{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/113298"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/113298","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Three essays on housing economics","abstract":"This dissertation includes two chapters that investigate the spatial behavior of housing price and its volatility and one chapter that focuses on a more fundamental housing issue. The first chapter examines the spatial variation of housing price volatility. I develop a flexible spatial volatility model for squared returns using a Box-Cox transformation for simultaneously testing space-varying volatility and its functional form. The maximum likelihood (ML) method is used to estimate this model and Monte Carlo simulations are conducted to investigate the finite sample performance of the ML estimator. Using housing price data from Chicago, I empirically demonstrate substantial evidence of spatial dependence in volatility and explicitly address the validity of a log-linear specification, after which I propose a new practical indicator, called neighborhood elasticity, which determines how volatility in one neighborhood is linked to that in surrounding neighborhoods. The average annual elasticity is found to be 0.4 across different spatial weight matrices, which can be used as a benchmark to compare different housing markets and a tool for policy makers to assist them to avoid volatility transmission and the risk of contagion in the housing market. Finally, to identify whether the neighborhood elasticity remains constant over time, adjusted quasi score (AQS) tests for testing the presence of temporal heterogeneity in spatial parameters in spatial panel data models are considered. The test results reveal that the neighborhood elasticity becomes homogeneous after controlling for both spatial and temporal heterogeneity in the intercepts of the model. The second chapter explores the implication of spatial volatility in the context of market efficiency in finance literature. More specifically, this study investigates whether the housing market is spatially efficient by examining linear and nonlinear spatial dependence patterns in housing returns. The spatial ARCH-type model and its extension to the quantile model that allows for possible heterogeneous effects of spatial dependence are applied to house price data in the broader Chicago area. Our study reveals a number of interesting new insights into the spatial market efficiency of the housing market. Specifically, we find: i) while housing returns are not correlated over space, squared returns, which represent volatility, exhibit significant spatial dependence, i.e., spatial market inefficiency and, therefore, the neighborhood housing returns contain information for spatial prediction and ii) the degree of inefficiency varies over quantiles; the spatial dependence is conspicuously distinct from the lower quantiles to the higher quantiles with a gradually increasing trend. The third chapter investigates spillover effects of house supply on nearby house prices across the housing cycle. Over the past five years, housing inventory shortages have been a primary factor in rising house prices. At the same time, demand for housing has risen aggressively as the job market has improved and millennials are aging into homeownership. The combination of limited homes on the market with high buyer demand has pushed house prices above what they were at the peak of the housing boom in early 2006. The underlying reasoning behind this idea - low supply should lead to price increases - is clear and some influential research has strongly suggested the inverse relationship by applying matching models to the housing market. However, there is a surprising lack of empirical work done on this inverse relationship, in particular, at the level of the local housing market. Furthermore, no empirical research has addressed the role of spatial spillovers between different regions in the relationship. Using a spatial panel model for 77 community areas within the Chicago area between 2009 and 2018, the study shows substantial asymmetric spatial effects; for instance, how the nearby house supply can serve as a key determinant on house prices in a boom period. However, these effects may not hold during a market downturn.","abstract_html":"This dissertation includes two chapters that investigate the spatial behavior of housing price and its volatility and one chapter that focuses on a more fundamental housing issue. The first chapter examines the spatial variation of housing price volatility. I develop a flexible spatial volatility model for squared returns using a Box-Cox transformation for simultaneously testing space-varying volatility and its functional form. The maximum likelihood (ML) method is used to estimate this model and Monte Carlo simulations are conducted to investigate the finite sample performance of the ML estimator. Using housing price data from Chicago, I empirically demonstrate substantial evidence of spatial dependence in volatility and explicitly address the validity of a log-linear specification, after which I propose a new practical indicator, called neighborhood elasticity, which determines how volatility in one neighborhood is linked to that in surrounding neighborhoods. The average annual elasticity is found to be 0.4 across different spatial weight matrices, which can be used as a benchmark to compare different housing markets and a tool for policy makers to assist them to avoid volatility transmission and the risk of contagion in the housing market. Finally, to identify whether the neighborhood elasticity remains constant over time, adjusted quasi score (AQS) tests for testing the presence of temporal heterogeneity in spatial parameters in spatial panel data models are considered. The test results reveal that the neighborhood elasticity becomes homogeneous after controlling for both spatial and temporal heterogeneity in the intercepts of the model. The second chapter explores the implication of spatial volatility in the context of market efficiency in finance literature. More specifically, this study investigates whether the housing market is spatially efficient by examining linear and nonlinear spatial dependence patterns in housing returns. The spatial ARCH-type model and its extension to the quantile model that allows for possible heterogeneous effects of spatial dependence are applied to house price data in the broader Chicago area. Our study reveals a number of interesting new insights into the spatial market efficiency of the housing market. Specifically, we find: i) while housing returns are not correlated over space, squared returns, which represent volatility, exhibit significant spatial dependence, i.e., spatial market inefficiency and, therefore, the neighborhood housing returns contain information for spatial prediction and ii) the degree of inefficiency varies over quantiles; the spatial dependence is conspicuously distinct from the lower quantiles to the higher quantiles with a gradually increasing trend. The third chapter investigates spillover effects of house supply on nearby house prices across the housing cycle. Over the past five years, housing inventory shortages have been a primary factor in rising house prices. At the same time, demand for housing has risen aggressively as the job market has improved and millennials are aging into homeownership. The combination of limited homes on the market with high buyer demand has pushed house prices above what they were at the peak of the housing boom in early 2006. The underlying reasoning behind this idea - low supply should lead to price increases - is clear and some influential research has strongly suggested the inverse relationship by applying matching models to the housing market. However, there is a surprising lack of empirical work done on this inverse relationship, in particular, at the level of the local housing market. Furthermore, no empirical research has addressed the role of spatial spillovers between different regions in the relationship. Using a spatial panel model for 77 community areas within the Chicago area between 2009 and 2018, the study shows substantial asymmetric spatial effects; for instance, how the nearby house supply can serve as a key determinant on house prices in a boom period. However, these effects may not hold during a market downturn.","abstract_has_math":false,"creators":["Chae, Jiyoung"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Economics","degree_department":null,"school":null,"contributors":["Bera, Anil","Hewings, Geoffrey","McMillen, Daniel","Lee, JiHyung"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-01-12T22:55:03Z","date_published":"2022-01-12T22:55:03Z","updated_at":"2026-07-22T22:24:53Z","subjects":["Spatial dependence","Spatial models","Spatial volatility clustering."],"languages":["en"],"rights":["Copyright 2021 Jiyoung Chae"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/113298","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Bera, Anil","Hewings, Geoffrey","McMillen, Daniel","Lee, JiHyung"]},{"key":"dc:creator","label":"Author","values":["Chae, Jiyoung"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-01-12T22:55:03Z","2024-01-12T22:56:20Z","2021-07-13","2021-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Economics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Spatial dependence","Spatial models","Spatial volatility clustering."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Jiyoung Chae"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/113298"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This dissertation includes two chapters that investigate the spatial behavior of housing price and its volatility and one chapter that focuses on a more fundamental housing issue. The first chapter examines the spatial variation of housing price volatility. I develop a flexible spatial volatility model for squared returns using a Box-Cox transformation for simultaneously testing space-varying volatility and its functional form. The maximum likelihood (ML) method is used to estimate this model and Monte Carlo simulations are conducted to investigate the finite sample performance of the ML estimator. Using housing price data from Chicago, I empirically demonstrate substantial evidence of spatial dependence in volatility and explicitly address the validity of a log-linear specification, after which I propose a new practical indicator, called neighborhood elasticity, which determines how volatility in one neighborhood is linked to that in surrounding neighborhoods. The average annual elasticity is found to be 0.4 across different spatial weight matrices, which can be used as a benchmark to compare different housing markets and a tool for policy makers to assist them to avoid volatility transmission and the risk of contagion in the housing market. Finally, to identify whether the neighborhood elasticity remains constant over time, adjusted quasi score (AQS) tests for testing the presence of temporal heterogeneity in spatial parameters in spatial panel data models are considered. The test results reveal that the neighborhood elasticity becomes homogeneous after controlling for both spatial and temporal heterogeneity in the intercepts of the model. The second chapter explores the implication of spatial volatility in the context of market efficiency in finance literature. More specifically, this study investigates whether the housing market is spatially efficient by examining linear and nonlinear spatial dependence patterns in housing returns. The spatial ARCH-type model and its extension to the quantile model that allows for possible heterogeneous effects of spatial dependence are applied to house price data in the broader Chicago area. Our study reveals a number of interesting new insights into the spatial market efficiency of the housing market. Specifically, we find: i) while housing returns are not correlated over space, squared returns, which represent volatility, exhibit significant spatial dependence, i.e., spatial market inefficiency and, therefore, the neighborhood housing returns contain information for spatial prediction and ii) the degree of inefficiency varies over quantiles; the spatial dependence is conspicuously distinct from the lower quantiles to the higher quantiles with a gradually increasing trend. The third chapter investigates spillover effects of house supply on nearby house prices across the housing cycle. Over the past five years, housing inventory shortages have been a primary factor in rising house prices. At the same time, demand for housing has risen aggressively as the job market has improved and millennials are aging into homeownership. The combination of limited homes on the market with high buyer demand has pushed house prices above what they were at the peak of the housing boom in early 2006. The underlying reasoning behind this idea - low supply should lead to price increases - is clear and some influential research has strongly suggested the inverse relationship by applying matching models to the housing market. However, there is a surprising lack of empirical work done on this inverse relationship, in particular, at the level of the local housing market. Furthermore, no empirical research has addressed the role of spatial spillovers between different regions in the relationship. Using a spatial panel model for 77 community areas within the Chicago area between 2009 and 2018, the study shows substantial asymmetric spatial effects; for instance, how the nearby house supply can serve as a key determinant on house prices in a boom period. However, these effects may not hold during a market downturn.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2023-08-01","The student, Jiyoung Chae, accepted the attached license on 2021-07-10 at 09:20.","The student, Jiyoung Chae, submitted this Dissertation for approval on 2021-07-10 at 09:39.","This Dissertation was approved for publication on 2021-07-13 at 16:30.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16845 on 2022-01-12 at 13:04:29","Made available in DSpace on 2022-01-12T22:55:03Z (GMT). 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The first chapter examines the spatial variation of housing price volatility. I develop a flexible spatial volatility model for squared returns using a Box-Cox transformation for simultaneously testing space-varying volatility and its functional form. The maximum likelihood (ML) method is used to estimate this model and Monte Carlo simulations are conducted to investigate the finite sample performance of the ML estimator. Using housing price data from Chicago, I empirically demonstrate substantial evidence of spatial dependence in volatility and explicitly address the validity of a log-linear specification, after which I propose a new practical indicator, called neighborhood elasticity, which determines how volatility in one neighborhood is linked to that in surrounding neighborhoods. The average annual elasticity is found to be 0.4 across different spatial weight matrices, which can be used as a benchmark to compare different housing markets and a tool for policy makers to assist them to avoid volatility transmission and the risk of contagion in the housing market. Finally, to identify whether the neighborhood elasticity remains constant over time, adjusted quasi score (AQS) tests for testing the presence of temporal heterogeneity in spatial parameters in spatial panel data models are considered. The test results reveal that the neighborhood elasticity becomes homogeneous after controlling for both spatial and temporal heterogeneity in the intercepts of the model. The second chapter explores the implication of spatial volatility in the context of market efficiency in finance literature. More specifically, this study investigates whether the housing market is spatially efficient by examining linear and nonlinear spatial dependence patterns in housing returns. The spatial ARCH-type model and its extension to the quantile model that allows for possible heterogeneous effects of spatial dependence are applied to house price data in the broader Chicago area. Our study reveals a number of interesting new insights into the spatial market efficiency of the housing market. Specifically, we find: i) while housing returns are not correlated over space, squared returns, which represent volatility, exhibit significant spatial dependence, i.e., spatial market inefficiency and, therefore, the neighborhood housing returns contain information for spatial prediction and ii) the degree of inefficiency varies over quantiles; the spatial dependence is conspicuously distinct from the lower quantiles to the higher quantiles with a gradually increasing trend. The third chapter investigates spillover effects of house supply on nearby house prices across the housing cycle. Over the past five years, housing inventory shortages have been a primary factor in rising house prices. At the same time, demand for housing has risen aggressively as the job market has improved and millennials are aging into homeownership. The combination of limited homes on the market with high buyer demand has pushed house prices above what they were at the peak of the housing boom in early 2006. The underlying reasoning behind this idea - low supply should lead to price increases - is clear and some influential research has strongly suggested the inverse relationship by applying matching models to the housing market. However, there is a surprising lack of empirical work done on this inverse relationship, in particular, at the level of the local housing market. Furthermore, no empirical research has addressed the role of spatial spillovers between different regions in the relationship. Using a spatial panel model for 77 community areas within the Chicago area between 2009 and 2018, the study shows substantial asymmetric spatial effects; for instance, how the nearby house supply can serve as a key determinant on house prices in a boom period. However, these effects may not hold during a market downturn.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2023-08-01","The student, Jiyoung Chae, accepted the attached license on 2021-07-10 at 09:20.","The student, Jiyoung Chae, submitted this Dissertation for approval on 2021-07-10 at 09:39.","This Dissertation was approved for publication on 2021-07-13 at 16:30.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16845 on 2022-01-12 at 13:04:29","Made available in DSpace on 2022-01-12T22:55:03Z (GMT). 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