This study documents that over 10% of the presale contracts in the Hong Kong housing market between 1996 and 2014 were rescinded, resulting in a loss of HKD 436.67 million per year. We then investigate potential determinants of contracts rescission from a novel perspective of option theory. We find out-of-the-money presale contracts (with market price being lower than the outstanding payment at settlement) have a 12.2% higher rescission rate. The rescission rate is also higher when presale homebuyers bear more of the price risk as proxied by option delta and time-induced risk as proxied by time-to-maturity. Moreover, we find rescission rates drop significantly after the Hong Kong government’s housing market macroprudential measures. Our findings shed light on understanding the mechanism of presale contracts rescission, homebuyers’ strategic default behaviour, and the role of housing market regulation in mitigating rescissions.
{"title":"Contract Rescission in the Real Estate Presale Market","authors":"Quan Gan, M. Hu, Wayne Xinwei Wan","doi":"10.2139/ssrn.3738130","DOIUrl":"https://doi.org/10.2139/ssrn.3738130","url":null,"abstract":"This study documents that over 10% of the presale contracts in the Hong Kong housing market between 1996 and 2014 were rescinded, resulting in a loss of HKD 436.67 million per year. We then investigate potential determinants of contracts rescission from a novel perspective of option theory. We find out-of-the-money presale contracts (with market price being lower than the outstanding payment at settlement) have a 12.2% higher rescission rate. The rescission rate is also higher when presale homebuyers bear more of the price risk as proxied by option delta and time-induced risk as proxied by time-to-maturity. Moreover, we find rescission rates drop significantly after the Hong Kong government’s housing market macroprudential measures. Our findings shed light on understanding the mechanism of presale contracts rescission, homebuyers’ strategic default behaviour, and the role of housing market regulation in mitigating rescissions.","PeriodicalId":12014,"journal":{"name":"ERN: Microeconometric Studies of Housing Markets (Topic)","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88364424","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 assess racial discrimination in mortgage approvals using new data on mortgage applications. Minority applicants tend to have significantly lower credit scores, higher leverage, and are less likely than white applicants to receive algorithmic approval from race-blind government automated underwriting systems (AUS). Observable applicant-risk factors explain most of the racial disparities in lender denials. Further, we exploit the AUS data to show there are risk factors we do not directly observe, and our analysis indicates that these factors explain at least some of the residual 1-2 percentage point denial gaps. Overall, we find that differential treatment has played a limited role in generating denial disparities in recent years.
{"title":"How Much Does Racial Bias Affect Mortgage Lending? Evidence from Human and Algorithmic Credit Decisions","authors":"Neil Bhutta, Aurel Hizmo, Daniel R. Ringo","doi":"10.2139/ssrn.3887663","DOIUrl":"https://doi.org/10.2139/ssrn.3887663","url":null,"abstract":"We assess racial discrimination in mortgage approvals using new data on mortgage applications. Minority applicants tend to have significantly lower credit scores, higher leverage, and are less likely than white applicants to receive algorithmic approval from race-blind government automated underwriting systems (AUS). Observable applicant-risk factors explain most of the racial disparities in lender denials. Further, we exploit the AUS data to show there are risk factors we do not directly observe, and our analysis indicates that these factors explain at least some of the residual 1-2 percentage point denial gaps. Overall, we find that differential treatment has played a limited role in generating denial disparities in recent years.","PeriodicalId":12014,"journal":{"name":"ERN: Microeconometric Studies of Housing Markets (Topic)","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88045514","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}
Felix Lorenz, Jonas Willwersch, Marcelo Cajias, F. Fuerst
While Machine Learning (ML) excels at predictive tasks, its inferential capacity is limited due to its complex non-parametric structure. This paper aims to elucidate the analytical behavior of ML through Interpretable Machine Learning (IML) in a real estate context. Using a hedonic ML approach to predict unit-level residential rents for Frankfurt, Germany, we apply a set of model-agnostic interpretation methods to decompose the rental value drivers and plot their trajectories over time. Living area and building age are the strongest predictors of rent, followed by proximity to CBD and neighborhood amenities. Our approach is able to detect the critical distances to these centers beyond which rents tend to decline more rapidly. Conversely, close proximity to hospitality facilities as well as public transport is associated with rental discounts. Overall, our results suggest that IML methods provide insights into algorithmic decision-making by illustrating the relative importance of hedonic variables and their relationship with rental prices in a dynamic perspective.
{"title":"Interpretable Machine Learning for Real Estate Market Analysis","authors":"Felix Lorenz, Jonas Willwersch, Marcelo Cajias, F. Fuerst","doi":"10.2139/ssrn.3835931","DOIUrl":"https://doi.org/10.2139/ssrn.3835931","url":null,"abstract":"While Machine Learning (ML) excels at predictive tasks, its inferential capacity is limited due to its complex non-parametric structure. This paper aims to elucidate the analytical behavior of ML through Interpretable Machine Learning (IML) in a real estate context. Using a hedonic ML approach to predict unit-level residential rents for Frankfurt, Germany, we apply a set of model-agnostic interpretation methods to decompose the rental value drivers and plot their trajectories over time. Living area and building age are the strongest predictors of rent, followed by proximity to CBD and neighborhood amenities. Our approach is able to detect the critical distances to these centers beyond which rents tend to decline more rapidly. Conversely, close proximity to hospitality facilities as well as public transport is associated with rental discounts. Overall, our results suggest that IML methods provide insights into algorithmic decision-making by illustrating the relative importance of hedonic variables and their relationship with rental prices in a dynamic perspective.","PeriodicalId":12014,"journal":{"name":"ERN: Microeconometric Studies of Housing Markets (Topic)","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80552188","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}
Abstract This paper studies the impact of home purchase restrictions on China's housing market. We estimate a structural model of household preference for housing, real estate developers' pricing decisions, and equilibrium market outcome in five large cities. By comparing the estimation results from pre- and post-policy intervention, we find that, after home purchase restrictions are implemented, overall housing demand in most cities becomes weaker and less price elastic; meanwhile, real estate developers face higher holding costs and thus are willing to lower prices and sell more quickly. Counterfactual analyses show that in some cities alternative policy designs that cause less structural change of demand could achieve larger consumer welfare and social welfare than the implemented policy.
{"title":"Examining the Impact of Home Purchase Restrictions on China's Housing Market","authors":"Zhentong Lu, Sisi Zhang, Jian Hong","doi":"10.2139/ssrn.3503541","DOIUrl":"https://doi.org/10.2139/ssrn.3503541","url":null,"abstract":"Abstract This paper studies the impact of home purchase restrictions on China's housing market. We estimate a structural model of household preference for housing, real estate developers' pricing decisions, and equilibrium market outcome in five large cities. By comparing the estimation results from pre- and post-policy intervention, we find that, after home purchase restrictions are implemented, overall housing demand in most cities becomes weaker and less price elastic; meanwhile, real estate developers face higher holding costs and thus are willing to lower prices and sell more quickly. Counterfactual analyses show that in some cities alternative policy designs that cause less structural change of demand could achieve larger consumer welfare and social welfare than the implemented policy.","PeriodicalId":12014,"journal":{"name":"ERN: Microeconometric Studies of Housing Markets (Topic)","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89019454","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}
Rhiannon L. Jerch, P. Barwick, Shanjun Li, Jing Wu
Canonical urban models postulate transportation cost as a key element in determining urban spatial structure. This paper examines how road rationing policies impact the spatial distribution of households using rich micro data on housing transactions and resident demographics in Beijing. We find that Beijing's road rationing policy significantly increased the demand for housing near subway stations as well as CBD. The premium for proximity is stable in the periods prior to the driving restriction, but shifts significantly in the aftermath of the policy. The composition of households living close to subway stations and Beijing's CBD shifts toward wealthier households, consistent with theoretical predictions of the monocentric city model with income-stratified transit modes. Our findings suggest that city-wide road rationing policies can have the unintended consequence of limiting access to public transit for lower income individuals.
{"title":"Road Rationing Policies and Housing Markets","authors":"Rhiannon L. Jerch, P. Barwick, Shanjun Li, Jing Wu","doi":"10.2139/ssrn.3766254","DOIUrl":"https://doi.org/10.2139/ssrn.3766254","url":null,"abstract":"Canonical urban models postulate transportation cost as a key element in determining urban spatial structure. This paper examines how road rationing policies impact the spatial distribution of households using rich micro data on housing transactions and resident demographics in Beijing. We find that Beijing's road rationing policy significantly increased the demand for housing near subway stations as well as CBD. The premium for proximity is stable in the periods prior to the driving restriction, but shifts significantly in the aftermath of the policy. The composition of households living close to subway stations and Beijing's CBD shifts toward wealthier households, consistent with theoretical predictions of the monocentric city model with income-stratified transit modes. Our findings suggest that city-wide road rationing policies can have the unintended consequence of limiting access to public transit for lower income individuals.","PeriodicalId":12014,"journal":{"name":"ERN: Microeconometric Studies of Housing Markets (Topic)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85093785","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}
Millions of properties in the U.S. are exposed to increasing threats from natural disasters. Yet, a large majority of at-risk homes are uninsured against the costliest disaster: flooding. Floods cause elevated rates of mortgage delinquency and default that can impact the broader housing finance system. In this paper, we explore the connection between homeowners' stake in their homes and their demand for flood insurance. To isolate the causal effect of home equity on food insurance demand, we study the response of flood insurance take-up to sudden house price changes over the housing boom and bust in the 2000s. We find that flood insurance take-up follows the dynamics of house prices in each market over the boom-bust cycle, with a home price elasticity around 0.33. A series of heterogeneity and robustness checks suggest that the role of mortgage default as implicit insurance is the most plausible mechanism for the positive relationship. We conclude by discussing the implications of our results for the effects of climate change on real estate and financial markets as well as for optimal disaster insurance policy.
{"title":"What's at Stake? Understanding the Role of Home Equity in Flood Insurance Demand","authors":"Yanjun Liao, P. Mulder","doi":"10.2139/ssrn.3756332","DOIUrl":"https://doi.org/10.2139/ssrn.3756332","url":null,"abstract":"Millions of properties in the U.S. are exposed to increasing threats from natural disasters. Yet, a large majority of at-risk homes are uninsured against the costliest disaster: flooding. Floods cause elevated rates of mortgage delinquency and default that can impact the broader housing finance system. In this paper, we explore the connection between homeowners' stake in their homes and their demand for flood insurance. To isolate the causal effect of home equity on food insurance demand, we study the response of flood insurance take-up to sudden house price changes over the housing boom and bust in the 2000s. We find that flood insurance take-up follows the dynamics of house prices in each market over the boom-bust cycle, with a home price elasticity around 0.33. A series of heterogeneity and robustness checks suggest that the role of mortgage default as implicit insurance is the most plausible mechanism for the positive relationship. We conclude by discussing the implications of our results for the effects of climate change on real estate and financial markets as well as for optimal disaster insurance policy.","PeriodicalId":12014,"journal":{"name":"ERN: Microeconometric Studies of Housing Markets (Topic)","volume":"122 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88023288","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 develop a unified framework with schools and residential choices and study the welfare and distributional consequences of switching from the traditional neighborhood assignment to the celebrated Deferred Acceptance mechanism. We show that when families receive higher priorities at neighborhood schools, the Deferred Acceptance mechanism improves aggregate or average welfare compared to the neighborhood assignment. Moreover, the Deferred Acceptance mechanism improves the welfare of lowest-income families, both with and without neighborhood priorities. Our work also lays theoretical foundations for analyzing general assignment games with externalities.
{"title":"School Choice and the Housing Market","authors":"A. Grigoryan","doi":"10.2139/ssrn.3848180","DOIUrl":"https://doi.org/10.2139/ssrn.3848180","url":null,"abstract":"We develop a unified framework with schools and residential choices and study the welfare and distributional consequences of switching from the traditional neighborhood assignment to the celebrated Deferred Acceptance mechanism. We show that when families receive higher priorities at neighborhood schools, the Deferred Acceptance mechanism improves aggregate or average welfare compared to the neighborhood assignment. Moreover, the Deferred Acceptance mechanism improves the welfare of lowest-income families, both with and without neighborhood priorities. Our work also lays theoretical foundations for analyzing general assignment games with externalities.","PeriodicalId":12014,"journal":{"name":"ERN: Microeconometric Studies of Housing Markets (Topic)","volume":"123 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85678701","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}
Multivariable regression may be the most prevalent and useful task in social science. Empirical legal studies rely heavily on the ordinary least squares method. Conventional regression methods have attained credibility in court, but by no means do they dictate legal outcomes. Using the iconic Boston housing study as a source of price data, this Article introduces machine-learning regression methods. Although decision trees and forest ensembles lack the overt interpretability of linear regression, these methods reduce the opacity of black-box techniques by scoring the relative importance of dataset features. This Article will also address the theoretical tradeoff between bias and variance, as well as the importance of training, cross-validation, and reserving a holdout dataset for testing.
{"title":"Split Decisions: Practical Machine Learning for Empirical Legal Scholarship","authors":"J. Chen","doi":"10.2139/ssrn.3731307","DOIUrl":"https://doi.org/10.2139/ssrn.3731307","url":null,"abstract":"Multivariable regression may be the most prevalent and useful task in social science. Empirical legal studies rely heavily on the ordinary least squares method. Conventional regression methods have attained credibility in court, but by no means do they dictate legal outcomes. Using the iconic Boston housing study as a source of price data, this Article introduces machine-learning regression methods. Although decision trees and forest ensembles lack the overt interpretability of linear regression, these methods reduce the opacity of black-box techniques by scoring the relative importance of dataset features. This Article will also address the theoretical tradeoff between bias and variance, as well as the importance of training, cross-validation, and reserving a holdout dataset for testing.","PeriodicalId":12014,"journal":{"name":"ERN: Microeconometric Studies of Housing Markets (Topic)","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90300517","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}
A. Carella, Federica Ciocchetta, F. Signoretti, V. Michelangeli
We exploit a novel dataset on mortgages offered by banks through Italy’s main online mortgage broker, which works with banks representing over 80 per cent of mortgages granted, to gain an up-to-date assessment of loan supply conditions. Characteristics of mortgages are reported for about 85,000 borrower-contract profiles, constant over time, available at the beginning of each month starting from March 2018. We document that riskier applications, characterized by high loan-to-value ratios and long maturity, are, on average, offered by a smaller number of banks that charge higher interest rates. Online banks tend to provide better price conditions than traditional intermediaries. We use the online rates offered to nowcast bank-level official (MIR) interest rate statistics, available only several weeks later. By using both regression analysis and machine learning algorithms, we show that the rates offered have significant predictive content for fixed-rate contracts, also after controlling for time-varying demand conditions, market reference rates, and unobserved time-invariant bank characteristics. Machine learning algorithms provide further improvements over regression models in out of sample predictions.
{"title":"What Can We Learn About Mortgage Supply from Online Data?","authors":"A. Carella, Federica Ciocchetta, F. Signoretti, V. Michelangeli","doi":"10.2139/ssrn.3746190","DOIUrl":"https://doi.org/10.2139/ssrn.3746190","url":null,"abstract":"We exploit a novel dataset on mortgages offered by banks through Italy’s main online mortgage broker, which works with banks representing over 80 per cent of mortgages granted, to gain an up-to-date assessment of loan supply conditions. Characteristics of mortgages are reported for about 85,000 borrower-contract profiles, constant over time, available at the beginning of each month starting from March 2018. We document that riskier applications, characterized by high loan-to-value ratios and long maturity, are, on average, offered by a smaller number of banks that charge higher interest rates. Online banks tend to provide better price conditions than traditional intermediaries. We use the online rates offered to nowcast bank-level official (MIR) interest rate statistics, available only several weeks later. By using both regression analysis and machine learning algorithms, we show that the rates offered have significant predictive content for fixed-rate contracts, also after controlling for time-varying demand conditions, market reference rates, and unobserved time-invariant bank characteristics. Machine learning algorithms provide further improvements over regression models in out of sample predictions.","PeriodicalId":12014,"journal":{"name":"ERN: Microeconometric Studies of Housing Markets (Topic)","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90403319","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}
This paper studies housing markets with price posting, directed search, and heterogeneity. One contribution is theoretical: in a novel model, we provide sharp results on existence, uniqueness and comparative statics. Another is empirical: we explore a new dataset from Vancouver. Then we confront theory and data. The framework is broadly consistent with evidence, and, in particular, generates price dispersion and stickiness. Beyond broad consistency, we develop formal methods for identification and estimation. Structural estimation reveals that accounting for price dispersion by search requires extreme assumptions. Hence we explore implications of specifications where search and unobserved heterogeneity both contribute to dispersion.
{"title":"How Well Does Search Theory Explain Housing Prices?","authors":"M. Rekkas, Randall Wright, Yu Zhu","doi":"10.2139/ssrn.3706329","DOIUrl":"https://doi.org/10.2139/ssrn.3706329","url":null,"abstract":"This paper studies housing markets with price posting, directed search, and heterogeneity. One contribution is theoretical: in a novel model, we provide sharp results on existence, uniqueness and comparative statics. Another is empirical: we explore a new dataset from Vancouver. Then we confront theory and data. The framework is broadly consistent with evidence, and, in particular, generates price dispersion and stickiness. Beyond broad consistency, we develop formal methods for identification and estimation. Structural estimation reveals that accounting for price dispersion by search requires extreme assumptions. Hence we explore implications of specifications where<br>search and unobserved heterogeneity both contribute to dispersion.","PeriodicalId":12014,"journal":{"name":"ERN: Microeconometric Studies of Housing Markets (Topic)","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80146705","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}
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