Hong Kong, China’s housing market witnessed dramatic appreciations recently, with the price index for private domestic housing units being 3 times higher than 10 years ago. This trend is supported by both internal and external factors. By providing a theoretical model and empirical analysis on the key variables influencing housing prices, we find that changes in housing price index reinforce price trends in the long term. Hong Kong, China’s dollar quantitative easing, and the gross domestic product of the People’s Republic of China (PRC) are positively related to housing prices and negatively to lending. The inability to increase supplies in response to rising demand since 2003 has also much to do with the skyrocketing prices. Moreover, mortgage-to-total loans value is shrinking due to the unaffordability of housing units at current prices. This trend has to be tackled in time, otherwise the PRC may incur severe consequences similar to Japan’s experience in the 1990s.
{"title":"Internal and External Determinants of Housing Price Booms in Hong Kong, China","authors":"Farhad Taghizadeh‐Hesary, N. Yoshino, Alvin Chiu","doi":"10.2139/ssrn.3470064","DOIUrl":"https://doi.org/10.2139/ssrn.3470064","url":null,"abstract":"Hong Kong, China’s housing market witnessed dramatic appreciations recently, with the price index for private domestic housing units being 3 times higher than 10 years ago. This trend is supported by both internal and external factors. By providing a theoretical model and empirical analysis on the key variables influencing housing prices, we find that changes in housing price index reinforce price trends in the long term. Hong Kong, China’s dollar quantitative easing, and the gross domestic product of the People’s Republic of China (PRC) are positively related to housing prices and negatively to lending. The inability to increase supplies in response to rising demand since 2003 has also much to do with the skyrocketing prices. Moreover, mortgage-to-total loans value is shrinking due to the unaffordability of housing units at current prices. This trend has to be tackled in time, otherwise the PRC may incur severe consequences similar to Japan’s experience in the 1990s.","PeriodicalId":12014,"journal":{"name":"ERN: Microeconometric Studies of Housing Markets (Topic)","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77375357","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}
Jack Y Favilukis, P. Mabille, Stijn Van Nieuwerburgh
Housing affordability is the main policy challenge for most large cities in the world. Zoning changes, rent control, housing vouchers, and tax credits are the main levers employed by policy makers. How effective are they at combatting the affordability crisis? We build a dynamic stochastic spatial equilibrium model to evaluate the effect of these policies on the well-being of its citizens. The model endogenizes house prices, rents, construction, labor supply, output, income and wealth inequality, the location decisions of households within the city as well as inter-city migration. Its main novel features are risk, risk aversion, and incomplete risk-sharing. We calibrate the model to the New York MSA. Housing affordability policies carry substantial insurance value but affect aggregate housing and labor supply and cause misallocation in labor and housing markets. Housing affordability policies that enhance access to this insurance especially for the neediest households create substantial net welfare gains.
{"title":"Affordable Housing and City Welfare","authors":"Jack Y Favilukis, P. Mabille, Stijn Van Nieuwerburgh","doi":"10.2139/ssrn.3265918","DOIUrl":"https://doi.org/10.2139/ssrn.3265918","url":null,"abstract":"\u0000 Housing affordability is the main policy challenge for most large cities in the world. Zoning changes, rent control, housing vouchers, and tax credits are the main levers employed by policy makers. How effective are they at combatting the affordability crisis? We build a dynamic stochastic spatial equilibrium model to evaluate the effect of these policies on the well-being of its citizens. The model endogenizes house prices, rents, construction, labor supply, output, income and wealth inequality, the location decisions of households within the city as well as inter-city migration. Its main novel features are risk, risk aversion, and incomplete risk-sharing. We calibrate the model to the New York MSA. Housing affordability policies carry substantial insurance value but affect aggregate housing and labor supply and cause misallocation in labor and housing markets. Housing affordability policies that enhance access to this insurance especially for the neediest households create substantial net welfare gains.","PeriodicalId":12014,"journal":{"name":"ERN: Microeconometric Studies of Housing Markets (Topic)","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77841880","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 Regression discontinuity estimates indicate that home buying is highly responsive to interest rates in a large segment of the population. A surprise 50 basis point cut in the effective interest rate for mortgages insured by the Federal Housing Administration (FHA) led to an immediate 14 percent increase in home buying among the FHA-reliant population. We show that this large, extensive-margin effect arises from the rate cut helping borrowers overcome maximum debt payment to income (DTI) thresholds. We conclude that binding DTI constraints are an important feature of the mortgage market that amplify the effect of interest rate shocks.
{"title":"The Effect of Interest Rates on Home Buying: Evidence from a Shock to Mortgage Insurance Premiums","authors":"Neil Bhutta, Daniel R. Ringo","doi":"10.2139/ssrn.3085008","DOIUrl":"https://doi.org/10.2139/ssrn.3085008","url":null,"abstract":"Abstract Regression discontinuity estimates indicate that home buying is highly responsive to interest rates in a large segment of the population. A surprise 50 basis point cut in the effective interest rate for mortgages insured by the Federal Housing Administration (FHA) led to an immediate 14 percent increase in home buying among the FHA-reliant population. We show that this large, extensive-margin effect arises from the rate cut helping borrowers overcome maximum debt payment to income (DTI) thresholds. We conclude that binding DTI constraints are an important feature of the mortgage market that amplify the effect of interest rate shocks.","PeriodicalId":12014,"journal":{"name":"ERN: Microeconometric Studies of Housing Markets (Topic)","volume":"84 4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76124079","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 article studies the evolution of housing rents in St. Petersburg between 1880 and 1917, covering an eventful period of Russian and world history. We collect and digitize over 5,000 rental advertisements from a local newspaper, which we use together with geo-coded addresses and detailed structural characteristics to construct a quality-adjusted rent price index in continuous time. We provide the first pre-war and pre-Soviet index based on market data for any Russian housing market. In 1915, one of the world's earliest rent control and tenant protection policies was introduced in response to soaring prices following the outbreak of World War I. We analyze the impact of this policy: while before the regulation rents were increasing at a similar rapid pace as other consumer prices, the policy reversed this trend. We find evidence for official compliance with the policy, document a rise in tenure duration and strongly increased rent affordability among workers after the introduction of the policy. We conclude that the immediate prelude to the October Revolution was indeed characterized by economic turmoil, but rent affordability and rising rents were no longer the dominating problems.
{"title":"Housing Rent Dynamics and Rent Regulation in St. Petersburg (1880-1917)","authors":"K. Kholodilin, L. Limonov, S. Waltl","doi":"10.2139/ssrn.3334471","DOIUrl":"https://doi.org/10.2139/ssrn.3334471","url":null,"abstract":"This article studies the evolution of housing rents in St. Petersburg between 1880 and 1917, covering an eventful period of Russian and world history. We collect and digitize over 5,000 rental advertisements from a local newspaper, which we use together with geo-coded addresses and detailed structural characteristics to construct a quality-adjusted rent price index in continuous time. We provide the first pre-war and pre-Soviet index based on market data for any Russian housing market. In 1915, one of the world's earliest rent control and tenant protection policies was introduced in response to soaring prices following the outbreak of World War I. We analyze the impact of this policy: while before the regulation rents were increasing at a similar rapid pace as other consumer prices, the policy reversed this trend. We find evidence for official compliance with the policy, document a rise in tenure duration and strongly increased rent affordability among workers after the introduction of the policy. We conclude that the immediate prelude to the October Revolution was indeed characterized by economic turmoil, but rent affordability and rising rents were no longer the dominating problems.","PeriodicalId":12014,"journal":{"name":"ERN: Microeconometric Studies of Housing Markets (Topic)","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77036895","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}
Before 2008, the government?s ?implicit guarantee? of the securities issued by the government-sponsored enterprises (GSEs) Fannie Mae and Freddie Mac led to practices by these institutions that threatened financial stability. In 2008, the Federal Housing Finance Agency placed these GSEs into conservatorship. Conservatorship was intended to be temporary but has now reached its tenth year, and policymakers continue to weigh options for reform. In this article, the authors assess both implicit and explicit government guarantees for the GSEs. They argue that adopting a legislatively defined ?explicit guarantee,? as advocated by some, may be problematic for a variety of reasons, including the difficulty of pricing such a guarantee and the potential high cost for mortgage holders or the government. In addition to the creation of an explicit guarantee, they recommend that steps be taken to limit systemic risk in housing markets. To that end, they advocate the wider adoption of mortgages?such as the ?Fixed-COFI? mortgage?that build homeowner equity faster than the thirty-year fixed-rate mortgage favored by the GSEs. With such mortgages, homeowners are better able to weather economic downturns.
{"title":"GSE Guarantees, Financial Stability, and Home Equity Accumulation","authors":"S. W. Passmore, A. V. von Hafften","doi":"10.2139/ssrn.3298883","DOIUrl":"https://doi.org/10.2139/ssrn.3298883","url":null,"abstract":"Before 2008, the government?s ?implicit guarantee? of the securities issued by the government-sponsored enterprises (GSEs) Fannie Mae and Freddie Mac led to practices by these institutions that threatened financial stability. In 2008, the Federal Housing Finance Agency placed these GSEs into conservatorship. Conservatorship was intended to be temporary but has now reached its tenth year, and policymakers continue to weigh options for reform. In this article, the authors assess both implicit and explicit government guarantees for the GSEs. They argue that adopting a legislatively defined ?explicit guarantee,? as advocated by some, may be problematic for a variety of reasons, including the difficulty of pricing such a guarantee and the potential high cost for mortgage holders or the government. In addition to the creation of an explicit guarantee, they recommend that steps be taken to limit systemic risk in housing markets. To that end, they advocate the wider adoption of mortgages?such as the ?Fixed-COFI? mortgage?that build homeowner equity faster than the thirty-year fixed-rate mortgage favored by the GSEs. With such mortgages, homeowners are better able to weather economic downturns.","PeriodicalId":12014,"journal":{"name":"ERN: Microeconometric Studies of Housing Markets (Topic)","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81590093","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 study investigates the spatial statistics of house prices in Beijing, China. We examine whether the house prices in one region is affected by the house price in neighbouring regions. We also investigate how the house prices in one region is affected by unknown characteristics of the neighbouring regions. Moreover, we analyse whether the explanatory factors of house prices in one region are affected by explanatory factors of house prices in neighbouring regions. Subsequently, we attempt to investigate the spatial spill-over effects of explanatory factors. Initially, we use Lagrange Multiplier (LM) test to examine the significance of spatial autocorrelation. After this we apply the spatial autoregressive model (SAR), spatial Durbin model (SDM), spatial autoregressive model with autoregressive disturbances (SAC) and spatial error model (SEM) into spatial regression methods. The paper overcomes the shortcomings of the previous studies by extending the range of examining spatial models, providing reasonable spatial model selection procedures, and employing improved spatial weights to analysing spillover effects of explanatory factors. On the aspect of analysing direct and indirect (spill-over) effects, this study examines the partitioning of direct and indirect effects and finds out the impacts of the neighbouring factors. Evidence is found for spatial dependence of house prices: house prices in one region are influenced by the house prices in neighbouring regions positively and significantly. Evidence is found for spatial heterogeneity of house prices across the space: house prices in neighbouring regions spill over more in times of increasing neighbouring house prices, then when neighbouring house prices are declining. Evidence is found for spatial spillover effects of explanatory factors: increases of average wage of real estate staff, income , tax, urban population and the house prices of the previous year increases the house prices positively in neighbouring regions; a decrease of unemployment drives down the house prices in neighbouring regions.
{"title":"Is It a Curse or a Blessing to Live Near Rich Neighbors? Spatial Analysis and Spillover Effects of House Prices in Beijing","authors":"K. Vergos, Hui Zhi","doi":"10.2139/ssrn.3289359","DOIUrl":"https://doi.org/10.2139/ssrn.3289359","url":null,"abstract":"This study investigates the spatial statistics of house prices in Beijing, China. We examine whether the house prices in one region is affected by the house price in neighbouring regions. We also investigate how the house prices in one region is affected by unknown characteristics of the neighbouring regions. Moreover, we analyse whether the explanatory factors of house prices in one region are affected by explanatory factors of house prices in neighbouring regions. Subsequently, we attempt to investigate the spatial spill-over effects of explanatory factors. Initially, we use Lagrange Multiplier (LM) test to examine the significance of spatial autocorrelation. After this we apply the spatial autoregressive model (SAR), spatial Durbin model (SDM), spatial autoregressive model with autoregressive disturbances (SAC) and spatial error model (SEM) into spatial regression methods. The paper overcomes the shortcomings of the previous studies by extending the range of examining spatial models, providing reasonable spatial model selection procedures, and employing improved spatial weights to analysing spillover effects of explanatory factors. On the aspect of analysing direct and indirect (spill-over) effects, this study examines the partitioning of direct and indirect effects and finds out the impacts of the neighbouring factors. Evidence is found for spatial dependence of house prices: house prices in one region are influenced by the house prices in neighbouring regions positively and significantly. Evidence is found for spatial heterogeneity of house prices across the space: house prices in neighbouring regions spill over more in times of increasing neighbouring house prices, then when neighbouring house prices are declining. Evidence is found for spatial spillover effects of explanatory factors: increases of average wage of real estate staff, income , tax, urban population and the house prices of the previous year increases the house prices positively in neighbouring regions; a decrease of unemployment drives down the house prices in neighbouring regions.","PeriodicalId":12014,"journal":{"name":"ERN: Microeconometric Studies of Housing Markets (Topic)","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78439393","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}
Professional sports facilities and teams generate local amenity flows in cities that may affect property values. Previous research shows evidence of important positive and negative local amenity flows based on case studies of changes in residential property values in specific cities. We analyze changes in residential property values in Oklahoma City over a period, 2000 to 2016, where both temporary and permanent exogenous shocks to local sports-related amenities occurred. Results from hedonic price models and repeat sales regression models show that nearby residential property prices increased after the opening of a new arena and the arrival of a new, permanent NBA team in the city. The presence of a temporary NBA team visiting the city also had a positive impact.
{"title":"Sports Arenas, Teams and Property Values: Temporary and Permanent Shocks to Local Amenity Flows","authors":"Yulia Chikish, B. Humphreys, Adam D. Nowak","doi":"10.2139/ssrn.3254241","DOIUrl":"https://doi.org/10.2139/ssrn.3254241","url":null,"abstract":"Professional sports facilities and teams generate local amenity flows in cities that may affect property values. Previous research shows evidence of important positive and negative local amenity flows based on case studies of changes in residential property values in specific cities. We analyze changes in residential property values in Oklahoma City over a period, 2000 to 2016, where both temporary and permanent exogenous shocks to local sports-related amenities occurred. Results from hedonic price models and repeat sales regression models show that nearby residential property prices increased after the opening of a new arena and the arrival of a new, permanent NBA team in the city. The presence of a temporary NBA team visiting the city also had a positive impact.","PeriodicalId":12014,"journal":{"name":"ERN: Microeconometric Studies of Housing Markets (Topic)","volume":"190 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72797576","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 While a timely, accurate house price index with broad coverage is of significant importance in housing market research and analysis, the lack of reliable raw data sources remains a major constraint in the house price index construction in nascent housing markets such as China. In this study, we introduce online listing information as an innovative data source for house price index construction, using China's housing resale markets as an example. Compared with alternative data sources, such as the officially-registered transaction information of housing resales, our analysis shows that online listing data provide a better trade-off between accuracy, reliability, and feasibility, especially after the resolution of potential replicated and/or manipulated data issues using our proposed procedures. Based on the cleaned online listing information, we calculate the first housing resale price indices covering almost all (274) Chinese cities. In particular, for around 200 relatively smaller cities, the index provides the first regular house price indicator, which shows a significant divergence in house price dynamics between different tiers of cities. We also briefly discuss the potential extensions of the listing price index, including the daily house price index and the housing rental price index.
{"title":"House Price Index Based on Online Listing Information: The Case of China","authors":"Xiaodan Wang, Keyang Li, Jing Wu","doi":"10.2139/ssrn.3223256","DOIUrl":"https://doi.org/10.2139/ssrn.3223256","url":null,"abstract":"Abstract While a timely, accurate house price index with broad coverage is of significant importance in housing market research and analysis, the lack of reliable raw data sources remains a major constraint in the house price index construction in nascent housing markets such as China. In this study, we introduce online listing information as an innovative data source for house price index construction, using China's housing resale markets as an example. Compared with alternative data sources, such as the officially-registered transaction information of housing resales, our analysis shows that online listing data provide a better trade-off between accuracy, reliability, and feasibility, especially after the resolution of potential replicated and/or manipulated data issues using our proposed procedures. Based on the cleaned online listing information, we calculate the first housing resale price indices covering almost all (274) Chinese cities. In particular, for around 200 relatively smaller cities, the index provides the first regular house price indicator, which shows a significant divergence in house price dynamics between different tiers of cities. We also briefly discuss the potential extensions of the listing price index, including the daily house price index and the housing rental price index.","PeriodicalId":12014,"journal":{"name":"ERN: Microeconometric Studies of Housing Markets (Topic)","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74113477","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 article, we analyze to what extent the influence of housing market determinants and especially the credit market vary across countries and time. We do this by means of an international panel data set, consisting of quarterly data for 18 industrialized countries between 1975/01 and 2017/02. Moreover, we test whether our findings hold true when controlling for periods of house-price booms and busts relative to normal phases, a structural break in 1985 (Great Moderation), as well as selected characteristics of housing finance. Five results are worth highlighting. First, house-prices are best explained by disposable income, residential investment, the unemployment rate, the credit market, a business cycle dummy, and the cpi inflation. Second, the cpi inflation and the business cycle dummy are only significant after 1985, but are insignificant before. Third, the credit market only influences house-prices in normal and boom times, and the cpi inflation only in normal and bust times. Fourth, the LTV ratio has a strong and positive impact on house-price growth, and fifth, the degree to which an increase in credit growth is passed on to the housing market is strongest in countries with high LTV ratios in normal times, and in countries with more developed secondary mortgage markets in boom times.
{"title":"House-Prices and the Credit Market - Evidence from an International Panel of Industrialized Economies","authors":"M. Kern, H. Wagner","doi":"10.2139/ssrn.3220328","DOIUrl":"https://doi.org/10.2139/ssrn.3220328","url":null,"abstract":"In this article, we analyze to what extent the influence of housing market determinants and especially the credit market vary across countries and time. We do this by means of an international panel data set, consisting of quarterly data for 18 industrialized countries between 1975/01 and 2017/02. Moreover, we test whether our findings hold true when controlling for periods of house-price booms and busts relative to normal phases, a structural break in 1985 (Great Moderation), as well as selected characteristics of housing finance. Five results are worth highlighting. First, house-prices are best explained by disposable income, residential investment, the unemployment rate, the credit market, a business cycle dummy, and the cpi inflation. Second, the cpi inflation and the business cycle dummy are only significant after 1985, but are insignificant before. Third, the credit market only influences house-prices in normal and boom times, and the cpi inflation only in normal and bust times. Fourth, the LTV ratio has a strong and positive impact on house-price growth, and fifth, the degree to which an increase in credit growth is passed on to the housing market is strongest in countries with high LTV ratios in normal times, and in countries with more developed secondary mortgage markets in boom times.","PeriodicalId":12014,"journal":{"name":"ERN: Microeconometric Studies of Housing Markets (Topic)","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75682710","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 exploit a unique dataset that features both un-intermediated mortgage requests and independent responses from multiple banks to each request. We show that households typically are not prudent risk managers, but prioritize minimizing current mortgage payments over insurance against future rate increases. Contrary to assumptions in the previous literature, we find that banks do also influence contracted rate fixation periods. They trade off their own exposure to interest rate risk against household requests and against credit risk.
{"title":"How Do Banks and Households Manage Interest Rate Risk? Evidence from Mortgage Applications and Banks’ Responses","authors":"Christoph Basten, B. Guin, Cathérine Koch","doi":"10.2139/ssrn.3192943","DOIUrl":"https://doi.org/10.2139/ssrn.3192943","url":null,"abstract":"We exploit a unique dataset that features both un-intermediated mortgage requests and independent responses from multiple banks to each request. We show that households typically are not prudent risk managers, but prioritize minimizing current mortgage payments over insurance against future rate increases. Contrary to assumptions in the previous literature, we find that banks do also influence contracted rate fixation periods. They trade off their own exposure to interest rate risk against household requests and against credit risk.","PeriodicalId":12014,"journal":{"name":"ERN: Microeconometric Studies of Housing Markets (Topic)","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87534132","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}