mayer, Neil, and Matt Piven. 2012. Experience of People of Color, Women and Low-Income Homeowners in the Home Affordable Modification Program. Washington, DC: Urban Institute.

Mayer, Neil, Peter A. Tatian, Kenneth Temkin, and Charles A. Calhoun. 2009. National Foreclosure Mitigation Counseling Program Evaluation: Preliminary Analysis of Program Effects. Washington, DC: Urban Institute.

Moody’s Investor Service. 2014. Moody’s Servicer Reports and Ratings, 2008–2014. Boston:

Moody’s Investor Service.

Nichols, Joseph, Anthony Pennington-Cross, and Anthony Yezer. 2004. “Borrower Self-Selection, Underwriting Costs, and Subprime Mortgage Credit Supply,” The Journal of Real Estate Finance and Economics 30 (2): 197–219.

Office of the Comptroller of the Currency (OCC). 2014. OCC Mortgage Metrics Report for the Fourth Quarter 2013. Tech. Rep. Washington, DC: Office of the Comptroller of the Currency.

Oliver, Melvin L., and Thomas M. Shapiro. 2006. Black Wealth/White Wealth: A New Perspective on Racial Inequality. New York: Routledge.

Piskorski, Tomasz, Amit Seru, and Vikrant Vig. 2010. “Securitization and Distressed Loan Renegotiation: Evidence From the Subprime Mortgage Crisis,” Journal of Financial Economics 97 (3): 369–397.

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Quercia, Roberto G., and Lei Ding. 2009. “Loan Modifications and Redefault Risk: An Examination of Short-Term Impacts,” Cityscape 11 (3): 171–193.

Reid, Carolina. 2014. “The Promises and Pitfalls of Homeownership.” In The Assets Perspective:

The Rise of Asset Building and Its Impact on Social Policy, edited by Reid Cramer and Trina R.

Williams Shanks. New York: Palgrave Macmillan: 123–149.

U.S. Department of the Treasury (Treasury). 2014. Making Home Affordable: Program Performance Report Through January 2014. Washington, DC: U.S. Department of the Treasury.

Voicu, Ioan, Vicki Been, Mary Weselcouch, and Andrew John Tschirhart. 2011. Performance of HAMP Versus Non-HAMP Loan Modifications: Evidence From New York City. NYU Law and Economics Research Paper No. 11-41. http://ssrn.com/abstract=1950430.

Voicu, Ioan, Marilyn Jacob, Kristopher Rengert, and Irene Fang. 2012. “Subprime Loan Default Resolutions: Do They Vary Across Mortgage Products and Borrower Demographic Groups?” Journal of Real Estate Finance and Economics 45: 939–964.

White, Alan M. 2009a. “Deleveraging the American Homeowner: The Failure of 2008 Voluntary Mortgage Contract Modifications,” Connecticut Law Review 41 (4): 1107–1131.

———. 2009b. “Rewriting Contracts, Wholesale: Data on Voluntary Mortgage Modifications From 2007 and 2008 Remittance Reports,” Fordham Urban Law Journal 36 (3): 509–535.

Wyly, Elvin K., and Steven R. Holloway. 2002. “Invisible Cities: Geography and the Disappearance of ‘Race’ From Mortgage Lending Data in the USA,” Social and Cultural Geography 3 (3):

247–282.

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Cityscape 189 Cityscape: A Journal of Policy Development and Research • Volume 17, Number 1 • 2015 U.S. Department of Housing and Urban Development • Office of Policy Development and Research 190 Departments Data Shop Data Shop, a department of Cityscape, presents short articles or notes on the uses of data in housing and urban research. Through this department, the Office of Policy Development and Research introduces readers to new and overlooked data sources and to improved techniques in using well-known data. The emphasis is on sources and methods that analysts can use in their own work. Researchers often run into knotty data problems involving data interpretation or manipulation that must be solved before a project can proceed, but they seldom get to focus in detail on the solutions to such problems. If you have an idea for an applied, data-centric note of no more than 3,000 words, please send a one-paragraph abstract to david.a.vandenbroucke@hud.gov for consideration.





Data Sources for U.S. Housing Research, Part 2: Private Sources, Administrative Records, and Future Directions Daniel H. Weinberg Virginia Tech This article is the second of a two-part article about data sources for U.S. housing research. The first part, which appeared in the previous issue of Cityscape (Volume 16, Number 3), addressed public sources.

Abstract For practitioners and policymakers to make a serious attempt to affect housing policy, they must cite evidence-based research. Part 2 of this article summarizes many of the private sources of housing data for researchers that can provide such evidence.

It then summarizes the challenges of using administrative records (AR) and proposes to construct new data sources by marrying survey data with AR and by constructing synthetic databases. The article concludes with a brief discussion of some data issues.

Cityscape 191 Cityscape: A Journal of Policy Development and Research • Volume 17, Number 1 • 2015 U.S. Department of Housing and Urban Development • Office of Policy Development and Research Weinberg Introduction The basis for good housing policy is evidence-based research, and the only way to do good research on housing is to base that research on appropriate data. Whereas part 1 of this article focused on government data sources for U.S. housing statistics, part 2 describes private data sources and administrative records (AR). It concludes with suggestions for future data production activities and mentions two unresolved data issues.

Private-Sector Data Sources The National Association of Realtors® (NAR) and the National Association of Home Builders (NAHB) both issue housing affordability indexes (the latter is known as the NAHB/Wells Fargo Housing Opportunity Index). Such an index typically indicates whether a family with median income can afford the median-priced existing single-family home at prevailing mortgage rates (NAR uses the national median income and NAHB uses the U.S. Department of Housing and Urban Development’s [HUD’s] Area Median Incomes).

National Association of Realtors NAR also provides monthly data series that track housing market sales: monthly sales volumes for existing homes by region, monthly sales volumes for single-family and cooperative apartments, monthly sales inventories of existing single-family and condominium homes, and the monthly pending home sales index (a forecast of existing home sales in the subsequent 1 to 2 months).

National Association of Home Builders On a subscription basis, NAHB also offers 43 sets comprising various data series of interest to its constituency. These sets include data on building material prices (for example, framing lumber), employment, and permits. NAHB surveys multifamily developers and property managers to produce a Multifamily Production Index and a Multifamily Vacancy Index.

Mortgage Bankers Association For subscribers, the Mortgage Bankers Association Weekly Applications Survey offers a comprehensive analysis of mortgage application activity. Historical index data are available back to the original start date of the survey in 1990. The survey’s 15 indexes cover fixed-rate, adjustable-rate, conventional, and government loans for purchases and refinances.

RealtyTrac RealtyTrac® Inc. has a website with foreclosure listings covering more than 2 million default, auction, short-sale, and bank-owned homes. Access is available for subscribers, and bulk downloads can be licensed. RealtyTrac describes its data as covering more than 100 million homes in 2,200 counties, accounting for 85 percent of all properties in the largest 200 metropolitan areas in the United States. For each property, RealtyTrac provides detailed housing characteristics (equity,

–  –  –

foreclosure details, comparable sales and listings, trends, lot size, square footage, price, and year built) and sales history (historical loan positioning, loan-to-value ratio, loan amount, estimated market value, property information, default amount, owner name, trustee, and lender name).

HUD and the U.S. Census Bureau began joint research using these records matched to the American Housing Survey (AHS). RealtyTrac is distinct from other companies providing online foreclosure information in that it has historical data back to 2005 and provides analytic reports.

Other Sources of Information Companies such as CoreLogic, Inc., Black Knight Financial Services, Inc., and Zillow can provide basic property tax data, including parcel boundaries, for a large subset of the United States. As CoreLogic notes—1 CoreLogic is the nation’s largest provider of advanced property and ownership information, analytics and solutions. Our databases cover more than 99 percent of the U.S. properties.

CoreLogic obtains property records, tax assessments, property characteristics, and parcel maps from tax assessors and county recorders offices across the nation. This information is combined with flood, demographics, crime, site inspection neighborhood, document images and other information from proprietary sources to further enrich our databases.

Zillow provides property-level data, including historical sales price and year, taxes, and number of bedrooms and bathrooms; demographic data at the city and neighborhood level; and neighborhood information, including the Zillow Home Value Index, median single-family home and condominium values, and average tax rates. The Census Bureau is looking into whether such commercial data sources can reduce the cost of updating the Master Address File (MAF).

MPF Research analyzes the rental housing market for clients. The company advertises that, “With exclusive access to a completely unique data source and a solid foundation of sound statistical methodologies, MPF Research publishes 72 individual apartment market reports covering the top 100 markets nationally.”2 MPF Research presents little about its methodology on its website.3 The National Council of Real Estate Investment Fiduciaries (NCREIF) is a cooperative organization that publishes information provided by its members. Its website indicates that, “NCREIF was established to serve the institutional real estate investment community as a non-partisan collector, processor, validator and disseminator of real estate performance information.”4 NCREIF bases its reports on its database of all-equity properties begun in 1977. In 2013, NCREIF has information on “approximately 30,000 properties historically, and approximately 10,000 current properties.

NCREIF collects 67 data fields each quarter that consist of financial information such as Market Quoted from http://www.corelogic.com/solutions/property-information-analytic-solutions.aspx.

Quoted from https://www.realpage.com/mpf-research/?src=AdWords&medium=PPC&campaign=AdGroupName& Network=Search&kw=mpf&gclid=CNfo5ayInsMCFdgKgQodtj0AmQ.

They note at https://www.realpage.com/mpf-research/methodology/ that “Data collected in the MPF Research quarterly survey is collected through various sources. Where available, MPF Research can incorporate data from RealPage software products. MPF Research also collects data through direct relationships with management companies, through telephone surveys, and through e-mail surveys that are completed by apartment community owners or managers.” Quoted from https://www.ncreif.org/about.aspx.

Cityscape 193Weinberg

Value, [Net Operating Income], Debt, and [Capital Expenditures], as well as descriptor data such as Property Type and Subtype, Number of Floors, Square Footage, Number of Units, and Location” (NCREIF 2013–2014: 2). It also publishes the NCREIF Property Index (NPI), “which is a quarterly, index tracking the performance of core institutional property markets in the U.S.” (NCREIF, 2013–2014: 2), and the NCREIF Transaction-Based Index (NTBI)—“The NTBI is an equal-weighted transaction and appraisal index while the NPI is a value weighted index calculated using appraised values” (NCREIF 2013–2014: 7). Using a subset of the included properties, NCREIF also publishes, the NCREIF Timberland and Farmland Property Indices and provides other products to its clients such as a quarterly property index trends analysis report and operations data categorized by “four subcategories within income and eight categories within expense, as well as four capital expenditures subgroups” (NCREIF, 2013–2014: 3).

The Promise and Challenge of Administrative Records Researchers have probably reached the limits of what government survey data collection can accomplish. As federal budgets get tighter, fewer surveys (and reduced sample sizes) are likelier than increased coverage of topics or additional samples to provide separate information for more metropolitan areas. That likelihood suggests that a fruitful area for federal statistical agencies to create value added is to take advantage of existing AR data sources; that is, data collected primarily or exclusively for administrative purposes rather than for research, also known as third party data.

As the HUD Research Roadmap points out—



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