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«DiscoVeriNg HomelessNess Volume 13, Number 1 • 2011 U.S. Department of Housing and Urban Development | Office of Policy Development and Research ...»

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Research and Evaluation Questions NSP has truly been a work in progress, with each round of funding modifying the statutory framework of the program, and with HUD continually updating policy guidance and regulations to more effectively advance the program’s goals. Meanwhile, the housing market has continued to change;

at the national level, prices continued to decline for approximately three quarters after the passage of HERA but have been fairly steady since mid-2009. Foreclosure starts remain elevated, but the problem is shifting from subprime loans to prime loans, and foreclosures now result more from unemployment than from resetting interest rates. Amid this changing environment, it is important to continue research and debate about the need for NSP and the particular form it should take. The

following questions are intended to spur that discussion:

1. To what extent is the relationship between foreclosures and neighborhood decline mediated through vacancy and other housing market dynamics? This question is particularly important given the way the foreclosure crisis has progressed from subprime loans to loans previously assumed to be of a higher quality. Do foreclosures of Alt-A loans in high-income neighborhoods have similar negative effects on surrounding properties?

2. How has HUD’s decision to guide NSP funding to the hardest hit neighborhoods (based on HUD estimates of foreclosure and abandonment risk) helped or hindered the program in stabilizing housing markets? For a given neighborhood, is there a nonlinear relationship between the size of an investment and the effect on neighborhood outcomes?

3. The use of NSP funds is limited by statute to properties that are foreclosed, vacant, or abandoned.

Could the program be more effective in stabilizing neighborhoods if properties of all types were eligible, as long as they are in a qualifying target neighborhood or submarket?

4. What has been the relationship between NSP investments and private investments? Do they complement each other, with private capital following after NSP investments bringing a neighborhood to a positive tipping point? Or has competition with private investors simply thwarted the planned investments of NSP grantees, without improving neighborhood conditions?

5. Context is extremely important to NSP. How effectively did grantees analyze the context of their local housing markets, set appropriate program goals, and choose the best strategies to achieve those goals?

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Conclusion NSP1 grantees first began obligating funds in March and April 2009, and NSP2 grantees began obligating funds in May 2010. With NSP3 funds just making their way out to grantees, an opportunity and a need still exist for continued research on how foreclosures affect neighborhoods and how government action can mitigate any negative effects. Foreclosures show little sign of slowing down, so lessons from this research will be very important for years to come.

Acknowledgments The author thanks Mark Shroder for his comments on this article and Todd Richardson for his general guidance on the Neighborhood Stabilization Program.

Author Paul A. Joice is a social science analyst at the U.S. Department of Housing and Urban Development, Office of Policy Development and Research, Program Evaluation Division.

References Apgar, William, and Chris Herbert. 2009. Report to Congress on the Root Causes of the Foreclosure Crisis. Report prepared for the U.S. Department of Housing and Urban Development. Cambridge, MA: Abt Associates Inc.

Immergluck, Dan, and Geoff Smith. 2006a. “The Impact of Single-Family Mortgage Foreclosures on Neighborhood Crime,” Housing Studies 21 (6): 851–866.

––––. 2006b. “The External Costs of Foreclosure: The Impact of Single-Family Mortgage Foreclosures on Property Values,” Housing Policy Debate 17 (1): 57–79.

National Association of Home Builders (NAHB). 2009 (January 21). “Builder Confidence Edges Down Further in January.” http://www.nahb.com/news_details.aspx?sectionID=134&newsID=8519 (accessed January 11, 2011).

Schuetz, Jenny, Vicki Been, and Ingrid Gould Ellen. 2008. “Neighborhood Effects of Concentrated Mortgage Foreclosures,” Journal of Housing Economics 17: 306–319.

Shiller, Robert J. 2008. “Understanding Recent Trends in House Prices and Homeownership.” In Housing, Housing Finance and Monetary Policy, Jackson Hole Conference Series, Federal Reserve Bank of Kansas City, 2008: 85–123.

Short, Doug. 2010 (July 27). “Conference Board Consumer Confidence Index: Evaluating Historical Performance.” http://seekingalpha.com/article/216708-conference-board-consumer-confidenceindex-evaluating-historical-performance (accessed January 11, 2011).

Cityscape 141Joice

142 Policy Briefs Graphic Detail Geographic Information Systems organize and clarify the patterns of human activities on Earth’s surface and their interaction with each other. GIS data, in the form of maps, can quickly and powerfully convey relationships to policymakers and the public. This department of Cityscape includes maps that convey important housing or community development policy issues or solutions. If you have made such a map and are willing to share it in a future issue of Cityscape, please contact david.e.chase@hud.gov.

Satisfaction With Local Conditions and the Intention To Move Richard N. Engstrom Nathan Dunkel Kennesaw State University The recent economic downturn has presented many challenges to local communities and policymakers. Foreclosed properties, job losses, and other challenges that local residents face can threaten the economic viability of local communities. Another consequence of the economic downturn is decreased government budgets, forcing policymakers to make decisions about how to allocate scarce resources effectively. When making decisions about local and regional policy, it would be useful to know how local characteristics contribute to the decisions residents make about whether to remain in a local community or to relocate. Exhibits 1 through 4 present maps created to investigate the relationship between residents’ perceptions of local conditions and the intentions of residents to move. The maps are of the ZIP Codes in the five core counties in the Atlanta metropolitan area (Clayton, Cobb, DeKalb, Fulton, and Gwinnett), combined with data from a public opinion survey conduced by the A.L. Burruss Institute of Public Service at Kennesaw State University.

All maps identify the approximate locations, within ZIP Codes, of respondents who express an intention to move sometime during the next year. Exhibit 1 presents this location information (as point data) along with ZIP Code-level measures of residents’ overall satisfaction with local conditions in the Atlanta metropolitan area (shaded regions). Darker region shadings represent higher levels of satisfaction. Exhibit 2 shows the level of satisfaction residents have with local police protection, exhibit 3 shows residents’ level of satisfaction with local schools, and exhibit 4 shows residents’ level of satisfaction with local opportunities for employment.

–  –  –

These four maps indicate that a summary measure of satisfaction seems to be associated with intent to move. The darkest regions, indicating ZIP Codes in which residents are the most satisfied with local conditions in general, contain no respondents who express an intention to move. The second level of shading, however, contains several respondents who intend to move. The lightest shading, indicating areas where people are least satisfied with local conditions, contains ZIP Codes in which most of those intending to move currently live.

It seems that residents’ level of satisfaction with local conditions is related to residents’ intentions to move. What, in particular, might it be about local conditions that cause people to want to move?

Exhibits 2 through 4, taken together, show that, although attitudes about police protection and school quality do not seem to be associated with the intent to move, employment opportunities do influence peoples’ intentions to move. Areas with high levels of satisfaction for police and school quality also contain many respondents who intend, nonetheless, to move from the area. This trend suggests that these elements of local conditions are not crucial factors in residents’ decisions to relocate. On the other hand, areas with residents who express both high and moderate levels of satisfaction with local employment opportunities contain no residents who intend to move. All those respondents in the survey who intend to move reside in ZIP Codes that are also characterized by low levels of satisfaction with local employment opportunities, suggesting that economic conditions (and the perception of economic conditions) are particularly worthy of the attention of those who wish to understand, or influence, local residents’ decisions to remain in or leave their communities.

This analysis does not fully explain why residents move, but it provides evidence that all public service provisions may not be equal in terms of their effect on residents’ moving decisions. Additional analysis could help further researchers’ understanding of how local conditions and the policies that affect perceptions of those conditions are related to community stability and residential mobility.

Authors Richard N. Engstrom is a an assistant professor with the Department of Political Science and International Affairs at Kennesaw State University, where he is also director of the A.L. Burruss Institute of Public Service and Research.

Nathan Dunkel is a researcher with the A.L. Burruss Institute of Public Service and Research, Kennesaw State University.

146 Graphic Detail 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, PD&R 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


to david.a.vandenbroucke@hud.gov for consideration.

Home Maintenance and Investment Decisions Jonathan D. Fisher New York Census Research Data Center, Baruch College Elliot D. Williams Bureau of Labor Statistics Any opinions and conclusions expressed herein are those of the authors and do not necessarily represent the views of the U.S. Census Bureau or the Bureau of Labor Statistics. The research in this article does not use any confidential Census Bureau information.

Abstract The owned home is often the largest asset in a household’s portfolio. To maintain its value, the home requires continual reinvestment, and a homeowner can increase its value through renovations and additions. Empirical research on these home maintenance and investment decisions of the household has relied almost exclusively on the American Housing Survey (AHS). The research presented in this article added a new data set to this literature, the Consumer Expenditure (CE) Survey, using quarterly household data from 1984 to the first quarter of 2005. In the article, we first compare results between the AHS and CE Survey using some stylized facts identified in the literature. Then we move beyond this comparison and highlight some strengths of the CE Survey, including the distinct time-series patterns observed in the quarterly data.

–  –  –

Introduction In the typical homeowner’s financial portfolio, the home is a singular beast. Owned homes are part consumption good and part investment good. Unlike purely financial assets, the home requires periodic maintenance to retain both its consumption and asset values, and the home can be expanded as necessary in lieu of incurring the substantial transaction costs associated with selling the current home, purchasing a new home, and moving.

In the research presented in this article, we constructed a new data set from the Consumer Expenditure (CE) Survey that enables researchers to investigate the household’s housing maintenance and additions decisions, confirming some of the stylized facts already in the housing literature, and expanding the results to take advantage of the long time-series of higher frequency data.

This article falls into the category of literature that investigates the microeconomic determinants of maintenance and investment decisions; at the same time, it adds to the literature by using a new, complementary data set that has different strengths (and weaknesses). Understanding home maintenance and additions behavior is an important component in understanding household borrowing, saving, and investment decisions. Because routine maintenance can be forgone (for a time) without substantial depreciation of the consumption and asset values, the owned home provides an internal capital market for homeowners and a means of short-term borrowing. Conversely, additions expenditures may be a means of saving––in that these expenditures increase the home’s capital stock.

Most research on the microeconomic determinants literature has relied on a single data set, the American Housing Survey (AHS). As the first pass at the data set created using the CE Survey, the research presented in this article took a fresh look at a number of the stylized facts, which have emerged from the microeconomic approach to analyzing homeowners’ additions and maintenance expenditures, described in the following paragraphs. Because this research uses a new data set, we decided to compare results in the CE Survey with the existing literature. This article also highlights some of the strengths of the CE Survey, mainly by showing the interesting time-series properties of the homeowners’ maintenance and additions expenditure data.

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