While the world went through rapid urbanization in the last half century, house prices in many densely populated metropolitan regions are becoming increasingly unaffordable. As a result, many families turn to rental housing. However, the high rents in some markets also place significant burdens on low-income households. Thus, a lot of housing policies and strategies have been introduced by national and local governments to subsidize low-income families to improve their rental housing affordability. The low-income housing tax credit program (LIHTC) in the USA and the national rental affordability scheme (NRAS) in Australia are such examples. Both policies aim to increase supply of affordable rental housing for low-income families. LIHTC finances the development of affordable rental housing through a tax credit system, whereas NRAS provides an annual tax-free incentive for investors to purchase new affordable dwellings and rent them at 20% below market rents to low-income families. LIHTC has been implemented in US for more than a quarter century since 1986, while NARS has relatively short history since 2008. However, whether these programs will increase the long-term housing supply, or will they simply “crowd out” other type of affordable rental housing remains an open question.This paper first studies the long-term impact of LIHTC on housing supply, using the property level LIHTC data from 1986 to 2011, as well as other housing subsidy and housing supply data, including non-LIHTC rental subsidy programs, housing vouchers, housing permits, etc. An empirical linear OLS model is estimated to find the long-run sensitivity of housing supply to LIHTC program, controlling for other supply/demand variables. We find LIHTC has strong positive effect on overall housing supply.Then we compare LIHTC to NRAS program and try to forecast the effectiveness of implementing NARS for increasing affordable rental housing supply, with limited historical data. Similar results show that NRAS has fully compensated for traditional public rental units decline. The comparative study is important because it makes it possible to evaluate the effectiveness of such different approaches, and would enable decision makers to put the tax payers’ money for better use. The research results will also be useful for national and local governments when designing low-income housing subsidy programs.
{"title":"Will Tax Credit Increase Housing Supply? Experience from U.S. and Prospect for Australia","authors":"Jian Chen, X. Ge","doi":"10.2139/SSRN.2294715","DOIUrl":"https://doi.org/10.2139/SSRN.2294715","url":null,"abstract":"While the world went through rapid urbanization in the last half century, house prices in many densely populated metropolitan regions are becoming increasingly unaffordable. As a result, many families turn to rental housing. However, the high rents in some markets also place significant burdens on low-income households. Thus, a lot of housing policies and strategies have been introduced by national and local governments to subsidize low-income families to improve their rental housing affordability. The low-income housing tax credit program (LIHTC) in the USA and the national rental affordability scheme (NRAS) in Australia are such examples. Both policies aim to increase supply of affordable rental housing for low-income families. LIHTC finances the development of affordable rental housing through a tax credit system, whereas NRAS provides an annual tax-free incentive for investors to purchase new affordable dwellings and rent them at 20% below market rents to low-income families. LIHTC has been implemented in US for more than a quarter century since 1986, while NARS has relatively short history since 2008. However, whether these programs will increase the long-term housing supply, or will they simply “crowd out” other type of affordable rental housing remains an open question.This paper first studies the long-term impact of LIHTC on housing supply, using the property level LIHTC data from 1986 to 2011, as well as other housing subsidy and housing supply data, including non-LIHTC rental subsidy programs, housing vouchers, housing permits, etc. An empirical linear OLS model is estimated to find the long-run sensitivity of housing supply to LIHTC program, controlling for other supply/demand variables. We find LIHTC has strong positive effect on overall housing supply.Then we compare LIHTC to NRAS program and try to forecast the effectiveness of implementing NARS for increasing affordable rental housing supply, with limited historical data. Similar results show that NRAS has fully compensated for traditional public rental units decline. The comparative study is important because it makes it possible to evaluate the effectiveness of such different approaches, and would enable decision makers to put the tax payers’ money for better use. The research results will also be useful for national and local governments when designing low-income housing subsidy programs.","PeriodicalId":448093,"journal":{"name":"SRPN: Housing (Topic)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126069074","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}
The U.S. mortgage crisis that began in 2007 generated questions about the role played by Fannie Mae and Freddie Mac, the Government-Sponsored Enterprises (GSEs), in its causes. Some have claimed that the Affordable Housing Goals (AHGs), introduced by Congress through the GSE Act of 1992, and the resulting purchases of single-family mortgages the GSEs made to meet those goals, drove lending to high-risk borrowers. Using regression discontinuity analysis, I measure the effect of one of the goals, the Underserved Areas Goal (UAG), on the number of whole single-family mortgages purchased by the GSEs in targeted census tracts from 1996 to 2002. Focusing additionally on tracts that became UAG-eligible in 2005-2006, when the Department of Housing and Urban Development (HUD) began to determine eligibility using the 2000 Census, I measure the effect of the UAG on the GSEs' whole single-family mortgage purchases during peak years for the subprime mortgage market. Under the first approach, I estimate that the GSEs purchased 0 to 3 percent more goal-eligible mortgages than they would have without the UAG in place. Under the second approach, I estimate this effect to be 2.5 to 5 percent. The results suggest a small UAG effect and challenge the view that the goals caused the GSEs to supply substantially more credit to high-risk borrowers than they otherwise would have supplied. Although the goals may have spurred the GSEs to purchase more multi-family mortgages and REMICs than they otherwise would have, my analyses suggest that the GSEs' purchases of whole single-family mortgages to satisfy the goals did not drive the subprime lending boom of 2002-2006.
2007年开始的美国抵押贷款危机引发了人们对房利美(Fannie Mae)和房地美(Freddie Mac)这两家政府支持企业(gse)在危机起因中所扮演角色的质疑。一些人声称，1992年国会通过《政府支持企业法案》(GSE Act of 1992)引入的“经济适用住房目标”(Affordable Housing Goals, ahg)，以及由此产生的政府支持企业为实现这些目标而购买的单户抵押贷款，推动了向高风险借款人放贷。使用回归不连续分析，我测量了其中一个目标，服务不足地区目标(UAG)对gse在1996年至2002年目标人口普查区购买的整个单户抵押贷款数量的影响。另外，当住房和城市发展部(HUD)开始使用2000年人口普查确定资格时，我将重点放在2005-2006年成为UAG合格的土地上，我测量了UAG对次级抵押贷款市场高峰时期gse整个单户抵押贷款购买的影响。根据第一种方法，我估计gse购买的符合目标的抵押贷款比没有UAG的情况下多0%到3%。在第二种方法下，我估计这种影响为2.5%至5%。结果表明，UAG效应很小，并挑战了这样一种观点，即这些目标导致gse向高风险借款人提供的信贷比它们本来可以提供的要多得多。尽管这些目标可能刺激了gse购买更多的多户抵押贷款和REMICs，但我的分析表明，gse为满足这些目标而购买整个单户抵押贷款并没有推动2002-2006年的次贷热潮。
{"title":"The Government‐Sponsored Enterprises and the Mortgage Crisis: The Role of the Affordable Housing Goals","authors":"Valentin Bolotnyy","doi":"10.1111/1540-6229.12031","DOIUrl":"https://doi.org/10.1111/1540-6229.12031","url":null,"abstract":"The U.S. mortgage crisis that began in 2007 generated questions about the role played by Fannie Mae and Freddie Mac, the Government-Sponsored Enterprises (GSEs), in its causes. Some have claimed that the Affordable Housing Goals (AHGs), introduced by Congress through the GSE Act of 1992, and the resulting purchases of single-family mortgages the GSEs made to meet those goals, drove lending to high-risk borrowers. Using regression discontinuity analysis, I measure the effect of one of the goals, the Underserved Areas Goal (UAG), on the number of whole single-family mortgages purchased by the GSEs in targeted census tracts from 1996 to 2002. Focusing additionally on tracts that became UAG-eligible in 2005-2006, when the Department of Housing and Urban Development (HUD) began to determine eligibility using the 2000 Census, I measure the effect of the UAG on the GSEs' whole single-family mortgage purchases during peak years for the subprime mortgage market. Under the first approach, I estimate that the GSEs purchased 0 to 3 percent more goal-eligible mortgages than they would have without the UAG in place. Under the second approach, I estimate this effect to be 2.5 to 5 percent. The results suggest a small UAG effect and challenge the view that the goals caused the GSEs to supply substantially more credit to high-risk borrowers than they otherwise would have supplied. Although the goals may have spurred the GSEs to purchase more multi-family mortgages and REMICs than they otherwise would have, my analyses suggest that the GSEs' purchases of whole single-family mortgages to satisfy the goals did not drive the subprime lending boom of 2002-2006.","PeriodicalId":448093,"journal":{"name":"SRPN: Housing (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126720073","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 chapter reviews literature on the distributional effects of environmental and energy policy. In particular, many effects of such policy are likely regressive. First, it raises the price of fossil-fuel-intensive products, expenditures on which are a high fraction of low-income budgets. Second, if abatement technologies are capital-intensive, then any mandate to abate pollution may induce firms to use more capital. If demand for capital is raised relative to labor, then a lower relative wage may also hurt low-income households. Third, pollution permits handed out to firms bestow scarcity rents on well-off individuals who own those firms. Fourth, low-income individuals may place more value on food and shelter than on incremental improvements in environmental quality. If high-income individuals get the most benefit of pollution abatement, then this effect is regressive as well. Fifth, low-income renters miss out on house price capitalization of air quality benefits. Well-off landlords may reap those gains. Sixth, transition effects could well hurt the unemployed who are already at some disadvantage. These six effects might all hurt the poor more than the rich. This paper discusses whether these fears are valid, and whether anything can be done about them.
{"title":"Distributional Effects of Environmental and Energy Policy: An Introduction","authors":"D. Fullerton","doi":"10.3386/W14241","DOIUrl":"https://doi.org/10.3386/W14241","url":null,"abstract":"This chapter reviews literature on the distributional effects of environmental and energy policy. In particular, many effects of such policy are likely regressive. First, it raises the price of fossil-fuel-intensive products, expenditures on which are a high fraction of low-income budgets. Second, if abatement technologies are capital-intensive, then any mandate to abate pollution may induce firms to use more capital. If demand for capital is raised relative to labor, then a lower relative wage may also hurt low-income households. Third, pollution permits handed out to firms bestow scarcity rents on well-off individuals who own those firms. Fourth, low-income individuals may place more value on food and shelter than on incremental improvements in environmental quality. If high-income individuals get the most benefit of pollution abatement, then this effect is regressive as well. Fifth, low-income renters miss out on house price capitalization of air quality benefits. Well-off landlords may reap those gains. Sixth, transition effects could well hurt the unemployed who are already at some disadvantage. These six effects might all hurt the poor more than the rich. This paper discusses whether these fears are valid, and whether anything can be done about them.","PeriodicalId":448093,"journal":{"name":"SRPN: Housing (Topic)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121227612","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}
Housing Supply in Manhattan has fallen relative to total US housing supply over the last 45 years. This time trend is entirely explained away by a combination of the fall of Robert Moses's urban renewal empire and the decreasing national share of construction that is multifamily. Similar results over a shorter period hold for metropolitan New York and San Francisco.
{"title":"As the Nation's Multifamily Goes, so Goes Manhattan: Are Tightening Local Regulations Really to Blame for Reduced Coastal Housing Supply?","authors":"T. Davidoff","doi":"10.2139/ssrn.978517","DOIUrl":"https://doi.org/10.2139/ssrn.978517","url":null,"abstract":"Housing Supply in Manhattan has fallen relative to total US housing supply over the last 45 years. This time trend is entirely explained away by a combination of the fall of Robert Moses's urban renewal empire and the decreasing national share of construction that is multifamily. Similar results over a shorter period hold for metropolitan New York and San Francisco.","PeriodicalId":448093,"journal":{"name":"SRPN: Housing (Topic)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125613897","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}
The authors provide an economic framework to analyze investment in informal housing in developing countries. They consider a simple model of investment in the housing market where investors can choose between two sectors-the formal sector, where physical investment faces no risk of destruction, and the informal sector, where investment in each period is subjected to an exogenous risk of destruction. Construction costs differ between the two sectors. All households are renters. Renters shop for dwelling attributes and do not care about the sector (formal or informal) itself. The model implies that returns on investment, measured by the rent-to-value ration, will be higher in the informal sector. The authors use a survey conducted by the World Bank in Pune, India in 2002. The sample comprises 2,850 households. This survey had the peculiarity of asking the households, regardless of tenure status, questions about the market rent and value of their dwelling. Thus they can calculate individual rates of return for each unit without facing the typical selection bias problems. Comparing the distributions of returns in the informal and formal sectors, the authors obtain the following results: 1) Rates of return are significantly higher in the informal sector, as predicted by the model. 2) These figures imply a perceived risk on housing investment in the informal sector equivalent to an annual destruction rate ranging between 1 and 2 percent. 3) The two distributions of rates of return present highly idiosyncratic components and are not well explained by variables proxying either the strength of informal property rights or lower perceived risks of eviction.
{"title":"Measuring the Risk on Housing Investment in the Informal Sector: Theory and Evidence from Pune, India","authors":"M. Kapoor, David Leblanc","doi":"10.1596/1813-9450-3433","DOIUrl":"https://doi.org/10.1596/1813-9450-3433","url":null,"abstract":"The authors provide an economic framework to analyze investment in informal housing in developing countries. They consider a simple model of investment in the housing market where investors can choose between two sectors-the formal sector, where physical investment faces no risk of destruction, and the informal sector, where investment in each period is subjected to an exogenous risk of destruction. Construction costs differ between the two sectors. All households are renters. Renters shop for dwelling attributes and do not care about the sector (formal or informal) itself. The model implies that returns on investment, measured by the rent-to-value ration, will be higher in the informal sector. The authors use a survey conducted by the World Bank in Pune, India in 2002. The sample comprises 2,850 households. This survey had the peculiarity of asking the households, regardless of tenure status, questions about the market rent and value of their dwelling. Thus they can calculate individual rates of return for each unit without facing the typical selection bias problems. Comparing the distributions of returns in the informal and formal sectors, the authors obtain the following results: 1) Rates of return are significantly higher in the informal sector, as predicted by the model. 2) These figures imply a perceived risk on housing investment in the informal sector equivalent to an annual destruction rate ranging between 1 and 2 percent. 3) The two distributions of rates of return present highly idiosyncratic components and are not well explained by variables proxying either the strength of informal property rights or lower perceived risks of eviction.","PeriodicalId":448093,"journal":{"name":"SRPN: Housing (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130628182","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}
1. INTRODUCTION In terms of housing issues, the primary public policy focus of economists has been the affordability of homes, mortgage availability, land-use regulation, and rent control. Studies of land-use regulation focus on the effects of regulation on the price of owner-occupied housing. Work on low-income housing has concerned itself more with issues of measurement and the debate over supply-side versus demand-side subsidies. In this paper, we look at the relationship between these two issues to examine how government regulation affects the dynamics of the low-income housing stock. We find that, consistent with theoretical models of housing, restrictions on the supply of new units lower the supply of affordable units. This occurs because increases in the demand for higher quality units raise the returns to maintenance, repairs, and renovations of lower quality units, as landlords have a stronger incentive to upgrade them to a higher quality, higher return housing submarket. This result is disturbing because it highlights how policies targeted toward new, higher income owner-occupied suburban housing can have unintended negative consequences for lower income renters. Our research differs from most studies of affordable housing in that we are not concerned with identifying the size of the affordable stock or matching it to the number of low-income households. The gap between the housing needs of low-income households and the stock of units deemed affordable has been demonstrated in a considerable amount of other research. (1) Here, we build on the Somerville and Holmes (2001) study of the effects of the unit, neighborhood, and market characteristics on the probability that a unit will stay in the stock of rental units affordable to low-income households; we do so by looking at how government regulations affect this probability. Our approach is to look at individual units in successive waves of the American Housing Survey (AHS) metropolitan area sample. In doing so, we follow Nelson and Vandenbroucke (1996) and Somerville and Holmes (2001), who use the panel nature of the AHS metropolitan area survey data to chart the movements of individual units in and out of the low-income housing stock. The remainder of the paper is structured as follows. First, we lay out the theoretical framework for our analysis. We follow with a discussion of our data. Finally, we present our empirical results, both for measures of constraints on the supply of new residential units and for the pervasiveness of rent control in an area. 2. THEORETICAL FRAMEWORK We model movements of units in and out of the stock of affordable housing as the filtering down of units through successive housing submarkets. The filtering model describes the housing market as a series of submarkets differentiated by unit quality. Rents fall as quality declines, so units that are lower on the quality ladder have lower rents than units of the same size in the same location at the top. Without e
{"title":"Government Regulation and Changes in the Affordable Housing Stock","authors":"C. Somerville, C. Mayer","doi":"10.14288/1.0052286","DOIUrl":"https://doi.org/10.14288/1.0052286","url":null,"abstract":"1. INTRODUCTION In terms of housing issues, the primary public policy focus of economists has been the affordability of homes, mortgage availability, land-use regulation, and rent control. Studies of land-use regulation focus on the effects of regulation on the price of owner-occupied housing. Work on low-income housing has concerned itself more with issues of measurement and the debate over supply-side versus demand-side subsidies. In this paper, we look at the relationship between these two issues to examine how government regulation affects the dynamics of the low-income housing stock. We find that, consistent with theoretical models of housing, restrictions on the supply of new units lower the supply of affordable units. This occurs because increases in the demand for higher quality units raise the returns to maintenance, repairs, and renovations of lower quality units, as landlords have a stronger incentive to upgrade them to a higher quality, higher return housing submarket. This result is disturbing because it highlights how policies targeted toward new, higher income owner-occupied suburban housing can have unintended negative consequences for lower income renters. Our research differs from most studies of affordable housing in that we are not concerned with identifying the size of the affordable stock or matching it to the number of low-income households. The gap between the housing needs of low-income households and the stock of units deemed affordable has been demonstrated in a considerable amount of other research. (1) Here, we build on the Somerville and Holmes (2001) study of the effects of the unit, neighborhood, and market characteristics on the probability that a unit will stay in the stock of rental units affordable to low-income households; we do so by looking at how government regulations affect this probability. Our approach is to look at individual units in successive waves of the American Housing Survey (AHS) metropolitan area sample. In doing so, we follow Nelson and Vandenbroucke (1996) and Somerville and Holmes (2001), who use the panel nature of the AHS metropolitan area survey data to chart the movements of individual units in and out of the low-income housing stock. The remainder of the paper is structured as follows. First, we lay out the theoretical framework for our analysis. We follow with a discussion of our data. Finally, we present our empirical results, both for measures of constraints on the supply of new residential units and for the pervasiveness of rent control in an area. 2. THEORETICAL FRAMEWORK We model movements of units in and out of the stock of affordable housing as the filtering down of units through successive housing submarkets. The filtering model describes the housing market as a series of submarkets differentiated by unit quality. Rents fall as quality declines, so units that are lower on the quality ladder have lower rents than units of the same size in the same location at the top. Without e","PeriodicalId":448093,"journal":{"name":"SRPN: Housing (Topic)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115121604","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}
The U.S. Department of Housing and Urban Development (HUD) finds dramatic increases in worst case housing needs during the 2009–2011 period that cut across demographic groups, household types, and regions. This rise in hardship among renters is due to substantial increases in rental housing demand and weakening incomes that increase competition for already-scarce affordable units. Given the severely challenged economic conditions that the United States confronted during this period, particularly surrounding the housing market, it is not surprising that the need for housing assistance continues to outpace the ability of federal, state, and local governments to supply it. The forthcoming Worst Case Housing Needs 2011: Report to Congress will examine the causes of and trends in worst case needs for affordable rental housing.
{"title":"Worst Case Housing Needs 2011: Report to Congress - Summary","authors":"U.S. Department of Housing and Urban Development","doi":"10.2139/ssrn.2284183","DOIUrl":"https://doi.org/10.2139/ssrn.2284183","url":null,"abstract":"The U.S. Department of Housing and Urban Development (HUD) finds dramatic increases in worst case housing needs during the 2009–2011 period that cut across demographic groups, household types, and regions. This rise in hardship among renters is due to substantial increases in rental housing demand and weakening incomes that increase competition for already-scarce affordable units. Given the severely challenged economic conditions that the United States confronted during this period, particularly surrounding the housing market, it is not surprising that the need for housing assistance continues to outpace the ability of federal, state, and local governments to supply it. The forthcoming Worst Case Housing Needs 2011: Report to Congress will examine the causes of and trends in worst case needs for affordable rental housing.","PeriodicalId":448093,"journal":{"name":"SRPN: Housing (Topic)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116854572","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：