Assessing the Early Impact of the Hardest Hit Fund on. Foreclosures, Mortgage Delinquencies, and Homeownership. A senior thesis presented by

Transcription

1 Assessing the Early Impact of the Hardest Hit Fund on Foreclosures, Mortgage Delinquencies, and Homeownership A senior thesis presented by John Macke to The Department of Economics and The Glynn Family Honors Program at The University of Notre Dame under advisement of Professor James Sullivan..

2 Macke 2 Thanks to all those who supported me throughout this project. It would not have been possible without you, and I am very grateful. -JPM

3 Macke 3 I. Introduction In 2007, the American housing market entered a severe crisis that saw steep declines in house prices and skyrocketing mortgage delinquency and foreclosures rates. This crisis played a key role in throwing the United States into a deep recession from which it is still recovering. In response to this crisis, the Obama Administration launched the Hardest Hit Fund in The fund would eventually provide 18 states and the District of Columbia with $7.6 billion to be spent on foreclosure prevention programs targeted at unemployed homeowners and homeowners that saw a large decrease in the value of their home. This program is unparalleled in US history not only in amount spent, but also in method, as it is the only program introduced in response to the housing crisis that includes payments to homeowners to avoid foreclosure, moving beyond loan modifications and advisory assistance. This paper investigates the early impact of the Hardest Hit Fund on foreclosure rates, mortgages delinquencies, and homeownership rates. In order to evaluate the Hardest Hit Fund, I exploit strict cutoffs in state eligibility that resulted in hundreds of millions of dollars being given to some states while otherwise similar states received nothing. States could qualify for the Hardest Hit Fund based on meeting any of three criteria: a drop in house prices of over 20%, large concentrations of residents living in areas with unemployment over 12%, and sustained unemployment above the national average. These cutoffs were rigid: all else equal, a state with a 20.5% drop in house prices would qualify for Hardest Hit, while a state with a 19.5% drop in house prices would not qualify and receive nothing. 19 states qualified based on the criteria detailed above, receiving an average of $400,000,000. I exploit this variation through the use of a difference-in-difference model, allowing me to generate estimates of the impact of the Hardest Hit Fund on foreclosure rates, mortgage

4 Macke 4 delinquency rates, and homeownership rates. I find that the Hardest Hit Fund led to a statistically significant 2.8 percentage point decrease in the subprime foreclosure rates in large cities of Hardest Hit states when compared to similar non-hardest Hit states from 2010 to Compared to an average subprime foreclosure rate of about 22 percent nationally in 2012, this estimate suggests that the Hardest Hit Fund decreased subprime foreclosure rates by over 10 percent over this time period. I also find suggestive evidence of a negative impact of Hardest Hit on mortgage delinquencies over the same time period, but no evidence of any substantial effect on homeownership. II. Background The housing crisis of 2007 and subsequent general financial crisis had severe and longlasting consequences that stretched across the global economy. While the proposed causes of the crisis were widespread and have been widely debated, the rise in subprime lending certainly played a central role. Subprime mortgages are those loans granted to individuals with poor credit histories who would not be able to qualify for conventional mortgages. The share of mortgage originations that were subprime rose from a historical average of approximately 8 percent form 1993 to 2003 to 20 percent from 2004 to 2006 (Joint Center for Housing Studies, 2007). As many as 90 percent of these subprime loans were adjustable rate mortgages, or ARMs, that started at a low interest rate and then increased to a higher rate after some number of years (Zandi, 2010). These subprime mortgages were typically awarded to people with low credit. They were able to afford the initial, lower interest payments, but as interest rates started to increase, foreclosures and mortgage delinquencies skyrocketed (See Figure 1).

5 Macke 5 Foreclosures have widespread negative effects on homeowners, lenders, and neighbors. Homeowners lose their homes and are forced to find a new place to live. They also typically have much lower credit than previously, which can lead to difficulty finding a job or purchasing essential goods (Graves, 2012). Lenders also stand to lose large amounts of money if they are forced to auction the house in a market with rapidly falling house prices, as was the case with the most recent crisis. Beyond even these effects, however, Scheutz, Been, and Ellen (2008) show the proximity to foreclosed homes decreases home value, even when controlling for baseline price differentials across neighborhoods with different levels of foreclosure exposure. The negative consequences of foreclosure to homeowners are especially dangerous for this last reason, as foreclosures can lead to a downward spiral for entire neighborhoods leading to higher levels of crime and instability (Stucky et al, 2012).

6 Macke 6 Figure 1. Source: Federal Crisis Inquiry Report, page 217. In response to rising delinquencies and foreclosures, the United States government has enacted a variety of potential policy solutions. Most of these policy solutions offered different methods of refinancing loans, with a focus on adjustable rate mortgages. One such program run through the Federal Housing Administration (FHA) was the FHA-Secure program, which allowed homeowners with non-fha adjustable rate mortgages to refinance into an FHA mortgage with lower interest rates. A similar refinancing plan was the HOPE for Homeowners program that allowed homeowners to refinance into fixed rate mortgages, provided they agreed to equity sharing. Another plan was the so-called Teaser Freezer, which locked in interest

7 Macke 7 rates at their initial rates for a certain number of years, essentially refinancing adjustable rate mortgages into fixed rate mortgages temporarily. There was also the FDIC Mod in a Box loan modification program that set standards for loan modifications. The government also provided a HOPE NOW support network to assist homeowners in navigating through their many options as they faced foreclosure. For more information, see (Federal Housing Administration, 2014) and (Federal Deposit Insurance Corporation, 2008). 1 While the United States government clearly tried many approaches to address the housing crisis, none went as far as the Hardest Hit Fund (HHF). HHF was implemented as part of the Emergency Economic Stabilization Act of 2008 under the Troubled Asset Relief Program (TARP), which set aside a total of $46 billion to help struggling families avoid foreclosure. First announced in February 2010, the Hardest Hit Fund provides $7.6 billion to 18 hardest hit states and the District of Columbia to develop locally-tailored foreclosure prevention solutions (Department of the Treasury, 2014). The Treasury additionally notes that HHF targets families in these areas that have been particularly affected by the crisis, and states that most HHF programs seek to aid unemployed homeowners and homeowners with homes that are worth less than the value of their mortgage. The Hardest Hit Fund is so named because it targets homeowners in states hardest hit by the housing crisis. This forms a crucial distinction between the Hardest Hit Fund and other programs that were implement to address the housing crisis. HHF awards are given at the state level, and they are only given to certain states, while other states receive no money. There were 3 rounds of awards for the Hardest Hit Fund, and eligibility was determined by a different criteria each round. The first round saw 5 states declared eligible for funding on the basis of 1 Notably, each of these programs was administered to residents of all 50 states, so they do not pose a threat to the variation I exploit in my difference-in-difference model.

8 Macke 8 having experienced a drop in housing prices of over 20% from pre-crisis peak to trough, as measured by the FHFA seasonally-adjusted house price index. The second round saw eligibility extended to an additional 5 states for having a high concentration of residents living in counties with unemployment above 12%. Finally, the third round resulted in eligibility being given to an additional 9 states and the District of Columbia. Additionally in this third round, 8 states that had already received money were eligible to apply for more funding. This final round of funding was given to states that had suffered periods of sustained unemployment above the national average in the 12 months from July 2009-June The states awarded funding in each round are displayed in Table 1. Table 1: States Awarded Funding in Each Round of HHF Round 1 Round 2 Round 3 Michigan Rhode Island Michigan Nevada South Carolina Nevada California Oregon California Florida North Carolina Florida Arizona Ohio Rhode Island South Carolina Oregon North Carolina Ohio Illinois Kentucky Indiana Tennessee Mississippi Georgia District of Columbia Alabama New Jersey While the funding for Hardest Hit comes from the federal government, the programs to be instituted with Hardest Hit money are designed and administered at the state level. Upon

9 Macke 9 being declared eligible based on one of the criteria above, states were required to submit proposals for programs to address the foreclosure problems in their communities. While each state s programs differ slightly, they typically include mortgage payment assistance for the unemployed, principal reduction, and payments to eliminate second lien loans. These programs all offer ways to reduce the likelihood of foreclosure that offer direct payments to help homeowners, unlike the previous refinancing programs that merely allowed homeowners to change the terms of their loan. Money disbursed through the Hardest Hit Fund was awarded to state Housing Finance Agencies in October 2010 with the requirement that it be spent by the end of Table 2 details the amount of the funds disbursed, as well as the amount that was spent through the end of While several states spent only a small portion of their allocations through this time period, it is important to keep in mind that they may have guaranteed more money than had actually been spent. For example, Nevada lists over $8,000,000 in funds guaranteed to homeowners through the end of 2011.Thus, even though it had only awarded around $500,000, Nevada had, in some sense, used over 15 times this amount. Since the Treasury does not collect any uniform measure of how much money state guaranteed through a given time period, however, the amount spent is the only measure available that can be compared across states.

10 Macke 10 Table 2: Amount Allocated to and Spent by Each Hardest Hit State through December 2011, in millions State Allocation Amount Spent through December 2011 Alabama $ $7.51 Arizona $ $1.06 California $1, $38.63 Florida $1, $11.72 Georgia $ $1.80 Illinois $ $4.80 Indiana $ $1.47 Kentucky $ $7.00 Michigan $ $5.59 Mississippi $ $1.33 Nevada $ $0.52 New Jersey $ $0.22 North Carolina $ $30.66 Ohio $ $21.42 Oregon $ $36.14 Rhode Island $79.35 $1.38 South Carolina $ $2.46 Tennessee $ $2.44 Washington, D.C. $20.70 $1.77 Total $7,600 $178 Source: State HFA reports to the US Department of the Treasury

11 Macke 11 III. Literature Review This paper fits into current strands of academic research on foreclosures and policy solutions. Perhaps most relevantly, Gerardi and Li (2010) discuss the empirical effects of many of the programs enacted before Hardest Hit: FHA-Secure, Mod in a Box, the Teaser Freezer, the Hope Now Alliance, and HOPE for Homeowners. Looking at foreclosure rates before and after the institution of the programs, as well as numbers of homeowners served by each program, they conclude that these programs fell short of their stated goals and generally had fairly poor results. This investigation was mainly descriptive, however, and lacked any comparison group to estimate the impact of the program. In fact, there do not appear to be any rigorous evaluations of the Hardest Hit Fund or the United States government s previous responses to the housing crisis of There are, however, several relevant theoretical papers that have looked into foreclosure solutions. Adelino, Gerardi, and Wilson (2009) offer a reason to be pessimistic about loan modification programs. They note that lenders rarely renegotiate normally, and propose high redefault risk as a potential reason. They argue that even after homeowners restructure their mortgages, they may still default, leaving lenders in the same position as before they refinanced, or perhaps an even more unfavorable position if the value of the home in question has dropped. If redefault risk is high, then loan modification programs will only delay the inevitable. Foote et al (2009) propose a constructive solution to foreclosures based on the relationship between job loss and foreclosure. One common measure of mortgage affordability is the ratio of monthly mortgage payment to gross income. They note that in markets where unemployment is increasing rapidly, as was the case in 2008 and 2009, gross income is very volatile. Mortgages that were once affordable are no longer so. Based on this observation,

12 Macke 12 they suggest that the government focus a program on the effects of income volatility The government could replace a portion of lost income for a period of 1 or 2 years. This investigation of the Hardest Hit Fund contributes to the existing academic literature in several ways. It provides a first-of-its-kind evidence of the impact of a government program to address foreclosures, using a difference-in-difference model to isolate the impact of Hardest Hit funding on the effects of foreclosure. Moreover, it provides such an estimate for a $7.6 billion program unprecedented in magnitude and method. Additionally, since HHF programs typically include mortgage payment assistance for the unemployed, principal reduction, and payments to eliminate second lien loans, this investigation of HHF also tests empirically the claims from the theoretical literature that a program that helps replace income of the unemployed may be more effective than loan modifications. IV. Data I estimate several measures of the impact of the Hardest Hit Fund in this paper. In all models, I use yearly data from the Federal Housing and Finance Administration on the statelevel FHFA seasonally adjusted House Price Index and yearly data from the Bureau of Labor Statistics on state-level seasonally adjusted unemployment rates. In estimating the effect of the Hardest Hit Fund on foreclosure rates and mortgage delinquency rates, the unit of observation is a Metropolitan Statistical Area, using the 2000 Census-based PUMAs. I aggregate data from the Urban Institute on quarterly foreclosure and delinquency rates to develop yearly foreclosure and delinquency rates for each MSA. Unfortunately, the mortgage delinquency and foreclosure rates are not available at the MSA-level before 2010, so the analysis is limited to years

13 Macke 13 In estimating the effect of the Hardest Hit Fund on homeownership rates, the unit of observation is a household. To determine homeownership rates, I use data from the One-Year American Community Survey (ACS), accessed via IPUMS. The one-year ACS is a statistical survey administered by the Census Bureau of approximately 1% of the population of the United States that serves as a yearly stand-in for the long form decennial census. I use the OWNERSHP variable in ACS to determine homeownership. OWNERSHP refers to the ownership of one s dwelling. It takes the value 00 if the data is unavailable, 10 if the home is owned or being bought by the head of household, 21 if the home is being rented without cash, and 22 if the home is being rented with cash. In line with the official Census Bureau definition, the homeownership rate is calculated as the number of households for which OWNERSHP=10 divided by the total number of households for which data is available. Ideally, the ACS data could be used to provide additional controls when I estimate the effect of Hardest Hit on foreclosure rates and mortgage delinquencies. The ability to do this depends on the ability to link the MSAs from the Urban Institute s foreclosure data with the MSAs from the American Community Survey. Unfortunately, in 2012, the Census Bureau began the transition over to new PUMAs based on the 2010 Census. While IPUMS is working to harmonize the new MSA definitions with the old where possible, the MSA variable for the Year American Community Survey is currently unavailable, preventing city-level analysis of the 2012 ACS data. V. Methods As described in Section II, the eligibility criteria for the Hardest Hit Fund are steep house price drops above 20%, concentrated population in counties with unemployment above 12%, and

14 Macke 14 sustained unemployment above the national average. These criteria provide sharp discontinuities in funding amounts: states meet at least one of the criteria and receive funds, or states meet none of the criteria and receive no funds. These sharp cutoffs in funding based on subtle changes in state characteristics provide the basis for my analysis. I estimate two difference-in-difference models to determine the impact of the Hardest Hit Fund to this point. My main results come from estimating a model of the form Y it = β 1 time t + β 2 HHF i + β 3 time * HHF it + β 4 X it +ε it (1) where i represents a Metropolitan Statistical Area, t represents a year (either 2010 or 2012), is an outcome related to foreclosures (subprime foreclosure rate, foreclosure rate, 90+ days mortgage delinquency rate, serious delinquency rate) in city 2 i and year Y it t. time t is an indicator variable that takes the value 0 in 2010 and 1 in 2012, HHF i is an indicator variable that takes the value 0 if the city is in a state that did not receive Hardest Hit funds and 1 if it is in a state that did receive Hardest Hit funds, and the X it are controls for the state FHFA seasonally-adjusted house price index and the state unemployment rate in year t and city i. I also estimate a linear probability model that has essentially the same form: Z it = β 1 time t + β 2 HHF i + β 3 time * HHF it + β X it +ε it (2) where the only changes are that i represents a household now, so HHF i is an indicator that takes value 0 if the household is in a state that did not receive Hardest Hit funds and 1 if it is in a state that did receive Hardest Hit funds, and Z it is a dummy that takes on the value 1 if the household 2 For the rest of the paper I use city and Metropolitan Statistical Area interchangeably. In every case, I am referring to a Metropolitan Statistical Area as determined by the 2000 Censusbased PUMAs. Additionally, whenever I refer to the Hardest Hit states, Washington, D.C. is included.

15 Macke 15 lives in a home that the head of household owns (this includes anyone making regular mortgage payments) and 0 otherwise. Using cities and households from all 50 states in the sample would likely be problematic. Since HHF states are chosen because they are thought to be the states hardest hit by the crisis, it is likely that they would experience larger drops in homeownership and larger rises in foreclosure and delinquency rates than other states in the absence of intervention. Thus, the coefficient on β 3 would be biased against showing a beneficial effect of the Hardest Hit Fund (i.e. it would be biased positively when I estimate foreclosure and delinquency rates and negatively when I estimate homeownership rates). To solve this problem, I create treatment and comparison groups, exploiting the aforementioned sharp cutoff in funding. In order to address this, I first sort all of the states in the US, as well as the District of Columbia, by average unemployment rates over the period during which Hardest Hit eligibility was determined. I form treatment and comparison groups by taking the 10 states receiving Hardest Hit with the lowest unemployment in the July 2009 to June 2010 and the 10 states not receiving Hardest Hit that have the highest unemployment (See Table 3). As shown in Table 1, all of the states that received money through the Hardest Hit Fund (other than Arizona) received funding at least partially due to high sustained unemployment. In fact, the 19 Hardest Hit states were all in the top 20 states with the highest average unemployment over the period from July 2009 to June 2010, so the states in the treatment group do have higher unemployment on average, but it is close. I argue that the states are otherwise similar enough to form a good comparison once the determinants of Hardest Hit selection (unemployment, house prices) are controlled for.

16 Macke 16 Some evidence of this comes from the similarities of the two groups with respect to several descriptive characteristics related to housing. As Table 4 shows, heads of household in treatment and comparison states are, on average, about the same age, nearly equally likely to be married, have graduated high school, and have lived in the same house 1 year ago. Additionally, they have about the same number of rooms in their house on average. There are some

17 Macke 17 differences between the two groups with respect to descriptive characteristics as well, as racial composition and average household incomes differ somewhat, but in general the descriptive characteristics are similar.

18 Macke 18 The strongest support for this model, however, comes from looking at trends in the dependent variables before the introduction of Hardest Hit. In a difference-in-difference model, the key trait of the two groups is not that they have similar levels of homeownership, foreclosure, or mortgage delinquency before the Hardest Hit Fund is introduced, but that the trends in these rates were similar. As Figure 2 shows, Hardest Hit states saw a larger drop in homeownership rates from 2007 to 2010 than non-hardest Hit states: while non-hhf states saw homeownership rates drop 2.1 percentage points, HHF states saw homeownership rates drop 2.8 percentage points, a 33% larger decrease. Once the sample is limited to the treatment and comparison states I selected, however, the difference nearly completely disappears. Non-HHF states experience a homeownership rate decrease of 2.3 percentage points, and HHF states have a homeownership rate decrease of 2.4 percentage points (See Figure 3).

19 Macke 19 Ideally, a similar analysis could be done for foreclosure rates and mortgage delinquency rates to identify if similar trends exist in treatment and comparison states before the introduction of the Hardest Hit Fund. Unfortunately, as discussed in section IV, the data I use on mortgage delinquencies and foreclosures is unavailable before 2010, Nonetheless, the similar descriptive characteristics of the two groups and the similar trends in homeownership provide some support for this difference-in-difference identification strategy. One criticism of limiting the sample to this smaller set of states would be that it limits the ability to determine the universal applicability of the results. Even if Hardest Hit were to be successful, if the 20 states used in the model proved to be significantly different from the rest of the country in some way, then the generalizability of these results would be limited. Fortunately, this is not the case. As Appendix Table 1 shows, the states used in the model are quite similar to the rest of the country with respect to a variety of descriptive characteristics, including food stamp receipt, percentage of residents with a high school diploma, and household income.

20 Macke 20 In my main estimates of the effect of Hardest Hit on subprime foreclosure rates, foreclosure rates, 90+ days mortgage delinquency rates, and serious delinquency rates, I also restrict the sample to large cities. As Figure 4 shows, people living in counties with higher populations (i.e. above 250,000 or 500,000 people) are more likely to receive assistance through the Hardest Hit Fund than people living in less populated cities. For this reason, I focus on metropolitan statistical areas with populations of over 500,000 in my main specification. The choice of 500,000 is somewhat arbitrary, but I settled on that number because it is large enough to capture only substantial cities while small enough to not limit the sample excessively. I test this restriction by examining the results of the same regression for all cities with population at least 250,000 and all cities regardless of population. One issue with using cities as a unit of observation in a state-level analysis is how to handle cities that stretch across multiple states. I handle the problem in the following way. In the main specification, I omit all cities that stretch across the border of multiple states. In a

21 Macke 21 secondary specification, I include cities that stretch into any of the 10 treatment states as cities with HHF i =1. Cities that stretch into no treatment states but at least one of the 10 comparison states are included with HHF i = 0. In addition to my main results, I also present estimates of the effect of the Hardest Hit Fund on homeownership rates, using household-level data from the American Community Survey. In generating these estimates, I cannot restrict the sample to large cities, due to the absence of the MSA variable in I am able to limit the analysis to one subset of the population that was more likely to benefit from Hardest Hit, however: low-income households. Thus, I estimate equation (2) with 3 different restrictions on the sample: first with all households, regardless of annual gross income, second limited to households with annual gross income under $100,000, and third limited to households with annual gross income under $50,000. VI. Results A. Foreclosure and Delinquency Rates The results for my estimates of the effect of Hardest Hit on subprime foreclosure rates, foreclosure rates, 90+ days delinquency rates, and serious delinquency rates are shown in Table 3. As discussed in the methods section, these results come from estimating equation (1) with a sample that includes cities from 10 treatment states and 10 comparison states, chosen for their proximity to the eligibility threshold. Border cities are omitted and only cities with at least 500,000 people are included. Standard errors are clustered by state in all cases. Row 1 of Table 5 shows the estimated impact of Hardest Hit assistance on subprime foreclosure rates, foreclosure rates, 90+ day mortgage delinquency rates, and serious mortgage delinquency rates. When the determinants of Hardest Hit selection are not included as controls,

22 Macke 22 the receipt of Hardest Hit assistance shows up as having a significantly negative impact on foreclosure and delinquency rates. That is, Hardest Hit assistance slowed the rise of foreclosure and delinquency rates significantly. When controls are added to the model, the effect of Hardest Hit decreases in magnitude across all foreclosure and delinquency rates. It is still negative in all cases, however, and the effect on subprime foreclosure rates is significantly negative. The coefficient on the interaction term suggests that the introduction of Hardest Hit Fund decreased subprime foreclosure rates by 2.78 percentage points compared to what would have been expected in absence of HHF. Compare to a national average subprime foreclosure rate of about 22 percent in 2012, this represents almost a 10 percent decrease. The coefficient on state level unemployment is miniscule and magnitude and not significant, suggesting that state unemployment rates have little impact on foreclosure and delinquency rates. The coefficient on the house price index does enter significantly in several of the regressions, but its magnitude is also very small the model suggests a 10 point increase in house price index would lead only to a.8 percentage point increase in the subprime foreclosure rate. The impact the other dependent variables is even smaller. In order to examine the robustness of this result, I also estimate a number of slightly differently specified versions of the main model. The estimates of the interaction term ( β 3 ) are shown in Table 6. The first change in specification is tests the importance of the number of states included in the treatment and control groups. First I remove 5 states on each side of the eligibility cutoff, including only the 5 Hardest Hit states with the lowest unemployment and the 5 non-hardest Hit states with the highest unemployment. When I do this, the magnitude on each of the coefficients changes very little, but the standard errors increase due to the smaller sample size, so the estimated effect on subprime foreclosures is no longer significantly different from 0,

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24 Macke 24 even though it moves to 3.1 percentage point decrease. Secondly, I add 5 states on each side, including the 15 Hardest Hit states with the lowest unemployment and the 15 non-hardest Hit states with the highest unemployment. Upon this change, none of the estimated effects of the Hardest Hit Fund are significantly different from 0, and three of them change sign. This is likely due to the fact that the 5 states added on each side are quite different from each other; I add 5 high unemployment states on the treatment side and 5 low unemployment states on the comparison side. Even controlling for unemployment and house prices, these states may be too different. Specifically, the Hardest Hit states may have suffered more seriously the effects of the crisis, leading to greater increases in foreclosure and delinquency rates and positively biasing the coefficients on the interaction term. I then investigate the inclusion of smaller cities. As discussed in the section V, I initially limit the sample to cities with over 500,000 residents. I do this because residents of more heavily populated counties have been helped by the Hardest Hit Fund at higher rates than residents of other counties. In order to examine the effect on a larger scale, I first include all cities over 250,000 people. I then extend the sample to include cities of all populations. The effects of the two expansions of the sample are similar. The magnitude of the coefficient on the interaction term decreases slightly and is no longer significantly different than 0, although it remains negative. This is likely due to the fact that the effect of Hardest Hit Fund receipt on foreclosure and delinquency rates for many households outside of large cities is small many people are neither not in need of such assistance or not eligible (See Figure 4). When cities with very few people receiving Hardest Hit are included, the effect is even more muted and hard to detect. Finally, I estimate the model including border states in the sample, as discussed in section V. The inclusion of border states has nearly no effect on the coefficients of interest.

25 Macke 25 B. Homeownership Rates The results of the estimation of equation (2) are displayed in Table 7. I consider three samples: all households, households with under $100,000 in gross yearly income, and households with under $50,000 in gross yearly income. When controls for state house price index and state unemployment are not included, interestingly, the coefficients are negative, implying that Hardest Hit has negative effect on homeownership rates. The coefficients on the interaction term are small, however, (under 1 percentage point), but significant due to the large sample size and correspondingly small standard error. When controls are added, the magnitude gets even smaller, and the effect is not significant for households with under $100,000 in income. Given that changes in homeownership rates are a lagging effect of reductions or increases in foreclosures, it is unsurprising that there would be no major effect of the Hardest Hit Fund on homeownership rates at this stage in the disbursement of funds. The lack of disbursement of funds through Hardest Hit is one limitation of analyzing its success at this point. Since states have until 2017 to spend funds, many states had only spent a small fraction of their total funds by the end of Because of this, the effects of Hardest Hit are likely to be small at this point, making them somewhat hard to detect statistically. Another limitation of this study is the lack of MSA-level data for 2012 due to the discrepancy in MSA definitions described in section IV. This lack of data precludes the use of stronger MSA-level demographic controls in the city-level model. Future research should address this, however, because each of these problems will be solved in time; more funds are being dispersed each day, and MSA-level data for the 2012 ACS will be available within the year. This model can easily be adapted and improved by making these changes when possible.

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28 Macke 28 VII. Conclusion This paper addresses the early impact of the Hardest Hit Fund, a government program that saw over $7.5 billion dollars allocated to 18 states and the District of Columbia in order to prevent foreclosures. I estimate a difference-in-difference model controlling for the determinants of selection and find that the fund decreased subprime foreclosure rates by 2.8 percentage points from 2010 to 2012 in large cities of Hardest Hit states when compared to large cities in similar non-hardest Hit states. Additionally, I find some evidence that the Hardest Hit fund decreased overall foreclosure rates, 90+ days mortgage delinquency rates, and serious delinquency rates, although the estimated effects are not statistically significant when state house prices and unemployment levels are controlled for. I do not find any evidence that the Hardest Hit fund has increased homeownership rates; my estimates suggest that Hardest Hit had essentially no effect on homeownership from 2010 to This paper is novel in approach in that it is the first paper to generate an estimate of the true impact of the Hardest Hit Fund using a control group, and its findings suggest moderate success thus far, in spite of slow implementation. It lays out a model for exploiting cutoffs in funding eligibility that could easily be adapted to evaluate the Hardest Hit Fund 3, 5, or 7 years down the road, when more funds have been spent. With the findings as they are now, this paper provides suggestive evidence that the Hardest Hit Fund has achieved its state goal of decreasing foreclosures, demonstrating that government interventions can have a significant impact in the housing market, especially when they are on the scale of Hardest Hit.

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30 References Adelino, Manuel Kristopher Gerardi, and Paul S. Willen. Why Don't Lenders Renegotiate More Home Mortgages? Redefaults, Self-Cures, and Securitization Public Policy Discussion Paper No Department of the Treasury. Hardest Hit Fund: Archived Program Information Programs/housing/hhf/Pages/Archival-information.aspx?Program=Hardest+Hit+Fund Federal Crisis Inquiry Report Accessed at Federal Deposit Insurance Corporation. FDIC Announces Availability of IndyMac Loan Modification. November 20, Federal Housing and Finance Administration. FHA Secure Refinancing Foote, Christopher et al. Reducing Foreclosures: No Easy Answers National Bureau of Economic Research. Gerardi, Kristopher and Wenli Li. "Mortgage Foreclosure Prevention Efforts" FRB Atlanta Economic Review, 95(2). Graves Jr, Earl. "Your Credit Score is Your GPA for Life." Black Enterprise, 43.3 (2012): 12. Joint Center for Housing Studies. The State of the Nation s Housing: Harvard University, 2008.

31 Macke 31 Schuetz, Jenny and Been, Vicki and Ellen, Ingrid Gould, Neighborhood Effects of Concentrated Mortgage Foreclosures (September 18, 2008). NYU Law and Economics Research Paper No Stucky, Thomas, John Ottensmann, and Seth B. Payton. Indiana University-Purdue University Indianapolis "The Effect of Foreclosures on Crime in Indianapolis, *." Social Science Quarterly (Wiley-Blackwell), 93.3 (2012): Zandi, Mark. Financial Shock. FT Press, 2010.