Profitwise News and Views

September 2003 Issue

Seeds of Growth Conference Summary

Seeds of Growth Sustainable Community Development: What Works, What Doesn't, and Why

Session Two - Housing and Neighborhood Development
The four papers in the second session explore the impacts and benefits of housing programs on neighborhood development.

The first paper in this session, by Rex LaMore, Susan Cocciarelli, Jose Gomez, John Melcher, John Metzger, Faron Supanich-Goldner, and Matt Syal, Organizational Capacity and Housing Production: A Study of Nonprofit Organizations in Michigan, explores the organizational capacity/productivity link among nonprofit housing development groups in Michigan.

"A critical question in community development is how best to organize, fund, and otherwise support affordable housing development by nonprofit organizations," said John Metzger, Ph.D., assistant professor of geography, Michigan State University, who, along with Jose Gomez, Ph.D., specialist, Center for Urban Affairs, Michigan State University, presented the paper. "The purpose of our research was to identify relationships that might exist between the components of organizational capacity and the efficient production of affordable housing."

The research team developed a questionnaire designed to create an organizational profile of nonprofit housing organizations in five different regions of Michigan.¹ The questionnaire was organized into nine sections: community assessment and participation activities, financial packaging skills, construction management skills, project management skills, homeownership programs, organizational administration and development, professional development activities, linkages to educational institutions, and public policy and housing advocacy skills.

Surveys were obtained from 37 organizations in the 5 regions of Michigan. Nine Habitat for Humanity groups were included (at least one in each region) to permit comparisons by type of organization. All of the groups were nonprofit housing organizations; many were involved in related community building activities.

The 37 groups in this study produced a total of 4,385 housing units over a 32-year span: 34 percent were multifamily rehabilitation units, 29 percent were multifamily new construction units, 19 percent were single-family rehabilitation units, and the remaining 18 percent single-family were new construction units.

Fifteen of the 37 organizations account for 92 percent of the total production in the survey. Less than half of the organizations reported having produced more than 50 housing units in their entire organizational history. While production from the organizations surveyed averaged ten units per year, the median production per year was three housing units - again, reflecting the concentration of production volume among a small segment of the groups.² The organizations involved in multi-unit construction and rehabilitation averaged 241 units each, nearly 10 times the number of units produced by single housing unit developers.

The study also analyzes on-time and on-budget efficiency. The organizations averaged 72 percent on-budget performance and 58 percent on-time performance for a combined overall efficiency score of 64 percent. The 20 organizations reporting above-average performance in both categories are considered "high-proficiency," while the remaining groups, performing at or below the average in one or both categories, are classified as "low-efficiency."

The authors build on the five components of capacity model developed by Glickman and Servon3 to quantify the organizational capacity of each group and to assign a capacity index score to each group in the survey. The capacity index score is simplified into the following equation:

Resource capacity (CRES) reflects an organization's ability to attract, manage, and maintain funding.
Organizational capacity (CORG) refers to the capability of a group's internal operations.
Programmatic capacity (CPRG) measures the types of services offered.
Networking capacity (CNET) reflects the organization's ability to work with other institutions.
Political capacity (CPOL) is the ability to credibly represent and advocate for its residents.

The surveys generated index scores for the five components of capacity. The annual average of units produced is calculated as a production measure, and the comparative on-time performance and under-budget performance of each group are calculated as efficiency measures. Comparisons are drawn between high- and low-production organizations, high- and low-capacity organizations, and high- and low-efficiency organizations. Those comparisons are summarized in the following table.

The authors conclude that while the characteristics of high-capacity and high-productivity groups are positively correlated, high-efficiency and high-capacity do not coincide. These findings suggest that increasing capacity will increase productivity, but increasing efficiency is likely more dependent on factors such as the funding and regulatory environment.

Based on the survey, increasing capacity requires further technical assistance and training, specifically in construction management, board development, human resource management, and financial management.

The paper concludes that nonprofit producers likely do not have the capacity to meet demand for affordable housing in their communities. The authors suggest the need for further refinement of the Glickman and Sevron model,⁴ and they call for further research into both the maximum productivity attainable by nonprofit housing development groups and communities' sustainable "carrying capacity" for nonprofit housing groups. Finally, further research and analysis needs to balance housing productivity goals with community building goals.

In the next paper, Neighborhood Externality Risk and the Homeownership Status of Properties, Christian Hilber, an economist at Fannie Mae, begins with the observation that homeownership rates are extremely low in many inner-city neighborhoods and distressed places. While the overall national homeownership rate is about 68 percent, many inner city neighborhoods' homeownership rates are below 50 percent with extreme cases like West Philadelphia as low as 12 percent.

Research has linked low homeownership rates to lower investment in social capital,⁵ bad environments for raising children,⁶ low quality schools in densely populated neighborhoods,⁷ and decaying housing stock.⁸ These are characteristics of many inner-city communities, and many inner-city communities also have low homeownership rates. This research explores the characteristics that affect homeownership rates in inner cities.

Low homeownership rates in inner cities may result from a preference of center-city residents to rent rather than own their dwelling. Other noted characteristics are: "sorting" - elderly households move nearer to services for the elderly, young households move to good school districts, and other logical trends, or location specificity - higher costs and the need for land use efficiencies in densely populated areas may generate demand for more rental units. While noting that the housing literature demonstrates the impact of children on the housing tenure choice, Hilber finds that the sorting and location characteristics fail to fully explain the low homeownership rates in inner cities.

Hilber explains that uncertain neighborhood conditions and, as a consequence, uncertainty about property values negatively affect the likelihood that a housing unit is owner- occupied. His proposition is based on a portfolio diversification argument: "Single owner occupiers have to pay a risk premium that increases with neighborhood uncertainty." Since homebuyers avoid risky neighborhoods, and lenders cannot fully price and thus avoid risky neighborhoods, that uncertainty negatively affects homeownership rates.

Hilber gathered data from the American Housing Study, a panel study including 55,000 repeatedly evaluated housing units, to measure neighborhood externalities including: junk, litter, and trash; street noise; neighborhood noise; and crime. He developed neighborhood externality risk measures based on the standard deviation of the four conditions as perceived by residents or as assessed by interviewers. The purpose was to test the hypothesis that externality risk negatively affects the probability that a specific housing unit is owner-occupied.

Neighborhoods in the study exhibited a random variation in these risk measures rather than a steady improvement or decline. "There are bad neighborhoods that have a high level of externalities but they are pretty much stable," Hilber said. "And there are very good neighborhoods that have some variation in their neighborhood externalities. So it's not uniformly distributed."

The author then analyzes four risk measures to assess the degree to which each measure could shed light on whether or not a housing unit is owner-occupied. The risk measures include: neighborhood externalities risk measures, neighborhood externality levels (to control for the possibility that the level rather than the variation affects the homeownership status of properties), demographic variables, and housing type variables.

All four risk measures have a negative effect on the likelihood that the housing unit is owner-occupied, and they are statistically significant at the 1 percent level. Hilber notes that the results are also quantitatively meaningful. Increasing the measure for junk and litter by one standard deviation reduces the likelihood of homeownership by about 5 percent. The other three quantitative effects are smaller but still quite meaningful. Hilber concludes that one can explain low homeownership rates by demographic factors, location characteristics, the housing structure/ composition of these places, and finally by neighborhood uncertainty.

Estimating the External Effects of Subsidized Housing Investment on Property Values, by Ingrid Gould Ellen, Michael Schill, Amy Ellen Schwartz, and Ioan Voicu, takes advantage of the unique opportunities presented by the creation of thousands of housing units under New York City's Ten Year Housing Plan. Here, the authors explore whether housing investments generate benefits to the surrounding community as measured by increases in property values.

The authors found that by reducing neighborhood uncertainty - specifically by removing a "disamenity" such as a vacant lot or an abandoned, boarded-up building - subsidized housing development can have positive spillover effects for other neighborhood properties. In the face of the "conventional wisdom" that subsidized housing accelerates neighborhood decline, these authors have five hypotheses on why subsidized housing investment may generate positive external effects:

Creation of new housing often involves the removal of a "disamenity" - a vacant lot or abandoned building.
Physical condition effect: "A nice, physically attractive new home might raise the value of surrounding properties."
Demonstration effect: "Successful investments in new housing beget more housing investment."
Population growth effect: "The increase in population from the new housing may increase street traffic, improve neighborhood safety levels, and increase demand for other commercial development."
Population mix effect: "All of these effects can be heightened by the particular mix of the characteristics of the inhabitants. In particular, higher income residents may be viewed by some as more desirable neighbors. Many people have posited that homeowners have stronger incentives to be "good" neighbors and invest in their homes, so homeownership properties may deliver more positive spillovers."

Between 1987 and 2000, New York City invested more than $5 billion to renovate more than 100,000 occupied housing units and create 66,000 new, affordable housing units. Of the 66,000 new units, 22,000 were new construction and the other 44,000 were created through gut rehabilitation of formerly vacant buildings. This study focuses on the 66,000 units of new housing produced under New York City's Ten Year Plan, "The largest municipally supported housing production program in the history of the U.S."

The study uses a host of data including a database of 294,000 property transactions in the city from 1980 to 1999, data on the property characteristics of each of those properties, and data on the location and characteristics of the 66,000 new units created under the Ten Year Plan. The data allowed the research team to estimate a difference-in-difference model, "And we think the results are fairly persuasive," according to Ingrid Gould Ellen, an assistant professor of Urban Planning and Public Policy at New York University's Wagner Graduate School of Public Service.

For every assisted housing unit that was built under the Plan, the research team drew a 2,000-foot ring around that project. They compared the prices of properties that sold within the ring with prices of comparable properties that sold outside the ring. They then compared that difference before and after the construction of the assisted housing, allowing those impacts to vary with both distance (from the project) and time. Finally, the analysis controlled for the structural characteristics of the housing units.

The results of the analysis are striking. For the "average" project - 250 units, 50 percent of which are in multifamily rental buildings - while the initial price differential for properties located adjacent to the project was 28 percent, it fell to about 13 percent after project completion, and the effect gets bigger over time, perhaps as people begin moving into the new units. The impacts are smaller for smaller projects.

The study also reveals larger impacts for projects with fewer units in multifamily rental buildings. "This is not to say that rental housing has the smaller spillover effect," cautioned Ioan Voicu, Furman fellow at the Center for Real Estate and Urban Policy of New York University. "There is almost a perfect overlap between rental housing and multifamily housing in our data, so we could not separately estimate the impacts of rental and multifamily housing."

In sum, creation of subsidized housing in New York appears to have increased the value of surrounding properties. These effects appear to be larger for larger projects and properties closer to the project site, and they persist over time. The effect for multifamily rental housing is less pronounced.

The next question was whether neighborhood characteristics affected the spillover effects. "The average project (250 units) seems to have a greater impact in more distressed neighborhoods," according to Voicu. "But, it is also true looking at the first row in Table 2, that a small number of units have a smaller impact in more distressed neighborhoods. Building a few units on one block when surrounding blocks are filled with blight will probably make little difference."

Most of the effect is felt at the start of the project, suggesting that much of the effect may be due to the removal of the original "disamenity," or that the market immediately incorporates people's expectations. There are also larger impacts following completion of larger projects.

This research suggests that subsidized housing can significantly benefit low-income neighborhoods, though it is unclear whether housing investment in stable neighborhoods would have similar impact in the absence of some blight being removed. The research also finds that some public costs of housing investments can be recouped through increased property tax revenue. And finally, the authors suggest that, "We might want to steer small projects to neighborhoods that are doing okay, because the smaller projects won't have the desired effects in the more distressed neighborhoods."

In the fourth and final paper of the session, Charles Capone, Jr., a senior analyst in the Microeconomic and Financial Studies division of the Congressional Budget Office (CBO), and Albert Metz, a principal analyst for the CBO, investigate Mortgage Default and Default Resolutions: Their Impact on Communities.

Credit flows are vital to community development. An array of programs attest to the federal government's pursuit of community development through increased credit flow: Community Reinvestment Act, Federal Housing Administration (FHA) and Small Business Administration loan guarantees, Federal Home Loan Bank Affordable Housing Program, HUD, Community Development Financial Institution, empowerment zones (EZ), and Section 8 funds.

A home purchase is inherently risky. It involves taking a highly leveraged financial position in an illiquid asset and covering high transaction costs that are only recovered over an extended period of appreciation in the asset. These risks can and do sometimes result in real losses. A foreclosure is more than a loss just to the homeowner; the lender (and/or guarantor) and community location of the home also experience losses.

Capone and Metz raise three "unasked questions:"

What rate of default and foreclosure is too high? How far can we really push the envelope of homeownership before it truly becomes a net loss rather than a net gain to households?
What rate of foreclosure destabilizes a neighborhood?
How much financial risk is appropriate to ask of low- and moderate-income households when we encourage them to become homeowners? Who explains the risks to them?

A number of risk mitigation tools came about in the 1990s. Homebuyer counseling and education programs, automated underwriting, early intervention servicing, and post-default intervention and loss mitigation programs have all helped reduce risk for lenders.

In 1991, Fannie Mae initiated a loss mitigation strategy on the basic premise that it is in everyone's best interest to avoid foreclosure, so there must be a provision for negotiations between borrowers and lenders. While this strategy was a novel addition to the home mortgage market in 1991, Capone pointed out that, "Actually, these kinds of interventions have always been done in the commercial real estate market because there's always lots of money on the table if one of those loans goes bad."

When lenders' priorities shifted from payment collection to negotiating workouts, foreclosures dropped dramatically. The lenders who engaged in negotiated workouts found that most borrowers in 90-day default only need temporary help. Most have the willingness and ability to continue homeownership. So now, Capone said, "More than half of all [of Fannie Mae's] loans that 12 years ago would have gone to foreclosure, don't."

Capone and Metz analyze data gathered on all FHA-insured borrowers with a 90-day default reported between 1998 and mid-2002, who were unable to cure the default on their own. The FHA loss mitigation program allows three ways⁹ for a borrower in default to stay in the home: a special forbearance with extended repayment plan, a loan modification that capitalizes some arrearages and refinances the property, or a partial claim¹⁰ that is unique to the HUD's FHA program. For those borrowers with a 90-day default, the home retention rate rose from 22.5 percent in 1998 to 85 percent in 2002.

The authors explore each of the factors that could influence foreclosure and affect actual outcomes. The research estimated the probability that the home retention outcome would change with each of the following factors:

Year of default
Gender of primary borrower
Marital status
Race
Underserved area
Mortgage type
Payment-to-income ratio
House price growth
Interest rates
Mortgage age
Initial loan-to-value ratio
Unemployment rate
Loan purpose
Property value

The study finds that the FHA loss mitigation program itself is the most important influence on the success rate of people keeping their homes. Other than that, house price appreciation has the most significant ongoing influence on success probabilities.

The paper also shows smaller, measurable influences. Blacks, for instance, have a marginally higher probability of success (6 percent) than other non-white homeowners. Factors that portend a marginally lower probability of success include low-valued properties11 (5 percent), unmarried homeowners (5 percent), graduated payment mortgage (10 percent), and HUD-defined underserved areas (4 percent).

To emphasize the point that having the loss mitigation program itself is fundamental, the researchers, "...threw everything into the mix and asked, Who's the least likely to keep their home?" Capone said that the least likely "person" to get a successful loan workout would be an unmarried or separated white person with a low-priced home in an underserved area and a high payment-to-income ratio (64 percent success probability). "If we gave the same person as above a graduated payment mortgage, we can knock their probability of successfully keeping their home in a workout down under 50 percent to a 49 percent success probability," Capone said.

The discussant, Wyman Winston, deputy director of the Portland Development Commission, argued that, while the Michigan study did an excellent job of describing the contributions of Michigan CDCs, and of answering the questions it poses, his question was more direct: "What will it take to solve America's housing crisis?"

Winston identified five key issues that relate to Glickman and Servon's five components of organizational capacity:

The market economy - Affordable housing is, in part, a function of our economy. There has not been an honest debate as to whether or not affordable housing is valuable in our society.
The political, economic disparity - Winners and losers are inherent in the market economy. Those disparities show up in housing, wages, and income. The housing problems of this nation cannot be solved through housing. The fundamental solution for housing is centered upon issues of wages and incomes.
CDC inefficiency - The private sector is boycotting affordable housing and, by default, CDCs have been left to fill that vacuum. The fact is they simply do not have the ability to achieve the national goals of affordable housing for all American citizens.
The CDC cartel - The CDC cartel is a political alliance among affordable housing practitioners resulting in some of the highest per unit prices and some of the most inefficient operating costs.
The network - Policymakers, practitioners, and those who finance affordable housing development are not having an honest discussion about solutions. So, what has been created is a segregated market in which the private sector, for the most part, is not involved in the production, management, and maintenance of the affordable housing that's being produced.

Winston noted the relatively small number of housing units being produced by Michigan CDCs, and expressed his concerns over the fundamental weaknesses in the affordable housing development system. Winston drew on his personal experience with the Wisconsin Housing and Economic Development Authority (WHEDA) to confirm Christian Hilber's findings on neighborhood externality risks. Winston, a former director of Emerging Markets for WHEDA, highlighted the Lindsey Heights project in Milwaukee, a 40-square block, subdivision-type project that tried to address issues of low property values and low homeownership rates in central Milwaukee.

In spite of millions of dollars of investment by WHEDA, the success of the project hinged on organizing block clubs, regular volunteer clean-up campaigns, and a push to get the city to help neighborhoods in clean-up efforts. This approach ultimately led to success at Lindsey Heights, where units are exhibiting price appreciation. Winston's Lindsey Heights experience seemed to affirm the findings of the third paper regarding external effects of subsidized housing

Notes
¹The Detroit metropolitan area, including Wayne, Oakland, and McComb counties - a large urban region; the Lansing area, including Clinton, Eden, and Ingam counties - a mid-sized urban region; northern lower Michigan including six counties that comprise a rural region; the Grand Rapids metropolitan area comprised of four counties; and the Flint area, focused on Genesee County.

²Thirty-six percent of the reported production in this study came from just one organization that produced 1,590 units, nearly all of which were multifamily, new construction. The analysis adjusts for this "statistical outlier" group.

³N. Glickman and L. Servon, 1998, "More Than Bricks and Sticks: Five Components of Community Development Corporation Capacity," Housing Policy Debate, 9:497-540.

⁴Ibid

⁵ D. DiPasqaule and E.L. Glaeser, 1999, "Incentives and Social Capital: Are Homeowners Better Citizens?" Journal of Urban Economics, 45:354-384.

⁶R.K. Green and M.J. White, 1997, "Measuring Benefits of Homeowning: Effects on Children," Journal of Urban Economics, 41:441-61.

⁷C.A.L. Hilber and C. J. Mayer, 2002, "Why do Households without Children Support Local Public Schools? Linking House Price Capitalization to School Spending," The Wharton School Working Paper, June 2002.

⁸G.C. Galster, 1983, "Empirical Evidence on Cross-Tenure Differences in Home Maintenance and Conditions," Land Economics 59:107-13.

⁹The FHA also allows a pre-foreclosure sale in which the FHA will cover the losses on the sale - a loss mitigation option for borrowers who do not wish to (or for some reason cannot) keep the home.

¹⁰In a "partial claim" the FHA will pay all the arrearages to the loan servicer and take on a soft second mortgage lien on the property to get paid back if and when the property is sold.

¹¹Low-valued properties are defined as those properties in the bottom quartile of any given market.