Three essays investigating post-bubble housing market: prices and foreclosures

Foreclosure;Housing prices;Program evaluation;Spatial and temporal analysis

Bak, Xian Fang

关键词: Foreclosure;    Housing prices;    Program evaluation;    Spatial and temporal analysis;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/97362/BAK-DISSERTATION-2017.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

This dissertation investigates the impacts of foreclosures on the housing price dynamics after the bursting of the housing bubble in 2007 in the United States.Following a sharp dip in housing prices, the number of foreclosures increased dramatically.The housing market experienced an unprecedented crisis accompanied by a wave of foreclosures across the country. While the decrease in housing prices triggered a large number of foreclosures initially, foreclosures in turn have slowed down the recovery of the housing market.An understanding of how foreclosures influence housing prices is important in the provision of a more complete interpretation of post-bubble housing market conditions, as well as providing a guide for the consideration of future government responses as well as the provision of reasonable predictions for the recovery trajectory.The following three chapters of this dissertation study the impact of foreclosures since 2008 in Chicago area that was severely hit by the real estate market collapse. Each chapter investigates the impacts of foreclosures on housing prices from different perspectives.In Chapter 1, the heterogeneous impact of foreclosures on nearby property values is examined.In Chapter 2, a governmental program that targeted at reducing the foreclosure impacts is evaluated.In the last chapter, the persistence of foreclosure impacts on housing prices is evaluated through estimating a impulse response function.The first chapter aims to identify the causal effects of foreclosures on housing prices and the heterogeneity of these effects across different neighborhoods.Foreclosures have negative impacts not just for the homeowner, but also on neighboring properties (e.g., Campbell, Giglio, & Pathak, 2011, CGP hence-forth; Gerardi, Rosenblatt, Willen, & Yao, 2015; Anenberg & Kung, 2014).Due to the heterogeneous characteristics of the housing market, different neighborhoods can be impacted differently by foreclosures.This study focuses on the spatially heterogeneous impacts of foreclosures on nearby property values, an issue that has been overlooked in the literature.First, using a standard ordinary least square model, each additional foreclosure within 0.1 mile that goes through an auction process is found to decrease nearby home sale prices by about 2.0% on average.In addition, conditional parametric quantile regression (McMillen, 2013) model is applied to explore the spatial heterogeneity in foreclosure impacts.Not surprisingly, the impact of foreclosures is found to vary across space and the spatial variation is most obvious for homes priced in the lower quantiles than in the higher quantiles.Poorer neighborhoods are the most impacted and the differences between different quantiles are largest for these neighborhoods.The second chapter evaluates the federal Neighborhood Stabilization Program (NSP) using a quasi-experimental design, to determine whether the government’s response effectively reduced the negative impact of foreclosures on the prices of neighboring homes.NSP aims to bring foreclosed and abandoned properties back to productive use through property purchase and rehabilitation.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).First of all, the NSP can only target a limited number of neighborhoods, while there are still large numbers of foreclosures in non-targeted areas (Immergluck, 2012).Moreover, since few foreclosure-related stabilization programs have been implemented in the past (Kingsley et al., 2009), the implementation of NSP had little prior experience to follow.This chapter is one of the first studies evaluating the NSP and also provides evidence that disamenity effects are a source of the negative impacts of foreclosures that is controversial in the literature (Gerardi, et al., 2015).Using a 2008-2014 repeated cross-section dataset for housing sales in the city of Chicago, the difference-in-differences estimates reveal that the average sales prices of homes within 0.1 miles of the NSP projects increased by 14.3% and these effects do not appear until the completion of the rehabilitation.The results vary under different contexts of NSP implementation, but the analytical approach presented in this study is reproducible for NSP studies in other regions.The third chapter explores the persistence of foreclosures on housing prices.This chapter aims to fill the gap in the literature by analyzing how long the shock in foreclosures can have impact on the housing price. The trajectory of the housing price corresponding to foreclosure shock are estimated using quarterly data at the community level in the city of Chicago.Since housing price can diffuse upon shocks at two dimensions – both time and space. We are going to explicitly control for both diffusions and estimate for the impulse response function for housing prices upon foreclosure shocks.This paper applies a new time series technique - local projection (Jorda, 2005) to a spatial dynamic panel model for measuring the impulse response function (Brady, 2011 & 2014).Using the housing and foreclosure data in the city of Chicago between 2008 and 2016, the results show a one-standard-deviation increase in the number of foreclosures can lead declines in the housing prices up to eleven quarters with a cumulative impact of 18.7% at the community level.In addition, spatial diffusion of housing prices is found within the city.A one percent positive shock in neighboring communities’ housing prices can induce increases in the housing prices of a community up to eleven quarters, with a cumulative response of 1.8%.