Introduction

An unprecedented number of foreclosures flooded the U.S housing market starting in 2008 after the housing bubble burst. Foreclosures not only bring financial andpsychological damages to families but also generate social and economic impacts to the community (Kingsley, et al., 2009). Many studies have identified the negative impact of foreclosures on nearby property values.8 The Neighborhood Stabilization Program (NSP) is a federal governmental program established under the Housing and Economic Recovery Act (HERA) in 2008 to relieve stress in foreclosure-concentrated neighborhoods. This study aims to evaluate this program in terms of elevating neighborhood property values.

The implementation of the NSP involves cooperation between governments and the third parties. A total of $6.92 billion was allocated to state and local governments during three rounds between 2009 and 2012 (hereafter, NSP1, NSP2 and NSP3). There are 5 eligible usages for this grant, including financing, acquiring and rehabilitation, land banking, demolition and redeveloping. This study focuses on the NSP rehabilitation. Since the absence of maintenance and the generally poor conditions of many foreclosed properties can be major sources of foreclosure impacts (see, for example, Gerardi, et al., 2015; Mikelbank, 2008), rehabilitation on these properties is expected to bring positive improvements than otherwise would be the case.

It has been controversial in the literature as for the nature of the channel through which a foreclosed property impacts nearby property values. Those promoting supply effects (Anenberg and Kung, 2014) and disamenity effects (Gerardi, et al., 2015) have argued that these are the primary sources of the negative impacts of foreclosures. Since the NSP rehabilitation focuses on properly maintaining and preventing foreclosed properties from falling into disrepair, the

8 _{In a range between -2.0% and -0.5% within 0.1-0.33 miles buffer areas (Anenberg & Kung, 2014; Campbell,}

Giglio, & Pathak, 2011; Gerardi, Rosenblatt, Willen, & Yao, 2015; Harding, Knight, & Sirmans, 2003; Immergluck & Smith, 2006

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evaluation of the program provides an alternative approach to test whether the deteriorating appearance of foreclosed properties is a source of foreclosure impacts on nearby property values. It is also important to evaluate the neighborhood stabilization program in order to provide future suggestions for untreated areas. While the NSP program was restricted to certain targeted neighborhoods, there were still large numbers of foreclosures in non-targeted areas (Immergluck, 2012). Due to limited number of foreclosure-related stabilization programs (Kingsley et al., 2009), the implementation of NSP had little prior experience to follow. There is a clear need for research that explores the impact of NSP in order to guide future government action on addressing foreclosure impacts (Joice, 2011). However, this national program has not yet been thoroughly analyzed. Spader et al. (2015) extensively analyzed the second round of NSP2 using information from 19 counties and provided valuable information to understand NSP2 implementation from many different perspectives. Regarding the effects of NSP2 on nearby property values, Spader et al. (2015) were unable to find consistent results and they concluded that the cause could be omitted variable bias. Furthermore, Spader et al. (2016) evaluated the NSP effects on the crimes in the city of Cleveland, Chicago and Denver. They did not find effects of NSP rehabilitations in all three cities, although they did find positive effects of NSP demolitions on reducing crimes in Cleveland. Schuetz et al. (2016) evaluated the impact of NSP2 on several housing outcomes in multiple urban areas. They found a decreasing inventory of distressed properties in all counties in their study, while other housing market outcomes (e.g. sales volume and vacancy) experienced uneven recovery across the space.

To identify the causal effects of NSP, it will be important to tackle the challenges of endogeneity resulting from the treatment itself and other omitted variables. This study applies a difference-in-difference (DD) approach, treating homes near NSP properties as the treatment group and homes in more distant areas as the control group. Difference-in-difference estimators control for time-invariant unobserved factors and common trend between the control and treatment groups. To test the common trend assumption that is crucial to the difference-in-differences model, placebo models across time and space are used.

Using a 2008-2014 repeated cross-section dataset of house transactions in the city of Chicago, the DD estimates reveal significantly positive effects of the NSP program. The average sales price of homes with at least one NSP project within 0.1 miles increased by 14.3%, due to the

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clustered investment of NSP projects which both removes disamenities and introduces amenities. Homes located more than 0.1 miles away from NSP projects are not affected and program effects do not appear until the completion of the rehabilitation. Furthermore, program effects are found large to normal homes but not to foreclosure-related homes. When discrete counts of project units are used for measuring program intensity effects, an additional project unit within 0.1 miles is found to bring 1.4% positive effects on the housing sale prices. This is not inconsistent with the externality of an additional foreclosure unit (negative 1~2%) estimated in the literature. Our findings also provide evidence that foreclosed properties do have a negative externality on nearby property values due to its disamenity effects, since through NSP program, the removal of these disamenities brought by foreclosures elevated nearby property values.

The remainder of the paper is organized as follows. In Section 2.1, some background information on the NSP grants allocation procedure is provided. Section 2.3 introduces the base model specification with the DD estimator and placebo models for common trend checking. Section 2.4 describes the data used, followed by DD regression results in section 2.5 and conclusions are drawn in section 2.6.