We propose a sequential monitoring scheme to find structural breaks in real estate markets. The changes in the real estate prices are modeled by a combination of linear and autoregressive terms. The monitoring scheme is based on a detector and a suitably chosen boundary function. If the detector crosses the boundary function, a structural break is detected. We provide the asymptotics for the procedure under the stability null hypothesis and the stopping time under the change point alternative. Monte Carlo simulation is used to show the size and the power of our method under several conditions. We study the real estate markets in Boston, Los Angeles and at the national U.S. level. We find structural breaks in the markets, and we segment the data into stationary segments. It is observed that the autoregressive parameter is increasing but stays below 1.
{"title":"Sequential Monitoring of Changes in Housing Prices","authors":"Lajos Horváth, Zhenya Liu, Shan Lu","doi":"10.2139/ssrn.3529058","DOIUrl":"https://doi.org/10.2139/ssrn.3529058","url":null,"abstract":"We propose a sequential monitoring scheme to find structural breaks in real estate markets. The changes in the real estate prices are modeled by a combination of linear and autoregressive terms. The monitoring scheme is based on a detector and a suitably chosen boundary function. If the detector crosses the boundary function, a structural break is detected. We provide the asymptotics for the procedure under the stability null hypothesis and the stopping time under the change point alternative. Monte Carlo simulation is used to show the size and the power of our method under several conditions. We study the real estate markets in Boston, Los Angeles and at the national U.S. level. We find structural breaks in the markets, and we segment the data into stationary segments. It is observed that the autoregressive parameter is increasing but stays below 1.","PeriodicalId":21047,"journal":{"name":"Real Estate eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79273840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Michael B. Imerman, J. Mason, Rajesh P. Narayanan, Meredith E. Rhodes
This paper analyzes price discovery among residential mortgage-backed securities (MBS), their credit default swaps (ABCDS), and the associated ABX contracts. VECM regressions show that the MBS and ABX markets lead price discovery over the ABCDS market. Neither the MBS nor the ABX market consistently dominate one another so that MBS and ABX markets respond to information simultaneously. Thus, while there is evidence that ABCDS were mispriced, there is no evidence for ABX market “overshooting” that was previously thought to have helped cause the recent mortgage market bubble and bust.
{"title":"Price Discovery in the Residential Mortgage-backed Security, Credit Default Swap, and ABX Markets","authors":"Michael B. Imerman, J. Mason, Rajesh P. Narayanan, Meredith E. Rhodes","doi":"10.2139/ssrn.3074878","DOIUrl":"https://doi.org/10.2139/ssrn.3074878","url":null,"abstract":"This paper analyzes price discovery among residential mortgage-backed securities (MBS), their credit default swaps (ABCDS), and the associated ABX contracts. VECM regressions show that the MBS and ABX markets lead price discovery over the ABCDS market. Neither the MBS nor the ABX market consistently dominate one another so that MBS and ABX markets respond to information simultaneously. Thus, while there is evidence that ABCDS were mispriced, there is no evidence for ABX market “overshooting” that was previously thought to have helped cause the recent mortgage market bubble and bust.","PeriodicalId":21047,"journal":{"name":"Real Estate eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82477368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent local price growth explains differences in search behavior across prospective homebuyers. Those experiencing higher growth in their postcode of residence search more broadly across locations and house characteristics, without changing attention devoted to individual sales listings, and have shorter search duration. Effects are stronger for homeowners, in particular those living in less wealthy areas and looking for a new primary residence. We use a quantitative equilibrium model and reduced-form analysis to show that the expansion of search breadth translates into widespread spillovers onto house sales prices and inventories of listings across postcodes within a metropolitan area.
{"title":"Local Experiences, Search and Spillovers in the Housing Market","authors":"Antonio Gargano, M. Giacoletti, Elvis Jarnecic","doi":"10.2139/ssrn.3519635","DOIUrl":"https://doi.org/10.2139/ssrn.3519635","url":null,"abstract":"Recent local price growth explains differences in search behavior across prospective homebuyers. Those experiencing higher growth in their postcode of residence search more broadly across locations and house characteristics, without changing attention devoted to individual sales listings, and have shorter search duration. Effects are stronger for homeowners, in particular those living in less wealthy areas and looking for a new primary residence. We use a quantitative equilibrium model and reduced-form analysis to show that the expansion of search breadth translates into widespread spillovers onto house sales prices and inventories of listings across postcodes within a metropolitan area.","PeriodicalId":21047,"journal":{"name":"Real Estate eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91356997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaozhou Ding, C. Bollinger, Michael Clark, W. Hoyt
In the past fifty years, a voluminous literature estimating the value of schools through capitalization in home prices has emerged. Prior research has identified capitalized value using various approaches including discontinuities caused by boundaries. We use changes in school boundaries and the proposal of a new school. Our findings from redistricting in the Fayette county school district (KY) show that prices for homes redistricted from a lower-performing (based on test scores) school into the proposed school catchment area increase by six percent. For houses in higher-performing school catchment areas redistricted to the proposed new school district, there is a smaller increase in value. Houses redistricted from higher-performing schools to lower-performing schools decrease in value by three to five percent. However, many of the redistricted properties see little or no significant change, suggesting that only extreme changes in school quality are capitalized. We estimate that homes in the redistricted areas increased by $108 million relative to homes that were not redistricted.
{"title":"How Do School District Boundary Changes and New School Proposals Affect Housing Prices","authors":"Xiaozhou Ding, C. Bollinger, Michael Clark, W. Hoyt","doi":"10.2139/ssrn.3531436","DOIUrl":"https://doi.org/10.2139/ssrn.3531436","url":null,"abstract":"In the past fifty years, a voluminous literature estimating the value of schools through capitalization in home prices has emerged. Prior research has identified capitalized value using various approaches including discontinuities caused by boundaries. We use changes in school boundaries and the proposal of a new school. Our findings from redistricting in the Fayette county school district (KY) show that prices for homes redistricted from a lower-performing (based on test scores) school into the proposed school catchment area increase by six percent. For houses in higher-performing school catchment areas redistricted to the proposed new school district, there is a smaller increase in value. Houses redistricted from higher-performing schools to lower-performing schools decrease in value by three to five percent. However, many of the redistricted properties see little or no significant change, suggesting that only extreme changes in school quality are capitalized. We estimate that homes in the redistricted areas increased by $108 million relative to homes that were not redistricted.","PeriodicalId":21047,"journal":{"name":"Real Estate eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86690163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cuong Nguyen, Ilan Noy, D. E. Sommervoll, Fang Yao
On the 22nd of February 2011, much of the residential housing stock in the city of Christchurch, New Zealand, was damaged by an unusually destructive earthquake. Almost all of the houses were insured. We ask whether insurance was able to mitigate the damage adequately, or whether the damage from the earthquake, and the associated insurance payments, led to a spatial re-ordering of the housing market in the city. We find a negative correlation between insurance pay-outs and house prices at the local level. We also uncover evidence that suggests that the mechanism behind this result is that in some cases houses were not fixed (i.e., owners having pocketed the payments) - indeed, insurance claims that were actively repaired (rather than paid directly) did not lead to any relative deterioration in prices. We use a genetic machine-learning algorithm which aims to improve on a standard hedonic model, and identify the dynamics of the housing market in the city, and three data sets: All housing market transactions, all earthquake insurance claims submitted to the public insurer, and all of the local authority’s building-consents data. Our results are important not only because the utility of catastrophe insurance is often questioned, but also because understanding what happens to property markets after disasters should be part of the overall assessment of the impact of the disaster itself. Without a quantification of these impacts, it is difficult to design policies that will optimally try to prevent or ameliorate disaster impacts.
{"title":"Redrawing of a Housing Market: Insurance Payouts and Housing Market Recovery in the Wake of the Christchurch Earthquake of 2011","authors":"Cuong Nguyen, Ilan Noy, D. E. Sommervoll, Fang Yao","doi":"10.2139/ssrn.3699240","DOIUrl":"https://doi.org/10.2139/ssrn.3699240","url":null,"abstract":"On the 22nd of February 2011, much of the residential housing stock in the city of Christchurch, New Zealand, was damaged by an unusually destructive earthquake. Almost all of the houses were insured. We ask whether insurance was able to mitigate the damage adequately, or whether the damage from the earthquake, and the associated insurance payments, led to a spatial re-ordering of the housing market in the city. We find a negative correlation between insurance pay-outs and house prices at the local level. We also uncover evidence that suggests that the mechanism behind this result is that in some cases houses were not fixed (i.e., owners having pocketed the payments) - indeed, insurance claims that were actively repaired (rather than paid directly) did not lead to any relative deterioration in prices. We use a genetic machine-learning algorithm which aims to improve on a standard hedonic model, and identify the dynamics of the housing market in the city, and three data sets: All housing market transactions, all earthquake insurance claims submitted to the public insurer, and all of the local authority’s building-consents data. Our results are important not only because the utility of catastrophe insurance is often questioned, but also because understanding what happens to property markets after disasters should be part of the overall assessment of the impact of the disaster itself. Without a quantification of these impacts, it is difficult to design policies that will optimally try to prevent or ameliorate disaster impacts.","PeriodicalId":21047,"journal":{"name":"Real Estate eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87156448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We focus on the housing market and examine why nonlocal home buyers (NLBs) pay 15 percent more for houses than local home buyers (LBs). We estimate a housing demand model that returns heterogeneous willingness to pay parameters for housing attributes. Our results show that NLBs are willing to pay more for specific housing attributes, especially for house size and school quality. We also find that gratification and reward arguments, and imperfect price information explain the price differential to a large extent. Search cost and house age arguments have an adverse effect on NLBs’ house spending.
{"title":"Why Do Buyers Pay Different Prices for Comparable Products? Evidence from the Housing Market","authors":"R. Siebert, Michael J. Seiler","doi":"10.2139/ssrn.3619686","DOIUrl":"https://doi.org/10.2139/ssrn.3619686","url":null,"abstract":"We focus on the housing market and examine why nonlocal home buyers (NLBs) pay 15 percent more for houses than local home buyers (LBs). We estimate a housing demand model that returns heterogeneous willingness to pay parameters for housing attributes. Our results show that NLBs are willing to pay more for specific housing attributes, especially for house size and school quality. We also find that gratification and reward arguments, and imperfect price information explain the price differential to a large extent. Search cost and house age arguments have an adverse effect on NLBs’ house spending.","PeriodicalId":21047,"journal":{"name":"Real Estate eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80726099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we analyse the impacts of low interest rates and lax underwriting standards on the US housing boom around the beginning of the new millennium. We suggest a time‐varying mean of the log price‐to‐rent ratio (PtR) to capture the persistent changes in housing prices. We show that the increasing latent trend in the PtR was significantly affected by the increased securitization of residential mortgage loans and decreasing interest rates, with the former effect being about three times larger than the latter. In the absence of securitization, negative interest rates would have been needed to reproduce an equally large housing boom since 2003.
{"title":"Low Mortgage Rates and Securitization: A Distinct Perspective on the US Housing Boom","authors":"H. Herwartz, Fang Xu","doi":"10.1111/sjoe.12320","DOIUrl":"https://doi.org/10.1111/sjoe.12320","url":null,"abstract":"In this paper, we analyse the impacts of low interest rates and lax underwriting standards on the US housing boom around the beginning of the new millennium. We suggest a time‐varying mean of the log price‐to‐rent ratio (PtR) to capture the persistent changes in housing prices. We show that the increasing latent trend in the PtR was significantly affected by the increased securitization of residential mortgage loans and decreasing interest rates, with the former effect being about three times larger than the latter. In the absence of securitization, negative interest rates would have been needed to reproduce an equally large housing boom since 2003.","PeriodicalId":21047,"journal":{"name":"Real Estate eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88907190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Los Angeles County's homeless population has increased by approximately 40 percent in the past five years. While county voters have supported the goal by approving billions of dollars in bonds that would provide tens of thousands of affordable housing units and services for the homeless, there remains a substantial gap in affordable housing for the homeless and low-income individuals who are at risk of homelessness, drive by fears and stigma in local communities. I investigate the effect of such housing sites on street homelessness, crime, and property values. I construct a comprehensive data that geocodes the locations of all designated homeless housing sites in Los Angeles County. Using spatial and time variation in homeless housing sites, I estimate the exposure of a community to designated homeless housing sites over time and use changes in this exposure to recover the causal relationship. I find that communities that had an increase in homeless housing in their boundaries and vicinity experience a sizable decline in homeless encampments, overall crime, and homeless-related crimes, and that housing values in these communities had increased.
{"title":"The Effects of Designated Homeless Housing Sites on Local Communities: Evidence from Los Angeles County","authors":"Elior Cohen","doi":"10.2139/ssrn.3513625","DOIUrl":"https://doi.org/10.2139/ssrn.3513625","url":null,"abstract":"Los Angeles County's homeless population has increased by approximately 40 percent in the past five years. While county voters have supported the goal by approving billions of dollars in bonds that would provide tens of thousands of affordable housing units and services for the homeless, there remains a substantial gap in affordable housing for the homeless and low-income individuals who are at risk of homelessness, drive by fears and stigma in local communities. I investigate the effect of such housing sites on street homelessness, crime, and property values. I construct a comprehensive data that geocodes the locations of all designated homeless housing sites in Los Angeles County. Using spatial and time variation in homeless housing sites, I estimate the exposure of a community to designated homeless housing sites over time and use changes in this exposure to recover the causal relationship. I find that communities that had an increase in homeless housing in their boundaries and vicinity experience a sizable decline in homeless encampments, overall crime, and homeless-related crimes, and that housing values in these communities had increased.","PeriodicalId":21047,"journal":{"name":"Real Estate eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75069363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Search engines play an important role in providing us with the main information of our daily life. The research on the search behavior on the Internet enjoys greater and greater popularity, for the search behavior has been proved to affect our daily decisions in purchasing, traveling, and even defining beauty. However, there is still a lack of full appreciation of the relation between the search behavior itself in terms of the emotional meaning and the decisions thus generated. Therefore, this study was carried out to analyze the emotional meanings of 13,915 English words obtained from Google Trends and the profits gained from the US house market by automatic transactions and discovered that the emotional meanings of the search contents could modulate the financial decision with unsupervised machine learning methods.
{"title":"The Power of Words: A Study of How Search Contents Can Affect Financial Decisions","authors":"Du Ni, Xingzhi Li, Zhi Xiao, Ke Gong","doi":"10.2139/ssrn.3508053","DOIUrl":"https://doi.org/10.2139/ssrn.3508053","url":null,"abstract":"Search engines play an important role in providing us with the main information of our daily life. The research on the search behavior on the Internet enjoys greater and greater popularity, for the search behavior has been proved to affect our daily decisions in purchasing, traveling, and even defining beauty. However, there is still a lack of full appreciation of the relation between the search behavior itself in terms of the emotional meaning and the decisions thus generated. Therefore, this study was carried out to analyze the emotional meanings of 13,915 English words obtained from Google Trends and the profits gained from the US house market by automatic transactions and discovered that the emotional meanings of the search contents could modulate the financial decision with unsupervised machine learning methods.","PeriodicalId":21047,"journal":{"name":"Real Estate eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74210908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Using novel shadow bank funding data, I find that shadow banks are funded by the very banks they compete with when originating mortgages. Evidence suggests that banks have market power in the upstream market for shadow banks’ funding, which in turn softens mortgage market competition through their strategic behaviors in both markets. I build and calibrate a quantitative model of vertical integration and competition to show that those consumers who would most benefit from shadow bank services are the ones to bear the costs. Secondary market innovation could increase downstream competition by reducing shadow banks’ reliance on their competitors.
{"title":"Financing Competitors: Shadow Banks' Funding and Mortgage Market Competition","authors":"E. Jiang","doi":"10.2139/ssrn.3556917","DOIUrl":"https://doi.org/10.2139/ssrn.3556917","url":null,"abstract":"\u0000 Using novel shadow bank funding data, I find that shadow banks are funded by the very banks they compete with when originating mortgages. Evidence suggests that banks have market power in the upstream market for shadow banks’ funding, which in turn softens mortgage market competition through their strategic behaviors in both markets. I build and calibrate a quantitative model of vertical integration and competition to show that those consumers who would most benefit from shadow bank services are the ones to bear the costs. Secondary market innovation could increase downstream competition by reducing shadow banks’ reliance on their competitors.","PeriodicalId":21047,"journal":{"name":"Real Estate eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80051209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：