the impact of secondary mortgage market and gse purchases on

Impact of Secondary Market and GSEs on Underserved Neighborhoods, January, 2003

1

The Impact of Secondary Mortgage Market and GSE Purchases

on Underserved Neighborhood Housing Markets: A Cleveland Case Study

Lance Freeman,* George Galster,** and Ron Malega***

January, 2003

This research is supported by grant H-21303RG from the U.S. Department of Housing and Urban Development. The views expressed in this report are the authors’, and do not necessarily reflect those of the U.S. Department of Housing and Urban Development or our respective universities. The authors gratefully acknowledge information provided by David Fynn, Isaac Megbolugbe and Jay Schultz. The helpful comments of Stanley Longhofer, participants in: the Fisher Real Estate Research Seminar, Haas School of Business, University of California-Berkeley, a seminar group at HUD, the 2003 AREUEA Annual Meetings, and three anonymous, HUD-sponsored reviewers are also acknowledged with appreciation. * Assistant Professor, Graduate School of Architecture, Planning, and Preservation Columbia University, New York, NY 10027 ** Hilberry Professor of Urban Affairs, College of Urban, Labor, and Metropolitan Affairs Wayne State University, Detroit, MI 48202 ***Doctoral Candidate, Department of Geography, University of Georgia, Athens, GA 30602


2

The Impact of Secondary Mortgage Market and GSE Purchases on Underserved Neighborhood Housing Markets:

A Cleveland Case Study

Abstract Since 1992 the federal government has established regulations encouraging the Government-Sponsored Enterprises (GSEs) Fannie Mae and Freddie Mac to purchase home mortgages originated in neighborhoods traditionally underserved by financial institutions, with the aim of stimulating housing market activity there. This research tests this proposition empirically, based on an econometric analysis of single-family home sales volumes and prices in underserved census tracts in Cleveland, OH during the 1993-1999 period. Our analysis reveals a positive relationship between home transaction activity and the actions of the secondary mortgage market. Based on our robustness tests, we are most confident in concluding that secondary mortgage market (and the non-GSE sector in particular) purchases of mortgages have a positive effect on the number of sales transactions one year later. We observe no consistent impact of purchasing rates on sales prices, although non-GSE purchases of non-home purchase mortgages appear to boost prices one and two years later. There is no robust evidence however, that GSE purchasing rates are positively associated with single-family home transactions volumes or sales prices during any periods.


3

The Impact of Secondary Mortgage Market and GSE Purchases on Underserved Neighborhood Housing Markets:

A Cleveland Case Study

Lance Freeman and George Galster, Co-Principal Investigators Introduction

Credit is the lifeblood of neighborhoods, providing the resources to upgrade and

prosper, or, in its absence, decline and decay. That credit is important to the health of neighborhoods has long been recognized among students of neighborhood change. It was not until the late 1960s and early 1970s, however, that policy makers began taking steps to insure that credit was accessible to all without regard to race or geographic location. Prodded by community activists who saw a lack of access to credit as a major cause of decline in their neighborhoods, policymakers have enacted a series of policies designed to increase the flow of credit into low income and minority communities. These include the Equal Credit Opportunity Act of 1974, which forbade racial or ethnic discrimination in any aspect of a credit transaction, the Home Mortgage Disclosure Act of 1975 and subsequent amendments, which required lenders to provide demographic data on their loans, and the Community Reinvestment Act of 1977, which obligated lenders to meet the needs of low and moderate income neighbors (Yinger 1995). With the reorganization of the financial services industry during the 1980s, the secondary mortgage market came to play an increasingly important role in the allocation of housing credit through the increasing volume of mortgages they purchased and by raising capital by issuing mortgage backed securities (Macdonald, 1996). Indeed, many primary market financial institutions have essentially become mortgage processors who originate and service mortgages, while the secondary mortgage market assumes the risk. As a result, the underwriting standards of the secondary market have become increasingly important determinants of access to credit in all communities, but especially in low income and minority communities. “If we can’t sell it, we can’t make it” has become the guiding principle of many primary market mortgage lenders (U.S. Congress, Senate 1991, 114).

Cognizant of the role the secondary market plays in determining access to credit, the policies of the Government-Sponsored Enterprises (GSEs) Fannie Mae and Freddie Mac have come under increased scrutiny as possible obstacles to increasing credit for low income and minority communities (Temkin, Quercia, Galster, and O’Leary, 1998). The GSEs’ quasi-governmental status makes them a natural focus of government policy aimed at influencing the impact of the secondary mortgage market on low-income and minority neighborhoods. Moreover, the substantial benefits that the GSEs receive from the federal government creates an obligation on their part to serve the public interest. Increasingly, this public interest has come to include making credit more accessible to low income and minority communities.

The increasing focus on the GSEs making credit accessible to low income and minority communities culminated in the Federal Housing Enterprises Financial Safety and Soundness Act (FHEFSSA) of 1992. The FHEFSSA granted the HUD Secretary authority to establish and enforce goals for GSE purchases of mortgages on homes purchased by low- and moderate-income households, housing located in central cities, rural areas, and other “underserved areas,” and “special affordable housing” meeting the needs of families earning less than 60% of the area median income. Interim purchasing


4

goals were established for 1993 and 1994, with final rules written in the fall of 1995 (U.S. Dept. of Housing and Urban Development, 1995).

Implicit in all of the policies designed to increase credit to low income and minority neighborhoods is the notion that not only is this the equitable thing to do, but the end result will be healthier neighborhoods and less urban decline. Although this appears to be a reasonable assumption, until now it was untested. As the literature review in the next section makes clear, there is scant empirical evidence on the nature of the relationship between the secondary mortgage market components-- the GSEs (Fannie Mae and Freddie Mac) or and other non-GSE institutions-- and housing market activity in disadvantaged neighborhoods. The research reported here attempts to fill this gap by developing a theoretical framework for viewing the relationship between housing market conditions in underserved neighborhoods and the secondary mortgage market and empirically testing this relationship. Given the focus of the policy reforms on underserved groups (both households and areas) we focus our attention on underserved neighborhoods. For this study we rely on the federal definition of an “underserved” metropolitan neighborhood: a census tract with median family incomes less than or equal to 90 percent of the area median income, or tracts having 30 percent or more non-white population with median family income less than or equal to 120 percent of the area median income ( Code of Federal Regulations [Title 24, Volume 1] [Revised as of April 1, 2001] From the U.S. Government Printing Office via GPO Access [CITE: 24CFR81]). The GSEs and Underserved Neighborhoods

With one notable exception (Thibodeaux and Ambrose 2002), the literature on the secondary mortgage market in general and GSEs has focused on how well the GSEs are meeting the goals set forth under the FHEFSSA, and how the GSEs compare to other secondary market players and primary market institutions in terms of serving underserved neighborhoods and populations. The literature has consistently found that the GSEs mortgage purchases to underserved neighborhoods and populations have been increasing, and are meeting the FHEFSSA targets (Bunce 2002; Manchester 2002; McClure 1999; Neal and Bunce 1998; Williams 1999; RFGA, p. 24.; Department of Housing and Urban Development, 2000). A general consensus also appears to be emerging from the second strand of GSE-related research that looks at how well the GSE’s efforts to serve underserved markets compares to other actors in the secondary mortgage market. The findings from this body of literature have typically found the GSEs to trail behind the rest of the secondary market in terms of serving the underserved markets (Bunce 2002; Bunce and Scheessele 1996; Freddie Mac 1998; Lind 1996; Manchester 1998; Manchester, Neal and Bunce 1998; McClure 1999; Williams 1999; Van Order 1996).

Canner and Passmore (1995) and Canner, Passmore, and Surette’s (1996) found depository institutions to serve FHA-eligible borrowers to a larger degree than either Fannie Mae or Freddie Mac. The authors suggest this is because of the GSEs’ lack of knowledge about local neighborhoods, especially when compared to depository institutions. Two recent qualitative studies based on the opinions and perceptions of knowledgeable informants in the home lending industry echo Canner et al.’s speculation on the causes of GSE’s “lagging” performance and sheds some light on why the GSEs tend to “follow” the market when it comes to serving underserved areas (Temkin et al. 1998, 2001; Fosberg (2001). As a result, the GSEs have minimal opportunity to underwrite mortgages without using formulaic guidelines. The literature thus paints a picture of the GSEs increasing their purchases from underserved areas in both absolute and relative terms. Although the consensus


5

emanating from the literature is that the GSEs do not serve underserved markets as well as other financial institutions in the primary or secondary sectors, the gap between them and the rest of the industry in serving underserved markets is narrowing (Manchester, Neal and Bunce 1998; McClure 1999; Williams 1999). What remains less clear, however, is what impacts, if any, are the GSEs’ targeting efforts having on the revitalization and or upgrading of underserved neighborhoods. To our knowledge, only one study has attempted some answers.

Thibodeaux and Ambrose (2002) found that borrowing costs were lower in MSAs where GSEs purchased a higher proportion of conventional loans,1 and overall mortgage lending volumes were higher when GSEs purchased more seasoned loans that had been held some years in primary lenders’ portfolios. Moreover, they found that the gap in ownership rates between low- and high-income households dropped in MSAs where GSEs expanded their purchases more strongly, though in the national sample they appeared to have no impact on the growth of low-income homeownership rates 1991-1997.

Our study asks a different set of questions than those posed by Thibodeaux and Ambrose (2002). We focus on underserved neighborhoods, not low-income households, and ask whether greater GSE purchasing rates (as well as those of other secondary market institutions) in underserved census tracts are associated with greater home sales volumes and price levels, and whether GSE or non-GSE purchases seem to have greater impacts. Unlike Thibodeaux and Ambrose’s cross-MSA perspective, our unit of analysis is the census tract observed annually over a seven-year period in the 1990s in a particular city.

The Relationship Between Secondary Mortgage Market Purchase Activity and Underserved Neighborhood Housing Markets: A Conceptual Model

The central theoretical contribution of our research is its formulation of secondary mortgage market impacts in the context of a structural model of housing market/mortgage market interactions occurring at the neighborhood level. In this section we present the model, which provides the foundation for our empirical work delineated in the next sections. In this research we are interested in two key indicators of neighborhood housing market impact: the number of single-family home sales (presumably intended for owner-occupancy) and the prices associated with these sales. The best way to understand how these indicators may be causally connected to the mortgage purchasing activities of the secondary market is to draw upon economic models of housing and mortgage markets.2 Specifically, the neighborhood impact of secondary mortgage market activities in general, and those by the GSEs in particular, can be elucidated by the specification of neighborhood-specific owner-occupied housing stock supply, owner-occupied housing stock demand, and mortgage supply functions, which are indexed for a particular neighborhood j during a particular year t. Using these functions we develop a model of home sales transactions, which sets the stage for our econometric specifications. 1 They authors acknowledge, however, that this may merely be an artifact insofar as GSEs must purchase “conforming” mortgages under a maximum principal amount, and there is a market-wide interest rate spread between conforming and larger, “jumbo” loans. 2 Our model draws heavily on those of Blackley and Follain (1991), Rothenberg, Galster, Butler, and Pitkin (1991), and Galster (1998).


6

Demand for Owner-Occupied Housing in the Neighborhood The neighborhood demand function we focus on in our work is the function showing the stock of single-family, owner-occupied units that households (both those who are currently homeowners in the neighborhoods and others who may become so) would be willing and able to occupy during year t in neighborhood j (HDEMAND jt), at alternative prices per unit of housing “quantity” implicit in the homes (PRICEjt). “Quantity” in this context refers to the multidimensional bundle of attributes comprising the housing package: structure, lot, neighborhood, public services/taxes, natural environment, amenities, etc. The same housing bundle can be priced differently over time and, due to spatial segmentation into housing submarkets,3 across space as well. Not only does the price per unit of housing quantity potentially vary across neighborhoods, but so, too, does the average quantity embodied in the neighborhoods’ housing packages. This quantity is often referred to in the housing economics literature as “hedonic value.” We focus on the purchase of single-family homes for owner-occupancy because this is the segment of the mortgage market where most GSE purchases occur. This is not to suggest that other sectors of the housing market may not also be important for underserved neighborhoods. However, in Cleveland where we operationalize our model, single-family units represent nearly half of the total housing stock in underserved neighborhoods. Our neighborhood owner-occupied stock demand function represents the aggregation of individual households’ decisions regarding four interrelated issues: (1) how much housing (quantity or hedonic value) to consume, (2) where (in what neighborhood) to consume it, (3) whether to rent or own it, and (4) whether (and how often) to move. We believe that most households make these decisions in an interactive, quasi-simultaneous fashion. For illustration: ? Tenure – neighborhood – hedonic value joint selection: If low economic status

constrains a household to a set of “affordable” neighborhoods, but in all these there are many social problems and concomitant expectations of property value deflation, there will be little motivation to buy. Conversely if a household is willing and able to buy, certain neighborhoods may not be selected if they hold the prospect for little property appreciation. Similarly, if the quantity of housing in these neighborhoods were far from the optimal bundle configuration sought by the household, such locations would be less likely to be chosen.

? Tenure - neighborhood - mobility joint selection: If a household expects to remain

long in a home, given its employment and life-cycle stage situation, it may be more likely to bear the high transactions costs of buying, and it will try harder to avoid weak/declining neighborhoods. Conversely, if a household can purchase a preferred home, and succeed in doing so in a good neighborhood, it will probably move less in the future.

3 See Rothenberg et al. (1991)


7

The empirical modeling of housing tenure choice and mobility as a joint decision has become quite conventional; see Zorn (1988) and Ioannides and Kan (1996) for seminal work. Similarly, modeling tenure choice jointly with expected future mobility has been undertaken for some time; see Boehm (1981), Ioannides (1987) and Rosenthal (1988). In the most recent and ambitious work of this genre, Kan (2000) models three simultaneous equations predicting: current year’s tenure choice, current year’s mobility choice, and expected future mobility behavior. Only one extant work has modeled the joint tenure/neighborhood choice process: Deng, Ross, and Wachter (2000). It is beyond the scope of our study to review this literature in detail, but it does provide guidance for the sorts of variables that should appear in our aggregate neighborhood homeownership demand equation. Demanders choose neighborhoods not only on the basis of getting more quantity of housing per dollar (PRICEjt), but also whether the total package of hedonic value matches their preferences and ability to pay. Thus, demand for owner-occupied housing (both for homeowners currently living there and potential ones) will depend on the average characteristics of single-family homes in the neighborhood as well as characteristics of the neighborhood itself: QUANTITYjt.4 A priori, one cannot predict how QUANTITY will affect demand for a stock of owner-occupied housing in a neighborhood. It will be contingent upon changes in the number of households in the metropolitan area that have the income and preferences for which the given value of QUANTITY approximates their optimum consumption pattern. Of course, economic and demographic characteristics of the metropolitan area’s population not only will influence how the market evaluates the given neighborhood’s bundle of attributes, but also will influence the demand for homeownership in general across all neighborhoods. These characteristics would include: the average household size, household distribution across stages in the life-cycle, and income and wealth distributions (Goodman, 1990; Megbolugbe and Cho, 1993). Of course, all neighborhoods within a particular metropolitan area will face these same parameters during a given period, so they can be summarized in terms of inter-temporal variation only, [YEARt]. One other demographic variable does vary across neighborhoods, however, and is likely inversely related to ownership demand: the percentage of neighborhood households in various racial-ethnic minority groups (%MINORITYjt).5 Given the racial-ethnic steering and other potential discriminatory barriers to minority homeownership erected by housing agents, minority-occupied neighborhoods may be expected to evince an attenuated flow of prospective purchasers, all else equal. The same relationship would be manifested to the extent that all prospective homebuyers perceive that neighborhoods with a larger proportion of minority households are less likely to appreciate in value (Ellen, 2000).6 Prospective homeowners, as explained above, will be interested in locating neighborhoods with better prospects for property appreciation, as this effectively reduces

4 For details on the procedures for operationalizing PRICE and QUANTITY, see Appendix 1. 5 Hispanics may define themselves as either white or nonwhite. Note that in our study site, Cleveland, there are only trivial numbers of other racial-ethnic groups besides Hispanics, non-Hispanic whites and non-Hispanic blacks. 6 We recognize that the inclusion of racial composition and expectations as distinct explanatory variables is implicitly assuming that real estate markets are not completely efficient, inasmuch as these factors are not already perfectly capitalized in PRICE.


8

their user cost of capital (Follain and Ling, 1988).7 We therefore include in the demand model the assessments of future home appreciation that current prospective homeowners would make for the neighborhood, EXPECTATIONS jt. Most importantly for the current research, demand for a stock of single-family, owner-occupied dwellings must include a measure of the degree to which prospective buyers into a particular neighborhood are constrained by: inability to acquire the minimum downpayment, personal credit blemishes, unstable incomes, and/or potentially by illegal practices of mortgage lenders that ration credit on the basis of applicant race or the racial or economic composition of neighborhoods. The net results of this interplay of factors could be thought of as mortgage supply: the willingness of primary lenders to approve mortgage loan applications from a given profile of applicants trying to purchase homes in the given neighborhood (LSUPPLYjt). It is through loan supply that secondary mortgage market activity creates a potential impact on neighborhoods, as we will amplify below. In summary, the demand for single-family, owner-occupied housing stock for a particular neighborhood j in a particular period t can be expressed symbolically: (1) HDEMANDjt = c + a PRICEjt + b QUANTITYjt + [d] [YEARt] + f %MINORITYjt +

g EXPECTATIONS jt + h LSUPPLYjt + ? where ? is a random error term whose statistical properties will be discussed further below. Supply of Owner-Occupied Housing in the Neighborhood The neighborhood supply function shows the stock of dwelling units that current owners of single-family homes would wish to have occupied by owners (perhaps themselves) during year t in neighborhood j (HSUPPLYjt), at alternative prices per unit of housing “quantity” implicit in the homes being offered to the homeowner market (PRICEjt). The actors currently constituting this supply function are both those who own single-family, renter-occupied units and homeowners. The owner-occupied stock supply function can thus be thought of as embodying both the willingness of absentee landlords to sell properties to prospective owner-occupants and current homeowners either to remain as owners of the unit they occupy or to sell to another and move out. In specifying a owner-occupied stock supply function, one must distinguish among time frames implicit in various sorts of supply (Rothenberg et al., 1991). A short-run or market period involves a period so short that the only supply choice is whether owners of existing units will offer them for sale. The long run involves a period long enough that the housing stock can adjust completely to shifts in consumer demands, via new construction and conversion of existing units. Such a long-run supply function appears to be perfectly elastic (Blackley and Follain, 1991). Inasmuch as this implies the potential replacement or modification of most of the standing stock—a long period indeed and one not likely to be manifested often when we observe a location—it seems most reasonable to specify an intermediate period owner-occupied stock supply equation. This equation allows for some relationship between quantities offered for owner occupancy and price, but fundamentally

7 Property taxes and cross-tenure differences in price per unit of hedonic value may also affect tenure choice. Inasmuch as our empirical work confines itself to one municipality there is no variation in property tax rates. Unfortunately, we were unable to estimate the latter factor on a neighborhood-by-neighborhood basis.


9

is under girded by the current stock of single-family units (STOCK jt) and their costs of operation (COSTjt), as explained below. Owners of existing single-family units in the neighborhood will offer them to the homeownership market if PRICE bid by demanders exceeds their reservation prices. Reservation prices will vary across neighborhoods and time according to the out-of-pocket costs of holding/maintaining homes and the opportunity costs of rental property (COSTjt).8 Current owners should be less likely to offer their property to the market if they anticipate a rapid inflation of values of single-family dwellings in the future (EXPECTATIONS jt). In summary, the stock supply function by providers of single-family, owner-occupied housing in a neighborhood j during period t, is expressed: (2) HSUPPLYjt = c’ + m PRICEjt + n STOCKjt + p COSTjt + q EXPECTATIONS jt + ? The Supply of Mortgage Loans to the Neighborhood As explained by Megbolugbe and Cho (1993), the total value of mortgages supplied (originated) in a geographic area (LSUPPLYjt ) results from the aggregation of individual underwriting decisions to accept or reject applications and aspects of the offer (involving fees and charges) and potential counteroffers of mortgage terms. Given an exogenously determined interest rate for a given mortgage product (IRATEt), the underwriting decision is made on the basis of assessed risk of the applicant (credit worthiness), pre-payment risk (related to future changes in interest rates), and risk of the property (future collateral value). Underserved neighborhoods may be distinguished in these regards both by having applicants typically evincing higher-than-average risk profiles and properties that may be more risky because of their price volatility. Let the credit risk characteristics of the pool of applicants for home purchase mortgages in a given neighborhood be labeled [APPLICANTSjt]. The property risk in the neighborhood is related to the current and future expected collateral value of single-family properties, modeled by (PRICEjt*QUANTITYjt) and EXPECTATIONS jt, respectively, as defined above. Let cross-sectional variations in prepayment risk be measured implicitly by a vector of tract fixed-effect dummy variables ([TRACTj]). Of central importance for the current research is how the secondary mortgage market may influence mortgage supply in the primary market in underserved neighborhoods. The conceptual model for making this link was originally developed by Van Order (1996) and expanded by Thibodeaux and Ambrose (2002). We provide a heuristic summary of its main facets and further adapt it to the context of underserved neighborhoods and HUD’s GSE purchasing goals. In principle, the secondary mortgage market provides a mechanism for transferring credit from the financial markets to the primary mortgage market. When such credit is provided to the primary lender it permits additional lending without additional deposits of capital into the lender. The Van Order-Thibodeaux-Ambrose formulation shows how this process can potentially benefit underserved neighborhood markets even under the 8 Owner-occupants will probably evince higher reservation prices the higher the degree they are socially attached to the neighborhood, schools, etc. and find the hedonic value of their dwelling close to what they assess as their optimum consumption bundle. Absentee owners of single-family rental dwellings will perceive opportunity costs influenced by the present discounted values of the stream of net revenues accruing if the property were to be maintained as a rental facility.


10

extreme situation where lenders had a preference not to lend there and the secondary market only bought mortgages originated in non-underserved areas. The model starts with the following assumptions about the primary market: (1) two groups of borrowers with equal credit worthiness, one (U) applying primarily for loans on properties in underserved neighborhoods, the other (non-U) in different neighborhoods; (2) all primary lenders are competitive but prefer to lend to the non-U group for reasons unrelated to default risk; (3) each group has its own mortgage supply and demand curves, and the supply to each is a function of interest rates in both own and substitute sectors; and (4) loans from each sector are imperfect substitutes. Because of (2), equilibrium results in a higher effective cost of borrowing (interest rate and/or underwriting requirements) for U. Now even if a secondary market were introduced that bought only mortgages originated in the non-U sector, the model predicts that the U sector would ultimately have reduced costs of borrowing. As loan supply in non-U increased, it would drive down interest rates in the sector. As lenders sought superior rates of return they would switch some lending to the U sector, eventually driving down interest rates there. If, instead, the secondary market were also to start buying loans originated in the U sector (as might result from HUD’s affordable housing goals for the GSEs), even a greater reduction in borrowing costs for the underserved sector would transpire. These lower costs (greater loan supply) mean that potential bidders on properties in underserved neighborhoods who previously would have been unwilling or unable to obtain a mortgage can now obtain one after the introduction of a secondary market, especially if it was required to purchase loans originated in U. In turn, this should result in stronger housing demand (via equation 1), more sales transactions and higher prices (explained below), and an increase in the number of mortgages issued, precisely the sorts of impacts we test for in underserved neighborhoods. These prospective benefits of secondary market purchases of mortgages in underserved neighborhoods are premised on two key assumptions, however. First, the primary lending market is competitive. Second, both sectors have equal default risk profiles. If these assumptions were relaxed, the implications for underserved neighborhoods are not nearly so salutary. As noted by Thibodeaux and Ambrose (2002), the competitiveness of the local primary mortgage market will be directly related to the degree to which borrowing cost savings associated with strong secondary market activity will be transferred to borrowers. In particular, if the claims of many community activists who allege redlining are valid, the market may indeed be less competitive in underserved neighborhoods. Moreover, the primary and secondary market may well assess prospective mortgages on homes in underserved areas as systematically more risky (due either to the weaker credit worthiness of the applicants or greater price volatility there) and the secondary market may price mortgage purchases accordingly. In this case the imposition of HUD underserved neighborhood mortgage purchasing regulations would increase the aggregate risk profile of conventional mortgages. Insofar as this risk is primarily born by private mortgage insurers, an increase in PMI premiums would follow. As a consequence, the impact might be to raise borrowing costs for all underserved neighborhoods, though likely only in the long run. Findings by Thibodeaux and Ambrose (2002) provide some support for this caveat.


11

Yet another complication should be noted about the potential impacts of the secondary market on underserved neighborhoods: an intra-secondary market substitution effect. As will be emphasized below, the secondary market has several important participants besides the GSEs. This raises the possibility that HUD purchasing requirements for the GSEs may merely result in the same supply of mortgages, but a substitution from non-GSE to GSE purchasers. Now, insofar as this substitution may also be associated with a different, superior mortgage product being originated (e.g., a conventional instead of a FHA or a sub-prime loan), there may be some benefit to the neighborhood. However, such benefits will be unlikely to be manifested in the form of increased transactions or home prices. The aforementioned three points being noted, our point is that it is not at all clear theoretically that enhanced activity by the secondary mortgage market, either GSE or non-GSE sectors, will have a significantly beneficial impact on local housing markets in underserved neighborhoods. The underlying rationale for HUD’s GSE purchasing rules thus needs to be tested empirically. As a final point in the discussion of primary-secondary market interactions, we note the element of uncertainty and expectations. A primary lender will be more likely to originate any given loan with given terms if they can pass on its risks to the secondary market at an acceptable price. In many circumstances the primary lender can be virtually certain that a prospective loan to the applicant in question can be readily sold in the secondary market because it clearly meets all underwriting criteria. But in other cases, such as more idiosyncratic characteristics of the property or applicant, or marginal credit rating scores for the applicant, the expectation is probably less clear-cut. Perhaps the prospective loan will need to be seasoned in portfolio before it can be sold. We believe that a primary lender’s expectations about the eventual probability of purchase of a prospective loan by the secondary market will be related to the latter’s recent past performance. As perceived by primary lenders, the probability of the secondary market buying a mortgage originated in neighborhood j during year t can thus be specified as a lag function of the secondary market’s recent past purchases in neighborhood j.9 Past GSE and non-GSE performance (in, for example, year t-1) can be expressed as the percentage of the value of all home mortgage loans originated in the neighborhood that were purchased by these institutions, either separately (GSE$jt-1, NON-GSE$jt-1 ) or combined (TOTAL$jt-1 ). In summary, the aggregate dollar supply of home mortgages in neighborhood j during year t is expressed: (3) LSUPPLYjt = c’’ + s IRATEt + u (PRICEjt*QUANTITYjt) + [v] [APPLICANTSjt] + + [r] [TRACTj] + w TOTAL$jt + w’ TOTAL$jt-1 + w’’ TOTAL$jt-2 + ? Or, alternatively, if source of secondary market purchasers matters to primary lenders: (3’) LSUPPLYjt = c’’ + s IRATEt + u (PRICEjt*QUANTITYjt) + [v] [APPLICANTSjt] + + [r] [TRACTj]+ w GSE$jt + w’ GSE$jt-1 +w’’ GSE$jt-2 + z NON-GSE$jt + z’ NON-GSE$jt-1 + z’’ NON-GSE$jt-2 +?

9 Given the relatively limited period of GSE data availability since the FHEFSSA (1993-1999), we are only able to test for a two-year lag effect.


12

Owner-Occupancy Sales Transactions One key indicator of how a neighborhood housing market is performing is how many homes sales transactions are taking place during a given period.10 This is especially true in underserved neighborhoods where, in some cases, there historically have been so few transactions that no viable market is deemed to exist.

We model transactions as a function of the difference between single-family homeowner stock demand and supply measured at current supplier offer prices. How stock demand and supply interact to generate sales transactions can be explained with the help of Figure 1, which portrays a hypothetical situation during time t in a particular neighborhood j that is assumed to have single-family dwellings of similar hedonic value (quantity of attributes). The two alternative single-family, owner-occupancy stock demand functions portrayed, D1 and D2, embody the considerations summarized in equation (1) above. The single-family owner-occupancy stock supply function suggests that no dwellings will be offered for sale at prices below PRICE0, the lowest reservation price of any owner in neighborhood j. Progressively more homes will be offered for sale into owner-occupancy as demanders’ bid prices rise, until all extant single-family homes have become owner-occupied at QT. Consider first the case of a strong demand for owner-occupancy in j. Suppose that QC represents the current stock of single-family, owner-occupied homes in the neighborhood. The number of sales transactions will depend on the relationship between the stock demanded and the current stock supplied (QC) at the suppliers’ current offer price (PC). In the case of D2, the stock demanded is Q*, given current offer price (PC). This difference will trigger sales (by bidding up prices above PC) that increase the stock of single-family homes that are owner-occupied to Q*, designated by the intersection of the stock demand and supply functions.11 As transactions occur, in-moving owner-occupants buy some single-family homes that previously were owner-occupied, and others that were previously renter-occupied, producing a net increase in owner-occupancy to an equilibrium, Q*. An equilibrium between single-family owner-occupied stock demand and supply does not imply zero transactions subsequently. In virtually all neighborhoods there is an “ambient” level of transactions that will occur due to non-market forces, such as morbidity or mortality of owners, or owners being transferred to jobs in other cities. However, we would expect more observed cross sectional variations in transactions to be associated with adjustments spawned by disequilibrium situations like that portrayed above. In the case of D1, a weak demand for single-family, owner-occupied stock, the stock demanded Q1 is less than the current stock QC at offer price PC. Now, current owners will not be induced to sell by the market; rather, they will try to sell only as other, more personalized reasons ensue, as noted above. There thus should be observed only a low, ambient level of transactions. Some current homeowners offering their units to the market (those with the lowest reservation prices) may be able to find buyers who will

10 We recognize that transactions in and of themselves may not be an indicator of a healthy neighborhood housing market, such as in the case of panic sales engendered by fears of racial tipping. 11 Equivalently, at QC the willingness of demanders to bid for units (P2 in Figure 1) far exceeds the price at which current owners will offer their units for sale (PC), thereby inducing them to sell.


13

keep the property in owner-occupancy. But most will not, because bid price P1 is less than offer price PC at QC (equivalently, QC > Q1 at PC). As these sales from homeowners to absentee owners ensue (and offer prices drop), the stock of single-family, owner-occupied homes will eventually decline to a new equilibrium, Q**. Of course, it is possible that demand is so low that the desired stock for owner-occupancy falls near zero, regardless of offer price (i.e., no demander is willing to bid P0). In such extreme circumstances, the homeownership market ceases to exist in the neighborhood. The upshot of the foregoing is that the number of single-family homeownership sale transactions in neighborhood j during time t can be expressed: (4) #SALESjt = c’’’ + k ( QDjt - QSjt ) Substituting (1) for QD, (2) for QS, and (3) into (1) and simplifying, yields the transactions equation:12 (5) #SALESjt = ? + k([a-m] PRICEjt + b QUANTITYjt + [d] [YEARt] + f %MINORITYjt + [g-q] EXPECTATIONS jt + hs IRATEt + hu (PRICEjt*QUANTITYjt) + h[v] [APPLICANTSjt] + h[r] [TRACTj] + hw TOTAL$jt + hw’ TOTAL$jt-1 +hw’’ TOTAL$jt-2 - n STOCKjt - p COSTjt )+ ? where ? = [c - c’ + hc’’ + c’’’] and TOTAL$ may also be replaced by GSE$, NON-GSE$ to distinguish the sector of the secondary market. Note that PRICE in (5) is not endogenous, or simultaneously determined with #SALES, as (5) should not be confused with a stock demand or supply equation, but rather as a model of a disequilibrium adjustment process. Sales Prices of Owner-Occupied Homes The second key indicator of how a neighborhood housing market is performing is the sales prices of homes transacting during a given period. Higher prices not only signal increases in wealth for the homeowner, they also have been shown to capitalize a wide variety of neighborhood externalities (Grieson and White, 1989; Palmquist, 1992). Thus, higher home prices may not merely signal pure inflation but also real improvements in the overall quality of the environs. Although in principle prices could be modeled in the aggregate for the neighborhood as a whole, we think a more precise model can be developed by exploiting information on individual home sales and their associated characteristics. Our approach involves a conventional “hedonic value” regression, wherein the (natural logarithm of) market sales value of the ith single-family home sold in neighborhood j during year t (VALUE jit) is expressed as a function of its myriad individual structural characteristics and neighborhood attributes, as explained above ([QUANTITYjit]). Of central interest to this research, however, is the addition of secondary mortgage market activity to this hedonic value equation. The hedonic price function has been described as a joint envelop of offer price functions by owners and bid price functions by potential buyers (Rosen, 1974). Of these two factors, Muellbauer (1974) argues that the latter dominates in second-hand durable goods markets where 12 In preliminary trials the semi-log form of the equation performed better than the linear one.


14

aggregate supply is fairly stable and particular supplies are held in a deconcentrated fashion, such as housing. As such, it follows that the buyers’ bid price function underlying the hedonic equation (i.e., owner-occupied stock demand) should be boosted in neighborhoods receiving more supply of mortgage loans, as per (1). Mortgage supply, in turn, should be boosted by GSE and other secondary mortgage market purchasing activity, as per (3). Expressed differently in terms of Figure 1, at any given current quantity of single-family, owner-occupied stock in the neighborhood (QC), the willingness and ability to bid for units should be enhanced the greater the supply of mortgage loans. Any observed sales transaction thus should evince a higher price per unit of housing quantity (PRICEjit) or, equivalently, a higher overall market value (VALUE jit ) when ([QUANTITYjit]) is held constant. Symbolically: (6) ln(VALUE jit) = c + [a] [QUANTITYjit ] + d [YEAR-QUARTERt] + f [TRACTj] + b TOTAL$jt + b’ TOTAL$jt-1 +b’’ TOTAL$jt-2 + ? where TOTAL$ may also be replaced by GSE$, NON-GSE$, and [YEAR-QUARTERt], denoting both calendar year and quarter, serves as a proxy for unmeasured variations in the regional economy, consumer preferences or expectations, and seasonality. As with (5), the sign, size, and statistical significance of the coefficients of the TOTAL$, GSE$ and NON-GSE$ variables provide the test measure of secondary mortgage market impact on neighborhood housing markets. Potential Indirect Neighborhood Effects of Secondary Market Activities From the foregoing theoretical exposition, it should be clear that the main hypothesized neighborhood impact of secondary mortgage market loan purchases is through its effect on the supply of mortgages intended to purchase single-family homes. This, in turn, is theorized to affect the demand for the stock of single-family, owner-occupied units in the neighborhood during the period. More aggressive mortgage purchasing activities by GSEs and non-GSE players in a neighborhood, therefore, would be predicted to increase both the volume of single-family home sales for owner occupancy, and the market values of those homes.

There may be a further consequence as well. Intensified purchases by the secondary market may boost the overall homeownership rate in a neighborhood, which may itself provide a series of positive, neighborhood-wide externalities (such as improved home maintenance, enhanced social control of public spaces and youth activities, etc.) that will be reflected in an additional fillip to single-family home prices. The secondary market also purchases home improvement loans as well. Should intensified purchases succeed in raising the supply of such lending in underserved neighborhoods, additional spillovers may occur through the enhancements of the exterior conditions of the homes. For this reason we also investigate the impact of the secondary market’s purchasing of all loans, not merely those intended for home purchase.


15

Data, Study Site, and Variable Specifications The core of our analysis involves operationalizing equations (6) and (7). We estimate variants that consider secondary mortgage market purchases as a whole, and distinguished by GSE and non-GSE institution. We also test for the robustness of results by employing two alternative data sources for measuring GSE purchases and by considering purchases of all mortgage loans and just home purchase mortgages. We then supplement the core analysis by exploring the extent to which the secondary market is leading or lagging the primary mortgage market in responding to or influencing neighborhood changes. For our empirical work we specified “neighborhood” as the census tract. All the analyses will be conducted on “underserved” neighborhoods and the single-family home sales occurring within them in the City of Cleveland, OH. These areas are identified using the standard criteria defined by HUD, as available on HUD’s website: http://www.huduser.org/datasets/gse.html. Cleveland is uniquely appropriate for our study because it has: (1) a large set (200) of underserved neighborhoods that vary widely in demographic composition (white, black, Hispanic); (2) high-quality, vendor-ready data on single-family property transfers; and (3) an unusually rich set of tract-level, annually updated administrative data that will be critical in several facets of the modeling. Data Sources

We obtained data for operationalizing (5) and (6) primarily from the following sources:

? Administrative records from the City of Cleveland, contained in the Urban Institute’s

National Neighborhood Indicators Project data base [1993-1999 data] ? HUD’s Public Use Data Base of GSE activity, PUDB [1993-1999 data] ? Home Mortgage Disclosure Act (HMDA) Public Use Data [1993-1999 data] ? Single-Family Property Transfer Records (vendor-supplied) [1989-1999 data] ? 1990 and 2000 Census of Population and Housing: STF-3 (Cleveland Tracts) Administrative data from the City of Cleveland were obtained from the Urban Institute through its National Neighborhood Indicators Project.13 This unusual database assembles demographic, economic, crime, and housing data tabulated at the census tract level by several administrative agencies and combines them into a consistent series for the period 1993-1999. Indicators from this database that we employed in our hedonic value equation included: % births of low-weight babies, % birth mothers who are not married, birthrate of women under age 20, % parcels that are non-residential, % residential and commercial parcels that are vacant, % parcels tax delinquent, % of non-residential parcels, % of parcels occupied by single-family dwellings, % commercial properties that are vacant, % of residential properties that are vacant, property crime rate, violent crime rate, and welfare receipt rate.

13 Thanks to Peter Tatian, Jennifer Johnson, and Chris Hayes of the Urban Institute for their help in obtaining the data.


16

HUD’s PUDB provides detailed information on the borrowers and census tract locations of properties backing GSE loan purchases. The GSEs have provided HUD with loan-level data on each of their mortgage transactions since the beginning of 1993. From this database, HUD has extracted a PUDB for each calendar year from 1993 to 1999. We employed the single-family loan record file, which we aggregated to the census tract level to provide measures of GSE loan purchase activity, undifferentiated by the type of mortgage. HMDA public use datasets were used to obtain information about the characteristics of loan applicants, primary market dispositions, non-GSE secondary market purchases, and as an alternative source of information about GSE purchases.14 HMDA records allow differentiating between home purchase loans and other types of loans that were originated and purchased during that calendar year. HMDA loan level records were aggregated to the census tract level. In this paper we focus on secondary market purchases of home purchase mortgages, and thus must rely on HMDA data by default. We also assess how robust results are to using information on all loans purchased, which is available from both PUDB and HMDA. Thus, the comparative advantages of and distinctions between PUDB- and HMDA-based information about GSE activity are important to elucidate. HMDA files show for each mortgage loan originated whether it was purchased by one of the secondary market institutions during that same year. This overlooks, therefore, loans that were purchased at a later date, perhaps after they were “seasoned” in portfolio. The GSE variable operationalization in this case is the percentage (based on dollar volume) of single-family mortgage loans (either for home purchase or all types, depending on variant) originated for homes in the census tract that were purchased by GSEs during the same year. HMDA files also have been criticized for overlooking significant numbers of originations and inaccurately reporting sales of mortgages (Bunce and Scheessele, 1996; Fannie Mae, 2000). PUDB, on the other hand, shows the total number of loans purchased by the secondary market during each year, regardless of when originated, but does not distinguish between home purchase loans and other types. The operationalization in this case for GSE is the ratio (based on dollar volume) of GSE-purchased mortgage loans during the given year for single-family homes in the given census tract to all single-family mortgage loans originated in the census tract (according to HMDA data) that year. We recognize that using HMDA data may cause us to underestimate GSE activity. Bunce and Scheessele (1996) and Williams (1999) found substantively similar results when comparing HMDA-reported and GSE-based loan purchase patterns, however. We follow this lead and conduct parallel analyses for both PUDB- and HMDA-based information on GSE activity for our robustness tests. Patterns of secondary market activity in the 200 underserved census tracts in Cleveland during the period 1993-1999 are portrayed in Figure 2. For sake of comparison we show the ratio of all home mortgages purchased by GSEs to those originated (both expressed as values of loans) during the given calendar year as measured both by the PUDB and HMDA data. The GSE purchasing rates track similarly with PUDB and HMDA data; regardless of data source they are substantially below those of non-GSE institutions. For example, based on PUDB data the GSE purchasing

14 Thanks to Jay Schultz of HUD for help in obtaining the PUDB and HUD data for Cleveland. In the HMDA data we counted originations as any loan application register flagged with a “1” action code (“originated”). If all such originations had an indicator in the action code “6” field (purchased by”) we flagged it as purchased.


17

rate in 1999 was 9.3% whereas the purchasing rate for non-GSE institutions was 45.3%. Note that the share of mortgages backed by Ginnie Mae, another federally sponsored financial organization dealing exclusively with federally insured FHA and VA mortgages, constituted a minor share of the non-GSE market in Cleveland.15

Also noteworthy are the overall trends in purchasing rates by the GSEs. Although the 1990s were a period that witnessed significant initiatives by the GSEs to increase their purchases of mortgages from underserved markets, these efforts have not translated into an increasing share of all mortgage loans originated in underserved Cleveland neighborhoods being purchased by the GSEs. The overall trend does not suggest an increase in the proportion of mortgages being purchased by the GSEs. In contrast, except for a dip between 1995-1996, the trend of the overall purchasing rate by non-GSE institutions is strongly upward.

This upward trend in the proportion of mortgages being purchased by non-GSE

institutions may reflect changing perceptions of the underserved market by financial institutions, including non-GSE secondary mortgage market purchasers. Others have documented the upward trend in originations in underserved markets during the 1990s due, in part, to the prodding resulting from a strengthened CRA (Evanoff and Segal (1996). But while lenders may have originally viewed serving the underserved market as an obligation, the increasing competitiveness of the financial services market in conjunction with the relatively robust growth of the underserved market perhaps has switched the perception to that of an opportunity. Indeed, qualitative research undertaken by scholars suggests that the perceived profitability of previously underserved markets has sustained forays by financial institutions into serving low income and minority populations (Listokin and Wyly, 2000). Although much of this evidence is related to the primary mortgage market, it seems plausible that these perceptions would have changed at the secondary level as well, hence the dramatic increase in secondary mortgage market purchases in underserved Cleveland neighborhoods during the 1990s.

We caution that these patterns evinced in Cleveland may not be general across other cities. For example, comparison to national statistics suggests that the GSE purchasing shares in underserved neighborhoods in Cleveland are only roughly one-third those in other MSAs (Pearce, 2001: exhibit 14; Thibodeaux and Ambrose, 2002: table 3).

The most complete source of home sales data available is the property tax rolls maintained by local property tax assessment offices. We employed the property tax roll records for the City of Cleveland provided by the private data vendor Experian. The Experian data contain all of the information available from the tax rolls on the property itself (including address, number of rooms, square footage, and type of construction), as well as the dates and amounts of the last two sales for each property.16 Files were

15 A potential reason why other financial institutions purchase so many mortgages from underserved neighborhoods is that their federal regulators give credit towards their Community Reinvestment Act ratings should they do so. 16 The tax roll data may not be sufficient to obtain a complete sales history for each property, however. If a property was sold more than two times during the period of interest, then the sales record will not be complete, as only the two most recent sales will be recorded. Therefore, these tax roll data were supplemented with a sales history data file, also obtained from Experian, which had a listing of the dates and amounts of every sale of the properties in the city, though no property characteristics. This sales history file


18

geocoded to match street addresses with latitude and longitude coordinates and Census tract identifiers. From the final set of sales data, we selected only sales of single-family homes. To help ensure that we were only dealing with homes conveyed in arms-length transactions, we eliminated all sales under $2,000. Patterns of single-family home transactions and values for underserved census tracts in Cleveland are presented in Figure 3. Both volume of sales and their average prices rose consis tently and sharply throughout the 1993-1999 period.17 Constant-quantity home prices rose 50 percent and the sales volume more than doubled during the six-year period. Nevertheless, mean values of single-family homes in our study area were less than $40,000 in 1999, thus analytical complications created by non-conforming loan limits and jumbo loans are moot here. The degree to which the enhanced secondary market activity portrayed in Figure 2 contributed to this clear market surge is the subject of our investigation that follows. Census tract data for 1990 and 2000 were used in preliminary procedures for developing PRICE and QUANTITY variables; see Appendix 1. Their use in the estimation of (5) and (6) was to provide measures of racial composition of census tracts 1993-1999. These measures employed straight-line interpolation of 1990 and 2000 statistics. Variable Specifications Consider first how we operationalize the elements in transactions volume equation (5): ? #SALES: We measure directly the sales of single-family homes occurring during

a year in each underserved census tract in our sample, using local property tax assessors/deed records offices data collected by Experian. No readily available data indicate whether a given sale is intended for owner-occupancy, however.18

? PRICE: The price per unit of housing quantity for single-family homes selling in the neighborhood is estimated by aggregating predicted values from a preliminary hedonic regression. This preliminary hedonic regression estimates its parameters from a sample of single-family home sales in underserved neighborhoods before the study period (1989-1991). Coefficients of this preliminary regression were used to construct a cross-neighborhood, cross-time, constant-quantity price index, following Galster (1998). For details on the procedures for operationalizing PRICE, see Appendix 1. Note that the construction of PRICE is equivalent to creating an instrumental variable, so the risk of any simultaneity bias in its coefficient is minimized.

? QUANTITY: There are no readily available data on the characteristics of the pool of structures in the neighborhood during a given period that may be offered for sale to owner-occupants. The best proxy is to measure the average characteristics of the single-family homes that actually sold during the entire study period (the 1990s), such as square footage of structure and yard, age of unit, numbers of bathrooms,

permitted the creation of a complete record of sales back to 1989. 17 Constant-quantity home prices were computed from the coefficients of year dummy variables in a preliminary estimate of equation (6). 18 Similarly, these data typically do not allow one to ascertain whether purchases in multiple-unit structures involve owner-occupancy. Thus, we focus on single-family home sales, assuming that such structure types have the highest average propensity for use by owner-occupiers.


19

etc.19 These characteristics are available from the same Experian-supplied database providing sales prices. Attributes of the neighborhood that contribute to hedonic quality are readily available from the 1990 Census, and for 1993-1999 from administrative database. For details on the procedures for operationalizing QUANTITY, see Appendix 1.

? EXPECTATIONS: We model the average of the annual changes in PRICE in the neighborhood in the preceding two years as a proxy for the current expectations of future home appreciation held by both prospective homebuyers and current property owners.

? [YEAR]: One can typically obtain only sketchy inter-census information on the multiple demographic and economic indicators of metro area conditions that may affect the regional housing market as a whole. Thus, we operationalize this element in summary form as a series of dummy variables denoting calendar year.

? %MINORITY: The racial-ethnic composition of the neighborhood is measured by 1990-2000 interpolated census tract information on percentage of population that is: non-Hispanic black, Hispanic, and all others but non-Hispanic whites.

? IRATE: Published annual average mortgage interest rates for 30-year fixed mortgages are available (Joint Center for Housing Studies, 2000: A4) to measure this variable directly. However, it proved too highly collinear with the [YEAR] variables to be employed in the final model.

? [APPLICANTS]: We use the following HMDA-based characteristics for mortgage applicants aggregated to tracts: % female, % black, % Asian and other non-white, % Hispanic, % who ultimately were rejected with “poor credit history” as the reason entered.

? STOCK: the number of single-family homes in the neighborhood during each year of the study period is measured directly with Cleveland administrative data.

? COST: The out-of-pocket and opportunity costs of supplying existing single-family properties for homeownership can be measured by a variety of utility, building cost, and rental price indices, but all proved so highly collinear with each other and [YEAR] that they all were subsumed under [YEAR] in the final equation. The only aspect of cost that we could measure with distinct cross-tract variability was property crime rates, which serves as a proxy for owners’ costs of holding/maintaining homes associated with damage due to burglary, arson, and vandalism.

? [TRACT]: A set of 199 dummy variables was specified, measuring the time-invariant idiosyncrasies of each underserved census tract in Cleveland (less one).

? GSE$: The share of value of home mortgages purchased by GSEs is measured in alternative forms from HMDA and PUDB, as explained above. Given the relatively constrained period of analysis (1993-1999), we experiment only with two lags in the GSE$ variable.

? NON-GSE$: The share of value of home mortgages purchased by other secondary market entities is measured directly from HMDA data, either for home purchase loans or all types. Contemporaneous, one-year, and two-year lagged values are employed.

? TOTAL$: GSE$ + NON-GSE$. Descriptive statistics of all variables are presented in Appendix 2.

19 We recognize that the homes sold may constitute a non-representative sample of the pool of homes potentially offered for sale.


20

Specification of Lag Effects In our modeling we experimented with three versions of the secondary market purchasing rate variables: contemporaneous, one-year lag and two-year lag. Our rationale for doing so arises from two sources: the nature of how primary lenders form expectations about the likelihood of selling a prospective loan and the nature of how extra liquidity generated by a loan purchase is lent out subsequently over time. The former was addressed already: purchasing rates evinced in the past in a tract may serve as a basis for lenders’ making assessments about the future prospects for selling (and hence their current assessment of risk of) a loan being currently considered. The latter relates to the time path of lending marginal increases in liquidity generated when a loan is purchased by the secondary market at time t. This may be portrayed by a hazard-like function showing how much of the marginal liquidity has, in fact, been lent for various times subsequent to t. We do not know the nature of this function, but if sizeable shares of the marginal liquidity remain to be lent as additional mortgages many months after t, impacts on home transaction volumes or sales prices might well be measured with a lag. Econometric Issues Equation (5) is estimated for a sample of 200 underserved neighborhoods (census tracts) within Cleveland, each of which (having complete information) is observed annually for the five-year period 1995-1999. Because the estimation sample varies both cross-sectionally and over time, econometric procedures appropriate for pooled samples were employed to obtain robust standard errors (Kmenta, 1986: 616-625). The specification of tract fixed effects with the [TRACT] dummies not only serves as a way to measure unobserved variables but also a means of correcting for any heteroskedasticity and serial correlation associated with a combined cross-sectional/panel dataset such as ours (Hsiao, 1986: 29-32).

The sales value equation (6) includes variables to control for spatial heterogeneity.20 Spatial heterogeneity, sometimes known as spatial submarket segmentation, refers to the systematic variation in the behavior of a given process across space. Here, the issue is whether the parameters of the equation are invariant across space or whether they assume different values according to the local socioeconomic, demographic, and/or physical contexts of the various neighborhoods across a metropolitan area. If such were the case, the error term ? would be heteroskedastic. To deal with this issue we employed the “spatial contextual expansion with quadratic trend” specification as suggested by Can (1997). This method involves adding to the models the latitude (X) and longitude (Y) coordinates of each observation in the following variables (adjusted so that zero values represent the center of the city): X, Y, XY, X2, and Y2. Higher numerical values of X (Y) signify increasing distance from the center of the city heading west (north).

20 We could not apply this technique in the case of the transactions equation because the latitude and longitude coordinates of the centroid of each tract are collinear with the tract fixed-effect dummy variables. Our previous work with this sort of price equation suggests that a spatial lag variable is both computationally burdensome and adds little explanatory power, so we do not use it here (Galster et al, 1999).


21

Econometric Results Consequences of Secondary Market Purchases of Home Purchase Loans The estimated parameters for the transaction volume equation based on HMDA data for home purchase loans are presented in Table 1. They indicate that single-family home sales were greater in Cleveland underserved census tracts having higher average hedonic quality single-family homes, a higher percentage of black applicants for mortgages, and later in the decade. The number of home sales were lower in census tracts with higher percentages of Asian, Hispanic, or female mortgage applicants and applications denied because of bad credit. The parameters estimated for the price equation based on HMDA data for home purchase loans are presented in Table 2 and demonstrate the usual relationships. Single-family home prices were higher in units with more bathrooms, a garage, larger interior and yard spaces (but at diminishing returns), more stories, and were built more recently. They were lower if the census tract had more births to unmarried mothers, more violent crimes, and a larger percentage of the population receiving welfare. Of more import to our study, however, are findings related to purchasing rates of mortgages originated for the purpose of buying a home. In general, the results support the notion that the secondary mortgage market as a whole plays some role in affecting the trajectory of underserved neighborhood housing markets, but there is little evidence that the GSEs in particular are crucial to these impacts. Consider the results displayed in Table 1, which reports the parameters of the purchasing rates of GSE and non-GSE sectors separately (columns four and five) and the combined secondary market (columns two and three) for home purchase loans. The results in columns two and three show that an underserved Cleveland tract with a ten percentage point higher rate of secondary mortgage market purchases of the home purchase loans originated there is predicted to have 2.6 percent more transactions one year later, ceteris paribus. When we examine the effects of the GSEs and non-GSEs separately in columns four and five, however, it appears that the non-GSEs are solely responsible for the impact described above. In contrast, the GSE purchasing rate evinces no statistically significant relationship to the number of transactions for any of the lags modeled, though large coefficients are estimated for one- and two-year lags that are significant at the 10 percent level (one-tailed test). The results of the price equations presented in Table 2, however, find no evidence of a relationship between the purchasing rate of the secondary mortgage market as a whole for mortgages intended for home purchase, or the GSEs and non-GSEs separately, and the price of homes sold in underserved Cleveland tracts. None of our coefficients representing secondary mortgage market purchasing rates are statistically significant. Robustness of Results We tested the robustness of our results above in several ways. First, we defined key variables differently, specifying secondary market purchase ratios in terms of numbers of loans instead of their aggregate values, and an alternative home price index. Second, we tested alternative functional forms and additional controls. Third, we estimated both fixed-effects and random-effects models. Finally, we considered all mortgage purchases, not only home purchase loans. Consider each one of these robustness checks.


22

Using Alternative Variable Specifications. The results proved virtually identical when secondary market purchasing rates were specified using numbers of loans instead of their aggregate dollar values. The same was true when we employed an alternative specification for the PRICE variable in the transactions equation; for details see Appendix 1. Functional Form Tests. We estimated trial linear regressions for both transactions and home values, though they generally had modestly less explanatory power and were theoretically less appealing than the semi-log forms presented here. The results for the secondary market purchasing variables in the linear regressions generally were less statistically significant. Random Effects Models. The results reported above were also estimated using a random effects approach. Although the Hausman test suggested that there were significant differences between the coefficients of the random effects and fixed effects models, substantively, the results of interest were virtually the same. That is, there was little difference in the magnitude or direction of the relationships between the number of sales or sales prices and secondary mortgage market purchasing rates, whether combined or estimated separately by sector. Using PUDB and HMDA Data on All Mortgages. Though secondary market purchases of home mortgages intended for home purchase seem to us the operationalization most consistent with our theory, we also experimented with measures based on all mortgages, undifferentiated by type. After all, the purchase of any mortgage, whether originated for home purchase or not, provides additional liquidity to the lender who might potentially use it for subsequent home purchase loans. Appendix Tables A1 and A2 present our estimated transaction equations of the relationship between the secondary mortgage market purchasing rate for all mortgage types collectively, as measured alternatively by PUDB and HMDA data, respectively, and single-family home sales transactions the same year and one and two years later. The results for the one-year lag variables are robust, but for other periods appear quite different when more than home purchase mortgages are considered. In particular, there often appears (especially when HMDA data are used) a statistically significant and large negative relationship between combined, GSE, and non-GSE purchasing rates and transactions measured during the same year. Moreover, when PUDB information is used there appears a statistically significant positive relationship between combined or GSE purchases of all types of mortgages and transactions two years hence. When all three periods’ coefficients are considered as a group, they tend to cancel each other out, with the contemporaneous negative coefficient being offset by the positive ones in the following two periods, though whether the net impact is positive or negative is sensitive to which database is used and whether GSE or non-GSE purchases are considered. For either data source, the net impact of non-GSE purchases after two years appears more positive than those of the GSEs, consistent with findings using only home purchase mortgages. At first glance, the evidence that GSE and non-GSE purchasing rates for all mortgages are associated with lower transactions volume contemporaneously appears counter-intuitive. We suspect that this result is spurious, though we cannot be definitive. One possible explanation could be that the secondary mortgage market purchasing


23

rates of non-home purchase mortgages were spuriously associated with residential stability intentions by current homeowners in a way that is not fully captured by our model. We would expect that homeowners who intended to stay for a considerable period would be more likely to obtain home improvement and refinance mortgages, and the secondary sector in Cleveland underserved neighborhoods purchased a slightly higher rate of these mortgages than those intended for home purchase, on average. . A further reason for this anomalous finding may be that the “home improvement” component of our “all loans” measure is notoriously ambiguous, in some cases recording unsecured loans issued in the names of financial institutions though builders’ supply firms.21 Appendix Tables A3 and A4 present alternative estimates of our home price equations, using purchases of all mortgage types as measured by PUDB and HMDA, respectively. Only the insignificant contemporaneous relationships are robust across all data sources and types of mortgages. Unlike the situation when only home purchase mortgages are considered, higher purchasing rates of all mortgages by the non-GSE sector is associated with higher home prices one and two years hence (the latter persisting in the combined purchasing model), regardless of which data source is employed. By contrast, higher purchasing rates of all mortgages by the GSEs either have no relationship with prices or, in the one case when purchases are measured with PUDB, a negative relationship with prices one year later. Again, we have no convincing explanation for this anomalous result.

Conclusions and Caveats Our econometric analysis of the housing market in underserved census tracts in Cleveland during the 1990s reveals a positive relationship between home sales transaction activity and the actions of the secondary mortgage market. Based on our robustness tests, we are most confident in supporting the hypothesis that secondary mortgage market (and the non-GSE sector in particular) purchases of mortgages have a positive effect on the number of transactions one year later. We observe no consistent impact of purchasing rates on sales prices, although non-GSE purchases of non-home purchase mortgages appear to boost prices one and two years later. There is no robust evidence however, that GSE purchasing rates are positively associated with transactions volumes or sales prices during any periods. The findings thus provide qualified support our model of local homeownership demand developed above. This model posited that homeownership demand depended upon the supply of mortgages, which, in turn, depended upon the ability (liquidity) and willingness (the perceived risk of the loan as adjusted by the prospects of selling it in the secondary mortgage market) of primary lenders. Our findings are consistent with the notion that more aggressive secondary market purchasing behavior by non-GSE entities can stimulate sales volumes and (in the case of mortgages not used for home purchase) prices of homes in low-income and predominantly minority-occupied neighborhoods in the inner city.

But two caveats are in order. First, the estimates of the implied impacts were made in a city undergoing substantial economic expansion and generalized home price

21 We thank Stanley Longhofer for this information.


24

inflation during the period, yet remained a low-cost housing market. We have no evidence about whether the impact of secondary market activity would be similar in neighborhoods that are otherwise in decline or in the broader context of a city-wide recession. Second, although the relationships were not manifested consistently for GSE purchasing activity, GSEs represented an unusually small share of the secondary market in Cleveland underserved neighborhoods.

We suspect that the greater magnitude and consistency of the non-GSE impacts

is due to two factors. First, GSEs’ charters and underwriting standards constrain them from purchasing many mortgage types, particularly those associated with innovative “affordable homeownership” programs, which multiplied during the 1990s (Pearce, 2001) and may have been primarily responsible for the upsurge in housing market activity observed. These new credit products have been developed by federal, state and local governments, private non-profit entities, and primary lenders themselves, perhaps in response to intensified CRA pressures. We suspect that the purchase of such unconventional mortgage products constituted a significant share of non-GSE purchasing in Cleveland’s underserved neighborhoods, though we have no way of ascertaining this from available data.22 If true, this would readily explain why the purchases of such mortgages by non-GSE entities proved such an apparent stimulus to home sales transactions in underserved neighborhoods on the margin.

Second, the non-GSEs clearly purchased a much larger portion of all mortgages

in Cleveland’s underserved neighborhoods during the 1990s (see Figure 2). To the extent that lending to the underserved market is especially sensitive to the signals given by the secondary mortgage market, it is the non-GSE institutions in Cleveland that appear to be the major players whose actions would more strongly shape the expectations of primary lenders. As suggested earlier, cities in which the GSEs are larger players may perform differently.

22 Several key informants have told us that, indeed, CRA provides a powerful motive for private financial institutions to purchase and hold mortgages from underserved areas, as reflected by premiums on mortgage-backed securities based on CRA loans.


25

REFERENCES Blackley, D.M. and J.R. Follain. 1991. An Econometric Model of the Metropolitan Housing

Market, Journal of Housing Economics 1:140-67. Boehm, T. P. 1981. Tenure Choice and Expected Mobility: A Synthesis. Journal

of Urban Economics 10: 375-389. Bunce, H. L. 2002. The GSEs’ Funding of Affordable Loans: A 2000 Update. U.S.

Department of Housing and Urban Development, Office of Policy Development and Research, Housing Finance Working Paper Series, Working Paper No. HF-013 (April).

Bunce, H. L. and R.M. Scheessele. 1996. The GSEs’ Funding of Affordable Loans.

U.S. Department of Housing and Urban Development, Office of Policy Development and Research, Housing Finance Working Paper Series, Working Paper No. HF-001 (December).

Burnett, K. and L. B. Fosburg. 2001. Study of Multifamily Underwriting and the GSEs’ Role in the Multifamily Market. Washington, DC: Abt Associates, Inc. report to U.S. Department of Housing and Urban Development, Aug.

Canner, G. B. and W. Passmore. 1995. Credit Risk and the Provision of Mortgages to

Lower-Income and Minority Homebuyers. Federal Reserve Bulletin, 81(November): 989-1016.

Canner, G. B., W. Passmore and B. J. Surette. 1996. Distribution of Credit Risk among

Providers of Mortgages to Lower-Income and Minority Homebuyers. Federal Reserve Bulletin, 82(December): 1077-1102.

Can, A. 1997. Spatial segmentation in urban house prices: Alternative approaches.

Unpublished working paper of the Policy, Research, Evaluation, and Training Division, Fannie Mae Foundation, Washington, D.C.

Deng, Y., S. L. Ross, and S. M. Wachter 2000. “Racial Differences

in Homeownership: The Effect of Residential Location.” Paper presented at the American Economics Association meetings, Boston, Jan.

Ellen, I. G. 2000. Sharing America’s Neighborhoods . Cambridge, MA: Harvard University

Press. Evanoff, D. D. and L. Segal. 1996. CRA and Fair Lending Regulations:

Resulting Trends in Mortgage Lending. Economic Perspectives . 19-46. Follain, J. and D. C. Ling. 1988. Another Look at Tenure Choice, Inflation, and Taxes.

Journal of the American Real Estate and Urban Economics Association 16 (3): 207-229.

Fannie Mae. 200. Comment letter to the U.S. Department of Housing and Urban

Development (HUD) on HUD’s notice of proposed rulemaking on the affordable


26

housing goals for 2000 and beyond. <http://www.fanniemae.com/neighborhoods/comment_letter.html>

Freddie Mac. 1998. We Open Doors for America’s Families: Annual Housing Activities

Report. McLean, VA.: Freddie Mac. Galster, G. C. 1998. An Econometric Model of the Urban Opportunity Structure:

Cumulative Causation among City Markets, Social Problems, and Underserved Areas. Washington DC: Fannie Mae Foundation.

. 1996. Future Directions in Mortgage Discrimination Research and Enforcement. In

John Goering and Ron Wienk (eds.) Mortgage Lending, Racial Discrimination, and Federal Policy. Washington, DC: Urban Institute Press.

Galster, G. C., Santiago, A. M., Smith, Robin and Tatian, Peter A. 1999. Assessing property value impacts of dispersed housing subsidy programs. (Urban Institute Report). Washington, DC: U.S. Department of Housing and Urban Development.

Goodman, A. C. 1990. Demographics of Individual Housing Demand. Regional Science

and Urban Economics 20:83-102. Granger, C. W. L. 1969. Investigating Causal Relationships by Econometric Models and

Cross-Sectional Methods. Econometrica 37: 424-438. Grieson, R. and J. White. 1989. The Existence and Capitalization of Neighborhood

Externalities, Journal of Urban Economics 25: 68-76. Halvorsen. R., and R. Palmquist. 1980. The Interpretation of Dummy Variables in

Semilogarithmic Equations, American Economic Review 70 (3) :474-475. Horne, D. 1994. Evaluating the Role of Race in Mortgage Lending. FDIC Banking Review

7:1-15. Hsiao, C. 1986. Analysis of Panel Data. Cambridge, UK: Cambridge University

Press.

Ioannides, Y. M. 1987. Residential Mobility and Housing Tenure Choice, Regional Science and Urban Economics 17: 265-287.

Ioannides, Y. M. and K. Kan 1996. Structural Estimation of Residential

Mobility and Housing Tenure Choice, Journal of Regional Science 36 (3): 335- 363.

Joint Center for Housing Studies. 2000. The State of the Nation’s Housing 2000.

Cambridge, MA: author. Kershaw, S. 2002. Failed Mortgages Soar in New York. New York Times , May 27: A1,

C1. Kmenta, J. 1986. Elements of Econometrics. 2nd ed. New York: Macmillan.


27

Lind, J. E. 1996. Community Reinvestment and Equal Credit Opportunity

Performance of Fannie and Freddie Mac from the 1994 HMDA Data, San Francisco: CANICCOR, (February 12).

Listokin, D., and E. K. Wyly. 2000. Making New Mortgage Markets: Case Studies

of Institutions, Home Buyers, and Communities. Housing Policy Debate 11(3):575-644.

MacDonald, H. 1996. Expanding Access to the Secondary Mortgage markets: The Role of Central City Lending Goals. Growth and Change 27:298-312.

Manchester, P. B. 1998. Characteristics of Mortgages Purchased by Fannie Mae and Freddie Mac: 1996-97 Update. Washington, DC: Office of Policy Development and Research, U.S. Department of Housing and Urban Development, Housing Finance Working Paper No. HF-006.

________. 2002. Goal Performance and Characterisitcs of Mortgages Purchased by

Fannie Mae and Freddie Mac, 1998-2000. U.S. Department of Housing and Urban Development, Office of Policy Development and Research, Housing Finance Working Paper Series, Working Paper No. HF-015 (May).

Manchester, P.B., S. G. Neal, and H.L. Bunce. 1998. Characteristics of

Mortgages Purchased by Fannie Mae and Freddie Mac, 1993-95. Department of Housing and Urban Development, Office of Policy Development and Research, Housing Finance Working Paper Series, Working Paper No. HF-003, (March).

McClure, K. 1999. The Twin Mandates Given to the GSEs: Which Works Best, Helping

Low Income Homebuyers or Helping Underserved Areas? Lawrence, KA: Graduate Program in Urban Planning, University of Kansas.

Megbolugbe, I., and M. Cho. 1993. An Empirical Analysis of Metropolitan Housing and

Mortgage Markets. Journal of Housing Research (4)2:191-224. Muellbauer, J. 1974. Household Production Theory, Quality, and the ‘Hedonic

Technique.’ American Economic Review 64: 977-994. Munnell, A. H., L. E. Browne, J. McEneaney, and G.M.B. Tootell. 1996. Mortgage

Lending in Boston: Interpreting HMDA Data. American Economic Review 86(1):25-53.

Palmquist, R. 1992. Valuing Localized Externalities, Journal of Urban Economics 31: 59-

68. Pearce, J. E. 2001. Fannie Mae and Freddie Mac Mortgage Purchases in Low-Income

and High-Minority Neighborhoods, 1994-96. Citiscape 5: 265-316. Pindyck, R., and D. Rubinfeld. 1981. Econometric Models and Economic Forecasts, 2nd

ed. New York: McGraw-Hill.


28

Pogge, J. and D. Flax-Hatch. 1991. The Invisible Lenders: The Role of Residential Credit in Community Economies. In Philip W. Nyden and Wim Wiewel (eds.) Challenging Uneven Development: An Urban Agenda for the 1990s . New Brunswick, NJ: Rutgers University Press.

Rosen, S. 1974. Hedonic Prices and Implicit Markets. Journal of Political Economy 82:

34-55. Rosenthal, S. S. 1988. A Residence Time Model of Housing Markets, Journal of

Public Economics 36: 87-109. Ross, S. L., and J. Yinger. 1999. Other Evidence of Discrimination: Recent Studies of

Redlining and Discrimination in Loan Approval and Loan Terms. In Turner, Margery Austin and Felicity Skidmore, eds. 1999. Mortgage Lending Discrimination: A Review of Existing Evidence. Washington, DC: Urban Institute Press.

Rothenberg, J. G. C. Galster, R. V. Butler, and J. Pitkin. 1991. The Maze of Urban

Housing Markets: Theory, Evidence, and Policy. Chicago: The University of Chicago Press.

Schill, M. H., and S. M. Wachter. 1993. A Tale of Two Cities: Racial and Ethnic

Geographic Disparities in Home Mortgage Lending in Boston and Philadelphia. Journal of Housing Research 4(2):245-275.

Temkin, K., R. Quercia, and G. Galster. 2001. The impact of secondary

mortgage market guidelines on affordable and fair lending: A reconnaissance from the front lines. Review of Black Political Economy 28(2) pp.29-52.

Temkin, K., R. Quercia, G. Galster, and S. O’Leary. 1998. A Study of the GSEs’ Single

Family Underwriting Guidelines. Urban Institute Report to U.S. Department of Housing and Urban Development.

Thibodeaux, T. and B. Ambrose. 2002. An Analysis of the Effects of the GSE

Affordable Goals on Low- and Moderate-Income Families, Washington, DC: U.S. Department of Housing and Urban Development, Office of Policy Development and Research (May).

Turner, M. A. and F. Skidmore, eds. 1999. Mortgage Lending Discrimination: A Review of

Existing Evidence. Washington, DC: Urban Institute Press. U.S. Congress. House of Representatives. Subcommittee on Housing and Community

Development, Committee on Banking, Finance and Urban Affairs. 1991. Treasury, CBO and GAO reports on FNMA, FHLMC and the Federal Home Loan Bank system 102nd Congress, 1st Session, 15 and 29 May.


29

U.S. Department of Housing and Urban Development, Office of Policy Development and Research. 1995. Economic Analysis for the Secretary of HUD’s Regulation of the Federal National Mortgage Association and the Federal Home Loan Mortgage Corporation. Washington, DC.

Van Order, R. 1996. Discrimination and the Secondary Mortgage Market. Pp. 335-363

in J. Goering and R. Wienk (eds.) Mortgage Lending, Racial Discrimination, and Federal Policy. Washington, DC: Urban Institute Press.

White, H. A heteroscedasticity-consistent covariance matrix estimator and a direct test

for heteroscedasticity. Econometrica 48:817-830. Williams, R. 1999. The Effect of GSEs, CRA, and Institutional Characteristics on Home

Mortgage Lending to Underserved Markets . Washington DC: U.S. Department of Housing and Urban Development.

Yezer, A., R.F. Phillips, and R. Trost. 1994. Bias in Estimates of Discrimination and

Default in Mortgage Lending: The Effects of Simultaneity and Self-Selection. Journal of Real Estate Finance and Economics 9(3): 197-215.

Yinger, J. 1996. Discrimination in Mortgage Lending: A Literature Review. In J.

Goering and R. Wienk (eds.) Mortgage Lending, Racial Discrimination, and Federal Policy. Washington, DC: Urban Institute Press.

. 1995. Closed Doors, Opportunities Lost: The Continuing Costs of Housing Discrimination. New York: Russell Sage Foundation. Zorn, P. M. 1988. An Analysis of Household Mobility and Tenure Choice, Journal of

Urban Economics 24: 113-128.


30

Table 1 Regression Results for Home Sales Transactions Equation: Combined, GSE and Non-GSE Secondary Market Purchases of Home Purchase Mortgages [GSE and non-GSE purchases measured from HMDA data] Dependent Variable: Ln (# single-family dwelling sales in tract during year) Variables Coefficient t-statistic Coefficient t-statistic Intercept 1.514 2.76** 1.496 2.72** Price per unit of quantity 0.003 0.81 0.003 0.81 Number of single family homes 3.14E-04 0.12 2.04E-04 0.08 Expectations: ave. home inflation prior 2 years 0.220 1.43 0.223 1.44 Ave. Hedonic Value 1.93E-05 2.68** 1.87E-05 2.59** Ave. Hedonic Value*Price per unit of quantity -1.94E -07 -1.79 -1.85E -07 -1.70 Percent of Tract Black -0.242 -0.28 -0.203 -0.23 Percent of Tract Hispanic -2.764 -1.07 -2.764 1.07 Percent of mortgage applicants Asian or Other -1.564 -5.05** -1.546 -4.87** Percent of mortgage applications denied: bad credit -0.280 -1.12 -0.280 -1.12 Percent of mortgage applicants Black 0.591 3.39** 0.622 3.52** Percent of mortgage applicants Hispanic -0.714 -2.76** -0.705 -2.72** Percent of mortgage applicants Female -3.032 -4.76** -3.056 -4.79** Total property crime per 100,000 Popn -6.35E -06 -0.36 -1.01E -05 -0.55 Year -1996 0.054 0.83 0.060 0.92 Year -1997 0.268 2.16* 0.268 2.14* Year -1998 0.535 3.15** 0.529 3.09** Year -1999 0.580 2.27* 0.570 2.21* HMDA_Combined Purchases t 0.075 0.76 NA NA HMDA_Combined Purchases t-1 0.258 2.62** NA NA HMDA_Combined Purchases t-2 0.060 0.68 NA NA HMDA_GSE Purchases t NA NA 0.085 0.41 HMDA_GSE Purchases t-1 NA NA 0.304 1.42 HMDA_GSE Purchases t-2 NA NA 0.285 1.49 HMDA_Non-GSE Purchases t NA NA 0.076 0.73 HMDA_Non-GSE Purchases t-1 NA NA 0.263 2.55** HMDA_Non-GSE Purchases t-2 NA NA 0.030 0.33 Adjusted R-sq. 0.843 0.843 F-statistic (DF = 186, 596 / 189, 593) 23.62** 23.21** * = statistically significant at 5% level, one-tailed test if expected sign (two-tailed test otherwise) ** = statistically significant at 1% level, one-tailed test if expected sign (two-tailed test otherwise) All above regressions include tract fixed effect dummies; results not shown.


31

Table 2 Regression Results for Home Sales Price Equation: Combined, GSE and Non-GSE Secondary Market Purchases of Home Purchase Mortgages [GSE and non-GSE purchases measured from HMDA data] Dependent Variable: Ln (individual single-family dwelling sales price) Variables Coefficient t-statistic Coefficient t-statistic Intercept 7.667 3.92** 7.861 4.01** Number of Baths / Number of Beds -0.006 -0.13 -0.006 -0.13 1.5 Baths - vs. 1 0.045 2.57** 0.045 2.57** 2+ Baths - vs. 1 0.017 0.66 0.017 0.67 Garage 0.168 14.32** 0.168 14.30** Building 1 Story - vs. more -0.055 -5.00** -0.054 -4.98** Built 1900 - 1919 (vs. pre-1900) 0.120 6.41** 0.120 6.41** Built 1920 - 1939 (vs. pre-1900) 0.224 10.60** 0.224 10.61** Built 1940 -1949 (vs. pre-1900) 0.350 13.20** 0.350 13.21** Built 1950 - 1959 (vs. pre-1900) 0.337 12.59** 0.337 12.60** Built 1960 - 1969 (vs. pre-1900) 0.472 13.04** 0.472 13.06** Built 1970 - 1979 (vs. pre-1900) 0.332 4.06** 0.331 4.05** Built 1980-1989 (vs. pre-1900) 0.670 5.88** 0.668 5.87** Built 1990 or later (vs. pre-1900) 0.995 22.85** 0.997 22.88** Lot Size - sq. ft. 2.17E-05 9.85** 2.16E-05 9.82** Square of Lot Size -1.01E -10 -8.59** -1.01E -10 -8.57** Lot Width - ft. 1.73E-04 2.15* 1.72E-04 2.14* Pool 0.090 0.71 0.092 0.72 Square feet / Number of Rooms 1.50E-04 0.90 1.49E-04 0.90 Square feet 3.98E-04 8.32** 3.99E-04 8.33** Square of Square Feet -2.47E -08 -2.21* -2.48E -08 -2.22* Latitude 3.108 2.45* 3.112 2.46* Longitude -1.965 -1.16 -2.027 -1.19 Latitude * Latitude 44.299 5.45** 44.313 5.45** Longitude * Longitude -2.934 -0.11 -2.775 -0.11 Latitude * Longitude -65.422 -3.16* -65.975 -3.19* Percent of Tract Hispanic 3.236 1.45 2.974 1.33 Percent of Tract Black 1.403 0.72 1.212 0.62 Percent of Tract White 1.191 0.62 1.006 0.52 % births that are low birth weight 0.002 1.46 0.002 1.56 % non-residential parcels -0.001 -0.07 -0.001 -0.06 Total property crime per 100,000 Popn 5.15E-08 0.01 -1.09E -06 0.15 Birth to unmarried mom/100 live births -1.46E -04 -1.58 -1.57E -04 -1.69* % all home single family 0.002 0.57 0.002 0.64 % all parcels tax delinquent -0.005 -1.17 -0.005 -1.24 Birth to teen/1000 teen females LE 19 yrs. 9.06E-05 0.58 1.06E-04 0.68 % all commercial parcels vacant 0.003 1.00 0.002 0.77 Total violent crimes per 100,000 Popn -3.46E -05 -1.69* -3.60E -05 -1.75*


32

% all residential parcels vacant 0.004 0.77 0.003 0.70 % of Popn receiving welfare -0.007 -2.98** -0.007 -2.84** Sale April - June 0.050 4.06** 0.050 4.04** Sale July - September 0.049 3.97** 0.049 3.95** Sale October - December 0.079 6.30** 0.079 6.27** Sale year 1996 0.038 2.11* 0.041 2.29* Sale year 1997 0.085 3.77** 0.090 3.94** Sale year 1998 0.074 2.34** 0.078 2.46** Sale year 1999 0.141 3.73** 0.146 3.84** HMDA_Combined Purchases t 0.010 0.26 NA NA HMDA_Combined Purchases t-1 0.027 0.68 NA NA HMDA_Combined Purchases t-2 0.015 0.42 NA NA HMDA_GSE Purchases t NA NA -0.078 -0.82 HMDA_GSE Purchases t-1 NA NA -0.075 -0.81 HMDA_GSE Purchases t-2 NA NA 0.067 0.83 HMDA_Non-GSE Purchases t NA NA 0.023 0.57 HMDA_Non-GSE Purchases t-1 NA NA 0.044 1.06 HMDA_Non-GSE Purchases t-2 NA NA 0.008 0.21 Adjusted R-sq. 0.396 0.396 F-statistic (DF = 215, 16255 / 218, 16255) 50.57** 49.89** * = statistically significant at 5% level, one-tailed test if expected sign (two-tailed test otherwise) ** = statistically significant at 1% level, one-tailed test if expected sign (two-tailed test otherwise) NA = Not Applicable All above regressions include tract fixed effect dummies; results not shown.


33

Table A1 Regression Results for Home Sales Transactions Equation: Combined, GSE and Non-GSE Secondary Market Purchases of All Mortgage Types [GSE mortgage purchases measured using PUDB, non-GSE purchases from HMDA data] Dependent Variable: Ln (# single-family dwelling sales in tract during year) Variables Coefficient t-statistic Coefficient t-statistic Intercept 1.639 2.91** 1.695 2.97** Price per unit of quantity 0.002 0.46 0.001 0.26 Number of single family homes -6.43E -06 -0.00 -5.95E -05 -0.02 Expectations: ave. home inflation prior 2 years 0.176 1.12 0.191 1.21 Ave. Hedonic Value 1.62E-05 2.25* 1.52E-05 2.08* Ave. Hedonic Value*Price per unit of quantity -1.87E -07 -1.73 -1.77E -07 -1.63 Percent of Tract Black 0.440 0.51 0.397 0.46 Percent of Tract Hispanic 0.950 0.35 0.794 0.29 Percent of mortgage applicants Asian or Other -1.073 -2.12* -1.123 -2.21* Percent of mortgage applications denied: bad credit -0.801 -2.17* -0.850 -2.27* Percent of mortgage applicants Black -0.004 -0.01 -0.007 -0.02 Percent of mortgage applicants Hispanic -0.760 -1.65 -0.765 -1.66 Percent of mortgage applicants Female -1.885 -3.38** -1.715 -2.99** Total property crime per 100,000 Popn 1.44E-05 0.86 1.44E-05 0.86 Year -1996 0.017 0.25 0.023 0.33 Year -1997 0.276 2.20* 0.291 2.30* Year -1998 0.575 3.31** 0.583 3.35** Year -1999 0.623 2.40** 0.659 2.52** HMDA_Combined Purchases t -0.295 -2.36* NA NA HMDA_Combined Purchases t-1 0.256 2.85** NA NA HMDA_Combined Purchases t-2 0.186 2.04* NA NA HMDA_GSE Purchases t NA NA -0.498 -1.74 HMDA_GSE Purchases t-1 NA NA 0.154 1.21 HMDA_GSE Purchases t-2 NA NA 0.269 1.99* HMDA_Non-GSE Purchases t NA NA -0.245 -1.74 HMDA_Non-GSE Purchases t-1 NA NA 0.350 2.67** HMDA_Non-GSE Purchases t-2 NA NA 0.096 0.77 Adjusted R-sq. 0.842 0.8423 F-statistic (DF = 198, 638 / 201, 635) 23.55** 23.21** * = statistically significant at 5% level, one-tailed test if expected sign (two-tailed test otherwise) ** = statistically significant at 1% level, one-tailed test if expected sign (two-tailed test otherwise) All above regressions include tract fixed effect dummies; results not shown.


34

Table A2 Regression Results for Home Sales Transactions Equation: Combined, GSE and Non-GSE Secondary Market Purchases of All Mortgage Types [GSE and non-GSE purchases measured from HMDA data] Dependent Variable: Ln (# single-family dwelling sales in tract during year) Variables Coefficient t-statistic Coefficient t-statistic Intercept 1.600 2.77** 1.658 2.87** Price per unit of quantity 0.003 0.80 0.002 0.49 Number of single family homes 1.32E-04 0.05 2.03E-04 0.07 Expectations: ave. home inflation prior 2 years 0.134 0.85 0.143 0.90 Ave. Hedonic Value 1.53E-05 2.10* 1.38E-05 1.87 Ave. Hedonic Value*Price per unit of quantity -1.88E -07 -1.72 -1.69E -07 -1.53 Percent of Tract Black 0.455 0.52 0.617 0.71 Percent of Tract Hispanic 0.485 0.18 0.694 0.25 Percent of mortgage applicants Asian or Other -0.864 -1.71 -0.831 -1.64 Percent of mortgage applications denied: bad credit -0.519 -1.43 -0.598 -1.62 Percent of mortgage applicants Black 0.068 0.25 0.010 0.03 Percent of mortgage applicants Hispanic -0.713 -1.54 -0.815 -1.74 Percent of mortgage applicants Female -2.153 -3.80** -2.011 -3.52** Total property crime per 100,000 Popn 1.28E-05 0.76 1.12E-05 0.65 Year -1996 0.014 0.21 0.025 0.36 Year -1997 0.252 2.00* 0.260 2.03* Year -1998 0.523 3.00** 0.526 3.01** Year -1999 0.537 2.05* 0.571 2.17* HMDA_Combined Purchases t -0.336 -2.42* NA NA HMDA_Combined Purchases t-1 0.268 2.03* NA NA HMDA_Combined Purchases t-2 0.134 1.14 NA NA HMDA_GSE Purchases t NA NA -0.650 -2.33* HMDA_GSE Purchases t-1 NA NA -0.125 -0.47 HMDA_GSE Purchases t-2 NA NA 0.113 0.49 HMDA_Non-GSE Purchases t NA NA -0.305 -2.10* HMDA_Non-GSE Purchases t-1 NA NA 0.319 2.33** HMDA_Non-GSE Purchases t-2 NA NA 0.117 0.92 Adjusted R-sq. 0.8408 0.841 F-statistic (DF = 198, 638 / 201, 635) 23.30** 23.01** * = statistically significant at 5% level, one-tailed test if expected sign (two-tailed test otherwise) ** = statistically significant at 1% level, one-tailed test if expected sign (two-tailed test otherwise) All above regressions include tract fixed effect dummies; results not shown.


35

Table A3 Regression Results for Home Sales Price Equation: Combined, GSE and Non-GSE Secondary Market Purchases of All Mortgage Types [GSE mortgage purchases measured using PUDB, non-GSE purchases from HMDA data] Dependent Variable: Ln (individual single-family dwelling sales price) Variables Coefficient t-statistic Coefficient t-statistic Intercept 7.470 3.82** 7.911 3.99** Number of Baths / Number of Beds -0.001 -0.03 -0.002 -0.03 1.5 Baths - vs. 1 0.042 2.39** 0.042 2.40** 2+ Baths - vs. 1 0.019 0.74 0.019 0.74 Garage 0.171 14.61** 0.171 14.57** Building 1 Story - vs. more -0.052 -4.76** -0.053 -4.79** Built 1900 - 1919 (vs. pre-1900) 0.116 6.25** 0.116 6.23** Built 1920 - 1939 (vs. pre-1900) 0.222 10.50** 0.221 10.49** Built 1940 -1949 (vs. pre-1900) 0.347 13.06** 0.347 13.05** Built 1950 - 1959 (vs. pre-1900) 0.334 12.46** 0.334 12.45** Built 1960 - 1969 (vs. pre-1900) 0.469 12.90** 0.469 12.91** Built 1970 - 1979 (vs. pre-1900) 0.332 4.03** 0.332 4.02** Built 1980-1989 (vs. pre-1900) 0.664 5.79** 0.666 5.80** Built 1990 or later (vs. pre-1900) 1.024 24.07** 1.023 24.05** Lot Size - sq. ft. 2.10E-05 9.52** 2.09E-05 9.48** Square of Lot Size -9.84E -11 -8.32** -9.81E -11 -8.30** Lot Width - ft. 1.75E-04 2.16* 1.74E-04 2.15* Pool 0.096 0.75 0.098 0.77 Square feet / Number of Rooms 1.75E-04 1.05 1.68E-04 1.01 Square feet 3.99E-04 8.30** 3.99E-04 8.30** Square of Square Feet -2.45E -08 -2.18* -2.43E -08 -2.17* Latitude 2.970 2.34* 2.982 2.35* Longitude -1.967 -1.15 -1.993 -1.17 Latitude * Latitude 43.766 5.35** 44.003 5.37** Longitude * Longitude 1.458 0.06 1.749 0.07 Latitude * Longitude -67.757 -3.26* -68.170 -3.28* Percent of Tract Hispanic 3.318 1.49 2.988 1.32 Percent of Tract Black 1.639 0.84 1.167 0.59 Percent of Tract White 1.337 0.69 0.908 0.46 % births that are low birth weight 0.002 1.63 0.002 1.55 % non-residential parcels -0.005 -0.37 -0.010 -0.81 Total property crime per 100,000 Popn 4.40E-06 0.61 3.13E-06 0.43 Birth to unmarried mom/100 live births -1.60E -04 -1.73* -1.56E -04 -1.69* % all home single family 0.003 0.74 0.002 0.68 % all parcels tax delinquent -0.009 -2.42** -0.009 -2.37** Birth to teen/1000 teen females LE 19 yrs. 1.00E-04 0.65 1.52E-04 0.98 % all commercial parcels vacant 0.002 0.98 0.003 1.09 Total violent crimes per 100,000 Popn -3.49E -05 -1.75* -3.67E -05 -1.84*


36

% all residential parcels vacant 0.002 0.48 0.003 0.69 % of Popn receiving welfare -0.008 -3.32** -0.007 -2.75** Sale April - June 0.053 4.32** 0.054 4.35** Sale July - September 0.049 3.98** 0.050 4.00** Sale October - December 0.082 6.48** 0.082 6.51** Sale year 1996 0.046 2.46** 0.044 2.31* Sale year 1997 0.082 3.68** 0.074 3.18** Sale year 1998 0.096 3.02** 0.076 2.32* Sale year 1999 0.150 3.98** 0.128 3.25** HMDA_Combined Purchases t 0.044 0.86 NA NA HMDA_Combined Purchases t-1 0.016 0.36 NA NA HMDA_Combined Purchases t-2 0.143 3.45** NA NA HMDA_GSE Purchases t NA NA -0.011 -0.09 HMDA_GSE Purchases t-1 NA NA -0.198 -2.05* HMDA_GSE Purchases t-2 NA NA 0.089 1.24 HMDA_Non-GSE Purchases t NA NA 0.082 1.41 HMDA_Non-GSE Purchases t-1 NA NA 0.117 2.17* HMDA_Non-GSE Purchases t-2 NA NA 0.155 3.01** Adjusted R-sq. 0.406 0.407 F-statistic (DF = 221, 16419 / 224, 16419) 51.85** 51.23** * = statistically significant at 5% level, one-tailed test if expected sign (two-tailed test otherwise) ** = statistically significant at 1% level, one-tailed test if expected sign (two-tailed test otherwise) NA = Not Applicable All above regressions include tract fixed effect dummies; results not shown.


37

Table A4 Regression Results for Home Sales Price Equation: Combined, GSE and Non-GSE Secondary Market Purchases of All Mortgage Types [GSE and non-GSE purchases measured from HMDA data] Dependent Variable: Ln (individual single-family dwelling sales price) Variables Coefficient t-statistic Coefficient t-statistic Intercept 7.638 3.89** 7.884 4.01** Number of Baths / Number of Beds -0.002 -0.04 -0.002 -0.05 1.5 Baths - vs. 1 0.042 2.39** 0.042 2.39** 2+ Baths - vs. 1 0.019 0.76 0.019 0.76 Garage 0.171 14.61** 0.171 14.58** Building 1 Story - vs. more -0.052 -4.78** -0.052 -4.77** Built 1900 - 1919 (vs. pre-1900) 0.116 6.24** 0.116 6.25** Built 1920 - 1939 (vs. pre-1900) 0.221 10.49** 0.222 10.51** Built 1940 -1949 (vs. pre-1900) 0.347 13.05** 0.347 13.06** Built 1950 - 1959 (vs. pre-1900) 0.334 12.44** 0.334 12.45** Built 1960 - 1969 (vs. pre-1900) 0.468 12.88** 0.469 12.90** Built 1970 - 1979 (vs. pre-1900) 0.330 4.00** 0.330 4.00** Built 1980-1989 (vs. pre-1900) 0.664 5.79** 0.665 5.80** Built 1990 or later (vs. pre-1900) 1.024 24.07** 1.027 24.14** Lot Size - sq. ft. 2.10E-05 9.51** 2.10E-05 9.48** Square of Lot Size -9.84E -11 -8.32** -9.80E -11 -8.29** Lot Width - ft. 1.78E-04 2.20* 1.78E-04 2.20* Pool 0.094 0.73 0.095 0.74 Square feet / Number of Rooms 1.81E-04 1.09 1.83E-04 1.10 Square feet 3.98E-04 8.28** 3.97E-04 8.26** Square of Square Feet -2.44E -08 -2.18* -2.40E -08 -2.14* Latitude 3.019 2.38* 2.997 2.36* Longitude -2.004 -1.18 -1.975 -1.16 Latitude * Latitude 43.911 5.36** 44.146 5.39** Longitude * Longitude 0.999 0.04 1.824 0.07 Latitude * Longitude -67.397 -3.24* -68.331 -3.29* Percent of Tract Hispanic 3.207 1.43 3.084 1.37 Percent of Tract Black 1.456 0.74 1.224 0.62 Percent of Tract White 1.139 0.59 0.952 0.49 % births that are low birth weight 0.002 1.59 0.002 1.60 % non-residential parcels -0.006 -0.50 -0.009 -0.77 Total property crime per 100,000 Popn 2.28E-06 0.32 2.16E-06 0.30 Birth to unmarried mom/100 live births -1.52E -04 -1.65* -1.49E -04 -1.61 % all home single family 0.003 0.74 0.003 0.75 % all parcels tax delinquent -0.009 -2.36** -0.010 -2.65** Birth to teen/1000 teen females LE 19 yrs. 9.49E-05 0.61 1.28E-04 0.82 % all commercial parcels vacant 0.002 1.00 0.003 1.05 Total violent crimes per 100,000 Popn -3.53E -05 -1.77* -3.55E -05 -1.78*


38

% all residential parcels vacant 0.003 0.54 0.003 0.62 % of Popn receiving welfare -0.008 -3.38** -0.007 -2.58** Sale April - June 0.054 4.35** 0.054 4.36** Sale July - September 0.050 4.02** 0.050 4.01** Sale October - December 0.082 6.50** 0.082 6.50** Sale year 1996 0.043 2.36** 0.039 2.02* Sale year 1997 0.087 3.97** 0.081 3.51** Sale year 1998 0.087 2.73** 0.087 2.66** Sale year 1999 0.138 3.58** 0.143 3.55** HMDA_Combined Purchases t 0.028 0.49 NA NA HMDA_Combined Purchases t-1 0.084 1.54 NA NA HMDA_Combined Purchases t-2 0.140 2.89** NA NA HMDA_GSE Purchases t NA NA -0.139 -1.05 HMDA_GSE Purchases t-1 NA NA -0.157 -1.22 HMDA_GSE Purchases t-2 NA NA 0.006 0.06 HMDA_Non-GSE Purchases t NA NA 0.055 0.92 HMDA_Non-GSE Purchases t-1 NA NA 0.102 1.83* HMDA_Non-GSE Purchases t-2 NA NA 0.149 2.87** Adjusted R-sq. 0.406 0.406 F-statistic (DF = 221, 16419 / 224, 16419) 51.84** 51.18** * = statistically significant at 5% level, one-tailed test if expected sign (two-tailed test otherwise) ** = statistically significant at 1% level, one-tailed test if expected sign (two-tailed test otherwise) NA = Not Applicable All above regressions include tract fixed effect dummies; results not shown.


39

Appendix 1

Method for Computing PRICE and QUANTITY

The market value (sales price) of a home is a function of its structural/parcel characteristics and the natural and human-made characteristics of the surrounding neighborhood. In other words, both structural/parcel and neighborhood characteristics influence the hedonic value of a property. Thus, in order to accurately measure a pure inflation effect, one must control for changes in both types of characteristics as homes sell over the course of time.

Home structural and parcel characteristics are measurable directly from the public property records generated at the time of sale, and available to us from a vendor-supplied database. In particular, our sales transaction database for Cleveland contains information of: number of bedrooms, bathrooms, and stories, year built, lot size, floor area, air conditioning, and the latitude and longitude (which permit control variables for spatial heterogeneity).

Neighborhood characteristics in 1990 are available in profusion by using the census tract data from the decennial census. Characteristics typically are more difficult to measure with a consistent annual time series between census enumeration years. Cleveland, however, represents a rare exception in this regard, as it has a publicly available, tract-level, annually updated (1993-1999) set of administrative data that give a rich, multidimensional portrait of many demographic, economic, and land use features of neighborhoods. In the dataset are included such indicators as: % of births to residents of low-weight babies, % birth mothers who are not married, birth rate of women under age 20, % parcels that are non-residential, % residential and commercial parcels that are vacant, % parcels tax delinquent, property crime rate, violent crime rate, and welfare receipt rate. Estimating PRICE, the constant-quantity price index for each tract, involves the following procedure: ? Using Cleveland single-family home sales 1992-1999, estimate a hedonic value

regression like (6), wherein ln(sales value) is regressed on all available structural/parcel characteristics, key 1990 decennial census tract information, administrative database information for the corresponding year as the sale

? Compare for each sale the foregoing model’s predicted value to actual value; sum these residuals for each tract in each year; the tract/year average represents the preliminary measure of PRICE, when converted out of log form.

To avoid ascribing minor inter-tract differences to differences in real prices instead of

measurement error, we sought to ascertain whether there was any statistically significant variation across tracts within any single year. We thought it most likely that it would occur across submarkets defined by poverty rate in tract (or, our proxy, TANF usage rates). So, each tract preliminary price observation was coded as belonging to one of the three terciles, based upon its appropriate TANF value, and ANOVA and SHEFFE (post hoc) procedures were performed.

From these results we coded two alternative PRICE variables. The first variant was coded as the mean of poverty tercile appropriate to the tract that year, regardless of the overall ANOVA statistical significance. I.e., we used the “best guess of the mean price” for each tercile each year, regardless if they were significantly different or not. Results in


40

the paper are based on this first variant, but did not substantively change when the second was employed.

The second variant was coded as the mean of the inflation terciles where the overall

ANOVA test was statistically significant and where the SCHEFFE test indicated which poverty terciles were significantly different from each other. When the overall ANOVA was not statistically significant the second variant of price was determined using the city’s overall mean of the price variable for that year. If at least one poverty tercile proved statistically significant, as indicated by the SCHEFFE test, then that tercile’s mean price was used; the mean price for the remaining (not significantly different) two poverty terciles was computed and these values used as the value for the individual tract’s price. Note: for both versions of PRICE, ALL tracts were so assigned, even if they had zero sales that year. I.e., we assumed that the inflation in the given tract was equal to that in all similar tracts in that poverty tercile. We used in experimental regressions the count of the number of sales in the tract as a filter to cut out certain tracts as “unreliable”, but his did not substantively change the results. The QUANTITY variable was computed from the 1992 to 1999 sales data by taking the mean value of the estimated log of the sales price by year and tract that was produced by the hedonic value equation (6). This value (transformed out of its log form) was assigned to the appropriate census tract in the tract variables data set used to estimate (5).


41

Appendix 2: Descriptive Statistics Table A: Home Sales Transactions Equation (unit of observation = census tract) Variables Mean Std Dev Log of number of sales 2.460 1.10 Price per unit of quantity 59.604 23.18 Number of single family homes 339.414 339.19 Expectations: ave. home inflation prior 2 years 0.364 0.15 Ave. Hedonic Value 23376.990 9365.79 Ave. Hedonic Value*Price per unit of quantity 1380863.000 767433.70 Percent of Tract Black 54.746 39.95 Percent of Tract Hispanic 6.780 9.85 Percent of mortgage applicants Asian or Other 3.296 10.42 Percent of mortgage applications denied: bad credit 8.643 11.34 Percent of mortgage applicants Black 44.452 35.92 Percent of mortgage applicants Hispanic 7.294 14.08 Percent of mortgage applicants Female 6.163 7.48 Total property crime per 100,000 Popn 9294.930 16941.43 Year -1996 0.200 0.40 Year -1997 0.200 0.40 Year -1998 0.200 0.40 Year -1999 0.200 0.40 PUDB_Combined Purchases t 0.475 0.24 PUDB_Combined Purchases t-1 0.439 0.24 PUDB_Combined Purchases t-2 0.400 0.28 PUDB_GSE Purchases t 0.081 0.15 PUDB_GSE Purchases t-1 0.076 0.15 PUDB_GSE Purchases t-2 0.082 0.21 PUDB_Non-GSE Purchases t 0.392 0.19 PUDB_Non-GSE Purchases t-1 0.361 0.20 PUDB_Non-GSE Purchases t-2 0.316 0.19 * HMDA_Combined Purchases t 0.458 0.20 * HMDA_Combined Purchases t-1 0.424 0.20 * HMDA_Combined Purchases t-2 0.389 0.21 * HMDA_GSE Purchases t 0.065 0.09 * HMDA_GSE Purchases t-1 0.062 0.10 * HMDA_GSE Purchases t-2 0.073 0.11 * HMDA_Non-GSE Purchases t 0.392 0.19 * HMDA_Non-GSE Purchases t-1 0.361 0.20 * HMDA_Non-GSE Purchases t-2 0.316 0.19 " HMDA_Combined Purchases t 0.446 0.24 " HMDA_Combined Purchases t-1 0.419 0.24 " HMDA_Combined Purchases t-2 0.417 0.25 " HMDA_GSE Purchases t 0.061 0.10 " HMDA_GSE Purchases t-1 0.059 0.10 " HMDA_GSE Purchases t-2 0.067 0.12


42

" HMDA_Non-GSE Purchases t 0.385 0.24 " HMDA_Non-GSE Purchases t-1 0.360 0.23 " HMDA_Non-GSE Purchases t-2 0.350 0.24 * All mortgage types " Home purchase only


43

Appendix 2: Descriptive Statistics (Continued) Table B: Home Sales Price Equation (unit of observation = home sale) Variables Mean Std Dev Log of sale amount 10.676 0.70 Number of Baths / Number of Beds 0.399 0.14 1.5 Baths - vs. 1 0.086 0.28 2+ Baths - vs. 1 0.076 0.27 Garage 0.808 0.39 Building 1 Story - vs. more 0.499 0.50 Built 1900 - 1919 (vs. pre-1900) 0.386 0.49 Built 1920 - 1939 (vs. pre-1900) 0.278 0.45 Built 1940 -1949 (vs. pre-1900) 0.103 0.30 Built 1950 - 1959 (vs. pre-1900) 0.102 0.30 Built 1960 - 1969 (vs. pre-1900) 0.023 0.15 Built 1970 - 1979 (vs. pre-1900) 0.003 0.06 Built 1980-1989 (vs. pre-1900) 0.001 0.04 Built 1990 or later (vs. pre-1900) 0.022 0.15 Lot Size - sq. ft. 5078.300 4352.41 Square of Lot Size 44731413.940 756693911.00 Lot Width - ft. 41.077 51.87 Pool 0.001 0.03 Square feet / Number of Rooms 203.841 40.63 Square feet 1270.360 377.85 Square of Square Feet 1756587.080 1360733.68 Latitude -0.003 0.08 Longitude -0.004 0.04 Latitude * Latitude 0.006 4.81E-03 Longitude * Longitude 0.002 2.21E-03 Latitude * Longitude 0.002 3.20E-03 Percent of Tract Hispanic 7.472 9.39 Percent of Tract Black 42.482 40.04 Percent of Tract White 47.311 34.57 % births that are low birth weight 10.879 6.48 % non-residential parcels 10.670 7.79 Total property crime per 100,000 Popn 5093.910 2357.64 Birth to unmarried mom/100 live births 615.924 207.10 % all home single family 48.389 23.29 % all parcels tax delinquent 12.495 7.09 Birth to teen/1000 teen females LE 19 yrs. 105.236 50.63 % all commercial parcels vacant 23.185 11.70 Total violent crimes per 100,000 Popn 1144.780 723.75 % all residential parcels vacant 7.856 8.79 % of Popn receiving welfare 11.446 7.80 Sale April - June 0.272 0.45 Sale July - September 0.274 0.45


44

Sale October - December 0.253 0.43 Sale year 1996 0.163 0.37 Sale year 1997 0.191 0.39 Sale year 1998 0.239 0.43 Sale year 1999 0.258 0.44 PUDB_Combined Purchases t 0.515 0.13 PUDB_Combined Purchases t-1 0.485 0.14 PUDB_Combined Purchases t-2 0.448 0.16 PUDB_GSE Purchases t 0.096 0.07 PUDB_GSE Purchases t-1 0.091 0.08 PUDB_GSE Purchases t-2 0.096 0.10 PUDB_Non-GSE Purchases t 0.419 0.12 PUDB_Non-GSE Purchases t-1 0.394 0.13 PUDB_Non-GSE Purchases t-2 0.352 0.12 * HMDA_Combined Purchases t 0.494 0.12 * HMDA_Combined Purchases t-1 0.463 0.13 * HMDA_Combined Purchases t-2 0.428 0.12 * HMDA_GSE Purchases t 0.074 0.05 * HMDA_GSE Purchases t-1 0.069 0.05 * HMDA_GSE Purchases t-2 0.076 0.06 * HMDA_Non-GSE Purchases t 0.419 0.12 * HMDA_Non-GSE Purchases t-1 0.394 0.13 * HMDA_Non-GSE Purchases t-2 0.352 0.12 " HMDA_Combined Purchases t 0.498 0.18 " HMDA_Combined Purchases t-1 0.456 0.16 " HMDA_Combined Purchases t-2 0.440 0.17 " HMDA_GSE Purchases t 0.064 0.06 " HMDA_GSE Purchases t-1 0.061 0.06 " HMDA_GSE Purchases t-2 0.066 0.07 " HMDA_Non-GSE Purchases t 0.434 0.17 " HMDA_Non-GSE Purchases t-1 0.395 0.16 " HMDA_Non-GSE Purchases t-2 0.374 0.17 * All mortgage types " Home purchase only


45

Figure 1

Illustration of Neighborhood j Single-Family Homeownership Stock Demand, Supply, and

Transactions in Time t

Supply of Single-Family, Owner-Occupied Stock in jt:

S1

Demand for Single-Family, Owner-Occupied Stock in jt: D2

Quantity of Single-Family Stock in jt

Price

Per unit housing

quantity in jt

P2

D1

Q** QC Q* QT

P0

P1

PC

Q1 Q2


46

Figure 2Secondary Mortgage Market Activity in Underserved Cleveland Census Tracts, 1993-1999

0.000

0.100

0.200

0.300

0.400

0.500

0.600

1993 1994 1995 1996 1997 1998 1999

Sh

are

of

all H

om

e M

ort

gag

es O

rig

inat

ed t

hat

wer

e P

urc

has

ed

(bas

ed o

n v

alu

e)

GSEs (PUDB)GSEs (HMDA)

Ginnie Mae (HMDA)

Other Non-GSEs (HMDA)

Combined (HMDA + PUDB)


47

Figure 3Housing Market Activity in Underserved Cleveland Census Tracts

1993-1999

0

50

100

150

200

250

300

350

400

450

500

1993 1994 1995 1996 1997 1998 1999

# Single-family Home Sales (/10)

Constant-Quality Single-family Home SalesPrice Index (1993 = 100)

the impact of secondary mortgage market and gse purchases on

Documents